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Speech Backup Alphabetical Files
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Originally Processed With FOIA(s):
FOIA Number:
S
S
FOIA
MARKER
This is not a textual record. This is used as an
administrative marker by the George Bush Presidential
Library Staff.
Record Group/Collection:
George H.W. Bush Presidential Records
Collection/Office of Origin:
Speechwriting, White House Office of
Series:
Speech File Backup Files
Subseries:
Alpha File, 1987-1991
OA/ID Number:
13845
Folder ID Number:
13845-001
Folder Title:
Labor, 1989 [4]
Stack:
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Section:
Shelf:
Position:
G
26
23
3
2
000
OCCUPATIONAL OUTLOOK QUARTERLY
U.S. Department of Labor
Bureau of Labor Statistics
Fall 1987
I
1L
OF LABOR
UNITED STATES OF AMERICA
U.S. Department of Labor
Ann McLaughlin, Secretary
Bureau of Labor Statistics
Janet L. Norwood, Commissioner
The Occupational Outlook Quarterly is published four times
per year by the Occupational Outlook Service, Bureau of Labor
Statistics, U.S. Department of Labor, Washington, D.C. 20212.
Second-class postage paid at Washington, D.C., and at
additional mailing offices. Postmaster: Send address changes to
the Superintendent of Documents, U.S. Government Printing
Office, Washington, D.C. 20402.
For Sale by the Superintendent of Documents, U.S. Govern-
ment Printing Office, Washington, D.C. 20402. Subscription price
$5 domestic, $6.25 foreign; single copy $2 domestic, $2.50
Editor's Note: Articles contributed by authors outside the U.S.
Department of Labor do not necessarily represent the views of
the Department. Questions on occupational and career
information should be sent to Inquiries and Correspondence,
Bureau of Labor Statistics, GAO Building, Washington, D.C.
20212. Communications on editorial matters should be addressed
to the Editor, Occupational Outlook Quarterly, Bureau of Labor
Statistics, Washington, D.C. 20212. Phone: (202) 272-5298.
The Secretary of Labor has determined that the publication of
this periodical is necessary in the transaction of the public
business required by law of this Department.
ISSN 01994786
Bureau of Labor Statistics
Regional Offices
Boston
Suite 1603
Phone:
John F. Kennedy Federal Bldg.
(617) 565-2327
Government Center
Boston, MA 02203
New York
Room 808
Phone:
201 Varick St.
(212) 337-2400
New York, NY 10014
Philadelphia
P.O. Box 13309
Phone:
Philadelphia, PA 19101
(215) 596-1154
Atlanta
1371 Peachtree St., NE.
Phone:
Atlanta, GA 30367
(404) 347-4418
Chicago
9th Floor
Phone:
Federal Office Bldg.
(312) 353-1880
230 South Dearborn St.
Chicago, IL 60604
Dallas
Federal Bldg.
Phone:
525 Griffin St., Room 221
(214) 767-6971
Dallas, TX 72502
Kansas City
911 Walnut St.
Phone:
Kansas City, MO 64106
(816) 347-2481
2000
San Francisco
71 Stevenson St.
Phone:
P.O. Box 3766
(415) 995-5602
San Francisco, CA 94119
000
OCCUPATIONAL OUTLOOK QUARTERLY
Fall 1987
Melvin Fountain, editor
Volume 31, Number 3
Neale Baxter, managing editor
Michael Stanton, staff writer
Richard Mathews, art director
Projections 2000
Elinor Abramson
2
Highlights of the Projections
3
The Changing Labor Force
4
The Changing Demand for Goods and Services
14
Changing Employment in Industries
17
Changing Employment in Occupations
28
The Growing Need for Education
34
2000
job opportunities for occupations covered
in the Occupational Outlook Handbook.
The information in this issue of the
Quarterly provides such readers a
background for analyzing and discussing
job opportunities in individual
occupations and fields of work. In
addition, counselors and others may wish
to reproduce or tear out some of the
more interesting charts and post them
on bulletin boards or use them as wall
charts to draw the interest of students
and others.
The projections were developed
through a series of models that estimate
the growth and changing composition of
the population, labor force, GNP,
industry employment, and occupational
employment. The models used are complex
and relate economic theory and behavior
to a variety of labor market and economic
data. Like any projections, they incorporate
specific assumptions, targets, and goals.
Three alternative sets of assumptions were
(GNP) and its major components.
used by the Bureau in order to develop
Following these are charts on expected
scenarios for high, moderate, and low
changes in the industrial structure of the
growth between 1986-the base year-
economy. Several pages are then devoted
and 2000. The projections presented here
to occupational employment. (The "Job
are from the moderate alternative. Some
by Elinor Abramson
Outlook in Brief," which presents the
of the assumptions and goals of this
projected change for each of the
alternative are a decline in the unemploy-
The year 2000 has just come into range.
occupations covered by the Occupational
ment rate from 7 percent in 1986 to 6
As readers of the Quarterly know,
Outlook Handbook, will appear in the
percent in 2000, a gradual curtailment in
the Bureau of Labor Statistics projects
Spring 1988 issue of the Quarterly.)
the rise of defense expenditures in the
occupational employment and other
The final section provides data on the
1990's, an increase in the rate of growth
economic activity 10 to 15 years in the
educational attainment of the population
of State and local government expenditures
future. The newest projections have 2000
and its relationship to occupational
on education spurred by the growth of
as their target.
employment.
the school age population, and a decline
This issue of the Quarterly is devoted
The projections have uses in many
in the value of the dollar combined with
to a graphic summary of these
activities, including planning curriculum
greater productivity, which will create a
projections, along with historical data
and program offerings in educational
more favorable balance of trade than in
from 1972 and 1986. The presentation
institutions, formulating policy by
recent years. In more general terms, the
begins with a series of charts on the
government agencies, and conducting
projections also assume no major war, oil
labor force and projected changes in
market research and personnel planning
embargo, or other economic shocks or
its composition. Next are charts on
by business organizations. However,
catastrophes over the projection period.
economic growth-gross national product
readers of the Quarterly are probably
The September 1987 issue of the
most familiar with their use in career
Monthly Labor Review contains a series
Elinor Abramson is an economist in the Division
guidance because the Bureau's projections
of articles on the projections and their
of Occupational Outlook, BLS.
provide the basic data for discussions of
implications, as well as additional data.
2
Occupational Outlook Quarterly/Fall 1987
Highlights of the Projections
The Changing Demand
Changing Employment in Occupations
for Goods and Services
Occupations will grow
The Changing Labor Force
The gross national product (GNP),
an average of 19 percent;
a measure of demand
of the broad occupational groups,
The labor force-
for goods and services,
technician and service occupations
job holders and job seekers-
will exceed $5 trillion in 2000.
will grow the fastest.
will continue to grow,
rising 21 million, from 1986 to 2000.
Every major category of GNP will grow.
A small number of occupations
The rate of growth-18 percent-will be
A larger share of
will account for more than
personal consumption expenditures
one-half of total job growth.
slower than during the previous 14 years.
-the largest category of GNP-
Twelve of the fastest growing
The labor force will look very different
will be spent on services than on goods.
occupations provide health services.
in 2000 than in 1986.
Changes in technology and business
Younger and older workers will become
Changing Employment in Industries
practices and increased use of imports
a smaller part of the labor force.
will cause some occupations to decline.
Women will continue to increase
Goods and services are produced
their share of the labor force.
in industries classified by sector,
division, and group.
The Growing Need for Education
The proportion of whites
in the labor force will decrease;
Driven by a rising demand for services,
The projected growth
the proportion of blacks and of
the service-producing sector
of the broad occupational groups
Asians and others will increase.
will provide 20 million new jobs.
shows the increasing need for education.
Asians and others will have
Workers with more education
the fastest percentage growth
Every industry division
also earn more
between 1986 and 2000, although
in the service-producing sector
their numerical growth will be small.
will continue to grow.
and are less likely to be unemployed.
The Hispanic labor force
Four divisions in the service-producing
will grow very rapidly.
sector will grow faster than average.
Two divisions-services and retail trade-
Like the labor force, employment
will provide 75 percent of job growth.
will continue to grow, although
more slowly than in the recent past.
In the services division,
health and business services will account
for more than one-half the growth.
In the retail trade division,
eating and drinking places will account
for about one-half the growth.
In the goods-producing sector,
construction is the only division
that will grow as a whole.
Projections
2000
The Changi. 1g
Labor Force
The Changing Labor Force
The labor force-
Labor force
(each figure = 1 million)
139
jobholders
million
and jobseekers-
will continue to grow,
118
rising 21 million,
million
from 1986 to 2000.
87
million
1972
1986
2000
1986
The rate of growth-
18 percent-
will be slower
Labor force growth
35.4
(percent)
than during the
previous 14 years.
1972
17.8
1986
2000
Growth will slow largely because population growth has slowed.
Occupational Outlook Quarterly/Fall 1987 5
The Changing Labor Force
The labor force
will look very different
in 2000 than in 1986.
People under 25 will have a.
smaller share.
People 55 and over will have a
smaller share.
Women will have a larger share.
Blacks will have a larger share.
Asians and others will have a
larger share.
Hispanics will have a larger
share.
6
Occupational Outlook Quarterly/Fall 1987
The Changing Labor Force
Younger and older workers will become
a smaller part of the labor force.
Share of the
labor force
73
by age (percent)
67
60
23
20
16
17
25 to 54 years
13
11
16 to 24 years
55 years and older
1972
1986
2000
The share of workers.
16 to 24 years of age will decline because the population of
their age group will decline.
25 to 54-which includes the large baby-boom
generation-will increase.
55 and over will decline because their labor force
participation rate will continue to decline, even though the
population of their age group will increase.
8
Occupational Outlook Quarterly/Fall 1987
The Changing Labor Force
Women will continue to increase
their share of the labor force.
Share of the labor force by sex (percent)
61
56
53
47
44
39
Men
Women
1972
1986
2000
The number of women in the labor force will rise from
52 million to 66 million.
The number of women will rise twice as fast as the number of
men because the proportion of women who participate in the
labor force-especially women 25 to 54 years of age-will
continue to rise.
Occupational Outlook Quarterly/Fall 1987 9
The Changing Labor Force
The proportion of whites in the labor force
will decrease; the proportion of blacks
and of Asians and others will increase.
Share of the labor force by race (percent)
88
86
84
10
11
12
White
Black
4
Asian
1
3
and other
1972
1986
2000
10
Occupational Outlook Quarterly/Fall 1987
The Changing Labor Force
Asians and others will have the fastest percentage
growth between 1986 and 2000, although their
numerical growth will be small.
Percent growth
71
Numerical growth (millions)
29
2.4
14.9
Asian
20.9
and other
3.6
Black
15
White
18
Total
Blacks will grow faster than whites
because of higher birth rates.
Asians and others will grow faster than whites
because of immigration and higher birth rates.
Occupational Outlook Quarterly/Fall 1987 11
The Changing Labor Force
The Hispanic labor force
74
will grow very rapidly.
Labor force growth,
1986-2000
(percent)
Hispanic
18
Total
1986
2000
The Hispanic labor force will rise
from 8 million in 1986 to 14 million in 2000.
Growth will occur because of immigration
and the rise in the native-born Hispanic population.
As a result of this very rapid growth,
the Hispanic share of the labor force
will increase from 7 percent in 1986
to 10 percent in 2000.
12
Occupational Outlook Quarterly/Fall 1987
The Changing Labor Force
Like the labor force,
employment will continue to grow,
although more slowly
than in the recent past.
Employment
(millions)
80
106
124
Wage and
salary
workers
Self-employed
and unpaid
family workers
6
7
8
1972
1986
2000
Employment will increase by 21 million,
a rise of 19 percent.
Almost all of the employment growth
will be in wage and salary jobs.
Between 1972 and 1986, employment growth
was bigger numerically (27 million)
and showed a larger percentage increase (32 percent)
than is projected for 1986-2000.
Occupational Outlook Quarterly/Fall 1987 13
Projections
2000
The Changing
Demand for
Goods and Services
The Changing Demand for Goods and Services
The gross national product (GNP),
a measure of demand
$5,161 billion
for goods and services,
will exceed $5 trillion in 2000.
Gross national product,
5
5
1982 dollars
$3,679 billion
I
NOTE
5
5
THE UNITED STATES OFAMERICA
94535022G
WASHINGTON.D.C.
