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Bureau o f Labor Statistics
,S. Department of Labor
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Federal Reserve Bank of St. Louis
Expenditures
of Retirees
Producer Prices in 2001
Expenditures of Single Parents
U.S. Department of Labor
Elaine L. Chao, Secretary
Bureau of Labor Statistics
Lois L. Orr, Acting Commissioner
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Cover designed by Keith Tapscott
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Federal Reserve Bank of St. Louis
MONTHLY LABOR
REVIEW__________________ _____
Volume 125, Number 7
July 2002
Producer price highlights during 2001
3
The PPI for finished goods experienced its largest decline in 15 years;
prices for natural gas and crude pertoleum dropped to 1999 levels
William F. Snyders, Jon Weinhagen, and Amy Popick
Expenditures of single parents: howdoesgender figure
in?
16
For the most part, expenditures patterns are the same for both
families headed by single fathers and families headed by single mothers
Geoffrey D. Paulin and Yoon G. Lee
Planning ahead: consumerexpenditurepatterns
inretirement
38
The ‘graying’ of the population creates a need to examine
the role of retirement on expenditures of various groups of retirees
Geofrey D. Paulin and Abby L. Duly
Departments
Labor month in review
Précis
Book reviews
Current labor statistics
2
59
60
61
Editor-in-Chief: Deborah P. Klein • Executive Editor: Richard M. Devens • Managing Editor: Anna Huffman H ill • Editors: Brian
I. Baker, Richard Hamilton, Leslie Brown Joyner • Book Reviews: Roger A. Comer, Richard Hamilton, • Design and Layout: Catherine
D. Bowman, Edith W. Peters • Contributors: Joshua Klick
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Federal Reserve Bank of St. Louis
The July Review
This issue leads off with William F.
Snyders, Jon Weinhagen, and Amy
Popick’s account o f price changes at
the producers’ level in 2001. Most
sim ply put, p ricing was tig h t for
producers last year; at the finished
goods level, the Producer Price Index
fell 1.6 percent overall, mostly in the
energy sector. As they trace prices
changes back through the intermediate
and crude materials stages, not only
did the index declines become bigger,
they spread to food and the “core”
indexes as well.
The rest o f the articles are on one
asp e ct or an o th er o f consum er
expenditure studies. G eoffrey D.
Paulin and Yoon G. Lee compare the
way single parents—both male and
female— spend their money. Single
fem ale
p a re n ts
spend
often
substantially larger shares o f their
budget on items like food and clothing,
while single fathers spend larger share
on things that are more discretionary.
M uch o f these d ifferences reflect
som ew hat h ig h er incom es for the
single men.
Paulin and Abby L. Duly do a similar
comparison o f the spending patterns
o f pre-retired (working and 55 to 64
years old) and retired (no labor income
and 65 to 74 years old) persons. Again,
one o f the big d ifferences across
g ro u p s is in incom e. N ot very
surprisingly, the pre-retired had higher
total incomes, in general because they
had su b sta n tia l averag e lab o r
earnings. On the expenditure side,
c o n c lu sio n s ab o u t the ro le o f
retirement in expenditure plans were
more difficult to draw, in part because
o f the degree o f similarity o f pre- and
p o st-re tire m en t p attern s th at was
ap p a ren t w hen v aria b les such as
incom e and dem ography had been
accounted for.
2 Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
Injuries in eatin g an d
drinking places
Approximately 304,000 nonfatal oc
cupational injuries and illnesses
occurred in the eating and drinking
places industry in 1999, down from
about 397,000 in 1992. Most of the onthe-job injuries and illnesses that occur
in eating and drinking places tend to be
relatively minor. In 1999, about a third
involved lost work time, compared with
almost half of injuries and illnesses for
all private industry workers.
However, there were 147 fatal occ
upational injuries at eating and drinking
places in 1999. Homicides were the
leading cause of worker fatalities in the
eating and drinking places industry:
almost two-thirds o f fatalities were
homicides in 1999. More information is
available in “Occupational Hazards in
Eating and Drinking Places,” by Timothy
Webster, Compensation and Working
Conditions.
Average compensation
$23.15 an hour
In March 2002, employer costs for
employee compensation for civilian
workers in the United States averaged
$23.15 per hour worked. Wages and
salaries, which averaged $16.76,
accounted for 72.4 percent of these costs,
while benefits, which averaged $6.39,
accounted for the remaining 27.6 percent.
Legally required benefits were $ 1.80
per hour on average, representing the
largest nonw age em ployer cost.
Employer costs for insurance benefits
were $1.61 per hour, paid leave benefits
were $1.59 per hour, and retirement and
savings benefits were 80 cents per hour.
For additional inform ation see
“Em ployer Costs for Employee
Compensation, March 2002,” news release
USDL 02-346. Publication of this news
release will change to a quarterly basis
beginning with June 2002 data.
W om en’s earning and
educaton
Earnings for female full-time wage and
salary workers vary considerably by
educational level. In 2001, those with
less than a high school diploma had
median earnings of $314 per week. This
compares with $784 per week for those
with a college degree. Women who
graduated high school but did not
attend college earned $441 a week at the
median, while those with some college
or an associate degree earned $525.
New productivity series
Labor productivity—defined as output
per hour—increased 3.0 percent from 1999
to 2000 in wholesale trade. This rise was
below the 4 percent annual increase for
the 1995-2000 period, but exceeded the 2.7percent annual growth of 1990-95.
These figures are from a new
productivity series for the wholesale trade
industry introduced this month. In
addition, there are now productivity series
for durable-goods wholesale trade and
nondurable-goods wholesale trade, and
for all three-digit SIC (Standard Industrial
Classification) industries in wholesale
trade. Unit labor costs series are also now
available for each of these industries.
The wholesale trade sector includes
establishm ents involved in selling
merchandise to retailers; to industrial,
commercial, institutional, farm, con
struction contractors, or professional
business users; or acting as brokers in
purchases or sales o f m erchandise
between businesses. See “ BLS Releases
New Series on Productivity and Costs in
Wholesale Trade Industries, 1990-2000”
news release USDL 02-347.
□
Producer prices, 2001
Producer price highlights
during 2001
The decline o f the PPI fo r finished goods in 2001
was the largest in 15 years; prices fo r natural gas
and crude petroleum fe ll back to 1999 levels
William F. Snyders,
Jon Welnhagen,
and
Amy Popick
William F. Snyders,
Jon Weinhagen,
and
Amy Popick
are economists for the
Producer Price Index
Program, Bureau of
Labor Statistics.
Email:
Snyders_W@bls.gov
Welnhagen_J@bls.gov
Poplck_A@bls.gov
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Federal Reserve Bank of St. Louis
he Producer Price Index (PPI) for Finished
Goods declined 1.6 percent in 2001, the
largest calendar year decrease since a 2.3percent drop in 1986. This index rose 3.6 percent
in 2000, and 2.9 percent in 1999. Finished goods
are commodities that are ready for sale to the fi
nal-demand user, either an individual consumer
or a business firm. The majority of the 2001 de
cline in finished goods prices can be traced to a
17.1 -percent drop in finished energy prices. Ex
cluding energy, the index for finished goods ad
vanced 1.2 percent in 2001. Following a 1.7-per
cent gain in the prior year, the index for finished
consumer foods rose 1.8 percent in 2001. Prices
for finished goods less foods and energy—a cat
egory that includes both consumer goods and
capital equipment—increased 0.9 percent in 2001,
following a 1.3-percent advance throughout the
previous 12 months.
Prices for commodities at the overall crude
and intermediate stages of processing also ex
perienced declines for the 2001 calendar year.
The PPI for intermediate materials, supplies,
and components fell 4 percent in 2001, after
posting a 4.1 -percent gain in 2000. Intermedi
ate goods in the PPI reflect material inputs to
the manufacturing process, as well as various
supplies consumed in the production process.
Prices for crude materials for furthering pro
cessing dropped 32.5 percent, following a 35.5percent jump in the prior calendar year. Crude
goods are unprocessed goods that are prima
T
rily outputs from mining industries and agri
cultural production.
Throughout 2001, energy prices turned down
at both the crude and intermediate stages of pro
cessing. The index for intermediate foods and
feeds rose at a much slower rate in 2001 than it
did in 2000. Prices for crude foodstuffs and
feedstuffs turned down, after falling a year ear
lier. Excluding foods and energy, the indexes for
intermediate goods and crude materials posted
declines. (See table 1.)
Energy goods
Falling prices for both natural gas and petroleumbased commodities pushed energy prices down
in 2001 at all three stages of processing. The
crude energy index dropped 52.9 percent, com
pared with an 85.6-percent jump in 2000. This
decrease was primarily the result of declining
prices for natural gas and crude petroleum. Prices
for energy goods at the intermediate stage of
processing fell 16.9 percent, subsequent to a 19percent gain a year earlier. The indexes for jet
fuels, diesel fuel, and industrial and commercial
natural gas registered declines in 2001, after ris
ing in the prior year. At the final stage of pro
cessing, the index for finished energy goods de
creased 17.1 percent, following a 16.6-percent
advance in 2000. Falling prices were observed
for gasoline, residential natural gas, liquefied pe
troleum gas, and home heating oil. (See table 2.)
Monthly Labor Review
July 2002
3
Producer Prices, 2001
Table 1.
Annual percent changes for major categories of the Producer Price Index by stage of processing, 1992-2001
Index
1992
1993
1994
1995
1996
1997
Finished goods..............................................
Foods .........................................................
Energy.......................................................
Other..........................................................
1.6
1.6
-.3
2.0
0.2
2.4
—4.1
.4
1.7
1.1
3.5
1.6
2.3
1.9
1.1
2.6
2.8
3.4
11.7
.6
-1.2
-.8
-6.4
0
Intermediate materials, supplies,
and components........................................
Foods and feeds........................................
Energy.......................................................
Other..........................................................
1.0
-.5
.7
1.2
1.0
5.5
—4.2
1.6
4.4
—4.5
2.9
5.2
3.3
10.3
1.1
3.2
.7
2.1
11.2
-.9
Crude materials for further processing.........
Foodstuffs and feedstuffs........................
Energy.......................................................
Other..........................................................
3.3
3.0
2.3
5.7
.1
7.2
-12.3
10.7
-.5
-9.4
-.1
17.3
5.5
12.9
3.7
-4.2
14.7
-1.0
51.2
-5.5
Table 2.
1999
2000
2001
0.0
.1
-11.7
2.5
2.9
.8
18.1
.9
3.6
1.7
16.6
1.3
-1.6
1.8
-17.1
.9
-.8
-1.7
-7.0
.3
-3.3
-7.3
-12.1
-1.6
3.7
-4.2
19.6
1.9
4.1
3.6
19.0
1.6
-4.0
.3
-16.9
- 1 .6
-11.3
-4.0
-23.1
0
-16.7
-11.0
-23.8
-16.0
15.3
-.1
36.9
14.0
35.5
7.4
85.6
-5.5
-32.5
-7.6
-52.9
-9.9
Annual percent changes in Producer Price Indexes for selected energy items, 1996-2001
Index
1996
1997
Finished energy goods...........................................
Gasoline................................................................
Residential natural g a s .........................................
Liquefied petroleum g a s........................................
Home heating o il...................................................
Residential electric power.....................................
11.7
27.1
11.2
71.4
25.0
.6
-6.4
-15.0
2.4
-29.3
-21.7
-.2
Intermediate energy goods.....................................
Jet fuels................................................................
Diesel fuels...........................................................
Industrial natural g a s ............................................
Commercial natural gas.........................................
Natural gas to electric utilities..............................
Residuaifuel.........................................................
Industrial electric power........................................
Commercial electric power....................................
11.2
26.1
26.2
22.3
16.8
6.1
32.8
0
-.1
Crude energy materials...........................................
Natural g a s ...........................................................
Crude petroleum....................................................
Coal........................................................................
51.2
92.0
35.8
-1.1
1999
2000
2001
-11.7
-33.1
-2.4
-32.6
-36.1
-2.5
18.1
74.8
.9
87.0
89.4
-.5
16.6
17.2
41.8
49.3
37.0
3.2
-17.1
-33.1
-22.1
-55.3
-42.9
3.6
-7.0
-22.3
-22.5
3.1
.9
9.3
-7.6
.5
0
-12.1
-35.8
-33.8
-9.7
-4.7
-24.3
-39.8
-1.3
-1.8
19.6
90.9
86.4
7.4
4.1
15.6
91.1
-.1
.6
19.0
42.6
39.8
91.9
56.0
83.1
29.8
4.9
4.4
-16.9
-44.3
-44.7
-36.7
-24.3
-39.9
-29.1
3.2
4.4
-23.1
-27.9
-28.3
4.9
-23.8
-17.8
-48.6
-1.2
36.9
7.9
172.0
-9.3
85.6
192.6
11.0
0
-52.9
-65.6
-42.4
10.1
Natural gas. The last 5 years have shown volatile price move
ments within the natural gas market, especially looking at the
years 2000 and 2001. Prices were relatively lower and more
stable in 1998 compared with 1997. Throughout the 1998-99
winter heating season, the natural gas index fell, but at a smaller
magnitude compared with the previous season. Mild winter
weather caused less demand for consumption, and therefore
resulted in higher storage levels. By the winter of 1999-2000,
weather conditions continued to be much warmer than ex
pected; therefore, prices remained low.
Natural gas prices began to rise considerably in 2000 when
the combination of decreasing supplies, high crude oil prices,
and weather-related demand pushed natural gas prices to new
heights. Demand for natural gas rose as consumers began
switching from higher-priced crude oil to lower-priced natural
gas. During the spring and early summer of 2000, supplies
began to tighten, causing the price of natural gas to climb
throughout the rest of the year.
After surging 192.6 percent in 2000, the PPI for natural gas
decreased 65.6 percent for the 2001 calendar year. Prices be
4
1998
Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
1998
gan to drop significantly in February and March as milder
temperatures moved into the high consumption regions, caus
ing storage levels to rebound. Mild temperatures continued
through October, which allowed prices to continue to decline.
In November, the natural gas index shot up 56.4 percent as a
result of colder weather. By the end of the year, mild weather
pushed gas storage stocks to record levels, which helped
push prices down again.1
For the 12 months ended in December 2001, industrial and
commercial natural gas prices declined 36.7 and 24.3 percent
respectively, falling to their lowest levels since May 2000. A
sharp decrease in spot natural gas prices pulled gas prices
substantially lower from their record highs in January 2001,
when the conjunction of falling supplies, rising crude oil
prices, and higher weather-related demand helped push natu
ral gas prices to unprecedented heights. After an 83.1-per
cent jump in 2000, the index for natural gas to electric utilities
fell 39.9 percent in 2001. The index for residential natural gas
decreased 22.1 percent in 2001, following a 41.8-percent gain
in 2000. After January 2001—the peak of residential natural
gas prices—this index declined sharply as a result of increased
production levels and record supply numbers. In December
2001, residential natural gas prices were at their lowest point
since May 2000.
The liquefied petroleum gas (LPG) index decreased 55.3
percent in 2001, after an increase of 49.3 percent in 2000. Prices
fell with the help of mild weather throughout the summer and
fall of 2001. Over the last 6 years, liquefied petroleum gas
prices have experienced a period of volatility. In 1996, prices
increased as a cold winter season created higher demand and
a large drop in inventories. By the start of 1997, however,
prices began to fall and continued in a downward trend until
the end of 1998. This period was marked by decreasing de
mand due to warmer-than-normal temperatures, restored in
ventories, higher production levels, and strong imports in the
LPG market. Lower prices in the crude petroleum and natural
gas markets also helped lower the LPG index for 1997 and
1998. From January 1999 until February 2001, LPG prices ral
lied with rising prices for crude oil and natural gas.
Petroleum-based products. Prices for crude petroleum
dropped 42.4 percent in 2001, following an 11 -percent gain in
2000 and a 172-percent surge in 1999. In March of 1999, both
Organization of Petroleum Exporting Countries (OPEC) and
major non-OPEC member countries announced an agreement
to reduce oil production, in order to bolster lackluster prices
from previous years. Throughout 1999, oil prices increased
dramatically, which ultimately resulted in OPEC receiving in
ternational pressure to raise their output. In March of 2000,
OPEC decided to raise output by 1.7 million barrels per day.2
Consequently, production from OPEC members increased
throughout the year 2000. However, prices continued to climb
as worldwide economic growth generated demand that out
paced the increased supply. By 2001, the combination of im
proved oil supplies and lower demand due to the economic
recession in the United States helped bring prices down from
their previous year’s level.
Looking further down the pipeline for petroleum-based
products, the index for jet fuels fell 44.3 percent in 2001, after
advancing 42.6 percent a year earlier. From the spring of 1999
through the summer of 2000, jet fuel prices rose due to the
large increase in crude oil prices during the same period. After
peaking in September 2000, prices finally leveled off with the
increase in inventories and began falling in the early part of
2001 as a result of declining oil prices. Starting in April, vola
tile supply levels created a price roller coaster for jet fuels
causing the index to jump up in May, fall in July, and rebound
in early September. Following the events surrounding the
September 11, 2001, terrorist attacks, prices once again
dropped due to falling demand.
The PPI for gasoline declined 33.1 percent, following a 17.2percent increase in 2000. Prices were relatively stable up until
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Federal Reserve Bank of St. Louis
April, when gasoline shortages caused a significant rise in
prices. Refineries then greatly advanced production, pulling
prices down considerably during the late spring. In response
to lower prices, refineries once again adjusted and set pro
duction levels lower. These production cuts, combined with a
late summer surge in demand, put upward pressure on gaso
line prices. Demand, however, declined due to the events of
September 11th and the end of the driving season. Further
more, OPEC’s inability to reduce global output resulted in an
excess supply of oil in the world market, causing a substantial
drop in gasoline prices.
In 2001, prices for diesel fuel decreased 44.7 percent, com
pared with a 39.8-percent rise in the previous year. For the
first quarter of 2001, prices declined as distillate supplies in
creased. By April, distillate production began to taper off
because refineries shifted more of their resources to gasoline
production. Hence, prices climbed in the spring. Prices were
then pulled back down in the summer as a result of oversup
ply. Due to the slowing of the U.S. economy, prices collapsed
more rapidly in the last quarter o f2001.
The downward trend in the natural gas market helped lower
residual fuel prices. The index for residual fuel fell 29.1 per
cent, following a 29.8-percent gain in 2000. Also adding to the
downward price pressure were the events of September 11th,
the contracting economy, the drop in oil prices, and unsea
sonably warm temperatures.
Home heating oil prices decreased 42.9 percent in 2001,
compared with a 37-percent rise in the prior year. The supply
of distillates remained quite low throughout the summer of
2000, and consequently put upward pressure on home heat
ing oil prices. Supplies then rebounded in the beginning of
October and continued through March o f2001, pulling down
prices for this period. In April and May, the rising tide of
gasoline prices carried other petroleum-based products along
with it, including home heating oil. Prices then dropped in the
summer months as supplies once again began to climb. The
months of August and September saw a brief rebound in the
home heating oil index resulting from increased demand. Like
other petroleum-based commodities, prices then collapsed
following the events of September 11th.
Electric power. The PPI for residential electricity increased
3.6 percent in 2001, after rising 3.2 percent in the previous
year. Subsequent to price increases in 2000, the indexes for
commercial electric power and industrial electric power ad
vanced 4.4 percent and 3.2 percent, respectively. Residential
electricity prices continued to increase through the first half
of 2001 due to the ongoing crisis in California. Contrary to
predictions, the crisis in California failed to widen in the sum
mer o f2001 as lower fuel costs, cooler summer temperatures,
and increased conservation within the State prevented the
crisis from escalating further. However, the higher prices
Monthly Labor Review
July 2002
5
Producer Prices, 2001
brought on by the crisis did not subside much by the end of
the year. Electricity prices as a whole continued to be high
due to the rate increases in California and the Pacific North
west. Drought conditions in the Pacific Northwest lowered
reservoirs, putting a strain on the hydroelectric power indus
try found in that region.
Foods and related products
The producer price index for finished consumer foods ad
vanced 1.8 percent in 2001, following a 1.7-percent increase in
the previous year. Leading this gain, prices for fresh fruits
and melons rose 24 percent during 2001. The indexes for dairy
products, soft drinks, processed fruits and vegetables, and
pork also rose, contributing to the overall increase. By con
trast, prices for eggs for fresh use, beef and veal, and finfish
and shellfish turned down in 2001, partially offsetting the rise
in finished consumer food prices. (See table 3.)
Prices for intermediate foods and feeds rose 0.3 percent in
2001, after increasing 3.6 percent in 2000. Most of this decel
eration can be traced to the index for prepared animal feeds
that fell 3.6 percent in 2001, following an 8.3-percent gain in
the preceding year. The index for beef and veal also fell in the
current year, after posting an increase in 2000, while prices for
fluid milk products and flour rose at a slower pace in 2001 than
they did in the year before. Partly offsetting the intermediate
foods and feeds deceleration, crude vegetable oils; refined
sugar; and natural, processed, and imitation cheese prices
turned up in 2001, after falling in the prior year. The index for
1 0 3 ^ 9
confectionery materials escalated at a faster rate in 2001 than
in the year before.
Following a 7.4-percent increase in 2000, the index for crude
foodstuffs and feedstuffs posted a 7.6-percent decline in 2001.
Contributing most significantly to this deceleration, the index
for slaughter cattle fell 15.1 percent in 2001, after advancing
9.1 percent in the previous year. Prices for slaughter hogs and
soybeans also posted declines in the current year, after in
creasing in 2000. Fluid milk, wheat, and com prices moved
upward at a slower rate in 2001 than they did in 2000. Alterna
tively, the indexes for fresh and dry vegetables and for fresh
fruits and melons turned up in 2001.
Fruits and melons. Prices for fresh fruits and melons turned
up 24 percent in 2001, following a 1.3-percent drop in the pre
vious year. Underlying this increase, the index for strawber
ries shot up 95.7 percent in 2001, after rising 25 percent in
2000. The index for citrus fruits turned up as a result of price
increases for navel oranges, lemons, and grapefruits. Price
increases in 2001 were also registered for red delicious apples
and pears. On the other hand, prices for McIntosh and Granny
Smith apples turned down in 2001.
Grains. Com prices rose 2.8 percent in 2001, after increasing
7.8 percent in the preceding year. The index for com exhibited
a spike in July, rising 16.2 percent as a result of decreased
supplies due to abnormally hot weather. In addition, elevated
com prices for 2001 were the result of increased foreign con
sumption of U.S. com due to diminished production from for-
Annual percent changes in Producer Price Indexes for selected food items, 1996-2001
----------------------- ,............... ... .. ......................................
Index
1996
1997
1998
1999
2000
2001
Finished consumer fo o ds....................
Fresh fruits and melons....................
Dairy products...................................
Soft drinks.........................................
Pork...................................................
Fresh and dry vegetables.................
Bakery products................................
Processed poultry.............................
Unprocessed and packaged fis h ......
Beef and v e a l....................................
Eggs for fresh use.............................
3.4
37.2
2.4
.1
21.9
-24.3
3.6
2.6
5.1
7.4
15.0
-0.8
-8.2
4.7
-1.0
-13.6
21.6
1.1
-6.3
4.7
-5.4
-15.6
0.1
-19.0
10.7
1.9
-27.3
8.8
1.0
3.8
-3.4
-2.7
-6.2
0.8
8.2
-11.1
3.3
29.8
4.4
1.6
-3.7
8.8
10.8
-27.4
1.7
-1.3
3.2
3.6
5.0
-23.7
2.7
1.1
.6
8.2
46.3
1.8
24.0
2.3
3.0
4.7
9.7
2.1
1.4
-7.8
-4.5
-27.5
Intermediate foods and feeds...............
Crude vegetable o ils .........................
Refined sugar....................................
Confectionery materials....................
Flour...................................................
Prepared animal feeds......................
2.1
-9.3
4.2
2.2
-9.0
5.4
-1.7
13.9
-4.5
-15.8
-8.2
-3.1
-7.3
-2.7
.6
-1.0
-5.6
-20.4
-4.2
-37.5
-2.2
1.7
-7.5
-2.7
3.6
-16.5
-9.6
.7
7.9
8.3
.3
15.9
6.3
15.1
4.1
-3.6
Crude foodstuffs and feedstuffs..........
Slaughter cattle.................................
Slaughter h o g s ..................................
Soybeans ..........................................
W heat................................................
Corn...................................................
Fluid m ilk............................................
-1.0
-2.5
23.2
-3.7
-19.3
-21.0
1.1
^f.O
2.0
-21.7
1.8
-11.3
2.2
2.8
-11.0
-12.0
-76.8
-21.3
-15.0
-22.5
25.6
-.1
19.4
266.9
-17.5
-13.9
-12.4
-31.3
7.4
9.1
14.9
9.9
13.9
7.8
7.0
-7.6
-15.1
-24.9
-12.5
1.7
2.8
3.0
6
Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
eign suppliers. Following July’s upsurge, com prices deceler
ated through October 2001.
The index for wheat posted a 1.7-percent gain for the 12
months ended December 2001, following a 13.9-percent in
crease in 2000. Prices fluctuated throughout 2001; however,
the yearly increase can be attributed mostly to a 9.4-percent
rise in May. Wheat supplies, which were at their lowest level
since 1988 due to adverse weather conditions, caused prices
to increase.3 Due to planting flexibility allowed for under
current Government programs, wheat crops were substituted
by other crops that yielded higher returns.
Soybean prices fell 12.5 percent over the past 12 months,
compared with a 9.9-percent advance in 2000. In addition to
declines in January, February, and April, the soybean index
posted price decreases for the last 4 months o f2001. Most of
this annual decline can be attributed to record supplies that
exceeded 3 billion bushels in 2001.4 Soybean production in
creased, “in part because the soybean loan rate has supported
expected returns and because per-acre costs of fertilizer and
energy inputs are lower than those of com,” according to the
U SD A .5
Lower prices for soybeans led to depressed prices for pre
pared animal feeds in 2001. The index for prepared animal
feeds fell 3.6 percent in 2001, following an 8.3-percent gain in
the prior year. U.S. feed grain production increased 4 percent
in 2001 from the year before, contributing to the fall in pre
pared animal feed prices.6
Meats. The PPI for slaughter livestock fell 16.7 percent in
2001, after moving up 9.8 percent in the previous year. Driv
ing this downturn, prices for slaughter cattle dropped 15.1
percent over the 12 months ended December 2001, compared
with a 9.1 -percent advance in 2000. This significant decrease
resulted from the record numbers of cattle remaining in feed
lots without bids from packing houses. Decreased demand,
both foreign and domestic, could not meet the overwhelming
supply of slaughter cattle in 2001. Due to reduced travel and
dining in the United States throughout the fall, domestic de
mand for slaughter cattle waned. Japan, which makes up ap
proximately 50 percent of foreign beef exports, had reduced
demand for slaughter cattle since the detection of mad cow
disease (bovine spongiform encephalopathy), which had
raised concern about the safety of beef products.
Transmission of weakened slaughter cattle prices led to
declines in beef and veal prices over 2001. The PPI for beef
and veal dropped 4.5 percent in 2001, following an 8.2-percent
gain in the preceding year. Excess supply due to increased
slaughter numbers and weights put downward pressure on
beef and veal prices. In addition, the weakened economy in
the second half of 2001 reduced demand by the restaurant
and hotel industry for beef products. Similar to the slaughter
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cattle market, foreign demand dropped in 2001 due to anxiety
about mad cow disease and hoof-and-mouth disease in Japan
and Europe.
Slaughter hog prices also exhibited a significant drop, fall
ing 24.9 percent in 2001, after advancing 14.9 percent in 2000.
This decline, like that of slaughter cattle prices, can be traced
to reduced demand from the restaurant and hotel industry
throughout the second half of 2001. Increased supplies as
well as record weights of existing hogs also applied down
ward pressure on slaughter hog prices. In spite of declining
slaughter hog prices, the index for pork rose in 2001, although
at a slower rate than it did in the prior year.
Dairy products. Posting monthly price increases from March
through September, fluid milk prices moved up 3 percent in
2001, compared with a 7-percent gain in the previous year.
Most of this price increase can be traced to diminished sup
plies as a result of inclement weather in top milk-producing
States. Bad winter weather makes dairy cattle less productive
and increases the cases of mastitis (inflammation of the ud
der) among cattle, which also hinders their ability to produce
milk. Winter weather conditions can also stop milk from arriv
ing at the processor before it spoils. Excessive heat in sum
mer months, especially in California, also caused stress to
dairy cattle, decreasing the milk-per-cow ratio. In addition to
weather problems, the tight supplies of top forage, such as
high-quality alfalfa hay, added to the overall reduction of fluid
milk supplies. The high prices of replacement cattle also left
many dairy farms operating below capacity.
At later stages of processing, dairy product prices rose 2.3
percent over the 12 months ended December 2001. The in
crease in fluid milk prices, as well as energy problems in Cali
fornia, the top milk-producing State, led to decreased sup
plies of dairy products. The rolling blackouts and high en
ergy prices in California cut milk processing times and spoiled
some milk in refrigeration. Increased demand for cheese; ice
cream; butter; and dry, condensed and evaporated milk prod
ucts also contributed to the rise in dairy product prices in
2001.
Finished goods other than foods
and energy
The PPI for finished goods other than foods and energy—the
core index—rose 0.9 percent in 2001, after increasing 1.3 per
cent in the previous year. Capital equipment prices showed
no change, following a 1.2-percent gain in 2000. The index for
finished consumer goods other than foods and energy in
creased 1.5 percent in 2001, after rising 1.4 percent a year
earlier.
Monthly Labor Review
July 2002
7
Producer Prices, 2001
ger cars; however, prices for heavy trucks rose. A 3.7-percent
fall in the October passenger car index represented the largest
monthly decline in the index since a 5.2-percent decrease in
October 1972. Manufacturer incentives, including 0 percent
financing, were primarily responsible for driving down light
truck and passenger car prices, helping to boost U.S. automo
bile sales to their second highest level on record.8 Light truck
sales finished 2 percent higher in 2001 than in the prior year.
Within capital equipment, the index for civilian aircraft
moved up at a slower pace in 2001 than it did in the previous
year. Prices for light motor trucks, metal-cutting machine tools,
truck trailers, and construction machinery and equipment
turned down, after increasing in 2000. The indexes for elec
tronic computers and passenger cars fell more than they did
in the prior year. By contrast, prices for x-ray and
electromedical equipment, and for office and store machines
and equipment advanced in 2001, after declining a year earlier.
The communications and related equipment index fell less than
it did in the previous year. Prices for pumps, compressors,
and equipment, and agricultural machinery and equipment
advanced at a faster rate than in 2000.
The index for finished consumer goods other than foods
and energy moved up 1.5 percent in 2001, after advancing 1.4
percent a year earlier. Rising prices for cigarettes, alcoholic
beverages, book publishing, newspaper circulation, house
hold furniture, sanitary papers and health products, pet food,
and periodical circulation outweighed falling prices for light
motor trucks, passenger cars, m en’s and boy’s apparel,
women’s apparel, and floor coverings. (See table 4.)
Electronic computers. Prices for electronic computers
dropped 29.9 percent in 2001, after showing a 14.2-percent
decline in the previous year. Prices fell sharply for personal
computers and workstations (31.6 percent); mid-range gen
eral purpose computers (34.5 percent); large-scale general
purpose computers (31.2 percent); and portable computers
(31.5 percent). Manufacturers of electronic computers ben
efited from declining input costs in 2001, as MOS memory
prices dropped 40.7 percent, MOS microprocessor prices fell
38.7 percent, and prices for computer storage devices de
creased 12.9 percent.
Tobacco products and alcohol. The index for tobacco prod
ucts rose 12.6 percent in 2001, following a 2.3-percent gain in
2000. The majority of the increase in tobacco prices was due
to a 14.1-percent jump in the index for cigarettes, which fol
lowed a 1.9-percent gain in 2000. In January and May, to
bacco manufacturers instituted two 14-cent-per-pack price
increases to offset losses from a $206 billion legal settlement
with 46 U.S. States, causing the index to rise significantly in
these 2 months.9 Tobacco producers also raised prices in
anticipation of a 5-cent-per-pack increase in the Federal ex
cise tax on cigarettes that went into effect January 2002. Alco
hol prices advanced 2.6 percent in 2001, following a 4.2-per
cent rise in 2000. Accounting for the majority of this gain, the
index for malt beverages increased at a 3.3-percent rate, after
rising at a 4.4-percent rate in 2000. Advancing prices for grains
Civilian aircraft. The index for civilian aircraft increased 3.8
percent in 2001, following a 6.7-percent gain in 2000. Prices
for civilian aircraft rose throughout the majority o f2001; how
ever, price increases slowed toward the end of the year, and
the index declined in September and November (the index had
not fallen since August 1999). The slower rate of increase for
civilian aircraft prices in the latter months of 2001 resulted
from declining sales, as civilian aircraft shipments fell to 3,483
in 2001, down from 3,780 in 2000.7 Within civilian aircraft,
sales for general aviation aircraft and helicopters decreased,
and sales for transport aircraft advanced.
Motor vehicles. Prices for motor vehicles declined signifi
cantly in 2001, as prices fell for both light trucks and passen
| Annual percent changes in Producer Price Indexes for selected finished goods other than foods and energy,
1996-2001
1997
1996
Index
1998
1999
2000
2001
Finished goods other than foods and energy........
0.6
0.0
2.5
0.9
1.3
0.9
Finished consumer goods less foods and energy ..
Cigarettes..........................................................
Alcoholic beverages..........................................
Books................................................................
Newspapers.......................................................
Sanitary papers and health products...............
Men’s and boys’ apparel....................................
Passenger ca rs.................................................
Light trucks.......................................................
.8
3.3
3.8
3.2
4.2
2.6
.3
10.0
4.2
49.4
1.5
4.1
1.1
- .6
.6
.5
1.0
1.2
9.6
.6
1.8
1.4
1.0
- .3
1.2
.3
1.4
1.9
4.2
3.4
4.3
2.7
.2
- .7
1.8
1.5
14.1
2.6
3.4
3.2
1.6
- 1.7
- 1.6
- 3.3
.3
2.1
19.7
1.4
- 1.9
1.4
1.2
6.7
14.2
.9
- 1.3
0
3.8
29.9
-.1
Capital equipment...................................................
Civilian aircraft...................................................
Computers.........................................................
Construction machinery.....................................
Communication and related equipment.............
Heavy trucks.....................................................
8
Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
-
-.5
-
1.1
- .8
.2
-
.4
3.2
22.3
1.8
1.5
- 4.5
-
-
3.3
.1
2.0
.2
2.6
3.6
- .6
.5
21.6
1.9
.8
.6
-
0
.5
26.6
1.7
- 1.1
3.9
-
-
-
.7
-
-.7
.3
in 2001 may have contributed to the rise in the malt beverage
index. The indexes for wine and brandy and for distilled spir
its also increased in 2001.
Newspaper circulation and book publishing. In 2001, the
index for newspaper circulation rose 3.2 percent, after increas
ing 4.3 percent a year earlier. Price increases were observed
for subscriptions and sales of both daily and weekly publica
tions. The book publishing index advanced 3.4 percent, fol
lowing a similar increase in 2000. Prices moved up for the
publication of text books; technical, scientific, and profes
sional books; general books; pamphlets; and religious books;
however, prices fell for the publication of general reference
books.
Interm ediate industrial m aterials
The PPI for intermediate materials other than foods and en
ergy decelerated, falling 1.6 percent in 2001, following a 1.6percent gain in the previous year. Prices also turned down for
nondurable manufacturing materials and durable manufactur
ing materials. The index for construction materials showed no
change after inching up in 2000. (See table 5.)
Nondurable manufacturing materials. Prices for nondurable
manufacturing materials dropped 5.5 percent in 2001, follow-
Table 5.
ing a 4.1 -percent increase in 2000. Not since 1951, when the
index closed down 6.1 percent, has the nondurable manufac
turing materials index fallen at such a steep rate over a calen
dar year. Much of the 2001 deceleration was the result of a
downturn in prices for basic organic chemicals. The indexes
for nitrogenates, plastic resins and materials, paperboard,
woodpulp, and paper also decreased in 2001, after advancing
in the prior year. By contrast, prices for fats and oils (inedible)
turned up in 2001, after falling a year earlier. Prices for phos
phates declined less than they did in 2000.
Prices for basic organic chemicals moved down 11.6 per
cent in 2001, after rising 5.8 percent a year earlier. The index
for primary basic organic chemicals decreased 29.5 percent,
following a 13.4-percent gain in 2000. Intermediate basic or
ganic chemical prices fell at a faster rate than they did in the
previous year. Petroleum is a major input to primary basic
organic chemicals, which include aromatics (not made in a
refinery), liquefied refinery gases, and other basic organic
chemicals, making primary basic organic chemical prices es
pecially sensitive to changes in the petroleum market. In 2001,
a 42.4-percent drop in crude petroleum prices coupled with
weak demand put downward pressure on basic organic chemi
cal prices.
Price decreases were widespread within the pulp and pa
per products industry in 2001. Woodpulp prices plummeted
24.3 percent, falling each month of the year with the excep-
Annual percent changes in Producer Price Indexes for selected intermediate and crude materials other than
foods and energy, 1996-2001
2000
2001
1.9
1.6
-1.6
4.0
6.9
2.2
15.9
12.1
13.0
2.8
4.1
5.8
44.9
2.2
14.8
10.6
4.1
-5.5
-11.6
-25.5
-9.8
-24.3
-7.0
-3.1
2.4
-2.4
4.2
8.6
1.6
-.2
10.3
.2
-.6
4.7
3.8
-.9
-6.2
-9.3
-4.0
-6.1
-2.9
-9.5
1.0
-1.9
-3.0
.1
-2.2
-4.6
-1.2
-10.1
.2
7.3
2.2
5.6
.3
3.5
10.1
2.4
23.1
.1
1.6
4.6
2.4
-14.5
.5
-27.1
0
-2.7
-4.0
-5.0
-2.4
1.7
.4
-16.0
-8.0
-10.0
-28.9
-39.9
14.0
-20.8
6.6
110.5
40.0
-5.5
30.2
4.4
-18.5
-28.8
-9.9
-46.7
-11.2
-30.2
-5.6
1996
1997
1998
Intermediate goods other than foods and
energy...............................................................
-0.9
0.3
-1.6
Nondurable manufacturing materials..................
Basic organic chemicals....................................
Nitrogenates.......................................................
Plastic resins and materials...............................
Woodpulp...........................................................
Paperboard........................................................
Paper.................................................................
-3.3
3.6
5.9
4.2
-33.0
-19.0
-14.2
.3
-1.7
-13.5
-2.8
4.1
5.8
3.8
-5.3
-6.4
-19.0
-13.4
-12.5
-8.0
-4.1
Durable manufacturing materials.........................
Steel mill products.............................................
Aluminum mill shapes.........................................
Copper and brass mill shapes............................
Cement...............................................................
Plywood.............................................................
Building paper and b oard...................................
-1.4
-1.4
-7.9
-10.6
5.0
-1.3
-5.8
0
.5
6.8
-6.5
3.5
-1.1
-2.0
-5.5
-6.5
-8.5
-11.5
5.2
4.9
-1.3
Construction materials........................................
Plastic construction products............................
Nonferrous wire and cable.................................
Hardwood lumber................................................
Softwood lum ber................................................
Millwork..............................................................
Gypsum products..............................................
1 .&
-1.1
-3.1
1.6
19.6
3.5
6.6
1.2
-2.0
-2.2
7.4
-3.8
1.0
7.1
Crude nonfood materials less energy.................
Raw cotton.........................................................
Nonferrous metal ores........................................
Wastepaper........................................................
Iron and steel scrap...........................................
-5.5
-13.0
-16.8
-1.3
-11.1
0
-11.2
-18.0
11.6
14.5
Index
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Federal Reserve Bank of St. Louis
1999
Monthly Labor Review
July 2002
9
Producer Prices, 2001
tions of November and December. Weak demand, resulting
from a slow domestic economy, in conjunction with high sup
plies of woodpulp drove down prices. Paper prices turned
down 3.1 percent in 2001, following a 4.1 -percent increase in
the previous year. The paper index rose in the first 4 months
o f2001 in spite of falling prices for woodpulp, a major input to
paper, but then showed eight consecutive decreases—more
closely reflecting the decline in price for woodpulp. Within
paper, the indexes for newsprint, writing and printing papers,
and packaging and industrial converting paper all decreased
in 2001. The index for paperboard moved down 7 percent in
2001, after rising 10.6 percent in the previous year, as prices
fell for the first 11 months in 2001. Price changes for woodpulp,
paper, and paperboard are closely related because woodpulp
is a major input for both paper and paperboard. (See chart 1.)
Prices for plastic resins and materials declined in 2001, fall
ing 9.8 percent. Both the indexes for thermoplastic resins and
thermosetting resins decreased, after advancing a year earlier.
Falling prices for crude petroleum and the economic down
turn were the most likely causes of declining prices for plastic
resins and materials.
Durable manufacturing materials. The index for durable
manufacturing materials fell 4 percent in 2001, after increasing
0.2 percent in the prior year. Prices for aluminum mill shapes,
cold rolled sheet and strip, primary aluminum (except extru
sion billet), copper and brass mill shapes, and hardwood lum
ber turned down, after rising in 2000. The indexes for hot
rolled sheets and strip and hot rolled bars, plates, and struc
tural shapes declined at a faster pace in 2001 than in the previ
ous year. On the other hand, prices for building paper and
board, plywood, and semi-finished steel mill products fell less
in 2001 than they did a year earlier. The index for cement
advanced, after moving down in 2000.
Prices for steel mill products declined substantially in 2001
as the indexes for cold rolled sheet and strip; hot rolled sheet
and strip; hot rolled bars, plates, and structural shapes; and
semi-finished steel mill products fell 9.5, 8.2,4.3, and 0.2 per
cent, respectively. Steel producers faced fierce import compe
tition throughout 2001, causing the steel mill product index to
decline 11 months out of the year. Domestic steel manufactur
ers complained of unfair foreign competition, prompting the
Bush Administration to request the International Trade Com
mission to investigate whether restrictions on steel imports
were needed.
The index for aluminum dropped 14.5 percent in 2001, reg
istering 8 monthly price declines during the year, after moving
up 3.3 percent in 2000. The American Metal Market reported
that primary aluminum production levels declined to a 3 3-year
low in 2001, reflecting extremely weak demand conditions. In
Chart 1. Paper-related indexes
Index level
Index level
225
225
Paperboard
200
200
Woodpulp
175
175
150
150
125
125
100
100
Jan-82
10
Jan-84
Monthly Labor Review
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Federal Reserve Bank of St. Louis
Jan-86
Jan-88
July 2002
Jan-90
Jan-92
Jan-94
Jan-96
Jan-98
Jan-00
addition, high producer inventories contributed to falling alu
minum prices in 2001. Prices for aluminum mill shapes fell 2.9
percent in 2001, following a 4.7-percent increase in the previ
ous year. Decreasing aluminum prices may have been par
tially responsible for the fall in the index for aluminum mill
shapes.
In 2001, the index for building paper and board fell 3.0 per
cent, following a 9.3-percent drop a year earlier. Accounting
for the majority of the decline in building paper and board
prices, the hardboard, particleboard, and fiberboard product
index moved down 3.3 percent. The index for plywood de
creased 1.9 percent in 2001, after declining 6.2 percent in the
previous year. Falling prices for softwood plywood were pri
marily responsible for the drop in plywood prices. Manufac
turers of softwood plywood benefited from lower prices for
softwood lumber, which decreased 2.4 percent in 2001. Al
though the index for plywood finished down for the year,
prices exhibited a high degree of volatility, resulting from un
certainty surrounding the March 31,2002, termination of the
Canada-U.S. Softwood Lumber Agreement.10 Under the agree
ment, Canada was entitled to unlimited access to the U.S.
market without threat of trade action.
Materials and Components fo r Construction. The PPI for
materials and components for construction showed no change
in 2001, after edging up 0.1 percent in the prior year. Falling
prices for plastic construction products, nonferrous wire and
cable, hardwood lumber, softwood lumber, fabricated struc
tural metal products, plywood, and wiring devices offset ris
ing prices for millwork, asphalt felts and coatings, switchgear
and switchboard equipment, air conditioning and refrigera
tion equipment, and metal valves (except fluid power).
From December 2000 to December 2001, prices for plastic
construction products fell 2.7 percent. Manufacturers were
able to lower output prices as input costs declined. Prices for
plastic resins and materials fell substantially in 2001 due to
weak demand resulting from a slow economy and plummeting
prices for crude petroleum.
Prices for nonferrous wire and cable declined 4 percent in
2001, following a 4.6-percent increase in 2000. Within nonfer
rous wire and cable, price declines for electric wire and cable,
telephone and telegraph wire and cable, control and signal
wire and cable, building wire and cable, apparatus wire and
cordage, power wire and cable, copper and copper alloy wire
and cable, aluminum wire and cable (bare), and fiber optic
cable outweighed price increases for magnet wire and for
appliance and flexible cord sets. Manufacturers of nonfer
rous wire and cable may have been able to pass decreasing
costs forward through the chain of production as prices for
copper base scrap, nonferrous metal ores, and primary nonferrous metals (except precious) all declined in 2001. Over
the same time period, prices for fabricated structural metal
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Federal Reserve Bank of St. Louis
products declined 0.4 percent, after increasing 0.3 percent in
2000.
The index for lumber decreased 3.2 percent in 2001, after
falling 9.6 percent in the previous year. Prices for hardwood
lumber moved down 5 percent, following a 2.4-percent increase
in 2000. After rising 10 percent in May and falling 5.9 percent
in July, the index for softwood lumber decreased 2.4 percent in
2001. As mentioned in the previous section, uncertainty sur
rounding the end of the Canadian-U.S softwood lumber agree
ment resulted in larger than normal price swings for softwood
lumber. Canadian producers, fearing retroactive tariffs on U.S.
exports, limited their supplies to U.S. markets, and U.S. buyers
held off purchasing in anticipation of a surge in Canadian
imports.
Basic industrial m aterials
Prices for basic industrial materials dropped 9.9 percent in
2001, following a 5.5-percent decline in the preceding year.
Contributing most significantly to this overall deceleration,
the index for raw cotton plunged 46.7 percent, after advancing
30.2 percent in 2000. Prices for nonferrous metal ores, copper
base scrap, and leaf tobacco turned down in 2001, while prices
for wastepaper, aluminum base scrap, and pulpwood fell at a
faster rate in 2001 than they did in the prior year. By contrast,
the index for iron and steel scrap fell 5.6 percent in 2001, com
pared with a 28.8-percent drop in 2000. Prices for softwood
logs, bolts, and timber also fell at a slower pace in 2001 than
they did in the previous year. The index for hardwood logs,
bolts, and timber turned up in 2001. (See table 5.)
Raw cotton prices dropped considerably in 2001, register
ing declining monthly prices from January through October.
Supplies of raw cotton increased approximately 17 percent in
2001 from the year before due to larger planting areas and
increased yield. Despite the drought in the southwestern
States, other areas of the Cotton Belt experienced decreased
abandonment levels and increased harvested areas. Cotton
exports rose in 2001; however, overall demand declined as a
result of decreased U.S. cotton mill consumption. Increases
in cotton textile and apparel imports, as well as the general
slowdown of the U.S. economy, weakened the demand for
cotton from the spinning and from the textile and apparel in
dustries. The gain in supplies and reduction in demand has
increased ending stocks to their highest level since 1986.11
Prices for nonferrous metal ores fell 11.2 percent in 2001,
turning down from a 4.4-percent gain in 2000. The index for
nonferrous scrap decreased 14.7 percent, following a 6.4-per
cent drop a year earlier. Prices for both nonferrous ores and
scrap declined as a result of weak demand from the automo
tive, construction, and aerospace industries. This diminished
consumption, which primarily affected the copper and alumi
num sectors, had been sliding since 1999. However, with the
Monthly Labor Review
July 2002
11
Producer Prices, 2001
downturn of the U.S. economy and the effects of the terrorist
attacks on September 11th, demand softened even further than
had been expected. The high inventories that remained from
2000 magnified the effects of the waning demand for nonferrous metals in 2001.
After dropping 18.5 percent in 2000, the PPI for wastepaper
decreased 30.2 percent over the 12 months ended December
2001. The index registered declining prices for the first 6
months of 2001 due to weakened demand. Decreased over
seas shipments and the weakening U.S. economy softened
demand for wastepaper, while large stocks leftover from 2000
forced consumers of wastepaper to reduce orders. During the
second half o f2001, prices rose minimally as demand began to
increase.
Iron and steel scrap prices declined at a slower rate in 2001
than in the prior year. The iron and steel scrap market experi
enced another year of falling prices due to continued weak
demand from the steel industry. This diminished demand was
most significant in the automobile and appliances sectors that
felt the effects of the weakening U.S. economy. A reduction in
scrap exports from Eastern Europe— especially Russia,
Ukraine, and Romania—helped to increase foreign demand
for U.S. steel scrap, and kept prices from falling further in
2001.
Selected service industries
A majority of the service industries tracked in the PPI exhib
ited advancing prices in 2001. Rising prices were registered
by the following indexes: property and casualty insurance;
grocery stores; offices and clinics of doctors of medicine;
general medical and surgical hospitals; new car dealers; skilled
and intermediate care facilities; real estate agents and manag
ers; legal services; drug stores and proprietary stores; opera
tors and lessors of non-residential buildings; United States
Postal Service; engineering design, analysis, and consulting
services; air transportation (scheduled); and home healthcare
services. Alternatively, prices declined for security brokers,
dealers, and investment bankers; telephone communications
(except radiotelephone); travel agencies; truck rental and leas
ing (without drivers); camera and photographic supply stores;
catalog and mail order houses; optical goods stores; and
trucking (except local) during 2001. (See table 6.)
The index for property and casualty insurance moved up
3.7 percent over the 12 months ended December 2001 due
partially to rising prices for private passenger automobile in
surance and homeowner’s insurance. Private passenger au
tomobile insurance advanced 5.9 percent in 2001, with signifi
cant increases in California and Florida. In Florida, cases of
automobile insurance fraud are currently being investigated
as causes for rising premiums. Prices for homeowner’s insur
ance rose 5.1 percent in 2001, with large gains in California,
12 Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
Florida, and Texas. The gain in prices resulted from an in
crease in claims surrounding mold problems that arise in warm
climates after water damage.
Prices for health services climbed upward in 2001, rising
3.1 percent over the 12 months. Contributing most signifi
cantly to this price increase was the index for offices and
clinics of doctors of medicine, which rose 2.8 percent in 2001.
Medicare increased payments to physicians by approximately
4.5 percent in 2001, allowing doctors to increase prices.12 The
Medicare increase affected not only direct Medicare payments
to physicians, but also the payments by private payers and
State Medicaid agencies that adjust contracts according to
Medicare rates. This increase in the payment rate, along with
a change in the Medicare payment system, caused prices for
physician’s offices to advance in 2001. The index for general
medical and surgical hospitals rose 2.7 percent in 2001 due to
increased labor, pharmaceutical and supply expenses, and in
creased liability insurance costs. The weakening economy
also decreased investment returns used to subsidize hospital
operating losses. Similarly to physician offices, prices for
skilled and intermediate care facilities and home healthcare
services rose partly as a result of increased Medicare pay
ments in 2001.
The grocery store index rose 5.6 percent in 2001. Margin
increases were influenced by rising produce, bakery, dairy,
and health and beauty care margins. On the other hand, the
index for convenience food stores dropped due to falling mar
gins for convenience food/gasoline stores.
From December 2000 to December 2001, prices for real es
tate agents and managers rose 1.6 percent. Leading this price
increase, prices for real estate brokerage (residential sales)
advanced 4.1 percent in 2001, due partly to an increase in the
median price of existing homes—to $ 147,500 in 2001, up from
$139,000 in 2000.13 This index also registered a decline in
prices between the third and fourth quarters o f2001, mirroring
the trend of median housing prices in those quarters. The
index for operators and lessors of nonresidential buildings
also moved up in 2001, posting a 1.3-percent gain.
Prices for the United States Postal Service advanced 7.5
percent from December 2000 to December 2001. The indexes
for first class mail, periodicals (second class mail), standard
class A mail (third class mail), and standard class B mail (fourth
class mail) moved up in 2001 as the United States Postal Ser
vice implemented rate increases in January and July. Postal
rates were raised to offset large projected losses for 2001.
Even after the January rate increase, the Postal Service pro
jected losses of up to $2.4 billion for the year 2001, leading to
the July rate hike.14
The index for security brokers, dealers, and investment
bankers dropped 13.2 percent in 2001. Falling share prices in
the stock market translated into lower fees for security bro
kers and dealers in 2001 as the S&P 500 index declined 14.4
Percent change in Producer Price Indexes for the net output of selected service industries, 1996-20G 1
SIC
code
Industry
Distribution:
Railroads, line-haul operating......................................................
Local trucking without storage....................................................
Trucking, except local.................................................................
Local trucking with storage.........................................................
Courier services, except by a ir...................................................
Farm product warehousing and storage......................................
Refrigerated warehousing and storage.......................................
General warehousing and storage..............................................
United States Postal Service......................................................
Deep sea foreign transportation of freight..................................
Deep sea domestic transportation of freight...............................
Freight transportation on the Great Lakes-St. Lawrence
Seaway.....................................................................................
Water transportation of freight, n.e.c..........................................
4449
Marine cargo handling.................................................................
4491
Tugging and towing services.......................................................
4492
Air courier services.....................................................................
4513
Airports, flying fields, and airport services.................................
4581
Crude petroleum pipelines...........................................................
4612
Refined petroleum pipelines........................................................
4613
Freight transportation arrangement.............................................
4731
Grocery stores.............................................................................
5411
Meat and fish (seafood) markets................................................
5421
Fruit and vegetable market.........................................................
5431
Candy, nut, and confectionery stores.........................................
5441
Retail bakeries.............................................................................
5461
Miscellaneous food stores..........................................................
5499
New car dealers..........................................................................
5511
Drug stores and proprietary stores.............................................
5912
Liquor stores................................................................................
5921
Sporting goods stores................................................................
5941
Book stores.................................................................................
5942
Stationery stores........................................................................
5943
Jewelry stores.............................................................................
5944
Hobby, toy, and game shops......................................................
5945
Camera and photographic supply stores....................................
5946
Gift, novelty, and souvenir shops...............................................
5947
Luggage and leather goods stores.............................................
5948
Sewing, needlework, and piece goods stores.............................
5949
Catalog and mail-order houses...................................................
5961
Automatic merchandising machine operators.............................
5962
Fuel dealers.................................................................................
598
Florists.........................................................................................
5992
Optical goods stores..................................................................
5995
Miscellaneous retail stores, n.e.c................................................
5999
4011
4212
4213
4214
4215
4221
4222
4225
4311
4412
4424
4432
2000-01
1996-97
1997-98
1998-99
1999-2000
1.0
.2
2.6
.6
3.8
2.0
.1
.7
0
-3.7
-.6
0.5
1.7
3.4
.5
4.2
.6
.5
2.9
0
4.7
.2
0.1
1.1
3.4
.5
3.4
5.3
1.2
2.6
2.2
22.9
1.2
1.8
4.2
6.3
1.4
4.4
1.6
1.7
3.0
0
12.8
4.8
2.3
2.8
-.4
1.0
2.6
3.3
1.2
3.0
7.5
7.4
2.1
1.4
-.4
1.2
2.2
-3.9
3.0
-3.7
1.2
-1.4
-
.8
-2.2
1.8
2.8
3.1
3.0
1.4
-1.1
-.6
—
-
-.1
8.1
1.5
2.9
5.1
3.9
-1.7
.3
-2.8
-.1
9.8
2.6
4.1
8.3
5.8
6.1
1.0
4.5
4.7
6.9
5.2
5.0
1.0
10.0
1.0
-
.1
-.5
1.1
.9
3.3
0
11.1
5.0
-2.7
5.6
2.1
1.3
3.4
3.3
15.6
3.0
6.3
1.9
8.8
-1.5
2.1
-1.6
1.0
-13.9
-1.0
1.3
-9.2
-1.5
.8
5.7
5.2
-8.9
-.6
“
-
-
—
—
“
—
—
—
”
-
-
-
—
—
“
—
—
—
—
“
”
“
—
—
—
—
—
“
-
4812
4813
4832
4841
Communications:
Wireless telecommunications......................................................
Telephone communications, except radiotelephone...................
Radio broadcasting.....................................................................
Cable and other pay television services.....................................
_
-.4
3.1
4.7
_
-1.7
.8
3.7
_
-3.0
7.7
3.3
-6.1
-1.7
4.9
5.7
-1.2
-4.0
-2.3
.8
6512
6531
Real estate:
Operators and lessors of nonresidential buildings.....................
Real estate agents and managers..............................................
2.2
1.4
1.2
2.6
5.7
1.5
1.3
4.6
1.3
1.6
7311
8111
8711
8712
8721
Professional, scientific, and technical:
Advertising agencies..................................................................
Legal services.............................................................................
Engineering design, analysis, and consulting services.............
Architectural design, analysis, and consulting services............
Accounting, auditing, and bookkeeping services......................
2.5
4.1
3.1
3.0
2.1
1.3
2.5
2.9
5.3
3.0
2.8
2.9
3.1
4.9
3.5
4.0
3.9
3.1
2.5
3.3
2.5
4.2
5.9
1.2
.6
8011
8053
8062
8063
8069
8071
8082
Healthcare:
Offices and clinics of doctors of medicine.................................
Skilled and intermediate care facilities.......................................
General medical and surgical hospitals......................................
Psychiatric hospitals........................................... .......................
Specialty hospitals, except psychiatric......................................
Medical laboratories....................................................................
Home healthcare services..........................................................
1.2
4.2
.5
-6.7
.6
.9
6.2
2.6
4.4
1.3
.5
2.3
.2
.5
2.1
4.0
1.8
.9
2.7
-.8
4.0
1.6
6.3
3.7
-.6
2.6
4.6
1.0
2.8
5.4
2.7
2.2
3.1
2.3
3.2
Monthly Labor Review
July 2002
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Federal Reserve Bank of St. Louis
13
Producer Prices, 2001
Table 6.
Continued— Percent change in Producer Price Indexes for the net output of selected service industries,
1996-2001
SIC
code
Industry
4512
4522
4724
6211
6311
6331
7011
7349
7361
7363
7372
7513
7514
Other:
Air transportation, scheduled......................................................
Air transportation, nonscheduled................................................
Travel agencies............................................................................
Security brokers, dealers, and investment bank companies.....
Life insurance carriers................................................................
Property and casualty insurance................................................
Hotels and motels........................................................................
Building cleaning and maintenance services, n.e.c....................
Employment agencies.................................................................
Help supply services..................................................................
Prepackaged software................................................................
Truck rental and leasing, without drivers....................................
Passenger car rental, without drivers.........................................
1996-97
1997-98
1998-99
1999-2000
2000-01
.9
-1.6
1.5
2.5
2.6
-2.3
6.7
2.0
.3
18.6
8.1
14.6
2.0
1.1
-7.8
-13.2
1.4
3.7
.8
3.7
1.8
0
-2.6
-4.2
-1.0
-
-
-
-
-
-
4.1
1.4
1.0
1.8
4.2
1.1
2.9
2.2
.9
-.9
^1.0
-
.5
13.7
-
-
-.3
1.1
2.8
2.6
2.2
1.8
-2.4
.3
3.8
-.6
1.1
5.7
3.9
2.4
1.2
2.4
4.5
2.8
Calculations are based on a 12-month change from December to December of indicated years. Dashes indicate index was not used in estimation,
n.e.c. = not elsewhere classified.
N ote:
Chart 2.
S&P 500
Closing points
Closing points
1-
1350
T‘
1300
1250
1200
-■
1050
1000
4-Dec-00
2-Jan-01
1-Feb-01
1-Mar-01
2-Apr-01
1-May-01
1-Jun-01
percent during the same period. (See chart 2.) Security bro
ker fees are often based on a percentage of stock prices; thus,
decreasing share prices lead to lower commissions for secu
rity brokers.
□
14
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
2-Jul-01
1-Aug-01
4-Sep-01
1-Oct-01
1-Nov-01
3-Dec-01
N otes______________________________________
1 On the Internet at http://ww w.eia.doe.gov/em eu/aer/txt/tab0607.htm
2 On the Internet at http://abcnews.go.com/sections/business/DailyNews/
opec000328.htm l
3 “U .S . Corn & W heat A creage D eclin e, W hile Soybean & Cotton
R ise " A g ricu ltu ra l O u tlo o k , (USDA Economic Research Service, August
2001 ).
4 “Abundant Field Crop Supplies Expected in 20 0 1 /0 2 ,” A g r ic u ltu r a l
O u tlo o k , (USDA Econom ic Research Service, June-July 2001).
9 On the Internet at h ttp ://w w w .m o n ey .cn n .co m /2 0 0 1 /0 4 /2 5 /n ew s/
philipm orris/index.htm
10 On the Internet at h ttp ://w w w .d fait-m aeci.gc.ca/~ eicb /so ftw o o d /
Archive/background-e.pdf
5 Ibid.
11 C otton a n d W ool S itu ation a n d O u tlo o k Y earbook 2 0 0 1 , (USDA Eco
nomic Research Service).
6 F e e d S itu a tio n a n d O u tlo o k Y ea rb o o k , (USD A Econom ic Research
Service, April 2001).
12 F e d e ra l R e g is te r, November 2000.
7 “2001 Year-End R eview and Forecast,” A e ro sp a c e In d u s trie s A s s o
13 National Association o f Realtors, “2001 A N ew Record, December
Existing Home Sales Strong - NAR Reports,” January 25, 2002.
c ia tio n .
8 “2001 auto sales were second highest ever,” M ilw a u k ee J o u rn a l Sen
tinel, January 4, 2002.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
14 “P ostal M e s s,” M ay 15, 2 0 0 1 , on the Internet at
h ttp ://
w w w .cb sn ew s.com /stories/2001/05/15/n ation al/m ain291449.shtml
W h e re are you p u b lishing y o u r re se a rc h?
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
Monthly Labor Review
July 2002
15
Expenditures of single parents:
how does gender figure in?
Regression analysis indicates that, fo r the most part,
expenditure patterns are the same fo r both families
headed by a single father and families headed
by a single mother; among the few differences found
were effects due to income, marital status, and age
Geoffrey D. Paulin
and
Yoon G. Lee
Geoffrey D. Paulin is a
senior economist in
the Division of
Consumer Expendi
ture Surveys, Bureau of
Labor Statistics; Yoon
G, Lee is assistant
professor, Department
of Human
Environments, the
Utah State University,
Logan, Utah, The
views expressed are
those of the authors
and do not reflect the
policies of the Bureau
of Labor Statistics ( bls)
or the Utah State
University, or the views
of other bls staff
members or Utah
State University
employees,
16
ver the last few decades, the proportion
of traditional two-parent families has
been declining. In 1980, married couples
headed 81 percent of all family households with
their own children under 18. By 1999, the figure
had fallen to 72 percent.1 The change was due
mostly to the growth in the number of single-par
ent households. For example, in 1980, the marriedcouple households just described numbered
slightly under 25 million. In 1999, the figure was
slightly over 25 million, a small change.2 By con
trast, households headed by a single parent grew
fromjust under 6.1 million in 1980 to nearly 7.8 mil
lion in 1999.3 In total, single-parent families with
their own children under 18 accounted for 20 per
cent of family households in 1980 and 28 percent in
1999.4
One explanation for the increase in single-par
ent families is the high divorce rate in the Nation
today. Between 1980 and 1999, the number of di
vorced persons doubled, from 9.9 million to 19.7
million.5 Divorce undoubtedly has contributed
to the increasing number of single fathers in the
United States. In 1980, approximately 616,000 fam
ily households with their own children under the
age of 18 included a father, but no mother. By
1999, the figure had risen to 1,706,000, an increase
of 177 percent.6 Similarly, over the same period,
single-mother households grew from 5.4 million
to 6.6 million, an increase of 21 percent.7 Put an
other way, single fathers accounted for 2 percent
of family households with their own children un
der 18 in 1980 and 5 percent in 1999. Single moth
ers accounted for 18 percent of these households
Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
in 1980 and 23 percent in 1999.8
Child rearing is difficult even when two par
ents are present. Yet, single mothers and single
fathers face the same tasks that married parents
do (for example, making sure that children are
clean, clothed, and fed; helping with homework;
preparing children for school; earning enough
money to pay bills; disciplining children; and
comforting them when they are upset), but with
fewer resources: not only is there no other adult to
share in the time spent with children, but in 1998
single parents received less than half the income
($24,530) that husband-and-wife families reported
($59,653).9 According to Douglas B. Downey,
ample literature supports the claim that children
from single-parent families are outperformed in the
classroom by their counterparts from two-parent
families.10 Downey reports that a leading explana
tion for this phenomenon is the lower economic
status of families headed by a single mother, com
pared with the economic status of two-parent fami
lies.11 However, he finds that, despite higher levels
of education and income for single fathers com
pared with single mothers,12 children in single-fa
ther families do no better in school than those from
single-mother families.13
Because an increasing proportion of children
in the United States reside with one parent only,
and because the economic status of single-par
ent families remains relatively low, research on
the economic status of these families is impor
tant, regardless of the gender of the parent. For
example, profiling the basic economic situation
of families in which parents are raising children
without a spouse can provide useful information for public
policymakers. Furtherm ore, understanding the income
sources, expenditure levels, budget shares, and characteris
tics of single-parent families is useful for those who provide
financial, economic, or other counseling to families headed by
single parents. Moreover, given that the proliferation of single
father households in the past decade was even more dramatic
than that of single-mother households, and in view of the fact
that single-father families grew more rapidly than either twoparent or single-mother families in the 1980s, it is important for
family researchers to appreciate the heterogeneity among
single-parent families.14 That is, it is useful to ascertain whether
there are important differences between consumption levels and
budget shares by single mothers and single fathers for vari
ous categories of consumption.
Literature review
Expenditure patterns o f single-parent families. There is a vast
literature examining single-parent families from different per
spectives. Using the 1984-85 Consumer Expenditure Survey,
Mark Lino examined the allocation of expenditures of single
parent households.15 His findings show that these households
spent 35 percent (the largest share) of their total expenditures on
housing, 20 percent on transportation, and 13 percent on food at
home. He also found that single-parent households spent 5 per
cent of their total expenditures on entertainment, 3 percent on
health care, and 2 percent on education. In another work, Lino
analyzed the expenditures of single-parent families by marital
status and found that the total expenditures of single-parent
families maintained by a widowed parent reached $22,071, those
headed by a divorced or separated parent summed to $16,426,
and those maintained by a never-married parent amounted to
$7,741.16 In addition, he found that the shares of total expendi
tures for all categories compared in the study were similar for the
divorced or separated families and the widowed families, but
were substantially different for the categories of housing, trans
portation, and food for never-married parents.17
In yet another article, Lino reported on factors influencing
the housing, transportation, food, and clothing expenditures
of single-parent households, also using data from the 1984-85
Consumer Expenditure Survey.18 He found that household
size, automobile ownership (for transportation), and the gen
der, age, race, education, and employment status of the single
parent were significant factors affecting expenditures. Not
surprisingly, he also found that the larger the family size, the
greater were the expenditures on transportation and food. The
following other significant socioeconomic characteristics of
single-parent households were revealed in Lino’s study: (1)
households headed by women spent 148 percent more on
clothing than did households headed by men, all else held
constant; (2) the higher the educational level of the single
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Federal Reserve Bank of St. Louis
parent, the greater were the expenditures on housing, all else
held constant; and (3) whether a single-parent household re
sided in an urban or a rural area had no significant effect on
expenditures for housing, transportation, food, or clothing.
Although Lino found that homeownership had no significant
effect on housing expenditures for single-parent households,
he also found that those who owned an automobile had trans
portation expenditures higher than did those who did not own
an automobile, all else held constant.
A year later, using the 1987 Consumer Expenditure Survey,
Lino examined child-rearing expenses in single-parent fami
lies.19 In the database, 91 percent of single-parent households
are headed by a woman. The findings indicate that child-rear
ing expenses increase with the age of the child and with family
income. Lino also found that single-parent households spent
slightly more per child than did married-couple households in
the same income group. Estimated total expenditures for the
younger child in a two-child, single-parent household ranged
from $3,800 to $5,650 per year for households in the lower
income group and from $7,830 to $ 10,030 per year for house
holds in the higher income group.20 For both income groups,
the largest proportion of child-related expenditures was allo
cated to housing, while the second-largest proportion was
allocated to transportation. This was also the case within each
income group, regardless of the age of the child. The smallest
share was allocated to health care in each group. The other
categories Lino considered were food; clothing; and educa
tion, child care, and other expenditures, but no clear patterns
emerged for these expenditures.21
Comparisons o f single- and two-parent families. Sally E.
Horton and Jeanne L. Hafstrom compared differences in con
sumption expenditures between families headed by a single
mother (that is, families maintained by a woman without a hus
band present) and two-parent families, using the 1972-73 Con
sumer Expenditure Survey.22 The authors modeled total ex
penditures and expenditures on six consumption categories
(total food, food at home, shelter, household expenses, cloth
ing and cleaning, and recreation and reading) as functions of
current and permanent income.23 The major focus of the study
was to examine whether families headed by a single mother
would change their expenditures on selected items by the
same percentage as two-parent families, given the same per
centage increase in income for each type of family. The major
finding was that only the two families’ expenditures for shelter
differed significantly. That is, the authors estimated that mar
ried couples would increase their expenditures for shelter by a
larger percentage (0.60 percent), given a 1-percent increase in
(current) income, than would single mothers (0.26 percent).
However, the authors also found that, for each of the two
types of family, a 1-percent increase in current income was
associated with a 1-percent increase in expenditures for recre-
Monthly Labor Review
July 2002
17
Expenditures of Single Parents
ation and reading.24 Lino’s study, which included single-par
ent families maintained by fathers and used data from the more
recent 1984-85 Consumer Expenditure Survey,25 found that
families maintained by single fathers did not have different
expenditure patterns for housing, transportation, or food, all
else held equal, than did families maintained by single moth
ers. However, Lino did find a significant gender difference in
expenditures for clothing. (Families headed by single mothers
spent more.)
Using the 1984-86 Consumer Expenditure Survey, Maureen
Boyle compared the spending patterns and income of single
parents and married parents.26 Married parents, on average,
had more than twice as many vehicles as single parents had,
and they also had a higher rate of homeownership. Single par
ents spent less than married parents for major expenditure cat
egories (food, housing, transportation, and apparel), even
when “per capita” expenditures were compared. However, on
a per capita basis, single parents spent more than married par
ents on some items, such as utilities, fuels, and public services
($545, compared with $519); babysitting and day care ($142,
compared with $ 106), and clothing for boys aged 2 to 15 years
($43, compared with $33). Single parents also spent less on
food away from home, entertainment, personal care, reading,
personal insurance, and pensions than did married parents.
However, single parents spent more on miscellaneous expend
itures, which included legal fees, than did married parents. The
expenditures for education, tobacco and smoking supplies,
and cash contributions were not significantly different be
tween single and married parents. Similarly, single parents ap
peared to spend more per capita ($68) than did married parents
($6) on public transportation, but the difference was not statisti
cally significant.
Mohamed Abdel-Ghany and F. N. Schwenk also examined
differences in consumption patterns of single-parent and twoparent families for six major expenditure categories.27 The ma
jor hypothesis of their study was similar to that of Horton and
Hafstrom: the consumption patterns of single- and two-parent
families differ as regards major expenditure categories. How
ever, Abdel-Ghany and Schwenk analyzed more recent data,
obtained from the 1989 Consumer Expenditure Survey. They
compared the influence of permanent income, family size, geo
graphical region, race, gender, age, and education of the head
of the family on the major expenditure categories. Using the
Chow test for equality of the entire set of single-parent and
two-parent regression coefficients, they found that the five
expenditure categories of total food, food at home, household
expenses, apparel, and recreation and reading had a signifi
cant F-statistic. This means that the consumption patterns of
the two groups with regard to those five categories were sig
nificantly different. (Only expenditures for shelter were found
to be essentially the same.) This finding contrasts with Horton
and Hafstrom’s that only expenditures for shelter differed sig
18 Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
nificantly between the two groups. The discrepancy may lie in
the fact that Horton and Hafstrom compared one specific de
terminant of expenditures (income), whereas Abdel-Ghany and
Schwenk compared models as a whole, through the Chow test.
In sum, several studies have analyzed the expenditures of
single-parent families, and a number of studies have compared
differences in consumption expenditures between families
headed by single mothers and two-parent families. Yet, de
spite the fact that single parenting has become commonplace,
only limited scholarly attention has been paid to the expendi
ture patterns of single fathers compared with those of single
mothers. Nevertheless, the gender of single parents may play
a critical role in a family’s expenditure patterns. Understanding
the differential expenditures between the two sexes is impor
tant, especially given the increasing number of single-father
households. Indeed, one study suggests that using the char
acteristics of female-headed single-parent families to repre
sent all single-parent families is no longer possible, consider
ing the rapid increase in the number of single-father families
during the past two decades.28
The analysis in this article
By comparing levels of expenditures and budget shares of
single-mother and single-father households, this article exam
ines whether there are differences in household consumption
patterns based on the gender of the parent. If there are, such
differences may translate into differences in economic well
being in single-mother and single-father households, particu
larly for children in those households.
One reason for the aforementioned lack of attention to gen
der-related differences is the absence of separate data on
single-mother and single-father households. This article uses
data from a nationwide survey to compare major expenditures
for the two kinds of household. The data for the survey are
collected from national probability samples of households in
theU.S. population.29 Selected for study are 221 single-father
and 1,660 single-mother families.
The data. The data used in this article are from the Interview
component of the Consumer Expenditure Survey. The Inter
view component is a panel survey designed to collect expend
iture information from families over five consecutive quarters.
During each interview, the respondent is asked to recall the
family’s last 3 months’ expenditures for most items listed in the
survey. The first interview is used for bounding purposes—
that is, to make sure that the expenditures subsequently re
ported actually took place during the reference period. (For
example, a family that purchased a refrigerator during the 3
months prior to the first interview should report the purchase
during the first interview. If the respondent for that same fam
ily then reports purchasing a refrigerator in the second inter-
view, the interviewer can make sure that the respondent is not
referring to the same refrigerator reported in the first inter
view.) The Interview component of the Consumer Expendi
ture Survey is designed primarily to collect accurate informa
tion on recurring (for example, rent or insurance) and “big
ticket” (for instance, automobiles or major appliances) expend
itures, because outlays for such items tend to be remembered
for long periods. As it turns out, the Interview component
actually covers up to 95 percent of all expenditures.30 (The
Interview component is also the source of Consumer Expend
iture Survey data used in the works described in the previous
section.)
The sample that is examined in this article consists of single
parents, interviewed in 1998 or 1999, who live with their own
children only. That is, no other relatives or unrelated persons
live with these individuals, so that no one (other than, per
haps, their children) shares in or otherwise directly affects
their expenditure decisions. The parents are also between the
ages of 25 and 49, and their oldest child is under 18 years. The
parents’ age range of 25 to 49 years is used for both a theo
retical and an empirical reason. The theoretical reason is to
narrow the focus to parents who are old enough to have es
tablished themselves economically. That is, they are not fi
nancially dependent on someone else strictly because of their
age, and they are legally old enough to obtain substantial
employment, to own or rent a home, to purchase, rent, or lease
a vehicle, and to have been “of age” for at least a few years. In
addition, although they may have children preparing for col
lege or other events, the parents themselves are probably not
expecting major events in their own careers, such as imminent
retirement, nor are they experiencing age-related health prob
lems that may have a great impact on their spending patterns.
The empirical reason is that the sample for men is extremely
small below age 25: during the 2 years covered in the survey,
only 11 single fathers under age 25 participated. By compari
son, during the same period, there were 13 single fathers be
tween the ages of 25 and 27 alone. The children’s age was
selected to ensure that the children would be financially de
pendent on their parents.
Demographic analysis. Table 1 shows the demographic
composition of single parents in the sample selected for study.
The vast majority is female; in fact, women outnumber men in
the sample by more than 7 to 1. Obviously, women are repre
sented in the single-parent category at a much higher rate
than they are in the general population, and males are under
represented. But this is only one of many differences across
gender.
Despite the deliberate selection of men and women in the
same age range (25 to 49 years old), men are still 4 years older
than women, on average. They also have fewer children (1.4)
than women have (1.8), and about twice as many vehicles (2.1,
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compared with 0.9). It is interesting to note that although both
men and women own about one automobile, on average, men
have many more “other” vehicles—primarily recreational ve
hicles (such as boats, campers, and motorcycles), but also
trucks and vans. In addition, men are more likely than women
to own at least one vehicle (91 percent, compared with 72
percent).
The circumstances of single parenthood also differ dramati
cally by gender. Three-fourths of all single fathers have be
come single due to divorce, compared with a bit more than half
(54 percent) of single mothers. The death of a spouse is equally
likely for both groups (6 percent) and could be a function of
age, given that both groups presumably have similar mortality
rates under age 50. Single mothers are twice as likely as single
fathers never to have been married, but still, a substantial pro
portion of the fathers—nearly 1 in 5—has never been married.
Race and ethnicity play an interesting role in this analysis.
Of all interviews conducted in 1998-99, 11.2 percent involve
families whose reference person is black, and 8.5 percent report
Hispanic ethnicity.31 However, in the distribution by gender
among single parents, blacks are overrepresented (30.7 percent
of women and 13.1 percent—only a slight overrepresentation—
of men). In contrast, Hispanic men are underrepresented (5.4
percent), although Hispanic women also are overrepresented
(13.7 percent).
Single fathers are much more likely than single mothers to
own their homes. In fact, the numbers are almost exactly oppo
site with regard to owning and renting: nearly two-thirds of
single fathers (64 percent) own their homes, while nearly twothirds of single mothers (63 percent) rent their homes. Like
income, homeownership is an important measure of economic
well-being. For example, because owners can build equity in
their property, they have greater access to loans in case of
emergency or even planned-for events, such as their children’s
education.
Income. Income is an important measure of the ability of
parents to provide basic goods and services for their children.
Table 2 shows that there are large differences in income be
tween single fathers and single mothers, at least for complete
reporters.32
The income distribution by gender is quite different for
single mothers and single fathers. Men are underrepresented
in the two lowest quintiles, with slightly more than one-fourth
of single fathers reporting incomes placing them there. By
contrast, five-eighths of single mothers are found in that part
of the distribution. Single fathers also are about 3 times as
likely (47 percent) to appear in the highest two quintiles than
are single mothers (15 percent).
Similarly, single fathers report almost twice as much in
come ($44,634) as do single mothers ($23,188). Also, while
single fathers report more income from employment (wages
Monthly Labor Review
July 2002
19
Expenditures of Single Parents
^
D e m ° 9 ra p h ic c h a ra c te ris tic s o f s in g le p a re n ts , C o n s u m e r E x p e n d itu re In te rv ie w S urvey, 19913-99
___________________________________________________________
Single parents
Variable
Men
Women
Number of consumer units (sample size)..........................
Characteristics of consumer units:
Age of reference person.......................................
Average number per consumer unit:
Persons...........................................................
Children underage 1 8 .......................................
Earners.............................................................
Vehicles.......................................................
Automobiles..............................................
Other vehicles1...................................................
Rooms other than bedrooms........................................
Bedrooms.................................................
Bathrooms and half baths.........................................
Percent distribution:
Marital status of reference person:
Divorced........................................................
Widowed...................................................
Never married....................................................
Age of oldest child:
Under 6 years..........................................................
6 to 11 years..............................................................
12 to 17 years.................................................
Housing relation:
Homeowner.......................................................
With mortgage....................................................
Without mortgage..............................................
Renter..............................................................
Race of reference person:
B lack..........................................................................
Ethnic origin of reference person:
Hispanic.................................................................
Education of reference person:
Less than high school graduate...........................
High school graduate..............................................................
Attended college (did not graduate)2 ...................................
College graduate.....................................................
Number of earners:
No earners................................................
One earner....................................................
Two or more earners........................................................
Earner composition:
Reference person only.................................................
Reference person and at least one child..................................
Child(ren) only.................................................
No earners...........................................................
Occupation of reference person:
Wage and salary earners...........................................
Manager or professional.................................................
Technical/sales......................................
Service.............................................................
Laborer/operator..............................................
Self-employed.....................................................
Not working...........................................................
Taking care of home or fam ily........................................
Retired, unemployed, and other not working.................................
Region of residence:
Northeast.........................................................
Midwest.................................................
South............................................................
W est..............................................................
Degree of urbanization:
Rural.....................................................................
At least one vehicle owned.....................................
11ncludes truck or van; motorized, trailer-type, or attachable camper; motor
cycle, motor scooter, or moped; boat, with or without motor; trailer (other than
20 Monthly Labor Review
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July 2002
/-statistic
(absolute
value)
221
1,660
39.7
35.3
10.60
2.4
1.4
1.2
2.1
.9
1.2
2.8
1.8
1.0
.9
.8
.2
9.77
9.77
5.80
10.78
2.59
9.59
3.2
2.7
1.6
2.8
2.6
1.5
4 33
1 61
2.68
75.1
6.3
18.6
54.3
6.4
39.3
7.7
33.9
58.4
16.2
34.9
48.9
63.8
52.5
11.3
36.2
37.2
28.6
8.6
62.9
13.1
30.7
5.4
13.7
10.0
29.0
32.6
28.5
16.5
34.8
33.4
15.2
1.8
82.4
15.8
14.9
74.6
10.5
81.9
15.8
.5
1.8
73.6
10.4
1.0
14.9
85.5
32.1
17.2
7.2
29.0
12.2
2.3
.5
1.8
80.6
21.1
33.3
16.7
9.5
3.4
16.0
10.9
5.1
20.8
24.0
24.9
30.3
14.5
25.7
34.5
25.4
8.6
91.4
6.4
72.1
camper type); private plane; and other vehicles.
2 Includes those who earned an associate-of-arts (AA) degree.
and salaries or self-employment) and savings and investment
(interest, dividend, rental, and other property income), single
mothers report much more income from assistance sources
(for example, unemployment, workers’ compensation, public
assistance, alimony, and child support). Whereas, on average,
about 1 percent of single fathers’ total income comes from
assistance sources, nearly 18 percent of single mothers’ total
income comes from these sources.
There are several factors that may explain these differ
ences. First, as shown in table 1, although the average num
ber of earners is similar for single fathers (1.2) and single moth
ers (1.0), the likelihood of having at least one earner is quite
different: less than 2 percent of consumer units headed by
single fathers have no earner, compared with 15 percent of
consumer units headed by single mothers.33 Also, families
headed by single fathers are more likely to have multiple
earners (16 percent) than are families headed by single moth
ers (11 percent).
Second, single fathers have a higher level of educational
attainment than single mothers. About 61 percent of single
fathers have at least attended college, compared with about
49 percent of single mothers. Similarly, 1 in 6 single mothers
has not graduated high school, compared with 1 in 10 single
fathers. Lower levels of education may also explain lower in
comes for single mothers.
Expenditure patterns. Given differences in income, it is not
surprising that single fathers spend more each quarter on many
items, such as shelter and utilities, than do single mothers.
Even so, the two genders spend about the same on a large
number of items.
According to table 3, single mothers spend a little bit less,
on average, each quarter for food at home ($847) than do single
fathers ($883).34 However, this difference is not statistically
significant. Similarly, for most apparel and services, both types
of family spend about the same, on average. The lone excep
tion is that single mothers spend significantly more ($44) for
children’s apparel than do single fathers. Expenditures for
babysitting and day care are also similar by gender, and so are
expenditures for public transportation, despite the fact that
single mothers are less likely to have a vehicle than are single
fathers, as noted earlier.
Levels of expenditure are not the only important measure of
spending patterns: expenditure shares—the portion of the
Income sources of single parents, Consumer Expenditure Interview Survey, 1998-99
Single parents
Variable
Men
Number of consumer units (complete income reporters only).................................
Women
177
1,347
31.3
31.6
21.7
12.1
3.3
Share of total income before taxes (complete reporters only, percent).................
Wages and salaries...................................................................
Self-employment..................................................................
Interest, dividend, rental, and other property income.........................................
Unemployment, workers’ compensation, and veterans’ benefits.........................
Public assistance, supplemental security income, and food stam ps.................
Regular contributions for support (such as alimony and child support)..............
Other income......................................................................
7.3
18.1
27.1
29.9
17.5
$44,634
37,796
6,135
203
104
87
55
254
100.0
84.7
13.7
.5
.2
.2
.1
.6
$23,188
17,835
965
115
164
1,499
1,702
908
100.0
76.9
4.2
.5
.7
6.5
7.3
3.9
Percent reporting:1
Wages and salaries..........................................................
Self-employment....................................................................
Interest, dividend, rental, and other property income.........................................
Unemployment, workers’ compensation, and veterans’ benefits.........................
Public assistance, supplemental security income, and food stamps.................
Regular contributions for support (such as alimony and child support)..............
Other income............................................................................
91.0
13.6
28.2
6.8
4.5
5.1
6.2
82.4
5.2
7.9
3.9
29.5
33.7
11.3
Income distribution (percent in each quintile):
Quintile 1 ...............................................................
Quintile 2 .........................................................................
Quintile 3 ..................................................................
Quintile 4 .....................................................................
Quintile 5 ......................................................................
Income before taxes...............................................................
Wages and salaries..........................................................................
Self-employment...........................................................
Interest, dividend, rental, and other property income....................................
Unemployment, workers’ compensation, and veterans’ benefits.......................
Public assistance, supplemental security income, and food stam ps.................
Regular contributions for support (such as alimony and child support)..............
Other income.........................................................
f-statlstic
(absolute
value)
7.36
7.98
2.38
.80
1.06
14.86
15.93
4.67
1 Numbers add to more than 100 percent, because some families report more than one source of income.
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Monthly Labor Review
July 2002
21
Expenditures of Single Parents
A v e ra g e q u a rte rly e x p e n d itu re s o f s in g le p a re n ts , C o n su m e r E xp e n d itu re In te rv ie w Survey, 1 9 9 8-9 9
Single parents
Men
Women
f-statlstlc
(absolute
value)
Average quarterly outlay...........................................................................................
Food at home (less trips).....................................................................................
$9,435
883
$6,074
847
7.18
1.11
Variable
Shelter and utilities (less trip s)............................................................................
2,725
2,059
3.88
Apparel and services...........................................................................................
Adults’ apparel (for members 16 years and o lder)...........................................
Children’s apparel (for members 15 years and younger).................................
Footwear...........................................................................................................
Other apparel and services..............................................................................
295
109
85
40
61
315
103
129
38
45
.74
.39
3.93
.44
1.34
Transportation (less trip s )....................................................................................
New-car or -truck purchases............................................................................
Used-car or -truck purchases..........................................................................
Other vehicle purchases...................................................................................
1,558
206
668
50
3.39
2.94
1.95
Gasoline and motor o il......................................................................................
Other vehicle expenses (licenses, insurance, rentals, etc.)...........................
221
400
766
104
219
(’)
155
272
5.23
3.47
Public transportation (local, less trips)............................................................
13
17
1.02
Health care...........................................................................................................
Health insurance...............................................................................................
Medical services...............................................................................................
Prescription drugs.............................................................................................
Medical supplies...............................................................................................
350
238
85
19
8
227
108
86
22
11
2.90
3.58
.06
.62
1.19
Entertainment and recreation...............................................................................
Local entertainment..........................................................................................
Food away from home (less trips)................................................................
Fees and admissions (less trip s).................................................................
Pets, toys, and playground equipment.........................................................
Other entertainment equipment and services (less trip s)...........................
Reading.........................................................................................................
Trips and travel.................................................................................................
1,096
858
361
96
55
322
24
238
599
474
185
51
57
161
21
125
4.72
4.85
6.50
3.80
.15
2.35
1.18
1.87
Miscellaneous child-related expenditures............................................................
Personal-care products and services..............................................................
Babysitting and day care..................................................................................
191
53
138
226
65
161
1.12
2.16
.73
Personal insurance and pensions.......................................................................
Life and other insurance...................................................................................
Pensions and Social Security..........................................................................
920
70
850
415
39
377
8.35
3.47
7.96
All other outlays...................................................................................................
1,417
620
3.44
Alcohol (less trips)............................................................................................
90
23
6.10
Housing upkeep................................................................................................
Domestic services.........................................................................................
Other household expenses...........................................................................
Household furnishings and equipment.........................................................
283
11
22
249
248
27
17
204
.64
3.64
1.45
.85
Education..........................................................................................................
98
77
.63
Tobacco and smoking supplies.........................................................................
97
53
3.72
Cash contributions (including alimony and child support)................................
