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1
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Cover designed by Bruce Boyd
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MONTHLY LABOR
REVIEW _ _ _ _ _ _ _ __
Volume 127, Number 12
December 2004
What can time-use data tell us about hours of work?
3
CPS hours-worked estimates are very close to estimates from ATUS
even though the CPS data are not representative of the whole month
Harley Frazis and Jay Stewart
OSHS: new recordkeeping requirements
Comparing old OSHS data with those resulting from changes
in recordkeeping rules in 2002 presents some challenges
William J. Wiatrowski
Hedonic regression models using two data sources
25
The two data sources-BLS and outside of BLS-used to create
the models have their own distinct advantages and disadvantages
Craig Brown
Reports
New and emerging occupations
39
Jerome Pikulinski
Fatal occupational injuries at road construction sites
43
Stephen Pegula
Departments
Labor month in review
Research summaries
Workplace safety and health
Precis
Book review
Current labor statistics
Index to volume 127
2
39
43
48
49
51
129
Editor-in-Chief: William Parks II • Executive Editor: Richard M. Devens • Managing Editor: Anna Huffman Hill • Editors: Brian
I. Baker, Kristy S. Christiansen, Richard Hamilton, Leslie Brown Joyner • Book Reviews: Richard Hamilton • Design and Layout:
Catherine D. Bowman, Edith W. Peters • Contributor: Scott Berridge
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Federal Reserve Bank of St. Louis
The December Review
Time-use studies in general, ar,d the
American Time-Use Survey (Arns) in
particular, are valued primarily for what
they tell labor economists about the time
workers spend away from the job.
Harley Frazis and Jay Stewart remind us
that time-use surveys do, in fact,
contribute to our understanding of time
on the job as well. Their analysis of
working hours shows that Ams and the
Current Population Survey ( C PS)
produce similar estimates of hours at
work for the weeks containing the 12th
day of a month, but that the week
containing the 12th is often the week in
which people work the most hours. To a
great extent, this reflects a deliberate
choice: The week containing the 12th
was selected as the government's
standard reference week for surveys
because it was the week in which the
fewest holidays fall.
William J. Wiatrowski explores the
impact of recent changes in the
Occupational Safety and Health
Administration's recordkeeping requirements on the analysis of the Bureau's
workplace safety and health statistics.
Craig Brown reports on the
advantages and disadvantages of using
data from sources outside the Consumer
Price Index (CPI) relative to using inhouse data to perform hedonic
regressions for the purpose of quality
adjustment.
Jerome Pikulinski summarizes the new
and emerging occupations identified in
the 2001 Occupational Employment
Survey.
In a new department, "Workplace
Safety and Health," Stephen Pegula
reports on fatal work injuries at road
construction sites.
Factory compensation
In the United States, hourly compensation costs for production workers
in manufacturing increased to $21.97 in
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2003. These costs were higher than
those in all the economies covered
outside Europe , but ten European
countries had higher hourly compensation costs (expressed in U.S. dollars)
than did the United States. (Hourly
compensation costs include hourly
direct pay, employer social insurance
expenditures , and other labor taxes.)
The strength of the European
currencies in 2003 drove costs in those
countries, as measured in U.S. dollars,
up. Costs in the countries which use the
euro as a currency rose the most, while
costs in several of the European
countries that do not use the euro (for
example, the United Kingdom) rose less.
The average for the 18 European
countries in 2003 rose to more than $24
per hour. Costs in Germany nearly
reached the $30 level.
Compensation costs in U.S. dollars
in Canada al so grew. In contrast ,
Mexican costs dropped. The drop was
associated with a weakening peso and
an increase in compensation costs on a
national currency basis that was lower
than average. For more information, see
"International Comparisons of Hourly
Compensation Costs for Production
Workers in Manufacturing, 2003 ," news
release USDL 04-2343.
Car quality 2005
The value of quality changes in 2005
model-year cars averaged $283.12. This
figure represents 73.8 percent of the
average increase in manufacturers'
invoice prices. In the light truck
segment, $306.26 in quality changes
accounted for 75.7 percent of average
increases in invoice price.
The retail equivalent value of the
quality changes to 2005 model-year
passenger cars averaged $310.50, or 7 4.3
percent of the over-the-year increase in
manufacturers ' suggested list prices.
The retail value of the quality changes
broke down to $193 .11 for safety
December 2004
improvements and $117 .39 for other
quality change s, such as emission
improvements, changes in audio
systems , and changes in levels of
standard or optional equipment.
The retail equivalent value of quality
changes for domestic light trucks
averaged $345.38 , representing 75.2
percent of the average increase in
manufacturers' suggested list prices.
The quality changes broke down to
$ 18.30 for federally mandated safety
improvements, $120.43 for nonmandated
safety improvements, and $206.65 for
other quality changes such as powertrain
improvements, theft protection,
changes in audio systems, and changes
in levels of standard or optional
equipment.
Estimates of the value of quality
change are based on a review of data
supplied by producers for similarly
equipped 2004 and 2005 domestic vehicles
priced in the Producer Price Index (PPI).
For more information, see "Report on
Quality Changes for 2005 Model
Vehicles," news release UDSL 04-2351.
Clarification
In Jonathan A. Schwabish 's September
article on wages and benefits, footnote
1 8 suggests that the distinction
between defined benefit and defined
contribution plans is unclear in the
Employment Cost Index (ECI) survey.
While that may have been true in the
past, for the last decade, data used to
compile the ECI has distinguished
between defined benefit and defined
contribution retirement plans using
definitions consistent with other BLS
surveys. Prior to 1995, ECI retirement
costs used a different allocation scheme
that classified retirement costs as
"pensions" or "savings and thrift plans."
Despite the change in definitions, the
total cost of retirement benefits was not
affected~ what changed was the
distribution of the costs among the
types of retirement benefits.
D
What can time-use data
tell us about hours of work?
Estimates of hours worked from the CPS are very close
to estimates from the ATVS for CPS reference weeks;
however, CPS reference weeks
are not representative of the entire month
Harley Frazis
and
Jay Stewart
Harley Frazis
and Jay Stewart
are research
economists on the
Employment
Research and
Program
Development Staff,
Office of Employment
and Unemployment
Statistics,
Bureau of Labor
Statistics.
E-mail:
Frazis. Harley@bls.gov
Stewart.Jay@bls.gov
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Federal Reserve Bank of St. Louis
T
he number of hours people work for pay is
an important economic measure. In addition to being a measure of labor utilization,
it is a component of other economic statistics.
For example, productivity measures are computed
by dividing total output by total hours worked,
and hourly wages are often computed by dividing usual weekly earnings by usual weekly hours
worked. 1 There are two major sources of hours
data for the United States-the BLS Current Population Survey (CPS) and the BLS Current Employment Statistics survey (CES)-and estimates of
weekly hours from these two surveys behave differently for a variety of reasons. The goal of this
article is to use data from the new American Time
Use Survey (ATUS) to shed light on the accuracy
of hours-worked reports in the CPS. Because the
purpose of this study is to determine whether
respondents report hours correctly in CPS, it does
not examine other factors that could result in differences in estimates of hours worked from CPS
and ATUS. In addition to differences in the reporting of hours, differences in estimates can be due
to differences in sample composition and differences in the reporting of other variables. 2 We
control for these other factors, but do not analyze
their effects on differences in estimates. We examine the effect of these other factors on comparisons of weekly hours from CPS and ATUS in a
forthcoming publication. 3
Previous studies
Previous studies that assess the accuracy of
hours data from establishment surveys either
compare hours data for the same industries
across surveys, or evaluate accuracy using cognitive methods such as focus groups and interviews with respondents. 4 The former approach
allows researchers to document differences between surveys (after accounting for differences
in concepts), while the latter provides information on how respondents compile their data.
Studies that are directed at verifying hours measures from household surveys such as the CPS
typically take one of two approaches: they compare weekly hours reports from a CPS-like question either to ( 1) records from the individual's
employer or (2) data collected from the individual
using a time diary.
Studies by Wesley Mellow and Hal Sider5
and Willard L. Rodgers, Charles Brown, and
Greg J. Duncan 6 took the first approach. Both
studies assumed that employer-reported hours
were correct, and that any difference between
the two measures was due to respondent error.
The Mellow and Sider study found that, compared with employer reports, respondents
overreported hours by 3.9 percent on average,
and that overreporting was greater for self respondents than when a proxy provided the
information ( 4.3 percent versus 3 .4 percent).
They also found that overreporting was
greater among managerial and professional
workers (11 percent). However, because these
workers tend to be salaried, it seems unlikely
that their employers kept records of their actual hours worked and instead reported the
hours of a standard workweek.
Monthly Labor Review
December
2004
3
Time-Use Data
In contrast, Rodgers, Brown, and Duncan found v !ry little
measurement error on average. However, differences in the
samples provide the most likely explanation for the different
results. The sample in the Rodgers, Brown, and Duncan study
was restricted to hourly-paid workers at a single firm. Further,
all of the workers in their study were unionized, and most were
full time. All of these characteristics would lead to more stable
work schedules, which should reduce reporting errors. In
contrast, the data in the Mellow and Sider study came from a
special supplement to the CPS in which respondents' employers were contacted and asked to provide hours and earnings
information. Thus, their sample is representative of the entire
employed civilian population.
The study that most closely resembles the one in this article was done by John Robinson and Ann Bostrom in 1994. 7
They compared time-diary estim~tes of hours worked from
surveys conducted during 1965, 1975, and 1985, to estimates
from CPS-like questions about hours worked last week asked
during the same surveys. One drawback of using time-diary
data from these surveys is that the data were collected only
for a single day. To overcome this, they constructed synthetic weeks by combining diaries of demographically similar
respondents. Another drawback is that the reference periods
for the two measures of hours worked do not cover the same
time period. The reference day for the time diary is the day
prior to the interview, while the reference period for the CPSlike question is the week prior to the interview. Their results
indicated that respondents overreport hours in the CPS-like
question, that women tend to overreport more than men, and
that the extent of this overreporting has increased over time.
These authors also found that overreporting was greater
among those who reported the longest hours in the CPS-like
question. However, Jerry A. Jacobs in 1998 argued that the
relationship between overreporting and reported hours
worked is due to regression to the mean. 8 Regression to the
mean arises because people who worked unusually long hours
during the previous week (the reference period for the CPS-like
question) were more Hkely to work more-normal hours during
the week in which the time diary was collected. Jacobs's analysis indicates that estimates of time spent at work are very
close to estimates of work'hours from a retrospective question, suggesting that self-reported hours are fairly accurate. 9
One possible explanation for the disagreement on the extent of overreporting between these two studies is that they
used different measures of work. The Robinson and Bostrom
study used actual work time as collected in the time diary,
whereas Jacobs used time spent at work. If respondents take
time off in the middle of the day to eat lunch or run errands,
then the time-spent-at-work measure will overstate time spent
working. On the other hand, work that is done at home after
hours will be missed by this measure, but will be captured by
the actual-time-worked measure.
4
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Federal Reserve Bank of St. Louis
December 2004
The current study contributes to this literature by using
ATUS data to examine the accuracy of reporting in the CPS.
Because the ATUS sample is drawn from households that recently completed their participation in the CPS, it is possible
to link ATUS respondents' interviews to their final CPS interviews. Thus, we can compare estimates of hours worked
generated from ATUS time diaries to those generated from the
actual CPS questions, rather than from a CPS-like question.
One difference between ATUS and CPS survey methods
turns out to be unexpectedly important in this comparison.
CPS respondents report their labor force activity for the week
containing the 12th of the month. This reference week was
chosen to avoid holidays. In contrast, ATUS interviews are
conducted over the entire month. We find that CPS hours
reports are, on average, quite similar to those from ATUS for
the CPS reference week, but the reference week is not representative of the entire month.
About the data
The data used in this study are from the new American TimeUse Survey. The ATUS sample is a stratified random sample
that is drawn from households that have completed their participation in the CPS and is representative of the U.S. civilian
population. The data cover the January-December 2003 period. Interviews were conducted every day during the year
except for a few major holidays. Thus, the data cover the
entire year, except for the days before these holidays. About
1,725 diaries were collected each month for a total sample size
of 20,720. The response rate for 2003 was about 58 percent.
Interviews with fewer than five activity spells or more than 3
hours of uncodeable activities were dropped from the sample.
As in other time-use surveys, respondents are asked to
sequentially report their activities on the previous day. The
diary day starts at 4 a.m. and goes through 4 a.m. of the following day (the interview day), so each interview covers a 24hour period. The respondent describes each activity spell,
which the interviewer either records verbatim or, for a limited
set of common activities (such as sleeping or watching television), enters a numerical code. These responses are translated into 3-tier activity codes. 1 For each episode, the ATUS
collects the start and stop times along with other information. 11 The ATUS does not collect information about secondary activities (for example, listening to the radio while driving)
in the time diary. This lack of information on secondary activities should have only a minor impact on time spent in paid
work, because most paid work is done as a primary activity.
Th~ ATUS also contains labor force information about the
respondent that was collected using a slightly modified version of the basic CPS questionnaire. These questions allow
analysts to determine whether the respondent is employed,
unemployed, or not in the labor force. 12 One notable differ-
°
ence between ATVS and CPS employment questions is that the
reference period in ATVS is the 7 days prior to the interviewthe last day being the diary day-instead of the previous calendar week as in CPS. The sample for this study is respondents 16 years and older who worked at a job during the 7 days
prior to their ATVS interview and reported usual hours.
The ATVS collects usual hours worked on respondents'
main and other jobs, but does not collect actual hours. Having
data on actual hours would be an advantage, because the time
diary collects actual hours-and because using actual hours
would make our results more comparable to those of other
studies. But there is a potential problem with using time-diary
estimates of actual hours collected during the ATVS interview:
the procedure used for contacting respondents in ATVS could
impart bias into estimates of actual hours for the previous 7
days. Each designated person is assigned an initial calling
day. If he or she is not contacted on that day, the interviewer
makes the next call I week later, thus preserving the assigned
day of the week. Individuals who are unusually busy during a
particular week (perhaps because they worked long hours) are
less likely to be contacted during that week, making it more
likely that they are contacted the following week (and asked to
report hours for the busy week). Hence, long work weeks
would tend to be oversampled, resulting in a correlation between hours worked during the previous week and the probability that that week is sampled.
Definitions of hours worked
For our comparisons, we consider three alternative measures
of hours worked and one measure of time at work from the time
diary data:
•
definition 1: Time spent in activities coded as
paid work in the time diary.
•
definition 2: Definition 1 plus breaks of 15
minutes or less and work-related travel (travel
between work sites).
•
definition 3: Definition 2 plus time spent in
work-related activities.
•
definition 4: Total elapsed time between the
start time of the first episode of paid work and
the stop time of the last episode of paid work. 13
definitions were chosen for comparison because they
represent possible ways that respondents might report hours
of work, although, conceptually, one can make a strong argument for using any of definitions 1-3. Of these three definitions, definition 1 is the most restrictive and, based on the
descriptions in John Robinson and Ann Bostrom and John
Robinson and Geoffrey Godby, 14 is the one used in the earlier
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studies to verify hours. This definition was also used by BLS
in its recent ATVS press release. Definition 2 corresponds to
the definition of hours used for productivity measurement.
The inclusion of breaks is appropriate because, as Daniel
Hamermesh 15 argues, breaks are productive. 16 On a more practical level, not all respondents report breaks as separate episodes, so definition 2 imposes more consistency across respondents. Definition 3 includes work-related activities-activities that are done for the respondent's job or business, but
may have a leisure component and take place outside normal
work hours (for example, dining or playing golf with clients or
customers). Empirically, there is very little difference between
definitions 2 and 3. Definition 4, which is similar to Jacobs 's
1998 time-at-work measure, 17 is potentially problematic because
it could include time spent doing nonwork activities, such as
running personal errands during work hours. Abraham,
Spletzer, and Stewart 18 speculated that the pattern of
overreporting found in Robinson and Bostrom 19-that hours
are overreported in retrospective questions and that
overreporting has increased over time-could be due to the
increased flexibility and variability of work schedules. For
example, a worker who arrives at work at 8 a.m. and leaves at 6
p.m. might report working a 50-hour week, even though he or
she usually takes a 2-hour break each day to run personal
errands. Under definition 4, these workdays would be 10 hours
long. Jacobs's result ofv~ry little difference between the timeat-work measure and the CPS-like measure is consistent with
Abraham, Spletzer, and Stewart's speculation.
Comparing ATUS and CPS hours measures
We compare the four definitions of time-diary measures of
hours worked from the ATVS to retrospective measures from
these respondents' final CPS interview. First, hours worked in
the time diary are compared to usual hours worked per week
from the employment section of the ATVS questionnaire. The
advantages of this comparison are that the questions were
asked in the same interview and that reference periods for the
different measures are close in time. For each of the time-diary
estimates, responses were reweighted so that each day of the
week receives equal weight (1/7 of the total) and used these
reweighted responses to compute the average number of hours
worked per day for workers. In order to make the time diary
measure-which records hours worked per day--comparable
with hours worked per week, these estimates were multiplied
by 7. Thus, sample averages should be an unbiased estimate
of average hours worked per week for the population.
Row (a) of table 1 shows the comparisons for our base
sample. Usual hours worked per week reported in the ATVS
(using a slightly modified version of the CPS question) are, on
average, about 2-3 hours higher than the diary-based measures. The closest figure is 40.9 hours using definition 4--
Monthly Labor Review
December 2004
5
Time-Use Data
Comparison of time-diary estimates of average weekly hours to estimates from
Arus definitions of paid work
Respondent
Definition 3:
Work plus
CPS
questions
cPs definitions of paid work
Definition 1:
Definition 2:
Work only
Work plus
breaks plus
work-related
travel
breaks plus
work-related
travel plus
work-related
activities
Start time
37.6
38.0
38.2
40.9
40.3
...
...
(ATUS
definition)
Definition 4:
minus
Usual hours
in
ATUS
Usual hours
incPS
Actual hours
in CPS
stop time
(a) Worked last week, usual hours
reported in ATUS (N = 11,988) .................
(b) (a) and worked during CPS
reference week and reported
usual hours reported in CPS
(N = 10,036) ... ..... .. .... .... .. ...................... ..
(c) (b) and usual hours in cPs and ATus
were within 5 hours of each other
(exclusive). (N = 6,268) .........................
38.7
39.2
39.3
42.2
41 .3
40.0
39.4
37.3
37.8
37.9
40.7
39.3
39.3
38.6
(d) (c) and Arus diary day not during
CPS reference week (N = 4,767) .............
36.8
37.3
37.4
40.2
39.2
39.2
38.3
(e) (c) and ATUS diary day during CPS
reference week (N = 1,501) ....................
38.8
39.3
39.5
42.3
39.7
39.7
39.3
(f) (d) and Arus diary day not a
holiday 1 (N = 4,703) ........ .......... ........ .....
37.4
37.9
38.0
40.8
39.3
39.2
38.4
1
Holidays include New Year's Day, Easter, Memorial Day, Independence Day, Labor Day, Thanksgiving, and Christmas. Interviews were collected for
all except Thanksgiving and Christmas.
the time work stopped minus the time work started. This
definition yields an estimate 0.6 hours greater than the usual
hours worked figure.
Another interesting, and possibly more appropriate, comparison is to compare the time-diary measures with retrospective reports of actual hours worked per week. The ATUS does
not collect such a measure. However, the CPS does; so, if a
respondent worked during the period covered by his or her
last CPS interview, his or her reported actual weekly hours from
CPS can be compared with hours worked from the ATUS time
diary. The results of this comparison are shown in row (b).
Actual hours reported in the CPS are much closer to diary
hours for definitions 1-3, but are about 3 hours less than the
diary hours for definition 4.
One problem with comparing actual CPS hours to ATUS
hours from the time diary is that the CPS interviews occurred,
on average, 3 months before the ATUS interview, and respondents' work schedules may have changed during that time. 20
Indeed, usual weekly hours reported in ATUS are on average
1.3 hours greater than usual hours reported in the last CPS
interview. This change in usual hours presents problems in
interpreting the comparison of CPS actual hours to the timediary measures, because one would expect that actual hours
may have changed as well. Looking at the CPS as a whole (not
just the sample matched with ATUS) , there is no evidence that
usual (or actual) hours worked increased between October
2002 (the period that the January 2003 ATUS sample was drawn)
and the end of 2003. 21 Together, these facts suggest either
6
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December 2004
that persons whose work hours increased were more likely to
respond in ATUS, or that persons were more likely to report a
high number of hours in ATUS than in CPS for the same jobs
despite the fact that the questions are the same in both surveys.22 If the first explanation is correct, the two measures are
not comparable because they were reported at different times.
To control for changes in usual hours, the sample is further
restricted to individuals whose reported usual hours in the
CPS and in ATUS were within 5 hours (exclusive) of each other. 23
This comparison is shown in row (c) . The results show a
greater difference between definitions 1-3 and CPS actual hours
than in row (b). Diary hours corresponding to definition 1 are
less than CPS actual hours by 1.3 hours, whereas they are only
0.8 and 0.7 hours less than CPS actual hours for definitions 2
and 3. All of these differences are statistically different from
CPS actual hours at the 10-percent level using a 2-tailed test.
One factor that could affect these comparisons is that the
reference week for the CPS ( the week containing the 12th of the
month, as mentioned above) was chosen to avoid holidays.
Therefore, one might expect hours of work to be greater, on
average, in CPS reference weeks than in nonreference weeks.
Rows (d) and (e) show that this is indeed the case: for all
measures, diary hours in reference weeks exceed diary hours
in nonreference weeks by approximately 2 hours. Using the
entire base sample (as in row (a)), rather than the restricted
sample (as in row (c)), shows an even larger difference: about
2.6 hours for definitions 1-3 and 2.9 hours for definition 4.
Thus, it is more appropriate to compare actual hours from CPS
to time-diary estimates that include only diaries that are in the
12 CPS reference weeks.
Comparing the CPS measure of actual hours to the timediary measures in row (e) indicates that they are quite close for
definitions 1---3 when the diary day is in a CPS reference week.
Except for definition 4, none of the diary measures are statistically different from the CPS measure. In contrast, when the
diary day is not in a CPS reference week, CPS actual hours
exceed time-diary hours for definitions 1-3 by 0.9 to 1.5 hours
(all of these differences are statistically significant at the 5percent level using a 2-tailed test). To illustrate the effect of
holidays, we recomputed the time-diary estimates for
nonreference weeks excluding major holidays and reweighting
the remaining diaries so that each day of the week is weighted
the same. This exclusion reduced the difference between CPS
actual hours and the diary measure by about half an hour, with
only the definition I difference remaining significant.
In summary, estimates of hours worked from time diaries are
significantly lower than estimates of usual hours worked.
However, when the sample is restricted to respondents whose
usual hours did not change much between their final CPS interview and their ATVS interview, average time-diary hours are
close to average actual hours as reported in CPS. These estimatPS are indistinguishable from each other when the ATVS
diary day falls in a CPS reference week. When the diary day
falls outside the CPS reference week, time-diary estimates are
significantly lower than estimates of actual hours worked from
CPS. The implications of these results are discussed later.
Table 2 shows comparisons for individuals whose usual
hours changed by less than 5 hours (the sample in rows (c)
■ r•••u--..-
through (f) of table 1), tabulated by sex, education, and full- or
part-time status. Men's diary hours are quite close to actual
hours reported from CPS, with definition 2 hours being equal to
CPS actual hours to one decimal place. Women report fewer
hours in the time diary than in CPS for definitions 1-3 (all differences are statistically significant at the 5-percent level). This
pattern of differential overreporting is also found in Robinson
and Bostrom. 24 They argue that women may be more likely to
work part time and have variable schedules, which would make
it harder to report their work hours. Although we do not know
why women's hours are overreported, we can rule out differences in reporting behavior between men and women. The
difference between CPS hours and diary hours is virtually identical between women who self-reported hours in CPS and those
whose hours were reported by proxy respondents (who are
often spouses). Table 2 also shows comparisons between
measures for different educational groups. The sample is further restricted to those ages 25 and older in order to minimize
the influence of respondents who are still in school. The results show a consistent pattern, although the differences between CPS actual hours and diary hours are not very precisely
estimated. More education is associated with more
overreporting of hours in CPS relative to the diary. For high
school dropouts, diary hours are slightly higher than CPS actual hours, although the difference is not significant. For high
school graduates and those with some college, diary hours are
quite close to CPS actual hours, at least for definitions 2 and 3.
For college graduates, diary hours are less than CPS actual
hours by 1.6 to 2.0 hours per week for definitions 1-3; these
differences are statistically significant at the 5-percent level.
Comparison of time-diary estimates of average weekly hours to estimates from
demographic characteristics
ATUS
definitions of paid work
CPS
CPS
questions, by selected
definitions of paid work
Definition 2: Definition 3:
Definition 4:
Work plus
Work plus
Usual hours
Usual hours Actual hours
Start time
breaks plus
breaks
(ATIJS
in ATUS
incPS
mn.is
in CPS
plus work- work-related
definition)
time
stop
travel plus
related
work-related
travel
- - - - -- - - - - - - - - + - - - - - - - + - - ----+-a_c_ti_v_iti_e s_----l--------l- ------+----- - + - - - - Respondent
Definition 1:
Work only
Sex
Men (N = 2,874) ... ... ... ........ ..... ........ ........
Women (N = 3,394) .................................
40.4
34.2
40.9
34.6
41.0
34.7
43.9
37.4
41.6
37.1
41.5
37.1
40.9
36.2
38.5
37.5
37.8
37.8
39.1
38.2
38.3
38.1
39.2
38.2
38.4
38.2
41.9
40.7
41.2
41.7
39.6
39.4
39.7
41.0
39.6
39.4
39.6
41 .0
39.0
38.7
38.9
39.8
39.4
23.0
39.9
23.2
40.0
23.3
43.0
24.5
42.0
21.8
42.0
21.8
41.2
21.3
Education (age 25 and older)
No high school diploma (N = 417) ... ..... ..
High school diploma (N = 1,678) ............
Some college (N = 1,793) ...... ... ... .... .......
College graduates (N = 1,989) .............. ..
Full• /Part-time status
Full time (N = 5,408) .. .. .. ........................ ..
Part time (N= 860) .... ... ........ ....................
NOTE: The universe for this table is the restricted sample as defined
in row (c) of table 1 (individuals who worked during the reference week in
Arns and the reference week in cPs, and whose usual hours in cPs and
ATUS were within 5 hours of each other (exclusive). For the education
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comparisons, the sample was restricted to respondents age 25 and older.
Full- / part-time status is determined using the response to usual hours
worked in CPS. Respondents who usually work 35 or more hours per week
are full time .
Monthly Labor Review
December
2004
7
Time-Use Data
Finally, table 2 compares work hours by full-time and parttime status based on the usual-hours question in ATUS (full
time is defined as 35 hours or more usually worked). Timediary hours for part-timers are above actual hours reported in
CPS, while for full-timers they are below. Differences between
ATUS hours and CPS actual hours are significant at least at the
10-percent level for definitions 1-3 for part-timers, and at the Ipercent level for full-timers. One obvious explanation for this
is regression to the mean, as those with unusually high or low
hours in CPS revert to more typical values. However, note that
our procedure of limiting the sample to those with similar usual
hours in CPS and ATUS should help limit this problem. By way
of comparison, performing a similar procedure with CPS data
by comparing actual hours 3 months apart for those whose
usual hours have changed less than 5 hours shows no increase in hours for part-timers or decrease for full-timers.
Implications. Our results indicate that, for ATUS respondents,
estimates of actual hours worked from the CPS are very close
to time-diary estimates for the CPS reference week. On the
other hand, it also appears that the CPS reference week is not
representative of the month as a whole, as there is a significant
difference in hours between reference and nonreference
weeks. The fact that CPS reference weeks avoid holidays simplifies the task of tracking employment and hours trends using
CPS data. However, a measure of monthly hours worked constructed from CPS average weekly hours data would overstate
actual hours worked during the month.
The fact that hours for some groups (such as women
and college graduates) are significantly overreported has
implications for measuring differences in hourly wages between groups. Typically, studies that examine betweengroup differentials use usual hours worked in the denominator of their hourly earnings measure. 25 To illustrate the
effect of overreporting by college graduates, if actual hours
from the time diary (under definition 2) are used instead of
usual hours from the CPS, the college-high school hourly
earnings ratio would be 4.1 percent higher. Performing a
similar experiment, the female-male hourly earnings ratio
would increase by 5.4 percent. It is worth noting that, unless reporting patterns have changed over time, this differential overreporting should have a relatively small impact
on trends.
Various important economic indicators, including the BLS
average hourly earnings series and productivity measures, use
data on work hours from the BLS CES program. The CES collects data from establishments for the pay period that includes
the 12th of the month; unlike the CPS, this period is longer than
the week including the 12th. In addition, the CES measures
hours paid rather than hours worked. Thus, the CES hours
paid will be much more representative of hours paid over the
entire month than the CPS is of hours worked over the entire
month-both because the CES covers a longer period and because much of the time off for holidays is paid. 26
OUR COMPARISON OF HOURS WORKED IN ATUS AND CPS indicates
that the CPS measure of actual hours is, on average, fairly close
to all three of the time-diary definitions of hours worked when
the diary day is in the CPS reference week (the week that includes the 12th of the month). However, for the other 3 weeks
of each month, the CPS measure of actual hours is approximately 5 percent higher than the hours collected in the ATUS.
There is variation in this correspondence between groups: for
women and college graduates, reported hours of work are
higher in CPS than in ATUS. Analysts should also keep in mind
that judging by ATUS, workers work longer hours on CPS reference weeks than other weeks.
Because we have only 1 full year of data, we are unable to
report on trends in the reporting of hours worked. In the future, as ATUS data accumulate over several years, we will determine to what extent there are changes in hours reporting in
CPS causing them to diverge from ATUS reports.
D
Notes
1
For a discussion of the importance of hours data for measuring real hourly
wages, see Katharine G. Abraham, James R. Spletzer, and Jay C . Stewart,
"Divergent Trends in Alternative Wage Series," in John Haltiwanger, Marilyn
E. Manser, and Robert Topel, eds., Labor Statistics MeasuremenJ Issues, NBER
Studies in Income and Wealth, Vol. 60, (Chicago, University of Chicago Press,
1998) pp. 293-324; Katharine G. Abraham, James R. Spletzer, and Jay C.
Stewart, "Why Do Different Wage Series Tell Different Stories?" American
Economic Review Papers and Proceedings, Vol. 89, No. 2, 1999, pp. 34-39;
and Lucy P. Eldridge, Marilyn E. Manser, and Phyllis Aohr Otto, "Hours Data
and Their Impact on Measures of Productivity Change." Paper presented to
the NBER Productivity Program meeting, Boston, March 2004.
2
For example, the ATVS has a higher multiple jobholding rate than
does CPS, which would tend to result in ATVS hours exceeding CPS hours.
8
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December 2004
3
Harley Frazis and Jay Stewart, "Where Does the Time Go? Concepts and Measurement in the American Time-Use Survey," in Ernst
Berndt and Charles Hulten, eds., Hard to Measure Goods and Services:
Essays in Memory of Zvi Griliches, NBER Studies in Income and Wealth
(Chicago, University of Chicago Press, forthcoming).
4
Sylvia Fisher, Karen Goldenberg, Eileen O'Brian, Clyde Tucker,
and Diane Willimack, "Measuring Employee Hours in Government Surveys." Paper presented to the Federal Economic Statistics Advisory
Council, Washington, DC, June 2001; and Karen L. Goldenberg and Jay
Stewart, "Earnings Concepts and Data Availability for the Current Employment Statistics Survey: Findings from Cognitive Interviews," in
Proceedings of the Section on Survey Research Methods, American
Statistical Association, 1999.
5
Wesley Mellow and Hal Sider, " Accuracy of Response in Labor
Market Surveys: Evidence and Implications," Journal of Labor Economics, Vol. 1, No. 4, 1983, pp. 331-44.
6
Willard L. Rodgers, Charles Brown, and Greg J. Duncan, "Errors in
Survey Reports of Earnings, Hours Worked , and Hourly Wages ," Journal of the American Statistical Association, December 1993, pp. 1208l 8.
7
John Robinson and Ann Bostrom, "The overestimated workweek?
what time diary measures suggest," Monthly Labor Review, January 1994,
pp . 11-23.
8
Jerry A . Jacobs, "Measuring time at work: are self-reports accurate?" Monthly Labor Review, December 1998, pp. 42-53.
9
The dataset that Jacobs used, the 1992 National Survey of the
Changing Workforce, collected information on when respondents typically left for and returned from work, typical commute times, and
number of days worked per week.
10
The verbatim responses are coded by coders, while the numerical
codes are translated into activity codes during processing. See a forthcoming issue of the Monthly Labor Review for a description of coding
procedures .
11
Frazis and Stewart, " Where does the time go?"; Michael Horrigan
and Diane Herz, " Planning, designing, and executing the BLS American
Time-Use Survey," Monthly Labor Review, October 2004, pp. 3-19.
12
ATUS distinguishes between " At Work" and " With Job But Absent
From Work" for the employed, and between " Looking" and "On Lay off' for the unemployed. It does not distinguish between different
reasons for being not in the labor force.
13
If the respondent reported that the first and last activities of the
day are paid work (the first activity begins at 4 a.m. and the last activity
ends 4 a.m. the following day) or if the respondent reported more than
4 hours of nonwork activities between the start and stop times, then we
assume that the respondent is doing shift work and calculate hours
worked using Definition 3 instead.
14
John P. Robinson and Geoffrey Godbey, Time for Life: The Surprising Ways Americans Use Their Time , 2nd edition (State College, PA,
Pennsylvania State University Press, 1997).
15
Daniel Hamermesh, " Shirking or Productive Schmoozing: Wages
and the Allocation of Time at Work," Industrial and Labor Relations
Review, Vol. 43 , No. 3, 1990, pp. 121S-133S.
16
An episode is considered to be a break if it is less than 15 minutes
in duration, occurs at the respondent ' s workplace, and the episodes
immediately preceding and immediately following the break are coded
as paid work. The two episodes of work that surround the break must
also pertain to the same job (either main job or other jobs).
2003 (inclusive) . To replicate the effect of the 3-month interval
between the final CPS interview and the ATUS interview, we examined a
sample of individuals who reported hours in CPS interviews 3 months
apart. This is possible because households are in the CPS for 4 consecutive months (Months-in-Sample 1 through 4), then out for 8 months,
then in for another 4 months (Months-in-Sample 5 through 8) , so we
can match responses from Month-in-Sample (MIS) 4 to MIS I and
responses from MIS 8 to MIS 5. We found that, in this matched sample,
both usual and actual hours worked declined by about half an hour.
However, we need to account for rotation group effects, the wellknown phenomenon that responses to certain questions vary systematically with the length of time that the respondent has been in the
survey. (For example, the unemployment rate is higher for respondents in their first month of the CPS than those in their second and
subsequent months. See Barbara A. Bailar, ·'The Effects of Rotation
Group Bias on Estimates from Panel Surveys," Journal of the American Statistical Association, Vol. 70, No. 349, 1975, pp. 23-30.) We
estimate that the average difference in both usual and actual hours
between MIS I and MIS 4, and between MIS 5 and MIS 8, within the same
survey month is about half an hour-virtually identical to the observed decline in the matched sample. Thus, after adjusting for rotation group effects, the expected change in hours between CPS and ATUS
is essentially zero.
22
One partial explanation for the change in usual hours is that CPS
accepts proxy responses (responses from someone else in the house hold) , whereas ATUS is strictly self-response. Persons whose CPS response was by proxy show a slightly greater increase in reported usual
hours worked than do CPS self-responders.
23
This restriction also helps control for differences between CPS and
ATUS in the reporting of other variables that might affect estimates of
hours worked. For example, this restriction controls for the difference
in multiple jobholding rates that was noted in footnote 3.
This restriction to respondents whose usual hours changed by fewer
than 5 hours resulted in a 38-percent drop in the sample from 10,036
observations to 6,268. This drop was larger than expected, which led to
further investigation. Several patterns emerged, although the decline
was large for all groups. The sample restriction was more likely to
exclude men and individuals whose MIS 8 CPS response was given by a
proxy. Compared with individuals whose usual hours were in the 35-44
hour range in the MIS 8 CPS interview, part-time workers and those who
usually worked 45 or more hours were more likely to be excluded. The
greater the length of time between the final CPS interview and the ATUS
interview the more likely it was that the observation was excluded.
Finally, people who reported having more than one job in ATUS, but not
in CPS, were more likely to be excluded.
24
Robinson and Bostrom, "The Overestimated Workweek ?"
25
Seasonality is not an issue because both hours measures cover an
entire year. The ATUS data cover calendar year 2003 , while the CPS data
approximately cover October 2002 through September 2003.
See three recent studies: Steve G. Allen, "Technology and Wage
Structure," Journal of Labor Economics, Vol. 19, No. 2, 2001, pp. 44083; John W. Budd and In -Gang Na, "The Union Membership Wage
Premium for Employees Covered by Collective Bargaining Agreements,"
Journal of Labor Economics , Vol. 18, No. 4, 2000, pp. 783- 807; and
Harley Frazis and Jay Stewart, "Tracking the Returns to Education in
the Nineties: Bridging the Gap Between the New and Old CPS Education
Items, " Journal of Human Resources, Vol. 34, No. 3, pp. 629- 41.
21
To determine the expected change in usual hours between the
final CPS interview and the ATUS interview, we examined both usual and
actual hours in the CPS data between October 2002 and December
26
In constructing hours measures for productivity estimates, BLS
uses , where possible, information on the ratio of hours worked to hours
paid from other BLS surveys to adjust CES hours paid data.
17
Jacobs , " Measuring Time at Work."
18
Abraham, Spletzer, and Stewart, " Divergent Trends in Alternative
Wage Series."
19
Robinson and Bostrom , "The Overestimated Workweek?"
20
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Monthly Labor Review
December
2004
9
.
Recordk eeping Requ1rements
' t~l6il
,:.~iill
Occupational injury and illness:
new recordkeeping requirements
Changes to OSHA recordkeeping rules in 2002
resulted in new BLS data; comparing the old
and new data series is challenging
William J. Wiatrowski
William J. Wiatrowski is
an economist In the
Office of Safety,
Health, and Working
Conditions, Bureau of
Labor Statistics. Email:
wiati0\,.,~•ki.william@bls.gov
E
2002, the Occupational Safety and Health
Administration (OSHA) implemented a number
f changes in the definitions of injury and illness
cases recorded by employers. The new definitions
in tum resulted in changes in occupational injury
and illness statistics provided by the Bureau of
Labor Statistics (BLS). As an example, in one
change, the old definition considered the application of a butterfly bandage to be medical treatment
and a recordable case; the new definition considers
such treatment to be first aid and not recordable.
Using the new definitions, the BLS reported that
there were 4.7 million nonfatal injuries and illnesses
in private-industry workplaces in 2002, resulting in
a rate of 5.3 cases per 100 equivalent full-time
workers. 1 While these data follow the trend of
declining cases and rates seen throughout the past
decade, because of the change in definition they
cannot be compared with data from prior years.
When the first data from 2002 were released in
late 2003 , the BLS cautioned readers of the differences between the 2002 data and data from previous
years and discouraged year-to-year comparisons.
Becaus~ employers were following the new rules
when recording cases throughout 2002, there was
no way that two sets of data (one maintained under
the old rules, the other under the new rules) could
be captured. Nonetheless, data users are interested
in the relationship of 2002 data to data from past
years. For example, among the questions they might
want answered are, Did the 10-year trend of reduced
injuries and illnesses continue in 2002? and What
effect did the change in recordkeeping rules have
on the data?
This article provides background on the BLS
survey and the change in the recordkeeping rule.
Both 2002 data and data from earlier years are
examined to determine what patterns might be
uncovered. While it will never be possible to
identify the rate of change in injuries and illnesses
l 0 Monthly Labor Review
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December 2004
from 2001 to 2002, it may be possible to identify
some patterns between the old and new data. These
patterns may provide insight into how the change
in recordkeeping affected estimates of occupational
injuries and illnesses. With only 1 year of data under
the new recordkeeping requirements , compared
with 30 years under the old system, this analysis
should be thought of as an initial attempt to identify
patterns and trends. As more years of data collected
under the new rules become available, patterns and
trends are likely to become clearer.
Background
For more than 30 years, the BLS has been
reporting on the number and rate of workplace
injuries and illnesses, an activity that was mandated with the passage, in 1970, of the Occupational Safety and Health Act, according to
which
the Secretary [of Labor] shall compile
accurate statistics on work injuries and
illnesses which shall include all disabling,
serious , or significant injuries and illnesses, whether or not involving loss of
time from work, other than minor injuries
requiring only first aid treatment and which
do not involve medical treatment, loss of
consciousnes s, restriction of work or motion, or transfer to another job. 2
injury and illness data are collected strictly for
statistical reporting purposes and undergo the
confidentiality and data security screening that
apply to all of the Agency's programs. These data
collection and reporting activities are independent
of the regulatory and inspection activities of OSHA.
The two agencies and their activities are linked in
many ways, however, including the definitions they
BLS
use to identify injury and illness "cases"-that is, what counts
as an occupational injury or illness.
Employers covered under the Occupational Safety and Health
Act are required to maintain records of injuries and illnesses that
meet OSHA definitions. 1 his requirement is known as the "recordkeeping rule." Certain employers are required to maintain a
recordkeeping log of injury and illness cases and, upon request,
must make that log available to OSHA inspectors and supply the
data contained in the log to the BLS. Other employers must
maintain such a log only whe_n they are selected to be part of
the BLS survey. In either case, the data the BLS gathers meet the
most recent definitions as specified in the OSHA recordkeeping
rule. When the rule changes, BLS data change. 3
The following introductory paragraph from the Federal
Register notice regarding the change in recordkeeping
provides the rationale for the change:
The Occupational Safety and Health Administration
is revising its rule addressing the recording and
reporting of occupational injuries and illnesses (29 CFR,
parts 1904 and 1952), including the forms employers use
to record those injuries and illnesses. The revisions to the
final rule will produce more useful injury and illness
records, collect better information about the incidence of
occupational injuries and illnesses on a national basis,
promote improved employee awareness and involvement
in the recording and reporting of job related injuries and
illnesses, simplify the injury and illness recordkeeping
system for employers, and permit increased use of
computers and telecommunications technology for OSHA
recordkeeping purposes. 4
(OSHA)
The 2002 recordkeeping rule included many changes. For
example, under the old rule, recurrences of injuries or illnesses
after a 30-day period were recorded as separate cases. Under the
new rule, a time frame is no longer specified. Accordingly,
employers may now consider recurrences that are not brought
on by a new event or exposure in the workplace to be the same
case. In another example, the old rule considered the application
of a butterfly bandage to be medical treatment and a recordable
case; by contrast, the new rule considers such treatment to be
first aid and not recordable. Intuitively, these two changes are
likely to result in a decline in the number of recordable cases, but
that is not the case for all the recordkeeping changes. For
example, under the old rules, needle sticks were recorded only if
they resulted in medical treatment; now needle sticks are
recorded if there is the potential to be contaminated with another
person's blood, regardless of whether the affected person is or
is not treated.
In its annual reports on occupational injuries and illnesses,
the BLS has monitored the trend in rnjury and illness counts and
rates. Both the actual number of cases and the rate of occupa-
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tional injuries and illnesses generally have been declining over
the past decade. 5 (See chart 1 and table 1.) The wide variety of
changes to the recordkeeping rule made it impossible for the
BLS to compare the 2002 data with data from previous years.
Survey of Occupational Injuries and
Illnesses
Participation in the BLS Survey of Occupational Injuries and
Illnesses is mandatory; indeed, the survey is the only Federally
mandated one conducted by the BLS. 6 The survey covers
private-sector employers, regardless of the number of workers,
with a few exceptions.7 Data also are available for State and local
government workers in a number of States. Each year, the BLS
selects a sample of employers covered under OSHA regulations,
including those which must maintain a log of workplace injuries
and illnesses under the OSHA rules and those which do not have
such requirements, typically because of their small employment.
At the end of the year prior to which data are to be recorded, all
sampled establishments are notified of their selection for the
survey and are provided instructions for maintaining injury and
illness records. A year later, these establishments are contacted
again and are asked to provide the BLS with data from the
records they maintained over the past year. Among the data to
be provided are information on employment and hours, a
summary of the number of recordable cases, and detailed
characteristics of cases that involve days away from work.
The BLS publishes two sets of national and State data based
on information provided by employers. 8 The first release of data
contains summary estimates of the number and rate of injuries
and illnesses by industry, with some details provided on the
type of case, such as that resulting in a job transfer or restricted
work activity. The second release contains details on the
demographics of the injured or ill worker and the circumstances
surrounding the case. This detailed information is available only
for those cases which involve days away from work-one of
the types of cases recorded by employers.
Number of occupational injuries and illnesses,
private industry, 1992-2001
[In millions]
Year
1992 ....... ... .. ........ ... ......
1993 ....... ... ................. ..
1994 ... .. .. ... ..... ............ ..
1995 .... ... ........ ..... ....... ..
1996 ······· ······ ······· ·········
1997 ··· ······· ·· ·· ·· ···· ··· ······
1998 ... .... ... .. .. .. ..... ........
1999 ···· ····· ··· ··· ····· ··· ······
2000 .. ....... .......... .... .. .. ..
2001 ..... ............. .... .. .... .
Number of
occupational
injuries and
illnesses
Number of
occupational
Injuries
6.7994
6.7374
6.7669
6.5754
6.2389
6.1456
5.9228
5.7072
5.6501
5.2156
Monthly Labor Review
6.3420
6.2553
6.2522
6.0806
5.7999
5.7158
5.5309
5.3350
5.2876
4.8818
Injuries as a
percent of
total
93.3
92.8
92.4
92.5
93.0
93.0
93.4
93.5
93.6
93.6
December 2004
11
Recordkeepi ng Requirements
lncidence1 of occupation al injuries and illnesses, private industry, 1976-2001
Rate
Rate
10
10
9
9
8
8
7
7
6
6
5
5
4
4
3
3
2
2
0
1976
1
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
0
. .
.
. . .
.
.
The incidence 1s the number of in Junes
and illnesses per 100 full-time workers.
The survey began in I 971 and has produced annual data
since 1972, with a major revision in 1992. That revision
resulted in the inauguration of a separate program to track
workplace fatalities: the Census of Fatal Occupationa l
Injuries. 9 The revision also introduced the current survey
output of detailed characteristic s of cases involving days
away from work. Prior to that time, there was no comprehensive nationwide study of the details of injury and
illness cases. Instead, a number of special studies were
conducted that explored certain industries or certain types of
injuries. 10
The two Federal agencies
The BLS and the OSHA play very different roles with regard to
worker safety, as indicated in the mission statement of each
agency:
The Bureau of Labor Statistics (BLS) is the principal
fact-finding agency for the Federal Government in the
broad field of labor economics and statistics ... BLS data
must satisfy a number of criteria, including relevance to
current social and economic issues, timeliness in reflecting
today's rapidly changing economic conditions, accuracy
and consistently high statistical quality, and impartiality in
12
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December 2004
both subject matter and presentation. 11
OSHA 's mission is to assure the safety and health of
America's workers by setting and enforcing standards;
providing training, outreach, and education; establishing
partnerships; and encouraging continual improvement in
workplace safety and health. 12
The BLS is a nonpartisan statistical organization that provides
data on a wide range of labor-related issues, including occupational safety and health. The agency does not have any
regulatory or enforcement functions.
OSHA uses BLS data in setting standards and identifying areas
of emphasis for inspection. The rate of injuries and illnesses in a
specific industry, as published by the BLS, is used as a standard
for targeting reductions in workplace injuries and as a benchmark
for individual employers. For example, OSHA has as one of its
goals to "reduce the rate of lost workday injuries and illnesses
by at least 5 percent annually." 13 Whether this goal has been met
is determined with the use of BLS data. In addition, OSHA has
established a number of cooperative programs to work with
businesses and other organizations. Among these programs are
the OSHA Voluntary Protection Programs, which use BLS data as
a benchmark that participating employers must meet to be eligible
for certain safe-worksite designations. 14
BLS
data
While the BLS has captured and reported on occupational injuries
and illnesses since the early years of the 20th century, there were
few standards in place regarding the reporting of occu-pational
injury and illness data prior to the Occupational Safety and Health
Act of 1970. The current BLS data series began soon after the Act
was passed. Early revisions to the program reflected changes in
industrial classifications and OSHA recordkeeping rules. The BLS
survey was completely redesigned in 1992, the result of a detailed
analysis of the existing program by the National Academy of
Sciences. 15 The redesign resulted in the separate collection of
fatalities 16 and the collection of detailed case characteristics.
Despite these changes, the BLS has been able to produce a
largely consistent data series showing the number of cases and
the rate of occupational injuries and illnesses. That series ended
with the 2001 data, although the rate of 5.3 injuries and
illnesses per 100 full-time workers in 2002 is consistent with
the trend seen in previous years.
But the inability to track total cases and incidence rates
before and after the recordkeeping change does not mean
that certain patterns in the injury and illness data cannot be
explored. Patterns involving the types of cases or events
leading to injury, among other characteristics, may provide
some indication of the effect the revised recordkeeping rules
had on employer reporting. For example, about 6 percent of
reported occupational injury and illness cases in 2002 were
illnesses, nearly identical to the proportion reported over the
previ0us several years.
Injury and illness cases are divided into two broad categories:
cases with days away from work, with a job transfer, or with a job
restriction; and other recordable cases. Prior to 2002, cases were
identified as either lost-workday cases or cases without lost
workdays. Despite the change in case classification and
definition, the division of cases between the two broad categories is generally consistent from 2000 to 2002, with about half
of the cases falling into each of the categories. (In both 2000 and
2001 , 49 percent of all cases were lost-workday cases, while in
2002, 53 percent of all cases were cases with days ·away from
work, with a job transfer, or with a job restriction.)
In the past, data were recorded in such a way that information
by type of case could be produced for injuries and illnesses
combined or for each of those categories separately. The 2002
recordkeeping change eliminates the ability to produce separate
case-type data either just for injuries or just for illnesses.
Industry data
Among most major industry groups, the number of cases
involving days away from work exceeds the number involving a
job transfer or job restriction, with the notable exception of
ma11ufa8turing. In manufacturing in 2002, about 25 percent of
cases involve days away from work, while 32 percent involve
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a job transfer or job restriction. (The remaining cases generally
involve medical treatment, but do not result in any time off,
restricted duty, or transfer.) This difference specific to
manufacturing continues a trend seen for the past several
years, even before the change in recordkeeping rules. (See
table 2.)
In 2002, there were six industries 17 that recorded I 00,000 or
more cases of occupational injuries. This figure compares with
nine such industries in 2000 and eight in 2001. (See table 3.)
The lists of industries in each of the 3 years are similar. Indeed,
the six industries with the greatest number of injuries were the
same for the last 3 years, although not in the same order.
Hospitals became the industry with the greatest number of
injuries in 2002, surpassing eating and drinking places, which
had been the industry with the highest count nearly every
year since the BLS began presenting data in this way in the
late 1980s. Among the six industries listed, there were variations in the numbers of cases between 2001 and 2002 that
could be the result of recordkeeping changes . For example,
hospitals may report more cases due to changes in reporting
requirements related to needle sticks. Of course, the many
recordkeeping changes may have affected specific industries
in a variety of, and perhaps offsetting, ways.
As noted, illnesses as a proportion of total cases remained
constant from 2001 to 2002, but the proportion in manufacturing
dropped from 54 percent of all illness cases in 2001 to 44 percent
in 2002. This change may be due to the recordkeeping changes
that altered the types of illnesses reported. Prior to these
changes, there were six specific types of illnesses, plus a category
for "all other illnesses." In 2002 and beyond, there are three
categories, plus "all other illnesses." 18 (See chart 2.) With the
elimination of a separate category for disorders associated with
repeated trauma, the proportion of cases recorded as "all other
illnesses" became the predominant type of illness. 19
Shifts in employment and in hours worked in certain industries
may influence the data on occupational injuries and illnesses.
For example, hospitals had the greatest number of cases in 2002,
surpassing eating and drinking places for the first time. The injury
and illness rate in hospitals also was higher in 2002 than in 2001 ,
but did not increase as much as the number of cases. This reversal
suggests that the increase in injuries and illnesses in hospitals
was not strictly a function of changes in employment. The
opposite may be true with the change in the proportion of
illnesses in manufacturing: manufacturing employment and
hours worked declined between 2001 and 2002, which may have
affected the proportion of illness cases in the industry.
Cases involving days away from work
Detailed case and demographic data are available only for those
cases involving days away from work. Once again, the definition
of a case differed from 2001 to 2002, as did the method used to
Monthly Labor Review
December 2004
13
Recordke eping Requirem ents
Incidence 1of occupational injuries and
illnesses by industry and type of case, private
industry, 2000-02
Industry and
type of case
2000
2001
2002
Total .............. ................ ............ .........
Cases with days away from work 2
Cases with restriction 3 •••. .. ... . . . ........
6.1
1.8
1.2
5.7
1.7
1.1
5.3
1.6
1.2
Agriculture, forestry, and fishing
Total ... .. ...... ... ........ .... ... ..... .............. .. .
Cases with days away from work 2 • •
Cases with restriction 3 ••. •.. •. .... ••.•. . . .
7.1
2.5
1.1
7.3
2.7
.9
6.4
2.1
1.2
4.7
2.4
.6
4.0
1.8
.6
4.0
2.0
.7
8.3
3.2
.9
7.9
3.0
.9
7.1
2.8
1.1
Total ... ................... ............... ..... .........
Cases with days away from work 2 .•
G;:is,=,s with restriction 3 . .. •.••.•.•.• • •• . ...
9.0
2.0
2.5
8.1
1.8
2.2
7.2
1.7
2.3
Transportation and public utilities
Total ............................................. ... .. .
Cases with days away from work 2 ••
Cases with restriction 3 ••.•.... . •.......•..
6.9
3.1
1.1
6.9
3.0
1.3
6.1
2.7
1.3
5.9
5.6
5.3
1.7
1.0
1.6
1.0
1.6
1.1
1.9
.6
.2
1.8
.6
.2
1.7
.5
.2
4.9
1.4
.9
4.6
1.3
.8
4.6
1.3
.9
Mining
Total ...................... .. ... ... ............ .........
Cases with days away from work 2 ••
Cases with restriction 3 ... . . • . .....•.••... .
Construction
Total ................. ....... ...........................
Cases with days away from work 1 • •
Cases with restriction 2 • • ••• ••••••.••••••••
Manufactur ing
Wholesale and retail trade
Total .... .............. .................. ...............
Cases with days away
from work 2 ••••• • ••••••• •• • ••.••• ••••••••••• ••.•
Cases with restriction 3 •... •.•.... .. •. .. ...
Finance, insurance, and real estate
Total .. .... ........ .....................................
Cases with days away from work 2 ••
Cases with restriction 3 . •............ .. . .. .
Services
Total ... ............... ............. ... .................
Cases with days away from work 2 ••
Cases with restriction 3 •••... .•. . •. . •• . •..•
1
The incidence of Injuries and illnesses represents the number of injuries
and illnesses per 100 full-time workers and is calculated by multiplying the
number of injuries and illnesses by the total hours worked by all employees
during the calendar year. The result of this calculation is then divided by
200,000 (100 workers , times 40 hours per week , times 50 weeks per year)
to determine the incidence.
2
In 2000 and 2001 , includes cases involving days away from work with
or without restricted work activity. In 2002 , includes cases involving days
away from work with or without job transfer or restriction .
3
In 2000 and 2001 , defined as cases with restricted work activity. In
2002, defined as cases with job transfer or restriction .
count the number of days away from work. Prior to 2002, days
were counted as workdays away from work. In 2002 and
subsequent years, the count is calendar days away from work.
For those cases with days away from work, demographic
characteristics that are captured by the survey include sex, age,
occupation, and other items. Ch~1 racteristics of the injury or
illness case include the nature of the injury or illness, the part of
the body involved, the event that led to the injury or illness, and
the source of the event. For example, an injury case with days
14
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Decembe r 2004
away from work involving a nurse who sprained her back while
lifting a patient would have the following characteristics:
•
•
•
•
Nature of disabling condition : sprain
Part of body affected: back
Event or exposure: lifting
Source directly producing disability: patient
In addition, these characteristics can be used to construct a
count of musculoskeletal disorders, which are defined as injuries
or disorders of the muscles, nerves, tendons, joints, cartilage, or
spinal discs. Musculoskeletal disorders are determined by the
nature of the condition and the event or exposure leading to that
condition. 20
Both the rate and the number of injury and illness cases
involving days away from work under the previous recordkeeping
definition have been declining steadily since the data were first
collected in 1992. (See charts 3 and 4.) The data for 2002-1. 4
million injury and illness cases involving at least 1 day away from
work and a rate of 1.6 cases per 100 equivalent full-time workers are consistent with the declining numbers over the previous
decade. Moreover, the distribution of these cases by sex follows
the same pattern as in the past: in 2002, 65 percent of cases
involving at least 1 day away from work affected men, a number
nearly identical to that for the previous 2 years. Furthermore, as
in the past, men had a greater proportion of injuries and illnesses
than their proportion of hours worked. The distribution of cases
by age also was consistent between 2002 and prior years, with
about three-quarters of the cases occurring among those aged
25- 54 years. (See table 4.)
The occupatio n with the greatest number of injuries and
illnesses involving days away from work in 2002 was truckdrivers,
as it has been since 1993.21 As table 5 indicates, many of the
occupations with the highest number of cases were the same in
2002 as they were in the 2 previous years, although there were
changes in the order. Two occupations are among the list of the
10 occupations with the greatest numbers of injuries and illnesses
for the first time in 2002: supervisors of sales workers and other
sales workers (those not in a specific sales occupation, such as
auto sales or apparel sales). The greater prevalence of injuries
and illnesses among these sales occupations may be due to the
recordkeeping change. For example, as of 2002, incidents that
occur on work property before or after work, such as assaults or
falls in a parking lot, are recordable cases.22 Conversely, two
occupations previously among the top 10, but which fell just
below that threshold in 2002, are cashiers and stock handlers.
Workers in these occupatio ns often suffer repetitive -motion
injuries. The change in the recordkee ping requireme nt that
eliminates the repeat recording of cases that recur after 30 days
may have led to a decline in cases in these occupations.
The characteristics of injuries and illnesses incurred in 2002
were nearly identical to those from 2001. The most prevalent kind
of injury was a sprain or strain, affecting 43 percent of all cases.
■ r• 1 •"'----
Number of cases of nonfatal occupational
injuries for industries with 100,000 or more
cases, 2000-02
2002
Industry
2000
2001
Hospitals .. .......... .........
Eating and drinking
places .. .... ................. .
Nursing and personal
care facilities .............
Grocery stores ............
Department stores .... ..
Trucking and courier
services, except c1.ir ..
Air tram,portation ,
scheduled .. ... .... .. ...... .
Motor vehicles and
equipment .......... .. ... ...
Hotels and motels .......
259.5
265.7
296.1
285.3
283.7
247.5
199.0
180.1
150.7
192.9
175.1
143.3
180.4
154.5
138.9
129.1
134.9
104.0
127.2
116.3
124.6
101.0
102.7
NOTE : Industries are based on three-digit Standard Industrial
Classification codes and are in order by the number of cases in 2002 . Dash
indicates industry did not have 100,000 or more cases in year shown.
prevalent involving floors, walkways, and ground surfaces;
containers; and worker motion or position. Finally, the two events
that were cited most often as leading to injury or illness were
contacts with objects and equipment (such as being struck
by an object) and overexertion (often due to lifting).
The number of assaults and violent acts, and their percentage
of all events, was slightly greater in 2002 than in 2001, a result
that may be due to the recording of events which occur prior to
and after work on employer property, such as incidents in parking
lots. (Looking beyond work-related incidents, overall rates of
violent crime dropped from 2001 to 2002, as did robbery and
assault rates. 23 ) By contrast, repetitive-motion events and their
proportion of all events were down slightly, due perhaps to the
lack of a specific category to capture disorders associated with
repeated trauma or to the change in rules for recording repeated
occurrences of an injury or illness. Musculoskeletal disorders
continue to account for about 1 in 3 injury and illness cases
involving days away from work, as they have consistently over
the past decade. (See chart 5.)
Median days
Body parts affected most frequently were the trunk (specifically,
the back), followed by both the upper and lower extremities.
Sources of injuries and illnesses were widespread, with the most
One of the changes in the OSHA recordkeepin g requirements
was the way in which employers were to count the number of
Percent distribution of occupational illnesses by type, private industry, 2001--02
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l
Poisoning
Respiratory
2001
·soning
/
Respiratory
2002
Monthly Labor Review
December 2004
15
Recordkeeplng Requirements
Number of occupational injuries and illnesses by selected types of cases, private industry,
1992-2001
Millions
Millions
12 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~ 12
11
■ Cases of days away from work ■ Total cases
11
10
10
9
9
8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
0
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
0
lncidence1 of occupational injuries and illnesses by selected types of cases, private
industry, 1992-200 l
Rate
Rate
14
14
■ Cases of days away from work ■ Total cases
12
12
10
10
8
8
6
6
4
4
2
2
0
1992
1
16
1993
1994
1995
1996
1997
1998
The incidence is the number of injuries and illnesses per 100 full-time workers.
Monthly Labor Review
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December 2004
1999
2000
2001
0
Percent distribution of occupational injuries
and illnesses involving days away from work,
by selected demographic characteristics, 2001
and 2002
Characteristic
2001
2002
Total .......... .. ..... ............ .
100.0
100.0
Men ......... ..... ... .. .... .. ......... ..... .... .
Women .......... .......... ....... .. .. ....... .
65.7
33.6
64.8
.1
2.9
11 .2
25.3
28.5
20.5
{' )
2.7
11 .1
25.0
27.9
21 .2
10.0
1.7
34.9
Age, years
14-15 .. ... .. .... ....... ... ....... ....... ..... .
16-19 .. .... .. .. ..... ... ........ ....... .. ......
20-24 ... ..... ...... .... .... ....... ... .........
25-34 ....... ..... .. .... .... .................. .
35-44 ··· ·· ·············· ···· ········ ·· ····· ···
45-54 ······· ····· ········· ·········· ········ ··
55-64 ............... .. .......... ..............
65 and older ... .... ....... .. ... ...........
1
8.8
1.6
Less than 0.1 percent.
days away from work. Previously, the count pertained to
workdays. Beginning in 2002, the count applies to calendar
days, a change intended to "ensure that a measure of the length
of disability is available, regardless of the employee's work
schedule. •>2 4 This modification may have the effect of increasing
the median number of days away from work recorded by the
survey. For example, in the past, if an injury occurred on a
Wednesday and the employee did not return to work until the
following Tuesday, the employer would count 3 days away from
work (Thursday, Friday, and Monday, assuming a standard 5day workweek). Under the new guidelines, the employer would
count 5 days (Thursday through Monday). This change may
especially affect those individuals or occupations which do not
work a standard workweek. For example, those aged 14 or 15
years may work only a few days per week, perhaps after school
or on weekends. In 2000 and 2001, such workers who suffered an
injury or illness that required time off from work had a median of
2 days away from work. In 2002, that median was 7 days, perhaps
reflecting the count of calendar days between their times at work.
A closer look at occupations that are typically thought of as
having irregular work hours or a large proportion of part-time
workers shows that the change in recordkeeping rules regarding
how days are counted may have affected different occupations
in different ways. For example, waiters and waitresses who
incurred injuries or illnesses involving days away from work were
off the job for a median of 5 days in 2002, compared with 7 days
in 2001. Cashiers, also a job with a large share of part-time
workers, saw their median days away from work remain at 6 days
from 2001 to 2002. These two examples suggest that other
recordkeeping changes, aside from the method of counting days,
are influencing the results.
Overall, the median number of days away from work was 7 in
2002. Between 1995 and 2001 , the median was always 5 or 6 days.
Chart 6 shows how the percent distribution of days has changed,
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with the proportion at 31 days or more a few percentage points
higher than in the past. Again, this effect may be the result of the
change in recordkeeping rules.
The distribution of days away from work for truckdrivers and
registered nurses provides an example of how the data have
changed. The median number of days away from work for
truckdrivers rose from 10 in 2001 to 13 in 2002. For registered
nurses, the median rose from 4 days to 6 days, and those with a
median number of days away from work greater than 10 rose from
30 percent to nearly 40 percent of all cases.
For injuries and illnesses requiring time off from work, the
median number of days away from work increased between 2001
and 2002 for injuries and illnesses with a variety of characteristics.
For example, cases of carpal tunnel syndrome led to a median 25
days away from work in 2001 and 30 days in 2002. Similarly,
amputations led to a median 18 days away from work in 2001,
compared with 26 days in 2002. In contrast, certain prevalent
events leading to injuries or illnesses showed only slight
increases in median days away from work: overexertion in lifting
led to a median 8 days away from work in 2002, compared with 7
in 2001; and being struck by an object led to a median 5 days
away from work in 2002, compared with 4 in 2001. The I-day
increase in these more frequently occurring events reflects the
overall 1-day increase in the median for all cases with days away
from work. Finally, the data on musculoskeletal disorders show a
slight increase in the median number of days away from work,
from 8 days in 2001 to 9 in 2002.
To compare or not to compare
The BLS has stated that the change in occupational injury and
11•1• 11
=---
Occupations with the highest number of
injury and illness cases involving days away
from work, 2000-02
Number of cases (in thousands)
Occupation
Truckdrivers ........ .........
Nursing aides, orderlies,
and attendants ...... .. ..
Laborers, nonconstruction ... ... .........
Janitors and cleaners ...
Construction laborers ..
Assemblers .. ........ ... .....
Carpenters .. ......... ........
Supervisors and
proprietors, sales ......
Cooks .. ......... ........... ... .
Sales workers , other
commodities ..... .... .... ..
Cashiers .. ... .. ........... .. ..
Registered nurses ... ... .
Stock handlers and
baggers .. ..... ....... .. .... .
Nor E:
2002
2000
2001
136.1
129.1
112.2
74.2
71.0
79.0
87.0
40.7
45.4
38.9
38.3
68.9
38.6
31 .1
32.7
76.6
42.0
41.9
34.4
28.3
24.1
27.8
23.1
27.8
26.1
24.7
24.1
26.9
24.5
22.2
22.2
24.7
24.7
22.5
21 .9
23.8
25.7
21 .5
44.1
Occupations are in order by the number of cases in 2002 .
Monthly Labor Review
December 2004
17
Recordkee ping ReqJiremen ts
Number of occupati onal injuries and illnesses involving days away from work and those
resulting in musculoskeletal disorders, private industry, 1992-200 l
Millions
3.5 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - --
Millions
- - - , 3.5
■ Musculosk eletal disorders ■ Days away from work
3.0
2.5
2.0
1.5
0.0
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Percent distribution of days away from work, private industry, 2001 and 2002
Year
2002
2001
0
18
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20
40
60
Percent
December 2004
80
100
illness recordkeeping requirements in 2002 resulted in a
discontinuity in the data series and that comparisons with
previous years should not be made. Nonetheless, data users are
eager to track trends and to determine the effect of the recordket>pif'lg changes. This article was written to provide some
guidance for those users. Tracking trends will be difficult,
because determining the exact effect of the recordkeeping
changes is not possible. But with some careful analysis and
some caveats, data users may be able to identify patterns.
Specifically, users who are comparing the data for multiple years
should keep the following suggestions in mind:
• Consider proportions, as well as counts.
• Consider multiple perspectives on the same data, such
as industry and occupation.
• Consider specific recordkeeping changes and how they
might have affected the particular industry, occupation,
worker, or injury/illness.
• Consider the combination of recordkeeping changes;
some modifications may counteract others.
• Look for a _c ontinuation of long-run trends-patterns
that developed for several years prior to and through
2001.
• Look for future trends as additional years of data become
available.
The BLS Survey of Occupational Injuries and Illnesses will
continue to report annually on the number and rate of incidents
by type of case and industry, with detailed information on the
characteristics of the workers and the incidents for cases
involving days away from work. 25 As more years of data under
the new recordkeeping requirements are accumulated, effects of
the recordkeeping changes and trends may become more
D
apparent.
Notes
1
See " Workplace Injuries and Illnesses in 2002," U.S. Department
of Labor news release 03-913, Dec. 18, 2003. Injury and illness rates
represent the number of injuries and illnesses per 100 full-time workers
and are calculated by multiplying the number of injuries and illnesses
by the total hours worked by all employees during the calendar· year.
This result is then divided by 200,000 ( I 00 workers, times 40 hours
per week, time s 50 weeks per year) to determine the rate per 100
eq uivale nt full-time workers.
Occupational Safety and Health Act of 1970, Public Law 91-596,
sec tion 24.
2
3
While the law does not actually specify that BLS data conform to
the OSHA recordkeeping requirements, such a procedure allows for the
efficient collection of data that in many cases are already being main tained by employers. In addition, by keeping the definitions consistent
with OSHA requirements, the BLS guarantees that its data can be used by
OSHA to monitor employers' progress in improving occupational safety
and health.
4
Federal Register, Jan. 19, 2001, p. 5916.
Changes to the program prior to 2002, including a major revision
in 1992, affected the type and amount of data available, but did not
change the basic definition of recordable cases of injuries and illnesses.
Thus, data on the number and rate of occupational injuries and illnesses
are consistent from 1972 through 2001 .
5
6
The BLS produces measures of employment, unemµloyment ,
compensation , price change, and productivity, among other things .
Participation in some of these data collection efforts is mandatory in
certain States, while participation in the Survey of Occupational
Injuries and Illnesses is mandated by the Federal Occupational Safety
and Health Act of 1970.
7
The BLS Survey of Occupational Injuries and Illnesses is designed to
cover all private-industry employers, not just those required by the
Occupational Safety and Health Administration to maintain records.
Farms with fewer than 1 I workers are excluded. Data on railroads and
on mecal and nonmetal mining are not collected directly by the survey.
Rather, they are provided to the BLS by the Federal Railway
Administration and the Mine Safety and Health Administration,
respectivel y.
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8
The BLS occupational safety and health statistics programs are
conducted in cooperation with the States, which jointly fund the
programs . Those States participating in the survey-42 in 2002together with the District of Columbia and three U.S. territories, collect
sufficient data to produce State estimates . No State data on occupational injuries and illnesses are available for nonparticipating
States.
9
The Census of Fatal Occupational Injuries uses multiple source
documents to identify and verify work-related fatalities. Data published
annually include demographic details, as well as information on the
circumstances surrounding the fatality and on the occupation, industry,
and geographic location of the worker. (See "National Census of Fatal
Occupational Injuries in 2003," U.S. Department of Labor news release
04-1830, Sept. 22, 2004. Additional data on occupational fatalities
are available on the Internet at http://www.bls.gov/iif/home.htm,
visited Sept. 30, 2004.)
1
° For exa mple , earlier BLS studies known as Work Injury Reports
used data captured from injured workers to identify the circumstances
surrounding specific types of injuries, such as falls from ladders or
scaffolds. Another program, the Supplementary Data System, compiled
case and demographic data from workers' compensation reports in
about 30 States.
11
The
BLS
mission statement is taken from the
BLS
Internet site ,
http://www.bls.gov/bls/blsmissn.ht m , visited Aug . 19, 2004.
12
The
OSHA
mission statement is taken from the
OSHA
Internet site,
http://www.osha.gov, visited Aug. I 9, 2004.
13
See http://www.osha.gov/StratPlanP ublic/factsheet.pdf,
visited Aug. 19, 2004.
14
See http://www.osha.gov/dcsp/vpp/index.html, visited Aug. 19,
2004.
15
Counting Injuries and Illnesses in the Workplace: Proposals for a
Better System (Washington, DC, National Academy Press, 1987). (See
also Katharine G. Abraham, William L. Weber, and Martin E. Personick,
" Improvements in the BLS health and safety statistical system,"
Monthly Labor Review, April 1996, pp. 3- I 2.)
Monthly Labor Review
December 2004
19
Recordkeepi ng Requirements
16
The redesign ended the practice of reporting workplace fatalities
collected in the Survey of Occupational Injuries and Illnesses. Because
fatalities are rare events, collecting information on them through a
sample survey did not provide reliable data. In place of the previous
survey, the Census of Fatal Occupational Injuries was introduced to
capture workplace fatalities.
17
Industry data are based on the three-digit Standard Industrial
Classification code.
18
For 2004 and beyond, a fourth specific illness category-heari ng
loss-was added to the recordkeeping requirement.
19
Despite the elimination of the specific illness category for
disorders associated with repeated trauma, the BLS Survey of
Occupational Injuries and Illnesses continues to provide some amount
of data on similar conditions. For cases that involve days away from
work, the survey records repetitive-moti on injuries and illnesses, as
well as musculoskeletal disorders.
20
Work-related musculoskeletal disorders include cases in which the
nature of the injury or illness is sprains, strains, tears; back pain, hurt back;
soreness, pain, hurt, except the back; carpal tunnel syndrome; hernia; or
musculoskeletal system and connective tissue diseases and disorders when
the event or exposure leading to the injury or illness is bodily reaction/
bending. climbing, crawling, reaching, twisting; overexertion; or repetition.
Cases of Raynaud 's phenomenon, tarsal tunnel syndrome, and herniated
APPENDIX:
spinal discs are not included: although these cases may be considered
musculoskeletal disorders, the survey classifies them into categories that
also include cases that do not involve musculoskeletal disorders.
21
In 1992, the first year that detailed occupation data were collected,
nonconstruction laborers were the occupation with the greatest number
of injuries and illnesses involving days away from work, with truckdrivers
second. Since 1993, truckdrivers have had the greatest number of cases
involving days away from work each year.
22
Prior to 2002, incidents in parking lots and recreation facilities were
presumed not to be work related. Under the new rules, only motor
vehicle accidents in parking lots are presumed not to be work related.