1
$2,609 billion
5
5
NOTE
NITED STATES OFAMERICA
E94535022G
WASHINGTON,D.C.
ONE DO AR
OPAMERICA
E94535022G
WASHINGTON.D.C.
ONE DOLLAR
NOTE TENDER
DEBTS PUBLIC PRIVATE
E94535022G
chatherine Devalor Ortige
DLLAR
5
5
EGAL TENDER
BLIC AND PRIVATE
2G
1972
1986
2000
Every major category
of GNP will grow.
Billions of 1982 dollars
Percent growth,
1986
2000
1986-2000
Gross national product
$3,679
$5,161
40
Personal consumption
expenditures
2,419
3,429
42
Gross domestic
660
932
41
private investment
Exports
371
635
71
Imports*
521
733
41
Federal, State, and
748
local government
898
20
*Imports are subtracted
from the other
major categories.
The percentage growth of GNP between 1986 and 2000
(40 percent) is about the same as for the 1972-86 period.
Exports will increase faster than any other major category
of GNP, but their value will still be less than that of imports.
Personal consumption expenditures account for two-thirds
of total GNP.
Occupational Outlook Quarterly/Fall 1987 15
The Changing Demand for Goods and Services
A larger share of personal consumption expenditures
will be spent on services than on goods.
Personal consumption expenditures (billions of 1982 dollars)
1972
$896
$756
1986
Goods include
automobiles, furniture,
Services include health care,
$1,241
$1,177
food, clothing, gasoline,
education, utilities, and
and fuel oil.
transportation.
2000
The growth of the economy
as a whole will result in more
$1,644
$1,786
money being spent on
goods even though they will
account for a smaller share of
personal consumption
expenditures.
16
Occupational Outlook Quarterly/Fall 1987
Projections
2000
Changing
Employment
in Industries
WOOD
Changing Employment in Industries
Goods and services are produced in industries
classified by sector, division, and group.
All industries can be
divided into two sectors,
service-producing
Goods-producing industries
Service-producing industries
and goods-producing.
Agriculture, forestry,
and fishing
Each sector can
The
Mining
The
be further split
goods-producing
service-producing
into divisions.
sector
Construction
sector
Manufacturing
Changing Employment in Industries
Transportation,
communications, and
public utilities
Business services
Wholesale trade
Personal, automotive,
and other services
Retail trade
Each division
The
Legal services
has several groups
services
Finance, insurance,
of industries.
division
and real estate
Education services
Services
Social services
Government
Health services
Offices of physicians
Offices of dentists
Offices of osteopathic
physicians
Offices of other health
practitioners
And a group
The
of industries
health
Nursing and personal
has many
services
care facilities
individual industries.
industries
Hospitals
Medical and dental
laboratories
Outpatient care
facilities
Health and allied
services not
elsewhere classified
Occupational Outlook Quarterly/Fall 1987 19
Changing Employment in Industries
95.7
Driven by rising demand for services,
the service-producing sector
will provide 20 million new jobs.
Wage and salary
75.6
employment
(millions)
51.5
27.9
27.2
27.6
Service-producing
Goods-producing
1972
1986
2000
Employment trends among the industry divisions in the
service-producing and goods-producing sectors will be very
different, reflecting the types of goods and services
purchased by individuals, businesses, and governments.
20
Occupational Outlook Quarterly/Fall 1987
ASD
Changing Employment in Industries
33.8
Wage and salary employment
22.7
(millions)
2000
1986
5.7
17.8
1972
5.2
Transportation,
4.5
communications,
and public utilities
7.3
5.7
11.8
23.8
4.1
Wholesale trade
7.9
13.8
6.3
Retail trade
18.3
16.7
3.9
13.3
Finance,
insurance, and
real estate
Every industry division
in the service-producing sector
will continue to grow.
Services
Government
1972 1986 2000
22
Occupational Outlook Quarterly/Fall 1987
Changing Employment in Industries
Four divisions in the service-producing sector
will grow faster than average.
Division
Percent growth, 1986-2000
Transportation,
communications,
9.1
and public utilities
Wholesale trade
26.7
Retail trade
27.2
Finance, insurance,
25.7
and real estate
Services
42.0
Government
9.7
Two divisions-
services and
retail trade
will provide
Retail
75 percent of
trade
job growth.
24.6
percent
Services
50.6
percent
All other
industry
divisions
24.8
percent
Share of total job growth, 1986-2000
Occupational Outlook Quarterly/Fall 1987 23
Changing Employment in Industries
In the services division,
health and business services
will account for more than one-half the growth.
Employment growth,
1986-2000,
shown as a share
of total growth
in the services
division.
Health services
Business services
3.2 million
3.3 million
Legalillion services
Other
services
Social
1.5 million
services
1.3 million
The fastest growing individual industries in the economy
are in this division. Among them are the following:
Computer and data processing services
Outpatient care facilities
Offices of physicians, including osteopaths
Personnel supply services, including temporary
help supply services
Each of these four industries will increase 70 percent
or more in employment.
24
Occupational Outlook Quarterly/Fall 1987
Changing Employment in Industries
In the retail trade division,
eating and drinking places
will account for about one-half the growth.
Employment growth,
1986-2000,
shown as a share
of total growth
in the retail trade
division.
Eating and drinking places
2.5 million
Grocery
stores
0.6 million
Other
retail
trade
1.8 million
Employment in eating and drinking places will grow more than
in any other industry. This industry and nine others will account
for more than 50 percent of all job growth between 1986 and 2000.
Here are the 10 industries and the numerical growth of each:
Eating and drinking places
2,486,000
Miscellaneous business services
1,342,000
Education, public and private
971,000
Offices of physicians, including osteopaths
886,000
Nursing and personal care facilities
847,000
Personnel supply services
834,000
Wholesale trade, machinery and equipment
614,000
Computer and data processing services
612,000
Grocery stores
598,000
Legal services
519,000
Occupational Outlook Quarterly/Fall 1987 25
THENT
an
Changing Employment in Industries
In the goods-producing sector,
construction is the only division that
will grow as a whole.
Employment (millions)
19.2
19.0
18.2
1972
1986
3.5
2000
3.3
2.9
Agriculture,
forestry, and fishing
0.6
0.8
0.7
Mining
5.8
4.9
3.9
Construction
Manufacturing
1972 1986 2000
Despite the overall employment decline in
manufacturing, some manufacturing industries will grow;
the following will each add at least 50,000 workers:
Miscellaneous plastic products
Office computing and accounting machines
Commercial printing and business forms
Newspapers
Occupational Outlook Quarterly/Fall 1987 27
Projections
2000
Changing
Employment
in Occupations
Changing Employment in Occupations
Different industries employ workers
who have different occupational skills.
Hospitals require registered nurses, nursing aides, and
workers in other occupations that provide health care.
Construction requires bricklayers, carpenters, and
other building trades workers.
Consequently, industry employment growth has a
significant effect on the growth of occupations.
Changes in the way an industry produces goods or
services also affect occupational employment.
For example, the increasing use of computers to
process records increases the need for computer
programmers and computer systems analysts, but
reduces the need for recordkeeping clerks.
The opportunities provided by occupational
employment growth can be viewed in two ways:
Rate of growth (percent)
Numerical increase of workers.
Occupations with fast growth rates generally offer good
opportunities.
However, large occupations, such as retail sales worker,
may offer many more jobs than a small, fast-
growing occupation, such as medical assistant.
Both rate of growth and numerical change should be
looked at to assess future job prospects.
1,201,000
Percent change,
Numerical change,
1986-2000
1986-2000
90
34
Retail
sales workers
119,000
Medical assistants
Occupational Outlook Quarterly/Fall 1987 29
Changing Employment in Occupations
Occupations will grow an average of 19 percent.
Of the broad occupational groups, technician
and service occupations will grow the fastest.
Percent change in employment, 1986-2000
Technicians and
related support workers
38
Service workers
31
Sales workers
30
Executive, administrative,
29
and managerial workers
Professional workers
27
Precision production,
craft, and repair workers
12
Administrative support
11
workers, including clerical
Operators, fabricators,
3
and laborers
Agriculture, forestry,
and fishing workers
-5
Employment will increase most, in terms of number of workers, in
service and sales occupations, but professional and executive
occupations will also add millions of jobs.
The following is a list of the major occupational groups and the
number of jobs each will add between 1986 and 2000:
Service workers
5,381,000
Sales workers
3,728,000
Professional workers
3,655,000
Executive, administrative, and managerial workers
3,033,000
Administrative support workers, including clerical
2,258,000
Precision production, craft, and repair workers
1,669,000
Technicians and related support workers
1,403,000
Operators, fabricators, and laborers
443,000
Agriculture, forestry, and fishing workers will decline by 163,000
over the projected period.
30
Occupational Outlook Quarterly/Fall 1987
Changing Employment in Occupations
A small number of occupations will account
for more than one-half of total job growth.
Percent
Numerical growth, 1986-2000
growth,
1986-2000
Sales workers, retail
1,201,000
34
Waiters and waitresses
752,000
44
Registered nurses
612,000
44
Janitors and cleaners
604,000
23
General managers and
582,000
top executives
24
Cashiers
575,000
27
Truckdrivers
525,000
24
General office clerks
462,000
20
Food counter and related
449,000
workers
30
Nursing aides, orderlies,
433,000
and attendants
35
Secretaries
424,000
13
Guards
383,000
48
Accountants and auditors
376,000
40
Computer programmers
335,000
70
Food preparation workers
324,000
34
Teachers, kindergarten
299,000
and elementary
20
Receptionists and
282,000
information clerks
41
Computer systems
251,000
analysts
76
Cooks, restaurant
240,000
46
Licensed practical nurses
238,000
38
Gardeners and
groundskeepers
238,000
31
Maintenance repairers,
general utility
232,000
22
Stock clerks, sales floor
225,000
21
Clerical supervisors and
205,000
managers
21
Dining room attendants
197,000
and related workers
26
Electrical and
192,000
electronics engineers
48
Lawyers
191,000
36
Most of these occupations are growing faster than the average
for all occupations.
Those growing less rapidly are very large, SO that the numerical
increase is still great.
Occupational Outlook Quarterly/Fall 1987 31
Changing Employment in Occupations
Twelve of the twenty fastest growing occupations
provide health services.
Growth of employment, 1986-2000 (percent)
Numerical
Average of all occupations
growth,
1986-2000
Paralegal personnel
104
64,000
Medical assistants
90
119,000
Physical therapists
87
53,000
Physical and corrective
82
therapy assistants and aides
29,000
Data processing
80
equipment repairers
56,000
Home health aides
80
111,000
Podiatrists
77
10,000
Computer systems
76
analysts
251,000
Medical records
75
technicians
30,000
Employment
71
interviewers
54,000
Computer programmers
70
335,000
Radiologic technologists
65
and technicians
75,000
Dental hygienists
63
54,000
Dental assistants
57
88,000
Physician assistants
57
15,000
Operations and
54
systems researchers
21,000
Occupational therapists
52
15,000
Peripheral electronic data
processing equipment
51
operators
24,000
Data entry keyers,
51
composing
15,000
Optometrists
49
18,000
The number of paralegal personnel-the fastest growing
occupation-is expected to double.
Four of the twenty fastest growing occupations are in the
computer field.
32
Occupational Outlook Quarterly/Fall 1987
Changing Employment in Occupations
Changes in technology and business practices
and increased use of imports
will cause some occupations to decline.
Numerical
Decline in employment, 1986-2000 (percent)
decline in
employment,
1986-2000
Electrical and
54
electronic assemblers
133,000
Electronic semiconductor
51
processors
15,000
Railroad conductors
41
and yardmasters
12,000
Railroad brake system
40
and switch operators
17,000
Gas and petroleum plant
34
and system occupations
11,000
Industrial truck and
34
tractor operators
143,000
Shoe sewing-machine
32
operators and tenders
9,000
Station installers and
repairers, telephone
32
18,000
Chemical equipment
controllers, operators,
30
and tenders
21,000
Chemical plant and
30
system operators
10,000
Stenographers
28
50,000
Farmers
28
332,000
Statistical clerks
26
19,000
Textile draw-out and
25
winding machine operators
55,000
Central office and PBX
23
installers and repairers
17,000
Farm workers
20
190,000
Coil winders, tapers, and
19
installers
6,000
Central office
18
operators
8,000
Directory assistance
operators
18
5,000
Compositors, typesetters,
17
and arrangers, precision
5,000
Most of these occupations have been declining for several
years.