568
52
2.53
Miscellaneous outlays......................................................................................
281
167
1.51
' No data reported.
average dollar allocated to a particular expenditure category—
also are important. One of the most famous applications in
economics is known today as Engel’s law. In 1857, Prussian
economist Ernst Engel found that as income increases, the
share of income allocated to food decreases. The implication
of this finding is straightforward: essentially, there are some
goods and services that all persons must consume to survive,
but the quantity needed is limited; therefore, as income in
creases, less and less of it needs to be allocated to these items,
and more of it is available for spending on other items. Thus,
22
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Federal Reserve Bank of St. Louis
July 2002
families that allocate larger portions of their income to basic
items like food have less to spend on “electives” such as
entertainment. With Engel’s law in mind, shares analysis may
give a more meaningful description of family expenditure pat
terns than can levels alone.
For example, as noted, families headed by single mothers
spend less for food at home than do those headed by single
fathers, although the difference is not statistically significant.
However, the share of total outlays is greater for the single
mother families by nearly 5 percentage points.35 (See table 4.)
Similarly, spending for children’s apparel by families headed
by single mothers exceeds spending by families headed by
single fathers by about 52 percent; however, the share of total
expenditures allocated to children’s apparel in single-mother
families is double (2 percent) the share spent in single-father
families (1 percent). And again, despite similar levels allocated
to babysitting and day care, families headed by single mothers
allocate nearly double the share (2.7 percent) that families
headed by single fathers allocate (1.5 percent). Finally, total
spending for shelter and utilities by single fathers accounts
for less than 3 of every 10 dollars spent, whereas shelter and
utilities accounts for 3 of every 9 dollars spent (that is, onethird of total expenditures) by single mothers.
For goods and services that are more “discretionary” in
nature, such as recreation, the reverse obtains: shares are
closer, but expenditures by women are much smaller. For ex
ample, single fathers allocate 9 percent of their total expendi
tures to food away from home, compared with 8 percent by
single mothers. However, single mothers actually spend about
one-half ($185) of the amount that single fathers spend on this
item ($361) each quarter. And the same holds true for fees and
admissions: both groups allocate about 1 percent of their total
expenditures to these items, but the households headed by
women again spend about half ($51) of what those headed by
men spend ($96).
Methodology: regression analysis
So far, several differences in expenditure patterns have been
observed for single-father and single-mother families. But at
the same time, several demographic differences have been
observed. Perhaps more important, large differences in income
and total spending are evident. Therefore, it is impossible to
say how much of the difference in expenditure patterns is due
to the difference in gender of the single parent and how much
is due to other socioeconomic phenomena.
To help understand these relationships, regression analy
sis is often used. In regression analysis, comparisons can be
made under “ceteris paribus” assumptions—that is, all char
acteristics are held equal except the one under study. In this
article, then, regression analysis may help to uncover how
single fathers and single mothers might allocate their expendi
tures, given the same total income, age, family size, and other
factors.
In what follows, several items are selected for regression
analysis. Some (for example, food at home; shelter and utili
ties; and apparel and services) are chosen because they repre
sent basic goods and services that any family or individual
needs to meet the essentials of existence. Others (for instance,
transportation; and babysitting and day care), while not nec
essary for the preservation of life, are still goods and services
that most families with children would find difficult to forego.36
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Table 4.
Expenditure shares of single parents,
Consumer Expenditure Interview Survey,
1998-99
[In percent]
Single parents
Variable
Men
Women
Average quarterly outlay................................
Food at home (less trips)............................
100.0
9.4
100.0
13.9
Shelter and utilities (less trips)..................
28.9
33.9
Apparel and services..................................
Adults’ apparel (for members 16 years
and older)............................................
Children’s apparel (for members
15 years and younger).......................
Footwear.................................................
Other apparel and services.......................
3.1
5.2
1.2
1.7
.9
.4
.6
2.1
.6
.7
Transportation (less trips)...........................
New-car or -truck purchases.................
Used-car or -truck purchases...............
Other vehicle purchases.......................
16.5
2.2
7.1
.5
Gasoline and motor o il..........................
Other vehicle expenses (licenses,
insurance, rentals, e tc .)....................
2.3
12.6
1.7
3.6
(’)
2.6
4.2
4.5
Public transportation (local, less trips) ..
.1
.3
Health ca re .................................................
Health insurance....................................
Medical services....................................
Prescription drugs..................................
Medical supplies.....................................
3.7
2.5
.9
.2
.1
3.7
1.8
1.4
.4
.2
Entertainment and recreation.....................
Local entertainment................................
Food away from home (less trips)......
Fees and admissions (less trips).......
Pets, toys, and playground
equipment........................................
Other entertainment equipment and
services (less trips)........................
Reading...............................................
Trips and travel.......................................
11.6
9.1
3.8
1.0
9.9
7.8
3.0
.8
.6
.9
3.4
.3
2.5
2.7
.3
2.1
Miscellaneous child-related expenditures ...
Personal-care products and services ....
Babysitting and day ca re .......................
2.0
.6
1.5
3.7
1.1
2.7
Personal insurance and pensions..............
Life and other insurance.......................
Pensions and Social Security...............
9.8
.7
9.0
6.8
.6
6.2
All other outlays..........................................
15.0
10.2
Alcohol (less trips).................................
1.0
.4
Housing upkeep.....................................
Domestic services..............................
Other household expenses...............
Household furnishings and
equipment.........................................
3.0
.1
.2
4.1
.4
.3
2.6
3.4
Education...............................................
1.0
1.3
Tobacco and smoking supplies.............
1.0
.9
Cash contributions (including alimony
and child support)................................
Miscellaneous outlays............................
1No data reported.
6.0
.9
3.0
2.7
The remaining items (food away from home; fees and admis
sions; pets, toys, and playground equipment; and trips and
travel) may not be necessary to sustain life or the basic daily
functioning of the family, but they represent activities that are
important for other reasons. For example, families may occa
sionally consume food away from home for reasons of conven-
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Expenditures of Single Parents
ience. This category includes all food purchased at restau
rants or carryouts, regardless of where it is consumed. A single
parent who works long hours might find it more convenient,
then, to purchase a pizza from a local establishment, rather
than coming home and cooking (and thus delaying the
children’s meal even longer). Moreover, the availability of food
away from home may allow the parent time to earn extra in
come to help purchase other goods and services for the fam
ily. Similarly, the other items tested are, arguably, important for
a child’s physical or mental and emotional development. For
instance, a child may learn responsibility by caring for a pet,
may obtain social skills by sharing games and toys with oth
ers, and may get exercise from using playground equipment.
Finally, taking trips and traveling may be a means of relaxation
for adults, but can be opportunities for children to learn about
the world outside their neighborhoods.
In this analysis, one expenditure category that could easily
be defined as “basic” has been purposely omitted: health care.
The reason for this omission is that the results of such an
analysis are not easily interpreted. In the Consumer Expendi
ture Survey, it is information on total out-of-pocket expendi
tures that is collected for health care items, rather than infor
mation on the actual amount of health care that is consumed.
That is, if a child in an “insured” family receives the same
inoculations and other treatments as a child in an “uninsured”
family, the actual amount of health care consumed is the same.
However, the insured family might report no expenditures for
health care— other than, possibly, an insurance premium—
while the uninsured family would report the amount paid to
Table 5.
the health care professional administering the services. Fur
thermore, differences in other kinds of health care expendi
tures may not be clearly ascribable. For example, two families
may have identical health insurance policies, but one policy
may be employer sponsored and the other may not. Therefore,
the health care expenditure for the employer-assisted family
will be lower than that for the unassisted family. In addition,
some facts about the policy are not clear. For instance, infor
mation on the number of persons covered by the policy is
collected in the survey, but information on the identity of each
person covered is not. Thus, if one person in a single-parent
family is covered by health insurance, it is not clear whether it
is the parent or a child who is covered. Even if two or more
persons are covered by different policies, it is possible that
the policies all cover the same person. Because of these is
sues, a thorough examination of health care expenditures is
beyond the scope of this article.
In what follows, two types of regression analysis are per
formed. The method o f ordinary least squares is used to ana
lyze all of the selected expenditure categories. That way, the
basic relationships mentioned earlier (such as the relationship
of expenditure to income) can be examined. The method of
ordinary least squares works well enough for expenditures
that are universally purchased, such as food at home or shel
ter and utilities. However, for other items, far less than 100
percent of families report the expenditure. (See table 5.) This
can be for several reasons. For example, some items, such as
clothing, are reasonably durable, and it may be that the family
did not need to purchase those items during the previous 3
Percent of single parents reporting selected expenditure categories, Consumer Expenditure Interview Survey,
1998-99
Single parents
Variable
Men
Women
Chl-square
Average quarterly outlay...........................................................................................
Food at home (less trips).......................................................................................
100.0
100.0
100.0
99.6
(’)
0
Shelter and utilities (less trips)
Homeowners.......................................................................................................
Renters...............................................................................................................
100.0
100.0
100.0
99.4
(’)
(’)
Apparel and services:
Adults’ apparel (for members 16 years and older).............................................
Children’s apparel (for members 15 years and younger)...................................
54.3
50.2
58.0
67.9
1.07
227.05
Transportation (less trips)......................................................................................
96.4
89.9
29.72
Entertainment and recreation:
Local entertainment:
Food away from home (less trips)..................................................................
Fees and admissions (less trips)...................................................................
Pets, toys, and playground equipment..........................................................
Trips and travel...................................................................................................
91.4
66.5
43.4
36.7
81.7
47.8
44.0
25.2
212.95
227.41
.02
213.01
Miscellaneous child-related expenditures.............................................................
Babysitting and day c a re ...................................................................................
20.4
29.9
28.72
1The chi-square test is invalid when 100 percent of at least one group
reports the expenditure in question.
2The chi-square statistic is statistically significant at the 99-percent
confidence level. Note that chi-square values between 3.84 and 6.63 are
24
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July 2002
significant at the 95-percent confidence level. By coincidence, for this set
of data, all chi-square statistics are significant either at the 99-percent level
or not at all.
_______________________________________________________
months. Other items, such as fees and admissions or food
away from home, may be infrequently purchased due to the
tastes and preferences of the family itself or because the
family’s income may be too low (temporarily or permanently)
to afford those items on any but the rarest occasions. What
ever the reason, for several items, logistic regression, or “logit”
is used to predict the probability of their purchase. The logit
results are then used to weight the ordinary-least-squares re
sults so that a more accurate picture of the family’s spending
patterns develops. If the aim is truly to measure the expected
outcome for the average family, one needs to take into account
the fact that the average family has a less-than-100-percent
chance of purchasing several items, as well as the possibility
that probability is influenced by demographics, just as the
level of expenditure (once a decision is made to purchase some
thing) may be so influenced. The resulting process is essen
tially a modified version of the Cragg model. (See Appendix A for
more information on the methodology.) The expenditure category
of shelter and utilities offers a special case. Homeowners are
expected, a priori, to have different expenditures than renters
have for shelter and utilities, even if the dwelling is the same
size and at the same location. However, each group is expected
to have some expenditure for this item. In this case, logit analy
sis is also used to predict the probability of renting the home.
Then the method of ordinary least squares is employed in
separate models for owners and renters, and the results are
analyzed, comparing single mothers who own with single fa
thers who own and, similarly, single mothers who rent with
single fathers who rent.
In addition, ordinaiy-least-squares regressions can be affected
by problems such as heteroscedasticity, a condition in which the
error produced in the regression is not random for the dependent
variable, so that the observed values will not vary consistently
around the regression line. One case in which heteroscedasticity
appears is when the dependent variable is not normally distrib
uted. However, if the underlying distribution is known, it is pos
sible to convert the variable to something that is—or at least that
approaches being—normally distributed. For example, if the data
are lognormally distributed, then regressing the logarithm of the
dependent variable on various characteristics should result in
unbiased ordinary-least-squares estimators.37 In the analysis to
be presented here, a program was run to find the appropriate
Box-Cox transformation of the data. The results showed that in
all cases, the fourth root was an appropriate transformation of
the data. (That is, before any regression was carried out, the
square root of the square root of each dependent data point was
obtained; then, that fourth root was subsequently used in the
regression.)
The Box-Cox transformation is also used for total quarterly
outlays, which are employed as a proxy for permanent income
in this study. “Permanent” income is used in the regressions
instead of current (that is, annual pretax) income because, ac
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cording to the “permanent-income hypothesis,” expenditures
are usually made with expectations of future earnings in
mind.38 In the present situation, the distinction is particularly
interesting, because, as shown in table 2, the sources of in
come acquired by the two groups under study are quite dif
ferent and may lead to very different expectations of future
income. Other factors, such as homeownership, might also
influence expectations in different ways, even if current in
comes (and sources) are identical. (See the earlier section, “De
mographic analysis,” for some examples.) According to the
permanent-income hypothesis, total outlays reflect rational de
cisions based on levels of wealth (rather than income alone)
that are available to the consumer unit; therefore, such out
lays serve as a better indicator of the consumer unit’s tastes
and preferences for particular goods and services than does
income.
Most of the logit regressions contain identical independent
variables, most of which are binary. These variables are used
to estimate the relationship between the probability of pur
chasing a given item and various characteristics, including the
age of the reference person39 (3 5 to 44 years or 45 to 49 years);
the reference person’s marital status (widowed or never mar
ried); the number of children of the reference person (two chil
dren or three or more children); the age of the oldest child
(under 6 years or 12 to 17 years); homeownership (homeowner
with mortgage, homeowner without mortgage, or omitted from
the regression for which the probability of renting is estimated);
race of the reference person (black) ; ethnic origin of the refer
ence person (Hispanic); educational attainment of the refer
ence person (less than high school graduate, attended col
lege, or college graduate); number and composition of earners
(one child or children only earn, or reference person and at
least one child earn); occupational status of the reference per
son (self-employed, taking care of home or family and so not
working, or not working for some other reason) ; region of resi
dence (Northeast, Midwest, or West); degree of urbanization
of residence (family lives in a rural area); and gender of the
reference person (male). (For an explanation of omitted cat
egories in the preceding list, see “Control group,” later in this
section.) There is one continuous variable, as noted earlier:
the fourth root of total outlays, used as a proxy for permanent
income. Also included is an interaction term created by multi
plying the binary variable “male” by the permanent-income
proxy. This interaction term allows the probability of purchase
of an item to change with income at a different rate for men and
women. If the coefficient of the interaction term is statistically
significant, then there is a difference in the income effect for
single fathers compared with single mothers.
The same variables also are used in the ordinary-least-squares
regressions. However, a few other variables are added. Some of
these variables are model specific. For example, in the transpor
tation model, a binary variable is added indicating that the con-
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July 2002
25
Expenditures of Single Parents
sumer unit owns no vehicles. Obviously, this would affect trans
portation expenditures by cutting costs, for example, for gaso
line and driver’s licenses, and possibly raising costs for public
transportation, automobile rentals, and other, similar expenses.
However, it is not clear a priori whether owning no vehicles would
directly affect other expenditures. Similarly, in the model for shel
ter and utilities for homeowners, a binary variable is included
indicating that the family owns its home with no mortgage. The
shelter and utilities model also has variables that account for the
size of the dwelling (total number of rooms and total number of
bathrooms or half baths). Both expenditures for mortgages and
expenditures for rents are expected to increase with the number
and size of the rooms, as are expenditures for utilities, because,
presumably, more fuel and electricity are required to manage a
larger dwelling. (There is more of a need for temperature control,
more space to vacuum, etc.). Some variables are excluded from
specific models. For example, the binary variable for “renter” is
removed from all shelter and utility ordinary-least-squares re
gressions, because, by definition, the value of that variable
would be 0 for all families in the homeowner model and 1 for all
families in the renter model. Similarly, the variable for homeowners
with mortgage is excluded from both shelter and utilities regres
sions, as is the variable for homeowners with no mortgage from
the renters-only model. Also, as it turns out, all families who
reported trips and travel had a working reference person. There
fore, the binaiy variable indicating that only children work in the
family is excluded from the associated regression. Finally, two
sets of interaction terms are added to each of the models: male
and marital status (widowers or bachelors); and male and age
(men 35 to 44 years old or men 45 to 49 years old).
The selection of these variables was based on a combination
of intuition and empiricism. First, variables were selected for gen
eral control of variance. For example, a priori, one can assume
that characteristics such as the age of the reference person af
fect the tastes and preferences of the family decisionmaker. (This
is because, presumably, the reference person is the family
decisionmaker as far as expenditures are concerned.) And simi
larly, the location of the consumer unit (for example, the geo
graphical region of the residence and the degree of urbanization
of the surrounding area) may affect prices or the availability of
goods and services, in which case they will also affect the prob
ability of purchasing an item, as well as expenditure levels. At
first, all binary variables were interacted with “male” to test
whether any of them might be differently related to the expendi
tures of single fathers compared with single mothers (for ex
ample, to test whether single fathers in the Northeast spend
money differently from single mothers in the Northeast). How
ever, the coefficients for the interaction terms were rarely statis
tically significant, so, to reduce potential problems from
multicollinearity or overspecification, these variables were
dropped from the models. In the logit models, only the binaiy
variable “male” and the male-income interaction term were re
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Federal Reserve Bank of St. Louis
July 2002
tained (the former to control for “general” differences by gen
der, the latter, as noted, to test whether single fathers and
single mothers respond differently to changes in permanent
income). In the ordinary-least-squares model, the interactions
for marital status, age, and number of children were retained
because these variables had at least one statistically signifi
cant coefficient in several models. That is, in one model, only
age 35 to 44 might have a statistically significant coefficient,
and in another model, only age 45 and older might, but clearly,
in either case age was an important factor.
Control group. As noted earlier, in order to make compari
sons, it is important for “ceteris paribus” to hold; that is, “all
other things” must be “held equal.” Therefore, a control group is
defined for the purposes of analysis. In this article, the control
group consists of single mothers who are between 25 and 35
years old; are divorced; rent their homes; are neither black nor
Hispanic; are high school graduates; are the sole earner in their
consumer unit; work for a wage or salary; live in the urban South;
own at least one vehicle; have average permanent income; and
have an only child between 6 and 11 years old. These families are
compared with single fathers with the same characteristics. In
both cases, as regards shelter and utilities, renters are assumed
to five in a dwelling containing five rooms (including bedrooms)
and one bathroom, while owners are assumed to have a mort
gage and live in a home with six rooms and two bathrooms if the
household is headed by a woman and seven rooms and two
bathrooms if the household is headed by a man.
Note that single fathers have a much larger permanent in
come, on average ($9,435), than single mothers have ($6,074)
and that, for owners, the number of rooms differs by gender.
Thi s actually violates the ceteris paribus condition, in that it is
not clear how much of the differences that are observed are due
purely to gender and how much are due to differences in perma
nent income or the size of the dwelling. Indeed, these differences
may be due to some of the underlying characteristics discussed
earlier. (For example, on average, single fathers have higher lev
els of education than single mothers have, but perhaps those
with identical education have the same permanent income.)
Nonetheless, the results of the analysis are found with the use of
these differences so that the “typical” family headed by a single
father can be compared with the “typical” family headed by a
single mother. Even though there may actually be no family with
exactly the characteristics of the “typical” family, many may at
least be close. (For the reader who is interested in pure ceteris
paribus comparisons, such results are presented in tables B -l
and B-2 of Appendix B.)
Analysis of results
Probability o f purchase. In examining the probability that a
certain item will be purchased, one readily finds that there is
little difference between single fathers, on the one hand, and
single mothers, on the other, with respect to the goods studied
in this article. Some purchases may appear to be substantially
different; for example, single fathers are predicted to be fairly
likely to purchase fees and admissions (62-percent probability
of doing so), while single mothers are predicted to have nearly
even odds of purchase (53 percent). (See table 6.) Still, despite
the 9-point difference in these probabilities, neither the binary
variable “male” nor the interaction with permanent income has
a statistically significant coefficient.40 In other words, there is
no “underlying” difference between single fathers and single
mothers that causes a change in the probability of their pur
chasing an item, nor does a change in income affect their like
lihoods of purchasing the item in any different way. In fact, in
only one case examined is the difference in probability of pur
chase based on any statistically significant coefficients: for
apparel and services for children, the male-permanent income
interaction variable is statistically significant at the 95-percent
confidence level. The results of the analysis show that single
mothers are much more likely (63 percent) to have purchased
apparel and services for children in the 3 months prior to the
survey than are single fathers (48 percent).
Another set of logit results warrants analysis: probability of
homeownership. As mentioned earlier, homeownership has
implications for the economic well-being of the consumer unit.
The regression results predict the probability of being a renter.
Several factors influence this probability for single parents.
For example, the older the reference person is, the less likely
the family is to rent.41 This is probably because older parents
have had the time to save for a downpayment on a home, to
obtain (and maintain) secure employment, and other factors.
They may also earn more income than their younger counter
parts, but this condition is controlled for in the regression
Probabilities of purchase
Single parents
Variable
Men
Women
Permanent income (quarterly outlays).....
Probability of purchase (percent):
Apparel and services (adults)..............
$9,435
$6,074
47.1
55.9
Apparel and services (children)............
47.6
'63.2
Transportation (less trips).....................
98.8
99.0
Food away from home (less trip s)........
98.3
95.2
Fees and admissions (less trip s ).........
62.5
52.8
Pets, toys, and playground equipment..
46.5
50.3
Trips and travel......................................
37.1
31.0
Babysitting and day c a re .....................
28.3
36.2
1 Male-income interaction coefficient is statistically significant at the 95percent confidence level.
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analysis. By contrast, having a large family substantially in
creases the probability of renting. For single fathers, the odds
rise from about even (51 percent) for those with small families
to probable (61 percent) for those with large families; for single
mothers, the probability rises from 2 out of 3 (67 percent) for
those with small families to 3 out of 4 (75 percent) for those
with large families. These results are calculated for families
that are identical to the control group, but that have at least
three children. This is again probably a “savings” effect, al
though the data do not include information on how long the
existing family structure has prevailed. Still, the presence of
two (or more) additional children presumably adds to a family’s
expenditures, but not to its income.
Marital status also plays an important role. Single-parent wid
ows and widowers are less likely to rent than divorcees, but
those who have never been married are more likely to rent. This
may be because in the first case, when there was a spouse present,
the family decided to purchase a home. In the event of the death
of the spouse, the family would presumably still live in the home
(or purchase another, rather than permanently renting). How
ever, those who were never married would not have had the
potential for receiving extra income, for example, to help improve
the chances that their request for a loan would be approved.
Education is also related to homeownership. For instance,
college graduates are much less likely than others to rent their
homes, and although the coefficient for those who did not
graduate from college is not statistically significant, the coef
ficient for those who did not graduate from high school is
large (about one-half the size of the three-or-more children
coefficient, which has already been shown to have a profound
effect on the probability of renting), and the coefficient for
those who have had some college is fairly small, indicating
little difference in the probability of renting (even if it were
statistically significant). Assuming that the income of a hypo
thetical college graduate is the same as that of a nongraduate,
it may be that the college graduate is more aware than the
nongraduate is of issues such as tax benefits and the accumu
lation of assets that accrues to homeowners.
In addition, there is strong evidence pointing toward under
lying differences between single fathers and single mothers in
respect of the decision to own a home. Both the binary vari
able “male” and the male-permanent income interaction have
statistically significant coefficients, albeit of opposite sign.
The coefficient on “male” is negative, indicating that something
inherent in single fathers makes them less likely to rent than
single mothers. However, the male-income interaction effect
is positive. When summed with the permanent-income “main
effect” (that is, the coefficient on permanent income before
any interaction has been performed), the income effect for men
is found to be negative, but not statistically significantly differ
ent from zero, according to a chi-square test. This means that
while there is a strong (negative) income effect for women
Monthly Labor Review
July 2002
27
Expenditures of Single Parents
regarding the probability of renting, the income effect for men
may be negligible. To phrase it more simply, the data suggest
that the probability of renting declines for single mothers as
their income increases, and while the probability of renting
also declines with income for single fathers, it does so at a
lesser rate; in fact, for single fathers, the choice to own a home
may be independent of their level of income. Put yet another
way, because the coefficient of “male” is negative and signifi
cant, single fathers with low levels of income will have a lower
probability of renting than will single mothers with the same
income. However, because single mothers have a stronger
(negative) income effect, eventually they will have a lower
probability of renting than will single fathers with similar in
comes. Given this finding, it is not surprising that if the “typi
cal” single father and single mother are compared (that is, the
fathers have higher permanent income ($9,435 versus $6,074,
quarterly), but the other characteristics are held to be the same),
the mothers have a much greater probability of renting, as
noted earlier (67 percent, compared with 51 percent). However,
it turns out that the probability functions cross at the level of
permanent income associated with “typical” single-father
families. That is, for single mothers with the same permanent
income as “typical” single fathers ($9,435), the probability of
Chart 1.
renting is, coincidentally, identical across the two genders.
Nevertheless, if the men are compared with the women by
reducing the men’s family income so that it is equal to the
women’s family income ($6,074), then the men are still sub
stantially less likely (55 percent) to rent than the women (67
percent). (See Appendix B, table B-3.) The two probability
functions are shown in chart 1.
Ordinary-least-squares results. Unlike the logit results, in
which only one expenditure examined (apparel and services
for children) was found to have a statistically significant dif
ference for single fathers and single mothers, several items
exhibit such differences when the predicted expenditures are
examined.42 One-third of the expenditure categories examined
with the logit regression (food at home; apparel and services
for adults; and pets, toys, and playground equipment) show
statistically significant differences across genders in both the
intercept and the income effect. For food away from home, the
coefficient “male,” but not the male-income interaction coeffi
cient, is statistically significant. Further, when the separate
housing regressions are examined, it turns out that expendi
tures for shelter and utilities do not differ by gender for own
ers, but do differ for renters. In each of these cases, including
Predicted probability of renting, by income, single fathers and single mothers
Permanent income (quarterly data)
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July 2002
shelter and utilities for renters, the income effect is smaller for
men than for women.
Despite the smaller income effect, single fathers are pre
dicted to spend more than single mothers for all expenditures
with a statistically significant difference in the income effect,
except for rent. (The resulting expenditure for shelter and utili
ties is substantially smaller for single fathers, who are pre
dicted to spend more than two-thirds—69 percent—as much
as single mothers for that item.)
Marginal propensity to consume (MPC) and elasticity. Two
important measures of tastes and preferences are the mar
ginal propensity to consume (m p c ) and the income elasticity
of a particular good or service. The m p c describes how ex
penditures would change if a consumer unit’s permanent in
come were to increase by 1 dollar; elasticity describes how
expenditures would change if a consumer unit’s permanent in
come were to increase by 1 percents These quantities can be
more enlightening when one examines observed or predicted
expenditure patterns, rather than actual levels of expenditures.
The actual expenditure for a given item may differ by gender
because of differences in income or other factors, as noted.
Indeed, even the predicted expenditure for the item may differ
by gender because of differences in income, at least in the
tables examined here, for reasons described earlier. (However,
the predicted expenditures, given true ceteris paribus condi
tions, are shown in Appendix B.) But the m p c and income
elasticity measure how important a good is to consumers by
showing how much more they would purchase if given the
means to do so.
In the case of universally purchased goods (that is, food at
home and shelter and utilities in this article), the calculation of
the m p c and income elasticity is straightforward. However, for
goods and services that are less frequently purchased, the prob
ability of purchase must be taken into account in calculating
these quantities. (See Appendix A for details in both cases.) The
reason is that it is reasonable to assume that whether an expend
iture takes place is a function of income, just as how much the
purchase is for is a function of income. Therefore, the expected
expenditure for a member of the control group is equal to the
actual expenditure (if a purchase is made), weighted by the
probability of incurring the expenditure. Accordingly, the
tables showing m p c and elasticity calculations also show the
predicted probability of purchase (which equals 100 percent
in the case of universal expenditures).
For most expenditures with statistically significant income
differences by gender, the m p c ’ s are fairly small, ranging from
0.4 cent per additional dollar (for apparel and services for chil
dren, purchased by single fathers) to 4.5 cents per additional
dollar (for apparel and services for adults, purchased by single
mothers). (See table 7.) The exception is shelter and utilities
for renters, for which, for single fathers, the m p c is 4.6 cents
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I
P re d ic te d e x p e n d itu re s , m a rg in a l p ro p e n s itie s to
c o n s u m e ( mpc ’s), a n d e la s tic itie s o f “ t y p ic a l”
s in g le p a re n ts
Variable
Men
Women
Permanent Income ( ! ) .................................
$9,435
$6,074
Food at home:
Probability, percent ( P ) .............................
P ' .............................................................
100.0
0
100.0
0
E ( Y ) ...........................................................
E ' ( Y ) .........................................................
,2$826
.0237
,2$649
.0405
MPC = P' E( Y) + P E ' ( V) ...........................
.024
.041
( l/E ( Y ) ) ........................
.27
.38
Apparel and services (adults):
P ' .............................................................
2.22E-05
4.09E-05
E ( Y ) ...........................................................
E ' ( Y ) .........................................................
1,2$514
.0216
1,2$413
.0500
MPC = P' £( V) + P E ' ( V) ...........................
Elasticity = MPC x
.022
.045
( l/E ( Y ) ) ........................
.40
.66
Apparel and services (children):
Probability, percent (P )...........................
P'
...........................................................
247.6
9.80E-06
263.2
3.25E-05
E ( Y ) ...........................................................
E ' Y ) ..........................................................
$101
.0073
$94
.0120
MPC = P'E(V) + PE'(V) ...........................
.004
.011
Elasticity = MPC x(//E (V ))........................
.42
.69
Transportation (less trips):
Probability, percent (P )...........................
P' ...........................................................
98.8
1.70E-06
99.0
1.27E-06
$1,602
.1873
$788
.1437
Elasticity = MPC x
E ( Y ) ...........................................................
E ' ( Y ) ........................................................
MPC =
..........................
.188
.143
Elasticity = MPC x(//E( y » ........................
1.11
1.10
Food away from home (less trips):
Probability, percent ( P ) .............................
P ' .............................................................
98.3
4.35E-06
95.2
1.48E-05
E ( Y ) ...........................................................
E ' Y ) ..........................................................
1$1,217
.0523
'$572
.0510
P ' E(Y ) + P E ' (Y )
MPC = P'
(V) ..........................
.057
.057
Elasticity = MPC x(//E (V ))........................
.44
.61
Fees and admissions (less trips):
Probability, percent (P ) .............................
P .............................................................
62.5
2.05E-05
52.8
4.78E-05
E ’ ( Y ) .........................................................
$389
.0202
$216
.0246
MPC = P'E(Y) + PE'(Y) ..........................
.021
.023
Elasticity = MPC x(//E( VO)........................
.50
.66
Pets, toys, and playground equipment
(less trips):
Probability, percent ( P ) .............................
P ' ..............................................................
46.5
1.17E-05
50.3
2.64E-05
E ( Y ) ...........................................................
E ' ( Y ) .........................................................
1,2$524
.0033
1,2$405
.0388
MPC = P' E( V) + P E ’ ( V) ..........................
.008
.030
Elasticity = MPC x (//E(V))........................
Trips and travel:
Probability, percent (P )...........................
P ' ..............................................................
.14
.45
37.1
1.78E-05
31.0
3.88E-05
E(Y ) + P E '
E ( Y ) ...........................................................
Monthly Labor Review
July 2002
29
Expenditures of Single Parents
Table 7.
Continued— Predicted expenditures, marginal
propensities to consume ( mpc ’s ), and elasticities
of “typical" single parents
Variable
Men
Women
E(Y)...........................................................
E '(Y ).........................................................
$933
.0979
$619
.0903
M P C = P £ (y ) + PP (V ) ...........................
.053
.052
( I I E ( Y ) ) ......................
.54
.51
Babysitting and day care:
Probability, percent (P )...........................
P ’ ..............................................................
28.3
1.16E-05
36.2
3.91 E-05
$273
.0110
$365
.0465
Elasticity = MPC X
E ( Y ) ...........................................................
E ' ( Y ) .........................................................
MPC
...........................
.006
.031
( l / E ( Y ) ) ......................
.22
.52
Shelter and utilities (owners, with
mortgage):3
Probability, percent (P )............................
P ' ...............................................................
100.0
0
100.0
0
$2,589
.1513
$2,258
.2005
.151
.201
.55
.54
100.0
0
100.0
0
1,2$1,248
.0458
1,2$1,807
.2394
.046
.239
.35
.80
= P ' E (Y ) + P E '( Y )
Elasticity - MPC X
E ( Y ) ...........................................................
E ’ ( Y ) .........................................................
MPC -
P ' E {Y ) + P B [Y )
Elasticity = MPC X
...........................
( I I E ( Y ) ) .....................
Shelter and utilities (renters):3
Probability, percent (P )............................
P' .............................................................
E ( Y ) ...........................................................
E ' { Y ) .........................................................
MPC -
P 'E ( Y ) + P E '( Y )
Elasticity - MPC x
...........................
( l / E ( Y ) ) .....................
1Binary variable used to calculate this value tor men is statistically signifi
cant at the 95-percent confidence level.
2 Men’s income effect used to calculate this value is statistically signifi
cantly different from the women's income effect at the 95-percent confidence
level.
3mpc’s and elasticities for homeowners are calculated assuming that single
fathers have seven rooms and two bathrooms or half baths and that single
mothers have six rooms and two bathrooms or half baths. For renters, both
types of parents are assumed to have six rooms and one bathroom or half
bath.
Note: Values are calculated from detailed regression coefficients, with
results rounded for presentation.
per additional dollar. For single mothers, the mpc is 23.9 cents
per additional dollar. In this example, the gap between the
elasticities of single mothers and single fathers is also large:
single fathers have an elasticity of 0.35, compared with 0.80
for single mothers. When single mothers are assumed to have
the same level of permanent income as single fathers, the
estimated elasticity for those of the mothers who rent actually
increases slightly, to 0.82. For homeowners, the parameter
estimate of income for single fathers is not significantly dif
ferent from that for single mothers. For both genders, the
estimated income elasticity is in the middle 0.50’s. This sug
gests that both single fathers and single mothers who own
30 Monthly Labor Review
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July 2002
homes are more similar to each other with respect to housing
decisions than they are to renters of the same gender. (That
is, single fathers who are homeowners are different from single
fathers who are renters, and single mothers who are homeowners are different from single mothers who are renters.) At
the same time, single-parent renters differ substantially by
gender in their expenditures.
The expenditure category with the largest income elasticity
is transportation. For both single fathers and single mothers,
the elasticity is about 1.1. This may at first be surprising, be
cause other categories, such as trips and travel, with which
one might associate high elasticities a priori have elasticities
less than unity. In the terminology of economists, transporta
tion is a “luxury” good, while trips and travel constitute a
“necessity” good.44 However, one must recall that the elastic
ity measured in this article is total elasticity; that is, it is not
just the elasticity for persons who purchase the good, but
rather, it is the elasticity for all consumers, whether they pur
chase or not, weighted by their probability of purchase. So as
income rises, the increase affects purchases both indirectly
(through a consumer unit’s probability of purchase) and di
rectly (through affordability for those who do purchase). Note
that for both single fathers and single mothers, the mpc for
trips and travel for purchasers only is estimated to be about
double (10 cents for men and 9 cents for women) what it is for
the overall group (about 5 cents each). Thus, for purchasers,
the income elasticity for trips and travel would be about double
what it is for the overall group, making it larger than unity (that
is, a “luxury good”) for both single mothers and single fathers.
Finally, one should not confuse the significance of the dif
ference of the income effect with the significance of the in
come effect in general. If there is no significant difference in
the income effect, it just means that there is no evidence to
support the hypothesis that single fathers and single mothers
have different m p c ’s, given the same level of income. How
ever, it does not mean that the income effect is nonexistent for
the good in question. To use a specific example, transporta
tion shows no difference in the income effect across gender
when either probabilities or expenditures are predicted. How
ever, the mpc — 19 cents for single fathers and 14 cents for
single mothers—is significantly different from 0 cents. That is,
given extra income, expenditures for transportation will in
crease for both genders, but not by a very different amount,
ceteris paribus.
a r t ic l e has e x a m in e d e x p e n d it u r e patterns for
single parents. To aid in the analysis presented, demographics
were compared first, followed by expenditure levels and ex
penditure shares. Although many differences in the expend
itures of single fathers and single mothers were found, they
could be due to differences in demographic characteristics—
especially income. To obtain more precise comparisons, two
T his
forms of regression analysis were performed: logistic (logit)
regression, to estimate the probability of reporting certain
items, and ordinary-least-squares regression, to estimate the
marginal propensity to consume, income elasticity, and similar
relationships of expenditure to various characteristics.
The logit regressions showed that, although some of the
characteristics that were examined definitely account for dif
ferences within gender groups, there were not many differ
ences across gender for single parents. That is, characteristics
such as family size affect the probabilities of purchasing vari
ous goods and services equally for both families headed by
single fathers and families headed by single mothers. How
ever, some differences were found in the ordinary-leastsquares analysis. For example, the income effect was frequently
significantly different by gender, but the effects of marital sta
tus and age were also different in some models. In using ordi
nary-least-squares results to calculate some factors of inter
est, such as marginal propensities to consume and income
elasticities, it was noted that some of the differences that were
found may again be due to differences in income assumed to
hold for the “typical” male-headed and female-headed single
parent family. However, table B-2 of Appendix B shows that
even if single mothers are assumed to have the same income
as single fathers, they would not substantially change the
proportion of total income allocated to most goods and serv
ices, as evidenced through only minimal changes in their mar
ginal propensity to consume or their income elasticity. (How
ever, a hypothetical increase in income would increase their
expected expenditures, and in some cases, they would exceed
expected expenditures by single-father-headed households by
a large amount.)
It may be surprising that more differences were not found
in the analysis, especially in the coefficients for the interac
tion terms. That is, the results show that there are differences
of some sort between families headed by single fathers and
those headed by single mothers, but single-father-headed
families in the Northeast are not significantly different from single
mother-headed families in the Northeast. The lack of evidence
of differences, though, should not be interpreted to mean
that there is a lack of differences themselves. It is important
to remember that single fathers are still a small, but notice
able, portion of the single-parent population. Therefore, it
may be that differences in certain characteristics of single
mothers (such as their region of residence) are not pro
nounced enough to be readily seen at this time. Still, as
noted earlier, single fathers are a rapidly growing group, and
they have not yet been studied in great detail. Thus, further
research into their expenditure patterns will be useful as
their numbers increase both absolutely and relatively to
the population of single mothers.
□
N otes__________________________________________
1 Statistical Abstract o f the United States: 2 0 0 0 (U.S. Bureau o f the
Census, 2000), p. 58, table 68.
2 Ibid. The precise numbers were 2 4 ,9 6 1 ,0 0 0 in 1980 and
25 ,066,000 in 1999, according to table 68.
3 Ibid. The precise numbers were 6,061,000 in 1980 and 7,752,000
in 1999.
4 Ibid.
5 I b i d ., p. 51, table 53.
6 Ibid., p. 58, table 68.
7 Ibid. The precise numbers were 5,445,000 in 1980 and 6,599,000
in 1999.
8 Ibid.
9 Consumer Expenditures in 1998, Report 940 (Bureau o f Labor
Statistics, February 2000), table 5.
10 Douglas B. Downey, “The School Performance o f Children From
Single-M other and Single-Father Families: Economic or Interpersonal
Deprivation?” Journal o f Family Issues, March 1994, pp. 129-47.
11 I b i d . , p. 130.
12 Ibid., table 1, pp. 13 9-40 .
13 Ibid., p. 144.
14 I b i d . , pp. 1 2 9 -3 0 .
15 M ark Lino, “Financial Status o f Single-Parent Households,” Fam
ily Economics Review, February 1989, pp. 2 -7 .
16 M ark Lino, “ Financial Status o f Single-Parent Households Headed
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
by a N ever-M arried, Divorced/Separated or W idowed Parent,” in R.
Walker (ed.), “Families in Transition: Structural Changes and Eifects on
Family Life,” Proceedings o f the 1989 Pre-Conference Workshop o f the
Family Economics Home Management Section o f the American Home
Economics Association, pp. 1 5 1-60 .
17 D ivorced or separated parents and widowed parents each a llo
cated 35 percent o f their total expenditures to housing, whereas neverm arried parents allocated 40 percent o f their total expenditures to that
category. For comparison, the expenditure share for never-married par
ents for transportation, 11 percent, was about h a lf that o f divorced or
separated parents (21 percent) and widowed parents (22 percent). Food at
home also accounted for a larger share o f the never-married parents’ total
expenditures. One in 5 dollars (20 percent) went to food at home for that
group, compared with 1 in 7 dollars (14 percent) for divorced or separated
parents and 1 in 8 dollars (12 percent) for widowed parents. Clothing,
health care, entertainment, education, child care, and “ other” expendi
tures all accounted for similar shares for each type o f single parent. (See
Lino, “Financial Status o f Single-Parent Households,” table 3, p. 160.)
18 M a rk Lino, “ Factors A ffe ctin g Expenditures o f Single-Parent
Households,” Home Economics Research Journal, M arch 1990, pp.
1 9 1 -2 0 1 .
19 M a rk Lino, “Expenditures on a Child by Single-Parent Families,”
Family Economics Review, March 1991, pp. 2 -7 .
20 The lower income group included single parents reporting less than
$ 2 9 ,9 0 0 in income before taxes; the upper group reported at least
$ 2 9,90 0. Lino explains that the figures are based on 1987 data for
husband-wife families, approximately one-third o f which reported in
come less than $2 6,00 0. Although relatively few single parents (15
percent) reported incomes more than $26,000, Lino retained that dol-
Monthly Labor Review
July 2002
31
Expenditures of Single Parents
lar amount to facilitate comparisons between single parents and hus
band-wife parents with similar income. The $29,900 was obtained from
the Consumer Price Index for A ll Urban Consumers ( c p i -u ) in order to
adjust the $26,000 from 1987 dollars to 1990 dollars. Apparently, Lino
made this adjustment because, at the time he was writing, the 1987 data
were the most recent available. (See Lino, “Expenditures on a C hild,”
esp. pp. 2, 3, and 5.)
21 Ibid., table 1, p. 5.
22 Sally E. Horton and Jeanne L. Hafstrom, “Income Elasticities for
Selected Consumption Categories: Comparison o f Single Female-Headed
and Two-Parent Families,” Home Economics Research Journal, March
1985, pp. 2 9 2 -3 0 3 .
23 Current income was defined as income earned within a given desig
nated “ recent” period— for example, this week’s income or this year’s
income. Permanent income was defined as current income plus ex
pected future income.
24 Specifically, they estimated that the increase in expenditures was 1.2
percent for single-parent women and 0.99 percent for married couples.
Flowever, they did not find this difference statistically significant.
25 Lino, “Factors A ffecting Expenditures.”
26 Maureen Boyle, “Spending patterns and income o f single and mar
ried parents,” Monthly Labor Review , March 1989, pp. 3 7 -4 1 .
27 Mohamed Abdel-Ghany and F. N. Schwenk, “Differences in Con
sumption Patterns o f Single-Parent and Two-Parent Families in the
United States,” Journal o f Family and Economic Issues, winter, 1993,
pp. 2 9 9 -3 1 5 .
28 David J. Eggebeen and Anastasia R. Snyder, “Children in SingleFather Families in Demographic Perspective,” Journal o f Family Is
sues, July 1996, pp. 44 1 -6 5 .
29 Consumer Expenditure Survey, 1996-97, Report 935 (Bureau o f
Labor Statistics, September 1999), p. 257.
30 Ibid., p. 256. The report indicates that the “Interview [compo
nent] collects detailed data on an estimated 60 to 70 percent o f total
household expenditures. In addition, global estimates, that is, expense
patterns for a 3-month period, are obtained for food and other selected
items. These global estimates account for an additional 20 to 25 percent
o f total expenditures.”
31 The categories defined as Hispanic in the survey are M exican,
Mexican-Am erican, Chicano, Puerto Rican, Cuban, Central and South
American, and other Spanish.
32 In general, complete reporters are those consumer units which
provide a value for at least one major source o f income, such as wages
and salaries, self-employment, or Social Security. However, even com
plete reporters do not necessarily provide a full accounting o f income
from all sources.
33 For the purposes o f this study, a consumer unit is defined as mem
bers o f the same household related by blood, marriage, adoption, or
some other legal arrangement. Also, only single-parent consumer units—
that is, those with one person aged 18 or older living with his or her own
children and no other persons— are examined. For convenience, the
terms “fam ily” and “household” are used interchangeably with the term
“consumer unit” throughout.
34 The table lists this expenditure as “less trips.” This is because food at
home is included in the expenditure for “trips and travel.” The term
“ food at home on trips” may sound self-contradictory, but in the Con
sumer Expenditure Survey the “at home” designation refers to the type of
business from which the food was purchased; that is, it distinguishes pur
chases at restaurants and carryouts from purchases at supermarkets or
similar establishments. Expenditures for “shelter and utilities on trips”
refer to hotel or motel payments or payments for vacation homes. The
other “on trips” expenditure categories are straightforward.
35 In the standard
bls
publications o f Consumer Expenditure Survey
32 Monthly Labor Review
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July 2002
data, certain items, such as mortgage principal payments, are not in
cluded as expenditures. This is due to a technical definition whereby
principal payments are considered an investment in housing rather than
a payment for the consumption o f housing services. (A ccording to
Consumer Expenditure Survey, 1996-97 [pp. 2 5 0 -5 1 ], “Mortgage prin
cipal repayments are payments o f loans and are shown in Other finan
cial information .” ) In contrast, the mortgage interest payment is con
sidered an expenditure, because it is the price one pays for the ability to
“invest” in the housing. Similarly, when vehicles are purchased, it is the
total price o f the vehicle, less its trade-in value, that is recorded in the
survey results, rather than the amount o f monthly payments made. In
the standard published tables, this makes sense, because, on average,
those who purchase a vehicle during the reference period w ill have a
large expenditure recorded, while those who already own a car, but make
payments on it, w ill have only the interest payments reported. There
fore, on average, recent purchasers’ expenditures for new cars w ill bal
ance out with payments made by those currently financing vehicles.
However, in examining individual families, a large expenditure is shown
for any family that purchases a new automobile, and a small expenditure
is shown for any that make payments each month. In this study, the
actual amount that leaves the fa m ily’s hands, including payments for
mortgages and regular payments for vehicles, is analyzed. Only “true”
payments for assets or liabilities (such as investments in stocks and
bonds) are omitted from the analysis. Technically, this is called a “total
outlays” approach; however, for convenience, the terms “outlays” and
“expenditures” w ill be used interchangeably throughout the article.
36 Even a parent who does not leave home frequently may still
occasionally have to hire a babysitter or day-care provider for an emer
gency or to enable him- or herself to hold a job.
37 Sometimes, authors use a “double log” specification, in which case
the dependent variable and a selected independent variable (frequently
income in expenditure studies) are converted to logarithmic form be
fore the regression is carried out. For example, Horton and Hafstrom
use such a form. The “double log” specification has a dual advantage: in
addition to reducing heteroscedasticity, it allows the coefficient on the
transformed independent variable to be interpreted as a measure o f
elasticity. In other words, i f the natural logarithm o f expenditure X is
regressed on the natural logarithm o f income, and the income coeffi
cient is 2.0, then, if the coefficient is statistically significant, the ana
lyst can validly infer that a 1-percent increase in income is associated
with a 2-percent increase in X .
38 See M ilto n Friedman, A Theory o f the Consumption Function
(Princeton, n j , Princeton University Press, 1957).
39 The reference person is the first person identified when the re
spondent is asked who is responsible for owning or renting the home. In
this article, the reference person is assumed to be the parent in all cases.
40 See additional table, “Expenditure logit results,” on the Internet at
h ttp ://w w w .b ls .g o v /c e x /c s x a rt.h tm
41 See additional table, “Housing tenure logit parameter estimates,”
on the Internet at http ://w w w .b ls.g o v/cex/csxa rt.h tm
42 See additional table, “O rdinary-least-squares results,” on the
Internet at h ttp ://w w w .b ls .g o v /c e x /c s x a rt.h tm
43 Horton and Hafstrom ’s findings in “Income Elasticities for Se
lected Consumption Categories” are examples o f income elasticities.
Their finding that a 1-percent increase in income yields a 0.59-percent
increase in expenditures for shelter for married couples can be more
simply stated by saying that, for married couples, the income elasticity
for shelter is 0.59. Similarly, Horton and Hafstrom find that the income
elasticity for single mothers is 0.25.
44 Goods with an income elasticity o f exactly unity are known as
“unitary elastic.” (For example, Horton and Hafstrom found income
elasticities for recreation and reading to be unitary elastic.) Goods with
elasticities greater than unity are “ luxuries,” because the increase in
spending is disproportionately large compared with the increase in in
come. Goods with positive elasticities less than unity are “necessities,”
because the increase in expenditure is disproportionately small. A ll goods
with positive elasticities are considered “normal” goods, because their
expenditures increase with income. There are some goods for which the
income elasticity is negative— the so-called inferior goods, because their
expenditure actually decreases as income increases. An example is used
A ppendix A:
goods: because most consumers prefer new products to used products (for
example, automobiles, clothing, and furniture), but used goods usually
have lower prices than new goods, it can be assumed that used goods w ill be
purchased disproportionately by lower income consumers, compared with
new goods. Thus, as income increases, fewer used goods are purchased.
M e th o d s o f a n a ly sis
B o x - C o x tr a n s f o r m a tio n s . E xp en d itu re data are n ot o ften norm ally
d istrib u ted , a situ ation that can ca u se bias in regression resu lts.1
H o w e v e r , ex p en d itu re data can be tran sform ed so that th e y are
a p p roxim ately n orm ally distributed. O n e m eth od that h as b een used
is th e B o x - C o x t r a n s f o r m a tio n .2 P erh ap s the m ost freq u en tly cited
v ersio n is
Y * = ( Y X- 1)/A,,
w here
Y * is th e t r a n s fo r m e d v e r s io n o f th e v a r ia b le , Y d e n o te s
ex p en d itu res for a sp e c ific go o d or serv ice (fo r ex a m p le, fo o d at
h o m e or apparel),
and
A, is a param eter u sed to n orm alize the data.
T h is v ersio n o f the eq u ation is m o st u sefu l in d em onstratin g tw o
sp e c ia l c a s e s for th e v a lu e o f X. T hat is, i f X is u n ity, then no
tran sform ation o f th e in d ep en d en t variable is n ecessary. (T h e net
resu lt is that Y * eq u a ls 7 - 1 , and subtracting a con stan t from each
o b serv a tio n o f 7 w ill n ot a ffe c t the d istrib u tion .) In contrast, i f X
a p p ro a ch es z e r o , th en 7* is ap p ro x im a tely eq u al to th e natural
logarithm o f 7.
A lthou gh this specification is useful for deriving the value o f 7*
w h en X approaches zero, it d oes not yield an intuitive interpretation
w h en X takes on any other valu e.3 H ow ever, in their original article,4
B o x and C o x point out that the equation can be sim plified to
y* = Yk
T h is le a d s to a sim p le interpretation o f both X and the eq u ation as
a w h o le . In the te x t o f th e cu rren t stu d y, X is fo u n d to be V*, in
d ic a tin g th a t th e tra n sfo rm e d v a ria b le is th e n sim p ly th e fou rth
ro o t o f 7.
T h e o b v io u s q u e stio n raised is h o w th e v a lu e o f X is fo u n d .
C o n v e n tio n a lly , th is is d o n e b y trial and error. S e v e r a l v a lu e s for
X are u se d , and w h ic h e v e r y ie ld s th e m o d e l w ith th e lo w e s t m ea n
sq u a r e error is th e s e le c t e d v a lu e . H o w e v e r , th e m e th o d is
e x tr e m e ly tim e c o n s u m in g an d is s e e n to b e n ea rly im p o s sib le
w h e n o n e t a k e s in t o a c c o u n t t h e f a c t t h a t t w o v a r ia b le s
(e x p e n d itu r e s and p erm an en t in c o m e ) are b ein g tran sform ed o v er
se v e r a l m o d e ls. In th e te x t, X is estim a te d th r o u g h a m a x im u m lik e lih o o d p r o ced u re u se d b y S c o tt and R o p e in th eir stu d y o f
C o n su m e r E x p e n d itu r e S u r v e y d a ta .5
S o m e exp en d itu res, su ch as food at h o m e
or sh elter and u tilities, are reported by virtu ally all participants in
the C o n su m er E xp en d itu re Survey. For th e se item s, the c h o ic e o f
reg r e ssio n te c h n iq u e is straigh tforw ard: ord in ary lea st sq u ares.
H o w e v e r , m a n y ex p en d itu re s are n ot u n iversal and m ay n ot be
m ade b eca u se o f ta stes and p referen ces (for ex a m p le, tob acco and
sm ok in g su p p lies) or b ecau se the item is a durable good (for exam p le,
R e g r e s s i o n te c h n iq u e s .
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
v e h icles). In th e stu d y set ou t in the text, four su ch variab les are
exam in ed . Three (fo o d a w ay from h o m e , entertain m en t, and o u t-o fto w n trips) are p robably ex a m p les o f the first situ ation (ta stes and
p referen ces d issu ad e so m e co n su m ers from p u rch asin g the item ),
w h ile the fourth (apparel) m ay be an exam p le o f the seco n d situation
(perhaps the co n su m er had su ffic ien t a m ou n ts o f apparel during the
p reviou s quarter or did n ot n eed se rv ices su ch as d ryclean in g or
repair). T h ese k inds o f exp en d itu res require sp ecial treatm ent.
O n e set o f m o d els d esig n ed to han d le su ch situ ation s is called the
“ d o u b le -h u r d le ” se t. T h e m o d e ls g e t th e ir n a m e b e c a u s e th e
co n su m er m u st first d ecid e w h eth er to p u rch ase the item and, i f so ,
then determ in e h o w m u ch to p urchase. In th e se m o d e ls, th e h urdles
appear in tw o stages: stage o n e m o d e ls th e prob ab ility o f p u rch a se,
stage tw o the lev el o f p u rch ase for th o se w h o b u y th e g o o d . R esu lts
o f the tw o stages are u sed togeth er to predict the exp en d itu re for a
given consum er.
O n e p op u lar form o f d o u b le -h u r d le m o d e l is th e T o b it m o d e l ,
in w h ic h th e h u rd les are estim ated w ith the sa m e in d ep en d e n t
variab les. T h e sta g e s are estim ated in su ch a w a y that a set o f
param eters is p roduced that can then be u tilized to estim a te the
p erson ’s probability o f purchasing a g iv en item (u sin g the cu m ulative
d e n s ity f u n c t io n , as w ith th e p rob it t e c h n iq u e ) and m a rg in a l
p r o p e n sity to c o n s u m e (a s w ith o rd in a ry le a s t sq u a r e s ). T h e
p redicted exp en d itu re is eq u iv a len t to the p redicted exp en d itu re for
th o s e w h o p u r c h a se th e ite m , w e ig h t e d b y th e p ro b a b ility o f
p u r c h a s in g i t .6 H o w e v e r , a m a jo r d r a w b a c k o f T o b it is th e
restriction s it p laces on th e resu lts o f th e an alysis. First, b eca u se
o n e particular set o f in d ep en d en t variab les is u se d , the m o d el is
u sefu l o n ly w h e n th e ex a ct sam e set o f variab les p red icts both the
probability o f p u rch asin g an item and th e lev el o f exp en d itu re on
th e item . T h is is n ot a lw a y s th e case. For ex a m p le, th e prob ab ility
o f p u rch asin g health in su ran ce m ay d ep en d on the size o f o n e ’s
fam ily. H o w ev er, i f a particular p o licy ch arges o n e p rem iu m for
“ fa m ily ” c o v e r a g e , reg a r d le ss o f th e n u m b er o f m e m b e r s in th e
fa m ily , th e Tobit m o d e l h as a w e a k n e s s in p red ictin g e x p e n d itu r e s
for th at p o licy . F u rth erm ore, th e T obit m o d e l a s s u m e s th a t th e
“ d ir e c tio n ” o f e a c h v a ria b le is th e sa m e for th e p ro b a b ility and
for th e le v e l o f c o n su m p tio n , w h ic h m ay n ot be true. For in sta n c e ,
an article d e sc r ib in g w in e c o n su m p tio n b y U .S . m en fo u n d that
m en w h o had at le a st a h ig h s c h o o l ed u c a tio n w e r e m ore lik e ly to
drink w in e than m en w ith lo w e r le v e ls o f e d u c a tio n ; h o w e v e r ,
th e a r tic le a ls o fo u n d th a t m e n w it h at le a s t a h ig h s c h o o l
e d u c a t io n d ra n k l e s s w in e th a n t h o s e w it h lo w e r l e v e l s o f
e d u c a tio n .7
O th er m o d e ls a lso h a v e b een p ro p o sed to h a n d le th e “ d o u b le
h u rd le” situ a tio n . T h e m o d e ls u sed in th is article are b a sed o n a
ty p e d e s c r ib e d b y J o h n G. C r a g g .8 In C r a g g ’s m e th o d , th e
probability o f p u rch ase is estim ated separately from th e le v e l o f
exp en d itu res. C ragg’s approach has m any ad van tages over the T obit
m eth od . T h e ab ility to separate th e p ro b a b ility -o f-p u rch a se and
lev el-o f-ex p en d itu re eq u a tio n s a llo w s d iffe r e n c e s in variab les and
sig n s across th e tw o s ta g e s o f th e a n a ly s is , p r o v id in g C r a g g ’s
a p p roach w ith a “ c o n sid e r a b le in te rp reta tio n a l a d v a n ta g e ” o v e r
th e T ob it m o d e l.9 In a d d itio n , n o t o n ly d o e s “ T o b it...fo r c e z e r o
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Expenditures of Single Parents
o b serv a tio n s to represent corn er so lu tio n s ,” b u t it a lso “ p r e su m e s
th a t th e sa m e se t o f v a r ia b le s and p aram eter e s tim a te s d eterm in e
b o th th e d isc r e te p ro b a b ility o f a n o n z e r o o u tc o m e and th e le v e l
o f p o s itiv e e x p e n d itu r e s .” 10
A lth o u g h C ra g g ’s m o d e ls u se probit to p redict p rob ab ilities o f
p u rch ase, h e n o te s that lo g it can be u sed in stea d .11 M a n y standard
eco n o m e tr ic s te x tb o o k s p oin t ou t that lo g it p rod u ces probability
estim a te s that are n early id en tical to probit estim a tes. H o w e v e r ,
lo g it resu lts are m u ch easier to u se and interpret. T h e eq u ation for
p red ictin g th e p rob ab ility o f p u rch ase (P ) o f an item is
P = exp(a + p X )/[l + ex p (a + (IT)],
rea so n a b le to a ss u m e th at, g iv e n e n o u g h tim e , 1 0 0 p ercen t o f
co n su m er u n its w ill ev e n tu a lly p u rch ase th o se item s. H o w e v e r ,
T ob it still s u f f e r s th e w e a k n e s s e s d e s c r ib e d ea r lie r , an d fo r
con ven ien ce as w ell, the Cragg m odel is u sed for all variables analyzed
in this a rticle.13
(MPC). T he m argin al p ro p en sity
to c o n su m e ( m p c ) is d efin ed as th e ch a n g e in ex p en d itu re, g iv e n a
unit ch an ge in in com e. In this case, perm anent in co m e is th e relevant
variable for ch an ge.
In the o rd in ary-least-sq u ares-on ly reg re ssio n s d escrib ed in th e
tex t (for food at h o m e , sh elter and u tilities, and tran sp ortation ), the
eq u a tio n s h ave th e form
M a r g i n a l p r o p e n s i t y to c o n s u m e
w here
E ( Y /4) = a + b P '4 + c X ,
is th e in tercep t o f the lo g it eq u ation ,
is a v ecto r o f p aram eter estim a te s,
E ( X ' 4) is th e p red icted (or e x p e c te d ) v a lu e o f th e d ep en d en t
and
variable,
X is a v ecto r o f in d ep en d en t variables.
T h is fo rm u la can be entered in to a standard sp read sh eet to estim ate
prob ab ilities o f p u rch ase for d ifferen t co n su m ers. F urtherm ore, the
eq u a tio n is e a sily d ifferen tiated to find th e m arginal relation sh ip o f
prob ab ility to a particular variab le. (For ex a m p le , i f in c o m e rises by
$ 1 , b y h o w m u ch d o es th e probability o f p u rch ase ch a n g e? ) W ith
probit, an eq u a tio n m u st be estim ated and th e results look ed up in a
sta tistica l table to fin d o u t th e overall p rob ab ility o f an e v e n t ’s
o c c u r r in g , a s w e ll as th e m argin al e f fe c t on p rob ab ility d u e to
ch a n g in g a variable.
In th e v ersio n o f the C ragg m od el u sed in th e tex t o f this article,
th e prob ab ility o f p u rch asin g an item is estim ated as su g g ested w ith
a lo g istic regression . Separately, the m eth od o f ordinary least squares
is u sed to estim a te exp en d itu res for th o se w h o p u rch ase th e ite m .12
To g e t th e fin a l r e s u lts , th e p red icted p r o b a b ility o f p u rch a se
o b ta in e d fr o m th e fir s t s t a g e is m u lt ip lie d b y th e p r e d ic te d
e x p en d itu re for th o s e w h o p u rch a se th e item . T h is ca lcu la tio n
esse n tia lly p ro d u ces an average p redicted ex p en d itu re, w eig h ted by
th e p ro b a b ility o f p u r c h a se . To illu str a te th e in tu itio n b eh in d
o b ta in in g su ch a w eig h ted -a v er a g e predicted ex p en d itu re, su p p ose
that a large sa m p le o f co n su m ers is se lecte d random ly. S u p p ose
further that 2 5 p ercen t o f the participants pu rch ased a particular
item th a t so ld for $ 1 0 0 . T h en th e a v era g e e x p en d itu re for all
c o n su m ers is $ 2 5 , or 2 5 p ercen t m ultip lied by $ 1 0 0 . I f a sm aller
sa m p le is ra n d om ly selecte d from th is large grou p , th e ex p ected
v a lu e o f the average o f that sm aller sam p le is also $ 2 5 . T h e reason is
that i f a large n u m b er o f random sam p les w ere pulled from the total
s a m p le , a n d e a c h tim e th e s a m p le s w e r e p u lle d th e a v e r a g e
exp en d itu re w a s recorded, then the “grand a verage” (th at is, the
a v era g e o f th e a v era g es) is ex p ected to be $ 2 5 .
In e s tim a tin g th e m a rg in a l p r o p e n sity to c o n s u m e an d th e
ela sticity in C ragg m o d e ls, the lo g it resu lts are taken in to a cco u n t,
b eca u se in c o m e is a ssu m ed to in flu e n c e exp en d itu res both d irectly
(th ro u g h th e le v e l o f exp en d itu re) and in d irectly (b y ch a n g in g the
p rob ab ility o f p u rch a se). (T h e m a th em a tica l d etails b eh in d th is
statem en t are provided in the n ext tw o su b sectio n s o f this ap p en d ix.)
A s a final p o in t, there are so m e exp en d itu res for w h ich Tobit
m a y be ap p ro p ria te, in that th e te c h n iq u e a ss u m e s th at, g iv e n
e n o u g h tim e, all co n su m ers w ill ev en tu a lly p u rch ase th e g iv en item .
For ex a m p le, le s s than 1 0 0 percent o f all co n su m er u nits report
e x p e n d itu r e s fo r ap p arel an d s e r v ic e s e v e r y q u arter, b u t it is
34
w h ere
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a is the in tercep t,
b is a param eter estim a te,
I d en o te s total o u tlays (th e p roxy for p erm an en t in c o m e ),
and
c X rep resen ts all other in d ep en d en t variab les, m ultip lied by their
regression co effic ie n ts.
In th is c a se , th e m p c is calcu lated b y fin d in g th e ch a n g e in the
predicted exp en d itu re, g iv e n a $1 in crease in p erm an en t in c o m e , or
d E (Y )/d I . A lth o u g h the m o d el is sp ec ified to ca lcu la te E ( Y 1/4), the
d esired resu lt is e a sily ob tain ed . To sim p lify th e a rith m etic, it is
e a siest to co n v ert £'(T 1/4) t o i i( 7 ) :
E ( Y ) = E i X y = ( a + b P '4 + c X } 4
d E ( Y ) /d I = 4 (a + b P '4 + c X ) \ ( \ I A ) b P V4] = [ b { a + b P '4 + c X f ] ! P ' 4
= ( b /P 14) x E i r y
= b [ E ( T ) / r \ V4.
T his result h as an in terestin g property in that th e m p c is a fu n ctio n
o f the exp ected budget share (that is, the sp ecific ou tlay E ( Y ) , divid ed
by th e total ou tla y s I).
T he C ragg-b ased m o d e ls h a v e a m ore co m p lic a ted sp ec ifica tio n ,
but th e y are n ev erth eless so lv a b le for th e m p c . N o te that th e m p c is
still d e fin e d and rep resen ted m a th e m a tic a lly in th e sa m e w ay;
h o w ev er, th e in itial form u lation is m ore co m p lica ted . T h e desired
resu lt is actu ally
E p (Y )
= P x [£(H/4)]4,
where P is the probability of observing an expenditure.
To find d E p( Y ) /d I , the product rule o f ca lcu lu s is u sed . T hat is,
dE p( Y ) / d I =
P'
[E (Y )]
+P
[E '
(7)].
N o w , recall that
P = e x p ( a + p /1/4 + 8 * ) / [ l + exp (a + p /1/4 + 5A )]),
w h ere 8 X is a v ecto r o f all in d ep en d en t variab les ex c e p t in c o m e ,
each m ultip lied by their param eter estim ates.
T h erefo re, to find P ' , th e q u otien t rule is u sed . T h u s,
P ' = ( f ' g - f g ' ) t g 2,
w h ere
f = e x p (a + p /174 + 8 X ),
g = 1 + e x p (a + p /174 + 8 X ),
and
f
= g' = [(% x p)//374] x exp(a + p/174 + 8Y).
B e c a u s e f 7' and g ' are eq u al in th is c a se , the fo reg o in g eq u ation
sim p lifie s a lg eb ra ica lly to
P ' = [l:’ ( g - f ) ] / c f -
and b e c a u se g eq u a ls f + 1, th e eq u ation red u ces e v e n further to
P' = [ f ( f + 1 - f ) \ / g 2 = f
the sum o f the rem aining p ieces. H ow ever, the form ula is left the w a y it
is for the m om en t, to illustrate an intuitive point: the m p c is derived
from the predicted value o f the expenditure for th ose w h o actually
purchase, w eigh ted by the probability o f purchasing. N o te that the
second term on the right-hand side ( P * b \E {Y )U ]yA, is the sam e m p c as
w a s found b efore, ex cep t that it is w eigh ted by the probability o f
purchase. T he rem aining term on the right-hand side is a result o f the
fact that the predicted expenditure is affected indirectly b ecau se o n e ’s
probability o f purchasing som ething ch an ges as a result o f a ch an ge in
income.
I n c o m e e la s tic ity (or m ore p ro p erly in th is c a s e ,
p erm an en t-in com e elasticity) is th e p ercen t ch a n g e in exp en d itu re
for a sp ecific good (su ch as food at h o m e ), g iv en a 1 -percent increase
in (p erm an en t) in co m e. For ex a m p le, for sin g le fath ers, th e in co m e
elasticity for fo o d at h o m e is estim ated to be 0 .2 8 , m ea n in g that for
e v e r y 1-p e rcen t in c r e a se in p erm a n en t in c o m e , th e s e m en are
p redicted to in crease their fo o d -a t-h o m e exp en d itu res b y m ore than
E la s tic itie s .
one-quarter o f 1 percent.
T h e eq u a tio n for calcu latin g th e ela sticity r\ is
/ g 2.
r| =
m pc
x I /E (Y ) .
N o w , w ith th e m u c h sim p lified resu lt, it can be sh o w n that
P ' = { [ ( lA x p)//374] x ex p (a + PT/4 + 8 J 0 }/[1 + exp (a+ pi174 + 5X )]2.
A g a in , by su b stitu tio n , th is red u ces to
P x {{Q A x fS)/P,4] / [ 1 + e x p (a + p /174 + 8 X ) ] } .
T h erefore,
m pc
= p x {[(y4 x p)//3/4] /[ l + ex p (a + p /174 + 5Y )]} >' ii(T ) + P y b
x [£(T )/7]3/4.
B e ca u se the term s P and E ( Y ) are com m on to both pieces o f the
complicated right-hand side o f this equation, the MPC can be simplified
m athem atically by factoring these term s out and m ultiplying them by
In the ca se o f the o rd in ary-least-sq u ares-on ly reg re ssio n s, the
e la s tic ity is c o n sta n t an d e q u a l to th e p aram eter e s tim a te fo r
perm anent in com e. To sh o w this m ath em atically, recall that the m p c
in th is ca se is a fu n ction o f the p redicted exp en d itu re share; that is,
m p c = b \ E ( Y ) / I ] V4. T h u s, m u ltip ly in g th e m p c b y I /E ( Y ) y ie ld s
b [ E ( Y ) / i y m , or b [ I /E ( Y ) ] VA. S o w h ile th e MPC is a fu n ctio n o f the
ex p ected b u d get share, ela sticity is a fu n ctio n o f the in v e r s e o f the
b u d get share. H e n c e , as the b u d get share in crea ses, so d o es the m p c ,
but ela sticity d eclin es.
For th e C ra g g -b a sed m o d e ls, th e fu ll fo rm u la is m u c h m o re
co m p lica ted , d u e to the co m p le x ity o f the m p c eq u ation . H o w e v e r ,
o n ce th e v alu e o f the m p c is o b tain ed , m u ltip lyin g that v a lu e b y the
in v erse o f the p redicted ex p en d itu re share still y ie ld s th e estim a te
o f elasticity.
Notes to Appendix A
1 S tu a rt Scott and D a n ie l J. Rope, “ D is trib u tio n s and T ra n s
formations for Fam ily Expenditures,” 1993 Proceedings o f the Section
on Social Statistics (Washington, DC, American Statistical Association,
1 9 93), pp. 7 4 1 -4 6 .
2 G. E. P. Box and D . R. Cox, “ An Analysis o f Transformations,”
Journal o f the Royal Statistical Society, Series B, 1964, pp. 2 1 1 -4 3 ,
esp. p. 214.
3 Even i f X is unity, it is hard to imagine why Y is transformed to 7 - 1.
4 Box and Cox, “Analysis,” p. 214.
3 Scott and Rope, “ Distributions and Transformations.”
6 See John M cD onald and Robert A. M o ffitt, “The Uses o f Tobit
Analysis,” Review o f Economics and Statistics, M a y 1980, pp. 3 1 8 21, esp. p. 318.
7 J. R. Blaylock and W. N. Blisard, “Wine consumption by US men,”
A pplied Economics, M a y 1993, pp. 6 4 5 -5 1 , esp. p. 649.
8 John G Cragg, “ Some Statistical Models for Lim ited Dependent
V ariab les w ith A p p lic a tio n to the D em and fo r D u ra b le G oods,”
Econom etrica, September 1971, pp. 8 2 9 -4 4 .
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Federal Reserve Bank of St. Louis
9 M o ham ed A b d e l-G h a n y and J. L e w S ilv e r, “ E conom ic and
D em ographic D eterm inants o f C an adian H ouseholds’ Use o f and
Spending on A lc o h o l,” Fam ily and Consum er Scien ces Research
Journal, September 1998, pp. 6 2 -9 0 , esp. p. 65.
10 Deanna L. Sharpe, Mohamed Abdel-Ghany, Hye-Yeon Kim , and
Gong-Soog Hong, “Alcohol Consumption Decisions in Korea,” Journal
o f Family and Economic Issues, Spring 2001, pp. 7 -2 4 , esp. p. 14.
11 See Cragg, “ Some Statistical Models,” footnotes 5 (p. 830) and 6
(p. 832).
12 To reduce heteroscedasticity, the ordinary-least-squares models
used in this study actually predict the fourth root o f the expenditure
for those individuals with positive expenditures.
13 Experiments run with the data presented in the text confirm that
Tobit does not yie ld consistently plausible results fo r apparel and
services. To test how Tobit and Cragg results compare in the present
situation, expenditures for both apparel fo r adults and apparel for
children were regressed on various characteristics, using a Tobit model.
The first problem in doing so is that, as described earlier, the variables
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Expenditures of Single Parents
differ in the first and second stages o f the Cragg model. That is, several
interaction terms for single fathers are included in the second stage
that are not included in the first stage. To make the models consistent,
these extra variables were excluded from the Tobit m odel. (In the
second stages o f the Cragg models, only two variables were found to be
statistically significant: the variable denoting single fathers with two
children was significant in both models, and the variable denoting
single fathers aged 45 to 49 years was significant only for expenditures
for children’s apparel.) W hen the results o f the Tobit model are used
to predict the probability o f purchase, however, they are not consistent
w ith the results produced by the Cragg model, nor do they resemble
values expected from the data themselves. For example, the actual
percentage o f single mothers in the sample who reported expenditures
for adult apparel and services is 58 percent, and for children’s apparel,
the percentage is about 68 percent. (See table 5.) However, for each o f
these items, the Tobit model predicts v irtu a l certainty o f purchase
(greater than 99 percent) in each case. This prediction is not consistent
with the Cragg model’s first-stage results, which are far more similar to
the observed data. (Single mothers w ith average permanent income
are predicted to have a 56-percent probability o f purchasing apparel
for adults and a 63-percent chance o f purchasing children’s apparel,
according to the C ragg m odel.) W hen the results o f the firs t and
second stages o f the Cragg models are compared, it is found that
several variables change signs. H ow ever, only one sign-changing
pa ra m eter estim ate is s ta tis tic a lly s ig n ific a n t at the 9 5 -p e rce n t
confidence level in both stages: the intercept. In the first stage o f the
Cragg model, it is negative, whereas in the second, it is positive. The
effect o f the intercept in the first stage, then, is to lower the predicted
probability o f purchase in these models. However, in the second stage,
the intercept acts as a “ starting point” for expenditures. (In effect, it
can be in terpreted as saying, “ Even i f the control group has no
permanent income, it is still predicted to spend at least this much on
A ppendix B:
apparel and services for children or adults.”) As mentioned earlier, one
o f the weaknesses o f Tobit is that the parameter cannot change signs
across stages. Because the To b it-d e riv ed intercept is “ larg e” and
po sitive, this forces the pred icted p ro b a b ility o f purchase to be
extremely high for both types o f apparel. In fact, even i f a fa m ily ’s
permanent income is zero, the predicted p rob ab ility o f purchasing
apparel for children is nearly 96 percent! For single fathers (again,
even those w ith zero permanent income), the predicted probability is
slightly higher, at 98 percent. Sim ilar results are observed for apparel
fo r children: single m others w ith zero perm anent incom e have a
predicted p rob ab ility o f purchase o f 83 percent, and single fathers
with zero permanent income have a predicted probability greater than
99 percent. In each case, w hen re a lis tic perm anent incomes are
assumed, the predicted p ro b ab ility o f purchase is greater than 99
percent. Given that the probability o f purchase in these cases is strongly
“ up w ardly biased,” the prob ab ility-w eig hted estimates o f both the
marginal propensity to consume and permanent-income elasticity w ill
undoubtedly also be biased. (T h e direction is impossible to know
w ithout any other measure by which to compare the intercepts. For
example, i f it is assumed that the p ro b ab ility intercept in To bit is
biased upward, it m ay be that the level-of-expenditure intercept is
biased downward, because both events are measured in one parameter.
W hich effect dominates presumably determines in what direction the
two parameters are also biased.) Hence, it is not surprising to find that
the results for marginal propensities to consume and income elasticities
obtained from the Tobit analyses in this experiment are, for the most
part, not consistent with those obtained from the Cragg model. A t any
rate, this again demonstrates a weakness o f Tobit— that is, that both
events (probability and level o f expenditure) are analyzed w ith the use
o f one set o f parameter estimates. Thus, this article uses the Cragg
model and leaves further examination o f the Tobit model for future
research.
Ceteris Paribus results
T h e tables in th is ap p en d ix s h o w h o w sin g le m oth ers com p are w ith
sin g le fath ers, a ssu m in g the sam e p erm an en t in co m e and d w ellin g
Table B-l.
Expenditures of single parents on selected
categories
size. It is in terestin g to n o te that adding th e extra p erm an en t in co m e
to fem a le-h ea d ed fa m ilies— an in crease o f m ore than 55 p ercen t—
h a s a n o tic ea b le e ffe c t on th o se fa m ilie s’ ex p ected prob ab ilities and
le v e ls o f sp en d in g for m o st g o o d s and se r v ic e s, but d o e s little to
ch a n g e their ex p ected m arginal p rop en sities to co n su m e or their
in c o m e ela sticities.
Variable
Permanent income (quarterly outlays, dollars).....
Apparel and services (adults)..............................
Apparel and services (children)1..........................
Transportation (less trips)....................................
Food away from home (less trip s ).......................
Fees and admissions (less trip s )........................
Pets, toys, and playground equipment...............
Trips and travel.....................................................
Babysitting and day care.....................................
Men
Women
$9,435
47.1
47.6
98.8
98.3
62.5
46.5
37.1
28.3
$9,435
67.0
71.8
99.3
98.0
65.8
57.7
42.9
47.8
1Male-income interaction coefficient is statistically significant at the
95-percent confidence level.
36
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July 2002
I
O rd in a ry le a s t sq u a re s results, s in g le p a re n ts
Variable
Permanent income (/).............................................
Men
Women
$9,435
$9,435
Food at home:
Probability, percent (P )......................................
P ' ........................................................................
100.0
0
100.0
0
£ (v y 2...................................................................
p (VO.................................................... ...............
$826
.0237
$772
.0332
.024
.033
.27
.41
MPC -
P'
E(Y) +
P E ' ( Y ) .....................................
Elasticity = MPC x
l/ E ( Y ) ) ...................................
Apparel and services (adults):
Probability, percent (P )......................................
P' ........................................................................
47.1
2.22E-05
67.0
2.64E-05
E(VT'2...................................................................
F ( V ) ....................................................................
$514
.0216
$574
.0459
.022
.046
MPC -
P ' E(Y )
+ P E ' (V ).....................................
Elasticity = MPC x (IIE (Y ) ) .................................
Apparel and services (children):
Probability, percent (P ).......................................
P ’ ........................................................................
.40
.75
71.8
47.6
9.80E-06 2.03E-05
.....................................................................
F (VO....................................................................
MPC - P ’ E(Y) + P E ' ( Y ) .....................................
$101
.0073
$133
.0111
.004
.011
Elasticity - MPC x (IIE (Y ) ) .................................
.42
.76
E(Y)
Transportation (less trips):
Probability, percent (P )......................................
P ' .......................................................................
E(Y) .....................................................................
F (VO....................................................................
99.3
98.8
1.70E-06 6.44E-07
$1,280
$1,602
.1486
.1873
M PC- P'£(V0 + PE' (VO .....................................
.188
.148
Elasticity = MPC x (l/E(Y)).................................
1.11
1.09
Food away from home (less trips):
Probability, percent (P )......................................
P' ........................................................................
....................................................................
F (V)....................................................................
E (Y )'
98.0
98.3
4.35E-06 4.50E-06
$1,217
.0523
$731
.0440
(Y) .....................................
.057
.046
Elasticity - MPC x (l/E(Y)).................................
.44
.60
Fees and admissions (less trips):
Probability, percent (P )......................................
P'
.................................................................
62.5
2.05E-05
65.8
3.10E-05
....................................................................
F (Y)....................................................................
$389
.0202
$295
.0223
MPC - P'
E (Y ) + P E '
E(Y)
.021
.024
( I I E ( Y ) ) .................................
.50
.76
Pets, toys, and playground
equipment (less trips):
Probability, percent ( P ) ......................................
P'
.................................................
46.5
1.17E-05
57.7
1.85E-05
...............................................................
.............................................................
$524
.0033
$526
.0339
MPC=P'E(VO + P F VO ......................................
.008
.029
MPC -
P 'E ( Y ) + P E ' ( Y ) .....................................
Elasticity = MPC x
HYP
(VO
E'
Elasticity = MPC x (l/E ( Y )) .................................
.14
.53
Trips and travel:
Probability, percent (P )......................................
P'
.........................................................
37.1
1.78E-05
42.9
3.20E-05
.........................................
.........................................
$933
.0979
$917
.0872
E(Y)
E
Y' )/ .....................................................
*—' (V
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Table B-2.
C o n tin u a tio n — O rd in a ry le a s t s q u a re s
results, s in g le p a re n ts
Variable
Men
Women
MPC - P' E(Y) + PE ' (V ).....................................
.053
.067
Elasticity = MPC x (//E(V)).................................
54
.69
Babysitting and day care:
28.3
Probability, percent (P )......................................
P'
...................................................... 1.16E-05
47.8
3.03E-05
..........................................................
...........................................................
$273
.0110
$515
.0434
MPC - P' E(Y) + PE' (V) .......................................
.006
.036
Elasticity = MPC x (//E(V)).................................
.22
.67
..........................................................
100.0
0
100.0
0
..........................................................
.................................................................
$2,589
.1513
$2,880
.1730
M P C -P 'E (Y ) + P F ( Y ) ........................................
.151
.173
Elasticity = MPC x (IIE (Y )) ....................................
.55
.57
Shelter and utilities (renters):3
Probability, percent (P ) ........................................
P'
...................................................
100.0
0
100.0
0
E(Y)
E 'l Y )
Shelter and utilities
(owners, with mortgage):3
Probability, percent (P )......................................
p'
EtY)
£ ' ( Y)
..............................................................
$1,248
$2,585
E' ( Y) .......................................................................
.0458
.2251
M P C -P 'E (Y ) + P F ( Y ) ........................................
.046
.225
Elasticity = MPC x (l/E(Y) ) ....................................
.35
.82
E lW '2
1 Binary variable used to calculate this value for men Is statistically signifi
cant at the 95-percent confidence level.
2 Men’s income effect used to calculate this value is statistically signifi
cantly different from the women’s income effect at the 95-percent confidence
level.
3 mpcIs and elasticities for homeowners are calculated assuming that single
fathers have seven rooms and two bathrooms or half baths and that single
mothers have six rooms and two bathrooms or half baths. For renters, both
types of parents are assumed to have five rooms and one bathroom or half
bath. For single mothers who are homeowners, the estimated expenditure
E (Y ) increases to $2,943 when they are assumed to have seven rooms, and
the mpc increases slightly, to 0.176. The elasticity estimate is unaffected by
this “total” c e te ris p a rib u s assumption, falling to 0.56.
Note: Values are calculated from detailed regression coefficients, with
results rounded.
Table B-3.
H o u sin g te n u re , s in g le p a re n ts
Variable
Men
Women
Probability of renting calculated by raising average
permanent income of single mothers to match
that of single fathers
Permanent income (quarterly outlays, dollars)......
Probability of outcome (renter, percent)...............
$9,435
50.7
$9,435
50.7
Probability of renting calculated by lowering average
permanent income of single fathers to match
that of single mothers
Permanent income (quarterly outlays, dollars)......
Probability of outcome (renter, percent)...............
6,074
55.2
6,074
66.7
Monthly Labor Review
July 2002
37
Planning ahead: consumer
expenditure patterns in retirement
The ‘g raying ’ o f the population creates
a need to examine the role
that retirement plays on expenditure decisions
o f various demographic groups o f retirees
Geoffrey D. Paulin
and
Abby L. Duly
Geoffrey D. Paulin
is a senior economist,
and Abby L. Duly
is an economist,
Division of Consumer
Expenditure Surveys,
Bureau of Labor
Statistics.
Email:
Paulin_G@bls.gov
Duiy_A@bls.gov
he fastest growing segment of the U. S.
population is composed of those aged 65
and older. The Bureau of the Census re
ported that in 1994,1 in 8 Americans was in this
age group, but projects that the ratio may be as
high as 1 in 5 by 2050. Furthermore, with in
creases in life expectancy, today’s adults will live
an average of 17 additional years after reaching
age 65.1
As this demographic pattern shifts, an in
creasing demand for research and data on the
older population— specifically, on retired per
sons and their roles on consumers— is con
stantly in evidence: “baby b o om ers,”
“privatization of Social Security,” “Medicare,”
and tips on financial planning are common top
ics of the daily print and video media. The sheer
growth in numbers suggests that the spending
patterns of this older population will also play
an increasingly important role in the future
economy, an assumption supported by recent
trends in expenditure levels. A study of real (that
is, inflation-adjusted) expenditures from 1984 to
1997 finds that “spending by older consumers
has risen from 12.6 percent to 14.6 percent of all
consumer spending.”2
In addition to the concerns these issues may
raise for policymakers, especially those involved
with providing adequate care and protection for
older consumers, the decision to retire has major
implications for individuals and families. Under
standing differences in spending patterns for
38 Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
preretired and retired consumers can help work
ers plan for the future.
Taken together, these items suggest that a
study of expenditure patterns of retirees is war
ranted. Differences in expenditure patterns for
preretirees and retirees are expected for many rea
sons. For example, income presumably will de
cline upon retirement. Given the relationship of
income to expenditures, it is important to see how
income differs— in level as well as in sources of
receipt. Also, other demographic characteristics
presumably play an important role in expenditure
decisions, both before and after retirement.
Therefore, examining the role these characteris
tics play is also important. In looking at spend
ing patterns for families who are near retirement
and comparing them with the patterns of those
individuals who have actually exited from the
workforce, this article provides valuable informa
tion about the impact of retirement on consumer
spending.
Several issues are addressed here. First, back
ground describing related research is presented.
Second, data from the U.S. Consumer Expendi
ture Survey, which provide the basis for the
analysis, are described. Third, demographic char
acteristics of “preretired” and “retired” consum
ers in this sample are presented and compared.
Fourth, income and expenditure patterns are de
scribed for these groups. Finally, regression
analysis is used to explore differences in expen
diture patterns given that demographics and in-
come levels are different for preretired and retired consumers.
(Logit and ordinary least squares results for the two groups
are presented in a detailed appendix.)
Related research
Many previous studies related to the population aged 65 and
older can be divided into two groups: those that focus on
age, and those that focus on retirement. Both groups are
important, and both have contributed to the analyses pre
sented here.
Expenditure patterns by age. Rose Rubin and Kenneth Koelin
examine how elderly households spend on necessities, com
pared with nonelderly households.3 Using data from the 198081 and 1989-90 Consumer Expenditure Survey, they examine
expenditures for housing, food at home, and healthcare, as
well as income, demographics, and receipt of cash assistance
( afd c or SSI). The methodology used to examine the relation
ship between their variables of interest is based on the life
cycle theory of consumption, with total expenditures acting
as a proxy for permanent income. Rubin and Koelin’s results
indicate that, in general, older consumers spend a higher pro
portion of their budget on housing and healthcare than do the
nonelderly, and that the receipt of financial assistance does
play a role in the spending decisions of both age groups.
In a study o f age groups within the older population,
Mohammed Abdel-Ghany and Deanna Sharpe use Tobit analy
sis to determine whether tastes and preferences differ for those
aged 65 to 74 and those aged 75 and older.4 Using indepen
dent variables such as total expenditures (once again as a
surrogate for permanent income), region of residence, educa
tion of reference person,5 household size, race, and family
type, the authors find differences between the “young-old”
and “old-old” (as they term the groups) across all major cat
egories of expense. Furthermore, the effect of the socioeco
nomic variables on spending patterns differed between the
two age groups, and among spending categories.
Studies based on retirement status. Because this study com
pares retired households with those that have members near
ing retirement, previous studies based on work status are dis
cussed in more detail. Among the studies reviewed here, an
article by Nancy E. Schwenk is unique in its focus on the
levels and sources of income of retirees, using multiple gov
ernment surveys as sources.6 Schwenk provides some dis
cussion of expenditures, specifically the fact that the alloca
tion of total spending for retirement, pensions, and Social
Security is significantly less for households in which the ref
erence person has “reached retirement age (65 years or older)”
than for those in which the reference person is aged 45 to 54.