23 Information
on overall crime statistics are from the U.S.
Department of Justice, Bureau of Justice Statistics. (See http://
www.ojp.usdo j.gov/bjs/glanc e/viort.htm, visited Oct. 14, 2004.
24
Federal Register, Jan. 19, 2001, p. 5969.
25
Beginning with data for 2003, the survey will use the North
American Industry Classification System to classify industries and the
Standard Occupational Classification System to classify occupations.
Prior to 2003, the survey used the Standard Industrial Classification
System and the Bureau of the Census Occupational Classification
System, respectively. This change will result in another break in series
among specific industries and occupations, but not for the overall
private-industr y data.
Recordkeeping under the OSHA regulations
Employer recordkeep ing requirements
The Occupational Safety and Health Act of 1970 requires the
Secretary of Labor to issue regulations requiring employers to
maintain accurate records of, and make periodic reports on, workrelated deaths, injuries, and illnesses. The Occupational Safety and
Health Administration (OSHA) maintains those regulations, known
as the employer recordkeeping requirements. Employers not exempt
from OSHA 's recordkeeping requirements must prepare and maintain
records of work-related injuries and illnesses as follows:
• Use the Log of Work-Related Injuries and Illnesses (Form 300)
to list injuries and illnesses and to track days away from work,
work or motion restrictions, and transfers to another job.
• Use the Injury and Illness Report (Form 301) to record
supplementar y information about recordable cases. A
workers' compensation or insurance form may be used if it
contains the same information.
• Use the Summary (Form 300A) to show totals for the year in
each category. The Summary is posted from February 1 to
April 30 of each year.
Recordkeeping is a critical part of an employer's safety and health
efforts for several reasons:
• Keeping track of work-related injuries and illnesses can help
prevent them in the future.
• Using injury and illness data helps identify problem areas. The
more the employer knows, the better the employer can identify
and correct hazardous workplace conditions.
• The employer can better administer company safety and health
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programs with accurate records.
• As employee awareness about injuries, illnesses, and hazards in
the workplace improves, workers are more likely to follow safe
work practices and report workplace hazards. OSHA compliance
officers can rely on the information thus reported to help them
properly identify and focus on particular types of injuries and
illnesses. The agency also asks about 80,000 establishments each
year to report information directly to OSHA, which uses the
information as part of its site-specific inspection-targeting
program. The Bureau of Labor Statistics also uses injury and
illness records as source data for its Annual Survey of
Occupational Injuries and Illnesses, which shows nationwide and
industrywide safety and health trends. 1
Changes to the recordkeep ing requirement
Among the changes in the OSHA recordkeeping rule are the following:
• changes in coverage
• changes in the OSHA forms
• changes in the recording criteria for determining the work
relationship
• the elimination of different recording criteria for injuries and
illnesses
Exhibit A-I offers a look at the old and new recordkeeping rules.
This listing of an employer ' s obligations under OSHA 's
recordkeeping rule is not comprehensive. (See 29 CFR Part 1904 and
other parts of that instruction for details pertaining to all
recordkeeping obligations.)
Changes in types of cases
December 2004
Exhibit A- 1. Changes to OSHA record keeping requirement from 2001 to 2002
New rule, 2002 and beyond
Old rule, through 2001
Forms (§ 1904.29)
200, Log and Summary; OSHA
101, Supplemental Record
OSHA
OSHA
OSHA
300, Log; OSHA 300 A: Summary;
301, Incident Report
Work related (§1904.5)
Any aggravation of a preexisting condition by a workplace event or exposure makes the case work related.
Significant aggravation of a preexisting condition by a
workplace event or exposure makes the case work related.
Exceptions to the presumption of a work relationship:
1. Member of the general public
2. Symptoms arising on premises and due totally to
outside factors
3. Parking lot/recreational facility
Exceptions to the presumption of a work relationship:
1. Member of the general public
2. Symptoms arising on premises and due totally to
outside factors
3. Voluntary participation in wellness program
4. Eating, drinking, and preparing one's own food
5. Personal tasks outside working hours
6. Personal grooming, self-medication, self-infliction
7. Motor vehicle accident in parking lot or access road
during commute
8. Cold or flu
9. Mental illness, unless the employee voluntarily
presents a medical opinion stating that he or she has
a mental illness that is work related.
New case(§ 1904.6)
New event or exposure; new case
30-day rule for cumulative trauma disorders
Aggravation of a case in which signs or symptoms have
not resolved is a continuation of the original case.
No such criteria
General recording criteria(§ 1904.7)
All work-related illnesses are recordable.
Restricted work activity occurs if the employee
1. Cannot work a full shift
2. Cannot perform all of his or her normal job duties,
defined as any duty the employee would be expected
to perform throughout the calendar year
Restricted work activity limited to the day of injury makes
the case recordable.
Day counts:
Count workdays
No cap on count
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Work-related illnesses are recordable if they meet the
general recording criteria.
Restricted work activity occurs if the employee
1. Cannot work a full shift
2. Cannot perform all of his or her routine job functions,
defined as any duty the employee regularly performs
at least once a week
Restricted work activity limited to the day of injury does
not make the case recordable.
Day counts:
Count calendar days
180-day cap on count
Monthly Labor Review
December 2004
21
Recordkeepi ng Requirements
Exhibit A- 1. Continue d-chang es to OSHA recordke
eping requirement from 2001 to
2002
Old rule, through 2001
New rule, 2002 and beyond
Medical treatment does not include
1. Visits to a medical doctor for observation only
2. Diagnostic procedures
3. First aid
First-aid list in Bluebook 1 is a list of examples and is not
comprehensive.
Two doses of a prescription medicine qualifies as medical
treatment.
Any dosage of over-the-counter medicine qualifies as
first aid
Two or more hot or cold treatments qualifies as
medical treatment.
Drilling a nail qualifies as medical treatment.
Applying a butterfly bandage or an adhesive skin closure
qualifies as medical treatment.
Recordable nonminor injuries:
1. Fractures
2. Second- and third-degree burns
Medical treatment does not include
1. Visits to a medical doctor for observation and
counseling only
2. Diagnostic procedures (including the administration
of prescription medication for diagnostic purposes)
3. First aid
First-aid list in the regulation is comprehensive. Any other
procedure is a medical treatment.
One dose of a prescription medicine qualifies as medical
treatment.
An over-the-counter medicine at prescription strength
qualifies as medical treatment.
Any number of hot or cold treatments qualifies as first aid.
Drilling a nail qualifies as first aid.
Applying a butterfly bandage or an adhesive skin closure
qualifies as first aid.
Recordable significant diagnosed injuries or illnesses:
1. Fractures
2. Punctured eardrums
3. Cancer
4. A chronic irreversible disease
Specific disorders
Hearing loss: Federal enforcement for a 25-dB shift in
hearing from original baseline
Needle sticks and sharps injuries are recorded only if the
case results in medical treatment, days away from work,
days of restricted work, or seroconversion.
All medical removal2 procedures that are under the
provisions of other OSHA standards are recordable.
A po"jtive skin test for tuberculosis is recordable when
it is known to be a workplace exposure to active
tuberculosis disease. In five industries, the presumption
is of a work relationship.
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December 2004
Beginning January 1, 2003, record all work-related cases of
hearing loss that meet both of the following conditions on
the same audiometric test for either ear:
1. The employee has experienced a standard threshold shift
and
2. The employee's total hearing level is 25 dB or more above
audiometric zero (averaged at 2,000, 3,000, and 4,000 Hz)
in the same ear(s) affected by the standard threshold
shift.
Beginning January 1, 2004, a separate hearing-loss column
appears on the OSHA 300 Log.
Needle sticks and sharps injuries that may be contaminated
with another person's blood or with infectious material
are recorded.
All medical removaF procedures that are under the
provisions of other OSHA standards are recordable.
A positive skin test for tuberculosis is recordable when it
is known to be a workplace exposure to active tuberculosis
disease. There is no presumption of a work relationship
in any industry.
Exhibit A- 1. Continued-changes to OSHA recordkeeping requirement from 2001 to
2002
Old rule, through 2001
New rule, 2002 and beyond
Other issues
The employer must enter the employee's name on all cases.
Employees have access to the entire log, including names;
they do not have access to supplementary form OSHA 101.
The employer must enter "Privacy Cases," rather than the
employee's name, and must keep a separate list of the case
number and corresponding names.
Employees and their authorized representatives have
access to the entire log, including names. Employees have
access to their own Incident Reports (OSHA 301).
Authorized representatives have access to a portion of all
OSHA301 's.
Employers must report all work-related fatalities to OSHA.
The employer, or the employee who supervised the
preparation of the log and summary, can certify the annual
summary.
The employer must post the annual summary during the
month of February.
Employers need not inform employees regarding how they
are to report an injury or illness.
1
Re co rdkeeping Guidelines for Occupational Injuries and
Illn esses (0MB no. 1220-0229).
2
Medical removal is the required removal of an employee from
a work location when certain criteria are met (for example, the
Employers need not report fatalities resulting from motor
vehicle accidents on public streets or highways that do
not occur in a construction zone.
A company executive must certify the annual summary.
The employer must post the annual summary anytime from
February 1 to April 30.
Employers must inform each employee regarding how he
or she is to report an injury or illness.
amount of lead in the hood reaches a specific level).
SOURCE : U.S. Department of Labor, Occupational Safety and
Health Administration; on the Internet at http://www.osha.gov/
recordkeeping/RKside-by-side.html , visited Sept. 30, 2004.
Exhibit A-2. Designation of types of occupational injury and illness cases under the
OSHA record keeping rules, 2001 and 2002
2001 and prior years
Total injury and illness cases
Lost-workday cases
Cases with days away from work 1
Cases with restricted work activity only
Cases without lost workdays
2002 and future years
Total injury and illness cases
Cases with days away from work, with a job transfer, or
with restricted work activity
Cases with days away from work 2
Cases with a job transfer or restricted work activity
Other recordable cases
Total injuries
Lost-workday cases
Cases with days away from work 1
Cases with restricted work activity only
Cases without lost workdays
Total injuries
Total illnesses
Lost-workday cases
Cases with days away from work 1
Cases with restricted work activity only
Cases without lost workdays
Total illnesses
1
May include days of restricted work activity, as well as days
away from work.
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2 May include days
with a job transfer or with restricted work
activity, as well as days away from work.
Monthly Labor Review
December 2004
23
Recordkeeplng Requirements
In 2001 and previous years, the type of occupational injury and
illness case was recorded separately for total injuries and illnesses,
for injuries only, and for illnesses only. This kind of separate
calculation resulted in the ability to tabulate detailed case-type
information for each of the three categories. Beginning in 2002,
case-type data are recorded only once, which limits the amount of
detail that can be tabulated. ExhibitA-2 indicates the available data
by type of case before and after the recordkeeping change.
Note to the appendix
1
The source of the preceding information is the Occupational Safety
and Health Administration, U.S. Department of Labor. (See the OSHA
notice in the Federal Register, Jan. liJ, 2001, p. 5916; and OSHA Fact
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December 2004
Sheet: Highlights of OSHA 's Recordkeeping Rule. Links to these
documents are available on the Internet at http://www.osha.gov/
recordkeeping/index.html, visited Aug. 19, 2004.
Hedonic Regression Models
;
"<~
_< >ii~
"X*N~l~
Hedonic regression models using
in-house and out-of-house data
Two data sources used by the Bureau of Labor Statistics
to create hedonic regression models have their own distinct
advantages and disadvantages; the Bureau performs research
on each data source in an effort to meet its current
and ever-evolving future needs
Craig Brown
Craig Brown Is an
economist in the
Office of Prices and
Living Conditions,
Bureau of Labor
Statistics. E-mail:
brown .cralg@bls.gov
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Federal Reserve Bank of St. Louis
T
he research leading up to the publication of
this article was conducted under the CPI
initiative to expand the scope of developing
hedonic regression models for quality adjustment
purposes to more items within the CPI market
basket. The primary focus of the article is to provide
a detailed analysis of the hedonic modeling process
and to illustrate the characteristics of two data
sources the Bureau of Labor Statistics has chosen
to utilize in its ongoing research on hedonic-based
quality adjustment methods.
Early research by BLS personnel and a significant
portion of the current research done by the CPI
staff in this area rely upon the existing sample of
CPI data for the creation of hedonic regression
models.' When it was recommended that the
Bureau expand its use of hedonic models for quality
adjustment purposes to more items within the CPI,
situations arose in which the existing sample size
of the items chosen were deemed insufficient to
support the creation of hedonic models. To alleviate
this problem, supplemental samples were designed
and collected exclusively for hedonic modeling
purposes.2 Despite the Bureau's having full control
over this type of sample data, such an "in-house"
prescription was not seen as a cure-all, because
designing and collecting these data exhausts many
BLS resources. Accordingly, the Bureau was led to
investigate the use of hedonic models created with
market data purchased from private firms that
specialize in collecting point-of-sale observational
data. 3 Purchased, or out-of-house, data offer many
enhancements over in-house data, but are costly
and have their own sets of limitations.
With home-based telephones (corded or cordless), the Bureau has an opportunity to compare
the process and results of using both in-house and
out-of-house data in the creation of hedonic
regression models. This article discusses the issues
of data quality, the specification of a model, and the
application of hedonic quality adjustments to
substitutions in the CPI sample. Empirical evidence
and quantitative data support the topics addressed.
The next section examines the characteristics of
the data. Following that section, the results of the
models are presented, and a discussion illustrates
how they could have been used in qualityadjusting substitutions in the CPI. The final
section is a follow-up of what has changed with
the data and presents a brief conclusion.
The data
Sources. The price data used in the analysis
that follows were collected by BLS data collectors
for the CPI and by the NPD Group, a private firm
that collects and sells point-of-sale marketing
information. The CPI sample consists of price data
for home-based telephones from the official CPI
sample and a specially designed supplemental
research sample that was created and collected in
order to increase the robustness of the existing
sample to facilitate hedonic modeling research.
CPI statisticians drew the supplemental sample
Monthly Labor Review
December 2004
25
Hedonic Regression Models
on the basis of current CPI sampling procedures. The CPI sample
is designed to be representative of consumer spending habits
and is distributed across the country and across all types of
retail outlets. The NPD Group sampie is a collection of point-ofsale data for home-based telephones from various partnered
retail outlets from across the country. The Bureau purchased
these data from NPD to explore the use of out-of-house data in
developing hedonic regression models. NPD offers point-of-sale
data for a wide range of consumer goods and services. Most of
the company's clients are private firms that use the sales data to
make marketing decisions.
Sample design. All of the price data for this study were collected in August and September of 2000. It was during these months
that the Bureau was able to collect its supplemental sample. The
agency purchased roughly 2 years of telephone data from the
NPD Group, but in order to make timely comparisons with the
CPI sample, only data from the aforementioned months were
used in the upcoming analysis.
The CPI sample consists of price quotes, each of which
represents a single item sold in a retail outlet. Each quote consists
of the item's listed price at the time of collection, a description of
the item's physical characteristics, and information about where
the item is sold. Under current CPI sampling procedures, data
collectors initiate price quotes by using a multistage probability
selection technique. 'The probabilities of selecting items for
pricing are proportional to the sales of the items. A slightly
augmented item selection procedure was followed in pricing the
supplemental sample. In this procedure, data collectors were
told how many cellular, corded, and cordless telephones to price
in each outlet. Respondents in the outlet were asked to rank
unique model numbers according to their sales figures and the
length of time they had been available for sale in the outlet.
Unique models that were both good sellers and fairly recent
arrivals to the outlet were chosen for the sample.
The supplemental sample added 398 observations to the
existing 115 observations from the official CPI sample. BLS data
collectors were unable to collect 21 percent of the supplemental
sample. The final sample consisted of 314 supplemental
observations and the 115 observations from the official CPI
sample, for a total of 429 observations.
Telephones are included in the information and information
processing CPI expenditure class (coded EE). Specifically,
telephones represent one cluster in the entry-level item (ELI)
category telephones, peripheral equipment, and accessories
(EE041). This cluster includes both home-based telephones and
cellular telephones. After observations for cellular telephones
and observations with duplicate model numbers from within a
unique retail outlet were excluded, the CPI sample for homebased telephones totaled 261 observations.
The NPD sample is a collection of aggregated and averagepriced point-of-sale observations representing a group of unique
26
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December 2004
product model numbers. Specifically, data are collected in
various cooperating retail chain outlets. NPD classifies these
outlets by type, or "channel," and then aggregates the data
across channels to arrive at a national estimate of the average
price for each unique product model number for which data are
collected. 4 Each observation includes an average price (total
expenditures for a unique product model number, divided by
total number of units of that model sold) and information on the
item's physical characteristics. NPD has more than 400 participating retail partners that provide the market data it sells. This
group of retailers includes department stores, mass merchants,
specialty stores, and other venues. Because the data are collected from transactions, they represent actual consumer purchases in those retail outlets which cooperated with the survey.
As stated earlier, the Bureau purchased approximately 2 years
of monthly point-of-sale data for corded and cordless telephones from NPD, but only data from August and September of
2000 were used in this study. The combined sample of those 2
months for both corded and cordless telephones consisted of
669 observations.
The NPD sample was further reduced by eliminating
observations pertaining to unique product model numbers
collected in August when data on those same numbers also
were collected in September. Unique product model numbers
with data for only August or only September were kept in the
sample. After these reductions, the final data set totaled 371
NPD observations.
Cleaning. Cleaning the data sets was a tedious exercise, but
one that had to be regarded with importance. The CPI sample
was cleaned by attempting to match the descriptions of all
collected manufacturer model numbers with descriptions found
on manufacturers' Internet Web sites. Corrections were made to
the data when inconsistencies were found. Not all model
numbers were capable of being verified through the manufacturers' sites, however. Because old models are discontinued
and replaced by new models every few months, it becomes
difficult to find data for older model numbers after a long time
has passed since their discontinuation. For those model
numbers for which primary data were not available, credible
secondary Internet Web sites were used for verification.
A unique characteristic of the CPI data is that they contain
information about the type of business or retail outlet where the
data were collected. The variable that gives this information is
included in many hedonic regression models created by the
CPI. Its primary use is as a control variable to account for the
effects that varying business practices may have on the price of
goods offered in an outlet. Each type of business category is
assigned a code that BLS data collectors use to classify the retail
outlet in which data are collected. Inconsistencies and coding
errors between the classifications and actual retail outlet names
were evaluated and corrected as needed.
Cleaning the NPD data was surmised to be a task less tedious
than cleaning the CPI data. The reasoning behind this assumption
begins with the fact that describing the characteristics of an item
correctly is difficult, given the diversity of work each CPI data
collector is responsible for, the complexity of the data collection
forms , the variety of the items themselves, and the paucity of
knowledgeable respondents within outlets. The CPI data thus
needed to be reviewed. By contrast, the NPD Group is a company
whose business is collecting and selling market data. The
accuracy of the NPD data is improved by electronic scanners in
the retail outlets that supply data to the company. These scanners
record price and quantity information, along with a minimal
amount of characteristic information in the form of SKU numbers
(unique identifying numbers manufacturers assign to their
products). NPD maintains a library of SKU's and characteristic
information on each one. Some degree of quality control is
employed by NPD in packaging the company's data. Collecting
and maintaining data in this way would seem to result in fewer
errors in the measurement of characteristic variables.
To test this theory, a random sample (5 percent of the total)
was generated from each "precleaned" data set, and the item descriptions of those model numbers were checked against either
manufacturers' information or information from credible
secondary sources. The following tabulation of the quality of
the samples before cleaning shows the results of this analysis
(entries are percentages):
Item
descriptions
Correct .................................................. .
Missing or incorrect
specifications ....................................... .
Could not verify ......... .... ................. .......
CPI,
NPD,
5 percent,
n = 14
36
5 percent,
n = 18
82
43
12
21
6
Because more than 80 percent of the NPD sample had correct
descriptions, compared with roughly one-third of the CPI sample,
verifying each model number in the NPD sample might have
proven inefficient and unwise. Accordingly, another element of
the NPD sample was selected for investigation: the description
field.
Found in the NPD sample purchased by the Bureau, the
description field consists of a highly general description of each
unique model number. The latter is likely to be a product identifier
used by the manufacturer or retailer. A random selection of model
numbers was cross-checked by comparing the information in the
description field with the item's actual description. The
consistency between the two was similar enough to justify
simply verifying the item description by consulting the
description field for all the model numbers in the sample.
The NPD sample had to be further reduced after the initial
cleaning phase. A total of 10 unique product model numbers had
no price, rendering them unsuitable for modeling purposes. Seven
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model numbers were for items that were outside the scope of the
CPI and therefore were ineligible for pricing. 5 Finally, two model
numbers had average prices that were well below average for the
types of telephones they represented. After deletion of those 19
unique model numbers, the final NPD sample stood at 352.
Market and product background. The market for home-based
telephones is an established, mature market. Most consumers
who have such telephones already have replaced their old ones
with more technologically advanced models as they arrive in the
marketplace (for example, corded to cordless or analog cordless
to digital cordless phones). Consumers routinely accept new
generations of advanced technology, and a few premium features
are well known and valued by consumers. Manufacturers of
home-based telephones bundle the different varieties of their
product with features the consumer is thought to value.
Corded telephones allow for a clear, static-free conversation,
but lack the convenience and mobility of a cordless telephone.
Still, many people choose to keep corded telephones in their
homes because they are inexpensive and reliable. Consumers of
cordless telephones are met with a wide variety from which to
choose. The most basic and inexpensive type of cordless
telephone is the 46- to 49-MHz analog phone. For more security,
900-MHz analog telephones are available, although they provide
less clarity and security than their 900-MHz digital counterparts.
Home-based telephones underwent a substantial technological
improvement in 1995 with the introduction of digital spreadspectrum (oss) technology. This feature allows the signal to
randomly jump channels, making it less susceptible to interference and eavesdropping. As the popularity of cordless
telephones grew, the 900-MHz frequency range became overcrowded. In response, the Federal Communications Commission
opened up the 2.4-GHz frequency to cordless telephones in 1998,
whereupon the operating range of such telephones increased
substantially. 6 However, even though the capability of a telephone to function at larger bandwidths is a valued characteristic,
it is not as valuable as the ability to operate through oss
technology. While there are fewer signals to interfere with in the
higher frequencies and the operating range is increased, without
oss technology interference and decreased security can still be
problematic. 7 5.8-GHz technology was not available during the
period covered by this study, but has recently become available
in the U.S. market for cordless phones.
Home-based telephone manufacturers will typically produce
many models of a telephone, with each model including certain
valued features. For example, a manufacturer may produce four
types of 900-MHz digital phones: a basic model, a digital phone
with caller ID, a digital speakerphone with caller ID, and a digital
speakerphone with caller ID and a digital answering machine. It is
presumed that the prices of these different telephones will become progressively higher as each additional feature is added to
the basic model.
Monthly Labor Review
December 200'1
27
Hedonic Regression Models
Comparing the samples. A closer look at the CPI and NPD data
sets reveals a similar distribution in the number of observations
of each type of telephone. Chart 1 shows the distribution of
unique model numbers for each type of telephone by the percent
of each sample. Four different samples will be analyzed in this
section: the CPI, the unweighted NPD, the quantity-weighted
NPD (NPDQ), and the value-weighted NPD (NPDV). 8 ln the August
and September 2000 data set, most of the cordless telephones
operate at 900 MHz. This type of telephone was, and still is, very
popular. Corded telephones account for a large percentage of
each sample, a fact that is interesting because the NPD sample
for corded telephones is collected independently of the company's sample for cordless telephones and the NPD samples
reflect transaction spending behaviors by consumers. In contrast, the CPI substitution procedure of pricing another item of
similar quality when the original item is no longer available could
result in a biased sample consisting of a disproportionate number
of corded telephones or older generations of cordless telephones
that are available for sale, but of which few are actually sold.
However, because of the similarities in the percentages of each
type of telephone in the CPI and NPD samples, it appears that the
sample is unbiased toward any one type of telephone. Another
point of interest is that the percentage of 2.4-GHz oss telephones
in the CPI sample is more than twice as large as the percentage of
those telephones in the NPD and NPDQ samples, but less than
the percentage in the NPDV sample. The reason for these
disp~rities could be that the CPI sample is disproportionately
representing a new good. It is also important to note that the CPI
sample had no 2.4-GHz analog telephones and the NPD sample
had no 2.4-GHz digital telephones. Whether these omissions wili
result in any specification bias is unclear.
Overall, a priori expectations of the average prices of telephones were met. The newer, more sophisticated telephones
clearly have higher average prices than the older telephones.
Chart 2 shows the distribution of average prices by type of
telephone for all samples. The average price of each type of
telephone is usually higher in the CPI sample. This pattern is
expected, because the Bureau collects list-price data for the CPI
sample whereas NPD collects transaction prices, which usually
are lower than list prices. The NPDV sample has a slightly higher
average price than the NPDQ sample for each type of telephone.
This difference has implications in the debate over whether to
use quantity weighting or value weighting. Erwin Diewert suggests that quantity weighting tends to give too little weight to
high-priced models and too much weight to less expensive
models that have fewer newer characteristics.9
Another perspective from which to compare the similarities
between the CPI and NPD samples is to examine the distribution
of brand names and unique model numbers found in those
samples. The final CPI sample of 261 observations contains 18
brand names and 156 unique model numbers. The final NPD
sample of 352 observations contains 22 brand names and 352
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December 2004
unique model numbers. 10 In total, 13 brand names and 67 model
numbers were common to both samples.
Quality characteristics. Three unique value-adding characteristics of the CPI and NPD data sets are of particular interest to
the analysis presented. The first is the vintage variable found in
the NPD sample. The vintage represents the date each unique
model number first appeared in the sample. It marks the introduction and prevalence of new, innovative technologies in the
telephone market. The vintage variable is useful because it makes
it possible to observe the changing prices of models over time.
As stated earlier, only data for August and September of 2000
were used for this study, but approximately 2 years of telephone
data were purchased from NPD. Looking at data over this longer
period may reveal trends in the pricing behavior over the product
life cycle.
Chart 3 graphs the average vintage (or age) and the average
(unweighted) price of each type of telephone. Vintage is measured by the number of months each model number has been in
the sample. Newer model numbers will have lower vintage values.
Model numbers that were introduced in September 2000 have a
vintage value of unity. The vintage values were averaged for
each type of telephone. In this small sample of data, it is apparent
that those telephone models which are likely to have the lowest
vintage values (the 2.4-GHz models) and the most advanced
features (the oss models) also have the highest average prices.
The vintage variable has many uses as a diagnostic tool and may
indeed be valuable in modeling the NPD data.
Another valuable feature of the NPD sample is the ability to
weight each model number in it by either the quantity of units
sold or the value of expenditures on that model number. As
illustrated earlier in chart 2, the average prices of the various
types of telephone can differ under all three weighting methods,
a feature whose implications will be examined later in the article.
It seems clear that, by the nature of the NPD data, a weighted
sample would be preferred over an unweighted sample. Unweighted, the NPD sample is simply an equally weighted list of
model numbers~ weighted, however, it becomes a more informative sample, indicating consumers' preferences for telephones.
The importance of weighting is obvious in the case of the 46- to
49-MHz telephones: as shown in chart 1, this type of telephone
represents a sizeable percentage of the unweighted NPD sample,
but its presence in the weighted sample is minuscule. The opposite is
true with the 900-MHz analog telephones, whose impact in the
weighted sample is far greater than in the unweighted sample.
The CPI sample does not weight quantity or expenditure data,
but it is designed in a manner that gives more popular models a
greater chance of being included in the sample. It could be argued
that this is a satisfactory weighting method, but it is unlikely that
it is as capable of tracking consumer spending preferences as
accurately as the electronic scanners found in the checkout lines
of NPD's retail partners.
Percent of sample by type of telephone for each sample
Percent
Percent
55
55
■ CPI ■ NPD □ NPDQ □ NPDV
50
50
45
45
40
40
35
35
30
30
25
25
20
20
15
15
10
10
5
5
0
Corded
46-49
MHz
0
900-MHz
analog
900-MHz
digital
2.4-GHz
analog
900-MHz
DSS
2.4-GHz
digital
2.4-GHz
DSS
Type of telephone
Average prices of telephones by type for each sample
Dollars
Dollars
180
180
■ CPI ■ NPD
160
NPDQ [§1 NPDV
160
140
140
120
120
100
100
80
80
60
60
40
40
20
20
0
0
Corded
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Federal Reserve Bank of St. Louis
46-49
MHz
900-MHz
analog
900-MHz
digital
900-MHz
DSS
2.4-GHz
analog
2.4-GHz
digital
2.4-GHz
DSS
Type of telephone
Monthly Labor Review
December 2004
29
Hedonlc Regression Models
Average vintage and average price for types of telephone, NPD sample 1
Months
or dollars
120
Months
or dollars
~-------------------------------------------,
120
■ Average vintage (in months) ■ Average price (in dollars)
100
100
80
80
60
60
40
40
20
20
0
Corded
46-49
MHz
900-MHz
analog
900-MHz
digital
900-MHz
DSS
2.4-GHz
analog
0
2.4-GHz
DSS
Type of telephone
1
There are no 2.4-GHz digital telephones in the
NPD
samples.
The final quality characteristic worth mentioning is the set of
control variables available in the CPI sample. The CPI uses the
list price advertised for each type of telephone at the time the
data are collected. CPI data collectors report that price and
indicate whether it was classified as a sale price or a regular price.
This information is used as a control variable in some hedonic
models employed by the CPI to control for the negative effect
sale price indicators have on prices, other things being equal.
The CPI also includes information on the types of retail outlets
in which the data are collected. This feature of the CPI sample is
important, because it aids in controlling for the effects each type
of retail outlet may have on the price of the items offered there.
Usually, when these control variables are included in hedonic
models, the resulting parameter estimates are logical and in
accordance with a predicted order of relative value.
The last control variable of note in the CPI sample is that
indicating the region and city size for each observation. The CPI
divides the United States into four regions, and the cities in
which the Bureau collects data are assigned one of three size
labels based on their populations. Variables for region and city
size are used to control for the possible effects location may
have on the price of goods sold. The usefulness of the geography
variables is difficult to quantify, but they do aid in minimizing the
unexplained variation in those price-determining variables which
are being estimated.
30
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December 2004
Data reservations. Despite the number of positive attributes
associated with both data sets, four lingering reservations about
the data deserve attention:
• The representativeness of the samples
• The absence of area, outlet (or channel), and type-of-price
indicators from the NPD sample
• The quality of the characteristic data
• The age of the data
The discussion of the representativeness of the data is best
addressed by splitting it into two issues. The first is whether the
data are representative of the market for home-based telephones.
Mary Kokoski, Keith Waehrer, and Patricia Rozaklis note that,
because the NPD sample is not collected under the probability
sampling procedures that are used for the CPI sample, the relative
degrees of representation of specific models in the respective
samples are different. 11 The NPD sample is capable of reflecting
changes in consumer purchasing habits, because it is a measure
of actual telephone purchases. In this regard, the NPD sample
could be more representative of the market for telephones than
the CPI sample, which has the potential to be skewed toward
older models due to the CPI 's procedure that instructs data
collectors to substitute an item of similar quality when the one
they were originally pricing becomes unavailable. 12 As shown in
chart 1, however, such a concern over representativeness is not
entirely warranted in this situation, because the CPI and NPD
samples are fairly similar in their distribution of the various types
of telephone.
'fhe second issue with regard to representativeness has to do
with where the data are collected. The outlets from which the
Bureau collects price quotes for the CPI differ from those sampled
by NPD, thus affecting both the product mix and prices. The CPI
data are collected in a wide variety of retail outlets across the
country. These outlets, which represent answers by respondents
of the Consumer Expenditure Survey to questions about where
they make their purchases, are chosen through statistical sampling procedures. 13 NPD, by contrast, collects its data from retail
outlets it has partnerships with. This approach introduces some
bias into the NPD sample, because some types of retail outlets
may not be represented in the national sales figures NPD
produces. In particular, the Bureau is aware that certain major
discount department stores do not contribute data to NPD. The
exclusion of important retailers from the NPD sample is a critical
shortcoming of the NPD data.
The second reservation regarding the data-the absence of
area, outlet, and type-of-price indicators from the NPD sampledeals with factors that contribute to the quality of the models.
Each price quote in the CPI is accompanied by information stating
where it was collected. This information consists of the specific
outlet name, a type of business classification code, and the size
and rf'gion category of the city in which the quote was collected.
The information is converted into variables that control for the
effects different types of business practices and geographic
locations may have on the product mix and price. Such control
usually helps minimize the variation in parameter estimates for
price-determining characteristics in the model. The NPD sample
does not have any control variables; therefore, even if the NPD
sample happened to be collected in the same mix of retail outlets
that the CPI sample used, there would still be no way of
accounting for the effects of geography and outlet characteristics
in regression models constructed from NPD data.
The NPD sample also lacks a sale or markdown price indicator.
Unlike the CPI sample, the NPD sample uses an aggregated
average price of the units sold for a unique model number in the
outlets that supply data to NPD. Such an average, however, is a
national average and has no indication of whether the units were
sold at a sale price or a regular price. Because items sold at a sale
price generally are of a lower price than other items of similar
quality, all other things being equal, it is appropriate to control
for the effects of sale prices in the regression model through the
use of a dummy variable.
The third reservation about the data concerns the quality of
characteristic data. Well-defined characteristic data are essential
to ct"eating reliable models. CPI researchers spend considerable
amounts of time and resources preparing and cleaning the data
they use for modeling. The NPD characteristic data were defined
more precisely than the CPI characteristic data. However, it may
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Federal Reserve Bank of St. Louis
be unwise to assume that NPD data for all other goods would be
of the same level of quality as the telephone data. By the same
token, while the CPI sample for telephones was not as well
defined, CPI data for other goods may be of a more acceptable
quality. More importantly, the collection of CPI data is under the
control of the Bureau and is capable of being manipulated in
order to increase the quality of the data. CPI data collectors can
be directed as to what data to collect, and data coliection forms
can be updated to capture innovations and other valuable quality
characteristics. The Bureau forgoes this level of involvement
with NPD data.
Besides having this obvious concern about the accuracy of
each data set, analysts may worry about the possibility that the
definitions, categories, and quality characteristics identified by
both data sets will differ from one another. Because hedonic
models created with NPD data could eventually be used to make
quality adjustments to CPI data, the variables denoting quality
characteristics in those models would need to be similar enough
to the specifications found in the CPI data for the desired quality
adjustments to be made. For the most part, the CPI and NPD data
are fairly similar, although there are a few differences wortl1
mentioning in the subsequent discussion of the hedonic mode1s. 14
The last major reservation about the data has to do with their
age. Both samples represent data from August and September of
2000. The CPI sample was cleaned for this study during the first
half of 2002, and the NPD sample was cleaned during 1he first
quarter of 2003. Given the amount of time between collection and
cleaning, difficulty arose in verifying the descriptions of each
model number through Internet sites. Many model numbers were
either no longer current or completely out of production by the
time the data were cleaned. Also, the telephone market has
changed considerably since 2000. 2.4-GHz telephones are far
more prevalent now, and new technologies are available. Accordingly, using hedonic models created with old data to qualityadjust substitutions in current samples is questionable, especially
because the market for telephones is so different now from what
it was then. If the coefficients of the variables were created from
current data, they would likely be quite different from what they
were a number of years ago.
Model specification
The first step in building the hedonic model is creating variables
from the telephone data. Dummy variables were constructed for
all characteristic data, except for the vintage variable in the NPD
models. Vintage is a continuous variable, and its parameter
estimate must be multiplied by the age of the unique model
number (in months) to be interpreted correctly.
Once the variables are created, a functional form for the
regression model is chosen and the model is specified on the
basis of a priori assumptions about price-influencing characteristics. The most common functional form recommended in the
Monthly Labor Review
December 2004
31
Hedonic Regression Models
hedonic literature is the semilogarithmic form 15 lnis form is
preferred because it fits the data particularly well and because
the coefficient estimates generated from the model can be
interpreted as being the proportion of a good's price that is
directly attributable to the respective characteristics of that
good. The semilogarithmic form is given by the equation
lnpi =Po+ plxli + p~2i + ... +
pnxni + Ei,
where 1n pi is the natural logarithm of the price of each good,
P.J is the coefficient of the characteristic variable X.,J and E.I is the
error term. The value of p0 is interpreted as the value of the
base good without any of the quality characteristics that add
value to or subtract value from that good. 16 The dependent
variable is defined differently in both samples: for the CPI sample,
it is the list price collected for each observation; the NPD sample
uses the average transaction price of each unique model number.
As stated earlier, the NPD sample is capable ofbeing weighted
by the quantity of units sold or by the value of expenditures for
each model number. Given this capability, regression models were
calculated for the earlier mentioned four different data sets: the
CPI sample, the unweighted NPD sample (NPD ) , the NPD sample
with value weighting (NPDV), and the NPD sample with quantity
weighting (NPDQ).
Preliminary models. A set of preliminary models, each of
which included only variables for the types of telephones, a
variable controlling for prices collected on sale, and a continuous
variable for vintage produced the results shown in table 1. The
order of the relative values of the coefficients for the telephonetype variables matches a priori expectations. All of the cordless
types of telephone are of greater value than the corded telephones. Cordless 46-to 49-MHz analog telephones are statistically insignificant in all models except the NPDQ. The other
cordless types of telephone are in an expected order, with 2.4GHz oss types having the largest coefficient in all models. The
CPI sample did not have any 2.4-GHz analog telephones, and the
NPD, NPDV, and NPDQ samples did not have any 2.4-GHz digital
telephones. The sign on the coefficient for the sale price variable
in the CPI model is negative, meeting a priori expectations, but
the variable is statistically insignificant. The sign on the
coefficient for the vintage variable is negative, also meeting
expectations, and is statistically significant in all three of the NPD
models.
Final models. Additional characteristic variables were included
in the final models, along with a few brand-name variables. Control
variables for the type of business, the region, and the size of the
city in which the data are collected were included in the final CPI
model. The final models yielded the results shown in table 2.
Other than the variables for the type of telephone and the
brand name, only the caller ID, handset keypad, and dual keypad
characteristic variables appear in all four models. The caller ID
32
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December 2004
variable is statistically significant in all models, and its coefficient
values in the CPI, NPDV, and NPDQ models are similar. The same
is true for the keypad placement variables. Corded telephones
with keypads only on the handset are typically featureless and
inexpensive, which explains the negative sign on the coefficient
of the associated variable. By contrast, cordless telephones with
keypads on both the base unit and the handset are generally
more sophisticated than those with keypads only on the handset,
resulting in large positive coefficients for this variable.
Other characteristic variables are similar, but not exactly the
same, in all models. For example, the speakerphone feature
variable applies only to corded telephones in the CPI model, but
only to corded telephones without three or more line capabilities
in the NPD models. Multiple line capability also is modeled
differently in the various models. In the CPI model, the variable
for multiple line capability does not distinguish between
telephones with exactly two lines and telephones with three or
more lines. By contrast, the NPD models make this distinction.
Still, the variable for two line capabilities in the NPD models does
not apply to telephones with conference call capability, and the
variable for three or more lines applies only to corded telephones.
Some characteristic variables are unique to one data set. For
instance, the CPI model includes a variable for telephones with
digital answering machines attached. Oddly, there are no
variables for these types of telephones in the NPD sample. The
NPD models, however, include variables for conference call and
Preliminary models
Variable name
CPI
Intercept ............ ... ... ............
1
3.236
(-.0542)
Base
...
.157
(-.1829)
1
.697
(-.0827)
1
1.103
(-.1004)
1
1.392
(-.1614)
(2)
...
1
1.492
(-.3074)
1
1.885
(-.1104)
- .155
(-.0921)
(2)
...
.586
53.56
261
Corded .. .......... ..... ................
Cordless 46-49 MHz .... ......
Cordless 900-MHz analog ..
Cordless 900-MHz digital ...
Cordless 900-MHz
DSS ...... .
Cordless 2.4-GHz analog ...
Cordless 2.4-GHz digital ....
Cordless 2.4-GHz
DSS ........
Sale price ...... ............. .. .... ...
Vintage .... ...................... ......
Adjusted R 2 .. .. ... ... . .............
Fvalue .. .. ............................
Number of observations .....
1
2
NPD
1
3.647
(-.0874)
Base
...
- .028
(-.0978)
1
.304
(-.0897)
1
.653
(-.1451)
1
.796
(-.1209)
1
.703
(-.3618)
(2)
...
1
1.122
(-.1712)
(2)
...
1
- .008
(-.0016)
.344
27.35
352
Statistically significant at the p = .05 level.
Not in model.
NPDV
NPDQ
1
3.839
(-.0845)
1
3.263
(-.0653)
Base
Base
...
...
1
- .087
.304
(-.1799) (-.1473)
1
.101
.418
(-.0783) (-.0623)
1
1.655
1.039
(-.1033) (-.1099)
1
1.602
1.011
(-.1253)
(-.139)
1
.500
1.002
(-.2883) (-.3444)
(2)
(2)
...
. ..
1
1
1.271
1.786
(-.1011) (-.1358)
(2)
(2)
...
...
1
1
- .012
- .008
(-.0018) (-.0013)
.551
.546
62.43
61 .36
352
352
Final models
Variable name
Intercept ..... .. .. .. ....... ......
Corded ...... .... .. ...............
Cordless
46-49 MHz .. ... ..............
Cordless
900-M Hz analog .. .........
Corl.lies:::
900-MHz digital ... .. .. .... .
Cordless
900-MHz DSS ..... . . .. . . .. . ..
Cordless
2.4-GHz analog ...... ......
Cordless
2.4-GHz digital .............
Cordless
2.4-GHz DSS. ....... . .. .. .. ..
Sale price .. .. ................. .
Vintage ..... .. ...... ....... .... ..
Digital answering
machine .. .. ...... ........ ... ..
Speakerphone
(corded) .... .. ......... .. .... ..
Speakerphone (not
three-line capable) .. ... ..
• 1
CPI
1
3.157
(.0725)
Base
.105
(.1236)
1
3.101
(.0997)
Base
1
.213
(.0959)
1
3.014
(.0718)
Base
1
.202
(.0940)
1
2.857
(.0615)
Base
.205
(.0830)
Headset jack ... .. ... ... ......
(2)
Brand B ... ......................
Base
Brand C ......... ................
1.083
(.1237)
1.207
(.1527)
Brand F .... ...... ... .... .... .. ...
1
.861
(.1978)
(2)
(2)
(2)
Brand N .. ... ...... .... ..... .....
1
1.413
(.0891)
1
1.211
(.1533)
1
1.420
(.0826)
1
1.466
(.0951)
Audio/video store .. .... ... .
Base
- .070
(.0585)
(2)
(2)
(2)
(2)
Full-price department
store .. .... .. ... ..... ... ... ... ...
1
- .004
(.0012)
-.001
(.0008)
.0001
(.0007)
Discount appliance
store .... .. ....... ... ..... .. .... .
(2)
(2)
(2)
Discount department
store ....... ...... ...............
1
.491
(.0711)
1
.488
(.0605)
Brand
1
1
1
1
A ........ . ...... .. . . . .....
.642
.758
(.0881)
.783
(.1334)
.696
(.0796)
(.0765)
Brand M ........... ..............
1
1.044
(.1106)
1
1.060
(.1129)
1
.964
(.0808)
1
.949
(.0819)
Brand H ... .... .... .... .... ......
Brand G ... ......................
(2)
1
1
.406
(.0549)
1
1
1
.235
(.0796)
(2)
Warehouse ............. .. .... .
1
.379
(.1054)
1
.334
(.0784)
1
.276
(.0681)
.453
(.1129)
.657
(.0723)
Intercom (not dual
keypad) ... ..... ........ ...... ..
(2)
Clock radio .. .. .. ... .... .......
(2)
Caller ID ....... ... . ...... .. ... ...
1
.456
(.0449)
.134
(.1223)
1
.677
(.1407)
1
.330
(.0554)
.040
(.0512)
1
.565
(.0903)
1
.466
(.0233)
.074
(.0900)
1
.587
(.0853)
1
.504
(.0259)
1
1
Midwest region .. .......... ..
C-sized city ..... ......... .....
1
Adjusted R 2 • • • •• ••• •• •• ••• • • • •
Fvalue ... ..... ............... .. ..
Number of
observations ...... ......... ..
1
2
1
NPDV
NPDQ
1
.905
(.1377)
.075
(.0700)
1
.799
(.0674)
1
.105
(.0285)
1
.824
(.1051)
Base
Base
Base
- .154
(.0753)
.142
(.0789)
(2)
-.036
(.0384)
1
.182
(.0421)
(2)
1
.087
(.0396)
1
.294
(.0487)
(2)
1
-.161
(.0724)
1
- .216
(.0815)
1
- .263
(.1045)
1
1.010
(.1823)
1
-.243
(.0314)
1
- .233
(.0386)
1
-.205
(.0345)
1
1.000
(.3014)
1
- .153
(.0303)
1
-.110
(.0450)
1
-.083
(.0377)
1
1.088
(.3524)
(2)
(2)
(2)
1
1
.154
(.0336)
1
- .140
(.0743)
1
-.196
(.0735)
1
-.289
(.0551)
1
-.330
(.0679)
1
.104
(.0477)
1
-.414
(.1076)
.852
60.62
261
Statistically significant at the p
Not in model.
= .05
(2)
(2)
(2)
(2)
(2)
(2)
.693
31.49
.940
210.94
.922
160.58
352
352
352
level.
1
.420
(.0811)
Two-line capability (no
conference calls) .. .......
(2)
1
.451
(.0868)
1
.509
(.0418)
1
.506
(.0616)
Three-or-more-line
capability (corded/ ... ... .
(2)
1
1.860
(.1597)
1
1.759
(.0864)
1
1.783
(.1046)
1
-.312
(.0794)
1
- .317
(.1597)
1
-.424
(.0785)
1
-.414
(.0621)
1
.274
(.0665)
1
.224
(.0716)
1
.305
(.0308)
1
.341
(.0437)
Corded handset on
base (cordless) ............
(2)
-.122
(.3171)
- .133
(.0962)
- .178
(.1417)
Extra-handset
capability (coraIessJ ....
(2)
.334
(.2374)
- .055
(.0628)
- .151
(.1223)
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(2)
1.073
(.2595)
1
.507
(.0959)
(2)
Keypad on base and
handset (cordless) .. .... .
Headset style
design.. .. ...... ... ......... ....
.200
(.0595)
1
.189
(.0726)
1
- .851
(.1522)
-.098
(.0752)
-.092
(.0787)
.021
(.1008)
(2)
1
.545
(.0718)
.706
(.0751)
Keypad on handset
only (corded) ... ....... .. ....
NPD
CPI
1
Conference call (-not
three-line capable) .. .....
Two-or-more-line
capability. ................ .....
Continued-Final models
Variable name
NPDQ
NPDV
NPD
•t•ir~...-
intercom capabilities, clock radio units, headset jacks, and
headset-style designed telephones. Generally, the values of
these variables are statistically significant and have the expected
signs on the coefficients.
Two variables unique to the NPD samples have values that are
not statistically significant in the final NPD models, but that
should be noted because of their significance in the current
market for telephones. Three cordless telephone models include
a corded handset on their base unit, and four cordless telephone
models are capable of adding extra cordless handsets to their
operation. Telephones with either of these features had average
prices 2 to 3 times greater than prices for those without the
Monthly Labor Review
December 2004
33
Hedonic Regression Models
features, yet, because relatively few of these telephones were
sold, they were not statistically significant in the models.
A comparison of the adjusted R 2 values from each model
reveals some interesting results. First, the unweighted NPD model
has a much lower adjusted R 2 value than do the value- and
quantity-weighted models. This is expected, because, in general,
weighted regressions produce much better fits. Of the two
weighted models, it is unclear which is preferred. The argument
by Diewert that quantity weighting ignores higher priced models
with innovative features in favor of lower priced models with
fewer advanced features 17 is not tully realized, because both
models have the same number of characteristic variables with
statistically significant values and the quantity-weighted model
actually has one more brand-name variable with a value that is
statistically significant. In fact, most of the variation in the
coefficients of these two models appears to be found in the
brand-name variables. Variations in the coefficients of other
variables appear negligible.
The other point of interest is the adjusted R 2 value of the CPI
model. Comparing adjusted R 2 values from two different models
is not econometrically sound, but if the values are compared in
terms of how well each model explains the variation in its own
data set, then it is fair to comment on the results. Under this
analytical framework , it is interesting to note that the CPI model
explains more of the variation in its data set than the unweighted
NPD model does in its data set, while the weighted models explain
more variation in price than the other two do.
Experimental models. To compare the samples further,
experimental models were run on modified data sets consisting
of the observations for unique model numbers common to the
CPI and NPD samples. 18 As stated earlier, there were 67 unique
model numbers common to both samples. These model numbers
represent 118 observations in the CPI sample and 67 observations
in the NPD samples. Because the data sets consist of the common
model numbers, the experimental models are specified with the
same variables, which represent the quality characteristics
present in the 67 common model numbers. These quality
characteristics are found to be most consistently statistically
significant in the final models in table 2. Variables for 12 of the 13
brand names common to all of the data sets also were included. 19
The experimental models yielded the results shown in table 3.
The variables for the type of telephone and many of the feature
variables remain very strong in these models. Two exceptions
are the variables for handset placement, which are significant in
the final models, but not in the experimental models. Also,
although many of the telephone type and feature variables are
significant, there are large differences in the parameter estimates
for these variables. For example, the parameter estimates for
cordless 900-MHz digital telephones range from 0.730 to 0.933,
and the parameter estimates for the speakerphone feature range
from 0.302 to 0.498. There is also much variation in the statistical
34
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['ecember 2004
·••1•11=--- Experimental models
Variable name
Intercept ........................
Corded .......... .................
Cord less 46-49 MHz .....
Cordless
900-MHz analog ...... .....
Cordless
900-MHz digital ....... .. ...
Cordless
900-MHz
DSS . . .. ........ ... .
Cordless
2.4-GHz oss .. ..............
Caller ID ... ......... ........... ..
Two-or-more-line
capability ... ........ ........ .. .
Speakerphone .... ..... .... ..
Conference call
capability ....... ...... .. .......
Keypad on handset
only (corded) .... .. ... .......
Keypad on base and
handset (cordless) ... ....
Brand A .... ... .... .. ... ..... ....
Brand B .... ..... ....... .........
Brand C ............ ... ... .... .. .
Brand D ...... .... ...... .. ... ....
Brand E .............. ....... ....
Brand F .................. ........
Brand G .... ............... ..... .
Brand H ..... ..... ............ ...
Brand
1 •••••• •••• •••• •••• ••• ••••••
Brand J ... ....... ....... .........
Brand K ..... ............. ... ....
Brand L .... ... ...... .. .... ... ....
Adjusted R 2
F Value .. ..... .... ... ........ .. .
Number of
observations ....... ..........
1
CPI
NPO
NPOV
NPOQ
1
3. 124
(.1255)
Base
1
3. 08
(.1823)
Base
1
3.144
(.2086)
Base
1
3.121
(.1635)
Base
.221
(.1944)
1
.528
(.2269)
.463
(.2396)
1
.497
(.1908)
1
.645
(.1073)
1
.617
(.1646)
1
.569
(.2045)
1
.571
(.1550)
1
.920
(.1290)
1
.933
(.2287)
1
.760
(.2220)
1
.730
(.1947)
1
1.277
(. 1272)
1
1.084
(.1653)
1
.964
(.2149)
1
.981
(.1822)
1
1.460
(.1476)
1
1.611
(.2148)
1
1.403
(.2132)
1
1.407
(.1854)
1
.480
(.0585)
1
.377
(.0843)
1
.499
(.0437)
1
.506
(.0510)
1
.388
(.1147)
1
.302
(.1103)
1
.478
(.1648)
1
.414
(.1680)
1
.518
(.1 032)
1
.482
(.2209)
(.1480)
1
.498
(.1695)
.078
(.1588)
.087
(.3543)
.125
(.3751)
.211
(.4098)
-.158
(.1010)
- .242
(.1737)
- .221
(.2099)
- .193
(.1543)
-.180
(.1288)
-.308
(.2036)
- .253
(.2240)
-.176
(.1868)
.005
(.1083)
Base
.1040
(.1 374)
Base
.0750
(.0811)
Base
- .001
(.0948)
Base
-.023
(.1093)
-.053
(.2783)
-.155
(.2820)
-.177
(.1282)
1
- .359
(.1137)
1
-.400
(.1204)
,-.499
(.2229)
-.516
(.2901)
,-.547
(.1570)
1
- .753
(.1691)
.8834
41.28
-.112
(.1441)
- .074
(.3 121)
-.050
(.2993)
-.243
(.1785)
1
-.291
(.1427)
,- .373
(.1544)
-.266
(.2609)
- .130
(.2993)
- .175
(.1893)
1
-.628
(.2232)
.8447
17.31
- .144
(.0743)
- .053
(.1629)
-.189
(.1960)
1
-.368
(.0667)
1
- .405
(.0681)
1
-.583
(. 0630)
,-.500
(.1423)
-.269
(.1509)
1
-.272
(.1062)
1
- .735
(.1656)
.9688
94.24
-.141
(.0807)
-.040
(.3325)
- .174
(.2477)
,- .372
(.0709)
1
- .396
(.0809)
,- .595
(.0660)
,- .501
(.1114)
-.254
(.1842)
- .262
(.1368)
,- .738
(.1159)
.9542
63.46
118
67
67
67
Statistically significant at the p = .05 level.
'.45e
significance of, and parameter estimates for, the brand-name
variables.
The adjusted R 2 values of the experimental models are quite
high. As suggested earlier, they are highest in the weighted
models, with the CPI model and the unweighted NPD model
following after.
Quality adjustment results
To assess the usefulness of a hedonic model for home-based
telephones, the model 's parameter estimates were applied to
home-based telephone substitute price quotes (items selected
by CPI data collectors to replace previously collected items that
are no longer available) with quality changes. Substitutions from
the months of May and June 2003 were chosen for this exercise.
There were 18 home-based telephone substitutions during
those 2 months. In the published index, 22 percent of the
subsi.itutions were compared directly with the previous item. The
price changes for the remaining noncomparable substitutions
were imputed, or "linked," essentially by assuming them to be
the same as the elementary item price change for the same
geographic area that month. 20
The comparability decisions of the 18 substitutions were
reassessed on the basis of the four hedonic models. Nearly 67
percent of the substitutions underwent quality changes that
could be adjusted for with the use of one of the hedonic
models. After the reassessment was complete, 39 percent of
the substitutions were adjusted with the CPI model, 28 percent
were adjusted with the NPD models, and 33 percent of the
prices were directly compared under all four models. 2 1 The
remaining substitutions (28 percent under the CPI model and
39 percent under the NPD models) were deemed noncomparable, and the price changes would have been imputed with
the link method. The substitution comparability ratio (the ratio
of directly compared and quality-adjusted substitute quotes
to the total number of substitute quotes) jumped from 22
percent in the published index to 72 percent under the CPI
model and 61 percent under the NPD models. Most of the
quality adjustments were calculated for changes in the caller
ID variable and brand-name changes, and most of the noncomparable substitutions had unadjustable quality changes,
such as an expandable phone system capability and extra
handsets included with the system. Table 4 summarizes the
specification and quality change s that occurred in the
substitutions.
The hedonic models were analyzed further through a
comparison of the unweighted mean price changes for the
home-based telephone substitutions in the published index
with the mean price changes for the quality-adjusted substitutions. The mean price change for directly compared
substitutions increased dramatically after the reassessment.
These substitutions were primarily those wherein only the
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■ 1•I•ir~••
Specification and quality changes in
substitution price quotes
Type of change
Number of occurences
Specification change
Model number change or other, minor
specification change ............ ........ .. .. ........ .. .....
Change in quality .. ... ...... ............. .. .... .. ........ .... ..
6
12
Quality Change 1
Type ... ...... ....... .. ....... ....... ...................................
Brand .. .. ............. .... .................... ....................... .
Digital answering machine ..... ... .. .. .... ... ........... ...
Speakerphone ..... ..... ......... ..... .. ...... .. ............. ... .
Keypad on base and handset (dual keypad) .. .
Two-line capability .... ............ .......... .... .. ..... ..... .. .
Caller ID ......... ............. ....................... ........ .. .... ..
Expandable phone system ...... ....... ... ..... .... ..... .
Extra handset(s) included .... ... .. ... ... ....... ...... .. .. .
Headset included .... ..... ...... ... ..... .. ... .. .. .... ... .......
1
For each specification listed , more than one could have changed for a
given substitution .
model number, with or without additional minor price factors,
changed. The mean price changes for the quality-adjusted
substitutions were mostly negative, with an average downward price change (across all models) of 4.7 percent. The overall mean price change for all substitutions with comparisons
(directly compared and quality-adjusted) varied greatly, with
the largest difference occurring between the unweighted NPD
model and the weighted NPD models. Table 5 compares the
mean price changes of the home-based te lephone substitutions under the published CPI with those calculated in the
four hedonic models.
On average, there were only nine home-based telephone
substitutions each month . The small proportion of substitution price quotes for home-based telephones in the item
index limits the impact of quality-adjusting the CPI for homebased telephones. Also, because cellular telephones account
for roughly 40 percent of the entire telephone sample used in
index calculations, the effect of quality-adjusting only homebased telephones is marginal. A side from the se points,
however, applying any one of the home-based telephone
models significantly decreases the number of noncom parable
(imputed) substitution price quotes, the fewer of which exist
in the index, the more likely it is that the index will measure
true price change over time.
What's changed?
Since the data presented in this article were collected , new
5.8-GHz telephones have entered the market and the prices of
older types of telephones have fallen . New innovations, accompanied by decreas ing prices, have nearly driven 46-49MHz telephones out of existence. Consumers seem to be
accepting new technology, such as the 2.4-GHz models, and
Monthly Labor Review
December 2004
35
Hedonic Regression Models
....... -~·-
Mean price changes of substitution price quotes
Published cP1
Total substitutions(= 18)
Number
Substitutions with comparison (nonlink)
Directly compared substitutions .......
Quality-adjusted substitutions ..........
Noncomparable (linked) ........... .. .... .. .... ..
1
4
4
0
14
Mean
price
change
(percent)
-0.65
-.65
CPI qualityadjusted index
Number
Mean
price
Number
change
(percent)
13
6
7
5
are hc>nefiting from the lower prices. Clear evidence of this
trend can be found by comparing the CPI sample in September
2000 with the CPI sample in June 2003. 22 Chart 4 compares the
percentage of the sample each type of telephone accounts
for, and chart 5 compares the average prices of each type of
telephone.
Expandable telephone systems and telephones sold with
two or more handsets are becoming very popular, as evidenced by the number of substitute price quotes having those
characteristics. The impact of the popularity of cellular
telephones on the home-based telephone market is also of
interest. Some consumers are now choosing cellular telephones as their primary communication device and abandoning their home-based telephone service altogether.
With regard to changes in the data sources, the CPI sample
has grown dramatically since September 2000. The size of the
sample for telephones increased from 115 observations in
September 2000 to 290 in June 2003, with the number ofhomebased telephones increasing from 84 to 167 observations.
The 2003 figures for home-based telephones are large enough
to support the creation of hedonic models without the aid of
supplemental samples.
Since September 2000, NPD's data on cordless telephones has changed to include 2.4-QHz digital models and
5.8-GHz models. This modification leads to the assumption
that NPD's sample now includes model numbers of those types
of telephone. NPD also has improved its control methods
for its consumer electronics data and now offers a variety
of data delivery options that may be of interest to the
Bureau. 23
AN ARGUMENT CAN BE MADE FOR AND AGAINST the use of either
of the two data sources examined in this article in creating
hedonic regression models. The CPI sample is relatively small
and is not the best item-oriented representation of consumer
purchases. Moreover, its characteristic data are not as well
defined as the NPD characteristic data. The CPI sample is,
however, more representative of the entire market for
telephones than are any of the NPD samples, becaus"'e it
36
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Federal Reserve Bank of St. Louis
December 2004
NPD qualityadjusted index
1.48
5.74
-2.17
11
6
5
7
Mean
price
change
(percent)
3.19
5.74
.14
NPDV qualityadjusted index
Number
11
6
5
7
Mean
price
change
(percent)
-0.34
5.74
-7.62
NPDQ qualityindex adjusted
Number
11
6
5
7
Mean
price
change
(percent)
-0.97
5.74
-9.02
includes many more retail outlets than those samples do and
because the Bureau has direct control over data collection
and maintenance of the CPI sample. 24 In addition, the results
of the study presented indicate that the CPI model can be
used to make quality adjustments more often than any of the
NPD models can. In comparison, NPD data offer a large,
representative sample of actual consumer preferences, contain
accurate characteristic data, and have options for weighting
the data, but suffer a bias in retail outlet representation. Further,
the Bureau has little oversight in the upkeep of NPD data. An
ideal research sample would meld the coverage and
manageability of the CPI sample with the accuracy and
robustness of the NPD sample.
In this research, the age of the data examined is somewhat of a cause for concern, because applying the models
discussed to quality-adjusting substitutions used in calculating the CPI may not now be appropriate. Doubtless, the
parameter estimates of the telephone characteristics have
changed as prices for telephones and the value consumers
place on quality features have changed since the data were
collected. Further research is recommended to determine
whether using the by-now-outdated model for quality
adjustment is preferable to simply linking substitutions with
quality change. Aside from this issue, however, the challenging characteristics of the data sources still exist, and the
benefits of each sample continue to define its appeal to
researchers.
Currently, BLS researchers still use NPD data in hedonic
research studies, and new methods of model creation and
application are being considered. 25 The Bureau continues to
rely on CPI data to create hedonic models that are used to
adjust the prices of apparel items. As the course ofBLS hedonic
research continues to be charted, the future data needs of the
Bureau will evolve, perhaps rendering one data source more
suitable than another. Until a single source of satisfactory
data is found for its research needs, the Bureau will likely
further explore the use of other data providers, in addition to
continuing to use its own in-house data.
D
Percent of CPI sample by type of telephone, September 2000 and June 2003
Dollars
Dollars
45 ~ - - - - - - - - - - - - - - - - - - - --
- - - - - - - - - - - - - - - ---, 45
■ September 2000 ■ June 2003
40
40
35
30
25
20
15
10
5
0
Corded
900-MHz
analog
46-49
MHz
900-MHz
900-MHz
digital
DSS
2.4-GHz
analog
2.4-GHz
digital
2.4-GHz
5.8-GHz
DSS
DSS
Type of telephone
Average price of CPI sample by type of telephone, September 2000 to June 2003
1
Dollars
Dollars
200
200
■ September 2000 ■ June 2003
180
180
160
160
140
140
120
120
100
100
80
80
60
60
40
40
20
20
0
0
Corded
46-49
MHz
900-MHz
analog
900-MHz
digital
900-MHz
DSS
2.4-GHz
analog
2.4-GHz
digital
2.4-GHz
5.8-GHz
DSS
DSS
Type of telephone
1
The June 2003
that model.
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Federal Reserve Bank of St. Louis
CPI
sample included one 2.4-GHz analog telephone; hence, the average price is the price of
Monthly Labor Review
December 2004
37
Hedonic Regression Models
Notes
ACKNOWLEDGMENT: The author thanks Nicole Rope, Paul
Liegey, and Mary Kokoski for their guidance. The views and
opinions expressed herein are solely those of the author and do
not necessarily reflect the policies of the Bureau of Labor Statistics.
1
Most notably, the hedonic models for items of apparel and their
continual upkeep. (For more information on early work with hedonic
models for apparel items in the CPI, see P. Liegey, "Adjusting Apparel
Indexes in the Consumer Price Index for Quality Differences," in
Price Measurements and Their Uses, National Bureau of Economic
Research Studies in Income and Wealth, 57 (Chicago, University of
Chicago Press, 1993), pp. 209- 26, and "Apparel price indexes:
effects of hedonic adjustment," Monthly Labor Re view, May 1994,
pp. 38-45; and N. Shepler " Analysis of Hedonic Regression : Applied
to Women's Apparel in the Consumer Price Index," manuscript
(Bureau of Labor Statistics , 1994).
2
Between April and September in each of 1999 and 2000,
supplemental research samples were collected for motor vehicle painting,
film processing, refrigerators, microwave ovens, videocassette recorders
(vCR's), digital video disk (DVD) players, camcorders, telephones, and
dental restorations.
3
See M. Kokoski , K. Waehrer, and P. Rozaklis, " Using Hedonic
Methods for Quality Adjustment in the CPI: The Consumer Audio
Products Component," BLS working paper (Bureau of Labor Statistics,
2000), for an example of research conducted with this type of data.
4
A detailed explanation of NPD's aggregation procedure can be
found in Kokoski , Waehrer, and Rozakli s, " Using Hedonic Methods,"
or on NPD's Web site at www.npd.com .
5
The seven model numbers were either conference call stations or
extra handsets that are used with expandable tel ephone systems.
6
A complete history of cordless telephones is available on the
Internet at www.affordablephone s.net/HistoryCordless. htm .
Since the time of this report, the Commission has opened up the 5.8GHz frequency to cordless telephones as well.
7
More information on oss and other cordless telephon e
technologies can be found on the Internet at www.howstuffworks.
com/question326.htm .
8
The next section discusses the weighting methods of the NPD data
in further detail.
9
E. Diewert, " Hedonic Regressions: A Review of Some Unresolved
Issues," presentation given at the Seventh Ottawa Group Meeting on
Price Indices, Paris, May 2003.
10
The NPD sample is designed so that each observation represents
the average price of a unique model number, as calculated from I
month's total expenditure and sales volume for that model number.
11
Kokoski, Waehrer, and Rozaklis , " Using Hedonic Methods. "
12
The CPI rotates approximately 25 percent of its sample each
38 Monthly Labor Review
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Federal Reserve Bank of St. Louis
December 2004
year. It is during this process that the sample is most likely to incorporate
the newest and most popular products in the market.
IJ For a more detailed discussion of this survey, see BLS Handbook of Methods,
Bulletin 2490 (Bureau of Labor Statistics, April 1997), chapter 16.
14
Guides to the characteristic data in each sample are available from
the author.
15
Recommendations on functional forms in the hedonic model
literature can be found in Z. Griliches, "Introduc tion : Hedonic Price
Indexes Revisited," in Z. Griliches, ed., Price Indexes and Quality Change
(Cambridge, MA, Harvard University Press, 1971 ); and J. Triplett, "The
Theory of Hedonic Quality Measurement and Its Use in Price Indexes,"
BLS staff paper 6, 1971.
16
See E. V. Georges and P. Liegey, " An Examination Using Hedonic
Regression Techniques to Measure the Effects of Quality Adjustment on
Apparel Indexes," internal report (Bureau of Labor Statistics, 1988).
17
Diewert, " Hedonic Regressions."
18
The experimental models are intended to highlight the variation
found in the datasets and are not suggested to be superior to the final
models. Part of the reason for the latter claim is that the experimental
models do not utilize the available data to their fullest extent.
19
One of the 13 common brand names did not appear in the 67
common model numbers.
20
The imputation or " link" methodology is explained in greater
detail in the BLS Handbook of Methods, chapter 17, p. 186.
21
An additional benefit of employing hedonic models in evaluating
substitutions is that the analyst is able to rely on statistical tools, rather
than expert judgment alone, when making comparability de cisio ns .
Because of this approach , th e percentage of directly compared sub stitu tions increased under the reevaluation.
22 This
type of analysis was not possible with NPD data, because the
Bureau does not have access to a current NPD sample. Also, the September
2000 CPI sample is from the official CPI sample used in calculating indexes.
The su pplemental observations added to increase the sample size for
modeling were excluded from the analysis in order to compare official
CPI samples.
23
This information was reported to the Bureau by employees of NPD at
a meeting on March 24, 2003. The meeting was scheduled to discuss the
future relationship between the two organizations.
24
Specifically, the Bureau has the ability to find and correct erro· , in
the data in a timely manner by communicating directly with the data
collectors. It also has the capability of tailoring its data collection forms
to fit the marketplace and its research needs.
25 Research
projects using NPD data for televisions and stereo receivers
are currently underway. Ariel Pakes 's new method of model estimation
and application also is being studied. (See Ariel Pakes , " A Reconsideration of Hedonic Price Indexes with an Application to PC ' s,"
America n Economic Review, December 2003 , pp . I 578-96.)
,'x,,?
Research Summary
',,Bl:
New and emerging
occupations
Jerome Pikulinski
A ccording to the OES survey, in
~001 , most new and emerging
(N&E) occupations were in firms with
fewer than 100 employees. No single
industry dominated in the creation and
growth of these occupations. (See chart
1.) More than one-half of these were
distributed among human services,
transportation, communications, business and personal services, and a wide
Jerome Pikulinski is an economist formerly in
the Division of Occupational Employment Statistics, Bureau of Labor Statistics.
variety of wholesale and retail trade activities. Slightly more than half of all
N&E occupations were paid in a range
of $8.50 to $17. (See chart 2.) No
single State or single occupational classification dominated in the creation of
N&E occupations; however, healthcare,
management, and production occupations were the three most frequent occupation classifications observed. (See
chart 3.) Information on specific occupations that are new or emerging is presented below. 1
Construction field
• Metal stud framer
• Epoxy floor installers
New building systems, particularly in
commercial construction, and increased
use of new materials explain the appearance of new occupations in the traditional construction industry.
Educational services
• School diagnosticians
• Adaptive physical education
specialist
• Distance learning coordinators
• Poison information specialist
• Poison information technician
• Home-school liaison
• Technology infusion specialist
• Director of technology
• Technology coordinator
• Athletic compliance coordinator
Education continues to create N&E occupations. Some of these arise in connection with the objective of tailoring
Percent
45
Percent
45
40
40
35
35
30
30
25
20
•
N&E
•
National
25
20
15
15
10
10
5
5
0
1-49
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Federal Reserve Bank of St. Louis
50-99
100-249
250-999
1,000 and over
0
Employment size
Monthly Labor Review
December 2004
39
Research Summary
Relative distribution of N&E occupations by survey wage range
Percent
Percent
18 ~ - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - ~ 18
16
16
14
14
12
12
10
10
8
8
6
6
4
4
2
2
0
Under
6.75
6.75
to
8.49
8.50
to
10.74
10.75
to
13.49
13.50
to
16.99
0
17.00
to
21.49
21 .50
to
27 .24
27.25
to
34.49
34 .50
to
43.74
43.75
to
55.49
55.50
to
69.99
70.00
and
over
Survey wage range
Regional distribution of N&E occupations and national employment
Percent
30
Percent
30
-
•
N&E
National
25
25
20
20
15
15
10
10
5
5
0
Western
Southwestern
New England
Mid-Atlantic
Midwest
Southeastern
0
Region
NOTE : Regions are defined as follows : Western: Alaska ,
Arizona, California, Guam , Hawaii , Idaho, Nevada, Oregon, and
Washington ; Southwestern: Arkansas , Colorado, Kansas ,
Louisiana, Missouri, Montana, New Mexico, Oklahoma, Texas ,
Utah, and Wyoming ; Midwest: Illinois, Indiana, Iowa, Michigan ,
Minnesota, Nebraska, North Dakota, Ohio, South Dakota, and
40
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Federal Reserve Bank of St. Louis
December
2004
Wisconsin ; Southeastern :_Alabama, Florida, Georgia, Kentucky,
Mississippi, North Carolina , South Carolina, and Tennessee ; MidAtlantic: Delaware, District of Columbia, Maryland , New Jersey,
Pennsylvania , Virginia , and West Virginia ; New England :
Connecticut, Maine, Massachusetts, New Hampshire, New York,
Rhode Island , and Vermont.
educational services to students' special
needs. Others deal with the use of improved telecommunications applications to deliver education. Technology
and its general uses in education explain
the creation of other specialist occupations. Governmental regulations governing athletic and other physical education programs have contributed to other
occupations in special education and the
administration of athletic programs.
Heal th services
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Monitor technicians
Medical specimen couriers
Patient-care technicians
Urine sample collectors
Polysomnographic technicians
Tissue process technicians
CRN anesthesiologist
Tissue & eye bank technicians
Spiritual care giver
Tissue service coordinator
Genetic counselor
Sanitization technician
Medical certification clerk
Plasma processor
Schedulers for surgical cases
Night monitors
In the health field , N&E occupations
have addressed specialized patient
care, continuing responses to advancing medical technologies, improved
scheduling of surgical procedures, and
alternative medical service delivery
approaches. Increased attention has
been directed toward management and
care of tissue banks. In light of recent
genome developments, genetic counselors are appearing upon the medical
scene.
Social service
•
•
•
•
•
•
Bill review nurse
Adult protective services
Energy auditor
HazMat drivers
Weatherization director
Director information
management
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
• Cheer workers
• Disaster preparedness staff
There are several groups of occupations
in social services - nurses and information management workers employed
in new fields; workers helping seniors
and others in their homes; and disaster
preparedness staff. Nurses continue to
be employed in areas other than those
directly related to providing clinical
care services, primarily in the control
of medical costs. In addition, a new
occupation for nurses was found in the
legal field where they are employed as
legal nurse staff specialists. Information management, like nursing, is not a
new field, but one that continues to appear in a number of new industry settings. Senior and disabled persons are
creating situations calling for cheer
workers who provide opportunities for
therapeutic interaction and others who
investigate charges and complaints of
mistreatment. Drivers qualified to deliver hazardous materials may provide
oxygen to residences and other service
locations. Providing services for the
insulation and heating of residences has
created occupations in weatherization
and heating cost recovery. Finally, increased awareness of disaster preparedness is driving the creation of related
positions.
Transportation
• Horsedrawn carriage drivers
• Handicapped bus aides
Clearly carriage drivers is not a new
occupation; however, its appearance in
connection with the development of urban entertainment districts makes this
occupation noteworthy. Attention to the
mobility needs of the handicapped has
contributed to the creation of aide occupations to assist them on buses.