Occupational Outlook Quarterly/Fall 1987
33
Projections
2000
The Growing
Need for
Education
TOWER
A-B
The Growing Need for Education
The projected growth of the
broad occupational groups shows
the increasing need for education.
Educational attainment of workers age 25 to 54
Change in
by occupational group, March 1986 (percent)
employment, 1986-2000
(percent)
Less than
4 years
1 to 3
4 or more
4 years of
of
years of
years of
high school
high school
college
college
All workers,
15
age 25 to 54
40
20
26
19
Executive,
administrative, and
5
26
23
47
29
managerial workers
Professional
1
8
15
workers
76
27
Technicians and
related support
3
30
35
33
38
workers
Sales workers
8
40
24
28
30
Administrative
support workers,
5
55
27
13
11
including clerical
Service workers
28
49
17
7
31
Precision
production, craft,
22
53
19
7
12
and repair workers
Operators,
fabricators, and
34
50
13
4
3
laborers
Agriculture, forestry,
and fishing workers
33
45
13
9
-5
Occupations in which a large proportion of workers have college
training are among the fastest growing.
Occupations in which a large proportion of workers have less
than 4 years of high school are generally among the slowest
growing.
Occupational Outlook Quarterly/Fall 1987 35
The Growing Need for Education
$33,443
$23,154
Average annual earnings, 1986
Workers with
more education
$19,844
also earn more. ..
$16,605
4 or more
1 to 3
4 years
Less than
years of
years
of
4 years of
college
of college
high school
high school
2.3
4.5
6.9
11.6
.and are less
likely to be
unemployed.
Unemployment rate
for workers age 25 to 64,
March 1986
36
Occupational Outlook Quarterly/Fall 1987
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United
Years of
States
of Labor 75
Working for
Department
America's
Future
UNEMPLOYMENT RATES IN EIGHT COUNTRIES, CIVILIAN LABOR FORCE BASIS,
APPROXIMATING U.S. CONCEPTS, SEASONALLY ADJUSTED, 1972-1989
UNITED
UNITED
PERIOD
STATES
CANADA
JAPAN
FRANCE
GERMANY
ITALY
SWEDEN
KINGDOM
(1)(2)
1972
5.6
6.2
1.4
2.9
0.7
3.8
2.7
4.2
1973
4.9
5.5
1.3
2.8
0.7
3.7
2.5
3.2
1974
5.6
5.3
1.4
2.9
1.6
3.1
2.0
3.1
1975
8.5
6.9
1.9
4.1
3.4
3.4
1.6
4.6
1976
7.7
7.1
2.0
4.5
3.4
3.9
1.6
5.9
1977
7.1
8.1
2.0
5.1
3.5
4.1
1.8
6.4
1978
6.1
8.3
2.3
5.3
3.3
4.1
2.2
6.3
1979
5.8
7.4
2.1
6.0
3.0
4.4
2.1
5.4
1980
7.1
7.5
2.0
6.4
2.9
4.4
2.0
7.0
1981
7.6
7.5
2.2
7.6
4.1
4.9
2.5
10.5
1982
9.7
11.0
2.4
8.3
5.8
5.4
3.1
11.3P
1983
9.6
11.8
2.7
8.5
(3)7.1
5.9
3.5
11.9P
1984
7.5
11.2
2.8
10.0
7.4
5.9
3.1
11.7P
1985
7.2
10.5
2.6
10.4
7.5
6.0
2.8
11.2P
1986
7.0
9.5
2.8
10.6
7.0
(3)7.5
2.6
11.2P
1987
6.2
8.8
2.9
10.8
7.1R,P
7.9
(3)1.9P
10.3P
1988
5.5
7.8
2.5
10.5P
7.1R,P
7.9P
1.6P
8.3P
I
5.7
7.8
2.7
10.6
7.1R
7.9
1.7
9.0
II
5.5
7.7
2.5
10.5
7.2R
7.9
1.6
8.6
III
5.5
7.8
2.6R
10.6
7.1R
8.0
1.6
8.0
IV
5.3
7.7
2.4
10.4
7.0R
7.9R
1.4
7.6
NOV
5.4
7.7
2.4
10.3
7.0R
1.4
7.6
DEC
5.3
7.6
2.3
10.5
6.8R
1.2
7.3
1989
JAN
5.4
7.6
10.5
6.6
1.5
7.2
R = REVISED.
P
= PRELIMINARY.
(1) QUARTERLY RATES ARE FOR THE FIRST MONTH OF THE QUARTER. (2) MANY ITALIANS REPORTED AS UNEMPLOYED DID NOT
ACTIVELY SEEK WORK IN THE PAST 30 DAYS, AND THEY HAVE BEEN EXCLUDED FOR COMPARABILITY WITH U.S. CONCEPTS.
INCLUSION OF SUCH PERSONS WOULD ABOUT DOUBLE THE ITALIAN UNEMPLOYMENT RATE IN 1985 AND EARLIER YEARS AND
INCREASE IT TO 11-12 PERCENT IN 1986-1988. (3) BREAK IN SERIES. BASED ON THE FORMER SERIES, THE GERMAN RATE
FOR 1983 WAS 7.4 PERCENT AND THE ITALIAN RATE FOR 1986 WAS 6.3 PERCENT. FOR SWEDEN, THE 1986 RATE BASED ON THE
NEW SERIES WAS 2.2 PERCENT.
NOTE: QUARTERLY AND MONTHLY FIGURES FOR FRANCE, GERMANY, AND THE UNITED KINGDOM ARE CALCULATED BY APPLYING
ANNUAL ADJUSTMENT FACTORS TO CURRENT PUBLISHED DATA AND THEREFORE SHOULD BE VIEWED AS LESS PRECISE INDICATORS
OF UNEMPLOYMENT UNDER U.S. CONCEPTS THAN THE ANNUAL FIGURES. FOR FURTHER QUALIFICATIONS AND HISTORICAL DATA,
SEE BLS BULLETIN 1979, "INTERNATIONAL COMPARISONS OF UNEMPLOYMENT" AND ITS METHODS AND STATISTICAL SUPPLEMENTS.
SOURCE: BUREAU OF LABOR STATISTICS, U.S. DEPARTMENT OF LABOR, MARCH 1989.
UNEMPLOYMENT RATES IN EIGHT COUNTRIES, TOTAL LABOR FORCE BASIS,
APPROXIMATING U.S. CONCEPTS, SEASONALLY ADJUSTED, 1972-1989
UNITED
UNITED
PERIOD
STATES
CANADA
JAPAN
FRANCE
GERMANY
ITALY
SWEDEN
KINGDOM
(1)(2)
1972
5.5
6.2
1.4
2.8
0.7
3.7
2.7
4.2
1973
4.8
5.5
1.3
2.7
0.7
3.6
2.4
3.2
1974
5.5
5.3
1.4
2.8
1.6
3.1
2.0
3.1
1975
8.3
6.9
1.9
4.0
3.3
3.4
1.6
4.5
1976
7.6
7.1
2.0
4.4
3.4
3.8
1.6
5.9
1977
6.9
8.0
2.0
4.9
3.4
4.0
1.8
6.3
1978
6.0
8.3
2.3
5.2
3.3
4.1
2.2
6.2
1979
5.8
7.4
2.1
5.9
2.9
4.3
2.0
5.3
1980
7.0
7.4
2.0
6.3
2.8
4.3
2.0
6.9
1981
7.5
7.5
2.2
7.4
4.0
4.8
2.5
10.4
1982
9.5
10.9
2.4
8.1
5.7
5.3
3.1
11.3P
1983
9.5
11.8
2.7
8.3
(3)7.0
5.8
3.4
11.8P
1984
7.4
11.2
2.7
9.7
7.2
5.8
3.1
11.6P
1985
7.1
10.4
2.6
10.2
7.3
5.9
2.8
11.1P
1986
6.9
9.5
2.8
10.4
6.9R
(3)7.4
2.6
11.2P
1987
6.1
8.8
2.9
10.5
7.0R,P
7.7
(3)1.9P
10.2P
1988
5.4
7.7
2.5
10.3P
7.0R,P
7.8P
1.6P
8.3P
I
5.6
7.8
2.7
10.3
7.0R
7.8
1.7
9.0
II
5.4
7.6
2.5
10.3
7.0R
7.8
1.6
8.6
III
5.4
7.8
2.6R
10.4
7.0R
7.8
1.6
8.0
IV
5.3
7.7
2.4
10.2
6.8R
7.8
1.4
7.5
NOV
5.3
7.7
2.4
10.1
6.9R
1.4
7.5
DEC
5.3
7.6
2.3
10.3
6.7R
1.2
7.3
1989
JAN
5.4
7.5
10.2
6.5
1.5
7.1
R = REVISED.
P = PRELIMINARY.
(1) QUARTERLY RATES ARE FOR THE FIRST MONTH OF THE QUARTER. (2) MANY ITALIANS REPORTED AS UNEMPLOYED DID NOT
ACTIVELY SEEK WORK IN THE PAST 30 DAYS, AND THEY HAVE BEEN EXCLUDED FOR COMPARABILITY WITH U.S. CONCEPTS.
INCLUSION OF SUCH PERSONS WOULD ABOUT DOUBLE THE ITALIAN UNEMPLOYMENT RATE IN 1985 AND EARLIER YEARS AND
INCREASE IT TO 11-12 PERCENT IN 1986-1988. (3) BREAK IN SERIES. BASED ON THE FORMER SERIES, THE GERMAN RATE
FOR 1983 WAS 7.3 PERCENT AND THE ITALIAN RATE FOR 1986 WAS 6.2 PERCENT. FOR SWEDEN, THE 1986 RATE BASED ON THE
NEW SERIES WAS 2.2 PERCENT.
NOTE: QUARTERLY AND MONTHLY FIGURES FOR FRANCE, GERMANY, AND THE UNITED KINGDOM ARE CALCULATED BY APPLYING
ANNUAL ADJUSTMENT FACTORS TO CURRENT PUBLISHED DATA AND THEREFORE SHOULD BE VIEWED AS LESS PRECISE INDICATORS
OF UNEMPLOYMENT UNDER U.S. CONCEPTS THAN THE ANNUAL FIGURES. FOR FURTHER QUALIFICATIONS AND HISTORICAL DATA,
SEE BLS BULLETIN 1979, "INTERNATIONAL COMPARISONS OF UNEMPLOYMENT" AND ITS METHODS AND STATISTICAL SUPPLEMENTS.
SOURCE: BUREAU OF LABOR STATISTICS, U.S. DEPARTMENT OF LABOR, MARCH 1989.
H
62
R26
R-3331
DOL/RC
Private Sector Training
Who Gets If and What Are Its Effects?
Lee A. Lillard, Hong W. Tan
Rand
The research described in this report was sponsored by the U.S.
Department of Labor under Contract No. J-9-M-2-0170.
ISBN: 0-8330-0719-X
The Rand Publication Series: The Report is the principal
publication documenting and transmitting Rand's major
research findings and final research results. The Rand Note
reports other outputs of sponsored research for general
distribution. Publications of The Rand Corporation do not
necessarily reflect the opinions or policies of the sponsors of
Rand research.
Published by The Rand Corporation
H
62
M.
R26
K-3331
11
LOL/RC
report ; 'R2
R-3331-DOL/RC
Private Sector Training
Who Gets It and What Are Its Effects?
Lee A. Lillard, Hong W. Tan
March 1986
Prepared for the
U.S. Department of Labor
RAPY
Rand
1700 MAIN STREET
P.O. BOX 2138
SANTA MONICA, CA 90406-2138
- iii -
PREFACE
This report was funded under Contract No. J-9-M-3-0170 with the
Office of the Assistant Secretary for Policy, Evaluation, and Research,
U.S. Department of Labor. The study uses measures of reported training
from the Current Population Survey, three cohorts from the National
Longitudinal Surveys, and the Employment Opportunities Pilot Projects
Survey to draw a broad picture of post-school training in the United
States. The research findings on who receives training, how much, and
why, and on how training affects earnings and employment, should be of
interest both to employers and to decisionmakers entrusted with
developing training policy.
- vi -
"type" we mean the nature or content of the training (such as
managerial, professional and technical, skilled manual, and clerical).
The CPS elicited information on the sources of training needed to get
the current (or last) job, and on training to improve skills on the
current job. In the NLS, repeated measures of both sources and types of
training taken since the last interview are available.