In terms of demographics, she notes that the majority of con
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sumers aged 65 years and older own their home, and that “of
those who are homeowners, most owned their home free and
clear (81 percent).” Finally, Schwenk finds that in 1991, in
come from dividends, interest, and rent provided about 20
percent of retirees’ total income.7
An earlier article by Frankie N. Schwenk uses data from the
1987 Consumer Expenditure Survey to examine whether there
are differences between those who opt for “early retirement”
and those who continue to work beyond the age of 65.8 In
this study, F. Schwenk specifically compares the two groups
in terms of family characteristics, asset levels, income, and
expenditures. Using Probit analysis, the author finds that
age, spouse’s employment status, education, housing tenure,
household size, marital status, and gender are significant fac
tors in predicting the likelihood of being retired. Other com
parisons show that “average dividend and interest [income]
amounts were higher for retired than for working families,”
and that “health was the only category of expenditures for
which households with a retired reference person spent more
than those with an employed person.”9
In a May 1990 article, Thomas Moehrle uses the Con
sumer Expenditure Survey to compare the average annual
expenditures of elderly working and nonworking consumer
units10 across low, medium, and high income groups. 11
Moehrle finds that (1) “Nonworking elderly households
spend more on food prepared at home than do working eld
erly households, regardless of income level,” and (2) “Re
gardless of income level, nonworking elderly households
spend more on health care than do working elderly house
holds.”12 Note that Moehrle analyzes one age group, those
with a reference person aged 62 to 74, and that the working
status of the consumer unit is based solely on that of the
reference person, regardless of whether any other members
are working or not. Also, he does not specifically limit the
nonworking households to those whose reference person is
retired (for example, “nonworking” can mean the reference
person is disabled, taking care of the home or family, or going
to school). However, he finds that “79 percent [of the non
working consumer units studied] had reference persons who
classified themselves as retired.”13
Rose Rubin and Michael Nieswiadomy compare demo
graphic characteristics, income, and expenditures of retirees
and nonretirees aged 50 or older from the 1986 and 1987 Con
sumer Expenditure Survey.14 Their sample consists of com
plete income reporters only, with the retirement status based
on that of the respondent.15 Rubin and Nieswiadomy also
divide their sample into three household types: single men,
single women, and husband-wife couple households. Using
Tobit regression analysis, they find “that the retired have a
higher marginal propensity to spend (than the nonretired) for
food, alcohol, housefumishings, apparel, transportation, gas
and motor oil, other vehicles, public transportation, health
Monthly Labor Review
July 2002
39
Expenditures in Retirement
care, entertainment, and cash gifts.”16 Also noteworthy is
their conclusion that for both the retired and nonretired
households, healthcare expenditures increase with educa
tional attainment.
About the sample
This article uses data from the 1998 and 1999 Consumer Ex
penditure Interview Surveys. The Interview Survey is a rotat
ing panel survey designed to collect information on major
items of expense, household characteristics, and income. The
questionnaire is administered to sample consumer units once
per quarter for five consecutive quarters. The main goal of
the initial household interview is to collect inventory informa
tion to be used for bounding purposes, that is, to ensure that
expenditures reported in subsequent interviews took place
during the appropriate reference period (in most cases, this
will be the 3-month period prior to the interview date). While
it is primarily designed to collect large (vehicles or appliances,
for example) and recurring (such as, rent or utilities) expendi
tures that can be easily recalled on a quarterly basis, the Inter
view Survey captures up to 95 percent of all expenditures.17
In order to examine the effect of retirement on consumer
spending patterns, the sample is divided into two groups: a
preretired group and a retired group. Ultimately, it would be
most useful to have data for the same family over some pe
riod of time to observe their expenditures both before and
after retirement and compare them directly. Unfortunately, as
discussed, the survey is not designed to follow families for
extended time periods. Even using multiple years of data, it
would be difficult to find families who are “working” in at
least one quarter and then “retired” for the remaining
quarter(s) of their participation. The results described here,
then, must be interpreted cautiously, bearing this in mind.
Nevertheless, the sample has been selected in such a way as
to make these comparisons as appropriately as possible, given
the data constraints.
To this end, a preretired consumer unit is defined as one
whose reference person is aged 55 to 64, and is earning at
least one type of labor income (that is, wage and salary in
come or self-employment income). This age group is chosen
because, for many, it is the last stage of their working lives.
Although some may choose to retire prior to reaching age 65,
this study excludes any consumer unit from the “preretired”
category in which there is a retired person (including a
spouse). In contrast, a “retired” consumer unit is defined as
one whose reference person is aged 65 to 74 and who is re
tired; that is, when asked about the occupation for which they
received the most income, they report that they are not work
ing due to retirement. Additionally, there are no earners in the
“retired” households. Excluded from both groups (preretired
and retired) are families in which the spouse (if present) is not
40 Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
working either due to illness or disability, or due to unemploy
ment. This omission is made because a consumer unit with a
disabled member may have some vastly different spending
patterns than an otherwise similar household, such as medi
cal expenses. Furthermore, in the case of illness or disability,
the decision not to work is not necessarily a voluntary one,
but rather is the result of circumstances that make work im
possible.18 Similarly, an unemployed person presumably would
like to work, and may eventually do so; therefore, these fami
lies may not display the same consumer expenditure patterns
as those in which the spouse is not working for voluntary
reasons (such as retirement or taking care of the home or
family).19 The age groups are chosen to compare those on the
verge of retirement with those consumer units who have re
cently retired, allowing these analyses to focus on the effect
of retirement as a single discrete event. Furthermore, previ
ous research has shown that there are significant differences
between those aged 65 to 74 and those aged 75 and older in
terms of household characteristics, income, and expendi
tures.20 Therefore, the consumer units whose reference per
son is aged 75 or older are removed from the retired sample in
order to eliminate this age effect.21
To facilitate the analysis, the sample for this study is lim
ited in scope. First, the sample is limited to three types of
households: single men, single women, and husband-andwife couples. These groups are selected in order to reduce
the effect of family size on expenditure patterns. Additionally,
the effects of other family member characteristics on expendi
tures are eliminated. For example, preretired families with chil
dren may be spending differently than those without chil
dren, because they may be expecting to send the children to
college soon. Retired families with children may be supported
by these children.22 In either case, expenditures would be
different from those who have children of different age, future
plans, and so forth.23 Even so, families with children are pre
sumably the exception, rather than the rule for these families,
especially those who are retired.
The separation of single men and single women is done in
order to examine the effect of gender-related differences on
spending patterns. For example, in terms of income, the life
time earnings of men and women are expected to be quite
different, especially given the generation being examined. Also,
marital status is affected by differences in life expectancy (that
is, there are more widowed single women than there are male
widowers, as shown in table 1). These factors presumably
will have an influence on spending patterns.
The type of household is determined by two pieces of
information: the number of family members and the marital
status of the reference person. For husband-and-wife couples,
the values for these variables are obvious: that is, there are
two persons in the consumer unit (one of which, by defini
tion, must be the reference person) and the marital status of
I Demographics of preretirees and retirees, by composition of consumer unit, Consumer Expenditure Interview
survey, tyys-yy
Preretired
Retired
Married couples
Single women
Single men
Characteristic
t-value'
Preretired
Retired
t-value’
Preretired
Retired
t-value’
Number of consumer u n its ....................
260
222
-
547
725
-
1,325
1,220
-
Age of reference person.......................
59
70
41.976
59
70
73.192
59
70
99.809
4.0
5.8
3.0
5.9
4.590
.650
4.4
5.7
3.9
5.8
3.396
.823
4.8
6.9
4.6
6.4
.795
7.494
1.1
1.7
1.1
1.7
.102
.113
1.3
1.8
1.2
1.7
1.458
1.261
1.4
2.2
1.6
2.0
2.307
5.020
Vehicles...................................................
Automobiles.........................................
Other vehicles.....................................
1.9
1.3
.6
1.9
1.2
.7
.272
2.397
.859
1.2
1.1
.1
1.2
1.1
.1
1.961
2.053
1.159
2.7
1.6
1.1
2.3
1.4
.9
6.384
6.501
3.419
Percent
Housing tenure:
Homeowner:
With mortgage...............................
With no mortgage.........................
Renter...............................................
31.9
29.6
38.5
7.7
64.0
28.4
_
11.5
68.3
20.3
_
-
40.0
35.8
24.1
51.6
41.0
7.4
16.6
78.2
5.3
Occupation of reference person:
Working for wage or sa la ry................
Self-employed......................................
Retired.................................................
91.1
8.9
0
0
0
100.0
-
94.1
5.9
0
0
0
100.0
-
85.6
14.4
0
0
0
100.0
-
Marital status of reference person:
Married.................................................
Widowed...............................................
Divorced...............................................
Separated............................................
Single (never married).........................
3.5
11.9
56.2
7.7
20.8
6.3
43.2
32.9
3.6
14.0
-
4.6
27.4
53.0
3.1
11.9
4.0
71.7
17.2
.7
6.3
-
100.0
0
0
0
0
100.0
0
0
0
0
-
Race/ethnicity of reference person:
Black....................................................
Hispanic...............................................
White and o th er...................................
12.7
4.6
82.7
13.5
3.2
83.3
-
13.2
2.2
84.6
7.6
1.5
90.9
-
5.3
3.0
91.7
4.3
1.8
93.9
-
10.8
30.8
30.6
27.5
-
11.3
29.6
20.0
38.6
-
“
9.2
33.0
18.8
33.0
—
26.9
22.3
Average number of:
Rooms:
Renter...............................................
Homeowner.......................................
Bathrooms (including halfbaths):
Renter...............................................
Homeowner.......................................
-
-
—
—
-
-
Education of reference person:
Did not graduate high school.............
High school graduate...........................
Some college
(including A.A. degree)....................
College graduate (B.A. degree,
and so fo rth )....................................
Graduate/professional degree............
23.5
16.2
~
33.6
24.7
22.3
12.7
15.3
10.4
-
-
14.6
10.8
10.9
5.9
-
16.2
14.7
17.9
8.1
-
Degree urbanization:
Rural.....................................................
Urban....................................................
6.9
93.1
9.5
90.5
-
10.8
89.2
11.6
88.4
-
13.2
86.8
13.9
86.1
-
Region of residence:
Northeast.............................................
Midwest................................................
South....................................................
W est.....................................................
18.8
17.3
39.2
24.6
23.0
28.8
22.1
26.1
-
20.3
23.2
36.3
20.3
-
-
13.2
24.5
39.3
23.0
18.2
29.9
33.4
18.6
20.6
25.3
33.5
20.6
Income distribution:
1st quintile...........................................
2nd quintile..........................................
3rd quintile...........................................
4th quintile...........................................
5th quintile...........................................
10.2
20.4
27.3
26.9
15.3
36.4
35.8
13.9
8.1
5.8
-
17.1
33.1
26.3
16.7
6.8
50.2
35.0
12.1
2.2
.5
-
4.1
6.4
16.6
26.9
46.2
9.2
46.0
28.6
12.4
3.8
-
-
-
-
-
1 Absolute values are displayed.
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Monthly Labor Review
July 2002
41
Expenditures in Retirement
the reference person is married. For single-member consumer
units, however, there are a variety of possible values for the
marital status variable. A single man or woman may be wid
owed, divorced, separated, never married, or in a small num
ber of cases, married. Even though a “married single person”
seems oxymoronic, some plausible explanations exist. Con
sidering that the household type is determined at the time of
the interview, a married person whose spouse is living else
where (perhaps on a long-term work assignment, such as a
military tour of duty) may be counted as a single person con
sumer unit. It could also be that some o f these “married
singles” are actually separated, though perhaps not legally
so. In that case, the respondent may identify himself or her
self as married, rather than separated. Either way, the spend
ing patterns of a married person living alone for an extended
period are assumed to mirror the spending patterns of a “true”
single person more closely than those of a married couple.
The sample also includes only those consumer units that
report ownership of at least one automobile, so that expendi
tures will be more comparable. The most obvious effect of
automobile ownership is on transportation expenditures. Pre
sumably, some retirees choose to sell or give away their auto
mobiles due to a lack of need for personal transportation (for
example, they are no longer going out to work every day).
Maintaining an automobile can add many dollars of expendi
ture to the household budget. Not only are there costs for
gasoline, motor oil, and the occasional repair, but automobile
insurance may be expensive, and may increase as the driver
grows older. Age-related health reasons may also play a part
in this decision. Whatever the reason, lack of automobile
ownership presumably limits mobility, and thus may affect
other expenditures, such as those for food away from home,
entertainment, and vacation and travel.
The above qualifications result in the following sample
sizes: 260 preretired single men and 222 retired single men;
547 preretired single women and 725 retired single women;
and 1,325 preretired couples and 1,220 retired couples. Note
that these data are not weighted to reflect the population.
First, this article compares demographics, income, and quar
terly expenditures of preretired and retired consumer units,
within each household type examined (that is, single person
or married couple). Some of the results of these comparisons
may be expected based on the parameters set for each group.
For example, the lower income levels reported for retirees are
not surprising given that no one is earning labor income in
those households. Thus, an important question is how retire
ment itself affects expenditure patterns: that is, whether tastes
and preferences change in retirement, even if incomes are held
constant. To this end, regression analysis is performed (us
ing ordinary least squares and a modified Cragg method where
necessary) to examine differences in marginal propensity to
Monthly Labor Review
Digitized for 42
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July 2002
consume and income elasticity. These analyses help to es
tablish whether or not differences in expenditure patterns are
related to retirement, per se, or to an income effect associated
with retirement.
Dem ographics
As previously noted, some of the household characteristics
are determined by the sample selection criteria. For example,
the average age of the reference person is constrained to be
within the allowed ranges for the preretired group (55 to 64)
and retired group (65 to 74). Across the three household
types studied, the average age for preretired reference per
sons is 59 years, and that for retired reference persons is 70
years. (See table 1.) Additionally, because automobile owner
ship is a condition of the sample selection process, the aver
age number of vehicles is greater than one in each case.
However, some findings are not so predictable. For ex
ample, contrary to the popular notion that “everyone” moves
to Florida (or at least the “Sunbelt”) upon retirement, single
preretirees are more likely to be located in the South than
single retirees. This difference is most pronounced for single
men: 39 percent of preretirees live in the South, compared with
22 percent of retirees. For single women, the difference is less
pronounced: 39 percent of preretirees live in the South, com
pared with 36 percent of retirees. However, for married couples,
almost no difference exists; about one-third of married couples
studied live in the South both before and after retirement.
Single men. Single retired men are more likely to be
homeowners (72 percent) than are single preretired men (62
percent). The difference is even more pronounced if the ho
meowner holds no mortgage against his property: 64 percent
of single male retirees own their homes outright, compared
with only 30 percent of the preretired. Regardless of work
status, more than 90 percent of single men live in urban areas.
Additionally, despite the large plurality of preretired single
men in the South (39 percent), after retirement, single men
have the most even distribution of the study sample. Ironi
cally, the South has the lowest percentage of retired men—22
percent. It is the Midwest that claims the highest percentage
of single retired men (29 percent).
There is little difference between single male retirees and
single male preretirees in terms of race or ethnicity. More
than 80 percent of both groups have reference persons who
are white (or other race, including Asian, Pacific Islander, and
others), and the least represented race for both groups is
Hispanic (3 percent of retired and 5 percent of preretired single
men).
For single retired men, the distributions among levels of
education and among income quintiles follow the same nega-
tive slope. For example, the largest percentage of single re
tired men (31 percent) has attained the least education, that is,
they did not graduate from high school. Similarly, the largest
proportion of single male retirees are also in the lowest in
come quintile (36 percent). Furthermore, the highest category
of educational attainment (graduate or professional degree)
accounts for the smallest proportion of single retired men (10
percent), and the highest income quintile contains the small
est proportion of single retired men (6 percent). Given the
expected correlation between income and education, this pat
tern is not surprising. The correlation also appears to hold for
single preretired men, although the ordering of categories is
reversed: single preretired men are more likely to have at least
a high school degree than are single retired men, and they are
also more likely to be in one of the top three quintiles than are
single retired men. This may reflect a generational effect, as
educational opportunities have become more available and
more socially and economically valuable for each successive
generation.
Single women. The housing tenure and degree of urbaniza
tion for single women follow the same patterns as those de
scribed for single men, that is, retirees are more likely to be
homeowners without a mortgage than are preretirees, and re
gardless of work status the majority of the sample resides in
urban rather than rural areas. However, unlike single men, a
higher percentage of single women, both retired and working,
live in the South (36 percent of single retired women and 39
percent of single preretired women) compared with other re
gions. It is also interesting to note that the largest difference
in the proportion of retired and preretired single female resi
dents is in the Northeast. Only 13 percent of (or about one in
eight) single female preretirees live in this region, compared
with 20 percent of (or one in five) single female retirees.
In terms of race, again, white and other is the predominant
group for both single female retirees (91 percent) and single
female preretirees (85 percent). There is, however, a notable
difference in the proportion of single female retirees who are
black (8 percent) and single female preretirees who are black
(13 percent). Roughly 2 percent of both groups of single
women are Hispanic.
Unlike single retired men, the largest percentage of single
retired women have completed high school (39 percent), com
pared with other levels of education, but only 6 percent have
obtained a graduate or professional degree. Again, those in
the preretired group are more likely than retirees to have at
least attended college. While the income distribution for single
retired women is similar to that of single retired men, the dis
parity between the lowest and highest quintiles is much greater
for single women. In fact, half of all single retired women fall
into the lowest quintile, and less than 1 percent fall into the
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highest quintile. More single preretired women are in the
second income quintile (33 percent) than are in any other
quintile, and a much higher percentage of preretirees (7 per
cent) than retirees fall into the highest income quintile.
Husband-and-wife couples. Once again, homeownership is
more likely in the retired sample than in the preretired sample
of married couples. Furthermore, there is a lower percentage
of renters in the married couple sample (5 percent of retirees
and 7 percent of the preretired households) than in the singles
samples. Roughly one-third of husband-and-wife consumer
units live in the South, regardless of work status, and the
Midwest is the only region in which the proportion of retired
married couples (25 percent) is smaller than that of preretired
married couples (30 percent).
There is little difference between retired married couples
and preretired married couples in the percentage of reference
persons who are white or other races, which is once again the
most represented category in the sample.
Approximately one-third of the reference persons in both
retired and preretired husband/wife consumer units are high
school graduates. The largest differences between the two
groups are found at the lowest and highest levels of educa
tional attainment. While 19 percent of the retirees in this
sample did not graduate from high school, the same is true for
only 9 percent of the preretired married couples. At the other
end of the scale, only 8 percent of reference persons in retired
couples have earned a graduate or professional degree, com
pared with 15 percent of preretired couples.
The comparison of income distribution among retired and
preretired married couples is different from that of single men
and that of single women. First, the highest percentage of
married retirees (46 percent) fall into the second quintile, not
the first quintile as is the case for single male and single fe
male retirees. In fact, only 9 percent of retired husband-andwife households are in the lowest quintile. For the preretired
married couples, the income distribution is more concentrated,
that is, only 4 percent of the sample are in the lowest quintile
and 46 percent are in the highest income quintile.
Income
Before discussing the comparative results, it is important to
provide a more detailed definition of some of the income
sources examined in this study. For example, with income as
with demographics, there are some results that are determined
by the sample selection criteria. Specifically, no retired house
holds have labor income, including wages and salaries and
self-employment income. For this reason, a new income cat
egory is created in order to make the total income for retirees
and preretirees more comparable (income before taxes, which
Monthly Labor Review
July 2002
43
Expenditures in Retirement
is commonly used as a measure of total income, includes labor
income). The components of comparable income are those
income sources that are available to both retired and preretired
consumer units: that is, comparable income includes interest
and property income, unemployment insurance and workers’
compensation, public assistance, and several other sources,
but it excludes wages and salaries, self-employment income,
or income from Social Security, and private and government
retirement. It should be noted that more than 20 percent of
preretirees in all three household types report some retire
ment income, which could be explained by early retirement.
(See table 2.) Specifically, some persons may choose to retire
from a career before age 65, but continue to earn some labor
income from another job; in this event, they are classified as
preretired in this study.24 Even so, retirement income is not
included in the comparable measure, because it may be a
supplemental source for the preretired, but it is the main (or
perhaps sole) source of income for retirees, and thus it is not
comparable. Another important consideration regarding the
income analysis is that the figures presented are for average
annual income per consumer unit. To ensure more meaningful
comparisons, only incomes from complete income reporters
are shown.
Single men. Not surprisingly, single male retirees have sig
nificantly lower total incomes ($24,738) than do preretired
single men ($42,033). Approximately 77 percent of the
preretirees’ income is from wages and salaries ($32,196), while
Percent reporting and average annual income, preretirees and retirees, by composition of consumer unit,
consumer Expenditure Interview Survey, 1998-99 (complete income reporters only)
Single men
Single women
Married couples
Category
Percent reporting income source:
Income before ta x e s ..........................
Wages and salaries.........................
Self-employment income.................
Social Security, private,
and government retirement..........
Interest, dividends, rental income,
and other property income...........
Unemployment,
workers’ compensation,
and veterans’ benefits.................
Public assistance,
supplemental security income,
and food stam ps...........................
Regular contributions for support
(including child support
and alimony)..................................
Other incom e...................................
Comparable income2 ...........................
Annual means:
Income before ta x e s .........................
Wages and salaries.........................
Self-employment income3 ...............
Social Security, private,
and government retirement..........
Interest, dividends, rental income,
and other property income...........
Unemployment,
workers’ compensation,
and veterans’ benefits.................
Public assistance,
supplemental security income,
and food stam ps...........................
Regular contributions for support
(including child support
and alimony)..................................
Other incom e...................................
Comparable income2 ...........................
Preretired
Retired
t-value'
100.0
89.4
14.8
100.0
0
0
-
25.9
98.3
37.5
35.3
5.6
3.5
.5
6.4
.5
3.2
42.6
0
1.7
41.6
-
$42,033
32,196
$24,738
0
0
3,482
Preretired
Retired
t-value'
100.0
92.7
6.1
100.0
0
0
-
~
22.8
—
Preretired
Retired
99.9
94.1
19.9
100.0
0
0
_
-
99.3
25.1
100.0
-
31.2
27.4
-
32.3
36.9
-
3.8
.3
-
3.1
2.7
-
1.2
5.2
.8
1.7
2.8
.2
36.4
2.1
.2
34.3
-
.2
1.6
36.0
.3
.8
39.8
_
-
5.137
14.929
3.833
$30,443
25,376
$15,690
0
0
10.919
21.736
3.453
$74,816
59,068
$27,570
o
0
15.669
30.893
4.232
17,815
10.722
2,177
13,758
24.149
4,533
25,038
33.288
1,321
5,813
3.127
840
1,678
2.164
1,939
2,285
.878
392
172
.817
106
37
1.574
62
80
.607
2
106
2.027
14
60
2.243
44
94
0.888
5
1,894
3,614
0
832
6,923
1.000
.929
1.662
425
0
1,386
156
1
1,932
1.553
.948
1.285
57
40
2,142
51
21
2,532
.093
1.157
.961
-
_
-
-
' Absolute values are displayed.
2 Income before taxes less wages and salaries; self-employment income; and Social Security, private and government retirement income.
3 Mean incomes from this source are less than $1
44
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t-valu e1
July 2002
retirement income ($17,815) contributes 72 percent of the retir
ees ’ income. However, when considering only comparable
income sources, the relationship between preretired and re
tired single men reverses. From those sources that are avail
able to both groups, retirees earn more ($6,923) than do
preretirees ($3,614). Yet, the percentage of single men report
ing these sources of comparable income is similar for the re
tired sample (42 percent) and the preretired sample (43 per
cent). Nevertheless, this “reversal of fortune” can be at least
partially explained by the higher income earned by retired single
men from dividends, interest, rental and other property—
$5,813 compared with $1,321 earned by preretired single men.
In fact, the average member of the single-male-retiree group
earns more income from this source than does any other de
mographic group in the study. Interestingly, there is no great
difference in the percent reporting this source of income (35
percent of single retired men and 37 percent of preretired single
men). Presumably, the retirees have had their investments
longer and are thus enjoying the time value of money. In
addition, retirees may have different types of investments than
preretirees based on their needs and goals: income generat
ing investments versus growth funds, for example. Finally,
retired single men are much more likely to receive public assis
tance, which includes supplemental security income and food
stamps (6 percent report income from this source), than are
preretired single men (less than 1 percent receive this type of
income).
Single women. As with single male households, total income
before taxes is significantly higher for the preretired single
women ($30,443) than for the single retired women ($ 15,690),
but comparable income is higher, albeit less so, for retirees:
$1,932 compared with $1,386. Also, a higher percentage of
retired single women report income from public assistance (5
percent) than do preretired single women (1 percent). Single
women in both groups derive a higher proportion of their
income from one primary source than do single men. In the
case of female retirees, 88 percent of their income comes from
retirement sources, while 83 percent of preretirees’ earnings
come from wages and salaries. In addition, single women,
regardless of work status, are the only household type of
which more than 1 percent of the sample reports income from
alimony and child support.
Husband-and-wife couples. Income before taxes is $74,816
for preretired married couples and $27,570 for retired married
couples. Wages and salaries account for 79 percent of the
preretirees’ income, while 91 percent of retirees’ income comes
from retirement sources. The figures for comparable income
show the same inverse relationship as those in the single
households discussed above. Married couples, however,
differ from the singles in that the difference between the re-
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tired and preretired couples’ income from interest and divi
dends is not significant. Another difference is that where the
percent reporting income from public assistance is substan
tially higher for retirees in the single samples, 2 percent of
retired couples and 1 percent of preretired couples report this
source of income.
O utlays
As with the analysis of income, there are some important meth
odological distinctions that should be discussed before the
comparison of outlays is presented. First and foremost is the
decision to use an outlays approach, which differs from the
average annual expenditures shown in the standard Bureau
of Labor Statistics publications of the Consumer Expenditure
Survey data. Specifically, in these publications, certain items
of expense are excluded, such as mortgage principal which is
listed as a reduction of liabilities, not an expenditure. The
housing expenditures do include the mortgage interest paid
by the consumer unit. The same is true for vehicle payments
made during the reference period on financed vehicles (only
the interest is included as an expenditure). However, if a ve
hicle is purchased during the reference period, the total price
(less any trade-in value) is recorded as an expenditure. As a
result of this approach, the mean vehicle expenditure value
will approximate the average annual payments made by those
who finance their vehicles because, presumably, there will be
a relatively small number of actual vehicle purchases during
any one quarter, and these will balance out vehicle payments
for those individuals who are still making them. However, this
method is not suitable when regression analysis involving
outlays is employed, as it is in this study. The reason is that
those consumer units that happened to purchase during the
interview period will have a huge expenditure imputed to them,
even if they financed the automobile. Those who are still
making payments on their automobile will have their expendi
tures artificially deflated, because the principal payments will
not be counted as expenditures. Therefore, in this study, the
actual amounts paid out by consumer units are examined, in
cluding regular mortgage and vehicle principal payments.
Although, technically, this may be called an “outlays ap
proach,” in this text, the terms “outlay” and “expenditure” are
used interchangeably for convenience.
For these analyses, it is particularly important to include
mortgage principal payments in the comparison of housing
expenditures. As previously noted in the demographics sec
tion, the majority of retirees in all three household types are
homeowners without mortgages, while a higher proportion of
preretirees are still making payments on their homes. There
fore, in order to allow for an accurate comparison of housing
expenditures in pre- and post-retirement families, the “true”
housing payment must be examined. In addition, the outlay
Monthly Labor Review
July 2002
45
Expenditures in Retirement
for housing in this study is comprised of shelter (mortgage
principal and interest, rental payments, property taxes, and
maintenance and repair) and utilities. Presumably, some rent
ers may have utility costs included in their regular rental pay
ment. Therefore, utilities are included so that homeowners
and renters have comparable housing expenses.
In addition to housing, some other spending categories
have been modified from their standard publication formats to
better fit this study. For instance, marketers and advertisers
often promote the notion that travel is a popular pastime for
retired persons. Presumably this is because of the free time
that retirees would have spent working, and perhaps because
they now have fewer familial and financial obligations (for
example, any children they have are grown, and any home
mortgage is likely to be paid off). In order to capture these
vacation and trip outlays, a new category is created, which
includes such items as housing expenses for a vacation prop
erty, and food, alcoholic beverages, lodging and transporta
tion on trips.
Also, it is important to note that expenditures for pensions
and Social Security (that is, payroll deductions and other de
posits to government, railroad, or private retirement plans) are
excluded from this analysis. This omission allows for a more
comparable measure of total outlays, as these expenditures
are negligible for post-retirement households. The reason is
that for preretirees, these “expenditures” are actually a form
of “savings,” which are then a source of “dissavings” for
retirees. That is, rather than contributing to a pension fund, a
retiree is more likely to “draw it down.” In other words, the
same pension plans to which a family contributes prior to
retirement will likely be the main source of income for that
family after retirement. In addition, no other forms of savings
are included as “expenditures” in this analysis.25 Therefore,
for the same reason that retirement sources are omitted from
“comparable” income (as previously discussed), contributions
to pension plans are omitted as a category of expenditure.
Finally, note that the analyses presented here use average
quarterly outlays per consumer unit.
In general, the results indicate that the preretired and re
tired households do spend differently, across all family types
examined. (See table 3.) For the majority of spending catego
ries within each household type (single male, single female,
and married couple), the differences are statistically signifi
cant. In fact, the following categories are significant for all
three groups: total quarterly outlays, food away from home,
shelter and utilities, total transportation, private transporta
tion, apparel and services, total healthcare, health insurance,
prescription drugs, education, alcoholic beverages, tobacco,
and life and other insurance. Many of these differences are
easily intuited: for instance, one expects significant differ
ences in total outlays due to the significant differences in
total income (as measured by income before taxes). Also,
46
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Federal Reserve Bank of St. Louis
July 2002
given the homeownership rates and mortgage status com
parisons, it is not surprising that preretired consumer units
spend more than retirees on shelter and utilities. Additionally,
private transportation (expenses for the consumer unit’s
owned vehicles) is significantly higher for preretired singles
and couples than for retirees. Even though the sample has
been restricted to those households who own at least one
vehicle, retirees may have paid off their vehicles, and may
have lower maintenance and gasoline expenditures due to
less use of the vehicle than preretirees, who may be driving to
work every weekday.
Single men. Preretired single men spend more overall
($6,804)— and for most categories of interest—than do single
male retirees ($5,050 total quarterly outlays). The only excep
tions are healthcare, for which retirees spend almost twice as
much ($560) as the preretired households spend ($293), and
cash contributions, for which retired men spend $649 com
pared with $268 spent by preretirees. Within the category of
healthcare, outlays are higher by retirees for each compo
nent, but are only significantly so for insurance and prescrip
tion drugs.
Interestingly, expenditures for food at home are not sig
nificantly different for retired and preretired single men,
but preretirees spend significantly more for food away from
home ($372) than retired single men spend ($224). Con
comitantly, retired single men (73 percent) report foodaway-from-home purchases less frequently than preretirees
(90 percent). Thus, even the average expenditure for re
tired single men who purchase food away from home is
substantially smaller ($305) than the average expenditure
for similar preretired single men ($415).26 The most obvi
ous explanation is, once again, the difference in income for
these groups. But perhaps this is a mobility issue, as retir
ees are older and may have health-related barriers to going
out. This would seem to be supported by their signifi
cantly smaller outlays for vacations and trips, contrary to
the proposed notion o f increased leisure and travel after
retirement. Furthermore, retirees spend significantly less
on entertainm ent items and services ($178) than do
preretirees ($311)— entertainment expenditures also include
some items related to mobility, such as tickets to sporting
and cultural events (theater, concerts, and so forth).
Outlays for apparel and services are also significantly
lower in the post-retirement single male households: $ 123
compared with $208 spent by preretirees. Presumably, at
least part of the preretired m ale’s purchases will be for
work clothing, a cost no longer applicable to the retirees.
Also, deductions for employer-sponsored plans may ac
count for some o f the relatively higher outlays for life in
surance by the preretired sample— $94 compared with $40
spent by retired single men.
Quarterly outlays anci t-values, preretirees and retirees, by composition of consumer unit,
Consumer Expenditure Interview Survey, 1998-99
Single men
Single women
Married couples
Item
Preretired
Retired
Total quarterly outlays...........................
$6,804
$5,050
Food at hom e......................................
580
Food away from home.........................
t-valu e’
Preretired
Retired
t-valu e1
Preretired
Retired
t-valu e1
2.941
$6,222
$4,911
3.941
$10,482
$7,705
8.471
536
1.139
513
559
2.384
961
880
3.448
372
224
4.249
182
110
6.357
449
245
5.770
Shelter and utilities.............................
2,250
1,286
7.795
2,283
1,496
6.730
3,082
1,831
10.592
T ransportation.....................................
Private transportation.....................
Public transportation.......................
1,145
1,135
9
643
639
4
2.666
2.640
1.241
809
802
7
530
528
2
3.916
3.855
2.565
1,700
1,685
15
1,131
1,130
2
4.478
4.373
5.682
Vacation/trips......................................
387
212
2.219
271
211
1.485
623
577
.791
Apparel and services.........................
208
123
2.973
297
217
2.613
428
231
9.195
Healthcare...........................................
Health insurance..............................
Medical services..............................
Prescription d ru g s...........................
Medical supplies..............................
293
149
100
33
12
560
271
201
58
31
3.214
5.337
1.340
2.284
1.140
333
132
123
61
17
542
294
127
101
21
6.986
11.340
.177
4.537
.765
617
293
206
89
30
970
542
204
187
37
8.453
14.735
.046
8.626
1.033
Entertainment......................................
311
178
3.910
238
196
2.325
572
435
1.146
All other outlays.................................
Housing while attending school2 ....
Personal care...................................
Reading............................................
Education.........................................
Alcoholic beverages........................
Tobacco ...........................................
Cash contibutions............................
Life and other insurance................
Miscellaneous expenditures3 .........
940
1,050
.255
1.409
.845
1.661
2.453
3.347
2.622
.948
3.649
.253
917
721
.976
1.635
.874
.514
2.239
3.156
3.424
.220
3.266
0.221
1,331
25
98
67
155
90
83
428
201
275
947
1
84
55
17
51
39
484
120
153
2.915
2.881
4.169
4.074
3.742
6.394
7.795
.514
5.517
2.110
-
33
36
123
86
91
268
94
244
-
30
28
6
46
58
649
40
222
1 Absolute values are displayed.
2 Mean outlays for this category are less than $1.
3 Includes legal fees; accounting fees; miscellaneous fees, parimutuel
losses; funeral expenses; cemetery lots, vaults, maintenance fees; safe
Single women. The comparisons o f outlays by pre- and
post-retirement women are similar to those of men de
scribed above. Preretired single women spend significantly
more than retired women on food away from home, shelter
and utilities, transportation (both private and public), ap
parel and services, entertainment, education, alcoholic
beverages, tobacco, and life and other insurance. Retir
ees, on the other hand, generally have higher outlays for
healthcare.
Unlike in the analysis o f single men, however, single
female retirees spend significantly more than their preretired
counterparts for food at home— $559 versus $513, and they
spend less for cash contributions (although this differ
ence is not statistically significant). Also notable is the
lack o f significance in the difference between vacation
spending by female retirees ($211) and that spent by fe
male preretirees ($271).
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-
70
45
108
37
49
365
79
209
-
65
43
17
20
30
328
36
224
deposit box rental; checking accounts, other bank service charges; finance
charges excluding mortgage and vehicle; credit card memberships;
miscellaneous personal services; occupational expenses; expenses for
other property; interest paid, home equity line of credit (other property);
Husband-and-wife couples. The analysis o f outlays by
married couples yields some interesting results that are
different than the previous discussions o f single men and
women. For example, the difference in entertainment spend
ing is not significant, with preretired couples spending $572
and retired couples spending $435. There are also a few
categories o f outlays for which the differences are signifi
cant in the couples sample, but are not so in the singles
samples, namely, all other outlays and its components—
housing while attending school, personal care, reading,
and miscellaneous expenditures. It is also interesting to
note that like the single female results, spending by mar
ried retirees for food at home is significantly different from
that spent by preretired consumer units. However, in the
case of married couples, preretirees spend more ($961) than
do retirees ($880), the opposite as is seen in the single
female comparison.
Monthly Labor Review
July 2002
47
Expenditures in Retirement
1 Percent reporting expenditures that are analyzed using regression analysis
Single women
Single men
Married couples
Outlay category
Food at hom e...................................
Food away from home.....................
Shelter and utilities (owners)..........
Shelter and utilities (renters)..........
Apparel and services......................
Healthcare less insurance1.............
Transportation..................................
Entertainment...................................
Out-of-town trip s ..............................
Preretired
Retired
Preretired
Retired
Preretired
99.2
89.6
100.0
100.0
81.5
49.6
98.9
89.6
40.8
100.0
73.4
100.0
100.0
68.5
60.4
98.7
73.9
32.4
99.8
80.1
100.0
100.0
86.3
73.1
99.8
88.1
41.7
99.9
73.1
100.0
100.0
77.0
75.0
97.7
84.7
36.4
99.9
89.4
100.0
99.0
88.3
80.1
99.6
95.3
55.6
N ote: These figures are calculated from the full sample. Therefore,
the values for percent reporting may differ slightly from those
observations actually used in the regression. Missing values for some
independent variables cause a few observations to be removed from
the regressions, as described in the main text.
1 Percent reporting positive values only. Those reporting net
reimbursements—that is, negative values—and those reporting no
Regression analysis and results
Thus far, the results presented have examined differences
between the preretired and retired groups in general ways.
For example, retirees may spend differently on certain goods
or services than might preretirees. But how much of this ef
fect is due to the lifestyle differences (such as additional free
time) that accompany retirement, and how much is due to
other differences, such as lower income or other factors? To
help discern the effect that retirement has, regression analy
sis is useful.
In this study, two types of regressions are performed: lo
gistic regressions, or “logits,” and ordinary least squares (OLS)
regressions.27 Each has a different purpose. The logits are
used to ascertain the probability that an event (such as a
particular expenditure) will occur, given characteristics of the
consumer unit. The logits are only necessary for expendi
tures that are not universally made. The OLS regressions de
scribe how expenditure levels are related to certain character
istics. (For example, most expenditures are expected to in
crease with income, but by how much?) Table 4 shows the
percent reporting expenditures that are used for regression
analysis, and table 5 shows the number of observations used
for ordinary least squares regressions.
The expenditures selected for study are either those that
are basic goods and services (food at home, shelter and utili
ties, apparel and services, healthcare less insurance, and
transportation) or items that might be expected a priori to
differ with retirement (food away from home, entertainment,
and out-of-town trips) due to the increased availability of lei
sure time. All categories are examined using OLS. Of the basic
goods, only apparel and services requires a logit analysis.
However, the “leisure” expenditures all require logit analysis.
48
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July 2002
Retired
99.9
80.7
100.0
100.0
79.5
84.8
99.5
90.8
48.0
iexpenditure are treated as “nonexpenditures.” Reimbursements are rare,
Ihowever. The largest percentage occurs for retired single males, and accounts
for 3.6 percent of the group. Reimbursements are reported for 1.5 percent of
|preretired single males, and 1.4 percent of preretired married couples. For all
others, reimbursements account for percentages greater than 0.9 but less
than 1.0 percent.
Healthcare is the one basic expenditure group that requires
special consideration. Only the “out-of-pocket” expenditures
for actual medical goods and services are examined, because
the quality of health insurance coverage can differ so much
for these groups. Presumably, all the retirees in our sample are
eligible for Medicare coverage. This is not true of the
preretirees. Thus, the utility of comparing probability of cov
erage is limited. However, even if one only examines expendi
tures for actual drugs, medical supplies, and services, the re
sults are still unclear: if the expenditures for “noninsurance”
healthcare are higher for retirees, is this due to health reasons,
or to less adequate coverage? The analysis in this study shall
not attempt to answer these questions; even so, because
healthcare is an important factor in maintaining quality of life,
the results are reported for those who may find its inclusion
useful (such as those who only want to see the “bottom
line”—that is, the expected difference in spending associated
with retirement, whatever the reason may be).
The independent variables for each of the regression mod
els are similar. For the logistic regressions, the independent
variables used describe occupation of the reference person
(retired or preretired, self-employed); marital status for singles
(divorced, separated, or never married); race (black) and
ethnicity (Hispanic) of the reference person; educational at
tainment of the reference person (high school graduate, some
college, college graduate, attended graduate school); degree
of urbanization for the consumer unit (that is, urban or rural
location); region of residence of the consumer unit; housing
tenure (home owned without mortgage or renter); and total
outlays that are used as a proxy for “permanent” income.
(Also, an interaction term is included to see if the relationship
of expenditure to “permanent” income differs in retirement.)
This study uses “permanent” instead of “current” (that is,
1 Number of observations for ordinary least
squares regressions
O u tla y
category
Food at hom e.....................................
Food away from home.......................
Shelter and utilities (owners)............
Shelter and utilities (renters)............
Apparel and services........................
Healthcare, less insurance...............
Transportation....................................
Entertainment.....................................
Out-of-town trip s ................................
Single
men
Single
women
Married
couples
480
396
317
160
364
263
476
397
161
1,270
968
985
279
1,030
944
1,254
1,096
467
2,542
2,168
2,354
153
2,139
2,096
2,532
2,370
1,206
Note: The married couple regressions are missing one observation due
to one negative observation for permanent income; presumably, this couple
had a relatively large reimbursement for healthcare that overwhelmed their
other expenditures in the quarter in which it was received.
annual) income because, according to the “permanent income
hypothesis,” expenditures are often made with expectations
of future earnings in mind.28 In this study, it is particularly
important to use “permanent income” as opposed to “current
income,” because table 2 shows current income is vastly dif
ferent for the preretired and retired groups. This is because
the retiree by definition has ceased working, and so he or she
must live off of savings and other assets that have been accu
mulated. Any income received will presumably be based on
these assets (such as interest or dividends), or will be from
some source related to previous labor (such as Social Security
or pension income). Even so, these income sources by them
selves may not be enough to sustain a comfortable living situ
ation for most consumers (retired or otherwise), and would be
an unrealistic measure of the consumer unit’s actual economic
status.29 Expenditures reflect rational decisions based on lev
els of wealth (rather than income alone) that are available to
the consumer unit, and therefore serve as a better indicator of
the consumer unit’s tastes and preferences for particular goods
and services. (Additionally, by using “permanent income”
instead of “current income,” there is no need to distinguish
“complete” and “incomplete” reporters, as virtually all respon
dents provide some information on outlays.)
The purpose of regressions, as noted earlier, is to allow
“ceteris paribus” comparisons. That is, given that two con
sumer units are identical except for the issue in question (in
this case, retirement), how does this issue influence the ex
pected outcome for the affected consumer? To aid compari
sons, a control group is selected, and its characteristics are
used with the regression coefficients to predict the outcomes
for each consumer unit (that is, preretired or retired). In this
study, the control group consists of consumer units who are:
currently working for a wage or salary; widowed (if single);
neither black nor Hispanic; lacking a high school degree; liv
ing in an urban area of the South; and homeowners with a
mortgage. In a few of the OLS regressions, additional controls
are applied. For example, it is assumed that single homeowners
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live in a dwelling with six rooms (including bedrooms) and two
bathrooms (including half baths), compared to four rooms and
one bathroom for single renters. For couples, owners are as
sumed to have seven rooms and two bathrooms, while renters
are assumed to have five rooms and one bathroom. It is also
assumed for all consumer units that they own one automobile
and no other vehicles. These characteristics play roles in
different models; for example, outlays for shelter and utilities
will obviously vary with the size of the dwelling; transporta
tion outlays will depend on number of vehicles owned (auto
mobile or otherwise). Some other outlays, such as entertain
ment, may also depend on numbers of vehicles. One enter
tainment expenditure category specifically accounts for ex
penditures on vehicles like boats or motorcycles. In some
cases, the consumer unit owns these vehicles (such as a boat)
specifically for recreational purposes; in other cases, having
access to certain vehicles (such as motorcycles) may make
access to certain areas a greater possibility, and the opportu
nity may drive the expenditure.
Also, before performing the regressions, all expenditure
values (including permanent income) were transformed by tak
ing their natural log. This was done to m inim ize
heteroscedasticity, which can be a problem in regression mod
els. However, it has a convenient side-effect in that the mar
ginal propensities to consume (M P C ) and income elasticities
have special properties: For all the basic goods (except ap
parel and services), the M PC becomes proportional to the ex
pected budget share for the item under study; the elasticities
simply equal the coefficient on natural log of permanent in
come. (For more information, see the appendix.)
Before examining the results, two caveats are in order: First,
for the “ceteris paribus” analysis, note that average total out
lays are used as the “control” amount, and that the average
for preretired consumers is the operative value. This may not
seem realistic, since the tables clearly show that outlays de
cline with retirement. There are several reasons for this: Even
if tastes and preferences do not change in retirement, retirees
are more likely to have paid off their mortgage, which would
substantially reduce outlays. Additionally, as noted earlier,
because the Consumer Expenditure Survey is not longitudi
nal, it is impossible to obtain a large sample whereby the act of
retirement may be observed, let alone one where several years
(or at least time periods) of expenditures both prior to and after
retirement may be observed. Given the method used to define
the sample, then, it could be that some selection bias is intro
duced into the data; that is, perhaps a substantial amount of
the “preretirees” are consumers who plan to continue to work
during retirement, though not necessarily at their original ca
reer job. These consumers may have different characteristics
(including tastes) than those who retire completely, and thus
they “select” themselves out of the retiree sample. However,
assuming this problem is minimal, the issue still remains that
Monthly Labor Review
July 2002
49
Expenditures in Retirement
expenditures decline in retirement for those in the sample. The
“ceteris paribus” results are concerned with the effect of the
retirement decision itself, so in this discussion there is no
problem. (See tables 6 and 7.) However, some readers may be
interested in learning how expenditures differ in reality as a
total result of retirement and its concomitant decisions that
result in lower total outlays. For that purpose, tables are in
cluded in Appendix A that show the “total effect” of retire
ment. (That is, most characteristics, such as region of resi
dence, are held constant, but permanent income is allowed to
decrease.)
Second, one other factor cannot be separated out from the
retirement decision: by definition, the retirees in this sample
are older than the preretirees. Therefore, some of the retire
ment effect may be increased or decreased by an age effect.
(This may be especially true for an expenditure such as
healthcare less insurance.)
Finally, the number of observations differs from the full
sample size in a few cases. This is generally due to missing
data; for example, occasionally a consumer unit does not pro
vide information on number of rooms or bathrooms in the
household, and those records are deleted from the regres
sion. Also, in the case of healthcare less insurance, the ex
penditure can be reported as negative because of reimburse
ments made by insurance companies. If a consumer unit
made an expenditure for healthcare in one quarter and re
ceived reimbursement in a subsequent quarter, the healthcare
expenditure during the “reimbursement” quarter will appear
as a negative value. Although on average the reimburse
ments and the expenditures will cancel each other out, in the
P r e d ic te d p ro b a b ilitie s , “c e te r is p a rib u s ”
[In percent]
Probability of purchase
Ceteris paribus criteria
Preretired
|
Retired
Single men:
Food away from home.....................
Apparel and services......................
Healthcare........................................
Entertainment...................................
Out-of-town trips..............................
94.6
60.6
39.8
90.7
33.2
93.0
70.3
71.6
88.2
29.6
Single women:
Food away from home.....................
Apparel and services......................
Healthcare........................................
Entertainment...................................
Out-of-town trips..............................
81.4
82.0
84.2
92.8
33.8
83.6
74.1
87.8
90.2
27.5
Married couples:
Food away from home.....................
Apparel and services......................
Healthcare........................................
Entertainment...................................
Out-of-town trips..............................
92.7
90.5
89.1
96.7
45.4
86.9
85.6
93.4
93.8
46.6
1 Significant at the 95-percent confidence level.
Dash indicates result not significant at the 95-percent confidence level.
50
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Federal Reserve Bank of St. Louis
July 2002
regression results they can be problematic.30 Fortunately,
these occurrences are infrequent.
Table 5 shows the total number of observations used in
the OLS regressions.31 For apparel and services and the “lei
sure” regressions, observations are less than the total sample
size because only those who had positive outlays are included
in the OLS stage, as explained in the appendix.
Single men. In the case of single men, retirement status ap
pears to play an indirect role in expenditure patterns. Although
mpcs and elasticities appear to differ in several of the “basic”
goods cases, none of these is associated with a statistically
significant retirement effect, either for retirement in general or
for the interaction of retirement and income, except for trans
portation. In this case, the predicted expenditure is signifi
cantly related both to the “event” of retirement and to a change
in the income/expenditure relationship. Outlays are predicted
to drop significantly both in economic and statistical terms.
(The difference is $265 per quarter.) The MPC declines sub
stantially—from less than $0.18 to more than $0.09. The de
crease in elasticity indicates that this good falls from “luxury”
status for preretirees to “necessity” status for retirees. This
may indicate that before retirement, single men, if given more
income, will buy vehicles more frequently or more expensive
vehicles than they would upon retirement. Again, retirees
may also have less need to drive (therefore, they pay less for
gasoline and other travel expenditures), as they do not have
to go to work every day. (Note that single women and married
couples also experience declines in predicted expenditures for
transportation in retirement, although in those cases the dif
ference is not statistically significant.)
As for the “leisure” goods tested,
two show a difference related to the
probability of purchase. In the first
Significance indicator
case, food away from home, the over
Retirement
incom e
all difference in predicted probability
is not meaningful—falling from less
than 95 percent for preretirees to 93
(1)
percent for retirees; the bottom line is
most single men are predicted to pur
chase food away from home at least
once every few months in retirement.
Nor is the effect on MPC meaningful;
it remains under $0.02 regardless of re
tirement status. However, for out-oftown trips, the results are more inter
esting. The probability of purchase
declines 3 percentage points, due
both
to the retirement effect and a dif
ference in the income/probability rela
tionship after retirement. The pre
dicted expenditure for actual buyers
1 Elasticities, and so forth under “ceteris paribus”
[Probabilities in percent]
Married couples
Single women
Single men
Ceteris paribus criteria
Preretired
Retired
Preretired
Retired
Preretired
Retired
Variables:
Permanent income..................................
Log incom e.............................................
$6,804
8.825266
$6,804
8.825266
$6,222
8.735847
$6,222
8.735847
$10,482
9.257415
$10,482
9.257415
Owners:
Rooms/bedrooms....................................
Bathrooms/halfbaths..............................
6
2
6
2
6
2
6
2
7
2
7
2
Renters:
Rooms/bedrooms....................................
Bathrooms/halfbaths..............................
4
1
4
1
4
1
4
1
5
1
5
1
Food at home:
Probability of purchase.........................
Predicted expenditure (buyers only).....
Marginal propensity to consum e...........
Elasticity.................................................
100.0
$536
.014
.18
100.0
$503
.024
.32
100.0
$470
.019
.26
100.0
'■2$546
.034
.39
100.0
$897
.020
.24
100.0
$878
.022
.27
Food away from home:
Probability of purchase...........................
Predicted expenditure (buyers o n ly ).....
Marginal propensity to consume............
E lasticity.................................................
94.6
$193
.013
.45
'93.0
$162
.015
.65
81.4
$169
.017
.64
83.6
$119
.012
.63
92.7
$305
.022
.76
86.9
1,2$252
.014
.57
Shelter and utilities (owners):
Probability of purchase..........................
Predicted expenditure (buyers only).....
Marginal propensity to consum e...........
Elasticity.................................................
100.0
$2,509
.216
.59
100.0
$2,005
.137
.46
100.0
$2,185
.246
.70
100.0
$1,947
.206
.66
100.0
$3,090
.166
.56
100.0
$2,972
.148
.52
Shelter and utilities (renters):
Probability of purchase..........................
Predicted expenditure (buyers only).....
Marginal propensity to consum e...........
Elasticity.................................................
100.0
$1,523
.096
.43
100.0
$1,769
.147
.57
100.0
$2,088
.240
.71
100.0
$1,923
.248
.80
100.0
$1,992
.103
.54
100.0
$1,570
.068
.45
Apparel and services:
Probability of purchase..........................
Predicted expenditure (buyers only).....
Marginal propensity to consum e...........
Elasticity.................................................
60.6
$111
.012
.73
70.3
$99
.013
.92
82.0
$142
.024
1.08
74.1
2$99
.013
.83
90.5
$253
.024
1.00
85.6
12$183
.015
.83
Healthcare (less insurance):
Probability of purchase..........................
Predicted expenditure (buyers only).....
Marginal propensity to consum e...........
Elasticity.................................................
39.8
$226
.012
.35
71.6
$370
.045
.82
84.2
$158
.014
.55
87.8
1'2$218
.033
.95
89.1
$228
.016
.72
93.4
$336
.020
.61
Transportation:
Probability of purchase..........................
Predicted expenditure (buyers only).....
Marginal propensity to consum e...........
Elasticity.................................................
100.0
$1,018
.175
1.17
100.0
1'2$753
.094
.85
100.0
$476
.052
.68
100.0
$373
.043
.71
100.0
$1,197
.110
.96
100.0
$889
.083
.98
Entertainment:
Probability of purchase..........................
Predicted expenditure (buyers only).....
Marginal propensity to consume...........
Elasticity.................................................
90.7
$188
.021
.76
88.2
$155
.014
.63
92.8
$139
.015
.67
90.2
$134
.015
.69
96.7
$284
.026
.95
93.8
$236
.021
.91
Out-of-town trips:
Probability of purchase.........................
Predicted expenditure (buyers only).....
Marginal propensity to consum e...........
Elasticity.................................................
33.2
$98
.012
.82
29.6
$96
.006
.43
33.8
$157
.012
.48
27.5
1,2$155
.012
.49
45.4
$435
.030
.73
46.6
$530
.047
.92
1 Retirement coefficient is statistically significant at the 95-percent confidence level.
2 Coefficient for retired income term is statistically significant at the 95-percent confidence level.
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Monthly Labor Review
July 2002
51
Expenditures in Retirement
does not differ much, but the MPC is cut in half—from $0,012
to $0,006, as is the income elasticity— from 0.82 to 0.43.32
Single women. The probabilities of purchase are not signifi
cantly affected by retirement for single women, according to
the logit results. However, in several cases, retirement is di
rectly and indirectly related to differences in expenditures for
those who purchase. Food at home, healthcare (less insur
ance), and out-of-town trips all exhibit such differences, and
apparel and services exhibits an indirect difference (that is,
the income coefficient is statistically significant, but not the
retirement variable itself). For food at home, a sizable increase
in expenditures is predicted—about $76 per quarter. Although
not statistically significant, food away from home also shows
a decline in predicted expenditure for single female retirees
($50). It is interesting to note that the table in the appendix, in
which retirees are assumed to have lower permanent incomes
than preretirees, shows that the situation reverses. Although
food-at-home expenditures are predicted to rise (by $28), the
difference is less than the predicted decrease in food-awayfrom-home expenditures ($65).
An interesting difference occurs for apparel and services
for this group. After retirement, the MPC for this item is cut in
half. As a result, the elasticity falls substantially as well. Be
fore retirement, apparel and services are treated as “luxury”
goods for single women; afterward, they become “necessity”
goods, although they still have a higher elasticity than most
of the other expenditure items. It is also interesting to note
that although preretired single women are predicted to spend
more ($142) than preretired single men ($111) each quarter,
male and female retirees have the same predicted expenditure
($99) for apparel and services. This is also roughly true when
incomes are assumed to decline for retirees—both single male
and female retirees are predicted to spend about $80 on ap
parel and services. (See appendix.)
M arried couples. As with singles, married couples appear
to have some substantial differences either in probability
o f purchase or level o f purchase, but not many are statisti
cally significant. The only two expenditures that show
significant differences are food away from home and ap
parel and services. Both show decreases in the predicted
expenditure due to the direct retirement effect and changes
in the income effect. The apparent difference in probabil
ity for food away from home is the largest o f the three
groups studied, falling nearly 13 percentage points. Simi
larly, the expenditure for those who report purchases falls
by $85 per quarter. Nevertheless, the difference in MPC is
not even noticed when rounded to the full cent (that is,
$0.02 before and after retirement). The elasticity declines
somewhat, from 0.76 to 0.62, but still remains in the moder
ately high level o f inelastic expenditures.
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Apparel and services, though, show a pattern very similar
to single women. Although all groups show declines in pre
dicted expenditures, probably because of less need for work
attire or uniforms as noted before, apparel and services fall
from unitary elasticity for preretired couples to inelasticity
(0.83) for retirees. The MPC is also substantially reduced (from
$0,024 to $0,015). Predicted expenditures fall by $70 for this
group.
This study has analyzed expenditure patterns by preretirees
to help understand how expenditure patterns
differ upon retirement for single men, single women, and mar
ried couples. Many differences have been found. Some of
these are undoubtedly due to differences that are to be ex
pected upon retirement. For example, retirees have lower in
comes than preretirees, and therefore would naturally be ex
pected to spend less on many items. However, preretirees are
found to have different demographic characteristics than re
tirees, even when examining carefully selected groups (single
men, single women, and married couples with no children).
Again, some of these are expected; age is by definition greater
for retirees than preretirees, and retirees are more likely to own
their home outright (that is, the mortgage is paid off) than are
preretirees. Others are not necessarily predictable a priori,
such as differences in proportions of each group that are lo
cated in various regions of the country. Nevertheless, each of
these characteristics could have an effect on expenditure pat
terns. To control for these differences, and to attempt to as
certain whether income differences are solely responsible for
expenditure differences or whether tastes and preferences dif
fer in retirement, regression analyses are performed.
From the regression results, it is difficult to draw general
conclusions about the role of retirement in expenditure deci
sions. For example, the results for single men showed few
statistically significant differences in probability of reporting
expenditures or in the predicted outlay for items. However,
more were significant for single women and married couples.
Nevertheless, some interesting findings are presented. For
example, in each group studied, both the probability of pur
chase and predicted expenditure for food away from home are
lower for retirees than preretirees. Because these results are
calculated assuming income is equal for the pre- and post
retirees, it may indicate that the “utilitarian” purpose of food
away from home outweighs the “recreational” purpose of food
away from home. That is, the preretirees may be purchasing
more food away from home more frequently because they do
not have the same amount of leisure time as the retirees. How
ever, given the lack of statistical significance of many of the
parameters used to compute these results, this interpretation
should be viewed with caution.
Retirement is a major event in a working person’s life,
accompanied by many lifestyle changes, such as a reducand retirees
tion in labor income and an increase in leisure time. This
article documents some o f the potential consequences of
these changes. These issues are particularly important
today with the “graying” o f the population; it is only a few
years until the “baby boomers” reach retirement age. This
analysis should be useful not only to professionals and
policymakers who study the effects of changing demo
graphics on the economy at large, but also to retirement
planners and counselors, as well as to those who plan to
retire soon themselves.
□
N o te s
Note: Additional tables can be obtained on the Internet version of this
article at h ttp ://w w w .b ls.g o v/cex/csxa rt.h tm
Respondents may select only one o f the categories— “retired” or “not
working due to disability.”
1 See 65+ in the United States, Current Population Reports, Special
Studies (U.S. Bureau o f the Census, 1996), pp 23-190.
19 The other possible occupational statuses for the spouse are “working
without pay” or “not working” because they are either going to school
or doing something else (that is, not working for a reason not already
described).
2 Geoffrey D. Paulin, “Expenditure patterns o f older Americans, 19 8497,” Monthly Labor Review, M ay 2000, pp. 3 -2 8 .
3 Rose M . Rubin and Kenneth Koelin, “Elderly and nonelderly expen
ditures on necessities in the 1980s,” Monthly Labor Review, September
1996, pp. 2 4 -3 1 .
4 Mohammed Abdel-Ghany and Deanna L. Sharpe, “Consumption Pat
terns Among the Young-Old and O ld -O ld ,” Journal o f Consumer Af
fairs, Summer 1997, pp. 90 -1 1 2 .
5 The reference person is the first person mentioned by the survey
respondent when asked: “Start with the name o f the person or one o f
the persons who owns or rents this home.”
6 N an cy E. Schwenk, “ Trends in the Econom ic Status o f Retired
People,” Family Economic Review, 1994, 7(2), pp. 19 -27.
7 Ibid., pp. 2 4 -2 5 .
8 Frankie N. Schwenk, “A Comparison o f Households Headed by Per
sons 55 to 65 Years o f Age: Retired and Employed,” Family Economic
Review, 1990, 3(3), pp. 19-25.
9 Ibid., pp. 22, 24.
10 A consumer unit is defined as members o f a household related by
blood, marriage, adoption, or other legal arrangement; a single person
living alone or sharing a household with others but who is financially
independent; or two or more persons living together who share respon
sibility for at least two out o f three major types o f expenses— food,
housing, and other expenses. In this article, consumer unit and house
hold are used interchangeably.
11 Thomas Moehrle, “Expenditure patterns o f the elderly: workers and
nonworkers,” Monthly Labor Review, M ay 1990, pp. 3 4 -4 1 .
12 Ibid., p. 34.
13 Ibid., p. 36.
14 Rose M . Rubin and Michael Nieswiadomy, “Expenditure patterns o f
retired and nonretired persons,” Monthly Labor Review , April 1994, pp.
1 0 -2 1 .
15 A complete income reporter is a consumer unit that provides values
for at least one o f the major sources o f its income, such as wages and
salaries, self-employment income, and Social Security income. A com
plete reporter may not provide a full accounting o f all income from all
sources, however.
16 Rubin and Nieswiadomy, “Expenditure patterns...,” p. 36.
17 The 1 9 9 6 -9 7 Consumer Expenditure Survey 2-year report notes
that the “Interview survey collects detailed data on an estimated 60 to
70 percent o f total household expenditures. In addition, global esti
mates— that is, expense patterns for a 3-month period— are obtained
for food and other select items. These global estimates account for an
additional 20 to 25 percent o f total expenditures.” Source: Bureau o f
Labor Statistics, Consumer Expenditure Survey, 1996-97, Report 935
(U.S. Department o f Labor, September 1999), p. 256.
18 It is im portant to note that some retirees in our sample may be
“retired due to disability.” However, in the Consumer Expenditure Sur
vey, there is no way to identify those who are both retired and disabled.
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20 Beth Harrison highlights the differences in expenditure levels and
shares between these two age groups from the 1984 Consumer Expen
diture Interview Survey, finding them to be distinct in most m ajor
categories. (See “Spending patterns o f older persons revealed in expen
diture survey,” Monthly Labor Review, October 1986, p. 1 5 -1 7 .) In
addition, in a study following up on Harrison’s findings, Pamela Hitschler
(p. 3) finds that “consumer units in the younger group spent, on aver
age, a significantly larger amount on every major expenditure category
except housing and healthcare in both years [1980 and 1990 are com
pared].” (“Spending by older consumers: 1980 and 1990 compared,”
Monthly Labor Review, M ay 1993, pp. 3 -1 3 .)
21 This “age effect” is assumed to include differences by age in health
status. Although health status can be an important influence on the
expenditures o f older consumers, there are no concrete measures o f
health status available in the Consumer Expenditure Survey.
22 As described previously, the definition o f “preretired” and “retired”
depends on the occupational status o f the reference person and spouse,
in the case o f married couples. It is possible that one o f the parents
owns the home, and is therefore the reference person, but the child
moves back in with them to provide economic support.
23 Even eliminating families with children does not guarantee that the
couple is childless. College students, when living in university-spon
sored housing, are considered to be separate consumer units from their
parents. Additionally, children may have reached the age o f majority,
and may have moved out to establish consumer units o f their own.
However, the survey does not collect information in such a way as to
allow the selection o f singles or couples who do not have children at all.
Therefore, although it is possible that some o f these families purchase
items for their children that those without children would not, it is not
possible to identify those families with the possibility o f such additional
spending.
24 Recall that the definition o f retirement in this study is based on the
self-reported occupation o f the reference person. Thus, it is possible to
retire from one’s life-long work and to pursue other avenues o f em
ployment. The “retiree” may choose to work for pay in a field that
was previously a hobby, or perhaps may seek a low-wage job to keep
active, but not for income, per se.
25 W hile it is true that some “expenditures,” such as mortgage princi
pal, may be considered an “ investm ent” in some cases, most
homeowners do not own their home solely for investment purposes, as
they might a stock or bond; they also consume the housing services the
home provides. Sim ilarly, some consumers may own life insurance
policies that pay annuities at some point; however, the policy is not
merely a savings vehicle; it is prim arily a purchase o f protection o f
one’s estate in case o f unexpected events.
26 Calculated by dividing the average expenditure for the whole group
($372 for preretirees, and $224 for retirees) by the percent reporting,
shown in table 6 (89.6 percent for preretirees, and 73.4 percent for
retirees).
27 See the Technical Notes section for a detailed explanation o f the
regression methodology, including the model specifications.
Monthly Labor Review
July 2002
53
Expenditures in Retirement
28 See M ilto n Fried m an, A T h e o r y o f t h e C o n s u m p t i o n F u n c t i o n
(Princeton, nj : Princeton U niversity Press, 1957).
29 There are also empirical reasons for using “permanent” income in
this case. Respondents do not always provide information on “ cur
rent” income, and even those who do may not provide a full account
ing o f all income from all sources. Furthermore, data regarding assets
and liabilities are only collected on a lim ited basis in the Interview
survey. H ow ever, the prim ary goal o f the In terview Survey is to
collect expenditures.
30 One possible solution is to use four complete quarters for each
consumer unit, rather than treat each quarter independently as is done
in this article. However, even this solution does not provide a bal
anced treatment o f medical expenditures and reimbursements. For
example, a reimbursement reported in the second interview (the first
interview during which these data are collected) w ill have no matching
expenditure because that expense would have been incurred by the
consumer unit prior to its participation in the survey. Likew ise, a
medical expenditure reported in the fifth and final interview may very
well be reimbursed afterward, when the consumer unit is no longer a
survey participant. There is no way to capture these prior expenses
or future reimbursements.
31 Because the logit models share the same specification, and because
they predict the probability o f an expenditure occurring, nearly all o f
them have the same number o f observations as the sample size for the
group under study. The exception is the set o f healthcare less insur
ance models. The lo g it models have few er observations than the
sample for the group under study in this case, because the negative
healthcare outlays are om itted from the sample before running the
regression. (For single men, the total is 470 observations; for single
women, it is 1,260 observations; and for married couples, it is 2,515
observations.)
32 A t first glance, the predicted value for out-of-town trips may appear
low, but there are at least two reasons for this. First, out-of-town trips
are defined in the survey either as trips that last at least overnight for
recreation purposes, or “day trips” in which the participant travels at
least 75 miles from home. Therefore, they may be short in duration
and not costly. Second, this phenomenon may be due to the economet
rics underlying the model. The specification may be inaccurate due to
omitted variables, improper transformation o f the dependent or inde
pendent variables, or other reasons. However, the standard errors o f
the relative coefficients are wide enough to encompass an extremely
large range o f predicted values. This is because, as noted, E ( ln Y ) is the
predicted value resulting from the regression, and e x p [ E ( l n Y ) ] is the
predicted value for the expenditure. A very small deviation in E ( ln Y )
can lead to a very large difference in e x p [ E ( l n Y ) ] . For example, as
shown in the table, the current predicted value for preretirees is $98.
This is based on E ( l n Y ) o f approximately 4.58. However, i f E ( l n Y )
increases by 1 to 5.58, e x p [ E ( ln Y ) ] increases to $265. Even at the 90percent confidence level, an estimate o f 5.58 is plausible; i f all relevant
parameters are evaluated at the lowest level in the 90-percent confi
dence interval, E ( ln Y ) is approximately -3 .8 8 ; i f all are evaluated at the
highest level in the 90-percent confidence interval, E ( ln Y ) is approxi
mately 12.99. The same reasoning applies to travel expenditures for
single women. Applying the confidence intervals to their parameters
yields an estimated range from 0.51 to 9.59 for E ( ln Y ) .
A ppendix A : Re sults o f re g re ssio n a n a ly sis
In tables 6 and 7, results w ere sh ow n assum ing “ceteris paribus.” That
is, all characteristics (inclu d in g perm anent in com e) excep t retirement
w ere assum ed to be constant for the groups com pared and the results
w ere com puted on that basis. In reality, perm anent in com e d eclin es
substantially in retirement. For the reader’s con ven ien ce, the follow in g
[In percent]
tables sh ow the “full effect” o f retirem ent as estim ated from the regres
sion s discu ssed in the text. O nly characteristics that are n ot ex p licitly
related to retirement (such as whether one lives in an urban or rural area)
are held constant. H ow ever, permanent incom e is evaluated at its mean
for retirees in the fo llo w in g calculations. (S e e tables A - l a n d A -2 .)
1 Probabilities of pijrchasing selected goods and services for preretired and retired consumers, allowing full
retirement effec t, 1998-99
Probability of purchase
Significance indicator
Consumer type
Pre-retired
Retired
94.6
60.6
39.8
90.7
33.2
89.3
57.3
63.6
83.2
23.7
81.4
82.0
84.2
92.8
33.8
79.9
68.6
86.1
87.6
23.2
92.7
90.5
89.1
96.7
45.4
80.1
77.9
89.7
89.3
34.9
Retirement
Income
Single men:
Food away from hom e.........
Apparel and services..........
Healthcare (less insurance).
Entertainment......................
Out-of-town trip s .................
1
_
_
_
_
_
_
_
-
-
_
_
Single women:
Food away from hom e.........
Apparel and services..........
Healthcare (less insurance).
Entertainment......................
Out-of-town trip s .................
_
_
-
_
_
_
-
-
-
_
Couples:
Food away from hom e.........
Apparel and services..........
Healthcare (less insurance).
Entertainment......................
Out-of-town trip s .................
1 Significant at the 95-percent confidence level.
Dash indicates result not significant at the 95-percent confidence level.
54
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-
_
-
_
-
_
-
-
Predicted outcomes given full retirement effect
Preretired
I
Couples
Single women
Single men
Variables
Retired
Preretired
Retired
Preretired
Retired
Variables:
Permanent income..............................
Log incom e.........................................
$6,804
8.825266
$5,050
8.527144
$6,222
8.735847
$4,911
8.499233
$10,482
9.257415
$7,705
8.949625
Owners:
Rooms/bedrooms...............................
Bathrooms/halfbaths.........................
6
2
6
2
6
2
6
2
7
2
7
2
Renters:.................................................
Rooms/bedrooms...............................
Bathrooms/halfbaths.........................
4
1
4
1
4
1
4
1
5
1
5
1
Food at home:
Probability of purchase (percent).....
Predicted expenditure.......................
Marginal propensity to consume.......
Elasticity..............................................
100.0
$536
0.014
0.18
100.0
$457
0.029
0.32
100.0
$470
0.019
0.26
100.0
$498
0.040
0.39
100.0
$897
0.020
0.24
100.0
$809
0.028
0.27
Food away from home:
Probability of purchase (percent).....
Predicted expenditure (buyers only) .
Marginal propensity to consume.......
Elasticity..............................................
94.6
$193
0.013
0.45
289.3
$136
0.018
0.68
81.4
$169
0.017
0.64
79.9
$104
0.013
0.63
92.7
$305
0.022
0.76
80.1
1'2$220
0.018
0.62
Shelter and utilities (owners):
Probability of purchase (percent).....
Predicted expenditure.......................
Marginal propensity to consume.......
Elasticity..............................................
100.0
$2,509
0.216
0.59
100.0
$1,746
0.161
0.46
100.0
$2,185
0.246
0.70
100.0
$1,666
0.223
0.66
100.0
$3,090
0.166
0.56
100.0
$2,531
0.171
0.52
Shelter and utilities (renters):
Probability of purchase (percent).....
Predicted expenditure.......................
Marginal propensity to consume.......
Elasticity..............................................
100.0
$1,523
0.096
0.43
100.0
$1,494
0.167
0.57
100.0
$2,088
0.240
0.71
100.0
$1,591
0.260
0.80
100.0
$1,992
0.103
0.54
100.0
$1,365
0.081
0.45
Apparel and services:
Probability of purchase (percent).....
Predicted expenditure (buyers only) .
Marginal propensity to consume.......
Elasticity..............................................
60.6
$111
0.012
0.73
57.3
$79
0.014
0.89
82.0
$142
0.024
1.08
68.6
2$81
0.013
0.81
90.5
$253
0.024
1.00
77.9
12 $146
0.016
0.86
Healthcare less insurance:
Probability of purchase (percent).....
Predicted expenditure (buyers only) .
Marginal propensity to consume.......
Elasticity..............................................
39.8
$226
0.012
0.35
63.6
$292
0.046
0.79
84.2
$158
0.014
0.55
86.1
1,2$172
0.033
0.94
89.1
$228
0.016
0.72
89.7
$284
0.023
0.64
Transportation:
Probability of purchase (percent).....
Predicted expenditure.......................
Marginal propensity to consume.......
Elasticity..............................................
100.0
$1,018
0.175
1.17
100.0
’•2$584
0.098
0.85
100.0
$476
0.052
0.68
100.0
$316
0.046
0.71
100.0
$1,197
0.110
0.96
100.0
$659
0.083
0.98
Entertainment:
Probability of purchase (percent).....
Predicted expenditure (buyers only) .
Marginal propensity to consume.......
Elasticity..............................................
90.7
$188
0.021
0.76
83.2
$132
0.017
0.65
92.8
$139
0.015
0.67
87.6
$115
0.016
0.69
96.7
$284
0.026
0.95
89.3
$182
0.022
0.95
Out-of-town trips:
Probability of purchase (percent).....
Predicted expenditure (buyers only) .
Marginal propensity to consume.......
Elasticity..............................................
33.2
$98
0.012
0.82
12 23.7
$77
0.005
0.35
33.8
$157
0.012
0.48
23.2
1,2$120
0.010
0.42
45.4
$435
0.030
0.73
34.9
$373
0.037
0.76
1 Coefficient for retired income term is statistically significant at the 95-percent confidence level; retirement coefficient is statistically significant at the 95percent confidence level.
2 Retirement coefficient is statistically significant at the 95-percent confidence level.
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55
Expenditures in Retirement
A ppendix
B. Regression techniques
S o m e exp en d itu res, su ch as fo o d at h om e, or shelter and u tilities, are
reported by virtu ally all participants in the C on su m er Expenditure
Survey. For these item s, the c h o ice o f regression technique is straight
forward: O rdinary least squares (OLS) su its them w ell. H ow ever,
m any exp en d itu res are n ot u n iversal. T h ese pu rch ases m ay n ot be
m ade b eca u se o f tastes and p referen ces (for exam p le, to b a cco and
sm o k in g su p p lies) or b eca u se o f durability o f the item (for exam p le,
v e h ic le p u rch ases). In th is study, four su ch variab les are exam in ed .
Three (fo o d aw ay from h om e, entertainm ent, and o u t-o f-to w n trips)
are probably ex a m p les o f the first situation (tastes and p referen ces
d issu a d e so m e co n su m ers from p urchase) w h ile the fourth m ay b e an
ex a m p le o f the seco n d situ ation (perhaps the con su m er had su ffi
cien t am oun ts o f apparel during the last quarter, and did n ot n eed
serv ices, su ch as dry clean in g or repair). T h ese k inds o f expenditures
require sp ecia l treatm ent in their an alysis.
O n e set o f m o d e ls d esign ed to han d le th ese situ ation s is called the
“d o u b le h urdle” set o f m od els. T h e set g ets its nam e b ecau se the
co n su m er m ust first d ecid e w h eth er to purchase the item , and then
h o w m uch to purchase. In these m o d els, the hurdles are m od eled in
tw o stages: stage o n e m o d e ls the prob ab ility o f purchase; and stage
tw o m o d e ls the lev el o f purchase for th o se w h o buy the g ood . R e
su lts o f the tw o stages are u sed togeth er to p redict the expenditure
for a g iv en consum er.
O ne popular form o f d ou b le hurdle m od el is the Tobit m odel. In
th is m o d el, the “h urdles” are estim ated w ith the sam e in d ep en dent
variab les. T he sta g es are estim ated in su ch a w a y that on e set o f
param eter estim ates is produced, and th ese param eters can b e used
to estim ate p rob ab ility o f purchase (u sin g the cu m u lative d en sity
fu n ction , as w ith probit) and the m arginal p rop en sity to co n su m e (as
w ith o l s ). T h e predicted exp en d itu re is eq u ivalen t to the predicted
ex p en d itu re for th o se w h o purchase w eig h ted by the probability o f
p u rch a se.1 H ow ever, a m ajor draw back o f T obit is the restrictions it
m akes on the results. First, b ecau se o n e set o f in d ep en dent variables
is u sed , the m od el is o n ly u sefu l w h en the exact sam e set o f variables
predicts both the probability o f purchase and the lev el o f ex p en d i
ture. T h is is n o t a lw a y s the case. For exam p le, the prob ab ility o f
p u rch asin g h ealth insurance m ay d epend on the siz e o f o n e ’s fam ily.
H o w ev er, i f a particular p o lic y ch arges o n e prem ium for “fam ily”
co v era g e, regard less o f the num ber o f m em b ers o f the fam ily, the
Tobit m o d el has a w eak n ess in p redicting expenditures for that policy.
Furtherm ore, the T obit m o d el assu m es that the “d irection ” o f each
variab le is the sam e for the p rob ab ility and for the lev el o f con su m p
tio n . T h is m ay n o t b e true. For exam p le, an article d escrib in g w in e
co n su m p tio n b y U .S . m en fin d s that th o se w h o h ave at least a high
sc h o o l ed u ca tio n are m ore lik ely to drink w in e than m en w h o h ave
lo w er le v e ls o f education; h ow ever, th ey also find that m en w ith at
least a h ig h sc h o o l ed u cation drink less w in e than th ose w h o have
lo w er le v e ls o f ed u ca tion .2
Other m o d els have b een proposed, h ow ever, to handle the “double
h urdle” situation. T he m o d e ls u sed in th is study are b ased on a type
d escrib ed b y John G C ragg.3 In C ragg’s m ethod, the probability o f
p u rch ase is estim a ted separately from the le v e l o f exp en d itu res.
C ra g g ’s approach has m any advantages over the Tobit. T he ab ility to
separate th e p rob ab ility o f purchase and lev el o f expen d itu re eq u a
tio n s a llo w s d ifferen ces in variab les and sig n s across the tw o stages
o f the a n alysis, p ro v id in g C ragg’s approach w ith a “con sid erab le
in terp reta tio n a l a d v a n ta g e” o v er th e T ob it m o d e l, a cco rd in g to
M oh am ed A b d el-G h a n y and J. F e w S ilver.4 A d d ition ally, “T o b it ...
fo rces zero ob serv a tio n s to represent co m er so lu tio n s,” accord in g to
other researchers, w h o g o on to d iscu ss a w ea k n ess in T obit already
56
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addressed— nam ely, that it “p resum es that the sam e set o f variab les
and param eter estim ates d eterm ine both the discrete p rob ab ility o f a
n on zero o u tcom e and the level o f p o sitiv e ex p e n d itu r e s....”5
A lth ou gh C ragg’s m o d els u se probit to p redict the prob ab ility o f
purchase, he n otes that lo g it can b e u sed in stead .6 M an y standard
econ om etric textb ook s p oin t out that logit, w h en applied, p rod u ces
prob ab ility estim ates that are nearly id en tical to probit estim ates.
H ow ever, lo g it is m uch easier to u se and interpret. T h e eq u ation for
p red icting probability o f purchase (P ) is:
P = e x p ( a + b X ) /[ 1 + e x p ( a + b X )\
w here
a is the intercept o f the lo g it equation
b is a vector o f param eter estim ates
A is a vector o f in d ep en dent variables.
T he form ula can b e entered into a standard spreadsheet to estim ate
p rob ab ilities o f purchase for different con su m ers. Furtherm ore, the
eq u ation is ea sily differentiated to find the m arginal relation sh ip o f
probability to a particular variable. (F or exam p le, i f in co m e rises by
$ 1, h o w m uch d oes the probability o f purchase change?) W ith probit,
an equation m ust b e estim ated, and the resu lts m ust b e lo o k ed up in
a statistical table to fin d ou t the o verall p rob ab ility o f an ev en t
occuring, as w e ll as the m arginal effect on probability from ch an gin g
a variable.
In the version o f the C ragg m odel u sed in th is paper, the p rob ab il
ity o f purchase is estim ated as su g g ested w ith a lo g istic regression .
Separately, OLS is u sed to estim ate exp en d itu res for th o se w h o pur
ch ase the item .7 To get the final results, the predicted p rob ab ility o f
purchase ob tain ed from the first stage is m u ltip lied b y the p redicted
exp en d itu re for th ose w h o purchase. T h is essen tia lly p rod u ces an
average p redicted exp en d itu re, w eig h ted b y the p rob ab ility o f pur
chase. To illustrate the intuition behind obtaining th is w eigh ted aver
age p redicted expenditure, su p p ose that a large sam p le o f con su m ers
is selected random ly. S u p p ose that 25 p ercent o f the participants
purchased a particular item . S u p p ose that th is item so ld for $ 1 0 0 .
T h e average exp en d itu re for all con su m ers is then $ 2 5 , or 25 percent
m ultip lied by $ 1 0 0 . I f a sm aller sam p le is ran d om ly se lecte d from
th is large group, the ex p ected valu e o f the average o f that sm aller
sam p le is a lso $ 2 5 . T h is is b ecau se i f a large num ber o f random
sam p les w ere p u lled from the total sam ple, and each tim e the average
expenditure w as recorded, then the “grand average” (that is, the aver
age o f the averages) is ex p ected to be $ 25.
W hen estim atin g the m arginal prop en sity to con su m e and ela stic
ity for the Cragg m od els, the logit results are taken into account. T his
is b ecau se in com e is assum ed to in flu en ce exp en d itu res b oth directly
(through lev el o f exp en d itu re) and in d irectly (b y ch an gin g the prob
ab ility o f purchase). T h e m athem atical d etails are p rovid ed in the
fo llo w in g se ctio n s (“M arginal P rop en sity to C on su m e (MPC)” and
“E la sticities.”)
A s a final p oin t, there are so m e exp en d itu res for w h ich T obit m ay
be appropriate, in that this tech n iqu e assu m es that, g iv en en o u g h
tim e, all con su m ers w ill even tu ally purchase the g iv en item . For
exam p le, less than 100 p ercent o f all con su m er units report ex p en d i
tures for apparel and services every quarter, but g iv en en ou gh tim e, it
is reason able to assu m e that 100 p ercent w ill ev en tu a lly purchase
som e. H ow ever, Tobit still suffers the w e a k n esses d escrib ed earlier,
and for co n v en ien ce, the C ragg m od el is u sed for all variab les ana
ly zed in th is study. Further exam in ation o f the Tobit m od el w ill be
left for future research.
M a r g in a l P r o p e n s i t y to C o n s u m e (M P C ). T he m arginal p ropensity
to co n su m e (M P C ) is d efin ed as the ch an ge in expen d itu re g iv en a
unit ch a n g e in in com e. In th is case, “perm anent in co m e” is the rel
evant variable for change.
T h e “ OLS o n ly ” regression s d escrib ed in th e text (for fo o d at
hom e; sh elter and u tilities; and transportation) have the fo llo w in g
sp ecification :
E (ln Y ) = a + b l n l + c X
w here
E ( ln Y ) is the predicted (or “ex p ected ”) valu e o f the
d ep en d en t variable
a is the intercept
b is a param eter estim ate
I n i is the natural lo g o f perm anent in com e
c X represents all other in d ep en dent variables m ultip lied
by their reg ressio n c o effic ie n ts.
In th is case, the m p c is calcu lated b y fin d in g the ch an ge in the pre
d icted ex p en d itu re g iv en a $1 in crease in perm anent in com e, or
8 E (Y )/8 I . A lth o u g h the m od el is sp ec ified to calcu late E (ln Y ), the
d esired result is ea sily obtained:
Therefore, to find P
the q u otien t rule is used:
P ’= (f ’g -fg)l'g2
where
/ = e x p ( a + f i ln l + A X )
g = 1 + e x p (a + p in l + AX)
f ’= g ’= (fi/I )e x p ( a + p i n l + A X )
B e c a u se / ’ and g ’ are equal in this case, this sim p lifies algeb raically
to:
and, b ecau se g eq u als ( f + 1), this red u ces even further to:
P ’= [ f ’( f+i - J) Vg 2=fVgz.
N o w , w ith the m uch sim p lified result, it can b e sh o w n that:
P ’ = [ ( p l l ) e x p ( a + p i n l + A X )]/[ 1 + e x p ( a + p i n l + A X )]2.
A gain , b y substitution, this red u ces to:
P*{[P/I]/[\+exp(a + pinI+AX)]}.
8 E ( ln Y ) l d l = d ( a + b l n l + c X ) / d l
1 /[ E ( Y ) ]* d E ( Y )l 8 1 = b * ( l / f ) = b /I
T herefore,
d E ( Y )/ 8 1 = b * [E (Y )/T \
M P C = P * { [ p / I \ / [ l + e x p ( a + p i n l + A X ) ] } * e x p [E (ln Y )]
T h is result has an interesting property in that the m p c is propor
tion al to the b u d get share (that is, sp ec ific ou tlay d ivid ed by total
o u tla y s), w ith the proportion equal to the param eter estim ate for 1n l.
T h is still lea v es o n e question: I f the m od el p redicts E (ln Y ), w hat
is E ( Y )? T h is a lso is ea sily so lv e d , in that:
E (Y ) = e x p [E (ln Y )\
U s in g th is form u lation , on e n eed o n ly se lect a group o f interest, u se
the regression resu lts to determ ine E (ln Y ), and then fo llo w the p roce
dures indicated. In this study, the “group o f interest” is the control
group d escrib ed in the text.
T he C ragg-b ased m o d e ls h ave a m ore com p licated sp ecification ,
but it is n ev erth eless so lv a b le to y ield the m p c . T he m p c is still
d efin ed th e sam e w a y and is still represented the sam e w ay m ath
em atically; that is,
+ P e x p ’[E (ln Y )];
e x p ’[E (ln Y )] = e x p [E (ln Y )] * E ’( InY );
e x p [ E ( ln Y ) ] = E ( Y ) ;
E ’(lnY ) = S E ( ln Y ) /c l = 1/E ( Y ) * c E ( Y )/ c I
= 1 /E (Y ) * [b * E (Y )/I ] = b /I;
A ltern atively, b ecau se E (ln Y ) eq u als a + b ln l + cX ,
E ’(lnY ) = c E (ln Y )/d I = d ( a + b l n l + c X ) / d I = b * ( l/I ) = b/I;
M P C = P * { \P T ]/[1 + e x p i a + p i n l + A X )]\* E {Y ) + P * [£ ( Y)*(b/I)];
or
M P C = P * E ( Y ) * { [ p / I J / [ l + e x p ( a + p i n l + A X )] } +
P * b [E (Y )/I ]
To find d E ( Y )l 81, the product rule o f calcu lu s is used. That is:
B e ca u se the term s P and E (Y ) are com m on to both p ie c e s o f the
com p licated right-hand sid e o f this eq u ation , m athem atically, the
m p c can be sim p lified by factorin g th ese term s out, and m u ltip ly in g
them by the sum o f the rem aining p ieces. H o w ev er, the form ula is
left in this form for the m om ent, to illustrate an in tu itive point: N o te
that the m p c is derived from the p redicted valu e o f the expen d itu re
for th ose w h o purchase as w eigh ted by the prob ab ility o f purchase.
N o te that the secon d term on the right-hand sid e, that is, P * b [E (Y )/I ],
is the sam e m p c as w as foun d b efore, ex cep t that it is w eig h ted b y the
probability o f purchase. T he rem ain ing term is a result o f the fact
that the p redicted exp en d itu re is affected in d irectly b eca u se p rob
ab ility o f purchase ch an ges as a result o f in com e change.
8 E (Y )/ 8 1 = P ’e x p [E (ln Y )\ + P e x p \E { ln Y ) ]
E la s tic itie s . In com e ela sticity (or m ore properly in th is case, perm a
M P C = 8 E (Y )/8 I .
H o w ev er, the initial form ulation is m ore com p licated . T he desired
resu lt is actu ally
E (Y ) = P * e x p [E (ln Y )]
w h ere P is the prob ab ility o f ob servin g an expenditure.
R ecall that:
P = e x p ( a + p i n l + A X )I [\ + e x p ( a + J 3 ln l+ A X )])
w h ere
A X is a v ecto r o f all in d ep en dent variab les ex cep t in com e,
each m u ltip lied by their param eter estim ates.
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nent in com e elasticity) is the percent ch an ge in exp en d itu re for a
sp ec ific g o o d (su ch as fo o d at h om e) g iv en a 1-percen t in crease in
(perm anent) in com e. For exam p le, for retired sin g le m ales, the in
com e elasticity for fo o d at h om e is estim ated to b e 0 .3 2 , m ean ing that
for every 1-percent in crease in perm anent in com e, th ese m en are
predicted to increase foo d -a t-h o m e exp en d itu res b y about one-third
o f 1 percent.
Monthly Labor Review
July 2002
57
Expenditures in Retirement
T h e eq u ation for calcu latin g ela sticity ( tj) is:
tj= M P C * I /E ( Y )
In the ca se o f the “ o l s o n ly ” regression s, the elasticity in th is ca se is
constant, and equal to the param eter estim ate for perm anent in com e.