Service
• Surveillance person
• Producer-Internet provider (ISP)
•
•
•
•
•
•
•
•
•
•
•
Psychic counselors
Chief software architect
Match-makers
Web analyst
Bar-proof checker
Digital imagers and modelers
Customer insight analyst
Interactive media planner
Sr. supply chain manager
Televideo engineer
Divers-underwater inspectors
A variety of service occupations are
appearing . Some deal with security
needs. Others reflect cultural attitudes
about future uncertainties or finding a
mate. Behavioral science applications
to marketing are creating other kinds of
marketing research jobs. The continuing drive to improve the efficiency of
manufacturing operations through improved material management has created specialist positions. Increased attention to docks and ports has created
highly specialized underwater inspection jobs.
Special attention is called to the
Internet and telecommunications technologies. A variety of new specialized
occupations continue to appear as a
result of these, such as producers for
Internet service provider sites, web
analysts that study utilization patterns,
and interactive media planners. The
pattern in development of these N&E
occupations appears to have its parallel
in the development of new occupations
that followed the introduction of automotive technology. The latter industry
has continued to contribute to N&E
occupations for more than I 00 years.
The same pattern seems to be developing within the Internet and telecommunication industries.
Engineering services and manufacturing
•
•
•
•
•
•
Hazardous material engineer
Neon glass benders
Compliance engineer
Cultured marble caster
Laser engineer
Glue mixer
Monthly Labor Review
December 2004
41
Research Summary
•
•
•
•
•
•
•
•
Optical design engineer
Perfumers
Optical engineer
Translators
Roof truss designers
Missile specialists
Pharmacokineticist
Truss layout and assembly
workers
New materials and processes have contributed to the creation of new occupations while the regulatory concern for
the associated environmental and health
impacts of these have created additional
occupations. Lasers and various optical technologies continue to create new
occupations. Manufactured housing
components have created both design
and production occupations. The drug
industry has seen the creation of an occupation, pharmacokineticist, concerned with establishing dosage standards related to the drug availability of
retained drug dosages in patients. Some
occupations are not new but are once
again emerging due to consumer preferences. The cultural resurgence of
neon lighting has created the need for
neon benders. Market demand for cast
marble surfaces has resulted in more
work for those who cast it. Finally, the
growth of small-scale perfume distributors contributed to the appearance of
perfume mixers. Other occupations that
are not new but are emerging in engineering and sciences include translators
as manufacturers' foreign markets and
contacts increased. Maintenance and
renewal of the national defense capabilities is associated with the expanded
presence of missile specialists.
□
Note
1
For information on the concepts and methodology of identifying new and emerging occupations, as well as detailed data on the findings of
the Occupational Employment Statistics Survey,
see Jerome Pikulinski, "New and Emerging Occupations," Occupational Employment and
Wages , May 2003 (Bureau of Labor Statistics
Bulletin 2567, September 2004).
Where are you publishing your research?
The Monthly Labor Review will consider for publication studies of the labor force,
labor-management relations, business conditions, industry productivity, compensation,
occupational safety and health, demographic trends, and other economic developments.
Papers should be factual and analytical, not polemical in tone.
We prefer (but do not require) submission in the form of an electronic file in Microsoft
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text of the article; the tables; and charts. We also accept hard copies of manuscripts.
Potential articles should be mailed to: Editor-in-Chief, Monthly Labor Review, Bureau of Labor Statistics, Washington, DC 20212, or by e-mail to mlr@bls.gov
42
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December
2004
Workplace Safety and Health
,~~ ,
(?t!
Fatal occupational
injuries at road
construction sites
ries, it becomes even more important to
determine the types of workers involved
in road construction site fatalities and the
events that precipitate the fatalities. 4
Stephen Pegula
What is a road construction
site?
uring the 1995 to 2002 period, 844
workers were killed while working
at a road construction site.' More than
half of these fatalities were attributable
to a worker being struck by a vehicle or
mobile equipment. The range of these
fatal occupational injuries was a low of
93 in 1996 and a high of 124 in 1999,
as shown below:
D
1995
1996
1997
1998
········ 94
........ 93
........ 94
....... 113
1999 ········ 124
2000 ........ 106
2001 ........ 118
2002 ........ 102
Fatal workplace injuries at road construction sites were first identified as a
separate category in the Bureau of Labor Statistics Census of Fatal Occupational Injuries (cFot) in 1995. Since that
time, overall workplace fatalities have
generally declined, but fatalities at road
construction sites have fluctuated, staying in the low 100's since 1998. Workplace fatalities that occur at a road construction site typically account for 1.5
percent to 2.0 percent of all workplace
fatalities annually.
A number of safety measures exist
for road construction sites. For instance, the Federal Highway Administration's Manual on Uniform Traffic
Control Devices provides guidance
ranging from the types of signs to use
at a road construction site to the proper
use of rumble strips. 2 In addition, the
Federal Highway Administration offers
tips for motorists on traveling safely
through road construction sites. 3 As fatal work injuries at road construction
sites continue to account annually for a
large number of fatal occupational injuStephen Pegula is an economist in the Office of
Safety, Health, and Working Conditions, Bureau
of Labor Statistics.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
There are various definitions of what
constitutes a road construction site. According to the BLS Census of Fatal Occupational Injuries, a road construction site
includes, " ... road construction workers
and vehicle occupants fatally injured in
work zones. Work zones include construction, maintenance, and utility work
on a road, street, or highway." The Federal Highway Administration's Manual
on Uniform Traffic Control Devices
gives this definition, "A work zone is an
area of a highway with construction,
maintenance, or utility work activities. A
work zone is typically marked by signs,
channelizing devices, barriers, pavement
markings, and/or work vehicles. It extends from the first warning sign or highintensity rotating, flashing, oscillating, or
strobe lights on a vehicle to the END ROAD
WORK sign or the last TIC [temporary traffic control] device." 5
In this report, only fatal work injuries
that occurred at road construction sites as
defined by CFO! are included in the analysis. Fatal work injuries at road construction sites were identified in two ways.
First, all occupational fatalities that were
coded as having occurred at a road construction site were included. 6 Next, the
remaining CFO! record set was searched for
key variables that might indicate that a
fatal work injury did indeed occur at a
road construction site, but was not coded
as such. These variables include:
• Keywords. Records with narratives containing variations on the
following words were examinedzone, construction site, worksite,
pedestrian, road construction, road
site, flag, cone, road crew, high.way construction, street construction, barrel, manhole, road repair,
painting line, pothole, and sewer.
• Industry. All records in which the
decedent was employed in Standard Industrial Classification
(sic) 1611-Highway and Street
Construction; or sic 1622Bridge, Tunnel, and Elevated
Highway Construction; and
where the fatality occurred on a
roadway were examined.
• Occupation. All records in which
the decedent was employed, per
the U.S. Census Bureau Occupation Codes, as a construction laborer (869), operating engineer
(844), or paving, surfacing, and
tamping equipment operator (594),
and where the fatality occurred on
a roadway were examined.
• Worker activity. All records in
which the decedent was, as classified by the CFOI worker activity
codes, directing or flagging traffic ( 150); walking behind a vehicle (162); or resurfacing, blacktopping, etc. ( 140); and where the
fatality occurred on a roadway
were examined.
• Source and secondary source.
All records in which the source or
secondary source of the fatal
work injury, as classified by the
Occupational Injury and Illnesses
Classification System, was construction, logging, and mining
machinery (codes 3200 to 3299)
and where the fatality occurred on
a roadway were examined.
• Event. All records in which the
decedent was killed, as classified
in the Occupational Injury and Illnesses Classification System, by
being struck by a vehicle or mobile equipment and where the fatality occurred on a roadway were
examined.
Records found through this key variable search deemed to have occurred at
a road construction site (per the CFOI
definition), but not coded as road con£truction, were recoded for this report. 7
Monthly Labor Review
December 2004
43
Workplace Safety and Health
Limitations of the data. The consistency of the application of the road construction site location code in CFOI could
affect the data used for this analysis. An
examination of the CFOI narratives shows
that the road construction site location
code was applied more rigorously later
in the study period. 8 More cases in need
of recoding were found in the early
years of the study than in the latter years.
These different applications of the code
may skew the data; that is, the increase
in fatal work injuries at road construction sites over time may be partly due to
the more rigorous application of the location code in the latter years of the
study period.
country 's roads will mean that more road
construction sites will be needed. To
better protect workers, the Federal Government has taken steps to improve
safety in work zones. For example, in
2001 , the National Institute for Occupational Safety and Health (N1osH) published "Building Safer Highway Workzones: Measures to Prevent Worker Injuries From Vehicles and Equipment." 12
In addition, the National Work Zone
Safety Information Clearinghouse was
created in February of 1998 to improve
safety in highway work zones. 13 This
clearinghouse provides access to data,
training , and safety information for
workers at road construction sites.
Dangers at road construction
sites
Data analysis
Few work environments present the
multitude of risks as do road construction sites. For example, vehicles may
pass by at high speeds, and the work
conditions are constantly changing.
Data from the National Highway Traffic Safety Administration show that injuries at road construction sites are a
major concern. In 2001, 1,079 people
were killed at a road construction site. 9
This figure includes people who were
not at work at the time of their death,
such as occupants of vehicles passing
through road construction sites for
nonwork-related reasons.
Highway traffic is a concern for
workers at a road construction site, but
workers also face a similar danger from
vehicles and mobile equipment being
used at such sites. As shown later, fatally injured workers at road construction sites were more likely to be struck
and killed by construction vehicles and
equipment than by automobiles.
To improve the country's roads, Congress passed the Transportation Equity
Act for the 21st Century (TEA-21) in
1998. This act provided more than $200
billion dollars for transportation-related
programs. 10 This legislation is in the process of being renewed. 11 Improving the
44
Monthly Labor Review
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Federal Reserve Bank of St. Louis
December
sites, and 10 percent and 12 percent of
workplace fatalities to all workers.
In terms of age, approximately 70
percent (594) of the decedents were
between the ages of 25 and 54. Workers under age 25 made up 10 percent of
fatal work injuries incurred at a road
construction site and 11 percent of fatal
work injuries overall. Workers age 55
and older accounted for 20 percent of
the fatal work injuries incurred at a road
construction site and 22 percent of
workplace fatalities overall. Workers
killed at a road construction site were
largely working for wage and salary; approximately 96 percent (811) of the decedents were wage/salary workers,
while only 4 percent were self-employed. For overall workplace fatalities from 1995 to 2002, 80 percent of
the decedents were wage/salary workers and 20 percent were self-employed.
Demographics. As mentioned earlier,
over the 1995- 2002 period, 844 workers lost their lives due to
fatal work injuries inWorker fatalities at road construction sites
curred at a road construcover the 1995-2002 period, by selected
demographic characteristics
tion site. (See table 1.)
The workplace fatality
Characteristics
Number of fatalities
demographic breakTotal. .................... ....... ... ... ..... ......
down for this group was
844
Employee status:
very similar to the workWage and salary workers
811
place fatality demoSelf-employed
33
Gender:
graphic breakdown for
Male .... ........... .................. ..... ... ... ....
787
workers in general.
Female. ........ ... .................. ............ ..
57
Males accounted for 93
Age:
18 to 19 years .. ........ ....... ..... ........ ..
17
percent (787) of the
20 to 24 years .. .. .. ........ .... ..............
63
workplace fatalities at a
25 to 34 years ............. ......... .... ......
185
35 to 44 years ................................
213
road construction site ,
45 to 54 years ...... ........ .. ..... . .... ... ...
196
compared with 92 per55 to 64 years .. .... ..... ..... ...... .. ........
130
65 years and older ... .. ...... ........ ... ...
36
cent for all workplace
Race or ethnic origin:
fatalities. White workWhite .. ........... .. .. .............................
613
Black or African American .... .... ......
86
ers accounted for 73 perHispanic or Latino
118
cent (613) of the road
May include volunteers and other workers rece iving
construction site workcompensation.
2 Includes
paid and unpaid family workers, and may include
place fatalities and 73
owners of incorporated businesses, or members of partnerships.
percent of fatally injured
The categories "White" and "Black or African American" do not
include "Hispanic or Latino" persons.
workers overall. Black
Persons identified as Hispanic may be of any race.
workers and Hispanic
NoTE: Totals for 2001 exclude fatalities resulting from the
workers represented 10
September 11 terrorist attacks. Totals for major categories may
percent and 14 percent,
include subcategories not shown separately.
respectively, of workSouRcE: U.S. Department of Labor, Bureau of Labor Statistics,
in cooperation with State, New York City, District of Columbia, and
place fatalities occurring
Federal agencies, Census of Fatal Occupational Injuries.
at road construction
1
2
• •••• •• • • • •• •
• •• • • ••• •• •• •• • • • • •• • • • •• • • • •• •
3
4
1
3
4
2004
•• •• • •••• • • •• • • ••• • •• •• ••
...w:':':'"o-rk:-e-r':'fa"':t~a":li:tie_s_a~t_r_oa_d:--c-o-n-st'."'ru_c_t~io_n_s:it~e-s_o_v_e_r_t~h-e1
..,,
l!Bl!l'-::w::"o~r:'.':k':e':'r:fa:t~a:li:ti':'e~s~at=--roa"."':"d-,-c_o_n_s':'tr_u_c':'ti'."'o_n_s~it'."'e_s_o_v_e_r""'.t:""h_e,-~••l,,l.ullr_■
1995-2002 period, by State of incident
State of incident
Number of fatalities
Texas .. .... .............................. ..... .... ... .... ... .
California ... ... ...... ........... .. ........................ .
Florida ....... ........ ........ .. ........... ...... ...... ..... .
Ohio ...... .. ....... ........ ...... ..... .......... ....... .. ... .
Pennsylvania ... ... .... ..... ............................ .
71
51
46
46
44
New York ..... ... .................. ........ .................
Indiana ....... ........... ................................ ....
Illinois ..... ..... .............. ..... ................. ......... .
Virginia .. ............... ..................... ....... ....... ..
Georgia ... .. ...... ... ..... .. ...... ......................... .
40
38
36
36
32
NOTE: Totals for 2001 exclude fatalities resulting from the September 11
terrorist attacks.
SouRcE: U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies,
Census of Fatal Occupational Injuries.
■ r• 1 •u~-
Worker fatalities at road construction sites over
the 1995-2002 period, by event or exposure
Event or exposure
Number of fatalities
Transportation incidents .. .......... ...... ..... ......... .
693
Highway ........ ....... ............. ... ............... .....
Collision between vehicles, mobile
equipment ... ... .. .. .... ... ... .... .. .............. .
Moving in the same direction ..... ...... .
Moving and standing vehicle,
mobile equipment in roadway ..... ..
Noncollision .... .................. .. .... ............. .
Jack-knifed or overturned-no
collision .... ...... .. ...... ... ..... ........ ....... .
Nonhighway ............... ...... ......... .... .......... ..
Noncollision accident ............ ..... ... ....... .
Overturned .. ..................................... .
Worker struck by vehicle, mobile
equipment ......... .. ................ .. ..... ... ..... .. .
Worker struck by vehicle, mobile
equipment in roadway .... ...... ...... ...... .
Worker struck by vehicle, mobile
equipment on side of road ...... ... ..... ..
137
83
29
29
36
27
43
41
27
509
363
119
85
Contact with objects and equipment .. ..... ...... .
Struck by object .. ... ......................... ... ...... .
44
Falls ........ .... .. ... .......................................... .... .
28
Exposure to harmful substances and
environments ..... ... .. .................. ... ..... .. .... ....
Contact with electric current .... .... ... ......... .
Contact with overhead power lines ..... .
33
23
20
1995-2002 period, by industry and occupation
Number of
fatalities
Characteristics
Industry:
Private industry ......................... ... .. ................... ......
Construction .... ...... ......... ... ..... .... ........ ..... ... ... ... .. .
Heavy construction, except building ... .. .... ... ..
Highway and street construction ............... ..
Heavy construction, except highway .... ...... .
Bridge, tunnel, and elevated highway ..... .
Water, sewer, and utility lines .... ........... .. .
Special trade contractors ........ ... .. .... ........ ..... .
688
566
467
340
125
70
34
90
Transportation and public utilities .... ........ .... ...... .
Trucking and warehousing ... ........ .... ............ ..
Trucking and courier services,
except air .... ... ..... ... ..................... .... ......... .
Trucking , except local ....... ....... .. ......... ..... .
52
44
Services ....... ... ......... .. ... ... ........ .... ................. .. ....
34
Government 1 • • • •• •••• • ••• • ••• • •• • • ••• •• •••••••• • •• ••••••••• • •••• • • • ••••
State government ...... .. ....... .......... ..... ...... .......... .
Construction ... ... .. ..... ....... ..... .... ......... ... ......... .
Heavy construction , except building .... .... ...
Highway and street construction ..... ...... ...
Public administration ..................................... .
156
83
57
56
55
24
Local government .............. ................... .... .... ......
Construction .. .... .. .. ... ...................... .. ..... ..... ....
Heavy construction, except building .. ........ .
Highway and street construction ............. .
Public administration ........ ................... .......... .
70
Occupation :
Managerial and professional specialty ......... ............ .
Precision production, craft, and repair .............. ....... .
Construction trades ... ... ....... .. ... ... ..... .......... ... ..... .
Supervisors, construction occupations ............ .
Construction trades, except supervisors ......... .
Paving, surfacing, and tamping
equipment operators ............... ... .... ...... ... .
44
34
38
38
37
29
52
183
170
55
115
27
Operators, fabricators, and laborers ....... ....... ... ... .. .
Transportation and material moving
occupations .. ..................... .................... .... .... .
Motor vehicle operators .. ....................... ... ..... .
Truck drivers ..... ... ...... ... ... ....... ..... .......... ... .. .
558
Material moving equipment operators .......... .
101
Operating engineers ........ ...... ... .. ... ...... .... ... .
Grader, dozer, and scraper operators .... .....
54
27
Handlers, equipment cleaners , operators,
and laborers .... ... ......... .. ..... ............... ....... ..... .
Construction laborers .... ....... ...... .. ...... ............
359
335
186
85
83
1
Includes fatalities to workers employed in governmental organizations
regardless of industry.
NoTE: Totals for 2001 exclude fatalities resulting from the September 11
terrorist attacks. Totals for major categories may include subcategories not
shown separately.
NoTE: Totals for 2001 exclude fatalities resulting from the September 11
terrorist attacks. Totals for major categories may include subcategories not
shown separately.
SouRcE: U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies,
Census of Fatal Occupational Injuries.
SouRcE : U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies,
Census of Fatal Occupational Injuries.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
December 2004
45
Workplace Safety and Health
Texas had the largest number of
workplace fatalities at road construction
sites; 8 percent (71) of the workplace
fatalities occurred in this State. (See
table 2, page 45 .) Other States with a
large number of these types of occupational fatalities included California (6
percent), Florida (5 percent), Ohio (5
percent), Pennsylvania (5 percent), and
New York (5 percent).
operators (12 percent), and as truck
drivers (10 percent), among other occupations.14
Struck by vehicle or mobile equipment
incidents. Approximately 60 percent
sector, 82 percent (566) of the road construction site decedents worked in construction. (See table 4, page 45.) Most
of these construction fatalities (60 percent) were incurred by workers in highway and street construction. No other
major industry group in the private sector accounted for more than 8 percent
of the fatalities. Government workers
accounted for 18 percent ( 156) of the
workplace fatalities that occurred at a
road construction site. These fatalities
were incurred primarily by State and local government workers. As in the private sector, decedents working for a
government entity were most likely to
be working in highway and street construction.
Among occupations, 40 percent
(335) of the decedents worked as construction laborers. (See table 4, page
45.) The remaining decedents were employed in the construction trades (20
percent), as material moving equipment
(509 fatalities) of the occupational fatalities that occurred at road construction sites were the result of workers being struck by vehicles or mobile equipment. Construction laborers incurred
49 percent (247) of these fatalities. In
addition, 48 percent (242) of the decedents were working in the private highway and street construction industry.
Geographically, these incidents were
most likely to occur in Texas (9 percent,
or 46 fatalities, of all struck by vehicle
or mobile equipment workplace fatalities at road construction sites), Florida
(7 percent), California (6 percent),
Pennsylvania (6 percent) and Ohio (6
percent). (See table 5.)
For fatalities for which the time of incident was available, 29 percent of the
decedents who were struck by vehicles
or mobile equipment at a road construction site were struck between the hours
of 9:00 a.m. and 11:59 a.m., and 17 percent were struck between 6:00 a.m. and
8:59 a.m. These percentages were larger
than those for all fatal occupational injuries, where 23 percent occurred between
9:00 a.m. and 11 :59 a.m., and 13 percent
occurred between 6:00 a.m. and 8:59 a.m.
Fatalities at road construction sites from
being struck by a vehicle or mobile equipment also tend to be more clustered in the
daylight hours (6:00 a.m. to 5:59 p.m.)
than fatalities in general. Approximately
83 percent of the fatal work injuries incurred by workers at road construction
sites from being struck by a vehicle or
mobile equipment occurred in daylight
hours, while 75 percent of all fatal work
injuries occurred during these hours.
In struck by vehicle or mobile equipment cases, the vehicle or mobile equipment that struck the worker is the source
of the fatal injury. In 54 percent (274)
of the cases, a truck struck the worker.
Of these trucks, 36 percent were dump
46
2004
Event or exposure. More than four-fifths
(693) of occupational fatalities that occur at a road construction site were
caused by transportation incidents. Most
prevalent were workers who were struck
by 2 vehicle or mobile equipment, who
accounted for approximately 60 percent
(509) of all fatal work injuries that occurred at a road construction site. (See
table 3, page 45 .) Other fatal events of
note included highway collisions between vehicles or mobile equipment ( 10
percent of all fatal work injuries at a road
construction site), being struck by an object (5 percent), and falls (3 percent).
Industry and occupation. In the private
Monthly Labor Review
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Federal Reserve Bank of St. Louis
December
trucks, 21 percent were pickup trucks,
and 19 percent were semitrailer, tractor
trailer, or trailer trucks. Automobiles
were the source in 28 percent ( 143) of
all cases of struck by vehicle or mobile
equipment at road construction sites.
Finally, construction machinery, which
includes backhoes, levelers, planers,
scrapers, steamrollers, and road pavers,
accounted for 11 percent (56) of the
struck by vehicle or mobile equipment
fatalities. (See table 6.)
Note that workers at a road construction site faced a greater likelihood of being struck by a construction vehicle or
construction equipment than of being
struck by a car. While 28 percent of the
workers who were killed in struck by
vehicle or mobile equipment incidents at
a road construction site were struck by
automobiles, 31 percent were struck by
dump trucks or construction machinery.
With respect to the activity the decedent was performing when he or she
was struck by a vehicle or mobile equipment, 29 percent (147) were constructing, repairing, or cleaning. Approximately 28 percent were walking in or
near a roadway when they were struck,
11•1• 11
=--- Worker fatalities at road construction sites over the 1995-
2002 period, resulting from
being struck by a vehicle or
mobile equipment, by State
of incident
State
of
incident
Number
of
fatalities
Texas .......... ..... .... ............. .
Florida .. .. ... ....... .... .............
California ...... .. .. ........ ... ..... .
Pennsylvania ....... .......... ... .
Ohio ............. ......... ........... .
46
37
33
30
29
Illinois ... ..... .- .. ............... ..... .
Georgia ................. ....... .... .
NewYork ............. ........ ......
Virginia ...................... .. .... ..
North Carolina .. .. ...... ........ .
23
22
20
18
17
NOTE: Totals for 2001 exclude fatalities resulting from the September 11 terrorist attacks.
SouRcE: U.S. Department of Labor, Bureau
of Labor Statistics, in cooperation with State,
New York City, District of Columbia, and Federal
agencies, Census of Fatal Occupational Injuries.
Worker fatalities at road construction sites over the
1995-2002 period, resulting from being struck by a
vehicle or mobile equipment, by worker activity
Worker fatalities at road construction sites over the
1995-2000 period, resulting from being struck by a
vehicle or mobile equipment, by source of the fatality
Fatalities
Fatalities
Worker activity
Source
Number
All struck by vehicle or mobile equipment
fatalities 1 ••••••• ••••••••••• •• •••••••••••••••••••••••••••••••••••••
Vehicles .......... .... ... ... ................................... ....
Highway vehicle-motorized .. .. ....................... .
Automobile ....................... .............. ............. .
Truck ................................................ ............ .
Dump truck ........ .. ...................................... .
Pickup truck ......... .............. .. .. ............... ..... .
Semi-trailer, tractor trailer, or trailer
truck .... .... ... ............................................. .
Van ......................................... ..................... .
Machinery ........................................... .............. .
Construction, logging, and mining machinery
Excavating machinery ............... .. ........... ..... .
Backhoes ......... .. .................. ...................... .
Bulldozers ..... ......... ........ .......... .. ..... .. ......... .
Road grading and surfacing machinery ....... .
Graders, levelers, planers, and scrapers ....
Steam rollers and road pavers .................. .
Percent
509
446
441
143
274
100
57
100
88
87
28
53
14
63
56
21
10
54
20
11
3
12
11
4
2
9
1
6
6
30
20
4
6
1
' In struck by vehicle or mobile equipment fatalities, the source of the fatality
is the vehicle or mobile equipment that struck the decedent.
NorE: Totals for 2001 exclude fatalities resulting from the September 11
terrorist attacks. Totals for major categories may include subcategories not
shown separately.
SouRcE: U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies, Census of Fatal Occupational Injuries.
slightly more than 18 percent were directing or flagging traffic, and 7 percent
were resurfacing or blacktopping. (See
□
table 7.)
Notes
The author thanks
Dino Drudi , Samuel Meyer, Katharine Newman,
St ep hanie Pratt , Scott Ri c hardson , Bill
Wiatrowski , and Janice Windau for their assistance in the preparation of this article.
A C KNOWLEDGMENT:
Preliminary data for 2002 are used in thi s
analysis .
1
2
For more information on the Manual on
Uniform Traffic Control De vices, see http://
Number
All struck by vehicle or mobile equipment
fatalities ................................................... .
Vehicular and transportation operation ...... .
Resurfacing and blacktopping ................. .
Directing or flagging traffic ...... ... ..... ... .......
Walking in or near roadway ..................... .
Using or operating tools or machinery ...... .
Constructing, repairing, or cleaning .. .. .......
Construction, assembling, or
dismantling .... .. .... .... .... .. ............. ....... .. .
Constructing or assembling ....... ..... .. ... .
Installing .................. ............................ .
Dismantling or removing ...................... .
Repairs or maintenance ............ .............. .
Repairing .................... ... ......... ... ...... .....
Maintenance ....................... ................. .
Inspecting or checking ... .. ................. ....... .
Painting , etc . ............................... ............ ..
Material handling operations ......................
Physical activity, not elsewhere classified 1 •
See http://safety.fltwa.dot.gov/roaduser/
wzs.htm
4
For an examination of worker fatalities in
highway work zones from 1992 to 1998, see
pages 5 and 6 of Building Safer Highway Work
Zon es: M easures to Prevent Worker Injuries
from Vehicles and Equipment, on the Internet at:
http://www.cdc.gov/niosh/pdfs/O 1-128.pdf
5
See http://mutcd.fhwa.dot.gov/pdfs/
2003rl/Ch6A-E.pdf, page 6C-2.
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Federal Reserve Bank of St. Louis
8
The examination of the narratives should
mitigate the problems ari sing from the application of the location code. The breakdown for
added records is as follows:
1995
1996
1997
1998
......... 63
......... 47
......... 36
......... 37
66
10
14
8
30
13
2
3
2
6
3
2
4
2
2
9
17
9
18
11
12
46
SouRcE: U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies,
Census of Fatal Occupational Injuries.
7
Ascertaining whether a record should be
recoded as a road construction fatality was sometimes complicated by vague and/or incomplete
narratives. For the borderline cases, the determination as to whether a fatality occurred at a road
construction site was made by examining the combination of the narrative, industry, occupation, and
worker activity. Because there are various defini tions of what constitutes a road construction site,
different people may make different determinations as to whether a fatal work injury occurred at
a road construction site. For this analysis, the reclassifications were made by the author with input from CFOI staff. These reclassifications were
based on a consistent set of requirements foffiluIated by the author and CFOI staff.
Records
added
100
55
7
18
28
3
29
NoTE: Totals for 2001 exclude fatalities resulting from the September 11
terrorist attacks. Totals for major categories may include subcategories not
shown separately.
6 CFO I uses a location code of 65 to designate
fatal work injuries that occur at road construction sites.
Years
509
278
38
93
141
17
147
' Includes walking, sitting, running, and climbing ladders or stairs.
mu tcd. flt wa.dot.gov/
3
Percent
Years
1999
2000
2001
2002
Records
added
........
........
........
...... ..
53
43
45
24
In total , 496 records were included because
the location code was 65-road construction. An
additional 348 were added after examining
records.
9
See Table 61 in http://www-nrd.nhtsa.
dot.gov/pdf/nrd-30/NCSA/fSFAnn/fSF2001.
pdf
°
1
Camille Villanova, "Looking for Safety
Zones," Job Safety and Health Quarterly, Vol. 11,
No . 3, p. 19. For more information on TEA-2I,
see http://www.fltwa.dot.gov/tea2 l/
11
For more information on the reauthorization on TEA-2I, see http://www.fltwa.dot.gov/
reauthorization/index.htm
12
See http://www.cdc.gov/niosh/pdfs/Ol-
128.pdf
13
The National Work Zone Safety Information Clearinghouse was the product of collaboration between the American Road & Transportation Builders Association (ARTBA) and the Federal Highway Administration. Now, it is run
jointly by ARTBA and the Texas Transportation
Institute. For more information, access http://
wzsafety.tamu.edu and http://wzsafety.tamu.
edu/files/brochure.stm
14
Material moving equipment operators include occupations such as operating engineers;
excavating and loading machine operators; and
grader, dozer, and scraper operators.
Monthly Labor Review · December 2004
47
The business cycle and
earnings and incom e
inequality
electricity and increased mechani-zation
of production that occurred in the early
20th century, and the widespre ad
computerization that occurred later in
the century. Individuals differ in their
ability to incorpor ate the new
technology. As a result, they differ in
their ability to take advantage of itsome are able to benefit from the new
technolog y relatively quickly, while
others are slower to absorb it and thus
lose ground, economically, relative to
their peers. Eventually, however, these
"laggards " begin to catch up as they
embrace the new technolog y. Thus,
technological innovations first tend to
. increase earnings inequality, but in the
longer term they tend to decrease it-at
least until the next innovation comes
along.
In their empirica l analysis , the
authors "begin with the one episode of
declining earnings inequality during the
past century," the period from the late
1920s to the early 1950s. They then
examine two periods of increasin g
earnings inequalit y-the early 1900s to
the late 1920s and the late 1960s to "at
least the end of the century." The
empirica l findings support the
predictio ns of their model, which
attributes growth in earnings inequality
to "technol ogical upheava ls." Inequality increased in the early 20th
century due to the introduc tion of
electricity and mechanized production.
It decreased during the middle period,
as the benefits of the new technology
spread throughout the economy. Then,
in the latter portion of the century,
earnings inequality increased again with
the widespread computerization of U.S.
industry.
Economists and other analysts generally
agree that inequality in both earnings
and income increased over the last 40
years. Moreover, such inequality tended
to accelerate in periods of economic
downturn during that span. Together,
these facts have led to renewed interest
in the extent to which earnings and
income inequality are affected by the
business cycle, especially recessions.
In a recent study, "Earnings Inequality
and the Business Cycle," NBER Working
Paper 10469, economists Gadi Barlevy
(Federal Reserve Bank of Chicago) and
Daniel Tsiddon (Tel Aviv University)
examine this issue.
The authors develop a model in
which 1ong-term trends in earnings and
income inequality are amplified during
recessio ns, regardle ss of whether
inequalit y is generally increasin g or
decreasing. In other words, the model
predicts that during periods when
inequality is growing, recessions will
tend to exacerba te that trenu and
inequality will increase more rapidly.
However , during periods when inequality is decreasing, recessions will
tend to accelerate that trend as well, and
inequality will decrease more rapidly. To
test their model empirically, the authors
examine data from the entire 20th
century. They find that, indeed,
"downtur ns are associated with more
rapid growth in inequality in [long-term]
periods of rising inequality but with more
rapid reductions in inequality in periods
of falling inequality."
Barlevy and Tsiddon provide some
explanations for their findings. They
Siblings and earnings
argue, for example , that the U.S.
inequality
economy periodic ally experien ces
"waves of drastic technolo gical In the Decembe r 2004 Chicago Fed
innovation," such as the introduction of Letter, economist Bhashkar Mazumder
48
Monthly Labor Review
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Federal Reserve Bank of St. Louis
Decembe r 2004
presents new research on siblings and
earnings inequality. His essay, "What
similariti es between siblings tell us
about inequality in the U.S.," considers
the question, "How important is family
background in determining economic
success in the United States?"
To address this question, Mazumder
analyzes data from the National
Longitudinal Survey of Youth (NLSY).
The dataset consists of a sample of more
than 12,000 men and women who were
between ages 14 and 22 in 1979. (This
longitudi nal survey is ongoing; see
http://www.bls.gov/nls/nlsy79.htm for
more information). The sample includes
more than 4,000 pairs of siblings.
For men, Mazumder estimates that
the correlation among siblings in annual
earnings is 0.49 and the correlation for
hourly wages is 0.54. For women, he
notes that the results tend to be lower,
which he explains is "not surprisin g
given the more varied labor force
participa tion patterns for younger
women."
Mazumder concludes that about half
of earnings inequalit y in the United
States is accounte d for by family
background. He writes that his finding
about the correlation among siblings in
economi c outcome s "suggest s that
inequalit ies between families persist
strongly from generation to generation
and that the U.S. is a less mobile
society than is commonly believed. "
We are interested in your feedback
on this column. Please let us know
what you have found most
interesti ng and what essentia l
readings we may have missed. Write
to: Executive Editor, Monthly Labor
Review, Bureau of Labor Statistics,
Washington, oc 20212; fax: (202) 6915899, ore-mail: mlr@bls.gov
Israel's economy
The Israeli Economy, 1985-1998: From
Government Intervention to Market
Economics. Edited by Avi BenBassat. Cambridge, MA , Massachusetts Institute of Technology, 2002,
514 pp., $40/cloth.
As editor, Avi Ben-Bassat presents a
series of 14 essays based on a 1996
research project by variou s J,;;raeli
academic and financial institutions as
well as the International Monetary
Fund, plus his own contribution in the
form of an introductory essay discussing the obstacles Israel faced in its
transformative efforts. These essays
detail efforts to reform Israel from a
poorly performing centralized command economy to a dynamic market
economy. The Israeli Economy, 19851998 explores the obstacles faced and
transformations experienced during
this transition period. These essays
describe the fiscal and monetary
changes, changes in the markets , and
external developments that allowed
Israel to move from a situation where
it faced 400 percent inflation to relative stability and growth .
After the intensified conflicts of the
1967 Six Day War and the 1973 Yorn Kippur War, Israel reacted by centralizing
decisionmaking and controlling resources. These conflicts resulted in the
growth of the public sector and the public deficit. Lax fiscal discipline and the
growth of government, due to increased
defense expenditures and subsidies to
businesses, resulted in severe economic
decline. After a relaxation in the intensity of the Israeli-Arab conflict during
the latter part of the 1970s and early
1980s, Israel was in a better position to
address the economic difficulties it faced
in the forms of high inflation and almost
nonexistent growth of gross domestic
product (GDP).
To ~ddress these economic difficulties, Israeli policies needed some serious reform. How Israel addressed its
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Federal Reserve Bank of St. Louis
reform program is described in six major
headings: fiscal policy; monetary policy;
financial market reforms; reforms in the
goods and services markets; immigration
and the labor market; and changes in
growth, branch structure, and income
distribution.
Changes in fiscal policy are explained
in the first essay showing efforts at implementing tight fiscal discipline and a reduction in the size of government. Because of fiscal reform, three areas saw
the greatest change in an effort to reduce expenditures: defense spending ;
subsidies to the business sector and interest payments; and welfare transfers
to households. Another part of these
efforts was the drive to reduce public
expenditures through the elimination of
interest payments on external debts that
approached 80 percent of GDP.
Reform of monetary policy, as part
of these stabilization program efforts,
is addressed in three essays: reform
of the financial system; di sinflation
from 1985 to 1998 ; and the international view oflsraeli inflation. Israel's
greate s t accompli s hment was to
achieve disinflation. Liberalization is
shown to be a key to enhancing competition in the financial markets in this
accomplishment, as well as the Bank
of Israel's (Bol) increased independence and use of interest rates as an
instrument of monetary policy. These
measures were fairly successful when
combined with a reduction in the government budget deficit through fiscal
discipline . The program did not work
as well as it could have due to political pressures; changes in employment
due to increased immigration from the
former Soviet Union; and Israel's inability to reach a consensus about the
process as a whole , shown by a conflict between the BoI and the Treasury.
Achieving disinflation proved a
difficult process when compared to
nine other countries- all of which went
from high inflation to lowered inflation levels with varying results. Despite set-back periods of double-digit
inflation rates, Israel, by 1998, was
experiencing mid single-digit rates.
Liberalization of financial markets also
saw mixed results, but in general the
outcome has been positive.
Liberalization of financial markets
has allowed many projects to proceed
when previously they would not have
done so under government-provided
funding, while others of less likely
profit have been dropped. Because
of more reliance for funding from the
Tel Aviv Stock Exchange and less from
bank debt or government-provided
credits, credit crunches and reduced
output volatility have been reduced
over the business cycle , and results
of this program are fairly solid.
Pension reform, however, is a work in
progress. Balancing social needs of pensioners against relieving the younger
generation from the burden of a large
share of income support is difficult. The
solution is to accumulate more pension
savings while workers are young, thus
sharing the burden of risk between the
generations.
Fortunately for these workers, the
unemployment rate dropped from 12 percent to 6 percent during the 1990s. This
effect was produced by changes in immigration and liberalization of the goods
and services market. These two issues
are addressed separately, but are closely
intertwined. While immigration increased during the 1990s due to the fall
of the Soviet Union and an increase in
migrants from Eastern Europe, the labor
market grew as well. With the diminishment of economic centralization came
growth in the labor market as industries
increased efficiency efforts. In the first
half of the 1990s, growth was greatest in
exposed industries that were open to
competition from imported items and required lower skills. In the second half,
growth was seen most in more sophisticated industries that required skilled
workers.
Changes in capital accumulation and
increased productivity resulted in
changes to the labor market. It also re-
Monthly Labor Review
December 2004
49
Book Reviews
sulted in challenging questions of income distribution. While Israel was
growing in a dynamic market economy
with immigration increases, income inequalities were also growing. The approach to these questions, and how Israe 1 is going to answer them, is addressed in the book's final chapters.
A constant theme emerges as the authors describe the complex, and often
contradictory, events and efforts to reform. Sectors and industries that are
opened to competition thrive and be-
50
Monthly Labor Review
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Federal Reserve Bank of St. Louis
come more efficient, providing benefits
to both labor and consumers. Efficiency
aids efforts to globalize Israel's economy,
though it also is more exposed to external crises. Another recurring issue of
many essays is that success is not seen
in every effort to liberalize, as is the case
in some public utilities.
Because this book is a collection of
studies analyzing one country going
through one process, there are some redundancies. This is unavoidable because this one process is complex and
December 2004
has many levels that are affected by the
same events. The essays provide a nonpartisan view of an economic system in
transformation, and a good argument for
market solutions versus dirigiste philosophies toward providing a healthy
economic environment.
-Scott Berridge
Office of Publications
and Special Studies,
Bureau of Labor Statistics
',ii
',~~
Current Labor Statistics
Notes on labor statistics ..............................
Comparative indicators
52
30. Employment Cost Index, compensation.................... ......... 96
1. Labor market indicators .... .... .................... .... .................... 65
2. Annual and quarterly percent changes in
compensation, prices, and productivity ....................... 66
3. Alternative measures of wages and
compensation changes ................................................... 66
Labor force data
4. Employment status of the population,
seasonally adjusted ....... ................................................
5. Selected employment indicators,
seasonally adjusted .......................................................
6. Selected unemployment indicators,
seasonally adjusted .......................................................
7. Duration of unemployment,
seasonally adjusted .......................................................
8. Unemployed persons by reason for unemployment,
seasonally adjusted .......................................................
9. Unemployment rates by sex and age,
seasonally adjusted .......................................................
l 0. Unemployment rates by States,
seasonally adjusted .......................................................
11. Employment of workers by States,
seasonally adjusted .......................................................
12. Employment of workers by industry,
seasonally adjusted .......................................................
13. Average weekly hours by industry,
seasonally adjusted .......................................................
14. Average hourly earnings by industry,
seasonally adjusted........................................................
15. Average hourly earnings by industry ................................
16. Average weekly earnings by industry ...............................
17. Diffusion indexes of employment change,
seasonally adjusted .......................................................
18. Job openings levels and rates, by industry and regions,
seasonally adjusted.........................................................
19. Hires levels and rates by industry and region,
seasonally adjusted..........................................................
20. Separations levels and rates by industry and region,
seasonally adjusted..........................................................
21. Quits levels and rates by industry and region,
seasonally adjusted..........................................................
22. Quarterly Census of Employment and Wages,
IO largest counties.........................................................
23. Quarterly Census of Employment and Wages, by State
24. Annual data: Quarterly Census of Employment
and Wages, by ownership .............................................
25. Annual data: Quarterly Census of Employment and Wages,
establishment size and employment, by supersector ...
26. Annual data: Quarterly Census of Employment and
Wages, by metropolitan area .........................................
27. Annual data: Employment status of the population ........
28. Annual data: Employment levels by industry ..................
29. Annual data: Average hours and earnings level,
by industry............................... ......................................
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Federal Reserve Bank of St. Louis
Labor compensation and collective
bargaining data
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 firm s
and government .. .. .... .. .. .. .. .... .. .. .... ... .. ..... .. .. .. ...... .... .. .....
36. Work stoppages involving 1,000 workers or more ...........
67
Price data
68
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 .....................................................
69
69
70
71
72
73
74
77
78
79
80
98
99
l 00
101
l 02
l 03
104
I 07
I 08
I 09
110
111
112
113
114
115
116
Productivity data
81
82
82
83
83
84
86
87
48. Indexes of productivity, hourly compensation,
and unit costs, data seasonally adjusted .... .... ...............
49. Annual indexes of multifactor productivity .... ........ ..........
50. Annual indexes of productivity, hourly compensation,
unit costs, and prices ......... ................... ........................
51. Annual indexes of output per hour for select
industries........................ ....... ........................................
117
118
1 19
120
International comparisons data
52. Unemployment rates in nine countries,
53. Annual data: Employment status of the civilian
working-age population, 10 countries............ .............. .. 124
54. Annual indexes of productivity and related measures,
12 countries....... ................................ .... ........................ 125
88
89
94
94
95
Injury and Illness data
55. Annual data: Occupational injury and illness
incidence rates ............. .................. ..... .......... .................. . 126
56. Fatal occupational injuries by event or exposure.. ........... 128
Monthly Labor Review
December 2004
51
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 1 and 4-9
were revised in the February 2004 issue of
the Review. Seasonally adjusted establishment survey data shown in tables 1 12-14,
and 17 were revised in the March 2004 Review. A brief explanation of the seasonal
adjustment methodology appears in "Notes
on the data."
Revisions in the 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 mt1cte 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
52
Monthly Labor Review
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Federal Reserve Bank of St. Louis
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/lpd
For additional information on interna-
December 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 (ECI) program. The labor force
participation rate, the employment-popu lation ratio, and unemployment rates for major demographic groups based on the Current Population (" household") Survey are
presented, while measures of employment
and average weekly hours by major industry sector are given using nonfarm payroll
data. The Employment Cost Index (compensation), by major sector and by bargaining
status, is chosen from a variety of BLS
compensation and wage measures because
it provides a comprehensive measure of
employer costs for hiring labor, not just
outlays for wages, and it is not affected
by employment shifts among occupations
and industries.
Data on changes in compensation,
prices, and productivity are presented in
table 2. Measures of rates of change of compensation and wages from the Employment
Cost Index program are provided for all civilian nonfarm workers (excluding Federal
and household workers) and for all private
nonfarm workers . Measures of changes in
consumer prices for all urban consumers;
producer prices by stage of processing; overall prices by stage of processing; and overall export and import price indexes are
given. Measures of productivity (output per
hour of all persons) are provided for major
sectors.
Alternative measures of wage and
compensation rates of change, which reflect the overall trend in labor costs, are summarized in table 3. Differences in concepts
and scope, related to the specific purposes
of the series, contribute to the variation in
changes among the individual measures.
Notes on the data
Definitions of each series and notes on the
data are contained in later sections of these
notes describing each set of data.
Employment and
Unemployment Data
(Tables I; 4--29)
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 16 years of
age and older. Households are interviewed
on a rotating basis, so that three-fourths of
the sample is the same for any 2 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 businesses and government agencies, which represent approximately
400,000 individual worksites and represent
all industries except agriculture. The active
CES sample covers approximately one-third
of all nonfarm payroll workers. Industries
are classified in accordance with the 2002
North American Industry Classification System. In most industries, the sampling probabilities are based on the size of the establishment; most large establishments are
therefore in the sample. (An establishment
is not necessarily a firm; it may be a branch
plant, for example, or warehouse.) Self-employed persons and others not on a regular
civilian payroll are outside the scope of the
survey because they are excluded from establishment records. This largely accounts for
the difference in employment figures between
the household and establishment surveys.
Em_~>loyed persons include ( 1) all those
cps/rvcps03.pdf).
Definitions
who worked for pay any time during the
week which includes the 12th day of the
month or who worked unpaid for 15 hours
or more in a family-operated enterprise and
(2) those who were temporarily absent from
their regular jobs because of illness, vacation, industrial dispute, or similar reasons.
A person working at more than onl': 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.pdf) 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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Current Labor Statistics
in each establishment which reports them.
Production workers in the goods-prod uc ing industries cover employees, up
through the level of working supervisors,
who engage directly in the manufacture or
construction of the establishment's product.
In private service-providing industries, data
are collected for nonsupervisory workers,
which include most employees except those
in executive, managerial, and supervisory
positions. Those workers mentioned in
tables 11-16 include production workers in
manufacturing and natural resources and
mining; construction workers in construction; and nonsupervisory workers in all private service-providing industries. Production and nonsupervisory workers account
for about four-fifths of the total employment
on private nonagricultural payrolls.
Earnings are the payments production
or nonsupervisory workers receive during
the survey period, including premium pay
for overtime or late-shift work but excluding irregular bonuses and other special
payments. Real earnings are earnings adjusted to reflect the effects of changes in
consumer prices. The deflator for this series is derived from the Consumer Price Index for Urban Wage Earners and Clerical
Workers (CPI-W).
Hours represent the average weekly
hours of production or nonsupervisory
workers for which pay was received, and are
different from standard or scheduled hours.
Overtime hours represent the portion of average weekly hours which was in excess of
regular hours and for which overtime premiums were paid.
The Diffusion Index represents the percent of industries in which employment was
rising over the indicated period, plus onehalf of the industries with unchanged employment; 50 percent indicates an equal balance between industries with increasing and
decreasing employment. In line with Bureau
practice, data for the 1-, 3-, and 6-month
spans are seasonally adjusted, while those
for the 12-month span are unadjusted. Table
17 provides an index on private nonfarm
employment based on 278 industries, and a
manufacturing index based on 84 industries.
These indexes are useful for measuring the
dispersion of economic gains or losses and
are also economic indicators.
Notes on the data
Establishment survey data are annually adjusted to comprehensive counts of employment (called "benchmarks"). The March
2003 benchmark was introduced in February 2004 with the release of data for January 2004, published in the March 2004 is-
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sue of the Review. With the release in June
2003, CES completed a conversion from the
Standard Industrial Classification (S IC) system to the North American Industry Classification System cNAICS) 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
December 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 1) 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. Se~sonally adjusted unemployment rates are presented in table 10.
Insofar as possible, the concepts and definitions underlying these data are those
used in the national estimates obtained
from the CPS.
Notes on the data
Data refer to State of residence. Monthly
data for all States and the District of Columbia are derived using standardized procedures established by BLS. Once a year,
estimates are revised to new population controls, usually with publication of January
estimates, and benchmarked to annual average CPS levels.
FOR ADDITIONAL INFORMATION on data in
this series, call (202) 691-6392 (table 10)
or (202) 691-6559 (table 11).
Quarterly Census of
Employment and Wages
Description of the series
Employment, wage, and establishment data
in this section are derived from the quarterly tax reports submitted to State employment security agencies by private and
State and local government employers sub-
ject to State unemploymen t insurance (u1)
laws and from Federal, agencies subject
to the Unemployme nt Compensation for
Federal Employees (uCFE) program. Each
quarter, State agencies edit and process the
data and send the information to the Bureau of Labor Statistics.
The Quarterly Census of Employment
and Wages (QCEW) data, also referred as ES202 data, are the most complete enumeration
of employment and wage information by industry at the national, State, metropolitan
area, and county levels. They have broad economic significance in evaluating labor market trends and major industry developments.
Definitions
In general, the Quarterly Census of Employment and Wages monthly employment data
represent the number of covered workers
who worked during, or received pay for, the
pay period that included the 12th day of the
month. Covered private industry employment includes most corporate officials, executives, supervisory personnel , professionals, clerical workers, wage earners, piece
workers, and part-time workers. It excludes
proprietors, the unincorporate d self-employed, unpaid family members, and certain
farm and domestic workers. Certain types
of nonprofit employers, such as religious organizations, are given a choice of coverage
or exclusion in a number of States. Workers
in these organizations are, therefore, reported to a limited degree.
Persons on paid sick leave, paid holiday,
paid vacation, and the like, are included. Persons on the payroll of more than one firm
during the period are counted by each u1subject employer if they meet the employment definition noted earlier. The employment count excludes workers who earned no
wages during the entire applicable pay period because of work stoppages, temporary
layoffs, illness, or unpaid vacations.
Federal employment data are based on
reports of monthly employment and quarterly wages submitted each quarter to State
agencies for all Federal installations with
employees covered by the Unemploymen t
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
receive<l pay for the pay period that included
the 12th of the month.
An establishment is an economic unit,
such as a farm, mine, factory, or store, that
produces goods or provides services. It is
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typically at a single physical location and
engaged in one, or predominantly one, type
of economic activity for which a single industrial classification may be applied. Occasionally, a single physical location encompasses two or more distinct and significant
activities. Each activity should be reported
as a separate establishmen t if separate
records are kept and the various activities are classified under different NAICS
industries.
Most employers have only one establishment; thus, the establishment is the predominant reporting unit or statistical entity for
reporting employment and wages data. Most
employers, including State and local governments who operate more than one establishment in a State, file a Multiple Worksite Report each quarter, in addition to their quarterly u1 report. The Multiple Worksite Report is used to collect separate employment
and wage data for each of the employer's
establishments, which are not detailed on the
u1 report. Some very small multi-establishment employers do not file a Multiple
Worksite Report. When the total employment in an employer's secondary establishments (all establishments other than the largest) is 10 or fewer, the employer generally
will file a consolidated report for all establishments. Also, some employers either cannot or will not report at the establishment
level and thus aggregate establishments into
one consolidated unit, or possibly several
units, though not at the establishment level.
For the Federal Government, the reporting unit is the installation: a single location at which a department, agency, or other
government body has civilian employees.
Federal agencies follow slightly different criteria than do private employers when breaking down their reports by installation. They
are permitted to combine as a single statewide unit: 1) all installations with IO or fewer
workers, and 2) all installations that have a
combined total in the State of fewer than 50
workers. Also, when there are fewer than 25
workers in all secondary installations in a
State, the secondary installations may be
combined and reported with the major installation. Last, if a Federal agency has fewer
than five employees in a State, the agency
headquarters office (regional office, district
office) serving each State may consolidate
the employment and wages data for that State
with the data reported to the State in which
the headquarters is located. As a result of
these reporting rules , the number of reporting units is always larger than the number
of employers (or government agencies) but
smaller than the number of actual establishments (or installations).
Data reported for the first quarter are
tabulated into size categories ranging from
worksites of very small size to those with
1,000 employees or more. The size category
is determined by the establishment' s March
employment level. It is important to note that
each establishment of a multi-establish ment
firm is tabulated separately into the appropriate size category. The total employment
level of the reporting multi-establish ment
firm is not used in the size tabulation.
Covered employers in most States report
total wages paid during the calendar quarter, regardless of when the services were performed. A few State laws, however, specify
that wages be reported for, or based on the
period during which services are performed
rather than the period during which compensation is paid. Under most State laws or
regulations, wages include bonuses, stock
options, the cash value of meals and lodging, tips and other gratuities, and, in some
States, employer contributions to certain deferred compensation plans such as 401 (k)
plans.
Covered employer contributions for oldage, survivors, and disability insurance
(OASDI), health insurance, unemploymen t insurance, workers' compensation, and private
pension and welfare funds are not reported
as wages. Employee contributions for the
same purposes, however, as well as money
withheld for income taxes, union dues, and
so forth, are reported even though they are
deducted from the worker 's gross pay.
Wages of covered Federal workers represent the gross amount of all payrolls for
all pay periods ending within the quarter.
This includes cash allowances, the cash
equivalent of any type of remuneration, severance pay, withholding taxes, and retirement deductions. Federal employee remuneration generally covers the same types of
services as for workers in private industry.
Average annual wage per employee for
any given industry are computed by dividing total annual wages by annual average employment. A further division by 52 yie lds
average weekly wages per employee. Annual
pay data only approximate annual earnings
because an individual may not be employed
by the same employer all year or may work
for more than one employer at a time.
Average weekly or annual wage is affected by the ratio of full-time to part-time
workers as well as the number of individuals in high-paying and low-paying occupations. When average pay levels between
States and industries are compared, these
factors should be taken into consideration.
For example, industries characterized by
high proportions of part-time workers will
Monthly Labor Review
December 2004
55
Current Labor Statistics
show average wage levels appreciably less
than the weekly pay levels of regular fulltime employees in these industries. The opposite effect characterizes industries with
low proportions of part-time workers, or industries that typically schedule heavy weekend and overtime work. Average wage data
also may be influenced by work stoppages,
labor turnover rates, retroactive payments,
seasonal factors , bonus payments, and so on.
Notes on the data
Beginning with the release of data for 2001 ,
publications presenting data from the Covered Employment and Wages program have
switched to the 2002 version of the North
American Industry Classification System
(NAICS) as the basis for the assignment and
tabulation of economic data by industry.
NAICS is the product of a cooperative effort
on the part of the statistical agencies of the
United States, Canada, and Mexico. Due to
difference in NAICS and Standard Industrial
Classification (SIC) structures, industry data
for 2001 is not comparable to the SIC-based
data for earlier years .
Effective January 2001 , the program began assigning Indian Tribal Councils and related establishments to local government
ownership. This BLS action was in response
to a change in Federal law dealing with the
way Indian Tribes are treated under the Federal Unemployment Tax Act. This law requires federally recognized Indian Tribes to
be treated similarly to State and local governments. In the past, the Covered Employment and Wage (CEW) program coded Indian
Tribal Councils and related establishments
in the private sector. As a result of the new
law, CEW data reflects significant shifts in
employment and wages between the private
sector and local government from 2000 to
2001. Data also reflect industry changes.
Those accounts previously assigned to civic
and social organizations were assigned to
tribal governments. There were no required
ind11 ~try changes for related establishments
owned by these Tribal Councils. These tribal
business establishments continued to be
coded according to the economic activity of
that entity.
To insure the highest possible quality
of data, State employment security agencies verify with employers and update, if
necessary, the industry, location, and ownership classification of all establishments
on a 3-year cycle. Changes in establishment classification codes resulting from the
verification process are introduced with the
data reported for the first quarter of the year.
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Changes resulting from improved employer
reporting also are introduced in the first
quarter. For these reasons, some data, especially at more detailed geographic levels, may not be strictly comparable with
earlier years.
County definitions are assigned according to Federal Information Processing Standards Publications as issued by the National
Institute of Standards and Technology. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those areas designated by the Census Bureau where counties
have not been created. County data also are
presented for the New England States for
comparative purposes, even though townships are the more common designation used
in New England (and New Jersey).
The Office of Management and Budget
(0MB) defines metropolitan areas for use in
Federal statistical activities and updates
these definitions as needed. Data in this table
use metropolitan area criteria established by
0MB in definitions issued June 30, 1999
(0MB Bulletin No. 99-04). These definitions
reflect information obtained from the 1990
Decennial Census and the 1998 U.S. Census Bureau population estimate. A complete
list of metropolitan area definitions is available from the National Technical Information Service (NTIS), Document Sales, 5205
Port Royal Road, Springfield, Va. 22161 ,
telephone 1-800-553-6847.
0MB defines metropolitan areas in terms
of entire counties, except in the six New
England States where they are defined in
terms of cities and towns. New England data
in this table, however, are based on a county
concept defined by 0MB as New England
County Metropolitan Areas (NECMA) because county-level data are the most detailed
available from the Quarterly Census of Employment and Wages. The NECMA is a countybased alternative to the city- and town-based
metropolitan areas in New England. The
NECMA for a Metropolitan Statistical Area
(MSA) include: ( 1) the county containing the
first-named city in that MSA title (this county
may include the first-named cities of other
MSA, and (2) each additional county having
at least half its population in the MSA in
which first-named cities are in the county
identified in step 1. The NECMA is officially
defined areas that are meant to be used by
statistical programs that cannot use the regular metropolitan area definitions in New
England.
FoR ADDITIONAL INFORMATION on the
covered employment and wage data, contact
the Division of Administrative Statistics and
Labor Turnover at (202) 691 - 6567.
December 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 subm it job openings information for the last business day of the reference month. A job opening requires that ( 1)
a specific position exists and there is work
available for that position; and (2) work
could start within 30 day s regardless of
whether a suitable candidate is found; and
(3) the employer is actively recruiting from
outside the establishment to fill the position.
Included are full-time, part-time, permanent,
short-term, and seasonal openings. Active
recruiting means that the establishment is
taking steps to fill a position by advertising
in newspapers or on the Internet, posting
help-wanted signs, accepting applications,
or using other similar methods.
Jobs to be filled only by internal transfers,
promotions, demotions, or recall from layoffs are excluded. Also excluded are jobs with
start dates more than 30 days in the future,
jobs for which employees have been hired
but have not yet reported for work, and jobs
to be filled by employees of temporary help
agencies, employee leasing companies, outside contractors, or consultants. The job
openings rate is computed by dividing the
number of job openings by the sum of employment and job openings, and multiplying
that quotient by 100.
Hires are the total number of additions to
the payroll occurring at any time during the
reference month, including both new and rehired employees and full-time and part-time,
permanent, short-term and seasonal employees, employees recalled to the location
after a layoff lasting more than 7 days, oncall or intermittent employees who returned
to work after having been formally separated,
and transfers from other locations. The hires
count does not include transfers or promotions within the reporting site, employees
returning from strike, employees of temporary help agencies or employee leasing companies, outside contractors, or consultants.
The hires rate is computed by dividing the
number of hires by employment, and multiplying that quotient by I 00.
Separations are the total number of terminations 0f 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 100.
Notes on the data
The JOLTS data series on job openings, hires,
and separations are relatively new. The full
sample is divided into panels, with one panel
enrolled each month. A full complement of
panels for the original data series based on
the 1987 Standard Industrial Classification
(SIC) system was not completely enrolled in
the survey until January 2002. The supplemental panels of establishments needed to
create NAICS estimates were not completely
enrolled until May 2003. The data collected
up until those points are from less than a
full sample. Therefore, estimates from earlier months should be used with caution, as
fewer sampled units were reporting data at
that time.
In March 2002, BLS procedures for collecting hires and separations data were revised
to address possible underreporting. As a result, JOLTS hires and separations estimates for
months prior to March 2002 may not be comparable with estimates for March 2002 and
later.
The Federal Government reorganization
that involved transferring approximately
180,000 employees to the new Department
of Homeland Security is not reflected in the
JOLTS hires and separations estimates for the
Federal Government. The Office of Personnel Management's record shows these transfers were completed in March 2003. The
inclusion of transfers in the JOLTS definitions
of hires and separations is intended to cover
ongoing movements of workers between establishments. The Department of Homeland
Security reorganization was a massive onetime event, and the inclusion of these intergovernmental transfers would distort the
Federal Government time series.
Data users should note that seasonal adjustment of the JOLTS series is conducted with
fewer data observations than is customary.
The historical data, therefore, may be subject to larger than normal revisions. Because
the seasonal patterns in economic data series
typically emerge over time, the standard use
of moving averages as seasonal filters to capture these effects requires longer series than
are currently available. As a result, the stable
seasonal filter option is used in the seasonal
adjustment of the JOLTS data. When calculating seasonal factors, this filter takes an average for each calendar month after detrending
the series. The stable seasonal filter assumes
that the seasonal factors are fixed; a necessary assumption until sufficient data are avail-
able. When the stable seasonal filter is no
longer needed, other program features also
may be introduced, such as outlier adjustment
and extended diagnostic testing. Additionally,
it is expected that more series, such as layoffs and discharges and additional industries,
may be seasonally adjusted when more data
are available.
JOLTS hires and separations estimates cannot be used to exactly explain net changes in
payroll employment. Some reasons why it is
problematic to compare changes in payroll
employment with JOLTS hires and separations,
especially on a monthly basis, are: (I) the
reference period for payroll employment is
the pay period including the 12th of the
month, while the reference period for hires
and separations is the calendar month; and
(2) payroll employment can vary from month
to month simply because part-time and oncall workers may not always work during the
pay period that includes the 12th of the
month. Additionally, research has found that
some reporters systematically underreport
separations relative to hires due to a num ber of factors, including the nature of their
payroll systems and practices. The shortfall
appears to be about 2 percent or less over a
12-month period.
FOR ADDITIONAL INFORMATION on the Job
Openings and Labor Turnover Survey, contact the Division of Administrative Statistics
and Labor Turnover at (202) 961-5870.
Compensation and
Wage Data
(Tables 1-3; 30--36)
Compensation and waged data are gathered
by the Bureau from business establishments,
State and local governments, labor unions,
collective bargaining agreements on file
with the Bureau, and secondary sources.
Employment Cost Index
Description of the series
The Employment Cost Index (ECI) is a
quarterly measure of the rate of change in
compensation per hour worked and includes
wages, salaries, and employer costs of employee benefits. It uses a fixed market
basket of labor-similar in concept to the
Consumer Price Index's fixed market basket of goods and services-to measure
change over time in employer costs of employing labor.
Statistical series on total compensation
Monthly Labor Review
December 2004
57
Current Labor Statistics
costs, on wages and salaries, and on benefit costs are available for private nonfarm
wor!cers excluding proprietors, the self-employed, and household workers. The total
compensation costs and wages and salaries
series are also available for State and local
government workers and for the civilian
nonfarm economy, which consists of private industry and State and local government workers combined. Federal workers
are excluded.
The Employment Cost Index probability
sample consists of about 4,400 private nonfarm establishments providing about 23,000
occupational observations and 1,000 State
and local government establishments providing 6,000 occupational observations selected to represent total employment in each
sector. On average, each reporting unit provides wage and compensation information
on five well-specified occupations. Data are
collected each quarter for the pay period including the 12th day of March, June, September, and December.
Beginning with June 1986 data, fixed
employment weights from the 1980 Census
of Population are used each quarter to
calculate the civilian and private indexes
and the index for State and local governments. (Prior to June 1986, the employment
weights are from the 1970 Census of Population.) These fixed weights, also used to
derive all of the industry and occupation
series indexes, ensure that changes in these
indexes reflect only changes in compensation, not employment shifts among industries or occupations with different levels of
wages and compensation. For the bargaining status, region, and metropolitan/nonmetropolitan area series, however, employment data by industry and occupation are
not available from the census. Instead, the
1980 employment weights are reallocated
within these series each quarter based on the
current sample. Therefore, these indexes are
not strictly comparable to those for the aggregate, industry, and occupation series.
Definitions
Total compensation costs include wages,
salaries, and the employer's costs for employee benefits.
Wages and salaries consist of earnings
before payroll deductions, including production bonuses, incentive earnings, commissions, and cost-of-living adjustments.
Benefits include the cost to employers
for paid leave, supplemental pay (including nonproduction bonuses), insurance, retirement and savings plans, and legally required
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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=100) are available
on the Internet:
http://www.bls.gov/ect/
FOR ADDITIONAL INFORMATION on the
Employment Cost Index, contact the Office
of Compensation Levels and Trends: (202)
691-6199.
Employee Benefits Survey
Description of the series
Employee benefits data are obtained from
the Employee Benefits Survey, an annual
survey of the incidence and provisions of
selected benefits provided by employers.
The survey collects data from a sample of
approximately 9,000 private sector and State
and local government establishments. The
data are presented as a percentage of employees who participate in a certain benefit,
or as an average benefit provision (for example, the average number of paid holidays
provided to employees per year). Selected
data from the survey are presented in table
34 for medium and large private establishments and in table 35 for small private establishments and State and local government.
The survey covers paid le8.ve benefits
such as holidays and vacations, and personal,
funeral, jury duty, military, family, and sick
leave; short-term disability, long-term disability, and life insurance; medical, dental,
and vision care plans; defined benefit and
defined contribution plans; flexible benefits
plans; reimbursement accounts; and unpaid
family leave.
Also, data are tabulated on the incidence of several other benefits, such as
severance pay, child-care assistance, wellness programs, and employee assistance
programs.
December 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 (if any), and obligate the employer to provide those benefits. Benefits
are generally based on salary, years of service, or both.
Defined contribution plans generally
specify the level of employer and employee
contributions to a plan, but not the formula
for determining eventual benefits. Instead,
individual accounts are set up for participants, and benefits are based on amounts
credited to these accounts.
Tax-deferred savings plans are a type
of defined contribution plan that allow participants to contribute a portion of their salary to an employer-sponsored plan and defer income taxes until withdrawal.
Flexible benefit plans allow employees
to choose among several benefits, such as
life insurance, medical care, and vacation
days, and among several levels of coverage
within a given benefit.
Notes on the data
Surveys of employees in medium and large
establishments conducted over the 197986 period included establishments that employed at least 50, 100, or 250 workers,
depending on the industry (most service
industries were excluded). The survey conducted in 1987 covered only State and local governments with 50 or more employ-
ees. The surveys conducted in 1988 and
1989 included medium and large establishments with 100 workers or more in private
industries. All surveys conducted over the
1979-89 period excluded establishments
in Alaska and Hawaii, as well as part-time
employees.
Beginning in 1990, surveys of State and
local governments and small private establishments were conducted in even-numbered years, and surveys of medium and
large establishments were conducted in oddnumbered years. The small establishment
survey includes all private nonfarm establishments with fewer than 100 workers ,
while the State and local government survey includes all governments , regardless of
the number of workers. All three surveys incl u(IP- full- and part-time workers , and
workers in all 50 States and the District of
Columbia.
FOR ADDITIONAL INFORM ATION 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 = 100 for many Producer
Price Indexes (unless otherwise noted), 198284 = 100 for many Consumer Price Indexes
(unless otherwise noted), and 1990 = 100 for
International Price Indexes.
Consumer Price Indexes
Work stoppages
Description of the series
Description of the series
The Consumer Price Index (CPI) is a measure of the average change in the prices paid
by urban consumers for a fixed market basket of goods and services. The CPI is calculated monthly for two population groups,
one consisting only of urban households
whose primary source of income is derived
from the employment of wage earners and
clerical workers, and the other consisting of
all urban households. The wage earner index (CPI-W) is a continuation of the historic
index that was introduced well over a halfcentury ago for use in wage negotiations.
As new uses we're developed for the CPI in
recent years, the need for a broader and more
representative index became apparent. The
all-urban consumer index (CPI-U), introduced
in 1978, is representative of the 1993-95
buying habits of about 87 percent of the noninstitutional population of the United States
at that time, compared with 32 percent represented in the CPI-W. In addition to wage
earners and clerical workers, the CPI-U covers professional , managerial, and technical
workers, the self-employed , short-term
workers, the unemployed, retirees, and others not in the labor force.
The CPI is based on prices of food , clothing, shelter, fuel, drugs, transportation fares,
doctors ' and dentists' fees, and other goods
and services that people buy for day-to-day
living. The quantity and quality of these
items are kept essentially unch:rnged be-
Data on work stoppages measure the number and duration of major strikes or lockouts (involving 1,000 workers or more) occurring during the month (or year), the number of workers involved, and the amount of
work time lost because of stoppage. These
data are presented in table 36.
Data are largely from a variety of published sources and cover only establishments directly involved in a stoppage. They
do not measure the indirect or secondary
effect of stoppages on other establishments
whose employees are idle owing to material
shortages or lack of service.
Definitions
Number of stoppages:
The number of
strikes and lockouts involving 1,000 workers or more and lasting a full shift or longer.
Workers involved: The number of
workers directly involved in the stoppage.
Number of days idle: The aggregate
number of workdays lost by workers involved in the stoppages.
Days ofidleness as a percent of estimated
working time: Aggregate workdays lost as a
percent of the aggregate number of standard
workdays in the period multiplied by total employment in the period.
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tween major revisions so that only price
changes will be measured. All taxes directly
associated with the purchase and use of
items are included in the index.
Data collected from more than 23 ,000 retail establishments and 5,800 housing units
in 87 urban areas across the country are used
to develop the "U.S. city average." Separate
estimates for 14 major urban centers are presented in table 38. The areas listed are as indicated in footnote 1 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 IN FORMATION , 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 commodilies produced in the manufacturing; agriculture, forestry, and fishing; mining; and gas and electricity and public utilities sectors. The stageof-processing structure of PPI organizes
products by class of buyer and degree of fabrication (that is, finished goods, intermediate goods, and crude materials). The traditional commodity structure of PPI organizes products by similarity of end use or
material composition. The industry and
product structure of PPI organizes data in
accordance with the 2002 North American Industry Classification System and product
codes developed by the U.S. Census Bureau.
Monthly Labor Review
December 2004
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Current Labor Statistics
To the extent possible, prices used in calculating Producer Price Indexes apply to the
first significant commercial transaction in
the United States from the production or
central marketing point. Price data are generally collected monthly, primarily by mail
questionnaire. Most prices are obtained directly from producing companies on a voluntary and confidential basis. Prices generally are: reported for the Tuesday of the week
containing the 13th day of the month.
Since January 1992, price changes for
the various commodities have been averaged
together with implicit quantity weights representing their importance in the total net
selling value of all commodities as of 1987.
The detailed data are aggregated to obtain
indexes for stage-of-processing groupings,
commodity groupings, durability-of-product groupings, and a number of special composite groups. All Producer Price Index data
are subject to revision 4 months after original publication.
FOR ADDITIONAL INFORMATION, contact
the Division of Industrial Prices and Price
Indexes: (202) 691-7705.
International Price Indexes
Description of the series
The International Price Program produces
monthly and quarterly export and import
price indexes for nonmilitary goods and services traded between the United States and
the rest of the world. The export price index provides a measure of price change
for all products sold by U.S. residents to
foreign buyers. ("Residents" is defined as
in the national income accounts; it includes corporations, businesses, and individuals, but does not require the organizations to be U.S. owned nor the individuals to have U.S. citizenship.) The import
price index provides a measure of price
change for goods purchased from other
countries by U.S. residents.
The product universe for both the import
and export indexes includes raw materials,
agricultural products, semifinished manufactures, and finished manufactures, including both capital and consumer goods. Price
data for these items are collected primarily
by mail questionnaire. In nearly all cases,
the data are collected directly from the exporter or importer, although in a few cases,
prices are obtained from other sources.
To the extent possible, the data gathered
refer to prices at the U.S. border for exports
and at either the foreign border or the U.S.
border for imports. For nearly all products, the prices refer to transactions com-
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pleted during the first week of the month.
Survey respondents are asked to indicate
all discounts, allowances , and rebates applicable to the reported prices, so that the
price used in the calculation of the indexes
is the actual price for which the product
was bought or sold.
In addition to general indexes of prices for
U.S. exports and imports, indexes are also
published for detailed product categories of
exports and imports. These categories are defined according to the five-digit level of detail
for the Bureau of Economic Analysis End-use
Classification, the three-digit level for the Standard International Trade Classification (SITC),
and the four-digit level of detail for the Harmonized System. Aggregate import indexes by
country or region of origin are also available.
BLS publishes indexes for selected categories of internationally traded services,
calculated on an international basis and on
a balance-of-payments basis.
Notes on the data
The export and import price indexes are
weighted indexes of the Laspeyres type. The
trade weights currently used to compute
both indexes relate to 2000.
Because a price index depends on the
same items being priced from period to period, it is necessary to recognize when a
product's specifications or terms of transaction have been modified. For this reason, the
Bureau's questionnaire requests detailed descriptions of the physical and functional
characteristics of the products being priced,
as well as information on the number of units
bought or sold, discounts, credit terms, packaging, class of buyer or seller, and so forth.
When there are changes in either the specifications or terms of transaction of a product,
the dollar value of each change is deleted from
the total price change to obtain the "pure"
change. Once this value is determined, a linking procedure is employed which allows for
the continued repricing of the item.
FOR ADDITIONAL INFORMATION, contact
the Division of International Prices: (202)
691-7155.
Productivity Data
(Tables 2; 48-51)
Business and major sectors
Description of the series
The productivity measures relate real out-
December 2004
put to real input. As such, they encompass a
family of measures which include singlefactor input measures, such as output per
hour, output per unit of labor input, or output per unit of capital input, as well as measures of multifactor productivity (output per
unit of combined labor and capital inputs).
The Bureau indexes show the change in output relative to changes in the various inputs.
The measures cover the business, nonfarm
business, manufacturing, and nonfinancial
corporate sectors.
Corresponding indexes of hourly compensation, unit labor costs, unit nonlabor
payments, and prices are also provided.