We use several techniques to analyze the data. We document the
amounts of training for each demographic group through simple tabular
analysis. To study the determinants of training, we estimate probit
models for each source and type of training, controlling for a
comprehensive set of covariates such as personal attributes, labor
market experience, job characteristics, the industry rate of technical
change, and local labor market conditions. Finally, we use multiple
regression and nonlinear methods to examine how training affects
subsequent earnings and the likelihood of unemployment.
We found that post-school training is quite pervasive. Nearly 40
percent of both men and women in the CPS reported having taken training
to improve skills on the current job, a proportion that rises with time
on the job. When we average across job tenure, we find that training in
company programs, OJT, and training in regular schools are reported 10
to 15 percent of the time as a source of job-relevant training. For a
given two-year period in the NLS, the fractions of young men, career
women, and mature men reporting some training were approximately 30, 24,
and 10 percent, respectively. For all three populations, the employer
was the single most important source of training. By comparison, only
11 percent of the disadvantaged (EOPP) sample reported some training
over a similar time interval, with a relatively low proportion getting
training from company sources. Their training experience resembled that
of the sample of NLS women with low attachment to the labor force.
We reached the following conclusions regarding the determinants of
training:
Formal schooling is an important determinant of post-school
investments in training. In fact, those sources of "training"
are strongly complementary. The likelihood of getting most
kinds of training rises with the level of schooling attainment,
- vii -
with the exception of the most educated workers
(postgraduates), a finding that holds true for both men and
women. The lack of a formal education therefore limits access
to post-school investments in most kinds of job training and to
resulting improvements in productivity and income.
Training propensity varies systematically over the life-cycle.
The likelihood of getting most kinds of training is low in the
first five years in the labor market, coinciding with an
initial period of job search. In the absence of job
attachment, this likelihood continues to fall with work
experience but at a slower pace; however, the likelihood of
training then rises with time on the job. Although some job
switching is likely to be advantageous, the inability to
develop enduring job attachment increasingly reduces the
likelihood of getting training, especially for older workers.
Nonwhite men, in particular, are significantly less likely to
get most kinds of job training even after controlling for
observable worker attributes. However, no significant racial
differences are apparent among women. These relationships
suggest that job training may be partly responsible for the
observed earnings differentials among white and nonwhite men
and the absence of race differences among women.
Rapid technical change in the industry of employment increases
the probability of getting managerial training and training
from in-house sources such as company programs or OJT,
especially for the most educated, but decreases the probability
of getting professional, technical, and semiskilled manual
training, or training from external sources such as business,
technical, and traditional schools. Again, these findings are
replicated for females. It thus appears that rapid rates of
technical change are associated with an increased reliance on
in-house training, possibly because skills specific to new
technologies are not readily available outside the firm.
The transferability of prior work skills diminishes when new
jobs are created in industries with rapid technical change.
Both men and women working in high-tech industries are
- viii -
significantly less likely to report that previous company
training and OJT were important in getting the current (or
last) job. Only among postgraduates is previous OJT important.
The likelihood of getting most kinds of training is smaller in
local labor markets.with persistently high unemployment or
greater cyclic volatility relative to the nation as a whole.
Over time, a pro-cyclical pattern of training emerges for the
NLS samples of mature men and career women. Periods of high
national unemployment tend to be associated with a greater
likelihood of training from company sources, especially for
professional and technical types of training. One possible
interpretation is that employers are more likely to retrain
older workers during periods of slack economic activity when
the opportunity cost of their time is low.
To examine how training affects subsequent labor market outcomes,
we used the CPS and the NLS Young Men surveys to examine the
relationships between training, the level and growth of earnings, and
the probability of experiencing an unemployment spell in the previous
year. The principal findings are as follows:
Earnings rise with the level of schooling completed, especially
for highly educated people with college or advanced degrees in
industries experiencing rapid technical change. This finding
is consistent with, and may explain why, highly educated people
are also more likely to get company training and informal OJT
in "high-tech industries." This result provides empirical
support for Welch's (1970) hypothesis regarding the allocative
efficiency of schooling--namely, that better educated workers
are more adept at responding to technical change.
Among the sources of training, company training has the
greatest effect on increasing earnings, an effect that persists
for over 13 years. This is followed by training from business
and technical schools. When types of training are considered,
managerial training is the most important, followed closely by
professional and technical training.
- ix -
The effects of training on unemployment mirror the earnings-
augmenting effect of training. On average, vocational training
is associated with a decline in the likelihood of unemployment
lasting approximately 12 years. However, the effects of
training on unemployment vary systematically by source and type
of training.
Unlike the result for earnings, the industry rate of technical
change in the current job is not statistically correlated with
the probability of experiencing an unemployment spell in the
past year. In fact, for the sample of male youth studied,
rapid technical change is typically associated with a lower
probability of unemployment; and for high school graduates,
this relationship is statistically significant. At least for
this group of youth, the results suggest that concern over the
labor-displacement effects of technical change may be
misplaced. Whether or not this finding holds for other groups
is a subject for future research.
- xi -
ACKNOWLEDGMENTS
This research benefited from the contributions of a number of
people. As formal reviewers, Rand colleagues Michael Murray and
Elizabeth King provided careful and thoughtful comments, many of which
were incorporated into the report. Mary Layne supplied expert
programming assistance that was crucial in preparing the five surveys
for analysis. The unenviable task of typing the reams of tables and
text fell to Nora Wolverton who, as usual, did so cheerfully and
competently.
Earlier versions of the report were presented separately by the
authors to the Labor Economics Seminar at the University of California
at Los Angeles, and to the Bureau of Labor Market Research, Canberra,
Australia. We are grateful to the seminar participants on both sides of
the Pacific, in particular to Finis Welch, Michael Ward, and James P.
Smith from the UCLA seminar, to Terry Murphy from the BLMR, and to Bruce
Chapman from Australian National University. Their insightful and
illuminating comments enabled us to make several salutary revisions in
our final draft.
- xiii -
$
CONTENTS
PREFACE
iii
SUMMARY
V
ACKNOWLEDGMENTS
xi
TABLES
XV
Section
I. INTRODUCTION
1
II. DATA SOURCES AND AN OVERVIEW OF TRAINING
5
Data Sources
5
Overall Patterns of Training
8
Summary
17
III. THE DETERMINANTS OF TRAINING
18
Regular Schools as a Training Source After Beginning Work
20
Training from Sources Other than Regular School
26
Training Among the Disadvantaged
39
Summary
42
IV. THE ECONOMIC CONSEQUENCES OF TRAINING
43
The 1983 Current Population Survey (CPS)
44
The National Longitudinal Survey of Young Men (NLS)
49
The Effects of Training on Unemployment
59
V. SUMMARY AND CONCLUSIONS
65
APPENDIX
73
REFERENCES
83
- XV -
TABLES
2.1. Survey Points for Three NLS Cohorts
7
2.2. Sources and Types of Training in the Three Surveys
9
2.3. Prevalence of Training in the CPS: Men and Women
10
2.4. Prevalence of Training Among NLS Men
12
2.5. Prevalence of Training Among NLS Women
12
2.6. Prevalence of Training Among
14
2.7. Multiple Training Events in the NLS: Young Men
15
2.8. Training Over Extended Periods of Time: Percent Receiving
Training Over Given Intervals, by Source and Type of
Training
16
3.1. Regular Schools to Improve Current Job Skills: CPS Men and
Women
23
3.2. Training from Regular School Sources - NLS Young Men
24
3.3. Effects of Educational Attainment on the Probability of
Training, by Source: CPS Men and Women
27
3.4. Effects of Educational Attainment on the Probability of
Training, by Source: NLS Young Men, Mature Men, and
Career Women
28
3.5. Effects of Technological Change on the Probability of
Training, by Source: Men and Women in the CPS
31
3.6. Effects of Technological Change on the Probability of
Training, by Source and Type: Males in the NLS
32
3.7. Effects of Technological Change on the Probability of
Training, by Source and Type: Career Women in the NLS
33
3.8. Effects of Nonwhite Race on the Probability of Training, by
Source and Type
36
3.9. Effects of Economic Conditions on the Probability of
Training, by Source and Type
38
- xvi -
(1)
3.10. Prevalence of Training by Source Among Disadvantaged Workers
EOPP Sample
41
4.1. The Earnings and Training Equations for CPS Men
46
4.2. Summary Measures of Training Taken by NLS Young Men by 1969
and 1980; Selected Sources and Types of Training
52
4.3. Earnings and any-Training Equations
54
4.4. Effects of Training on Annual Earnings of NLS Young Men by
Source and Type of Training
57
4.5. Time Path of Training Effects on Annual Earnings of NLS
Young Men
58
4.6. Effects of any Training on the Unemployment Probability:
NLS Young Men Sample
61
4.7. Effects of Training on Unemployment Probability: NLS Young
Men Sample
63
A.1. Determinants of Training to Get Current Job and Improve
Skills: CPS Men
73
A.2. Determinants of Training to Get Current Job and Improve
Skills: CPS Women
74
A.3. Type Determinants of Training for NLS Young Men, by Source and
75
A.4. Type Determinants of Training for NLS Mature Men, by Source and
76
A.5. Type Determinants of Training for NLS Career Women, by Source and
77
A.6. Summary Statistics for the NLS Young Men Sample
78
A.7. Results for Earnings and Training by Source and Type:
NLS Young Men Sample
79
A.8. Probability of Unemployment and Weeks Unemployed Equations
by Source and Type of NLS Young Men
81
- 1 -
I. INTRODUCTION
In recent years, the federal government has become concerned about
the productivity slowdown in the United States, about the effects of
technological change on the labor market, about structural unemployment,
and about labor market responses to changing demographic patterns.
While these problems raise many kinds of policy questions, they raise
especially challenging questions about training. Policies aimed at
promoting worker training for the large baby-boom cohorts, for women
returning to the labor force, for workers requiring skill upgrading or
retraining, and for the disadvantaged, will strongly influence the
productivity and wages of the U.S. work force in the years ahead.
Since 1965, labor productivity in the United States has risen
sluggishly--1.7 percent per annum, as compared with 3 percent between
1950 and 1965. The emerging interest in training policies is rooted
largely in the belief that greater investments in training will halt
declines in labor productivity not only through enhancing the skills of
new labor market entrants, or upgrading existing worker skills, but also
through improving managerial and technical skills, which have a broader
impact on the efficiency of production (Goldstein, 1980). Of interest
also is whether such policies will be effective in offsetting declines
in the international competitiveness of U.S. industries, which some have
attributed to lower U.S. investments in equipment and in skill
acquisition through education and training, relative to its major
trading partners. 1
Technological change, and its impact on the labor market, further
motivate public and private interest in training. Over the last two
decades, this interest has shifted from concern over the potential
effects of automation on labor displacement and structural unemployment,
which recent research suggests is misplaced (Ayres and Miller, 1983), to
the changing skill requirements of new technologies. While public
¹The President's Commission on American Competitiveness, and the
Department of Labor's Bureau of International Labor Affairs, cited in
Carnevale and Goldstein (1985).
- 2 -
attention has tended to focus on skill shortages in "high-tech" jobs
(which account for less than 10 percent of the labor force), less
dramatic but more pervasive skill shortages arise in the workplace as a
consequence of technological change. As technologies evolve, job-
specific training and retraining are constantly necessary to supply the
technical and managerial skills required by new process and product
innovations. By identifying these skill shortages, policies may be
implemented to encourage greater investments in the skills required for
technological change.
Several demographic trends in particular, the postwar swings in
birth rates and the increasing numbers of working women--also have
implications for training policy. The future composition of the labor
force will change both as the large baby-boom cohorts that entered the
labor market in the 1970s mature and are replaced by smaller baby-bust
entry cohorts, and as female labor-force participation rates rise. How
well the labor market responds to these changes, and whether skill
shortages materialize, will depend on the degree to which older workers,
women, and youth are substitutable for each other (Tan and Ward, 1985).
The outcome will be strongly affected by the amounts and kinds of
training received by the different groups.
To address these issues, government officials will need
comprehensive information about the amounts and kinds of training
available for different demographic groups, the determinants of
training, and its effects on labor market outcomes. Their information
needs, however, already outstripped the available data. As Carnevale
and Goldstein (1983, p. 31) have noted, "Sound theoretical literature is
thin. Empirical analysis is thinner still. Those responsible for
practical decisions on employee training face ignorance and confusion
when they try to find reliable information."