To sh o w th is m ath em atically, recall that MPC in this case is propor
tional to the p redicted expen d itu re share; that is, M P C eq u als b [ E ( Y ) l
I \. It is ea sy to se e that m u ltip lyin g m p c b y I /E (Y ) y ie ld s b , w h ich is
the param eter estim ate for lo g o f in com e, as stated.
For the C ragg-b ased m o d els, the fu ll form ula is m uch m ore co m
p licated , due to the co m p lex ity o f the MPC equation. H ow ever, on ce
the v a lu e o f the MPC is obtained, m u ltip lyin g th is v alu e by the inverse
o f the predicted exp en d itu re share still y ie ld s the ela sticity estim ate.
R ecall that part o f the m p c equation in volved the probability-w eighted
exp en d itu re share. T h e elasticity w ill a lso b e sim ilar to the “ OLS
o n ly ” resu lts in that, i f the form ula is sp ecified , it con tain s the prob
a b ility -w eig h te d in co m e c o effic ie n t. That is,
Footnotes to A ppendix B
1 See John McDonald and Robert A. M offitt, “The Uses o f Tobit Analy
sis,” T h e R e v ie w o f E c o n o m ic s a n d S ta t is tic s , M ay 1980, pp. 3 1 8 -2 1 ,
especially p. 318.
2 J.R. Blaylock and W .N . Blisard, “W ine consumption by us men,”
pp. 6 4 5 -5 1 , especially p. 649.
A p p l i e d E c o n o m ic s , M ay 1993,
3 John G. Cragg, “ Some Statistical M odels for L im ited Dependent
Varibles w ith A p p lic a tio n to the D em and fo r D u rab le G oods,”
E c o n o m e tr ic a , September 1971, pp. 8 2 9 -4 4 .
4 Mohamed A bdel-G hany and J. Lew Silver, “Economic and Dem o
graphic Determinants o f Canadian Households’ Use o f and Spending on
Alcohol,” Family and Consumer Sciences Research Journal, September
1998, pp. 6 2 -9 0 , especially p. 65.
5 Deanna L. Sharpe, Mohamed Abdel-Ghany, Hye-Yeon Kim , and GongSoog Hong, “Alcohol Consumption Decisions in K orea,” Journal o f
Family and Economic Issues, Spring 2001, pp. 7 -2 4 , especially, p. 14.
6 See footnotes 5 (p. 830) and 6 (p. 832).
M P C * [ I /E ( Y )] = P * { p / [ 1 + e x p ( a + f i l n l + AY)]} + P * b
T h e se co n d term on the right-hand sid e, P * b , is the probabilityw eig h ted c o e ffic ie n t ju st m en tion ed .
58
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July 2002
7 To reduce heteroscedasticity, the ols model actually predicts the natu
ral log o f the expenditure for those with positive expenditures. Coinci
dentally, Cragg shows this as one o f the possible specifications for the
second stage model. (See Cragg, p. 831, eq. 10.)
Contingent “new
econom y” jobs?
Are “new economy” jobs more likely to
involve contingent or alternative
employment relationships? Before that
question can be answered, “new
economy” jobs need to be identified.
David Neumark and Deborah Reed, in
“Employment Relationships in the New
Economy” (NBER Working Paper 8910),
show that this is no easy task.
Neumark, of Michigan State University
and N B E R , and Reed, of the Public Policy
Institute of California, operationalized the
concept of “new economy” workers in
three ways for their analysis. One way is
to look at workers in high-tech industries;
for this, they used a classification from an
article by Daniel Hecker that appeared in
this Review a few years back (see “Hightechnology employment: a broader view,”
June 1999). A second way is to define
“new economy” workers as those who
reside in high-tech cities—the authors
based this classification on a recent
Brookings Institution study. The third
approach used by Neumark and Reed is
to look at workers in the fastest-growing
industries.
After defining “new economy” jobs in
these three different ways, Neumark and
Reed compared the nature of such jobs to
other jobs using the Contingent and
Alternative Employment Arrangement
Supplements of the Current Population
Survey. These Supplements are from
surveys conducted in February of 1995,
1997,1999, and2001.
The results obtained by Neumark and
Reed depend on the definition of “new
econom y” workers. With the first
definition, employment in high-tech
industries, the authors did not find greater
use o f nontraditional employment
relationships. Based on the second
definition, residence in high-tech cities,
there is evidence that contingent and
alternative employment relationships are
more common in the new economy. Finally
with the third definition, jobs in the
fastest-grow ing industries, “new
economy” workers are much more likely
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to have contingent or alternative
employment relationships, with much of
the difference driven by employment in
construction and personnel supply
services; it may be that employment in
these two particular industries is
inherently contingent or alternative.
Neumark and Reed do emphasize the
“provisional nature” of their conclusions.
They indicate that their paper may do more
to raise questions and stimulate research
than to supply definitive answers.
Pollution and
discrimination
A lthough less so than in the past,
occupations are still segregated by sex.
In a recent paper, Claudia Goldin of
H arvard U niversity develops a
“pollution” theory of discrimination in
an attempt to explain such segregation
(“A Pollution Theory of Discrimination:
M ale and Fem ale D ifferences in
O ccupations and E arnings,” N B E R
Working Paper 8985).
In Goldin’s model, discrimination is
treated as “the consequence of a desire
by men to maintain their occupational
status or prestige, distinct from the desire
to maintain their earnings.” The notion is
that the prestige of an occupation can be
“polluted” by entry into the occupation
of a person whose qualifications are
judged based on the average of the group
that the individual belongs to, rather than
on individual merits.
T herefore, men in an all-m ale
occupation might exhibit hostility towards
perm itting a woman to enter their
occupation, even if a particular woman
meets the entry qualifications. Her entry
could be perceived in the wider society as
a signal that the occupation has been
altered. A key aspect of this model is
informational asymmetry—in the model,
women know what their own levels of
qualifications are, and so do their
employers, but only their average or
median level is widely known.
Goldin notes that a “mechanism that
increases inform ation, such as the
credentialization of occupations, will
foster integration.” In addition, the
visibility of successful women “may
help shatter old stereotypes and in
crease know ledge about the true
distribution of female attributes.”
C a lifo rn ia ’s m inim um
w ag e workers
There were just over a million workers in
California who in 2000 were earning
somewhere between that year’s State
minimum wage of $5.75 and the new State
minimum wage of $6.25 enacted in 2001
according to a report, Minimum Wages:
The Econom ic Im pact o f the 2001
California Minimum Wage Increase, from
the California Department of Industrial
Relations.
The author, Jeffrey G Woods, describes
the typical minimum wage worker: “She is a
teenage, foreign-bom Hispanic without
U.S. citizenship. Havingneverbeenmarried,
she has no more than a high school
education. She is less likely to be a member
of a labor union and her total family income is
less than $20,000 per year.”
R etirem ent a n d w e ll
being
The raw correlation between retirement
status and subjective w ell-being is
generally negative. Correlation is not
causation, however, as a recent nber
Working Paper, “ Is Retirem ent
Depressing? Labor Force Inactivity and
Psychological Well-Being in Later Life,”
by Kerwin Kofi Charles, reminds us. In
the case of retirement and well-being,
Charles attempts to account for the fact
that the two are sim ultaneously
determined. “In particular, people with
idiosyncratically low well-being, or people
facing transitory shocks which adversely
affect well-being might disproportionately
select into retirement.” Once such factors
are taken into account, Charles finds that
retired men tend to report lower scores on
measures of depression and loneliness.
Monthly Labor Review
July 2002
59
Cost-of-living, price indexes
At What Price? Conceptualizing and
Measuring Cost-of-Living and Price
Indexes. By the National Research
Council. Washington, D C , National
Academy Press, 2002,332 pp., $49.95/
hardcover.
At What Price? Conceptualizing and
Measuring Cost-of-Living and Price In
dexes is the product of an 11-member
panel convened by the Committee of
National Statistics (C N S) and sponsored
by the Bureau of Labor Statistics (BLS).
BLS requested the panel to analyze the
development of a cost-of-living index
(C O L I) and evaluate the proper use of
consumer price indexes for Federal pro
grams such as Social Security, food
stamps, and Federal Government wages.
Discussion of the C O L I dates back to
1961 when the Stigler Committee of The
National Bureau of Economic Research
summarized the differences between a
consumer price index (CPI) based on a
cost-of-goods index (C O G I) and an in
dex that measures the cost of living. The
Stigler Committee recommended that BLS
conduct long-term research to improve
the CPI by transforming it into a better
approximation of a C O LI. In 1995, the
Senate Finance Committee appointed the
Boskin Commission to evaluate biases
in the C PI. The Senate reasoned that
upward bias caused overcompensation
to Social Security recipients. The Boskin
Commission determined that the C PI
overstates inflation by 1.1 percentage
points per year and recommended that
BLS change the CPI methodology from a
C O G I, or fixed market basket framework,
to a C O LL
The CNS panel promotes the C O L I,
similar to the previous commissioned
reports, but it diverges from the Boskin
Commission conclusions by highlight
ing the relevance of the C O G I. Katharine
Abraham, former Commissioner of the
60 Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
Bureau of Labor Statistics, supported the
production of both indexes. “An index
that is good for one purpose will not al
ways be good for another ... Each pur
pose leads to a somewhat different con
ceptual framework.” The panel concurs
with the former commissioner by recog
nizing the importance of the C O G I as an
indicator of the level of prices, and the
C O L I as a measure of the change in the
cost of living.
Specifically, the panel defines C O L I
as a measurement of “the percentage
change in expenditures a household
would have to make in order to hold con
stant some specified standard of living.”
According to economic theory, when
prices change, consumers generally shift
their purchases toward goods with rela
tively lower prices. For example, if the
price o f b ee f increases relative to
chicken, consumers will tend to pur
chase more chicken relative to beef.
Therefore, an advantage o f the C O L I
compared to the C O G I is how it accounts
for substitution between items, while
maintaining an equivalent standard of
living between two time periods. The
C O G I is markedly different from the C O LI
because it does not account for substi
tution that may occur between items.
At What Price? contains 18 recom
mendations from the panel. These range
from the development of a conditional
C O L I to conducting research on issues
like quality change and data collection.
The panel supports a conditional C O LI
where private goods and services are
accounted for, but environmental factors
are held constant. Accounting for
nonmarket prices presents numerous
conceptual problems such as measuring
price in changes to the environment,
quality of life, and public goods. The
panel uses temperature as an example.
When it is extremely hot or cold, people
tend to spend more money on heat or air
conditioning. If the price for heat and
air conditioning rem ains constant
throughout the shift in temperatures,
then the price index should not move re
gardless of the change in consumption.
The panel recognizes that BLS cur
rently evaluates what percent of a price
change is caused by quality and what
percent is caused by ‘real’ price change.
BLS is currently investigating a hedonically adjusted price change, where sta
tistical regressions are applied to mon
etary values based on changes in prod
uct characteristics. The panel is cau
tious about com pletely integrating
hedonics into the entire CPI market bas
ket and recommends further research.
Another area o f research recom
mended by the panel is the exploration
of new methods of data collection. Cur
rently, the Consumer Expenditure Sur
vey accounts for the level of expendi
tures across items. Given the high de
gree of aggregation, BLS is unable to
measure living costs for specific com
modities or demographic groups. One
means of disaggregating, or collecting
household level expenditures, is with
handheld computers and scanners. In
addition to new survey techniques, the
panel recommends researching the fea
sibility o f integrating expenditure
weights from the personal consumption
expenditure (PCE) survey prepared by
the Bureau of Economic Analysis.
BLS initiated the production of the
Chained Consumer Price Index (C -C P IU ) in August of 2002; this new index is
based on a C O L I framework. Successful
implementation, however, may depend
on understanding the methodology be
hind the C O LI and C O G I, and specific uses
for each index. At What Price? serves
as a good resource for business analysts
and economists to social science re
searchers and policymakers.
—Joshua Klick
Division of Consumer Prices
and Price Indexes,
Bureau of Labor Statistics
Notes on labor statistics
62
Labor compensation and collective
bargaining data—continued
74
2 8 . E m p loym ent C ost Index, private nonfarm w orkers,
by b argaining status, region , and area s i z e .........................
2 9 . P articipants in b en efit plans, m ed iu m and large f ir m s ......
30. P articipants in b en efits plans, sm all firm s
and g o v e r n m e n t..................................................................................
31. W ork stop p ages in v o lv in g 1 ,0 0 0 w ork ers or m o r e .............
Com parative indicators
1. Labor m arket in d ic a to r s ...................................
2. A n n u al and quarterly p ercent ch an ges in
co m p en sa tio n , prices, and prod u ctivity
3. A ltern a tiv e m easu res o f w a g e s and
co m p en sa tio n c h a n g e s .................................
75
75
Labor force data
76
seasonally adjusted....................................................
77
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. Establishment size and employment covered under ui,
private ownership, by major industry...................
19. Annual data establishment, employment, and wages,
covered unless ui and ucfe, by ownership..............
20. Annual data: Establishments, employment,
and wages covered under ui and ucfe, by State.....
21. Annual data: Employment and average annual pay of
ui- and ucFE-covered workers, by largest counties ..
22. Annual data: Employment status of the population ...
23. Annual data: Employment levels by industry...........
24. Annual data: Average hours and earnings level,
by industry.........................................................
78
78
79
79
80
80
81
83
84
85
86
87
88
89
90
91
95
96
96
Labor compensation and collective
bargaining data
2 5 . E m p lo y m en t C o st Index, com p en sation ,
b y occu p a tio n and industry g r o u p ........................................
2 6 . E m p lo y m en t C ost Index, w a g e s and salaries,
b y occu p a tio n and industry g r o u p ........................................
2 7 . E m p lo y m en t C ost Index, b en efits, private industry
w orkers, by o ccu p ation and industry g r o u p .....................
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Federal Reserve Bank of St. Louis
103
104
Price data
4. Employment status of the population,
seasonally adjusted...............................................
5. Selected employment indicators,
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...............................................
10. Unemployment rates by States,
101
102
32. C on su m er P rice Index: U .S . city average, b y exp en d itu re
category and commodity and service groups.............. 105
33. Consumer Price Index: U.S. city average and
local data, all items......................................................108
34. Annual data: Consumer Price Index, all items
and major groups.........................................................109
35. Producer Price Indexes by stage of processing.................110
36. Producer Price Indexes for the net output of major
industry groups...........................................................Ill
37. Annual data: Producer Price Indexes
by stage of processing................................................. 112
38. U.S. export price indexes by Standard International
Trade Classification.................................................... 113
39. U.S. import price indexes by Standard International
Trade Classification.................................................... 114
40. U.S. export price indexes by end-use category.................115
41. U.S. import price indexes by end-use category................115
42. U.S.international price indexes for selected
categories of services................................................... 115
Productivity data
43. In d exes o f p roductivity, hou rly com p en sation ,
and unit co sts, data sea so n a lly adjusted ...............................116
4 4 . A n n u al in d ex es o f m ultifactor p r o d u c tiv ity .............................117
4 5 . A nnual in d ex es o f productivity, h ou rly com p en sation ,
unit costs, and p r i c e s ....................................................................118
4 6 . A n n u al in d exes o f output per hour for selected
in d u str ie s............................................................................................119
International comparisons data
4 7 . U n em p loym en t rates in n in e countries,
data sea so n a lly a d ju ste d .............................................................. 122
48. A nnual data: E m p loym en t status o f the civilian
w ork in g-age p op u lation , 10 c o u n tr ie s................................... 123
49. A n n u al in d ex es o f p rod u ctivity and related m easures,
12 c o u n tr ie s .......................................................................................124
Injury and illness data
97
99
50. A nnual data: O ccu p ation al injury and illn ess
in cid en ce r a t e s ............................................................................... 125
5 1 . Fatal occu p ation al injuries by even t
100
or e x p o su r e ........................................................................................... 127
Monthly Labor Review
July 2002
61
Notes on Current Labor Statistics
T h is sectio n o f the R e v ie w presents the prin
cip al statistical series c o lle c te d and ca lcu
la ted b y th e B u rea u o f L ab or S ta tistic s :
series on labor force; em p loym en t; u n em
p loym en t; labor co m p en sa tio n ; con su m er,
producer, and international prices; p roduc
tivity; international com p arison s; and injury
and illn e ss statistics. In the n otes that follow ,
the data in each group o f tab les are b riefly
described; k ey d efin itio n s are given ; n otes
on the data are set forth; and sou rces o f addi
tion al inform ation are cited.
G eneral notes
T h e fo llo w in g n o te s ap p ly to several tables
in th is section :
Seasonal adjustment. Certain m onth ly
and quarterly data are adjusted to elim in ate
th e effect on the data o f such factors as c li
m atic co n d itio n s, industry p roduction sch ed
u les, o p en in g and c lo s in g o f sc h o o ls, h o li
d ay b u y in g p eriod s, and vacation p ractices,
w h ich m ight p revent short-term evalu ation
o f th e sta tistica l se ries. T ab les co n ta in in g
data that h ave b een adjusted are id en tified as
“ sea son ally adjusted.” (A ll other data are not
se a so n a lly adjusted.) S eason al effects are e s
tim a ted o n th e b a sis o f p a st e x p e r ie n c e .
W h en n ew se a so n a l factors are com p u ted
ea ch year, r e v isio n s m ay affect se a so n a lly
adjusted data for several p reced in g years.
S ea so n a lly adjusted data appear in tables
1 - 1 4 , 1 6 - 1 7 , 4 3 , and 4 7 . S eason ally adjusted
labor fo rce data in ta b les 1 and 4 - 9 w ere re
v ise d in the February 2 0 0 2 issu e o f the R e
v ie w . S ea so n a lly adjusted estab lish m en t sur
v e y data sh o w n in tables 1 ,1 2 - 1 4 and 1 6 - 1 7
w ere rev ised in the July 2 0 0 2 R e v ie w and
reflect the ex p erien ce through M arch 2 0 0 2 . A
b r ie f exp la n a tio n o f the season al adjustm ent
m eth o d o lo g y appears in “N o te s on the data.”
R ev isio n s in the p rod u ctivity data in table
4 9 are u su a lly introduced in the S eptem ber
issu e. S ea so n a lly adjusted in d ex es and per
c e n t c h a n g e s fro m m o n th -to -m o n th and
quarter-to-quarter are p u b lish ed for num er
o u s C on su m er and Producer P rice Index s e
ries. H o w ev er, se a so n a lly adjusted in d ex es
are n o t p u b lish ed for the U .S . average A llItem s CPI. O n ly se a so n a lly adjusted percent
ch a n g es are availa b le for th is series.
Adjustments for price changes. S om e
data— su ch as the “real” earnings sh o w n in
table 14— are adjusted to elim in ate the e f
fect o f c h a n g es in price. T h ese adjustm ents
are m ade by d iv id in g current-dollar v alu es
b y the C on su m er P rice In d ex or the appro
priate co m p o n en t o f the in d ex, then m u lti
p ly in g b y 100. For ex am p le, g iv en a current
hourly w a g e rate o f $3 and a current p rice
in d ex num ber o f 1 50, w h ere 1982 = 100, the
62
Monthly Labor Review
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Federal Reserve Bank of St. Louis
hourly rate exp ressed
($ 3 /1 5 0 x 100 = $2).
r esu ltin g v a lu e s) are
“con stan t,” or “ 1 9 8 2 ”
in 1982 dollars is $2
T he $2 (or any other
d esc rib ed as “r e a l,”
dollars.
C o m p a r is o n s o f U n e m p lo y m e n t, BLS B u lle
tin 1979.
D etailed data on the occu p ation al injury
and illn ess series are p u b lish ed in O c c u p a
tio n a l I n ju r ie s a n d I lln e s s e s in th e U n ite d
Sources of information
D ata that su p p lem en t the tab les in th is se c
tion are p u b lish ed by the Bureau in a variety
o f sou rces. D e fin itio n s o f each series and
n otes on the data are con tained in later se c
tio n s o f th ese N o te s d escrib in g each set o f
data. For detailed d escrip tion s o f each data
series, se e BLS H a n d b o o k o f M e th o d s , B u l
letin 2 4 9 0 . U sers also m ay w ish to con su lt
S ta te s , b y In d u s tr y , a BLS annual bulletin.
F inally, the M o n th ly L a b o r R e v ie w car
ries analytical articles on annual and lon ger
term d ev elo p m en ts in labor force, em p lo y
m ent, and u n em p loym en t; e m p lo y e e c o m
p en sation and c o lle c tiv e bargaining; prices;
productivity; international com p arison s; and
injury and illn ess data.
Symbols
M a j o r P r o g r a m s o f th e B u r e a u o f L a b o r S ta
tis tic s , R eport 9 1 9 . N e w s releases p rovid e
the latest statistical inform ation p u b lish ed by
the Bureau; the m ajor recurring releases are
p u b lish ed accord in g to the sch ed u le appear
in g on the back co v er o f this issu e.
M ore inform ation about labor force, em
p loym en t, and u n em p loym en t data and the
h o u seh o ld and estab lish m en t su rveys under
ly in g the data are availab le in the B u reau ’s
m onth ly pub lication , E m p lo y m e n t a n d E a r n
in g s. H istorical unadjusted and sea so n a lly
adjusted data from the h o u seh o ld su rvey are
availab le on the Internet:
http://www.bls.gov/cps/
H istorically com parable u nadjusted and sea
so n a lly adjusted data from the estab lish m en t
survey also are availab le on the Internet:
http://www.bls.gov/ces/
A d d ition al inform ation on labor force data
for areas b e lo w the national lev el are pro
vid ed in the BLS annual report, G e o g r a p h ic
P r o f ile o f E m p lo y m e n t a n d U n e m p lo y m e n t.
For a co m p reh en siv e d isc u ssio n o f the
E m p loym en t C ost Index, see E m p lo y m e n t
C o s t I n d e x e s a n d L e v e ls , 1 9 7 5 - 9 5 , BLS B u l
letin 2 4 6 6 . T he m ost recent data from the
E m p lo y ee B e n e fits S u rvey appear in the fo l
lo w in g Bureau o f Labor S tatistics bulletins:
E m p l o y e e B e n e f its in M e d iu m a n d L a r g e
F ir m s ; E m p lo y e e B e n e fits in S m a ll P r iv a te
E s ta b lis h m e n ts ; and E m p lo y e e B e n e f its in
S ta te a n d L o c a l G o v e r n m e n ts .
M ore detailed data on con su m er and pro
d ucer p rices are p u b lish ed in the m on th ly
p e r io d ic a ls, T h e CPI D e t a i l e d R e p o r t and
P r o d u c e r P r ic e I n d e x e s . For an o v erv iew o f
the 199 8 revision o f the C P I , se e the D e c e m
ber 19 9 6 issu e o f the M o n th ly L a b o r R e v ie w .
A d d ition al data on international p rices ap
pear in m on th ly n ew s releases.
L istin gs o f industries for w h ich p rod u c
tivity in d ex es are availab le m ay b e foun d on
the Internet:
http://www.bls.gov/lpc/
For ad d ition al in form ation on interna
tion al com p arison s data, se e I n te r n a tio n a l
July 2002
n .e.c. =
n .e.s. =
p =
r
=
n ot elsew h ere cla ssified ,
n ot elsew h ere sp ecified .
prelim inary. To in crease th e tim e
lin ess o f so m e series, prelim inary
figures are issu ed b ased on repre
sen tative but in co m p lete returns,
rev ised . G en erally, th is r e v isio n
r e fle c ts th e a v a ila b ility o f later
data, but a lso m ay reflect other ad
justm en ts.
Comparative Indicators
(T ables 1 - 3 )
C om p arative in d icators ta b le s p r o v id e an
o v erv iew and com p arison o f m ajor b l s sta
tistical series. C on sequ en tly, alth ou gh m any
o f the in clu d ed series are availab le m onthly,
all m easu res in th ese com p arative ta b les are
p resented quarterly and annually.
Labor market indicators in clu d e em
p loym en t m easu res from tw o m ajor su rveys
and inform ation on rates o f ch an ge in co m
p en sation provid ed b y the E m p loym en t C ost
In d ex (ECl) program . T he labor force partici
pation rate, the em p lo y m en t-to -p o p u la tio n
ratio, and u n em p loym en t rates for m ajor d e
m o g r a p h ic g ro u p s b a se d o n th e C u rren t
P o p u la tio n (“h o u se h o ld ”) S u rvey are pre
sented , w h ile m easu res o f em p loym en t and
average w e e k ly hours b y m ajor industry se c
tor are g iv en u sin g nonfarm p ayroll data. T he
E m p loym ent C ost In d ex (co m p en sa tio n ), by
m ajor sector and b y b argaining status, is c h o
sen from a variety o f b l s com p en sa tio n and
w a g e m easures b ecau se it p rovid es a co m
p reh en siv e m easu re o f em p lo y er c o s ts for
hiring labor, n ot ju st ou tlays for w a g es, and
it is not affected b y em p loym en t sh ifts am ong
occu p ation s and industries.
D ata on changes in compensation, prices,
and productivity are presented in ta b le 2.
M e a su r e s o f rates o f c h a n g e o f c o m p e n sa -
tio n an d w a g e s from th e E m p lo y m e n t C o st
In d e x p ro g ra m are p r o v id e d for a ll c i v i l
ia n n o n fa r m w o r k e r s ( e x c l u d in g F ed era l
an d h o u s e h o ld w o r k e r s) and for a ll p rivate
n o n fa rm w o r k e r s. M e a s u r e s o f c h a n g e s in
c o n su m e r p r ic e s fo r a ll urban c o n su m e r s;
p r o d u c e r p r ic e s b y s ta g e o f p r o c e s s in g ;
o v e r a ll p r ic e s b y sta g e o f p r o c e s sin g ; and
o v e r a ll e x p o rt and im p o rt p r ic e in d e x e s are
g iv e n . M ea su res o f p ro d u ctiv ity (ou tp u t per
h o u r o f a ll p e r s o n s ) are p r o v id e d fo r m ajor
se c to r s.
Alternative measures of wage and com
pensation rates of change, w h ich reflect the
overall trend in labor co sts, are sum m arized
in table 3. D iffer en ces in con cep ts and scop e,
related to the sp e c ific p u rp oses o f the series,
contribute to the variation in ch an ges am ong
the in d ivid u al m easures.
Notes on the data
D e fin itio n s o f each series and n otes on the
data are con tain ed in later se ctio n s o f th ese
n o te s d escrib in g each set o f data.
Employment and
Unemployment Data
(T ab les 1; 4 - 2 4 )
Household survey data
Description of the series
Employment data in th is se ctio n are o b
tained from the Current P op u lation Survey,
a program o f personal in terview s con d u cted
m on th ly b y the B ureau o f the C en su s for the
B ureau o f L abor S tatistics. T h e sam p le co n
sists o f about 6 0 ,0 0 0 h o u seh o ld s selected to
represent the U .S . p op u lation 16 years o f age
and older. H o u seh o ld s are in terview ed on a
rotatin g b a sis, so that three-fou rth s o f the
sa m p le is th e sa m e fo r an y 2 c o n se c u tiv e
m onths.
Definitions
Employed persons in clu d e (1) all th ose w h o
w o rk ed for pay an y tim e during the w eek
w h ich in clu d es the 12th day o f the m onth or
w h o w ork ed unpaid for 15 hours or m ore in
a fa m ily -o p era ted en terp rise and (2 ) th o se
w h o w ere tem porarily absent from their regu
lar jo b s b eca u se o f illn ess, vacation , in d u s
trial d isp u te, or sim ila r reason s. A p erson
w ork in g at m ore than o n e jo b is cou n ted on ly
in the jo b at w h ich h e or sh e w ork ed the
greatest num ber o f hours.
Unemployed persons are th o se w h o did
n o t w ork during the su rvey w eek , but w ere
av a ila b le for w ork ex cep t for tem porary ill
n e ss and had lo o k ed for jo b s w ith in the pre
ce d in g 4 w eek s. P erso n s w h o did n ot lo o k
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
for w ork b eca u se th ey w ere on la y o f f are
a lso cou n ted am on g th e u n em p lo y ed . The
unemployment rate rep resen ts th e n u m
ber u n em p lo y ed as a p ercen t o f the civ ilia n
labor force.
T he civilian labor force co n sists o f all
e m p lo y e d or u n e m p lo y e d p e r so n s in th e
civilian n oninstitutional pop u lation . P erson s
not in the labor force are those not cla ssifie d
as e m p lo y e d or u n e m p lo y e d . T h is grou p
in c lu d e s d isco u ra g ed w ork ers, d efin e d as
p ersons w h o w ant and are availab le for a jo b
and w h o h ave look ed for w ork som etim e in
the past 12 m onths (or sin ce the end o f their
last jo b i f they held on e w ith in the past 12
m o n th s ), b u t are n o t cu rren tly lo o k in g ,
b e c a u s e t h e y b e l i e v e th e r e are n o j o b s
availab le or there are n on e for w h ich they
w o u ld q u a lify . T h e civilian noninstitu
tional population com p rises all p ersons 16
years o f age and old er w h o are n ot inm ates
o f penal or m ental in stitu tion s, sanitarium s,
or h o m es for the aged, infirm , or needy. T he
civilian labor force participation rate is the
p roportion o f the c iv ilia n n on in stitu tion al
p o p u la tio n that is in the labor force. T h e
employment-population ratio is em p lo y
m en t as a p ercen t o f th e c iv ilia n n o n in
stitutional population.
Notes on the data
F rom tim e to tim e, and e sp e c ia lly after a
d ecen n ial cen su s, ad ju stm en ts are m ade in
th e Current P o p u la tio n S u rvey fig u r es to
c o r r e c t fo r e s tim a tin g errors d u rin g th e
in tercen sal years. T h ese ad ju stm en ts affect
the com p arab ility o f h istorical data. A d e
scrip tion o f th ese ad ju stm en ts and their e f
fe c t on the variou s data series appears in the
E x p la n a to r y N o t e s o f E m p l o y m e n t a n d
E a r n in g s .
L abor fo rce data in tab les 1 and 4 - 9 are
se a so n a lly ad ju sted. S in c e January 1 9 8 0 ,
n ation al labor force data h ave b een se a so n
a lly adjusted w ith a procedure ca lled X - l l
arima w h ich w a s d e v e lo p e d at S ta tistics
C anada as an ex ten sio n o f the standard X 11 m eth od p rev io u sly u sed by bls. A d e
tailed d escrip tion o f the procedure appears
in th e X - l l arima S e a s o n a l A d j u s t m e n t
M e th o d , b y E ste la B e e D agu m (S ta tistic s
C anada, C atalogu e N o . 1 2 -5 6 4 E , January
1 983).
A t th e b eg in n in g o f each calen d ar year,
h isto rica l se a so n a lly adjusted data u su a lly
are revised , and p rojected season al adjust
m ent factors are calcu lated for u se during
the Jan u ary-Ju n e period. T h e h istorical sea
so n a lly adjusted data u su a lly are rev ised for
o n ly the m o st recent 5 years. In July, n ew
season al adjustm ent factors, w h ich in corp o
rate the exp erien ce through June, are p ro
d u ced for the J u ly -D e c e m b e r p eriod , but n o
r e v isio n s are m ade in the h istorical data.
For additional information o n n a
tio n a l h o u se h o ld su rvey data, co n ta ct the
D iv is io n o f L ab or F o rce S ta tistics: ( 2 0 2 )
6 9 1 -6 3 7 8 .
Establishment survey data
Description of the series
Employment, hours, and
earnings data
in th is se c tio n are c o m p ile d from p ay ro ll
records reported m on th ly o n a volu n tary b a
sis to th e B u reau o f L abor S ta tistics and its
coop eratin g State a g en cies b y about 3 0 0 ,0 0 0
e sta b lish m e n ts rep resen tin g all in d u stries
ex cep t agriculture. In d u stries are c la s sifie d
in accord an ce w ith the 1 9 8 7 S ta n d a r d I n
d u s t r i a l C la s s if ic a tio n (SIC) M a n u a l. In m o st
in d u stries, th e sa m p lin g p r o b a b ilitie s are
b ased on the siz e o f th e estab lish m en t; m o st
large e sta b lish m e n ts are th e refo re in th e
sam p le. (A n esta b lish m en t is n o t n ecessa r
ily a firm; it m ay b e a branch p lan t, fo r e x
am p le, or w a r e h o u s e .) S e lf-e m p lo y e d p er
s o n s and o th e rs n o t o n a reg u la r c iv ilia n
p a y r o ll are o u ts id e th e s c o p e o f th e su r
v e y b e c a u se th e y are e x c lu d e d from e s ta b
lish m en t record s. T h is la rg ely a c c o u n ts for
th e d iffe r e n c e in e m p lo y m e n t fig u r e s b e
tw e e n th e h o u s e h o ld an d e s ta b lis h m e n t
su rv ey s.
Definitions
A n establishment is an eco n o m ic unit w h ich
p rod u ces g o o d s or se rv ices (su ch as a fa c
tory or store) at a sin g le location and is en
gaged in o n e typ e o f ec o n o m ic activity.
Employed persons are all p erso n s w h o
r e c e iv e d p a y ( in c lu d in g h o lid a y an d s ic k
p a y ) for a n y part o f th e p a y r o ll p e r io d in
c lu d in g th e 12th d ay o f th e m o n th . P er
s o n s h o ld in g m o re th an o n e j o b (a b o u t 5
p ercen t o f a ll p e r s o n s in th e lab or fo r c e )
are c o u n te d in e a c h e s ta b lis h m e n t w h ic h
rep o rts th em .
Production workers in m anu factu ring
in clu d e w o rk in g su p ervisors and n on su p erv iso r y w ork ers c lo s e ly a sso cia ted w ith p ro
d u c tio n o p e r a tio n s. T h o s e w o rk ers m e n
tio n ed in tab les 1 1 - 1 6 in clu d e p rod u ction
w ork ers in m anu factu ring and m in in g; c o n
s t r u c tio n w o r k e r s in c o n s t r u c t io n ; a n d
n on su p ervisory w orkers in the fo llo w in g in
dustries: transportation and p u b lic u tilities;
w h o le sa le and retail trade; fin a n ce , in su r
an ce, and real estate; and se rv ices. T h ese
grou p s a cco u n t for ab ou t fou r-fifth s o f the
to ta l e m p lo y m e n t on p riv a te n o n a g r ic u ltural p ayrolls.
Earnings are th e p aym en ts p rod u ction
or n o n su p erv iso ry w ork ers rec e iv e d uring
the su rvey p eriod , in clu d in g prem ium p ay
for o v ertim e or la te-sh ift w ork but ex c lu d -
Monthly Labor Review
July 2002
63
Current Labor Statistics
in g irreg u la r b o n u s e s an d o th e r s p e c ia l
p a y m e n ts . Real earnings are e a r n in g s
adjusted to reflect the e ffe c ts o f c h a n g es in
co n su m er p rices. T h e d eflator for th is series
is d eriv ed from the C on su m er P rice In d ex
fo r U rb a n W a g e E a r n e r s a n d C le r ic a l
W orkers (CPI-W).
Hours r e p r e s e n t th e a v e r a g e w e e k ly
hours o f production or n on su p ervisory w ork
ers for w h ich p ay w a s received , and are d if
feren t from standard or sc h e d u le d h ou rs.
Overtime hours represent the portion o f a v
erage w e e k ly hours w h ich w as in e x c e s s o f
regular h ours and for w h ich overtim e prem i
u m s w ere paid.
T h e Diffusion Index r e p r e s e n ts th e
p ercen t o f in d u stries in w h ich em p loym en t
w a s risin g o v er the in d icated p eriod , p lu s
o n e -h a lf o f the in d u stries w ith u n ch an ged
em p lo y m en t; 5 0 p ercen t in d icates an equal
b a la n ce b etw een in d u stries w ith in creasin g
and d ecreasin g em p loym en t. In lin e w ith B u
reau practice, data for the 1 -, 3-, and 6-m on th
sp an s are se a so n a lly adjusted, w h ile th o se
for th e 12-m on th span are u nadjusted. D ata
are cen tered w ith in the span. T able 17 pro
v id e s an in d ex on private n on farm e m p lo y
m en t b a sed on 3 5 6 in d ustries, and a m anu
fa ctu rin g in d e x b a sed on 1 3 9 in d u stries.
T h e se in d e x e s are u sefu l for m easu rin g the
d isp ersio n o f ec o n o m ic g a in s or lo s s e s and
are a lso e c o n o m ic in d icators.
Notes on the data
E sta b lish m en t su rv ey data are an n u ally ad
ju sted to co m p reh en siv e co u n ts o f em p lo y
m en t (c a lle d “b en ch m ark s”). T h e latest ad
ju stm en t, w h ich in corp orated M arch 2001
b en ch m arks, w a s m ade w ith the relea se o f
M a y 2 0 0 2 data, p u b lish ed in the July issu e
o f the R e v ie w . C o in cid en t w ith the b en ch
mark adjustm ent, h istorical se a so n a lly ad
ju s te d data w ere rev ised to reflect updated
season al factors. U nadjusted data from A pril
2 0 0 0 forw ard and se a so n a lly adjusted data
from January 1 9 9 7 forw ard w ere rev ised
w ith the relea se o f the M ay 2 0 0 2 data.
In ad d ition to the rou tine b enchm ark re
v is io n s and up d ated season al factors in tro
d u ced w ith the relea se o f the M ay 2 0 0 2 data,
the first estim ates for th e transportation and
p u b lic u tilities; retail trade; and fin a n ce, in
surance, and real estate in d ustries w ere p u b
lish ed from a n ew p rob ab ility-b ased sam p le
d esig n . T h ese in d ustries are the third group
to co n v e r t to a p r o b a b ility -b a se d sa m p le
un d er a 4 -y ea r p h a se-in plan o f a sam p le
r e d e sig n p ro ject. T h e c o m p le tio n o f the
p h a se-in for the red esign , in June 2 0 0 3 for
th e se r v ic e s industry, w ill c o in c id e w ith the
co n v ersio n o f n ation al estab lish m en t survey
se ries from in d ustry c o d in g b a sed on the
19 87 Standard Industrial C la ssific a tio n (SIC)
sy s te m to th e N o r th A m e r ic a n In d u stry
C la ssifica tio n S y stem (NAICS). For additional
64
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Federal Reserve Bank of St. Louis
in form ation , see the the June 2 0 0 2 issu e o f
E m p lo y m e n t a n d E a r n in g s .
R evision s in State data (table 11) occurred
w ith the publication o f January 2 0 0 2 data.
B e g in n in g in June 1996, the BLS u ses the
X-12-arima m eth o d o lo g y to se a so n a lly ad
ju s t estab lish m en t su rvey data. T h is p ro ce
dure, d ev e lo p e d b y the B u reau o f the C en
su s, co n tro ls for the effe ct o f varyin g sur
v e y in tervals (a lso k n o w n as th e 4 - versu s
5 -w e e k e ffe ct), thereb y p rovid in g im proved
m easurem ent o f over-the-m onth ch an ges and
u n d erlyin g e c o n o m ic trends. R e v is io n s o f
data, u su a lly for th e m o st recent 5-year p e
riod, are m ade o n c e a year co in cid en t w ith
the benchm ark rev isio n s.
In th e esta b lish m en t su rvey, e stim a te s
for the m o st recent 2 m on th s are b ased on
in co m p lete returns and are p u b lish ed as pre
lim inary in the tab les ( 12 - 1 7 in the R e v ie w ) .
W h en all returns h ave b een received , the e s
tim ates are rev ised and p u b lish ed as “fin a l”
(prior to an y b enchm ark r e v isio n s) in the
third m onth o f their appearance. T hus, D e
cem b er data are p u b lish ed as p relim in ary in
January and February and as fin al in M arch.
For the sam e reason s, quarterly esta b lish
m en t data (tab le 1) are prelim inary for the
first 2 m on th s o f p u b lication and fin al in the
third m onth. T h u s, fourth-quarter data are
p u b lis h e d as p relim in a ry in January and
F ebruary and as final in M arch.
For additional information on estab
lish m en t su rvey data, con tact the D iv isio n
o f C urrent E m p lo y m e n t S ta tistics: ( 2 0 2 )
6 9 1 -6 5 5 5 .
Unemployment data by
State
Description of the series
D ata p resen ted in th is se ctio n are ob tain ed
from the F o c a l A rea U n em p lo y m en t S ta tis
tic s (LAUS) program , w h ich is con d u cted in
co o p era tio n w ith S tate e m p lo y m en t se c u
rity a g en cies.
M o n th ly e stim a te s o f th e lab or fo rce,
em p loym en t, and u n em p loym en t for States
and su b -S tate areas are a k ey indicator o f lo
cal eco n o m ic co n d ition s, and form the b asis
for determ in in g the elig ib ility o f an area for
b en efits under Federal eco n o m ic assistan ce
program s su ch as the Job Training Partner
ship A ct. S eason ally adjusted u n em p loym en t
rates are p resen ted in table 10. Insofar as
p o ssib le , the con cep ts and d efin ition s under
lyin g these data are th ose u sed in the national
estim ates ob tain ed from the cps.
Notes on the data
D ata refer to State o f residence. M onthly data
for all States and the D istrict o f C olum bia are
d e r iv e d u s in g s ta n d a r d iz e d p r o c e d u r e s
July 2002
established b y bls. O nce a year, estim ates are
revised to n ew population controls, usually
w ith p u b lication o f January estim ates, and
benchm arked to annual average CPS levels.
For additional information on data in
this series, call (2 0 2 ) 6 9 1 - 6 3 9 2 (table 10) or
(2 0 2 ) 6 9 1 - 6 5 5 9 (table 11).
Covered employment and
wage data (ES-202)
Description of the series
E mployment, wage, and establishment data
in th is s e c tio n are d e r iv e d from th e q u ar
terly ta x rep o rts su b m itte d to S ta te e m
p lo y m e n t se c u r ity a g e n c ie s b y p riv a te and
S tate and lo ca l g o v ern m en t e m p lo y ers su b
j e c t to S ta te u n e m p lo y m e n t in su r a n c e (u i)
la w s and from F ed era l, a g e n c ie s su b je c t to
t h e U n e m p lo y m e n t C o m p e n s a t io n f o r
F ed era l E m p lo y e e s ( u c fe ) p rogra m . E a ch
quarter, S tate a g e n c ie s ed it and p r o c e s s the
d ata and se n d th e in fo r m a tio n to th e B u
reau o f F a b o r S ta tistic s .
T h e C o v e r e d E m p lo y m e n t an d W a g e s
d a ta , a ls o r e fe r r e d a s E S - 2 0 2 d a ta , are
th e m o s t c o m p le t e e n u m e r a tio n o f e m
p lo y m e n t an d w a g e in fo r m a t io n b y i n
d u str y at th e n a t io n a l, S ta te , m e t r o p o li
ta n a rea , an d c o u n t y l e v e l s . T h e y h a v e
b ro a d e c o n o m ic s ig n i f i c a n c e in e v a lu a t
in g la b o r m a rk et t r e n d s a n d m a jo r i n
d u str y d e v e lo p m e n t s .
Definitions
In g e n e r a l, es -2 0 2 m o n th ly e m p lo y m e n t
d a ta r e p r e s e n t th e n u m b e r o f covered
workers w h o w o rk ed d u rin g , or r e c e iv e d
p ay for, th e p ay p e r io d th at in c lu d e d th e
12th d ay o f th e m o n th . Covered private
industry employment in c lu d e s m o st c o r
p o ra te o f f ic ia ls , e x e c u t iv e s , su p e r v is o r y
p e r so n n e l, p r o fe ss io n a ls , c le r ic a l w o rk ers,
w a g e earners, p ie c e w ork ers, and p art-tim e
w o rk ers. It e x c lu d e s p ro p rieto rs, th e u n
in co rp o ra ted s e lf- e m p lo y e d , u n p a id fa m
ily m em b er s, and certain farm and d o m e s
tic w o rk ers. C ertain ty p e s o f n o n p r o fit
em p lo y ers, su ch as r e lig io u s o rg a n iza tio n s,
are g iv e n a c h o ic e o f co v e r a g e or e x c lu s io n
in a n u m b er o f S ta tes. W ork ers in th e se
o r g a n iz a tio n s are, th e r e fo r e , rep o rted to a
lim ite d d egree.
P e r s o n s o n p a id s ic k le a v e , p a id h o l i
d a y , p a id v a c a t io n , an d th e lik e , are i n
c lu d e d . P e r s o n s o n th e p a y r o ll o f m o r e
t h a n o n e f ir m d u r in g t h e p e r i o d a re
c o u n te d b y e a c h u i- s u b j e c t e m p lo y e r i f
t h e y m e e t th e e m p lo y m e n t d e f i n i t i o n
n o te d ea rlier. T h e e m p lo y m e n t c o u n t e x
c lu d e s w o rk ers w h o earn ed n o w a g es
p lo y e e s . F ed era l a g e n c ie s f o llo w s lig h t ly
d iffe r e n t criteria th an d o p riv a te e m p lo y
pay, w ith h o ld in g taxes, and retirem ent d e
d u r in g th e e n tir e a p p lic a b le p a y p e r io d
ers w h e n b rea k in g d o w n th e ir reports by
g en erally co v ers the sam e typ es o f se rv ices
installation. T hey are perm itted to com b ine as
as for w orkers in private industry.
Average annual wages per em p lo y ee for
b e c a u s e o f w o r k s t o p p a g e s , te m p o r a r y
la y o f f s , i l l n e s s , o r u n p a id v a c a t io n s .
Federal employment data are b a sed
o n r e p o r ts o f m o n th ly e m p lo y m e n t and
q u a rterly w a g e s su b m itte d e a ch quarter to
S ta te a g e n c ie s fo r a ll F ed era l in sta lla tio n s
w ith e m p lo y e e s c o v e r e d b y th e U n e m
p lo y m e n t C o m p e n s a t io n fo r F e d e r a l
a single statewide unit: 1) all installations with
10 or few er workers, and 2) all installations
d u ctio n s. F ed eral e m p lo y e e rem u n era tio n
any g iv en industry are com p u ted b y d iv id in g
that have a com bined total in the State o f few er
total annual w a g es b y annual average em p loy
than 50 workers. A lso , w hen there are few er
m ent. A further d iv isio n b y 52 y ield s average
than 25 workers in all secondary installations
in a State, the secondary installations m ay be
w eek ly w a g es per em p loyee. A nnual p ay data
com bined and reported w ith the m ajor instal
in d ivid u al m ay n ot b e em p lo y ed b y the sam e
em p loyer all year or m ay w ork for m ore than
o n e em p loyer at a tim e.
E m p l o y e e s ( ucfe) p r o g r a m , e x c e p t f o r
c e r ta in n a tio n a l s e c u r ity a g e n c ie s ,
w h ic h a re o m itte d fo r s e c u r ity rea so n s.
E m p lo y m e n t fo r a ll F ed era l a g e n c ie s for
lation. Last, if a Federal agency has few er than
fiv e em p lo y ees in a State, the a gen cy h ead
quarters o ffic e (region al o ffice, district o f
fice) servin g each State m ay co n so lid a te the
a n y g iv e n m o n th is b a sed o n th e n u m b er
o f p e r s o n s w h o w o rk ed d u rin g or r e c e iv e d
em p loym en t and w a g es data for that State
w ith the data reported to the State in w h ich
the headquarters is located . A s a result o f
on ly approxim ate annual earnings b eca u se an
Average weekly or annual pay is a f
fected by the ratio o f fu ll-tim e to part-tim e
w orkers as w ell as the num ber o f in d iv id u a ls
in h igh -p ayin g and lo w -p a y in g o ccu p a tio n s.
W h en average pay le v e ls b etw een States and
p a y fo r th e p a y p e r io d that in c lu d e d th e
12th o f th e m o n th .
th ese reporting rules, the num ber o f report
industries are com pared, th ese factors sh ou ld
A n establishment is an e c o n o m ic u n it,
su ch as a farm , m in e , factory, or sto re, that
in g units is alw ays larger than the num ber o f
e m p lo y e r s (or g o v ern m en t a g e n c ie s ) but
be taken into con sid eration . For exam p le, in
du stries characterized b y h igh p rop ortions
p r o d u c e s g o o d s or p r o v id e s s e r v ic e s . It is
sm aller than the num ber o f actual estab lish
o f part-tim e w orkers w ill sh o w average w a g e
t y p ic a lly at a s in g le p h y sic a l lo c a tio n and
e n g a g e d in o n e, or p red o m in a n tly o n e, typ e
o f e c o n o m ic a c tiv ity for w h ic h a s in g le in
m ents (or in stallation s).
D ata reported for the first quarter are tabula ted in to size c a t e g o r ie s r a n g in g from
le v e ls ap p reciab ly less than the w eek ly pay
le v e ls o f regular fu ll-tim e em p lo y ees in th ese
industries. T h e o p p o site e ffe ct ch aracterizes
d u stria l c la s s if ic a tio n m ay b e a p p lied . O c
c a s io n a lly , a s in g le p h y s ic a l lo c a tio n e n
w ork sites o f very sm all siz e to th ose w ith
1 ,0 0 0 em p lo y ees or m ore. T he siz e category
industries w ith lo w p rop ortions o f part-tim e
w orkers, or industries that ty p ic a lly sch e d
c o m p a s s e s tw o or m o re d istin c t and s i g
n ific a n t a c tiv itie s . E a ch a c tiv ity sh o u ld b e
is determ ined by the estab lish m en t’s M arch
em p lo y m en t le v e l. It is im portant to n ote
u le h eavy w eek en d and overtim e w ork. A ver
age w age data also m ay b e influenced b y w ork
r e p o r te d a s a se p a r a te e s ta b lis h m e n t i f
that each estab lish m en t o f a m u lti-estab lish
stop p ages, labor turnover rates, retroactive
sep a ra te r e c o r d s are k ep t and th e v a r io u s
m en t firm is tabulated separately into the
paym ents, season al factors, b on u s paym ents,
a c tiv itie s are c la s sifie d u nder d ifferen t fou r
appropriate siz e category. T he total em p lo y
m ent lev el o f the reporting m u lti-estab lish
and so on.
Notes on the data
d ig it sic c o d e s .
M o s t e m p lo y e r s h a v e o n ly o n e e s ta b
m en t firm is n ot u sed in the siz e tabulation.
lish m e n t; th u s , th e e s ta b lis h m e n t is th e
C overed em p loyers in m ost States report
p r e d o m in a n t re p o r tin g u n it or sta tistic a l
en tity for rep o rtin g e m p lo y m en t and w a g e s
total wages paid during the calendar quarter,
regard less o f w h en the se rv ices w ere per
form ed. A few State law s, h ow ever, sp ec ify
that w a g es be reported for, or based on the
period during w h ich services are perform ed
rather than the period during w h ich com p en
sation is paid. U nder m ost State law s or regu
lations, w ages include bonuses, stock options,
the cash valu e o f m eals and lod gin g, tips and
other gratuities, and, in som e States, em ployer
To insure the h igh est p o ssib le quality o f data,
State em p lo y m en t secu rity a g e n c ie s verify
w ith em p loyers and update, i f necessary, the
industry, location , and ow n ersh ip c la ssific a
tion o f all estab lish m en ts on a 3-year cy cle.
C h an ges in establishm ent classification co d es
resulting from the v erification p rocess are in
troduced w ith the data reported for the first
quarter o f the year. C h an ges resu ltin g from
im proved em p loyer reporting a lso are intro
p lo y e r s d o n o t f i le a M u ltip le W o rk site
contributions to certain deferred com p en sa
tion p lans such as 4 0 1 (k ) plans.
C overed em p loyer contributions for old -
d u ced in the first quarter. For th ese reasons,
so m e data, e sp e cia lly at m ore d etailed g e o
graphic lev els, m ay n o t b e strictly co m p a
R ep o rt. W h en th e to ta l e m p lo y m e n t in an
a g e, s u r v iv o r s, an d d is a b ility in su r a n c e
rable w ith earlier years.
e m p lo y e r ’s se c o n d a r y e s ta b lis h m e n ts (a ll
e s ta b lis h m e n ts o th e r than th e la r g e st) is
10 or fe w e r , th e e m p lo y e r g e n e r a lly w ill
( o a s d i ), health insurance, u n em p loym en t in
surance, w ork ers’ com p en sation, and private
T h e 199 9 cou n ty data u sed to calcu late
th e 1 9 9 9 - 2 0 0 0 ch a n g es w ere adjusted for
f ile a c o n s o lid a te d rep ort for all e s ta b lis h
m e n ts. A ls o , so m e e m p lo y e r s eith e r c a n
n o t or w ill n o t rep ort at th e e sta b lish m e n t
p en sion and w elfare fun d s are n ot reported
as w a g es. E m p lo y ee con trib u tion s for the
sam e pu rp oses, h o w ev er, as w e ll as m on ey
w ith h eld for in com e taxes, un ion d ues, and
ch an ges in industry and cou n ty classifica tio n
to m ake them com p arab le to data for 2 0 0 0 .
A s a result, the adjusted 1999 data d iffer to
so m e exten t from the data availab le on the
le v e l and thu s a ggregate esta b lish m en ts into
o n e c o n s o lid a te d u n it, or p o s s ib ly se v era l
u n its, th o u g h n o t at th e esta b lish m en t lev el.
F o r th e F e d e r a l G o v e r n m e n t, th e re
so forth, are reported even thou gh th ey are
ded u cted from the w ork er’s gross pay.
Wages of covered Federal workers rep
resent the gross am ount o f all payrolls for all
Internet at:
data. M o s t e m p lo y e r s, in c lu d in g S tate and
lo c a l g o v e r n m e n ts w h o o p era te m ore than
o n e e s ta b lis h m e n t in a S ta te, f ile a M u l
tip le W o rk site R ep o rt e a ch quarter, in a d
d itio n to th e ir q u a r te r ly u i rep o rt. T h e
M u ltip le W o rk site R ep o rt is u se d to c o l
le c t se p a r a te e m p lo y m e n t and w a g e d ata
fo r e a ch o f th e e m p lo y e r ’s e s ta b lish m e n ts ,
w h ic h are n o t d e ta ile d o n th e ui report.
S o m e v e r y sm a ll m u lti-e s ta b lis h m e n t e m
http://www.bls.gov/cew/home.htm.
C ou n ty d efin itio n s are a ssig n ed acco rd
in g to F ederal Inform ation P ro cessin g Stan
installation: a s in g le
pay p eriod s en d in g w ith in the quarter. T h is
dards P u b lication s as issu ed b y the N a tio n a l
lo c a tio n at w h ic h a d ep artm en t, a g en cy , or
in clu d es cash allow an ces, the cash eq u iva
Institute o f Standards and T ech n ology . A reas
o th e r g o v e r n m e n t b o d y h a s c iv ilia n e m
lent o f any type o f rem uneration, severan ce
sh ow n as cou n ties in clu d e th ose d esign ated
p o r tin g u n it is th e
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Monthly Labor Review
July 2002
65
Current Labor Statistics
as in d ep en d en t cities in som e ju risd iction s
and, in A la sk a , th o se areas d esign ated by the
C en su s Bureau w here cou n ties h ave n ot been
created. C o u n ty data a lso are presen ted for
the N e w E n glan d States for com parative pur
p o ses, ev en th o u g h tow n sh ip s are the m ore
co m m o n d esig n a tio n u sed in N e w E ngland
(and N e w Jersey).
F or additional inform ation on the c o v
ered em p lo y m en t and w a g e data, con tact the
D iv isio n o f A d m in istrative S tatistics and Labor T urnover at (2 0 2 ) 6 9 1 - 6 5 6 7 .________
Compensation and
Wage Data
(T ab les 1 -3 ; 2 5 - 3 1 )
Compensation and wage data are gathered
b y the B ureau from b u sin ess estab lish m en ts,
State and loca l govern m en ts, labor u n ions,
c o lle c tiv e bargaining agreem ents on file w ith
the B ureau, and secon dary sou rces.
Employment Cost Index
Description of the series
T he Employment Cost Index (ECI) is a quar
terly m easure o f the rate o f change in com
p en sa tio n p er h o u r w o r k e d and in c lu d e s
w a g e s, sa laries, and em p lo y er c o s ts o f em
p l o y e e b e n e f it s . It u s e s a f ix e d m ark et
basket o f labor— sim ilar in concept to the Con
sum er Price In d ex ’s fixed m arket basket o f
g o o d s and services— -to m easure change over
tim e in em p loyer co sts o f em p loyin g labor.
S tatistical series on total com p en sation
costs, on w a g es and salaries, and on benefit
co sts are available for private nonfarm w ork
ers ex clu d in g proprietors, the self-em ployed ,
and h ou seh old workers. The total com p en sa
tion co sts and w a g es and salaries series are
also available for State and local governm ent
w orkers and for the civilian nonfarm econom y,
w h ich co n sists o f private industry and State
and local governm ent workers com bined. F ed
eral w orkers are excluded.
T he E m p loym ent C ost Index probability
sam ple con sists o f about 4 ,4 0 0 private n on
farm establishm ents providing about 2 3 ,0 0 0
occupational observations and 1,000 State and
lo ca l g overn m en t estab lish m en ts p rovid in g
6 ,0 0 0 occupational observations selected to
represent total em ploym ent in each sector. On
average, each reporting unit provides w age and
com pensation inform ation on five w ell-sp eci
fied occupations. D ata are collected each quar
ter for the pay period including the 12th day
o f M arch, June, Septem ber, and D ecem ber.
66
Monthly Labor Review
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Federal Reserve Bank of St. Louis
B eg in n in g w ith June 1986 data, fixed em
p lo y m e n t w e ig h ts from the 1 9 8 0 C en su s o f
P o p u l a t i o n a re u s e d e a c h q u a r te r to
calcu late the civ ilia n and private in d ex es and
the in d ex for State and lo ca l g overn m en ts.
( P r io r t o J u n e 1 9 8 6 , t h e e m p lo y m e n t
w e ig h ts are from the 1 9 7 0 C en su s o f P o p u
la tio n .) T h e se fix e d w e ig h ts, a lso u sed to
d eriv e all o f th e in d ustry and o ccu p a tio n
series in d ex es, en su re that ch a n g es in th e se
in d e x e s reflect o n ly ch a n g es in c o m p en sa
tion , n ot em p lo y m en t sh ifts am on g in d u s
tries or o ccu p a tio n s w ith d ifferen t le v e ls o f
w a g es and com p en sation . For the b argaining
status, reg io n , and m etrop olitan /n on -m etrop o lita n area se ries, h o w ev er, em p lo y m en t
d ata b y in d u stry and o c c u p a tio n are n o t
a v a ila b le from the cen su s. Instead, th e 198 0
e m p lo y m en t w e ig h ts are reallocated w ith in
th e se series each quarter b ased on the cur
rent sam p le. Therefore, th e se in d ex es are n ot
strictly com p arab le to th o se for th e aggre
gate, industry, and o ccu p a tio n series.
Definitions
Total compensation c o sts in clu d e w a g es,
salaries, and the em p lo y e r ’s c o s ts for em
p lo y e e b en efits.
Wages and salaries co n sist o f earnings
b efore p ayroll d ed u ction s, in clu d in g p roduc
tion b o n u ses, in cen tiv e earn in gs, c o m m is
sio n s, and c o st-o f-liv in g adjustm ents.
Benefits in clu d e the co st to em p loyers
for p aid le a v e , su p p lem en tal p ay (in c lu d
in g nonproduction b onuses), insurance, retire
m ent and savin gs plans, and legally required
benefits (such as Social Security, workers’ com
pensation, and unem ploym ent insurance).
E xcluded from w ages and salaries and em
p lo y ee ben efits are such item s as paym ent-in
kind, free room and board, and tips.
Notes on the data
T h e E m p loym ent C ost In d ex for ch an ges in
w a g e s and salaries in the private nonfarm
eco n o m y w as p u b lish ed b egin n in g in 1975.
C h an ges in total com p en sation co st— w a g es
and salaries and b en efits com b in ed — w ere
p u b lish ed b eg in n in g in 1980. T h e series o f
ch an ges in w a g es and salaries and for total
com p en sation in the State and local govern
m en t s e c to r and in th e c iv ilia n n o n fa rm
econ om y (exclu d in g Federal em p loyees) were
p u b lish ed b egin n in g in 1981. H istorical in
d ex es (June 1 9 8 1 = 1 0 0 ) are availab le on the
Internet:
http://www.bls.gov/ect/
For additional information on th e
E m p lo y m en t C o st In d ex, con tact the O ffice
o f C om p en sation L e v e ls and Trends: (2 0 2 )
July 2002
6 9 1 -6 1 9 9 .
Employee Benefits Survey
Description of the series
Employee benefits data are o b ta in ed from
the E m p lo y e e B e n e fits S u rvey, an annual
su rvey o f the in c id e n c e and p r o v isio n s o f
se le c te d b e n e fits p r o v id ed b y em p lo y e r s.
T h e su rvey c o lle c ts data from a sa m p le o f
a p p r o x im a te ly 9 ,0 0 0 p r iv a te s e c to r a n d
State and lo ca l go v ern m en t esta b lish m en ts.
T he data are presented as a p ercentage o f em
p lo y ees w h o participate in a certain b enefit, or
as an average benefit p rovision (for exam ple,
the average num ber o f paid h olid ays provided
to em p loyees per year). S elected data from the
survey are presented in table 25 for m edium
and large private establishm ents and in table
2 6 for sm all private establishm ents and State
and local governm ent.
T h e su r v e y c o v e r s p aid le a v e b e n e fits
su ch as h o lid ays and v acation s, and personal,
fun eral, ju ry duty, m ilitary, fam ily, and sic k
leave; short-term d isab ility, lo n g -term d is
ab ility, and life insurance; m ed ica l, den tal,
and v is io n care plans; d efin e d b en efit and
d efin ed con trib u tion plans; fle x ib le b en efits
plans; reim b u rsem en t accou n ts; and un p aid
fa m ily lea v e.
A l s o , d a ta are ta b u la te d o n th e i n c i
d e n c e o f se v e r a l o th e r b e n e f its , su c h a s
se v era n ce pay, ch ild -ca re a ssista n ce, w e ll
n e s s p ro g ra m s, an d e m p lo y e e a s s is ta n c e
program s.
Definitions
Employer-provided benefits are b e n e fits
that are fin an ced either w h o lly or partly by
the em ployer. T h ey m ay b e sp o n so red b y a
u n ion or other third party, as lo n g as there is
so m e em p lo y er fin an cin g. H o w ev er, so m e
b en efits that are fu lly paid for b y the em
p lo y e e a lso are included. For exam p le, lo n g
term care insurance and postretirem en t life
insu ran ce p aid en tirely b y the e m p lo y e e are
in clu d ed b eca u se the guarantee o f insu rab il
ity and a vailab ility at group prem ium rates
are co n sid ered a b en efit.
Participants are workers w h o are covered
by a benefit, whether or not they use that benefit.
I f th e b e n e fit p lan is fin a n ce d w h o lly b y
em ployers and requires em p loyees to com plete
a m inim um length o f service for eligibility, the
workers are considered participants w hether or
not they h ave m et the requirem ent. I f w orkers
are required to contribute tow ards the co st o f
a plan, they are co n sid ered participants o n ly
i f they elect the plan and agree to m ake the
required contributions.
Defined benefit pension plans u se pre-
determ ined form ulas to calculate a retirement
b en efit ( i f any), and obligate the em ployer to
provide th o se benefits. B en efits are generally
based on salary, years o f service, or both.
Defined contribution plans g en era lly
s p e c ify the le v e l o f em p lo y er and e m p lo y e e
co n trib u tio n s to a plan, but n ot the form u la
for d eterm in in g ev en tu al b en efits. Instead,
in d iv id u a l a cco u n ts are set up for p artici
p an ts, and b e n e fits are b a sed o n am oun ts
cred ited to th e se acco u n ts.
Tax-deferred savings plans are a type o f
d e fin e d co n trib u tio n plan that a llo w par
ticipants to contribute a portion o f their sal
ary to an em ployer-sponsored plan and defer
in com e taxes until withdrawal.
Flexible benefit plans a llow em p loyees
to c h o o s e a m o n g sev eral b en efits, su ch as
life in su ran ce, m ed ica l care, and v acation
d ays, and a m o n g sev era l le v e ls o f co v era g e
w ith in a g iv e n b en efit.
Notes on the data
S u rv ey s o f e m p lo y e e s in m ed iu m and large
esta b lish m en ts co n d u cted o v er the 1 9 7 9 - 8 6
p e r io d in c l u d e d e s t a b l i s h m e n t s t h a t
e m p lo y ed at lea st 5 0 , 100, or 2 5 0 w ork ers,
d e p e n d in g on th e in d u stry (m o s t se r v ic e
in d u s t r ie s w e r e e x c l u d e d ) . T h e s u r v e y
co n d u cte d in 1 9 8 7 co v ered o n ly State and
l o c a l g o v e r n m e n t s w it h 5 0 o r m o r e
e m p lo y e e s. T h e su rv eys co n d u cte d in 198 8
a n d 1 9 8 9 i n c l u d e d m e d iu m an d la r g e
esta b lish m en ts w ith 1 00 w ork ers or m ore in
p riv a te in d u stries. A ll su r v e y s c o n d u c te d
o v e r t h e 1 9 7 9 - 8 9 p e r io d e x c l u d e d
esta b lish m en ts in A la sk a and H aw aii, as w e ll
as p art-tim e e m p lo y e e s.
B eg in n in g in 1990, surveys o f State and
lo c a l g o v e r n m e n t s an d s m a ll p r iv a te
e s ta b lis h m e n ts w e r e c o n d u c te d in e v e n num bered years, and surveys o f m edium and
large establishm ents w ere conducted in oddn u m b ered years. T h e sm all esta b lish m en t
s u r v e y i n c lu d e s a ll p r iv a te n o n fa r m
establishm ents w ith few er than 100 workers,
w h ile the State and local governm ent survey
in clu d es all g o vern m en ts, regardless o f the
num ber o f workers. A ll three surveys include
lu ll- and part-time w orkers, and workers in all
5 0 States and the District o f Colum bia.
For additional information on th e
E m p lo y e e B e n e fits S u rvey, con tact the O f
fic e o f C o m p en sa tio n L e v e ls and T rends on
the Internet: http://www.bls.gov/ebs/
Work stoppages
Description of the series
D ata on w ork sto p p a ges m easure the nu m
ber and duration o f m ajor strikes or lock ou ts
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
(in v o lv in g 1,000 w orkers or m ore) occurring
during the m onth (or year), the num ber o f
w orkers in volved , and the am ount o f w ork
tim e lo st b ecau se o f stop p age. T h ese data are
p resented in table 27.
D ata are largely from a variety o f p u b
lish ed sou rces and cover o n ly estab lish m en ts
d irectly in v o lv e d in a sto p p a g e. T h ey do
n ot m easu re the in d irect or secon d ary effe ct
o f stop p ages on other esta b lish m en ts w h o se
e m p lo y e e s are id le o w in g to m aterial short
a g es or lack o f service.
Definitions
Number o f stoppages:
T h e n u m b er o f
strik es and lo c k o u ts in v o lv in g 1 ,0 0 0 w o rk
ers or m ore and la stin g a fu ll sh ift or lon ger.
W orkers involved: T h e n u m b e r o f
w orkers d irectly in v o lv ed in the stop p age.
Number of days idle: T h e aggregate
n u m b er o f w o rk d a y s lo st by w ork ers in
v o lv ed in the stop p ages.
Days of idleness as a percent of estimated
working time: A ggregate w orkdays lost as a
percent o f the aggregate number o f standard
w ork d ays in the p eriod m u ltip lied by total
em ploym ent in the period.
Notes on the data
T h is series is n ot com parable w ith the on e
term inated in 1981 that covered strikes in
v o lv in g six w orkers or m ore.
For additional information on w ork
sto p p a g es data, con tact the O ffic e o f C o m
p en sation and W orking C on d ition s: (2 0 2 )
6 9 1 - 6 2 8 2 , or the Internet:
http :/www.bIs.gov/cba/
Price Data
(T ables 2; 3 2 ^ 1 2 )
Price
data are g a th e r e d b y th e B u re a u
o f L a b o r S ta t is t ic s fro m r e ta il an d p r i
mary markets in the U nited States. Price in
d exes are given in relation to a base period—
1982 = 100 for m any Producer Price Indexes,
1 9 8 2 -8 4 = 100 for m any C onsum er Price In
d exes (un less otherw ise noted), and 1990 =
100 for International Price Indexes.
Consumer Price indexes
Description of the series
T h e Consumer Price Index (CPI) is a m ea
sure o f th e average ch an ge in the p rices paid
b y urban co n su m ers for a fix e d m arket b a s
k et o f g o o d s and serv ices. T h e cpi is c a lc u
lated m o n th ly for tw o p o p u la tio n grou p s,
o n e c o n s is tin g o n ly o f urban h o u se h o ld s
w h o s e prim ary sou rce o f in co m e is d eriv ed
from the em p lo y m en t o f w a g e earners and
clerical w ork ers, and the other c o n sis tin g o f
all urban h o u seh o ld s. T h e w a g e earner in d ex
(CPI-W) is a con tin u ation o f the h isto ric in
d ex that w a s in trod u ced w e ll o v er a h a lfcentury ago for u se in w a g e n eg o tia tio n s. A s
n e w u se s w ere d ev e lo p e d for th e cpi in re
cen t years, th e n eed for a broader and m ore
rep resen tative in d ex b eca m e apparent. T he
all-urban con su m er in d ex (CPI-U), introduced
in 1 9 7 8 , is rep resen tative o f the 1 9 9 3 - 9 5
b u y in g h a b its o f ab ou t 8 7 p ercen t o f the
n o n in stitu tio n a l p o p u la tio n o f the U n ite d
S tates at that tim e, com p ared w ith 3 2 per
cen t rep resen ted in th e cpi-w. In ad d ition to
w a g e earners and clerica l w ork ers, the cpi-u
co v ers p ro fessio n a l, m anagerial, and tech n i
cal w ork ers, th e se lf-e m p lo y e d , short-term
w ork ers, the u n em p lo y ed , retirees, and o th
ers n ot in th e labor force.
T he CPI is b ased on p rices o f fo o d , clo th
ing, shelter, fuel, drugs, transportation fares,
d o cto rs’ and d en tists’ fe e s, and other g o o d s
and se rv ices that p e o p le b u y for d ay -to -d a y
liv in g . T h e q u a n tity and q u a lity o f th e se
item s are kept essen tially u n changed b etw een
m ajor rev isio n s so that o n ly p rice ch a n g es
w ill b e m easured. A ll ta x es d irectly a ss o c i
ated w ith the purchase and u se o f item s are
in clu d ed in the index.
D ata c o llected from m ore than 2 3 ,0 0 0 re
tail estab lish m en ts and 5 ,8 0 0 h o u sin g u nits
in 87 urban areas across the country are used
to d ev elo p the “U .S . city average.” Separate
estim ates for 14 m ajor urban centers are pre
sen ted in tab le 3 3 . T h e areas listed are as
ind icated in fo o tn o te 1 to the table. T he area
in d ex es m easure o n ly the average ch a n g e in
p rices for each area sin ce the b ase period, and
d o n ot in d ica te d iffe r e n c e s in the le v e l o f
p rices am on g cities.
Notes on the data
In January 1 9 8 3 , th e B u reau ch a n g ed the
w a y in w h ic h h o m e o w n e r s h ip c o s t s are
m eaured for the cpi-u . A rental e q u iv a len ce
m eth od rep la ced th e a sset-p rice ap p roach
to h o m eo w n ersh ip c o sts for that series. In
January 19 8 5 , the sam e ch a n g e w a s m ade in
the CPI-W. T h e central p u rp ose o f th e ch a n g e
w a s to separate sh elter c o s ts from th e in
v estm en t co m p o n e n t o f h o m e -o w n ersh ip so
that th e in d ex w o u ld reflect o n ly th e co st o f
sh elter s e r v ic e s p ro v id ed b y o w n e r -o c c u
p ied h o m es. A n up d ated CPI-U and cpi-w
w ere in trodu ced w ith release o f th e January
198 7 and January 199 8 data.
For additional information o n c o n
su m er p r ic e s, co n ta c t th e D iv is io n o f C o n
su m e r P r ic e s an d P r ic e I n d e x e s : ( 2 0 2 )
6 9 1 -7 0 0 0 .
Monthly Labor Review
July 2002
67
Current Labor Statistics
Producer Price Indexes
Description of the series
Producer Price Indexes (PPI) m easu re av
erage ch a n g es in p rices received b y d om estic
p rod u cers o f co m m o d itie s in all sta g es o f
p ro cessin g . T h e sa m p le u sed for ca lcu la tin g
th ese in d ex es currently con tain s about 3 ,2 0 0
co m m o d itie s and ab ou t 8 0 ,0 0 0 q u otation s
per m on th , se le c te d to represent the m o v e
m en t o f p rices o f all c o m m o d ities p rod u ced
in th e m a n u fa ctu rin g ; agricu ltu re, forestry,
and fish in g ; m in in g ; and g a s and e lectricity
and p u b lic u tilit ie s se c to r s. T h e s ta g e -o fp r o c e s s i n g s t r u c t u r e o f ppi o r g a n i z e s
p r o d u c ts b y c la s s o f b u y er and d e g r e e o f
fa b r ic a tio n (th a t is, fin is h e d g o o d s , in ter
m e d ia te g o o d s , and cru d e m a te r ia ls). T h e
tr a d itio n a l c o m m o d ity stru ctu re o f ppi or
g a n iz e s p r o d u c ts b y sim ila r ity o f en d u se
or m a teria l c o m p o s itio n . T h e in d u stry and
p ro d u ct stru ctu re o f ppi o r g a n iz e s d ata in
a c c o r d a n c e w ith th e S tan d ard In d u stria l
C la s s ific a tio n (SIC) and th e p ro d u ct c o d e
e x te n sio n o f the s ic d e v e lo p e d by the U .S .
B u reau o f th e C en su s.
To th e e x te n t p o s s ib le , p r ic e s u se d in
c a lc u la tin g P ro d u c er P r ic e In d e x e s ap p ly
to th e first sig n ific a n t co m m e r c ia l tra n sa c
tio n in th e U n ite d S ta te s from th e p r o d u c
tio n or cen tra l m a rk etin g p o in t. P r ic e d ata
are g e n e r a lly c o lle c t e d m o n th ly , p rim arily
b y m a il q u e stio n n a ir e . M o s t p r ic e s are o b
ta in ed d irectly from p rod u cin g co m p a n ie s
on a v olu n tary and co n fid en tia l b asis. P rices
g e n e r a lly are rep o rted for th e T u e s d a y o f
th e w e e k c o n ta in in g th e 13th d ay o f th e
m o n th .
S in ce January 1992, price changes for the
v a rio u s c o m m o d itie s h a v e b e e n a veraged
t o g e th e r w ith im p lic it q u a n tity w e ig h t s
representing their im portance in the total net
sellin g valu e o f all com m od ities as o f 1987.
T he d eta iled data are aggregated to obtain
in d e x e s for sta g e-o f-p ro cessin g grou p in gs,
com m o d ity groupings, durability-of-product
groupings, and a num ber o f special com p osite
groups. A ll P roducer P rice In d ex d ata are
su b ject to rev isio n 4 m onth s after origin al
p u b lica tio n .
For additional information o n p ro
d u cer p r ic e s , c o n ta c t th e D iv is io n o f In
d u stria l P r ic e s an d P r ic e In d e x e s: ( 2 0 2 )
6 9 1 -7 7 0 5 .
International Price Indexes
Description of the series
T h e International Price Program produces
m o n th ly and quarterly ex p o rt and im port
68
Monthly Labor Review
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Federal Reserve Bank of St. Louis
p rice in d ex es for nonm ilitary g o o d s and ser
v ic e s traded b etw een the U n ited States and
the rest o f the w orld. T he export price in d ex
p rovid es a m easure o f p rice ch an ge for all
p rod u cts so ld by U .S . resid en ts to foreign
buyers. (“R esid e n ts” is d efin ed as in the na
tional in com e accounts; it in clu d es corpora
tion s, b u sin esses, and in d ivid u als, but d oes
n o t req u ire th e o r g a n iz a tio n s to b e U .S .
ow n ed nor the in d ivid u als to h ave U .S . citi
zen sh ip .) T he im port price in d ex p rovid es a
m easure o f p rice ch an ge for g o o d s purchased
from other cou n tries b y U .S . residents.
T h e product u n iverse for both the im port
and exp ort in d ex es in clu d es raw m aterials,
agricultural products, sem ifin ish ed m anufac
tures, and fin ish ed m anufactures, in clu d in g
both capital and con su m er g o o d s. P rice data
for these item s are collected prim arily by m ail
questionnaire. In nearly all cases, the data are
c o llected d irectly from the exporter or im
porter, althou gh in a fe w cases, p rices are
obtained from other sources.
To the extent p ossib le, 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 im ports. For nearly all products,
the prices refer to transactions com pleted dur
in g the first w eek o f the m onth. Survey re
spondents are asked to indicate all discounts,
allow an ces, and rebates applicable to the re
ported prices, so that the price u sed in the
calculation o f the indexes is the actual price for
w h ich the product w as bought or sold.
In addition to general in d exes o f prices for
U .S . exports and im ports, in d ex es are also
p u b lish ed for detailed product categories o f
exp orts and im ports. T h ese ca teg o ries are
d efin ed accord in g to the fiv e -d ig it lev el o f
detail for the B ureau o f E c o n o m ic A n a ly sis
E n d -u se C lassification , the th ree-d igit lev el
for th e Standard In d u strial C la ss ific a tio n
(SiTC), and the four-d igit level o f detail for the
H a r m o n iz e d S y s te m . A g g r e g a t e im p o r t
in d ex es by coun-try or region o f origin are
also available.
bls publishes indexes for selected catego
ries of internationally traded services, calcu
lated on an international basis and on a balance-of-payments basis.
Notes on the data
T h e exp ort and im port p rice in d e x e s are
w eig h ted in d ex es o f the L asp eyres type. The
trade w eigh ts currently u sed to com pute both
in d ex es relate to 2 0 0 0 .
B ecau se a price index d epends on the sam e
item s b ein g priced from period to period, it is
n e c e ssa r y to r e c o g n iz e w h en a p r o d u c t’s
sp ec ifica tio n s or term s o f transaction have
been m odified. For this reason, the B ureau’s
July 2002
questionnaire requests detailed descriptions o f
the p h ysical and functional characteristics o f
the products b ein g priced, as w ell as informa
tion on the num ber o f units bought or sold,
d iscou n ts, credit term s, packaging, cla ss o f
buyer or seller, and so forth. W hen there are
ch an ges in either the sp ecification s or term s o f
transaction o f a product, the dollar v alu e o f
each ch an ge is d eleted from the total price
change to obtain the “pure” change. O nce this
value is determ ined, a linking procedure is em
p loyed w h ich a llow s for the con tinu ed repric
ing o f the item.
For additional information on inter
n ational prices, con tact the D iv isio n o f Inter
national Prices: (2 0 2 ) 6 9 1 - 7 1 5 5 .
Productivity Data
(T ables 2; 4 3 - 4 6 )
Business sector and major
sectors
Description of the series
T h e p rod u ctivity m easu res relate real output
to real input. A s such, th ey en com p ass a fam
ily o f m easu res w h ich in clu d e sin g le-fa cto r
input m easures, su ch as output per hour, ou t
put per un it o f lab or input, or ou tp u t per
unit o f capital input, as w e ll as m easu res o f
m ultifactor p rod u ctivity (output per un it o f
com b in ed labor and capital inputs). T he B u
reau in d ex es sh o w the ch an ge in output rela
tiv e to ch a n g es in the variou s inputs. T h e
m easu res cover the b u sin ess, nonfarm b u si
n ess, m anufacturing, and n on fin an cial corp o
rate sectors.
C orresponding in d exes o f hourly co m p en
sation, unit labor costs, unit n on labor p ay
m ents, and p rices are a lso provided.
Definitions
Output per hour of all persons (la b o r p ro
d u ctiv ity ) is th e quantity o f g o o d s and ser
v ic e s p roduced per hour o f labor input. Out
put per unit of capital services (ca p ita l
p rod u ctivity) is the qu an tity o f g o o d s and
se r v ic e s p rod u ced per u n it o f cap ital ser
v ic e s input. Multifactor productivity is the
quantity o f g o o d s and services produced per
com bined inputs. For private b u sin ess and pri
vate nonfarm b usiness, inputs include labor
and capital units. For manufacturing, inputs
include labor, capital, energy, non-energy m a
terials, and purchased b u sin ess ser-vices.
Compensation per hour is total c o m
p en sation d iv id ed b y h ou rs at w ork. Total
co m p en sa tio n eq u a ls the w a g e s and salaries
o f e m p lo y e e s p lu s e m p lo y e r s’ con trib u tion s
for socia l insurance and private b en efit plans,
p lu s an estim a te o f th e se p aym en ts for the
se lf-e m p lo y e d (e x c e p t for n o n fin a n cia l cor
p o r a tio n s in w h ic h th ere are n o s e lf- e m
p lo y e d ). Real compensation per hour is
c o m p e n s a t io n p er h o u r d e fla te d b y th e
ch a n g e in the C o n su m er P rice In d ex for A ll
U rban C on su m ers.
Unit labor costs are the labor co m p en
sa tio n c o s ts e x p en d ed in the p rod u ction o f a
un it o f ou tp u t and are d erived by d iv id in g
c o m p e n sa tio n b y o u tp u t. Unit nonlabor
payments in c lu d e p r o fits , d e p r e c ia tio n ,
in terest, and in d irect ta x es per u n it o f ou t
put. T h ey are com puted by subtracting com
pensation o f all p ersons from current-dollar
valu e o f output and d ivid in g by output.
Unit nonlabor costs c o n ta in a ll th e
c o m p o n e n t s o f u n it n o n la b o r p a y m e n ts
e x c e p t u n it p ro fits.
Unit profits in c lu d e c o rp o ra te p r o fits
w ith in v e n to r y v a lu a tio n and ca p ita l c o n
su m p tio n a d ju stm e n ts p er u n it o f ou tp u t.
Hours o f all persons are t h e to ta l
h o u r s at w o r k o f p a y r o ll w o r k e r s , s e lf e m p l o y e d p e r s o n s , a n d u n p a id f a m ily
w o rk ers.
Labor inputs are h o u rs o f a ll p e r s o n s
a d ju ste d fo r th e e f fe c ts o f c h a n g e s in th e
ed u ca tio n and e x p erien ce o f the labor force.
Capital services are th e f lo w o f se r
v ic e s from th e ca p ita l s to c k u se d in p ro
d u c tio n . It is d e v e lo p e d from m e a su res o f
th e n e t s to c k o f p h y s ic a l a s s e ts — e q u ip
m en t, stru ctu res, la n d , and in v e n to r ie s —
w e ig h te d b y ren ta l p r ic e s fo r e a ch ty p e o f
a sset.
Combined units of labor and capital
inputs are d e r iv e d b y c o m b in in g c h a n g e s
in la b o r an d c a p ita l in p u t w ith w e ig h t s
w h ic h rep resen t e a ch c o m p o n e n t ’s sh are
o f to ta l c o s t. C o m b in e d u n its o f lab or,
c a p ita l, en erg y , m a ter ia ls, an d p u rch a sed
b u s in e s s s e r v ic e s are s im ila r ly d e r iv e d b y
c o m b in in g c h a n g e s in e a c h in p u t w ith
w e ig h t s th a t rep resen t e a ch in p u t’s sh are
o f to ta l c o s ts . T h e in d e x e s for ea ch in p u t
a n d fo r c o m b i n e d u n it s are b a s e d o n
c h a n g in g w e ig h ts w h ic h are a v era g es o f the
sh a re s in th e cu rren t and p r e c e d in g year
(th e T o r n q u ist in d e x -n u m b e r fo rm u la ).
Notes on the d ata
B u s i n e s s s e c to r o u tp u t is an a n n u a lly w e ig h te d in d e x c o n str u c te d b y e x c lu d in g
from real g r o s s d o m e stic p ro d u ct ( g d p ) the
f o l lo w in g o u tp u ts: g e n e r a l g o v e r n m e n t,
n o n p r o fit in stitu tio n s, p a id e m p lo y e e s o f
p riv a te h o u s e h o ld s , and th e ren tal v a lu e
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Federal Reserve Bank of St. Louis
o f o w n e r -o c c u p ie d d w e llin g s . N o n fa r m
b u s in e s s a lso e x c lu d e s farm in g. P riv a te
b u s in e s s an d p r iv a te n o n fa r m b u s in e s s
fu rth er e x c lu d e g o v e r n m e n t e n te r p r ise s .
T h e m ea su res are su p p lie d b y th e U .S . D e
p artm en t o f C o m m e r c e ’s B u reau o f E c o
n o m ic A n a ly sis . A n n u al estim a tes o f m an u
fa ctu rin g se c to r a l o u tp u t are p r o d u c e d by
th e B u reau o f L ab or S ta tistic s . Q u arterly
m a n u fa c tu r in g o u tp u t in d e x e s fro m th e
F ederal R eserv e B oard are adjusted to th ese
an n u al o u tp u t m ea su r e s b y th e bls . C o m
p e n sa tio n d ata are d e v e lo p e d from d ata o f
th e B u rea u o f E c o n o m ic A n a ly s is an d th e
B u re a u o f L ab or S ta tis tic s . H o u rs d ata
are d e v e lo p e d from d ata o f th e B u rea u o f
L ab or S ta tistic s .
T h e p r o d u c tiv ity an d a s s o c ia te d c o s t
m ea su r e s in ta b le s 4 3 - 4 6 d e sc r ib e th e re
la tio n s h ip b e tw e e n o u tp u t in real term s
and th e lab or and ca p ita l in p u ts in v o lv e d
in its p r o d u c tio n . T h e y s h o w th e c h a n g e s
from p e r io d to p e r io d in th e a m o u n t o f
g o o d s and se r v ic e s p ro d u ced p er u n it o f
m ea su r e s d iffe r in m e th o d o lo g y and d ata
s o u r c e s fro m th e p r o d u c tiv ity m e a su r e s
fo r th e m ajor se c to r s b e c a u s e th e in d u stry
m ea su r e s are d e v e lo p e d in d e p e n d e n tly o f
th e N a tio n a l In co m e and P rod u ct A c c o u n ts
f r a m e w o r k u s e d fo r t h e m a jo r s e c t o r
in p u t.
A lth o u g h th e s e m ea su r e s rela te ou tp u t
to h o u rs and ca p ita l s e r v ic e s , th e y d o n o t
m ea su re th e c o n tr ib u tio n s o f lab or, c a p i
ta l, or a n y oth er s p e c ific fa cto r o f p r o d u c
tio n . R ath er, th e y r e fle c t th e j o in t e f fe c t
o f m an y in flu e n c e s , in c lu d in g c h a n g e s in
te c h n o lo g y ; sh ifts in th e c o m p o s itio n o f
th e la b o r fo rce; ca p ita l in v e stm e n t; le v e l
o f o u tp u t; c h a n g e s in th e u tiliz a t io n o f
c a p a c ity , en e r g y , m a ter ia l, an d resea rch
and d e v e lo p m e n t; th e o r g a n iz a tio n o f p ro
d u ctio n ; m a n agerial sk ill; and ch a ra cteris
industry.
t ic s and e ffo r ts o f th e w o rk fo r c e .
FOR ADDITIONAL INFORMATION On th is
p r o d u c tiv ity s e r ie s, c o n ta c t th e D iv is io n
o f P r o d u c t iv it y R e s e a r c h : ( 2 0 2 ) 6 9 1 —
5606.
Industry p ro d uc tivity
m e a sure s
Description of the series
T h e BLS i n d u s t r y p r o d u c t i v i t y d a ta
su p p le m e n t th e m ea su r e s fo r th e b u s in e s s
e c o n o m y an d m ajor se c to r s w ith an n u al
m ea su res o f lab or p ro d u ctiv ity for se le c te d
in d u stries at th e three- and fo u r -d ig it le v e ls
o f th e S tan d ard In d u stria l C la s s ific a tio n
sy ste m . In a d d itio n to lab or p r o d u ctiv ity ,
t h e in d u s t r y d a ta a ls o i n c lu d e a n n u a l
m ea su r e s o f c o m p e n sa tio n and u n it lab or
c o s ts fo r th r e e - d ig it in d u s tr ie s and
m ea su r e s o f m u ltifa c to r p r o d u c tiv ity for
th r e e -d ig it m a n u fa ctu rin g in d u str ie s and
r a ilr o a d t r a n s p o r t a t io n . T h e in d u s t r y
m ea su res.
Definitions
Output per hour is d e r iv e d b y d iv id in g
an in d e x o f in d u stry ou tp u t b y an in d e x o f
la b o r in p u t. F or m o st in d u s tr ie s, output
in d e x e s are d eriv ed from d ata o n th e v a lu e
o f in d u s t r y o u tp u t a d j u s t e d f o r p r ic e
ch a n g e. F or th e rem a in in g in d u stries, o u t
put in d e x e s are d e r iv e d from d ata o n th e
p h y sic a l q u a n tity o f p r o d u c tio n .