Definitions
Output per hour of all persons (labor productivity) is the quantity of goods and services produced per hour of labor input. Output per unit of capital services (capital productivity) is the quantity of goods and services produced per unit of capital services
input. Multi factor productivity is the quantity of goods and services produced per combined inputs. For private business and private nonfarm business, inputs include labor
and capital units. For manufacturing, inputs
include labor, capital, energy, nonenergy materials, and purchased business services.
Compensation per hour is total compensation divided by hours at work. Total compensation equals the wages and salaries of
employees plus employers' contributions for
social insurance and private benefit plans,
plus an estimate of these payments for the
self-employed (except for nonfinancial corporations in which there are no self-employed). Real compensation per hour is
compensation per hour deflated by the
change in the Consumer Price Index for All
Urban Consumers.
Unit labor costs are the labor compensation costs expended in the production
of a unit of output and are derived by dividing compensation by output. Unit nonlabor
payments include profits, depreciation,
interest, and indirect taxes per unit of output. They are computed by subtracting
compensation of all persons from currentdollar value of output and dividing by output.
Unit nonlabor costs contain all the
components of unit nonlabor payments except unit profits.
Unit profits include corporate profits
with inventory valuation and capital consumption adjustments per unit of output.
Hours of all persons are the total hours
at work of payroll workers, self-employed
persons, and unpaid family workers.
Labor inputs are hours of all persons adjusted for the effects of changes in the education and experience of the labor force.
Capital services are the flow of services
from the capital stock used in production. It
is developed from measures of the net stock
of physical assets-equipment, structures,
land, and inventories-weighted by rental
prices for each type of asset.
force; capital investment; level of output;
changes in the utilization of capacity, energy, material, and research and development; the organization of production; managerial skill; and characteristics and efforts
of the work force.
FOR ADDITIONAL INFORMATION on this
productivity series, contact the Division of
Productivity Research: (202) 691-5606.
Combined units of labor and capital
inputs are derived by combining changes in
labor and capital input with weights which
represent each component's share of total
cost. Combined units of labor, capital, energy,
materials, and purchased business services are
similarly derived by combining changes in
each input with weights that represent each
input's share of total costs. The indexes for
each input and for combined units are based
on changing weights which are averages of the
shares in the current and preceding year (the
Tornquist index-number formula).
Notes on the data
Business sector output is an annuallyweighted index constructed by excluding
from real gross domestic product (GDP) the
following outputs: general government, nonprofit institutions, paid employees of private
households , and the rental value of owneroccupied dwellings. Nonfarm business also
excludes farming. Private business and private nonfarm business further exclude government enterprises. The measures are supplied by the U.S. Department of Commerce's
Bureau of Economic Analysis. Annual estimates of manufacturing sectoral output are
produced by the Bureau of Labor Statistics.
Quarterly manufacturing output indexes
from the Federal Reserve Board are adjusted
to these annual output measures by the BLS.
Compensation data are developed from data
of the Bureau of Economic Analysis and the
Bureau of Labor Statistics. Hours data are
developed from data of the Bureau of Labor
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 tc>chnology; shifts in the composition of the labor
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Industry productivity
measures
ducing that output. Combined inputs include capital, labor, and intermediate purchases. The measure of capital input represents the flow of services from the capital
stock used in production. It is developed
from measures of the net stock of physical
assets-equipment, structures, land, and inventories. The measure of intermediate
purchases is a combination of purchased
materials , services, fuels, and electricity.
Notes on the data
Description of the series
The BLS industry productivity indexes measure the relationship between output and
inputs for selected industries and industry
groups, and thus reflect trends in industry
efficiency over time. Industry measures include labor productivity, multifactor productivity, compensation, and unit labor
costs.
The industry measures differ in methodology and data sources from the productivity measures for the major sectors because the industry measures are developed
independently of the National Income and
Product Accounts framework used for the
major sector measures.
The industry measures are compiled from
data produced by the Bureau of Labor Statistics and the Census Bureau, with additional data supplied by other government
agencies, trade associations, and other
sources.
FOR ADDITIONAL INFORMATION on this series, contact the Division of Industry Productivity Studies: (202) 691-5618.
International Comparisons
(Tables 52-54)
Labor force and
unemployment
Description of the series
Definitions
Output per hour is derived by dividing an
index of industry output by an index of labor input. For most industries, output indexes are derived from data on the value of
industry output adjusted for price change.
For the remaining industries, output indexes
are derived from data on the physical quantity of production.
The labor input series is based on the
hours of all workers or, in the case of some
transportation industries, on the number of
employees. For most industries, the series
consists of the hours of all employees. For
some trade and services industries, the series also includes the hours of partners, proprietors, and unpaid family workers.
Unit labor costs represent the labor
compensation costs per unit of output produced, and are derived by dividing an index
of labor compensation by an index of output. Labor compensation includes payroll
as well as supplemental payments, including both legally required expenditures and
payments for voluntary programs.
Multifactor productivity is derived by
dividing an index of industry output by an
index of combined inputs consumed in pro-
Tables 52 and 53 present comparative measures of the labor force, employment, and
unemployment approximating U.S. concepts for the United States, Canada, Australia, Japan, and six European count_ries. 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 diffe:r:ences, to the
extent that data to prepare adjustments are
available. Although precise comparability may
not be achieved, these adjusted figures provide a better basis for international comparisons than the figures regularly published by
each country. For further information on adjustments and comparability issues, see
Constance Sorrentino, "International unemployment rates: how comparable are they?"
Monthly Labor Review, June 2000, pp. 3-20
(available on the BLS Web site at http://
www.bls.gov/opu b/ml r/2000/06/
artlfull.pdf).
Definitions
For the principal U.S. definitions of the labor force, employment, and unemployment,
see the Notes section on Employment and
Monthly Labor Review
December 2004
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Current Labor Statistics
Unemployment Data: Household survey
data.
Notes on the data
The foreign country data are adjusted as
closely as possible to U.S. concepts, with the
exception of lower age limits and the treatment
of layoffs. These adjustments include, but are
not limited to: including older persons in the
labor force by imposing no upper age limit,
adding unemployed students to the
unemployed, excluding the military and family
workers working fewer than 15 hours from the
employed, and excluding persons engaged in
passive job search from the unemployed.
Data for the United States relate to the
popuiation 16 years of age and older. The U.S.
concept of the working age population has
no upper age limit. The adjusted to U.S.
concepts statistics have been adapted, insofar
as possible, to the age at which compulsory
schooling ends in each country, and the
Swedish statistics have been adjusted to
include persons older than the Swedish upper
age limit of 64 years. The adjusted statistics
presented here relate to the population 16
years of age and older in France, Sweden,
and the United Kingdom; 15 years of age and
older in Australia, Japan, Germany, Italy, and
the Netherlands. An exception to this rule is
that the Canadian statistics are adjusted to
cover the population 16 years of age and
older, whereas the age at which compulsory
schooling ends remains at 15 years. In the labor
force participation rates and employmentpopulation ratios, the denominator is the
civilian noninstitutionalized working age
population, except that the institutionalized
working age population is included in Japan
and Germany.
In the United States, the unemployed
include persons who are not employed and
who were actively seeking work during the
reference period, as well as persons on layoff.
Persons waiting to start a new job who were
actively seeking work during the reference
period are counted as unemployed under U.S .
concepts; if they were not actively seeking
work, they are not counted in the labor force.
In some countries, persons on layoff are
classified as employed due to their strong job
attachment. No adjustment is made for the
countries that classify those on layoff as
employed. In the United States, as in Australia
and Japan, passive job seekers are not in the
labor force; job search must be active, such
as placing or answering advertisements,
contacting employers directly,or registering
with an employment agency (simply reading
ads is not enough to qualify as active search).
Canada and the European countries classify
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passive jobseekers as unemployed. An
adjustment is made to exclude them in Canada,
but not in the European countries where the
phenomenon is less prevalent. Persons waiting
to start a new job are counted among the
unemployed for all other countries, whether
or not they were actively seeking work.
The figures for one or more recent years
for France, Germany, and the Netherlands are
calculated using adjustment factors based on
labor force surveys for earlier years and are
considered preliminary. The recent year
measures for these countries are therefore
subject to revision whenever more current
labor force surveys become available.
There are breaks in series for the United
States(1994, 1997, 1998, 1999,2000,2003),
Australia (2001), and Germany (1999).
For the United States, beginning in 1994,
data are not strictly comparable for prior years
because of the introduction of a major
redesign of the labor force survey questionnaire and collection methodology. The
redesign effect has been estimated to increase
the overall unemployment rate by 0.1
percentage point. Other breaks noted relate
to changes in population controls that had
virtually no effect on unemployment rates.
For a description of all the changes in the
U.S. labor force survey over time and their
impact, see Historical Comparability in the
"Household Data" section of the BLS publication Employment and Earnings (available
on the BLS Web site at http://www.bls.gov/
cps/eetech_methods.pdf).
For Australia, the 2001 break reflects the
introduction in April 2001 of a redesigned
labor force survey that allowed for a closer
application of International Labor Office
guidelines for the definitions of labor force
statistics. The Australian Bureau of Statistics
revised their data so there is no break in the
employment series. However, the reclassification of persons who had not actively
looked for work because they were waiting to
begin a new job from "not in the labor force"
to "unemployed" could only be incorporated
for April 2001 forward. This reclassification
diverges from the U.S. definition where
persons waiting to start a new job but not
actively seeking work are not counted in the
labor force. The impact of the reclassification
was an increase in the unemployment rate by
0.1 percentage point in 2001.
For Germany, the 1999 break reflects the
incorporation of an improved method of data
calculation and a change in coverage to
persons living in private households only.
For further qualifications and historical
data, see Comparative Civilian Labor Force
Statistics, Ten Countries, on the BLS Web site
at http ://www.bls.gov/fls/flslforc.pdf
December 2004
FOR ADDITIONAL INFORMATION on this
series, contact the Division of Foreign
Labor Statistics: (202) 691-5654 or
flshelp@bls.gov
Manufacturing productivity
and labor costs
Description of the series
Table 54 presents comparative indexes of
manufacturing labor productivity (output per
hour), output, total hours, compensation per
hour, and unit labor costs for the United States,
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 problems in comparing the levels of
manufacturing output among countries.
BLS constructs the comparative indexes
from three basic aggregate measures--output, total labor hours, and total compensation. The hours and compensation measures
refer to all employed persons (wage and salary earners plus self-employed persons and
unpaid family workers) 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-weighted 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 10
years).
To preserve the comparability of the U.S.
measures with those for other economies,
BLS uses gross product originating in manufacturing for the United States for these comparative measures. The gross product originating series differs from the manufacturing output series that BLS publishes in its
news releases on quarterly measures of U.S.
productivity and costs (and that underlies the
measures that appear in tables 48 and 50 in
this section). The quarterly measures are on
a '·sectoral output" basis, rather than a valueadded basis. Sectoral output is gross output
less intrasector transactions.
Total labor hours refers to hours worked
in all 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 average
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 Denmark 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
statistics 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 Kingdom, compensation is reduced between 1967
and 1991 to account for employment-related
subsidies. Self-employed workers are included in the all-employed-persons measures
by assuming that their 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, fanns with
fewer than 11 employees, employers regulated
by other Federal safety and health laws, and
Federal, State, and local government agencies
are excluded from the survey.
The survey is a Federal-State cooperative program with an independent sample
selected for each participating State. A stratified random sample with a Neyman allocation is selected to represent all private industries in the State. The survey is stratified
by Standard Industrial Classification and
size of employment.
Definitions
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 i:aused by inhalation, absorption, ingestion, or direct contact.
Lost workday injuries and illnesses are
cases that involve days away from work , or
days of restricted work activity, or both.
Lost workdays include the number of
workdays (consecutive or not) on which the
employee was either away from work or at
work in some restricted capacity, or both, because of an occupational injury or illness. BLS
measures of the number and incidence rate
of lost workdays were discontinued beginning with the 1993 survey. The number of
days away from work or days of restricted
work activity does not include the day of injury or onset of illness or any days on which
the employee would not have worked, such
as a Federal holiday, even though able to
work.
Incidence rates are computed as the
number of injuries and/or illnesses or lost
work days per 100 full-time workers.
Notes on the data
The definitions of occupational injuries and
illnesses are from Recordkeeping Guidelines for Occupational Injuri es 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 reported. These long-term latent ill-
Monthly Labor Review
December 2004
63
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 100 equivalent fulltime workers. For this purpose, 200,000 employee hours represent l 00 employee years
(2,000 hours per employee). Full detail on
the available measures is presented in the annual bulletin, Occupational Injuries and Illnesses: Counts, Rates, and Characteristics.
Comparable data for more than 40 States
and territories are available from the BLS Office of Safety, Health and Working Conditions. Many of these States publish data on
State and local government employees in addition to private industry data.
Mining and 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
64
Monthly Labor Review
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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/ii fl
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.
December 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 characteristics are included in a national news release that is available about 8 months after
the end of the reference year. The Census of
Fatal Occupational Injuries was initiated in
1992 as a joint Federal-State effort. Most
States issue summary information at the
time of the national news release.
FOR ADDITIONAL INFORMATION on the
Census of Fatal Occupational Injuries contact the BLS Office of Safety, Health, and
Working Conditions at (202) 691-6175, or
the Internet at:
http://www.bls.gov/iif/
1. Labor market indicators
2002
Selected Indicators
II
IV
Ill
2004
2003
2002
2003
II
IV
Ill
Ill
Employment data
Employment status of the civilian noninstitutional
population (household survey):
1
Labor force participation rate ................................... ......... .....
Employment-population rat io .... .... ......................... ..... ......... .
Unemployment rate ..... ............... .... ......... ... .... ... ...... ....... ..
Men ... .... .... ....... ....... .... ... ........... ..... ... ....................... .. .
16 to 24 years ........ .......................... ................. .. ........ .. .......... .
25 years and older ............ .. .......... .. .. ..... ................ ................ .
Women ...... .... .......... ... .... ....... ... ... .
16 to 24 years ..... .. .. . ... .................... ............ ... ..... .. .... ..
25 years and older ...................................................... ..
Employment, nonfarm (payroll data) , in thousands:
66.6
62.7
5.8
66.2
62.3
6.0
66.6
62.8
5.8
66.5
62.5
5.9
66.3
62.4
5.8
66.4
62.3
6.1
66.2
62.1
6.1
66 .1
62 .3
5.9
66.0
62 .2
5.6
65.9
62 .2
5.6
66.0
62.4
5.5
5.9
12.8
4.7
6.3
13.4
5.0
5.9
13.1
4.7
6.1
12.5
4.9
6.1
12.6
6.1
13.1
4.9
5.7
12.5
4.5
5.7
12.9
4.5
5.6
12.5
4.4
5.7
11.4
4.6
5.6
10.9
4.6
5.7
11 .4
4.6
5.0
5.5
11.2
6.4
13.8
5.1
5.6
11.1
6.5
14.0
5.2
5.7
11 .8
5.8
11 .5
5.6
10.9
5.6
11 .1
4.5
4.6
4.7
4.6
4.5
5.4
10.9
4.4
5.4
11 .0
4.3
4.6
1
Total nonfarm ...... .
Total private .. .
Goods-producing ................. ..... .
Manufacturing ........ ....... .. ...... ....... ....... ....... ... .... ... .
Service-providing .. ... ........ ......... .... .. ... ... ......... .......... ..... .
130,341
108,828
129,931
108,356
130,287
108,736
130,248
108,654
130,047
108,428
129,878
108,309
129,820
108,260
130,002
108,453
130,367
108,827
131 ,125
109,577
131 ,52 1
109,897
22 ,557
21,817
22,252
14,979
21 ,848
21,718
14,775
14,570
14,410
21,676
14,340
21,719
14,326
21,869
14,385
21 ,927
21,817
22,466
15,197
22 ,025
22 ,557
107,789
108,114
107,821
107,995
108,022
108,030
108,102
108,326
108,648
109,256
109,595
14,403
Average hours:
Total private ..
Manufacturing ....
Overtime ..
Employment Cost lndex
3
........ ............... .
Service-providing3 ...
State and local government workers
Workers by bargaining status (private industry):
Union ........ .. .............. .... ........................... ... ....... ......... .. .
Nonunion .... ..... ... ........ ...... ....... ....................................... .
1
2
33.7
33.9
33.8
33.8
33.7
33.6
33.7
33.8
33.7
40.5
4.2
40.4
4.2
40.4
4.3
40.4
4.2
40.4
4.2
40.2
4.1
40.2
4.1
40.6
4.4
41 .0
4.6
40 .9
4.6
33.8
40.8
4.6
3.4
3.8
.9
.6
1.1
.5
1.4
.9
1.0
4.0
.6
.4
1.4
1.7
.8
3.2
.8
1.0
.4
1.5
.9
.8
3.7
4.0
.6
.9
1.8
.9
.7
.5
2.3
.9
.9
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
4.2
3.2
4.6
3.9
1.2
.5
.9
.4
1.6
1.6
1.2
.8
1.0
1.0
.7
.4
2.8
1.3
1.5
.8
.8
.9
2
Percent change in the ECI , compensation :
All workers (excluding farm , household and Federal workers) .. .
Private industry workers.. ... . ... ...... .. ... ... .. ..... .. .. .... .... ....... ..
Goods-producing
33. 9
Quarterly data seasonally adjusted.
NOTE: Beginning in January 2003, household survey data reflect revised population
Annual changes are December-to-December changes. Quarterly changes are calculated
controls. Nonfarm data reflect the conversion to the 2002 version of the North American
using the last month of each quarter.
3
Goods-producing industries include mining, construction, and manufacturing. Service-
Industry Classification System (NAICS), replacing the Standard Industrial Classification (SIC)
system . NAICS-based data by industry are not comparable with sic-based data.
providing industries include all other private sector industries.
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Federal Reserve Bank of St. Louis
Monthly Labor Review
December 2004
65
Current Labor Statistics:
Comparati ve Indicators
2. Annual and quarterly percent changes in compensation, prices, and productivity
Selected measures
2002
2003
2002
Ill
2003
IV
2004
II
Ill
IV
II
Ill
Compensation data 1' 2
Employment Cost Index-compens ation (wages,
salaries, benefits) :
Civilian nonfarm .......................... ........................................
Private nonfarm ..............................................................
Employment Cost Index- wages and salaries:
Civilian nonfarm ...... ................. .... ..... ... ......... ..........
Private nonfarm................. ................. ..... .......................
3.4
3.2
3.8
4.0
0.9
.6
0.6
.4
1.4
1.7
0.8
.8
1.1
1.0
0.5
.4
1.4
1.5
0.9
.9
1.0
.8
2.9
2.7
2.9
3.0
.7
.4
.4
.3
1.0
1.1
.6
.7
.9
.8
.3
.4
.6
.7
.6
.7
.9
.9
Consumer Price Index (All Urban Consumers) : All Items ......
2.3
2.3
.6
-.1
1.8
- .3
- .2
-.2
1.2
1.2
.2
Producer Price Index:
Finished goods .......................... .................... '. .. ... .......... .. ...
Finished consumer goods ... ................................... ..........
Capital equipment. .. .. .. ... ...... ... ..... .. .. ... ... ........ ..... ....
Intermediate materials, supplies, and components .. .. .. ......
Crude materials ............... .......................... ...........................
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
-.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
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
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
Price data
1
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 compounded.
2
3
Annual rates of change are computed by comparing annual averages.
Quarterly percent changes reflect annual rates of change in quarterly indexes.
The data are seasonally adjusted.
Excludes Federal and private household workers.
4
Output per hour of all employees.
NOTE: Dash indicates data not available.
3. Alternative measures of wage and compensation changes
Quarterly change
Components
2003
Ill
Average hourly compensation: 1
All persons, business sector ........................................................ .
All persons, nonfarm business sector .............. .................... .. .. .. ..
Four quarters ending-
2004
IV
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-compens ation:
Civilian nonfarm 2 ........... .. .........
.. ...... ..... ..... ..... .
Private nonfarm ..................... ............. ...................................... .
Union .... .. ... ... .. .............. ........................... ............................. ..
Nonunion .... .. ... ..................................................................... ..
State and local governments .................................................... .
Employment Cost Index-wages and salaries:
Civilian nonfarm 2 ............. .. ............. .... ... .... . ... .... ... .. .. .. ... ... ............ .
Private nonfarm ........... .......... ....................................................
Union ... ......... .. ........... .. .......................... ........ .... ..... .............. ..
Nonunion ..... .. .................... ................................... .. ............... .
State and local governments ........................ ........................... ..
1
2
.9
.8
.6
.9
1.0
I
Seasonally adjusted. "Quarterly average" is percent change from a quarter ago, at an annual rate.
Excludes Federal and household workers.
66 Monthly Labor Review
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Federal Reserve Bank of St. Louis
December 2004
4. Employment status of the populaHon, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted
[Numbers in thousands]
2004
Annual average
2003
2003
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
221,HW
146,510
66.2
137,736
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
224,192
147,850
65.9
139,778
62.3
8,774
6.0
74,658
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
62.3
8,072
5.5
76 ,342
96,439
73,630
76.3
69,734
98,272
74,623
75.9
70,415
98,696
74,942
75.9
70,726
98,814
75,188
76.1
70,964
98,927
75,044
75.9
71 ,099
98,866
75,171
76.0
71 ,329
98,966
74,797
75.6
70,969
99,065
75,018
75.7
71 ,128
99 ,170
74,871
75.5
71 ,118
99,279
75,048
75.6
71,162
99,396
75,372
75.8
71,570
99,512
75,577
75.9
71 ,847
99,642
75,639
75.9
71,870
99,776
75,443
75.6
71 ,677
99,904
75,622
75.7
71 ,882
72.3
3,896
5.3
22,809
71.7
4,209
5.6
23,649
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
24,231
72.0
3,802
5.0
24,023
72.2
3,730
4.9
23,935
72.1
3,768
5.0
24,003
71.8
3,766
5.0
24,332
72.0
3,740
4.9
24,282
1
105,136
populalion ······· .........
63,648
Ci,iliar. labor force .............
60.5
Participation rate ...... ...
Employed ....................... 60,420
Employment-pop2
57.5
ulation ratio ...
3,228
Unemployed ...................
5.1
Unemployment rate ....
41,488
Not in the labor force .... ...
106,800
64,716
60.6
61,402
107,197
64,899
60.5
61 ,524
107,303
64,917
60.5
61 ,597
107,404
107,131
64,515
60.2
61,260
107,216
64,629
60.3
61,456
107,299
107,389
64,687
60.3
61,373
64,785
60.3
61,571
107,483
64 ,813
60.3
61 ,721
107,586
64,893
60.3
61,629
107,687
65,122
60.5
61 ,918
107,801
64,846
60.4
61,521
64,903
60.2
61,870
107,920
64,989
60.2
61,925
108,032
65,103
60.3
61,918
57.5
3,314
5.1
42,083
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
42,617
57.3
3,172
4.9
42,587
57.2
3,314
5.1
42,613
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
42,931
57.4
3,105
4.8
42,928
15,994
7,585
47.4
6,332
16,096
7,170
44.5
5,919
16,145
16,162
7,082
43.8
5,972
16,178
6,987
43.2
5,859
16,164
7,177
44.4
5,977
16,175
7,045
43.6
5,875
16,186
6,945
42.9
5,797
16,198
7,085
43.7
5,888
16,205
7,113
43.9
5,888
16,214
7,051
43.7
5,846
7,014
43.3
5,832
16,222
7,157
44.1
5,896
16,234
7,1 62
44.1
5,941
16,246
7,051
43.4
5,877
16,257
7,124
43.8
5,898
39.6
1,253
16.5
8,409
36.8
1,251
17.5
8,926
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
36.3
1,226
17.2
9,732
1
population .... .. ........ .......... 179,783
Civilian labor force ............. 120,150
66.8
Participation rate .........
Employed ...................... . 114,013
Employment-pop63.4
ulation ratio2 ........
6,137
Unemployed ...................
5.1
Unemployment rate ....
59,633
Not in the labor force .......
181 ,292
181 ,871
182,032
182,001
182,001
182,252
183,022
120,723
66.4
114,765
120,540
66.2
114,602
120,542
66.2
114,433
120,675
66.2
114,712
182,531
121,180
66.4
115,152
182,846
121,041
66.5
114,783
182,384
120,984
66.3
114,976
182,676
120,736
66 .4
114,535
182,185
120,751
66.3
114,678
181,879
120,546
66.5
114,235
121,428
66.5
115,623
121 ,300
66.3
115,547
121,016
66.1
115,323
183,188
121 ,240
66.2
115,572
63.0
6,311
5.2
60,746
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
61,577
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
63.1
5,668
4.7
61,948
25,578
16,565
64.8
14,872
25,686
16,526
64.3
14,739
25,825
16,589
64.2
14,696
25,860
16,524
63.9
14,812
25,894
16,365
63.2
14,679
25,867
16,602
64.2
14,886
25,900
16,404
63.3
14,804
25,932
16,595
64.0
14,909
25,967
16,485
63.5
14,878
26,002
16,442
63.2
14,818
26,040
16,506
63.4
14,833
26,078
16,755
64.3
14,926
26,120
16,724
64.0
14,983
26,163
16,703
63.8
14,981
26,204
16,839
64 .3
15,037
58.1
1,693
10.2
9,013
57.4
1,787
10.8
9,161
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
9,265
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
57.4
1,802
10.7
9,365
Employment status
2002
TOTAL
Civilian noninstitutional
1
217,570
population ..... .... ...
Civilian labor force ............. 144,863
66.6
Participation rate ...... ...
Employed ....................... 136,485
Employment-pop2
62.7
ulation ratio ...
8,378
Unemployed ...................
5.8
Unemployment rate ... .
Not in the labor force ..... ... 72,707
Men, 20 years and over
Civilian noninstitutional
1
population . .. ............. .
Civilian labor force ...... .......
Participation rate ....... ..
Employed ................... ....
Employment-pop2
ulation ratio ...
Unemployed ...................
Unemployment rate ....
Not in the labor force .. .....
Women, 20 years and over
Civilian noninstitutional
Both sexes, 16 to 19 years
Civilian noninstitutional
1
population ..
Civilian labor force .............
Participation rate .........
Employed .......................
Employment-pop2
ulation ratio .. .
Unemployed ...... .... .........
Unemployment rate .. ..
Not in the labor force .. ... ..
3
Whlte
Civilian noninstitutional
Black or African Amertcan
Civiii<in noninstitutional
3
1
population ... .........
Civilian labor force .............
Participation rate .........
Employed ......... .... ..........
Employment-pop2
ulation ratio ...
Unemployed ...................
Unemployment 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
December 2004
67
Current Labor Statistics:
Labor Force Data
4. Continued-Employment status of the ·population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted
[Numbers in thousands]
Annual average
Employment status
2003
2004
2002
2003
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
25,963
17,943
69.1
16,590
27,551
18,813
68.3
17,372
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
28,431
19,533
68.7
18,220
28,520
19,563
68.6
18,247
63.9
1,353
7.5
8,020
63.1
1,441
7.7
8,738
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
64.1
131
6.7
8,898
64.0
1,317
6.7
8,957
Hispanic or Latino
ethnicity
Civilian noninstitutional
1
oooulation .........................
Civilian labor force .............
Participation rate ...... ...
Employed .......................
Employment-population ratio 2 .............
Unemployed ...................
Unemployment rate ....
Not in the labor force ..........
1
The population figures are not seasonally adjusted.
2
Civilian errployment as a percent of the civilian noninstitutional population.
Beginning in 2003, persons who selected this race group only; persons who selected
more than one race group are not included. Prior to 2003, persons who reported more
than one race were included in the group they identified as the main race.
3
NOTE: Estimates for the above race groups (white and black or African American) do not sum
to totals because data are not presented for all races. In addition, persons whose ethnicity is
identified as Hispanic or Latino may be of any race and, therefore, are classified by ethnicity as
well as by race. Beginning in January 2003, data reflect revised population controls used in the
household survey.
5. Selected employment indic ators, monthly data seasonally a djusted
[In thousands]
Selected categories
Characteristic
Employed, 16 years and over ..
Men .... ................. ................
Women ................................
Annual average
2003
2002
2003
Oct.
Nov.
136,845
72,903
63,582
137,736
73 ,332
64,404
138,095
73 ,643
64,452
138,533
73,915
64,618
2004
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct..
138,479
74,085
64,394
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
139,778
74 ,837
64,941
Married men , spouse
present. ..............................
Married women, spouse
44,116
44,653
44,684
45,152
45,431
45,490
45,128
45,043
44,735
44,723
44,938
44,935
45,1 06
45,034
45,052
rrPSP'1I. .......................... . ...
34 ,155
34,695
34,993
35,076
35,034
34,585
34,502
34,256
34 ,339
34,522
34,461
34,599
34 ,448
34,601
34,798
Persons at work part tlme 1
All industries:
Part time for economic
reasons ...... ······· ··· ·····
·
Slack work or business
condnions .... ........ ........
Could only find part-time
work ......... .. ... .. .........
Part time for noneconomic
reasons .............. ...... ....
Nonagricultural industries:
Part time for economic
reasons .. .. ... .. ......... ......
Slack work or business
cond~ions ............ .........
Could only find part-time
work ........ ..................
Part time for noneconomic
reasons .......... .............. ...
4,213
4,701
4,800
4,880
4,788
4,714
4,437
4,733
4,574
4,665
4,513
4,490
4,504
4,452
4,732
2,788
3,118
3,030
3,226
3,205
2,996
2,865
3,011
2,819
2,853
2,803
2,660
2,812
2,808
3,053
1,124
1,279
1,356
1,350
1,295
1,380
1,347
1,427
1,439
1,467
1,404
1,500
1,461
1,312
1,371
18,843
19,014
18,935
19,1 10
18,561
18,905
18,900
19,006
19,000
19,621
19,531
19,741
19,680
19,386
19,710
4,119
4,596
4,690
4,782
4,727
4,613
4,328
4,622
4,471
4,605
4,442
4,400
4,391
4,379
4,627
2,726
3,052
2,964
3,153
3,144
2,911
2,778
2,927
2,756
2,812
2,762
2,605
2,714
2,753
2,995
1,114
1,264
1,349
1,353
1,279
1,399
1,340
1,414
1,431
1,476
1,387
1,496
1,442
1,315
1,357
18,487
18,658
18,628
18,752
18,367
18,636
18,691
18,693
18,664
19,220
19,072
19,290
19,213
19,025
19,305
' Excludes persons "with a job but not at work" during the survey period for such reasons as vacation , illness, or industrial disputes.
NOTE: Beginning in January 2003, data reflect revised population controls used in the household survey.
68 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
6. Selected unemployment indicators, monthly data seasonally adjusted
[Unemployment rates]
2002
2003
2004
2003
Annual average
Selected categories
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Characteristic
Tutal, 16 years and older. .......... ................
Both sexes, 16 to 19 years ...... .. ... .. .... ...
Men. 20 years and older .................. ... ...
Women. 20 years and older .. .................
5.8
16.5
5.3
5.1
6.0
17.5
5.6
5.1
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
5.5
17.2
4.9
4.8
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
4.4
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
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
4.7
15.2
17.4
12.7
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
11.4
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
21.6
10.4
8.9
10.7
34.5
36.9
32.3
10.2
8.9
Hispanic or Latino ethnicity ...... ...... ....
Married men. spouse present... .. ...... .. ..
Married women, 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
7.3
3.8
3.8
6.1
5.5
7.4
3.7
3.8
6.1
5.1
6.6
3.3
3.9
5.8
5.3
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
6.7
3.0
3.1
5.4
5.5
Educational attalnment2
Less than a high school diploma .. .. ...........
8.4
8.8
8.8
8.5
8.1
8.8
8.5
8.8
8.7
8.8
8.8
8.3
8.1
8.8
8.2
3
High school graduates. no college ......... .
Some college or associate degree ... .. ......
5.3
4.5
5.5
4.8
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
4.9
4.1
Bachelor's degree and higher 4. ......... ......
2.9
3.1
3.1
3.1
3.0
2.9
2.9
2.9
2.9
2.9
2.7
2.7
2.7
2.6
2.5
1
1
Beginning in 2003, persons who selected this race group only; persons who
selected more than one race group are not included. Prior to 2003, persons who
reported more than one race were included in the group they identified as the
main race.
3
Includes high school diploma or equivalent.
4
Includes persons with bachelor's, master's, professional. and doctoral degrees.
NOTE: Beginning in January 2003, data reflect revised population controls used in the
~ Data refer to persons 25 years and older.
household survey.
7. Duration of unemployment, monthly data seasonally adjusted
[Numbers in thousands]
Weeks of
unemployment
2002
2003
2004
2003
Annual average
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
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
2,612
3,378
1,442
1,936
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
2,612
2,394
3,365
1,467
1,898
2,468
2,412
3,274
1,403
1,871
2,589
2,414
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
1,211
1,667
2,604
2,521
2,903
1,239
1,664
2,790
2,255
2,954
1,253
1,747
2,749
2,288
3,043
1,253
1,790
Mean duration, in weeks .......... .......
Median duration, in weeks ..............
16.6
9.1
19.2
10.1
19.4
10.3
20.0
10.4
19.6
10.4
19.8
10.7
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
19.6
9.5
NOTE: Beginning in January 2003, data reflect revised population controls used in. the household survey.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
December 2004
69
Current Labor Statistics:
Labor Force Data
8. Unemployed persons by reason for unemployment, monthly data seasonally adjusted
[Numbers in thousands]
Reason for
unemployment
1
Job losers ......... . .... .... ..... ........ . .
On temporary layoff.. .............. .....
Not on temporary layoff................
Job leavers......................................
Reentrants ...................... ................
New entrants ..................................
Annual average
2002
2003
4,607
1,124
3,483
866
2,368
536
4,838
1,121
3,717
818
2,477
641
2003
2004
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
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
681
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
2,314
645
4,211
926
3,286
846
2,438
713
4,099
1,011
3,088
902
2,435
636
4,181
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
4,064
944
3,120
824
2,419
748
50.8
51 .7
49.4
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
50.3
11 .6
38.7
10.4
30.6
8.7
50.5
11.7
38.7
10.2
30.0
9.3
2.8
.6
1.7
.4
2.8
.6
1.6
.5
2.7
2.7
2.7
.6
1.7
.5
.6
1.6
.5
.6
1.6
.5
Percent of unemployed
1
Job losers .. .... .. ...... .... .. .......... ...
On temporary layoff ........ .............
Not on temporary layoff ...... ..........
Job leavers............................... .......
Reentrants ... .... .. .. .. ........... ........ .... ..
New entrants ............................. ... ..
55.0
55.1
54.2
12.1
42.1
10.7
28.0
7.1
52 .3
52.4
54.2
53.8
12.8
42 .4
9.3
28.2
7.3
55.2
12.4
42 .8
8.9
28.5
7.4
54.6
13.4
41.6
10.3
28.3
6.4
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
51.3
11 .3
40.0
10.3
29.7
8.7
3.2
3.3
3.3
3.2
3.1
2.9
.5
1.7
.4
.6
1.7
.4
.5
1.6
.5
3.0
.6
1.7
.5
3.0
.6
1.7
.4
3.0
.5
1.7
.5
3.1
.6
1.6
.4
.6
1.7
.4
.6
1.6
.4
.6
1.7
.5
Percent of clvlllan
labor force
1
Job losers .. .. ...... .... ... .. ..............
Job leavers... ...................................
Reentrants ......................................
New entrants ................................ ..
1
lndudes persons who completed temporary jobs.
NOTE: Beginning in January 2003, data reflect revised population controls used in the household survey.
70
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
9. Unemployment rates by sex and age, monthly data seasonally adjusted
[Civilian workers]
2002
2003
2004
2003
Annual average
Sex and age
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
Aug.
July
Sept.
Oct.
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
5.5
12.2
17.2
20 .5
15.2
9.8
4.3
4.3
3.8
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
6.0
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
MP.n. 11:i 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. ....... .......
5.9
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
6.2
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
5.6
13.0
19.0
21 .7
17.7
10.3
4.3
4.3
4.1
Women, 16 years and older. ..........
16 to 24 years ........... ............... ..
16 to 19 years .........................
16 to 17 years ....... ........ ... .
18 tO 19 years ........... ... .....
20 to 24 years .... ................... ..
25 years and older ............. .. ......
25 to 54 years ............... .......
5.6
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
5.7
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
5.3
11 .3
15.3
19.3
12.6
9.3
4.2
4.4
3.6
3.7
3.4
3.5
3.5
4.1
3.9
3.5
3.3
3.3
3.8
3.8
3.9
3.5
3.3
55 years and older
1
1
•• • •• • • • • • ••
Data are not seasonally adjusted.
NOTE: Beginning in January 2003, data reflect revised population controls used in the household survey.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
December 2004
71
Current Labor Statistics:
Labor Force Data
10. Unemploy ment rates by State, seasonally adjusted
State
Aug.
Sept.
2003
2004P
2004P
State
Sept.
Aug.
Sept.
2003
2004P
2004P
Alabama ....................... .. ............. ... ...... .
Alaska ...................................................... .
Arizona ........................... ................ ...... .
Arkansas .......... ........... .......... ....................
California ..................... .................. ....... .
5.8
8.0
5.5
6.6
6.7
6.0
7.6
4.4
5.4
5.9
5.7
7.6
4.8
5.5
5.9
Missouri
Montana .. ... ............... ............... ... ........ ..... .
Nebraska ....................................... ...... . .
Nevada........................... ......................... .
New Hampshire ... ............... ........ .. ......... .
5.7
4.8
4.1
5.3
4.3
5.5
4.8
3.6
4.0
3.7
5.6
5.1
3.7
3.9
3.5
Colorado ................................................ .. .
Connecticut.. ....... ... .. ............ .......... .... ... .
Delaware ... ......................................... ... ....
District of Columbia ............................. .... .
Florida............ .. ............. .. .. ....................... .
6.0
5.5
4.5
6.8
s.1
5.1
4.6
3.6
7.5
4.6
4.9
4.7
3.9
7.9
4.5
New Jersey ......................... .............. .. ..... .
New Mexico .................................. ........ .
New York ...................................... ........ ....
North Carolina...................... ....... .... ... ... .
North Dakota .... ................................... ..... .
5.8
6.6
6.4
6.6
4.0
4.8
5.4
5.6
5.4
3.3
4.8
5.3
5.5
5.3
3.6
Georgia............ ................. .... .... ..... ...... .
Hawaii ........ ...................... .... ............ .. ... ....
Idaho ..... .................... ... ................... .... .
Illinois ........................................................
Indiana..................... .................... ... ... .. .
4.5
4.5
5.3
6.9
5.2
4.2
2.9
5.0
6.1
5.1
4.1
3.1
5.0
6.0
5.2
Ohio ........ .................... .... ......... .. ......... .
Oklahoma........... .. ......... ... ... ... ... ... .. ..........
Oregon ............................. ......... ..... ...... .
Pennsylvania......................... ......... ..........
Rhode Island .. .... ........ ................. ... .. ..... .
6.1
5.8
8.2
5.4
4.9
6.3
4.1
7.4
5.6
5.5
6.0
4.4
7.3
5.3
5.0
Iowa.......... .. ..................... ......... ... ....... .
Kansas ...................... .......... ..................... .
Kentucky .................................... ... ... .... .
Louisiana .............. ............................. ...... .
Maine ...................... ....... ..... .. .. .. .... .. ... . .
4.6
5.4
6.2
6.5
4.7
4.5
4.8
5.1
5.0
4.5
4. 7
4.7
4.6
5.3
5.2
South Carolina .......... ................. ...... ...... .
South Dakota ....................... ........... ......... .
Tennessee ................... ... ... ........ .......... . .
Texas ........... ... ......... ................... ............. .
Utah ................. .. .......... ........... .... .... .... .
7.0
3.7
6.1
6.8
5.5
6.4
3.2
4.9
5.7
4.7
6.9
3.4
5.1
5.5
4.8
Maryland ................... ........... ... ..... .. .. .... .
Massachusetts .................. .. ..................... .
Michigan ...... ............... ................... ....... .
Minnesota ......................... .. ....... ........ ...... .
Mississippi.. ......................... ........ ... .. .... .
4.5
5.9
7.6
5.1
6.1
4.3
5.4
6.7
4.8
5.9
4.1
4.6
6.8
4.6
6.0
Vermont.. .. ... ......... ... .......... .... .... .... ...... .
Virginia ..... ............... ........... .......................
Washington .... ..... .............. ........ ..... ....... .
West Virginia ................................ ............ .
Wisconsin .. .................... ........ ...... ... ...... .
Wyoming ...... ..................... ................... .... .
5.0
4.1
7.7
6.0
5.6
4.3
3.3
3.6
6.2
5.5
4.8
3.7
3.3
3.2
5.6
5.0
5.0
3.9
P
72
Sept.
= preliminary
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Decembe r 2004
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
11. Employment of workers on nonfarm payrolls by State, seasonally adjusted
Sept.
State
2003
Aug.
2004P
Sept.
2004P
State
Sept.
Aug.
Sept.
2003
2004P
2004P
Alabama ............ ..... .... ... ........ .... . .
Alaska ................... ..... .... ............... .
Arizona .. .... .... ..... ... .......... . .... .. ... . .
Arkansas ............................. ...... .. .. .
California ................. ... ........... .... .. .
2,160,049
333,861
333,861
1,263,558
17,464,738
2,171 ,032
345,845
345,845
1,321 ,281
17,646,871
2,161 ,249
347,401
347,401
1,326,730
17,693,316
Missouri
Montana .......... .. ........................... .
Nebraska.............. ... ........... .... .... .
Nevada ........... ... .. .... ......... .... .. ...... .
New Hampshire .. ......... .. ... .... ... ..... .
3,029,677
477,507
979,224
1,146,920
722,716
3,048,875
483,962
990,212
1,185,851
730,469
3,038,268
483,831
991,725
1,184,592
729,402
Colorado ... ........... ..... .. ... .... ... .. ...... .
Connecticut... .. ...... ..... ..... .... ........ .
Delaware ...... ... ... ... ........... ... ... .. .... .
District of Columbia ..... .............. ... .
Florida ......... .............................. ... .
2,485,194
1,800,423
418,597
301 ,878
8,191 ,765
2,521,641
1,788,315
424,091
301 ,032
8,400,607
2,530 ,744
1,791 ,014
427,415
304,885
8,387,744
New Jersey ....... .. ......................... ..
New Mexico ..................... .... ... .... . .
New York ........ .. ... ... .. ... .. .......... ..... .
North Carolina .. ... ....... .. ... ............ . .
North Dakota .. .... .... .................... ...
4,379,131
900,779
9,603,949
4,253,937
347,271
4,425,145
910,889
9,308,448
4,183,628
350,563
4,409,429
911,138
9,327,402
4,156,918
351,278
Georgia .. ........ .. ....... ..... ... .. ...... ... .
Hawaii ................. ................... .. ..... .
Idaho ... ................... ....... . ..... . ...... .
Illinois ........... .............. ... ... ....... ...... .
Indiana .. ........................... .... .. .... .
4,440,823
624,018
693,057
6,340,126
3,188,438
4,439,453
630,197
710,466
6,388,300
3,147,244
4,417,986
630,197
708,364
6,426,479
3,152,373
Ohio .. ..... ........... .... ............ ... ... . .. .
Oklahoma .... ... .................. ..... ... .... .
Oregon ......................... .. ......... .. . .
Pennsylvania .. ... ...... ........ ... .......... .
Rhode Island ....... ..... .. ................ . .
5,923,631
1,695,200
1,845,115
6,142,682
573,558
5,875,960
1,698,816
1,850,802
6,275,025
568,893
5,867,088
1,704,629
1,831 ,288
6,289,636
567,061
Iowa ... ... .................. ... ... .. ... .... ... . .
Kansas .. ... .................... ... ........ ... ... .
Kentucky ... .............. ..... ..... ..... .. ... .
Louisiana ............................ .......... .
Maine .. .... .. ......... .... ... ....... ... ....... .
1,599,167
1,436,282
1,962,756
2,040,987
696,561
1,632,557
1,471 ,017
1,982,539
2,032,997
701 ,541
1,630 ,014
1,473,426
1,981,137
2,059,942
698,132
South Carolina ............. .......... ..... . .
South Dakota........... .................. ... .
Tennessee .. ... .... ... ... ...... ... ...... ... . .
Texas .. ... ........ ....... .............. ... ..... .. .
Utah .. .. ........ ............ ....... .. ........ .. .
2,019,922
425,674
2,606,564
10,935,870
1,190,270
2,068,869
424,034
2,931,130
10,963,157
1,211,405
2,082,012
425,250
2,941 ,877
10,976,345
1,214,025
Maryland .. ................... .... .... ...... .. .
Massachusetts ...... .... ... ..... ... ...... ... .
Michigan ... ... ...... ...... .. ................. .
Minnesota ................. .................... .
Mississippi. .. .. ................ ... ... ..... ... .
2,935,450
3,402,875
5,058,058
2,923,107
1,315,128
2,948,541
3,412,958
5,052,968
2,969,386
1,325,882
2,955,645
3,389,041
5,060,530
2,960 ,898
1,326,606
Vermont.. ... ... .... ................. ... .. . ... .
Virginia ... ... ............ .................... .. .. .
Washington .... ... ..... .. .. ........ .. .. .. ... .
West Virginia .. .......... .... .... ..... ....... .
Wisconsin ................ ..... ..... ... ... .... .
Wyoming .. ............... .... .......... ... ..... .
351,934
3,782,841
3,142,922
784,831
3,089,324
279,960
354,281
3,846,077
3,211 ,058
803,717
3,115,623
279,926
352,558
3,828,941
3,209,744
802,974
3,1 16,331
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
December 2004
73
Current Labor Statistics:
Labor Force Data
12. Employment of workers on nonfarm payrolls by Industry, monthly data seasonally adjusted
[In thousands]
Annual average
Industry
2002
TOT AL NONFARM ...............
TOTAL PRIVATE......................
2003
2003
Oct.
2004
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.P
Oct.P
130,341
129,931
129,944
130,027
130,035
130,194
130,277
130,630
130,954
131,162
131,258
131,343
131 ,541
131,680
132,017
108.828
22,557
108.356
21,817
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
109.771
21,906
109.912
21 ,939
110.007
21,935
110.303
22,000
583
70.4
512.2
121.9
571
68.5
502.3
122.9
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
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
591
64.1
526.9
133.1
Minina. exceot oil and aas •• •••
Coal minina ... ... .. ....... .. ......
Support activities for mining ....
210.6
74.4
179.8
202.7
70.4
176.8
202.1
69.6
175.3
202.4
69.5
177.1
202.0
69.8
177.7
200.0
69.6
178.2
200 .6
70.2
178.6
202.8
70.6
182.1
205.2
71 .8
182.3
207.8
72.9
183.1
207.9
73.5
182.8
211.2
75.0
184.1
209.2
74.6
184.7
208.8
74.4
185.4
208.5
74.2
185.3
Construction ..............................
6,716
1,574.8
930.6
4,210.4
15,259
6,722
6,791
6,853
6,872
6,909
6,930
6,945
7,016
1.593.3
928.0
4.290.2
14,314
1,590.9
924.0
4,276.5
14,321
1,607.6
926.8
4,318.9
14,344
1.609.8
924.7
4,337.3
14,365
1.622.9
924.3
4,362 .2
14,396
6,911
1,625.9
920.9
4,364.6
14,393
6,916
1,583.9
918.8
4.268.6
14,344
6,774
1,585.1
920.7
4,268.4
14,324
6,812
1,575.9
910.7
4 ,235.5
14.525
6,754
1,579.4
910.8
4,263.7
14,351
6,771
Construction of buildinas .........
Heaw and civil enaineerina ....
Soecialitv trade contractors .. ....
Manufacturing ............................
1.629.7
920.2
4.365.6
14,398
1,635.5
921.9
4,378.9
14,412
1,645.3
921.0
4,378.6
14,398
Production workers ......... .....
Durable goods.........................
10.766
9,483
10.200
8,970
10.058
8,854
10.048
8,874
10.044
8,868
10.035
8,869
10.038
8,882
10.085
8,924
6.157
536.1
492.6
476.7
1.478.4
1.153.5
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
6.126
540.0
497.8
462 .5
1.486.7
1,152.0
6.164
543.8
501.7
465.4
1.497.6
1,156.7
10.141
8,955
6,167
544.1
502.6
467.0
1,501.3
1.160.4
10.142
8,978
6.529
554.9
516.0
509.4
1,548.5
1,229.5
10.123
8,946
6,152
543.0
501.4
464.0
1.494.5
1,153.3
10.162
8,986
Production workers ..............
Wood oroducts ...... ........ ......... .
Nonmetallic mineral oroducts
Primarv metals ... ..... ...... ...........
Fabricated metal oroducts .......
Machinerv ...... ... ...... ...............
Comouter and electronic
10.058
8,889
6,101
539.7
493.2
462 .0
1.478.5
1,145.1
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
1.656.2
926.5
4.432.8
14,393
10,134
8,983
6,180
549.1
500.4
464.5
1.507.0
1,160.5
1,507.2
1,360.9
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
1,349.9
250.0
185.8
225.7
157.0
219.3
1 53.9
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
215.6
157.2
524.5
450.0
461 .8
429.3
449.4
425.1
451.2
425.2
451 .3
425.3
450.2
423.7
451.4
423.3
452.1
426.8
453.4
427.5
455.8
430.1
458.0
429.8
460.7
432.4
460.2
433.0
460.0
433.3
459.3
435.1
496.5
1,828.9
459.9
1,775.4
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
448.7
1,776.3
604.1
688.3
573.5
662.8
568.2
655.2
568.9
652.7
569.3
651.9
571 .3
652.0
571 .2
653.0
573.4
653.0
577.6
654.4
575.0
654.6
576.7
655.5
574.6
653.6
574.1
653.0
574.1
652.6
Nondurable goods...................
Production workers ..... .........
5,775
4,239
5,555
4,043
5,497
3,992
5,470
3,959
5,445
3,954
5,439
3,950
5,445
3,957
5,450
3,971
5,426
3,967
5,420
3,961
5,410
3,954
1,525.7
1,518.7
1,528.2
1,508.3
1,500.7
1,502.4
1,504.5
1,502.7
1,507.0
5,438
3,964
1,502.8
5,443
3,974
rood 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 ... ............... .. ........... .
5,456
3,965
1,506.3
576.5
653.0
5,441
3,959
1,508.0
1,499.6
1,497.5
1,497.3
207.4
290.9
194.6
359.7
50.2
546.6
200.6
260.3
179.8
312.7
45.2
519.0
201 .0
247.0
172.6
299.7
43.7
513.3
198.3
245.1
175.2
297.7
44.1
511 .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
197.0
232.9
180.6
278.4
45.3
507.5
706.6
118.1
927.5
848.0
680.0
! 1 4.6
7.9
815.9
673.3
112.6
899.1
806.3
673.1
112.0
897.6
806.5
670.1
112.4
895.9
805.8
667.6
·114.3
893.7
804.8
665.0
112.9
894.7
803.9
664.4
113.1
894.9
806.3
663.6
112.6
896.4
807.5
665.9
113.1
895.0
810 .2
667.0
113.8
895.2
808.6
663.8
113.6
894.2
811 .2
662.2
114.1
891.9
808.8
659.5
114.1
891 .5
809.0
658.0
114.3
889.9
808.5
SERVICE-PROVIDING ..................
107,784
108,114
108,270
108,341
108,367
108,498
108,593
108,852
109,132
109,268
109,367
109,437
109,602
109,745
110,017
PRIVATE SERVICEPROVIDING ............. .. ..........
86,271
86,538
86,710
86,797
86,823
86,971
87,054
87,299
87,560
87,724
87,839
87,865
87,973
88,072
88,303
25,497
5,652.3
3,007.9
2,015.0
25,275
5,605.0
2,949.2
2,002.1
25,272
5,581 .6
2,932.0
1,992.4
25,261
5,592.7
2,943.9
1,989.2
25,211
5,598.4
2,945.8
1,991 .8
25,312
5,611.4
2,954.9
1,993.7
25,331
5,612.2
2,953.8
1,994.5
25,415
5,623.5
2,963.4
1,995.3
25,448
5,632.5
2,967.5
1,996.3
25,477
5,636.7
2,969.7
1,997.2
25,497
5,639.5
2,975.6
1,994.3
25,499
5,649.6
2,986.0
1,994.3
25,516
5,652.8
2,989.6
1,992.1
25,530
5,662.9
2,992.9
1,992.5
25,566
5,668.5
2,997.5
1,997.2
GOODS-PRODUCING .......... .. ....
Natural resources and
mining .......... .........................
logging ... .... .... ... ... .................
Mining .... .. ..... ............. .. ....... .......
Oil and gas extraction .......... . ,
1
1
oroducts •••••••••• • •• • •• ••••••• ••
Comouter and oerioheral
equipment. .. ....... .... ..............
Communications equipment. .
Semiconductors and
electronic components .... .....
Electronic instruments ... ... .. ..
Electrical equipment and
appliances ........ .................. ....
Transportation equipment... .....
Furniture and related
products ...... .. ...... ... ......... ...
Miscellaneous manufacturing
Plastics and rubber products ..
Trade, transportation,
and utilities...............................
Wholesale trade .......................
Durable goods ................. .. ..
Nondurable goods ...... ... .. ....
Electronic markets and
agents and brokers ......... .. ..
629.4
Retail trade ............................ ... 15,025.1
Motor vehicles and parts
1
dealers • • •••• . •• . •••••••••••••• • • .
Automobile dealers ....... ...... .. .
Furniture and home
furnishings stores ................. ..
Electronics and appliance
stores ... ....... ....... .... ................
654.3
657.2
659.6
660 .8
662.8
663.9
664 .8
671 .5
668.7
669.8
669.6
670.7
674.0
673.8
14,911 .5 14,948.1 14.921.7 14,876.0 14,944.8 14.963.0 15,013.0 15,037.1 15,047.6 15,054.9 15,038.1 15.048.8 15,043.1 15,064.1
1,879.4
1,252.8
1,883.5
1,255.1
1,889.7
1,259.6
1,892.9
1,258.9
1,893.7
1,259.5
1,895.4
1,261 .3
1,900.9
1,262.9
1,906.9
1,263.9
1,910.9
1,264.7
1,911.4
1,263.6
1,908.5
1,262.3
1,908.1
1,259.2
1,904.9
1,256.8
1,904.9
1,253.3
1,904.8
1,251.8
538.7
542.9
540.2
544.8
547.2
546.4
544.5
544.8
544.5
545.7
546.3
546.4
548.7
548.5
549.4
525.3
511 .9
506.5
512.8
511 .9
509.3
508.2
511 .7
514.1
512.6
511 .5
510.7
511.6
512.7
519.7
See notes at end 0f table.
74 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
10.128
8,955
December 2004
12. Continued-Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted
[In thousands]
Annual average
Industry
2003
2004
2002
2003
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
1,176.5
2,881 .6
1,191.1
2,840.9
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
1,260.5
2,836.7
938.8
895.9
943.1
879.9
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
954.8
871 .8
1,312.5
1,296.7
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
1,354.2
661 .3
2,812.0
1,684.0
959.5
443.7
645.0
2,815.2
1,618.8
934.1
427 .5
642.0
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
639.1
2815.5
1,599.7
927.7
429.9
4,223.6
563.5
217.8
52.6
1,339.3
4,176.7
527 .3
215.4
52.5
1,328.0
4,162.9
506.1
215.2
52.5
1,329.3
4,168.0
511 .5
215.5
50.9
1,335.7
4,157.0
512.9
215.5
50.0
1,338.7
4,175.9
510.2
215.4
50.6
1,343.6
4,175.8
511 .6
215.7
48.8
1,344.1
4,197.0
512.9
216.0
49.2
1,346.4
4,196.5
513.3
216.3
50.6
1,352.2
4,209.9
514.7
216.4
51 .1
1,353.9
4220.9
513.8
217.3
51 .7
1,353.9
4228.3
512.4
217.8
51 .7
1,361 .9
4232.5
511 .8
217.4
50.3
1,363.7
4240.3
512.3
217.9
50.3
1,368.7
4249.9
513.7
217.9
51 .0
1,368.0
380.8
41 .7
380.3
40.0
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
377.0
38.5
25.6
28.0
29.0
28.7
29.4
28.7
29.7
31.4
31 .1
30 .6
32.6
32.6
32 .7
32.9
32.9
524.7
560.9
516.7
516.3
566.6
522 .3
514.3
565.0
522.6
512 .4
564.7
524.2
511 .6
559.0
516.1
514.1
566.9
525.8
515.5
567.7
524.4
519.1
570.9
532.6
523.7
579.2
536.3
525.1
580.4
538.1
579.2
3,166
578.9
3,172
579.3
3,175
580.2
3,163
580.0
3,169
582.1
3,173
581 .7
3,182
582.6
3,173
582.0
3,166
525.3
581 .1
538.5
583.3
3,158
527 .9
580.8
542.2
580.8
3,198
519.5
572 .8
531 .1
582 .3
3,177
520.8
578.2
534.0
596.2
3,395
518.5
572.1
531 .9
581 .2
3,169
964.1
926.4
918.0
918.4
917.4
914.0
915.1
915.3
916.3
916.2
916.6
914.7
914.3
914.3
913.6
387 .9
334.1
376.1
327 .0
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
395.9
338.1
33.7
1,186.5
30.0
1,082.6
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
35.7
1,024.3
441 .0
47.3
407 .5
48.1
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
406.4
49.1
7,847
5,817.3
7,974
5,920.5
7,990
5,930.2
7,985
5,922.7
7,981
5,916.5
7,981
5,917.1
7,989
5,924.7
8,003
5,933.0
8,015
5,947.7
8,029
5,946 .0
8,049
5,960.4
8,044
5,951 .9
8,053
5,962.4
8,077
5,973.6
8,094
5,989.7
23.4
22.7
22.5
22 .5
22 .5
22.4
22.4
22 .3
22.3
21 .8
21 .9
21 .8
21 .8
2 1.8
21 .6
.. ... ..
2,686.0
2,785.6
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
2,823.5
intermediation' .. . . .. . .. . .. ....
Commercial bankina ..
Securities, commodity
contracts, investments. ... ....
Insurance carriers and
related activities ........ .....
Funds, trusts, and other
financial vehicles ..... ... .. . . . ..
Real estate and rental
and leasing .... .... .. ...... .. .... ..
Real estate .. . . . .. .. . . . .. . . . . . .. ..
Rental and leasing services ....
Lessors of nonfinancial
intangible assets ...... ....
1,733.0
1.278.1
1,752.1
1,281 .1
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
1,776.2
1.290.2
789.4
764.4
762.0
769.1
771 .9
773.8
778.2
780.8
781 .0
782.8
787.2
787.8
791 .6
793.0
800.9
2,233.2
2,266.1
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
2,266.4
Building material and garden
supply stores .... ... .... ........... ...
Food and beverage stores ...
Health and personal care
stores .. ......... ...... ... .. ...... .
Gasoline stations ....... ·· ··· ·····
Clothing and clothing
accessories stores ... .... ......
Sporting goods, hobby,
book, and music stores .... ....
General merchandise stores1.
Department stores ·· ······"···
Miscellaneous store retailers ..
Nonstore retailers .. ..... ..... ..
Transportation and
warehousing ..........................
Air transportation ..... ....... .... ..
Rail transportation .. ... . .... .....
Water transportation ...... .... ...
Truck transportation ... ...... .... .
Transit and ground passenger
transportation ........ ........ .. ..
Pipeline transportation .. .. ... ..
Scenic and sightseeing
transportation ... .. ... .. .. . .. . ....
Support activities for
transportation .. ... ..... .... ... ...
Couriers and messengers .. ....
Warehousing and storage
Uiiilt1es .................... ...................
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 ... ..
Flnanclal activities ... ... .... ... ......
Finance and insurance ......... .. .
Monetary authoritiescentral bank ....... ... ........ .... .
Sept.P
Oct.P
583.5
3,163
Credit intermediation and
related activities' .. .. .
Deoositorv credit
Professional and business
services ..................... .. ..... ...
85.4
81 .7
80.0
79.6
80.7
79.8
79.5
79.0
78.8
77.9
77.6
78.0
77 .8
77.6
77.3
2,029.8
1,352.9
649.1
2,053.6
1,384.4
640.8
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
2,071 .6
1,409.2
633.2
2,083.1
1,418.7
635.4
2,088.1
1,418.8
640.5
2,092.0
1,422.1
641 .4
2,090.6
1,424.1
638.0
2,193.1
1,431 .1
643.7
2,104.0
1,433.4
642.5
27.6
28.4
29.7
29.2
30.0
29.9
30.1
29.6
29.2
29.0
28.8
28.5
28.5
28.3
28.1
15,976
15,999
16,070
16,114
16,159
16,172
16,196
16,237
16,363
16,432
16,457
16,490
16,518
16,562
16,659
6,675.6
1,115.3
6,623.5
1,136.8
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
1,148.2
6,762.0
1,146.2
6,788.5
1,149.3
6,817.2
1,149.1
837.3
815.6
801 .5
810.6
826.6
815.2
813.3
812.8
818.5
806.3
811 .6
811 .9
815.3
817.7
824.2
1,246.1
1,228.0
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
1,282.6
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
December 2004
75
Current Labor Statistics:
Labor Force Data
12. Continued-Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted
[In thousands]
Computer systems design
and related services ..........
Management and technical
consulting services ............
Management of companies
and enterprises .. .. ..... .... ... ..
Administrative and waste
services ........... ... ....... .... . ..
Administrative and suooort
services' .. .... .... ...............
Employment services
1
•••• • .
Temoorarv helo services. ..
Business suooort services ..
Services to buildinas
and dwellinas ..... .. .... .... ..
Waste management and
remediation services ......
Educational and health
services ... .. ............ ... .. ..... ...
Educational services ...............
Health care and social
assistance ............ ....... .. ...
2004
2003
Annual average
Industry
2002
2003
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
1,152.8
1,108.3
1,107.0
1,105.7
1,105.7
1,104.6
1,099 .8
1,103.5
1,103.5
1,110.1
1,1 17.7
1,120.5
1,129.7
1,136.4
Oct.P
1,143.3
734.4
747.3
755.6
760.6
764.0
765.4
767.9
774.0
780.9
785.9
791.4
792 .2
794.3
795.9
798.6
1,705.4
1,675.5
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
1,677.9
7,595.2
7,698.3
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
8,162.2
7,276.8
73,764.0
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
7,837.3
3,246.5
3,336.2
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
3,695.8
2.193.7
756.6
2.243.2
747.4
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
2.555.8
750.8
1.606.1
1.631 .7
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
1.690.4
318.3
321.9
320.3
320.8
322.9
321
322.8
323.3
325.3
324.5
327
326.2
326.4
325.9
326.9
16,199
2,642.8
16,577
2,688.5
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
2,731 .2
16,965
2,746.4
16,984
1,756.4
17,046
2,777.9
13,555.7
13,888.0
13,970.0
13,981 .5
14,003.2
14,077.1 14,113.1 14,140.1 14,169.8
14,169.3
14,218.3
14,227.9
14268.5
4,633.2
1,967.8
413.0
679.8
4,776.0
2,003.8
423.1
727.1
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
4,883.6
2,046.1
432.2
748.4
4,896.8
2,049.6
435.1
751 .7
4,909.6
2,053.9
436.0
754.2
4,920.8
2,057.5
437.6
756.8
4,935.1
2,062.1
438.0
760.1
4,939.3
2,068.5
437.0
760.7
4,961.4
2,076.6
437.5
765.4
4,159.6
4,252.5
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
4,337.6
2,743.3
2,784.3
2,794.2
2,792.1
2,791.1
2,798.4
2,802.8
2,806.3
1.585.2
2,094.1
1.581 .7
2,095.3
1.580.3
2,096.9
1.578.7
2,106.3
1.582.1
2 ,1 12.7
1.584.0
2,121 .6
1.585.3
2,121 .6
2,809.0
1.586.5
2,132.9
2,812.0
1.582.8
2,075.2
760.5
12,128
2,792.8
1.584.1
2,091 .9
2,793.0
1.573.2
2,019.7
744.1
11 ,986
2,814.0
1.586.3
2,138.7
2,819.5
1.586.2
2,137.1
2,821.4
1.586.3
2,148.1
14,017.1 14,036.8
Ambulatorv health care
services' .................... .....
Offices of physicians .......... .
Outpatient care centers ..... ..
Home health care services ...
Hospitals ..... .......................
Sept.P
Nursina and residential
r:~rA f~r:il itiA~ 1
Nursina care facilities ..........
1
Social assistance ••••••••• ......
Child day care services ..
1.586.7
2,114.5
788.8
792.7
782.8
752.1
786
777.1
777.6
773.7
772.2
766.3
770
766.3
771 .6
12,364
12,351
12,341
12,344
12,339
12,331
12,271
12,303
12,229
12,218
12,178
12,192
12,147
Leisure and hospitality ...... ... ..
Arts, entertainment,
1,786.3
1,792.7
1,785.6
1,791 .9
1,792.0
1,793.1
1,791 .1
1,798.7
1,796.7
1,801.4
1,795.2
1,799.4
1,796.9
1,801 .0
1,782.6
and recreation .........
Performing arts and
363.8
363.2
356.0
357.1
359.3
358.8
361 .4
364.6
366.5
369.4
368.8
371.7
369.6
370.2
363.7
spectator sports ...... ·· ···· ··
Museums, historical sites,
116.1
116.3
116.7
116.6
116.1
115.6
114.6
114.2
113.7
113.4
113.1
113.3
114.2
114.1
114.0
zoos, and parks ..................
Amusements, gambling, and
........ 1,305.0 1,316.6 1,313.1 1,314.4 1,313.3 1,318.6 1,316.5 1,3 19.9 1,315.1 1,318.7 1,316.6 1,318.2 1,312.9 1,313.2 1,306.4
recreation .. .. ... .. ...
Accommodations and
food services .. ········· ...... .... 10,203.2 10,324.4 10,350.4 10,378.9 10,396.3 10,416.5 10,432.3 10,742.0 10,511 .8 105,837.9 10,546.7 10,551 .7 10,555.6 10,558.1 10,577.9
1,766.4
1,765.3
1,767.9
1,764.4
1,764.7
1,758.5
1,758.5
1,753.4
1,754.4
1,752.1
1,763.0
1,751 .7
1,733.7
1,765.2
1,778.6
Accommodations ·· ·· · · ······· · ···
Food services and drinking
.5
8,811
.8
8,792
8,787.7
8,787.7
8,782.0
8,779.4
8,753.3
8,718.6
.9
8,677
8,664.4
8,633.3
8,627.2
8,616.7
8,559.2
8,424.6
places .......... . ... .. .. .......
5,411
5,410
5,414
5,414
5,418
5,407
5,404
5,391
5,376
5,374
5,379
5,382
5,387
5,393
5,372
Other services ...... ....... ...........
1,238.4
1,236.8
1,235.2
1,236.3
1,235.1
1,237.7
1,238.2
1,239.4
1,230.5
1,233.5
1,228.5
1,234.4
1,237.6
1,236.2
Repair and maintenance ..... ... 1,246.9
1,253.8
1,254.1
1,259.9
1,262.1
1,268.4
1,265.5
1,260.5
1,255.9
1,247.6
1,251.2
1,250.2
1,254.1
1,254.6
1,257.2
1,258.2
Personal and laundry services
Membership associations and
2,919.2
5,919.2
2,919.1
2,915.9
2,914.9
2,903.7
2,904.8
2,895.2
2,898.3
2,894.5
2,895.7
2,893.9
2,895.2
2,898.0
2,867.8
organizations .................. ...
21,714
21 ,673
21,629
21 ,572
21 ,528
21 ,544
21,572
21,553
21,539
21 ,544
21 ,527
21,544
21 ,560
21,575
21,513
Government. ...............................
2,704
2,710
2,712
2,710
2,716
2,712
2,727
2,710
2,716
2,715
2,720
2,736
2,723
2,767
2,756
Federal.. .. ..... ...... .......................
Federal , except U.S. Postal
1,920.9
1,926.3
1,926.3
1,922.5
1,930.5
1,925.7
1,939.5
1,921.1
1,923.8
1,921 .5
1,928.9
1,924.9
1,932.9
1,947.0
Service ......... ... ............. ....... 1,923.8
782 .9
784.0
785.3
787.2
785.4
786.5
787.3
789.1
791.7
793.1
791.4
798.1
803.3
809.1
842.4
U.S. Postal Service .. ... .. . .. .. . ..
5,065
5,052
5,035
5,019
5,004
5,004
5,019
5,023
5,018
5,027
5,007
5,031
5,023
5,017
5,029
State .........................................
2,312.5
2,302.3
2,285.2
2,271 .1
2,257.8
2,261.4
2,278.3
2,283.2
2,279.6
2,268.0
2,285.7
2,282.5
2,290.4
2,266.4
Education ....... ................ .. ..... 2,242.8
2,752.2
1,749.2
2,749.4
2,747.8
2,746.1
2,742.8
2,740.6
2,739.7
2,738.4
2,738.9
2,740.9
2,740.0
2,740.4
2,750.7
2,786.3
Other State government ........
13,945
13,911
13,882
13,843
13,808
13,828
13,826
13,820
13,805
13,805
13,797
13,798
13,793
13,802
Local. .. ............ ... ........ ..... .. ... ... .. 13,718
7,810.6
7,778.2
7,758.4
7,715.7
7,695.1
7,710.2
7,710.9
7,704.7
7,694.3
7,692.2
7,687.1
7,684.5
7,687.0
7,699.1
Education ......... ........ .. ... ... . ... . 7,654.4
6,133.9
6,132.7
6,123.2
6,116.8
6,113.3
6,117 .9
6,115.4
6,114.8
6,110.8
6,112 .7
6,113.1 6,109.7
6,105.9
6,104 0
6,063.2
Other local government... .....
• • • • • • • • • I • •
1
includes other industries not shown separately.
pg preliminary.
Classificat ion System (NAICS), replacing the Standard lndu~trial Classification (SIC) system .
NAICS-based data by industry are not comparable with sic-based data. See "Notes on the
NoTE: Data reflect the conversion to the 2002 version of the North American industry
data" for a description of the most recent benchmark revision .
Monthly Labor Review
76
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
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
2004
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.P
Oct.P
TOTAL PRIVATE .............................
33.9
33.7
33.7
33.8
33.6
33.8
33 .8
33.8
33.7
33.8
33.6
33.8
33.7
33.8
33.8
GOODS-PRODUCING .. ... .. ...................
39.9
39.8
39.9
40.1
39.9
40.2
40 .3
40.2
40.0
40.3
40.0
40.1
40.1
40.1
40.0
43.9
44.1
44.4
44.6
45.1
Natural resources and mining .... ........
43.2
43.6
43.7
43.9
43.6
44.5
44.1
44.2
44.3
44.2
Construction ........................... .... .....
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
38.3
Manufacturing ....................................
Overtime hours .................................
40.5
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
40.7
4.5
Durable goods .... ... ...... ..... .... ........ ... .
Overtime hours .......................... ... .. ..
Wood products ...................................
Nonmetallic mineral products ... ..........
Primary metals ............. .. ... ... ... ............
Fabricated metal products ............... ...
Machinery ......... ..... .. .. .... ..............
Computer and electronic products .. ...
Electrical equipment and appliances ..
Transportation equipment.. .................
Furniture and related products ..........
Miscellaneous manufacturing .............
40.8
4.2
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
40.9
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
41.2
4.7
40.3
42 .3
43.0
41 .1
42.2
40.3
40.8
42.3
39.0
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
39.9
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
39.9
4.2
39.2
38.2
40.0
38.9
35.6
37.7
42.0
38.4
43.0
42.3
40.6
38.2
44.5
42 .4
40.4
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
38.3
45.9
42.7
40.2
38.2
45.1
42.7
40.2
32.5
32 .4
32.3
32.4
32.2
32 .4
32 .4
32.4
32.3
32.4
32 .3
32.4
32.4
32.5
32.4
33.6
38.0
30.9
36.8
40.9
36.5
~5.6
33.5
37.8
30 .9
36.9
41 .1
36.2
35.5
33.6
38.0
30.9
37.1
41 .0
36.1
35.5
33.6
38.0
30 .9
37.0
41.4
36.3
35.5
33.5
37.8
30.8
36.7
40.8
36.2
35.3
33.6
37.9
31 .0
36.9
40.8
36.2
35.7
33.7
38.0
30 .9
37 .2
41 .0
36.3
35.5
33.6
38.0
30.8
36.9
41.2
36.3
35.5
33.5
38.0
30.7
36.9
41.2
36.3
35.6
33.5
37.8
30.7
37.3
41.3
36.4
35.8
33.3
37.6
30.5
36.9
41.1
36.5
35.5
33.4
37.8
30.6
37.1
41.0
36.4
35.6
33.5
37.6
30 .7
37.2
40.9
36.4
35.5
33.6
37.8
30.8
37.3
41 .4
36.3
35.5
33.6
37.7
30 .8
37.3
40.7
36.2
35.6
34.2
32 .4
25.8
32 .0
34.1
32.3
25.6
31.4
34.0
32.3
25.6
31 .3
34.1
32.4
25.7
31.2
33.8
32.4
25.6
31 .0
34.1
32.4
25.7
31.1
34.2
32 .4
25.8
31.1
34.1
32.4
25.7
31 .2
34.1
32.4
25.7
31 .1
34.2
32.5
25.7
31 .2
33.9
32.5
25.7
31 .0
34.2
32.6
25.6
31 .1
34.2
32.5
25.5
31 .1
34.5
32 .5
25.5
31 .1
34.3
32.6
25.6
31 .1
PRIVATE SERVICEPROVIDING .................... ... .... ... ....
Trade, transportation, and
utilities ..............................................
Wholesale trade ......... ....... .. .... ... ... ... ..
Retail trade .................................. ..
Transportation and warehousing .... .. ..