This information gap is at least partly attributable to the
perceived problems of using self-reported measures of training. In
addition to the issue of reliability, reported training measures may
suffer from a selective recall problem: namely, that only the most
memorable (i.e., formal) kinds of training are reported, while
potentially more important forms of on-the-job training are ignored.
Thus, past research in the human capital tradition has tended to focus
- 3 -
on formal schooling, government sponsored training programs, time in the
labor force, and firm tenure as proxies for training (for example, see
Mincer, 1974). These proxies have been important in testing various
theories, but their use has hampered understanding of the empirical
correlates of training and its effects, which can only come from using
actual measures of training taken.
We believe this report represents the first systematic attempt to
exploit current data on reported training and answer several questions:
who receives training, how much and why, and how does training affect
future earnings and employment stability? Most of our data are drawn
from several information-rich sources: the National Longitudinal
Surveys (NLS), the Current Population Surveys (CPS), and the Employment
Opportunities Pilot Projects survey (EOPP). Because detailed
information is available on the kinds of training provided, we are able
to pursue the analysis of training by source (in-house company programs,
on-the-job training, business and technical schools, traditional
schools) and by type of training (managerial, professional and
technical, and semiskilled manual training).
Self-reported training measures are not wholly reliable; they
clearly understate how much training actually goes on by failing to
report more informal kinds of training. One survey, which contained a
comprehensive set of training questions, found informal OJT to be as
prevalent as training in formal company programs. This caveat
notwithstanding, a remarkably consistent picture of training emerges
when we compare the surveys. Patterns of training observed in the data--
by source and by type, across demographic groups, and over the life
cycle--are consistent both with theory and with our a priori
expectations. Indeed, as the rest of the report demonstrates, analysis
of reported training measures can yield important insights into behavior
in areas where government policies may have their greatest impact
through training.
Our study covers an extensive set of issues and brings together a
number of different lines of research. It investigates how workers
decide to take, and employers decide to give, training, the extent of
various kinds of training, their sensitivity to technological change and
labor market conditions, and the effects of training on subsequent
- 4 -
earnings level and growth, and on employment and employment stability.
Many of the hypotheses investigated are guided by other developments in
human capital theory; others, such as the relationships between
training, technological change and local labor market conditions,
represent tests of theories developed by the authors (Tan, 1980 and
1986; Lillard, 1981 and 1986).
Section II describes the five surveys we used and provides an
overview of the magnitude and kinds of training obtained by different
groups. Section III lays out the main hypotheses to explain the
decisions to take (or give) training, followed by a summary of the most
important empirical correlates of training in the five surveys. Our
analysis of the effects of training on earnings, earnings growth, and
employment stability are the subject of Section IV. Section V
summarizes the main findings and their implications for training policy.
- 5 -
II. DATA SOURCES AND AN OVERVIEW OF TRAINING
We first describe the main features of the five surveys used in our
analysis.¹ The surveys differ substantially in both the form and
content of their training questions, and in the time period when they
were conducted.
DATA SOURCES
The Current Population Survey (CPS) surveys a nationally
representative sample of the non-institutional population several times
a year. It implemented a special supplement in January 1983 to elicit
information on occupational mobility, job tenure, and training. For
this study, we merged one-half of the respondents in the supplement with
the March 1983 demographic file to take advantage of the wealth of labor
force information on weeks worked and previous year's earnings.
The National Longitudinal Surveys (NLS) included cohorts of Young
Men, Women, and Mature Men. Panel data on the training and labor market
experiences of these three cohorts are available (in versions that we
use) for the period from 1966 to 1981, with four breaks in the panel.
The Young Men and Mature Men samples comprised individuals aged 14-24
and 45-59 in 1966, respectively, and the Women sample those aged 30-44
in 1967.
The Employment Opportunities Pilot Projects Surveys (EOPP) were
fielded primarily between May and September 1980. The EOPP was designed
to evaluate the impact of participation and nonparticipation in a job-
search/work-and-training program. The sample included both low-income
and control households, but because the poverty group was oversampled
relative to the nonpoor group, we will refer to the EOPP sample as the
"economically disadvantaged" population.
¹A sixth potential source is the PSID (Panel Study of Income
Dynamics), a family- or household oriented-longitudinal data file
covering 1968 to 1981. However, the PSID includes only crude
information about whether training was taken by household heads with
less than a high school degree.
- 6 -
Each data set has several important features that make it unique.
The surveys ask different but overlapping sets of training questions,
with more detail on certain kinds of training in some (informal OJT in
the CPS and types of training in the NLS) than in others. The relevant
reference period for training also differs both within and across
surveys: fixed intervals in the NLS (ranging from one to five years),
and a variable interval (years on the current job) for the CPS. These
are discussed here and used throughout the report.
The CPS asked what training was needed to get the current or last
job and about training to improve skills on the current job. Thus, the
reference job may have begun many years ago or as recently as the past
month. To mitigate potential recall errors, our analysis is restricted
to people who entered their current jobs in 1959 or later. The CPS
questions on training to improve skills refer to the period implied by
the phrase "since you obtained your present job." In the analysis of
training propensity, we will want to control for this since the
individual is exposed to increased training possibilities the longer the
time spent on the current job. Fortunately, the survey includes
information on years of job tenure. Finally, while the questions allow
only two intervals, the current job and all prior jobs, it does allow
multiple responses about sources of training--a response is given for
each source.2
The NLS asked about training taken since the last interview, a
fixed period of time. However, the interval may be of varying length
(one, two, or five years) depending on the elapsed time since the last
interview. As such, we use subsamples based on the length of the
reference period, pooling data for reference periods of the same length.
Furthermore, because the person may have changed jobs during the
interval, been unemployed, and so forth, we will want to control for job
changes or entry and exit from the labor force, when they are known.
Unlike the CPS, which allows multiple responses, the NLS training
questions refer only to the "longest" training event in the interval.
²In the analysis of the CPS data, each source of training is
treated independently, i.e., we do not estimate the joint probability of
getting training from multiple sources.
- 7 -
Therefore, only one event in an interval is known, but there is
information on multiple intervals for each person in the panel. This
will be an advantage for the analysis of training effects (Sec. IV).
The survey points in the three NLS cohorts and their corresponding
reference periods for the training questions are shown in Table 2.1.
The EOPP survey elicited information on up to four training events
occurring any time between January 1, 1979, and the interview date,
which fell primarily between May and September of 1980. The training
data, though detailed, are limited by the short time period considered.
As in the NLS, people may have changed jobs, been unemployed, or have
left the labor force in between reported training events.
The substantive content of the training questions is broadly the
same across surveys, with more detail in some than in others. The
surveys first ascertained whether any occupational or job-related
training had been taken (termed ANY). Then the CPS, NLS, and EOPP asked
about the sources of training. By source, we mean where the training
was obtained, whether from regular schools, company training programs,
informal OJT, business and technical schools, or from "other" sources.
The NLS then also asked about types of training. By type, we mean the
nature or content of training, which included managerial, professional
Table 2.1
SURVEY POINTS FOR THREE NLS COHORTS
NLS Sample
Length
Survey Years (19--)
Young Men
1-year
67 68 69 70 71 76
2-year
73 75 78 80
Mature Men
2-year
67 69 71
5-year
76 81
Mature Women a
1-year
69 72
2-year
71
5-year
77
a Training questions for 1967 and 1979 were
flawed and not comparable.
- 8 -
and technical, clerical, semiskilled manual, or "other" types of
training. Unlike the other surveys, the CPS also elicited information
on the sources of training that respondents felt were important in
getting the current or last job. Though clearly subjective, such
information may provide insights into the job-related content of
training from formal schooling (Sec. III) or the extent to which prior
job skills are transferable to new jobs (Sec. IV).
Table 2.2 summarizes the information on the sources and types of
training that are reported in each of the different surveys.
OVERALL PATTERNS OF TRAINING
We now turn to an overview of the general level of post-school
training, the various forms it takes, and differences among the
demographic groups suggested by the five surveys. We examine the
training patterns of men and women in each survey separately. Recall
that both the CPS and EOPP samples include persons of all ages. The
NLS, on the other hand, includes separate surveys for Young Men, Women,
and Mature Men aged 14-24, 30-44, and 45-59, respectively, in the first
interview year (1966 or 1967). It should be kept in mind that these are
more formal dimensions of training and that more informal training and
learning on the job are covered less adequately. However, we will see
later that these forms of training have real consequences for earnings
and employment.
Traditional schooling is the most widely studied source of
training. In studies that have looked at post-school training, the
research focus has typically been on the effects of school curriculum on
labor market outcomes, standardizing for subsequent training events
(Grasso and Shea, 1979; Tannen, 1984; Meyer and Wise, 1982). To date,
with the exception of the study by Carnevale and Goldstein (1985), our
knowledge of post-school job training remains fragmentary.³ To get a
more comprehensive picture of training, we now turn our attention to
training of any source or type, in addition to traditional schooling,
that various groups receive.
³The Carnevale and Goldstein study uses the 1981 Survey of
Participation in Adult Education to provide the first broad description
of the size and scope of employee training in the United States.
- 9 -
Table 2.2
SOURCES AND TYPES OF TRAINING IN THE THREE SURVEYS
Survey
Sources and Types
Sources of Training
NLS CPS EOPP
Company schools or courses
NLS EOPP
Business, technical and vocational schools
NLS CPS
Traditional schools, colleges and universities
CPS
Current job: informal OJT
CPS
Past jobs: informal OJT or experience needed
to get current job
NLS
Other sources, such as training under Title V
or the Manpower Development Act
CPS
Other sources to improve current job skills,
such as Armed Forces, correspondence schools;
and other sources to get job, such as friends
and relatives, or other non-work-related
experience
Type of Training
NLS
Managerial
NLS
Professional and technical
NLS
Clerical--Women only
NLS
Manual, skilled and semiskilled, men only
NLS
Other types, such as nontechnical and general
courses not required to obtain a certificate
or degree
NOTE: CPS = Current Population Survey; NLS = National Longi-
tudinal Surveys; EOPP = Employment Opportunities Pilot Projects
Survey.
- 10 -
First, consider the prevalence of training necessary to get a job.
More than half of all men and women in the labor force in the CPS
job. reported 4 that some training was necessary to get their current or last
(See Table 2.3.)
About one quarter of the CPS sample--and slightly more women than
men reported regular schooling as important, with the proportion rising
dramatically with education for each. 5 This suggests that the job-
related content of formal schooling rises with the level of education.
Informal OJT from prior jobs is of comparable importance, but only 12
percent of men and 8 percent of women thought that previous formal
Table 2.3
PREVALENCE OF TRAINING IN THE CPS: MEN AND WOMEN
Source of Training
Any
Regular
Sample
Training
School
Company
OJT
Other
Training Prior to the Current Job Needed to Get the
Current or Last Job
Men
55.5
22.2
Women
11.7
30.8
55.0
8.5
27.9
7.5
26.0
2.7
Training to Improve Skills on the Current Job
(Respondents working at the Survey)
Men
38.0
13.6
Women
11.6
15.1
36.7
5.4
10.3
13.1
15.1
4.5
SOURCE: January 1983 CPS.
reporting the sum proportions over all sources adds to more than training, the so that
"Individuals of could report more than one source of
survey. any source. Types of training were not ascertained proportion in the CPS
over increases 79 just under 2 percent for non-high-school graduates get a job to
The from fraction reporting school training as needed to
percent for those with graduate degrees.
- 11 -
company training was needed. Reporting miscellaneous other training
sources were 9 percent of men and 3 percent of women. While the overall
proportions were the same, women tended to cite regular school and men
tended to cite company training and other forms of training.
Table 2.3 suggests that nearly 40 percent of both men and women got
training to improve skills on their current job. The proportion rises
steadily with tenure on the current job, since the person has been
exposed to the possibility of training for a longer period. Note that
the following numbers represent an average based on the tenure
distribution in the CPS sample. Regular schools, company training, and
OJT each are reported 10 to 15 percent of the time as a source of
training to improve or upgrade job skills.' Clearly, the traditional
school system continues to be an important source of job training for
workers who have completed their formal schooling. Only about 5 percent
reported training from other sources.
Now consider the prevalence of training in fixed-length intervals
in the three NLS cohorts. Training information is reported for the
longest training event in the interval, the length of the reference
period being determined by the survey questions relating to time "since
the last survey. " These values are reported in Table 2.4 for young and
mature men and in Table 2.5 for women with varying degrees of attachment
to the labor force.