T h e labor input s e r ie s c o n s is t o f the
hours o f all em p lo y ees (produ ction w o rk ers
and n on p rod uction w orkers), the hours o f all
p erson s (paid e m p lo y ees, partners, propri
etors, and u n p aid fa m ily w o rk ers), or the
num ber o f em p lo y ees, d ep en d in g up on the
Unit labor costs re p r e se n t th e la b o r
c o m p e n s a t io n c o s t s p e r u n it o f o u tp u t
p ro d u ced , and are d e r iv e d b y d iv id in g an
in d e x o f lab or c o m p e n s a tio n b y an in d e x
o f o u tp u t. Labor compensation in c lu d e s
p a y r o ll a s w e l l a s s u p p l e m e n t a l p a y
m en ts, in c lu d in g b o th le g a lly req u ired e x
p e n d itu r e s an d p a y m e n ts fo r v o lu n ta r y
p rogram s.
M ultifactor productivity is d e r iv e d
b y d iv id in g an in d e x o f in d u stry o u tp u t
b y an in d e x o f th e c o m b in e d in p u ts c o n
su m ed in p r o d u c in g th at o u tp u t. Com
bined inputs in c lu d e ca p ita l, lab o r, and
in te r m e d ia te p u r c h a se s. T h e m ea su re o f
capital input u se d rep r e se n ts th e f lo w o f
s e r v ic e s fro m th e c a p ita l s to c k u s e d in
p ro d u ctio n . It is d e v e lo p e d from m ea su res
o f th e n et sto c k o f p h y s ic a l a s s e t s —
e q u ip m e n t, stru ctu res, lan d , and in v e n to
ries. T h e m e a su r e o f intermediate pur
chases is a c o m b in a tio n o f p u rch a sed m a
te r ia ls, s e r v ic e s , f u e ls , an d e le c tr ic ity .
Notes on the data
T h e in d u stry m e a su r e s are c o m p ile d fro m
d ata p ro d u ced b y th e B u reau o f L a b o r S ta
t is t ic s and th e B u rea u o f th e C e n s u s ,w ith
a d d itio n a l d ata su p p lie d b y o th e r g o v e r n
m e n t a g e n c ie s , tra d e a s s o c i a t i o n s , a n d
oth er so u r c e s.
F or m o st in d u s tr ie s, th e p r o d u c tiv ity
in d e x e s refer to th e ou tp u t per h o u r o f a ll
e m p lo y e e s . F o r so m e trad e and s e r v ic e s
in d u str ie s, in d e x e s o f o u tp u t p er h o u r o f
a ll p e r s o n s ( in c lu d in g s e lf- e m p lo y e d ) are
Monthly Labor Review
July 2002
69
Current Labor Statistics
co n str u c te d . F or so m e tra n sp o rta tio n in
d u stries, o n ly in d e x e s o f o u tp u t p er e m
p lo y e e are p rep ared .
FOR ADDITIONAL INFORMATION o n th is
s e r ie s , c o n ta c t th e D iv is io n o f In d u stry
P r o d u c tiv ity S tu d ies: ( 2 0 2 ) 6 9 1 - 5 6 1 8 .
International
Comparisons
(T a b le s 4 7 - 4 9 )
Labor force and
u n e m p lo y m e n t
Description of the series
T ab les 4 7 and 4 8 present com parative m eas
ures o f the labor force, em p loym en t, and un
e m p lo y m e n t — a p p r o x im a tin g U .S . c o n
cep ts— for the U n ited States, Canada, A u s
tralia, Japan, and several European countries.
T h e u n e m p lo y m e n t s t a t is t ic s (a n d , to a
le sse r ex ten t, e m p lo y m en t sta tistics) p u b
lish ed by other industrial cou n tries are not,
in m o st ca ses, com parable to U .S . u n em p loy
m en t sta tistics. T h erefore, the B ureau ad
ju sts the figures for selected countries, w here
n ecessary, for all k n ow n m ajor d efin ition al
d ifferen ces. A lth o u g h p recise com p arab ility
m ay n ot be a ch iev ed , th e se adjusted figures
p rovid e a better b a sis for international co m
p arison s than the fig u res regularly p u b lish ed
b y each country. For further inform ation on
ad ju stm en ts and co m p arab ility issu e s, se e
C on stan ce Sorrentino, “ International u n em
p lo y m en t rates: h o w com p arab le are th ey?”
M o n th ly L a b o r R e v ie w , June 2 0 0 0 , pp. 3 -2 0 .
Definitions
For the principal U .S . definitions o f the labor
force, employment, and unemployment, see
the N o tes section on E m ploym ent and U n em
p loym ent Data: H o u seh old survey data.
Notes on the data
T he adjusted statistics h ave b een adapted to
the ag e at w h ich co m p u lsory sc h o o lin g en d s
in each country, rather than to the U .S . stan
dard o f 16 years o f age and older. T herefore,
the adjusted statistics relate to the p op u la
tion aged 16 and older in France, Sw ed en , and
the U n ited K ingdom ; 15 and older in A ustra
lia, Japan, Germ any, Italy from 1993 onward,
and the N etherlands; and 14 and old er in Italy
prior to 1993. A n ex cep tio n to th is rule is
that the C anadian statistics for 197 6 onward
are a d ju sted to c o v e r a g e s 16 and o ld er,
70
Monthly Labor Review
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Federal Reserve Bank of St. Louis
w hereas the age at w h ich com p ulsory sc h o o l
in g en d s rem ain s at 15. T h e in stitu tio n a l
p op u lation is in clu d ed in the denom inator o f
the labor force participation rates and em
p lo y m e n t-p o p u la tio n ratios for Japan and
Germ any; it is exclu d ed for the U n ited States
and the other countries.
In the U .S . labor fo rce survey, persons on
la y o ff w h o are aw aitin g recall to their jo b s
are cla ssifie d as u n em p loyed . European and
Japanese la y o ff p ractices are quite different
in nature from th o se in the U n ited States;
therefore, strict ap p lication o f the U .S . d efi
n ition has n ot b een m ade on this point. For
further inform ation, se e M o n th ly L a b o r R e
v ie w , D ecem b er 1981, pp. 8 -1 1 .
T h e figu res for on e or m ore recent years
for F rance, G erm any, Italy, the N eth erlan d s,
and the U n ited K in gd om are calculated u sin g
adjustm ent factors b ased on labor fo rce sur
v e y s for earlier years and are co n sid ered pre
lim inary. T he recent-year m easu res for th ese
cou n tries, therefore, are su b ject to revision
w h en ev er data from m ore current labor force
su rveys b eco m e available.
There are breaks in the data series for the
U nited States ( 1 9 9 0 ,1 9 9 4 ,1 9 9 7 ,1 9 9 8 ,1 9 9 9 ,
2 0 0 0 ), C anada (1 9 7 6 ) France (1 9 9 2 ), G er
m any (1 9 9 1 ), Italy (1 9 9 1 , 1993), the N e th
erlands (1 9 8 8 ), and S w ed en (1 9 8 7 ).
For the U n ited States, the break in series
reflects a m ajor red esign o f the labor force
su rvey qu estion n aire and co llectio n m eth od
o lo g y introduced in January 1994. R ev ised
p op u lation estim ates b ased on the 19 9 0 c en
su s, adjusted for the estim ated undercount,
a lso w ere incorporated. In 1996, p reviou sly
p u b lish ed data for the 1 9 9 0 -9 3 period w ere
r e v is e d to r e fle c t th e 1 9 9 0 c e n s u s -b a s e d
p o p u la tio n c o n tr o ls, a d ju sted for th e u n
dercount. In 1997, revised pop u lation co n
trols w ere introduced into the h o u seh o ld sur
v e y . T h e r e fo r e , th e d ata are n o t str ic tly
conparable w ith prior years. In 1998, n ew
co m p o site estim ation p rocedures and m inor
rev isio n s in p op u lation con trols w ere intro
du ced into the h o u seh o ld survey. T herefore,
the data are n ot strictly com parable w ith data
for 1997 and earlier years. S ee the N o te s se c
tio n on E m p lo y m e n t an d U n e m p lo y m e n t
D ata o f th is R e v ie w .
bls recently introduced a n ew adjusted
series for Canada. B e g in n in g w ith the data
for 1976, C anadian data are adjusted to m ore
c lo s e ly approxim ate U .S . con cep ts. A d ju st
m en ts are m ade to the u n em p loyed and labor
force to exclu d e: (1 ) 15-year-old s; (2 ) p as
siv e jo b seek ers (p erson s o n ly reading n e w s
paper ads as their m eth od o f jo b search); (3 )
p erson s w aitin g to start a n ew jo b w h o did
n ot seek w ork in the past 4 w eeks; and (4 )
p ersons u n available for w ork due to personal
or fa m ily resp o n sib ilities. A n adjustm ent is
July 2002
m ade to in clu d e full-tin e students lo o k in g for
fu ll-tim e w ork . T h e im pact o f the a d ju st
m ents w as to low er the annual average u nem
p loym en t rate by 0 .1 - 0 .4 p ercen tage p oin t
in the 1 9 8 0 s and 0 .4 - 1 .0 percen tage p oin t in
the 1990s.
For F rance, the 1992 break reflects the
substitution o f standardized E uropean U n io n
S tatistical O ffice (eurostat) u n em p lo y m en t
sta tistics for th e u n em p lo y m en t data e s ti
m ated accord in g to the International Labor
O ffice (ilo) d efin ition and p u b lish ed in the
O rganization for E c o n o m ic C oop eration and
D ev elo p m en t (oecd) annual yearb o o k and
quarterly update. T h is ch an ge w as m ade b e
cau se the eurostat data are m ore u p -to-date
than the OECD figures. A lso , sin ce 1 9 9 2 , the
eurostat d efin itio n s are clo ser to the U .S .
d efin itio n s than th ey w ere in prior years. T he
im pact o f th is revision w a s to lo w er the u n
em p loym en t rate b y 0.1 p ercen tage p o in t in
1992 and 1993, by 0 .4 p ercen tage p oin t in
1994, and 0.5 percentage p oin t in 1995.
For G erm any, the data for 1991 onw ard
refer to u n ified Germ any. D ata prior to 1991
relate to the form er W est G erm any. T h e im
pact o f in clu d in g the form er E ast G erm any
w as to in crease the u n em p loym en t rate from
4 .3 to 5 .6 p ercent in 1991.
For Italy, the 1991 break reflects a rev i
sio n in the m eth od o f w eig h tin g sam p le data.
T h e im pact w as to in crease the u n em p lo y
m en t rate b y ap p roxim ately 0 .3 p ercen tage
point, from 6 .6 to 6 .9 percent in 1991.
In O ctob er 1992, the su rvey m eth o d o l
o g y w a s revised and the d efin itio n o f u n em
p loym en t w as ch an ged to in clu d e o n ly th o se
w h o w ere a ctively lo o k in g for a j o b w ith in
the 3 0 days p reced in g the su rvey and w h o
w ere a v a ila b le for w ork . In a d d itio n , th e
lo w er age lim it for the labor force w a s raised
from 14 to 15 years. (Prior to th ese ch a n g es,
bls ad ju sted Ita ly ’s p u b lish e d u n e m p lo y
m ent rate dow n w ard b y e x clu d in g from the
u n e m p lo y e d t h o s e p e r s o n s w h o h ad n o t
a ctiv ely sou gh t w ork in the p ast 3 0 d ays.)
T h e break in the series also reflects the in cor
poration o f the 1991 p op u lation c en su s re
su lts. T h e im pact o f th e se ch a n g es w a s to
raise Ita ly ’s adjusted u n em p loym en t rate by
ap p roxim ately 1.2 p ercen tage p oin ts, from
8.3 to 9 .5 p ercen t in fourth-quarter 1 9 9 2 .
T h ese ch a n g es d id n ot affect em p lo y m en t
sign ifican tly, ex cep t in 1993. E stim ates by
the Italian S tatistical O ffice in d icate that em
p lo y m e n t d e c lin e d b y a b ou t 3 p ercen t in
1993, rather than the nearly 4 percent in d i
cated by the data sh o w n in table 4 4 . T h is
d ifferen ce is attributable m ain ly to the in co r
poration o f the 1991 pop u lation benchm arks
in the 1993 data. D ata for earlier years h ave
n ot b een adjusted to incorporate the 1991
cen su s results.
For the N eth erlan d s, a n ew su rvey q u es
tionnaire w a s introduced in 1992 that allow ed
for a c lo s e r a p p lica tio n o f ILO g u id elin e s.
Eurostat h as rev ised the D u tch series back
to 19 8 8 based on the 1992 changes. T he 1988
rev ised u n em p lo y m en t rate is 7 .6 percent;
the p rev io u s estim ate for the sam e year w as
9.3
percent.
There h a v e b een tw o breaks in series in
th e S w e d ish lab or fo rce survey, in 1987 and
1 9 9 3 . A d ju stm en ts h a v e b een m ade for the
19 9 3 break b ack to 1 9 8 7 . In 1987, a n ew
q u e stio n n a ir e w a s in tr o d u ce d . Q u e s tio n s
re g a r d in g cu rren t a v a ila b ility w e r e a d d ed
and th e p e r io d o f a c tiv e w o r k s e e k in g w a s
r e d u c e d fro m 6 0 d a y s to 4 w e e k s . T h e s e
c h a n g e s lo w e r e d S w e d e n ’s 1 9 8 7 u n e m
p lo y m e n t rate b y 0 .4 p e r c e n ta g e p o in t,
fro m 2 .3 to 1 .9 p ercen t. In 1 9 9 3 , th e m e a
su re m en t p e r io d fo r th e lab or fo r c e su r
v e y w a s c h a n g e d to rep resen t a ll 5 2 w e e k s
o f th e y e a r ra th er th a n o n e w e e k e a c h
m o n th and a n e w a d ju stm en t for p o p u la
t io n t o t a ls w a s in tr o d u c e d . T h e im p a c t
w a s to r a is e th e u n e m p lo y m e n t rate b y
a p p r o x im a te ly 0 .5 p e r c e n ta g e p o in t, from
7 .6 to 8.1 p ercen t. S ta tis tic s S w e d e n re
v is e d its la b o r fo r c e su r v e y d ata for 1 9 8 7 —
9 2 to ta k e in to a c c o u n t th e b reak in 1 9 9 3 .
T h e a d ju stm en t ra ised th e S w e d is h u n e m
p lo y m e n t rate b y 0 .2 p e r c e n ta g e p o in t in
1 9 8 7 an d g ra d u a lly r o se to 0 .5 p e r c e n ta g e
p o in t in 1 9 9 2 .
B eg in n in g w ith 1987, bls has adjusted the
S w ed ish data to c la ssify students w h o also
so u g h t w ork as u n em p loyed . T h e im pact o f
th is ch a n g e w a s to in crease the adjusted un
em p lo y m en t rate b y 0.1 p ercen tage p oin t in
19 8 7 and b y 1.8 p ercen tage p oin ts in 1994,
w h en u n em p lo y m en t w a s higher. In 1998,
th e ad ju sted u n em p lo y m en t rate had risen
from 6 .5 to 8 .4 percent d u e to the adjustm ent
to in clu d e students.
T h e n e t e f fe c t o f th e 1 9 8 7 an d 1 9 9 3
c h a n g e s an d th e bls a d ju stm en t fo r stu
d e n ts s e e k i n g w o r k lo w e r e d S w e d e n ’s
1 9 8 7 u n e m p lo y m e n t rate from 2 .3 to 2 .2
p ercen t.
FORADDITIONAL INFORMATION On th is se
ries, con tact the D iv isio n o f F oreign Labor
Statistics: (2 0 2 ) 6 9 1 - 5 6 5 4 .
M anufacturing productivity
and labor costs
Description of the series
T able 4 9 p resen ts com p arative in d e x e s o f
m anufacturing labor p rod u ctivity (output per
hour), output, total hours, com p en sation per
hour, and u n it la b o r c o s ts for th e U n ited
S tates, C anada, Japan, and n in e E uropean
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
countries. T h ese m easures are trend com pari
son s— that is, series that m easure ch an ges
o v er tim e— rather than le v e l com p arison s.
There are greater tech n ical p rob lem s in co m
paring the le v e ls o f m anu factu ring output
am ong countries.
BLS constructs the com parative in d exes
from three basic aggregate m easures— output,
total labor h ou rs, and total co m p en sa tio n .
T h e hours and com p en sation m easures refer
to all em p lo y ed p erson s (w a g e and salary
earners p lus se lf-em p lo y ed p erson s and u n
paid fam ily w ork ers) in the U n ited States,
Canada, Japan, F rance, Germ any, N orw ay,
and S w ed en , and to all em p lo y ees (w a g e and
salary earners) in the other countries.
Definitions
Output, in g e n e r a l, refers to v a lu e ad d ed
in m a n u fa c tu r in g from th e n a tio n a l a c
c o u n ts o f e a c h co u n tr y . H o w e v e r , th e
o u tp u t se r ie s fo r Japan p rior to 1 9 7 0 is
an in d e x o f in d u stria l p r o d u c tio n , and th e
n a tio n a l a c c o u n ts m ea su res for th e U n ite d
K in g d o m are e s s e n t ia lly id e n tic a l to th eir
in d e x e s o f in d u stria l p ro d u ctio n .
T h e 1 9 7 7 - 9 7 o u t p u t d a ta f o r t h e
U n ite d S ta tes are th e g r o s s p ro d u ct o r ig i
n a tin g (v a lu e a d d ed ) m ea su r e s p rep ared
b y th e B u re a u o f E c o n o m ic A n a ly s is o f
th e U .S . D ep a rtm e n t o f C o m m er ce. C o m
p a ra b le m a n u fa c tu r in g o u tp u t d ata cu r
ren tly are n o t a v a ila b le p rior to 1 9 7 7 .
U .S . g ross p roduct origin atin g is a ch ain ty p e a n n u a l-w e ig h te d se ries. (F or m ore in
fo rm a tio n o n th e U .S . m ea su re, se e R ob ert
E . Y u s k a v a g e , “ I m p r o v e d E s tim a t e s o f
G r o s s P r o d u c t b y In d u s tr y , 1 9 5 9 - 9 4 , ”
S u r v e y o f C u r r e n t B u s i n e s s , A u g u st 1 9 9 6 ,
pp. 1 3 3 - 5 5 .) T h e J a p a n ese v a lu e ad d ed
s e r ie s is b a se d u p o n o n e se t o f fix e d p rice
w e ig h ts for th e y ea rs 1 9 7 0 th ro u g h 1 9 9 7 .
O u tp u t s e r ie s fo r th e o th e r fo r e ig n e c o n o
m ie s a lso e m p lo y f ix e d p r ic e w e ig h t s , but
th e w e ig h t s are u p d a ted p e r io d ic a lly (fo r
e x a m p le , e v e r y 5 or 10 y e a r s).
To p reserve the com p arab ility o f the U .S .
m easures w ith th ose for other econ om ies, bls
u se s gross product origin atin g in m anu fac
turing for the U n ited States for th e se co m
parative m easures. T h e gross product orig i
n atin g series d iffers from the m anufacturing
output series that bls p u b lish es in its n ew s
releases on quarterly m easures o f U .S . pro
d u ctivity and co sts (and that u n d erlies the
m easures that appear in tables 43 and 45 in
th is section ). T he quarterly m easures are on
a “sectoral output” b asis, rather than a valu eadded b asis. Sectoral output is gross output
less intrasector transactions.
Total labor hours refers to hours w orked
in all countries. T h e m easu res are d ev elo p ed
from statistics o f m anufacturing em p loym en t
and average hours. T he series u sed for France
(from 197 0 forw ard), N orw ay, and S w ed en
are o fficial series p u b lish ed w ith the n ational
accounts. W here o fficial total hours series are
n ot availab le, the m easu res are d ev elo p ed by
bls u sin g em p loym en t figures p u b lish ed w ith
the n ational accou n ts, or other com p reh en
siv e em p loym en t series, and estim ates o f an
nual hours w ork ed . For G erm any, bls u ses
estim ates o f average hours w ork ed d ev elo p ed
b y a research institute con n ected to th e M in
istry o f L abor for u se w ith the n ation al a c
co u n ts e m p lo y m en t fig u r es. F or the other
countries, bls con stru cts its o w n estim ates
o f average hours.
Denm ark has not p ublished estim ates o f
average hours for 1994—97; therefore, the bls
m easure o f labor input for D enm ark ends in
1993.
Total compensation (labor cost) includes
all paym ents in cash or in-kind m ade directly
to em p loyees p lus em ployer expenditures for
legally required insurance program s and co n
tractual and private benefit plans. T he m ea
sures are from the national accounts o f each
country, excep t th ose for B elgiu m , w h ich are
d evelop ed b y bls u sin g statistics on em p loy
m ent, average hours, and hourly com p en sa
tion. For Canada, France, and S w eden, co m
pensation is increased to account for other sig
nificant taxes on payroll or em ploym ent. For
the U nited K ingdom , com pensation is reduced
betw een 1967 and 1991 to account for em
p loym en t-related su b sid ies. S e lf-e m p lo y e d
workers are included in the all-em ployed-per
son s m easures by assum ing that their hourly
com pensation is equal to the average for w age
and salary e m p lo y ees.
Notes on the data
In general, the m easu res relate to total m anu
facturing as d efined b y the International Stan
dard Industrial C lassification . H ow ev er, the
m easu res for France (for all years) and Italy
(b egin n in g 19 7 0 ) refer to m in in g and m anu
factu rin g le s s en ergy-related p roducts, and
the m easures for Denm ark inclu d e m inin g and
exclu d e m anufacturing handicrafts from 1960
to 1966.
T h e m easu res for recen t years m ay b e
based on current indicators o f m anufacturing
ou tp u t (su c h as in d u strial p ro d u ctio n in
d e x e s ), e m p lo y m e n t, a v er a g e h o u rs, and
hourly com p en sation until national accou n ts
and other statistics u sed for the lon g-term
m easures b ecom e available.
For additional information o n this se
ries, con tact the D iv isio n o f F oreign Labor
Statistics: (2 0 2 ) 6 9 1 - 5 6 5 4 ._________________
Monthly Labor Review
July 2002
71
Current Labor Statistics
c o n t in u e d b e g in n in g w ith th e 1 9 9 3 su r
v e y . T h e n u m b e r o f d a y s a w a y fr o m
w o r k or d a y s o f r e s tr ic te d w o r k a c t iv ity
d o e s n o t in c lu d e th e d a y o f in ju r y or
o n s e t o f i l l n e s s or a n y d a y s o n w h ic h
th e e m p lo y e e w o u ld n o t h a v e w o r k e d ,
su c h a s a F e d e r a l h o lid a y , e v e n th o u g h
a b le to w o rk .
Incidence rates are c o m p u t e d a s th e
n u m b e r o f in ju r ie s a n d /o r i l l n e s s e s or
lo s t w o r k d a y s p er 1 0 0 f u l l - t im e w o r k
ers.
Occupational Injury
and Illness Data
(T ables 5 0 - 5 1 )
Survey of Occupational
Injuries and Illnesses
Description of the series
The Survey o f O ccupational Injuries and Ill
n esses co llects data from em ployers about their
w orkers’ job-related nonfatal injuries and ill
nesses. T he inform ation that em ployers pro
v id e is based on records that they m aintain un
der the O ccupational Safety and Health A ct o f
1970. S elf-em p loyed individuals, farms with
few er than 11 em p loyees, em ployers regulated
by other Federal safety and health law s, and
Federal, State, and local governm ent agencies
are excluded from the survey.
T h e su rv ey is a F ed eral-S tate c o o p era
tiv e program w ith an in d ep en d en t sam p le
se lecte d for each participating State. A strati
fied ran d om sa m p le w ith a N ey m a n a llo c a
tio n is se le c te d to represent all private in
d u stries in the State. T h e su rvey is stratified
b y Standard In d u strial C la ss ific a tio n and
siz e o f em p lo y m en t.
Definitions
U n d er the O ccu p ation al Safety and H ealth
A ct, em p lo y ers m aintain records o f nonfatal
w ork -related injuries and illn e ss e s that in
v o lv e o n e or m ore o f the fo llo w in g : lo ss o f
c o n sc io u sn ess, restriction o f w ork or m otion,
transfer to another jo b , or m ed ical treatm ent
other than first aid.
Occupational injury is any injury such as
a cut, fracture, sprain, or amputation that re
sults from a work-related event or a single, in
stantaneous exposure in the work environment.
Occupational illness is an abnormal con
dition or disorder, other than one resulting from
an occu p ational injury, caused by exp osu re to
factors a sso cia ted w ith em p loym en t. It in
clu d es acute and chronic illn esses or d isease
w h ich m ay b e caused by inhalation, absorp
tion, in gestion , or direct contact.
Lost workday injuries and illnesses are
ca se s that in v o lv e d ays aw ay from w ork, or
d ays o f restricted w ork 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 dis
72
Monthly Labor Review
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Federal Reserve Bank of St. Louis
Notes on the data
T h e d efin ition s o f occu p ational injuries and
illn esses are from R e c o r d k e e p in g G u id e lin e s
f o r O c c u p a tio n a l I n ju r ie s a n d I lln e s s e s (U .S .
D epartm ent o f Labor, Bureau o f Labor Sta
tistics, S eptem ber 1986).
Estim ates are m ade for industries and em
ploym ent size classes for total recordable cases,
lo st w ork d ay ca ses, days aw ay from w ork
cases, and nonfatal cases w ithout lost work
days. T h ese data also are show n separately for
injuries. Illness data are available for seven cat
egories: occupational skin diseases or disorders,
dust diseases o f the lungs, respiratory condi
tions due to toxic agents, p oison in g (system ic
effects o f toxic agents), disorders due to p hysi
cal agents (other than toxic materials), disor
ders associated with repeated trauma, and all
other occupational illnesses.
The survey continues to m easure the num
ber o f n ew w ork-related illn ess cases w h ich
are recognized, diagnosed, and reported during
the year. S om e conditions, for exam ple, lon g
term latent illn esses caused by exposure to
carcinogens, often are difficult to relate to the
w orkplace and are not adequately recognized
and reported. T h ese long-term latent illn esses
are b elieved to be understated in the su rvey’s
illness measure. In contrast, the overw helm ing
m ajority o f th e reported n ew illn e ss e s are
th ose w h ich are easier to directly relate to
w orkplace activity (for exam ple, contact der
m atitis and carpal tunnel syndrom e).
M ost o f the estim ates are in the form o f
incid en ce rates, defined as the number o f inju
ries and illn esses per 100 equivalent full-tim e
workers. For this purpose, 2 0 0 ,0 0 0 em p loyee
hours represent 100 em p loyee years (2 ,0 0 0
hours per em p loyee). Full detail on the avail
able m easures is presented in the annual b u lle
tin , O c c u p a t i o n a l I n j u r ie s a n d I l ln e s s e s :
M in in g and railroad data are furnished to
bls b y the M in e S afety and H ealth A d m in is
tration and the Federal R ailroad A d m in istra
tion. D ata from th ese organ izations are in
clu d ed in both th e n ation al and State data
p u b lish ed annually.
W ith the 19 9 2 survey, bls b egan p u b lish
in g d etails on seriou s, nonfatal in cid en ts re
su ltin g in days aw ay from w ork. In clu d ed are
so m e m ajor characteristics o f the injured and
ill w orkers, su ch as occu p ation , age, gender,
race, and len gth o f service, as w e ll as the cir
cu m stan ces o f their injuries and illn esses (na
ture o f the d isab lin g con d ition , part o f b od y
affected , even t and exp osu re, and the sou rce
directly p roducing the con d ition ). In general,
th ese data are availab le n a tio n w id e for d e
tailed industries and for in d ivid u al States at
m ore aggregated industry lev els.
For additional information on o c c u
pational injuries and illn esses, contact the O f
fice o f O ccupational Safety, H ealth and Work
in g C on d ition s at (2 0 2 ) 6 9 1 - 6 1 8 0 , or a ccess
the Internet at:
http://www.bls.gov/iip/
Census of Fatal
O ccupational Injuries
T h e C en su s o f Fatal O ccu p ation al Injuries
co m p ile s a co m p lete roster o f fatal jo b -re
lated injuries, in clu d in g d etailed data about
th e fa ta lly in ju red w o rk ers an d th e fatal
e v e n t s . T h e p ro g ra m c o l l e c t s an d c r o s s
ch e c k s fa ta lity in form ation from m u ltip le
sou rces, in clu d in g death certificates, State
and F ederal w ork ers’ com p en sation reports,
O ccu p ation al S afety and H ealth A d m in istra
tion and M in e S afety and H ealth A d m in is
tration records, m ed ica l exam in er and au
top sy reports, m ed ia accou n ts, State m otor
v e h ic le fatality records, and fo llo w -u p q u es
tionn aires to em p loyers.
In ad d ition to private w a g e and salary
w orkers, the self-em p loyed , fam ily m em bers,
and F ed eral, S tate, and lo c a l g o v ern m en t
w orkers are covered by the program . To be
in clu d ed in the fatality cen su s, the d eced en t
m ust h ave b een em p loyed (that is w ork in g
for pay, com p en sation, or profit) at the tim e
o f the even t, en gaged in a legal w ork activity,
or present at the site o f the in cid en t as a re
quirem ent o f h is or her jo b .
C o u n ts, R a te s , a n d C h a r a c te r is tic s .
C om parable data for m ore than 4 0 States
and territories are availab le from the bls O f
fic e o f Safety, H ealth and W orking C on d i
tion s. M an y o f th ese States pub lish data on
State and local govern m en t em p lo y ees in ad
d ition to private industry data.
July 2002
Definition
A fatal work injury is any intentional or un
intentional w ound or dam age to the bod y re
sulting in death from acute exposure to energy,
such as heat or electricity, or kinetic energy
Notes on the data
the en d o f the referen ce year. T h e C en su s o f
Fatal O ccu p ation al Injuries w a s in itiated in
199 2 as a jo in t F ed eral-S tate effort. M o st
States issu e sum m ary inform ation at the tim e
o f the n ation al n e w s release.
For additional information o n the
C en su s o f Fatal O ccu p ation al Injuries c o n
tact th e bls O ffic e o f S afety, H ealth , and
W orking C on d ition s at (2 0 2 ) 6 9 1 - 6 1 7 5 , or
the Internet at: http://www.bls.gov/iip/
from a crash, or from the ab sen ce o f such es
sentials as heat or o x y g en caused by a sp ecific
even t or incident or series o f events w ithin a
sin gle w orkday or shift. Fatalities that occur
during a p erson ’s com m u te to or from w ork
are exclu d ed from the census, as w ell as workr e la te d i l l n e s s e s , w h ic h can b e d iff ic u lt
to identify due to lon g latency periods.
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Federal Reserve Bank of St. Louis
T w e n ty -eig h t data elem en ts are c o lle c te d ,
cod ed , and tabulated in the fatality program ,
in clu d in g in form ation ab ou t the fatally in
ju red w orker, the fatal in cid en t, and the m a
ch in ery or eq u ip m en t in v o lv ed . Sum m ary
w ork er d em ograp h ic data and ev en t ch arac
teristics are in clu d ed in a n ation al n e w s re
le a se that is availab le ab ou t 8 m on th s after
LABSTAT available via World Wide Web
L abstat, the B ureau o f Labor S tatistics p u b lic database, p rovid es current and historical
data for m any B L S su rveys as w e ll as n u m erous n ew s releases.
labstat P u b lic A c c e ss has introduced a n ew production Internet service o ver the W orld
W ide W eb.
bls and region al o ffic e s program s are d escrib ed u sin g h yp ertext p ages.
A c c e ss to labstat data and n ew s releases is p rovid ed by a link to the bls gop h er server.
T he url is:
http://www.bls.gov/blshome.html
I f y o u h ave q u estion s or com m ents regarding the labstat system on the Internet, address
e-m ail to: lab stat.h elp d esk @ b ls.gov
Monthly Labor Review
July 2002
73
Current Labor Sta tistic s:
Comparative Indicators
1. Labor market indicators
Selected indicators
2000
2000
2001
I
II
2001
III
IV
I
II
2002
III
IV
I
E m p lo y m e n t d a ta
Employment status of the civilian noninstitutionalized
population (household survey):1
Labor force participation rate....................................................
Employment-population ratio....................................................
Unemployment rate..................................................................
Men...........................................................................................
16 to 24 years.......................................................................
25 years and over................................................ ................
Women.....................................................................................
16 to 24 years.......................................................................
25 years and over.................................................................
67.2
64.5
66.9
67.3
63.8
4.0
3.9
9.7
2.8
4.1
4.8
4.8
11.4
3.6
4.7
64.6
4.0
3.9
9.7
8.9
3.2
9.7
3.7
67.3
64.6
4.0
3.9
9.7
2.8
4.2
2.8
4.1
9.5
9.0
3.2
3.1
67.0
67.1
64.3
4.1
3.9
9.8
2.8
64.4
4.2
8.5
4.0
8.4
3.3
3.0
4.0
4.0
9.6
2.9
67.2
64.4
4.2
4.2
10.6
3.1
4.1
66.9
63.9
4.5
4.6
11.2
3.4
8.7
4.3
9.2
3.3
3.4
66.8
63.6
4.8
4.9
11.5
3.7
4.8
10.0
3.7
66.9
63.1
5.6
5.7
12.7
4.4
5.5
10.6
4.4
66.5
62.8
5.6
5.7
12.9
4.5
5.5
11.0
4.4
Employment, nonfarm (payroll data), in thousands:1
Total.............................................................................................
131,720
131,922
Private sector...........................................................................
111,018
Goods-produclng...................................................................
Manufacturing....................................................................
Service-producing..................................................................
25,649
18,473
106,051
110,989
24,949
17,695
106,978
130,995
110,461
25,701
18,502
105,293
131,819
110,860
25,690
18,510
106,129
131,876
111,219
25,681
18,494
106,195
132,185
111,551
132,559
111,687
132,193
111,332
25,626
18,400
106,559
25,493
18,196
106,941
25,136
17,872
107,057
131,943
110,939
24,786
17,538
107,157
131,130
110,035
24,375
17,174
106,755
130,759
109,594
24,049
16,883
106,711
Average hours:
Private sector...........................................................................
Manufacturing........................................................................
Overtime..............................................................................
34.5
41.6
4.6
34.2
40.7
34.5
41.8
3.9
4.8
4.1
1.3
4.2
1.5
34.4
34.4
41.8
4.7
41.5
4.5
34.3
41.1
4.4
34.3
41.0
4.1
34.2
40.8
3.9
34.1
40.7
3.9
1.0
1.2
1.0
.9
.7
1.3
1.4
.9
1.0
1.2
.7
34.1
40.5
3.8
34.2
40.8
4.0
.8
.8
1.0
1.1
E m p lo y m e n t C o s t In d e x 2
Percent change in the E C I, compensation:
All workers (excluding farm, household and Federal workers).
Private industry workers.........................................................
4.1
4.4
.9
Goods-producing3...............................................................
4.4
3.8
1.6
1.2
.9
.6
1.3
.9
.7
.8
1.2
Service-producing3..............................................................
State and local government workers......................................
4.4
3.0
4.3
4.2
1.4
.6
1.2
.3
1.0
1.3
.7
.7
1.4
.9
1.0
.6
1.0
2.1
.8
.6
1.1
.6
Workers by bargaining status (private industry):
Union...........................................................................................
Nonunion................................................................................ .
4.0
4.4
4.2
4.1
1.3
1.5
1.0
1.2
1.2
1.0
.5
.7
.7
1.5
1.1
1.0
1.0
.9
1.4
.7
1.1
1.1
’ Quarterly data seasonally adjusted.
2 Annual changes are December-to-December changes. Quarterly changes are calculated using the last month of each quarter.
3 Goods-producing industries include mining, construction, and manufacturing. Service-producing Industries include all other private sector industries.
74
Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
2. Annual and quarterly percent changes in compensation, prices, and productivity
Selected measures
2000
I
II
2002
2001
2000
2001
III
IV
II
I
III
IV
1
Compensation data1,2
Employment Cost Index— compensation (wages,
salaries, benefits):
Civilian nonfarm........................................................................
4.1
4.1
1.3
1.0
1.0
0.7
1.3
0.9
1.2
0.8
1.0
Private nonfarm....................................................................
4.4
4.2
1.5
1.2
.9
.7
1.4
1.0
.9
.8
1.1
3.8
3.7
1.1
1.0
1.1
.6
1.1
.9
1.0
.7
.9
3.9
3.8
1.2
1.0
1.0
.6
1,2
1.0
.8
.8
.9
1.6
3.4
1.7
.7
.8
.2
1.3
1.0
.2
- .9
.7
.4
.1
.9
.8
- .3
-3 .2
1.1
1.2
1.0
- .3
-4 .3
1.5
2.9
Employment Cost Index—wages and salaries:
Private nonfarm....................................................................
Price data1
Consumer Price Index (All Urban Consumers): All Items......
Producer Price Index:
Finished goods...........................................................................
3.5
-1 .8
1.5
1.8
.6
4.3
-2 .4
1.9
1.3
.8
Capital equipment..................................................................
1.2
1.0
.1
.1
-7 .2
1.1
-.1
-7.1
-.1
.1
Intermediate materials, supplies, and components...............
4.0
-.2
1.8
1.4
1.0
- .3
.2
.6
-1 .0
-3 .6
.9
Crude materials...........................................................................
31.1
-8 .8
9.0
-6 .0
2.1
9.4
-3 .5
-6 .6
-12.0
-12.2
8.0
8.3
Productivity data3
Output per hour of all persons:
Business sector...........................................................................
3.0
1.1
.3
6.7
.4
2.1
-1 .5
-.2
1.8
7.6
Nonfarm business sector...........................................................
2.9
1.1
.2
6.0
.6
1.7
-1 .5
-.1
2.1
7.3
8.6
Nonfinancial coroorations4........................................................
2.1
1.0
5.3
.3
2.6
- .7
-2 .6
2.3
3.2
10.8
5.1
1
Annual changes are December-to-December changes.
Quarterly changes are
cent changes reflect annual rates of change in quarterly indexes.
calculated using the last month of each quarter. Compensation and price data are not
The data are seasonally adjusted.
seasonally adjusted, and the price data are not compounded.
4 Output per hour of all employees.
2 Excludes Federal and private household workers.
3 Annual rates of change are computed by comparing annual averages. Quarterly per-
3.
Alternative measures of wage and compensation changes
Quarterly average
Components
Four quarters ending
2002
2001
I
II
III
IV
I
2002
2001
I
II
III
IV
I
Average hourly compensation:1
All persons, business sector..............................................................
All persons, nonfarm business sector..............................................
3.1
2.8
0.5
.1
0.9
1.0
1.4
1.5
3.8
3.6
4.5
4.2
3.9
3.6
2.0
1.8
1.5
1.4
1.6
1.6
1.3
1.4
.7
1.5
.9
.9
1.0
1.1
1.0
.6
1.2
.9
1.0
.9
2.1
.8
.8
1.4
.7
.6
1.0
1.1
1.1
1.1
.6
4.1
4.2
3.4
4.3
3.3
3.9
4.0
3.5
4.2
3.6
4.1
4.0
3.4
4.1
4.4
4.1
4.2
4.2
4.1
4.2
3.9
3.9
4.7
3.8
3.9
1.1
1.2
.6
1.2
.7
.9
1.0
1.1
.9
.5
1.0
.8
1.0
.8
1.9
.7
.8
1.6
.7
.5
.9
.9
.7
1.0
.5
3.8
3.8
3.6
3.9
3.5
3.7
3.8
3.8
3.7
3.7
3.6
3.6
3.6
3.6
3.9
3.7
3.8
4.4
3.6
3.6
3.5
3.5
4.4
3.4
3.4
Employment Cost Index— compensation:
Civilian nonfarm2..................................................................................
Private nonfarm.................................................................................
Union.................................................................................................
Nonunion..........................................................................................
State and local governments...........................................................
Employment Cost Index—wages and salaries:
Civilian nonfarm2..................................................................................
Private nonfarm.................................................................................
Union.................................................................................................
Nonunion..........................................................................................
State and local governments...........................................................
1 Seasonally adjusted. "Quarterly average" is percent change from a quarter ago, at an annual rate.
2 Excludes Federal and household workers.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
75
Current Labor Statistics:
4.
Labor Force Data
Employment status of the population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted
[Numbers in thousands]
Employment status
Annual average
2000
2002
2001
2001
May
June
July
Aug.
Sept
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
211,864
211,525
141,445
66.9
135,235
211,725
141,468
66.8
135,003
211,921
212,927
142,279
66.9
134,253
141,390
66.8
134,055
213,089
141,390
66.4
133,468
213,306
142,211
66.7
134,319
213,334
142,068
66.9
135,004
212,581
142,280
66.9
134,615
212,767
141,651
66.8
135,106
212,135
141,380
66.6
134,408
212,357
141,815
66.9
135,073
142,005
66.6
133,894
213,492
142,570
66.8
133,976
213,658
142,769
66.8
134,417
63.8
6,742
4.8
70,050
63.9
6,210
4.4
70,080
63.8
6,465
4.6
70,257
63.8
6,545
4.6
70,270
63.4
6,972
4.9
70,755
63.6
7,064
5.0
70,289
63.3
7,665
5.4
70,301
63.1
8,026
5.6
70,488
63.0
8,259
5.8
70,613
62.6
7,922
5.6
71,699
63.0
7,891
5.5
70,995
62.8
8,111
5.7
71,329
62.8
8,594
6.0
70,922
62.9
8,351
5.8
70,889
TOTAL
Civilian noninstitutional
population1.......................... 209,699
Civilian labor force.............. 140,863
Participation rate.........
67.2
135,208
Employment-pop64.5
5,665
Unemployed...................
Unemployment rate....
4.0
Not in the labor force.......
68,836
M en , 20 y e a rs a n d o v er
Civilian noninstitutional
population1..........................
Civilian labor force..............
Participation rate.........
Employed........................
Employment-popAgriculture...................
Nonagricultural
industries..................
Unemployed...................
Unemployment rate....
92,580
93,659
93,541
93,616
93,708
93,810
93,917
94,015
94,077
94,161
94,228
94,262
94,315
94,414
94,479
70,930
76.6
68,580
71,590
76.4
68,587
71,468
76.3
68,698
71,429
76.3
68,535
71,500
76.3
68,610
71,523
76.2
68,388
71,805
76.5
68,696
71,940
76.5
68,486
71,935
76.5
68,204
71,988
76.5
68,276
71,534
75.9
67,818
71,718
76.1
68,157
71,723
76.0
68,013
72,098
76.4
68,193
72,428
76.7
68,647
74.1
73.2
2,102
73.4
73.2
73.2
2,057
2,035
83.1
2,138
72.8
2,132
72.5
2,082
72.5
2,141
72.0
2,207
72.3
2,185
72.1
2,084
72.7
2,168
72.9
2,129
72.2
2,252
2,213
2,125
66,328
2,350
3.3
66,485
3,003
4.2
66,530
2,770
3.9
66,478
2,894
4.1
66,575
2,890
4.0
66,259
3,135
4.4
66,558
3,109
4.3
66,354
3,454
4.8
66,122
3,731
5.2
66,135
3,712
5.2
65,611
3,716
5.2
65,973
3,560
5.0
65,929
3,710
5.2
65,980
3,905
5.4
66,522
3,781
5.2
101,078
102,060
101,938
102,023
102,067
102,165
102,277
102,371
102,550
102,651
102,728
102,847
62,148
60.9
59,596
62,068
60.9
59,716
61,961
60.7
59,555
62,103
60.8
59,640
62,142
60.8
59,526
62,222
60.8
59,463
62,269
60.8
59,302
102,438
62,321
60.8
59,288
102,492
61,565
60.9
59,352
62,481
61.0
59,205
62,056
60.5
59,102
62,703
61.1
59,588
62,320
60.7
59,227
62,724
61.0
59,333
102,936
62,597
60.8
59,337
58.7
818
58.4
82
58.6
816
58.4
772
58.4
784
58.3
781
58.1
823
57.9
842
57.9
852
57.8
859
57.6
824
58.0
829
57.7
804
57.7
732
57.6
760
58,535
2,212
3.6
58,779
2,551
4.1
58,900
2,352
3.8
58,783
2,406
3.9
58,856
2,463
4.0
58,745
2,616
4.2
58,640
2,759
4.4
58,460
2,967
3.8
58,436
3,303
4.9
58,346
3,276
5.2
58,277
2,954
4.8
58,759
3,116
5.0
58,423
3,093
5.0
58,602
3,391
5.4
58,577
3,260
5.2
16,042
8,369
52.2
7,276
16,146
8,077
50.0
6,889
16,046
16,086
8,078
50.2
6,913
16,145
8,048
49.8
6,856
16,161
7,715
47.7
6,494
16,163
8,041
49.7
6,845
16,195
8,071
49.8
6,827
16,252
8,023
49.4
6,761
16,275
7,845
48.2
6,574
16,310
16,293
7,800
47.8
6,548
7,790
47.8
6,575
16,292
7,962
48.9
6,655
16,231
7,909
49.3
6,821
7,748
47.7
6,450
16,243
7,744
47.7
6,434
45.4
235
42.7
225
42.5
209
43.0
215
42.5
236
40.2
216
42.3
220
42.2
229
41.6
220
40.4
246
40.1
241
40.4
233
40.8
239
39.7
209
39.6
213
7,041
1,093
13.1
6,664
1,187
14.7
6,612
1,088
13.8
6,698
1,165
14.4
6,620
1,192
14.8
6,278
1,221
15.8
6,625
1,106
14.9
6,598
1,244
15.4
6,541
1,262
15.7
6,328
1,271
16.2
6,307
1,252
16.1
6,342
1,215
15.6
6,416
1,308
16.4
6,240
1,298
16.8
6,221
1,310
16.9
174,428
175,888
175,653
175,789
175,924
176,069
176,220
176,372
176,500
176,607
176,713
176,783
176,866
176,972
177,087
117,574
67.4
113,475
118,144
67.2
113,220
117,714
67.0
113,185
117,854
67.0
113,037
117,986
67.1
113,237
117,813
66.9
112,703
118,274
67.1
113,147
118,506
67.2
112,878
118,566
67.2
112,652
118,403
67.0
112,388
117,759
66.6
111,876
118,472
67.0
112,632
118,159
66.8
112,286
118,661
67.1
112,426
118,742
67.1
112,563
65.1
4,099
3.5
64.4
64.4
64.4
4,923
4.2
4,541
3.9
4,728
4.0
64.3
4,810
4.1
64.0
5,073
4.3
64.2
5,127
4.3
64.0
5,628
4.7
63.8
5,914
5.0
63.6
6,015
5.1
63.3
5,883
5.0
63.7
5,840
4.9
63.3
5,873
5.0
63.5
6,236
5.3
53.6
6,179
5.2
25,218
25,559
25,501
25,533
25,565
25,604
25,644
25,686
25,720
25,752
25,785
25,813
25,839
25,868
25,898
16,603
65.8
15,334
16,719
65.4
15,270
16,644
65.3
15,311
16,739
65.6
15,330
16,685
65.3
15,337
16,720
65.3
15,210
16,827
65.6
15,339
16,748
65.2
15,144
16,687
64.9
15,040
16,833
65.4
15,122
16,769
65.0
15,119
16,747
64.9
15,131
16,758
64.9
14,969
16,941
65.5
15,045
16,887
65.2
15,168
60.8
1,269
7.6
59.7
1,450
8.7
60.0
1,333
8.0
60.0
1,409
8.4
60.0
1,348
8.1
59.4
1,510
9.0
59.8
1,488
8.8
59.0
1,604
9.6
58.5
1,647
9.9
58.7
1,711
10.2
58.6
1,650
9.8
58.6
1,616
9.6
57.9
1,789
10.7
58.2
1,896
11.2
58.6
1,718
10.2
W o m e n , 20 y ea rs a n d o ver
Civilian noninstitutional
Civilian labor force..............
Employed........................
Employment-popAgriculture...................
Nonagricultural
industries..................
Unemployed....................
Unemployment rate....
B o th s e x e s , 1 6 t o 1 9 y e a rs
Civilian noninstitutional
population1..........................
Civilian labor force..............
Employed........................
Employment-popAgriculture...................
Nonagricultural
Industries..................
Unemployed...................
Unemployment rate....
W h it e
Civilian noninstitutional
population1..........................
Civilian labor force.............
Participation rate.........
Employment-popUnemployed...................
Unemployment rate....
B la c k
Civilian noninstitutional
Civilian labor force.............
Employment-population ratio2.............
Unemployed...................
Unemployment rate....
See footnotes at end of table.
76 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
4. Continued—Employment status of the population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted
[Numbers in thousands]
Employment status
2002
2001
Annual average
2000
2001
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
22,393
23,122
23,021
23,090
23,157
23,222
23,288
23,351
23,417
23,478
23,542
23,604
23,664
23,732
23,797
15,368
68.6
14,492
15,751
68.1
14,714
15,656
68.0
14,684
15,602
67.6
14,574
15,753
68.0
14,776
15,788
68.0
14,771
15,811
67.9
14,785
15,956
68.3
14,824
15,932
68.0
14,751
16,013
68.2
14,753
15,988
67.9
14,700
16,011
67.8
14,867
15,908
67.2
14,743
16,156
68.1
14,877
16,085
67.6
14,963
64.7
876
5.7
63.6
1,037
6.6
63.8
972
6.2
63.1
1,028
6.6
63.8
977
6.2
63.6
1,017
6.4
63.5
1,026
6.5
63.5
1,132
7.1
63.0
1,181
7.4
62.8
1,260
7.9
62.4
1,288
8.1
63.0
1,143
7.1
62.3
1,165
7.3
62.7
1,279
7.9
62.9
1,122
7.0
Hispanic origin
Civilian noninstitutional
Employment-pop-
UnemDlovment rate....
1 The population figures are not seasonally adjusted.
NOTE: Detail for the above race and Hlspanlc-orlgin groups will not sum to totals
2 Civilian employment as a percent of the civilian noninstitutional population.
5.
becausedata for the "other races" groups are not presented and Híspanles are included in
both the white and black population groups.
Selected employment indicators, monthly data seasonally adjusted
[In thousands]
Selected categories
2002
2001
Annual average
Dec.
Jan.
Feb.
Mar.
Apr.
May
133,894
71,299
62,595
133,976
71,397
62,579
134,417
71,894
62,524
2000
2001
May
June
July
Aug.
Sept.
Oct.
Nov.
Employed, 16 years and over..
Men.......................................
Women.................................
135,208
72,293
62,915
135,073
72,080
62,992
135,235
72,131
63,104
135,003
72,012
62,991
145,106
72,093
63,013
134,408
71,705
62,703
135,004
72,177
62,827
134,615
71,871
62,744
134,253
71,570
62,683
134,055
71,577
62,478
133,468
71,114
62,354
134,319
71,457
62,862
Married men, spouse
present................................
43,368
43,243
43,633
43,357
43,264
43,143
43,099
42,983
42,861
42,772
42,823
43,275
43,317
43,167
43,548
33,703
33,552
33,446
33,371
Characteristic
Married women, spouse
present................................
Women who maintain
families................................
Class of worker
Agriculture:
Wage and salary workers.....
Self-employed workers.......
Unpaid family workers.........
Nonagricultural industries:
Wage and salary workers.....
Government.........................
Private households.......
Other...............................
Self-employed workers......
Unpaid family workers........
33,708
33,613
33,692
33,466
33,571
33,685
33,604
33,227
33,330
33,209
33,174
8,387
8,364
8,335
2,513
1,558
8,328
8,274
8,256
8,331
8,458
8,396
8,417
8,320
8,266
8,397
2,034
1,233
38
1,884
1,233
27
1,957
1,208
34
1,803
1,193
32
1,798
‘ 152
23
1,852
1,239
29
1,882
1,278
24
1,898
1,290
26
1,865
1,276
12
1,879
1,313
27
1,917
1,311
49
1,930
1,293
21
1,825
1,264
29
1,896
1,216
34
1,911
1,156
4
123,128
19,053
104,076
890
103,186
8,674
101
123,235
19,127
104,108
803
103,305
8,594
101
123,530
19,068
10,442
795
103,667
8,540
111
123,069
18,934
104,135
760
103,375
8,720
102
123,204
18,999
104,205
790
103,415
8,568
98
122,685
19,150
103,535
814
102,721
8,503
111
123,186
19,290
103,896
804
103,092
8,556
101
122,710
19,223
103,487
867
102,620
8,505
95
122,507
19,172
103,335
790
102,545
8,507
77
122,196
19,183
103,013
736
102,277
8,524
92
122,145
19,047
103,098
725
102,373
8,213
97
122,770
19,286
103,485
709
102,775
8,257
86
122,545
19,218
103,327
677
102,650
8,200
89
122,366
19,347
103,019
791
102,228
8,234
103
123,071
19,811
103,260
775
102,485
8,305
105
3,190
3,672
3,388
3,649
3,571
3,389
4,148
4,329
4,206
4,267
3,973
4,228
3,997
4,151
3,996
2,115
2,796
2,983
2,796
2,809
2,549
2,755
2,721
2,690
2,626
952
1,064
1,108
1,121
1,161
1,089
1,120
1,021
1,131
1,064
Persons at work part time1
All industries:
Part time for economic
reasons..............................
Slack work or business
1,927
2,355
2,205
2,276
2,174
944
1,007
921
1,008
1,011
Could only find part-time
Part time for noneconomic
reasons.............................
Nonagricultural industries:
Part time for economic
reasons..............................
Slack work or business
18,722
18,707
18,634
18,482
18,812
19,011
18,798
18,644
18,587
18,540
18,201
18,395
18,530
18,793
18,887
3,045
3,529
3,231
3,556
3,425
3,246
4,015
4,222
4,017
4,119
3,781
3,998
3,848
4,009
3,818
1,835
2,266
2,101
2,215
2,111
2,025
2,704
2,898
2,679
2,717
2,448
2,615
2,605
2,587
2,515
1,096
1,138
1,068
1,089
1,001
1,122
1,033
18,007
17,960
17,717
17,886
18,004
18,274
16,350
Could only find part-time
Part time for noneconomic
reasons.............................
924
989
899
990
993
927
1,045
1,082
18,165
18.177
18,097
18,066
18,283
18,485
18,232
18,065
1 Excludes persons "with a job but not at work" during the survey period for such reasons as vacation, Illness, or Industrial disputes.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
77
Current Labor Statistics:
Labor Force Data
Ó. Selected unemployment indicators, monthly data seasonally adjusted
[Unemployment rates]
2000
2002
2001
Annual average
Selected categories
2001
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Characteristic
Total, 16 years and over..............................
Both sexes, 16 to 19 years......................
Men, 20 years and over...........................
Women, 20 years and over.....................
4.0
13.1
3.3
3.6
4.8
14.7
4.2
4.1
4.4
13.8
3.9
3.8
4.6
14.4
4.1
3.9
4.6
14.8
4.0
4.0
4.9
15.8
4.4
4.2
5.0
14.9
4.3
4.4
5.4
15.4
4.8
4.8
5.6
15.7
5.2
4.9
5.8
16.2
5.2
5.2
5.6
16.1
5.2
4.8
5.5
15.6
5.0
5.0
5.7
16.4
5.2
5.0
6.0
16.8
5.4
5.4
5.8
16.9
5.2
5.2
White, total................................................
Both sexes, 16 to 19 years................
Men, 16 to 19 years.........................
Women, 16 to 19 years...................
Men, 20 years and over.....................
Women, 20 years and over................
3.5
11.4
12.3
10.4
2.8
3.1
4.2
12.7
13.8
11.4
3.7
3.6
3.9
12.0
13.3
10.7
3.4
3.4
4.0
12.7
14.3
11.0
3.6
3.4
4.1
13.2
13.8
12.6
3.5
3.5
4.3
13.8
15.1
12.4
3.8
3.6
4.3
12.7
13.6
11.7
3.8
3.8
4.7
23.1
14.7
11.5
4.4
4.1
5.0
13.5
15.8
11.1
4.7
4.2
5.1
13.7
14.6
12.8
4.6
4.5
5.0
14.2
13.7
14.6
4.7
4.2
4.9
14.0
15.4
12.6
4.4
4.4
5.0
14.5
16.3
12.7
4.5
4.3
5.3
14.0
15.4
12.5
4.8
4.6
5.3
14.8
15.4
14.2
4.8
4.5
Black, total................................................
Both sexes, 16 to 19 years................
Men, 16 to 19 years.........................
Women, 16 to 19 years...................
Men, 20 years and over.....................
Women, 20 years and over................
7.6
24.7
26.4
23.0
7.0
6.3
8.7
29.0
30.5
27.5
8.0
7.0
8.0
25.7
30.0
21.5
7.6
6.4
8.4
28.0
30.5
25.7
7.8
6.7
8.1
26.6
28.1
25.2
7.9
6.2
9.0
30.1
31.4
28.7
8.8
7.0
8.8
28.5
30.8
26.1
7.8
7.7
9.6
30.2
31.2
29.1
8.2
8.5
9.9
32.1
31.6
32.6
8.7
8.4
10.2
33.4
32.0
34.8
9.1
8.7
9.8
30.7
32.1
29.0
8.9
8.4
9.6
27.9
30.0
25.6
8.7
8.5
10.7
31.0
36.9
44.7
10.1
9.0
11.2
35.4
37.3
33.5
9.3
10.2
10.2
30.2
36.8
22.3
8.6
9.5
5.7
6.6
6.2
6.6
6.2
6.4
6.5
7.1
7.4
7.9
8.1
7.1
7.3
7.9
7.0
2.0
2.7
5.9
3.9
4.8
2.7
3.1
6.6
4.7
5.1
2.6
2.9
6.2
4.3
4.8
2.6
3.0
6.3
4.5
5.2
2.7
2.9
6.3
4.5
5.1
2.8
3.1
6.8
4.8
5.4
2.8
3.3
7.1
5.0
4.6
3.1
3.6
6.8
5.4
5.5
3.3
3.6
8.0
5.6
5.6
3.4
3.7
8.0
5.8
5.6
3.5
3.4
8.9
5.7
5.2
3.4
3.8
8.0
5.7
4.8
3.4
3.7
7.3
5.8
5.2
3.9
3.9
8.6
6.2
5.2
3.6
3.9
8.1
5.9
5.6
4.1
3.9
6.4
3.6
3.4
4.0
3.1
5.0
2.3
3.8
2.1
7.5
5.1
4.7
7.3
5.2
5.3
5.1
4.1
5.6
2.8
4.6
2.2
9.7
4.3
4.9
5.7
4.7
4.8
4.5
3.3
5.1
2.1
4.0
1.8
6.1
4.8
4.9
5.4
4.9
4.9
4.8
3.8
5.6
2.4
4.6
2.6
7.2
4.8
3.3
4.9
5.5
5.4
5.7
3.8
5.2
3.1
4.6
2.8
7.8
5.1
4.8
6.0
5.6
5.9
5.3
3.9
5.4
2.8
5.1
2.7
7.5
5.0
4.2
5.9
5.5
5.6
5.3
3.9
5.7
2.9
4.8
2.2
5.5
5.4
5.9
6.3
5.6
6.0
5.1
5.4
5.8
2.9
5.2
2.1
7.3
5.7
3.8
8.0
6.1
6.6
5.3
5.4
6.0
3.6
5.3
2.1
10.4
5.8
5.9
9.5
6.6
7.2
5.7
5.7
6.3
2.8
4.9
2.1
13.2
6.9
8.7
13.9
7.1
7.8
6.1
7.0
7.1
2.4
5.9
2.4
15.2
6.6
6.4
12.3
7.0
7.6
6.1
6.3
7.2
3.0
5.7
2.5
14.0
6.5
6.4
11.9
7.3
7.5
6.9
5.7
7.2
3.0
5.5
2.3
16.8
6.3
5.6
9.9
7.0
7.3
6.6
5.9
7.0
3.1
5.3
2.1
8.0
6.0
4.3
7.5
6.6
6.3
7.0
5.3
6.8
3.6
5.4
2.4
6.6
6.4
3.5
7.3
4.2
5.8
3.6
6.3
3.7
6.4
4.1
6.6
4.3
7.1
4.0
7.1
4.3
7.9
4.8
9.0
4.8
10.1
6.2
9.7
6.0
9.4
5.9
8.6
5.5
7.4
5.1
2.7
1.7
3.3
2.3
2.8
1.9
3.0
2.3
3.2
2.4
3.4
2.7
3.3
2.5
3.8
2.5
4.0
2.7
4.0
2.8
4.6
3.0
4.5
2.9
4.5
2.7
4.6
2.7
4.5
2.7
Hispanic origin, total.............................
Married men, spouse present..............
Married women, spouse present........
Full-time workers...................................
Part-time workers............................ ......
Industry
Nonagricultural wage and salary
Construction..............................................
Manufacturing...........................................
Transportation and public utilities.........
Wholesale and retail trade.....................
Finance, insurance, and real estate......
Services.....................................................
Agricultural wage and salary workers.......
Educational attainment1
Less than a high school diploma................
High school graduates, no college............
Some college, less than a bachelor’s
degree..........................................................
College graduates........................................
1 Data refer to persons 25 years and over.
7.
Duration of unemployment, monthly data seasonally adjusted
[Numbers in thousands]
Weeks of
78
2000
2001
May
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
1,503
862
641
2,955
2,152
1,798
980
818
2,807
2,366
1,907
1,084
823
3,084
2,522
2,042
1,136
906
3,090
2,573
2,317
1,207
1,110
3,024
2,724
2,410
1,295
1,115
2,978
2,586
2,546
1,418
1,127
2,828
2,515
2,561
1,383
1,178
3,078
2,411
2,688
1,355
1,333
2,793
2,818
2,854
1,360
1,494
2,876
2,531
2,952
1,316
1,636
12.4
6.4
12.9
6.3
12.7
6.7
13.2
6.6
13.3
7.3
13.0
7.4
14.4
7.6
14.5
8.2
14.6
8.8
15.0
8.1
15.4
8.1
16.6
8.9
17.1
9.8
2,833
2,163
1,746
949
787
2,714
2,021
Mean duration, in weeks..................
Median duration, in weeks..............
12.6
5.9
13.2
6.8
July 2002
July
2,647
2,170
1,630
948
682
2,543
1,803
1,309
665
644
Monthly Labor Review
June
2,809
2,098
1,571
843
728
Less than 5 weeks............................
5 to 14 weeks....................................
15 weeks and over...........................
15 to 26 weeks...............................
27 weeks and over........................
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
2002
2001
Annual average
unemployment
8.
Unemployed persons by reason for unemployment, monthly data seasonally adjusted
[Numbers in thousands]
2000
2001
2,492
842
1,650
775
1,957
431
2002
2001
Annual average
Reason for
unemployment
3,428
1,044
2,379
832
2,029
453
May
June
3,132
1,055
2,077
818
1,827
467
3,249
990
2,259
807
1,921
470
July
Aug.
3,294
1,020
2,274
791
1,948
442
3,438
1,071
2,367
877
2,162
488
Sept.
3,595
1,114
2,481
819
2,102
466
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
4,297
1,288
3,009
880
2,113
466
4,501
1,157
3,344
848
2,197
497
4,492
1,107
3,385
908
2,361
495
4,354
1,124
3,231
879
2,191
479
4,326
1,106
3,220
877
2,268
485
4,370
1,066
3,204
862
2,471
557
4,525
1,095
3,430
1,017
2,450
519
4,598
1,091
3,506
902
2,433
499
Percent of unemployed
44.1
50.8
50.2
50.4
50.9
49.4
51.5
55.4
56.0
54.4
55.1
54.4
52.3
53.2
54.5
14.9
29.2
13.7
34.6
7.6
15.6
35.3
12.3
30.1
6.7
16.9
33.3
13.1
29.3
7.5
15.4
35.0
12.5
29.8
7.3
15.8
35.1
12.2
30.1
6.8
15.4
34.0
12.6
31.0
7.0
16.0
35.5
11.7
30.1
6.7
16.6
38.8
11.3
27.2
6.0
14.4
41.6
10.5
27.3
6.2
13.4
41.0
11.0
28.6
6.0
14.2
40.9
11.1
27.7
6.1
13.9
40.5
11.0
28.5
6.1
13.1
39.3
10.6
30.3
6.8
12.9
40.3
12.0
28.8
6.1
12.9
41.6
10.7
28.9
5.9
1.8
2.4
2.2
2.3
2.3
2.4
2.5
3.0
3.2
3.2
3.1
3.0
3.0
3.2
3.2
.6
1.4
.3
.6
1.4
.3
.6
1.3
.3
.6
1.4
.3
.6
1.4
.3
.6
1.5
.3
.6
1.5
.3
.6
1.5
.3
.6
1.5
.3
.6
1.7
.3
.6
1.5
.3
.6
1.6
.3
.6
1.7
.4
.7
1.7
.4
.6
1.7
.3
Percent of civilian
labor force
New entrants.....................................
1 Includes persons who completed temporary jobs.
9.
Unemployment rates by sex and age, monthly data seasonally adjusted
[Civilian workers]
Sex and age
2000
2001
2002
2001
Annual average
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Total, 16 years and over...................
16 to 24 years.................................
16 to 19 years.............................
16 to 17 years..........................
18 to 19 years..........................
20 to 24 years.............................
25 years and over..........................
25 to 54 years..........................
4.0
9.3
13.1
15.4
11.5
7.1
3.0
3.1
2.6
4.8
10.6
14.7
17.1
13.2
8.3
3,7
3.8
3.0
4.4
10.0
13.8
15.8
12.5
7.9
3.4
3.5
2.6
4.6
10.4
14.4
16.5
13.0
8.2
3.5
3.6
2.8
4.6
10.2
14.8
19.0
12.4
7.7
3.5
3.7
2.9
4.9
11.3
15.8
18.6
14.4
8.9
3.8
3.9
3.1
5.0
10.8
14.9
16.6
13.9
8.6
3.8
3.9
3.2
5.4
11.5
15.4
17.4
14.2
9.3
4.2
4.4
3.4
5.6
11.7
15.7
17.5
14.8
9.5
4.4
4.6
3.5
5.8
11.9
16.2
18.8
14.8
9.6
4.5
4.7
4.0
5.6
11.9
16.1
17.0
15.2
9.7
4.4
4.7
3.5
5.5
11.6
15.6
16.5
14.7
9.5
4.5
4.6
3.8
5.7
12.5
16.4
16.5
15.1
10.3
4.5
4.7
3.5
6.0
12.3
16.8
19.4
15.1
10.0
4.9
5.0
4.0
5.8
11.6
16.9
20.7
14.8
8.9
4.8
5.0
4.2
Men, 16 years and over..................
3.9
9.7
14.0
16.8
12.2
7.3
2.8
2.9
2.7
4.8
1 i.4
15.9
18.8
14.1
8.9
3.6
3.7
3.3
4.5
11.0
15.4
17.9
13.9
8.7
3.3
3.4
2.9
4.7
11.6
15.8
18.5
14.2
9.3
3.4
3.5
3.0
4.7
10.7
15.6
19.1
13.4
8.1
3.6
3.6
3.1
5.1
12.3
17.4
21.9
15.0
9.5
3.8
3.9
3.3
5.0
1.5
16.0
18.7
14.5
9.1
3.7
3.8
3.3
5.5
12.4
17.2
20.3
15.1
9.8
4.2
4.3
3.7
5.9
13.0
17.7
20.4
16.2
10.5
4.5
4.6
4.1
5.8
12.8
17.2
20.0
15.6
10.5
4.5
4.5
4.2
5.8
12.5
16.3
17.6
15.1
10.6
4.5
4.7
3.8
5.6
12.4
16.8
19.6
15.4
10.2
4.4
4.5
4.1
5.9
13.7
18.5
20.8
16.7
11.1
4.5
4.7
3.6
6.1
13.0
18.1
19.6
17.2
10.3
4.8
4.9
4.3
5.9
12.5
18.6
23.7
15.6
9.4
4.8
4.9
4.5
Women, 16 years and over............
16 to 24 years..............................
4.1
8.9
12.1
14.0
10.8
7.0
4.7
9.7
13.4
15.3
12.2
7.5
4.3
8.8
12.1
13.8
11.0
7.0
4.4
9.2
13.0
14.4
11.8
7.0
4.6
9.7
14.0
18.8
11.3
7.3
4.8
10.3
14.1
15.4
13.7
8.2
5.0
10.1
13.6
14.3
13.3
8.1
5.3
10.5
13.6
14.5
13.3
8.7
5.4
10.3
13.7
14.5
13.3
8.3
5.8
11.0
15.1
17.6
14.0
8.7
5.4
11.3
15.8
16.4
15.2
8.7
5.5
10.7
14.3
13.6
13.9
8.7
5.5
11.2
14.3
15.3
13.4
9.4
6.0
11.6
15.4
19.2
12.9
9.6
5.8
10.7
15.2
17.4
14.1
8.3
25 years and over........................
3.2
3.3
3.7
3.8
3.4
3.6
3.5
3.7
3.5
3.7
3.8
3.9
4.0
4.0
4.2
4.4
4.4
4.7
4.6
4.8
4.3
4.6
4.6
4.7
4.4
4.6
5.0
5.1
4.8
5.1
55 years and over.................
2.6
2.7
2.4
2.6
2.6
2.8
3.2
3.2
2.8
3.7
3.0
3.5
3.4
3.7
3.7
25 years and over........................
25 to 54 years........................
55 years and over.................
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
79
Current Labor Statistics:
Labor Force Data
10. Unemployment rates by State, seasonally adjusted
Apr.
2001
State
Alabama.........................................................
Alaska............................................................
Mar.
Apr.
2002p
2002p
5.0
6.3
4.2
5.0
5.0
6.0
6.3
5.9
5.3
6.5
5.6
6.6
5.7
5.3
6.5
3.2
2.9
3.5
6.2
4.3
5.6
3.5
3.8
6.7
5.4
Hawaii.............................................................
Idaho..............................................................
Illinois..............................................................
3.8
4.5
4.9
5.2
3.9
Kansas............................................................
Kentucky........................................................
Louisiana.......................................................
Arkansas........................................................
California........................................................
Colorado........................................................
Florida.............................................................
Apr.
2001
State
Missouri
Mar.
Apr.
2002p
2002p
4.6
4.6
3.1
4.8
3.3
5.2
46
36
58
4.1
5.2
4.6
39
5.5
40
5.3
3.8
4.2
6.4
5.3
40
47
4.5
5 1
30
5
6
5
6
3
6
1
9
6
1
5.6
6.0
6 1
6.9
4.6
4.6
5.5
6.1
4.9
4.6
4.3
5.2
6.4
5.1
4 1
3.4
58
4.6
47
58
4.2
79
5R
5.6
42
4.4
75
5.4
46
3.3
4.2
5.2
5.8
3.9
3.4
4.4
S8
Utah...............................
52
32
43
4.5
4 1
60
5.3
5.6
4.1
3.6
4.5
5.3
5.8
4.0
32
5.7
58
54
34
53
6.2
54
3.9
3.3
4.9
3.7
5.1
5.3
4.3
6.0
4.4
6.6
5.4
4.7
6.1
4.3
7.1
Wyoming........................................................
3.4
30
60
5.1
45
3.9
3.9
4.2
68
5.9
57
3.9
39
4.6
72
60
54
4.4
36
p = preliminary
11. Employment of workers on nonfarm payrolls by State, seasonally adjusted
[In thousands]
State
Apr.
2001
Mar.
2002p
Apr. 2002
State
Alabama...................
Alaska.......................
Arizona.....................
Arkansas..................
California..................
1,919.2
288.3
2,273.8
1,160.0
14,720.7
1,899.9
291.7
2,243.4
1,155.7
14,672.0
1,899.1
290.6
2,243.4
1,152.8
14,667.7
Missouri..........................................
Montana..........................................
Nebraska.........................................
Colorado..................
Connecticut..............
Delaware..................
District of Columbia.
Florida.......................
2,241.4
1,685.8
421.5
649.5
7,200.4
2,190.1
1,673.3
416.6
649.2
7,178.8
2,195.6
1,673.6
414.6
651.6
7,191.6
New Jersey.....................................
New Mexico...................................
Georgia....................
Hawaii.......................
Idaho.........................
Illinois........................
Indiana.....................
3,987.9
555.9
569.9
6,032.4
2,947.3
3,867.7
549.0
568.3
5,922.3
2,910.5
Iowa..........................
Kansas.....................
Kentucky..................
Louisiana................. .
Maine........................
1,472.1
1,352.8
1,815.8
1,928.0
608.8
Maryland..................
Massachusetts........
Michigan...................
Minnesota................
Mississippi................
2,464.0
3,350.6
4,602.7
2,689.8
1,134.4
Apr.
2001
2,691.1
393.2
911.8
1,066.3
626.5
2,693.1
394.5
911.0
1,068.6
627.4
North Dakota...................................
4,026.8
756.6
8,645.6
3,897.9
331.0
4,014.6
763.0
8.541.3
3.882.3
330.5
4,010.7
760.9
8,534.5
3,877.2
329.6
3,880.2
544.8
569.8
5,916.3
2,902.6
Ohio.................................................
Oklahoma.......................................
Oregon.............................................
Pennsylvania..................................
Rhode Island..................................
5,581.5
1,510.7
1,605.9
5,713.8
479.7
5,534.9
1,518.6
1,575.7
5,650.8
480.3
5,520.9
1,520.6
1,576.6
5,645.1
483.3
1,461.3
1,362.1
1,823.0
1,923.3
609.0
1,461.4
1,358.1
1,823.6
1,930.4
609.9
South Carolina...............................
South Dakota..................................
Tennessee......................................
Texas...............................................
Utah.................................................
1,834.4
379.2
2,715.4
9,550.5
1,083.6
1,827.1
375.4
2,717.2
9,455.7
1,072.4
1,828.6
378.1
2,707.5
9,458.7
1,069.2
2,456.5
3,395.6
4,562.6
2,659.9
1,133.1
2,454.2
3,299.2
4,554.4
2,655.7
1,131.4
Vermont..........................................
Virginia............................................
Washington.....................................
West Virginia..................................
Wisconsin.......................................
Wyoming.........................................
299.5
3,537.0
1,714.2
737.6
2,834.0
244.4
296.1
3,497.4
2,651.6
736.7
2,816.6
248.9
295.6
3,494.8
2,648.3
734.2
2,821.8
247.2
New Hampshire.............................
p = preliminary.
80
Monthly Labor Review
Apr.
2002
2,747.6
392.3
908.8
1,056.2
628.1
NOTE: Some data in this table may differ from data published elsewhere because of the continual updating of the data base.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Mar.
2002p
July 2002
12. Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted
[In thousands]______________________________________________________________________________
Industry
T O T A L ........................................
P R IV A T E S E C T O R ......................
G O O D S - P R O D U C IN G ......................
M i n i n g '................................................
Metal mining..............................
Oil and gas extraction................
Nonmetallic minerals,
except fuels.............................
C o n s tr u c tio n .....................................
General building contractors.....
Heavy construction, except
building....................................
Special trades contractors........
M a n u fa c tu r in g ..................................
Production workers..............
D u r a b le g o o d s ...............................
Production workers..............
Lumber and wood products....
Furniture and fixtures..............
Stone, clay, and glass
products.................................
Primary metal industries.........
Fabricated metal products......
Industrial machinery and
equipment.............................
Computer and office
equipment...........................
Electronic and other electrical
equipment.............................
Electronic components and
accessories..........................
Transportation equipment.......
Motor vehicles and
equipment............................
Aircraft and parts...................
Instruments and related
products................................
Miscellaneous manufacturing
industries................................
Annual average
2002
2001
2000
2001
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.p
Mayp
131,739
111,079
131,922
110,989
132,229
111,375
132,108
111,204
132,045
111,074
131,966
110,968
131,819
110,776
131,414
110,349
131,087
109,987
130,890
109,768
130,871
109,734
130,706
109,544
130,701
109,505
130,680
109,495
130,702
109,496
25,709
543
41
311
24,944
25,147
25,012
24,907
24,261
565
33
339
24,130
568
33
342
564
32
339
560
32
336
23,905
564
32
339
23,870
570
35
342
24,353
566
34
340
23,975
567
35
341
24,675
571
35
343
24,041
566
37
340
24,776
571
35
343
24,511
565
36
338
566
34
340
558
32
334
114
111
111
111
112
111
111
110
110
111
111
111
111
112
112
6,698
1,528
6,685
1,462
6,714
1,465
6,697
1,462
6,680
1,457
6,679
1,461
6,674
1,462
6,643
1,456
6,629
1,454
6,634
1,459
6,615
1,459
6,597
1,458
6,593
1,462
6,541
1,452
6,541
1,454
901
4,269
922
4,300
921
4,328
921
4,314
925
4,298
925
4,293
924
4,288
922
4,265
925
4,250
924
4,251
919
4,237
914
4,225
908
4,223
901
4,188
908
4,179
18,469
12,628
17,695
11,933
17,867
12,065
17,748
11,971
17,657
11,901
17,526
11,797
17,430
11,719
17,302
11,620
17,158
11,513
17,062
11,437
16,947
11,362
16,880
11,305
16,822
11,264
16,800
11,250
16,758
11,245
11,138
7,591
10,636
7,126
10,769
7,230
10,684
7,162
10,606
7,101
10,516
6,026
10,445
6,971
10,343
6,889
10,237
6,809
10,166
6,753
10,070
6,690
10,023
6,653
9,976
6,625
9,976
6,620
9,963
6,619
832
558
786
519
788
529
788
524
786
519
783
513
784
507
777
500
772
495
770
494
771
492
771
491
769
491
767
497
770
494
579
698
1,537
571
656
1,483
574
666
1,493
572
660
1,482
569
665
1,478
568
649
1,471
566
643
1,465
564
637
1,455
561
625
1,438
558
617
1,437
555
607
1,427
551
601
1,425
550
596
1,422
551
598
1,425
549
597
1,428
2,120
2,010
2,049
2,025
2,003
1,976
1,957
1,935
1,909
1,887
1,868
1,855
1,846
1,842
1,826
361
343
353
347
341
336
331
328
325
322
317
315
315
313
308
1,719
1,631
1,672
1642'
1,611
1,586
1,565
1,542
1,520
1,499
1,478
1,459
1,445
1,443
1,437
682
1,849
661
1,760
684
1,771
667
1,765
652
1,763
635
1,760
628
1,750
616
1,729
605
1,720
595
1,709
582
1,680
571
1,682
566
1,674
566
1,671
567
1,675
1,013
465
947
461
952
464
948
464
950
464
945
463
937
463
921
458
921
452
920
449
902
437
913
427
915
419
912
416
914
416
852
830
845
844
842
837
832
829
825
822
818
816
813
811
807
394
380
382
382
380
373
376
375
372
373
374
372
370
371
372
7,331
5,038
7,059
4,808
7,098
4,835
7,064
4,809
7,051
4,800
5,010
4,771
6,985
4,748
6,959
4,731
6,921
4,704
6,896
4,684
6,877
4,672
6,857
4,652
6,846
4,639
6,824
4,630
6,808
4,626
Food and kindred products.....
Tobacco products....................
Textile mill products.................
Apparel and other textile
products................................
Paper and allied products.......
Printing and publishing............
Chemicals and allied products.
Petroleum and coal products...
Rubber and miscellaneous
plastics products....................
Leather and leather products..
1,684
34
528
1,691
34
478
1,691
34
485
1,691
34
478
1,689
34
475
1,685
35
469
1,690
34
464
1,690
34
459
1,690
34
451
1,685
34
448
1,686
34
444
1,686
33
441
1,685
34
440
1,689
33
436
1,687
34
434
633
657
1,547
1,038
127
566
834
1,490
1,022
126
575
636
1,503
1,022
125
566
635
1,494
1,021
126
566
632
1,487
1,024
126
555
630
1,480
1,022
126
551
628
1,471
1,019
126
546
627
1,463
1,018
127
537
626
1,453
1,015
127
537
624
1,444
1,012
126
536
622
1,437
1,008
126
531
621
1,428
1,011
126
527
620
1,419
1,010
126
523
615
1,413
1,008
125
520
612
1,407
1,006
125
1,011
71
958
60
964
61
959
60
959
59
950
58
945
57
939
56
932
56
930
56
928
56
924
56
929
56
927
55
928
55
S E R V IC E -P R O D U C IN G ...................
106,050
106,978
107,082
107,096
107,138
107,190
107,144
106,903
106,734
106,629
106,741
106,665
106,726
106,775
106,832
7,019
4,529
236
7,065
4,497
234
7,131
4,546
235
7,121
4,540
234
7,110
4,535
233
7,088
4,522
233
7,044
4,487
232
6,974
4,427
232
6,907
4,367
232
6,856
4,332
233
6,850
4,343
235
6,837
4,341
234
6,814
4,330
233
6,799
4,330
230
6,793
4,328
228
476
1,856
196
1,281
14
471
480
1,848
192
1,266
15
462
480
1,856
192
1,295
15
473
477
1,855
195
1,291
15
473
484
1,850
196
1,288
15
469
480
1,845
194
1,291
15
463
477
1,841
192
1,268
15
462
478
1,831
193
1,236
15
442
480
1,831
189
1,187
15
433
481
1,827
188
1,159
15
429
481
1,824
188
1,171
15
429
479
1,826
187
1,171
15
429
478
1,819
186
1,172
15
427
476
1,830
190
1,162
15
427
475
1,827
193
1,165
15
425
2,490
1,639
2,570
1,716
2,585
1,732
2,581
1,726
2,575
1,721
2,566
1,714
2,557
1,706
2,547
1,696
2,540
1,689
2,524
1,679
2,507
1,660
2,496
1,652
2,484
1,643
2,469
1,628
2,465
1,626
N o n d u ra b le g o o d s .......................
Production workers..............
T r a n s p o r t a t io n a n d p u b lic
u tilitie s ...........................................
Transportation...........................
Railroad transportation............
Local and interurban
passenger transit...................
Trucking and warehousing.....
Water transportation................
Transportation by air...............
Pipelines, except natural gas..
Transportation services.........
Communications and public
utilities.....................................
Communications......................
Electric, gas, and sanitary
851
852
853
855
854
852
851
851
851
845
847
844
841
841
839
W h o le s a le tr a d e ...............................
7,024
6,776
6,794
6,781
6,773
6,762
6,747
6,728
6,693
6,702
6,702
6,689
6,681
6,678
6,681
R e ta il tr a d e .........................................
23,307
23,522
23,566
23,581
23,577
23,553
23,509
23,470
23,449
23,318
23,396
23,331
23,332
23,345
23,327
1,016
2,837
2,491
1,044
2,897
2,559
1,041
2,916
2,577
1,054
2,917
2,579
1,047
2,911
2,574
1,049
2,901
2,566
1,051
2,902
2,567
1,052
2,888
2,552
1,049
2,877
2,540
1,050
2,853
2,520
1,049
2,856
2,520
1,048
2,892
2,550
1,053
2,901
2,560
1,061
2,915
2,575
1,068
2,897
2,560
Building materials and garden
General merchandise stores....
Department stores...................
See footnotes at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
81
Current Labor Statistics:
Labor Force Data
12. Continued—Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted
[In thousands]________________________________________________________________________________________________________________
Industry
Annual average
2000
Food stores...............................
Automotive dealers and
service stations.......................
New and used car dealers......
Apparel and accessory stores...
Furniture and home furnishings
stores......................................
Eating and drinking places.......
Miscellaneous retail
establishments.......................
Finance, insurance, and
real estate................................
Depository institutions............
Commercial banks................
Savings institutions...............
Nondepository Institutions......
Security and commodity
brokers..................................
Holding and other investment
Insurance...................................
Insurance carriers...................
Insurance agents, brokers,
Real estate................................
Services1...................................
Hotels and other lodging places
Personal services......................
Business services......................
Services to buildings...............
Personnel supply services......
Help supply services.............
Computer and data
processing services..............
Auto repair services
and parking.............................
Miscellaneous repair services....
Motion pictures..........................
Amusement and recreation
services..................................
Health services..........................
Offices and clinics of medical
doctors...................................
Nursing and personal care
facilities..................................
Hospitals..................................
Home health care services.....
Legal services...........................
Educational services.................
Social services...........................
Child day care services..........
Residential care.......................
Museums and botanical and
zoological gardens................
Membership organizations.......
Engineering and management
services..................................
Engineering and architectural
services.................................
Management and public
relations................................
Federal, except Postal
Service.................................
State..........................................
Education................................
Other State government.........
Local..........................................
Education................................
Other local government..........
2001
June
July
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.p
M ayp
3,541
3,453
3,448
3,439
3,432
3,438
3,442
3,448
3,430
3,421
3,402
3,392
3,392
3,397
2,412
1,114
1,193
2,425
1,121
1,189
2,421
1,118
1,199
2,425
1,120
1,195
2,426
1,119
1,191
2,438
1,123
1,196
2,434
1,123
1,188
2,426
1,123
1,177
2,434
1,126
1,173
2,438
1,131
1,163
2,436
1,133
1,187
2,430
1,134
1,172
2,426
1,131
1,175
2,429
1,129
1,170
2,434
1,133
1,169
1,134
8,114
1,141
8,256
1,135
8,270
1,135
8,277
1,131
8,304
1,137
8,272
1,141
8,234
1,136
8,239
1,156
8,224
1,156
8,190
1,138
8,238
1,143
8,161
1,143
8,154
1,141
8,152
1,146
8,130
3,080
317
3,131
3,130
3,128
3,128
3,121
3,110
3,086
3,038
3,069
3,083
3,088
3,085
3,086
7,560
3,710
2,029
1,430
253
681
7,712
3,800
2,053
1,434
256
720
7,719
3,807
2,052
1,433
255
713
7,719
3,812
2,059
1,437
256
720
7,718
3,803
2,056
1,434
255
724
7,728
3,809
2,059
1,435
256
728
7,739
3,813
2,061
1,437
258
733
7,743
3,812
2,061
1,439
257
740
7,751
3,821
2,068
1,442
260
747
7,748
3,818
2,070
1,444
261
752
7,748
3,819
2,070
1,450
262
755
7,745
3,812
2,072
1,446
263
754
7,740
3,809
2,074
1,447
264
753
7,743
3,813
2,075
1,446
264
756
7,732
3,813
2,075
1,446
264
756
748
769
785
777
765
763
758
750
745
734
729
726
722
723
723
251
2,346
1,589
257
2,369
1,595
257
2,367
1,596
256
2,369
1,596
258
2,369
1,597
259
2,371
1,599
261
2,375
1,598
261
2,379
1,600
261
2,377
1,597
262
2,372
1,594
259
2,372
1,594
260
2,376
1,593
260
2,375
1,591
259
2,374
1,989
261
2,369
1,583
757
1,504
773
1,544
771
1,545
773
1,538
772
1,546
772
1,548
777
1,551
779
1,552
780
1,553
778
1,558
778
1,557
783
1,557
784
1,556
785
1,556
786
1,550
40,460
832
1,914
1,251
9,858
994
3,887
3,487
40,970
849
1,870
1,269
9,572
1,016
3,446
3,084
41,018
848
1,889
1,267
9,646
1,021
3,519
3,146
40,990
850
1,876
1,271
9,590
1,020
3,457
3,092
40,989
852
1,874
1,272
9,528
1,016
3,400
3,041
41,061
854
1,866
1,273
9,537
1,018
3,412
3,050
41,062
857
1,852
1,274
9,522
1,020
3,383
3,029
40,923
859
1,814
1,272
9,393
1,022
3,249
2,906
40,834
860
1,810
1,266
9,277
1,025
3,126
2,799
40,883
865
1,805
1,284
9,265
1,025
3,107
2,782
10,908
865
1,811
1,290
9,231
1,022
3,080
2,761
40,901
868
1,811
1,282
9,207
1,018
3,070
2,758
40,963
872
1,811
1,289
9,237
121
3,107
2,795
41,025
857
1,796
1,286
9,312
1,027
3,175
2,857
41,093
856
1,789
1,279
9,330
1,023
3,198
2,888
2,095
2,225
2,232
2,237
2,237
2,230
2,233
2,232
2,221
2,219
2,213
2,208
2,198
2,190
2,190
1,248
366
594
1,257
374
583
1,262
374
578
1,259
373
588
1,265
372
585
1,262
374
583
1,261
375
580
1,253
375
575
1,259
375
577
1,259
376
574
1,262
376
581
1,262
379
574
1,260
377
572
1,261
377
574
1,262
375
578
1,728
1,721
1,747
1,724
1,722
1,714
1,700
1,702
1,685
1,680
1,699
1,649
1,635
1,611
1,621
10,197
10,381
10,333
10,365
10,393
10,424
10,452
10,476
10,502
10,530
10,551
10,575
10,602
10,611
10,626
1,924
2,002
1,995
2,003
2,006
2,012
2,016
3,018
2,025
2,029
2,033
3,041
2,046
2,044
2,050
1,795
3,990
643
1,010
2,325
2,903
712
806
1,847
4,096
636
1,037
2,433
307
716
864
1,837
4,072
633
1,036
2,450
3,036
713
857
1,845
4,087
635
1,035
2,434
3,054
719
863
1,848
4,101
634
1,038
2,439
3,076
723
868
1,852
4,117
637
1,041
2,449
3,094
727
873
1,858
4,129
639
1,046
2,452
3,097
722
878
1,862
4,141
639
1,047
2,454
3,110
721
884
1,866
4,153
640
1,049
2,458
3,121
721
888
1,871
4,164
641
1,051
2,463
3,135
723
891
1,876
4,174
643
1,053
2,473
3,149
723
896
1,875
4,134
642
1,054
2,485
3,155
722
899
1,879
4,193
643
1,056
2,489
3,162
723
902
1,883
4,199
643
1,059
2,501
3,167
925
903
1,886
4,207
644
1,066
2,518
3,164
722
901
106
2,475
110
2,468
110
2,466
111
2,471
111
2,464
111
2,473
111
2,479
110
2,474
109
2,473
110
2,473
110
2,471
109
2,471
109
2,470
109
2,477
108
2,480
3,419
3,593
3,582
3,595
3,604
3,612
3,610
3,616
3,620
3,621
3,624
3,629
3,631
3,636
3,649
1,017
1,053
1,054
1,056
1,057
1,058
1,057
1,056
1,051
1,048
1,047
1,044
1,044
1,041
1,042
1,090
1,166
1,160
1,165
1,166
1,171
1,175
1,178
1,182
1,184
1,192
1,193
1,191
1,202
1,209
20,681
2,777
20,933
2,616
20,854
2,612
20,904
2,617
20,971
2,622
20,998
2,624
21,043
2,622
21,065
2,622
21,100
2,622
21,122
2,616
21,137
2,615
21,162
2,609
21,196
2,608
21,185
2,611
21,206
2,600
1,917
4,785
2,032
2,753
13,119
7,440
5,679
1,767
4,885
2,096
2,789
13,432
7,646
5,786
1,755
4,866
2,081
2,785
13,376
7,607
5,769
1,769
4,884
2,096
2,788
13,376
7,621
5,782
1,770
4,912
2,120
2,792
13,403
7,644
5,793
1,771
4,910
2,116
2,794
13,437
7,668
5,796
1,774
4,938
2,140
2,798
13,464
7,679
5,804
1,778
4,925
2,118
2,807
13,483
7,693
5,825
1,776
4,925
2,121
2,804
13,518
7,710
5,849
1,776
4,932
2,124
2,808
13,559
7,723
5,852
1,776
4,935
2,127
2,808
13,575
7,732
5,861
1,777
4,937
2,130
2,807
13,593
7,746
5,871
1,782
4,940
2,133
2,807
13,617
7,767
5,878
1,784
4,942
2,135
2,807
13,645
7,754
5,879
1,777
4,945
2,141
2,804
13,661
7,770
5,891
1 Includes other industries not shown separately.
82
Aug.