Utilities .... ... ....... .. ... ........ ..... ... ... .. ..
Information ... ... ........................ .........
Financial activities ............................
Professional and business
services .. ...... ..... ...... ......................
Education and health services ............
Leisure and hospitality ......................
Other services ......................................
1
Data relate to production workers in natural resources and mining and manu-
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
December 2004
77
Current Labor Statistics:
Labor Force Data
1
14. Average hourly earnings of production or nonsupervisory workers on private nonfarm payrolls, by industry,
monthly data seasonally adjusted
2004
Annual average
Industry
2002
2003
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.P
Oct.P
TOTAL PRIVATE
Current dollars .. ... ....... ...... .......
Constant (1982) dollars .. ... ........ .
$14.95
8.24
$15.35
8.27
$15.43
8.28
$15.46
$15 .45
8.30
$15.49
8.27
$15.52
8.27
$15.55
8.24
$15.59
8.25
$15.63
8.21
$15.66
8.20
$15.71
8.23
8.23
$15.76
8.26
$15.78
8.26
$15.83
8.25
GOODS-PRODUCING .............................
16.33
16.80
16.90
16.94
16.97
17.00
17.06
17.08
17.13
17.13
17.16
17.19
17.24
17.30
17.31
Natural resources and mining .............
Construction .........................................
Manufacturing .......................................
Exduding overtime ............. .. ... .......
Durable goods .... .. ...... .. ... .... ....... ...
Nondurable goods .. .... ......... ........ ...
17.19
18.52
15.29
14.54
16.02
14.15
17.58
18.95
15.74
14.96
16.46
14.63
17.72
19.06
15.83
15.03
16.54
14.72
17.79
19.06
15.89
15.06
16.58
14.79
17.91
19.04
15.93
15.09
16.64
14.81
17.95
19.11
15.94
15.11
16.63
14.85
18.01
19.18
15.99
15.14
16.68
14.89
18.10
19.17
16.01
15.16
16.69
14.93
18.08
19.20
16.08
15.24
16.75
15.00
18.10
19.20
16.08
15.23
16.75
15.02
18.24
19.19
16.13
15.27
16.78
15.08
18.15
19.22
16.16
15.30
16.81
15.12
18.12
19.25
16.23
15.37
16.90
15.15
18.09
19.28
16.30
15.43
16.99
15.19
18.20
19.33
16.37
15.42
16.98
15.12
PRIVATE SERVICE·
PROVIDING ................... .... .. ........ .. .....
14.56
14 .96
15.03
15.06
15.05
15.08
15.10
15.13
15.17
15.23
15.26
15.31
15.36
15.38
15.43
14.02
16.98
11 .67
15.76
23.96
20.20
16.17
14.34
17.36
11 .90
16.25
24 .76
21 .01
17.13
14.41
17.47
11 .95
16.32
25.17
21 .21
17.29
14.44
17.47
11.97
16.35
25.36
21 .10
17.30
14.41
17.46
11 .95
16.33
25 .13
20 .99
17.30
14.45
17.53
11 .95
16.46
25 .32
21 .15
17.35
14.49
17.54
11 .98
16.52
25.35
21.24
17.32
14.50
17.54
11 .99
16.53
25.38
21 .25
17.41
14.57
17.60
12.01
16.71
25.67
21 .29
17.46
14.61
17.63
12.06
16.75
25.46
21 .42
17.49
14.65
17.67
12.10
16.82
25.44
21 .30
17.50
14.70
17.71
12.12
16.89
25.57
21.45
17.55
14.73
17.70
12.16
16.99
25.54
21 .53
17.58
14.75
17.76
12.16
16.95
25.73
21 .61
17.62
14.76
17.82
12.16
17.01
25.75
21 .59
17.73
16.81
17.20
17.25
17.29
17.25
17.24
17.25
17.27
17.29
17.36
17.42
17.44
17.56
17.52
17.64
15.21
8.58
13.72
15.64
8.76
13.84
15.73
8.78
13.80
15.77
8.82
13.81
15.81
8.84
13.80
15.87
8.85
13.84
15.90
8.86
13.84
15.96
8.87
13.87
15.99
8.86
13.84
16.06
8.86
13.85
16.12
8.85
13.88
16.18
8.87
13.90
16.19
8.91
13.92
16.22
8.95
13.96
16.24
9.00
13.98
Trade,transportatlon, and
utilities ........................................
Wholesale trade ..................................
Retail trade .........................................
Transportation and warehousing .......
Utilities ..... ................. .................. .
Information ............................................
Financial activities................................
Profe~slonal and business
services...............................................
Education and health
services...............................................
Leisure and hospitality........................
Other services.......................................
1
Data relate to production workers in natural resources and mining and manufac-
NOTE:
Data reflect the conversion to the 2002 version of the North American industry
turing, construction workers in construction , and nonsupervisory workers in the
Classification System (N AICS) , replacing the Standard Industrial Classification (SIC) system. NAICS
service-providing industries.
based data by industry are not comparable with SIC-based data. See "Notes on the data" for a
p = preliminary.
description of the most recent benchmark revision.
78 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
15. Average hourly earnings of production or nonsupervisory work~rs 1 on private nonfarm payrolls, by industry
Industry
TOTAL PRIVATE ...........................
Seasonally adjusted ... ..... ... .......
Annual average
2002
2003
$14 .95
$15.35
15.18
15.47
2003
2004
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.P
Oct.P
$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
$15.84
15.78
GOODS-PRODUCING ................... ..........
16.33
16.8
16.95
16.98
17.03
16.94
16.95
17.00
17.09
17.10
17.14
17.18
17.28
17.41
17 .37
Natural resources and mining ...........
17.19
17.58
17.69
17.15
17.97
18.00
18.05
18.17
18.14
18.06
18.18
18.07
18.01
18.03
18.18
Construction ................................... .. ..
18.52
18.95
19.13
19.08
19.19
19.01
19.07
19.07
19.15
19.15
19.12
19.25
19.33
19.42
19.46
Manufacturing .............................. .. .
15.29
15.74
15.81
15.92
16.05
15.98
15.99
16.01
16.07
16.05
16.09
16.04
16.17
16.37
16.25
Durable goods ... ···· ······ ···· ··· ···········
Wood products ............. .. ... ... ..... ... .. ..
Nonmetallic mineral products .. .. . . . .
Primary metals ....... .............. ...... ......
!" dbricated metal products .. .............
M.achinery ...... ............ ... ... ....... ...
Computer and electronic products ...
Electrical equipment and appliances
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
16.46
12.71
15.77
18.13
15.01
16.30
16.68
14.35
21 .25
12.98
13.30
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
21 .34
12.96
13.78
16.72
13.00
16.19
18.52
15.21
16.53
17.01
14.80
21.36
13.09
13.70
16.71
13.03
16.18
18.48
15.20
16.53
17.11
14.83
21.29
13.04
13.76
16.75
12.98
16.24
18.51
15.23
16.56
17.21
14.88
21.36
13.10
13.81
16.61
13.03
16.38
18.66
15.26
16.68
17.29
14.88
20.77
13.11
13.89
16.85
1::i.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
16.99
13.03
16.33
18.68
15.4 1
16.82
17.46
14.93
21 .86
13.25
13.90
Nondurable goods ..... ... .. ......... ..... .
Food manufacturing ... ... ......... .....
Beverages and tobacco products ....
14.15
12.55
1; 73
14.63
12.80
17.96
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
15.08
12.81
19.15
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
12.00
11 .24
9.56
11 .67
17.32
12.02
11 .37
9.69
11 .83
17.44
15.41
12.21
11 .44
9.80
11.90
17.60
15.56
12.11
11.45
9.74
11 .94
17.63
15.53
12.13
11 .40
9.58
11.76
17.55
15.57
12.09
11 .37
9.60
11 .64
17.59
15.61
12.23
11 .33
9.71
11 .65
17.84
15.54
12.24
11 .53
9.78
11 .55
18.20
15.97
12.17
11 .50
9.86
11 .75
17.98
15.99
24.00
18.77
14.27
24.06
18.79
14.47
24.13
18.83
14.43
24.32
18.85
14.45
24.82
18.87
14.45
24.48
19.02
14.58
12.15
11 .29
9.60
11 .59
17.86
15.54
24.24
19.20
14.59
12.08
11 .46
9.73
11 .68
17.84
15.86
23.63
18.66
14.19
12.08
11 .30
9.55
11.49
17.88
15.51
24.41
19.05
14.55
12.07
11.48
9.74
11 .68
17.91
15.71
23.04
17.97
13.55
15.37
23.64
18.52
14.18
12.08
11.35
9.71
11 .87
17.58
15.48
24.35
19.36
14.69
24.07
19.29
14.66
24.52
19.51
14.75
24.34
19.45
14.51
PRIVATE SERVICEPROVIDING ..... ....... ... ... ........ ..........
14.56
14.96
15.01
15.13
15.07
15.1 9
15.24
15.16
15.20
15.24
15.14
15.17
15.24
15.37
15.42
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
14.38
17.42
11 .91
16.31
14.44
25.23
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
14.76
177.80
12.16
17.02
25.77
20.20
21.01
21 .25
21 .28
21.10
21 .21
21 .28
21 .17
21 .24
21.41
21 .18
21 .30
21 .44
21 .73
21 .69
16.17
17.13
17.25
17.42
17.26
17.35
17.47
17.37
17.45
17.62
17.38
17.44
17.58
17.60
17.72
16.81
17.20
17.13
17.41
17.29
17.38
17.47
17.28
17.26
17.45
17.28
17.31
17.46
17.43
17.55
16.23
Financial activities ...... .. ..... ......... .......
Professional and business
services .............................. ......... .
Education and health
services .................................... ...
15.21
15.64
15.73
15.79
15.86
15.94
15.95
15.94
15.99
16.00
16.06
16.18
16.16
16.24
Leisure and hospitality ....................
8.58
8.76
8.78
8.83
8.94
8.89
8.92
8.89
8.84
8.85
8.78
8.78
8.80
8.94
9.03
Other services ...................................
13.72
13.84
13.78
13.85
13.88
13.89
13.90
13.85
13.87
13.90
13.82
13.78
13.84
13.98
13.97
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 constriJction, and nonsupervisory workers in
Classification System (NAICS), replaci ng the Standard Industrial Classification (SIC)
the service-providing industries.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
system. NAICS-based data by industry are not comparable with sic-based data. See
"Notes on the data" for a description of the most recent benchmark revision .
Monthly Labor Review
December 2004
79
Current Labor Statistics:
Labor Force Data
16. Average weekly earnings of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry
Industry
Annual average
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.P
Oct.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
$535.39
535.05
669.23
681 .39
684.29
682.90
674.21
674.61
681 .70
678.47
690.84
689.03
687.20
698.11
691 .18
698.27
711 .82
618.75
766.83
727.11
636.07
778.36
744.16
643.47
784.55
730.76
655.90
781 .70
714.34
662.87
784.80
712.88
650.39
786.98
711.31
652.39
797.66
732.29
653.21
794.53
721 .96
652.44
798.25
741.11
659.66
809.01
738.03
659.69
802.31
754.60
646.41
806.85
755.80
661.35
796.93
730.19
664.62
829.01
755.05
661 .38
652.97
492.00
646.91
749.32
596.38
645.55
671 .53
513.92
665. 11
767.63
610.33
664 .79
680.2 1
525.62
679.47
771 .98
616.23
667.08
692.22
537.43
681 .17
785.93
621 .98
682.69
703.08
531.42
669.56
799.97
635.09
696.38
688.06
517.29
663.64
796.29
626.24
689.30
688.88
521 .56
664.00
787.64
623.90
691 .35
690.97
524 .96
680.85
790.02
625.25
690.93
687.19
530.40
684.84
800.06
620.27
987.65
695.14
544.65
684.41
803.88
627.76
700.87
695.13
533.48
690.20
808.89
627.48
698.83
674.37
531 .62
694.51
791 .18
621 .08
692.22
695.91
538.61
700.47
798.94
627.60
697.22
698.91
521 .26
708.28
809.35
628.00
698.45
699.99
527.72
697.29
799.50
634.89
708.12
642.87
674.68
684.22
693.01
695.91
680.81
695.41
690.74
683.80
694 .67
698.73
696.79
700.01
701 .49
703.64
560.24
877.87
582.68
890.32
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
613.62
926.86
494 .01
505.23
508.69
523.20
528.43
510.23
505.17
510.62
517.06
517.69
521 .38
515.22
529.47
518.76
515.43
-
651 .61
Natural resources
and mining ... ...... .. ..... .......... ... 741 .97
Construction ..........................
Manufacturing ..................... ...
Durable goods .. .. .... .... .... ....... .
Wood products .............. .. ..... ....
Nonmetallic mineral products ... .
Primary metals ... ........ ... .... ...
Fabricated metal products ... .... .
Machinery ................... ... ..... .
Computer and electronic
products .. .............. ... ....... ........
Electrical equipment and
appliances ................. .... ..........
Transportation equipment.. ... ...
Furniture and related
products .. ..... ..... ........ .........
Miscellaneous
manufacturing ........... .. .... .. .... ..
499.13
510.69
515.90
530.38
533.12
532.15
533.50
534.66
524.71
535.26
530.30
527.82
534.00
529.46
533.76
566.84
Food manufacturing .... .... ... .... ... 496.91
582.65
502.61
588.27
505.69
600.88
515.11
602.64
514.12
594.11
504.78
595.20
499.36
596.00
498.84
595.90
497.66
602.20
511.13
604.21
512.20
602.57
512.87
606.62
514 .40
611 .12
521 .89
603.20
507.28
698.39
476.52
429.01
333.66
412 .99
705.62
702.75
469.47
445.08
340.22
458.26
719.21
707.56
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
725.79
484.37
448.50
350.03
442.98
756.96
573.05
587.42
597.91
603.72
602.17
593.25
597.89
600.99
593.63
594.03
593.63
600.12
610.61
611 .65
615.62
Nondurable goods...... ..... ......... ...
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 ...... ... .... ....... ... .. ... .
Ci,t:imicals ... ..... ......... .... ... ....
Plastics and rubber
products ... .. .... . . . . . . . . . . . .. . . . . . .
990.88
759.53
1,052.97 1,068.08 1,099.20 1,061 .05 1,068.96 1,074.94 1,079.67 1,062.43 1,091 .13 1,095.65 1,120.10 1,097.59 1,125.47 1,102.60
831 .13
824.68
811.41
814.06
815.34
819.84
816.99
823.68
808.99
806.09
804.04
816.21
784.56
785.59
549.85
572.23
578.95
586.50
596.16
585.86
588.12
589.56
594 .86
595.10
599.65
583.19
589.33
590.00
583.30
PRIVATE SERVICEPROVIDING ..... ...........................
472.88
484 .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
499.61
Trade, transportation,
and utilities ...... ........ .. .... ... ... .
Wholesale trade .... .. ..................
Retail trade ......... .... .... ... .... ....
471 .27
644 .38
360.81
481.10
657.12
367.28
483.17
661 .96
366.83
486.63
676.06
365.94
480.82
659.99
367.97
477.05
656.74
361 .80
488.43
670.56
368.42
482.40
658.62
365.71
486.05
665.28
367.23
493..37
674.99
372.06
489.44
661.01
372.37
494.46
665.78
376.58
498.44
672.22
378.42
496.94
667.02
377.91
494 .46
668.53
373.31
Transportation and
warehousing ........ ... ....... .. .....
Utilities ........ ....... ... ... ... ....... ...
579.75
979.09
1
641 .84
628.47
634.85
627.00
621 .60
627.19
613.46
604 .27
610.65
603.47
615.00
602.58
597.50
597.79
1,016.94 1,039.48 1,068.45 1,028.08 1,032.97 1,039.42 1,039.76 1,053.29 1,054.39 1,046.13 1,032.46 1,030.93 1,074.44 1,053.99
Information ..... ...... ... ............ ...
738.17
761 .13
769.25
783.10
761 .71
763.56
776.72
760.00
764.64
777.18
775.19
773.19
788.99
788.80
785.18
Financial activities ........ .. ... .....
575.51
608.87
608.93
628.86
607.55
612.10
630.67
611 .42
615.99
637.84
613.51
617.38
634.64
619.52
627.29
Professional and
business services ..................
582.67
583.97
602.72
587.52
588.57
603.77
587.52
590.27
604.12
592.62
600.21
574.66
586.68
580.71
597.16
Education and
health services ............. .. .... ...
492.74
505.76
506.51
516.33
512.28
514.86
519.97
513.27
516.48
521 .60
520.34
527.47
530.05
526.18
527.48
Leisure and hospitality ............
221 .26
224 .35
223.89
226.05
223.29
221 .36
230.14
225.80
224 .81
229.22
227.40
230.91
234.08
226.18
230.27
Other services ......... ...............
439.76
434.49
431.31
434 .89
430.28
429.20
433.68
428.73
428.58
435.07
428.42
429.94
434.58
431 .98
434.47
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:
80
2004
Oct.
TOTAL PRIVATE ...... ,. ........ .... $506.07
Seasonally adjusted.. .. .. ....
GOODS-PRODUCING ... ........ ... ..
2003
2003
2002
Data reflect the conversion to the 2002 version of the North American
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
Dash indicates data not available. p = preliminary.
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 ........ ... ... ... .......... ............ ...... .
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
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
56.3
36.2
39.9
61.5
37.9
40.8
36.5
54.0
32.6
55.2
36.3
62.8
35.1
70.0
40 .5
74.5
58.1
34.9
39.4
42.6
68.7
37.4
64.6
35.4
57.2
56.5
34.7
38.7
40.1
61.0
53.2
35.3
37.1
45.5
58.1
52.9
30.8
34.4
50 .5
56.8
32.0
34.7
51.1
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
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
64.0
59.7
31.5
35.3
42.6
54.0
31.1
33.3
41.7
Over 12-month span :
2000 ..............................................
2001 ..............................................
2002 ..............................................
2003... ..... .. ............... .. ...................
2004. .............................................
70.9
59.5
33.6
34.5
37.81
73.2
69.2
53.4
59.5
30.2
31.7
32.9
31 .5
43.2 1 47.3
71.0
49.3
30.4
33.5
50.7
69.8
48.6
30.2
36.2
54.9
70.3
70.3
39.9
43.9
31.3
30.0
37.6
33.1
65.3
63.8
industries
65.6
37.8
29.5
37.4
65.6
63.8
37.1
32.9
33.1
62.1
34.9
34.7
35.4
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
60 .7
13.7
28.6
14.3
25.0
19.0
35.1
17.9
39.9
14.9
41 .1
10.1
32.7
25.0
54.2
40.5
28.0
57.1
28.0
26.2
48.2
31.0
27.4
42.3
11.9
28.6
42.3
15.5
51 .2
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
42.3
23.2
8.9
6.5
27.4
38.7
10.1
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
53.6
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
9.5
40.5
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
56.0
35.1
8.3
4.8
9.5
33.3
6.0
8.3
11.3
Over 3-month span:
2000 .... .... .. ................................... .
2001 ............................................. .
2002 ........ ... ........ .. ........................ .
2003 ................... ....................... ... .
2004 ............................... .. .... .. ..... . .
Over 6-month span:
2000 ..... ... .... .. ... ............................ .
2001 ....... .. .. ... ... ................. .. ... ...... .
2002 ..................................... .. ...... .
2003 ............................................. .
2004 ..................................... .. ... ... .
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.
70.0
71.0
43.3
45.0
32.0
29.1
34.7
34.4
64.0
60.3
Manufacturing payrolls, 84
4.8
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
December 2004
81
Current Labor Statistics:
Labor Force Data
18. Job openings levels and rates by industry and region, seasonally adjusted
Levels 1 (in thousands)
Industry and region
Rates
2004
Apr.
Totai2 .... ... ......... ..... ......... .... ............. .. ...
May
June
July
2004
Aug.
Sept.
Oct.P
Apr.
May
June
July
Aug.
Sept.
Oct.P
3,135
3,105
3,022
3,237
3,195
3,294
3,330
2.3
2.3
2.3
2.4
2.4
2.4
2.5
Total private 2 ••• • ••••••••••••••••.• • •• . ••• • ••••••. •
2,778
2,746
2,640
2,894
2,859
2,934
2,950
2.5
2.4
2.3
2.6
2.5
2.6
2.6
Construction ..... ... ... ... ... ..... ... ... .......
105
108
94
88
121
113
121
1.5
1.5
1.3
1.3
1.7
1.6
1.7
Manufacturing .... ..... .... ..... ..... ..... .. ..
251
244
247
240
234
251
259
1.7
1.7
1.7
1.6
1.6
1.7
1.8
Trade, transportation, and utilities .......
531
521
503
567
551
591
592
2.0
2.0
1.9
2.2
2.1
2.3
2.3
Professional and business services ....
518
494
583
594
564
585
3.1
3.1
2.9
3.4
3.5
3.3
3.4
Education and health services ....... ... .
576
530
542
496
537
3.3
3.1
2.9
3.1
3.1
3.1
3.1
376
391
421
435
543
425
543
Leisure and hospitality .. .. ... ..... ..... ....
536
410
356
3.0
3.1
3.3
3.4
3.2
3.3
2.8
Government. .... ...... ........ ... ... .... ..........
354
360
380
343
337
350
382
1.6
1.6
1..7
1.6
1.5
1.6
1.7
Northeast. .... ............ .. ... ......... ... ....
South .. ... ..... ........... .. ... .. ........... ....
560
1,191
526
1,164
546
1,164
545
1,280
540
562
1,245
599
1,311
2.2
2.0
2.1
2.5
2.5
2.4
2.1
2.7
2.1
2.6
2.2
2.6
2.3
1,259
Midwest. .......... ....... ......... ..... ..... ...
692
688
631
635
613
699
640
2.2
2.2
2.0
2.0
1.9
2.2
2.0
West. .. ... .... .......... ..... ....... .. .... ... ....
694
765
677
738
771
790
756
2.4
2.6
2.3
2.5
2.6
2.7
2.6
Industry
Region'
Detail will not necessarily add to totals because of the independent seasonal
adjustment of the various series.
2.7
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 0 1
the month; the job openings rate is the number of job openings on the last business day 0 1
the month as a percent of total employment plus job openings.
P = preliminary.
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,
19. Hires levels and rates by industry and region, seasonally adjusted
Levels 1 (in thousands)
Industry and region
Rates
2004
Apr.
Total 2 • •• ••• • • ••• • • • • • •• • ••• • •••••• • •••••••••••••••••• • • • •
May
June
July
2004
Sept.
Aug.
Oct.
1
Apr.
May
June
July
Aug
Sept.
Oct.P
4,398
4,206
4,433
4,229
4,375
4,253
4,317
3.4
3.2
3.4
3.2
3.3
3.3
3.3
4,090
421
3,938
406
4,110
3,930
3,987
383
323
3.7
6.1
2.5
3.6
5.3
3.7
5.8
3.6
5.0
945
957
356
984
388
379
3.7
6.3
354
1,032
368
352
3.6
5.9
Manufacturing ........ ..... ... ....... .... .....
Trade, transportation, and utilities .......
436
370
4,058
401
3,906
Construction ... ........... ... ... .. ...... .... ...
864
951
4.1
Professional and business services ... ..
Education and health services ........ ...
609
460
692
428
689
401
3.7
418
690
470
720
451
409
2.7
Leisure and hospitality ... ... ..... ........ ...
766
739
749
760
760
782
747
Government. ...... ................. ..... ...... ... .
300
272
328
310
322
337
Industry
Total private 2 • ...•. •••.•••••••••••• . •••• . ••••• . .• . .
Reglon
336
938
631
621
2.6
2.4
2.5
3.6
5.5
2.6
3.8
3.9
3.4
3.8
3.7
4.2
3.8
4.2
4.2
3.7
4.3
2.7
2.5
2.5
2.8
2.4
2.4
6.0
6.1
6.2
6.1
301
6.2
1.4
1.3
1.5
1.4
1.5
6.3
1.6
6.0
1.4
3.2
2.8
2.8
2.9
3.0
2.9
2.9
3.4
3.5
3.7
3.5
3.5
3.5
3.5
2.3
3.7
2.2
3
Northeast. .. ..... .......... .. ..... .. ... ...... ..
810
708
703
720
763
745
South ......... ...................... .......... ...
1,582
1,606
1,709
1,640
1,643
1,635
736
1,646
Midwest. ..... ... .... .. .. ... .... ......... .. .... ..
991
956
1,009
935
945
942
1,010
3.2
3.1
3.2
3.0
3.0
3.0
3.2
West. ............... ...... ........... ... .... .....
1,093
951
1,023
685
1,018
942
893
3.8
3.3
3.6
3.0
3.5
3.3
3.1
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.
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.
3
Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New
York, Pennsylvania, Rhode Island, Vermont; c;outh: Alabama, Arkansas, Delaware,
District ot Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi,
North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia;
82
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
NOTE: The hires level is the number of hires during the entire month; the hires rate
is the number of hires during the entire month as a percent of total employment.
P
= preliminary.
20. Total separations levels and rates by industry and region, seasonally adjusted
1
Rates
Levels (in thousands)
Apr.
Total
2
2004
2004
Industry and region
• .... •.• ....•.•..•.. • . •.. .•. • ••. . ..•••.. • • . .........
Oct.P
Sept.
Aug.
July
June
May
4,088
4,040
4,069
4,074
4,134
4,158
Apr
Oct.P
Sept.
Aug.
July
June
May
4,159
3.1
3.1
3.1
3.1
3.1
3.2
3.2
Industry
3,843
3,761
3,789
3,793
3,894
3,856
3,891
3.5
3.4
3.5
3.5
3.5
3.5
3.5
Construction ...... ... ........ ... .......... ....
391
367
382
364
391
350
469
5.7
5.3
5.5
5.3
5.6
5.0
6.7
Manufacturing ..... ..... .. ....................
353
377
343
367
379
381
352
2.5
2.6
2.4
2.5
2.6
2.6
2.4
Trade, transportation, and utilities ..... ..
1,013
917
927
972
951
909
935
4.0
3.6
3.6
3.8
3.7
3.6
3.7
Professional and business services ....
606
556
607
613
575
590
539
3.7
3.4
3.7
3.7
3.5
3.6
3.2
Education and health services ... .... ....
386
379
362
363
380
384
361
2.3
2.2
2.1
2.1
2.2
2.3
2.1
Leisure and hospitality .....................
679
696
734
694
760
756
746
5.5
5.6
5.9
5.6
6.2
6.1
6.0
Government. ........................ .. ....... .....
245
268
270
273
246
306
269
1.1
1.2
1.3
1.3
1.1
1.4
1.2
Total private
2
...•. ...•.. ... ........•.. •. .. .. • ....••
Reglon
3
Northeast. .. ... ... .. ... .. ····· ········· ···· ···
716
648
704
674
717
730
693
2.9
2.6
2.8
2.7
2.8
2.9
2.7
South .... ....... ......... ........... ....... .. ...
1,524
1,504
1,533
1,545
1,527
1,506
1,595
3.3
3.2
3.3
3.3
3.3
3.2
3.4
Midwest. ....... ........ ... ........ ...... ..... ..
877
833
853
935
831
931
894
2.8
2.7
2.7
3.0
2.7
3.0
2.9
West. ......... ... ... ... ........... ..... ... ... ....
959
1,008
979
945
1,087
978
952
3.4
3.5
3.4
3.3
3.8
3.5
3.3
Kansas, Michigan, Minnesota, Missouri , Nebraska,
' Dtltail will not necessarily add to totals because of the independent seasonal adjustment
Midwest: Illinois, Indiana, Iowa,
of the various series.
North Dakota, Ohio , South Dakota, Wisconsin; West: Alaska, Arizona, California,
Includes natural resources and mining, information, financial activities, and other
3
Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington,
Wyoming.
services, not shown separately.
Northeast: Connecticut, Maine, Massachusetts, New Hampshire , New Jersey, New
York, Pennsylvania, Rhode Island, Vermont; South: Alabama, Arkansas, Delaware,
District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi,
North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, 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
month as a percent of total employment.
P
= preliminary.
21. Quits levels and rates by industry and region, seasonally adjusted
1
Rates
Levels (in thousands)
Industry and region
Apr.
Total 2 .. . .. . ..... . ··· ·· ·· ·· ··· ············ ······· .......
2004
2004
May
June
July
Aug.
Sept.
Oct.P
June
May
Apr.
Sept.
Aug.
July
1.7
1.7
Oct.P
1.7
1.7
1.7
1.7
2.0
1.9
2.0
2.0
1.9
1.9
1.9
2.2
2.1
2.3
1.5
2.1
2.0
2.8
2,278
2,173
2,284
2,265
2,252
2,248
2,259
1.7
. •... . •.•...•.•.... . •.. .• ••.. .. • .•....
2,151
2,026
2,162
2,141
2,140
2,118
2,130
Construction ...... ......... .. ......... .. ......
149
144
156
101
147
138
198
Industry
Total private
2
Manufactu ring ....... .... ....... .. .... .. .. ....
189
171
171
174
165
183
173
1.3
1.2
1.2
1.2
1.1
1.3
1.2
Trade, transportation, and utiliti es .......
Professional and busi ness services ....
525
259
536
322
2.1
1.6
1.3
2.1
2.0
1.3
2.1
2.0
1.4
2.0
1.7
1.4
429
455
480
442
3.5
3.7
3.9
1.6
3.6
2.2
1.9
1.4
Leisure and hospitality ..... ... ...... .. .....
239
476
2.2
2.0
1.5
2.2
2.0
225
536
325
240
520
284
223
559
322
271
552
308
Education and health services ...........
563
323
245
3.9
3.6
3.7
Government. ............ .......... ........... ·: ...
129
129
123
126
.6
.6
.5
.6
.6
Reglon
235
454
116
130
124
.6
.6
3
Northeast. ..................... .. .. .. .... .. ....
390
318
334
338
339
325
333
1.6
1.3
1.3
1.3
1.3
1.3
1.3
South .............. .................. ... ........
888
857
910
901
897
903
888
1.9
1.8
2.0
1.9
1.9
1.9
1.9
Midwest. .... ... ..... ..... .... ....... ... ...... ..
479
479
485
505
447
472
480
1.5
1.5
1.6
1.6
1.4
1.5
1.5
524
521
573
519
566
546
561
1.8
1.8
2.0
1.8
2.0
1.9
1.9
West. ....... .... .... ......... ... .......... ......
1
439
Detail will not necessarily add to totals becaL,se of the independent seasonal adjustment
of the various series.
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 Caroli na, Tennessee, Texas, Virginia, West Virginia;
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Kansas, Michigan, Minnesota, Missouri ,
Illinois, Indiana, Iowa,
Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona,
California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah,
Washington, Wyoming.
Midwest:
NOTE: The quits level is the number of quits during the entire month; the quits rate
is the number of quits during the entire month as a percent of total employment.
P
= preliminary.
Monthly Labor Review
December 2004
83
Current Labor Statistics:
Labor Force Data
22. Quarterly Census of Employment and Wages: 10 largest counties, fourth quarter 2003.
County by NAICS supersector
Average weekly wage 1
Employment
December
2003
(thousands)
Percent change,
December
2002-03 2
Fourth
quarter
2003
Percent change,
fourth quarter
2002-03 2
United States3 .....................................................•.•.............•...••.•.
Private industry .. .... ................ ..... ........... .. ........... .................... .
Natural resources and mining ..............................................
Construction .........................................................................
Manufacturing ............... ........... ............ ... ............................ .
Trade, transportation, and utilities ....................................... .
Information ....................................................... .. ................. .
Financial activities .............. .... .. ......... ... ..................... ... .. ... .. .
Professio;ial and business services ........ ...................... ...... .
Education and health services .. ............................. .. .. ... .. .. ...
Leisure and hospitality ............. .. .... .. .. .............. .............. ... .. .
Other services .................. .. .. ............................................... .
Government ................................ ................................ ... .. ........
8,314.1
8,048.7
123.7
804.9
376.8
1,853.6
145.2
767.0
1,329.4
732.2
669.9
1,080.6
265.3
129,341.5
108,215.1
1,557.8
6,689.5
14,307.8
25,957.3
3,165.9
7,874.7
16,113.2
15,974.0
12,042.8
4,274.1
21,126.3
0.0
.0
.1
1.2
-4.2
-.3
-4.0
1.2
.6
2.1
1.7
-.1
-.2
$767
769
703
837
943
665
1,139
1,138
945
731
335
494
757
3.6
3.9
4.9
2.3
6.7
3.4
3.9
5.9
3.8
3.8
3.4
3.1
2.4
Los Angeles, CA ................................................ ..........................
Private industry ....................................................................... .
Natural resources and mining ............................................. .
Construction ....... ... .... ....... ....................... .. .. ........................ .
Manufacturing ....................................... ........................ ...... .
Trade, transportation, and utilities ....................... ................ .
Information ....... ................. ........... .... ........ .... .................. ... .
Financial activities .. ................................................... ... .. ... .. .
Professional and business services ....... .. .......... ................. .
Education and health services ................... .... .. ... ................ .
Leisure and hospitality .........................................................
Other services ........................ ... ... ..................................... .. .
Government .......... ............................. .......................... .. ......... .
356.0
352.2
.6
12.9
17.8
53.9
9.2
23.0
40.1
26.6
25.6
142.1
3.8
4,075.3
3,486.3
11.0
133.9
485.2
794.6
194.9
237.9
575.0
456.5
375.9
220.7
589.0
-.5
-.2
.7
-1 .1
-7.1
-1.2
-2.0
.9
1.6
1.9
5.6
3.5
-2.3
903
898
955
883
900
735
1,627
1,258
1,043
820
766
422
930
4.2
4.2
16.9
1.7
6.5
2.7
5.2
7.0
3.7
3.9
6.5
5.0
3.3
Cook, IL ...................................................................................... ..
Private industry ....................................................................... .
Natural resources and mining ................ .............. ... ............ .
Construction .......... ............................... ... ......... .... ........ .. ..... .
Manufacturing ...................................................... ....... .. .... ...
Trade, transportation, and utilities .................................. ......
Information ........... ... .... ... ... ............. ... .. ............ ........... .. ....... .
Financial activities ................... ..................................... .. .. ... .
Professional and business services .................................... .
Education and health services ....................................... .. ... .
Leisure and hospitality ........................................................ .
Other services .............................................................. ....... .
Government ............. ............................................................ ... .
126.7
125.5
.1
10.5
7.9
26.7
2.5
13.8
26.1
12.3
10.5
12.6
1.2
2,539.8
2,221.9
1.3
96.7
265.7
499.4
66.1
219.4
405.5
350.8
217.7
95.1
317.9
-1 .2
-.9
-3.6
.0
-5.1
-.8
-4.1
-.8
-1.3
1.0
2.8
-2.0
-3.1
922
929
1,037
1,169
975
753
1,164
1,471
1,206
791
375
655
871
3.0
3.2
3.2
-.8
6.3
.4
.1
8.1
4.1
3.7
-.3
3.0
.9
New York, NY .............................................................................. .
Private industry ............................... ........................................ .
Natural resources and mining .................... ......................... .
Construction ........................................................................ .
Manufacturing ........................ .. ..................... .. ................ .... .
Trade, transportation, and utilities .......................... .... .. .... ... .
Information ...... .................... ........ ............... ......................... .
Financial activities ............................................................... .
Professional and 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
-2.2
1,480
1,623
1,197
1,567
1,290
1,164
1,751
3,034
1,702
918
787
871
912
7.2
8.1
-6.5
3.4
6.4
5.5
7.9
16.1
2.6
7.6
6.1
6.1
.1
Harris, TX .............................................................. ........... .. .... .... ..
Private industry .............................................. ......................... .
Natural resources and mining ....................................... ...... .
Construction ................................. . ......................... ... .........
Manufacturing ..................................................................... .
Trade, transportation, and utilities ....................................... .
Information ...........................................................................
Financial activities ..................................................... ... .. ..... .
Professional and business services ........... ................ .. ....... .
Education and health services .... ... ... .... ....... .. ........ ............. .
Leisure and hospitality ................................ ........................ .
Other services ........................................................... .. ........ .
Government .................... ....... .. .......................... .. .. ................. .
89.4
89.0
1.2
6.3
4.7
21 .1
1.4
9.7
17.0
8.8
6.5
10.3
.4
1,841.5
1,595.2
62.5
135.5
164.0
403.2
33.8
113.1
279.0
188.3
155.2
56.3
246.3
-.9
-1.2
8.7
-5.0
-4.9
-2.1
-3.9
1.7
-1.7
1.5
.7
-3.1
1.1
906
929
2,185
919
1,106
821
1,098
1,181
1,073
812
335
539
759
2.1
2.1
-.9
2.6
2.3
1.0
.4
4.9
3.2
1.8
-.9
.4
3.1
Maricopa, AZ ................... ............................................ .. ...............
Private industry .......... ................................. .................. ...... ... ..
Natural resources and mining .................... ... ................... ... .
Construction .. .... ........ ................ ......... ... ..... ............... .. ........ .
Manufacturing ..................................................................... .
Trade, transportation, and utilities ................. ..... ................. .
Information .................................................... ............ ...... .. .. .
Financial activities .............. ............... .. ....................... ......... .
Professional and business services .............. .......................
Education and health services ........... ................................. .
Leisure and hospitality ........................................................ .
Other services .............. ... .......... ........ .. ................................ .
Government ....................... .. ......... .......................................... .
80.9
80.5
.5
8.4
3.3
18.6
1.6
9.5
18.1
7.6
5.6
5.7
.5
1,621.2
1,401 .8
9.8
131.7
128.0
336.4
36.6
133.3
261.5
160.5
155.8
44.7
219.4
(4)
2.2
-2.6
5.9
-2.5
1.5
-4.1
1.5
4.2
5.6
.8
-2.6
1.6
757
755
545
779
1,050
712
872
933
776
842
364
500
766
4.0
3.9
4.4
2.1
8.2
3.2
.5
3.7
3.5
5.0
2.8
2.2
3.7
See footnotes at end of table.
84
Establishments,
fourth quarter
2003
(thousands)
tviur 1tr1ly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
-1.1
22. Continued-Quarterly Census of Employment and Wages: 10 largest counties, fourth quarter 2003.
County by NAICS supersector
Establlshments,
fourth quarter
2003
(thousands)
Average weekly wage 1
Employment
December
2003
(thousands)
Percent change,
December
Fourth
quarter
Percent change,
fourth quarter
2002-03 2
2003
2002-03 2
Dallas. TX ................................................ .. ..... .. ........ ............ .. ... . ..
Private industry ................. .......... ............ ............................ ... ..
Natural resources and mining ........ ............................. .. ..... ..
Construction .. ... .............. .................... ...................... ....... .... .
Manufacturing ............................. ....... .. ...... .. ........... ............ .
Trade. transportation. and utilities ........................ .. .... .... .. .. ..
Information .. ... ... .. ... ........ .......................................... ... .. .. .... .
Financial activities ............................ .... ................ ......... ..... ..
Professional and business services ...................... .. .......... .. .
Education and health services .......... .... .. ............ .. ........ ..... ..
Leisure and hospitality ........................................................ .
Other services ........... .... ... .. .............................. ....................
Government ............................................. .... .. ........................ ..
68.6
68.2
.5
4.5
3.5
15.8
1.9
8.6
14.0
6.3
5.2
6.7
.4
1,450.8
1.294.6
6.8
73.0
144.9
326.1
64.0
140.0
237.7
131.4
127.5
40.5
156.2
-1 .4
-1.4
-20.5
-2.2
-3.1
-3.3
-5.1
1.2
.0
2.4
.0
-3.4
-1.8
$952
970
2.680
909
1.075
898
1.272
1.215
1.152
887
432
587
800
4.3
4.8
22.7
5.5
6.8
5.2
8.7
2.9
4.2
2.7
4.3
2.8
-.1
Orange. CA ......................... .. ......... .. ... .... ...... ... ....................... .... .
Private industry ....................... .............. ............................ ... ... .
Natural resources and mining .. .. ... .. .......... ... .. ......... ....... .. ... .
Construction .............................. ... ............... ............ ... ... .... .. .
Manufacturing .. ....................................................................
Trade. transportation. and utilities .. ............... .... .. ............... ..
Information .. ... ... ...... ......... ... ..... .. ............... .. ............... ... ... ... .
Financial activities ......................... .. .......... ... ..... .. ... .. .. ... ... ... .
Professional and business services .. .... .. ..... .. .. ... ....... ..... .. ...
Education and health services ....... .. ....................... ............ .
Leisure and hospitality ....... .. .... .. ................. ........................ .
Other services ......... .. .............................................. .. ....... ... .
Government .. ............. .............. ........... ......................... ... ........ .
88.8
87.4
.3
6.4
6.1
17.3
1.5
9.7
17.4
9.1
6.6
12.9
1.4
1,436.6
1.305.5
6.1
85.5
179.9
278.8
33.8
127.8
261 .0
126.6
159.9
46.0
131 .1
1.3
2.1
8.3
4.4
-3.0
.6
-4.4
9.9
1.0
6.1
2.5
6.3
-5.7
874
875
579
969
1.036
802
1.152
1.354
942
849
358
518
859
5.3
5.2
.2
5.9
11 .4
2.7
5.3
6.2
2.8
3.7
3.8
3.0
6.0
San Diego. CA ......... ........ .... .............. ..... ....... ..... ... ... ..... .... ... ...... .
Private industry ....................................................................... .
Natural resources and mining ..................... .. ...................... .
Construction ............ .. .. .......................... .. ... ................... .. ... ..
Manufacturing .. ..... ... .... .. .... ........ .. ...... ... .. ... ............... .......... .
Trade. transportation. and utilities ... ......................... .. .. ... .... .
Information ..... .. ................ ............. ... ...... ... ....................... .. . .
Financial activities ................. .. ............................... ....... ...... .
Professional and business services ....... ........... ..... ..............
Education and health services ............ ........ ..... ....................
Leisure and hospitality ................... ............. .. ............... ....... .
Other services ..................... ...... ...... .. ... .... ... ... .. ............... ... . .
Government ... .... ............. .. ..... ....... ..................................... .. .. ..
85.3
83.9
.9
6.4
3.6
14.2
1.4
8.8
14.9
7.6
6.5
19.5
1.3
1.278.2
1.060.2
11 .0
81.1
105.4
220.4
36.7
81 .6
208.1
122.6
141 .5
51.6
218.0
1.3
1.5
-5.4
4.7
-4.2
2.2
-4.5
4.8
1.5
1.6
3.5
1.8
.1
815
809
491
869
1.129
655
1.582
1.058
989
778
346
449
843
2.6
2.5
1.0
.7
11 .5
.9
-2.0
.4
2.8
5.7
2.4
2.7
2.9
King. WA ...... .............................. .... ..... .. ... ........ ............. ......... ... ...
Private industry ........ .. ... ...... .................... .......... .. .................... .
Natural resources and mining .. ............................. .. ...... .. .... .
Construction ....................................................... ... .... .. ... ..... .
Manufacturing . .. ... ... .. .......... ..... . ......... ..... .. ........... ... ....... .
Trade. transportation. and utilities ...... .. ............. ........ .... ..... ..
Information ..... ... .................................. ............... ..... .... ...... .. .
Financial activities ........ ................................................ ........
Professional and business services .................................... .
Education and health services ... ...................... ................... .
Leisure and hospitality ........... ........ .......... .. ............. ............ .
Other services .................................. ..................... ... .... ....... .
Government .......................... .. ........ .. .. ... .. ................ ... .......... ...
81 .6
81 .0
.4
6.2
2.7
14.8
1.5
6.1
11.7
5.9
5.4
26.4
.6
1.100.6
945.5
2.8
53.4
101 .9
225.5
69.2
77.5
158.3
108.3
100.5
48.1
155.1
.2
.1
-11.3
-.4
-8.2
1.1
.8
2.4
.7
1.5
2.9
1.2
1.0
935
944
1.109
921
1.176
804
1.829
1.114
1.1 60
746
390
463
882
.2
-.3
.8
1.4
-2.1
2.6
-15.7
3.5
8.4
4.8
3.7
.4
3.6
Miami-Dade. FL ... ........... ... ....... ... ...... .... .. .... ....... .................... .. ... .
Private industry ... ......... ........ ................ .... .. ........................... ...
Natural resources and mining ............................................ ..
Construction .. ....... ..... ........ ... ....... ................... ... .... ..... .... ... .. .
Manufacturing ............................ ..... ........ .. ........... ... ............ .
Trade. transportation. and utilities ..................... .................. .
Information ...........................................................................
Financial activities .. .. ... .. .... ................ .... ....................... ... .... .
Professional and business services .. ...... ... ............. ........... ..
Education and health services ....... ........ .. ... .................. .. .... .
Leisure and hospitality .............................. ................. .. ...... . .
Other services .... ................ ....................... ... ... ... .......... .. ..... .
Government .............. .... ....... ............... .................................... .
80.2
79.9
.5
4.9
2.8
23.2
1.7
8.2
15.9
7.8
5.3
7.5
.3
980.8
827.5
9.9
40.7
49.4
247.2
28.5
65.5
132.0
123.4
92.8
34.5
153.3
-.5
-.7
-1.8
.3
-9.8
-1 .7
-3.2
.7
-.2
1.4
2.1
-1 .8
.5
765
742
421
788
695
689
990
1.062
948
748
432
450
886
3.5
3.6
4.0
2.7
5.8
4.2
1.7
-1 .1
5.2
2.3
9.9
3.0
2.8
1 Average weekly wages were calculated using unrounded data.
2
Perce nt changes were computed from quarterly employment and pay data
adjusted for noneconomi c county reclassifications. See Notes on Current Labor
Statisti cs.
3
Totals for the United States do not include data for Puerto Rico or the
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Virgin Islands.
4
Data do not meet BLS or State agency disclosure standards.
NOTE: Includes workers covered by Unemployment Insurance (UI) and
Unemployment Compensation for Federal Employees (UCFE) programs. Data are
preliminary.
Monthly Labor Review
December 2004
85
Current Labor Statistics: Labor Force Data
23. Quarterly Census of Employment and Wages: by State, fourth quarter 2003.
State
Establishments,
fourth quarter
2003
(thousands)
Average weekly wage 1
Employment
December
2003
Percent change,
December
Fourth
quarter
Percent change,
fourth quarter
(thousands)
2002-03
2003
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
-.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 .......................... ... ... ..... ....
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
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
.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
202.6
222.7
47.2
157.6
1,781.0
365.4
2,648.0
9,300.1
1,066.2
300.7
3,477.5
2,654.7
685.2
2,715.4
.3
.3
.4
-.3
1.2
.3
1.2
1.0
.1
.0
623
559
689
754
630
661
786
759
587
683
3.1
4.1
4.2
3.1
2.3
5.1
5.2
1.3
2.1
4.1
~:::~~:f:.'..:::::::::::::::::::::::::::::: : :::::::::
~~~~~~~~~'.~.:::::::::::::: ::::::::::::: ::::::::::
1.1
2.2
.5
.0
-1.1
1.1
Wyoming .. .......................... .. .. ..... .... . .
22.0
241 .6
1.7
616
4.1
Puerto Rico ........................ .......... ... . .
Virgin Islands ..................... ............ .. .
50.2
3.2
1,074.1
42.5
3.5
-.2
450
629
4.7
2.4
1 Average weekly wages were calculated using unrounded
data.
2 Totals for the United
States do not include data for Puerto Rico
or the Virgin Islands.
86 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
NOTE: Includes workers covered by Unemployment Insurance (UI)
and Unemployment Compensation for Federal Employees (UCFE)
programs. Data are preliminary.
24. Annual data: Quarterly Census of Employment and Wages, by ownership
Year
Average
establishments
Average
annual
employment
Total annual wages
(In thousands)
Average annual wage
per employee
Average
weekly
wage
Total covered (UI and UCFE)
1993 ................ .. .... .... .......... .. ... ....... ..
1994 ... ···· ···· ····· ··· ···· ············· ···· ···· ·· ···
1995 ·· ······ ·· ········ ···· ··········· ··· ·· ··· ···· ·· ···
1996 ....... ... .... ......... ..... ... ..... ............. .
1997 ·· ····· ·· ·········································
1998 ... ..... ............ ...... ... .. .................. .
1999 .. .... ... ...... .. .......... .... ... .... ... .... .... .
2000 ··················· ·············· ·· .. ·· ····· ······
2001 ········ ··············· ·· ········ ····· ······· ···· ·
200? ..................... ... ..... .. ... .... ... .. .... .. .
6,679 ,934
6,826,677
7,040 ,677
7,189,168
7,369,473
7,634,018
7,820,860
7,879 ,116
7,984,529
8,101 ,872
109,422 ,571
112,611,287
115,487,841
117,963,132
121,044,432
124,183,549
127,042,282
129,877,063
129,635,800
128,233,919
$2,884,472,282
3,033,676 ,678
3,215,921,236
3,414,514,808
3,674,031,718
3,967,072,423
4,235,579,204
4,587,708,584
4,695,225,123
4,714,374,741
$26,361
26,939
27,846
28,946
30,353
31 ,945
33,340
35,323
36,219
36 ,764
$507
518
536
557
584
614
641
679
697
707
$26,055
26 ,633
27,567
28,658
30 ,058
31 ,676
33,094
35,077
35,943
36 ,428
$501
512
530
551
578
609
636
675
691
701
$25,934
26,496
27 ,441
28,582
30,064
31 ,762
33,244
35,337
36 ,157
36 ,539
$499
510
528
550
578
611
639
680
695
703
$28,643
29,518
30,497
31 ,397
32,521
33,605
34,681
36 ,296
37 ,814
39 ,212
$551
568
586
604
625
646
667
698
727
754
$26,095
26,717
27,552
28.320
29,134
30 ,251
31 ,234
32,387
33,521
34, 605
$502
514
530
545
560
582
601
623
645
665
$36 ,940
38,038
38 ,523
40,414
42 ,732
43,688
44 ,287
46,228
48,940
52,050
$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
7,137,644
7,317,363
7,586,767
7,771,198
7,828,861
7,933,536
8,051 ,117
106,351,431
109,588,189
112,539,795
115,081 ,246
118,233,942
121,400,660
124,255,714
127,005,574
126,883,182
125,475,293
$2,771,023,411
2,918,684 ,128
3,102,353,355
3,298,045,286
3,553,933,885
3,845,494,089
4,112,169,533
4,454,966,824
4,560,511,280
4,570,787,218
Private industry covered
1993 ......... ..... .. .............. .. ... ...... .. ... .. . .
1994 ........ .. ................... ................ .... .
1995 .............. ..... .... .. ...... .... ........ ... ... .
1996 ··· ·· ·· ···· ····· ·· ···· ·········· ···· ··· ········ ···
1997 .... ..... ... ... .... ....... ....................... .
1998 ... ... ... .... .......... ... .. ........ ... ..... .. ... .
1999
2000
2001
2002
··············· ···· ···························· ···
········ ··· ··· ···· ·········· ···· ··· ·· ·· ··· ··· ·····
···· ··· ·· ·· ·· ···· ··········· ··········· ·· ··· ······
..... ... ..... .... ... ....... ..... .. .... ...... .. ... .
6,454,381
6,596,158
6,803,454
6,946,858
7,121 ,182
7,381 ,518
7,560,567
7,622,274
7,724 ,965
7,839 ,903
91,202,971
94,146,344
96,894,844
99,268,446
102,175,161
105,082,368
107,619,457
110,015,333
109,304,802
107,577,281
$2,365,301 ,493
2,494,458,555
2,658,927,216
2,837,334,217
3,071 ,807,287
3,337,621,699
3,577,738,557
3,887,626,769
3,952,152,155
3,930,767,025
State government covered
1993 ............... .... .... ..................... ..... .
1994 .... .. .. ... ..... ... ... .. ... ... ... ......... ... .. .. .
1995 .. ................. .............. .. .... .... .. ... ..
1996 .. ... ..... ... .... ..... .... .... ............. .. .. .. .
1997 ............. ... .... ...................... ....... .
1998 .............. ... ... .... .. ... .... ... .. ... ........ .
1999 ······ ········ ··· ··· ······ ···· ········ ······ ······
2000 ... ... ... ..................... ................... .
200 ·1 .... ... ....... ...... ... .......... ..... ... ........ .
2002 .. .... ... ..... ...... ........................ ..... .
59 ,185
60 ,686
60 ,763
62,146
65,352
67 ,347
70,538
65 ,096
64 ,583
64,447
4,088,075
4,162,944
4,201,836
4,191,726
4,214,451
4,240 ,779
4,296,673
4,370,160
4,452 ,237
4,485,071
$11 7,095,062
122,879,977
128,143,491
131,605,800
137,057,432
142,512,445
149,011 ,194
158,618,365
168,358 ,331
175,866 ,492
Local government covered
1993 ····· ·········· ··· ··· ·· ·· ···· ·· ····· .. ··· ....... .
1994 .. .. .... ....... .. .................. ... ............
1995 ········ ···· ···· ········· ··· ···················· ··
1996 ··· ·············· ·· ··· ·· ····· ··· ·· .. ··· ···········
1997 .. ..... ....... .... ..... ...... ........ ........ .. ...
1998 ·· ····················· ···· ········· ··· ··· ··· ·····
1999 ...... ... .... .... .......... .................... .
2000 ·· ··· ··· ········· ······ ····· ······ ·· ········· ···· ·
2001 ..... ..... ... ...... ... .......... ..... ..... ... .... .
2002 ........... ..... .. .. .... ... ....... ... ..... ....... .
118,626
121,425
126,342
128,640
130,829
137,902
140,093
141,491
143,989
146,767
11 ,059,500
11,278,080
11 ,442 ,238
11,621 ,074
11,844,330
12,077,513
12,339,584
12,620,081
13,126,143
13,412,941
$288,594,697
301 ,315,857
315,252 ,346
329,105,269
345,069,166
365,359,945
385,419,781
408,721,690
440,000,795
464,153,701
Federal Government covered (UCFE)
1993 ··· ···················· ····· ·· ····· ········· ··· ···
1994 ············ ······· ······· · ..... .. ..... ......... . .
1995 .. .......... ..... ..... .......... ................. .
1996 ................ .... .................... ...... ... .
1997 ··· ···· ····· ···· ··· ············· ··· ··· ····· ··· ····
1998 ...... .. .... ...... ... ... .. ....... .. ..... .... ... ...
1999 ...... ... ... .... .. ........... ... ....... ..... ..... .
2000 .. .... .................. .... ... ..... ... ... .. .. ... .
2001 ·········· ····· ·········· ······ ··· ··· ····· ········
2002 ...... ....... ..... .... ...... ... ... ........ .. ..... .
47,714
48,377
50 ,083
51,524
52,110
47,252
49,661
50,256
50,993
50,755
3,071 ,140
3,023,098
2,948,046
2,881,887
2,810,489
2,782,888
2,786,567
2,871,489
2,752,619
2,758,627
$ 113,448,871
114,992,550
113,567,881
116,469,523
120,097,833
121 ,578,334
123,409 ,672
132,741 ,760
134,713,843
143,587,523
NOTE: Detail may not add to totals due to rounding. Data reflect the movement of Indian Tribal Council establ ishments from private industry to
the public sector. See Notes on Current Labor Statistics.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
December 2004
87
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, estahllshments, and
employment
Total
Fewer than
5 workers 1
5 to 9
workers
10 to 19
workers
20 to 49
workers
50 to 99
workers
100 to 249
workers
250 to 499
workers
500 to 999
workers
1,000 or
more
workers
Total all industries 2
Establishments, first quarter ..................
Employment, March ·······························
7,933,974
105,583,548
4,768,812
7,095,128
1,331,834
8,810,097
872,241
11,763,253
597,662
18,025,655
203,030
13,970,194
115,598
17,299,058
28,856
9,864,934
10,454
7,090,739
5,487
11 ,664,490
Natural resources and mining
Establishments, first quarter
··················
Employment, March ······ ·························
124,527
1,526,176
72,088
110,155
23,248
153,629
14,773
198,895
9,226
275,811
2,893
198,122
1,593
241,559
501
171,063
161
108,563
44
68,379
Construction
Establishments, first quarter
··················
Employment, March ...............................
795,029
6,285,841
523,747
746,296
129,201
846,521
76,215
1,021,722
46,096
1,371 ,071
12,837
872,274
5,604
823,846
1,006
338,107
262
172,944
61
93,060
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
801,1'10
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, fir::;t quarter
··················
E,·,1pl, ;yment, March ...............................
720,207
15,680,834
338,139
629,968
164,622
1,092,329
103,683
1,392,099
65,173
1,955,861
24,086
1,679,708
17,122
2,558,300
3,929
1,337,188
1,761
1,220,921
1,692
3,814,460
Leisure and hospitality
Establishments, first quarter ··················
Employment, March ...............................
657,359
11,731,379
260,149
411,192
110,499
744,144
118,140
1,653,470
122,168
3,683,448
34,166
2,285,550
9,718
1,372,780
1,609
545,304
599
404,831
311
630,660
Other services
Establishments, first quarter ..................
Employment, March ...............................
1,057,236
4,243,633
851,231
1,037,360
116,940
761,518
56,238
740,752
24,235
703,957
5,451
371,774
2,561
376,832
454
150,421
109
71,453
17
29,566
Manufacturing
Establishments, first quarter ..................
Employment, March
·······························
1
Includes establishments that reported no workers in March 2003.
2
Includes data for unclassified establishments, not shown separately.
88 Monthly Labor Review
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Federal Reserve Bank of St. Louis
Decen 1ber 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 area 1
2001
2002
Percent
change,
2001-02
Metropolitan areas3 ...... .. .......... ......... .. .. ... .... ... .... ........ .. .. .... .
$37,908
$38,423
1.4
Abi lene, 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-N eenah, 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-Passai c, NJ .. ..... .......... .. .... .. .... .. ..... ..... ... .. ................ ..
Bi llings, 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 ......... .. ........... ......... ... ... ............. ....................... .
Bl oomington, IN ...... .. ....... .. .. ... .. ........... .. ... .. ... ........ ........... .. ... .
Bloomington-Normal , IL .............................. .. ......................... .
Boise City, ID ............. ...... .. ................................................... ..
Boston-Worcester-Lawrence- Lowell-Brockton , MA-NH .... .. .. .
Boulder-Longmont, CO ......................... .. ... ........................... ..
Brazoria, TX ........................ ... .. ................................... ........... .
28,35 1
31,187
34 ,519
27,116
28.013
35.111
31 .624
45.766
44.310
35,655
28.515
31 .832
35.940
27.993
28.855
36.133
31 .955
45.685
44.037
36.253
Bremerton . WA ... ...... .. ... ... .. .......... .............. .. ... ... ... .... .... ... .. ... .
Brownsville-Harlingen-San Benito. TX .... ............. ...... .. ... ...... .
Bryan-College Station, TX ............. .............. ........... ......... ...... .
Buffalo-Niagara Falls. NY .... .. ..... .. ... .... .. ........ ................ ....... ..
Burlington , VT .................... ... ................. ........ .. .. .. .... .... .... .. ... ..
Canton-Massillon. OH ..................... ...... ... ........... .... ............ .. .
Casper, WY ... ..... ... .. .... ... .... ........... .... ... ....... ............... .... .. .. ....
Cedar Rapids, IA ... ......... ... .. ............. ... ... ..... ... ........... .. .. .. .......
Champaign-Urbana. IL .... ................................... ... .. .. ... ... ..... ..
Charleston-North Charleston. SC ............ .. ... .... .. .................. ..
31,525
22 ,142
25,755
32,054
34 ,363
29,020
28,264
34.649
30,488
28,887
33.775
22.892
26.05 1
32.777
35.169
29,689
28,886
34,730
31 ,995
29,993
Charleston, WV .. ... ..................... ........ .. ............ .................... ..
Charlotte-Gastonia-Rock Hill . NC-SC ................ ................... ..
Charlottesville. VA ..... .. .. .. .. ... .............................. .. .... .... .. ....... .
Chattanooga, TN-GA .... .... ................. ... ............ .... ... .. ... ......... .
Cheyenne. WY .. .... .. .... .. .. ........... .. .. ...... .. ... .. ... ......... ... .... ...... ..
Chi cago, IL .. ... ...... .. .. .... ... ...... .. ... ... ..... .. .... ....... .. ... .. .... ... .. .... ...
Chico-Paradise. CA ..... .. .. .. .......................... ... ... ........... .. ...... ..
Cinci nnati, OH-KY-IN .................... .. ............. ... ................... ... ..
Clarksville-Hopkinsville . TN-KY ..... .... .............................. .. ... ..
Cleveland-Lorain-Elyria, OH .... ....... .. ... ..... ... .. ......... .. ..... .... .... .
31 ,530
37,267
32,427
29,981
27.579
42 ,685
26,499
36 ,050
25.567
35.514
32,136
38,413
33,328
30,631
28,827
43,239
27,190
37,168
26,940
36,102
Colorado Springs. CO ........... .. .. ......................... .. .. ...... ........ ..
Columbia, MO ... ... .......... ... ... ... ... ... ..... ... ............... .............. .... .
Columbia, SC .. .. .... .... .. ....... .. ......... .... .. .... ... .... ....... .. .............. .
Columbus. GA-AL ................. ........ ........... .. .. ... ... ... .. ...... .. ... ... ..
Colu mbus. 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
7.1
3.4
1.1
2.3
2.3
2.3
2.2
.2
4.9
3.8
1.9
3.1
2.8
2.2
4.5
1.3
2.6
3.1
5.4
1.7
.8
2.3
2.7
2.8
3.2
2.7
3.5
4.7
.7
2.6
See footnotes at end of tabl e.
Monthly Labor Review
December 2004
89
Current Labor Statistics:
90
Mrinthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Labor Force Data
26. Continued-Annual data: Quarterly Census of Employment and
Wages, by metropolitan area, 2001-02
Average annual wage2
Metropolitan area 1
2001
2002
Percent
change,
2001-02
Davenport-Moline-Rock Island, IA-IL ........... .. ..... ........... ........ .
Dayton-Springfield, OH ................ ..................... ...... ... ......... .. ..
Daytona Beach, FL ........................... ..................................... .
Decatur, AL .................................................................... ....... ..
Decatur, IL ........ ........................ ... ... .... ... ... .... .. .. ..................... .
Denver, CO ................ .................... .... .................................... .
Des Moines, IA ..... ............... .. ......... ... .................. .................. .
Detroit, Ml .. .......................... ........ .......................................... .
Dothan, AL .... .............................................. ............... ..... ... .... .
Dover, DE .............................................. ... ... ..................... .... ..
$31 ,275
33,619
25,953
30,891
33,354
42,351
34,303
42,704
28,026
27,754
$32,118
34,327
26,898
30,370
33,215
42,133
35,641
43,224
29,270
29,818
2.7
2.1
3.6
-1.7
-.4
Dubuque, IA .... ... .. .. ... .... ..... .... ............................ .............. .... .. .
Duluth-Superior, MN-WI ... ..................................................... .
Dutchess County, NY ... .. ........ .. .............................. .... .... ....... .
Eau Claire, WI .. ..... .. .................. ..... ....... ................. ... ... ....... .. .
El Paso, TX ... .... ........................... .. ........... .... .. ... .. ... ................
Elkhart-Goshen, IN .. .... ........... ..... ... ........................... .............
Elmira, NY ... ........................................... .... ......... .................. .
Enid, o:< .................................................................................
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-Hig h 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
3.5
3.3
2.0
Greenville, NC ............. .... ........... .. .... .......... ................... .. ...... .
Greenville-Spartanburg-Anderson, SC .. ......... ............ .... .. .... ..
Hagerstown, MD ................... ................ .... .. ......................... ...
Hamilton-Middletown, OH .... .. .. ............ .. ..... ................... ........ .
Harrisburg-Lebanon-Carlisle, PA .......... ... .. ............................ .
Hartford, CT .... .. ... ... .. .... .. .............. .. ... ..... .............. ..... ... .... ..... .
Hattie~hurg, 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
3.1
5.0
1.7
2.9
2.5
2.4
3.5
4.2
4.1
See footnotes at end of table.
December 2004
-.5
3.9
1.2
4 .4
7.4
3.4
-.2
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
26. Continued-A nnual 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 .... .. ............... .... ......... ................ .. ... .............. . .
Johnsl)n 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
Lansing-East Lansing, Ml .... .... .... ......... ..................... .. ... ....... .
Laredo, TX ....... ............. .. ... .......... ... ....... .............. .. ................ .
Las Cruces, NM ............ .... ... ...................... ... ... ... ........... ...... .. .
Las Vegas, NV-AZ ... .. ... ........... .............. ............. ................... .
Lawrence, KS .... ......... ... ... ... ............ ............................ ..... .. ... .
Lawton, OK ...... ... .. ....... .. ............................... .. ... .... ....... .... .... ..
Lewiston-Auburn, ME ... ... ............................... ... .. ......... ..... .... .
Lexington, KY ............. .. .......... ..... .. .............................. ...........
Lima, OH ........... .... .. ..... ......... ...... .. ... ... ................ .. ................ .
Lincoln, NE .......................... ....................... .. ......................... .
34,724
24,128
24,310
32,239
25,923
24,812
27,092
31 ,593
29,644
29,352
35,785
24,739
25,256
33,280
26,621
25,392
28,435
32,776
30,379
30,614
3.1
2.5
3.9
3.2
2.7
2.3
5.0
3.7
2.5
4.3
Little Rock-North Little Rock, AR ... ..... ... .......... .... ... .. ... .. ........ .
Longview-Marshall, TX .................... .... ................ .. ......... ... .... .
Los Angeles-Long Beach, CA .................. .... ...... ... .. ... ... ..... ....
Louisville, KY-IN ...... ..... ..... .... ........ .... .. .... .... ... ... .. ...... ... .. ... .....
Lubbock, TX ...... ..... ... ..... .. ....... ........ ... ...... .... ..... ........ .. .......... .
Lynchburg, VA ... .... .... .... ..... ............ .... ... .. .. ... ......... .................
Macon, GA .. ............................. .... .. .... ....... ... .. ... ... ...................
Madison, WI ... .. .. ..................... .................... ..................... .... .. .
Mansfield, OH ......... ... .. .. ..... .. ........... .......... .. ........ ...... .. .......... .
McAllen-Edinburg-Mission, TX ... .................. ...... .......... ......... .
30,858
28,029
40,891
33,058
26,577
28,859
30,595
34,097
28,808
22,313
31 ,634
28,172
41 ,709
33,901
27,625
29,444
31,884
35,410
30,104
23,179
2.5
Medford-Ashland, OR ...... .................... ..................... ... .. .. ... ... .
Melbourne-Titusville-Palm Bay, FL ... .. ... ........................ ..... ... .
Memphis, TN-AR-MS .. .... ... .. ............................. ...... .. ............ .
Merced, CA .... ... .. .. ... ........... ........... ... ... .............. .. .. .... .... .. .... .. .
Miami , FL .... .......................... .. ........ ... .... ... .... ... ... .. .... .. ... ....... ..
Middlesex-Somerset-Hunterdon. NJ ........ .. ........................ .. ..
Milwaukee-Waukesha, WI .. ................................................... .
Minneapolis-St. Paul, MN-WI ........, ...... ... .............................. .
Missoula, MT .... .................. .. .... .... ... ... ................... ... .... ... ...... .
Mobile , AL ············ ···································································
27,224
32,798
34,603
25,479
34,524
49,950
35,617
40,868
26,181
28,129
28,098
33,913
35,922
26,771
35,694
50,457
36,523
41 ,722
27,249
28,742
3.2
3.4
3.8
5.1
3.4
1.0
2.5
2.1
4.1
2.2
Modesto, CA ...... .. .... .. .... ............. .. .. .......... ...... .... ....................
Monmouth-Ocean, NJ .... ... ............. ................. .. ......... .. ......... .
Monroe, LA ... .... ... ..... ... ..................... .. .... ............ ............ ....... .
Montgomery, AL ................. ..... .... ......... ..... ... .. ... .......... ...........
Muncie, IN ... .... .. .. ... .......... ..... .. ...... .. ..... .... .. ...... .. .... ............... .
Myrtle Beach, SC ... .. ... .. .......... ... ... .... .............. ....................... .
Naples, FL ...... ..... ...... ... ..... ... .... ............... .... ........ .. .. .. ... .... ..... .
Nashville, TN ............................................ .. ....................... .... .
Nassau-Suffolk, NY .. ...................................... ............. .... .... .. .
New Haven-Bridgeport-Stamford-Waterbury-Danbury, CT ... .
29,591
37,056
26,578
29,150
28,374
24,029
30,839
33,989
39,662
52,198
30,769
37,710
27,614
30,525
29,017
24,672
31 ,507
35,036
40,396
51 ,170
4.0
1.8
3.9
4.7
2.3
2.7
2.2
3.1
1.9
-2.0
New London-Norwich, CT .. .. .... .... .. .............. .. .............. .. ....... .
New Orleans, LA .......... ..... ............. ... .. .... ............... ... .. ... ....... .
New York, NY .............. .... .. .... .............. ..... ...... ... ... .... .. ... ........ .
Newark, NJ ...... .... ........... .. ........ ..... .................... ... .. ..... ........ .. .
Newburgh, NY-PA ... ..... .... .. ............. .... ...... ..................... .... .. ..
Norfolk-Virginia Beach-Newport News. VA-NC .. ................... .
Oakland, CA .. .. .. .. ... ... ....... ............. ........... ........ .. ............. .... .. .
Ocala, FL ................ ......... ... .... ... .... .... ... .. .. ................... ..... ..... .
Odessa-Midland, TX .... .... ....... ... ...... .... ..... ..... ............ ... .. ... .... .
Oklahoma City, OK .... ..... .. .. ... ......................... ........... ... ... ...... .
38,505
31 ,089
59,097
47,715
29,827
29,875
45,920
26,012
31,278
28,915
38,650
32,407
57,708
48,781
30,920
30 ,823
46,877
26,628
31 ,295
29,850
.4
4.2
-2.4
2.2
3.7
3.2
2.1
2.4
.1
3.2
.7
1.9
2.1
2.2
.5
2.0
2.6
3.9
2.0
4.2
3.9
4.5
3.9
See footnotes at end of table.
Monthly Labor Review
December 2004
91
Current Labor Statistics:
92
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Labor Force Data
26. Continued-Annual data: Quarterly Census of Employment and
Wages, by metropolitan area, 2001-02
Average annual wage2
Metropolitan area1
2001
2002
Percent
change,
2001-02
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 ..... ... .... .... ............. ... .... ... ..... .... .. ... .
RiversidP.-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
36,71 2
Salem, OR .. .... .. ............... ............... ... ... ... ...... .. .. .......... .... .. .... .
Salinas, CA .. .. .. .......... ....... .......... ......... ...... .. .. ....... .. ............... .
Salt Lake City-Ogden , UT ... ................................................. .. .
San Angelo, TX ..................................................................... .
San Antonio, TX ................................................................... ..
San Diego, CA ................ ............ ................ .... .. ........... ...... .... .
San Francisco, CA ............................ .. ... .... .. ...... .. .... .. ... ... .. ... ..
San Jose, CA .. .. ......... .. ............ ... ........... .. ... .................. .. ....... .
San Luis Obispo-Atascadero-Paso Robles, CA .......... ......... ..
Santa Barbara-Santa Maria-Lompoc, CA ........................ ...... .
28,336
31,735
31 ,965
26,147
30,650
38,418
59 ,654
65,931
29,092
33,626
29,210
32,463
32,600
26,321
31 ,336
39,305
56,602
63,056
29,981
34,382
2.2
2.3
-5.1
-4.4
3.1
2.2
Santa Cruz-Watsonville, CA ........................ ...... .. .... .............. .
Santa Fe, NM ..................... .. .. ... ...... .. ... ................................. .
Santa Rosa, CA .... .... .... .. .. ... ...... .. .. ............... ........................ ..
Sarasota-Bradenton, FL ... ... ... ... ... ... .. .. ............................... ... .
Savannah, GA ...................... .......... ............. .... ................... .. ..
Scranton-Wilkes-Barre--Hazleton , PA .... ... ... ... ...... ............... .
Seattle-Bellevue-Everett, WA .. .............................................. .
Sharon, PA ............................... .. ........................... .. .... .... .... .. .
Sheboygan, WI ......... ... ... .. .... .... ................... .. ..... .. ................. .
Sherman-Denison, TX ........................................................... .
35,022
30,671
36 ,145
27,958
30,176
28,642
45,299
26,707
30,840
30,397
35,721
32,269
36,494
28,950
30,796
29,336
46,093
27,872
32,148
30,085
2.0
5.2
1.0
3.5
2.1
2.4
1.8
4.4
4.2
-1.0
Shreveport-Bossier City, LA ..................... .......... .................. ..
Sioux City, IA-NE .............. ... ... ......................... .. ... .. ..... ... ....... .
Sioux Falls, SD .............. .. ... ..... .......... .. .............. ............ ... ..... .
South Bend , IN ........... .............. ..... .... .. .. ........... ..... .... .... .... .... .
Spokane, WA ... ............. ..... ..... .. .................. ... ... .. ... ........... ..... .
Springfield, IL .... .... ... ... .......... .... ..... .... .... ............... ... .............. .
Springfield, MO ............... ... .... .... .. ......... .. ..... : ................ .. ... ... . .
Springfield, MA ....... ....... .... .. ..... .. ....... .... ...... .. ....... .. ... ... ........ . .
State College, PA ... ..... .. .... .. ... .. ...................... ... ... .. ... .... ... ... ...
Steubenville-Weirton, OH-WV ... .. ................ .................. ..... .. ..
27,856
26,755
28,962
30 ,769
29 ,310
36 ,061
27,338
32,801
29 ,939
28,483
28,769
27,543
29,975
31 ,821
30,037
37,336
27,987
33,972
30,910
29,129
3.3
2.9
3.5
3.4
2.5
3.5
2.4
3.6
3.2
2.3
See footnotes at end of table.