The proportions reporting any training in the NLS surveys differ
among groups substantially more than in the CPS. In part, this may be
attributed to age differences across the three NLS cohorts, and changes
in training patterns over the life-cycle are likely to be important.
Because each NLS group includes a 2 year interval, these are most
directly comparable. Young men and career women (who always worked
throughout the 12-year panel) have the greatest proportions receiving
any training--3 percent and 24 percent, respectively--which is more
than twice that of mature men (10 percent). However, a closer
examination reveals differences in the kinds of training received by the
6.5 percent of men and 14.9 percent of women reported multiple
sources of training to improve skills. Company training and OJT were
the multiple sources most frequently reported jointly.
- 12 -
Table 2.4
PREVALENCE OF TRAINING AMONG NLS MEN
(In percent)
Source of Training
Type of Training
Business
Sample
Skilled
and
and Time
Any
and
Technical
Interval
Regular
Profes-
Training
Company
Manage-
Semi-
School
School
Other
sional
rial
Skilled
Other
Young Men
1 year
24.3
7.6
3.7
3.6
9.4
2 year
29.7
8.6
2.7
10.4
8.2
5.2
4.8
5.2
9.0
11.4
4.0
6.8
7.5
Mature Men
2 year
10.2
3.3
.4
--
6.4
5 year
4.2
17.2
2.0
5.6
1.7
1.0
2.2
--
10.6
8.9
2.7
2.3
3.4
Table 2.5
PREVALENCE OF TRAINING AMONG NLS WOMEN
(In percent)
Source of Training
Type of Training
Business
Time
Any
and Tech
Interval
Profes-
Training
Manage-
Company
School
Other
sional
rial
Clerical
Other
Always Work
1 year
22.9
3.6
1.1
18.1
2 year
10.1
23.7
1.6
3.2
3.0
1.3
6.9
19.2
5 year
9.4
36.1
1.5
3.2
7.6
8.2
2.9
25.6
19.8
3.8
4.9
5.7
Works Intermittent| Working at Survey
1 year
16.3
3.0
.8
12.6
2 year
6.0
20.1
.4
1.6
2.1
.6
6.3
17.9
5 year
5.2
31.1
.4
4.0
2.7
3.6
9.2
23.4
14.1
2.2
5.3
6.9
Works Intermittent| - Not Working at Survey
1 year
12.4
.8
.4
11.2
3.2
2 year
9.4
2.0
.5
1.7
.1
6.0
8.8
5 year
2.4
.0
18.0
2.4
1.8
1.5
3.8
14.2
6.4
.2
3.2
6.0
Never Worked
1 year
5.6
.1
.2
5.3
2 year
.5
.1
5.0
.0
.6
.5
3.8
4.5
5 year
1.2
10.7
.0
.5
.3
1.0
3.2
9.2
2.3
.2
1.6
5.7
- 13 -
different groups. In particular, compared with both young and mature
men, a smaller fraction of career women reported getting company or
managerial training, and the prevalent source of training was from
unspecified "other" sources. 7
In general, the proportion reporting any training increases with
the length of the interval, but not even close to proportionately.
Moving from a 1-year to a 2-year interval, young men reported a greater
increase than women; and moving from a 2-year to a 5-year interval,
mature men report a greater increase (proportionately) than mature
women.
Briefly consider women who are less attached to the labor force
than career women. Those women who did not work at any time (except
possibly once) through the panel years reported very little training.
The training they did report tended to be of the miscellaneous "other"
source or type. Some women work intermittently, entering and exiting
the labor force frequently. These women had training patterns much like
those of career women in the periods in which they worked, and reported
patterns somewhat like those of women who never worked in the periods in
which they (the same women) did not.
Turning to the disadvantaged (EOPP) sample, only about 11 percent
of men and women reported getting training from any source. (See Table
2.6.) These proportions pertain to a reference period of approximately
17 to 21 months, and are therefore most comparable to the NLS 2-year
intervals. While the proportion getting business and technical school
training is roughly comparable to those in the other groups, only a
small percentage report OJT. In fact, their training experience most
closely resembles that of the sample of NLS women with low attachment to
the labor force.
Cross-tabulations of training by source and by type (not reported
here) are informative about the sources of various types of training in
the NLS. Nearly half of managerial training is provided by company
sources, the proportion being a bit lower for women. The remainder
comes from ambiguous "other" sources, with relatively little coming from
business, technical, and vocational schools. Surprisingly little
professional training comes from business, technical and vocational
schools. A much larger share comes from company schools, but most comes
from other sources.
- 14 -
Table 2.6
PREVALENCE OF TRAINING AMONG
DISADVANTAGED WORKERS
Source
Business and
Any
Technical
Sample
Training
School
OJT
Other
Men
10.9
4.0
2.6
4.6
Women
12.0
4.4
2.3
5.7
SOURCE: EOPP.
NOTE: Interval length is between 17 and
21 months.
Let us return to the issue of the training taken over successively
longer intervals of time. We have provided some insights from the NLS
Young Men sample. Table 2.7 shows the probability of engaging in
training other than from regular schools, over intervals several periods
(years) long. 8 The period begins with the first observed period of
work--either upon completion of full-time schooling (upper panel of the
table) or the first period observed already working (lower panel).
Reading across columns, the first row reports the proportion receiving
no training. In the upper panel, this figure declines from over 86
percent in the first year of work to 33 percent after nine periods
(survey years). Reading down the rows, after (say) 5 periods, the table
suggests that about 54 percent receive training in at least one period;
over 28 percent report at least two training episodes. These training
patterns are mirrored for the sample in the lower panel.
8 Each individual enters the calculation several times, until he
either drops out of the sample or reaches the maximum length of his
longest interval of continuous participation in the panel. Note that
the NLS youth in the upper and lower panels are mutually exclusive and
exhaustive samples.
- 15 -
Table 2.7
MULTIPLE TRAINING EVENTS IN THE NLS: YOUNG MEN
Number of
Periods
in which
Number of Potential Periods of Work
Received
Training
1
2
3
4
5
6
7
8
9
From First Period of Full-Time Work
None
86.2
72.5
59.7
51.6
45.9
44.0
38.5
35.2
33.0
1
13.8
21.8
25.5
26.1
25.6
23.5
26.4
18.8
17.6
2
5.7
11.2
13.5
15.1
16.2
15.4
23.2
18.5
3
3.6
7.2
8.5
9.3
9.1
8.9
12.9
4
1.6
4.3
4.3
6.1
8.3
9.9
5
0.6
5.5
3.1
2.9
5.2
6
0.3
1.2
1.6
2.6
7
0.2
1.0
0.4
8
0.3
Sample size
1870
1540
1298
1078
867
680
519
384
233
From First Period Observed, Already Working
None
90.0
78.0
67.7
59.4
53.9
46.5
41.8
37.2
32.6
1
10.0
17.4
22.3
23.2
23.2
24.8
24.8
25.0
22.8
2
4.7
7.5
11.2
12.0
12.9
13.0
14.2
15.7
3
2.6
4.9
7.0
7.9
9.2
9.4
10.3
4
1.3
3.4
5.6
6.3
6.5
8.1
5
0.6
2.1
3.6
4.2
4.2
6
0.2
1.3
2.8
4.0
7
0.7
1.6
8
0.1
0.6
9
0.1
Sample size
2719
2343
2036
1874
1751
1598
1486
1343
1210
SOURCE: NLS Young Men.
What do these patterns look like when the data are disaggregated by
source and type of training? Several patterns of training over time are
revealed in Table 2.8. If we exclude miscellaneous sources, company
training programs are the largest source of training. The upper panel
indicates that the cumulative probability of receiving any company
- 16 -
training rises steadily, to over 27 percent by the ninth year in the
labor market. Over the same interval, about 20 percent get training in
business and technical schools, but most of the increase is concentrated
in the first five years. Perhaps because these are skills useful to
many firms, employers have few incentives to provide general training
and individuals must get (and pay for) this training themselves prior to
joining the firm or early in their careers (Becker, 1975).
The cumulative probabilities of getting different types of
occupational training are displayed in the lower panel of Table 2.8.
Professional and technical training is by far the commonest form of
Table 2.8
TRAINING OVER EXTENDED PERIODS OF TIME: PERCENT RECEIVING TRAINING
OVER GIVEN INTERVALS, BY SOURCE AND TYPE OF TRAINING
Number of Potential Periods of Work
Sources
1
2
3
4
5
6
and Types
7
8
9
Source of training
Company
3.1
8.2
15.0
20.2
24.2
27.5
29.5
34.9
36.9
Business or
technical
1.7
7.2
10.8
13.1
15.1
15.9
17.7
18.5
19.7
Miscellaneous
other
5.3
3.5
21.3
26.9
31.5
32.2
36.1
39.9
44.0
Type of training
Managerial
.5
3.2
5.9
7.3
9.0
10.0
11.2
16.4
19.7
Professional
technical
2.2
17.8
27.3
32.6
38.8
38.8
40.8
43.7
46.8
Semiskilled
manual
5.4
7.9
11.4
15.0
19.1
21.0
23.5
25.3
26.2
Miscellaneous
other
3.2
7.8
13.4
16.8
19.0
22.4
25.6
27.3
32.3
SOURCE: NLS Young Men.
- 17 -
training reported, and managerial training the least common. Profes-
sional and technical training tends to be concentrated early in the
career. The probability of managerial training is low initially, but
rises over time, as might be expected if long promotion times are
required to attain managerial rank.
SUMMARY
The surveys provide a wealth of detail on the training experiences
of different demographic groups. While there are important differences
in the nature and form of the training questions asked, they overlap and
permit comparisons of the magnitude and sources of training across
surveys. Taken together, these surveys provide a much broader
perspective on the dimensions of training than is available with any one
data source.
This brief overview revealed several important stylized facts about
the magnitude and composition of post-school training received by the
different demographic groups.
First, almost 40 percent of men and women report having taken some
training to improve existing skills while on the current job.
Second, over a fixed time interval, young men and career women
received more training than mature men, which is consistent with a life-
cycle pattern of training predicted by human capital theory. However, a
smaller fraction of career women receive company or managerial training
than of either cohort of young or mature men.
Third, women with intermittent labor market experience receive
little training, and when they do, much of it is from miscellaneous
"other" sources.
Finally, the economically disadvantaged group is characterized by a
low likelihood of training, much like the sample of women with weak
attachment to the labor force.
- 18 -
III. THE DETERMINANTS OF TRAINING
Several patterns of training emerged from the overview in Sec. II.
First, career women in the NLS appear to receive relatively less company
and managerial training and more of other kinds of training than do
young or mature men. These differences are not apparent in the CPS
samples, at least for training sources. Second, relative to other
groups, a smaller proportion of the disadvantaged (EOPP) group report
getting any training, and those that do receive relatively less company
training. Finally, as much as a third of the young men in the NLS get
no training, even after nine survey periods, while many report getting
multiple training events.
To determine the reasons for those differences, this section
explores the factors that determine the probability of getting training
in a fixed interval (NLS and EOPP surveys) or over a variable reference
period (time on the current job in the CPS). For each sample, we
estimate separate probit models for each source and type of training.¹
As suggested in Sec. II, economic forces are likely to affect individual
or employer decisions to take or provide the various kinds of training.
Each probit model includes a common set of regressors on schooling,
race, labor force experience, the industry rate of technical change, and
local and national labor market conditions. Schooling effects are
captured by separate variables for each of five levels of educational
attainment: less than 12, 12, 13-15, 16, and more than 17 years of
school. Ethnic origin is controlled for using an indicator variable,
NONWHITE, with a value of 1 if nonwhite, 0 otherwise. Two measures of
labor market experience are included: years on the current job and
years of potential labor market experience, the latter being computed as
age minus years of schooling minus 5.2
Thus, as noted earlier, we do not estimate the joint probability
of training from multiple sources (CPS), or of multiple training
episodes over time (NLS).
2As is widely known, this potential experience measure is an
especially poor measure of actual work experience for women who exit and
enter the labor force more frequently than do males. For the NLS Young
Men sample, only years of potential work experience is included. Close
- 19 -
Technical change plays a central role in our analysis. The
perspectives on technical change, and its relationship to training
requirements, come from research by Tan (1980, 1986). The argument is
that many job-relevant skills are technology-specific and are acquired
through working with particular production technologies and specialized
equipment. As technological change advances, technology-specific skill
requirements also grow apace. To the extent that few of these skills
are readily available outside the firm, we might expect the demand for
in-house company training to increase with the industry rate of
technical change. Second, it. has been argued that workers with more
education are more adept at critically evaluating new information, and
therefore respond to technical change more readily (Welch, 1970). This
"allocative efficiency" of schooling hypothesis suggests that innovative
firms in high-technology industries are more likely to use highly
educated and technically skilled workers.