3,521
p = preliminary.
Note : See "Notes on the data" for a description of the most recent benchmark revision.
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
2002
2001
May
July 2002
13. Average weekly hours of production or nonsupervisory workers on private nonfarm payrolls, by industry, monthly
data seasonally adjusted
Industry
2000
P R IV A T E S E C T O R .........................................
2001
2002
2001
Annual average
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.p Mayp
34.5
34.2
34.2
34.2
34.2
34.1
34.1
34.0
34.1
34.1
34.1
34.2
34.2
34.2
34.2
40.4
40.3
40.3
40.1
40.2
40.2
40.3
40.4
40.5
40.4
40.3
41.0
40.4
40.5
40.4
M I N I N G ......................................................................
43.1
43.5
43.8
43.5
43.4
43.5
43.6
43.0
43.5
43.8
43.0
43.4
43.3
42.4
43.0
M A N U F A C T U R I N G ............................................
41.6
4.6
40.7
3.9
40.8
3.9
40.7
3.9
40.8
3.9
40.7
4.0
40.6
3.9
40.5
3.8
40.4
3.8
40.8
3.8
40.6
3.9
40.7
3.9
41.0
4.1
40.9
4.2
40.9
4.2
42.1
4.7
41.0
40.0
43.1
44.9
41.0
3.9
40.6
39.0
43.6
43.6
41.1
3.9
40.6
38.7
43.8
43.5
41.0
3.9
40.5
38.5
43.9
43.7
41.1
3.9
40.9
39.7
43.8
43.8
41.0
3.9
40.8
39.7
43.7
43.6
40.9
3.8
41.2
39.1
43.9
43.7
40.7
3.7
30.7
38.6
43.6
43.4
40.6
3.7
40.7
38.8
43.6
43.0
40.9
3.8
41.0
39.2
43.4
43.7
41.0
3.9
40.5
40.1
43.8
43.6
41.1
3.9
40.9
40.3
44.1
43.8
41.3
4.1
41.1
40.6
43.6
44.4
41.4
4.1
40.8
40.8
43.8
44.3
41.3
4.1
40.8
40.4
43.4
44.1
46.0
42.6
44.6
41.4
44.5
41.5
44.8
41.3
44.6
41.5
44.6
41.4
45.3
41.2
44.5
41.1
43.9
41.0
44.4
41.3
44.5
41.3
44.8
41.6
45.5
41.7
45.1
41.6
45.6
41.9
42.2
40.6
40.8
40.5
40.6
40.3
40.3
40.2
39.9
40.1
40.1
40.1
40.5
40.6
40.7
41.1
43.4
44.4
41.3
39.0
39.4
41.9
42.7
40.9
37.9
39.2
42.3
43.2
41.0
37.9
39.3
42.0
42.9
40.9
38.3
39.1
42.1
42.9
40.8
38.2
39.1
42.2
43.6
40.6
38.1
39.1
41.5
42.4
41.1
37.7
39.0
41.5
42.4
40.7
37.3
39.0
41.6
42.5
40.6
37.4
39.4
41.9
43.2
40.6
38.0
38.7
42.7
44.3
40.5
38.2
38.9
42.3
43.7
40.4
38.4
39.4
42.4
43.9
40.6
38.8
39.5
42.6
44.4
40.4
38.8
39.4
42.3
44.2
40.4
38.8
40.8
4.4
41.7
41.2
37.8
42.5
40.3
4.0
41.1
39.9
37.3
41.6
40.3
3.9
41.1
40.2
37.7
41.6
40.3
4.0
41.1
40.1
37.4
41.7
40.3
4.0
40.9
39.7
37.4
41.8
40.2
4.1
41.1
39.8
37.1
41.3
40.2
4.1
41.0
39.8
36.9
41.7
40.1
4.0
41.2
39.4
36.6
41.4
40.1
3.9
41.0
39.3
36.9
41.3
40.1
3.9
40.9
40.0
36.9
41.3
40.0
4.0
41.0
40.2
36.7
41.1
40.2
3.9
41.0
40.9
36.7
41.5
40.4
4.2
41.4
41.4
37.4
41.5
40.3
4.3
41.2
41.5
37.1
41.6
40.4
4.3
41.2
41.4
37.0
41.9
G O O D S - P R O D U C I N G ........................................
Overtime hours.....................................
Overtime hours....................................
Lumber and wood products.................
Furniture and fixtures............................
Stone, clay, and glass products..........
Primary metal industries.......................
Blast furnaces and basic steel
products..............................................
Industrial machinery and equipment...
Electronic and other electrical
equipment............................................
Transportation equipment....................
Motor vehicles and equipment..........
Instruments and related products.......
Miscellaneous manufacturing.............
N o n d u r a b l e g o o d s ..........................................
Overtime hours....................................
Food and kindred products..................
Textile mill products..............................
Apparel and other textile products......
Paper and allied products....................
Printing and publishing.........................
Chemicals and allied products............
Rubber and miscellaneous
plastics products..................................
Leather and leather products.............
38.3
42.5
38.1
42.3
38.1
42.4
38.0
42.2
38.3
42.5
38.0
42.2
38.0
42.1
37.9
42.0
37.8
41.9
37.8
41.9
37.3
41.9
37.4
41.9
37.5
42.0
37.2
41.8
37.5
42.3
41.4
37.5
40.7
36.3
40.6
36.1
40.7
36.3
40.7
36.0
40.6
36.3
40.8
36.4
40.5
36.2
40.7
36.6
40.8
36.9
40.5
37.0
40.9
37.2
41.1
37.3
41.6
37.5
41.2
36.7
S E R V I C E - P R O D U C IN G ......................................
32.8
32.7
32.7
32.7
32.7
32.7
32.7
32.6
32.6
32.7
32.7
32.7
32.8
32.7
32.8
38.6
38.2
38.2
38.2
38.1
38.1
37.9
38.0
38.9
38.2
38.1
38.2
38.2
38.3
38.4
38.3
38.4
38.3
38.3
29.0
29.1
29.0
29.1
T R A N S P O R T A T IO N A N D
P U B L IC U T I L I T I E S ........................................
W H O L E S A L E T R A D E ......................................
R E T A IL T R A D E ...................................................
38.5
28.9
38.2
28.9
38.3
28.8
38.2
28.8
38.2
28.8
38.3
28.8
38.3
28.8
38.0
38.2
38.3
38.2
28.8
28.8
28.9
28.9
p = preliminary.
NOTE: See "Notes on the data" for a description of the most recent benchmark revision.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
83
Current Labor Statistics:
Labor Force Data
14. Average hourly earnings o f production or nonsupervisory workers on private nonfarm payrolls, by industry,
seasonally adjusted
Annual average
Industry
2001
2002
2000
2001
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.p
Mayp
$13.75
$14.32
$14.24
$14.29
$14.33
$14.38
$14.43
$14.46
$14.52
$14.56
$14.59
$14.62
$14.65
$14.68
$14.70
G o o d s - p r o d u c i n g ...........................................
15.40
15.92
15.85
15.89
15.92
15.99
16.02
16.05
16.11
16.18
16.24
16.28
16.29
16.32
16.35
Mining......................................................
17.24
17.56
17.49
17.62
17.63
17.62
17.62
17,70
17.68
17.51
17.69
17.66
17.72
17.63
17.87
Construction............................................
17.88
18.34
18.23
18.30
18.29
18.37
18.39
18.40
18.47
18.60
18.65
18.68
18.74
18.83
18.77
Manufacturing........................................
14.38
14.83
14.78
14.81
14.86
14.91
14.95
14.99
15.03
15.08
15.13
15.17
15.19
15.19
15.27
Excluding overtime............................
13.62
14.15
14.09
14.13
14.19
14.22
14.28
14.31
14.36
14.39
14.42
14.46
14.45
14.43
14.53
14.18
14.21
14.24
P R IV A T E S E C T O R (in c u r r e n t d o lla r s )..
S e r v i c e - p r o d u c i n g ..........................................
13.24
13.85
13.76
13.82
13.86
13.91
13.97
14.00
14.06
14.10
14.11
14.14
Transportation and public utilities.......
16.22
16.79
16.71
16.77
16.81
16.81
16.87
16.96
17.03
17.09
17.13
17.16
17.26
17.26
17.31
Wholesale trade.....................................
15.20
15.86
15.75
15.89
15.87
15.88
15.99
15.97
15.98
16.07
16.10
16.19
16.23
16.11
16.12
Retail trade.............................................
9.46
9.77
9.69
9.75
9.77
9.79
9.81
9.84
9.90
9.89
9.90
9.92
9.95
9.97
9.99
Finance, insurance, and real estate....
15.07
15.80
15.71
15.78
15.85
15.88
15.93
15.97
16.00
16.00
16.06
16.08
16.14
16.18
16.17
Services...................................................
13.91
14.67
14.56
14.61
14.68
14.76
14.83
14.88
14.94
14.98
15.01
15.04
15.08
15.13
15.16
7.86
8.00
7.93
7.94
7.99
8.02
8.01
8.06
8.10
8.14
8.14
8.14
8.13
8.10
8.12
P R IV A T E S E C T O R (in c o n s t a n t (1 9 8 2 )
d o l l a r s ) ...................................................................
p = preliminary. Dash indicates data not available.
No t e : See "Notes on the data" for a description of the most recent benchmark revision.
84
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
15. Average hourly earnings of production or nonsupervisory workers on private nonfarm payrolls, by industry
2000
2001
2002
2001
Annual average
Industry
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.p
$14.62
$14.65
$14.67
$14.67
$14.69
$14.67
Mayp
PRIVATE SECTOR.....................................
$13.76
$14.21
$14.20
$14.26
$14.26
$14.50
$14.49
$14.54
MINING..........................................................
17.22
17.56
17.42
17.53
17.61
17.47
17.61
17.72
17.61
17.58
17.89
17.76
17.73
17.70
17.74
CONSTRUCTION.........................................
17.88
18.34
18.18
18.22
18.33
18.44
18.51
18.57
18.54
18.69
18.56
18.62
18.66
18.70
18.67
15.15
15.16
15.16
15.20
15.23
$14.32
MANUFACTURING.....................................
14.37
14.83
14.75
14.79
14.84
14.89
15.01
14.97
15.07
15.17
Durable goods...........................................
Lumber and wood products.................
Furniture and fixtures............................
Stone, clay, and glass products..........
Primary metal industries......................
Blast furnaces and basic steel
products..............................................
Fabricated metal products...................
14.82
11.94
11.74
14.53
16.41
15.28
12.26
12.24
15.00
16.92
15.19
12.16
12.13
15.01
16.78
15.24
12.19
12.19
15.11
16.93
15.26
12.32
12.27
15.10
17.07
15.38
12.37
12.33
15.16
17.02
15.49
12.44
12.39
15.21
17.23
15.46
12.37
12.42
15.09
17.08
15.55
12.40
12.45
15.13
17.24
15.66
12.42
12.56
15.10
17.19
15.61
12.38
12.61
15.12
17.15
15.63
12.39
12.59
15.17
17.15
15.63
12.35
12.57
15.12
17.20
15.66
12.33
12.54
15.35
17.25
15.68
12.43
12.59
15.43
17.36
19.82
13.87
20.41
14.25
20.26
14.22
20.39
14.25
20.48
14.26
20.62
14.34
20.90
14.42
20.52
14.33
20.66
14.42
20.53
14.56
20.53
14.57
20.63
14.51
20.66
14.60
20.69
14.66
20.81
14.64
Industrial machinery and equipment...
Electronic and other electrical
equipment............................................
Transportation equipment....................
Motor vehicles and equipment..........
Instruments and related products.......
15.55
15.89
15.76
15.79
15.88
15.93
16.01
16.07
16.16
16.23
16.31
16.33
16.31
16.30
16.35
13.79
18.46
18.80
14.41
11.63
14.51
19.06
19.40
14.81
12.16
14.36
18.88
19.23
14.67
12.11
14.49
18.96
19.31
14.74
12.07
14.56
18.85
19.09
14.91
12.12
14.70
19.13
19.43
14.93
12.23
14.82
19.36
19.73
15.00
12.38
14.78
19.41
19.83
14.97
12.24
14.88
19.54
19.96
14.98
12.35
14.97
19.71
20.19
15.09
12.39
14.86
19.57
19.99
15.09
12.46
14.90
19.69
20.05
15.10
12.42
14.93
19.65
20.09
15.12
12.39
14.87
19.68
20.22
15.11
12.36
14.91
19.65
20.17
15.11
12.37
Nondurable goods...................................
Food and kindred products..................
13.68
12.51
21.34
11.16
9.29
16.25
14.16
12.89
21.50
11.35
9.43
16.87
14.06
12.85
22.39
11.30
9.36
16.72
14.11
12.89
22.59
11.32
9.42
16.89
14.21
12.95
22.97
11.37
9.38
16.98
14.16
12.89
20.97
11.39
9.41
16.87
14.30
12.97
20.71
11.40
9.54
17.11
14.26
12.89
20.71
11.34
9.44
17.14
14.36
13.10
21.46
11.40
9.49
17.19
14.45
13.17
31.37
11.53
9.60
17.26
14.47
13.14
21.21
11.66
9.72
17.19
14.47
13.08
21.71
11.64
9.77
17.17
14.46
13.10
22.47
11.65
9.82
17.25
14.53
13.18
22.80
11.65
9.93
17.33
14.55
13.25
23.09
11.73
9.93
17.51
14.40
18.15
21.99
14.82
18.61
22.08
14.76
18.52
21.81
14.75
18.55
21.77
14.84
18.68
22.01
14.88
18.54
22.19
15.01
18.85
22.24
14.93
18.74
22.23
14.91
18.83
22.38
15.04
18.88
22.19
15.01
18.87
22.10
15.06
18.95
22.45
15.12
18.93
22.39
15.11
19.01
22.39
15.05
18.96
22.02
12.85
10.17
13.39
10.31
13.29
10.24
13.29
10.27
13.37
10.24
13.43
10.33
13.50
10.24
13.53
10.24
13.57
10.20
13.69
10.29
13.71
10.31
13.65
10.35
13.61
10.40
13.68
10.39
13.69
10.43
PUBLIC UTILITIES..................................
16.21
16.79
16.65
16.69
16L.81
16.78
16.91
16.98
17.05
17.11
17.18
17.18
17.24
17.31
17.24
WHOLESALE TRADE................................
15.22
15.86
15.71
15.81
15.92
15.80
16.08
15.95
15.96
16.21
16.11
16.21
16.13
16.11
16.08
RETAIL TRADE...........................................
9.46
9.77
9.67
9.70
9.70
9.71
9.86
9.87
9.91
9.89
9.96
9.95
9.98
10.00
9.98
15.14
15,80
15.72
15.68
15.82
15.77
15.96
15.91
15.97
16.14
16.07
16.13
16.17
16.23
16.26
14.45
14.52
14.52
14.85
14.87
14.99
15.15
15.14
15.17
15.16
15.16
15.12
Textile mill products..............................
Apparel and other textile products......
Paper and allied products....................
Printing and publishing.........................
Chemicals and allied products............
Petroleum and coal products...............
Rubber and miscellaneous
plastics products..................................
Leather and leather products.............
TRANSPORTATION AND
FINANCE, INSURANCE,
AND REAL ESTATE...............................
SERVICES....................................................
13.93
14.67
14.52
0 = preliminary.
No t e : See "Notes on the data” for a description of the most recent benchmark revision.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
85
Current Labor Statistics:
Labor Force Data
16. Average weekly earnings of production or nonsupervisory workers on private nonfarm payrolls, by industry
Industry
Annual average
2001
2002
2000
2001
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.p
Mayp
PRIVATE SECTOR
Current dollars............................
Seasonally adjusted..............
Constant (1982) dollars...........
$474.38
272.16
$489.74
273.45
$484.56
487.01
269.20
$488.48
488.72
271.08
$494.82
490.09
275,82
$491.97
490.36
274.23
$498.80
492.06
276.50
$492.66
491.64
274.31
$494.36
495.13
275.72
$502.93
496.50
281.91
$492.24
497.52
275.46
$497.31
500.00
277.36
$497.31
501.03
275.82
$497.99
502.06
274.53
$500,25
502.74
275.77
MINING...........................................
743.04
763.86
768.22
767.81
769.56
761.69
774.84
772.59
.764.27
771.76
754.96
761.90
757.07
750.48.
766.37
CONSTRUCTION...........................
702.68
720.76
730.84
730.62
740.53
741.29
738.55
737.23
724.91
719.57
714.56
716.87
716.54
723.69
728.13
MANUFACTURING
Current dollars...........................
Constant (1982) dollars............
598.21
343.21
603.58
337.01
600.33
333.52
603.43
334.87
599.54
334.19
609.00
338.46
616.91
341.97
607.78
338.41
613.35
342.08
625.00
350.34
612.06
342.51
610.95
340.74
620.04
343.89
620.16
341.87
622.91
343.39
Durable goods...............................
623.92
626.48
624.31
626.36
619.56
632.31
636.00
651.46
636.89
637.70
489.13
469.20
497.76
477.36
497.34
463.37
498.57
471.75
502.66
483.44
633.66
509.64
494.43
639.74
Lumber and wood products.....
Furniture and fixtures...............
Stone, clay, and glass
products.................................
Primary metal industries..........
Blast furnaces and basic
steel products........................
Fabricated metal products.......
Industrial machinery and
equipment.............................
Electronic and other electrical
equipment..............................
Transportation equipment........
Motor vehicles and
equipment............................
Instruments and related
products.................................
Miscellaneous manufacturing....
517.50
491.88
507.17
481.90
507.16
485.55
507.98
501.14
493.96
504.40
495.60
501.08
645.52
646.76
649.15
503.88
504.30
510.87
509.09 506 31/50 504 43/50
626.24
737.26
654.00
737.71
664.94
729.93
670.88
741.53
668.93
739.13
676.14
740.37
685.97
763.29
666.98
739.56
662.69
748.22
649.30
763.24
645.62
746.03
646.24
746.03
645.62
758.52
667.73
762.45
675.83
767.31
911.72
590.86
910.29
589.95
899.54
588.71
919.59
589.85
919.55
581.81
919.65
595.11
959.31
598.43
906.98
591.83
915.24
596.99
909.48
614.43
907.43
600.28
915.97
597.81
933.83
607.36
937.26
606.92
951.02
611.95
656.21
645.13
643.01
639.50
639.96
638.79
646.80
646.01
648.02
667.49
657.29
658.10
663.82
660.15
665.45
567.18
800.73
571.69
798.61
560.04
806.18
569.46
802.01
559.10
767.20
576.24
816.85
583.91
811.18
580.85
809.40
587.76
818.73
603.29
841.62
573.60
827.81
576.63
825.01
588.24
835.13
581.42
844.27
582.98
842.99
834.28
828.38
842.27
841.92
782.69
860.75
846.42
844.76
856.28
892.40
871.56
868.17
883.96
907.88
905.63
595.96
453.57
605.73
460.86
600.00
458.97
599.92
463.49
UUHIIIItttt
459.35
604.67
468.41
618.00
467.96
607.78
457.78
611.18
461.89
623.22
477.02
612.65
469.74
611.55
473.20
616.90
483.21
607.42
479.57
607.42
479.96
Nondurable goods......................
558.55
568.63
569.82
588.12
575.91
574.46
581.29
529.78
923.93
457.33
529.66
914.21
444.57
546.04
836.68
458.28
574.68
538.80
834.61
445.66
580.14
523.00
870.97
454.26
572.06
536.22
832.51
456.74
582.01
521.25
877.90
459.79
570.65
529.78
851.40
452.87
563.81
Food and kindred products......
Tobacco products.....................
Textile mill products.................
Apparel and other textile
products.................................
Paper and allied products.........
544.96
862.69
450.30
546.56
880.44
465.87
533.48
854.76
465.23
523.20
881.43
471.41
533.17
912.28
483.48
582.65
533.79
932.52
485.81
543.25
962.85
486.80
351.54
690.63
351.74
701.79
355.68
690.54
356.08
702.62
348.94
708.07
349.11
695.04
350.12
722.04
344.56
714.74
351.13
718.54
358.08
724.92
350.89
709.95
357.58
705.69
368.25
713.43
369.40
717.46
369.40
728.42
Printing and publishing.............
Chemicals and allied products..
Petroleum and coal products....
Rubber and miscellaneous
plastics products.....................
Leather and leather products....
551.52
771.38
932.80
564.64
787.20
945.02
556.45
783.40
911.66
557.55
782.81
933.93
563.92
790.16
953.03
568.42
780.53
954.17
577.89
797.36
954.10
568.83
787.08
926.99
572.54
793.74
939.96
576.02
800.51
934.20
555.37
790.65
932.78
558.73
790.22
938.41
568.51
793.17
920.23
560.58
794.62
900.23
559.86
800.11
887.41
531.99
381.75
544.97
374.25
539.57
370.69
543.56
377.94
534.80
361.47
543.92
379.11
556.20
376.83
549.32
372.74
553.66
376.38
568.14
380.73
555.26
378.38
556.92
380.88
559.37
386.88
564.98
388.59
564.03
382.78
TRANSPORTATION AND
PUBLIC UTILITIES......................
626.09
641.38
634.37
640.90
650.55
644.35
645.96
645.24
646.20
660.45
647.69
751.12
655.12
657.78
660.29
WHOLESALE TRADE....................
585.20
605.85
#######
603.94
612.92
605.14
620.69
606.10
611.27
627.33
608.96
615.98
614.55
615.40
615.86
RETAIL TRADE..............................
273.39
282.35
277.53
283.24
288.09
285.47
284.95
282.28
282.44
289.78
279.88
284.57
286.43
287.00
289.42
586.37
FINANCE, INSURANCE,
AND REAL ESTATE....................
547.04
570.38
559.63
567.62
579.01
567.72
585.73
569.58
573.32
592.34
575.31
582.29
580.50
581.03
577.63
SERVICES......................................
454.86
479.71
471.90
473.96
480.61
477.71
487.08
483.28
487.18
498.44
487.51
493.03
492.70
491.18
489.89
p= preliminary.
Note : See "Notes on the data" for a description of the most recent benchmark revision. Dash indicates data not available.
86
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
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
Aug.
July
Sept.
Oct.
Nov
Dec.
Private nonfarm payrolls, 356 industries
Over 1-month span:
1998..................................................
1999..................................................
2000..................................................
2001..................................................
2002..................................................
62.4
55.3
55.9
49.4
47.3
57.5
58.6
57.5
45.7
41.4
59.1
53.6
57.9
50.3
49.7
60.2
58.4
51.2
42.4
49.7
57.5
55.5
50.1
47.3
50.6
56.8
57.8
55.8
43.2
_
54.6
57.1
57.8
44.5
_
59.1
54.8
51.4
42.5
_
57.2
57.1
52.4
42.4
-
53.0
57.2
52.4
40.5
_
57.9
60.4
53.2
39.3
_
56.8
58.1
52.7
44.1
_
Over 3-month span:
1998..................................................
1999..................................................
2000..................................................
2001..................................................
2002..................................................
65.3
59.2
60.4
45.5
40.1
66.3
57.6
61.4
46.1
43.2
65.3
59.5
59.4
40.8
43.9
65.9
55.2
53.2
43.4
43.9
62.7
60.2
52.4
37.8
58.2
57.2
55.5
43.2
58.9
59.4
56.6
39.3
59.1
59.2
56.2
38.0
59.8
59.7
51.2
35.3
_
57.9
58.9
51.0
33.7
_
57.1
61.2
53.2
36.3
_
58.8
60.7
51.6
38.9
_
70.4
67.4
59.8
60.6
50.6
65.0
58.2
62.6
48.6
62.5
60.3
63.7
45.3
63.6
56.7
61.5
44.1
60.5
59.2
55.5
38.5
59.2
61.8
56.1
37.1
58.6
60.8
58.6
35.6
57.5
62.7
52.4
34.3
_
60.2
61.8
48.7
33.1
_
59.2
61.2
45.7
34.1
_
58.4
62.8
46.5
35.6
_
67.6
60.2
63.0
47.7
67.4
58.2
61.8
45.0
66.0
60.8
59.5
43.1
64.0
60.8
58.4
40.5
62.7
61.6
56.8
39.8
61.9
62.2
55.7
38.4
62.0
61.3
56.5
36.8
-
60.8
63.8
47.7
34.4
-
59.4
62.2
45.2
34.3
-
60.8
59.7
44.5
32.9
-
58.9
60.5
42.9
_
Over 6-month span:
1998..................................................
1999..................................................
2000..................................................
2001..................................................
2002..................................................
Over 12-month span:
1998..................................................
1999..................................................
2000..................................................
2001..................................................
2002..................................................
59.8
63.5
52.0
37.8
69.7
61.2
62.5
49.6
-
Manufacturing payrolls, 139 industries
Over 1-month span:
1998 ...............................................
1999..................................................
2000..................................................
2001..................................................
2002..................................................
57.0
47.4
44.9
34.9
35.3
52.6
41.2
52.2
26.8
37.9
52.2
42.6
49.3
38.2
40.4
52.9
46,0
46.0
29.0
47.1
44.9
46.3
49.3
28.3
46.7
47.4
43.4
50.7
30.5
38.2
50.0
57.4
34.9
52.9
42.6
36.8
25.7
44.9
46.0
39.0
31.6
_
38.6
45.6
42.3
31.3
_
42.3
51.5
47.1
25.0
_
41.5
49.3
40.8
30.9
_
Over 3-month span:
1998..................................................
1999 .................................................
2000..................................................
2001..................................................
2002..................................................
59.2
39.3
48.2
21.3
24.6
57.0
39.3
48.9
21.3
30.1
54.8
39.7
48.9
18.4
37.9
51.8
40.1
44.5
23.5
39.7
48.2
41.2
46.7
19.9
38.2
43.8
52.2
23.2
41.9
44.1
46.0
17.3
43.0
46.3
38.6
19.1
43.0
42.3
29.0
16.2
38.2
44.1
34.2
18.0
_
32.7
47.8
39.0
18.4
_
40.4
45.2
36.0
18.0
_
Over 6-month span:
1998..................................................
1999..................................................
2000..................................................
2001..................................................
2002..................................................
60.7
36.4
47.6
20.2
20.2
54.4
36.0
45.2
49.3
37.5
44.5
14.0
40.1
40.4
50.0
16.2
45.2
37.5
41.9
16.5
39.0
43.0
36.0
14.7
39.0
43.0
36.0
14.7
38.2
44.5
35.3
11.8
34.6
48.2
32.4
14.0
41.2
43.0
26.1
13.2
_
35.7
44.5
21.3
17.6
_
33.1
47.4
21.7
16.5
_
54.8
38.5
49.3
13.6
52.2
34.6
44.1
13.6
51.8
32.4
41.2
14.7
46.7
36.0
36.8
15.4
40.4
37.9
35.3
12.1
38.2
44.5
35.3
11.8
38.2
40.1
33.8
11.0
37.5
40.4
28.7
11.0
36.4
44.5
22.1
12.9
34.6
44.5
19.1
13.6
35.7
43.4
17.6
13.6
34.2
44.5
14.0
_
-
-
Over 12-month span:
1998.................................................
1999.................................................
2000.................................................
2001.................................................
2002.................................................
16.9
26.1
_
_
_
_
_
~
Dash indicates data not available.
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 inceasing and decreasing employment.
Data for the 2 most recent months shown in each span are
preliminary. See the "Definitions" in this section. See "Notes on
the data" for a description of the most recent benchmark
revision.
Monthly Labor Review
July 2002
87
Current Labor Statistics:
18.
Labor Force Data
Establishment size and employment covered under III, private ownership, by major industry division, first quarter 2000
S iz e o f e s t a b lis h m e n ts
In d u s t r y , e s t a b lis h m e n ts , a n d
T o ta l
e m p lo y m e n t
F e w e r th a n
5 to 9
1 0 t o 19
2 0 to 4 9
5 0 to 99
100 to 249
5 w o rk e rs '
w o rk e rs
w o rke rs
w o rk e rs
w o rk e rs
w o rk e rs
2 5 0 to 4 9 9
w o rk e rs
5 0 0 to 9 9 9
w o rk e rs
1 ,0 0 0 o r
m o re
w o rke rs
T o t a l, a ll in d u s t r ie s 2
Establishments, first quarter ..................
Employment, March ................................
7,531,330
108,195,174
4,413,181
6,831,146
1,302,488
8,615,974
850,411
11,471,927
590,662
17,878,154
206,415
14,212,796
119,172
17,895,603
31,311
10,658,780
11,713
7,965,372
5,977
12,665,422
200,289
1,702,493
123,880
179,158
37,646
248,989
22,736
302,599
11,179
326,510
2,875
196,681
1,473
216,628
370
126,181
106
69,476
24
36,271
27,284
524,514
14,102
22,082
4,323
28,959
3,728
51,183
3,202
97,241
1,023
69,762
591
89,714
214
74,836
76
52,916
25
37,821
747,563
6,310,456
477,549
703,310
126,844
831,405
76,253
1,024,819
46,543
1,389,870
13,242
898,785
5,748
846,893
1,053
347,400
272
182,357
59
85,617
405,838
18,433,795
147,029
251,154
67,385
453,397
61,150
842,691
61,487
1,922,360
30,568
2,144,676
24,264
3,739,308
8,646
2,977,743
3,598
2,446,323
1,711
3,656,143
315,413
6,678,516
174,645
272,380
49,173
325,334
36,475
498,572
30,720
945,800
12,952
895,012
7,913
1,190,459
2,127
726,615
892
618,630
516
1,205,714
664,094
6,947,770
400,335
621,924
110,091
729,753
77,321
1,046,983
52,153
1,565,359
15,187
1,035,060
7,019
1,035,170
1,478
496,350
414
274,988
96
142,183
1,458,626
22,807,395
623,529
1,154,942
329,260
2,204,569
235,941
3,190,042
179,053
5,437,335
57,988
3,943,391
26,380
3,880,016
4,982
1,659,975
1,169
764,056
324
573,069
671,294
7,379,831
438,402
714,292
114,349
751,197
62,141
826,817
35,549
1,065,116
11,618
797,168
6,025
912,396
1,799
621,570
898
615,246
513
1,076,029
2,890,313
37,110,557
1,879,338
2,772,133
451,715
2,967,673
271,168
3,643,823
169,867
5,102,854
60,864
4,225,937
39,727
5,980,102
10,640
3,627,319
4,286
2,939,641
2,708
5,851,075
A g r i c u lt u r e , fo r e s t r y , a n d fis h in g
Establishments, first quarter ..................
Employment, March ................................
M in in g
—
Establishments, first quarter ..................
Employment, March ................................
C o n s t r u c t io n
Establishments, first quarter ..................
Employment, March ................................
M a n u f a c t u r in g
Establishments, first quarter ..................
Employment, March ................................
T r a n s p o r t a t io n a n d p u b lic u tilitie s
Establishments, first quarter ..................
Employment, March ................................
W h o le s a l e tr a d e
Establishments, first quarter ..................
Employment, March ................................
R e ta il t r a d e
Establishments, first quarter ..................
Employment, March ................................
F in a n c e , i n s u r a n c e , a n d re a l e s t a te
Establishments, first quarter ..................
Employment, March ................................
S e r v ic e s
Establishments, first quarter ..................
Employment, March ................................
1 Includes establishments that reported no workers in March 2000.
NOTE: Detail may not add to totals due to rounding.
2 Includes data for nonclassifiable establishments, not shown separately.
88
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
19.
Annual data: establishments, employment, and wages covered under Ul and UCFE by ownership
Year
Average
establishments
Average
annual
employment
Total annual wages
(in thousands)
Average annual
wages
per employee
Average
weekly
wage
Total covered (Ul and UCFE)
1991 ......................................................
1992 ......................................................
1 9 9 3 ......................................................
1 9 9 4 ......................................................
1995 ......................................................
1996 ......................................................
1997 ......................................................
1998 ......................................................
1999 ......................................................
2000 ......................................................
6,382,523
6,532,608
6,679,934
6,826,677
7,040,677
7,189,-1^8
7,369,473
7,634,018
7,820,860
7,879,116
106,884,831
107,413,728
109,422,571
112,611,287
115,487,841
117,963,132
121,044,432
124,183,549
127,042,282
129,877,063
$2,626,972,030
2,781,676,477
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
$24,578
25,897
26,361
26,939
27,846
28,946
30,353
31,945
33,340
35,323
$473
498
507
518
536
557
584
614
641
679
$24,335
25,622
26,055
26,633
27,567
28,658
30,058
31,676
33,094
35,077
$468
493
501
512
530
551
578
609
636
675
$24,178
25,547
25,934
26,496
27,441
28,582
30,064
31,762
33,244
35,337
$465
491
499
510
528
550
578
611
639
680
$27,132
27,789
28,643
29,518
30,497
31,397
32,521
33,605
34,681
36,296
$522
534
551
568
586
604
625
646
667
698
$24,595
25,434
26,095
26,717
27,552
28,320
29,134
30,251
31,234
32,387
$473
489
502
514
530
545
560
582
601
623
$32,609
35,066
36,940
38,038
38,523
40,414
42,732
43,688
44,287
46,228
$627
674
710
731
741
777
822
840
852
889
Ul covered
1991 ......................................................
1 9 9 2 ......................................................
1993 ......................................................
1 9 9 4 ......................................................
1 9 9 5 ......................................................
1 9 9 6 ......................................................
1997 ......................................................
1 9 9 8 ......................................................
1999 ......................................................
2000 ......................................................
6,336,151
6,485,473
6,632,221
6,778,300
6,990,594
7,137,644
7,317,363
7,586,767
7,771,198
7,828,861
103,755,832
104,288,324
106,351,431
109,588,189
112,539,795
115,081,246
118,233,942
121,400,660
124,255,714
127,005,574
$2,524,937,018
2,672,081,827
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
Private industry covered
1991 ......................................................
1992 ......................................................
1 9 9 3 ......................................................
1 9 9 4 ......................................................
1995 ......................................................
1996 ......................................................
1997 ......................................................
1998 ......................................................
1 9 9 9 ......................................................
2000 ......................................................
6,162,684
6,308,719
6,454,381
6,596,158
6,803,454
6,946,858
7,121,182
7,381,518
7,560,567
7,622,274
89,007,096
89,349,803
91,202,971
94,146,344
96,894,844
99,268,446
102,175,161
105,082,368
107,619,457
110,015,333
$2,152,021,705
2,282,598,431
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
State government covered
1991 ......................................................
1 9 9 2 ......................................................
1993 ......................................................
1 9 9 4 ......................................................
1 9 9 5 ......................................................
1 9 9 6 ......................................................
1997 ......................................................
1998 ......................................................
1999 ......................................................
2000 ......................................................
58,499
58,801
59,185
60,686
60,763
62,146
65,352
67,347
70,538
65,096
4,005,321
4,044,914
4,088,075
4,162,944
4,201,836
4,191,726
4,214,451
4,240,779
4,296,673
4,370,160
$108,672,127
112,405,340
117,095,062
122,879,977
128,143,491
131,605,800
137,057,432
142,512,445
149,011,194
158,618,365
Local government covered
1991 ......................................................
1992 ......................................................
1993 ......................................................
1 9 9 4 ......................................................
1995 ......................................................
1996 ......................................................
1997 ......................................................
1998 ......................................................
1999 ......................................................
2000 ......................................................
114,936
117,923
118,626
121,425
126,342
128,640
130,829
137,902
140,093
141,491
10,742,558
10,892,697
11,059,500
11,278,080
11,442,238
11,621,074
11,844,330
12,077,513
12,339,584
12,620,081
$264,215,610
277,045,557
288,594,697
301,315,857
315,252,346
329,105,269
345,069,166
365,359,945
385,419,781
408,721,690
Federal Government covered (UCFE)
1991 ......................................................
1 9 9 2 ......................................................
1993 ......................................................
1 9 9 4 ......................................................
1995 ......................................................
1 9 9 6 ......................................................
1997 ......................................................
1998 ......................................................
1999 ......................................................
2000 ......................................................
NOTE:
46,372
47,136
47,714
48,377
50,083
51,524
52,110
47,252
49,661
50,256
3,128,999
3,125,404
3,071,140
3,023,098
2,948,046
2,881,887
2,810,489
2,782,888
2,786,567
2,871,489
$102,035,012
109,594,650
113,448,871
114,992,550
113,567,881
116,469,523
120,097,833
121,578,334
123,409,672
132,741,760
Detail may not add to totals due to rounding.
Monthly Labor Review
July 2002
89
Current Labor Statistics:
20.
Labor Force Data
Annual data: establishments, employment, and wages covered under Ul and UCFE, by State
Average
establishments
State
2000
2000
19992000
change
Total annual wages
(in thousands)
2000
19992000
change
Average weekly
wage
2000
19992000
change
Total United States ..........................................
7,879,116
58,256
129,877,063
2,834,781
$4,587,708,584
$352,129,380
$679
$38
Alabam a..............................................................
A lask a..................................................................
Arizona................................................................
Arkansas .............................................................
California.............................................................
112,328
18,820
115,171
72,240
1,026,568
454
32
2,589
406
-33,271
1,877,963
275,607
2,220,712
1,130,891
14,867,006
6,911
6,674
70,174
17,750
472,932
54,538,027
9,685,341
72,417,033
29,761,939
612,318,313
1,970,401
532,709
6,772,271
1,520,062
71,430,084
558
676
627
506
792
18
22
40
18
69
Colorado .............................................................
Connecticut.........................................................
Delaw are.............................................................
District of Columbia...........................................
Florida..................................................................
148,479
107,787
24,751
28,409
444,731
6,278
1,696
584
1,474
9,134
2,186,656
1,674,728
406,350
637,292
7,060,986
81,404
22,363
4,210
21,588
216,337
81,273,035
76,176,856
14,845,185
33,753,742
215,780,400
9,292,033
5,650,414
707,255
2,423,907
17,731,492
715
875
703
1,019
588
57
54
27
40
32
G eorgia...............................................................
Hawaii ..................................................................
Id a h o ....................................................................
Illinois...................................................................
Indiana ................................................................
225,040
34,027
45,399
322,324
152,846
6,628
1,564
1,128
2,721
-1,089
3,883,005
553,185
563,193
5,940,772
2,936,634
88,250
15,440
20,785
90,253
29,778
132,853,189
16,942,944
15,600,825
226,012,936
91,086,141
10,161,751
921,218
1,474,196
13,664,320
3,800,930
658
589
533
732
596
36
16
32
34
19
Iowa .....................................................................
Kansas ................................................................
Kentucky .............................................................
Louisiana.............................................................
Maine ...................................................................
97,091
80,477
107,740
118,216
44,865
2,479
1,036
2,403
1,549
956
1,443,394
1,313,742
1,762,949
1,869,219
590,818
12,412
14,945
31,482
21,317
17,005
40,312,331
38,571,763
50,774,667
52,131,235
16,344,365
1,743,623
2,164,568
2,669,580
1,838,194
916,386
537
565
554
536
532
19
26
20
13
15
Maryland .............................................................
Massachusetts...................................................
Michigan..............................................................
Minnesota ...........................................................
Mississippi...........................................................
146,559
187,391
260,885
155,711
63,970
1,117
344
2,244
4,932
229
2,405,510
3,275,135
4,585,211
2,608,543
1,137,304
58,631
83,493
82,445
57,751
-1,880
87,548,876
145,184,150
169,702,272
92,377,120
28,665,889
6,606,334
16,396,342
8,726,750
6,959,859
879,567
700
852
712
681
485
37
76
24
37
16
Missouri...............................................................
M ontana..............................................................
Nebraska.............................................................
Nevada ................................................................
New Hampshire .................................................
163,080
38,349
51,838
48,126
45,924
2,303
1,585
4
194
494
2,677,110
379,094
882,918
1,017,902
606,543
31,687
7,855
16,308
41,975
15,318
84,020,093
9,202,211
24,449,709
32,853,744
21,069,920
4,745,993
567,364
1,370,028
2,392,271
2,067,493
604
467
533
621
668
28
20
21
21
50
New J erse y.........................................................
New Mexico ........................................................
New York ............................................................
North Carolina....................................................
North D akota......................................................
270,384
47,987
529,103
222,234
23,297
-15,337
693
4,797
7,270
240
3,877,572
717,243
8,471,416
3,862,782
309,223
85,195
16,339
178,874
58,413
3,263
169,355,641
19,722,105
384,241,451
120,007,446
7,632,602
13,725,235
1,311,285
34,472,229
7,922,007
365,713
840
529
872
597
475
51
24
61
30
18
Ohio .....................................................................
Oklahoma............................................................
O regon................................................................
Pennsylvania......................................................
Rhode Island......................................................
280,988
89,298
109,050
315,284
33,327
1,073
1,368
-1,296
13,267
621
5,513,217
1,452,166
1,608,069
5,558,076
467,602
62,090
29,357
32,067
98,602
10,766
179,218,763
39,191,626
52,703,467
189,058,210
15,250,760
8,080,924
2,464,854
4,049,166
10,557,733
1,011,495
625
519
630
654
627
21
23
36
25
28
South Carolina...................................................
South Dakota .....................................................
Tennessee ..........................................................
Texas ...................................................................
Utah .....................................................................
109,370
27,145
125,247
489,795
66,144
-1,993
437
-51
8,425
2,282
1,820,138
364,119
2,667,230
9,289,286
1,044,143
27,993
8,334
40,186
272,645
26,519
51,289,516
9,030,727
81,495,110
324,579,638
30,518,822
2,664,765
574,920
4,055,765
27,952,132
2,131,853
542
477
588
672
562
20
20
21
39
26
Vermont ..............................................................
Virginia................................................................
Washington .........................................................
West Virginia......................................................
Wisconsin............................................................
W yom ing.............................................................
23,870
192,745
221,150
46,830
145,871
20,861
805
3,212
9,010
21
977
238
296,462
3,427,954
2,706,462
686,622
2,736,054
230,857
8,473
100,832
62,732
6,014
44,603
5,892
8,571,976
120,567,926
100,381,521
18,461,154
83,980,263
6,195,607
624,326
10,689,950
5,904,038
752,890
4,294,806
425,897
556
676
713
517
590
516
25
41
26
17
21
23
Puerto R ic o .........................................................
Virgin Islands .....................................................
52,371
3,255
202
32
1,026,175
42,349
23,785
1,411
19,306,364
1,173,955
709,126
104,996
362
533
5
31
NOTE: Detail may not add to totals due to rounding.
90
19992000
change
Average annual
employment
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
21. Annual data: Employment and average annual pay for all workers
covered under Ul and UCFE in the 316 largest U.S. counties
Employment
Average annual pay
Percent
change,
1999-20002
Ranked by
percent
change,
1999-20003
United States4 .................... 129,877,063
2.2
-
35,323
5.9
Jefferson, A L .....................
Madison, A L .......................
Mobile, A L ..........................
Montgomery, A L ...............
Tuscaloosa, A L ..................
Anchorage, AK ..................
Maricopa, A Z .....................
Pima, A Z .............................
Pulaski, A R ........................
Sebastian, A R ....................
384,662
154,356
169,469
131,988
76,499
129,700
1,544,971
328,426
243,157
75,197
.6
1.7
-.1
.2
.8
2.0
3.6
3.1
.4
1.1
256
186
291
285
244
164
48
77
272
228
34,026
35,837
28,623
28,894
29,064
36,659
35,110
29,194
30,799
27,011
3.9
5.0
2.4
3.2
2.5
2.7
7.8
3.5
3.8
4.8
Washington, A R .................
Alameda, CA .....................
Contra Costa, CA .............
Fresno, CA ........................
Kern, C A .............................
Los Angeles, C A ...............
Marin, C A ............................
Monterey, C A .....................
Orange, C A ........................
Placer, CA .........................
80,045
696,242
336,691
322,759
238,250
4,098,154
111,645
164,646
1,394,414
107,182
3.3
3.0
3.1
1.9
2.1
1.7
2.1
2.5
3.6
8.9
61
84
78
169
153
187
154
118
49
3
26,408
45,091
42,318
26,162
28,572
39,651
42,600
29,962
39,247
33,386
3.8
9.8
3.7
4.8
5.7
4.9
8.5
5.1
4.8
5.3
Riverside, C A .....................
Sacramento, C A ................
San Bernardino, C A ..........
San Diego, C A ...................
San Francisco, C A ............
San Joaquin, C A ................
San Luis Obispo, CA ........
San Mateo, CA ..................
Santa Barbara, CA ...........
Santa Clara, C A ................
469,467
573,942
528,437
1,195,116
609,138
201,070
94,883
378,494
176,901
1,030,633
5.3
2.6
3.0
3.0
3.7
3.1
3.6
5.3
3.0
6.1
12
107
85
86
43
79
50
13
87
9
29,136
37,732
29,901
37,535
57,532
29,237
28,096
67,051
32,566
76,213
4.7
7.2
3.8
8.1
12.0
4.7
6.2
30.4
8.2
24.7
Santa Cruz, C A ..................
Solano, CA ........................
Sonoma, C A ......................
Stanislaus, C A ...................
Tulare, CA .........................
Ventura, C A .......................
Yolo, CA .............................
Adams, C O ........................
Arapahoe, C O ....................
Boulder, C O .......................
101,833
117,217
190,946
160,948
132,986
287,611
84,565
144,806
284,236
179,719
3.3
3.7
3.1
1.7
3.6
3.4
1.5
3.6
3.9
8.2
62
44
80
188
51
57
201
52
38
4
35,819
31,670
35,715
28,201
23,750
37,069
33,438
33,428
46,254
45,564
15.5
8.4
11.3
4.4
4.6
9.1
3.3
4.8
7.8
13.9
Denver, C O ........................
El Paso, C O .......................
Jefferson, CO ....................
Larimer, C O .......................
Fairfield, C T .......................
Hartford, C T .......................
New Haven, CT .................
New London, C T ...............
New Castle, D E .................
Washington, DC ...............
469,137
237,739
210,519
119,155
427,557
501,562
367,343
123,039
281,920
637,292
3.2
3.4
2.6
5.1
1.1
1.1
1.1
.6
-.7
3.5
69
58
108
16
229
230
231
257
301
54
44,343
33,039
36,195
32,394
61,156
43,656
38,355
36,757
40,491
52,964
11.6
7.7
5.2
7.9
8.5
6.2
5.4
3.8
4.5
4.1
Alachua, FL .......................
Brevard, F L ........................
Broward, F L .......................
Collier, F L ...........................
Duval, FL ............................
Escambia, F L .....................
Hillsborough, F L ................
Lee, FL ...............................
Leon, FL .............................
Manatee, FL ......................
117,658
181,314
644,192
103,264
434,219
125,666
588,792
162,304
141,978
( 5)
2.5
3.3
3.3
6.9
4.1
1.0
2.5
4.4
2.2
( 5)
119
63
64
6
32
235
120
25
142
( 5)
26,155
32,101
33,234
29,962
32,777
26,709
31,707
28,148
29,249
( é)
3.9
7.2
6.5
6.9
4.6
4.5
4.8
6.4
4.1
(5>
Marion, FL .........................
Miami-Dade, F L .................
Orange, F L ........................
Palm Beach, F L .................
Pinellas, F L ........................
Polk, FL ..............................
Sarasota, F L ......................
Seminole, FL .....................
Volusia, F L .........................
Bibb, GA .............................
83,319
980,394
611,469
481,395
436,390
183,222
( 5)
139,610
141,652
88,790
1.7
2.3
3.2
4.1
4.2
2.6
( 5)
4.6
1.4
-1.2
189
135
70
33
29
109
( 5)
23
207
308
24,953
33,333
31,123
35,233
31,263
27,881
(5)
30,835
25,079
29,299
3.3
3.9
4.6
7.3
5.4
3.5
( 5)
6.9
5.5
3.2
Chatham, G A .....................
Clayton, G A .......................
Cobb, G A ............................
122,785
116,368
301,183
1.3
-.6
1.3
214
296
215
29,650
36,774
38,792
1.9
6.7
5.4
County'
2000
2000
Percent
change,
1999-20002
See footnotes at end of table.
Monthly Labor Review
July 2002
91
Current Labor Statistics:
Labor Force Data
21. Continued—Annual data: Employment and average annual pay for
all workers covered under Ul and UCFE in the 316 largest U.S.
counties
A v e ra g e a n n u a l pay
E m p lo y m e n t
C o u n ty 1
2000
P e rc e n t
change,
1 9 9 9 -2 0 0 0 2
R an ked by
p e rc e n t
change,
1 9 9 9 -2 0 0 0 3
2000
P e rc e n t
change,
1 9 9 9 -2 0 0 0 2
Dekalb, G A ........................
Fulton, GA ..........................
Gwinnett, G A .....................
Muscogee, G A ...................
Richmond, G A ...................
Honolulu, H I .......................
Ada, I D ................................
310,659
754,368
281,654
98,315
106,260
407,935
177,741
-.6
2.7
4.1
-.1
-.6
2.6
6.5
297
103
34
292
298
110
8
38,614
47,060
39,051
27,744
28,592
31,874
34,460
4.9
8.5
6.0
3.7
3.6
2.8
10.0
Champaign, I L ...................
Cook, I L ..............................
Du Page, IL ........................
Kane, IL ..............................
Lake,IL ...............................
McHenry, I L .......................
McLean, I L ..........................
Madison, I L ........................
Peoria, I L ............................
Rock Island, I L ...................
90,429
2,687,795
582,352
193,410
310,689
87,258
84,324
94,550
102,801
80,273
2.8
1.3
1.7
2.9
3.1
1.9
.6
.4
.1
.8
96
216
190
91
81
170
258
273
287
245
29,183
42,898
42,570
32,173
42,620
32,007
34,254
28,974
31,387
33,525
4.2
5.8
3.6
.1
6.7
2.0
4.1
2.9
1.6
4.5
St. Clair, I L ..........................
Sangamon, I L ....................
Will, I L .................................
Winnebago, IL ...................
Allen, IN ..............................
Elkhart, I N ...........................
Hamilton, I N .......................
Lake,IN ..............................
Marlon, I N ...........................
St. Joseph, IN ....................
89,963
144,286
142,355
143,760
189,425
122,468
77,452
199,421
605,903
129,558
2.2
4.4
3.5
.5
.3
.6
3.0
-.6
1.6
.5
143
26
55
265
281
259
88
299
194
266
26,878
34,764
32,313
31,499
32,279
30,339
37,931
31,564
36,473
29,657
2.6
1.7
2.1
2.0
3.0
2.3
7.9
4.0
3.2
3.5
Tippecanoe, I N ..................
Vanderburgh, IN ...............
Linn, IA ...............................
Polk, IA ...............................
Scott, I A ..............................
Johnson, KS ......................
Sedgwick, KS ....................
Shawnee, K S .....................
Wyandotte, K S ...................
Fayette, K Y ........................
77,377
109,904
121,968
263,940
87,113
287,797
249,846
100,223
79,746
172,031
1.1
.7
2.1
1.3
-.4
2.8
.0
2.4
1.8
1.8
232
251
155
217
295
97
289
130
177
178
31,083
29,569
34,097
33,666
29,067
37,247
32,696
29,375
34,592
30,713
4.0
3.2
4.9
2.5
3.9
6.7
2.9
3.2
2.9
3.8
Jefferson, K Y .....................
Caddo, L A ...........................
Calcasieu, LA ....................
East Baton Rouge, L A ......
Jefferson, LA .....................
Lafayette, LA .....................
Orleans, L A ........................
Cumberland, M E ...............
Anne Arundel, MD ............
Baltimore, M D ....................
439,103
119,449
83,976
246,434
214,680
114,059
263,551
166,757
194,018
358,117
1.4
.3
.1
2.7
-.7
2.3
1.9
3.7
5.3
1.2
208
282
288
104
302
136
171
45
14
222
33,334
28,767
28,226
29,257
28,051
29,911
31,694
30,752
35,461
34,119
3.9
3.2
.9
1.6
2.1
5.5
1.3
1.1
7.3
4.7
Frederick, M D ....................
Howard, M D .......................
Montgomery, M D ..............
Prince Georges, M D .........
Baltimore City, M D ............
Barnstable, M A ..................
Bristol, MA .........................
Essex, MA .........................
Hampden, M A ....................
Middlesex, M A ...................
77,323
128,678
447,314
303,262
386,411
88,589
221,539
305,382
204,303
846,931
4.9
3.2
5.0
3.3
.8
3.7
1.3
2.5
1.9
3.1
22
71
20
65
246
46
218
121
172
82
30,847
37,897
43,708
37,060
38,579
29,726
30,785
39,154
32,220
52,091
5.9
5.1
5.8
6.9
4.5
.0
4.6
8.8
4.8
11.8
Norfolk, M A ........................
Plymouth, M A ....................
Suffolk, M A ........................
Worcester, M A ...................
Genesee, M l ......................
Ingham, M l ..........................
Kalamazoo, M l...................
Kent, Ml ..............................
Macomb, M l .......................
Oakland, Ml .......................
325,018
166,482
608,285
321,131
165,297
174,315
118,342
347,707
337,504
768,629
2.4
1.3
3.3
2.5
-1.4
2.0
-.1
1.6
.3
1.0
131
219
66
122
313
165
293
195
283
236
43,368
33,931
56,699
37,657
36,324
34,963
32,675
33,996
40,904
44,500
10.4
6.3
11.6
10.8
1.4
5.6
2.3
2.6
3.5
4.2
Ottawa, Ml ..........................
Saginaw, M l .......................
Washtenaw, M l ..................
Wayne, Ml ..........................
Anoka, M N .........................
Dakota, M N ........................
Hennepin, M N ....................
Olmsted, M N ......................
118,711
95,474
195,624
866,282
108,989
153,364
874,693
82,670
1.8
-.8
.5
1.2
3.8
2.6
2.1
3.9
179
304
267
223
40
111
156
39
31,947
34,672
40,182
42,440
33,928
34,362
43,816
36,104
3.5
2.5
5.3
3.5
4.5
4.7
7.1
3.1
See footnotes at end of table.
Monthly Labor Review
92
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
21. Continued—Annual data: Employment and average annual pay for
all workers covered under Ul and UCFE in the 316 largest U.S.
counties
A v e ra g e a n n u a l p ay
E m p lo y m e n t
C o u n ty 1
2000
P e rc e n t
change,
1 9 9 9 -2 0 0 0 2
R a n k ed by
p e rc e n t
change,
1 9 9 9 -2 0 0 0 3
2000
P e rc e n t
change,
1 9 9 9 -2 0 0 0 2
Ramsey, M N ......................
St. Louis, M N .....................
332,929
94,926
1.6
1.4
196
209
39,069
28,903
5.8
4.6
Stearns, M N .......................
Harrison, M S ......................
Hinds, MS ...........................
Boone, MO ........................
Clay, M O .............................
Greene, M O .......................
Jackson, M O ......................
St. Charles, M O .................
St. Louis, M O .....................
St. Louis City, M O .............
76,292
89,745
136,949
75,785
84,159
142,508
393,761
95,799
646,858
250,878
3.1
.4
-1.2
2.8
.0
2.4
.4
3.2
.8
.4
83
274
309
98
290
132
275
72
247
276
27,584
25,442
30,578
27,361
32,207
26,971
36,056
29,515
38,145
38,612
4.2
4.8
4.6
3.1
6.4
3.2
6.2
3.8
5.6
4.1
Douglas, NE ......................
Lancaster, N E ....................
Clark, NV ............................
Washoe, NV ......................
Hillsborough, NH ..............
Rockingham, NH ..............
Atlantic, NJ ........................
Bergen, N J .........................
Burlington, N J ....................
Camden, N J .......................
330,128
146,433
697,575
189,102
193,796
129,494
140,141
448,513
180,165
199,768
2.1
1.8
5.3
3.2
2.7
4.1
-.2
.5
.8
-1.1
157
180
15
73
105
35
294
268
248
307
32,356
28,511
32,131
32,748
39,212
35,823
31,068
46,306
37,597
35,130
4.1
3.9
3.4
4.4
9.1
9.8
3.4
7.0
4.7
3.2
Essex, NJ ...........................
Gloucester, N J ...................
Hudson, N J ........................
Mercer, NJ .........................
Middlesex, N J ....................
Monmouth, NJ ...................
Morris, NJ ...........................
Ocean, N J ...........................
Passaic, N J ........................
Somerset, N J .....................
363,942
86,667
238,388
210,031
392,427
233,285
275,499
129,093
177,364
173,571
1.6
.7
3.4
3.3
.6
2.5
2.8
2.5
.6
4.1
197
252
59
67
260
123
99
124
261
36
44,653
32,055
47,427
44,658
46,487
39,695
60,487
30,447
37,759
54,781
3.5
2.8
10.2
5.2
5.8
5.4
19.0
4.6
2.0
5.1
Union, N J ............................
Bernalillo, NM ....................
Albany, N Y ..........................
Bronx, NY ...........................
Broome, N Y .......................
Dutchess, N Y .....................
Erie, N Y ..............................
Kings, N Y ............................
Monroe, NY .......................
Nassau, N Y .......................
237,176
307,705
230,962
212,982
99,613
109,949
459,828
441,916
399,602
598,538
2.2
2.6
1.4
2.2
1.2
1.9
1.0
2.3
.9
1.6
144
112
210
145
224
173
237
137
242
198
45,282
30,184
35,795
32,850
29,658
36,065
31,489
30,760
35,423
40,023
4.9
4.1
6.1
2.7
3.6
2.2
3.0
3.7
1.8
4.4
New York, N Y ....................
Niagara, N Y .......................
Oneida, N Y ........................
Onondaga, N Y ...................
Orange, N Y ........................
Queens, N Y .......................
Richmond, NY ...................
Rockland, N Y .....................
Suffolk, N Y ..........................
Westchester, N Y ...............
2,382,175
78,186
110,684
252,476
119,571
480,676
88,245
106,361
578,401
405,440
3.2
.2
1.4
.7
1.6
1.3
1.9
1.4
2.3
2.3
74
286
211
253
199
220
174
212
138
139
72,572
31,112
27,300
32,499
29,357
34,986
32,149
37,264
37,862
47,066
10.3
3.7
3.4
3.4
4.6
4.4
4.2
4.3
6.6
8.3
Buncombe, NC ..................
Catawba, NC .....................
Cumberland, N C ...............
Durham, N C .......................
Forsyth, NC .......................
Gaston, N C ........................
Guilford, N C .......................
Mecklenburg, N C ..............
New Hanover, N C .............
Wake, NC ...........................
106,036
101,321
109,858
167,191
181,619
77,176
279,889
514,223
87,019
383,705
.5
2.6
1.2
2.9
1.8
-3.6
.6
3.8
.4
3.3
269
113
225
92
181
314
262
41
277
68
27,652
28,210
26,112
49,359
34,011
28,335
32,216
40,538
28,560
35,377
3.8
4.0
3.9
12.6
6.3
4.0
2.5
5.4
4.3
7.4
Cass, ND ............................
Butler, O H ...........................
Cuyahoga, O H ...................
Franklin, OH ......................
Hamilton, O H .....................
Lake, OH ............................
Lorain, OH ..........................
Lucas, O H ...........................
Mahoning, OH ...................
Montgomery, OH ..............
81,823
126,189
817,572
701,913
566,965
102,320
105,988
238,450
112,531
303,352
2.2
2.6
.9
2.2
.8
1.5
2.3
.6
-.6
.4
146
114
243
147
249
202
140
263
300
278
27,801
31,502
36,520
34,970
37,598
30,735
32,013
32,255
25,966
34,532
4.1
1.7
4.2
4.6
3.9
2.1
1.9
2.3
3.0
2.6
Stark, O H ............................
Summit, O H .......................
175,535
266,001
1.7
.4
191
279
28,505
32,735
2.1
4.2
See footnotes at end of table.
Monthly Labor Review
July 2002
93
Current Labor Statistics:
Labor Force Data
21. Continued—Annual data: Employment and average annual pay for
all workers covered under Ul and UCFE in the 316 largest U.S.
counties
E m p lo y m e n t
C o u n ty 1
2000
94
R a n k ed by
p e rc e n t
change,
1 9 9 9 -2 0 0 0 3
2000
P e rc e n t
change,
1 9 9 9 -2 0 0 0 2
Trumbull, OH .....................
Oklahoma, O K ...................
Tulsa, O K ............................
Clackamas, OR .................
Lane, O R ............................
Marlon, OR ........................
Multnomah, OR .................
Washington, OR ...............
94,382
414,239
340,671
133,065
139,710
127,558
453,274
224,033
-1.3
2.9
2.5
2.2
1.1
2.0
2.1
4.3
311
93
125
148
233
166
158
27
32,785
29,216
31,157
32,482
27,877
28,116
36,796
44,459
1.0
4.6
3.7
4.0
3.5
2.9
6.2
13.4
Allegheny, P A ....................
Berks, P Â ............................
Bucks, P A ...........................
Chester, PA .......................
Cumberland, PA ...............
Dauphin, PA ......................
Delaware, P A .....................
Erie, PA ..............................
Lackawanna, P A ...............
Lancaster, P A ....................
711,068
168,068
244,317
216,777
123,998
172,465
212,540
131,700
98,383
218,280
1.2
1.8
2.5
2.5
-1.3
2.1
1.0
2.5
-.7
1.8
226
182
126
127
312
159
238
128
303
183
36,727
32,007
34,059
43,762
32,811
33,680
36,828
28,368
27,663
30,809
2.5
3.3
3.4
6.9
3.2
2.2
5.5
1.8
7.5
4.6
Lehigh, P A .........................
Luzerne, P A .......................
Montgomery, P A ...............
Northampton, P A ..............
Philadelphia, P A ...............
Westmoreland, P A ............
York, PA .............................
Providence, Rl ...................
Charleston, SC ..................
Greenville, SC ...................
171,175
143,066
481,011
87,846
668,793
134,436
167,757
290,809
182,793
233,062
2.0
2.2
2.3
3.0
1.5
1.0
2.2
1.7
1.3
2.6
167
149
141
89
203
239
150
192
221
115
35,274
27,855
43,810
30,767
39,700
27,992
30,926
33,410
27,680
31,281
2.5
2.7
6.5
3.1
4.5
1.3
3.3
4.0
4.8
4.0
Horry, S C ............................
Lexington, S C ....................
Richland, S C ......................
Spartanburg, S C ...............
Minnehaha, S D ..................
Davidson, T N .....................
Hamilton, T N ......................
Knox, T N .............................
Rutherford, T N ...................
Shelby, T N ..........................
99,124
81,341
207,508
119,791
105,837
434,901
188,161
202,688
76,993
500,255
1.7
2.0
.6
.5
3.2
1.5
1.8
3.4
2.5
1.0
193
168
264
270
75
204
184
60
129
240
22,883
27,505
29,627
30,596
28,212
34,863
30,574
30,090
31,132
34,357
5.4
3.5
4.1
3.4
3.7
5.4
4.0
4.1
3.6
2.5
Bell, TX ...............................
Bexar, T X ............................
Brazoria, T X .......................
Cameron, T X .....................
Collin, T X ............................
Dallas, T X ...........................
Denton, TX ........................
El Paso, T X ........................
Fort Bend, TX ....................
Galveston, T X ....................
87,850
648,942
75,417
109,115
167,956
1,567,626
119,722
251,557
87,763
86,844
2.1
2.2
2.8
5.4
5.9
4.2
3.7
1.5
2.4
-1.0
160
151
100
11
10
30
47
205
133
306
25,193
29,923
34,367
21,553
40,509
44,381
29,298
25,069
35,801
29,518
4.1
5.2
3.3
2.6
5.8
7.7
4.0
3.2
5.1
4.0
Harris, TX ...........................
Hidalgo, T X ........................
Jefferson, TX .....................
Lubbock, TX ......................
Me Lennan, TX ..................
Montgomery, T X ...............
Nueces, T X ........................
Potter, TX ...........................
Smith, T X ............................
Tarrant, TX ........................
1,840,442
163,443
120,815
115,422
98,076
76,865
142,309
75,572
83,353
703,025
2.8
7.1
1.1
1.9
1.0
5.0
.8
.7
2.8
3.5
101
5
234
175
241
21
250
254
102
56
41,869
21,671
31,277
26,297
27,034
32,119
28,187
26,552
29,509
35,438
7.7
2.7
.8
6.3
2.1
9.7
4.7
2.8
3.6
5.0
Travis, T X ...........................
Williamson, T X ...................
Davis, U T ............................
Salt Lake, U T .....................
Utah, UT .............................
Weber, UT ..........................
Chittenden, V T ...................
Arlington, V A ......................
Chesterfield, V A .................
Fairfax, V A .........................
538,193
76,588
84,640
531,240
142,369
86,404
95,343
157,906
107,932
537,647
5.1
9.5
3.2
2.6
4.5
.4
5.1
4.1
2.1
6.7
17
2
76
116
24
280
18
37
161
7
41,332
50,415
27,711
32,192
27,891
26,644
34,288
52,846
31,880
51,576
7.0
-4.5
7.2
5.0
5.0
2.5
4.2
7.1
3.5
10.3
Henrico, VA .......................
Loudoun, V A ......................
Prince William, V A ............
Alexandria, V A ...................
Chesapeake, V A ...............
Newport News, VA ...........
Norfolk, VA ........................
165,617
87,265
78,209
91,818
81,294
93,607
145,197
2.4
11.9
4.3
5.1
2.1
1.8
.3
134
1
28
19
162
185
284
36,138
54,141
28,986
42,101
26,069
30,261
32,179
5.8
3.6
5.5
6.1
4.2
5.4
4.9
See footnotes at end of table.
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
A v e ra g e a n n u a l p ay
P e rc e n t
change,
1 9 9 9 -2 0 0 0 2
July 2002
21. Continued—Annual data: Employment and average annual pay for
all workers covered under Ul and UCFE in the 316 largest U.S.
counties
A v e ra g e a n n u a l pay
E m p lo y m e n t
R an ked by
p e rc e n t
change,
P e rc e n t
change,
C o u n ty 1
2000
1999-20002
P erc en t
change,
2000
1999-20002
1999-20003
Richmond, V A ....................
Roanoke City, V A .............
Virginia Beach, V A ............
166,923
75,894
165,610
1.4
3.0
3.6
213
90
53
38,635
29,487
25,414
5.1
4.6
4.4
Clark, WA ...........................
King, W A .............................
Pierce, W A .........................
Snohomish, W A .................
Spokane, W A .....................
Thurston, W A .....................
Yakima, W A .......................
Kanawha, W V ....................
Brown, Wl ...........................
Dane, W l .............................
113,910
1,162,290
241,654
209,557
188,843
84,277
94,233
112,920
142,359
274,353
1.5
2.7
4.2
-1.2
2.9
1.6
1.9
.7
2.1
2.6
206
106
31
310
94
200
176
255
163
117
32,163
47,459
29,854
35,091
29,760
31,745
23,237
30,156
31,538
32,817
6.0
3.0
4.2
3.6
7.9
6.9
3.7
3.1
2.9
5.5
Milwaukee, W l ...................
Outagamie, W l ...................
Racine, Wl .........................
Waukesha, Wl ...................
Winnebago, W l ..................
528,837
94,364
79,160
222,877
90,256
.5
2.9
-.9
1.2
2.2
271
95
305
227
152
34,744
30,769
32,536
35,767
33,622
3.1
4.4
-.6
5.2
2.7
San Juan, PR ....................
327,187
3.8
42
21,312
3.5
1 Includes areas not officially designated as
counties.
See Notes on Current Labor
Statistics.
4 Totals for the United States do not include
data for Puerto Rico.
5 Data are not available for release.
2 Percent changes were computed from
annual employment and pay data adjusted for
noneconomic county reclassifications.
See
Notes on Current Labor Statistics.
3 Rankings
for
percent
change
in
employment are based on the 314 counties that
are comparable over the year.
22.
Note: Data pertain to workers covered by
Unemployment
Insurance
(Ul)
and
Unemployment Compensation for Federal
Employees (UCFE) programs. The 315 U.S.
counties comprise 70.8 percent of the total
covered workers in the United States
Annual data: Employment status of the population
[Numbers in thousands]
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Civilian noninstitutional population...........
192,805
194,838
196,814
198,584
200,591
203,133
205,220
207,753
209,699
211,864
Civilian labor force...................................
128,105
129,200
131,056
132,304
133,943
136,297
137,673
139,368
140,863
141,815
Labor force participation rate...............
66.4
66.3
66.6
66.6
66.8
67.1
67.1
67.1
67.2
66.9
Employed.............................................
118,492
120,259
123,060
124,900
126,708
129,558
135,208
135,073
61.5
61.7
62.5
62.9
63.2
63.8
131,463
64.1
133,488
Employment-population ratio..........
64.3
64.5
63.8
Agriculture......................................
3,247
3,115
3,409
3,440
3,443
3,399
3,378
3,281
3,305
3,144
115,245
117,144
119,651
121,460
123,264
126,159
128,085
130,207
131,903
131,929
6,742
Employment status
9,613
8,940
7,996
7,404
7,236
6,739
6,210
5,880
5,655
Unemployment rate..........................
7.5
6.9
6.1
5.6
5.4
4.9
4.2
4.0
4.8
Not in the labor force...............................
64,700
65,638
65,758
66,280
66,647
66,837
4.5
67,547
68,385
68,836
70,050
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
95
Current Labor Statistics:
23.
Labor Force Data
Annual data: Employment levels by industry
[In thousands]
Industry
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Total employment...........................................
108,601
114,163
117,191
119,608
89,956
23,231
95,036
23,908
97,885
100,189
24,493
125,865
106,042
25,414
128,916
108,709
25,507
132,213
111,341
24,265
122,690
103,133
24,962
131,759
Private sector...............................................
110,713
91,872
23,352
635
610
4,492
4,668
596
5,691
590
6,020
6,415
Manufacturing.........................................
18,104
18,075
18,321
5,160
18,524
580
5,418
539
Construction............................................
601
4,986
18,495
18,675
18,805
18,552
Service-producing.....................................
85,370
5,718
87,361
90,256
92,925
95,115
97,727
5,811
5,984
6,132
103,409
6,834
5,997
6,162
6,911
22,848
7,555
23,307
6,806
21,966
7,109
6,800
22,295
7,389
6,911
20,507
6,896
6,378
21,187
6,648
19,356
6,602
5,981
19,773
6,757
6,253
6,482
21,597
100,451
6,611
7,560
7,624
29,052
30,197
31,579
33,117
34,454
36,040
37,533
39,055
40,460
41,024
18,645
2,969
4,408
11,267
18,841
2,915
19,128
2,870
4,576
11,682
19,305
2,822
19,419
19,557
2,699
4,582
12,276
19,823
2,686
4,612
20,206
2,757
4,606
20,681
2,777
4,785
20,873
2,616
4,880
13,377
Goods-producing......................................
Mining......................................................
Transportation and public utilities........
Wholesale trade.....................................
Retail trade.............................................
Finance, insurance, and real estate....
Services..................................................
Government...........................................
Federal.................................................
State......................................................
Local.....................................................
4,488
11,438
581
4,635
11,849
12,056
6,408
12,525
2,669
4,709
12,829
111,079
25,709
543
25,122
6,698
18,469
6,861
106,050
7,019
7,024
13,119
563
17,698
107,092
7,070
7,014
23,488
NOTE: See "Notes on the data" for a description of the most recent benchmark revision.
24.
Annual data: Average hours and earnings of production or nonsupervisory workers on nonfarm
payrolls, by industry
In d u s try
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
P rivate sector:
Average weekly hours...................................................
Average hourly earnings (in dollars)..........................
Average weekly earnings (in dollars).........................
34.4
10.57
363.61
34.5
10.83
373.64
34.7
11.12
385.86
34.5
11.43
394.34
34.4
11.82
406.61
34.6
12.28
424.89
34.6
12.78
442.19
34.5
13.24
456.78
34.5
13.75
474.38
34.2
14.33
490.09
43.9
14.54
638.31
44.3
14.60
646.78
44.8
14.88
666.62
44.7
15.30
683.91
45.3
15.62
707.59
45.4
16.15
733.21
43.9
16.91
742.35
43.2
17.05
736.56
43.1
17.24
743.04
43.4
17.65
766.01
38.0
14.15
537.70
38.5
14.38
553.63
38.9
14.73
573.00
38.9
15.09
587.00
39.0
15.47
603.33
39.0
16.04
625.56
38.9
16.61
646.13
39.1
17.19
672.13
39.3
17.88
702.68
39.2
18.33
718.54
41.0
11.46
469.86
41.4
11.74
486.04
42.0
12.07
506.94
41.6
12.37
514.59
41.6
12.77
531.23
42.0
13.17
553.14
41.7
13.49
562.53
41.7
13.90
579.63
41.6
14.38
598.21
40.7
14.84
603.99
38.3
13.43
514.37
39.3
13.55
532.52
39.7
13.78
547.07
39.4
14.13
556.72
39.6
14.45
572.22
39.7
14.92
592.32
39.5
15.31
604.75
38.7
15.69
607.20
38.6
16.22
626.09
38.1
16.89
643.51
38.2
11.39
435.10
38.2
11.74
448.47
38.4
12.06
463.10
38.3
12.43
476.07
38.3
12.87
492.92
38.4
13.45
516.48
38.3
14.07
538.88
38.3
14.58
558.80
38.5
15.20
585.20
38.2
15.80
603.56
28.8
7.12
205.06
28.8
7.29
209.95
28.9
7.49
216.46
28.8
7.69
221.47
28.8
7.99
230.11
28.9
8.33
240.74
29.0
8.74
253.46
29.0
9.09
263.61
28.9
9.46
273.39
28.8
9.82
282.82
35.8
10.82
387.36
35.8
11.35
406.33
35.8
11.83
423.51
35.9
12.32
442.29
35.9
12.80
459.52
36.1
13.34
481.57
36.4
14.07
512.15
36.2
14.62
529.24
36.3
15.07
547.04
36.3
15.83
574.63
32.5
10.54
342.55
32.5
10.78
350.35
32.5
11.04
358.80
32.4
11.39
369.04
32.4
11.79
382.00
32.6
12.28
400.33
32.6
12.84
418.58
32.6
13.37
435.86
32.7
13.91
454.86
32.7
14.61
477.75
M ining:
Average weekly hours................................................
Average hourly earnings (in dollars)........................
Average weekly earnings (in dollars)......................
C o n s tru c tio n :
Average weekly hours................................................
Average hourly earnings (in dollars)........................
Average weekly earnings (in dollars)......................
M a n u fa c tu rin g :
Average weekly hours................................................
Average hourly earnings (in dollars).........................
Average weekly earnings (in dollars)......................
T ra n s p o rta tio n an d p u b lic utilities:
Average weekly hours................................................
Average hourly earnings (in dollars)........................
Average weekly earnings (in dollars)......................
W h o le s a le trade:
Average weekly hours................................................
Average hourly earnings (in dollars)........................
Average weekly earnings (in dollars)......................
R e tail trade:
Average weekly hours................................................
Average hourly earnings (in dollars)........................
Average weekly earnings (in dollars).......................
F in an ce , in s u ra n c e , a n d real estate:
Average weekly hours................................................
Average hourly earnings (in dollars)........................
Average weekly earnings (in dollars)......................
S ervices:
Average weekly hours................................................
Average hourly earnings (in dollars)........................
Average weekly earnings (in dollars).......................
96
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
25.
Employment Cost Index, compensation,1 by occupation and industry group
[June 1989 = 100]
2002
2001
2000
Series
Mar.
o
C iv ilia n w o rk e rs ..................................................................................
June
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
Percent change
12
3
months
months
ended
ended
Mar. 2002
146.5
148.0
149.5
150.6
152.5
153.8
155.6
156.8
158.4
1.0
3.9
148.4
146.7
150.5
148.6
142.7
146.0
149.9
148.3
151.9
150.1
144.1
147.1
151.5
150.0
153.7
151.8
145.6
148.5
152.5
151.3
154.6
152.8
146.5
150.0
154.4
153.2
156.6
155.3
148.2
152.0
156.0
154.3
158.6
156.8
149.3
153.3
157.7
156.7
159.6
158.8
151.1
155.0
158.9
157.5
161.2
160.0
152.0
156.9
160.5
158.5
163.7
162.0
153.7
158.4
1.0
.6
1.6
1.3
1.1
1.0
4.0
3.5
4.5
4.3
3.7
4.2
Goods-producing......................................................................
Manufacturing.........................................................................
Service-producing....................................................................
Services...................................................................................
Health services.....................................................................
Hospitals..............................................................................
Educational services............................................................
144.9
146.0
147.1
148.0
145.9
146.3
146.5
146.6
147.5
148.4
149.3
147.5
147.7
146.8
148.0
148.7
150.1
151.2
149.0
149.5
149.7
148.8
149.3
151.1
152.4
150.7
151.3
150.6
150.7
151.3
153.0
154.3
152.5
153.2
151.7
152.2
152.6
155.4
155.4
154.6
155.6
152.2
153.3
153.3
156.4
158.1
156.7
158.2
156.1
154.4
154.6
157.6
159.0
158.3
160.0
156.6
156.3
156.6
159.1
160.2
160.5
162.3
157.1
1.2
1.3
1.0
.8
1.4
1.4
.3
3.7
3.5
4.0
3.8
5.2
5.9
3.6
Public administration3.............................................................
Nonmanufacturing....................................................................
145.7
146.1
146.9
148.3
150.6
151.9
153.8
155.2
156.5
.8
3.9
146.6
148.0
149.6
150.7
152.6
154.0
156.0
157.2
158.7
1.0
4.0
P rivate in d u s try w o rk e rs ................................................................
146.8
146.5
148.5
148.2
149.9
149.8
150.9
150.9
153.0
153.0
154.5
154.4
155.9
156.0
157.2
160.9
158.9
159.0
1.1
1.1
3.9
3.9
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....
149.3
149.4
148.4
151.1
148.9
149.0
142.6
142.3
144.0
137.5
146.4
151.1
151.3
150.7
152.7
150.3
150.6
144.1
144.1
145.0
138.6
148.1
152.6
152.9
152.2
154.4
151.2
152.3
145.5
145.8
146.0
139.9
149.4
153.6
154.1
153.7
155.3
151.4
153.4
146.4
146.7
146.8
141.1
150.4
155.7
156.5
156.3
157.3
152.3
156.1
148.2
148.7
148.3
142.6
152.2
157.4
158.1
157.5
159.4
154.5
157.7
149.3
149.7
149.1
143.9
153.4
158.7
159.6
159.2
160.2
155.0
159.5
151.0
151.8
150.4
145.6
154.9
160.1
160.9
160.3
161.8
156.7
160.8
151.9
152.5
151.5
146.3
156.5
161.9
162.8
161.5
164.4
157.7
162.8
153.6
153.7
153.6
148.7
158.7
1.1
1.2
.7
1.6
.6
1.2
1.1
.8
1.4
1.6
1.4
4.0
4.0
3.3
4.5
3.5
4.3
3.6
3.4
3.6
4.3
4.3
Service occupations...............................................................
4
Production and nonsupervisory occupations ..................
143.9
145.4
146.6
148.1
150.0
151.3
152.6
154.8
156.4
1.0
4.3
145.3
146.9
148.4
149.5
151.4
152.7
154.3
155.5
157.1
1.0
3.8
Workers, by industry division:
Goods-producing....................................................................
Excluding sales occupations........................................
White-collar occupations..................................................
Excluding sales occupations........................................
Blue-collar occupations.....................................................
Construction..........................................................................
Manufacturing.......................................................................
White-collar occupations..................................................
Excluding sales occupations........................................
Blue-collar occupations.....................................................
Durables................................................................................
Nondurables.........................................................................
144.8
144.2
148.1
146.5
142.8
140.8
146.0
148.2
146.2
144.4
146.5
144.9
146.6
145.9
150.1
148.4
144.4
143.2
147.5
150.2
148.2
145.6
148.3
146.0
147.9
147.2
151.3
149.6
145.8
145.1
148.7
151.4
149.3
146.7
149.4
147.5
148.8
148.2
151.9
150.5
146.8
146.7
149.3
151.5
149.7
147.8
150.1
147.7
150.7
150.1
154.5
153.0
148.2
148.2
151.3
154.2
152.2
149.1
151.8
150.4
152.1
151.5
156.5
155.0
149.3
150.3
152.6
156.0
154.0
150.0
153.1
151.6
153.1
152.5
156.8
155.3
150.8
151.7
152.2
156.0
153.8
151.3
154.0
152.0
154.4
153.7
158.1
156.5
151.9
153.0
154.6
156.9
154.5
152.7
155.3
153.2
156.2
155.5
160.1
158.4
153.6
154.1
156.6
159.1
156.7
154.6
156.9
156.0
1.2
1.2
1.3
1.2
1.1
.7
1.3
1.4
1.3
1.2
1.0
1.8
3.6
3.6
3.6
3.5
3.6
4.0
3.5
3.2
3.0
3.7
3.4
3.7
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.......................................................................
147.4
147.7
149.3
150.3
141.8
143.6
143.9
140.4
148.6
148.4
148.9
145.6
146.4
150.0
149.6
143.2
139.7
140.1
149.1
149.4
151.0
152.1
143.1
145.1
145.7
141.8
150.9
150.9
151.0
147.3
148.1
151.8
151.1
144.8
141.0
142.5
150.6
151.1
152.6
153.9
144.5
146.3
147.4
142.8
153.5
153.9
152.9
148.3
149.6
152.1
152.7
146.2
142.2
143.4
151.7
152.2
153.7
155.1
145.3
147.9
148.3
143.9
154.1
154.7
153.4
149.4
150.6
154.4
154.9
146.6
144.4
153.8
154.6
155.8
157.5
147.7
149.6
150.5
145.4
157.3
158.3
156.0
151.0
152.6
155.1
156.9
148.7
147.3
146.1
155.3
156.0
157.4
159.1
148.7
150.8
152.4
146.9
159.8
161.1
158.1
152.6
153.9
157.8
158.5
149.7
149.4
148.2
156.9
157.8
159.0
160.9
150.9
152.2
153.5
148.2
160.7
162.8
158.1
153.7
155.4
158.6
160.0
150.9
149.7
149.7
158.2
159.0
160.3
162.2
151.0
154.2
155.5
151.1
161.5
163.4
159.1
155.5
159.5
160.6
153.2
150.9
151.7
159.9
160.9
162.1
164.1
153.2
155.9
157.3
152.5
163.9
166.0
161.3
156.5
161.9
162.3
153.5
152.4
152.9
1.1
1.2
1.1
1.2
1.2
1.1
1.2
.9
1.5
1.6
1.4
.6
1.5
1.1
.2
1.0
.8
4.0
4.1
4.0
4.2
3.7
4.2
4.5
4.9
4.2
4.9
3.4
3.6
4.4
3.4
3.2
3.5
4.7
Workers, by occupational group:
White-collar workers.................................................................
Professional specialty and technical...................................
Workers, by industry division:
Excluding sales occupations..............................................
Workers, by occupational group:
144.5
See footnotes at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
97
Current Labor Statistics:
Compensation & Industrial Relations
25. Continued—Employment Cost Index, compensation,1 by occupation and industry group
[June 1989 = 100]
2000
2001
2002
Series
Mar.
June
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
Percent change
12
3
months
months
ended
ended
Mar. 2002
Finance, insurance, and real estate..................................
152.0
153.1
155.2
155.7
157.9
159.5
160.9
161,3
165.2
2.4
4.6
Excluding sales occupations.........................................
Banking, savings and loan, and other credit agencies.
Insurance.............................................................................
Services..................................................................................
155.5
164.2
151.3
151.2
156.3
147.5
147.5
154.9
155.5
157.4
165.8
154.8
152.9
157.5
149.0
149.2
158.8
158.6
158.4
166.5
155.2
154.1
158.4
161.2
170.8
157.6
156.5
160.5
152.7
153.5
162.3
162.2
163.1
172.7
159.3
157.8
163.0
154.7
155.9
162.6
162.6
164.7
175.4
159.9
160.0
165.2
156.8
158.4
166.4
166.2
165.0
174.5
161.3
161.0
166.2
158.4
160.3
167.6
167.5
169.8
182.1
164.0
162.6
166.3
160.6
162.8
168.5
168.1
2.9
4.5
1.7
1.0
.1
Health services...................................................................
Hospitals............................................................................
Educational services..........................................................
Colleges and universities................................................
154.2
162.7
149.9
149.4
154.2
145.8
145.8
154.0
154.6
1.4
1.6
.5
.4
5.3
6.6
4.1
3.9
36
5.2
6.1
3.8
3.6
Nonmanufacturing................................................................
146.7
148.4
150.0
151.1
153.1
154.7
156.3
157.6
159.3
1.1
4.0
White-collar workers..........................................................
Excluding sales occupations........................................
Blue-collar occupations.....................................................
Service occupations..........................................................
149.2
150.2
140.6
143.5
151.0
152.0
142.3
145.1
152.6
153.8
143.9
146.3
153.7
155.1
144.8
147.8
155.8
157.5
146.9
149.5
157.5
159.1
148.1
150.7
159.0
160.9
150.2
152.1
160.5
162.3
150.6
154.1
162.2
164.2
152.2
155.9
1.1
1.2
1.1
1.2
4.1
4.3
3.6
4.3
S t a t e a n d lo c a l g o v e r n m e n t w o r k e r s ..............................................