December 2004
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
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
26. Continued-Annual data: Quarterly Census of Employment and
Wages, by metropolitan area, 2001-02
Average annual wage2
Metropolitan area ,
Percent
change,
2001-02
2001
2002
Stockton-Lodi, CA ................ ... ..... .. .... ..... ... .. .... .. ........ ....... .... ..
Sumter, SC ............................................................................ .
Syracuse, NY .... .................................................. .... ... ...... ... ... .
Tacoma, WA ............................ ... .. ........... ... ..... .. ..... .......... ..... .
Tallahassee, FL ................... ..... .. ... ......... .... ...... ...... ........... ... ..
Tampa-St. Petersburg-Clearwater, FL .... .. .... ............ ............ .
Terre Haute, IN ............................................................... ....... .
Texarkana, TX-Texarkana, AR ........................................ ...... .
Toledo, OH ....... .... .... ......... ... .... ..... .............. .... ........... ... ...... ...
Topeka, KS .. ....................................... ..... ...... .... ................ .... .
$30,818
24,450
32,254
31 ,261
29,708
31,678
27,334
26 ,492
32,299
30,513
$31,958
24,982
33,752
32,507
30,895
32,458
28,415
27,717
33,513
31,707
3.7
2.2
4.6
4.0
4.0
2.5
4.0
4.6
3.8
3 .9
Trenton, NJ ... ... .................... .. ... ...... ... ... .......................... .... .. . .
Tucson, AZ .. ...................... ................... ....... ..................... ..... .
Tulsa, OK .... ... .... ... .. .... ..... .. .. ...... ..... .... .. .. ...... ... ... ............. ...... .
Tuscaloosa, AL ..... ....................... ................... .................. ..... .
Tyler, TX .. . ......................................... .. ............ ................... .. .
Utica-Rome, NY .... .. ... ................. .... ............ .. ..... ...... ...... .... ... ..
Vallejo-Fairfield-Napa, CA ......... .. ..... ..... ...... ......................... ..
Ventura, CA ....................................... ... ............ ........ .. ........... .
Victoria, TX ............. ............................................ .................. ..
Vineland-Millville-Bridgeton, NJ ............................................ ..
46,831
30,690
31,904
29,972
30,551
27,777
33,903
37,783
29,068
32,571
47,969
31,673
32,241
30,745
31 ,050
28,500
34,543
38,195
29,168
33,625
2.4
3.2
1.1
2.6
1.6
2.6
1.9
1.1
Visalia-Tulare- Porterville, CA ............................ ................... ..
Waco, TX .................................. ... ............. .. ....... .............. ..... ..
Washington, DC-MD-VA-WV .............. .......... ............... ......... ..
Waterloo-Cedar Falls, IA .... ............................................ ...... ..
Wausau, WI ............. ...... ... ... ............................. ..................... .
West Palm Beach-Boca Raton, FL ...................... .................. .
Wheeling, WV-OH .. .............. .. ............................. ..... ..... ........ .
Wichita, KS ....................... ... .. .. .. ..... .................. .... ............ .... . .
Wichita Falls, TX ................... ................................ .. ..... .. ....... ..
Williamsport, PA ......... .. ................................................ .. ...... ..
24,732
28,245
47,589
29,119
29,402
35,957
26,282
32,983
25,557
27,801
25,650
28,885
48,430
29,916
30,292
36,550
26,693
33,429
26,387
27,988
3.7
2.3
1.8
2.7
3.0
1.6
1.6
1.4
3.2
Wilmington-Newark, DE-MD ............ .. .... .. ... .. ... ................ ...... .
Wilmington, NC ... ..... ........ .. ............ ........................................ .
Yakima, WA .......... ... ... .. ............ ... ....... .. ........ .... .... ... ...... ........ .
Yolo, CA .. ...................................... ................. .. .... ..... ..... ....... .
York , PA .................................... ......... .......................... ........ ..
Youngstown-Warren, OH ........ .. ................ ............. .............. ..
Yuba City, CA ............... ...................... .. ............................. ... ..
Yuma, AZ ..... .... ................... .... .... ................ ........................... .
42,177
2S,287
24,204
35,352
31,936
28,789
27,781
22,415
43,401
29,157
24,934
35,591
32,609
29,799
28,967
23,429
2.9
-.4
3.0
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 Bulleti n 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
December 2004
93
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
1994 1
1995
1996
199i
19981
1999 1
2000 1
2001
2002
2003
194,838
129,200
66.3
120,259
61.7
8,940
6.9
6<;,638
196,814
131 ,056
66.6
123,060
62 .5
7,996
6.1
65,758
198,584
132,304
66.6
124,900
62.9
7,404
5.6
66,280
200,591
133,943
66.8
126,708
63.2
7,236
5.4
66,647
203,133
136,297
67.1
129,558
63.8
6,739
4.9
66,836
205,220
137,673
67.1
131,463
64.1
6,210
4.5
67,547
207,753
139,368
67.1
133,488
64.3
5,880
4.2
212,577
142,583
67.1
136,891
64.4
5,692
4.0
69,994
215,092
143,734
66.8
136,933
63.7
6,801
4.7
71,359
217,570
144,863
66.6
136,485
62.7
8,378
5.8
72 ,707
221,168
146,510
66.2
137,736
62.3
8,774
6.0
74,658
68,385
Not strictly comparable with prior years.
28. Annual data: Employment levels by industry
[In thousands]
1993
Industry
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Total private employment... ..........................
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
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
18,989
19,275
19,432
19,539
19,664
19,909
20,307
20,790
21 ,118
21 ,513
21 ,575
Private service-providing ........... ..... ... .......
Trade, transportation, and utilities ..........
Wholesale trade ..................... .. ...........
Retail trade .... ................................... .
Transportation and warehousing ........
Utilities ..... ........... .............. .... ........ ...
Information ..... .. ........ .............. ... ... .... ..
Financial activities ....... ... ........... ...........
Professional and business services ... ..
Education and health services .. ... ...... .
Leisure and hospitality ........ ..... .........
Other services .. ..... ....... .... .. ..... .... ... .
Government. ..... ..... ........ .. ..... ... ... ..... ....
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 .
94 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
29. Annual data: Average hours and earnings of production or nonsupervisory workers on nonfarm
payrolls, by industry
2001
2000
1999
1998
1996
1997
1995
1994
1993
Industry
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
Natural resources and mlnlnQ
Average weekly hours ........... ....... ............. ............
Average hourly earnings (in dollars) .. .. ....... ..... .. ...
Average weekly earnings (in dollars) ... .... .. .... .. .....
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
Construction:
Average weekly hours .... .. ........................ ... .... ......
Average hourly earnings (in dollars) ... ........ ..........
Average weekly earnings (in dollars) ....... .. .... ... .. ..
Manufacturing:
Average weekly hours ... .... .... ... .. ................ ..... .... ..
Average hourly earnings (in dollars) .....................
Average weekly earnings (in dollars) ....................
38.4
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
Private service-providing:
Average weekly hours .. .... ........... ........ .. .............. .
Average hourly earnings (in dollars) ... .. ... ..............
Average weekly earnings (in dollars) ... ... .... .. .... .. ...
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
Trade, transportation, and utilities:
Average weekly hours ....... .......... ....... ....... .. .... .......
Average hourly earnings (in dollars) ............. .... .. ...
Average weekly earnings (in dollars) .....................
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
Transportation and warehousing:
Average weekly hours ... .....................................
Average hourly earnings (in dollars) ................. .
Average weekly earnings (in dollars) .. .... ... ...... ..
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
Utilities:
Average weekly hours .... ............... ... .......... ........
Average hourly earnings (in dollars) .... ............. .
Average weekly earnings (in dollars) ................ .
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
Education and health services:
Average weekly hours .... .. ...................... .......... ..
Average hourly earnings (in dollars) ...... ... ... ......
Average weekly earnings (in dollars) .................
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
Leisure and hospitality:
Average weekly hours .. .... .. .. ..... .. ... .. ..................
Average hourly earnings (in dollars) ................. .
Average weekly earnings (in dollars) .............. ...
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
Other services:
Average weekly hours .............. .. ............ .......... ..
Average hourly earnings (in dollars) .......... ........
Average weekly earnings (in dollars) ... ..............
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
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) .. .... .. .. .... .. .
Information:
Average weekly hours ................ .. ............ ..........
Average hourly earnings (in dollars) ..... .. .... ...... .
Average weekly earnings (in dollars) ... .. .. ... ........
Financial activities:
Average weekly hours ...................... ... ...... .... .....
Average hourly earnings (in dollars) .... ..............
Aver<1ge weekly earnings (in dollars) ............... ..
Professional and business 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
December 2004
95
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
Clvlllan workers2 ............................ ...... .................... ..... ...... ..
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
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
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
1.0
1.0
1.3
1.3
1.3
1.7
1.6
1.0
Wurkers, 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......................................................
Public administration ...... .... ... ...•. ....• ..•.. ...•..•..• .......... ........
Nonmanufacturing... ...........................................................
160.2
161 .7
169.2
160.5
162.8
163.9
164.5
167.6
162.8
161.7
162.4
165.8
165.8
166.5
168.2
168.5
169.3
173.1
166.9
167.3
167.8
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
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
.7
.8
.7
1.0
.7
Service occupations ...................................................... .
159.0
159.8
161.7
162.6
163.8
164.3
166.9
168.2
168.9
.6
3.1
Production and nonsupervisory occupations 4 . . . .. . . .• •.......
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
174.7
.8
175.6
.8
177.3
.9
179.4
.9
167.4
.6
168.1
.4
173.6
.6
166.2
.9
183.6
.3
183.6
.1
183.3
.5
169'. 1
.6
169.6
.6
177.8
1.1
175.3
.7
164.2
.3
168.8
1.6
163.5
. J . . _ - - _ j _ - - - - .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
---
3
L __
_
See footnotes at end of table.
96 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
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
..,___ __ j _ _ _- ' - - - - - - ' - - - - - - ' - - - _ _
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
_J'----
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
-'----.
.9
1.2
.6
1.1
- j ' - --
4.6
5.0
3.4
3.6
4.4
4.3
2.9
4.1
3.4
3.7
3.9
3.6
3.6
4.4
2.4
3.5
4.1
4.5
4.4
5.9
3.3
3.4
1
30. Continued-Emp loyment Cost Index, compensation, by occupation and industry group
[June 1989
= 100]
Series
Sept.
Dec.
Mar.
June
Percent change
2004
2003
2002
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 ... ....... .. .... ... ........................
168.0
172.1
184.6
167.1
164.9
167.2
163.2
166.2
173.5
172.0
168.5
173.1
185.3
167.9
165.4
167.5
164.4
168.1
175.2
173.7
176.7
182.0
204.3
172.1
167.1
168.5
166.5
170.8
176.3
174.5
180.2
178.3
184.0 1,853.0
207.6
206.3
175.1
173.9
170.4
168.4
171.9
169.2
169.4
167.9
173.9
171.9
180.2
177.1
178.4
175.4
180.9
186.1
209.0
176.2
171.4
172.6
170.8
175.9
181 .3
179.4
182.5
186.6
207.2
177.8
173.5
174.8
173.3
178.1
183.1
181 .2
183.6
188.7
208.9
180.5
175.1
176.9
174.8
179.7
184.2
182.5
184.8
190.9
210.5
182.1
176.9
178.5
177.0
181.8
187.0
185.2
0.7
.7
.8
.9
1.0
.9
1.3
1.2
1.5
1.5
2.6
2.5
1.4
4.0
3.8
3.8
4.5
4.5
3.8
3.8
Nonmanufacturing ...................................... ....................
White-collar workers ... ... ............. .. ............................. ...
Excluding sales occupations ... ........ .........................
Blue-collar occupations ..................... ...........................
Service occupations ...... ...... .......................................
162.0
164.8
166.6
155.4
158.4
162.5
165.3
167.1
155.9
159.2
164.9
168.0
170.0
157:5
161.1
166.4
169.3
171 .4
159.7
162.0
168.1
171.2
173.2
161 .1
163.2
169.0
172.1
174.2
161.7
162.4
170.9
174.1
176.2
163.4
166.0
172.5
175.7
177.7
165.5
167.3
173.9
177.2
179.3
166.4
168.0
.8
.9
.9
.5
.4
3.5
3.5
3.5
3.3
2.9
State 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
159.7
161.0
163.5
164.1
159.2
159.6
157.7
164.7
160.2
160.9
162.8
165.5
166.2
160.3
160.7
158.8
165.8
161 .7
161.8
164.0
166.4
167.0
161.1
161 .4
159.4
167.0
163.4
162.3
164.2
166.7
167.3
161 .7
162.0
160.0
167.5
164.3
164.9
166.8
169.5
170.3
164.3
164.7
163.0
169.2
167.3
165.7
168.2
171 .0
171.4
165.0
165.3
163.7
170.0
168.1
166.5
169.4
172.2
172.4
165.7
166.0
164.4
170.7
170.1
166.8
170.1
172.9
173.2
165.9
166.3
164.6
171 .0
171 .4
169.7
173.0
175.7
176.3
168.8
169.2
168.0
172.4
174.1
1.7
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. ....................
Administrative support, including clerical .. ...... ...............
Blue-collar workers ... ........ .. ................................ ...............
Workers, by industry division:
Services .................................. .................. ........ ................
5
Services excluding schools .... ... ....... .... .. ....... .. ...............
Health services ....................................... ........... ......... ..
Hospitals .......... ......... .............. ...................................
Educational services ...................................... .. ............
Schools .... ...... .. ................. ..................... ... .............. ...
Elementary and secondary ............. ........................
Colleges and universities ........................................
3
Public administration .. ... ......... ... ..
Cost (cents per hour worked) measured in the Employment Cost Index consists of
wages, salaries, and employer cost of employee benefits.
2
Consists of private industry workers (excluding farm and household workers) and
State and local government (excluding Federal Government) workers.
1
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
3
Consists of legislative, judicial, administrative, and regulatory activities.
This series has the same industry and occupational coverage as the Hourly
Earnings index, which was discontinued in January 1989.
4
5
Includes, for example, library, social, and health services.
Monthly Labor Review
December
2004
97
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
Civilian 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. ............... .. .. ........... .
Executive, 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
2.7
2.6
3.4
1.8
2.7
2.8
2.5
2.4
3.1
2.1
2.3
2
Service occupations ......... .... ... ...... ......... .... ... ... .... ... ... .. . .
Production and nonsupervisory occupations 3
Workers, by industry division:
Goods-producing ............................ ......... ....................... ..
Excluding sales occupations .......................... .......... .
White-collar occupations ............ .. ............... .. ....... ....... .
Excluding sales occupations .................................... .
Blue-collar occupations ....................... ........................ .
C('\nstruction ... ...... .............................................. .. ... ... ... .
Manufacturing .... .... ............... .... ... .. .. .. ..... .... .......... ... ... ...
White-collar occupations .. ........................................... .
Excluding sales occupations .. .......... ....... .. .... ......... .. .
Blue-collar occupations ......... ......... .. .... .......... ..... ..... ... .
Durables .. .................................... .. ..... .. .......... .. .............
Nondurables .......................................... .................... ... .
.7
.8
.6
1.1
.7
.6
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
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
156.3
155.4
160.0
158.0
153.8
150.6
158.0
160.1
157.7
156.3
158.8
156.6
157.4
156.5
161.4
159.2
154.8
152.4
159.0
161.6
158.9
156.9
159.7
157.8
158.3
157.4
161.9
159.9
155.9
153.6
159.7
162.0
159.5
157.9
160.6
158.3
158.7
158.0
162.1
160.4
156.4
154.0
160.1
162.1
160.0
158.5
160.9
158.7
159.9
159.2
163.2
161.5
157.7
155.1
161 .3
163.3
161.2
159.8
161.9
160.4
160.9
160.2
164.5
162.7
158.6
155.9
162.4
164.7
162.5
160.6
162.9
161.6
162.3
161.2
166.0
163.6
159.8
157.1
163.8
166.1
163.5
162.1
164.5
162.8
.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
Service-producing .......................................... ... .. ........... ...
158.4
158.6
160.6
161.7
163.3
163.9
165.0
166.1
167.5
.8
2.6
Excluding sales occupations .................................... .
159.3
159.6
161.7
164.2
162.8
165.0
166.0
167.1
168.5
.8
2.6
White-collar occupations ................. .............. .............. .
160.5
160.7
163.0
164.1
166.0
166.6
167.8
168.9
170.4
.9
2.7
Excluding sales occupations .................. .................. .
162.5
162.8
165.3
166.5
168.2
169.0
170.2
171.2
172.8
.9
2.7
Blue-collar occupations .... ..... ............................... .... .. . .
151 .8
152.0
153.2
154.3
155.1
155.4
156.2
157.8
158.9
.7
2.5
Service occupations ........ ............................................ .
153.5
154.1
155.1
155.6
156.6
157.4
158.0
158.8
159.4
.4
1.8
Transportation and public utilities ............. ......... .... .. ..... .
153.4
154.1
154.8
155.6
156.0
156.5
157.6
159.1
160.4
.8
2.8
Transportation ............... .. ............. ......... .... .. ..... .. ... .. .... .
149.6
150.1
150.5
150.6
150.4
150.8
151.7
153.4
155.0
1.0
3.1
Public utilities ....... ... ... ........ .. .. .......... .. .................... ...... .
158.2
159.3
160.4
162.1
163.4
164.1
165.3
166.4
167.5
.7
2.5
Communications ........................................ ... .......... ...
159.6
160.7
161.9
163.4
165.4
165.9
167.0
167.5
168.8
.8
2.1
Electric, gas, and sanitary services ........ ......... .. ...... ..
156.5
157.4
158.6
160.4
161 .0
161.8
163.3
165.1
165.9
.5
3.0
Wholesale and retail trade ...................... ....... ... ....... ... .. .
155.5
155.5
156.7
157.5
159.2
159.5
161.6
160.3
162.5
.6
2.1
Wholesale trade .............. ................... ............... .......... .
160.4
161 .0
163.4
164.7
164.8
165.3
166.2
167.8
169.7
1.1
3.0
Excluding sales occupations ... .... .. .. ... .. ......... ... ........ .
162.6
163.7
163.9
165.2
165.7
166.3
167.8
167.6
168.6
.6
1.8
Retail trade .. ............... .. .... .... ... ........... .... ............... ..... .
152.9
152.7
153.1
153.8
156.3
156.5
158.4
157.3
158.7
.2
1.5
General merchandise stores ...... ... ........ .. ........... ... .... .
150.1
149.2
149.8
152.0
153.1
153.6
154.1
154.9
157.5
1.7
2.9
Food stores .... ................................................ ..... ..... ..
150.1
150.3
151.0
151 .6
152.2
152.8
153.8
154.3
154.5
.1
1.5
See footnotes at end of table.
' - - - - - ' - - - - - ~ - - - ' - - - ~ - - - - ' - - - - - ' - - - ' - - - - - ' - - - - -- ' - -- - - - ' - - - - - -
98 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
31. Continued-Employment Cost Index, wages and salaries, by occupation and industry group
[June 1989
= 100]
Percent change
2004
2003
2002
3 months
12 months
ended
ended
Series
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
June
Sept.
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
0.7
.7
.9
1.0
1.1
.9
1.2
1.1
1.7
1.7
1.4
1.5
.2
3.1
3.1
3.1
3.8
3.5
3.1
3.2
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
.8
.9
.9
.6
.3
2.5
2.7
2.7
2.4
1.7
State and local government workers ............... .. ..... .. .......
160.1
161.5
162.6
163.2
165.9
166.8
168.0
168.7
171 .5
1.0
2.0
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
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
Workers, by industry division :
Services ....................... .. ....... .. ............................. .......... .. .
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
4
Services excluding schools .. ··············· ··· ···· ······· ·· ···· ·· ··
Health services ........ .................. .......... ..................... .. ..
Hospitals .. ............. .. ............................ .......................
Educational services ........ .. ........ .... .. ............................
Schools ....... ...... .. .. ................................. ................... .
Elementary and secondary ... ...... ... ... ... ... ... ... ..........
Colleges and universities ................. ...... .. .... ...........
Public administration
2
...
Consists of private industry workers (excluding farm and household workers) and
State and local government (excluding Federal Government) workers.
2
Consists of legislative, judicial, administrative, and regulatory activities.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
3
This 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.
Monthly Labor Review
December
2004
99
Current Labor Statistics:
Compensation & Industrial Relations
33. Employment Cost Index, private 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 size
1
Metropolitan areas ......................................... .. ......... ... ... .... ....
Other areas ......... .................................................................. .
1
The indexes are calculated differently from those for the occupation and industry groups. For a detailed description of the index calculation, see the Monthly Labor Review
Technical Note, "Estimation procedures for the Employment Cost Index," May 1982.
100 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
34. Percent of full-Hme employees partlclpattng In employer-provided benefit plans, and In selected features within plans,
medium and large private establishments, selected years, 198o-97
1995
1993
1991
1988
1989
1986
1984
1982
1980
Item
Scope of survey (in OOO 's) .. .. .. ........... ..... .
Number of employees (in OOO's) :
With medical care ... .. ..... ... ... ... ..... ... ....... .... ..... .
With life insurance .... ..... ... .... .. .... ... .. ... .... ..... ... .
With defined benefit plan ..... ...... ..... .
1997
21,352
21,043
21 ,013
21,303
31 ,059
32,428
31 ,163
28,728
33,374
38,409
20,711
20,498
17,936
20,412
20,201
17,676
20,383
20,172
17,231
20,238
20,451
16,190
27,953
28,574
19,567
29,834
30,482
20,430
25,865
29,293
18,386
23,519
26 ,175
16,015
25,546
29,078
17,417
29,340
33,495
19,202
10
27
72
26
88
10
26
71
26
8
80
81
3.7
89
9.3
3.3
9
29
68
26
83
3.0
91
9.4
21
3.1
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 ..... .... ... .. ... .. ..... ... ... .. .. . .
.d_vPrage days per year ...... .
Paid personal leave ......... ..... ... .......... .
Average days per year ..
Paid vacations ... ... . ...... .......... ... .................... .
1
Paid sick leave ••••••• •• •• • • •• • • • • ••• • •• • • • • •• •• •• • ••••
Unpaid maternity 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 facilities ...... ... .... ..... .... .
Physical exam ............................... . .
Percent of participants with employee
contribution required for:
Self coverage ................. ....... ..... .. ......... .. .
Average monthly contribution ..... ...... .. .. .. ... ... .. .
Family coverage .... ............... .... .
Average monthly contribution .. .. ... .. ... .
Participants in life insurance plans ....... ... .
Percent of participants with :
Accidental death and dismemberment
insurance ... ..... ...... .......... ................... ... .. .. ... .. . .
Survivor income benefits .... ... .. ..... ... ....... .. ....... .
Retiree protection available .... ...... .. ..... .. ...... .... . .
Participants in long-term disability
insurance plans .... ....... .......... .......... ... ............ .
Participants in sickness and accident
insurance plans ....... ... .... ............... ......... ...... .. ... . .
99
99
3.2
99
10.0
24
3.8
9.8
23
3.6
10.0
25
3.7
11
29
72
26
85
3.2
96
9.4
24
3.3
100
99
99
100
98
97
96
62
67
67
70
69
68
37
18
67
37
26
10
9
9
75
25
76
25
26
73
26
99
10.1
20
33
16
84
3.3
97
9.2
22
3.1
30
67
28
80
3.3
92
10.2
21
3.3
20
3.5
97
96
95
65
60
53
58
56
84
93
97
97
97
95
90
92
83
82
77
76
62
46
62
76
79
28
80
8
66
70
18
75
58
28
81
80
30
86
82
42
78
73
56
85
78
63
36
$11.93
58
$35.93
43
$12.80
63
$41.40
44
$19.29
64
$60.07
47
$25.31
66
$72.10
51
$26.60
69
$96.97
61
$31 .55
76
$107.42
67
$33.92
78
$118.33
69
$39.14
80
$130 .07
26
27
46
51
96
96
96
96
92
94
94
91
87
87
69
72
74
78
71
76
77
74
6
5
49
71
7
42
44
41
7
37
33
42
43
53
55
64
64
72
10
59
40
43
47
48
42
45
40
41
54
51
51
49
46
43
45
44
8
Participants in short-term disability plans ' ............. .
Retirement plans
Participants in defined benefit pension plans ..
P"'"Al"!t of participants with :
Normal retirement prior to age 65 ..... ....... .... .. .
Early retirement available ... ... ... ... .... ... .. .......... . .
Ad hoc pension increase in last 5 years ... ...... .. .. .
Terminal earnings formula ....... ... .... .... .
Benefit coordinated with Social Security ..... .. ... .. . .
3.3
89
9.1
22
6
84
84
82
76
63
63
59
56
52
50
55
58
97
98
7
56
52
96
6
4
54
61
48
58
62
62
97
22
64
63
52
95
52
45
64
98
35
57
62
59
98
26
53
45
63
97
47
54
56
55
98
51
52
95
10
56
49
60
45
48
48
49
55
57
33
36
41
44
43
54
55
2
5
10
12
12
13
12
23
36
52
38
32
7
Participants in defined contribution plans .. ... .. .. .
Participants in plans with tax-deferred savings
arrangements .. ... ........ .. .. .. ... ..... ..... .. ..
55
Other benefits
Employees eligible for:
Flexible benefits plans .... ...... .... ............ ... .
2
Reimbursement accounts ..• •• • . • • •••••••. . •••• .. . • •• .. .
Premium conversion plans .... .. ... ... ....... ....... ..... . .
' Th e definitions for paid sick leave and short-term disability (previously sickness and
5
5
fits at less than full pay.
accident insurance) were changed for the 1995 survey. Paid sick leave ·now includes only
2
plans that specify either a maximum number of days per year or unlimited days. Shortterms disability now includes all insured, self-insured, and State-mandated plans available
specifically allow medical plan participants to pay required plan premiums with pretax
on a per-disability basis, as well as the unfunded per-disability plans previously reported as
tabulated separately.
Prior to 1995, reimbursement accounts included premium conversion plans , which
dollars.
Also, reimbursement accounts that were part of flexible benefit plans were
sick leave. Sickness and accident insurance, reported in years prior to this survey, included
only insured, self-insured, and State-mandat ed plans providing per-disability bene-
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
NOTE: Dash indicates data not available.
Monthly Labor Review
December 2004
101
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
1987
1996
1992
1990
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
50
3.1
82
51
3.0
80
17
34
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
10.9
38
2 .7
72
13.6
39
2.9
67
14.2
38
2.9
67
11 .5
38
3.0
66
97
95
95
94
57
30
51
33
59
44
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 ........ .. ................................... .
8
9
37
48
27
47
2 .9
84
37
49
26
50
3.0
82
Averaqe days per year' .. ... .. .. .. ...... .. ............ .
Paid personal leave .... ...... ... .. ........ .. ... .. ... .... .
Average days per year .. .... ... .... .. .. .... .. .. ...... .
Paid vacations....
. .. ... .... ........ .. .... . .
9 .5
11
2.8
88
9.2
12
2 .6
88
7.5
13
2 .6
88
7.6
14
3.0
86
47
53
50
50
17
18
8
7
Paid sick leave
2
........ ... .. ............. ........ .. . ..... . .
Unpaid leave ............. ........... .. ............ .... ..
Unpaid paternity leave ............... .
Unpaid family leave .... .. .
47
48
66
64
93
Insurance plans
Participants in medical care plans .... .. ... .. .
Percent of participants with coverage for:
Home health care .................. .......... .. ........... .. . ..
Extended care facilities ........... .... .... .... .... .. .
Physical exam ................. ... ...... ....... . .
Percent of participants with employee
contribution required for:
Sell coverage ... .... .................................... .
.'.v.,,r;:,ge monthly contribution
Family coverage .......
. ..... ......... ... .... . ..
Average monthly contribut ion ..................... .
Participants in life insurance plans ................ ... .... .
Percent of participants with :
Accidental death and dism emberm ent
insurance .... ...................................... .
Survivor income benefits .. ........ .
Retiree protection available .. ... ... .
Participants in long-term disability
insurance plans .. .. ......................... .... ..... .. ... .
Participants in sickness and accident
insurance plans .. .... ............. .
Part icipants in short-term disability plans
2
69
71
79
83
26
80
84
28
42
$25.13
67
47
$36.51
73
52
$40.97
76
$109 .34
$ 150.54
64
64
78
1
19
76
1
25
19
93
93
90
87
76
78
36
82
79
36
87
84
47
84
81
55
52
$42.63
75
35
$15.74
71
38
$25.53
65
43
$28.97
72
47
$30.20
71
$ 159.63
$ 181 .53
$71 .89
$ 117.59
$139 .23
$149.70
61
62
85
88
89
87
79
67
1
55
67
1
45
74
1
46
64
20
77
1
13
23
20
22
31
27
28
30
26
26
14
21
22
21
15
93
90
87
91
47
92
89
8
92
89
10
100
10
92
87
13
44
92
90
33
100
18
2
2
46
29
.......
Retirement plans
Participants in defined benefit pension plans .....
Percent of participants with:
Normal retirement prior to age 65 .. ... ......... ... .
Early retirement available .... ........................... .
Ad hoc pension increase in last 5 years ............. .
Terminal earnings formula
Benefit coordinated with Social Sec urity .... ..
Participants in defined contribution plans ... .. .
Participants in plans with tax-deferred savings
arrangements ... ....................... ......... .. .. .
20
22
54
95
7
58
49
50
95
4
54
46
31
33
34
38
9
9
9
9
17
24
23
28
28
45
45
24
15
53
88
16
100
99
49
Other benefits
Employees eligible for :
Flexible benefits plans ...
Reimbursement accounts 3 ..................... ..
Premium conversion plans ........ ..... .... .. .... ..
1
1
2
3
4
5
5
5
5
8
14
19
12
5
31
50
64
7
Methods used to calculate the average number of paid holidays were revised
Sickness and accident insurance, reported in years prior to this survey,
in 1994 to count partial days more precisely. Average holidays for 1994 are
included only insured, self-insured, and State-mandated plans providing per-
no: cc,,nparable with those report ed in 1990 and 1992.
disabil ity benefits at less than full pay.
2
3
The definitions for paid sick leave and short-term disability (previously
sickness and accident insurance) were changed for the 1996 survey. Paid sick
Prior to 1996, reimbursement accounts included premium conversion plans,
which specifically allow medical plan participants to pay required plan
leave now includes only plans that specify either a maximum number of days
premiums with pretax dollars. Also, reimbursement accounts that were part of
per year or unlimited days. Short-term disability now includes all in sured, self-
flexible benefit plans were tabulated separately.
insured , and State-mandated plan s available on a per-disability basi s, as well
as the unfunded per-disability plans previously reported as sick leave.
102 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
NOTE: Dash indicates data not available .
36. Work stoppages involving 1,000 workers or more
2002
2003
2004P
2003
Annual totals
Measure
Dec.
Nov.
Oct.
Mar.
Feb.
Jan.
June
May
Apr.
Oct.
Sept.
Aug.
July
Number of stoppages:
Beginning in period ... .... ......................
In effect during period ..... ...................
19
20
14
15
5
5
0
3
0
2
0
1
1
2
1
1
0
1
2
2
3
4
0
1
2
2
2
3
-
Workers involved:
Beginning in period (in thousands) ....
In effect during period (in thousands) .
46
47
129.2
130.5
82.2
82 .2
8.0
76.7
.0
70.5
.0
61.3
6.5
66.5
2.2
2.2
.0
2.2
103.0
103.0
27 .6
28.6
.0
1.6
3.7
3.7
6.0
8.0
-
Days idle :
Number (in thousands) .............. .... .. ..
6,596
4,091 .2
1,168.5
1,219.0
1,473.4
1,203.9
1,146.5
44.0
26.4
204 .0
94.0
3.2
52 .5
60.0
(2)
.01
.04
.05
.05
.05
.05
.00
.00
.01
.00
.00
.00
.00
-
1
Percent of estimated workina time .. . .
1
Agricultural and government employees are included in the total employed and total
working time; private household, forestry , and fishery employees are excluded. An
-
Monthly Labor Review , October 1968, pp.54-56.
2
Less than 0.005 .
explanation of the measurement of idleness as a percentage of the total time worked
is found in "Total economy measures of strike idleness,"
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
NOTE: Dash indicates data not available. P = preliminary.
Monthly Labor Review
December 2004
103
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 ind icated]
Annual average
Series
2002
2003
2004
2003
Oct.
Nov.
Dec.
Jan
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
CONSUMER PRICE INDEX
FOR ALL URBAN CONSUMERS
All items ....... ......... ... ..... . .. ............. ..............
All items (1967 = 100) ..... ..... .. ..... .. ... ..... ········ .. ......
········ ··· · •• ·· ·· ··· · ...........
.......... .. ................ ······· ... ... . ... ..... ·· ··· ·" .....
at home .. ·· ··· ···· ···· ···· ····· ··· ··········· ·•· ..... .... .. ..
Food and beverages .....
Food ...
Food
Cereals and bakery products ······· ··• · .... ...... . ...... .
iv1 cais, riou ltry, fish. and eggs ... ....... ·············
1
Dairy and related prod ucts ······· ·· .... .... ..
Fruits and vegetables ... .. ..... ·····················
Nonalcoholic beverages· and beverage
materials .... ............ ..... ....... .. ... ....... .. ... .....
Other foods at home .. ....... .. ... .. ..... ..... . ... .. ..
Sugar and sweets ... .. ......... ..... . ............ ... ... ....
Fats and oils ... ...... . ... ... ··························
Ot her foods .......
...... . . . . . . .. . . . . . .. . . . . . . . . ........... ......
Oth er misce llaneous foods
Food away from home
12
· .. . . .. . .........•...
1
12
Other food away from home · .. ............ ... ..
Alcoholic beverages .. ... .. ... ..... . . .. .. . .. . .•• . . . •••. . ••• •..
Housing ... .. .......... .. .. ....... ... ... ... .... ... ... ..... ..
179.9
184 .0
538.8
551 .1
176.8
176.2
175.6
198.0
180.5
180.0
179.4
185.0
554 .3
185.2
554.9
186.2
187.4
188.0
189.1
189.7
189.4
189.5
557.9
561 .5
563.2
566.4
568.2
567.5
567.6
189.9
568.7
184.5
184.1
184.0
204.4
179.7
184.9
184.4
184.3
204 .8
185.0
184.5
184.1
205.5
186.5
186.1
186.6
186.8
186.3
186.8
202.9
181 .1
184.3
183.8
184.0
203.9
179.9
206.8
182.3
187.2
186.7
186.1
206.4
179.2
206.1
181 .1
187.3
186.8
186.7
207.2
179.5
187.2
186.8
187.1
207.2
183.7
184.5
184.3
552 .7
552 .1
182.9
182.4
182.4
202.5
184.7
180.0
184.1
179.3
190.9
571 .9
162.1
202.8
169.3
182.2
181 .7
181 .5
203.1
174.0
183.7
183.4
188.4
187.9
187.9
207.0
182.9
168.1
220.9
167.9
225.9
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
182.1
240.0
139.2
139.8
140.5
140.7
141.4
169.9
139.8
140.5
140.3
140.3
165.4
163.0
162.5
159.7
178.7
163.0
161 .0
157.7
179.6
162.8
163.0
160.7
178.0
163.7
163.9
162.3
178.9
140.8
165.1
163.3
166.2
180.4
139.7
162.6
162 .0
157.4
178.8
137.9
162.0
161 .7
157.3
177.9
139.3
160.8
159.0
155.4
177.1
165.0
162.6
166.2
180.4
165.4
163.5
169.4
180. 1
165.8
162.8
171 .3
180.5
166.0
163.8
171 .9
180.3
166.2
164.4
169.7
180.9
165.2
163.5
170.4
179.4
162.6
170.2
180.1
109.9
109.2
110.3
110.7
109.0
109.8
109.1
109.5
111 .7
110.5
110.8
110.9
109.4
111 .5
110.5
189.4
178.3
182 .1
183.3
183.8
184.3
184.9
185.5
185.8
186.2
186.7
187.0
187.8
188 .4
188 .9
126.8
117.7
183.6
121 .3
187.2
122.3
188.1
122.7
188.6
122.9
188.7
123.9
189.4
124.0
189.9
124.1
190.8
124.7
191 .8
124.8
191 .7
124.8
192.4
125.1
192.2
125.4
192.5
125.9
193.4
193.6
191 .0
180.3
184 .8
185.7
185.1
185.1
186.3
187.0
187.9
188.4
188.9
190.3
190.9
191.2
191 .0
220.6
208.1
213 .1
2 14.7
214.2
213.1
215.2
216.0
217.8
218.4
218.7
219.2
220.0
220.3
220 .2
212.8
199.7
205.5
206.9
207.5
205.5
208.3
208.8
209.2
209.7
210.2
210.7
2 11.2
21 1.9
212.4
212. 8
118.3
119.3
120.9
115.0
119.3
117.2
120.0
128.1
129.1
128.2
129.1
132.2
130.6
127 .0
128.0
214.7
219 .9
221 .4
221.9
219.9
222.6
222.9
223.3
223.9
224.3
224 .7
225.1
225.7
226.1
226.5
Tenants' and household insurance ·
Fuels and utilities .. .. ... .... ... . . ... . . ... . . . ..••....•. •• •.• .
Fuels .... ..... .... .......... ... ................... ·· ··· ·•···
Fuel oil and other fuels .. .........................
Gas (piped) and electricity ·· ······················· ·····
Household furnishings and operations .. .... .....
.. ..... . ....
Apparel ... ........ .. ......... ...
Men's and boys' apparel. .. .. ...... ....... ... ... .. ..
108.7
143.6
114 .8
154 .5
138.2
139.5
145.0
126.1
116.0
155.0
114.3
152.9
114.8
154.5
138.7
139.1
145.0
124.7
114.8
156.3
115.0
156.9
116.1
166.6
149.5
151 .1
156.9
125.2
150.5
157.4
157.6
124.8
149.3
161.6
156.0
125.0
116.3
162.8
144.9
125.4
116.2
165.5
148.5
150 .7
155.8
125.6
116.6
166.7
125.3
115.7
155.6
138.0
149.6
144.2
125.6
116.3
167.7
139.2
149.9
145.5
115.1
155.2
137.6
152.5
143.5
125.7
116.1
158.1
139.5
155.1
145.5
125.7
115.8
115.5
118.6
117.1
123.4
120.3
116.9
120.1
117.7
112.3
115.9
115.2
121 .2
116.2
114.4
124.1
11 8.3
Women's and girls' apparel ..... .... ......... .. .. .. ...
124.3
120.3
118.7
Shelter ... ... ........... .... .... ........... .. .......... .. .......
Rent of primary residence ........ ... ............... .. .... .
. ....... ..
lodgin g away from home ..
Owners' equivalent rent of primary residence
3
..
12
127.2
115.5
134.4
128.3
138.2
131 .4
145.6
135.7
134.8
142.6
125.1
124.9
124.8
120.8
123.1
121.4
140.4
150.4
146.8
177.3
150.0
126.1
115.8
120.9
118.0
113.1
118.8
115.7
119.0
118.0
110.9
105.7
110.3
123.5
119.8
117.6
106.1
116.5
113.8
107.5
Infants· and toddlers' apparel ...... .. . .... .... .
Footwear .. ................... ...... ... ..... . ...........
Transportation ... . ... . ..... .. ... .. . . . .. ........ ..... .... ..
126.4
122.1
125.2
123.0
119.2
117.7
119.3
121.9
120.5
11 8.1
116.2
114.5
115.0
119.5
120.6
121 .4
121 .8
157.1
121 .0
115.9
157.0
158.8
120.1
160.5
121 .0
161 .8
120.3
165.2
118.4
165.7
115. 1
164.0
117.3
162.9
121 .7
162.9
122.1
155.7
118.5
154.7
117.0
152.9
119.6
157.6
Private transportation ......................... ..• ..
148.8
153.6
153.0
151 .7
150.8
153.2
154.9
156.6
157.9
161 .5
161.9
160.0
159.1
159.4
162.9
1
.
124.0
121.7
119.2
166.4
99.2
96 .5
94.6
94.6
94.4
94 .3
94 .4
94 .2
94 .1
94.0
93.6
93.5
93.4
93 .9
94.3
140.0
137.9
136.5
137.5
138.0
138.0
138.3
137.9
137.6
137.4
137.2
135.9
134.9
134.9
135.9
' 1scd r.a rs and trucks
Motor fuel. ...... ......... . ........ .............. ··· ··· ·· ··
Gasoline (all types) . ......... ....... ..... .... .......... ...... .
152.0
116.6
135.1
136.6
131.0
127.8
127.2
131.2
150.5
149.8
130.6
173.3
172.7
164.5
133.8
162.0
161.2
136.5
161.2
155.3
131 .8
170.5
169.8
132.1
165.2
136.1
131.0
143.1
142.5
131 .3
155.9
136.0
132.0
131 .2
130.6
130.8
136.7
116.0
142.9
135.8
135.1
160.5
136.8
173.1
172.2
Motor vehicle parts and eq uipment.. .. ............ .....
Moto r vehicle maintenance and repair ..
Public tra nsportation ....
106.9
190.2
207.4
107.8
195.6
209.3
107.9
196.9
211 .3
107.9
197.2
207.9
107.8
198.0
205.6
108.0
198.2
206.3
108.0
198.2
208.1
107 .8
198 .5
209.9
107.9
198.6
211.5
107.9
199.0
210.7
108.2
199.7
212.3
108.8
200.3
2 14.4
109.0
200.8
209.7
109.3
200.7
205.3
109.5
20 1.7
206.5
Medica l care ........... .... ... . . . ... . ..... .. ..• .......... . .. ......
Medical care commodities .... ..... ........ .. .......
Medical care services ... ..... . . .. . •• ••• ··········· ·· ····
Professional services ............ ............. .... .....
Hospital and related services .. ..... .. ···· ·· ··· ··
· ·· ·1
285.6
256.4
292.9
253.9
367.8
297 .1
262 .8
306 .0
261.2
394.8
299.9
264.7
309.1
263.0
400.7
300.8
264.0
310.6
263.0
405.6
302 .1
265 .0
311 .9
261 .2
407 .0
303.6
265.5
313.8
262.5
409.7
306.0
266.7
316.6
268.0
412.5
307 .5
267.3
318.4
269.7
413.8
308.3
268.5
319.2
270.6
413.6
309.0
269.1
319.8
270.9
414 .6
310.0
269 .6
321.0
271.6
416.9
311. 0
269.9
322.3
272.3
4 19.1
311.6
270.0
323. 1
273.3
418.8
312 .3
270 .9
323.7
273.3
420.3
3 13.3
271 .7
324.8
273.7
422.5
New and used motor vehicles2
New vehicles .............. .. ... . . .. . .. . . . . .... ..... .
.
1
106.2
107.5
107.6
107.8
107.7
107.9
108.4
108.8
109.0
108.8
108.9
108.7
108.5
108.6
108.7
102.6
103.6
103.5
103.8
103.3
103.6
104.1
104.3
104.7
104.6
104.4
104.4
104.1
104 .0
104.2
107.9
109.8
110.9
110.8
110.9
111 .1
111 .2
111.1
110.9
110.6
110.8
110.9
111 .7
112.9
112.5
.... .... .. ..
126.0
317.6
134.4
335.4
139.1
339.7
139.0
336.0
139.4
342.8
140.1
345.4
140.4
348.6
140.6
348.9
140.7
349.5
140.9
349.6
141.6
350.6
142.1
349.5
145.1
353.3
147.9
352 .8
148.3
353.8
Tuition, other school fees. and child care ..........
362 .1
362.1
401 .1
401 .2
401 .7
403.6
404 .2
404 .7
404.9
405.6
407.6
409.4
428.2
89.7
88.4
88.2
88.2
88.1
88.1
87.7
87.4
86.9
86.8
86.5
418.3
86.1
427.4
92.3
86 .2
85.5
90.8
87.8
86.4
86 .2
86 .2
86.1
86.1
85 .7
85.4
84.8
84 .7
84 .5
84.0
84 .1
83.4
99.7
98.3
97.1
97.2
97 .2
97.0
97.1
96 .7
96.5
95.9
95.8
95.6
95.0
95.3
94.6
18.3
16.1
15.6 '
15.4
15.3
15.3
15.2
15.2
15.0
14.9
14 .9
14.8
14.7
14.7
14.5
RAr.rn;,itinn
2
Vir1AO ;,inr1 ;,i11r1io
12
·
2
Education and communication
2
Education ..
Ed ucational books and supplies .. .......
• ··
12
Comm11nir.;,ition ·
12
Information and information processinq · ..
•· •
12
services ·
Telepho ne
Information and information processing
14
nth Ar th;,in IAIAnhnnA ~Arvir.A~ ·
Personal computers and peripheral
12
eouipment ·
Other goods and services. ... . . . . . .. . . . .. .. . . . . . . . .. .. ... ..
Tobacco and smoking products . . .. .. . . . .. ... .. . ... .. ...
.
.
Personal care
1
Personal care products
Personal care services
1
1
.
.
·······
.....
... ... ...........
22.2
17.6
16.5
16.3
16.2
16.2
16.0
15.8
15.9
15.7
15.5
15.3
15. 1
15.0
14.6
293.2
461 .5
298 .7
469.0
300.2
469.5
300.0
469.1
300 .2
470.4
301.4
473.0
302 .3
472 .6
303 .1
473 .6
303.6
473.3
303.8
473.5
304 .1
476 .0
305.1
480.5
305.5
481 .6
306 .3
482 .9
306.8
482.3
174.7
178.0
179. 1
179.0
179.0
179.7
180.4
180.9
181.3
181.4
181.4
181 .7
181 .9
182.3
182.8
154.7
153.5
153.6
153.2
153.4
153.8
154.5
154.5
154.5
154.6
153.8
153.4
152.8
153.5
154.0
188.4
193.2
195.6
194.2
194.3
194.6
195.2
195.8
196.1
196.6
196.9
197.5
198.9
199.1
199.4
See footnotes at end of table .
104
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
37. Continue~onsumer 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
Miscellaneous personal services ....
Commodity and service group:
Commodities ... ..... ...... ........ ...............•. ·········
Food and beverages .. .......... ... ........... .. ........
Commodities less food and beverages .. ... .......
Nondurables less food and beverages .. .........
Apparel ........................................... ........ . ...
2003
2004
2003
Oct.
Nov.
Dec.
Jan.
Mar.
Feb.
Apr.
184.5
153.7
184.9
134.1
151.2
123.1
148.4
115.8
134.2
151.4
124.8
146.7
119.0
145.1
124.0
149.7
120.9
154.2
187.3
154.9
187.2
156.7
115.9
135.6
156.1
136.7
157.8
123.4
160.5
120.1
188.4
139.4
162.6
116.5
121 .2
124.1
124.3
152.3
184.3
132.6
182.2
296.3
154.5
123.5
151.1
184.1
131.7
151 .4
180.5
134.5
Oct.
187.2
136.1
118.6
150.4
182.9
132.9
149.0
151.2
176.8
134.2
Sept.
186.8
138.2
136.0
155.3
150.9
149.7
Aug.
155.8
156.0
186.5
138.6
160.9
292.7
July
295.9 1
185.0
136.9
157.2
291 .6
287.0
295.2
154.3
290.4
285.8
294 .4
293.6
288.8
283.5
June
293.1
287.1
274 .4
May
157.1
I
Nondurables less food, beverages,
and apparel .. ..... ...... .. ....... ... .. ...............
Durables ................. ....... ... ......... . ..... .•.... ......
162.2
171.5
121 .4
117.5
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
190.6
114.7
. .... ............. ............ .......
209.8
216.5
218.4
217.9
217.9
219.1
219.9
221 .0
221 .5
221.9
223.3
224 .1
224.5
224 .5
224.5
Rent of shelter3 ... ............................... ... ..
Transporatation services. ................ ..... ... .. .. ..
216.7
209.1
221 .9
216.3
254.4
222 .9
217.7
257.4
224.1
218.7
258.4
224.9
219.3
259.2
226.8
219.7
259.5
220.0
259.7
227.7
220.0
259.6
228.3
220.5
260.2
229.2
221 .6
260.5
229.4
246.4
223.0
218.6
257.3
227.4
............... ························ ······
223.5
218.9
257.2
220.8
261 .9
229.3
220.1
263.8
229.8
221.4
263.7
All items less food ............... .....................
All items less shelter ........... . . . .. . . . . .
All items less medical care .. ....................... ....
Commodities less food .. ... ... ... .. .. .. .. .. . . . . . . . ... . .
180.5
170.8
174.3
184.7
174.6
178.1
185.6
175.5
179.1
184.4
174.7
178.2
188.0
177.6
181 .3
188.6
178.2
181 .8
189.6
179.6
182.9
189.9
179.5
183.2
190.4
180.1
183.6
136.1
133.8
136.3
138.0
138.9
140.6
190.3
180.2
183.5
140.3
189.9
179.6
183.2
136.5
185.5
175.6
179.1
134.7
186.6
176.7
180.1
136.0
184.9
174.9
178.5
135.0
138.2
137.7
138.8
191 .4
181.4
184.6
141 .1
Nondurables less food . . ······ ················ ···· ........
Nondurables less food and apparel ... ···· ··· ·····•
Nondurables ... .. ..... .. .... .. .. .. ......... ....... ........
147.4
163.3
161.1
151 .9
172.1
165.3
153.3
172.2
166.8
151 .3
170.0
166.1
149.2
168.8
165.4
150.8
173.0
166.4
153.7
176.1
168.1
157.5
179.4
170.3
159.3
162.8
162.4
181.7
171.4
187.7
174.1
189.0
174.0
158.8
185.6
172.2
158.2
184.3
171 .9
159.9
184.4
172.8
190.0
175.8
Services less rent of shelter3. ............. ·•· ..
Services less medical care services ........ ... ....
Energy ........ ........... . . ... . .. .. .. . ... . . . . .. . . . ..•. . • .. ......
All items less energy . .. .... ....... ... ...... ..... ....
All items less food and energy .... .. .. .. ...........
217.5
226.4
228.7
228.2
228.4
229.7
230.6
230.7
231.1
231 .7
234.2
235.0
235.6
235.9
235.1
202.5
121 .7
208.7
136.5
190.6
193.2
210.5
136.9
191 .7
194.3
209.9
133.1
191 .6
209.9
131 .8
191 .5
193.6
211 .7
140.6
192.7
194.9
212.7
143.1
193.7
196.1
140.9
136.7
140.4
137.0
140.3
151 .3
215.0
159.7
194.4
196.6
139.4
215.8
156.3
194.5
196.6
138.2
225.8
226.6
228.9
229.4
229.6
172.8
230.2
165.1
231 .0
138.1
162.5
231.4
139.4
162.0
231.6
216.0
157.7
196.0
198.2
140.5
174.2
223.8
139.3
144.6
227.5
213.6
154.1
194.3
196.5
140.2
170.1
216.1
154.3
195.2
197.4
143.7
117.1
217.5
213.2
145.9
194.1
196.5
140.5
156.3
216.2
155.3
194.7
196.8
Commodities less food and energy .. ...........
Energy commodities ···························· .....
Services less energy ..................... .... ...... .. ..
211 .0
137.4
191 .9
194.0
138.5
Services .... ..
Other services ..
Special indexes:
.
.
187.7
190.5
193.9
139.9
132.1
225.6
139.0
129.0
225.5
138.2
164.2
232.1
CONSUMER PRICE INDEX FOR URBAN
WAGE EARNERS AND CLERICAL WORKERS
175.9
179.8
180.7
180.9
538.7
181 .9
541 .7
182.9
544 .8
185.3
184.9
185.0
185.4
538.2
179.9
536.0
184.7
535.6
180.2
536.7
183.5
523.9
546.5
550.2
551.9
550.8
551 .0
552.4
186.5
555.7
176.1
176.5
175.1
179.9
179.4
178.5
181 .7
181 .2
180.7
182.4
181 .9
181 .6
183.6
183.1
183.3
183.8
183.3
183.2
184.0
183.5
183.2
184.4
183.8
183.5
184.5
183.9
183.3
186.0
185.6
185.8
186.4
185.9
186.1
186.8
186.3
186.3
186.9
186.4
186.1
186.8
186.2
185.5
187.9
187.4
187.1
198.0
202.8
202.4
179.2
202.4
181 .0
203.8
179.9
204.4
179.7
204.9
179.6
206.0
179.1
181 .1
206.7
182.4
207.2
169.2
203.2
173.8
205.5
162.0
183.7
207.0
183.7
206.3
183.4
206.9
183.0
Dairy and related products .. ............. ...
Fruits and vegetables ........................ ... ....
Nonalcoholic beverages and beverage
167.2
222.9
167.6
224 .3
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
181.8
238.0
materials ........... .. .. ... .......
Other foods at home .. ....... ... ... .. ....... .. ... .. . . ... .
138.6
139.1
137.3
161 .6
138.6
162.5
140.0
162.3
139.3
160.5
157.7
162.4
160.7
162.6
166.0
180.0
178.4
179.4
180.8
180.8
180.8
180.7
163.8
169.9
181 .4
139.7
164.8
163.1
170.3
178.3
165.1
162.9
169.4
180.5
139.6
165.8
161 .4
157.3
164.6
161 .9
166.1
139.3
165.5
162.2
171.4
139.8
163.3
163.2
162.2
140.1
164.7
139.1
162.2
161.6
157.4
179.2
139.8
162.5
162.1
159.6
179.0
140.8
160.4
158.8
155.3
177.6
179.7
140.0
165.0
162.2
170.0
180.5
12
·
109.7
110.8
111 .2
109.5
110.3
109.6
110.1
112.2
111 .0
111 .2
111 .4
109.7
112.0
111 .0
110.3
. ..................... •• .. .
178.2
182.0
183.3
183.7
184.2
184.8
185.3
185.6
186.1
186.6
186.8
187.6
188.2
188.8
189.3
118.1
183.3
121 .5
187.1
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
126.8
193.9
175.7
201 .9
180.4
206.9
181 .3
208.3
180.9
208.2
181 .0
208.2
182.1
209.2
182.6
209.8
183.2
211 .0
183.6
211 .5
184.1
211 .8
185.6
186.2
213.0
186.6
186.5
186.2
212.2
213.4
213.4
213.8
199.0
118.4
204 .7
206.1
206.6
207.0
207.4
208.0
208.4
208.9
209.4
209.9
210.3
211 .0
211 .6
212.0
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
128.3
3
195.1
199.7
201 .0
201.4
201 .7
202.1
202.3
202.7
203.1
203.6
203.9
204.2
204.7
205.1
205.5
....
Tenants' and household insurance •
Fuels and utilities .. ............. ...........................
108.7
142.9
126.1
114.7
153.9
137.0
116.0
154.3
137.0
114.4
152.3
114.4
153.0
114.9
155.6
115.1
156.2
115.2
154.7
121 .9
130.7
144.6
120.9
121 .0
148.9
143.5
121 .3
149.6
146.1
121 .1
149.8
155.1
121 .3
148.4
150.2
156.2
120.7
156.8
156.8
120.4
161 .1
155.3
120.6
177.2
149.1
121 .7
123.1
121 .7
120.0
117.5
112.1
123.9
120.0
118.2
122.6
121 .1
118.7
117.8
110.5
115.7
115.6
105.5
118.3
117.4
109.8
136.6
152.0
142.9
121.4
122.9
116.8
166.2
148.2
138.7
144.1
138.3
154.5
144.7
121 .4
116.5
167.2
149.3
115.0
133.4
124.4
138.0
149.6
144.7
116.5
165.0
147.4
116.5
161 .9
135.4
136.2
142.5
120.4
116.4
157.4
139.3
116.3
166.1
134.7
134.4
141 .9
116.0
155.1
137.0
123.8
122.8
120.3
119.6
117.8
115.6
115.2
115.9
113.3
120.6
115.6
123.5
117.8
127.7
121 .1
155.4
152.5
125.0
120.4
153.6
149.0
124.1
119.1
156.3
153.5
150.8
121.4
117.8
152.5
149.7
120.1
115.6
154.9
152.2
99.4
96.0
93.5
93.1
92 .8
92.7
All items .....
·················· ................................ .....
.... ·····················
All items (1967 = 100) .. ....... .
Food and beverages ..
. . . .. . . . . . .
. .. ... ... ... . ....•..........
Food ..... ..... ...... .. ... .... .... ...• .. ········"·· ... ...... ......
Food at home ........................ ..... ........ . .. ....
Cereals and bakery products ......... .. ... .. ......
Meats, poultry, fish , and eggs ..... .. ..
.....
I
1
.
Sugar and sweets ..... ......... ........... .. ........ .. .
Fats and oils ···· ··· ··· ····· ··· ··· ····· ··· ..... ..........
Other foods ................................ .. ....
Other miscellaneous foods
Food away from home
1
.. ...... ...
1
2
Other food away from home • .......... . ...
Alcoholic beverages ........................ ...... .............
Housing .... ···························•·······--········--···--······--
Shelter .................................... ............ . .... .. ....
Rent of primary residence .. ············ ··"············
Lodqinq away from home
2
Owners' equivalent rent of primary residence
12
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
114.6
128.6
121.2
151 .8
120.7
115.3
165.6
162.9
172.0
143.5
120.0
117.4
120.6
118.4
116.7
112.2
106.0
106.9
114.0
119.3
122.2
116.4
156.8
125.2
118.6
158.5
123.4
119.6
159.9
120.9
119.0
163.6
118.8
117.0
164.0
117.0
114.4
162.2
117.6
116.3
161.4
122.3
120.4
161 .6
123.3
120.6
165.3
154.0
155.7
157.1
160.9
161 .3
159.3
158.6
159.1
162.7
92.8
92.6
92.6
92.5
92.1
92.1
92.2
92.3
93.3
See footnotes at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
December 2004
105
Current Labor Statistics:
Price Data
37. Continue~onsu mer 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
2003
Oct.
Nov.
2004
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
New vehicles .. .................... .... ....... .. ..............
141.1
139.0
137.8
138.7
139.2
139.2
139.5
139.0
138.7
138.5
138.2
137.0
136.0
136.0
136.9
1
152.8
117.0
116.4
106.1
191 .7
202.6
284.6
251 .1
292.5
256.0
363.2
104.6
102.0
107.6
125.9
318.5
354.8
93.7
92.7
99.9
143.7
136.1
135.5
107.3
197.3
206.0
296.3
257.4
305.9
263.4
391 .2
105.5
102.9
109.0
135.9
136.9
136.4
107.5
198.6
208.7
299.1
259.2
309.1
265.2
397.5
105.4
102.8
132.8
131.5
130.9
107.5
198.9
205.8
300.1
258.5
310.6
265.2
402.4
105.6
103.0
109.6
131 .7
128.1
127.6
107.3
199.8
203.6
301 .4
259.4
311 .9
266.5
403.4
105.5
102.5
109.7
138.0
343.8
390 .7
89.7
131.6
131 .7
143.6
143.0
107.6
200.1
206.2
305.4
260.9
316.8
270.6
408.7
106.2
103.2
110.0
132.6
171.1
170.4
107.5
200.8
208.8
308.4
263.3
320.0
273.5
410.7
106.6
103.9
109.2
139.9
350.4
394 .6
88 .. 4
88.2
97.2
87.0
96.1
131.4
173.8
173.2
107.8
201 .5
210.0
309.4
263.8
321 .2
274.1
413.0
106.7
103.7
109.4
140.6
351 .5
396.7
88.4
86.9
96.1
141.0
350.4
398.1
88.1
86.7
95.8
134.6
162.4
161 .7
108.4
202.7
208.0
311 .0
263.8
323.2
275.8
414.9
106.1
103.4
109.9
143.6
354.7
405.8
87.6
88.3
97.4
139.4
349.5
393.3
89.6
88.2
97.3
132.1
156.5
155.8
107.5
200.4
209.4
307.7
262.5
319.4
273.2
409.8
106.7
103.9
109.6
139.7
350.4
394.1
89.0
87.5
96.7
133.0
165.6
165.0
108.2
202.1
212.1
310.4
263.7
322.4
274.8
415.2
106.3
103.7
109.4
139.1
346.1
392.8
89.6
132.0
150.9
150.3
107.4
200.3
208.0
306.9
261 .5
318.6
272.3
409.9
106.5
103.5
109.8
139.6
349.9
393.8
89.3
87.9
96.9
86.2
95.2
137.3
161.7
161 .0
108.7
202.7
203.1
311 .7
264 .8
323.9
275.9
416.4
106.2
103.3
110.8
146.3
354.8
414 .0
87.8
86.3
95.5
137.6
173.6
172.9
108.9
203.8
204.2
312.7
265.4
325.0
276.3.
418.5
106.2
103.5
110.5
146.7
355.6
415.2
87.1
85.6
94.8
Used 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 ... .... •.. .... ..... .. ....
RocrAalion 2
Vic1Ao and audio 1•2
Education and communicalion 2 ....
.. ...
Education 2 .. .
.. ... . ·· ·····
Educational books and supplies ...................
137.1
136.6
107.6
199.9
204 .6
302.8
259.8
313.8
267.8
405.9
105.6
102.7
109.8
89.9
98.5
109.7
138.1
340.6
390.1
89.9
88.5
97.3
138.0
337.5
390.2
89.8
88.4
97.4
19.0
16.7
16.2
15.9
15.8
15.8
15.8
15.7
15.5
15.4
15.4
15.3
15.3
15.2
15.0
21.8
302.0
463.2
17.3
307.0
470.5
174.1
155.5
189.1
274.0
177.0
154.2
193.9
283.3
16.2
308.2
470.7
178.0
154.1
196.3
285.6
16.0
307.7
470.2
177.7
153.8
194.8
286.7
15.9
308.1
471.5
177.8
154. 2
194.9
286.6
15.8
309.3
473.8
177.4
154.3
195.1
288.4
15.7
310.0
473.2
179.1
155.0
195.7
290.2
15.5
310 .8
474.2
179.7
155.0
196.3
291.6
15.6
311 .3
474.1
180.1
155.1
. 196.6
292.9
15.4
311 .5
474.4
180.2
155.1
197.1
293.1
15.2
311 .8
476.9
180.0
154.3
197.5
293.5
15.0
313.2
481.6
180.3
153.9
198.1
294.7
14.9
313.5
482.6
180.5
153.1
199.5
295.4
14 .8
314.4
483.9
180.9
154.0
199.7
296.2
14.3
314 .7
483.0
181.4
154.3
199.9
296.6
150.4
176.1
135.5
147.0
123.1
151.8
179.9
135.8
152.1
120.0
151 .9
181.7
135.2
153.6
123.9
151 .3
182.4
133.8
151.4
122.6
150.7
183.6
132.5
149.0
118.7
151 .5
183.8
133.5
151.0
115.7
152.7
184.0
135.2
154.3
118.3
154.1
184.4
137.0
158.4
122.9
154.8
184.5
138.0
160.5
123.8
156.7
186.0
140.0
164.7
122.8
156.6
186.4
139.6
164.4
119.6
155.2
186.8
137.5
160.4
115.6
154.9
186.9
137.1
159.5
115.9
155.7
186.8
138.2
161 .2
120.6
158.0
187.9
141 .0
166.5
123.5
165.3
121.8
205.9
175.6
117.4
212.6
175.7
114.7
214.4
216.0
187.0
113.9
217.1
194.5
113.9
217.6
196.0
113.5
219.0
191 .8
113.2
219.7
190.2
113.1
220.2
190.1
113.7
220.3
196.9
114.3
220.0
200.6
219.0
250.7
176.5
114.0
215.3
201.4
219.1
251.8
184.1
114.0
216.7
199.2
216.2
248.5
171 .6
114.0
214 .2
200.6
218.0
250 .9
180.2
1142.0
194.5
207.7
241.6
172.9
114.2
214.1
200.5
218.8
250.7
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
205.9
222.0
255.9
All items less food ............................... ...........
All items less shelter .. . ····· ·····--···" ..... ... .. .......
All items less medical care ....... ... .. ...... ...... .
Commodities less food .......... .. .. ... ......... ... .... ...
Nondurables less food .. .. ............ .. .......... ....... ...
~J::nc:kuables less food and apparel ... .... .. ..... .. ..
Nondurables ....... .. ...... ..................... .. ...... ........
175.8
168.3
171.1
137.3
149.2
166.1
161.4
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 .... ...... ..... . ···· ··· ··· ········
193.1
198.9
120.9
183.6
185.6
144.4
17.3
213.9
179.7
171 .9
174.8
137.7
154.2
175.9
166.4
201 .3
205.2
135.9
186.1
187.9
141.1
136.8
220.2
180.4
172.6
175.6
137.0
155.7
176.1
168.1
203.2
206.9
136.3
187.0
188.6
140.3
137.2
222.1
179.7
171.9
175.0
135.8
153.7
173.6
167.3
202.7
206.5
132.4
187.0
188.4
139.7
132.1
222.1
179.2
171 .6
174.7
134.5
151.4
172.1
166.6
202.9
206.6
131 .1
186.9
188.0
141 .1
136.8
222.1
180.2
172.5
175.6
135.5
153.3
176.9
167.8
204.1
207.6
136.9
187.2
188.3
138.2
138.3
223.1
181.4
173.7
176.6
137.1
156.4
180.2
169.5
204 .9
208.2
140.2
187.9
189.1
139.0
144.7
223.9
182.6
174.7
177.6
138.9
160.4
184.0
171.8
204.9
208.8
143.0
188.7
190.1
140.0
151 .5
224 .9
183.2
175.3
178.2
139.9
162.4
186.6
173.0
205.2
209.2
146.0
189.0
190.4
140.1
156.7
225.3
184.4
176.8
179.4
141 .8
166.4
193.5
175.9
205.8
209.7
154.5
189.3
190.4
139.9
170.7
225.5
185.0
177.5
180.0
141 .5
166.2
194.8
175.9
208.2
211 .1
159.9
189.3
190.3
139.0
173.3
226.0
184.5
176.7
179.6
139.4
162.3
191 .0
174.0
208.9
211.8
156.2
189.3
190.3
138.0
165.5
226.7
184.5
176.6
179.6
139.0
161.5
189.6
173.6
209.3
212.2
155.1
189.5
190.5
138.0
162.8
2~7.1
185.1
177.3
180.0
140.2
163.2
189.7
174.5
209.5
212 .3
154.2
190.2
191.4
139.5
162.3
227.4
186.2
178.6
181.1
142 .2
168.2
195.6
177.7
208.6
212.0
157.8
191.0
192. 1
140.5
174.5
227.9
Tuition, other school fees, and child care ..
r.omm1inir.atinn 1•2
Information and information processini{ 2 ..
Telephone services 1 ·2 ...
Information and information processing
nthP.r than IAIAnhonA SArvir.As 1 .4
Personal computers and peripheral
1
equipment ' 2 .. ..
Other goods and services ... ... .... ....... .. .... ... .....
Tobacco and smoking products.... .......... . .........
1
Personal care ··· ··· ·· ·· ····· ·········· ······ ·· ·· ·•·· ···· ·
Personal care products 1 .. ..... .... ... . .....
1
Personal care services ..
. . . . . . . . . ... .
Miscellaneous personal services.. ........ ..........
Commodity and service group:
Commodities .... .... ····· ····· .................... . . . . . .. .... .. . .
Food and beverages .. ... .. ........ .. .......... ..............
Commodities less food and beverages ..... .......
Nondurables less food and beverages ···· ·
Apparel .................................... ...........
Nondurables less food, beverages,
and apparel .. .. .... .. .... ..........
Durables ··················· ......................
Services ........ .. ...... .. .
··········· ···
· ················· .. ······· .. .•.... ....
Rent of shelter3 . . . . . . . . . . . . . . . . . . . . . . .. .. . .. ... . .... ...
Transporatation services .... ....... ..............
Other services. ····· ·· . . . .. .. .. . .. . ........ .. ...
Special indexes:
1
Not seasonally adjusted.
2
Indexes on a December 1997 = 100 base.
3
2003
133.8
336.5
377.3
91.2
4
Indexes on a December 1988 = 100 base.
Dash indicates data not available.
NOTE: Index applied to a month as a whole, not to any specific date.
Indexes on a December 1982 = 100 base.
106
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 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 .............. .. ..... ............. .... ........ ....
Region and area slze
1
May
June
July
2004
Aug.
Sept.
May
Oct
June
July
Aug.
Sept.
Oct.
189.9
190.9
184.7
185.3
184.9
185.0
185.4
201.0
201 .2
202.5
196.4
197.5
197.3
197.2
197.7
199
203.1
203.2
204.5
197.1
198.3
198.0
198.1
198.4
199.7
189.1
189.7
189.4
189.5
M
199.9
201.1
201.0
M
202.0
203.3
203.0
M
Urban Wage Earners
186.5
2
Northeast urban ···· ········ ····· ··········· ···· ·· ·· ·· ························ ···
SizP. A-More than 1,500,000 ... .. .. ........,........... ...............
3
M
118.3
118.7
119.2
118.9
119.2
120.1
118.4
118.8
119.1
118.7
119.2
120.1
. . . ........ .. .... ..... .... . . . . .......•.. •............ . ...•......... •
M
182.9
183.3
183.2
183.3
183.6
184.5
177.8
178.2
178
178.2
178.6
179.5
Size A-More than 1,500,000 ....................................... ...
M
185.0
185.3
185.4
185.6
189.5
186.8
179.4
179.4
179.5
179.8
180.2
181 .1
Size B/C-50,000 to 1,500,000 ........... .... .. .... ...... . ... ... .. . .
Size D-Nonmetropolitan (less than 50.000) ......... .. ......
M
116.4
116.8
116.3
116.5
116.8
117.4
115.5
116.0
115.5
115.7
115.9
116.6
M
176.0
176.9
177.1
176.3
176.4
177.1
173.2
174.1
173.7
173.4
173.7
174.4
South urban .... ...................................................................
M
182.0
182.9
182.6
182.6
182.8
183.7
178.9
179.7
179.3
179.4
179.7
180.6
Size A-More than 1,500,000 .. ........................................
Size B/C-50,000 to 1,500,000 ... .... .............. ............ ....
Midwest urban
4
3
M
183.4
184.3
183.7
183.7
184.0
185.0
180.8
181.9
181 .2
181.2
181.4
182.5
Size B/C-50 ,000 to 1,500,000 . .... ............ .. .. ........ .........
Size D-Nonmetropolitan (less than 50,000) .. ... ... ... .... ..
M
116.4
117.0
116.9
116.9
116.9
117.4
114.8
115.3
115.2
115.3
115.4
115.9
M
179.4
180.5
180.1
180.0
181 .2
179
193.4
193.3
192.9
193.0
193.8
M
195.9
195.9
195.4
195.5
196.4
197.5
189.7
188.0
188.9
180.7
188.8
Size A-More than 1,500,000 ............... ..... ......................
188.6
189.6
179.5
188.0
182.3
M
180
188.6
179.4
West urban ........ ................................................................
182.8
195.0
188.9
189.9
191 .0
M
118.2
117.9
117.9
118.1
118.4
119.2
117.8
117.6
117.4
117.6
117.8
118.7
M
M
M
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
174.6
118.1
182.9
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
116.5
179.7
172.8
11 7.2
180.8
Chicago--Oary-Kenosha, IL-IN-WI. ........ ... .. .............. ...
Los Angeles-Riverside-Orange County, CA ..... .. .... ... ... .. .
M
M
188.7
193.3
189.1
193.7
189.2
193.4
190.2
193.1
190.0
194.5
190.8
196.3
182.2
186.8
182.5
187.4
182.4
186.8
183.2
186.5
183.1
187.8
184.0
189.8
New York, NY-Northern NJ-Long Island, NY-NJ--CT-PA ..
M
204.4
206.0
205.5
205.7
205.9
207.3
199.1
200.4
200.1
200.3
200.6
201 .9
Boston-Brockton-Nashua, MA- NH-M E--CT ........... .... ....
1
181 .3
208.9
-
209.8
-
207.9
-
179.1
181.7
183.8
172.6
1
118.9
-
172.8
Dallas-Ft Worth, TX ......... ......... ... ........ .. ........ ...... ......
-
-
208.8
174.8
-
1
-
207.9
Cleveland-Akron, OH .... .... .. .......... .. .. ... ................ ... ...
-
-
185.7
184.1
183.9
185.1
-
198.0
-
199.1
-
200.2
199.0
-
198.7
-
200.3
195.3
-
194.6
-
196.5
3
3
Size B/C-50,000 to 1,500,000 ···· ···· .. .. .... ...... .......... ...
Size classes:
As.
.............. .............................. .... .. ...... .... .....
3 ······
B/C .. ... .. .. .... ... . ............... ..... . ..... ... . .. .. ... .... .. .. .. ....... .. .. ...... .
0 ... ............ .. ......... ..... .. ................................. ......... ......
190.0
Selected local areas'
7
WashinQton-Baltimore, DC-MD-VA-WV ..... ... .... . ..... . .......
Atlanta, GA . ... .. . .... ..... ... .. .................... ..... ... .... ....... ...
1
118.9
2
Detroit-Ann Arbor-Flint, Ml. ............. .. .... ... .. ...... .... ..... ..
2
Houston-Galveston-Brazoria, TX ..... ... .. ....... .. ... .. .. .......
2
Miami-Ft. Lauderdale, FL. ... .. ....... ...... .... .. .. ............ .....
2
Ph1ladelphia-Wilmington-Atlantic City, PA-NJ-DE-MD .....
2
San Francisco--Oakland--San Jose, CA ... ..... ..... ... .. ..... ...
2
Seattle-Tacoma-Bremerton, WA .. .. ... .. ..... .... ..... .. ... ......
2
-
1
Foods, fuels, and several other items priced every month in all areas; most other
goods and services priced as indicated:
185.8
169.3
185.6
179.1
120.2
186.8
169.1
179.7
120.8
-
179.5
118.4
187.6
171.8
187
-
-
184.0
-
167.6
-
195.4
179.4
119.7
180.4
183.4
197.3
190.4
-
-
180.0
120.4
-
182.5
-
181.7
181.5
-
-
167.4
183.0
169.5
-
198.0
-
195.0
-
196.4
189.6
-
191 .6
182.9
185.1
199.8
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.