To test these hypotheses, we use the measures of technical change
developed by Gollop and Jorgenson (1980) for the most recent period
available at the time of this study, 1966-1973. The measures are
derived from CRS (constant returns to scale) translog production
functions, which include as inputs both quality-adjusted indices of
capital and labor and intermediate products. They are available for 45
two- and three-digit industrial groupings. Using these indices of
technical change, jobs may be characterized as being more or less
"technologically progressive." We interact the technical change
measures with schooling attainment. This allows us to investigate the
(potentially different) relationships between technical change and the
likelihood of getting training for more and less educated workers.
The role of local labor market conditions is considered in a
limited way. Ideally, we would consider the separate effects of both
cross-sectional and local time series labor market conditions as in
Lillard (1981, 1986). However, data limitations restrict what can be
done. While the CPS identifies the state in which an individual lives,
examination of the imputed job-tenure variable (respondents were not
asked job tenure directly in many years) revealed the measure to be
quite deficient.
- 20 -
the training questions are for a variable length of time determined by
the person's current job tenure. Therefore, we use only the state
indices of labor market conditions developed in the Lillard study.
These are the cyclic sensitivity of the state to national unemployment
cycles (denoted RHAT) and the state's relative long-run unemployment
level (denoted SHAT). While the NLS training variables are for a well-
defined period of time, the state of residence of the individual is not
available. Therefore, we use the national unemployment rate as an
aggregate measure of labor market conditions.
Other demographic control variables include region of residence,
and, for young men and women in the NLS, measures of job change or labor
force attachment when available. For the CPS the regions include South,
North East, North Central, and West (the omitted category). For the NLS
the regions are simply South and Non-South (the omitted category)
because that is all the locational information available.
We begin with an exploration of the determinants of training from
regular school sources. As shown in Sec. II, a sizeable fraction of
persons continue to get training from traditional schools even after
entering the labor force. This is followed by an analysis of training
from sources other than regular schools: company training programs,
informal OJT (CPS), business and technical schools, and other sources.
For the NLS surveys, probit estimates for types of training--managerial,
professional and technical, semiskilled manual, and other types--are
also reported. We then briefly examine the training experiences of the
findings. disadvantaged EOPP sample, before concluding with a summary of the main
BEGINNING WORK
REGULAR SCHOOLS AS A TRAINING SOURCE AFTER
Here we consider regular school training obtained after beginning
work for young men in the NLS, and to improve job skills on the current
job for men and women in the CPS.³
sources. survey, no information was elicited on training from regular school the
³In the other NLS surveys of women and mature men, and in EOPP
- 21 -
Individuals may get training from regular schools for different
reasons: to complete interrupted full-time schooling, to acquire
additional job-related skills, or both.4 The CPS does not distinguish
between these motives. For the NLS, we exploit the panel nature of the
survey to create two control variables, SCHWRK and SCHWRKT. The first
is an indicator variable, with a value of 1 if the individual attains a
higher level of schooling over the panel after beginning full-time work,
0 otherwise. Conditional on SCHWRK, the second variable indicates
whether the reference period falls within the interval in which the
person is completing school. In this schema, those who get regular
school training but do not increase their level of schooling (SCHWRK =
0) are interpreted as getting training to improve job skills.'
Before turning to the analysis, some broad figures on the extent of
regular school training are useful. First, 3.6 percent of young men
reported regular schooling as a training source in a 1-year period, and
5.2 percent in a 2-year period. However, remember that these training
events are for the "longest" event in the interval and may not be
associated with the same employer. Second, consider regular school
training to improve job skills in the CPS. Since obtaining their
current jobs, 3.4 percent of both men and women reported taking training
from this source. However, since the CPS training question implies a
cumulative period of exposure, the probability of training (of any kind)
should increase with job tenure, and it does.
"Standard models of human capital investment predict that full-
time schooling should occur as early as possible in the life cycle.
However, as a practical matter, students must sometimes interrupt full-
time schooling or finish school on a part-time basis. This is
especially true of college or graduate schooling. For those young men
in the NLS who completed college during the panel period 1967-1980, 35
percent found it necessary to interrupt their schooling with a period of
"work as primary activity" before finishing college. For those who
experienced a gap, approximately 80 percent finished college in 4 years
or less, and only 2.5 percent took more than 6 years. Of college
graduates, approximately 44 per cent completed some graduate schooling.
Only 20 percent of this group managed to complete their formal education
without interruption before starting work as their main activity.
⁵This would include those taking one or two courses without credit
in a regular school as well as those failing to complete course work for
a higher degree.
- 22 -
Tables 3.1 and 3.2 report the relationship of training from regular
school sources to covariates for the CPS and NLS samples, respectively.
The results are quite similar for men and women in the CPS, and between
them and the young men in the NLS.
The probability of receiving regular school training increases with
the level of schooling attainment, with a much higher likelihood of
training for those with postgraduate degrees. These relationships are
not only highly significant statistically in the CPS, but also are very
similar for both men and women. Highly educated women, however, are
more likely than men to obtain additional training from this source.
Broadly similar results are also found in both the 1-year and 2-year NLS
samples. For NLS young men, completion of interrupted schooling appears
to be an important motive for training, as suggested by the positive and
significant coefficient on the SCHWRK variable. This group, however, is
also more likely to get other forms of training (see Appendix), which
may indicate that SCHWRK is also picking up the effects of unobserved
ability or motivation.
Interestingly, the probability of regular school training is
diminished in industries associated with high rates of technological
change. Again, this relationship is statistically significant in the
CPS samples, especially for women. In the NLS, this relationship is
especially pronounced at the postgraduate level. Taken at face value,
this result might be interpreted as a repudiation of the "allocative
efficiency" of schooling hypothesis. However, two points should be
noted. First, regular schools are only one source of training; other
sources exist, both within the firm and outside. Second, and more
important, the hypothesized skill requirements are for more job-specific
skills in rapidly changing industries, a demand not likely to be met
from regular school sources. And it is not, as suggested by this
result. Indeed, this is part of an overall pattern of the effects of
technical change on training requirements to be discussed later.
Other job characteristics appear to affect the likelihood of
training, but for reasons that are unclear. For example, union jobs are
associated with more regular school training (statistically significant
for the CPS samples), while the self-employed group reported less of
- 23 -
Table 3.1
REGULAR SCHOOLS TO IMPROVE CURRENT
JOB SKILLS: CPS MEN AND WOMEN
Variable
Men
Women
Constant
-1.814 ***
-1.603 ***
(0.082)
(0.083)
Schooling <12 years
-0.514 ***
-0.457 ***
(0.075)
(0.090)
Schooling 13-15 years
0.464 ***
0.543 ***
(0.043)
(0.042)
Schooling 16 years.
0.618 ***
0.670 ***
(0.047)
(0.052)
Schooling 17+ years
1.044 ***
1.288 ***
(0.045)
(0.058)
Technical change interaction
Schooling <12 years
-4.135
-41.748 ***
(6.029)
(10.009)
Schooling 12 years
-9.999 ***
-28.146 ***
(2.912)
(3.329)
Schooling 13-15 years
11.506 ***
-15.350 ***
(3.128)
(3.427)
Schooling 16 years
-11.356 ***
-21.970 ***
(3.151)
(5.615)
Schooling 17+ years
-14.588 ***
-37.254 ***
(3.766)
(7.932)
Nonwhite
-0.192 ***
-0.200 ***
(0.061)
(0.054)
South region
-0.107 **
-0.082 *
(0.045)
(0.047)
North East region
-0.190 ***
-0.199 ***
(0.054)
(0.057)
North Central region
-0.014
-0.033
(0.045)
(0.049)
Union member
0.151 **
0.344 ***
(0.059)
(0.067)
Union missing
-0.008
0.029
(0.035)
(0.036)
First 5 years of work
0.100 **
-0.100 *
(0.045)
(0.051)
Potential work experience
-0.002
-0.006 **
(0.002)
(0.002)
Years of job tenure
0.034 ***
0.046 ***
(0.003)
(0.003)
Self-employed
-0.170 ***
-0.077
(0.053)
(0.069)
SHAT (long-run state
-1.104
2.033
unemployment rate)
(2.882)
(2.829)
RHAT (cyclical sensitivity
0.089 *
-0.003
of state unemployment)
(0.047)
(0.050)
SOURCE: January 1983 CPS.
NOTE: Probit specification, standard errors in parentheses.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 24 -
Table 3.2
TRAINING FROM REGULAR SCHOOL
SOURCES-- YOUNG MEN
Interval length
Variable
I-Year
2-Year
Constant
-2.236 ***
-1.904 ***
(0.111)
(0.134)
Schooling <12 years
-0.146 **
-0.371 ***
(0.075)
(0.094)
Schooling 13-15 years
0.298 ***
0.189 ***
(0.061)
(0.063)
Schooling 16 years
0.308 ***
0.374 ***
(0.081)
(0.069)
Schooling 17+ years
0.704 ***
0.567 ***
(0.070)
(0.066)
Technical change interaction
Schooling <12 years
1.675
-0.547
(5.703)
(5.925)
Schooling 12 years
-8.712 **
-3.683
(3.490)
(3.340)
Schooling 13-15 years
-5.186
-4.057
(3.426)
(3.674)
Schooling 16 years
-3.107
-4.520
(7.266)
(4.706)
Schooling 17+ years
-17.455 ***
-12.277 ***
(5.869)
(4.417)
Nonwhite
-0.138 **
-0.046
(0.056)
(0.056)
South region
-0.101 **
-0.107 **
(0.047)
(0.045)
Union member
0.041
0.007
(0.071)
(0.075)
Union missing
-0.144 **
-0.077
(0.068)
(0.068)
First 5 years of work
0.181 ***
0.039
(0.066)
(0.067)
Potential work experience
0.005
-0.008
(0.008)
(0.006)
Changed job in interval
-0.049
-0.059
(0.050)
(0.048)
Job change missing
-0.036
0.128 **
(0.063)
(0.060)
SCHWK (school completion)
0.200 ****
0.150 ***
(0.054)
(0.050)
SCHWKT (period in school
-0.036
0.159 **
completion interval)
(0.062)
(0.066)
NUR (national unemployment
0.042 **
0.021
rate)
(0.016)
(0.013)
SOURCE: NLS Young Men.
NOTE: Probit specification, standard errors in parentheses.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 25 -
this source of training. For the latter, one might speculate that to
the extent that employers use formal schooling as a screening device,
the self-employed would have few incentives to invest in such
credentials.
Consistent with the prediction of standard human capital models,
most training from regular schools is concentrated early in the work
career. Workers who were in their first five years in the labor force
when they first got the current job (CPS), or who are currently in the
first five years (NLS), are more likely to report regular school
training. This probability declines with potential work experience,
namely, as these times occur later in the work career. As time in the
current job increases, the probability rises but that is to be expected
since the person is exposed to training possibilities for a longer
period of time. In the NLS, the coefficient of potential work
experience is positive but not significant, possibly reflecting the net
(and opposite) effects of labor market experience and unmeasured job
tenure.
Three other determinants of regular school training are noteworthy.
First, nonwhite workers (NONWHITE) are significantly less likely to get
training, by about 20 percent for both men and women in the CPS, and by
a smaller amount among youth in the NLS. As we shall see, this race
effect persists as well for other sources and types of training.
Second, important regional variations in training are found: lower in
the South, and greatest in the West. Finally, NLS young men are more
likely to report getting school training in years of high national
unemployment rates (NUR). One possible interpretation is that the
incentives for training (or retraining) are greater when the opportunity
cost of time is low, i.e., in times of slack economic activity. No
significant effects of local labor market conditions (SHAT and RHAT) on
training are found for the CPS samples.
- 26 -
TRAINING FROM SOURCES OTHER THAN REGULAR SCHOOL
Training from sources other than regular schools include training
from company programs, informal OJT (reported in the CPS), business and
technical schools, and other sources. In the NLS, we also distinguish
between sources and types of training, the latter including managerial,
professional and technical, semiskilled manual, and other training
types.