145.5
145.9
147.8
148.9
150.3
151.2
154.3
155.2
156.1
.6
3.9
144.9
144.1
147.0
145.9
143.7
145.3
144.5
147.2
146.5
144.2
147.3
146.6
149.2
148.3
145.9
148.3
147.4
150.7
149.4
147.2
149.5
148.4
152.4
150.7
148.6
150.4
149.2
153.7
151.6
149.0
153.7
152.8
156.4
154.2
151.5
154.4
153.2
157.6
155.6
153.2
155.2
153.6
159.5
156.9
154.0
.5
.3
1.2
.8
.5
3.8
3.5
4.7
4.1
3.6
3.7
150.6
151.1
159.9
159.2
Workers, by occupational group:
White-collar workers.................................................................
Professional specialty and technical...................................
Executive, administrative, and managerial.........................
Workers, by industry division:
Services....................................................................................
145.2
145.5
148.0
148.9
149.9
150.6
154.4
154.9
155.5
.4
Services excluding schools5................................................
Health services...................................................................
Hospitals...........................................................................
Educational services.........................................................
Schools.............................................................................
Elementary and secondary.........................................
Colleges and universities............................................
145.2
145.8
147.6
148.8
150.1
151.9
154.5
156.1
157.9
1.2
5.2
147.3
147.9
145.0
145.3
144.5
147.4
147.9
148.4
145.2
145.5
144.7
147.6
150.0
150.7
147.9
148.2
147.3
150.5
151.6
152.0
148.7
149.0
148.1
151.7
152.1
152.2
149.6
149.9
148.5
153.7
154.4
154.7
150.1
150.5
149.0
154.3
157.1
157.4
154.1
154.4
152.8
153.8
158.5
159.1
154.5
154.8
153.1
159.6
160.4
160.7
154.8
155.1
153.4
160.0
1.1
1.0
.2
.2
.2
.3
5.5
5.6
3.5
3.5
3.3
4.1
Public administration .............................................................
145.7
146.1
146.9
148.3
150.6
151.9
151.9
155.2
156.5
.8
3.9
1 Cost (cents per hour worked) measured in the Employment Cost Index consists of
wages, salaries, and employer cost of employee benefits.
2 Consists of private industry workers (excluding farm and household workers) and
State and local government (excluding Federal Government) workers.
98
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
3 Consists of legislative, judicial, administrative, and regulatory activities.
4 This series has the same industry and occupational coverage as the Hourly
Earnings index, which was discontinued in January 1989.
5 includes, for example, library, social, and health services.
26.
Employment Cost Index, wages and salaries, by occupation and industry group
[June 1989 = 100]
2000
2002
2001
Series
Mar.
Civilian workers1..............................................................
June
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
Percent change
12
3
months
months
ended
ended
Mar. 2002
144.0
145.4
147.0
147.9
149.5
150.8
152.3
153.4
154.8
0.9
3.5
White-collar workers.................................................................
Professional specialty and technical...................................
Executive, adminitrative, and managerial...........................
Administrative support, including clerical...........................
Blue-collar workers..................................................................
Service occupations.................................................................
146.2
144.9
148.6
145.5
139.2
143.0
147.6
146.4
149.9
146.9
140.6
144.0
149.2
148.3
151.6
148.5
142.0
145.7
150.2
149.6
152.4
149.6
142.9
147.1
151.7
151.1
154.0
151.6
144.7
148.6
153.1
152.155.8
152,7
146.0
149.7
154.5
154.2
156.7
154.6
147.6
151.2
155.6
155.1
158.1
155.7
148.5
153.0
157.0
155.6
160.7
157.3
149.7
154.2
.9
.3
1.6
1.0
.8
.8
3.5
3.0
4.4
3.8
3.5
3.8
Workers, by industry division:
Goods-producing......................................................................
Manufacturing.........................................................................
Service-producing....................................................................
Services...................................................................................
Health services.....................................................................
Hospitals..............................................................................
Educational services...........................................................
141.3
142.9
145.0
146.6
143.8
142.6
145.3
143.0
144.4
146.3
147.9
145.3
143.8
145.6
144.3
145.7
148.0
149.9
146.7
145.6
148.9
145.3
146.5
148.9
151.0
148.3
147.3
149.6
147.0
148.5
150.5
152.6
149.8
148.8
150.5
147,6
150.0
151.7
153.6
151.8
151.2
151.0
149.5
150.7
153.4
156.2
153.7
15.5
154.6
150.5
151.7
154.5
157.1
155.5
155.5
155.1
151.8
153.1
155.9
158.1
157.3
157.2
155.3
.9
.9
.9
.6
1.2
1.1
.1
3.3
3.1
3.6
3.6
5.0
5.6
3.2
142.5
144.2
142.9
145.5
144.6
147.2
146.1
148.1
147.6
149.7
148.7
149.7
150.3
152.6
151.6
153.8
152.5
155.0
.6
.8
3.3
3.5
143.9
143.5
145.4
145.1
146.8
146.5
147.7
147.6
149.4
149.5
150.9
150.8
152.1
152.2
153.3
153.3
154.7
154.9
.9
1.0
3.5
3.6
146.6
146.7
145.1
149.2
146.7
146.0
139.1
138.9
140.7
134.1
141.8
148.3
148.5
147.3
150.7
147.9
147.5
140.5
140.6
141.6
135.2
143.6
149.7
149.9
148.6
152.3
149.0
149.1
141.9
142.0
142.9
136.5
145.0
150.6
151.1
150.2
153.0
148.7
150.1
142.8
142.8
143.7
137.6
146.2
152.3
153.0
152.1
154.7
149.2
152.3
144.6
144.6
145.6
139.5
148.0
153.8
154.4
153.2
156.5
151.5
153.6
145.9
145.7
146.9
140.7
149.8
154.8
155.7
154.8
157.2
151.2
155.3
147.5
147.7
148.1
142.1
151.0
156.1
156.9
155.9
158.6
152.6
156.5
148.3
148,4
149.0
142.8
152.4
157.7
158.6
156.7
161.3
153.6
158.2
149.6
149.2
150.5
144.8
154.2
1.0
1.1
.5
1.7
.7
1.1
.9
.5
1.0
1.4
1.2
3.5
3.7
3.0
4.3
2.9
3.9
3.5
3.2
3.4
3.8
4.2
Service occupations...............................................................
141.0
142.5
143.5
144.9
146.4
147.5
148.7
150.6
152.0
.9
3.8
Production and nonsupervisory occupations3..................
142.1
143.7
145.0
146.0
147.7
149.0
150.3
151.5
152.7
.8
3.4
141.3
140.5
145.0
143.2
139.0
136.0
142.9
145.8
143.7
140.8
143.0
142.7
143.0
142.1
146.8
144.9
140.5
138.0
144.4
147.7
145.6
142.0
144.7
143.9
144.3
143.4
147.9
146.0
142.0
139.4
145.7
148.7
146.6
143.4
146.1
145.0
145.2
144.6
148.7
147.2
143.1
140.7
146.5
149.2
147.5
144.6
147.3
145.4
147.0
146.3
150.5
148.9
144.7
142.1
148.5
151.1
149.9
146.4
149.0
147.5
148.6
147.8
152.3
150.5
146.1
143.9
150.0
152.7
150.5
147.8
150.5
149.0
149.5
148.7
152.6
150.8
147.4
145.1
150.7
152.8
150.5
149.1
151.5
149.3
150.5
149.7
153.6
151.7
148.4
146.3
151.7
153.3
151.0
150.3
151.7
153.9
151.7
150.9
155.0
152.9
149.6
147.0
153.1
154.9
152.3
151.7
153.9
151.9
.8
.8
.9
.8
.8
.5
.9
1.0
.9
.9
.9
1.1
3.2
3.1
3.0
2.7
3.4
3.4
3.1
2.5
2.1
3.6
3.3
3.0
145.0
145.3
146.9
147.8
139.1
141.1
138.5
134.9
143.2
143.4
143.0
143.8
145.2
147.4
147.9
142.1
137.8
136.7
146.5
146.9
148.5
149.6
140.3
142.5
140.0
136.2
144.9
145.0
144.7
145.5
146.8
149.4
149.7
143.5
138.5
139.5
147.9
148.3
150.0
151.2
141.6
143.5
141.3
137.4
146.4
146.7
145.9
146.4
148.2
149.6
151.3
144.8
139.7
140.2
148.9
149.4
150.9
152.3
142.2
144.8
142.3
138.6
147.1
147.4
146.6
147.4
149.0
151.6
153.2
145.2
142.2
141.6
150.5
151.3
152.5
154.3
144.3
146.1
143.7
139.8
148.7
149.2
148.1
148.4
150.7
151.6
154.9
146.9
143.8
143.3
151.9
152.6
154.0
155.6
145.3
147.2
145.7
141.6
151.0
151.8
149.9
150.1
151.9
154.5
156.5
147.8
145.5
144.5
153.2
154.2
155.2
157.2
147.5
148.4
146.7
142.6
152.0
153.3
150.4
150.6
153.1
154.1
157.4
148.8
145.7
145.7
151.9
156.1
157.2
158.2
148.1
149.4
149.2
145.7
153.6
155.2
151.7
152.1
154.8
157.9
150.7
146.5
146.7
156.1
157.2
158.2
160.4
149.4
151.6
150.5
147.4
154.3
155.3
153.0
153.0
157.2
159.4
150.9
147.9
148.0
1.0
1.1
1.1
1.1
.9
.9
.9
1.2
.5
.1
.9
.6
1.6
.9
.1
1.0
.9
3.7
3.9
3.7
4.0
3.5
3.8
4.7
5.4
3.8
4.1
3.3
3.1
3.7
2.9
2.7
2.9
3.3
Workers, by occupational group:
Public administration .............................................................
Nonmanufacturing....................................................................
P r iv a te in d u s t r y w o r k e r s .....................................................................
Excluding sales occupations..............................................
Workers, by occupational group:
White-collar workers...............................................................
Excluding sales occupations............................................
Professional specialty and technical occupations...........
Executive, adminitrative, and managerial occupations..
Sales occupations................................................................
Administrative support occupations, including clerical...
Blue-collar workers................................................................
Precision production, craft, and repair occupations.......
Machine operators, assemblers, and inspectors............
Transportation and material moving occupations...........
Handlers, equipment cleaners, helpers, and laborers....
Workers, by industry division:
Goods-producing....................................................................
Excluding sales occupations........................................
Excluding sales occupations........................................
Construction..........................................................................
Manufacturing.......................................................................
White-collar occupations..................................................
Excluding sales occupations........................................
Durables................................................................................
Service-producing...................................................................
Excluding sales occupations........................................
Excluding sales occupations........................................
Transportation and public utilities.....................................
Transportation.....................................................................
Public utilities......................................................................
Communications.............................................................
Wholesale and retail trade..................................................
Excluding sales occupations........................................
Wholesale trade.................................................................
Excluding sales occupations........................................
General merchandise stores..........................................
Food stores.......................................................................
*
See footnotes at end of table.
https://fraser.stlouisfed.org
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Monthly Labor Review
July 2002
99
Current Labor Statistics:
Compensation & Industrial Relations
26. Continued—Employment Cost Index, wages and salaries, by occupation and industry group
[June 1989 = 100]
2002
2001
2000
Series
Mar.
June
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
Percent change
12
3
months
months
ended
ended
Mar. 2002
Health services...................................................................
Hospitals............................................................................
Educational services.........................................................
Colleges and universities................................................
148.7
150.2
162.0
145.5
147.4
152.0
143.5
141.8
148.9
148.9
149.5
151.5
163.3
146.6
149.1
154.1
145.3
143.3
149.6
149.4
151.7
153.3
165.0
150.7
150.6
155.3
146.6
144.9
153.4
152.5
151.7
154.1
165.7
150.8
151.8
156.0
148.1
146.8
154.3
152.9
153.9
156.6
169.4
152.4
153.8
158.2
149.8
148.5
155.4
154.1
154.6
157.6
170.8
153.3
155.0
160.8
151.8
151.0
156.1
155.0
155.8
159.1
173.2
153.6
157.1
162.8
153.6
153.3
159.6
158.4
156.0
159.1
171.7
155.0
158.2
163.7
155.4
155.4
160.5
159.6
160.3
164.5
181.2
157.1
159.5
164.0
157.3
157.1
161.2
159.9
2.8
3.4
5.5
1.4
.8
.2
1.2
1.1
.4
.2
4.2
5.0
7.0
3.1
3.7
3.7
5.0
5.8
3.7
3.8
Nonmanufacturing................................................................
White-collar workers..........................................................
Excluding sales occupations........................................
Blue-collar occupations.....................................................
Service occupations..........................................................
143.9
146.5
147.4
137.4
140.9
145.5
148.2
149.1
138.9
142.4
146.9
149.6
150.7
140.3
143.4
147.9
150.6
151.9
140.9
144.7
149.5
152.3
153.9
142.8
146.0
150.9
153.8
155.3
143.9
147.1
152.2
155.0
156.9
145.8
148.2
153.5
156.4
158.3
146.4
150.1
155.0
158.0
160.1
147.5
151.4
1.0
1.0
1.1
.8
.9
3.7
3.7
4.0
3.3
3.7
State and local government workers...............................
144.3
144.7
147.2
148.3
150.2
151.2
154.3
155.2
156.1
.5
3.4
144.1
144.3
144.9
142.4
141.5
144.5
144.7
145.1
143.0
142.1
147.1
147.4
147.3
145.0
143.9
148.0
148.2
148.8
146.2
145.1
149.0
149.1
150.1
147.0
146.0
149.8
149.8
151.5
147.6
146.5
152.7
153.0
153.9
149.8
149.1
153.3
153.4
155.1
150.9
150.8
153.9
153.6
156.6
151.9
151.6
.4
.1
1.0
.7
.5
3.3
3.0
4.3
3.3
3.8
Finance, insurance, and real estate..................................
Excluding sales occupations.........................................
Banking, savings and loan, and other credit agencies.
Insurance.............................................................................
Services.................................................................................
Workers, by occupational group:
White-collar workers.................................................................
Professional specialty and technical...................................
Executive, administrative, and managerial.........................
Workers, by industry division:
Services....................................................................................
144.6
144.9
147.9
148.7
149.5
150.2
153.7
154.2
154.6
.3
3.4
Health services...................................................................
Hospitals..........................................................................
Educational services.........................................................
Schools............................................................................
Elementary and secondary.........................................
Colleges and universities............................................
144.3
145.3
145.3
144.5
144.7
144.5
144.9
144.8
145.7
145.6
144.8
144.9
144.6
145.6
146.7
147.7
147.7
148.0
148.1
147.9
148.3
147.9
149.3
149.2
148.7
148.9
148.5
149.5
149.1
149.9
149.5
149.5
149.7
149.0
151.4
150.7
151.9
151.8
150.0
150.2
149.5
151.8
153.2
154.2
154.2
153.6
153.8
152.8
156.5
154.9
155.8
155.7
154.0
154.1
153.1
156.7
156.7
157.8
157.7
154.2
154.3
153.4
156.8
1.2
1.3
1.3
.1
.1
.2
.1
5.1
5.3
5.5
3.1
3.1
3.0
3.6
Public administration .............................................................
142.5
142.9
144.6
146.1
147.6
148.7
150.3
151.6
152.5
.6
3.3
Services excluding schools4................................................
1 Consists of private industry workers (excluding farm and household workers) and
State and local government (excluding Federal Government) workers.
3 This series has the same industry and occupational coverage as the Hourly
Earnings index, which was discontinued in January 1989.
2 Consists of legislative, judicial, administrative, and regulatory activities.
4 Includes, for example, library, social, and health services.
27. Employment Cost Index, benefits, private industry workers by occupation and industry group
[June 1989 = 100]
2002
2001
2000
Series
Mar.
June
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
Percent change
12
3
months
months
ended
ended
Mar. 2002
153.8
155.7
157.5
158.6
161.5
163.2
165.2
166.7
169.3
1.6
4.8
156.3
150.0
158.5
151.6
160.4
153.1
161.5
154.1
165.2
155.7
167.4
156.7
169.5
158.3
171.2
159.2
173.5
162.2
1.3
1.9
5.0
4.2
152.3
154.0
152.3
154.0
154.2
156.0
153.9
156.1
155.7
157.9
154.9
158.1
156.2
159.4
154.8
159.7
158.5
162.6
157.1
162.9
159.6
164.6
157.9
164.9
160.8
167.1
158.5
167.4
162.6
168.4
160.4
168.6
165.8
170.7
163.7
171.1
2.0
1.4
2.1
1.4
4.6
5.0
4.2
5.0
Workers, by occupational group:
Workers, by industry division:
Nonmanufacturing...................................................................
100
Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
28.
Employment Cost Index, private nonfarm workers by bargaining status, region, and area size
[June 1989 = 100]
2002
2001
2000
Series
Mar.
June
Sept.
Dec.
Mar.
Sept.
June
Dec.
Mar.
Percent change
12
3
months
months
ended
ended
Mar. 2002
C O M P E N S A T IO N
W o r k e r s , b y b a r g a in in g s ta t u s 1
Union................................................................................................
Goods-producing........................................................................
Service-producing......................................................................
Manufacturing.............................................................................
Nonmanufacturing......................................................................
143.0
143.3
142.5
144.5
141.7
144.4
144.8
143.9
145.4
143.4
146.1
146.8
145.2
147.1
145.0
146.9
147.3
146.4
147.4
146.2
147.9
147.9
147.6
147.9
147.3
149.5
149.3
149.5
148.8
149.4
151.0
150.6
151.2
149.9
151.1
153.1
151.6
154.2
151.4
153.5
154.8
153.4
156.0
153.4
155.0
1.1
1.1
1.2
1.3
1.0
4.7
3.7
5.7
3.7
5.2
Nonunion..........................................................................................
Goods-producing.......................................................................
Service-producing......................................................................
Manufacturing.............................................................................
Nonmanufacturing......................................................................
147.4
145.4
148.0
146.5
147.4
149.1
147.2
149.6
148.2
149.1
150.6
148.4
151.2
149.2
150.7
151.6
149.3
152.3
149.9
151.8
153.8
151.6
154.4
152.4
153.9
155.3
153.1
155.9
153.7
155.4
156.7
154.0
157.5
154.4
157.0
157.8
155.3
158.6
155.5
158.2
159.6
157.2
160.3
157.6
159.9
1.1
1.2
1.1
1.4
1.1
3.8
3.7
3.8
3.4
3.9
146.3
145.0
148.9
147.0
147.6
146.7
150.7
148.8
149.3
147.6
152.2
150.8
150.3
148.6
153.3
151.8
151.6
151.1
154.8
154.3
153.7
152.3
156.0
156.0
155.2
153.5
157.4
157.6
156.3
154.6
158.6
159.4
158.3
156.2
161.1
160.4
1.3
1.0
1.6
.6
4.4
3.4
4.1
4.0
146.9
146.0
148.6
147.7
150.1
148.8
151.0
150.3
153.1
152.1
154.6
153.7
156.0
154.8
157.4
155.6
159.1
157.5
1.1
1.2
3.9
3.6
Union................................................................................................
Goods-producing........................................................................
Service-producing......................................................................
Manufacturing............................................................................
137.2
137.2
137.6
138.8
136.4
138.5
138.4
138.9
139.7
137.8
140.0
140.2
140.1
141.4
139.2
141.2
141.3
141.5
142.6
140.4
142.1
142.4
142.2
143.9
141.1
143.7
144.2
143.7
145.5
142.7
145.1
145.3
145.4
146.7
144.3
147.4
146.3
148.9
148.0
147.1
148.4
147.2
150.0
149.0
148.1
.7
.6
.7
.7
.7
4.4
3.4
5.5
3.5
5.0
Nonunion..........................................................................................
Goods-producing........................................................................
Service-producing......................................................................
Manufacturing............................................................................
Nonmanufacturing.....................................................................
145.1
142.9
145.8
144.4
145.0
146.7
144.7
147.3
146.1
146.6
148.1
145.8
148.7
147.2
148.0
149.0
146.8
149.6
148.0
148.9
150.8
148.8
151.4
150.1
150.7
152.2
150.3
152.7
151.6
152.0
153.4
151.1
154.1
152.2
153.3
154.4
152.1
155.1
153.1
154.4
155.9
153.5
156.7
154.7
155.9
1.0
.9
1.0
1.0
1.0
3.4
3.2
3.5
3.1
3.5
142.3
143.0
145.3
144.7
143.7
144.6
147.1
146.3
145.3
145.3
148.6
148.2
146.0
146.3
149.6
149.2
147.3
148.3
150.9
151.3
149.2
149.3
152.3
152.S
150.6
150.2
153.6
154.3
151.7
151.2
154.7
156.0
153.5
152.5
157.1
156.4
1.2
.9
1.6
.3
4.2
2.8
4.1
3.4
144.1
142.2
145.7
143.7
147.1
144.7
148.0
146.0
149.8
147.4
151.2
148.8
152.4
149.7
153.7
150.5
155.1
151.7
.9
.8
3.5
2.9
W o r k e r s , b y r e g io n 1
Northeast........................................................................................
South...............................................................................................
Midwest (formerly North Central)...............................................
W o r k e r s , b y a r e a s iz e 1
Metropolitan areas.........................................................................
W A G E S A N D S A L A R IE S
W o r k e r s , b y b a r g a in in g s ta t u s 1
W o r k e r s , b y r e g io n 1
Northeast.......................................................................................
South...............................................................................................
Midwest (formerly North Central)...............................................
W o r k e r s , b y a r e a s iz e 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.
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Monthly Labor Review
July 2002
101
Current Labor Statistics:
Compensation & Industrial Relations
29. Percent of full-time employees participating in employer-provided benefit plans, and in selected features within plans,
medium and large private establishments, selected years, 1980-97
Item
1980
Scope of survey (in 000's)...........................................
Number of employees (in 000’s):
With medical care......................................................
With life insurance.....................................................
With defined benefit plan..........................................
1982
1984
1986
1988
1989
1991
1993
1995
1997
21,352
21,043
21,013
21,303
31,059
32,428
31,163
28,728
33,374
38,409
20,711
20,498
17,936
20,412
20,201
17,676
20,383
20,172
17,231
20,238
20,451
16,190
27,953
28,574
19,567
29,834
30,482
20,430
25,865
29,293
18,386
23,519
26,175
16,015
25,546
29,078
17,417
29,340
33,495
19,202
Participants with:
Paid lunch time...........................................................
Average minutes per day........................................
Paid rest time............* ...............................................
Average minutes per day........................................
Paid funeral leave......................................................
Average days per occurrence.................................
Paid holidays...............................................................
Average days per year............................................
10
75
99
10.1
9
26
73
26
-
8
30
67
28
80
3.3
92
10.2
21
3.3
80
3.3
89
9.1
81
3.7
89
9.3
23
3.6
99
100
98
96
21
3.1
97
22
3.3
96
20
3.5
99
10
26
71
26
84
3.3
97
9.2
22
3.1
97
9
29
68
26
83
3.0
91
9.4
20
100
10
27
72
26
88
3.2
99
10.0
25
3.7
11
29
72
26
85
3.2
96
9.4
Paid personal leave....................................................
Average days per year............................................
Paid vacations............................................................
9
25
76
25
99
10.0
24
3.8
Paid sick leave1.........................................................
Unpaid maternity leave.............................................
Unpaid paternity leave...............................................
Unpaid family leave...................................................
62
-
67
-
67
-
70
-
69
33
16
68
37
18
67
37
26
65
60
53
58
56
-
-
-
-
-
-
-
-
84
93
97
97
97
95
90
92
83
82
77
76
58
62
46
62
8
66
70
18
76
79
28
75
80
28
81
80
30
86
82
42
78
73
56
85
78
63
26
46
27
51
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
Time-off plans
99
9.8
24
3.3
95
In s u ra n c e p la n s
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 montmy contribution................................
-
-
36
$11.93
58
$35.93
96
96
96
96
92
94
94
91
87
87
69
72
74
-
-
-
64
64
72
10
59
78
8
49
71
7
42
71
6
44
76
5
41
77
7
37
74
6
33
40
41
42
43
45
44
53
55
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..........................................................
40
43
47
48
42
45
54
51
51
49
46
43
Participants in short-term disability plans ' .................
-
-
-
-
-
-
-
-
R e tir e m e n t p la n s
Participants in defined benefit pension plans...........
84
84
82
76
63
63
59
56
52
50
Percent of participants with:
Normal retirement prior to age 65...........................
Early retirement available........................................
Ad hoc pension increase In last 5 years.................
Terminal earnings formula......................................
Benefit coordinated with Social Security................
55
98
53
45
58
97
52
45
63
97
47
54
56
64
98
35
57
62
59
98
26
55
62
62
97
22
64
63
55
98
7
56
54
52
95
6
61
48
52
96
4
58
51
52
95
10
56
49
-
-
-
60
45
48
48
49
55
57
33
36
41
44
43
54
55
Participants in defined contribution plans...................
Participants in plans with tax-deferred savings
arrangements.............................................................
O th e r b e n e fits
Employees eligible for:
Flexible benefits plans...............................................
Reimbursement accounts2.......................................
“
“
Premium conversion plans........................................
The definitions for paid sick leave and short-term disability (previously sickness and
accident insurance) were changed for the 1995 survey. Paid sick leave now includes only
plans that specify either a maximum number of days per year or unlimited days. Shortterms disability now includes all insured, self-insured, and State-mandated plans available
on a per-disability basis, as well as the unfunded per-disability plans previously reported as
sick leave. Sickness and accident insurance, reported in years prior to this survey, Included
only insured, self-insured, and State-mandated plans providing per-disability bene
102 Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
2
5
9
10
12
12
13
5
12
23
36
52
38
5
32
7
fits at less than full pay.
2 Prior to 1995, reimbursement accounts included premium conversion plans, which
specifically allow medical plan participants to pay required plan premiums with pretax
dollars. Also, reimbursement accounts that were part of flexible benefit plans were
tabulated separately.
No te : Dash indicates data not available.
30. Percent of full-time employees participating In employer-provided benefit plans, and in selected features
within plans, small private establishments and State and local governments, 1987, 1990,1992, 1994, and 1996
State and local governments
Small private establishments
Item
1994
1992
1990
1987
1996
1994
1992
1990
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
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
37
48
27
47
2.9
84
9
37
49
26
50
3.0
82
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
Average days per year1...........................................
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
10.9
38
2.7
72
13.6
39
2.9
67
14.2
38
2.9
67
11.5
38
3.0
66
Paid sick leave2.........................................................
47
53
50
50
97
95
95
94
Unpaid leave...............................................................
Unpaid paternity leave...............................................
Unpaid family leave....................................................
17
8
18
7
47
48
57
30
51
33
59
44
93
69
71
66
64
93
93
90
87
80
84
28
_
_
Physical exam..........................................................
79
83
26
76
78
36
82
79
36
87
84
47
84
81
55
Percent of participants with employee
contribution required for:
Self coverage...........................................................
Average monthly contribution...............................
Family coverage......................................................
42
$25.13
67
47
$36.51
73
52
$40.97
76
52
$42.63
75
35
$15.74
71
38
$25.53
65
43
$28.97
72
47
$30.20
71
Average monthly contribution................................
$109.34
$150.54
$159.63
$181.53
$71.89
$117.59
$139.23
$149.70
Participants in life insurance plans.............................
Percent of participants with:
Accidental death and dismemberment
insurance.................................................................
64
64
61
62
85
88
89
87
78
1
19
76
1
25
79
2
20
77
1
13
67
1
55
67
1
45
74
1
46
64
2
46
19
23
20
22
31
27
28
30
-
14
21
22
21
-
-
-
-
T im e -o ff p la n s
In s u ra n c e p la n s
Participants in medical care plans..............................
Percent of participants with coverage for:
Home health care.....................................................
Retiree protection available......................................
Participants in long-term disability
insurance plans.........................................................
Participants in sickness and accident
insurance plans..........................................................
6
26
26
-
-
-
Participants in defined benefit pension plans...........
20
22
15
15
93
90
87
91
Percent of participants with:
Normal retirement prior to age 65..........................
Early retirement available.......................................
Ad hoc pension increase in last 5 years................
Terminal earnings formula......................................
Benefit coordinated with Social Security...............
54
95
7
58
49
50
95
4
54
46
-
92
90
33
100
18
89
-
47
92
53
44
92
89
10
100
10
92
87
13
99
49
31
33
34
38
9
9
9
9
28
28
45
45
24
Participants in short-term disability plans 2.................
29
R e tire m e n t p la n s
Participants in defined contribution plans..................
Participants in plans with tax-deferred savings
arrangements...........................................................
17
24
23
88
16
100
8
O th e r b e n e fits
Employees eligible for:
Flexible benefits plans...............................................
Reimbursement accounts 3.......................................
Premium conversion plans ......................................
1
2
3
4
5
5
5
5
8
14
19
12
5
31
50
64
7
1 Methods used to calculate the average number of paid holidays were revised
in 1994 to count partial days more precisely. Average holidays for 1994 are
not comparable with those reported in 1990 and 1992.
Sickness and accident insurance, reported in years prior to this survey,
included only insured, self-insured, and State-mandated plans providing per-
2 The definitions for paid sick leave and short-term disability (previously
sickness and accident insurance) were changed for the 1996 survey. Paid sick
leave now includes only plans that specify either a maximum number of days
per year or unlimited days. Short-term disability now includes all insured, self-
3 Prior to 1996, reimbursement accounts included premium conversion plans,
which specifically allow medical plan participants to pay required plan
premiums with pretax dollars. Also, reimbursement accounts that were part of
insured, and State-mandated plans available on a per-disability basis, as well
as the unfunded per-disability plans previously reported as sick leave.
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Federal Reserve Bank of St. Louis
disability benefits at less than full pay.
flexible benefit plans were tabulated separately.
NOTE: Dash indicates data not available.
Monthly Labor Review
July 2002
103
Current Labor Statistics:
Compensation & industrial Relations
31. Work stoppages involving 1,000 workers or more
Measure
Annual totals
2000
2001
2001
May
June
July
Aug.
2002p
Sept.
Oct.
Nov.
Dec.
Jan
Feb
Mar
Apr
May
Number of stoppages:
Beginning in period...............................
39
29
7
3
2
3
2
1
0
2
In effect during period..........................
40
30
8
5
3
4
3
4
1
2
0
1
1
1
2
3
2
1
3
5
Workers involved:
Beginning in period (in thousands)....
394
99
22.1
4.7
2.2
5.8
3.0
24.9
.0
6.0
.0
1.5
2.9
4.1
5.1
In effect during period (in thousands).
397
102
23.4
9.0
3.3
6.9
4.1
29.0
1.6
6.0
1.0
2.5
2.9
7.0
9.2
Number (in thousands)........................
20,419
1,151
201.6
73.2
62.1
71.5
55.7
316.4
11.2
55.0
21.0
9.0
43.5
80.7
138.2
Percent of estimated workina time1....
.06
.00
.01
<2)
(2)
(2)
.01
(2)
<2)
,00
,00
,00
,00
.00
Days idle:
(2)l
' Agricultural and government employees are included in the total employed and total working time; private household, forestry, and fishery employees are excluded. An explanation of
the measurement of idleness as a percentage of the total time worked is found in " Total economy’ measures of strike idleness," Monthly Labor Review, October 1968, pp. 54— 56.
2 Less than 0.005.
p = preliminary.
NOTE: Dash indicates data not available.
104
Monthly Labor Review
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Federal Reserve Bank of St. Louis
July 2002
32. 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
[1 982-84 = 100, unless otherwise indicated]
Series
2000
2001
2002
2001
Annual average
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
C O N S U M E R P R IC E IN D E X
FOR A LL URBAN CO NSUM ERS
All Items......................................................................
All items (1967 - 100)...............................................
172.2
515.8
177.1
530.4
177.7
532.2
178.0
533.3
177.5
531.6
177.5
531.8
178.3
534.0
177.7
532.2
177.4
531.3
176.7
5292.0
530.6
177.8
532.7
178.8
535.5
179.8
538.6
179.8
538.5
Food and beverages................................................
168.4
167.8
167.9
188.3
154.5
173.6
173.1
173.4
173.4
173.0
173.3
194.2
161.7
174.0
173.5
173.9
194.9
162.3
174.4
173.9
174.2
195.9
162.4
174.6
174.1
174.3
195.1
162.4
175.3
174.9
175.2
195.2
163.5
175.2
174.6
174.7
194.9
162.7
175.2
174.7
174.7
195.3
162.0
176.2
175.8
176.2
196.7
162.1
176.4
175.9
176.0
197.6
161.8
176.6
176.1
176.3
193.8
161.3
172.9
172.5
172.8
193.2
160.8
197.0
162.8
176.7
176.2
176.4
198.1
162.5
175.8
175.5
198.2
162.4
160.7
204.6
167.1
212.2
164.7
213.1
166.9
211.8
168.3
210.7
168.9
208.8
169.4
212.1
170.8
213.5
171.2
212.9
170.8
214.4
169.9
224.8
170.1
223.3
169.4
225.8
168.7
223.4
169.0
221.0
139.2
Fats and oils......................................................
Other foods........................................................
137.8
155.6
154.0
147.4
172.2
159.6
155.7
155.7
138.6
159.5
155.7
156.7
175.7
138.9
160.4
156.1
157.8
176.8
140.0
161.0
156.1
158.5
177.6
139.2
160.2
156.6
158.5
176.2
139.9
160.9
156.4
159.5
177.0
139.5
160.3
154.9
155.6
177.6
18.5
160.9
156.1
156.9
177.9
139.5
161.3
158.4
158.3
177.4
140.0
160.4
158.5
157.2
176.3
140.1
159.9
157.2
156.4
176.0
138.1
159.6
155.8
154.7
176.4
175.9
140.1
161.5
159.6
156.5
177.8
138.0
160.0
157.9
155.9
176.1
Other miscellaneous foods1,2.........................
107.5
108.9
108.8
107.7
109.6
109.5
108.9
108.9
110.6
108.5
108.9
108.0
107.8
108.0
108.9
Food away from home1..........................................
169.0
173.9
173.1
173.6
174.1
174.7
175.1
176.4
177.0
177.1
109.0
174.7
113.4
179.3
112.4
178.5
112.6
179.1
113.8
179.7
114.3
180.0
115.3
180.4
175.8
115.5
181.2
176.0
Other food away from home1,2...........................
Alcoholic beverages................................................
Housing.....................................................................
175.6
115.4
180.8
115.5
180.9
115.5
181.8
115.8
182.6
116.3
182.5
177.2
116.9
182.9
177.6
117.1
183.3
169.6
193.4
176.4
200.6
175.9
199.6
177.3
200.7
177.6
201.4
178.0
202.4
177.4
202.0
176.7
202.4
176.9
202.9
176.9
203.2
177.6
204.5
178.5
206.1
179.1
207.0
179.5
207.5
207.5
191.6
123.7
205.7
192.3
193.1
193.9
194.7
198.2
198.5
198.8
206.3
207.3
116.8
208.1
114.5
209.0
210.1
108.0
210.9
197.0
113.1
211.6
197.7
125.2
195.5
111.6
196.4
124.0
119.3
212.2
121.9
212.8
122.1
213.3
120.1
213.7
106.6
154.8
140.5
123.8
148.6
129.2
106.6
152.7
106.9
144.6
106.9
143.5
138.0
122.1
146.0
129.1
106.7
150.6
135.7
125.3
143.1
129.4
129.1
121.5
135.9
129.0
127.8
118.3
134.7
129.1
106.3
142.2
126.2
112.7
133.5
128.9
106.4
141.5
125.3
112.9
132.4
128.7
106.8
140.0
123.7
112.3
130.6
128.6
106.8
140.2
123.8
112.8
130.7
128.7
107.2
140.3
123.8
115.1
130.6
128.9
107.6
141.5
125.1
114.4
132.1
128.9
126.8
123.7
120.3
129.5
127.5
122.1
128.0
127.4
119.4
123.7
120.4
122.8
114.8
120.8
109.7
123.5
122.0
115.3
128.2
125.2
121.3
128.8
125.6
122.2
127.1
124.3
229.4
127.4
124.5
Food.........................................................................
Food at home.........................................................
Cereals and bakery products..............................
Meats, poultry, fish, and eggs.............................
Dairy and related products1.................................
Fruits and vegetables..........................................
Nonalcoholic beverages and beverage
materials...........................................................
Other foods at home............................................
Sugar and sweets..............................................
Shelter....................................................................
Rent of primary residence...................................
177.1
176.4
179.7
183.9
192.1
191.0
Owners’ equivalent rent of primary residence3....
117.5
198.7
118.6
206.3
120.0
204.9
Tenants’ and household insurance1,2.................
Fuels and utilities................................................
103.7
137.9
106.8
151.3
Fuels...................................................................
Fuel oil and other fuels....................................
Gas (piped) and electricity..............................
Household furnishings and operations...............
122.8
129.7
128.0
128.2
106.2
150.2
135.4
129.3
142.4
129.1
136.8
131.9
143.8
128.9
107.0
155.7
141.6
129.6
149.4
129.2
Apparel.....................................................................
Men's and boys' apparel......................................
Women's and girls' apparel.................................
129.6
129.7
121.5
127.3
125.7
119.3
129.8
129.1
122.3
126.3
125.8
117.5
122.6
122.5
111.6
122.6
121.4
112.1
Infants’ and toddlers’ apparel1.............................
Footwear..............................................................
Transportation...........................................................
Private transportation............................................
130.6
129.2
127.3
122.1
158.3
127.2
129.9
198.9
123.7
117.1
154.0
122.9
155.5
151.2
128.5
120.6
125.0
121.9
153.3
148.8
131.5
124.9
152.3
148.1
150.2
146.1
148.5
144.3
148.6
144.4
119.5
148.4
144.1
123.5
150.5
146.3
124.5
153.7
149.6
153.8
149.5
New and used motor vehicles2...........................
New vehicles.....................................................
100.8
142.8
101.3
142.1
155.3
101.4
124.5
121.3
154.4
149.9
129.3
123.0
154.3
150.0
130.6
124.4
159.2
126.3
123.8
153.3
149.1
142.3
101.1
141.7
100.8
141.2
100.5
140.3
100.2
140.2
100.6
141.0
100.1
141.2
99.6
140.7
99.3
140.4
99.1
139.8
155.8
129.3
128.6
101.5
177.3
209.6
158.7
124.7
124.0
104.8
183.5
210.6
159.1
146.8
146.0
104.4
182.5
209.3
158.9
142.0
141.3
104.4
182.7
216.3
158.3
125.6
124.9
105.1
183.4
216.1
158.0
121.9
121.2
104.9
184.0
213.7
157.3
131.4
130.7
105.2
185.1
212.7
157.8
116.3
115.6
105.5
186.0
209.1
101.6
143.5
157.2
96.1
95.4
105.8
186.4
204.8
101.0
142.7
Used cars and trucks1.......................................
Motor fuel.............................................................
Gasoline (all types)............................................
Motor vehicle parts and equipment....................
Motor vehicle maintenance and repair...............
Public transportation..............................................
101.3
142.6
157.4
104.5
103.8
105.8
186.4
205.1
155.6
97.9
97.2
106.2
187.1
205.8
153.9
98.2
97.6
106.1
188.0
207.3
152.1
107.7
107.1
106.5
188.5
207.9
152.8
121.4
120.8
106.8
189.0
209.7
151.8
121.4
120.8
106.8
189.9
211.3
Medical care..............................................................
260.8
238.1
266.0
237.7
271.4
246.6
277.3
245.8
335.1
272.5
248.1
278.3
246.5
336.6
273.1
248.5
278.9
246.8
337.9
274.4
249.1
280.5
247.7
341.2
275.0
249.6
281.0
247.9
342.6
275.9
250.2
282.0
248.4
344.8
276.7
250.6
283.0
248.8
347.1
277.3
251.6
283.5
248.9
348.3
279.6
252.6
286.2
317.3
272.8
247.6
278.8
246.5
338.3
281.0
253.7
287.7
251.4
356.4
282.0
254.1
288.9
251.9
359.4
283.2
254.8
290.2
252.5
362.4
284.1
255.4
291.2
252.9
364.5
Lodging away from home....................................
Professional services...........................................
Hospital and related services..............................
132.4
250.6
353.1
103.3
104.9
105.0
104.8
105.0
105.1
105.2
105.3
105.7
105.9
106.1
106.5
106.4
101.5
101.6
101.3
101.7
101.7
101.3
101.3
105.5
101.4
105.3
101.0
101.2
102.1
102.9
102.9
102.9
103.1
102.5
105.2
104.0
104.4
104.8
105.8
106.6
107.1
107.0
106.9
107.2
107.3
106.6
106.2
106.6
112.5
279.9
118.5
295.9
116.4
290.7
116.9
293.9
117.2
295.1
119.5
298.0
121.7
305.4
122.2
307.2
122.3
304.7
122.0
294.7
122.6
303.0
123.2
314.4
123.3
314.2
123.3
314.4
123.5
315.6
324.0
93.6
341.1
93.3
335.0
92.9
336.2
93.1
337.2
93.6
343.9
93.5
350.0
93.1
351.5
93.6
352.0
93.3
352.2
93.4
353.2
93.4
353.9
93.1
354.1
92.0
354.1
91.2
354.6
91.9
Information and information processing1,2.......
92.8
92.3
91.8
92.1
92.5
92.4
92.0
92.5
92.2
92.3
92.2
92.0
90.8
90.0
90.7
Telephone services1,2....................................
Information and information processing
98.5
99.3
98.7
99.0
99.6
99.6
99.2
99.9
99.6
99.6
100.3
100.3
99.1
98.2
99.3
other than teleDhone services1,4.................
Personal computers and peripheral
25.9
21.3
21.7
21.4
21.3
20.7
20.3
20.2
20.0
19.8
19.4
19.0
18.8
18.6
18.5
29.3
285.8
441.2
27.8
26.7
26.4
25.8
25.3
24.6
23.8
23.1
22.9
23.0
283.3
424.6
287.8
444.0
285.6
429.9
289.2
446.7
286.4
431.7
287.2
432.8
290.2
449.3
288.5
433.4
292.9
461.4
291.5
449.0
Education and communication2...............................
Education2............................................................
equipment1,2...........................................
41.1
29.5
30.4
Tobacco and smoking products...........................
271.1
394.9
282.6
425.2
281.3
418.7
29.8
281.2
421.0
Personal care1......................................................
165.6
170.5
169.5
170.0
170.7
171.2
171.9
172.3
172.6
172.6
173.2
173.7
174.1
174.4
174.7
Personal care products1....................................
153.7
155.1
153.2
154.6
155.1
154.7
155.5
155.4
155.4
155.4
155.2
155.5
155.1
155.4
154.8
Personal care services1.....................................
178.1
184.3
184.1
184.1
184.8
185.2
185.5
185.9
186.8
186.4
186.3
186.4
187.3
187.9
188.3
See footnotes at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
105
Current Labor Statistics:
Price Data
32. Continued—Consumer Price Indexes for All Urban Consumers and for Urban Wage Earners and Clerical Workers: U.S. city
average, by expenditure category and commodity or service group
[1982-84 = 100, unless otherwise indicated]_______
Series
Miscellaneous personal services..................
Annual average
2000
2001
2001
May
June
July
Aug.
2002
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
252.2
263.1
261.C
261.8
263.2
265.5
266.4
267.C
268.C
268.5
270.4
271.6
272.S
273.2
274.2
150.7
152.1
173.4
151.5
174.6
138.0
149.6
126.8
149.5
175.2
147.9
175.2
147.6
176.2
148.1
176.4
176.6
151.0
176.7
150.5
176.4
139.4
151.3
126.3
149.6
174.4
135.4
144.6
122.6
150.5
175.2
140.8
153.5
129.8
150.4
174.C
136.5
146.3
122.6
149.4
173.6
137.2
147.1
127.3
152.9
172.9
Commodities less food and beverages............
Nondurables less food and beverages...........
Apparel.........................................................
149.2
168.4
137.7
147.4
129.6
136.1
146.0
129.5
134.6
142.6
128.0
132.3
138.4
123.7
131.6
137.9
120.4
132.1
139.6
123.6
133.7
143.6
128.2
136.0
148.4
128.8
135.4
147.4
127.1
Nondurables less food, beverages,
and apparel.................................................
Durables................................................
162.5
125.¿
163.4
124.6
172.0
124.9
170.4
124.5
164.5
124.2
162.1
123.6
167.5
123.4
160.4
123.6
156.2
124.2
151.6
124.3
152.6
123.6
153.6
122.7
157.3
122.1
164.7
121.9
164.1
121.7
Services..............................................................
195.3
203.4
202.5
204.0
204.5
205.2
204.9
204.7
205.1
205.3
206.3
207.3
208.0
208.4
208.8
Rent of shelter3.....................
Transporatation services.................................
Other services..................................................
Special indexes:
201.3
196.1
229.E
208.9
201.9
238.0
207.8
200.4
236.4
209.0
202.0
236.7
209.7
202.6
237.7
210.8
202.7
239.4
210.3
202.8
240.6
210.8
203.4
241.4
211.3
204.2
241.9
211.7
204.5
241.9
213.0
205.2
242.9
214.7
206.5
243.5
215.6
207.3
243.6
216.1
207.9
216.1
208.9
244.5
All items less food..........................................
All items less shelter.........................................
All Items less medical care...........................
173.0
165.7
167.2
139.2
149.1
162.9
158.2
177.8
169.7
171.9
179.0
171.0
172.9
178.2
170.0
172.3
138.2
148.3
165.2
160.3
178.2
169.7
179.0
170.9
173.0
178.2
169.9
172.4
137.8
148.1
161.5
160.8
177.8
169.3
172.0
136.4
145.1
157.7
159.1
177.0
168.2
171.3
134.1
140.9
153.4
156.8
177.4
168.4
171.7
178.2
168.7
172.4
179.2
169.7
173.3
160.6
178.6
170.9
172.6
142.4
155.1
172.0
163.6
133.5
140.5
154.5
157.0
133.9
142.2
155.4
158.0
135.6
145.9
158.7
160.2
180.4
170.9
174.3
137.8
150.4
165.5
162.7
202.9
212.3
211.4
195.7
140.1
182.9
185.5
145.7
145.6
208.4
197.8
132.4
183.6
186.2
144.4
125.6
210.1
214.0
198.4
129.4
184.1
186.6
143.8
122.0
211.2
145.2
131.0
211.2
213.0
197.8
122.1
185.1
187.6
145.6
116.9
211.7
213.3
198.2
116.0
185.4
188.1
146.0
105.8
212.3
198.3
111.4
185.2
187.8
144.7
97.6
212.6
213.9
199.2
111.7
185.7
188.2
143.7
99.3
213.8
214.3
196.6
129.3
183.5
186.1
145.3
125.2
209.6
213.3
197.2
140.5
183.3
185.9
144.9
141.1
209.4
213.2
188.9
124.6
178.6
181.3
144.9
129.5
202.1
200.2
111.0
186.5
189.2
144.2
99.5
215.1
214.8
200.8
115.6
187.1
189.8
144.6
108.6
215.9
201.2
122.2
187.5
190.3
145.1
121.6
216.3
201.6
201.6
122.9
187.4
190.2
144.4
121.6
216.6
All Items.....................................................
All items (1967= 100)............................................
163.2
486.2
173.5
516.8
174.4
519.4
174.6
520.0
173.8
517.8
173.8
517.6
174.8
520.6
174.0
518.3
173.7
517.3
172.9
515.0
173.2
515.0
173.7
517.5
174.7
520.2
175.8
523.7
175.8
523.6
Food and beverages.............................................
163.8
163.4
163.0
184.7
147.6
173.0
172.5
172.4
193.6
161.2
172.3
171.9
171.8
192.9
160.6
172.8
172.4
172.4
193.9
161.4
173.4
173.0
173.0
194.5
162.1
173.8
173.4
173.3
195.6
162.0
174.0
173.5
173.4
194.8
162.3
174.8
174.3
174.3
195.1
163.2
174.5
174.1
173.7
194.7
162.6
174.6
174.1
173.7
195.1
161.8
175.7
175.2
175.3
196.7
162.0
175.8
175.3
175.1
197.5
161.6
176.1
175.6
175.5
197.0
162.7
176.1
175.5
175.3
197.9
162.1
175.7
175.1
174.4
198.2
162.1
159.4
201.8
167.1
210.8
164.7
211.5
166.9
210.5
168.3
209.5
168.9
208.0
169.4
211.0
170.8
212.2
171.2
211.5
170.6
212.8
169.7
223.2
170.0
222.2
169.2
224.9
168.7
222.0
168.7
219.1
133.2
152.8
152.2
147.9
168.8
138.4
159.1
155.6
155.4
176.3
137.2
159.1
155.8
154.3
176.5
137.8
159.1
155.5
156.4
176.0
138.0
160.0
156.0
157.4
177.2
139.3
160.5
156.1
158.0
177.9
138.4
159.8
156.2
158.1
176.5
139.2
160.4
156.2
159.1
177.3
138.7
159.7
154.7
155.1
177.8
137.7
160.5
155.9
156.5
178.3
138.8
161.0
158.5
158.0
177.9
139.5
160.1
158.5
157.0
176.8
139.7
159.6
157.1
156.3
176.5
139.4
161.0
153.4
156.2
178.2
137.3
159.7
157.6
155.7
176.7
Commodity and service group:
Commodities......................................................
Food and beverages.........................................
Commodities less food....................................
Nondurables less food............................
Nondurables less food and apparel................
Nondurables...............................................
Services less rent of shelter3...............
Services less medical care services................
Energy....................................................
All items less energy....................................
All Items less food and energy.......................
Commodities less food and energy..............
Energy commodities...................................
Services less energy....................................
138.9
149.1
164.1
141.0
153.1
170.6
162.7
213.7
172.3
137.2
146.9
163.0
159.7
139.7
151.5
168.0
162.3
213.9
198.1
132.5
184.5
187.1
243.8
215.1
180.4
170.9
174.2
137.3
149.5
165.0
216.0
C O N S U M E R P R IC E IN D E X F O R U R B A N
W A G E E A R N E R S A N D C L E R IC A L W O R K E R S
Food................................................................
Food at home.....................................................
Cereals and bakery products...........................
Meats, poultry, fish, and eggs..........................
Dairy and related products1........................
Fruits and vegetables.....................................
Nonalcoholic beverages and beverage
materials......................................................
Other foods at home.........................................
Sugar and sweets...........................................
Fats and oils...................................................
Other foods.....................................................
Other miscellaneous foods1,2.....................
Food away from home1...............................
Other food away from home1,2........................
Alcoholic beverages...........................................
Flousing..........................................................
Shelter....................................................
104.6
109.1
108.7
108.0
109.9
109.7
109.2
109.5
110.8
109.0
109.3
108.5
108.3
108.5
109.5
165.0
173.8
173.1
173.5
174.0
175.8
177.1
177.5
112.8
178.4
114.0
179.2
115.7
180.5
115.8
180.8
176.9
116.0
182.1
177.0
112.5
178.0
176.0
115.8
180.5
176.4
113.6
178.8
175.0
115.6
180.1
175.6
105.1
168.8
174.7
114.4
179.7
116.8
182.2
117.4
182.8
117.7
183.1
160.0
172.1
171.7
173.0
194.4
173.3
173.5
173.2
172.5
172.9
173.4
173.9
174.4
174.8
175.1
195.0
195.9
196.0
196.6
172.8
197.2
197.7
198.7
199.8
200.6
201.0
201.2
197.0
197.8
115.8
181.4
181.6
194.5
193.5
Rent of primary residence...............................
177.1
190.4
191.0
191.7
192.4
Owners’ equivalent rent of primary residence3
122.2
175.7
119.9
186.3
123.2
187.0
123.7
187.5
124.4
188.5
193.3
116.8
189.2
194.0
114.8
190.0
194.9
111.8
190.9
195.7
Lodqinq away from home2...........................
191.5
118.4
187.6
108.8
191.7
196.3
113.2
192.3
119.4
192.9
197.5
122.2
193.3
122.0
193.9
98.1
120.7
194.2
Tenants’ and household insurance1,2.............
Fuels and utilities.............................................
Fuels......................................................
Fuel oil and other fuels................................
Gas (piped) and electricity...........................
Household furnishings and operations............
Apparel.........................................................
Men's and boys' apparel..................................
Women's and girls' apparel.............................
101.6
128.7
113.0
91.7
120.4
124.7
130.1
131.2
121.3
106.4
149.5
134.2
129.2
141.5
125.8
126.1
125.8
117.3
106.9
150.8
135.7
131.5
142.9
125.7
128.5
129.2
120.2
107.2
155.2
140.5
129.2
148.5
125.9
125.2
126.3
115.6
106.7
154.4
139.5
123.1
147.8
125.8
121.9
122.9
110.2
106.8
152.2
137.0
121.5
145.2
125.7
121.6
121.6
110.1
106.8
150.1
134.7
125.3
142.2
126.0
125.6
123.7
118.3
107.0
144.0
127.9
121.4
135.0
125.5
128.3
127.3
120.2
107.1
142.8
126.7
118.5
133.7
125.6
127.2
127.3
118.0
106.3
141.5
125.2
112.7
132.5
125.4
123.0
122.7
113.5
106.4
140.8
124.2
113.0
131.4
125.0
119.6
121.0
108.5
106.8
139.4
122.7
112.4
129.7
124.9
122.4
122.2
113.8
106.9
139.6
122.8
112.7
129.8
124.9
126.9
125.2
119.7
107.5
139.6
122.7
114.7
129.6
125.1
127.9
125.8
120.9
107.6
140.7
123.9
114.0
131.0
125.0
126.2
124.6
118.2
Infants’ and toddlers' apparel1.........................
Footwear...............................................
Transportation........................................................
Private transportation.........................................
130.3
130.9
123.1
153.6
150.8
132.0
124.5
159.2
156.6
128.6
122.1
157.9
155.1
126.2
121.4
153.4
150.4
128.3
122.0
152.5
149.5
131.1
123.0
155.1
152.3
133.5
124.9
151.4
148.6
134.3
124.2
149.2
146.4
130.3
121.0
147.4
144.5
126.7
117.7
147.5
144.6
128.4
119.3
147.1
144.2
131.7
126.2
143.4
140.7
122.8
149.2
146.4
131.7
124.4
152.7
149.8
129.9
124.4
152.7
149.8
100.4
101.9
102.0
101.7
101.4
101.0
100.7
101.1
101.7
102.0
101.31
100.3
99.7
New and used motor vehicles2.......................
See footnotes at end of table.
106
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
99.5 !
99.3
32. Continued—Consumer Price Indexes for All Urban Consumers and for Urban Wage Earners and Clerical Workers: U.S. city
average, by expenditure category and commodity or service group
[1982-84 = 100, unless otherwise indicated]
Series
Annual average
2000
2001
2001
May
June
July
2002
Aug.
Sept.
Nov.
Oct.
Dec.
Jan.
Feb.
Mar.
Apr.
May
143.9
143.2
143.4
142.7
142.3
141.4
141.3
142.1
143.8
144.7
143.8
142.3
141.8
141.5
140.9
Used cars and trucks1....................................
157.1
159.8
160.2
160.0
159.3
159.0
158.2
158.7
158.3
158.1
156.5
154.8
153.0
152.6
152.7
Gasoline (all types)........................................
Motor vehicle parts and equipment.................
129.5
128.8
100.9
124.9
124.2
104.0
122.0
121.3
104.1
104.4
103.8
105.0
96.3
95.7
104.9
98.2
97.6
105.3
185.0
209.5
185.6
207.7
207.0
187.5
203.7
187.8
200.4
187.9
200.1
188.6
201.0
98.5
97.9
105.3
189.5
202.5
108.0
107.5
105.7
185.1
204.9
132.4
131.7
104.4
186.7
116.2
115.5
104.7
178.8
203.4
142.1
141.1
103.6
184.4
209.5
124.9
124.2
104.3
Public transportation..........................................
147.4
146.7
103.6
184.1
203.5
189.9
203.0
121.7
121.2
106.0
190.5
204.5
121.8
121.2
106.0
191.4
206.3
259.9
233.6
265.9
271.8
242.7
270.4
241.7
273.4
244.1
280.2
273.9
244.6
280.7
274.9
245.2
281.7
275.6
245.6
282.6
283.0
278.5
247.6
285.7
279.8
248.5
287.2
280.9
249.0
288.4
281.9
249.6
289.6
282.9
333.8
277.0
248.0
330.6
272.0
243.6
278.5
249.0
276.2
246.7
278.5
248.7
271.5
243.2
278.0
248.7
332.0
249.9
337.0
250.1
338.3
250.5
340.5
250.9
342.7
251.0
343.6
252.8
348.2
253.6
351.4
254.0
354.3
254.6
357.1
250.3
290.6
255.3
359.4
104.9
Medical care..........................................................
Medical care commodities.................................
Medical care services.........................................
Professional services.......................................
Hospital and related services..........................
239.6
313.2
Recreation2..........................................................
102.4
103.6
103.7
103.5
333.5
103.7
103.9
103.8
103.8
104.0
103.8
104.2
104.5
104.6
105.0
Video and audio1,2............................................
100.7
100.9
101.1
100.7
101.1
101.0
100.6
100.6
100.7
100.5
101.4
102.2
102.1
102.2
102.3
Education and communication2...........................
102.7
105.3
104.1
104.5
104.9
105.8
106.5
107.1
106.9
106.9
107.1
107.2
106.5
106.0
106.5
Education2........................................................
Educational books and supplies....................
112.8
283.3
118.7
299.9
116.7
294.5
117.2
298.2
117.6
299.3
119.6
302.2
121.7
309.8
122.3
311.7
122.3
308.9
122.1
297.3
122.7
305.2
123.3
315.2
123.3
315.1
123.3
315.3
123.5
316.3
Tuition, other school fees, and child care......
318.2
94.6
334.7
94.5
329.1
94.0
330.3
94.3
331.3
94.8
337.3
94.7
342.9
94.3
344.4
94.9
344.9
94.5
345.2
94.6
346.2
94.7
347.0
94.5
347.2
93.3
347.2
92.6
347.7
93.3
Information and information processing1,2....
94.1
93.8
93.4
93.6
94.0
94.0
93.6
94.2
93.8
93.9
94.0
93.7
92.6
91.7
92.5
Telephone services1,2.................................
Information and information processing
98.7
99.4
98.8
99.2
99.7
99.8
99.4
100.1
99.7
99.9
100.4
100.5
99.3
98.4
99.4
other than teleDhone services1,4..............
Personal computers and peripheral
26.8
22.1
22.4
22.2
22.0
21.5
21.2
21.0
20.8
20.6
20.1
19.7
19.5
19.3
19.2
25.5
22.7
299.1
equipment1,2........................................
Other goods and services.....................................
Tobacco and smoking products........................
40.5
29.1
29.9
29.4
28.7
27.4
26.6
286.8
419.8
287.9
421.6
293.8
441.9
290.0
425.6
295.5
444.7
297.3
448.3
25.0
293.3
432.9
23.5
289.5
426.1
26.1
292.4
430.9
24.3
276.5
395.2
294.0
433.5
298.3
450.7
22.8
295.2
434.1
22.5
301.7
462.7
Personal care1...................................................
165.5
170.3
169.3
169.9
170.6
170.9
171.4
171.9
172.3
172.3
172.7
173.2
173.7
173.9
174.0
Personal care products1.................................
154.2
155.7
155.4
156.1
156.1
156.1
156.0
155.9
156.3
156.0
156.2
155.4
184.9
184.8
155.9
185.4
155.5
178.6
153.8
184.7
186.5
187.4
187.1
260.7
261.6
263.2
265.6
266.8
267.5
268.0
271.4
188.0
272.5
188.7
262.8
187.0
269.8
187.1
251.9
185.9
264.9
186.1
Miscellaneous personal services....................
Commodity and service group:
272.6
189.1
273.6
Commodities........................................................
Food and beverages..........................................
Commodities less food and beverages.............
Nondurables less food and beverages............
Apparel..........................................................
Nondurables less food, beverages,
149.8
167.7
139.0
149.1
128.3
151.4
173.0
138.7
149.0
126.1
153.9
172.3
142.6
156.2
128.5
153.0
172.8
141.1
153.6
125.2
151.2
173.4
138.0
148.2
121.9
150.5
173.8
136.9
146.5
121.6
152.5
174.0
139.8
152.0
125.6
151.2
174.8
137.4
147.4
128.3
150.1
174.5
135.9
144.2
127.2
148.4
174.6
133.4
139.4
123.0
148.3
175.7
132.7
138.9
119.6
148.6
175.8
133.1
140.7
122.4
149.8
176.1
134.7
144.8
126.9
151.7
176.1
137.5
150.5
127.9
151.2
175.7
136.8
149.3
126.2
and apparel..................................................
Durables...........................................................
165.3
125.8
166.3
125.3
176.3
125.5
174.1
125.2
167.3
124.8
164.8
124.3
171.4
124.1
162.7
124.3
158.2
124.8
153.1
124.9
154.2
124.1
155.4
123.1
159.4
122.3
168.1
122.1
167.2
122.0
205.8
Services................................................................
450.1
191.6
199.6
198.7
200.1
200.6
201.2
201.1
201.0
201.4
201.7
202.5
203.3
203.9
204.2
180.5
192.9
225.9
187.3
199.1
233.7
186.3
197.6
232.2
187.2
198.9
232.6
187.8
199.5
233.6
188.7
199.8
235.1
188.7
200.1
235.9
189.3
200.9
236.8
189.9
202.3
237.2
190.4
202.6
237.3
191.4
203.4
238.3
192.5
204.7
239.0
193.2
205.6
238.8
193.7
193.9
206.2 7.1239.7
283.9
283.9
All items less food.............................................
All items less shelter.........................................
All items less medical care...............................
Commodities less food.....................................
Nondurables less food......................................
Nondurables less food and apparel..................
Nondurables......................................................
169.1
163.8
164.7
140.4
150.7
165.4
158.9
173.6
167.6
169.1
140.2
150.8
166.7
161.4
174.7
169.1
170.0
144.1
157.6
175.9
164.8
174.9
169.0
170.2
142.6
155.3
173.9
163.8
173.9
167.8
169.4
139.6
150.1
167.7
161.2
173.7
174.9
167.5
169.3
138.5
148.5
165.4
160.5
168.8
170.3
141.3
153.8
171.5
163.5
173.8
167.6
169.5
139.0
149.4
163.5
161.5
173.4
166.9
169.1
137.6
146.4
159.5
159.7
172.5
165.7
168.3
135.1
141.8
154.7
157.3
172.7
165.8
168.5
134.5
141.8
154.7
157.5
173.3
166.1
169.0
134.8
143.1
157.0
158.5
174.3
167.1
170.0
136.5
147.0
160.7
160.8
175.7
168.5
171.1
139.1
152.5
168.7
163.7
175.8
168.4
171.0
138.5
151.4
167.9
162.9
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....................................
180.1
185.4
124.8
175.1
177.1
145.4
129.7
198.7
188.5
187.8
189.6
189.9
190.1
193.1
128.7
179.8
181.7
146.1
125.3
206.0
192.3
140.6
179.2
181.2
146.4
146.6
204.8
193.6
140.3
179.5
181.4
194.2
131.3
179.8
181.7
145.4
125.0
206.3
194.7
128.6
180.1
181.9
144.6
122.1
207.3
189.9
194.6
132.6
180.7
182.6
146.0
132.1
207.6
189.0
194.4
121.2
181.3
183.2
146.3
116.7
208.3
189.3
194.8
114.8
181.8
183.8
146.9
105.5
209.0
189.2
195.0
110.0
181.5
183.5
145.6
97.5
209.4
189.8
195.7
110.5
181.6
183.6
144.4
99.2
210.4
190.1
196.5
109.8
182.5
184.4
144.8
99.5
211.5
190.5
197.0
114.7
182.9
184.9
145.0
108.7
212.1
190.7
197.4
121.6
183.4
185.5
145.8
121.9
212.6
181.6
197.9
122.2
183.3
185.4
145.0
121.9
213.0
Transporatation services..................................
Other services...................................................
Special indexes:
1 Not seasonally adjusted.
2 Indexes on a December 1997 = 100 base.
3 Indexes on a December 1982 = 100 base.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
145.6
141.5
205.7
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.
Monthly Labor Review
July 2002
107
Current Labor Statistics:
Price Data
33. Consumer Price Index: U.S. city average and available local area data: all items
[1982-84 = 100, unless otherwise indicated]__________________
Pricing
U.S. city average.................................................................
All Urban Consumers
sched
2001
ule1
Dec
M
176.7
Urban Wage Earners
2001
2002
Jan.
Feb.
Mar.
177.1
177.8
178.8
Apr
May
179.9
179.8
Dec.
2002
Jan.
Feb.
Mar.
Apr.
172.9
173.2
173.7
174.7
175.8
May
175.8
Region and area size2
Northeast urban....................................................
M
184.2
184.9
186.1
187.0
187.8
187.7
181.0
181.4
182.3
183.1
184.2
184.1
Size A— More than 1,500,000............................................
M
185.4
186.2
187.8
188.6
189.3
189.2
181.1
181.6
182.8
183.6
184.5
184.3
Size B/C— 50,000 to 1.500.0003...............................
M
10.3
110.5
110.5
111.2
111.9
112.0
109.9
110.1
110.1
110.8
111 7
111 7
M
171.9
172.1
172.5
173.6
174.7
174.8
167.6
167.7
168 1
169 1
170 0
M
173.8
174.1
174.7
176.0
177.3
177.2
168.7
168.8
169.4
170.6
172.2
172.0
M
109.6
109.5
109.6
110.2
110.7
110.8
109.2
109.2
109.2
109 7
110 2
110 7
Midwest urban4...................................................
Size A— More than 1,500,000..............................................
Size B/C— 50,000 to 1,500,0003..................................
Size D— Nonmetropolitan (less than 50,000)...................
South urban...........................................................................
M
165.5
166.2
166.6
167.1
168.1
168.2
163.3
163.9
164.3
164.8
166.0
166.1
M
170.3
170.6
171.0
172.1
173.1
173.2
168.1
168.3
168.6
169.6
170.8
170.8
Size A— More than 1,500,000..............................................
M
171.7
171.7
172.4
173.3
172.4
174.6
169.0
169.0
169.5
170.5
171.7
171.9
Size B/C— 50,000 to 1,500,0003.................................
Size D— Nonmetropolitan (less than 50,000)....................
M
108.9
109.2
109.3
110.0
110.8
110.7
108.5
108.6
108.7
109.3
110.2
110.1
M
167.7
168.6
168.6
169.9
170.5
170.6
168.3
169.2
168.9
170.2
171.2
171.1
M
181.6
182.4
183.2
184.0
185.1
184.8
176.8
177.4
178.1
179.0
180.0
180.0
Size A— More than 1,500,000......................................
M
111.6
111.9
185.4
186.2
187.2
187.5
176.9
177.7
178.6
179.5
180.5
181.0
Size B/C— 50,000 to 1,500,0003........................................
M
111.6
111.9
112.4
112.8
113.7
112.5
111.2
111.4
111.8
112.2
112.9
112.3
M
M
M
161.1
109.7
169.8
161.6
109.9
170.5
162.5
110.1
170.7
163.4
110.7
171.5
164.2
111.4
172.4
164.3
111.2
172.4
159.4
109.3
168.5
159.7
109.9
169.7
160.5
109.5
169.3
161.3
110.1
170.2
162.4
110.9
171.3
162.5
110.7
171.1
175.3
West urban.......................................................................
Size classes:
A5.............................................................
B/C3...............................................................
D............................................................................
Selected local areas6
Chicago-Gary-Kenosha, IL—IN—W l........................................
M
177.9
177.9
178.7
179.8
180.9
181.4
171.7
171.6
172.4
173.5
174.8
Los Angeles-Riverside-Orange County, CA.........................
M
177.1
178.9
180.1
181.1
182.2
182.6
169.7
171.5
172.8
173.8
174.8
175.4
New York, NY-Northern NJ-Long Island, N Y -N J-C T-P A ..
M
187.3
188.5
189.9
191.1
191.8
191.4
182.8
183.5
184.7
185.6
186.6
186.4
Boston-Brockton-Nashua, M A -N H -M E -C T .........................
1
-
192.9
-
194.7
-
194.8
_
191.8
_
193.2
Cleveland-Akron, O H................................................................
1
-
171.4
-
173.7
-
173.0
-
162.8
_
164.1
_
_
164.0
193.3
Dallas-Ft Worth, T X .................................................................
1
-
170.6
-
172.1
-
172.9
-
170.0
-
171.4
-
172.5
Washinqton-Baltimore, D C -M D -V A -W V 7.............................
1
-
110.9
-
111.9
-
112.8
-
110.5
-
111.4
-
112.4
Atlanta, GA.................................................................................
2
174.8
-
176.1
-
178.6
-
172,0
_
173.2
_
175.5
Detroit-Ann Arbor-Flint, Ml......................................................
2
173.5
-
176.2
-
179.0
-
167.9
_
170.5
_
173.4
Houston-Galveston-Brazoria, T X ............................................
2
157.1
-
156.6
-
158.8
-
155.2
-
154.3
_
156.8
_
172.3
_
172.5
Miami-Ft. Lauderdale, FL.........................................................
2
173.1
-
175.0
-
175.0
-
170.5
Philadelphia-Wilmington-Atlantic City, P A -N J-D E -M D .....
2
179.9
-
182.0
-
183.1
-
179.2
San Francisco-Oakland-San Jose, CA.................................
2
190.6
-
191.3
-
193.0
-
Seattle-Tacoma-Bremerton, WA.............................................
2
186.1
187.6
-
188.8
-
1
Foods, fuels, and several other items priced every month in all areas; most other
goods and services priced as indicated:
M— Every month.
1—
January, March, May, July, September, and November.
February, April, June, August, October, and December.
7
2 Regions defined as the four Census regions.
181.1
-
_
186.8
_
_
188.8
_
_
_
_
182.5
-
183.6
-
181.4
182.3
MO-KS; Milwaukee-Racine, Wl; Minneapolis-St. Paul, MN-W I; Pittsburgh, PA;
Port-land-Salem, OR-WA;
Petersburg-Clearwater, FL.
2—
186.5
_
_
_
St Louis,
MO-IL;
San
Diego,
CA; Tampa-St.
Indexes on a November 1996 = 100 base.
Dash indicates data not available.
3 Indexes on a December 1996 = 100 base.
4 The "North Central" region has been renamed the "Midwest" region by the Census
NOTE: Local area CPI indexes are byproducts of the national CPI program. Each
Bureau. It is composed of the same geographic entities.
local Index has a smaller sample size and is, therefore, subject to substantially
more sampling and other measurement error. As a result, local area indexes
5 Indexes on a December 1986 = 100 base.
6 In addition, the following metropolitan areas are published semiannually and appear in
tables 34 and 39 of the January and July issues of the CPI Detailed Report-. Anchorage,
AK;
108
Cincinnati-Hamilton, O H-KY-IN; Denver-Boulder-Greeley,
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
CO; Flonolulu,
HI;
show greater volatility than the national index, although their long-term trends are
similar. Therefore, the Bureau of Labor Statistics strongly urges users to consider
adopting the national average CPI for use in their escalator clauses. Index applies
to a month as a whole, not to any specific date.
34.
Annual data: Consumer Price Index, U.S. city average, all items and major groups
[ 1982-84 =
100]
Series
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Consumer Price Index for All Urban Consumers:
All items:
140.3
3.0
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
138.7
1.4
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
137.5
2.9
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
131.9
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
126.5
2.2
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
190.1
7.4
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
183.3
6.8
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
138.2
2.9
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
Food and beverages:
Housing:
Apparel:
Transportation:
Medical care:
Other goods and services:
Consumer Price Index for Urban Wage Earners
and Clerical Workers:
All items:
Percent change.............................................................
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
109
Current Labor Statistics:
35.
Price Data
Producer Price Indexes, by stage of processing
[1982
=
100]
Grouping
Annual average
2002
2001
Mar.
Apr.
May
Finished consumer goods.........................
Finished consumer foods........................
138.0
138.2
137.2
140.7
141.5
141.3
142.5
143.8
141.8
142.1
143.3
141.9
140.7
141.5
141.2
141.1
142.0
142.6
141.7
142.9
142.9
139.6
139.9
141.8
139.7
138.4
140.5
137.2
136.8
140.4
137.4
137.2
141.1
137.7
137.5
142.3
138.7
138.9
143.4
139.0
139.4
139.2
138.8
139.2
139.4
Finshed consumer goods
excluding foods......................................
Nondurable goods less food................
Durable goods........................................
Capital equipment...................................
138.4
138.7
133.9
138.8
141.4
142.8
133.9
139.7
144.5
147.3
133.8
139.7
143.7
146.5
133.2
139.6
141.4
143.1
133.2
139.8
141.6
143.5
133.0
139.5
142.7
145.1
133.2
139.4
139.0
139.2
134.4
139.8
137.3
136.8
134.5
139.9
135.1
134.0
133.9
139.7
135.4
134.4
133.9
139.7
135.4
134.3
134.1
139.8
136.9
136.7
133.6
139.5
139.2
140.0
133.7
139.4
138.8
139.7
133.1
139.2
Intermediate materials,
supplies, and components..................
129.2
128.7
131.2
131.4
130.3
129.8
130.1
127.6
126.7
125.4
125.5
125.2
126.1
127.6
127.2
128.1
119.2
132.6
129.0
126.2
127.4
124.3
131.8
125.2
126.3
128.6
124.6
134.2
126.9
126.4
128.3
125.7
133.4
126.5
126.4
127.5
126.1
131.9
125.3
126.2
126.9
128.1
130.1
124.6
126.2
126.6
127.5
129.9
124.2
125.9
125.9
126.1
128.7
123.4
125.9
125.2
123.9
127.4
122.8
125.9
124.7
122.5
126.2
122.5
126.0
124.5
122.1
125.4
122.5
126.3
124.6
122.6
125.4
122.6
126.3
125.1
122.9
126.5
123.5
126.4
125.7
122.0
128.4
123.7
126.3
125.7
121.4
128.3
124.2
126.4
150.7
102.0
151.6
136.9
150.6
104.5
153.1
138.6
151.6
108.1
153.9
139.0
151.7
110.2
154.1
138.8
151.0
106.8
153.6
138.8
151.0
106.0
153.2
138.7
150.8
108.4
153.0
138.6
150.4
97.4
152.4
138.3
150.3
94.7
152.2
138.3
149.0
89.3
152.2
138.1
150.2
90.0
152.6
138.2
150.2
88.8
151.9
138.1
150.7
91.3
151.7
138.3
151.1
97.0
151.2
138.5
151.3
95.2
151.1
138.4
120.6
100.2
130.4
121.3
106.2
127.3
130.9
110.3
140.4
122.8
109.7
127.4
116.1
109.6
116.3
113.4
108.9
112.4
108.0
108.5
103.8
97.7
104.7
89.4
104.8
98.3
105.5
94.8
96.4
90.2
98.9
99.6
95.0
98.0
102.0
91.4
103.7
102.8
100.9
107.9
96.4
113.5
110.5
98.4
116.5
Finished consumer goods less energy......
Finished goods less food and energy........
138.1
94.1
144.9
147.4
148.0
140.4
96.8
147.5
150.8
150.0
142.6
104.1
147.7
151.6
150.0
142.0
102.7
147.6
150.9
149.9
140.5
97.0
147.5
150.7
149.9
140.5
97.8
147.7
151.1
149.7
141.3
100.1
147.9
151.4
149.8
138.8
90.1
147.9
151.3
150.4
137.7
85.5
147.7
151.0
150.6
136.1
80.7
147.6
150.9
150.4
136.3
81.3
147.7
151.1
150.4
136.3
81.3
148.1
151.6
150.4
137.2
85.0
148.2
151.9
150.2
138.7
89.3
147.3
150.6
150.5
138.4
88.9
147.2
150.5
150.2
Finished consumer goods less food
and energy..................................................
154.0
156.9
156.9
156.7
156.8
156.6
156.8
157.5
157.8
158.0
157.6
157.6
157.4
158.0
157.7
Consumer nondurable goods less food
and energy...............................................
169.8
175.1
175.4
175.5
175.5
175.3
175.6
175.8
176.4
176.4
176.4
176.2
176.3
176.4
177.4
Intermediate goods less energy.................
130.1
111.7
101.7
135.0
130.5
115.9
104.1
135.1
132.1
114.9
107.6
136.1
132.3
116.3
109.7
135.9
131.0
117.1
106.3
135.3
130.4
119.4
105.6
134.9
130.7
118.7
107.9
134.7
128.2
117.3
97.1
134.2
127.3
115.5
94.3
133.7
126.0
114.3
89.0
133.4
126.1
113.6
89.6
133.3
125.9
113.6
88.4
133.3
126.8
114.3
90.9
133.8
128.4
113.7
96.6
134.1
128.0
113.0
94.9
134.1
Intermediate materials less foods
and energy.................................................
136.6
136.4
137.5
137.2
136.5
136.0
135.8
135.3
134.9
134.6
134.6
134.6
135.0
135.5
135.5
109.0
114.3
129.4
104.2
113.6
128.4
93.1
113.3
128.5
75.2
109.8
125.8
96.5
104.8
124.5
76.7
103.4
124.2
82.8
106.2
126.1
76.9
108.5
128.1
89.9
109.3
129.0
106.7
105.3
131.4
109.1
107.9
136.1
2000
Finished goods.....................................
Materials and components
for manufacturing.......................................
Materials for food manufacturing..............
Materials for nondurable manufacturing...
Materials for durable manufacturing........
Components for manufacturing................
2001
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Materials and components
Containers....................................................
Crude materials for further
processing.........................................
Foodstuffs and feedstuffs............................
Crude nonfood materials.............................
Special groupings:
Finished goods, excluding foods................
Finished energy goods................................
Intermediate materials less foods
Crude energy materials...............................
Crude nonfood materials less energy........
Monthly Labor Review
110
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122.1
111.7
145.2
122.8
112.2
130.6
July 2002
139.8
115.3
130.9
123.1
114.8
130.6
36.
Producer Price Indexes for the net output of major industry groups
[December 1984 = 100, unless otherwise indicated]
Industry
SIC
Annual average
2000
-
10
12
13
14
-
20
21
22
23
24
25
26
2001
2001
May
June
July
Aug.
Sept.
T o ta l m in in g in d u s tr ie s ............................................
113.5
114.3
98.7
98.9
Coal mining (12/85 = 100)...............................
70.8
91.3
127.5
128.1
71.6
93.8
145.6
112.2
73.8
84.8
126.8
71.2
89.6
125.1
70.7
92.8
106.4
69.8
92.0
107.0
90.8
71.7
92.1
95.9
137.0
141.0
140.8
141.3
141.5
141.4
133.5
128.5
345.8
116.7
134.6
132.8
386.1
116.9
136.5
133.4
391.3
117.2
135.8
133.7
391.7
117.2
134.4
134.0
391.1
117.1
134.6
134.6
391.0
116.8
125.7
125.8
125.9
125.8
125.9
158.1
143.3
145.8
156.2
145.1
146.2
160.1
145.2
147.0
161.6
145.3
147.0
158.4
145.4
146.5
Mining and quarrying of nonmetallic
minerals, except fuels....................................
T o ta l m a n u fa c tu r in g in d u s trie s ............................
Food and kindred products.............................
Tobacco manufactures....................................
Textile mill products.........................................
Apparel and other finished products
made from fabrics and similar materials......
Lumber and wood products,
except furniture..............................................
Furniture and fixtures.......................................
Paper and allied products................................
2002
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
101.7
78.3
88.3
77.6
81.9
78.0
87.5
69.8
92.9
79.1
68.9
95.4
92.0
68.9
92.5
78.3
71.0
95.3
84.0
72.3
94.5
77.9
72.9
94.6
92.7
99.9
72.4
94.3
112.1
73.9
94.3
114.8
141.5
141.8
141.6
141.5
142.5
143.4
143.5
142.9
143.5
135.6
134.5
391.1
116.4
133.7
134.1
391.1
116.5
132.7
132.4
398.3
116.3
131.6
131.7
398.2
116.1
131.7
131.5
391.7
116.3
132.0
132.0
391.7
115.8
132.8
132.0
392.2
115.8
133.8
131.6
407.9
115.7
133.6
131.0
408.0
115.5
125.9
125.9
125.9
125.6
125.3
125.2
125.1
125.2
125.1
125.1
158.1
145.2
145.6
157.3
145.4
145.5
154.6
145.5
145.1
154.0
145.5
144.6
153.4
145.5
144.8
154.0
145.6
144.1
154.8
145.8
143.2
156.7
145.7
142.9
157.1
145.7
143.2
156.2
145.9
142.4
27
Printing, publishing, and allied industries.......
182.9
188.7
188.5
188.7
188.8
189.1
189.4
189.7
190.2
192.0
192.0
192.1
192.1
192.2
192.6
28
29
30
31
32
33
34
Chemicals and allied products........................
Petroleum refining and related products........
Rubber and miscellaneous plastics products.
Leather and leather products..........................
Stone, clay, glass, and concrete products.....
Primary metal Industries.................................
Fabricated metal products,
except machinery and transportation
equipment..............................
156.7
112.8
124.6
137.9
134.6
119.8
158.4
105.3
125.9
141.3
136.0
116.1
160.1
122.8
126.5
142.7
136.0
116.7
159.7
115.9
126.4
141.9
135.7
115.4
157.8
101.7
126.2
142.1
136.0
116.1
156.3
104.7
125.7
142.3
136.0
115.6
156.6
114.9
125.6
141.5
136.4
115.3
155.7
94.6
125.5
141.2
136.6
114.6
155.4
86.3
125.6
140.9
136.9
114.2
154.3
75.9
125.2
140.3
136.7
114.0
154.0
77.7
125.1
140.2
136.9
113.7
154.3
79.5
124.4
139.8
136.4
113.7
155.1
89.2
124.6
140.0
136.3
114.4
156.0
100.2
124.8
140.5
136.5
114.7
156.6
99.4
125.4
140.8
136.9
115.4
1,310.3
131.0
131.2
131.1
131.1
131.1
131.1
131.0
131.1
131.2
131.2
131.2
131.2
131.4
131.4
35
Machinery, except electrical...........................
117.5
118.0
118.1
118.1
118.1
117.9
117.9
117.9
117.9
117.8
117.7
117.6
117.7
117.6
117.6
36
Electrical and electronic machinery,
equipment, and supplies...............................
Transportation.................................................
Measuring and controlling instruments;
photographic, medical, and optical
goods; watches and clocks...........................
Miscellaneous manufacturing industries
industries (12/85 = 100).................................
108.3
136.8
107.0
137.9
107.2
137.4
107.0
137.1
106.8
137.5
106.4
137.4
106.5
137.3
106.4
138.5
106.5
138.3
106.6
138.6
106.7
138.0
106.6
138.5
106.6
137.9
106.5
137.7
106.3
137.1
126.2
127.3
127.3
127.2
123.2
127.4
127.5
127.6
127.8
127.7
128.3
128.6
128.9
128.1
128.2
130.9
132.4
132.5
132.5
132.6
132.7
132.8
132.7
132.6
132.4
132.7
133.4
132.9
133.1
134.0
119.4
135.2
122.6
147.7
102.3
123.1
143.4
129.8
157.2
110.3
122.9
141.3
129.2
156.7
109.0
123.1
141.3
129.2
157.6
109.0
123.2
145.4
133.1
158.7
110.9
123.5
145.4
133.2
159.0
111.2
123.8
145.4
133.9
158.5
111.7
123.6
145.4
133.5
158.9
111.8
123.4
145.4
130.2
156.8
112.0
123.1
145.4
129.7
157.1
112.0
123.2
145.4
129.3
157.1
111.1
123.4
145.4
128.9
157.1
111.3
123.5
145.4
128.7
156.8
111.6
123.8
145.4
127.6
160.2
111.3
123.8
145.4
131.5
156.4
111.3
37
38
39
S e r v ic e in d u s trie s ;
42
43
44
45
46
Motor freight transportation
and warehousing (06/93 = 100).....................
U.S. Postal Service (06/89 = 100)....................
Water transportation (12/92 = 100)..................
Transportation by air (12/92 = 100).................
Pipelines, except natural qas (12/92 = 100)....
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Federal Reserve Bank of St. Louis
Monthly Labor Review
June 2002
111
Current Labor Statistics:
37.
Price Data
Annual data: Producer Price Indexes, by stage of processing
[1982 = 100]
Index
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Finished goods
Energy.................................................................................
Other....................................................................................
123.2
123.3
77.8
134.2
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
114.7
113.9
84.3
122.0
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
100.4
105.1
78.8
94.2
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
Intermediate materials, supplies, and
components
Total......................................................................................
Foods..................................................................................
Energy.................................................................................
Crude materials tor further processing
Total......................................................................................
Foods..................................................................................
Energy.................................................................................
Other....................................................................................
Monthly Labor Review
112
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Federal Reserve Bank of St. Louis
July 2002
38.
[2000
U.S. export price indexes by Standard International Trade Classification
=
100]
S IT C
2001
Industry
R ev. 3
May
June
July
101.2
106.2
104.3
97.4
101.1
106.1
102.6
98.6
101.8
105.7
102.2
101.7
93.3
91.0
93.1
82.3
92.5
91.6
92.6
95.6
92.8
80.6
90.9
91.0
106.8
106.6
106.1
Medicinal and pharmaceutical products..........................
Essential oils; polishing and cleaning preparations........
Plastics in primary forms.................................................
Plastics in nonprimary forms............................................
Chemical materials and products, n.e.s..........................
Aug.
2002
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
102.6
106.4
104.5
102.4
103.3
107.8
106.4
100.8
102.7
107.8
103.9
102.1
100.9
99.2
105.2
99.7
101.2
97.8
107.2
100.6
102.7
93.1
108.4
110.5
100.0
91.3
106.0
102.4
100.3
93.2
105.4
102.5
100.6
92.0
105.2
103.7
99.7
91.6
103.8
103.8
92.4
102.5
93.4
78.2
90.4
87.8
91.1
104.3
92.9
76.6
89.3
86.2
89.5
99.0
90.2
77.3
87.7
85.1
87.1
89.8
89.7
77.7
84.5
82.7
86.3
89.1
88.7
77.4
82.0
81.4
87.1
90.9
88.0
77.2
84.0
81.3
87.1
91.6
88.1
75.8
85.3
84.9
86.9
89.4
87.6
73.9
86.6
87.0
87.7
92.0
87.2
74.1
86.2
87.3
89.7
93.8
87.3
77.1
86.8
91.7
90.9
95.1
87.4
81.4
84.9
92.4
103.2
106.9
101.8
96.7
106.8
93.7
97.5
107.9
95.2
103.3
108.8
103.6
93.4
108.9
88.4
88.3
108.9
80.9
82.4
108.8
74.6
87.1
109.5
80.1
84.3
109.7
76.5
89.8
110.8
83.6
99.7
111.4
95.8
95.4
111.4
90.2
96.9
99.5
99.7
94.9
97.0
98.9
96.2
99.5
99.7
93.9
97.4
99.1
94.9
100.2
99.1
91.2
98.0
98.7
94.1
100.8
99.0
90.0
96.9
98.7
93.8
101.1
99.1
88.6
97.2
99.0
93.8
100.9
99.0
89.2
95.9
98.6
93.6
100.9
98.9
88.5
95.8
98.7
92.8
100.9
98.8
86.5
95.8
97.6
92.2
101.1
97.5
85.4
95.9
98.1
92.3
100.8
97.1
85.8
95.7
97.6
93.2
100.5
97.6
87.6
95.8
98.0
94.7
100.3
97.5
89.9
95.1
97.5
95.0
100.2
97.0
92.2
95.6
97.4
M a n u fa c tu r e d g o o d s c la s s ifie d c h ie fly b y m a te ria ls .....
99.7
99.5
99.1
98.4
98.2
97.3
96.6
96.7
97.3
97.2
96.7
97.4
97.4
Rubber manufactures, n.e.s.............................................
Paper, paperboard, and articles of paper, pulp,
and paperboard...............................................................
Nonmetallic mineral manufactures, n.e.s........................
Nonferrous metals.................................................
99.8
99.8
100.5
101.0
101.0
100.6
100.5
100.9
100.4
100.4
100.8
101.1
101.6
98.0
100.4
100.0
97.4
100.8
98.0
95.1
100.8
97.0
95.1
101.0
93.0
95.6
101.1
90.2
95.1
101.1
86.9
95.2
101.4
81.8
95.2
102.1
83.1
95.3
101.7
85.3
94.1
101.4
85.9
92.5
102.1
85.1
93.1
101.9
86.5
93.1
102.0
86.5
F o o d a n d liv e a n im a ls ..................................................................
Meat and meat preparations...........................................
Cereals and cereal preparations.....................................
Vegetables, fruit, and nuts, prepared fresh or dry.........
C ru d e m a te ria ls , in e d ib le , e x c e p t fu e ls ................................
Oilseeds and oleaginous fruits.........................................
Cork and wood........................................................
Pulp and waste paper......................................................
Textile fibers and their waste...........................................