MO-IL; San Diego, CA; Tampa-St. Petersburg-Clearwater, FL.
1-January, March, May, July, September, and November.
7
Indexes on a November 1996 = 100 base.
2-February, April, June, August, October, and December.
2
Regions defined as the four Census regions.
NOTE: Local area CPI indexes are byproducts of the national CPI program. Each local
3
Indexes on a December 1996 = 100 base.
index has a 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
4
The "North Central" region has been renamed the "Midwesf' region by the
Census Bureau. It is composed of the same geographic entities.
5
6
Indexes on a December 1986 = 100 base.
In addition, the following metropolitan areas are published semiannually and
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
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Federal Reserve Bank of St. Louis
Dash indicates data not available.
Monthly Labor Review
December
2004
107
Current Labor Statistics:
Price Data
39. Annual data: Consumer Price Index, U.S. city average, all items and major 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 ................................ ..... ... .... ...... .
l 08 Monthly Labor Review
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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
271.1
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
December 2004
40. Producer Price Indexes, by stage of processing
(1982 = 100]
Annual average
Grouping
2002
2003
2004
2003
Oct.
Nov.
Dec.
Jan.
Feb.
1
Sept.P
Oct.P
148.7
152.0
152.1
148.6
151.9
152.2
148.7
152.0
152.2
151 .9
155.5
154.7
Mar.
Apr.
May
June
July"
148.9
152.5
155.5
148.7
152.0
155.0
Aug.
Finished goods.... ................................
Finished consumer goods ... ........ .. .... .......
Finish ed consumer foods .......................
138.9
139.4
140.1
143.3
145.3
145.9
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
Finshed consumer goods
excluding foods .. .... ....... .... .......... .. .. .....
Nondurable goods less food ...... ..........
Durable goods ............... .......................
Capital equipment.. ........ .... ... ....... ..... .. ...
138.8
139.8
133.0
139.1
144 .7
148.4
133.1
139.5
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
141 .1
151 .5
158.1
133.8
141.3
155.5
162.0
137.7
143.5
Intermediate materials,
supplies, and components............... ....
127.8
133.7
134.1
134.1
134.5
136.2
137.3
138.3
140.2
142.0
142.8
143.8
144.9
145.3
146.2
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
141.2
144.2
153.5
152.8
128.2
Materials and components
for manufacturing ..... ... .... .........................
Materials for food manufacturing .............
Materials for nondurable manufacturing ..
Materials for durable manufacturing ..... ...
Comrorients for manufacturing ........... .. ...
126.1
123.2
129.2
124.7
126.1
129.7
134.4
137.2
127.9
125.9
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
Materials and components
for construction ......... ............ .... ....... ... .....
Processed fuels and lubricants ...... ........... .
Containers ............... .. .................... .... .........
Supplies .......... ...... .......... .. ..... .... ...... ........ .
151.3
96.3
152.1
138.9
153.6
112.6
153.7
141 .5
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.o
171 .1
127.1
162.5
147.7
170.7
130.4
164.1
147.8
Crude materials for further
processing .......................... ... ...... ...... ..
Foodstuffs and feedstuffs .. ..... ...................
Crude nonfood materials ......... ........... .... ....
108.1
99.5
111.4
135.3
113.5
148.2
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
121 .7
174.1
159.7
119.9
186.1
Special groupings:
Finished goods, excluding foods ............ ....
Finished energy goods ........... .. ... .. .............
Finished goods less energy ......... ...... ........
Finished consumer goods less energy ......
Finished goods less food and energy ........
138.3
88.8
147.3
150.8
150.2
142.4
102.0
149.0
153.1
150.5
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
150.9
120.9
154.4
159.1
154.7
157.6
157.9
159.5
159.2
159.0
159.4
159.4
159.7
159.8
159.9
160.0
160.0
159.7
160.0
162.2
Finished consumer goods less food
and energy ................. ............ ............ ... ...
Consumer nondurable goods less food
and energy .... .... ...... .............. ......... .. ......
177.5
177.9
178.6
178.5
178.9
179.7
179.8
179.8
180.5
180.2
180.2
180.5
180.8
181 .3
181.6
Intermediate materials less foods
and feeds .................................... ......... ....
Intermediate foods and feeds .... ... ..... ...... ..
Intermediate energy goods .... ... .................
Intermediate goods less energy .......... .. .....
128.5
11 5.5
95.9
134.5
134.2
125.9
111 .9
137.7
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
121 .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
147.0
131.2
129.5
148.7
Intermediate materials less foods
and energy ............ .................................. .
135.8
138.5
139.0
139.2
139.5
140.4
141 .7
142.9
144.6
145.7
146.2
147.1
148.5
149.5
149.9
Crude energy materials ........................ ..... .
Crude materials less energy ..... .. ... ......... ...
Crude nonfood materials less energy ... .....
102.0
108.7
135.7
147.2
123.4
152.5
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
179.5
142.0
204.6
December 2004
109
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
Industry
Jan.
211
212
213
-
Total mining industries (December 1984:100)......................................
Oil and gas extraction(December 1985=100) .. ....... ...... .... ...... .... ..... .....
Mining, except oil and gas ....... ... ......... .. ......... .. ..... ....... .. ......... ....
Mining support activities ....... ..... .... ...... .. .. ....... .. .. ... ... ... ...... ... ......
Feb.
Apr.
May
June
JulyP
Aug.P
Sept.P
Oct.P
144.6
140.3
136.6
140.9
149.5
155.5
155.2
157.2
148.8
158.9
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
199.9
112.3
110.1
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
146.5
142.9
101.6
101 .7
100.1
Total manufacturing industries (December 1984:100) ........................
Food manufacturing (December 1984=100) ...... ..... ........ ....... ... .. .. ..
Beverage and tobacco manufacturing ............................. ........... ...........
Textile mills .............. ... .. .................................... ... ..... ....... ..... .................
Apparel manufacturing ................. ... .. ........ .... ...... ... ....... .............
Leather and allied product manufacturing (December 1984=100) .........
Wood products manufacturing ...... .. ..... ...... ...... ... ....... ..... .. ..... ... ...
Paper manufacturing ...... ........................................................... .............
Printing and related support activities ........ ........................... ............. ... .
Petroleum and coal products manufacturing (December 1984=100) ....
Chemical manufacturing (December 1984=100) .. .. .... ...... .... ... .. ..... ..
Plastics and rubber products manufacturing (December 1984=100) .. ..
Primary metal manufacturing (December 1984= 100) ... ..... ..... ... ... .... .
Fabricated metal product manufacturing (December 1984= 100) ....... ..
Machinery manufacturing .... ... ..... .................... .... ... .... .... .......... ...
Computer and electronic products manufacturino .. .. .. ...... .. .... ....... .. .
Electrical equipment, appliance, and components manufacturing .... , .. .
Transportation equipment manufacturing .... ....... .... ....... ... ..............
Furniture and related product manufacturing(December 1984=100) ... .
Miscellaneous manufacturing .... ......... ... ... .... ........ .......................
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
143.7
107.4
105.7
101.9
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
176.7
177.1
134.3
152.0
144.7
103.1
98.9
104.4
103.2
153.5
101 .6
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 ... ............. ... ... .. ...... .. .. .. .... ......... ... .... .. ...
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
104.2
104.0
100.6
106.8
42 .0
103.1
481
483
491
Transoortation and warehousino
Air transportation (December 1992= 100) ....... ... .. .......... ... .
Water transportation ... .. .. ... ......... ...... .... ... .. .. ..... ......... .. ... ....... .... .
Postal service (June 1989=100) ... .. ......... .... ..... . ..................... .....
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
161.6
103.6
155.0
221
Utilities
Utilities ... .. ........... .. .. .. ... ... .. ... ... .... ..
·· ···· ···· ··· ··· ··· ······ ················
101.7
102.5
101 .2
101 .8
103.1
106.9
107.1
107.5
105.1
104.0
Health care and social assistance
Office of physicians (December 1996=100) .. ..... .... ...... .... .... .......... .
Medical and diagnostic laboratories ... .. ... .. .. .... .. .. .......... .. ... ...........
Home health care services (December 1996=100) .. ... .. .... .. ... .. ....... .
Hospitals (December 1992=100) .. ....... ... .. ... .
·· ···· ·· ······· ··· ··· ····· ···· ·
Nursing care facilities ...... ........ .... ..... ........ ... .. ... .... ... .. .... .. ...........
Residential mental retardation facilities .......... ... ......... ...... ...
114.1
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
114.4
100.1
119.9
142.9
103.5
100.9
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
101.5
103.2
99.2
98.9
104.0
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
104.1
99.5
98.5
108.0
132.5
102.0
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
11 4.8
94.8
100.9
101 .3
125.4
127.4
100.4
115.3
96.9
101 .5
101.4
125.4
311
312
313
315
316
321
322
323
324
325
326
331
332
333
334
335
336
337
339
441
6211
6215
6216
622
6231
62321
511
515
517
5182
523
53112
5312
5313
5321
5411
541211
5413
54181
5613
56151
56172
5621
721
Other services Industries
Publishing industries, except Internet .... .. ....... ....... ...... ... .. .. .... ... ..
Broadcasting, except Internet. ... ..... .... ... ... .. ..... ..... .... ..... .... ...... ... .
Telecommunications .. ... ... ..... ... ... .... ..... .. ... .. ...... .... .... .. ............. ..
Data processing and related services ...............
·· ·· ·· ····················· ·
Securitv. commodity contracts. and like activitv .... ... .. .... .... ..... ........ .
Lessors or nonresidental buildings (except miniwarehouse) .. ... .... .. ....
Offices of real estate agents and brokers .. .. ...... ..... ..... ... ..
Real estate support activities ........ ... ..... .. ..... .. .. .. ... ... .. .... .... ......... .
Automotive equipment rental and leasing (June 2001=100) ...............
Legal services (December 1996=100) .. .. .. ... ....... .... ........ ... .. ... .......
Offices of certified public accountants ............. .... ..... ... ...... ..... . ..... .
Architectural, engineering, and related services
(December 1996= 100) ............ ... ... ..... ...... ........... ... .. ...............
Advertising agencies ....... ... .... ..... ...... ..... .. .. ... .... ...... ..... ......... .....
Employment services (December 1996=100) ... .... ...... ...... ... ... ..... ....
Travel agencies ..... .... ... ... .. ...................... ........ .. ...... ... ......... .....
Janitorial services ....... .. ........ ... .... ........... ... ...... ... .
·· ····· ············ ··
Waste collection ......... ....... .. .. ... .. .. ..... ... .. ... .. ...... ... .............. ..... ..
Accommodation (December 1996-100\ .............. ..... .. .. .. .. ... ...... .....
NOTE: Data reflect the conversion to the 2002 version of the North American Industry Classification System
(NAICS), replacing the Standard Industrial Classification (SIC) system .
110
Mar.
Monthly Labor Review
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Federal Reserve Bank of St. Louis
December 2004
42. Annual data: Producer Price Indexes, by stage of processing
[1982
= 100)
Index
1993
1994
1995
1996
1998
1997
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 further processing
Total. ... .... .... ... ..... ..... ..... .... .. ... ................................ ...... .
Foods .... .............. ..... .. ... . .... ............. ......... ........ . .. .
Energy .................... .......... .. .. ..... ..... ........ ........... .
Other .. ............... ............. .. ... ... ... . ..... .. . .... .... ... ..... .. .
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Federal Reserve Bank of St. Louis
Monthly Labor Review
December 2004
111
Current Labor Statistics:
Price Data
43. U.S. export price Indexes by Standard International Trade Classlflcatlon
[2000 = 100)
SITC
Rev.3
2003
Industry
2004
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Sept.
Oct.
0 Food and live animals ......... .. .................. .................
Meat and meat preparations ............. ......... .....................
01
Cereals and cereal preparations .....................................
04
Vegetables. fruit, and nuts, prepared fresh or dry ...........
05
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
118.5
125.9
115.5
131.2
2 Crude materials, Inedible, except fuels ..........................
Oilseeds and oleaginous fruits ............................. ... ....... .
22
24
Cork and wood ..... ...........................................................
Pulp and waste paper .... ... ............................... ...............
25
Textile fibers and their waste ..........................................
26
Metalliferous ores and metal scrap .................................
28
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
117.8
109.1
98.6
98.6
100.1
187.9
3 Mineral fuels, lubricants, and related products .............
Coal, coke, and briquettes ..............................................
32
Petroleum, petroleum products. and related materials ...
33
108.2
111 .6
104.1
106.3
111 .6
101.2
110.7
112.9
106.2
120.5
119.3
123.0
123.2
135.1
-
131 .8
-
137.5
139.3
116.8
-
-
156.1
-
-
141 .2
-
11 4.7
-
120.1
-
119.8
135.0
-
129.7
134.5
136.2
138.0
156.8
5 Chemicals and related products, n.e.s . .........................
Medicinal and pharmaceutical products ............ ..............
54
Essential oils; polishing and clean ing preparations .........
55
Plastics in primary forms .. ..............................................
57
Plastics in non primary forms ................................. ......... .
58
Chemical materials and products, n.e.s.........................
59
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
111 .9
107.8
106.0
113.6
97.9
105.1
6 Manufactured goods classlfled chiefly by materials.....
62
Rubber manufactures. n.e.s ............... .. ......................
64
Paoer. oaoerboard. and articles of oaoer. oulo.
100.3
100.7
100.8
101 .7
103.0
104.1
105.6
106.6
107.0
108.5
109.6
110.5
111 .1
109.2
109.5
109.9
110.4
110.9
110.4
110.9
110.8
111.2
111 .8
112.0
111 .2
111 .5
and oaoerboard ....... ..... ........... ... .. ... .. ..... ..... ..... .....
Nonmetallic mineral manufactures. n.e.s. ..... ......... ........
Nonferrous metals ............................. ......... .....................
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
103.8
100.5
99.2
7 Machinery and transport equipment...............................
71
Power generating machinery and equipment... ..... .........
Machinery specialized for particular industries ...............
72
74
General industrial machines and parts, n.e.s.,
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
98.6
109.6
107.2
and machine parts ............ .... .............. ............... ...........
Computer equipment and office machines ...... .. ........... ...
Telecommun ications and sound recording and
reproducing apparatus and equipment... ......................
Electrical machinery and equipment... ........... .................
Road vehicles .................. .. ....... ... ............................... ....
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
106.3
85.9
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
90.5
88.6
102.8
102.1
102.3
102.3
102.2
102.3
102.3
102.2
102.1
102.0
101 .7
101.9
101 .8
102.2
66
68
75
76
77
78
Aug.
87 Professional, scientific, and controlling
Instruments and apparatus .....................................
112
Monthly Labor Review
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December 2004
44. U.S. Import price Indexes by Standard International Trade Classlflcatton
[2000 = 100)
SITC
2004
2003
Industry
Oct.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Food and live animals ............................................. .
Meat and meat preparations .. ....... ......... ....... ..................
Fish and crustaceans, mollusks, and other
aquatic invertebrates .... .... .. ... ........... .................... ...
Vegetables, fruit , and nuts, prepared fresh or dry ...........
Coffee, tea, cocoa, spices, and manufactures
thereof. .. .. .. ..... .. .. .... ... ... ....... .... .. ... ... .... ... .. ..... ......
100.3
115.2
100.0
117.2
101 .0
120.4
102.2
117.7
104.7
11 8.0
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
109.9
133.9
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
85.4
110.2
95.5
93.1
96.0
100.1
101.9
101.7
103.6
102.4
107.0
102.7
103.3
105.6
104.5
1 Beverages and tobacco ............ ................ ....... ...... ..
11
Beverages ......... .. ... .................... ... .............................. .
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
106.5
106.9
2 Crude materials, Inedible, except fuels ..........................
24
Cork and wood ................................ ............ ....................
Pulp and waste paper ........................ .............................
25
Metalliferous ores and metal scrap .. ........... ......... ...........
28
Crude animal and vegetable materials, n.e.s ................ .
29
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
124.9
125.8
99.8
165.1
96.9
3 Mineral fuels, lubricants, and related products.............
Petroleum, petroleum products, and related materials ...
33
Gas, natural and manufactured ............ ... ....... ................
34
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
160.9
165.5
121 .1
5 Chemicals and related products, n.e.s. .........................
Inorganic chemicals .... ......................................... ...........
52
Dying, tanning, and coloring materials .... ........................
53
Medicinal and pharmaceutical products .. ................ ........
Essential oils; polishing and cleaning preparations ..... ....
Plastics in primary forms .... ................ ....................... ......
Plastics in nonprimary forms ... ........................................
Chem ical materials and products, n.e.s ................... ......
100.2
108.8
98.1
102.3
91 .2
105.6
101.7
92.3
100.8
111 .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
106.3
125.1
98.5
106.4
93.3
109.7
104.0
94.9
6 Manufactured goods classified chiefly by materials.....
Rubber manufactures, n.e.s.............. ....... ......................
62
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
101.0
Paper, paperboard, and articles of paper, pulp,
and paperboard ............ ....... ... ..... .......... .. ..............
Nonmetallic mineral manufactures, n.e.s . ............... .......
Nonferrous metals ................... .................... .............. ......
Manufactures of metals, n.e.s . ....................... ... ....... .. ....
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
100.6
106.5
104.1
7 Machinery and transport equipment. ..............................
Machinery specialized for particular industries ........ .......
72
General industrial machines and parts, n.e.s. ,
74
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
95.0
107.8
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
104.6
73.2
78
and machine parts ............................. ...........................
Computer equipment and office machines .....................
Telecommunications and sound recording and
reproducing apparatus and equipment... .......................
Electrical machinery and equipment... ...... ...... ................
Road vehicles ...... ....... .... ....... .... ................... ............. .....
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
83.5
94.6
103.5
85
Footwear ..... ..... ... ..•......... .... .............. .
························
100.0
100.1
100.1
100.5
100.5
100.6
100.6
100.6
100.4
100.4
100.1
100.5
100.5
88
Photographic apparatus, equipment, and supplies,
and ootical aoods, n.e.s . ... ................. ....... ...... ............
99.3
99.8
99.9
99.9
100.3
100.0
99.4
99.0
98.2
98.2
98.2
98.2
Rev. 3
0
01
03
05
07
54
55
57
58
59
64
66
68
69
75
76
77
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
99.3.
Monthly Labor Review
Aug.
Sept.
December 2004
108.6
98.6
113
Current Labor Statistics:
Price Data
45. U.S. export price indexes by end-use category
(2000 = 100)
2003
Category
2004
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
ALL COMMODITIES ..................................................
100.0
100.5
100.8
101.5
102.2
103.0
103.7
104.1
103.4
103.9
103.4
103.8
104.5
Foods, feeds, and beverages .. .................. ...... .... .
Agricultural foods, feeds, and beverages .. ............ ..
Nonagricultural (fish , beverages) food products .....
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
117.5
117.5
116.8
113.8
116.5
Oct.
Industrial supplies and materials .. .. ........................
101.0
101.7
102.5
105.1
106.4
108.1
109.1
110.2
109.9
112.0
113.1
Agricultural industrial supplies and materials ..........
113.3
119.0
117.5
118.6
116.6
117.2
114.8
113.7
110.7
109.0
108.4
109.4
108.7
Fuels and lubricants ....................................... .. .... .
Nonagricultural supplies and materials,
excluding fuel and building materials .... ..............
Selected building materials ... ..... ........................... ..
97.5
96.4
99.0
106.1
106.5
108.9
109.6
117.5
114.9
118.6
120.4
120.8
131 .1
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
116.4
103.8
Capital goods .... ... ... ........................... ... ... ..... .. ..
Electric and electrical generating equipment.. ........
Nonelectrical machinery ............................ .. ..........
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
98.3
103.1
94.4
Automotive vehicles, parts, and engines .................
101 .9
101 .9
101 .8
101.9
102.0
101.9
102.2
102.3
102.3
102.4
102.6
102.6
102.8
Consumer goods, excluding automotive ............. .....
Nondurables, manufactured .. ............... ..................
Durables, manufactured ........ .. ......... ... .. ..... .. .... ..
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
100.9
101.0
100.6
Agricultural commodit:es ..... .. .. ... ....... ... ...... ....... .. .
Nonagiic•Jltural commodities .. ... ....... .. ..... ... ........ ...
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
116.0
103.8
114
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
46. U.S. import price indexes by e nd-use category
(2000 = 100]
2004
2003
Category
Oct.
ALL COMMODITIES ..... ... ... ... ....................................
96.3
Nov.
96.8
Dec.
97.5
Jan.
99.0
Feb.
99.4
July
Aug.
Sept.
Oct.
Mar.
Apr.
May
June
100.2
100.4
101 .9
101 .7
102.1
103.5
104.0
105.6
107.3
114.0
92 .3
108.7
116.4
91.5
108.0
115.6
90.9
Foods, feeds , and beverages ..... .. .. ... ... ...... ...... ....
Agricultural foods , feeds , and beverages .............. ..
Nonagricultural (fish, beverages) food products ... ..
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
Industrial supplies and materials .. ..... .. .. ......... .. .... ..
99.5
100.7
103.6
108.5
110.0
112.7
113.9
119.7
119.3
120.6
126.4
128.1
134.4
Fuels and lubricants ....... ...................... ...... .... .. .... .
Petroleum and petroleum products .. .... ... .. .... ... .
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
160.2
165.6
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
93.9
93.9
94.1
94.2
95.6
96.8
98.2
99.0
100.0
100.4
101.2
100.8
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
108.5
115.2
133.4
98.6
Capital goods ....... .. .... ... ... ... .. ... ..... ...... ...... ... .....
Electric and electrical generating equipment. .........
Nonelectrical machinery .... ......... .................. ..... .....
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
Y7.5
90.0
92.1
97.5
89.9
92.0
97.4
89.8
91.8
97.2
89.6
Automotive vehicles, parts, and engines ... .. ....... .....
101 .2
101 .2
101.4
101 .6
101 .7
101 .8
102.0
102.0
102.2
102.3
102.5
102.6
103.0
Consumer goods, excluding automotive .. ... ..... ... .... .
Nondurables, manufactured .......... ... .. ... .. .... ...........
Durables, manufactured ...... .... .. .. ...... ... ... ... .. ......
Nonmanufactured consumer goods ..... .. ..... .... ... ..
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
98.4
100.8
96.0
97.9
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Federal Reserve Bank of St. Louis
Monthly Labor Review
December
2004
115
Current Labor Statistics: Productivity Data
47. U.S. international price Indexes for selected categories of services
(2000
= 100, unless indicated otherwise]
2002
Category
Sept.
2003
Dec.
Air freight (inbound) ........ ........... .. ..... ... .. ... ......... .. ... ....
Air freight (outbound) ............. ... ..... ..... .... .... ........ ...
100.3
97.3
Inbound air passenger fares (Dec. 2003 = 100) ......... .
Outbound air passenger fares (Dec. 2003 = 100)) .......
Ocean liner freight (inbound) ... ....... ........... .... ... ........
93.5
2004
Sept.
Dec.
Mar.
June
Sept.
105.9
95.4
108.8
97.2
109.4
95.4
112.5
95.5
112.9
94.9
116.2
96.1
116.6
99.0
118.7
100.7
-
-
-
-
-
-
-
-
93.3
94.0
116.1
116.2
100.0
100.0
117.7
105.1
99.3
119.1
106.1
114.2
121 .1
110.1
114.2
120.3
NOTE: Dash indicates data not available.
116 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
June
Mar.
December 2004
48. Indexes of productivity, hourly compensation, and unit costs, quarterly data seasonally adjusted
[1992 = 100]
Ill
IV
I
II
2004
2003
2002
2001
Item
Ill
I
IV
II
Ill
IV
I
II
Ill
Business
Output per hour of all persons ............... ............... .... .. ...
Compensation per hour .. ..... ............ ....... .. .. ... ....... .
Real compensation per hour .. ... ....... ......... ..... .... .....
Unit labor costs ......... ........... ..................... .. ...... ..... ...
Unit nonlabor payments ...... .......... .. ... ............ ... ...... .
Implicit price deflator ........... .. .... ... ...... ... ...... ........ ..
118.8
140.4
113.2
118.2
110.2
115.2
120.9
141.5
114.2
117.0
113.1
115.6
122.7
143.2
115.2
116.7
113.4
115.5
123.2
144.4
115.2
117.2
113.6
115.9
124.7
145.0
115.0
116.3
115.7
116.1
125.0
145.5
114.8
116.3
116.8
116.5
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
Nonfarm business
Output per hour of all persons .......................................
Compensation per hour ....... .... ......... .. ... ....... .. .. .....
Real compensation per hour ... ...... ...... ................. ...
Unit labor costs ................. .................... ..... ... .... ........
Unit nonlabor payments ..... .. ............... ............ ...... ...
Implicit price deflator ... ........ ...... ........................ ....
118.5
139.6
112.5
117.8
111 .9
115.6
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
Nonflnanclal corporations
Output per hour of all employees ... ... ............................
('')mpensation per hour. ... ...... ... .. ........ ..... ... .... ......
Real compensation per hour ..... ......... .... ........ ... ......
Total unit costs ......... .. .... .............. ...... .. ..... .............. .
Unit labor costs ........................... .................................
Unit nonlabor costs ....... ................ .......... ..... ... ...... .. .....
Unit profits .. .... .. ................ ... .. ............................ ...... .... ..
Unit nonlabor payments ................. ........................ ..
Implicit price deflator ....... ... .... ......... ........... ... ....... .
123.0
137.9
111 .1
112.8
112.1
114.7
79.4
105.2
109.8
126.3
123.9
139.9
139.3
112.6
112.5
111.6
113.4
112.4 1,110.8
114.0
116.2
89.1
75.8
107.4
105.4
109.6
110.1
127.9
141.3
112.7
111.2
110.5
112.9
94.7
108.1
109.7
129.2
131 .3
144.1
112.7
110.7
109.8
113.2
99.2
109.4
109.7
134.1
146.3
137.2
115.3
109.0
108.2
111 .1
118.7
113.1
109.9
138.9
150.9
115.9
108.8
108.6
109.5
128.1
114.5
110.6
141 .5
154.4
114.2
109.7
109.1
111 .4
111 .0
111.3
109.8
138.9
150.0
116.2
108.7
108.0
110.5
123.2
113.9
110.0
140.1
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
Manufacturing
Output per hour of all persons ...................... ........... ......
Compensation per hour .. ..... ... ... .. ... ...... ....... .. ... .... .
Real compensation per hour ..... ..... ... ... ........ .. ... ... ...
Unit labor costs ........... .. ...................... ... .... .. ... ..... .... .
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
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
142.1
148.5
Monthly Labor Review
December
152.9
116.1
109.4
109.2
109.9
116.7
109.4
109.1
110.3
134.3
134.6
116.4
111 .6
116.8
111 .7
161.5
163.7
124.3
101 .4
163.2
165.5
125.0
101.4
2004
117
Current Labor Statistics:
Productivity Data
49. Annual indexes of multifactor productivity and related measure
s, selected years
(1996 = 100)
Item
1980
1990
1991
1992
1993
1994
1995
1997
1998
1999
2000
2001
Private business
Productivity:
Output per hour of all persons ..... .. ... .... .... ................
Output per unit of capital services ....... ..... ..............
Multifactor productivity .... .. .... ......... ...... .... .. ..........
Output. ................... ............... ........... .. .. .... ... .......... ..
Inputs:
Labor input.. ....................................... .........................
Capital services ................. .................. .. .... .... .......
Combined units of labor and capital input.. .... ..... ... ..
Capital per hour of all persons ........... .............. .. ... .. ....
75.8
103.3
88.8
59.4
90.2
99.7
95.5
83.6
91 .3
96.5
94.5
82.6
94.8
98.0
96.7
85.7
95.4
98.7
97.1
88.5
96.6
100.4
98.2
92.8
97.3
99.8
98.4
95.8
102.2
100.3
101 .2
105.2
105.0
99.3
102.5
110.5
107.7
98.2
103.4
115.7
111.0
96.6
105.0
120.4
112.4
92.8
103.9
120.2
71 .9
57.6
67.0
73.4
89.4
83.8
87.5
90.4
88.3
85.7
87.4
94.6
89.3
87.5
88.7
96.8
91 .8
89.7
91 .1
96.6
95.6
92.5
94.6
96.2
98.0
96.0
97.3
97.5
103.5
104.9
104.0
101 .9
106.1
111 .3
107.9
105.8
109.0
117.9
110.9
109.7
110.1
124.5
114.7
114.8
109.5
129.6
115.7
121.1
77.3
107.6
91 .0
59.6
90 .3
100.4
95.8
83.5
91 .4
97.0
94.8
82.5
94.8
98.2
96.7
85.5
95.3
99.0
97.2
88.4
96.5
100.4
98.2
92.6
97.5
100.0
98.6
95.8
102.0
100.0
101.0
105.1
104.7
99.0
102.2
110.5
107.1
97.6
102.9
115.7
110.3
95.9
104.4
120.2
111 .6
92.0
103.3
120.1
70.7
55.4
65.5
71.8
89.2
83.2
87.2
89.9
87.9
85.1
87.0
94.3
89.0
87.0
88.4
96.5
91.8
89.4
91 .0
96.3
95.4
92.2
94.3
96.1
97.8
95.8
97.2
97.6
103.6
105.1
104.1
101.9
106.4
111.7
108.1
105.8
109.5
118.5
112.4
109.7
110.6
125.4
115.2
115.0
110.1
130.5
116.3
121.3
62.0
97.2
81.2
64.3
82.2
97.5
93.3
83.2
84.1
93.6
92.4
81 .5
88.6
95.9
94.0
85.5
90.2
96.9
95.1
88.3
93.0
99.7
97.3
92.9
96.5
100.6
99.2
96.9
103.8
101.4
103.1
105.6
108.9
101.7
105.7
110.5
114.0
101 .7
108.7
114.7
118.3
101 .0
111.3
117.4
119.7
95.1
110.3
112.1
103.7
66.1
86.1
63.9
65.8
79.2
101 .1
85.3
93.1
77.5
84.7
89.1
96.9
87.1
93.2
78.5
84.6
88.3
96.5
89.1
93.1
83.5
92.0
90.9
97.8
91.1
96.6
86.5
92.9
92.8
99.9
93.2
99.9
90.3
96.0
95.5
100.4
96.4
102.3
93.1
100.4
97.7
101 .7
104.1
97.5
101.9
103.9
102.4
101.5
108.7
100.6
107.5
103.1
104.6
100.7
112.8
102.9
107.9
105.4
105.5
99.2
116.2
104.3
106.9
106.5
105.5
99.6
117.9
98.9
105.5
97.7
101.6
Private nonfarm business
Productivity:
Output per hour of all persons ........ ..... .. .. ...... .........
Output per unit of capital services .............. ...... .. ....
Multifactor productivity ... .... .. ..... ... .. ....... .... ... ........
Output. ................... ................ ... ..... ... .. ... ... ...... .. ... ...
Inputs:
Labor input.. ................................................................
Capital services ....... .. .. .................. ...... ..... .. ....... ...
Combined units of labor and capital input.. .. .. ... .. .....
Capital per hour of all persons ....... ........ .. .......... .. ... .
Manufacturing
Productivity:
Output per hour of all persons .... ..... ..... ... ...... ...... ..
Output per unit of capital services .. ... .......... ..... ......
Multifactor productivity .......... ...... .... .. ...... .. .. .........
Output. ................................ .. ...... ... ... ...... ..... ..... .. ... .
Inputs:
Hours of all persons .... ................... ..............................
Capital services .. ................. .. ... .......... .. .. .......... ....
Energy .. ......... .. ... ...... .................. ................... .........
Nonenergy materials ....................................... ............
Purchased business services ......................................
Combined units of all factor inputs ................. .........
118
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Decemb er
2004
5Q. 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
2003
2002
Business
Output per hour of all persons ......... ... ... ... .......... ... ... .....
Compensation per hour ...... .. ....... ..... ... ...... ... ... .... ..
Real compensation per hour ...... ... ..... .. ..... .. ....... .....
Unit labor costs .............. .. ... ..... ... ......... ... ........ ...... ... .
Unit nonlabor payments .... .. ................ ......... .. .... .. .
Implicit price deflator .. .. ........ .... ... ......... .................
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
Nonfarm business
Output per hour of all persons ... .......... ........... .... .... ... ... .
Compensation per hour .. ................. ................. .. .. .
Real compensation per hour ................ ...... .... .. ... ... .
Unit labor costs ... ... ....... ... ........... .......... .... ... .............
Unit nonlabor payments ....... ......... .. ...... ... .... .. ... ... ... .
Implicit price deflator .... ..... ..... ..... ....... .. ....... ... .... ...
51 .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
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 non labor payments .... ... ......... ...... ... .. ...... ... ... ... .
Implicit price deflator ....... ... ..... ......... ·· ····· ········· ·· ··
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.()
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 deflator .. ......... .... ........ ... ........ ....... .. ..
41 .8
14.9
65.0
35.6
26.8
30.2
54.2
23.7
79 .2
43.8
29.3
35.0
70.1
55.6
91 .4
79.3
80.2
79.9
92 .9
90.1
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 available.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
December
2004
119
Current Labor Statistics:
Productivity Data
51. Annual indexes of output per hour for selected NAICS industries, 1990-2002
[1997-100)
NAICS
Industry
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Mining
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 ... ..... .. ... .. ..... .. ......... ....
86.0
78.4
79.3
68.1
79.9
92.3
86.8
78.8
80.0
69.3
82.7
89.5
95.2
81 .9
86.8
75.3
91 .7
96.1
96.2
85.1
89.9
79.9
102.2
93.6
99.6
90.3
93.0
83.9
104.1
96.9
101 .8
95.5
94.0
88.2
98.5
97.3
101 .7
98.9
96.0
94.9
95.3
97.1
100.0
100.0
100.0
100.0
100.0
100.0
103.4
101 .6
104.6
106.5
109.5
101.3
111 .1
107.9
105.9
110.3
112.7
101 .2
109.5
115.2
106.8
115.8
124.4
96.2
107.7
117.4
109.0
114.4
131 .8
99.3
112.3
119.3
111.7
112.2
143.9
103.8
71 .2
71.4
73.8
72.7
74.2
75.8
78.7
79.8
83.0
82.1
88.6
89.0
95.5
96.1
100.0
100.0
103.8
99.1
104.1
103.1
107.0
113.1
106.4
110.0
102.4
114.9
127.2
117.3
109.9
117.0
-
Utilities
Manufacturing
3111
3112
3113
3114
3115
Animal food ....... ....... ....... .... .......... ... ...... .. .. ..
Grain and oilseed milling .............. .. .. .... .... ... .. ..
Sugar and confectionery products ...... .. ... .. .. ......
Fruit and vegetable preserving and specialty ... ....
Dairy products ..... .. ... .... . ... .... ... ... ... ... ...... .... ...
90.1
89.0
91 .0
86.4
90.8
89.3
91 .2
93.8
89.7
92.1
90.2
91 .1
90.5
90.7
95.4
90.2
93.8
92 .5
93.8
93.9
87.3
94.7
94.0
94.9
95.4
94.0
99.1
94.3
97.1
98.7
87.5
91.3
98.2
98.2
98.0
100.0
100.0
100.0
100.0
100.0
109.4
107.5
104.0
106.8
99.1
109.5
114.2
107.1
108.4
94.5
109.7
112.5
111 .9
109.8
96.0
~116
Animal slaughtering and processing ...... .... ..... .. ..
Seafood product preparation and packaging ....... .
Bakeries and tortilla manufacturing ............... .. ...
Other food products .................. ... ...... ......... ....
Beverages .. ...... ......... .. ..... .. .. .. ... ... ... ..... ...... ..
94.5
117.5
92.6
91 .9
86.5
96.8
112.0
92.3
93.5
90.1
101 .5
115.3
95.6
95.9
93.8
100.9
113.9
96.0
102.8
93.2
97.4
114.1
96.7
100.3
97.7
98.5
108.4
99.7
101 .3
99.6
94.3
116.2
97.7
103.0
101.1
100.0
100.0
100.0
100.0
100.0
99.9
117.0
103.8
106.9
98.5
100.3
130.2
105.4
108.8
92.4
101.9
137.6
105.3
110.2
90.6
102.7
147.3
106.3
103.2
91 .7
3133
3141
Tobacco and tobacco products .... .. .... ..... .. ..... ...
Fiber, yarn, and thread mills .. .... .. . ... .. . .... .. ........
Fabric mills .... ... ... ....... ....... ... ..... .... ... .... .. .... ..
Textile and fabric finishing mills ...... .. .... .. ... .. .... ..
Textile furnishings mills .. ... .. .... .. .... ...... .. .... .. ....
81.4
73.9
75.0
81.7
88.2
77.3
74.7
77.7
80.4
88.6
79.6
80.1
81.5
83.7
93.0
73.7
84.6
85.0
86.0
93.7
89.8
87.2
91 .9
87.8
90.1
97.5
92.0
95.8
84.5
92.5
99.4
98.7
98.0
85.0
93.3
100.0
100.0
100.0
100.0
100.0
98.1
102.2
103.9
100.6
99.9
92.1
104.6
109.8
101 .7
101 .2
98.0
102.6
110.2
104.0
106.8
100.0
110.5
109.1
109.7
106.9
3149
3151
3152
3159
3161
Other textile product millsv
Apparel knitting mills .......... ..... ... .... .. .... ...........
Cut and sew apparel. .. ........ .... ..... ............. .... ..
Accessories and other apparel. .......... ... ..... ... .. ..
Leather and hide tanning and finishing ...... ..... ....
91 .1
85.6
70.1
100.9
60.8
90.0
88.7
72 .0
97.3
56.6
92.0
93.2
73.1
98.7
76.7
90.3
102.5
76.6
99.0
83.1
94.5
104.3
80.5
104.6
75.9
95.9
109.5
85.5
112.4
78.6
96.3
121 .9
90.5
112.6
91.5
100.0
100.0
100.0
100.0
100.0
97.0
96.6
104.0
110.8
98.0
110.4
102.0
118.8
103.3
101 .6
-
110.4
110.2
127.7
104.9
110.0
105.0
108.4
131 .7
114.8
109.7
-
-
3162
3169
3211
3212
3219
Footwear ..... ... .. .... ..... ..... ......... ... ... ....... ... .....
Other leather products .... .. ..... . .. ........... ........ ...
Sawmills and wood preservation ... ... .... ........ .. ...
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
Pulp, paper, and paperboard mills ... ... .... .... .......
Converted paper products .. ..... ....... . .... .. ..... .....
Printing and related support activities ....... .. .. ......
Petroleum and coal products .......... .... .... .... ..... .
Basic chemicals ...... .......... ... ..... .. ..... .... .. .. ... ...
88.5
90.5
96.6
76.7
91 .4
88.1
93.5
95.4
75.8
90.1
92.3
93.7
101 .3
78.9
89.4
92.9
96.3
100.1
84.5
89.9
97.6
97.6
98.3
85.6
95.1
102.0
97.2
98.8
90.1
92 .3
97.6
99.6
94.8
90.0
100.0
100.0
100.0
100.0
100.0
103.1
102.7
100.5
102.1
102.5
111.4
101.5
103.5
107.8
114.7
115.7
101 .9
104.9
113.2
118.4
117.5
101 .0
105.6
112.2
111 .0
-
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
3255
3256
Resin , rubber , and artificial fibers ....... .. ...... ... .. ..
Agricultural chemicals .... ...... ... .. .... ..... ....... ... ...
Pharmaceuticals and medicines ... ......... .. ... . ... ...
Paints, coatings, and adhesives ... .... .. ... .. ...... ....
Soap, cleaning compounds, and toiletries ........ ...
98.5
99.2
103.8
91.1
97.4
102.1
102.7
3253
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
94.8
96.5
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
90.1
89.3
81.7
95.9
95.0
87.8
90.5
87.2
100.0
98.2
88.8
3312
Cement and concrete products .... ... ........ ..... .. .. .
Lime and gypsum products ... .. . .. ..... .. .... ...........
Other nonmetallic mineral products .. ..... ......... ...
Iron and steel mills and ferroalloy production .......
Steel products from purchased steel. . ... ............
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 production ......... ...... .....
Foundries .. .... ... ...... .......... .. ....... .. ............ .... .
Forging and stamping .... ........ ....... ... ..... ....... ...
Cutlery and hand tools ..... .. .. .. ... ...... .... ... .... .....
91.9
95.6
85.3
88.6
85.1
93.3
95.8
84.5
86.5
85.4
96.8
98.8
85.8
91.7
87.2
96.0
101 .8
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
100.5
109.3
121 .8
110.2
3323
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
95.1
100.5
95.7
91 .5
91.6
93.9
97.8
97 .3
99.5
98.7
94.2
100.7
102.6
102.8
100.0
100.0
100.0
100.0
100.0
100.0
101 .1
101 .3
101 .0
111 .6
99.3
101 .8
98.9
106.5
112.9
103.9
101 .0
97.7
115.8
114.6
107.2
100.7
98.2
114.6
110.6
107.2
3117
3118
3119
3121
3122
3131
3132
3231
3241
3251
3252
3253
3254
3274
3279
3311
3324
3325
3326
3327
120
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
89.8
94.6
91 .7
93.4
94.8
89.6
95.3
86.9
98.3
93.4
96.2
96.9
-
-
-
-
-
51 . Continued- Annual Indexes of output per hour for selected NAICS industries, 1990-2002
(1997=100)
Industry
NAICS
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
100.0
100.0
100.0
100.0
100.0
101 .7
102.3
104.2
94.4
107.5
101 .5
100.2
95.0
105.2
111 .2
105.9
100.8
101.0
129.7
101.4
105.1
98.2
99.5
104.6
94.4
2002
3328
3329
3331
3332
3333
Coating, engraving, and heat treating metals .. .....
Other fabricated metal products ... ... ......... ...... ...
Agriculture , construction , and mining mach inery
Industrial machinery .. ...... . ..... . .... .. .... ..... ... ......
Commercial and service industry machinery ... .. ...
81 .6
86 .7
82 .8
80.6
91 .4
78.1
85.9
77.2
81.1
89.6
86.9
90.6
79.6
79.5
96.5
91 .9
92 .1
84 .1
84.9
101 .7
96.5
95.0
91 .0
90.0
101 .2
102.8
97.1
95.6
97 .9
103.0
102.9
98.9
95.9
98.8
106.3
..,..,..,...
3335
3336
3339
HVAC and commercial refrigeration equipment
Metalworking machinery ...... ............. ....... .. .. ....
Turbine and power transmission equipment.. ......
Other general purpose machinery ...... .... ... ........
88.8
85.3
85.1
85.9
88.2
82.3
84.6
85.2
90.8
89.3
81 .2
85.1
93.8
89.3
84.8
89.8
97.3
94.0
93.3
91 .5
96.6
99.1
92.1
94.6
97.8
98.1
97.9
95.1
100.0
100.0
100.0
100.0
106.6
99.1
106.4
103.2
110.4
100.5
113.3
105.6
108.3
106.4
117.1
113.0
110.8
102.0
130.2
109.4
3341
Computer and peripheral equipment.. ....... ..... . ...
14.3
15.8
20.6
27.9
35.9
51 .3
72 .6
100.0
138.6
190.3
225.4
237.0
3342
3343
3344
3345
3346
Communications equipment. .... .... .... .... .. .... .. .. ..
Audio and video equipment.. . .......... ... ..... .... ... ..
Semiconductors and electron ic components ... ... ..
Electronic instruments ........... .... ... ...... ... ......... .
Magnetic media manufacturing and reproduction
47.3
75 .5
21.4
76.0
86 .6
49.3
82.8
24.5
80.5
91 .2
59.3
92.1
29.6
83.1
93.0
62.1
98.8
34.1
85.8
96.8
70.1
108.5
43.1
88.8
106.1
74 .6
140.0
63.4
96.8
106.7
84.3
104.7
81 .8
97.7
103.8
100.0
100.0
100.0
100.0
100.0
102.7
103.1
125.2
101 .3
105.4
134.0
116.2
174.5
105.1
106.8
165.5
123.3
233 .3
114.3
104.0
155.2
126.3
231.6
116.1
98.6
3351
3352
3353
3359
3361
Electric lighting equipment. .. .. ... . ...... ... .. ......... . .
Household appliances ... .. ....... ......... .. ... ......... ..
Electrical equipment. .... . .... .................... ... .. ....
Other electrical equipment and components .... .. ..
Motor vehicles ... ..... ..... .. ... ........ ... .... .. .. ....... .. .
87 .3
76.4
73.6
75 .3
86.0
88.5
76.4
72.7
74.2
82.4
93.6
82.4
78.9
81.6
91 .2
90.8
88.9
85.8
86.8
89.8
94.5
95.0
89.0
89.4
90.3
92 .2
92.7
98.1
92 .0
88.6
95.6
93.1
100.2
96.0
91 .0
100.0
100.0
100.0
100.0
100.0
103.8
105.1
99.8
105.5
113.3
102.5
104.3
98.9
114.8
123.3
101 .9
117.5
100.6
120.5
110.4
105.4
122.6
101 .0
113.5
108.7
3362
3363
3364
3365
3366
Motor vehicle bodies and trailers .. .... .. ......... ... . ..
Motor vehicle parts ... .. .... ...... ..... . ... ... ....... ... .. ..
Aerospace products and parts ... .... .. .... .. ......... ..
Railroad rolling stock. ... ... .. .... ... . . . . . . . . . . . . . . . . . . . .
Ship and boat building ..... .... ... ... ..... .... ... .........
75 .8
75.7
87 .7
77.2
99.6
71.8
74.5
92.1
80.0
92.6
88.3
82.4
94.1
81 .1
98.5
96.3
88.5
98.2
82.3
101 .3
97.7
91 .8
93.8
83.1
99.0
97.3
92 .3
93 .7
82 .0
93.1
98.4
93.1
98.1
80.9
94.1
100.0
100.0
100.0
100.0
100.0
102.7
104.8
118.5
102.9
100.3
103.1
110.4
118.0
116.0
112.2
98 .4
112.7
101 .0
117.7
120.1
99.4
114.8
114.7
124.7
119.8
3369
3371
3372
3379
3391
3399
Other transportation equipment. ..... ... ... ........ . ... .
Household and institutional furniture ............. ... ..
Office furniture and fixtures .. ... ..... .. ......... .. .. .... .
Other furniture-related products ...... ..... ... .. .. .... ..
Medical equipment and supplies .... .. ... .. .. .... .... ..
Other miscellaneous manufacturing .. . ..... . ...... ....
62 .6
87.6
80.8
88.1
81 .2
90.1
62.0
88.2
78.8
88.6
83.1
90.6
88.4
92.9
86.2
88.4
88.1
90.0
99.8
93.8
87.9
90.5
91 .1
92.3
93.4
94.1
83.4
93.6
90.8
93.0
93.1
97.1
84 .3
94.5
95.0
96.0
99.8
99.5
85.6
96.7
100.0
99.6
100.0
100.0
100.0
100.0
100.0
100.0
110.8
102.7
100.1
107.2
108.9
101 .9
113.3
103.7
98.5
102.5
109.6
105.2
130.9
102.5
100.2
100.1
114.2
112.9
146.9
106.1
97.1
105.3
119.0
110.9
42
423
4231
4232
4,:JJ
Wholesale trade ... . ... .. ..... ..... ...... ... ... .. .... ..... ..
Durable goods .. .... ...... .... .. .. .. .. .. ... ....... ... .. .. .. ..
Motor vehicles and parts ... ... .. . .. .... ... . ··· ···· ··· ···
Furniture and furn ishings ..... ... ... ... . .. .... .. ... ...... .
Lumber and construction supplies .. ..... .. ... ... .... ..
77.8
65.7
76.6
82 .4
115.0
79.1
66.1
73.3
87.2
113.2
86.2
75.0
82.2
92.0
119.6
89.5
80.5
88.0
95.8
113.9
91 .3
84.5
94.1
93.3
111 .9
93 .3
88 .9
93 .6
96.8
103.6
96.2
94.0
94.9
97.0
103.0
100.0
100.0
100.0
100.0
100.0
104.4
105.6
104.7
97.5
102.9
110.9
115.3
119.8
100.8
104.8
114.1
119.6
114.0
105.5
101.7
117.1
120.3
114.1
105.4
108.6
123.6
127.7
121 .7
101 .8
119.2
4234
4235
4236
4237
4238
Commercial equipment. ... .. .. . ....... .. ... .......... ... .
Metals and minerals ..... ....... ... ..... .... .. .......... ...
Electric goods .... .. ... ... ............ ... ..... ... . ...... .. ...
Hardware and plumbing ......... ...... ..... .. ... .. ..... ..
Machinery and supplies .. .. ..... ... ... .. .. ... .. ....... ....
33.8
101 .6
46.8
88.8
78.9
37.3
102.6
47.6
86.5
74.2
48.2
109.1
51.4
95.6
79.7
56.2
111 .7
59.1
94 .3
84.3
60.5
110.1
68.2
101 .3
85.4
74.7
101.2
79.3
98.0
89.7
88.4
102.7
87.8
99.1
93.9
100.0
100.0
100.0
100.0
100.0
118.2
102.4
105.9
103.5
104.2
14 1.1
96.0
126.2
107.8
101.4
148.9
99.2
151 .7
111 .1
104.1
164.9
102.2
148.1
102.6
102.7
189.4
102.2
161 .2
107.9
100.2
4239
424
4241
4242
4243
Miscellaneous durable goods ...... .... .. .... .. .... .... .
Nondurable goods ... .... ...... .... .. . .. .... . .. . . .. . . .. .. . .
Paper and paper products ... ..... . .......... .... ... ....
Druggists' goods ..... ... .... ......... .. . ...... ........... ...
Apparel and piece goods .. ... .... ..... . .. .... ..... .... ...
89.5
98.4
81.0
81 .8
103.9
96.6
99.8
85.5
86.6
103.3
112.1
103.2
96.5
91 .8
100.1
113.2
103.0
97 .2
89.3
97 .7
106.1
101.8
101 .5
92 .8
103.8
99.2
99.7
99.0
95.4
92.2
101 .0
99.2
96.5
98.3
99.0
100.0
100.0
100.0
100.0
100.0
101 .8
102.8
100.4
99.6
104.1
112.6
104.1
105.5
101 .7
103.5
116.7
103.5
105.5
96.8
102.7
116.1
106.9
109.0
101 .2
102.4
125.5
112.6
120.2
116.0
111 .5
4244
Grocery and related products .. .......... ....... ..... ...
Farm product raw materials .... .... . .. .. ... ... ...........
Chemicals .. .... ..... .... .... .. ... .. .... ........ .. .. ..... ... ..
Petroleum ..... .. .... .. .... ... ... .... ... .. .... ..... ... ... .. .. ..
Alcoholic beverages ..... . .. . ... ....... ..... ... ....... .....
96 .4
80.6
107.3
97.3
109.4
98.2
85.9
106.6
107.0
111 .2
103.6
85.9
112.5
118.3
107.4
105.1
84 .0
110.0
119.1
105.6
103.3
80.4
110.5
115.8
105.9
103.0
87.7
102.1
108.7
102.5
99.8
90.6
100.0
105.9
104.5
100.0
100.0
100.0
100.0
100.0
101 .9
100.4
99.3
115.0
109.7
103.6
114.2
98.0
112.0
110.1
105.2
11 9.0
95.8
112.5
111 .0
109.4
120.0
93.6
116.5
111 .6
111 .8
4245
4246
4247
4248
4249
425
4251 1
42512
Miscellaneous nondurable goods .. .... ... ...... .. .... .
Electronic markets and agents and brokers ... .... ..
Business to business electronic markets ..... ..... ...
Wholesale trade agents and brokers ........ .. .. ... .. .
107.3
70.7
70.4
70.8
98.2
73.6
72 6
74.0
93.9
81 .5
80.3
82.3
97.5
85.9
84 .8
86.8
94 .8
88.0
88.3
88.4
96 .2
91 .1
90 .5
91 .8
98.7
95.7
95.3
96.1
100.0
100.0
100.0
100.0
101 .7
104.6
103.5
104.8
99.6
114.4
121 .7
110.5
106.2
124.1
141 .3
115.7
104.2
131 .3
169.4
114.2
97.0
132.6
205.0
109.3
44-45
441
4411
4412
4413
Retail trade ... .......... ........ ... ········ ·· ··· ·· . . .. . . . . . .
Motor vehicle and parts dealers ... .... ... ...... .... ....
Automobile dealers ........... . ... ..... ..... ..... ... ... .... .
Other motor vehicle dealers ....... .... .. ... ...... .......
Auto parts, accessories, and tire stores ..... ....... ..
83.2
89.7
92 .1
69.0
85.0
83.3
88.3
90.8
71 .7
84.0
86.8
92 .6
94.8
78.3
89.1
89.4
94.0
96.0
84.1
90.6
92 8
96.9
98.0
90.2
95.4
94.7
97.0
97.2
91 .0
97.9
97.7
98.8
98.9
97.7
98.3
100.0
100.0
100.0
100.0
100.0
104.3
102.7
102.7
105.9
105.7
110.3
106.4
106.4
113.0
110.0
114.2
107.2
106.6
108.6
112.0
117.4
110.0
109.1
112.6
109.3
122.7
109.7
106.0
11 6.4
115.8
442
4421
4422
443
Furniture and home furnishings stores ..... .... .. ... .
Furniture stores ..... . ... ... .. ... ....... ... ...... ....... .....
Home furnish ings stores ... ... . .... ..... ..... .... ... .... .
Electronics <Jnd appliance stores .. ... .... .... .. ...... ..
Building material and garden supply stores ..... . ...
80.7
82 .1
78.5
46.0
81.8
81.1
83.5
77.6
49.2
80.2
88.1
89.0
86.8
56.9
84 .0
88.3
89.0
87.2
65.5
88.0
90.4
88.9
92.1
77 .6
93.7
94 .1
92 .5
95.9
89.2
93.7
99.4
97.8
101.3
95.0
97.5
100.0
100.0
100.0
100.0
100.0
101 .7
102.1
101 .3
122.9
106.7
109.6
108.2
111 .4
152.2
112.3
115.7
114.8
116.8
177.7
113.1
118.5
121 .1
115.6
199.1
115.8
125.1
128.6
121 .4
240.0
119.9
Wholesale trade
Retail trade
4,14
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
December
2004
135.4
96.9
126.0
117.3
12 1
Current Labor Statistics:
Internationa l Comparison
51. Continued - Annual indexes of output per hour for selected NAICS industries, 1990-2002
(1997=100]
NAICS
Industry
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
4441
4442
445
4451
4452
Building material and supplies dealers ... ....... ... ..
Lawn and garden equipment and supplies stores
Food and beverage stores ... ... ... .. ..... .... .... .... ..
Grocery stores ... .... ... .... . .... .. ... ...... ......... .... ....
Specialty food stores ........ ... .. .. . . .. . .. . ..... ... .. ....
83.2
74.5
107.1
106.5
122.9
80.7
77.5
106.6
106.6
115.0
84.7
80.2
106.9
106.7
111.4
89.1
81 .5
105.4
105.9
107.6
94.8
86.9
104.3
104.9
104.5
94.8
87.0
102.5
103.0
101.1
97.6
97.1
100.3
100.8
95.5
100.0
100.0
100.0
100.0
100.0
107.6
101 .2
99.9
100.3
95.0
113.7
103.5
103.7
104.3
99.6
113.8
108.2
105.1
104.9
105.6
115.3
119.4
107.6
107.5
110.8
119.8
121 .2
110.3
110.3
114.2
4453
Beer, wine and liquor stores ........ .. .. .. .... ... .. ... ..
100.1
100.2
101 .0
94.4
92 .9
96.2
103.1
100.0
105.8
99.8
111.1
110.4
446
447
448
111 .8
Health and personal care stores ....... ... .. .. .. ... .....
Gasoline stations ... .. ...... .... .. ... .. .. ............ .. .... .
Clothing and clothing accessories stores .... ... ... .. .
92.0
84.8
69.5
91.6
85.7
70.5
90.7
88.5
75.3
91 .9
92 .8
78.9
91 .8
96 .8
83.3
93.0
99.7
91 .2
95.7
99.4
97.9
100.0
100.0
100.0
104.1
105.6
105.4
106.9
110.6
112.8
111.4
106.5
120.3
112.7
109.8
123.5
118.8
117.5
129.0
4481
Clothing stores ....... ....... . .. ..... .. ..... .. ......... .. ....
68.9
71.4
77.1
79.2
81 .9
90.1
97.1
100.0
106.7
113.3
120.9
125.2
132.7
4482
4483
451
4511
4512
Shoe stores .. .. ..... ..... ........ ......... ... ..... .... ... ... .
Jewelry, luggage, and leather goods stores ...... .. .
Sporting goods, hobby, book, and music stores ...
Sporting goods and musical instrument stores ....
Book, periodical, and music stores ..... ..... .... ...... .
73.7
68.6
80.8
77.1
89.0
73 .1
64 .5
85.6
82 .8
91.8
78.2
65.0
83.8
79.8
92.5
79.2
77.1
84.0
80.6
91 .6
88.3
85 .0
87.2
83.9
94.5
93.7
94 .1
93.0
92.3
94.5
102.4
97.3
94 .7
92.5
99.3
100.0
100.0
100.0
100.0
100.0
97.8
107.0
108.7
112.9
101.0
104.9
118.3
114.9
120.4
104.7
109.6
128.0
121.1
128.3
108.0
115.8
122.5
125.4
130.4
116.0
120.0
121.5
132.9
137.9
123.8
452
4521
4529
453
4531
General merchandise stores ............... .... ..... .....
Department stores .. ... ....... .. .. .... ... ... .. .... . ...... ..
Other general merchandise stores ......... .. .... .. ....
Miscellaneous store retailers .... ... .......... .. ... ... ...
Florists .. ... ... .... ..... ... ..... .. .............. .... ..... ..... ..
75.3
84 .0
61.4
70.6
75.1
79.0
88.3
64.8
68.0
75.9
83.0
91.6
69.7
74.2
85.1
88.5
95.0
77.8
79.1
91 .4
90.6
95.1
82.6
87 .0
85.4
92 .2
94.7
87.6
89.5
83.5
96.9
98.4
94.3
95.0
96.1
100.0
100.0
100.0
100.0
100.0
105.0
100.6
113.4
108.3
101 .2
113.1
104.5
129.8
109.8
117.3
119.9
106.3
145.9
111 .3
116.0
124.2
104.0
162.1
108.4
108.6
130.5
104.7
177.5
115.6
120.7
4532
4533
454
4541
4542
4543
Office supplies, stationery and gift stores .. .... .... ..
Used merchandise stores .. . ....... .. ... ... ..... ... . .... .
Other miscellaneous store retailers ..... ... . .... ..... ..
Non store retailers ..... ........... .. ..... ... ... ..... ... ... .
Electronic shopping and mail-order houses ....... ..
Vending machine operators .......... ... ... .... . .. . .
Direct selling establishments .. ... ... . ...... .. ..... ... ...
64.6
84.9
79.6
54.4
43.5
97.1
70.0
66.3
83.1
69.2
55.0
46.7
95.4
67.6
71 .5
89.7
74.7
63.4
50.6
95.1
82.1
75.8
88.9
80.5
66.7
58.3
92.8
79.7
87.5
87 .3
89.7
73.8
62 .9
94 .1
89.2
90.9
90.2
90.5
80.9
71.9
89.3
94.7
91.8
97.4
98.0
91.6
84.4
96.9
102.2
100.0
100.0
100.0
100.0
100.0
100.0
100.0
113.0
113.5
105.0
111.3
118.2
114.1
96.2
118.0
109.8
101 .6
125.4
141 .5
118.1
96.3
124.1
115.7
99.6
142.8
159.8
127.1
104.3
125.1
115.0
93.2
146.9
177.5
110.4
98.7
481
482111
48412
491
Transportation and warehousing
Air transportation .. ...... ... . ...... .. ..... .. ..... ...... .... ..
Line-haul railroads .. .. ................... .... ........ ...... .
General freight trucking, long-distance ........ ...... .
U.S. Postal service .... ...... .. .. . ... .... .. .. .. .... .. ......
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
5111
5112
51213
5151
5152
5171
5172
5175
Information
Newspaper, book, and directory publishers .. .. .....
Software publishers .... .... ............ . .... .. ... .. ..... ...
Motion picture and video exhibition .......... ... .. .... .
Radio and television broadcasting .. .... ... ..... .......
Cable and other subscription programming .........
Wired telecommunications carriers ............. ...... .
Wireless telecommunications carriers .... .. ...... ... .
Cable and other program distribution ....... ... .. . . . .
97.4
28.6
109.4
96.1
98.8
64.8
76.3
99.1
96.1
30.6
108.9
97.8
94.3
68.4
73.8
94 .3
95.8
42.7
104.1
102.8
96.0
74.5
85.6
95.9
95.3
51 .7
104.6
101.4
93.6
79.7
94.8
93.5
93.0
64.6
103.4
106.0
92.0
85.1
97 .1
91 .9
93.5
73.0
99.9
106.1
94.4
90.6
98.3
94.2
92 .7
88.0
100.0
104.1
93.7
97.5
103.0
93.5
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
104.5
115.9
99.9
99.1
129.3
105.5
114.2
95.7
108.5
113.0
102.0
99.4
133.2
112.7
134.3
94 .5
110.1
103.9
106.5
98.4
135.7
119.9
139.0
90.4
106.4
101.9
104.7
94.3
125.3
121.0
172.7
87.6
52211
Finance and Insurance
Commercial banking ... ... .. .... .. ....... ....... .
108.1
106.7
104.4
100.4
131.4
130.6
192.0
93.5
4 r:- ")('\
oJ v..J
····· ····
80.5
83 .2
83.3
90.3
92 .9
96.0
99.3
100.0
98.0
101.5
104.2
101.6
103.8
532111
53212
Real estate and rental and leasing
Passenger car rental ..... ........ .. ..... ...... ...... ......
Truck, trailer and RV rental and leasing ...... .........
89.8
70.7
97 .8
71.7
104.4
69.5
106.1
75.8
107.9
82.0
101 .1
90.3
108.9
96.7
100.0
100.0
101.2
93.7
113.1
97.8
112.0
95.9
112.1
93.6
113.3
91.4
541213
54181
Professional, scientific, and technical services
Tax preparation services ........ ....... ....... .. ..........
Advertising agencies ... .. . ... ........... ........ .... . ...... .
92.4
105.0
84 .7
99.7
99.5
111.9
119.1
111.3
119.9
106.8
96.2
101.4
92.1
102.1
100.0
100.0
105.1
95.8
99.2
110.1
91 .8
116.6
78.2
116.7
92 .1
123.9
7224
Accomodation and food services
Traveler accommodations .. .. ...... .... .. ... .. ..... ..... .
Food services and drinking places .......... ... .. .. . ..
Full-service restaurants ...... ... .. ........ .. .... .... ... ...
Limited-service eating places ....... ... .... .......... .. ..
' Special food services .... .... .. ..... .. .... .......... ... . ..
Drinking places, alcoholic beverages ......... .. . .... ..
82 .9
102.9
99.1
103.3
107.2
125.7
85 .4
102.3
98.3
103.3
106.9
121.2
92 .9
101 .7
97.5
102.7
106.4
121 .5
93.0
102.3
97.7
105.6
103.8
112.7
97 .0
100.8
97.8
103.6
101 .1
102.6
99.2
100.6
96.6
104.7
99.3
104.4
100.1
99.2
96.3
102.2
97 .6
102.4
100.0
100.0
100.0
100.0
100.0
100.0
100.0
101 .2
100.0
102.4
102.1
100.0
103.6
101 .1
99.2
102.5
106.0
99.4
107.7
103.5
100.8
105.1
111 .7
100.4
102.0
103.7
100.8
106.6
108.4
98.2
104.1
104.9
102.0
107.1
108.1
107.2
8111
81211
81221
8123
81292
Other services (except public administration)
Automotive repair and maintenance .. ... .. .... ........
Hair, nail and skin care services .. .......... ....... .....
Funeral homes and funeral services ....... ... ...... .. .
Drycleaning and laundry services ... ............. .. ..
Photofinishing .... .... ... ................... .. ... .... ........
92 .8
81 .6
96.1
95.6
117.3
86.5
79.8
94.3
93.2
115.6
90.0
85.6
104.7
94.9
116.2
91 .2
84.3
100.4
93.8
123.6
96.7
88.7
103.6
95.9
124.9
102.9
92.4
100.4
98.8
114.7
98.9
97.1
97.9
101 .6
103.2
100.0
100.0
100.0
100.0
100.0
105.0
102.7
103.8
105.0
99.4
106.9
103.6
100.4
109.5
106.9
108.6
103.0
94.5
113.7
107.6
109.3
109.5
93.9
121 .1
115.0
103.7
104.2
90.9
120.2
133.6
7211
722
7221
7222
7223
NOTE: Dash indicates data are not available.
122
Monthly Labor Review
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Federal Reserve Bank of St. Louis
December 2004
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
52. Unemployment rates, approximating U.S. concepts, in nine countries, quarterly data
seasonally adjusted
Annual average
Country
2002
2003
4.7
9.4
5.3
5.4
5.5
5.4
5.4
5.4
5.2
9.3
8.6
8.7
8.9
9.0
9.2
9.4
8.6
9.3
8.5
8.7
8.9
9.2
9.4
9.4
9.3
9.2
9.1
8.8
9.2
9.1
9.0
9.0
8.8
8.7
8.6
8.6
5.1
5.2
5.8
5.0
5.0
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
5.4
8.7
Germany ... ... ..... ...
1
5.0
9.4
6.1
6.9
6.2
France ....... .... ......
2
5.1
9.4
5.8
6.7
6.2
Japan .... ....... ... .. ..
Sweden • • • • ••• • • ••••••
United Kinadom .....
5.6
6.6
5.6
5.9
6.9
6.2
6.0
6.9
6.1
•••••••••• •• • •• • • ••
5.6
6.7
5.7
5.9
6.8
5.8
5.7
7.0
6.3
5.8
7.0
6.4
1
6.1
7.2
6.1
II
I
IV
Ill
II
I
IV
Ill
5.8
6.9
6.4
United States ........
Canada .............. ..
Australia ........ .... ...
ltaly
2004
2003
2002
II
Quarterly rates are for the first month of the quarter.
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
unemployment under U.S. concepts than the annual figures. See
9.9
6.8
4.8
"Notes on the data" for information on breaks in series. For further
qualifications and historical data, see Comparative Civilian Labor
Force Statistics, Ten Countries, 1959-2003 (Bureau of Labor
Statistics, June 23, 2004) , on the Internet at
http://www.bls.gov/fls/home.htm.
Monthly and quarterly unemployment rates , updated monthly, are
also on this site.
Monthly Labor Review
December 2004
123
Current Labor Statistics: International Comparison
53. Annual data: employment status of the working-age populaHon, approxlmaHng U.S. concepts, 10 countries
[Numbers in thousands]
EmDlovment status and countrv
Civilian labor force
1993
United States .. .... .......... .... .... .... .... ....... ............ .
Canada ...... ............. .. ... ... ........ .. .............. ... .... . .
129,200
14,308
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
131,056
132,304
14,517
133,943
14,669
136,297
14,958
137,673
14,400
139,368
15,536
142,583
15,789
143,734
16,027
144,863
16,475
146,510
16,819
66,010
15,237
Australia ... ... ................. ..... .................... ......... .
8,613
8,770
8,995
9,115
9,204
9,339
9,414
Japan .......... ... ....... ... ...... ... ..... .. .... .. ..... .. ...... ... .
France .. ..... ..... .. ... .. .. ... ............................... ..... .
9,590
9,752
65,470
24,480
65,780
24,670
65,990
67,200
25,130
67,240
67,090
9,907
66,240
Germany ... ..... .. ........ ... .. .... ........ ...... .. ..... ....... . .
39,102
39,074
38,980
39,142
39,415
25,460
39,754
25,790
39,375
66,990
26,070
66,870
24,760
66,450
25,010
Italy .. .. ...... ... ... .. ... ........ .. ...... ................... ..... .. .
39,302
26,350
39,459
39,413
26.730
39,276
22,570
22.450
22,460
22,570
22,680
22,960
23,130
Netherlands ......... ........... .. .......... ...... .. .. ........... .
Sweden ..... ..... ...... ....... . .. .. .. ..... ................ ... ... . .
23,340
23,540
23,750
23,880
7,010
7,150
7,210
7,300
7,540
7,620
7,850
8,150
8,340
8,300
8,330
4,444
4,418
4,460
4,459
4.418
4,402
United Kingdom ....... .... ... .. .................... ....... .... .
4,430
4,489
4,530
4,544
4,567
28,165
28,149
28,157
28,260
28,417
28,479
28,769
28,930
29,053
29,288
29,490
26,590
Participation rate 1
10,092
United States ........................ ... ..................... .. . .
Canada ...... ........ .......... .. .. ............ ........... .. .. .. . .
66.3
66.6
66.6
66.8
67.1
67 .1
67.1
67.1
66.8
66.6
66.2
65.5
64.9
64.7
65.0
Australia ...... .. ..... .. .... ... ........ .... .... .. .. ............... .
66.8
67.3
64.6
64.3
65.8
64.0
66.0
64.5
65.4
64.3
65.9
63.5
65.2
63.9
Japan ..... ... ... ... .. .. .. ..... ...... .. ........... ......... ....... . .
64.4
64.4
64.4
64.6
63.3
63.1
62.9
63.0
63.2
62.8
62.4
France .. .. ........ ... ........ ..... .. ... ..... ........ .. ....... .... .
Germany ..... .. ...... ... ....... ............................. .... .
62.0
61 .6
60.8
60.3
55.4
57.8
55.5
57.4
55.4
55.6
55.5
55.9
56.3
56.6
56.8
57.0
57.0
Italy ............ ...... ......... ... ...... .. ....... ................ .. .
Netherlands ............ .. ....... .... .. ... .. ... ..... .. ..... ... .. . .
Sweden ........ .. .. ... ........ ...... ... ...... .. ... ............... .
United Kingdom ... ... ... .... .. .. .. .......... ... ................ .
56.6
56.6
56.3
56.1
47.3
47.8
62.6
48.1
48.6
65.0
48.8
64.5
48.3
65.8
63.3
57 .7
47.6
61 .1
62 .8
56.8
47.9
57.9
62.8
63.8
63.7
64.0
64.6
64.0
64.5
58.6
63.7
62.7
United States ..... .... .......... .... ....... ..... ...... .. ..... ... .
Canada .................................. .. ............ ... ... ..... .
Australia ...... ................... ...... .. ..... .. .. ... ... ......... .
57.1
57.1
57.3
47.1
58.8
47.1
47.2
60.8
64.1
59.2
64.0
62.6
62.4
62.4
62.6
62.5
62.9
62.9
62.7
62.9
62.9
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
9,271
9,481
63,920
63,790
63,470
62,650
62,510
24,250
Employed
Japan ... .... ..... ... ... ... .. ... ..... .. .. . .... .... ................. .
63,810
63,860
63,890
64,200
64,900
64,450
France ............. ........... .. ..... ... ....... ... .... .. ..... ..... .
21 ,710
21,750
21,960
22,040
22,170
22,600
23,050
Germany .... .... ....... .. .. .... .. .. .... ... ..... .. .. ............. .
23,690
24,140
24,280
35,989
35,756
35,780
35,637
35,508
36,061
36,042
Italy ............... .. .. ... .. .... .... .. .... ................ ......... . .
36,236
36,350
36,018
35,615
20,270
19,940
19,820
19,920
20,460
20,840
21 ,270
21 ,580
21,790
6,570
6,660
6,730
6,860
19,990
7,160
20,210
Netherlands .. .. ... .......... ... .. .. ........ ..... .......... .... .. .
7,320
7,600
Sweden ..... .. ..... ..... .................................. ...... . .
7,910
8,130
8,070
8,010
4,028
3,992
4,056
4,019
3,973
4,034
4,117
United Kingdom .............. ... ............................... .
4,229
4,303
4,310
4,303
25,242
25,429
25,718
25,964
26,433
26,696
27,048
27,350
27,570
27,768
28,011
61 .7
58.5
62.5
59.0
62.9
63.2
63.8
64.4
63.7
62.7
62.3
59.1
59.7
59.0
64 .1
60.4
64.3
59.4
61 .3
62.1
61 .9
60.1
62.4
63.0
60.7
Employment-population ratio 2
United Stai es ... .. ...... ... ... ... .. .. .... .. ........ ... .......... .
Canada .............. .... ... ..... ... ........ ...... ... ... .......... .
Australia .. ......... .. .... ........................... ... ... ....... .
56.8
57.8
59.2
59.3
Japan ... .... ..... ... .......... ................. .. .. ... ... .. .. .... . .
France .. .. ....... ... ... ......... ....... .. ........ .... ... ........ .. .
61 .7
49.1
60.9
49.1
60.9
49.0
Germany ... .. ... ... ... .......................... ................ .
53.2
61 .3
49.0
52.6
52.0
Italy ... ... ..... .. .. ... .. ................ . ........ .... ... ... .... ..... .
Netherlands ........... ..... ..... .. .. ... ......... ...... .... ... ... .
43.0
42.0
52.4
41 .5
61 .0
49.0
51.6
41.6
41 .6
54.2
54.6
54.9
55.7
57.8
59.3
59.6
60.3
60 .2
49.7
59.4
59.0
51.4
52.2
60.3
52.3
41 .9
50.3
52.0
42.3
42.9
58.4
52 .0
52.2
43.6
58.7
60.6
62.6
64.2
63.2
62.1
57.5
52.0
51.5
44.1
57.1
51 .7
50.9
44.6
Sweden ...... ...... ..... ..... .. .... .... .... .. .. ... ............... .
58.5
57.6
58.3
57.7
56.9
57.6
58.4
United Kingdom .. ..... ......... ... . ... ... ...... ....... ... .