Metalliferous ores and metal scrap.................................
M in e ra l fu e ls , lu b r ic a n ts , a n d re la te d p ro d u c ts ...............
Coal, coke, and briquettes............................................
Petroleum, petroleum products, and related materials...
C h e m ic a ls a n d re la te d p ro d u c ts , n .e .s .................................
Apr.
May
M a c h in e r y a n d tr a n s p o rt e q u ip m e n t......................................
100.4
100.3
100.2
100.0
100.0
99.7
99.7
99.6
99.3
99.3
99.5
99.5
99.3
Power generating machinery and equipment..................
Machinery specialized for particular industries................
General industrial machines and parts, n.e.s.,
and machine parts.............................................
Computer equipment and office machines......................
Telecommunications and sound recording and
reproducing apparatus and equipment..........................
Electrical machinery and equipment................................
Road vehicles.............................................................
102.3
100.3
102.3
100.3
102.4
99.6
102.8
99.5
103.0
99.5
103.1
100.6
104.1
100.5
104.0
100.5
104.6
100.7
104.4
100.8
104.6
101.1
104.6
101.4
104.6
102.0
101.3
96.9
101.3
95.9
101.8
95.6
101.8
94.8
101.9
94.8
101.8
94.6
101.9
94.2
101.7
92.9
102.1
92.5
102.0
92.9
102.2
93.1
102.2
92.5
102.3
91.7
99.7
98.7
100.2
99.8
98.3
100.2
99.8
97.8
100.3
98.7
97.7
100.2
98.5
97.6
100.2
98.0
95.9
100.3
98.0
95.9
100.2
97.7
95.9
100.3
97.9
94.8
100.1
97.5
94.6
100.2
97.5
94.7
100.3
97.8
94.8
100.3
97.8
94.6
1,004.0
100.8
100.9
100.8
100.8
100.9
101.0
100.9
100.9
100.8
101.1
101.2
101.1
101.3
P ro fe s s io n a l, s c ie n tific , a n d c o n tro llin g
in s tr u m e n ts a n d a p p a r a tu s .......................................................
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
113
Current Labor Statistics:
39.
Price Data
U.S. import price indexes by Standard International Trade Classification
[2000 = 100]
SITC
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
95.8
94.3
96.4
97.0
96.4
0 Food and live animals.........................................................
97.3
96.0
95.1
94.9
95.1
94.7
95.1
94.8
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...............................................................................
106.3
106.2
109.3
108.9
113.5
114.8
118.0
109.8
105.5
107.4
109.8
110.1
105.4
90.7
101.1
90.0
97.6
87.0
98.4
86.8
98.2
86.3
98.5
84.6
99.1
82.8
101.5
82.9
99.3
82.3
106.8
82.0
98.1
80.4
104.0
80.1
104.9
80.0
108.1
87.4
85.8
81.2
78.8
80.1
77.3
77.2
78.5
77.5
78.8
83.3
88.5
83.8
1 Beverages and tobacco..................................................... .
102.0
101.7
101.7
102.1
1-2.0
102.7
102.6
103.0
102.9
102.9
102.1
102.0
102.7
Beverages..........................................................................
102.7
102.4
102.4
102.4
102.4
102.6
102.6
103.1
103.2
103.2
102.5
102.3
102.4
95.8
96.3
96.8
01
03
05
07
11
98.1
102.8
96.4
95.8
96.6
94.5
91.3
89.9
90.1
92.7
Cork and wood...................................................................
Pulp and waste paper.......................................................
Metalliferous ores and metal scrap..................................
Crude animal and vegetable materials, n.e.s..................
104.9
92.4
95.5
94.9
122.1
87.1
93.9
92.9
108.2
83.5
94.4
80.8
109.6
79.3
93.1
81.0
112.2
77.3
92.8
83.8
105.1
76.8
91.6
93.4
97.5
78.0
89.8
93.1
91.7
77.7
91.2
96.0
92.6
78.1
91.4
92.2
98.6
77.2
92.7
91.7
106.6
74.9
93.7
92.3
108.1
73.4
95.0
90.5
105.2
73.5
95.6
103.8
3 Mineral fuels, lubricants, and related products.............
Petroleum, petroleum products, and related materials...
33
Gas, natural and manufactured.......................................
34
93.1
90.0
113.7
90.4
89.3
97.4
94.4
84.4
82.8
85.6
86.1
80.9
85.8
86.8
77.8
72.3
73.0
65.7
65.0
63.0
75.9
61.2
59.8
68.7
64.0
62.6
70.8
65.2
65.6
58.2
76.4
77.4
64.8
87.1
86.8
86.0
89.0
89.1
84.3
5 Chemicals and related products, n.e.s............................
52
Inorganic chemicals...........................................................
Dying, tanning, and coloring materials.............................
53
54
Medicinal and pharmaceutical products..........................
Essential oils; polishing and cleaning preparations........
55
Plastics in primary forms...................................................
57
Plastics in nonprimary forms.............................................
58
Chemical materials and products, n.e.s...........................
59
101.6
101.2
100.2
96.7
98.7
101.1
103.6
100.1
100.5
100.1
98.1
96.7
98.4
102.1
102.4
99.9
99.3
99.4
95.6
99.0
98.1
102.1
100.7
99.1
98.4
98.0
95.7
97.3
98.1
100.5
100.7
99.0
98.3
98.1
96.3
97.0
99.7
99.7
99.3
99.0
98.8
99.4
97.1
97.5
99.8
99.8
101.6
99.2
97.8
98.9
96.8
97.3
99.7
99.8
101.1
98.6
97.5
97.6
97.1
97.0
100.1
99.8
100.9
97.8
97.7
97.0
97.8
97.1
100.1
98.6
100.8
96.1
96.7
97.1
97.4
96.3
99.9
97.1
100.6
95.2
96.3
97.8
97.2
96.0
99.8
91.5
100.6
93.6
97.3
98.5
95.6
96.6
98.9
91.4
101.8
94.5
98.0
98.7
95.6
96.7
99.1
96.8
1,002.0
94.3
2 Crude materials, inedible, except fuels...........................
24
25
28
29
6 Manufactured goods classified chiefly by materials....
98.2
98.0
96.8
95.0
94.8
93.8
92.4
92.0
92.4
92.3
92.2
92.6
92.3
62
64
Rubber manufactures, n.e.s..............................................
Paper, paperboard, and articles of paper, pulp,
99.4
99.0
98.8
98.7
98.7
98.5
97.8
97.9
97.3
97.6
97.6
97.9
98.1
66
68
69
Nonmetallic mineral manufactures, n.e.s.........................
Nonferrous metals..............................................................
Manufactures of metals, n.e.s..........................................
103.7
99.7
96.1
100.0
102.7
99.4
95.3
100.1
101.7
99.3
91.0
99.3
99.9
99.1
83.4
99.3
99.3
99.3
82.2
99.3
98.6
97.5
78.7
99.7
97.6
97.2
73.7
99.5
96.1
97.5
73.8
99.0
95.0
97.2
76.4
99.0
93.7
97.0
77.2
98.5
93.4
96.9
76.9
98.5
92.5
96.9
79.2
98.2
91.9
97.0
79.7
98.2
97.0
7 Machinery and transport equipment................................
72
74
75
76
98.5
98.5
98.2
98.1
98.0
98.0
97.9
97.7
97.4
97.2
97.1
97.2
99.2
99.1
98.5
98.6
99.1
99.2
99.0
98.7
98.5
98.5
98.5
98.6
98.8
General industrial machines and parts, n.e.s.,
Computer equipment and office machines.....................
Telecommunications and sound recording and
98.3
93.9
98.2
93.6
98.0
92.1
97.8
91.7
98.0
90.0
98.7
89.1
98.1
89.0
97.8
88.8
98.1
88.6
97.5
88.2
97.5
88.1
97.6
88.2
97.4
88.0
97.2
98.8
99.8
97.3
98.9
99.7
97.1
98.7
88.7
96.8
98.6
100.0
96.5
98.7
100.3
96.4
98.6
100.2
96.3
97.0
100.3
95.7
96.9
1,001.0
95.1
97.0
100.2
94.8
96.8
100.1
94.8
97.0
100.2
94.5
97.1
100.0
Road vehicles....................................................................
97.1
99.2
99.7
85
Footwear............................................................................
100.2
100.1
100.1
100.5
100.4
99.9
99.9
100.3
99.3
99.6
99.5
99.0
99.1
88
Photographic apparatus, equipment, and supplies,
and optical ooods. n.e.s.................................................
98.8
98.5
97.9
97.9
98.2
98.6
98.5
98.4
97.7
97.3
97.2
97.2
97.2
77
78
114
2002
2001
Industry
Rev. 3
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
40.
U.S. export price indexes by end-use category
[2000
=
100]
2002
2001
Category
Nov.
Oct.
Sept.
Aug.
July
June
May
Jan.
Dec.
May
Apr.
Mar.
Feb.
ALL COMMODITIES..........................................................
99.6
99.4
99.0
98.8
99.0
98.3
97.8
97.6
97.5
97.3
97.6
98.0
98.0
Foods, feeds, and beverages.........................................
Agricultural foods, feeds, and beverages.................
Nonagricultural (fish, beverages) food products.....
99.8
100.6
92.7
100.4
101.2
92.6
101.7
102.4
94.8
102.6
104.0
90.2
102.6
103.6
92.9
101.2
102.2
91.9
99.7
100.7
90.9
100.6
101.6
90.4
102.0
102.6
96.3
98.9
99.4
94.5
99.7
100.0
98.3
100.3
100.8
96.2
100.4
100.9
96.1
Industrial supplies and materials...................................
98.0
97.2
95.5
94.8
95.2
93.6
92.3
91.4
91.5
91.4
91.9
93.4
93.7
Agricultural industrial supplies and materials...........
102.1
99.3
98.5
97.2
96.8
93.8
92.1
93.3
92.3
92.9
93.6
93.6
93.0
106.0
102.8
96.9
97.6
103.2
93.6
88.5
83.5
85.6
83.8
85.6
90.3
87.9
96.5
96.3
96.1
97.0
94.9
97.0
94.0
96.8
93.8
95.5
93.4
95.1
92.8
94.4
92.3
94.1
92.3
94.4
92.2
94.4
92.6
94.2
94.0
94.3
94.7
94.1
100.4
101.7
99.4
100.3
101.7
99.1
100.2
101.8
98.9
100.0
101.5
98.6
100.0
101.6
98.6
99.7
101.6
98.2
99.7
101.6
98.1
99.4
101.5
97.7
99.1
102.1
97.2
99.2
102.0
97.3
99.4
102.1
97.5
99.5
101.8
97.6
99.2
101.8
97.3
100.5
100.4
100.5
100.5
100.4
100.5
100.4
100.5
100.7
100.8
100.9
100.7
100.9
99.8
99.1
100.5
99.9
99.1
100.5
99.5
98.2
100.6
99.1
98.2
99.9
99.1
98.1
99.7
98.9
98.2
99.3
98.9
98.2
99.2
99.2
97.7
100.2
97.3
100.9
97.2
98.3
97.2
98.9
97.5
99.6
97.8
99.5
97.8
Fuels and lubricants.....................................................
Nonagricultural supplies and materials,
excluding fuel and building materials......................
Selected building materials.........................................
Electric and electrical generating equipment...........
Durables, manufactured.............................................
99.4
98.9
99.9
99.4
99.0
100.0
99.5
98.9
100.2
99.5
98.9
100.2
99.7
99.1
100.4
99.7
99.0
100.6
Agricultural commodities.................................................
Nonagricultural commodities.........................................
100.8
99.5
100.9
99.3
101.8
98.8
102.8
98.5
102.5
98.6
100.7
98.1
Consumer goods, excluding automotive.....................
41.
U.S. import price indexes by end-use category
[2000 =
100]
2002
2001
Category
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Mar.
Feb.
Jan.
Apr.
May
ALL COMMODITIES..........................................................
98.0
97.6
96.1
96.0
95.9
93.7
92.3
91.4
91.6
91.6
92.8
94.3
94.4
Foods, feeds, and beverages.........................................
Agricultural foods, feeds, and beverages.................
Nonagricultural (fish, beverages) food products.....
96.6
98.4
92.9
95.4
97.0
92.2
94.4
96.7
89.7
94.5
96.9
89.5
95.0
97.8
89.2
94.5
97.8
87.8
95.2
99.5
86.4
94.6
98.3
86.8
95.7
99.9
87.0
93.8
97.2
86.8
95.0
99.5
85.5
96.0
100.9
85.5
97.2
102.7
85.2
Industrial supplies and materials...................................
96.5
95.5
91.4
91.0
91.0
84.3
79.9
77.6
79.1
79.8
84.9
90.3
90.9
Fuels and lubricants.....................................................
Petroleum and petroleum products.......................
93.4
90.3
90.9
89.4
84.8
84.6
86.0
86.1
86.1
86.7
72.9
73.4
65.7
63.6
61.6
59.9
64.5
63.0
65.9
65.7
76.4
76.9
87.1
86.7
88.4
88.4
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..............
102.2
100.0
98.0
95.1
93.9
93.1
92.3
90.7
90.0
88.8
88.0
87.0
86.4
101.4
100.1
94.2
100.9
100.3
111.1
93.6
100.6
98.6
103.0
91.4
100.1
98.0
102.9
87.4
100.2
97.9
103.7
87.1
100.4
98.0
99.9
85.1
99.9
96.7
96.1
82.1
98.9
96.2
92.9
82.1
99.0
96.3
93.1
83.2
98.4
96.0
96.1
83.8
97.6
95.9
100.7
83.8
97.2
97.4
101.0
86.2
97.6
97.8
99.6
86.6
96.7
97.8
101.8
96.9
97.7
101.8
96.7
97.3
101.6
96.2
97.1
101.3
96.0
96.8
101.4
95.6
96.7
101.4
95.4
96.5
101.2
95.3
96.2
100.6
94.9
95.7
97.3
94.8
95.4
96.7
94.5
95.2
95.5
94.4
95.2
95.3
94.5
95.1
94.9
94.4
Durables, manufactured.............................................
Nonmanufactured consumer goods..........................
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
42.
[2000
99.8
99.8
99.7
99.6
99.9
100.1
100.0
100.1
99.8
100.1
99.9
100.1
99.9
99.5
100.0
99.0
99.6
99.3
99.8
98.9
99.2
99.2
100.0
98.6
97.6
99.2
100.0
98.6
97.4
99.1
99.6
98.7
97.9
98.9
99.6
98.4
95.8
98.8
99.6
98.3
95.7
98.7
99.7
98.0
96.4
98.7
99.8
97.8
95.8
98.4
99.7
97.4
95.7
98.2
99.2
97.3
96.1
98.1
99.1
97.2
95.8
98.2
99.1
97.2
97.6
U.S. international price Indexes for selected categories of services
=
100]
2001
2000
Category
Mar.
June
Sept.
Air freight (inbound)............................................................
Air freight (outbound)..........................................................
100.7
99.2
100.1
100.3
100.2
100.2
Air passenger fares (U.S. carriers)...................................
Air passenger fares (foreign carriers)..............................
Ocean liner freight (inbound)............................................
95.8
97.1
96.6
101.2
103.1
103.2
101.1
102.1
101.3
Dec.
Mar.
June
2002
Sept.
Dec.
Mar.
99.0
100.2
97.9
100.1
95.1
98.0
94.9
97.6
95.2
97.9
93.8
95.3
99.9
97.6
101.0
101.9
100.7
102.8
106.4
107.6
110.2
98.1
103.5
103.3
99.4
91.7
103.8
100.8
Monthly Labor Review
100.8
93.6
July 2002
115
C urrent Labor S ta tistic s :
43.
Productivity Data
Indexes of productivity, hourly compensation, and unit costs, quarterly data seasonally adjusted
[1992 = 100]
Item
1999
2000
2001
I
II
III
IV
1
II
III
IV
I
112.7
124.1
107.7
110.2
112.9
111.2
112.5
124.3
107.1
110.5
113.2
111.5
113.6
123.4
107.3
110.4
114.1
111.8
115.2
127.0
107.8
110.2
115.3
112.1
115.3
131.4
110.5
114.0
110.7
112.8
117.2
132.4
110.5
113.0
114.1
113.4
117.3
135.0
111.7
115.1
111.2
113.7
117.9
136.8
111.9
117.5
112.1
123.2
106.9
109.9
114.3
111.5
111.9
123.4
106.3
“ 0.3
113.8
111.9
112.9
124.5
106.6
110.3
115.8
112.3
114.7
126.3
107.2
110.1
117.0
112.6
114.7
130.8
110.2
113.0
112.3
223.4
116.4
131.5
109.8
113.0
115.6
113.9
116.6
134.5
111.1
115.2
112.8
114.3
114.3
120.2
104.3
104.2
105.1
101.6
137.1
110.7
106.9
114.5
120.4
103.8
104.5
105.2
102.6
135.5
111.0
107.1
114.6
121.2
103.7
105.4
105.7
104.6
127.8
110.5
107.3
115.2
122.7
104.1
106.1
106.5
105.1
126.5
110.6
107.8
116.7
126.9
106.7
107.8
108.7
105.4
120.5
109.3
108.9
116.8
127.8
106.6
108.9
109.4
107.7
120.4
110.9
209.9
117.6
130.4
107.9
110.4
110.9
108.9
111.4
109.5
110.5
112.2
112.6
11 T
110.4
108.3
110.9
94.9
108.9
11 1
iii A
128.0
120.6
103.7
94.3
128.8
120.9
104.2
93.9
129.8
122.6
104.9
94.4
132.1
124.2
105.4
94.0
133.6
131.4
110.5
98.4
134.9
129.3
107.9
95.9
135.4
132.2
109.4
97.7
135.9
131.5
108.0
96.7
135.4
132.0
107.4
97.5
II
2002
III
IV
I
117.9
137.8
111.1
116.8
115.5
116.4
120.1
138.3
111.6
115.1
117.2
115.9
122.5
139.0
112.2
113.9
119.6
116.0
117.2
136.7
110.2
116.6
117.2
116.8
119.3
137.2
110.7
115.0
119.2
116.5
121.8
138.4
111.3
113.6
121.3
116.4
118.2
132.7
107.0
113.7
112.3
117.6
99.7
113.1
112.5
121.3
133.6
107.8
111.8
110.2
116.2
109.6
114.5
111.6
122.8
134.9
108.5
111.6
109.9
116.0
109.4
114.3
111.4
136.4
133.3
107.5
97.8
137.6
134.3
108.3
97.6
140.9
136.5
109.6
96.9
Business
Output per hour of all persons...........................................
Compensation per hour.....................................................
Real compensation per hour.............................................
Unit labor costs....................................................................
Unit nonlabor payments...................................................
Implicit price deflator.......................................................
111.8
112.0
137.5
111.0
113.6
115.8
Nonfarm business
Output per hour of all persons..........................................
Compensation per hour....................................................
Real compensation per hour.............................................
Unit labor costs...............................................................
Unit nonlabor payments.....................................................
Implicit price deflator........................................................
117.1
135.3
111.2
116.7
113.4
114.8
113.8
117.3
‘2‘ .7
108.2
116.6
110.1
Nonfinancial corporations
Output per hour of all employees.....................................
Compensation per hour.....................................................
Real compensation per hour.............................................
Total unit costs.........................................................
Unit labor costs..................................................................
Unit nonlabor costs...........................................................
Unit profits............................................................
Unit nonlabor payments.....................................................
Implicit price deflator...........................................................
106.9
11U.O
116.3
. 106.5
113.3
115.6
97.2
110.9
112.0
Manufacturing
Output per hour of all persons..........................................
Compensation per hour......................................................
Real compensation per hour.............................................
Unit labor costs....................................................................
116
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
135.4
98.2
44.
Annual indexes of multifactor productivity and related measures, selected years
[1996 = 100, unless otherwise indicated]
Ite m
1960
1970
1980
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Private business
Productivity:
95.8
100.0
100.0
100.0
100.0
102.0
100.5
101.1
105.2
104.8
100.1
102.6
110.6
104.8
100.1
102.6
110.6
95.6
92.6
94.6
96.3
98.0
96.0
97.3
97.6
100.0
100.0
100.0
100.0
103.7
104.7
104.0
101.5
106.4
110.4
107.7
104.7
106.4
110.4
107.7
104.7
95.3
98.8
97.1
88.4
96.5
100.3
98.1
92.6
97.5
99.9
98.6
95.8
100.0
100.0
100.0
100.0
101.7
100.2
100.9
105.1
104.5
99.8
102.4
110.6
104.5
99.8
102.4
110.6
89.0
87.3
88.4
96.8
91.8
89.5
91.0
96.5
95.4
92.3
94.4
96.3
97.8
95.9
97.2
97.6
100.0
100.0
100.0
100.0
103.8
104.9
104.2
101.5
106.6
110.8
108.0
104.7
106.6
110.8
108.0
104.7
95.0
97.5
98.3
95.4
100.0
100.0
100.0
100.0
101.9
101.1
100.4
103.3
105.0
104.0
102.6
108.7
109.0
105.0
105.0
113.4
112.8
104.5
106.1
116.9
117.1
105.6
109.8
123.5
124.3
106.5
113.2
130.7
124.3
106.5
113.2
130.7
100.4
97.9
100.1
93.6
92.1
97.0
100.0
100.0
100.0
100.0
100.0
100.0
101.4
102.2
103.7
105.7
103.0
102.9
103.6
104.5
107.3
111.3
105.1
106.0
104.0
108.0
109.5
112.8
110.0
107.9
103.7
111.9
107.0
120.4
108.9
110.2
105.5
116.9
103.9
120.4
114.2
112.5
105.2
122.8
109.2
127.2
116.8
115.5
105.2
122.8
109.2
127.2
116.8
115.5
45.6
110.4
65.2
27.5
63.0
111.1
80.0
42.0
75.8
101.5
88.3
59.4
90.2
99.3
95.3
83.6
91.3
96.1
94.4
82.6
94.8
97.7
96.6
85.7
95.4
98.5
97.1
88.5
96.6
100.3
98.1
92.8
97.3
99.7
98.4
54.0
24.9
42.3
41.3
61.0
37.8
52.4
56.7
71.9
58.6
67.3
74.7
89.4
84.2
87.7
90.8
88.3
86.0
87.5
95.0
89.3
87.7
88.8
97.0
91.8
89.8
91.1
96.8
48.7
120.1
69.1
27.2
64.9
118.3
82.6
41.9
77.3
105.7
90.5
59.6
90.3
100.0
95.6
83.5
91.4
96.6
94.7
82.5
94.8
97.9
96.6
85.5
50.1
22.6
39.3
40.5
59.3
35.5
50.7
54.8
70.7
56.4
65.9
73.1
89.2
83.5
87.3
90.3
88.0
85.4
87.1
94.7
41.8
124.3
72.7
38.5
54.2
116.5
84.4
56.5
70.1
100.9
86.6
75.3
92.8
101.6
99.3
97.3
92.0
30.9
51.3
38.2
28.2
52.9
104.2
48.5
85.4
44.8
48.8
67.0
107.5
74.7
92.5
75.0
73.7
87.0
104.8
95.8
99.9
92.5
92.5
98.0
Inputs:
Private nonfarm business
Productivity:
Inputs:
Manufacturing (1992 = 100)
Productivity:
Inputs:
Combined units of all factor inputs................................
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
117
C u rre n t Labor S ta tistic s :
45.
Productivity Data
Annual indexes of productivity, hourly compensation, unit costs, and prices, selected years
[1992 = 100]
Item
1960
1970
1980
1990
1993
1994
1995
1996
1997
1998
1999
2000
2001
Business
Output per hour of all persons.................................
Compensation per hour....................................
Real compensation per hour.................................
Unit labor costs................................................
Unit nonlabor payments....................................................
Implicit price deflator...........................................
48.8
13.7
59.8
28.0
25.2
27.0
67.0
23.5
78.6
35.1
31.6
33.9
80.4
54.2
89.2
67.4
61.5
65.2
95.2
90.7
96.3
95.3
93.9
94.8
100.5
102.5
100.0
101.9
102.5
102.2
101.9
104.5
99.9
102.6
106.4
104.0
102.6
106.7
99.6
104.1
109.4
106.0
105.4
110.1
100.1
104.5
113.3
107.7
107.8
113.5
101.0
105.3
117.1
109.7
110.6
119.7
105.0
108.2
114.5
110.6
113.5
125.2
107.6
110.3
113.9
111.8
116.9
133.8
111.2
114.4
112.0
1113.5
118.2
137.7
111.4
116.5
114.7
115.8
51.9
14.3
62.6
27.5
24.6
26.5
68.9
23.7
79.2
34.4
31.3
33.3
82.0
54.6
89.8
66.5
60.5
64.3
95.3
90.5
96.2
95.0
93.6
94.5
100.5
102.2
99.7
101.7
102.8
106.6
99.4
103.7
110.4
106.1
105.4
109.8
99.8
104.2
113.5
107.6
107.5
113.1
100.6
105.2
118.0
109.8
110.3
119.1
104.5
108.0
115.7
110.8
112.9
124.3
106.8
110.1
115.5
112.1
116.2
133.0
110.6
114.4
103.0
102.2
101.8
104.3
99.7
102.5
106.9
104.1
113.5
114.1
117.5
136.6
110.5
116.3
116.4
116.3
55.4
15.6
68.1
26.8
28.1
23.3
50.2
30.2
28.8
70.4
25.3
84.4
34.8
35.9
31.9
44.4
35.1
35.6
81.1
56.4
92.9
68.4
69.6
65.1
68.8
66.0
68.4
95.4
90.8
96.5
95.9
95.2
98.0
94.3
97.1
95.8
100.7
102.0
99.6
101.0
101.3
100.2
113.2
103.5
102.1
103.1
104.2
99.6
101.1
101.0
101.3
131.7
109.0
103.7
104.2
106.2
99.0
102.0
101.9
102.2
139.0
111.6
105.1
107.5
109.0
99.0
101.2
101.4
100.6
152.2
113.8
105.5
108.4
110.3
98.1
101.5
101.8
100.9
156.9
115.2
106.2
111.7
116.0
101.7
103.3
103.8
102.2
141.7
112.3
106.6
114.7
121.1
104.1
105.1
105.6
103.5
131.7
110.7
107.3
117.1
129.2
107.4
109.8
110.3
108.3
113.2
109.5
110.0
118.3
132.4
107.0
112.9
111.9
115.8
100.5
111.8
111.9
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
90.8
96.4
97.8
99.8
99.0
101.9
102.7
100.2
100.8
100.9
100.9
105.0
105.6
101.0
100.7
102.8
102.0
109.0
107.9
100.6
99.0
106.9
103.9
112.8
109.4
99.4
96.9
109.9
104.8
117.6
111.5
99.1
94.8
110.0
104.1
123.3
117.4
103.0
95.2
103.7
100.4
129.7
122.1
104.9
94.1
104.9
100.7
134.9
131.1
109.0
97.2
107.0
103.2
136.2
133.1
107.7
97.8
Nonfarm business
Output per hour of all persons.........................................
Compensation per hour................................................
Real compensation per hour............................................
Unit labor costs...............................................
Unit nonlabor payments........................................
Implicit price deflator.....................................................
Nonfinancial corporations
Output per hour of all employees.....................................
Compensation per hour................................................
Real compensation per hour.................................
Total unit costs......................................................
Unit labor costs..........................................................
Unit nonlabor costs...................................................
Unit profits.....................................................
Unit nonlabor payments................................................
Implicit price deflator...............................................
Manufacturing
Output per hour of all persons..........................................
Compensation per hour..................................................
Real compensation per hour.........................................
Unit labor costs...............................................
Unit nonlabor payments....................................................
Implicit price deflator...................................................
Dash indicates data not available.
118
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
-
46.
Annual indexes of output per hour for selected 3-digit SIC industries
[1987=100]
Industry
SIC
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Mining
97.9
99.8
115.2
141.6
133.0
102.1
105.0
118.1
159.8
141.2
105.9
103.6
126.0
160.8
148.1
112.4
108.7
117.2
144.2
155.9
119.4
105.4
116.5
138.3
168.0
123.9
107.2
118.9
158.5
176.6
125.2
112.6
118.3
187.6
188.0
127.5
110.2
110.0
197.5
194.9
134.5
105.0
122.6
239.9
207.0
142.5
101.9
97.1
107.3
95.6
105.4
92.7
99.6
108.3
99.2
104.9
90.6
104.6
111.4
100.5
107.8
93.8
104.3
109.6
106.8
109.2
94.4
101.2
111.8
107.6
108.4
96.4
102.3
116.4
109.1
115.4
97.3
97.4
116.0
109.2
108.0
95.6
102.5
119.3
110.7
118.2
99.1
102.3
119.3
117.8
126.2
100.9
101.8
112.7
120.4
129.3
106.4
102.9
113.5
123.5
127.5
107.6
206
207
208
209
211
103.2
118.1
117.0
99.2
113.2
102.0
120.1
120.0
101.7
107.6
99.8
114.1
127.1
101.5
111.6
104.5
112.6
126.4
105.2
106.5
106.2
111.8
130.1
100.9
126.6
108.3
120.3
133.5
102.9
142.9
113.7
110.1
135.0
109.1
147.2
116.7
120.2
135.5
104.0
147.2
123.0
137.3
136.4
112.4
152.2
127.0
154.4
129.7
113.9
137.7
130.5
151.4
128.6
116.3
139.1
Narrow fabric mills.......................................................
Knitting mills.................................................................
Textile finishing, except wool....................................
221
222
224
225
226
103.1
111.3
96.5
107.5
83.4
111.2
116.2
99.6
114.0
79.9
110.3
126.2
112.9
119.3
78.6
117.8
131.7
111.4
127.9
79.3
122.1
142.5
120.1
134.1
81.2
134.0
145.3
118.9
138.3
78.5
137.3
147.6
126.3
150.3
79.2
131.2
162.2
110.8
138.0
94.3
136.2
168.6
117.7
135.9
93.7
139.3
175.3
124.9
146.6
94.4
140.2
167.4
117.1
155.6
97.2
Carpets and rugs.........................................................
Yarn and thread mills..................................................
Miscellaneous textile goods.......................................
Men's and boys’ furnishings.......................................
Women's and misses' outerwear..............................
227
228
229
232
233
93.2
110.2
109.2
102.1
104.1
89.2
111.4
104.6
108.4
104.3
96.1
119.6
106.5
109.1
109.4
97.1
126.6
110.4
108.4
121.8
93.3
130.7
118.5
111.7
127.4
95.8
137.4
123.7
123.4
135.5
100.2
147.4
123.1
134.7
141.6
100.3
150.4
118.7
162.1
149.9
102.3
153.0
120.1
174.8
151.9
96.0
157.6
128.0
190.9
173.9
103.0
155.4
134.4
200.3
189.9
Women's and children's undergarments.................
Hats, caps, and millinery............................................
Miscellaneous apparel and accessories..................
Miscellaneous fabricated textile products
Sawmills and planing mills.........................................
234
235
238
239
242
102.1
89.2
90.6
99.9
99.8
113.7
91.1
91.8
100.7
102.6
117.4
93.6
91.3
107.5
108.1
124.5
87.2
94.0
108.5
101.9
138.0
77.7
105.5
107.8
103.3
161.3
84.3
116.8
109.2
110.2
174.5
82.2
120.1
105.6
115.6
208.9
87.1
101.5
119.2
116.9
216.4
98.7
108.0
117.3
118.7
294.7
99.3
105.8
128.8
125.4
352.3
106.1
111.3
132.5
124.4
Mlllwork, plywood, and structural members............
Wood buildings and mobile homes...........................
Miscellaneous wood products...................................
Household furniture.................................................... .
243
244
245
249
251
98.0
111.2
103.1
107.7
104.5
98.0
113.1
103.0
110.5
107.1
99.9
109.4
103.1
114.2
110.5
97.0
100.1
103.8
115.3
110.6
94.5
100.9
98.3
111.8
112.5
92.7
106.1
97.0
115.4
116.9
92.4
106.7
96.7
114.4
121.6
89.1
106.2
100.3
123.4
121.3
91.3
106.5
99.2
131.2
125.7
89.2
103.9
100.3
140.7
128.9
91.4
104.6
94.6
146.5
128.4
Office furniture.............................................................
Public building and related furniture........................
Partitions and fixtures.................................................
Miscellaneous furniture and fixtures........................
Pulp mills.......................................................................
252
253
254
259
261
95.0
119.8
95.6
103.5
116.7
94.1
120.2
93.0
102.1
128.3
102.5
140.6
102.7
99.5
137.3
103.2
161.0
107.4
103.6
122.5
100.5
157.4
98.9
104.7
128.9
101.1
173.3
101.2
110.0
131.9
106.4
181.5
97.5
113.2
132.6
118.3
214.9
121.1
110.7
82.3
113.1
207.6
125.6
121.9
86.6
108.9
222.4
125.9
119.1
84.8
111.2
202.0
131.9
110.5
78.8
Paper mills....................................................................
Paperboard mills.........................................................
Paperboard containers and boxes............................
Miscellaneous converted paper products................
Newspapers.................................................................
262
263
265
267
271
102.3
100.6
101.3
101.4
90.6
99.2
101.4
103.4
105.3
85.8
103.3
104.4
105.2
105.5
81.5
102.4
108.4
107.9
107.9
79.4
110.2
114.9
108.4
110.6
79.9
118.6
119.5
105.1
113.3
79.0
111.6
118.0
106.3
113.6
77.4
112.0
126.7
109.7
119.5
79.0
114.8
127.8
113.5
123.0
83.6
126.2
134.9
111.9
126.0
86.0
133.5
135.3
112.9
128.3
88.3
Periodicals....................................................................
Books.............................................................................
272
273
274
275
276
93.9
96.6
92.2
102.5
93.0
89.5
100.8
95.9
102.0
89.1
92.9
97.7
105.8
108.0
94.5
89.5
103.5
104.5
106.9
91.1
81.9
103.0
97.5
106.5
82.0
87.8
101.6
94.8
107.2
76.9
89.1
99.3
93.6
108.3
75.2
100.1
102.6
114.5
108.8
77.9
112.2
100.9
119.4
109.9
76.7
111.2
106.1
127.2
115.0
70.6
109.9
106.1
127.8
118.7
69.4
Plastics materials and synthetics.............................
277
278
279
281
282
100.6
99.4
99.3
106.8
100.9
92.7
96.1
100.6
109.7
100.0
96.7
103.6
112.0
109.7
107.5
91.4
98.7
115.3
105.6
112.0
89.0
105.4
111.0
102.3
125.3
92.5
108.7
116.7
109.3
128.3
90.8
114.5
126.2
110.1
125.3
92.2
114.2
123.3
116.8
135.4
104.1
116.5
126.7
145.8
142.2
109.3
123.8
121.5
148.5
148.6
105.1
126.2
119.6
141.3
151.0
Drugs.............................................................................
Soaps, cleaners, and toilet goods............................
Paints and allied products.........................................
Industrial organic chemicals.....................................
Agricultural chemicals................................................
283
284
285
286
287
103.8
103.8
106.3
101.4
104.7
104.5
105.3
104.3
95.8
99.5
99.5
104.4
102.9
94.6
99.5
99.7
108.7
108.8
92.2
103.8
104.6
111.2
116.7
99.9
105.0
108.7
118.6
118.0
98.6
108.5
112.5
120.9
125.6
99.0
110.0
112.4
126.4
126.4
111.3
119.8
104.3
122.7
126.8
105.7
118.0
105.6
114.8
122.7
120.6
104.6
106.2
124.8
124.6
127.8
112.0
102
104
122
131
142
102.7
122.3
118.7
100.5
127.4
122.4
97.0
102.2
Meat products...............................................................
Dairy products..............................................................
Preserved fruits and vegetables................................
Grain mill products.......................................................
Bakery products...........................................................
201
202
203
204
205
Sugar and confectionery products............................
Fats and oils.................................................................
Beverages.....................................................................
Miscellaneous food and kindred products...............
Copper ores.................................................................
Gold and silver ores....................................................
Bituminous coal and lignite mining...........................
Crude petroleum and natural gas.............................
Crushed and broken stone........................................
Manufacturing
Manifold business forms............................................
Greeting cards.............................................................
Blankbooks and bookbinding....................................
Printing trade services................................................
See footnotes at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
119
C urrent Labor S ta tistic s :
Productivity Data
46. Continued - Annual indexes of output per hour for selected 3-digit SIC industries
[1987=100]
Industry
SIC
Miscellaneous chemical products.............................
Petroleum refining.......................................................
Asphalt paving and roofing materials.......................
Miscellaneous petroleum and coal products...........
Tires and inner tubes.................................................
289
291
295
299
301
97.3
109.2
98.0
94.8
103.0
96.1
106.6
94.1
90.6
102.4
101.8
111.3
100.4
101.5
107.8
107.1
120.1
108.0
104.2
116.5
123.8
104.9
96.3
124.1
107.8
132.3
111.2
87.4
131.1
110.1
142.0
113.1
87.1
138.8
120.3
149.2
123.1
96.5
149.1
120.8
155.8
124.7
98.5
144.1
123.3
170.2
123.4
86.5
142.1
125.6
180.2
126.1
82.9
145.9
Hose and belting and gaskets and packing............
Fabricated rubber products, n.e.c............................
Miscellaneous plastics products, n.e.c....................
Footwear, except rubber............................................
Flat glass.......................................................................
305
306
308
314
321
96.1
109.0
105.7
101.1
84.5
92.4
109.9
108.3
94.4
83.6
97.8
115.2
114.4
104.2
92.7
99.7
123.1
116.7
105.2
97.7
102.7
119.1
120.8
113.0
97.6
104.6
121.5
121.0
117.1
99.6
107.4
121.0
124.7
126.1
101.5
113.5
125.3
129.9
121.4
107.6
112.7
132.3
133.8
110.9
114.0
110.6
136.9
140.9
132.6
129.4
115.4
144.7
145.4
146.2
140.4
Glass and glassware, pressed or blown.................
Products of purchased glass.....................................
Cement, hydraulic........................................................
Structural clay products.............................................
Pottery and related products.....................................
322
323
324
325
326
104.8
92.6
112.4
109.6
98.7
102.3
97.7
108.3
109.8
95.9
108.9
101.5
115.1
111.4
99.5
108.7
106.2
119.9
106.8
100.3
112.9
105.9
125.6
114.0
108.5
115.7
106.1
124.3
112.6
109.4
121.4
122.0
128.7
119.6
119.4
128.3
125.1
133.1
111.9
124.2
135.2
122.0
134.1
114.8
127.4
139.3
130.2
138.6
123.5
122.0
135.8
137.2
136.9
124.8
121.2
Miscellaneous nonmetalllc mineral products.........
Blast furnace and basic steel products....................
Iron and steel foundries.............................................
Primary nonferrous metals.........................................
327
329
331
332
333
102.3
95.4
109.7
106.1
102.3
101.2
94.0
107.8
104.5
110.7
102.5
104.3
117.0
107.2
101.9
104.6
104.5
133.6
112.1
107.9
101.5
106.3
142.4
113.0
105.3
104.5
107.8
142.6
112.7
111.0
107.3
110.4
147.5
116.2
110.8
107.6
114.7
155.0
120.8
112.0
112.8
114.9
151.0
121.1
118.9
111.1
113.3
155.6
128.9
117.7
105.1
116.1
160.1
132.1
111.9
Nonferrous rolling and drawing.................................
Nonferrous foundries (castings)................................
Miscellaneous primary metal products....................
Metal cans and shipping containers.........................
Cutlery, handtools, and hardware.............................
335
336
339
341
342
92.7
104.0
113.7
117.6
97.3
91.0
103.6
109.1
122.9
96.8
96.0
103.6
114.5
127.8
100.1
98.3
108.5
111.3
132.3
104.0
101.2
112.1
134.5
140.9
109.2
99.2
117.8
152.2
144.2
111.3
104.0
122.3
149.6
155.2
118.2
111.3
127.0
136.2
160.3
114.6
115.7
131.5
140.0
163.8
115.7
121.4
129.8
149.0
157.9
121.9
118.0
129.7
154.3
159.5
125.4
Plumbing and heating, except electric.....................
Fabricated structural metal products........................
Metal forgings and stampings....................................
Metal services, n.e.c....................................................
Ordnance and accessories, n.e.c.............................
343
344
346
347
348
102.6
98.8
95.6
104.7
82.1
102.0
100.0
92.9
99.4
81.5
98.4
103.9
103.7
111.6
88.6
102.0
104.8
108.7
120.6
84.6
109.1
107.7
108.5
123.0
83.6
109.2
105.8
109.3
127.7
87.6
118.6
106.5
113.6
128.4
87.5
127.3
111.9
120.2
124.4
93.7
130.5
112.7
125.9
127.3
96.6
125.7
112.8
128.3
126.1
91.0
132.2
112.8
129.8
135.7
92.8
Miscellaneous fabricated metal products................
Engines and turbines...................................................
Farm and garden machinery.....................................
Construction and related machinery........................
Metalworking machinery.............................................
349
351
352
353
354
97.5
106.5
116.5
107.0
101.1
97.4
105.8
112.9
99.1
96.4
101.1
103.3
113.9
102.0
104.3
102.0
109.2
118.6
108.2
107.4
103.2
122.3
125.0
117.7
109.9
106.6
122.7
134.7
122.1
114.8
108.3
136.6
137.2
123.3
114.9
107.7
136.9
141.2
132.5
119.2
111.6
146.1
148.5
137.6
119.8
109.3
151.5
128.6
133.6
123.0
109.2
164.5
139.6
139.8
129.8
Special industry machinery........................................
General industrial machinery.....................................
Computer and office equipment................................
Refrigeration and service machinery........................
Industrial machinery, n.e.c.........................................
355
356
357
358
359
107.5
101.5
138.1
103.6
107.3
108.3
101.6
149.6
100.7
109.0
106.0
101.6
195.7
104.9
117.0
113.6
104.8
258.6
108.6
118.5
121.2
106.7
328.6
110.7
127.4
132.3
109.0
469.4
112.7
138.8
134.0
109.4
681.3
114.7
141.4
131.7
110.0
960.2
115.0
129.3
124.5
111.2
1356.6
121.4
127.5
138.6
113.1
1862.5
124.0
135.8
172.2
118.7
2172.0
122.3
141.8
Electric distribution equipment..................................
Electrical Industrial apparatus...................................
Household appliances................................................
Electric lighting and wiring equipment.....................
Communications equipment.......................................
361
362
363
364
366
106.3
107.7
105.8
99.9
123.8
106.5
107.1
106.5
97.5
129.1
119.6
117.1
115.0
105.7
154.9
122.2
132.9
123.4
107.8
163.1
131.8
134.9
131.4
113.4
186.4
143.0
150.8
127.3
113.7
200.7
143.9
154.3
127.4
116.9
229.5
142.8
164.2
142.9
121.8
275.4
147.5
162.3
150.2
129.2
284.5
148.9
158.3
149.5
132.4
371.9
155.4
157.0
162.4
134.8
448.8
Electronic components and accessories.................
Miscellaneous electrical equipment & supplies...
Motor vehicles and equipment...................................
Aircraft and parts.........................................................
Ship and boat building and repairing.......................
367
369
371
372
373
133.4
90.6
102.4
98.9
103.7
154.7
98.6
96.6
108.2
96.3
189.3
101.3
104.2
112.3
102.7
217.9
108.2
106.2
115.2
105.9
274.0
110.5
108.8
109.5
103.8
401.5
114.1
106.7
107.8
98.1
515.0
123.1
107.2
113.1
99.3
613.4
128.3
116.3
114.7
105.5
768.6
135.3
125.2
140.1
102.5
1062.6
147.2
136.7
138.1
113.1
1440.1
156.0
127.1
132.2
121.6
Railroad equipment.....................................................
Motorcycles, bicycles, and parts...............................
Guided missiles, space vehicles, parts....................
Search and navigation equipment............................
Measuring and controlling devices...........................
374
375
376
381
382
141.1
93.8
116.5
112.7
106.4
146.9
99.8
110.5
118.9
113.1
147.9
108.4
110.5
122.1
119.9
151.0
130.9
119.4
129.1
124.0
152.5
125.1
114.9
132.1
133.8
150.0
120.3
116.9
149.5
146.4
148.3
125.5
125.1
142.2
150.5
184.2
120.4
133.6
149.5
142.4
189.1
127.7
138.9
149.1
143.5
212.8
122.4
156.1
149.6
152.4
218.4
119.4
113.3
163.7
158.5
Medical instruments and supplies............................
Ophthalmic goods........................................................
Photographic equipment & supplies.........................
Jewelry, silverware, and plated ware.......................
Musical Instruments....................................................
384
385
386
391
393
116.9
121.2
107.8
99.3
97.1
118.7
125.1
110.2
95.8
96.9
123.5
144.5
116.4
96.7
96.0
127.3
157.8
126.9
96.7
95.6
126.7
160.6
132.7
99.5
88.7
131.5
167.2
129.5
100.2
86.9
139.8
188.2
128.7
102.6
78.8
147.4
196.3
121.5
114.2
82.9
158.6
199.0
128.0
113.1
81.4
160.4
235.2
160.6
134.3
97.1
167.0
250.2
169.4
144.9
105.3
Concrete, gypsum, and plaster products.................
See footnotes at end of table.
Monthly Labor Review
20
Digitized for 1FRASER
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
1990
1991
1992
1993
1994
105.7
1995
1996
1997
1998
1999
2000
46. Continued - Annual indexes of output per hour for selected 3-digit SIC industries
[1987=100]
Industry
Toys and sporting goods...........................................
Pens, pencils, office, and art supplies.....................
Costume jewelry and notions....................................
Miscellaneous manufactures....................................
SIC
394
395
396
399
1990
108.1
118.2
105 3
106.5
1991
109.7
116.8
106.7
109.2
1992
104.9
111.3
110 8
109.5
1993
114.2
111.6
115 8
107.7
1994
1995
1996
1997
1998
1999
2000
109.7
129.9
12Q n
113.6
135.2
148 7
119.9
144.1
14? ?
125.7
127.5
131.6
132.5
126.6
123.4
140.4
124.9
106.1
108.1
112.8
109.4
108.5
114.9
115.9
Transportation
Railroad transportation...............................................
4011
118.5
127.8
139.6
145.4
150.3
156.2
167.0
169.8
173.3
182.5
195.8
111.1
104.0
92.9
116.9
103.7
92.5
123.4
104.5
96.9
126.6
107.1
100.2
129.5
106.6
105.7
125.4
106.5
108.6
130.9
104.7
111.1
132.4
108.3
111.6
129.9
109.8
108.4
131.6
110.9
109.1
131.2
113.6
110.7
481
483
484
491,3(pts.)
492,3(pts.)
113 3
104.9
92 6
110.1
105.8
119 8
106.1
87 6
113.4
109.6
127 7
185 5
142 2
108.3
88 5
106.7
85 8
110.1
88 4
109.6
105.8
101.7
104.5
108.4
109.9
115.2
111.1
24.1
121.8
50.5
125.6
80.8
137.1
116.8
145.9
150.0
158.6
159.6
144.4
162.0
147.2
169.6
160.6
Lumber and other building materials dealers.........
Paint, glass, and wallpaper stores............................
Hardware stores...........................................................
Retail nurseries, lawn and garden supply stores...
Department stores.......................................................
521
523
525
526
531
104.3
106.8
115.3
84.7
96.8
102.3
100.4
108.7
89.3
102.0
106.4
107.6
115.2
101.2
105.4
111.4
114.2
113.9
107.1
110.4
118.9
127.8
121.2
117.0
113.5
117.8
130.9
115.6
117.4
116.1
121.6
133.5
119.5
136.4
123.8
121.8
134.8
119.0
127.5
129.1
134.2
163.5
137.9
133.7
135.8
143.0
165.1
147.6
150.4
146.0
144.2
170.1
145.7
154.5
160.4
Variety stores...............................................................
Miscellaneous general merchandise stores............
Grocery stores.............................................................
Meat and fish (seafood) markets..............................
Retail bakeries.............................................................
533
539
541
542
546
154.6
118.6
96.6
98.9
91.2
159.0
124.8
96.3
90.8
96.7
173.9
140.4
96.5
99.2
96.5
191.9
164.3
96.0
97.7
86.5
197.9
164.8
95.4
95.7
85.3
212.4
167.4
93.9
94.4
83.0
240.4
167.7
92.1
86.4
75.9
260.1
170.4
91.7
90.8
67.6
271.2
185.9
92.2
95.7
68.1
315.0
199.6
95.3
97.4
83.1
330.9
224.3
96.1
110.0
88.4
New and used car dealers.........................................
Auto and home supply stores....................................
Gasoline service stations...........................................
Men's and boy's wear stores.....................................
Women's clothing stores............................................
551
553
554
561
562
106.7
103.7
103.0
115.6
106.6
104.9
100.2
104.8
121.9
111.2
107.4
101.6
110.2
122.3
123.6
108.6
100.8
115.9
119.5
130.0
109.7
105.3
121.1
121.7
130.4
108.1
109.1
127.2
121.4
139.9
109.1
108.2
126.1
129.8
154.2
108.8
108.1
126.1
136.3
157.3
108.7
113.1
133.9
145.2
176.0
111.6
115.5
141.7
154.5
190.2
112.5
119.3
139.0
165.0
205.7
Family clothing stores.................................................
Shoe stores...................................................................
Furniture and homefurnlshings stores.....................
Household appliance stores.......................................
Radio, television, computer, and music stores.......
565
566
571
572
573
107.8
107.9
104.6
104.6
120.8
111.5
107.8
105.4
107.2
129.3
118.6
115.5
113.9
116.1
139.3
121.5
117.3
113.3
118.7
153.8
127.7
130.7
114.7
122.4
178.2
141.8
139.2
117.4
139.6
198.1
146.9
151.9
123.6
142.2
206.6
150.2
148.4
124.2
155.2
216.8
153.1
145.0
127.3
184.2
258.3
155.9
152.9
134.5
186.4
309.1
160.4
160.2
141.1
209.3
359.4
Eating and drinking places.........................................
Drug and proprietary stores.......................................
Liquor stores.................................................................
Used merchandise stores...........................................
Miscellaneous shopping goods stores.....................
581
591
592
593
594
104.5
106.3
105.9
103.0
107.4
103.8
108.0
106.9
102.3
109.3
103.4
107.6
109.6
115.7
107.9
103.8
109.6
101.8
116.7
111.7
102.1
109.9
100.1
119.5
117.3
102.0
111.1
104.7
120.6
123.2
100.6
113.9
113.8
132.6
125.3
101.6
119.8
109.9
140.3
129.4
102.0
125.7
116.5
163.6
138.7
104.0
129.8
114.5
183.2
143.7
107.3
136.9
127.7
216.7
150.6
Nonstore retailers........................................................
Fuel dealers.................................................................
Retail stores, n.e.c.......................................................
596
598
599
111.1
84.6
114.5
112.5
85.3
104.0
126.5
84.3
112.5
132.2
91.9
118.1
149.0
99.0
125.8
152.5
111.4
127.0
173.5
112.5
140.2
186.8
109.1
147.8
208.3
105.8
157.4
220.6
115.2
162.5
263.2
117.3
168.1
Commercial banks.......................................................
Hotels and motels........................................................
Laundry, cleaning, and garment services................
Photographic studios, portrait....................................
Beauty shops................................................................
602
701
721
722
723
107.7
96.2
102.3
98.2
97.5
110.1
99.3
99.9
92.1
95.8
111.0
108.0
99.3
95.8
100.9
118.5
106.5
99.9
101.8
97.0
121.7
109.9
105.0
108.3
101.1
126.4
110.5
106.6
116.2
104.8
129.7
110.0
109.8
110.7
107.6
133.0
108.2
109.0
114.1
108.5
132.6
108.2
116.0
121.6
110.5
135.9
109.9
120.8
107.7
113.4
143.2
114.1
123.6
112.0
114.5
Barber shops................................................................
Funeral services and crematories.............................
Automotive repair shops.............................................
Motion picture theaters...............................................
724
726
753
783
100.7
91.2
107.9
118.1
94.9
89.9
100.1
118.2
113.2
103.8
105.1
114.8
121.9
98.7
105.7
113.8
118.8
104.3
114.3
110.4
115.7
100.2
121.6
105.0
128.8
97.6
116.1
104.1
150.4
101.9
117.2
103.4
157.4
104.2
124.9
106.1
132.8
100.2
126.4
108.7
129.9
93.9
128.5
112.3
July 2002
121
Trucking, except local1.............................................
unitea states postal service ■....................................
4213
431
Air transportation......................................................... 4512,13,22(pts.)
utilities
Telephone communications.......................................
Radio and television broadcasting...........................
Cable and other pay TV services.............................
Electric utilities.............................................................
Gas utilities...................................................................
Trade
Finance and services
Herers to output per employee.
" Heters to output per tun-time equivalent employee year on tiscai Dasis.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
n.e.c. = not elsewhere classified
Monthly Labor Review
Current Labor Statistics:
47.
International Comparison
Unemployment rates, approximating U.S. concepts, in nine countries, quarterly data
seasonally adjusted
2000
2001
2001
2000
Annual average
Country
I
III
II
IV
I
III
II
IV
United States........
4.0
4.8
4.0
4.0
4.1
4.0
4.2
4.5
4.8
5.6
Canada..................
6.4
6.7
5.1
8.7
6.1
6.5
4.8
9.9
6.1
6.4
4.7
9.5
6.1
6.1
4.7
9.3
6.1
6.2
4.8
9.0
6.2
6.5
4.8
8.6
6.3
6.9
4.9
8.5
6.4
6.8
5.2
8.7
6.8
6.8
5.5
8.9
France1..................
6.1
6.3
4.8
9.4
Germany1.............
12
Italy ' ....................
8.1
8.0
8.3
8.1
8.0
7.8
7.9
8.0
8.0
8.1
10.7
9.6
11.2
10.9
10.5
10.1
10.0
9.7
9.5
9.3
Sweden1................
United Kinndom1...
5.8
5.5
5.0
-
6.6
5.8
6.0
5.5
5.6
5.4
5.2
5.3
5.1
5.1
5.0
5.0
5.0
5.1
5.1
-
1 Preliminary for 2001 for Japan, France, Germany, Italy, Sweden,
and the United Kingdom.
See "Notes on the data" for information on breaks in series. For
further qualifications and historical data, see Comparative Civilian
2 Quarterly rates are for the first month of the quarter.
NOTE: Quarterly figures for France and Germany are calculated
Labor Force Statistics, Ten Countries, 1959-2001 (Bureau of Labor
Statistics, Mar. 25, 2002), on the Internet at
by applying annual adjustment factors to current published data,
h ttp ://w w w .b ls .g o v /fls /h o m e .h tm
and therefore should be viewed as less precise indicators
of Monthly and quarterly unemployment rates, updated monthly, are
unemployment under U.S. concepts than the annual figures.
Monthly Labor Review
122
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
also on ttlis si,e- Dash indicates data not available.
48.
Annual data: Employment status of the working-age population, approximating U.S. concepts, 10 countries
[Numbers in thousands]
Employment status and country
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
128,105
14,177
8,557
129,200
14,308
8,613
131,056
14,400
8,771
132,304
14,517
8,995
133,943
14,669
9,115
136,297
14,958
9,204
137,673
15,237
9,339
139,368
15,536
9,466
140,863
15,789
9,678
141,815
16,027
9,817
66,870
C iv ilia n la b o r fo rc e
United States.....................................................................
Canada...............................................................................
Australia..............................................................................
Japan..................................................................................
65,040
65,470
65,780
65,990
66,450
67,200
67,240
67,090
66,990
France................................................................................
Germany............................................................................
24,570
39,010
24,640
39,100
24,780
39,070
25,520
39,750
25,830
39,800
25,980
39,750
22,910
22,570
22,450
25,090
39,140
22,570
25,210
39,420
Italy......................................................................................
24,830
38,980
22,460
22,680
22,960
23,130
23,340
23,540
Netherlands........................................................................
Sweden...............................................................................
United Kingdom.................................................................
6,950
4,520
28,410
7,100
4,443
28,430
7,190
4,418
28,440
7,260
4,460
28,560
7,370
4,459
28,720
7,530
4,418
28,910
7,690
4,402
29,040
7,900
4,430
29,300
8,050
4,489
29,450
4.537
United States.....................................................................
Canada...............................................................................
Australia..............................................................................
Japan..................................................................................
France................................................................................
Germany............................................................................
66.4
65.9
63.9
63.4
55.9
58.2
66.3
65.5
63.5
63.3
55.8
57.7
66.6
65.2
63.9
63.1
55.8
57.4
66.6
64.9
64.6
62.9
55.6
57.1
66.8
64.7
64.6
63.0
55.8
57.1
67.1
65.0
64.3
63.2
55.7
57.3
67.1
65.4
64.3
62.8
56.1
57.7
67.1
65.8
64.2
62.4
56.4
57.6
67.2
65.9
64.7
62.0
56.4
57.5
66.9
66.0
64.7
61.6
-
Italy.....................................................................................
47.5
47.9
47.3
47.1
47.1
47.2
47.6
47.8
48.1
-
Netherlands........................................................................
Sweden...............................................................................
United Kinqdom.................................................................
57.8
65.7
63.1
58.6
64.5
62.8
59.0
63.7
62.7
59.2
64.1
62.7
59.8
64.0
62.8
60.8
63.3
62.9
61.7
62.8
62.9
62.8
62.8
63.2
63.5
63.8
63.3
64.2
-
-
-
-
P a rtic ip a tio n ra te 1
-
E m p lo y e d
United States.....................................................................
Canada...............................................................................
Australia..............................................................................
Japan..................................................................................
118,492
12,672
7,660
63,620
120,259
12,770
7,699
63,810
123,060
13,027
7,942
63,860
124,900
13,271
8,256
63,890
126,708
13,380
8,364
64,200
129,558
13,705
8,444
64,900
131,463
14,068
8,618
64,450
133,488
14,456
8,808
63,920
135,208
14,827
9,068
63,790
135,073
14,997
9,157
63,470
France................................................................................
Germany............................................................................
22,020
36,390
21,740
35,990
21,720
35,760
21,910
35,780
21,230
20,270
19,940
19,820
22,090
35,510
19,990
22,510
36,060
20,210
22,940
36,360
20,460
23,530
36,540
20,840
-
Italy......................................................................................
21,960
35,640
19,920
Netherlands.......................................................................
Sweden...............................................................................
United Kingdom.................................................................
6,560
4,265
25,530
6,630
4,028
25,450
6,670
3,992
25,720
6,760
4,056
26,070
6,900
4,019
26,380
7,130
3,973
26,880
7,380
4,034
27,210
7,640
4,117
27,530
7,810
4,229
27,830
21,280
4,309
-
E m p lo y m e n t-p o p u la tio n ra tio 2
62.9
59.4
59.2
60.9
49.0
52.4
63.2
59.1
59.3
60.9
48.8
52.0
63.8
59.7
59.0
61.0
48.8
51.6
64.1
60.4
59.3
60.2
49.5
52.3
42.0
41.5
41.6
41.6
41.9
42.3
42.9
54.7
57.6
56.7
55.1
58.3
57.2
56.0
57.7
57.6
57.5
56.9
58.5
59.2
57.6
58.9
60.8
58.4
59.4
61.6
60.1
59.4
61.7
58.5
56.8
61.7
49.2
53.2
62.5
59.0
57.8
61.3
48.9
52.6
44.0
43.0
54.5
62.0
56.7
54.7
58.5
56.2
United States......................................................................
Canada...............................................................................
Australia..............................................................................
Japan..................................................................................
61.5
58.9
57.2
62.0
50.1
54.2
Italy.....................................................................................
Sweden...............................................................................
United Kinqdom.................................................................
64.3
61.3
59.8
59.4
50.1
52.6
64.5
62.1
60.6
59.0
51.1
52.8
63.8
61.9
60.3
58.4
_
-
_
61.0
-
U n e m p lo y e d
United States......................................................................
Canada...............................................................................
Australia..............................................................................
Japan..................................................................................
9,613
1,505
897
1,420
8,940
1,539
914
1,660
7,996
1,373
829
1,920
7,404
1,246
739
2,100
7,236
1,289
751
2,250
6,739
1,252
760
2,300
6,210
1,169
721
2,790
5,880
1,080
658
3,170
5,655
962
611
3,200
6,742
1,031
661
3,400
Germany............................................................................
2,550
2,620
3,060
3,320
2,920
3,200
3,130
3,510
1,680
2,510
2,640
2,650
3,120
3,910
2,690
3,020
3,690
2,750
2,890
3,440
Italy......................................................................................
2,900
3,110
2,300
2,670
2,450
3,210
2,500
2,270
Sweden..............................................................................
United Kingdom.................................................................
390
255
2,880
470
415
2,980
520
426
2,720
500
404
2,490
470
440
2,340
400
445
2,030
310
368
1,830
270
313
1,770
240
260
1,620
-
228
-
U n e m p lo y m e n t ra te
4.9
8.4
8.3
3.4
12.4
9.9
4.5
7.7
7.7
4.1
11.8
9.3
4.2
7.0
7.0
4.7
11.2
8.6
4.0
6.1
6.3
4.8
9.4
8.1
4.8
6.4
6.7
5.1
8.7
8.0
11.8
5.4
8.8
8.2
3.4
12.5
9.0
11.7
11.9
12.0
11.5
10.7
9.6
6.9
9.1
8.7
6.4
9.9
8.1
5.3
10.1
7.0
4.0
8.4
6.3
3.4
7.1
6.0
7.5
10.6
10.5
2.2
10.4
6.7
6.9
10.8
10.6
2.5
11.8
8.0
6.1
9.5
9.4
2.9
12.3
8.5
5.6
8.6
8.2
3.2
11.8
8.2
Italy.....................................................................................
7.3
10.2
11.2
Sweden..............................................................................
United Kingdom.................................................................
5.6
5.6
10.1
6.6
9.3
10.5
7.2
9.6
9.6
United States......................................................................
Canada...............................................................................
Australia.............................................................................
Japan..................................................................................
Germany............................................................................
1 Labor force as a percent of the working-age population.
2 Employment as a percent of the working-age population.
NOTE: See notes on the data for information on breaks in series.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
3.0
5.8
5.5 -
_
5.0
For further qualifications and historical data, see Comparative Civilian Labor Force
Statistics, Ten Countries, 1959-2001 (Bureau of Labor Statistics, Mar. 25,2002),
on the Internet at http://www.bls.gov/fls/home.htm
Dash indicates data are not available.
Monthly Labor Review
July 2002
123
Current Labor Statistics:
International Comparison
49. Annual indexes of m anufacturing productivity an d related measures, 12 countries
[1992 = 100]
It e m a n d c o u n t r y
1960
1980
1970
1989
1990
1991
1993
1994
1995
1997
1996
1998
1999
2000
Output per hour
United States.........................................................
Canada..................................................................
Japan.....................................................................
Belgium.................................................................
Denmark................................................................
France...................................................................
Germany................................................................
Italy........................................................................
Netherlands...........................................................
Norway..................................................................
Sweden.................................................................
United Kingdom....................................................
38.5
13.8
18.0
29.9
21.9
29.2
22.5
18.5
37,0
27.3
30.0
-
56.0
37.5
32.9
52.7
43.0
52.0
42.2
37.9
58.3
52.2
43.2
70.5
74.4
63.2
65.4
90.3
66.2
77.2
70.8
68.8
76.7
73.1
54.3
95.7
93.2
88.5
96.9
99.6
91.9
94.6
91.3
96.9
94.6
93.2
86.2
96.9
94.7
94.4
96.8
99.1
93.6
99.0
93.9
98.5
96.6
94.6
89.1
97.8
95.5
99.0
99.1
99.6
96.9
98.3
95.9
99.6
97.5
95.5
93.8
102.1
104.9
101.7
102.5
104.5
100.6
101.8
101.8
101.6
100.6
107.3
103.9
107.3
109.7
103.3
108.4
108.6
109.6
106.1
113.2
101.4
119.4
107.1
114.7
112.3
111.2
118.2
102.0
121.9
104.9
115.3
114.0
110.8
120.2
102.0
124.5
103.8
-
113.8
111.3
111.0
113.2
-
117.0
110.1
116.1
117.0
-
121.2
113.2
121.0
127.0
123.8
119.5
113.7
122.3
103.0
132.3
105.2
126.5
113.1
121.2
129.2
129.5
120.4
113.1
125.0
103.6
139.5
106.9
135.3
114.9
126.9
129.5
132.9
120.5
113.5
128.5
103.1
143.5
111.6
142.8
116.3
134.1
133.4
141.1
128.0
117.8
133.8
104.2
150.4
117.6
-
Output
United States.........................................................
Canada.................................................................
Japan.....................................................................
Belgium..................................................................
Denmark................................................................
France..................................................................
Germany...............................................................
Italy........................................................................
Netherlands..........................................................
Norway..................................................................
Sweden................................................................
United Kingdom....................................................
34.0
10.7
30.7
40.8
31.0
41.5
23.0
31.5
57.0
45.9
67.3
60.0
39.2
57.6
68.0
64.1
70.9
48.1
59.1
89.9
80.7
90.2
75.8
85.2
60.4
78.2
91.3
88.7
85.3
84.4
76.8
103.6
90.7
87.2
102.4
112.1
90.9
99.1
104.3
97.2
94.0
98.3
96.6
101.3
110.9
105.5
101.6
107.5
97.1
101.0
102.7
99.1
99.1
99.4
99.9
100.2
110.1
105.3
98.3
99.2
102.0
100.7
101.7
99.8
102.3
99.3
100.4
98.3
104.1
100.0
103.5
105.0
96.3
97.0
99.0
95.7
92.5
96.5
98.4
102.7
101.9
101.4
111.1
113.0
94.9
101.4
109.3
100.3
95.2
102.4
104.6
106.7
117.1
106.1
118.4
118.5
98.9
104.2
114.7
104.9
95.3
107.2
108.1
109.0
128.4
107.8
121.3
120.0
103.0
106.6
109.7
104.6
92.6
105.4
108.7
110.1
131.1
108.5
127.9
127.3
106.5
113.8
118.5
109.7
95.7
108.8
111.5
115.7
138.0
109.9
133.1
132.5
100.2
116.4
120.8
115.0
97.2
110.5
114.8
117.7
147.6
110.8
141.2
140.8
101.9
118.0
119.8
117.3
95.9
110.2
118.1
114.0
153.6
111.1
147.0
148.8
107.6
122.2
125.8
121.2
101.7
113.9
123.7
110.9
163.4
113.3
92.1
88.3
77.8
170.7
136.5
141.2
142.3
102.3
170.5
154.1
168.3
224.6
104.4
107.1
104.4
174.7
129.0
148.9
136.3
113.8
156.1
154.3
154.7
208.8
107.5
114.6
95.6
119.7
101.1
133.2
110.5
119.3
111.7
135.0
124.0
160.5
107.1
120.2
102.7
102.3
104.7
105.8
99.3
107.6
99.7
107.1
119.0
122.4
104.8
113.5
102.9
104.3
103.7
105.9
100.1
105.9
101.4
103.7
116.4
118.1
100.4
103.9
103.1
101.5
102.1
103.1
104.1
103.6
100.9
100.8
109.0
106.6
101.4
100.1
94.7
94.7
94.8
95.1
90.8
94.9
96.8
102.1
94.9
97.6
103.6
103.0
91.9
93.6
92.4
86.8
96.5
92.4
105.2
98.1
99.1
104.0
106.4
89.1
92.0
91.5
84.9
96.4
91.5
106.8
105.3
102.7
103.6
109.0
88.7
91.1
90.7
81.2
95.1
90.4
107.9
105.3
104.5
105.5
112.4
88.0
89.6
88.6
80.1
95.7
91.1
112.3
104.3
104.5
105.2
117.1
82.7
90.1
88.8
80.7
97.7
91.8
113.6
105.8
103.6
104.3
122.6
80.3
91.1
88.3
79.6
97.1
92.0
110.6
107.1
99.5
102.9
128.0
80.2
91.7
85.9
79.5
96.7
92.5
106.4
108.6
96.3
14.9
10.0
4.3
5.4
4.6
4.3
8.1
1.7
6.4
4.7
4.1
3.0
23.7
17.1
16.4
13.7
13.3
10.3
20.7
5.0
20.2
11.8
10.7
6.1
55.6
47.6
58.5
52.5
49.6
40.8
53.6
29.0
64.4
39.0
37.3
32.1
86.6
82.6
84.0
85.9
87.7
86.0
83.2
77.4
88.6
87.2
79.4
73.8
90.8
88.3
90.5
90.1
92.7
90.6
89.4
85.8
90.9
92.3
87.8
82.9
95.6
95.0
96.4
97.3
95.9
96.2
91.5
94.2
95.3
97.5
95.5
93.8
102.7
102.0
102.8
104.8
104.6
103.1
106.4
106.1
103.8
101.5
97.4
104.7
105.6
103.7
104.9
106.1
105.6
111.7
108.1
108.2
104.4
100.0
106.8
107.9
106.0
108.3
109.2
108.5
117.6
114.6
110.7
109.2
106.5
107.9
109.4
107.0
109.2
110.9
110.3
122.4
122.0
113.0
113.6
114.4
109.5
111.4
109.3
112.9
114.9
113.1
124.7
127.2
115.8
118.7
119.4
113.8
117.4
111.6
115.8
116.6
115.7
126.5
125.6
120.6
126.1
124.4
120.5
122.1
113.1
115.2
118.3
118.7
129.3
129.4
124.0
133.4
127.5
129.6
130.7
117.0
114.5
121.1
125.7
133.5
133.6
131.0
140.1
130.7
134.7
25.9
31.3
30.1
15.4
19.4
27.8
7.5
34.6
12.8
15.0
9.8
30.5
43.8
41.7
25.2
24.0
39.8
11.9
53.3
20.3
20.6
14.1
78.8
63.9
92.5
80.3
55.0
61.3
69.4
41.0
93.7
50.8
51.0
59.0
90.5
88.6
94.9
88.7
88.1
93.5
87.9
84.8
91.4
92.2
85.1
85.6
93.7
93.3
95.9
93.0
93.6
96.8
90.3
91.5
92.3
95.6
92.8
93.0
97.6
99.5
97.4
98.1
96.3
99.3
93.1
98.2
95.6
100.0
100.0
100.1
100.6
97.2
101.1
102.3
100.1
102.4
104.5
104.3
102.1
100.9
90.8
100.8
98.5
94.5
101.5
97.9
93.0
97.3
101.9
101.9
95.6
102.9
83.8
99.7
94.8
95.2
97.6
96.4
93.8
94.6
104.7
103.0
93.7
107.0
87.4
102.9
93.5
97.2
94.0
94.7
100.9
95.7
107.4
110.0
94.0
111.4
91.9
105.5
91.9
96.5
93.3
90.5
96.9
91.4
104.3
111.9
94.7
115.2
90.2
108.2
92.8
98.6
95.5
90.2
98.7
89.4
105.1
111.1
96.5
121.7
89.2
112.7
90.2
98.4
90.8
91.4
101.9
89.3
107.4
1114.0
96.6
129.5
88.8
116.1
91.5
100.6
85.4
90.8
100.2
89.1
104.3
113.4
97.9
134.5
86.9
114.5
32.2
11.0
19.4
13.5
20.9
10.4
15.0
16.1
11.2
16.9
15.6
35.3
15.5
27.0
20.3
23.1
17.1
23.3
25.9
17.6
23.1
19.1
78.8
66.1
51.8
88.3
58.9
76.7
59.6
59.0
82.9
63.9
70.2
77.7
90.5
90.4
87.1
72.3
72.6
77.6
73.0
76.1
75.8
82.9
76.8
79.4
93.7
95.6
83.8
89.5
91.3
94.0
87.3
94.1
89.1
95.0
91.3
93.9
97.6
104.9
91.7
92.3
90.8
93.1
87.5
97.5
89.9
95.7
96.3
100.1
100.6
91.0
115.4
95.1
93.2
95.7
98.6
81.6
96.6
88.3
67.8
85.7
98.5
83.6
125.9
94.2
88.3
92.8
98.2
77.9
92.4
90.7
63.2
86.5
94.8
83.8
131.7
105.2
101.1
100.5
114.2
77.9
102.7
105.0
71.3
92.0
93.5
86.1
109.6
98.4
105.0
99.0
111.4
87.9
98.1
107.1
79.8
93.2
91.9
84.2
97.7
81.2
88.6
82.8
93.9
80.9
85.3
101.0
68.8
100.3
92.8
80.4
92.4
79.9
88.9
80.2
93.3
78.8
85.5
100.2
65.3
105.8
90.2
80.0
101.2
77.6
88.0
76.8
91.3
77.3
82.1
103.1
62.5
106.3
91.5
81.8
100.4
66.8
74.8
66.4
76.9
66.6
72.1
94.8
55.2
98.3
-
-
Total hours
United States........................................................
Canada.................................................................
Japan....................................................................
Belgium................................................................
Denmark...............................................................
France..................................................................
Germany...............................................................
Italy........................................................................
Netherlands..........................................................
Norway..................................................................
Sweden................................................................
United Kingdom....................................................
Compensation per hour
Canada..................................................................
Japan.....................................................................
Belgium................................................................
Denmark...............................................................
France..................................................................
Germany..............................................................
Italy........................................................................
Netherlands..........................................................
Norway.................................................................
United Kingdom....................................................
Unit labor costs: National currency basis
Canada.................................................................
Japan...................................................................
Belgium................................................................
Denmark...............................................................
France...................................................................
Germany..............................................................
Italy........................................................................
Sweden................................................................
United Kingdom....................................................
Unit labor costs: U.S. dollar basis
United States........................................................
Canada.................................................................
Japan...................................................................
Denmark..............................................................
France..................................................................
Germany..............................................................
Italy.......................................................................
Netherlands..........................................................
United Kingdom...................................................
-
NOTE: Data tor Germany for years before 1991 are for the former West Germany. Data for 1991 onward are for unified Germany. Dash indicates data not available.
124 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
July 2002
50. Occupational injury and illness rates by industry,1United States
Industry and type of case
1989 1
1990
1992
1991
1993 4 1994 4 1995 4 1996 4 1997 4 1998 4 1999 4 2000 4
P R IV A T E S E C T O R 5
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
-
-
-
-
-
-
-
-
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
-
-
-
-
-
-
-
-
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
-
-
-
-
-
-
-
-
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
-
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
-
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
-
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
-
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
-
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
-
11.6
5.1
-
11.3
5.1
-
10.7
5.0
-
10.1
4.8
-
-
-
12.8
5.6
-
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
-
Furniture and fixtures:
Total cases....................................................................................
Lost workday cases.......................................................................
Lost workdays.................................................................................
16.1
7.2
15.9
7.2
14.6
6.5
-
15.0
7.0
-
13.9
6.4
12.2
5.4
11.4
5.7
-
11.5
5.9
-
11.2
5.9
-
Lost workday cases...........................................................................
Lost workdays...................................................................................
A g r ic u ltu r e , fo r e s tr y , a n d fis h in g 5
Lost workday cases...........................................................................
Lost workdays...................................................................................
M in in g
Lost workday cases...........................................................................
Lost workdays...................................................................................
C o n s tru c tio n
Lost workday cases...........................................................................
Lost workdays....................................................................................
General building contractors:
Lost workday cases...........................................................................
Lost workdays....................................................................................
Heavy construction, except buildinq:
Lost workday cases...........................................................................
Lost workdays....................................................................................
Special trades contractors:
Lost workday cases...........................................................................
Lost workdays...................................................................................
M a n u fa c tu rin g
Lost workday cases...........................................................................
Lost workdays...................................................................................
Durable goods:
Total cases.......................................................................................
Lost workday cases...........................................................................
Lost workdays...................................................................................
-
Lumber and wood products:
-
16.9
7.8
-
-
14.8
6.6
128.4
-
-
12.0
5.8
-
Stone, clay, and qlass products:
Total cases....................................................................................
Lost workday cases........................................................................
Lost workdays................................................................................
15.5
7.4
149.8
15.4
7.3
160.5
14.8
6.8
156.0
13.6
6.1
152.2
13.8
6.3
-
13.2
6.5
-
12.3
5.7
-
12.4
6.0
-
11.8
5.7
-
11.8
6.0
-
10.7
5.4
-
10.4
5.5
-
Primary metal industries:
Total cases....................................................................................
Lost workday cases........................................................................
Lost workdays................................................................................
18.7
8.1
168.3
19.0
8.1
180.2
17.7
7.4
169.1
17.5
7.1
175.5
17.0
7.3
-
16.8
7.2
-
16.5
7.2
-
15.0
6.8
-
15.0
7.2
-
14.0
7.0
-
12.9
6.3
-
12.6
6.3
-
Fabricated metal products:
Total cases....................................................................................
Lost workday cases........................................................................
Lost workdays................................................................................
18.5
7.9
147.6
18.7
7.9
155.7
17.4
7.1
146.6
16.8
6.6
144.0
16.2
6.7
-
16.4
6.7
-
15.8
6.9
-
14.4
6.2
-
14.2
6.4
-
13.9
6.5
-
12.6
6.0
-
11.9
5.5
-
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
-
Electronic and other electrical equipment:
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
-
Transportation equipment:
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
-
Instruments and related products:
Total cases....................................................................................
Lost workday cases........................................................................
Lost workdays................................................................................
5.6
2.5
55.4
5.9
2.7
57.8
6.0
2.7
64.4
5.9
2.7
65.3
5.6
2.5
-
5.9
2.7
-
5.3
2.4
-
5.1
2.3
-
4.8
2.3
-
4.0
1.9
-
4.0
1.8
-
4.5
2.2
-
Miscellaneous manufacturinq 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
-
Industrial machinery and equipment:
See footnotes at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
July 2002
125
Current Labor Sta tistic s:
Injury and Illness
50. Continued—Occupational injury and illness rates by industry,1 United States
In d u s tr y a n d ty p e o f c a s e
1989 1
Nondurable goods:
Total cases.....................................................................................
Lost workday cases..........................................................................
Lost workdays...................................................................................
1990
1992
1 99 1
1993 4
1994 4
19954
1996 4
1997 4
1998 4
1999 4
2000 4
9.9
4.9
9.2
4.6
8.8
4.4
8.2
4.3
7.8
4.2
-
-
-
-
16.3
8.7
15.0
8.0
14.5
8.0
11.6
5.5
107.8
11.7
5.6
116.9
11.5
5.5
119.7
11.3
5.3
121.8
10.7
5.0
-
10.5
5.1
-
Total cases...................................................................................
Lost workday cases.......................................................................
Lost workdays................................................................................
18.5
9.3
174.7
20.0
9.9
202.6
19.5
9.9
207.2
18.8
9.5
211.9
17.6
8.9
17.1
9.2
-
-
-
-
-
Tobacco products:
Total cases...................................................................................
Lost workday cases......................................................................
Lost workdays................................................................................
8.7
3.4
64.2
7.7
3.2
62.3
6.4
2.8
52.0
6.0
2.4
42.9
5.8
2.3
5.3
2.4
5.6
2.6
6.7
2.8
5.9
2.7
-
-
-
7.8
3.6
-
Food and kindred products:
Textile mill products:
Total cases...................................................................................
Lost workday cases......................................................................
Lost workdays................................................................................
10.3
4.2
81.4
9.6
4.0
85.1
10.1
4.4
88.3
9.9
4.2
87.1
9.7
4.1
Apparel and other textile products:
Total cases...................................................................................
Lost workday cases......................................................................
Lost workdays...............................................................................
8.6
3.8
80.5
8.8
3.9
92.1
9.2
4.2
99.9
9.5
4.0
104.6
-
-
-
-
11.0
5.0
125.9
9.9
4.6
9.6
4.5
8.5
4.2
-
6.7
3.0
13.6
7.5
"
12.7
7.3
-
12.4
7.3
-
5.5
2.2
6.2
3.1
-
6.4
3.4
-
-
-
6.7
3.1
7.4
3.4
6.0
3.2
8.2
4.1
-
8.7
4.0
-
-
-
-
-
6.4
3.2
-
9.0
3.8
8.9
3.9
8.2
3.6
7.4
3.3
7.0
3.1
-
6.2
2.6
“
5.8
2.8
-
6.1
3.0
7.3
3.7
6.5
3.4
-
7.1
3.7
-
7.0
3.7
-
7.9
3.8
-
-
-
6.4
3.0
6.0
2.8
5.7
2.7
5.4
2.8
5.0
2.6
5.1
2.6
-
-
-
Paper and allied products:
Total cases...................................................................................
Lost workday cases......................................................................
Lost workdays...............................................................................
12.7
5.8
132.9
12.1
5.5
124.8
11.2
5.0
122.7
Printinq and publishinq:
Total cases...................................................................................
Lost workday cases......................................................................
Lost workdays...............................................................................
6.9
3.3
63.8
6.9
3.3
69.8
6.7
3.2
74.5
7.3
3.2
74.8
6.9
3.1
-
-
-
-
-
-
-
Chemicals and allied products:
Total cases...................................................................................
Lost workday cases......................................................................
Lost workdays...............................................................................
7.0
3.2
63.4
6.5
3.1
61.6
6.4
3.1
62.4
6.0
2.8
64.2
5.9
2.7
5.7
2.8
5.5
2.7
4.8
2.4
4.2
2.1
-
-
-
-
4.4
2.3
-
4.2
2.2
-
4.8
2.3
-
Petroleum and coal products:
Total cases...................................................................................
Lost workday cases......................................................................
Lost workdays................................................................................
6.6
3.3
68.1
6.6
3.1
77.3
6.2
2.9
68.2
5.9
2.8
71.2
5.2
2.5
4.7
2.3
4.8
2.4
4.6
2.5
4.3
2.2
3.9
1.8
4.1
1.8
-
-
-
-
-
-
-
3.7
1.9
-
Rubber and miscellaneous plastics products:
Total cases...................................................................................
Lost workday cases.......................................................................
Lost workdays...............................................................................
16.2
8.0
147.2
16.2
7.8
151.3
15.1
7.2
150.9
14.5
6.8
153.3
13.9
6.5
-
14.0
6.7
12.9
6.5
12.3
6.3
11.9
5.8
11.2
5.8
-
-
-
-
-
10.1
5.5
-
10.7
5.8
-
Leather and leather products:
Total cases...................................................................................
Lost workday cases......................................................................
Lost workdays..............................................................................
13.6
6.5
130.4
12.1
5.9
152.3
12.5
5.9
140.8
12.1
5.4
128.5
12.1
5.5
12.0
5.3
11.4
4.8
10.7
4.5
10.6
4.3
9.8
4.5
9.0
4.3
-
-
-
-
-
-
10.3
5.0
-
Transportation and public utilities
Total cases.....................................................................................
Lost workday cases........................................................................
Lost workdays..................................................................................
9.2
5.3
121.5
9.6
5.5
134.1
9.3
5.4
140.0
9.1
5.1
144.0
9.5
5.4
9.3
5.5
9.1
5.2
8.7
5.1
8.2
4.8
7.3
4.3
4.3
-
-
-
-
-
-
7.3
4.4
-
8.0
3.6
63.5
7.9
3.5
65.6
7.6
3.4
72.0
8.4
3.5
80.1
8.1
3.4
7.5
3.2
6.8
2.9
6.7
3.0
6.5
2.8
-
7.9
3.4
-
-
-
-
-
6.1
2.7
-
7.7
3.8
7.5
3.6
6.6
3.4
6.5
3.2
6.5
3.3
6.3
3.3
Wholesale and retail trade
Lost workday cases........................................................................
Lost workdays..................................................................................
Wholesale trade:
Lost workday cases........................................................................
Lost workdays..................................................................................
-
5.8
-
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
-
-
-
-
-
-
-
8.1
3.4
60.0
8.1
3.4
63.2
7.7
3.3
69.1
8.7
3.4
79.2
8.2
3.3
7.9
3.3
7.5
3.0
6.9
2.8
6.5
2.7
-
-
-
-
6.8
2.9
-
6.1
2.5
“
-
2.0
.9
17.6
2.4
1.1
27.3
2.4
1.1
24.1
2.9
1.2
32.9
2.9
1.2
2.7
1.1
2.4
.9
2.2
.9
.7
.5
-
-
2.6
1.0
-
-
-
-
1.8
.8
-
1.9
.8
-
5.5
2.7
51.2
6.0
2.8
56.4
6.2
2.8
60.0
7.1
3.0
68.6
6.7
2.8
6.5
2.8
6.4
2.8
6.0
2.6
5.6
2.5
5.2
2.4
4.9
2.2
4.9
2.2
-
-
-
-
-
-
-
-
Retail trade:
Lost workday cases........................................................................
Lost workdays..................................................................................
-
-
—
Finance, insurance, and real estate
Lost workday cases........................................................................
Lost workdays.................................................................................
Services
Lost workday cases.........................................................................
Lost workdays.................................................................................. I
1 Data for 1989 and subsequent years are based on the Standard Industrial Class
ification Manual, 1987 Edition. For this reason, they are not strictly comparable with data
for the years 1985-88, which were based on the Standard Industrial Classification
Manual, 1972 Edition, 1977 Supplement.
N = number of injuries and illnesses or lost workdays;
EH = total hours worked by all employees during the calendar year; and
200,000 = base for 100 full-time equivalent workers {working 40 hours per week, 50
weeks per year).
2 Beginning with the 1992 survey, the annual survey measures only nonfatal injuries and
illnesses, while past surveys covered both fatal and nonfatal incidents. To better address
fatalities, a basic element of workplace safety, BLS implemented the Census of Fatal
4 Beginning with the 1993 survey, lost workday estimates will not be generated. As of
1992, BLS began generating percent distributions and the median number of days away
from work by industry and for groups of workers sustaining similar work disabilities.
Occupational Injuries.
6 Excludes farms with fewer than 11 employees since 1976.
Dash indicates data not available.
3 The incidence rates represent the number of injuries and illnesses or lost workdays per
100 full-time workers and were calculated as (N/EH) X 200,000, where:
Monthly Labor Review
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51. Fatal occupational injuries by event or exposure, 1994-2000
Fatalities
Event or exposure1
1994-98
19992
Average
Number
2000
Number
Percent
Total..............................................................................................
6,280
6 054
5 915
100
Transportation incidents.....................................................................
Highway incident..................................................................................
Collision between vehicles, mobile equipment............................
2,618
1,496
714
129
270
161
334
390
322
352
206
228
377
102
56
2,571
1,363
694
136
243
153
279
356
304
399
213
280
370
84
71
43
23
12
2
Overturned.........................................................................................
Aircraft...................................................................................................
Worker struck by a vehicle.................................................................
Water vehicle incident..........................................................................
Railway..................................................................................................
2,640
1,374
662
113
240
136
272
368
280
387
215
304
382
104
78
Assaults and violent acts....................................................................
Homicides..............................................................................................
Shooting.............................................................................................
Stabbing.............................................................................................
Other, including bombing................................................................
Self-inflicted injuries..............................................................................
1,168
923
748
68
107
215
909
651
509
62
80
218
929
677
533
66
78
220
16
11
C o n t a c t w ith o b je c ts a n d e q u ip m e n t .........................................................
984
564
364
60
281
148
124
1,030
585
358
55
302
163
129
1,005
570
357
61
294
157
123
17
10
6
1
5
3
2
686
609
101
146
89
53
721
634
96
153
92
70
734
659
110
150
85
56
12
11
2
3
2
1
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.....................................................................
583
322
136
45
118
66
96
77
533
280
125
51
108
55
92
75
480
256
128
29
100
48
93
74
8
4
2
F ir e s a n d e x p l o s i o n s ..........................................................................................
199
216
177
3
O th e r e v e n ts o r e x p o s u r e s 3.............................................................................
21
27
19
-
Moving in opposite directions, oncoming...................................
Jackknifed or overturned— no collision......................................
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...................................
F a lls .................................................................................................................................
Fall to lower level.................................................................................
Fall from ladder.................................................................................
Fall from roof.....................................................................................
Fall from scaffold, staging................................................................
Fall on same level................................................................................
E x p o s u r e t o h a r m fu l s u b s t a n c e s o r e n v ir o n m e n t s .....................
1 Based on the 1992 bls Occupational injury and Illness
3
4
3
5
6
5
7
4
5
6
1
1
9
1
1
4
-
2
1
2
1
Includes the category "Bodily reaction and exertion."
Classification Structures.
2
The BLS news release issued August 17, 2000, reported a
NOTE:
Totals
for
major categories
may include sub
total of 6,023 fatal work injuries for calendar year 1999. Since
categories not shown separately. Percentages may not add to
then, an additional 31 job-related fatalities were identified,
totals because of rounding.
bringing the total job-related fatality count for 1999 to 6,054.
percent.
Dash indicates less than 0.5
Monthly Labor Review
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