60.1
60.5
60.7
60.3
56.2
56.5
57.0
57.4
58.2
58.6
59.1
59.4
59.5
59.6
59.8
United States .... ................... .... ... .... .. ...... .. ....... .
Canada ..... .. ....... .... ........... ..... .. ........... ... ..... ... .
8,940
1,539
7,996
1,373
7,404
1,246
5,692
6,801
8,378
8,774
962
1,031
1,150
829
1,920
602
3,200
661
3,400
636
3,590
2,920
2,970
721
2.790
2,870
652
3,170
2,770
3,113
739
2,100
2,800
6,739
1,252
759
2,300
2,960
5,880
1,080
914
1,660
7,236
1,289
751
2,250
6,210
1,169
Australia ...... ... .... ... ..... .. ...... ....... .. ............. ...... .
Japan ... .. ..... ... ............. .. ......... ........ .. ..... ... ...... .
France .. ... .. .. .................... ... ....... .. .. ... .. ... .... ... .. .
Germany ......... .. ... ... ... ....... ... ........... .... ..... ... ... .
2,740
2,380
2,310
3,318
3,200
3,505
3,907
3,693
3,333
Italy ............. .. .. ................... ... .... .... .. .... ... ........ .
3,065
2,210
3,110
1,159
611
3,500
2,480
3,396
3,661
2,300
2,510
2,640
2,690
2,750
2,670
Netherlands .. .. .. ... .... ... ... ... ...... ...................... ... .
2,500
2,270
2,160
2,100
440
490
480
2,650
440
300
Sweden ............................. .. ..... .. .... ... ............. .
210
230
320
404
440
368
United Kingdom ........ ... ..... ...... .......... .... .. ... .... ... .
260
227
2,916
426
2,716
250
313
240
416
370
445
2,439
2,297
1,985
1.783
1,721
1,580
1,483
234
1,520
264
1,479
United States .. ... ....... .. .... .......... .. .... ... .. .... ... ..... .
Canada ..... .... .. ... ... ... .. ..... ... ............. ... .... .. ...... .
Australia ... .... .................... ... ...... ............ ..... ... . .
6.9
10.8
6.1
9.5
5.4
8.8
4.9
4.5
4.2
5.8
7.0
6.0
6.9
8.2
7.7
7.7
4.0
6.1
9.4
8.4
8.3
7.0
10.6
5.6
8.6
8.2
Japan ... .. ....... ... ............ .. ... .... ............ ............. .
6.9
2.5
2.9
3.2
3.4
3.4
4.1
4.7
France .. ... ....... ...... ... .. ... ........... ... .............. ...... .
11.3
11 .8
11.3
11 .3
8.0
10.2
6.3
8.5
11 .2
6.9
8.2
11.9
9.0
11 .8
Germany ................. ............. .. ... .. ......... .. ........ .
Italy .. ... ...... ....... .... ....... .... ...... ... .......... ... ....... .. .
Netherlands ............ ... .. ........ .. ... ... ....... ... .... ...... .
9.9
9.3
11.7
6.0
11 .9
4.9
12.0
3.9
Unemployed
Unemployment rate
11.8
6.7
4.7
6.3
6.4
6.8
6.4
6.1
4.8
5.1
5.4
5.3
10.6
9.1
8.5
8.4
7.9
11.5
7.8
10.7
3.2
2.9
9.6
2.5
8.7
9.3
8.6
9.1
9.3
2.8
8.8
3.8
Sweden ..... ..... .... .. ... .... .. .............. .. ... ... .. .... ... . .
9.4
9.6
9.1
9.9
10.1
8.4
Unitc<:i Kingdom ......... ..... .................... ............. .
7.1
5.8
5.0
5.1
5.8
10.4
9.6
8.7
8.1
7.0
6.3
6.0
5.5
5.1
5.2
5.0
' Labor force as a percent of the working-age population .
2
Employment as a percent of the working-age population.
NOTE: See "Notes on the data" for information on breaks in series.
124
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
For further qualifications and historical data, see Comparative Civilian Labor Force Statistics,
Ten Countries, 1959-2003 (Bureau of Labor Statistics, June 23, 2004), on the Internet at:
http://www.bls.gov/fls/home.htm.
Table 54. Annual indexes of manufacturing productivity and related measures, 12 countries
mm
Item and country
fHlQ? =
1960
1970
1980
Output per hour
70.5
0.0
United States ......... ... .. .. .. ...
72 .9
37.8
54.9
Canada ... .... .. ..... ... . .. ....... ..
37.7
63.6
13.9
Japan .... ....... ... .. ....... .. .. ... .
32.9
65.4
18.0
Belgium ...... .. .. ... ... ... ..........
83.2
46.3
25.2
Denmark ......... .. . ... ... .........
61.6
19.9
39.0
France ...... ....... .. .. ............ .
77.2
...
29.2
52.0
Germany .... ........... ... ......
46.2
78.6
24.8
Italy .............. ..... ... . ... ... .... .
69.1
18.8
38.5
Netherlands .... .... ... .... .. . ... ..
77.9
37.6
59.1
Norway ... .. . ... .. . .. ......... ......
52.2
73.1
Sweden ...... .... ... .. .............. 27.3
43.2
54 .3
30.0
United Kinadom .. ......... ..... ..
Output
75.8
United States ............ ........ .
83.6
58.9
Canada ............... .. .... ..... ... 33.4
60.8
10.8
39.4
Japan .... ...... ...... ............. ..
78.2
57.6
Belgium ... .. .... ... .. ......... ... . .. 30.7
72.7
94 .3
42.0
Denmark .. ... ..... .... .. .. .. .......
57.7
81.6
27.9
France ........ . ......... ....... .... .
85.3
41 .5
70.9
Germany ... ... .............. .... ...
84.4
48.1
.
....
...
..
23.0
.....
...
Italy ... .. .... ... ....
76.9
59.8
Netherlands ....... .. ...... .. ....... 31 .9
91 .0
104.9
57.7
Norway .... ... ........ ......... .. ...
90.7
80.7
Sweden .. ....... .... ... ..... ....... . 45.9
87.2
90.2
67.5
United Kingdom ........ ..... .....
Total hours
107.5
92.1
104.4
United States .. .. ..... ........... .
114.6
107.1
Canada ...... .. ... ... ... ... ... .. .... 88.3
104.3
95.5
77.8
Japan ........ ...... .... ... .. ..... ...
119.7
174.7
Belgium .. . .. .... ........ .... ... ... .. 170.7
157.1
113.4
Denmark .. .... ... ... ...... ......... 166.7
132.5
147.8
France .... ... .. .... .. .... . . . .. .. . .. 140.3
136.3
110.5
Germany ... ..... ........... .. .. .... 142.3
107.4
104.0
93.5
Italy ........ ... ... .. .......... ........
111 .2
155.5
Netherlands ..... ... ...... .. .... ... 169.8
134.7
153.9
Norway ....... ....... .... .. ... . ... .. 153.6
124.0
154./
Sweden .... ... ....... ... ......... .. . 168.3
160.5
208.8
United Kingdom .. ....... ... .. .. .. 224.6
Hourly compensation
oasIs1
\nauonaI currency
23.f
ob.ti
14.\l
United States ..... .. ........ ... .. .
17.1
47 .5
10.0
Canada ..... ..... ... .. .. ....... .. ...
16.4
58.6
4.3
Japan ......... ...... ..... ....... .. . .
b2 .b
13./
b.4
Belaium .......... ...... ... .... .. .. ..
4!:J.1
.1
11
3.\l
Denmark .... ..... ..... ..... ... ... ..
41.2
4.3
10.5
France .. .. ... .. ...... .. ...... ..... ..
20.7
53.6
8.1
Germany .. ....... .. ... ...... ... ... .
5.3
30.4
1.8
Italy .. .. ....... . ..... ..... ... .... .. ...
tiU.b
1\l.4
ti.2
Netherlands .. ..... ... .. ... .... ....
3\l.U
11 .!S
4./
Norway ... ....... .. ..... .... .. .. ... .
37.3
10.7
4.1
Sweden .... ... ... .. ..... ... . ...... ..
32 .0
6.1
2.9
United Kinadom ... .. ... ..........
Unit labor costs
(national currencv basis)
78.8
United States ..... ...... .. ... .... ..
65.2
31 .1
26.4
Canada .... .... . .. . .... .... .. .... ...
92.1
.1
31
43.6
Japan .. ..... .. .... ... .. .. .. ..... ....
80.3
41 .7
Belgium .. . ... ... ...... ...... ..... . .. 30.1
54.2
23.9
15.3
Denmark ........... .... .. .. .. .... ..
67.0
26.8
21 .7
France .... .. ............ ...... ......
69.4
39.8
27.8
Germany .... .... ... ......... .... ...
38.7
11 .4
7.2
Italy .. .. ....... ... ...... . ..... . .......
87.6
50.4
32.9
Netherlands ......... .. . .... ..... ..
50.0
20.0
12.6
Norway ............. ...... ..... ... ..
51 .0
20.6
Sweden .. . ... ... ...... .... ... ... .... 15.0
14.1
59.0
9.8
United Kingdom .. ..... .. .... .. .. .
Unit labor costs
(U.S. dollar basis)
78.8
United States ... ................ ..
ti/.4
3ti.U
32.\l
Canada .. ........ .. ....... ..........
.b
bl
lb.4
.U
11
Japan ...... ... ... .... ... ....... .....
88.3
27.0
Belgium ...... ... ... .. .. ... ........ .. 19.4
58.1
19.3
13.4
Denmark ...... ......... ........ ....
2b . l
!S3.\l
23.4
France .. ... . ... ... .. ... .. ... .. ... .. .
59.6
17.1
10.4
Germany ... ... ... .. ..... .......... .
ob.I
22.3
14.3
Italy ............ ... .... ... ...... ..... .
77 .5
24 .5
15.3
Netherlands .... .... ... ..... .. .... .
62.9
11 .0
17.4
Norway .......... .. .. .... .... ..... ..
/U.2
23.1
lti.\l
Sweden .... .. ..... .... .... ...... ....
77.6
19.1
15.6
United Kinadom ... .. .... .... .... .
NOTE: Data for Germany for years before 1991 are for the
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
-
-
-
-
-
-
-
1990
1991
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
96.9
93.4
94.4
96.8
98.4
93.9
99.0
96.6
98.7
98.1
94.6
89.2
97.9
95.3
99.0
99.1
100.3
97.0
98.3
96.1
99.0
98.2
95.5
93 .9
102.1
105.8
101 .7
102.5
100.2
101 .0
101 .8
101 .2
102.0
99.6
107.3
103.8
107.3
110.8
103.3
108.4
112.6
108.9
109.6
104.8
113.1
99.6
117.8
108.0
113.8
112.4
111 .0
113.2
112.5
114.4
112.3
107.9
117.3
100.7
124.5
106.2
117.0
109.7
116.1
116.3
109.8
114.7
114.7
108.3
119.3
102.5
129.5
105.4
121 .3
113.5
121.0
125.5
118.0
121 .7
120.4
110.3
121.4
102.0
141 .0
106.9
126.5
115.5
121 .2
126.9
117.4
127.9
122.0
110.8
124.1
99.9
149.5
108.4
132.8
122.1
126.7
125.5
123.1
133.0
121 .4
110.6
127.0
103.6
162.7
113.6
143.5
129.3
135.9
130.8
126.6
142.5
127.0
113.5
132.7
106.6
175.5
121 .0
145.2
127.0
135.9
132.6
127.2
148.0
127.8
114.0
132.5
109.8
170.3
125.1
160.0
130.5
139.9
141.7
131 .3
155.1
131 .0
112.1
135.4
111.7
185.6
127.7
171 .0
132.1
146.2
146.2
136.9
158.0
134.4
110.9
101 .6
106.0
97.1
101 .0
101 .7
99.1
99.1
99.4
99.0
101.4
110.1
105.3
98.3
99.0
102.0
100.7
100.7
99.8
102.3
99.3
99.8
99.0
104.1
100.1
103.5
105.9
96.3
97.0
97.0
95.7
92.4
96.5
97.7
101 .7
101 .9
101 .5
111.1
114.1
94.9
101.4
107.3
100.3
95.1
102.4
104.5
104.6
117.0
106.2
118.4
119.6
98.9
104.2
112.6
104.9
95.2
107.2
108.2
107.3
131.9
107.8
121.3
119.6
103.0
105.9
107.7
104.6
92.5
105.4
108.9
110.3
136.4
108.6
127.9
127.7
106.5
112.7
115.9
109.7
95.7
108.8
111.6
114.2
146.5
110.7
133.1
133.9
100.2
114.4
116.7
115.0
97.7
110.7
114.9
113.7
158.3
111 .3
138.9
139.6
153.6
105.5
120.4
121 .6
128.0
99.9
113.0
121.9
112.3
183.1
113.4
142.9
101.9
114.4
117.9
118.7
95.8
110.3
117.6
113.6
172.5
112.1
147.6
159.2
109.2
119.9
121 .9
124.3
100.1
113.6
122.8
112.8
188.3
115.0
145.4
157.3
106.7
120.9
121.4
128.5
104.8
113.5
102.9
104.3
103.3
105.6
100.1
102.9
100.3
103.4
116.4
118.1
100.4
103.9
103.1
101 .5
100.5
102.9
104.1
103.3
100.8
100.8
109.0
106.6
101.4
100.1
94.7
94.7
96.7
94.7
90.8
95.4
95.8
102.1
94.9
97.7
103.6
103.0
91.9
93.6
95.2
92.1
86.8
97.7
92.4
105.0
99.4
98.4
104.0
106.4
89.1
92 .0
100.1
91.7
84 .8
99.4
92 .3
106.6
105.9
101 .5
103.6
109.0
88.7
91 .0
98.1
91.2
80.6
97.3
91 .2
107.6
105.3
103.1
105.4
112.4
88.0
89.8
98.2
90.2
79.5
98.6
91 .9
112.0
103.9
103.5
105.2
115.9
82.7
90.2
99.4
89.9
80.1
99.9
92.6
113.7
105.9
102.7
104.6
118.7
80.4
91 .2
95.8
89.2
78.9
99.8
92.6
109.6
106.0
98.7
102.9
123.1
80.3
91.7
96 .3
87.2
78 .8
100.1
92 .5
105.9
107.3
95 .0
\lU.!S
88.3
90.6
\lU.1
90.9
89.4
87.6
!S\l.!S
\lb.ti
95.0
96.5
\j/ .3
\l/ .\j
96.4
91 .5
94.2
\!4.!S
\!2.3
\!l .b
87.8
82.9
95.5
93.8
102./
102.0
102.7
104.!S
102.4
103.1
106.4
105.7
104.o
101.b
97.4
104.5
10!:J.ti
103.7
104.7
1Uti.1
lUti.U
106.5
111.8
106.8
10\l.U
104.4
99.8
107.3
10/.\j
106.0
108.3
10\l.2
1U!S.1
110.4
11 7.6
111 .3
112.1
10\l.2
106.8
108.8
10\l.4
107.0
109.1
111.1
112.!S
112.2
123.3
119.0
114.4
113.ti
115.2
111 .4
111 .b
109.3
112.6
11!:J.2
11ti.ti
111 .8
125.7
123.0
11 / .2
1 l!S./
121 .0
115.7
11 / .4
111 .7
115.4
11 / .U
11\l.ti
112.7
127.6
122.2
122.U
12!:J./
125.6
123.0
122.U
115.8
114.8
11!S.b
12/.3
116.6
130.6
124.2
12ti.U
133.U
130.3
129.9
93.7
94.6
95.9
93.0
95.0
96.8
90.3
90.7
91.1
94.2
92.9
93.0
97.6
99.6
97.5
98.1
97.6
99.3
93.1
98.0
95.7
99.2
100.0
100.0
100.6
96.4
101 .0
102.3
102.2
102.0
104.5
104.5
102.4
101 .9
90.8
100.7
98.5
93.6
101 .4
97.9
94.2
97.8
102.0
101.9
96.4
104.8
84.7
99.4
94.8
94 .3
97.5
96.4
96.1
96.5
104.7
103.2
95.6
108.4
85.8
102.5
93.5
97.5
94 .0
95.5
102.8
97.8
107.5
109.8
95.9
110.8
89.0
105.7
91 .9
96.2
93.0
91 .8
98.8
91.9
104.5
111.4
96.5
116.4
85.8
108.2
92.8
96.7
95.2
92.2
101 .9
88.1
104.6
110.3
98.3
125.7
84 .0
113.5
93.7
\l!S.U
!S3.\l
89.5
92.7
\!4.1
87.3
\!3.3
87.9
93.6
\ll .3
93.9
97.6
10!:J.1
\!1.!S
92 .3
92.0
\!3.1
87.5
\j/ .3
90.0
95.0
\lti.3
100.0
100.6
\lU.3
11!:J.3
95.1
95.1
\lo.3
98.7
!Sl.!S
96.9
89.2
ti/ .!S
85.6
98.5
!S2.!S
12!:J.!S
94.2
89.4
\!3.4
98.2
94.8
!S3.U
131 .ti
105.2
103.6
102.b
114.2
/!S.U
104.8
106.4
/U.U
91.6
93.5
!Sti.4
10\l.o
99.1
107.0
101 .2
111 .6
91 .9
!S4.U
\j/.4
82.4
90.2
!S3.3
94.0
!SU.ti
8 7.0
102.1
tib.4
100.4
81 .6
91 .7
/\l.1
92.9
/!S.2
87.2
103.5
til .b
106.5
\!3.b
II.\!
!SI.I
100.0
106.6
//.3
93.4
144.9
158.0
103.4
121 .6
120.8
129.1
113.5
196.5
134.8
99.6
99.8
111.7
121 .0
111 .5
190.6
109.9
110.2
117.6
107.3
96.2
120.9
77.7
90.8
95.6
86.5
78.2
99.1
92.0
102.3
107.5
90.7
89.3
121.1
74.0
85.8
92.0
83.2
76.1
99.7
89.4
99.8
102.7
86.0
85.0
119.1
73.0
82.7
88.7
81.3
74.3
99.3
133.2
119.6
113.7
120.ti
130.2
122.8
137.4
127.8
132.U
140.b
136.8
137.6
13ti.3
123.7
114.6
12/.2
13ti.b
128.3
142.0
132.5
13!S.2
14!S.\l
143.8
144.3
14!:J.4
126.8
122.8
13ti.b
143.2
135.2
145.5
135.7
14/.3
lb/.\!
148.8
152.2
1!:J/.!S
131 .4
123.8
91 .9
94.9
90.6
94.4
103.4
87.6
107.6
112.3
99.1
128.4
80.1
114.3
92 .8
92 .5
83.6
92 .2
102.8
86.2
108.1
112.6
99.5
131 .9
77.9
113.7
93.9
97.4
84.4
95.9
107.3
86.6
111 .2
116.2
104.3
135.6
84.4
115.4
90.9
97.2
87.8
96.4
109.0
87.2
111 .1
121 .1
108.8
141 .3
80.2
119.2
92.3
99.4
84.7
92.8
/ts.ts
91 .9
92 .8
11 .2
lb .2
\!2 .2
101 .U
80.2
89.3
\l!S.4
6i' .8
76 .7
ti4.2
79.7
titi.2
73.3
93.0
4\l.b
97.6
93.9
/ti.U
!S!S.U
68.4
77.8
ti2.ti
79.5
titi.2
74.5
93.7
4/.ti
94.0
90.9
/4.!S
!S!S.\l
72.6
83.5
titi.b
83.9
lb.3
91.5
/ti.2
84.3
102.2
!>ti.4
104.7
93.2
92 .3
ti4.U
86.2
former West Germany. Data for 1991 onward are for unified Germany. Dash 1nd1cates data not available.
Monthly Labor Review
/2.\!
82 .1
110.0
4!S.1
101.4
December 2004
194.4
110.3
94.5
98.9
81 .9
-
l!>U.U
139.1
148.9
140.0
-
1ti4.ti
154.3
160.3
109.6
88.0
110.8
126.2
112.6
144.9
78.6
118.9
92.3
!So.ts
\!2.ti
-
100.6
!SU.4
100.1
\lU.\l
101.7
127.2
!:>ti.ti
110.0
125
Current Labor Statistics:
Injury and Illness
55. Occupational Injury and illness rates by Industry, 1 United States
Incidence rates per 100 full-time workers 3
Industry and type of case2
1989
1
1990
1991
1992
1993
4
1994
4
1995
4
1996
4
1997
4
1998
4
1999
4
2000
4
2001
4
PRIVATE SECTORS
Total cases ..... ............................ ..... ... .... ..... ..... .. .. .. ..... .. .... .
Lost workday cases .................... ......... ..... ............................ ........ .
Lost workdays .............. ... ...... .... ... ....... ............ ............................ .
8.6
4.0
78.7
8.8
4.1
84.0
8.4
3.9
86.5
8.9
3.9
93.8
8.5
3.8
8.4
3.8
8.1
3.6
7.4
3.4
7.1
3.3
6.7
3.1
6.3
3.0
6.1
3.0
5.7
2.8
Agriculture, forestry, and fishings
Total cases .. ............................ .... ..... .... .... ...... .... ....... ......... .
Lost workday cases .......... ....... ......... .. ..... ........... .. .......... ..... ....... ...
Lost workdays ..... ........ .. ......... ... .... ...... ..... ............ ... ....... .. ....... ... ..
10.9
5.7
100.9
11 .6
5.9
112.2
10.8
5.4
108.3
11.6
5.4
126.9
11 .2
5.0
10.0
4.7
9.7
4.3
8.7
3.9
8.4
4.1
7.9
3.9
7.3
3.4
7.1
3.6
7.3
3.6
Mining
Total cases ......................... ....... .. .... .
Lost workday cases ..... .. ...................... ... .............. ................... ... .. .
Los, 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 ....... ... .... ..... ... ...................... ..... ... ..
General building contractors:
Total cases .......................... .... ... ..... ... ......... ... .. .
Lost workday cases ........... ........ ............. .............. ........ ......... ....... .
Lost workdays ....... .... .. ..... ............ ............. .... ..... ........................ .
14.3
6.8
143.3
14.2
6.7
147.9
13.0
6.1
148.1
13.1
5.8
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
13.9
6.5
137.3
13.4
6.4
137.6
12.0
5.5
132.0
12.2
5.4
142.7
11 .5
5.1
10.9
5.1
9.8
4.4
9.0
4.0
8.5
3.7
8.4
3.9
8.0
3.7
7.8
3.9
6.9
3.5
Heavv construction. exceot buildina:
T atal 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
Manufacturing
Total cases ........ ........ .............. .. ... .... .. .. .. .. ...... ... .... ... .... ... ... .
Lost workday cases ...... .............. ............................................. .... ..
Lost workdays ............................. ........ ... ......... .................... ...... ..
13.1
5.8
113.0
13.2
5.8
120.7
12.7
5.6
121 .5
12.5
5.4
124.6
12.1
5.3
12.2
5.5
11 .6
5.3
10.6
4.9
10.3
4.8
9.7
4.7
9.2
4 .6
9.0
4.5
8.1
4.1
Durable goods:
Total cases ................ .......... ....................... ... ... .. ... ... .... . .
Lost workday cases ................. .
Lost workdays .... .... .. ... ........ .... ... ........... .................... ................. .
14.1
6.0
116.5
14.2
6.0
123.3
13.6
5.7
122.9
13.4
5.5
126.7
13.1
5.4
13.5
5.7
12.8
5.6
11 .6
5.1
11.3
5.1
10.7
5.0
10.1
4.8
Lumber and wood products:
Total cases ............................... ... ..... .. ....... .. ...... ........... ... . .
Lost workday cases .. ........... .......................... ............. .... ........... .
Lost workdays ........ ....... ........ .. ......................................... .... .... .
18.4
9.4
177.5
18.1
8.8
172.5
16.8
8.3
172.0
16.3
7.6
165.8
15.9
7.6
15.7
7.7
14.9
7.0
14.2
6.8
13.5
6.5
13.2
6.8
13.0
6.7
12.1
6.1
10.6
5.5
16.1
7.2
16.9
7.8
15.9
7.2
14.8
6.6
128.4
14.6
6.5
15.0
7.0
13.9
6.4
12.2
5.4
12.0
5.8
11.4
5.7
11.5
5.9
11.2
5.9
11.0
5.7
Furniture and fixtures :
Total cases .................................... .. .... .. .. .. .. .... .. .... .. ...... ... .
Lost workday cases .... ... ...... .................................... ...... ............ .
Lost workdays ....... ........................... .... ...... .. .... ...................... ...
8.8
4.3
Stone. clav. and alass oroducts:
:::ases ... ........ ............................. .... ..... ................. ..... .
Lost workday cases ... ............. .. ..... ................ ................. - .... .... ..
Lost workdays .. ........... .. .. ................... ............... ....... .. .............. .
Primarv metal industries:
Total cases ............ ............. ........... .... ... .. ... .. .... ............... .. .
Lost workday cases .... ...... .... ... ...... .... ............ ............. .... .......... ..
Lost workdays ....... ...... ........ ....................... ......... ............. .... ... .
15.5
7.4
149.8
15.4
7.3
160.5
14.8
6.8
156.0
13.6
6.1
152.2
13.8
6.3
13.2
6.5
12.3
5.7
12.4
6.0
11 .8
5.7
11 .8
6.0
10.7
5.4
10.4
5.5
10.1
5.1
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
5.3
Industrial machinery and equipment:
Total cases ................................ .... .. .. ... ........... ........ ... .. .... .
Lost workday cases ........... ......... .......... ......... .......... ....... ...... ..... .
Lost workdays .............. ....... ................ ............. ....................... .
12.1
4.8
86.8
12.0
4.7
88.9
11.2
4.4
86 .6
11.1
4.2
87.7
11.1
4.2
11.6
4.4
11.2
4.4
9.9
4.0
10.0
4.1
9.5
4.0
8.5
3.7
8.2
3.6
11 .0
6.0
Electronic and other electrical eauioment:
Total cases .... ..... ........ ............. ....... ... ...... ........... .... ..... .
Lost workday cases ......... .... ... ....... ........ .............. ... ................... .
Lost workdays .. ............ ..... ....... ........................ ....... ........ .... .... .
9.1
3.9
77.5
9.1
3.8
79.4
8.6
3.7
83.0
8.4
3.6
81.2
8.3
3.5
8.3
3.6
7.6
3.3
6.8
3.1
6.6
3.1
5.9
2.8
5.7
2.8
5.7
2.9
5.0
2.5
Transoortation eauioment:
Total cases ................ ......... ... ... ...... .. .. .... ... .. .............. ..... .. .
Lost workday cases ...... ............ ............ ...... ... .... ........................ .
Lost workdays .. ........ ... .................... ....... ...... ...... .... ...... ........... .
17.7
6.8
138.6
17.8
6.9
153.7
18.3
7.0
166.1
18.7
7.1
186.6
18.5
7.1
19.6
7.8
18.6
7.9
16.3
7.0
15.4
6.6
14.6
6.6
13.7
6.4
13.7
6.3
12.6
6.0
Instruments and related 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
65.3
5.6
2.5
5.9
2.7
5.3
2.4
5.1
2.3
4.8
2.3
4.0
1.9
4.0
1.8
4.5
2.2
4.0
2.0
Miscellaneous manufacturina industries:
Total cases ... .............. ..... .. ............. .. ...... ......... ... .... ... .... ... .
Lost workday cases ............... .. ............ ......... ........... ...... ........... .
Lost workdays ..... ...................
. ...................... .......... .... .....
11 .1
5.1
97.6
11 .3
5.1
113.1
11 .3
5.1
104.0
10.7
5.0
108.2
10.0
4.6
9.9
4.5
9.1
4.3
9.5
4.4
8.9
4.2
8.1
3.9
8.4
4.0
7.2
3.6
6.4
3.2
T"'"'
~----'---~--~-----'------'---~---'-----'-------'--------'--------'-----'----
See footnotes at end of table.
126 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
1
55. Continue~ccupational injury and illness rates by industry, United States
3
Incidence rates per 100 workers
2
Industry and type of case
1989
Nondurable goods:
Total cases ....... .. ....... : ......... .... . .
Lost workday cases .......... ....... .. ...... ...... ... ...... .... ..... .. .... .
Lost workdays .............. .. ................................ .............. .. ...... .... .
Food and kindred products:
Total cases
Lost workday cases
Lost workdays ......................... .
1
11 .6
5.5
107.8
18.5
9.3
174.7
Tobacco oroducts:
Total cases ......... .. ................. . .
Lost workday cases ........ .. .... ...... .... ..
.. ..... .... ... ..... .. .
Lost workdays..............
64.2
Textile mill oroducts:
Total cases ...... ............ . .
Lost workday cases ........ .
Lost workdays ......
10.3
4.2
81.4
8.7
3.4
Aooarel and other textile oroducts:
Total cases .......... .. ...... .. ...... ..
Lost workday cases ..................... ..
Lost workdays .......... .... .. .... ..
1992
1993 4
1994 4
1995 4
1996 4 1997 4
2001.
2000.
1999.
1998.
11.7
5.6
116.9
11 .5
5.5
119.7
11 .3
5.3
121 .8
10.7
5.0
10.5
5.1
9.9
4.9
9.2
4.6
8.8
4.4
8.2
7.8
4.2
7.8
4.2
6.8
4.3
20.0
19.5
9.9
207.2
18.8
9.5
211 .9
17.6
8.9
17.1
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
9.2
6.4
6.0
5.3
2.4
5.9
2.7
6.4
3.4
6.2
2.6
6.7
2.8
5.5
2.4
42 .9
5.8
2.3
5.6
2.8
2.2
3.1
6.7
4.2
9.7
4.1
8.7
4.0
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
9.0
3.8
8.9
8.2
3.9
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
9.9
202.6
7.7
3.2
62 .3
52.0
10.1
9.9
85.1
4.4
88.3
4.2
87.1
8.8
3.9
92.1
9.2
4.2
99.9
9.5
3.8
80.5
12.7
12.1
5.8
5.5
11.2
5.0
122.7
125.9
8.6
Paoer and allied oroducts:
Total cases ............................. ..
Lost workday cases .......................... ... ... ... ..... .
Lost workdays ... .. .. .. .
1991
1990
9.6
4.0
4.0
104.6
8.2
3.8
6.3
11 .0
9.9
9.6
4.6
4.5
8.5
4.2
7.9
3.8
7.3
3.7
7.1
3.7
7.0
3.7
6.5
5.0
3.4
6.0
3.2
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
4.8
2.4
4.8
2.3
4.2
2.1
4.4
2.3
4.2
4.0
2.2
2.1
132.9
124.8
Printina and oubl ishina:
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
Chemicals and allied oroducts:
Total cases ... ..
Lost workday cases... .. .. ..... ..
Lost workdays .. ......... .
7.0
3.2
63.4
6.5
3.1
61 .6
6.4
3.1
62.4
6.0
2.8
64.2
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
Rubber and miscellaneous olastics oroducts:
Total cases ............. ............. ..
Lost workday cases
Lost workdays ...... .. ....... ..
16.2
8.0
147.2
16.2
7.8
151 .3
15.1
7.2
150.9
14.5
153.3
Leather and leather oroducts:
Total cases ................. .
Lost workday cases .. ........... ..
Lost workdays ............ .. ................ ..
13.6
6.5
130.4
12.1
5.9
152.3
12.5
5.9
140.8
9.2
5.3
121 .5
9.6
5.5
134.1
9.3
5.4
140.0
8.0
5.9
5.7
5.5
2.7
2.8
2 .7
5.9
5.2
4.8
2.4
4.3
3.9
4.1
3.7
2.9
2.5
4.7
2.3
4.6
2.8
71 .2
2.5
2.2
1.8
1.8
1.9
1.4
13.9
6.5
14.0
6.7
12.9
6.5
12.3
6.3
11 .9
11 .2
10.7
5.8
5.8
8.7
4.8
12.1
5.4
128.5
12.1
12.0
5.3
11 .4
4.8
10.7
4.5
10.6
4.3
9.8
4.5
10.3
9.0
5.5
5.0
4 .3
9.1
9.5
9.3
9.1
8.7
5.4
5.5
5.2
5.1
8.2
4 .8
7.3
4.3
7.3
4.4
6.9
4.3
6.9
5.1
144.0
6.5
6.1
2.7
5.9
6.6
2.8
2.7
2.5
6.5
3.3
6.3
3.3
5.8
5.3
3.1
2.8
6.8
1
10.1 1
5.5
5.8
Transportation and public utilities
Total cases ........................... .
Lost workday cases ...... ........ ..... ......... .
Lost workdays ........................ .. ... ........... ..
Wholesale and retail trade
8.7
4.4
4.3
Total cases ..... ..
Lost workday cases .... .. ..
Lost workdays .. ......... ...... .................... .......... ..
7.9
3.5
65.6
7.6
3.4
72.0
8.4
3.5
80.1
8.1
3.6
63.5
3.4
7.9
3.4
7.5
3.2
6.8
2.9
6.7
3.0
Wholesale trade :
Total cases ......... ............... .. .. .......... .. .... . ..
Lost workday cases ............ ............ ....... ....... ... .
.. ........ .... .. ....... .
Lost workdays
7.7
4.0
71 .9
7.4
3.7
71 .5
7.2
3.7
79.2
7.6
3.6
82.4
7.8
3.7
7.7
3.8
7.5
3.6
6.6
6.5
3.4
3.2
8.1
7.7
7.9
3.3
2.8
6.8
2.9
6.5
3.3
7.5
3.0
6.9
3.3
2.7
6.1
2.5
5.9
2.5
5.7
2.4
60.0
69.1
8.7
3.4
79.2
8.2
3.4
63.2
2.0
2.4
2.4
2.9
1.1
1.2
2.7
1.1
2.6
1.0
2.4
.9
2.2
.9
.7
.5
1.8
.8
1.8
1.1
2.9
1.2
1.9
.9
17.6
.8
.7
27.3
24.1
32 .9
5.5
6.0
6.2
6.0
2.6
4 .9
2.2
4.9
2.8
5.6
2.5
5.2
2.8
6.5
2.8
6.4
2.8
56.4
7.1
3.0
68.6
6.7
2.7
51 .2
4.6
2.2
Retail trade:
Total cases ..
Lost workday cases .... .
Lost workdays .......... ... ............. .
8.1
3.J :
Finance, insurance, and real estate
.. . .. ..... ... .. .
T otai cases .. .. .. .. .. .. .. .
Lost workday cases ...
Lost workdays ...
Services
Total cases
Lost workday cases ..
Lost workdays .......... .
1
Data for 1989 and subsequent years are based on the Standard Industrial Class-
60.0
2.8
2.4
2.2
N = number of injuries and illnesses or lost workdays ;
EH
= total
hours worked by all employees during the calendar year; and
ification Manual, 1987 Edition . For this reason, they are not strictly comparable with data
f0r the years 1985-88, which were based on the Standard Industrial Classification
200,000 = base for 100 full-time equivalent workers (working 40 hours per week, 50 weeks
Manual , 1972 Edition , 1977 Supplement.
per year) .
Beginning with the 1992 survey, the annual survey measures only nonfatal injuries and
• Beginn ing with the 1993 survey, lost workday estimates will not be generated . As of 1992,
illnesses, while past surveys covered both fatal and nonfatal incidents. To better address
BLS began generating percent distributions and th e median number of days away from work
fatalities, a basic element of workplace safety, BLS implemented the Census of Fatal
by industry and for groups of workers sustaining similar work disabilities.
2
5
Occupational Injuries.
3
Excludes farms with fewer than 11 employees since 1976.
The incidence rates represent the number of injuries and illnesses or lost workdays per
100
full-time
workers
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
and
were
calculated
as
(N/E H)
X
200,000,
where:
NOTE: Dash indicates data not available.
Monthly Labor Review
December 2004
127
Current Labor Statistics:
Injury and Illness
56. Fatal occupational injuries by event or exposure, 1997-2002
Fatalities
Event or exposure1
1997-2001
average
2001
2
2002
Number
Number
Percent
Total. ........ . .... ................. .................... ... .. ... .. ............ ......... .
6,036
5,915
5,524
100
Transportation incidents...............................................................
Highway incident. .. ..... .... .. .. .... ..................................................... .
Collision between vehicles, mobile equipment.. .. ................... ..
Moving in same direction .. ..... .... .. .................. .... .......... ... .... . .
Moving in opposite directions, oncoming ..... .. .. .............. ...... .
Moving in intersection .. .. ..... . ....................... ... .... ... .... ........... .
Vehicle struck stationary object or equipment. .. .... .... ............ ..
Noncollision incident. ........................ ........... .. .... .... ... ... ..... .... ... ...
Jackknifed or overturned-no collision ....... . ... ... ...................
Nonhighway (farm, industrial premises) incident... .. .................... ..
Overturned . ... ... .... ....... ....... ........ ..... ..... ... .... ............... ... .. ........ .
Aircraft. .......... ... .. ......... ............... .... ...... ... .... ... ... . ... . .... ... .
Worker struck by a vehicle ... ........... .... .. .......... ... ........ .... .. .. .
Water vehicle .. ......... .. ............ .... ......... ... .... ... ............. ..... .. ......... ..
Rail vehicle .. .. ....... . .. ... .. .. . .. .. . ..... .. .... ...... ... .. ...... .............. . .
2,593
1,421
697
126
254
148
300
369
300
368
202
248
382
99
68
2,524
1,409
727
142
257
138
297
339
273
326
158
247
383
90
62
2,381
1,372
635
155
202
145
326
373
312
322
164
192
356
71
64
43
25
11
3
4
3
6
7
6
6
3
3
6
Assaults and violent acts ....................................... .......................
Homicides .... .......... .. ... .. ..... ........................... .................. .......... ..
Shooting ........... ......... .. . .. .... .. .. .. .. .... .. ..... .. .... .. .. ... . .. ...... .
Stabbing .. .... ... .. .. .... .. .. ... . ..... ..... ... ... ........ ...... ... ... . ..... ... .
Other, including bombing ................ .. . ... ... ......... ... . ...... .. .. .
Self-inflicted injuries ............. . ... ................. ... .............. ...... .. ...... ....
964
709
567
64
78
221
908
643
509
58
76
230
840
609
469
58
82
199
15
11
8
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
16
9
5
122
873
506
303
38
23 1
110
116
Falls.... ............. .......................................................................... .
Fall to lower level. ................. ..................... ............... .. ............... .
Fall from ladder .. ... .. . .. ..... ............................. ....... .. .. .... ......... .. ..
Fall from roof . ... ...... . .... .. .. ..... .... .... .... ......... ...... .. ......................
Fall from scaffold, staging .... ........ .. ..... ... ... ... .. ..... ........... .. .. .... .
Fall on same level. .. ........... .......................... ............................. ..
737
111
155
91
61
810
700
123
159
91
84
714
634
126
143
87
63
13
11
2
3
2
Exposure to harmful substances or environments............... . .
Contact with electric current. . ................ ... ................................ .. .
Contact with overhead power lines . .... ... ................ ...... ......... .. .
Contact with temperature extremes . ... .. ..... ... ...... .. ............ .. .... ....
Exposure to caustic, noxious, or allergenic substances .. ... ......... .
Inhalation of substances .... ... .... .......... .... .... .... .. ................ ...... .
Oxygen deficiency .. .. .. .... .. .... ..... .. ....... ............. .... .............. ......... .
Drowning, submersion . .... .. ............... ... ..... ... ..... ... .. .... .... ... .... ... .
529
291
134
41
106
52
89
71
499
285
124
35
96
49
83
59
538
289
122
60
98
49
90
60
10
5
2
Fires and explosions ................................... ........................... .
197
188
165
3
Other events or exposures3 . . .... .. .. ... .... .. .. . ...... .. . . .. . .... ..... . .... ....... . . .
21
24
13
1
Based on the 1992 BLS Occupational Injury and Illness
Classification Structures.
3
4
2
2
1
2
1
2
Totals for 2001 exclude fatalities from the September 11
terrorist attacks.
2
The BLS news release issued Sept. 25, 2002, reported a
total of 5,900 fatal wcrk injuries for calendar year 2001 . Since
then, an additional 15 job-related fatalities were identified,
bringing the total job-related fatality count for 2001 to 5,915.
128 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
654
144
1
4
December 2004
3
Includes the category "Bodily reaction and exertion. "
NOTE:
Totals for major categories may include subcategories not shown separately. Percentages may not add
to totals because of rounding. Dash indicates less than 0.5
percent.
Employment outlook 2002-12:
Changes in State laws:
Labor
• Concepts and context
• U.S. economy
Workers' compensation
Unemployment insurance
• Labor force
Labor market in 2003
Self-employed job fatalities
International workplace
injuries
• Industries an_<;J ~mployment
• Occupational employment
January
- Mdrch·
February
t of 9/11 on
workers
Workforce investments
t(pnal Compen
Size
Self-e
in Mexico
b injuries
and benefits
Job demands among
workers
September
American Time-Use Survey
Work-related multiplefatalities
Public sector employment
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
October
Time-use data
lnit,i l
Oce'l.
economy
lndust
ity trends
Statistics o , althcare
benefits
Benefit replacement rates
November
OSHS recordkeeping
requirements
Hedonic regression models
December
Index to Volume 127
January 2004 through December 2004
Alaska
Current Employment Statistics
Alaska's ' brain drain': myth or reality? 2004 May 9-22.
Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28.
American Time-Use Survey
Planning, designing, and executing the BLS American Time-Use
Survey. 2004 Oct. 3-19.
What can time-use data tell us about hours of work? 2004 Dec. 3-
9.
Benefits
Accounting for wages and benefits using the EC!. 2004 Sept. 26-41.
Federal statistics on healthcare benefits and cost trends: an overview. 2004 Nov. 43-56.
Incidence benefits measures in the National Compensation Survey. 2004 Aug. 21-28.
Medical and retirement plan coverage: exploring the decline in recent years. 2004 Aug. 29-36.
National Compensation Survey, The: a wealth of benefits data.
2004 Aug. 3-5.
New benefits data from the National Compensation Survey. 2004
Aug. 6-20.
Trends in employer-provided prescription-drug coverage. 2004
Aug. 37-45.
Business Employment Dynamics series
Annual measures of gross job gains and gross job losses. 2004
Nov. 3-13.
Business employment dynamics: new data on gross job gains and
losses. 2004 Apr. 29-42.
Why size class methodology matters in analyses of net and gross
job flows. 2004 July 3-12.
Canada
Current Population Survey
Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28.
Self-employment in the United States: an update. 2004 July 13-23.
What can time-use data tell us about hours of work? 2004 Dec. 39.
Displaced workers
Worker displacement in 1999-2000. 2004 June 54--68.
Earnings and wages
9/ 11 and the New York City economy: a borough-by-borough
analysis. 2004 June 3-33.
Accounting for wages and benefits using the EC!. 2004 Sept.
26-41.
Alaska's 'brain drain': myth or reality? 2004 May 9-22.
Blacks, Asians, and Hispanics in the civilian labor force. 2004 June
69-76.
Employment and wage outcomes for North Carolina's high-tech
workers. 2004 May 31-39.
Employment and wages for the U.S. ocean and coastal economy.
2004 Nov. 24--30.
Job mobility and hourly wages: is there a relationship? 2004 May
23-30.
National Compensation Survey, The: a wealth of benefits data.
2004 Aug. 3-5.
Using wage records in workforce investments in Ohio. 2004 May
40-43 .
Worker displacement in 1999-2000. 2004 June 54--68.
International analysis of workplace injuries, An. 2004 Mar. 41-51.
Economic and social statistics
Compensation costs
9/11 and the New York City economy: a borough-by-borough
analysis. 2004 June 3-33.
Blacks, Asians, and Hispanics in the civilian labor force. 2004 June
69-76.
Business employment dynamics: new data on gross job gains and
losses. 2004 Apr. 29-42.
Employment and wages for the U.S. ocean and coastal economy.
2004 Nov. 24--30.
National Compensation Survey, The: a wealth of benefits data.
2004 Aug. 3-5.
New statistics for health insurance from the National Compensation Survey. 2004 Aug. 46-50.
Trends in employer-provided prescription-drug coverage. 2004
Aug. 37-45.
Computers (See Technological change.)
Construction
Fatal occupational injuries at road construction sites. 2004 Dec.
43-47.
Consumer Price Index
Consumer prices during 2003. 2004 Apr. 3-8.
Hedonic regression models using in-house and out-of-house data.
2004. Dec. 25-38.
130 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
Economic development and growth
Annual measures of gross job gains and gross job losses. 2004
Nov. 3-13.
Employment in the public sector: two recessions' impact on jobs.
2004 Oct. 38-47.
Post-recession trends in non-farm employment and related economic indicators. 2004 Sept. 49-56.
U.S. economy to 2012, The: signs of growth. 2004 Feb. 23-36.
Education and training
Alaska 's ' brain drain ' : myth or reality? 2004 May 9-22.
Blacks, Asians, and Hispanics in the civilian labor force. 2004 June
69-76.
Educational attainment of the labor force and jobless rates, 2003.
2004 July 57-59.
Employer Cost Index (ECI)
Accounting for wages and benefits using the
ECI.
2004 Sept. 26-41.
Employment (See also Unemployment; Labor force.)
Alaska's ' brain drain ': myth or reality? 2004 May 9-22.
Blacks, Asians, and Hispanics in the civilian labor force. 2004 June
69-76.
Business employment dynamics: new data on gross job gains and
losses. 2004 Apr. 29-42.
Employment and wage outcomes for North Carolina's high-tech
workers. 2004 May 31-39.
Employment and wages for the U.S. ocean and coastal economy.
2004 Nov. 24--30.
Employment in the information sector in March 2004. 2004 Sept.
42-47.
Employment in the public sector: two recessions ' impact on jobs.
2004 Oct. 38-47.
Employment projections to 2012: concepts and context. 2004 Feb.
3-22.
Multiple jobholding in States, 2003 . 2004 July 60--61.
Occupational employment projections to 2012. 2004 Feb. 80--105.
Post-recession trends in non-farm employment and related economic indicators. 2004 Sept. 49-56.
Trends in job demands among older workers , 1992-2002. 2004
July 48-56.
U.S. labor market in 2003 , The: signs of improvement by year 's
end. 2004 Mar. 3-29.
Why size class methodology matters in analyses of net and gross
job flows. 2004 July 3-12.
Exports
Federal statistics on healthcare benefits and cost trends: an overview. 2004 Nov. 43-56.
Incidence benefits measures in the National Compensation Survey. 2004Aug. 21-28.
Measuring defined benefit plan replacement rates with PenSync.
2004 Nov. 57-68.
Medical and retirement plan coverage: exploring the decline in recent years. 2004 Aug. 29-36.
National Compensation Survey, The: a wealth of benefits data.
2004 Aug. 3-5.
New benefits data from the National Compensation Survey. 2004
Aug. 6-20.
New statistics for health insurance from the National Compensation Survey. 2004 Aug. 46-50.
Trends in employer-provided prescription-drug coverage. 2004
Aug. 37-45.
Healthcare
Federal statistics on healthcare benefits and cost trends: an overview. 2004 Nov. 43-56.
Measuring defined benefit plan replacement rates with PenSync.
2004 Nov. 57-68.
Hispanics
Blacks, Asians, and Hispanics in the civilian labor force. 2004 June
69-76.
Labor force projections to 2012: the graying of the U.S. workforce.
2004 Feb. 37-57.
Hours of work
Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28.
Diurnal pattern of on-the-job injuries , The. 2004 Sept. 18-25.
What can time-use data tell us about hours of work? 2004 Dec.
3-9.
Immigration
U.S . import and export prices in 2003. 2004 Sept. 3-9.
Labor force projections to 2012: the graying of the U.S. workforce.
2004 Feb. 37-57.
Finland
Imports
International analysis of workplace injuries, An. 2004 Mar. 41-51.
U.S. import and export prices in 2003. 2004 Sept. 3-9.
Foreign-born workers
Income (See Earnings and wages.)
Foreign-born workers: trends in fatal occupational injuries, 19962001. 2004 June 42-53 .
Industry
Foreign trade
Industry output and employment projections to 2012. 2004 Feb.
58-79.
U.S. import and export prices in 2003. 2004 Sept. 3-9.
Industry studies
France
Employment and wages for the U.S. ocean and coastal economy.
2004 Nov. 24--30.
Employment in the information sector in March 2004. 2004 Sept.
42-47.
Industry productivity trends under the North American Industry
Classification System . 2004 Nov. 31-42.
New and emerging occupations. 2004 Dec. 39-42.
International analysis of workplace injuries, An. 2004 Mar. 41-51.
Fringe benefits (See Benefits.)
Gross domestic product
U.S. economy to 2012, The: signs of growth. 2004 Feb. 23-36.
Health and insurance plans
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Inflation
Monthly Labor Review
December 2004
131
Index to Volume 126
Consumer prices during 2003. 2004 Apr. 3-8.
U.S. economy to 2012, The: signs of growth. 2004 Feb. 23-36.
Information sector
Employment in the information sector in March 2004. 2004 Sept.
42-47.
International comparisons
International analysis of workplace injuries, An. 2004 Mar. 41-51.
Job creation
Annual measures of gross job gains and gross job losses. 2004
Nov. 3-13.
Employment in the public sector: two recessions' impact on jobs.
2004 Oct. 38-47.
New and emerging occupations. 2004 Dec. 39-42.
Job flow statistics (See Business Employment Dynamics series.)
Job Openings and Labor Turnover Survey
Job Openings and Labor Turnover Survey, The: what initial data
show. 2004 Oct. 14-23.
Job tenure
Job mobility and hourly wages: is there a relationship? 2004 May
23-30.
Industry output and employment projections to 2012. 2004 Feb.
58-79.
Job mobility and hourly wages: is there a relationship? 2004 May
23-30.
Job Openings and Labor Turnover Survey, The: what initial data
show. 2004 Oct. 14-23.
Measuring labor dynamics: the next generation in labor market
information. 2004 May 3-8.
New and emerging occupations. 2004 Dec. 39-42.
Occupational employment projections to 2012. 2004 Feb. 80-105.
Post-recession trends in non-farm employment and related economic indicators. 2004 Sept. 49-56.
Self-employment among older U.S. workers. 2004 July 24-47.
Trends in job demands among older workers, 1992-2002. 2004
July 48-56.
Using wage records in workforce investments in Ohio. 2004 May
40-43.
U.S. labor market in 2003, The: signs of improvement by year's
end. 2004 Mar. 3-29.
Worker displacement in 1999-2000. 2004 June 54-68.
Labor turnover
Job Openings and Labor Turnover Survey, The: what initial data
show. 2004 Oct. 14-23.
Mexico
Declining union density in Mexico, 1984-2000. 2004 Sept. 10-17.
Job vacancies
Migration
Job Openings and Labor Turnover Survey, The: what initial data
show. 2004 Oct. 14-23.
Alaska's 'brain drain': myth or reality? 2004 May 9-22.
Labor and economic history
Employment in the public sector: two recessions' impact on jobs.
2004 Oct. 38-47.
Labor force
Alaska's 'brain drain' : myth or reality? 2004 May 9-22.
Blacks,Asians, and Hispanics in the civilian labor force. 2004June
69-76.
Educational attainment of the labor force and jobless rates, 2003.
2004 July 57-59.
Labor force and unemployment, The: three generations of change.
2004 June 34-41.
Labor force projections to 2012: the graying of the U.S. workforce.
2004 Feb. 37-57.
Using wage records in workforce investments in Ohio. 2004 May
40-43.
Labor law
Changes in State unemployment insurance legislation in 2003.
2004 Jan. 37-52.
Changes in workers' compensation laws in 2003. 2004Jan. 30-36.
State labor legislation enacted in 2003. 2004 Jan. 3-29.
Labor market
9/11 and the New York City economy: a borough-by-borough
analysis. 2004 June 3-33.
Alaska's 'brain drain': myth or reality? 2004 May 9-22.
Blacks, Asians, and Hispanics in the civilian labor force. 2004 June
69-76.
Employment and wage outcomes for North Carolina's high-tech
workers. 2004 May 31-39.
132 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
Minnesota
Job mobility and hourly wages: is there a relationship? 2004 May
23-30.
Minorities
Blacks, Asians, and Hispanics in the civilian labor force. 2004 June
69-76.
Labor force projections to 2012: the graying of the U.S. workforce.
2004 Feb. 37-57.
Mobility
Job mobility and hourly wages: is there a relationship? 2004 May
23-30.
Multiple jobholders
Multiple jobholding in States, 2003. 2004 July 60-61.
National Compensation Survey
National Compensation Survey. 2004 Aug. 3-50.
National Compensation Survey, The: a wealth of benefits data.
New benefits data from the National Compensation Survey.
Incidence benefits measures in the National Compensation
Survey.
Medical and retirement plan coverage: exploring the decline in
recent years.
Trends in employer-provided prescription-drug coverage.
New statistics for health insurance from the National
Compensation Survey.
New York City
9/ 11 and the New York City economy: a borough-by-borough
analysis. 2004 June 3-33.
North American Industry Classification System
Industry productivity trends under the North American Industry
Classification System. 2004 Nov. 31-42.
North Carolina
Employment and wage outcomes for North Carolina's high-tech
workers. 2004 May 31-39.
Occupations
Employment projections to 2012: concepts and context. 2004 Feb.
3-22.
New and emerging occupations. 2004 Dec. 39-42.
Occupational employment projections to 2012. 2004 Feb. 80-105.
Occupational safety and health
Fatal occupational injuries at road construction sites. 2004 Dec.
43-47.
Foreign-born workers: trends in fatal occupational injuries, 1996-200 I. 2004 June 42-53.
International analysis of workplace injuries, An. 2004 Mar. 41-5 l.
Occupational fatalities: self-employed workers and wage and salary workers. 2004 Mar. 30-40.
Occupational injury and illness: new recordkeeping requirements.
2004 Dec. l 0-24.
Work-related multiple-fatality incidents. 2004 Oct. 20-37.
Ohio
Using wage records in workforce investments in Ohio. 2004 May
40-43.
Employment projections to 2012: concepts and context.
Industry output and employment projections to 2012.
Labor force projections to 2012: the graying of the U.S.
workforce.
Occupational employment projections to 2012.
U.S. economy to 2012, The: signs of growth.
Recession
Employment in the public sector: two recessions' impact on jobs.
2004 Oct. 38-47.
Post-recession trends in non-farm employment and related economic indicators. 2004 Sept. 49-56.
Regional comparisons
Educational attainment of the labor force and jobless rates , 2003.
2004 July 57-59.
Employment in the information sector in March 2004. 2004 Sept.
42-47.
Multiple jobholding in States, 2003. 2004 July 60-61.
Retirement
Incidence benefits measures in the National Compensation Survey. 2004Aug. 21-28.
Measuring defined benefit plan replacement rates with PenSync.
2004 Nov. 57-68.
Medical and retirement plan coverage: exploring the decline in recent years. 2004 Aug. 29-36.
National Compensation Survey, The: a wealth of benefits data.
2004 Aug. 3-5.
New benefits data from the National Compensation Survey. 2004
Aug. 6--20.
Older workers
Salaries (See Earnings and wages.)
Self-employment
Labor force projections to 2012: the graying of the U.S. workforce.
2004 Feb. 37-57.
Self-employment among older U.S. workers. 2004 July 24-47.
Trends in job demands among older workers, 1992-2002. 2004
July 48-56.
Occupational fatalities: self-employed workers and wage and salary workers. 2004 Mar. 30-40.
Self-employment among older U.S. workers. 2004 July 24-47.
Self-employment in the United States: an update. 2004 July 13-23.
Pensions
Measuring defined benefit plan replacement rates with PenSync.
2004 Nov. 57-68.
Prices
Consumer prices during 2003. 2004 Apr. 3-8.
U.S. import and export prices in 2003. 2004 Sept. 3-9.
Producer Price Index
Consumer prices during 2003. 2004 Apr. 3-8.
Productivity
Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28.
Industry productivity trends under the North American Industry
Classification System. 2004 Nov. 31-42.
U.S. economy to 2012, The: signs of growth. 2004 Feb. 23-36.
Projections
Employment outlook: 2002-12. 2004 Feb. 3-105.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Service sector
Industry output and employment projections to 2012. 2004 Feb.
58-79.
Small business
Why size class methodology matters in analyses of net and gross
job flows. 2004 July 3-12.
State government
Changes in State unemployment insurance legislation in 2003.
2004 Jan. 37-52.
Changes in workers' compensation laws in 2003. 2004Jan. 30-36.
State labor legislation enacted in 2003. 2004 Jan. 3-29.
Statistical programs and methods
Accounting for wages and benefits using the EC!. 2004 Sept. 26--41.
Annual measures of gross job gains and gross job losses. 2004
Nov. 3-13.
Federal statistics on healthcare benefits and cost trends: an overview. 2004 Nov. 43-56.
Hedonic regression models using in-house and out-of-house data.
2004 Dec. 25-38.
Monthly Labor Review
December 2004
133
Job Openings and Labor Turnover Survey, The: what initial data
show. 2004 Oct. 14-23.
Measuring defined benefit plan replacement rates with PenSync.
2004 Nov. 57-68.
Measuring labor dynamics: the next generation in labor market
information. 2004 May 3-8.
National Compensation Survey, The: a wealth of benefits data.
2004 Aug. 3-5.
New benefits data from the National Compensation Survey. 2004
Aug. 6-20.
New statistics for health insurance from the National Compensation Survey. 2004 Aug. 46-50.
Planning, designing, and executing the BLS American Time-Use
Survey. 2004 Oct. 3-19.
Using wage records in workforce investments in Ohio. 2004 May
40-43.
What can time-use data tell us about hours of work? 2004 Dec. 39.
Why size class methodology matters in analyses of net and gross
job flows. 2004 July 3-12.
Survey methods
Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28.
Hedonic regression models using in-house and out-of-house data.
2004 Dec. 25-38.
Job Openings and Labor Turnover Survey, The: what initial data
show. 2004 Oct. 14-23.
Occupational injury and illness: new recordkeeping requirements.
2004 Dec. 10-24.
Planning, designing, and executing the BLS American Time-Use
Survey. 2004 Oct. 3-19.
What can time-use data tell us about hours of work? 2004 Dec. 38.
Why size class methodology matters in analyses of net and gross
job flows. 2004 July 3-12.
Sweden
Union membership and elections
Declining union density in Mexico, 1984-2000. 2004 Sept. 10-17.
Wages (See Earnings and wages.)
Women
Labor force projections to 2012: the graying of the U.S. workforce.
2004 Feb. 37-57.
Workers' compensation
Changes in workers' compensation laws in 2003. 2004Jan. 30-36.
Work injuries and illnesses
Diurnal pattern of on-the-job injuries, The. 2004 Sept. 18-25.
Fatal occupational injuries at road construction sites. 2004 Dec.
43-47.
Foreign-born workers: trends in fatal occupational injuries, 19962001. 2004 June 42-53.
International analysis of workplace injuries, An. 2004 Mar. 41-51.
Occupational fatalities: self-employed workers and wage and salary workers. 2004 Mar. 30-40.
Occupational injury and illness: new recordkeeping requirements.
2004 Dec. 10-24.
Work-related multiple-fatality incidents. 2004 Oct. 20-37.
DEPARTMENTS
Book reviews. Jan., Mar., Apr., May, June, July, Sept., Oct., and
Dec. issues.
Current labor statistics. Each issue.
Labor month in review. Each issue.
Workplace safety and health. Dec. issue.
Precis. Each issue.
Publications received. Feb., May, Aug., and Nov. issues.
International analysis of workplace injuries, An. 2004 Mar. 41-51.
Technological change
Regional trends. July issue.
Report from the regions. Sept. issue.
Employment and wage outcomes for North Carolina's high-tech
workers. 2004 May 31-39.
Unemployment (See also Employment; Labor force.)
Blacks, Asians, and Hispanics in the civilian labor force. 2004 June
69-76.
Educational attainment of the labor force and jobless rates, 2003.
2004 July 57-59.
Labor force and unemployment, The: three generations of change.
2004 June 34-41.
Post-recession trends in non-farm employment and related economic indicators. 2004 Sept. 49-56.
U.S. labor market in 2003, The: signs of improvement by year's
end. 2004 Mar. 3-29.
Worker displacement in 1999-2000. 2004 June 54-68.
Unemployment insurance
Changes in State unemployment insurance legislation in 2003.
2004 Jan. 37-52.
Measuring labor dynamics: the next generation in labor market
134
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
information. 2004 May 3-8.
December 2004
Research summary. Dec. issue.
Visual essay. June and Sept. issues.
BOOK REVIEWS (Listed by author of book.)
Backhouse, Roger E. The Ordinary Business of Life: A History of
Economics from the Ancient World to the Twenty-First Century.
2004 Oct. 49-50.
Ben-Bassat, Avi, ed. The Israeli Economy, 1985-1998: From Government Intervention to Market Economics. 2004 Dec. 49-50.
Brisbin, Richard, Jr. A Strike Like No Other Strike: Law & Resistance during the Pittston Coal Strike of 1989-1990. 2004 May
45-46.
Costello, Cynthia B., Vanessa R. Wight, and Anne J. Stone, eds.
The American Woman 2003-2004: Daughters ofa Revolution-Young Women Today. 2004 June 78.
Darlington, Patricia S. E. and Becky Michele Mulvaney. Women,
Power, and Ethnicity: Working Toward Reciprocal Empowerment. 2004 Apr. 44.
Gornick, Janet C. and Marcia K. Meyers. Families that Work: Poli-
cies for Reconciling Parenthood and Employment. 2004 Sept.
57-58.
Holtz-Eakin, Douglas and Bruce D. Meyer, eds. Making Work Pay:
The Earned Income Tax Credit and Its Impact on American
Families. 2004 May 45.
Houseman, Susan and Machiko Osawa, eds. Nonstandard Work in
Developed Economies: Causes and Consequences. 2004 July
63-64.
Kochan, Thomas A., Richard M. Locke, Michael Piore, and Paul
Osterman. Working in America: A Blueprint for the New Labor
Market. 2004 Mar. 53-54.
Locke, Richard M., Michael Piore, Paul Osterman, and Thomas A.
Kochan. Working in America: A Blueprint for the New Labor
Market. 2004 Mar. 53-54.
Meyer, Bruce D. and Douglas Holtz-Eakin, eds. Making Work Pay:
The Earned Income Tax Credit and Its Impact on American
Families. 2004 May 45.
Meyers, Marcia K. and Janet C. Gornick. Families that Work: Policies for Reconciling Parenthood and Employment. 2004 Sept.
57-58.
Mulvaney, Becky Michele and Pastricia S. E. Darlington. Women,
Power, and Ethnicity: Working Toward Reciprocal Empowerment. 2004 Apr. 44.
Osawa, Machiko and Susan Houseman, eds. Nonstandard Work in
Developed Economies: Causes and Consequences. 2004 July
63-64.
Osterman, Paul, Thomas A. Kochan, Richard M. Locke, and
Michael Piore. Working in America: A Blueprint for the New
Labor Market. 2004 Mar. 53-54.
Piore, Michael, Paul Osterman, Thomas A. Kochan, and Richard
M. Locke. Working in America: A Blueprint for the New Labor
Market. 2004 Mar. 53-54.
Stone, Anne J., Cynthia B. Costello, and Vanessa R. Wight, eds.
The American Woman 2003-2004: Daughters ofa Revolution-Young Women Today. 2004 June 78.
Wight, Vanessa R., Cynthia B. Costello, and Anne J. Stone, eds.
The American Woman 2003-2004: Daughters ofa Revolution-Young Women Today. 2004 June 78.
Zimbalist, Andrew. May the Best Team Win: Baseball Economics
and Public Policy. 2004 Jan. 54.
AUTHORS
Barsky, Carl B. Incidence benefits measures in the National Compensation Survey. 2004 Aug. 21-28.
Berman, Jay M. Industry output and employment projections to
2012. 2004 Feb. 58-79.
Berridge, Scott. Book review. 2004 Dec. 49-50.
Blostin, Allan P. The National Compensation Survey: a wealth of
benefits data. 2004 Aug. 3-5.
Bowles, Robert. Employment and wage outcomes for North
Carolina's high-tech workers. 2004 May 31-39.
Brown, Craig. Hedonic regression models using in-house and outof-house data. 2004 Dec. 25-38.
Buckley, John E. and Robert W. Van Giezen. Federal statistics on
healthcare benefits and cost trends: an overview. 2004 Nov.
43-56.
Campbell, Jim. Multiple jobholding in States. 2004 July 60--61.
Clark, Kelly A. The Job Openings and Labor Turnover Survey:
what initial data show. 2004 Nov. 14-23.
Clayton, Richard L. and Jay A. Mousa. Measuring labor dynamics:
the next generation in labor market information. 2004 May 3-8.
Clayton, Richard L., R. Jason Faberman, Akbar Sadeghi, David M.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Talan, and James R. Spletzer. Business employment dynamics:
new data on gross job gains and losses. 2004 Apr. 29-42.
Colgan, Charles S. Employment and wages for the U.S. ocean and
coastal economy. 2004 Nov. 24-30.
Dietz, Elizabeth. Trends in employer-provided prescription-drug
coverage. 2004 Aug. 37-45.
DiNatale, Marisa, Rachel Krantz, and Thomas J. Kralik. The U.S.
labor market in 2003: signs of improvement by year's end. 2004
Mar. 3-29.
·
Dolfman, Michael L. and Solidelle F. Wasser. 9/11 and the New York
City economy: a borough-by-borough analysi3. 2004 June 3-33.
Drudi, Dino and Mark Zak. Work-related multiple-fatality incidents. 2004 Oct. 20--37.
Eldridge, Lucy P., Marilyn E. Manser, and Phyllis Flohr Otto. Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28.
Faberman, R. Jason, Akbar Sadeghi, David M. Talan, Richard L.
Clayton, and James R. Spletzer. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42.
Fairris, David and Edward Levine. Declining union density in
Mexico, 1984-2000. 2004 Sept. I 0--17.
Fitzpatrick, John J., Jr. and Richard R. Nelson. State labor legislation enacted in 2003. 2004 Jan. 3-29.
Fortson, Kenneth N. The diurnal pattern of on-the-job injuries.
2004 Sept. 18-25.
Frazis, Harley and Jay Stewart. What can time-use data tell us about
hours of work? 2004 Dec. 3-9.
Gordon, Rich, Mark Schaff, and Greg Shaw. Using wage records
in workforce investments in Ohio. 2004 May 40--43.
Hadland, Jeff. Alaska's 'brain drain': myth or reality? 2004 May
9-22.
Hammida, Mustapha. Job mobility and hourly wages: is there a
relationship? 2004 May 23-30.
Hatch, Julie. Employment in the public sector: two recessions'
impact on jobs. 2004 Oct. 38-47.
Hecker, Daniel E. Occupational employment projections to 2012.
2004 Feb. 80--105.
Helwig, Ryan. Worker displacement in 1999-2000. 2004 June
54-68.
Herz, Diane and Michael Horrigan. Planning, designing, and executing the BLS American Time-Use Survey. 2004 Oct. 3-19.
Hipple, Steven. Self-employment in the United States: an update.
2004 July 13-23.
Horrigan, Michael and Diane Herz. Planning, designing, and executing the BLS American Time-Use Survey. 2004 Oct. 3-19.
Horrigan, Michael W. Employment projections to 2012: concepts
and context. 2004 Feb. 3-22.
Johnson, Richard W. Trends in job demands among older workers,
1992-2002. 2004 July 48-56.
Karoly, Lynn A. and Julie Zissimopoulos. Self-employment among
older U.S. workers. 2004 July 24-47.
Krantz, Rachel, David Langdon, and Michael Strople. Post-recession trends in nonfarm employment and related economic indicators. 2004 Sept. 49-56.
Krantz, Rachel, Marisa DiNatale, and Thomas J. Kralik. The U.S.
labor market in 2003: signs of improvement by year 's end.
2004 Mar. 3-29.
Krolik, Thomas J. Educational attainment of the labor force and
jobless rates, 2003. 2004 July 57-59.
Kralik, Thomas J., Rachel Krantz, and Marisa DiNatale. The U.S.
labor market in 2003: signs of improvement by year 's end.
2004 Mar. 3-29.
Monthly Labor Review
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135
Lancaster, Loryn. Changes in State unemployment insurance legislation in 2003. 2004 Jan. 37-52.
Langdon, David, Rachel Krantz, and Michael Strople. Post-recession trends in nonfarrn employment and related economic indicators. 2004 Sept. 49-56.
Lettau, Michael. New statistics for health insurance from the National Compensation Survey. 2004 Aug. 46-50.
Levine, Edward and David Fairris. Declining union density in
Mexico, 1984-2000. 2004 Sept. 10-17.
Loh, Katherine and Scott Richardson. Foreign-born workers: trends
in fatal occupational injuries, 1996-2001. 2004 June 42-53.
Manser, Marilyn E., Lucy P. Eldridge, and Phyllis Flohr Otto. Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28.
Martin, Gary. Book review. 2004 Sept. 57-58.
Messing, Ellen. Book review. 2004 Oct. 49-50.
Mitchell, David. Book review. 2004 May 45.
Moore, James H., Jr. Measuring defined benefit plan replacement
rates with PenSync. 2004 Nov. 57-68.
Mousa, Jay A. and Richard L. Clayton. Measuring labor dynamics:
the next generation in labor market information. 2004 May 3-8.
Nelson, Richard R. and John J. Fitzpatrick, Jr. State labor legislat10n enacted in 2003. 2004 Jan. 3-29.
Okolie, Cordelia. Why size class methodology matters in analyses
of net and gross job flows. 2004 July 3-12.
Otto, Phyllis Flohr, Marilyn E. Manser, and Lucy P. Eldridge. Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28.
Pegula, Stephen. Fatal occupational injuries at road construction
sites. 2004 Dec. 43-47.
Pegula, Stephen M. Occupational fatalities: self-employed workers and wage and salary workers. 2004 Mar. 30-40.
Perrins, Gerald. Employment in the information sector in March
2004. 2004 Sept. 42-47.
Pfuntner, Jordan. New benefits data from the National Compensation Survey. 2004 Aug. 6-20.
Pikulinski, Jerome. New and emerging occupations. 2004 Dec. 3942.
Pinkston, Joshua C. and James R. Spletzer. Annual measures of
gross job gains and gross job losses. 2004 Nov. 3-13.
Reynolds, Joy K. Book review. 2004 Mar. 53-54.
Richardson, Scott and Katherine Loh. Foreign-born workers: trends
in fatal occupational injuries, 1996-2001. 2004 June 42-53.
Russell, Matthew, Paul Takac, and Lisa Usher. Industry productivity trends under the North American Industry Classification
System. 2004 Nov. 31-42.
Sadeghi, Akbar, David M. Talan, Richard L. Clayton, James R.
Spletzer, and R. Jason Faberman. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42.
Schaff, Mark, Rich Gordon, and Greg Shaw. Using wage records
in workforce investments in Ohio. 2004 May 40-43.
Schwabish, Jonathan A. Accounting for wages and benefits using
the ECI. 2004 Sept. 26-41.
136
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
December 2004
Schwartz, Melissa E. U.S. import and export prices in 2003. 2004
Sept. 3-9.
Shaw, Greg, Mark Schaff, and Rich Gordon. Using wage records
in workforce investments in Ohio. 2004 May 40-43.
Sincavage, Jessica _R. The labor force and unemployment: three
generations of change. 2004 June 34-41.
Skelly, Kevin. Book review. 2004 Jan. 54.
Sorrentino, Constance. Book review. 2004 July 63-64.
Spletzer, James R. and Joshua C. Pinkston. Annual measures of
gross job gains and gross job losses. 2004 Nov. 3-13.
Spletzer, James R., R. Jason Faberman, Akbar Sadeghi, David M.
Talan, and Richard L. Clayton. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42.
Stewart, Jay and Harley Frazis. What can time-use data tell us about
hours of work? 2004 Dec. 3-9.
Strople, Michael, David Langdon, and Rachel Krantz. Post-recession trends in nonfarm employment and related economic indicators. 2004 Sept. 49-56.
Su, Betty W. The U.S. economy to 2012: signs of growth. 2004
Feb. 23-36.
Takac, Paul, Matthew Russell, and Lisa Usher. Industry productivity trends under the North American Industry Classification
System. 2004 Nov. 31-42.
Talan, David M., Richard L. Clayton, James R. Spletzer, R. Jason
Faberman, and Akbar Sadeghi. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42.
Toossi, Mitra. Labor force projections to 2012: the graying of the
U.S. workforce. 2004 Feb. 37-57.
Usher, Lisa, Matthew Russell, and Paul Takac. Industry productivity trends under the North American Industry Classification
System. 2004 Nov. 31-42.
Ussif, Al-Amin. An international analysis of workplace injuries.
2004 Mar. 41-51.
Van Giezen, Robert W. and John E. Buckley. Federal statistics on
healthcare benefits and cost trends: an overview. 2004 Nov.
43-56.
Wald, Michael. Book review. 2004 May 45-46.
Wasser, Solidelle F. and Michael L. Dolfman. 9/11 and the New
York City economy: a borough-by-borough analysis. 2004 June
3-33.
Wasser, Solidelle Fortier. Book review. 2004 June 78.
Whittington, Glenn. Changes in workers' compensation laws in
2003. 2004 Jan. 30-36.
Wiatrowski, William J. Medical and retirement plan coverage: exploring the decline in recent years. 2004 Aug. 29-36.
Wiatrowski, William J. Occupational injury and illness: new
recordkeeping requirements. 2004 Dec. 10-24.
Wilson, Todd. Consumer prices during 2003. 2004 Apr. 3-8.
Yee, Charlotte. Book review. 2004 Apr. 44.
Zak, Mark and Dino Drudi. Work-related multiple-fatality incidents. 2004 Oct. 20-37.
Zissimopoulos, Julie and Lynn A. Karoly. Self-employment among
older U.S. workers. 2004 July 24-47.
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