As noted in Sec. II, the bulk of training reported after beginning
work comes from these sources. Beneath these aggregate figures,
however, important differences in the composition of training are found
across the various demographic groups, and across surveys. For example,
NLS young men are more likely than career women to get company training,
and the disadvantaged EOPP sample report informal OJT less frequently
than other groups in the CPS. Our objective here is to gain insights
into the causes for these differences, focusing in particular on the
role of schooling, technical change, race, and local and national
economic conditions. The effects of other control variables are broadly
similar to those reported earlier for regular school sources, and are
not elaborated on here.
As before, we estimate probit models of the likelihood of getting
training from each source, and, for the NLS, an additional set of
estimates for training by type. In addition, we distinguish between the
CPS questions on training needed to get the current job and training to
improve job skills. For each population, a comprehensive set of
regressors are included to control for worker characteristics, the
industry rate of technical change, and local labor market or national
economic conditions. In the discussion that follows, we summarize the
effects of the most important regressors on the likelihood of getting
training. The probit estimates on which these figures are based are
reported in full in the Appendix.
- 27 -
Schooling
The effects of schooling on training probability are reported in
Tables 3.3 and 3.4 for the CPS and NLS samples, respectively.
As before, formal schooling emerges as an important determinant of
most post-school investments in training. In fact, both sources of
"training" are strongly complementary. Compared with high school
graduates (the omitted group), the probability of getting most kinds of
training rises with education to a peak at 16 years or less of
schooling. The only exception, training from "other" sources, rises
steadily with schooling much like the earlier result for regular school
training. These results are qualitatively quite similar for men and
women in the CPS (Table 3.3), and between them and the three NLS
populations (Table 3.4). One implication of this complementarity is
that people with limited formal schooling also face limited training
opportunities in the workplace, and lower future productivity and income
growth.
Table 3.3
EFFECTS OF EDUCATIONAL ATTAINMENT ON THE PROBABILITY OF TRAINING,
BY SOURCE: CPS MEN AND WOMEN
CPS MEN
CPS WOMEN
Educational
Company
Informal
Other
Company
Informal
Attainment
Other
Training
OJT
Sources
Training
OJT
Sources
Schooling
-0.483 ***
-0.076 #
-0.392 ***
-0.414 ***
-0.095 #
<12 years
-0.568 ***
(0.059)
(0.046)
(0.090)
(0.076)
(0.055)
(0.129)
Schooling
0.229 ***
0.102 ***
0.317 ***
0.242 ***
0.081 ##
13-15 years
0.103 *
(0.040)
(0.038)
(0.059)
(0.041)
(0.037)
(0.056)
Schooling
0.478 ***
0.116 ***
0.552 ***
0.373 ***
0.099 ##
16 years
0.410 ***
(0.043)
(0.043)
(0.062)
(0.051)
(0.047)
(0.063)
Schooling
0.308 ***
-0.051
0.832 ###
0.301 ***
-0.116 *
17+ years
0.265 ###
(0.045)
(0.046)
(0.057)
(0.061)
(0.062)
(0.098)
NOTES: In the CPS, training refers to training to improve skills in current firm
of employment. The omitted group are high school graduates with 12 years of school-
ing. Standard errors of probit estimates in parentheses.
# Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
Table 3.4
EFFECTS OF EDUCATIONAL ATTAINMENT ON THE PROBABILITY OF TRAINING,
BY SOURCE: NLS YOUNG MEN, MATURE MEN, AND CAREER WOMEN
Young Men
Mature Men
Career Women
Business
Business
Business
and
and
and
Educational
Company
Technical
Other
Company
Technical
Other
Company
Technical
Other
Attainment
Training
Schools
Sources
Training
Schools
Sources
I
Training
Schools
Sources
Schooling
-0.437 ###
-0.522 ###
-0.385 ###
-0.331 ***
-0.154
-0.285 ###
-0.232
0.418
-0.270 ##
<12 years
(0.068)
(0.084)
(0.068)
(0.070)
(0.172)
(0.056)
(0.179)
(0.378)
(0.106)
I
Schooling
0.301 ###
0.047
0.186 ###
0.190 ##
0.177
0.279 ###
0.236 #
0.248
0.376 ###
13-15 years
(0.046)
(0.054)
(0.047)
(0.079)
(0.191)
(0.069)
(0.135)
(0.369)
(0.091)
Schooling
0.454 ###
-0.229 ###
0.203 ###
0.106
0.547 ###
0.401 ###
0.058
0.312
0.893 ***
16 years
(0.054)
(0.072)
(0.057)
(0.113)
(0.191)
(0.087)
(0.209)
(0.622)
(0.140)
Schooling
0.261 ###
-0.066
0.360 ###
-0.076
0.047
0.625 ***
-0.067
-0.224
1.105 ***
17+ years
(0.054)
(0.062)
(0.052)
(0.108)
(0.285)
(0.079)
(0.245)
(7.612)
(0.265)
NOTES: Probit estimates for 2-year intervals in the case of young and mature men, and for 1-year intervals for
career women. Standard errors are enclosed in parentheses. The omitted group are high school graduates with 12 years
of schooling.
# Significant, from zero, at 10 percent level.
## Significant, from zero, at 5 percent level.
### Significant, from zero, at 1 percent level.
- 29 -
Increases in the level of schooling also have quantitatively
different effects on training likelihood, varying by source of training
and across various subgroups. We discuss each of these in turn.
First, compared with men, higher schooling among CPS women is
associated with smaller increases in the probability of training from
any source except, as noted earlier, additional training from regular
school sources. For example, relative to high school graduates (the
omitted group), female college graduates are only .37 percent more
likely to get company training as compared with .48 percent for their
male counterparts. Unlike their male counterparts, women graduates
report training from "other" sources as the only statistically important
source across education groups, the one exception being a greater
likelihood of company training for college graduates.
A comparison of NLS young and mature men also points to important
life-cycle training patterns among various educational groups,
especially with regard to company training. The likelihood of company
training is significantly higher for more educated young men in the NLS,
differences not apparent among NLS mature men. This training pattern is
consistent with, and may explain, observed wage profile differences
among schooling groups reported in the literature in which the more
educated experience higher rates of wage growth (Mincer 1974)
Finally, mature college graduates in the NLS get more training from
business and technical schools than do their younger counterparts.
Plausibly, this may reflect a greater demand among older workers for
retraining and skill-upgrading.
Technological Change
How does technological change affect skill requirements? Earlier,
it was noted that the likelihood of regular school training was lower in
jobs characterized by rapid technological change, especially for the
most educated. We pursue this line of questioning for each source and
type of training, using the same specification of technological change
interacted with level of schooling attainment. In addition, using the
CPS we examine the issue of how transferable prior job skills are across
jobs, and whether this relationship (if any) is affected by the rate of
technological change in the current job.
- 30 -
Tables 3.5, 3.6, and 3.7 summarize the effects of technological
change for the CPS and NLS samples. Table 3.5 reports the results for
the CPS of two kinds of training questions: training needed for or
important to getting the current job, and training to improve current
job skills. Tables 3.6 and 3.7 include both source and type of training
for the three NLS groups.
A strikingly similar pattern of technological change effects on
skill requirements is found in all five samples of men and women in the
CPS and NLS. First, note that company training to improve skills is
significantly more prevalent for the most highly educated in high-tech
industries: postgraduates among CPS males (Panel A of Table 3.5) and
NLS mature men (Panel B, Table 3.6), and NLS youth with 16 or more years
of schooling (Panel A). We adopt the term "high-tech" for brevity even
though it is not strictly correct. With variations, the result holds as
well for female workers. In high-tech industries, CPS women with 17 or
more years of schooling are more likely to get company training, while
those with a high school education or less are significantly less likely
to do so (Panel B, Table 3.5). This pattern of company training is
repeated among NLS career women (Table 3.7).
Surprisingly, less formal kinds of company training in the CPS are
not related to the rate of technical change. Only male high school
graduates are significantly more likely to get informal OJT in high-
tech industries; no systematic pattern of informal OJT is found for
other schooling groups or for women. It appears that informal OJT is
poorly recalled and reported, except perhaps when the respondent
perceives it to be important (see below).
In contrast, training (taken to improve skills, in the CPS) from
sources other than the employer is less prevalent in high-tech
industries. In the CPS samples, four out of five technical-
change/schooling interactions for training from "other" sources are
negative, several significantly so. Similarly, in the NLS the
likelihood of training from business and technical schools or "other"
sources is generally diminished in high-tech industries, with the more
educated being less likely to get such training.
Table 3.5
EFFECTS OF TECHNOLOGICAL CHANGE ON THE PROBABILITY OF TRAINING,
BY SOURCE: MEN AND WOMEN IN THE CPS
Training Needed to Get Current Job
Training to Improve Job Skills
Educational
Company
Informal
Attainment
Other
Training
OJT
Company
Informal
Sources
Other
Training
OJT
Sources
A. CPS Men
Schooling
-5.106
-4.213
<12 years
-9.854 ***
(4.469)
1.925
(2.695)
-1.200
(3.798)
2.822
(4.539)
(3.131)
(7.712)
Schooling
-4.980 **
-7.058 ###
12 years
-3.202
(1.955)
0.408
(1.725)
5.525 ###
(2.347)
-9.097 ##
(2.323)
(2.124)
(3.800)
Schooling
-5.512 ##
-2.596
13-15 years
-7.850 ##
(2.738)
2.878
(2.337)
-0.809
(3.124)
-0.306
(2.736)
(2.823)
(4.151)
Schooling
-6.472 ##
0.520
16 years
3.005
(2.981)
3.561
(2.874)
-0.561
(4.067)
-3.261
(2.984)
(3.358)
(4.311)
Schooling
-0.311
14.338 ###
17+ years
-2.083
(4.894)
15.322 ###
(3.632)
6.632
(5.734)
-8.307
- 31 -
(3.951)
(4.487)
(5.411)
B. CPS Women
Schooling
-24.843 ###
6.782 #
<12 years
-3.786
(6.595)
-23.692 ###
(3.853)
2.337
(8.168)
-46.897 ##
(8.936)
(4.620)
(20.172)
Schooling
-16.008 ###
-3.469 #
12 years
-4.424
(3.140)
-14.787 ###
(2.055)
-2.191
(4.149)
-23.260 ###
(2.824)
(2.487)
(4.239)
Schooling
-9.553 ##
-5.639 #
13-15 years
-0.331
(3.875)
-5.505
(3.132)
-2.988
(5.649)
-13.579 ###
(3.628)
(3.636)
(4.838)
Schooling
6.569
6.514
16 years
7.717
(5.558)
0.091
(4.920)
2.282
(8.901)
4.687
(4.900)
(5.283)
(6.625)
Schooling
-4.699
25.745 ## #
17+ years
13.223
(10.039)
18.435 ##
(7.046)
7.627
(11.348)
-27.000
(8.170)
(9.927)
(18.085)
NOTE: Standard errors of probit estimates in parentheses.
# Significant, from zero, at 10 percent level.
## Significant, from zero, at 5 percent level.
### Significant, from zero, at 1 percent level.
Table 3.6
EFFECTS OF TECHNOLOGICAL CHANGE ON THE PROBABILITY OF TRAINING,
BY SOURCE AND TYPE: MALES IN THE NLS
Source of Training
Type of Training
Business
and
Educational
Professional
Company
Technical
Other
Attainment
and
Training
Skilled and
Schools
Sources
Managerial
Technical
Semiskilled
A. NLS Young Men
Schooling
4.250
18.005 ###
7.035
9.938
<12 years
9.914
(4.621)
15.638 ###
(5.364)
(5.431)
(10.018)
(7.361)
(4.486)
Schooling
1.250
-4.796 #
-5.062 #
-1.498
12 years
-2.545
(2.465)
-4.675 ##
(2.715)
(2.620)
(4.524)
(2.699)
(2.221)
Schooling
0.283
-3.219
-7.542 ##
5.064
13-15 years
-3.153
(2.583)
-9.867 ###
(3.124)
(2.956)
(3.709)
(2.746)
(2.909)
Schooling
9.866 ###
-6.554
-8.612 #
10.435 ##
16 years
-6.851 ##
(3.351)
4.132
(4.820)
(4.624)
(4.931)
(3.353)
(4.850)
32 I I
Schooling
16.877 ###
0.302
-13.354 ###
20.738 ###
17+ years
-10.974 ###
(3.462)
-0.018
(4.550)
(4.305)
(4.235)
(3.428)
(6.630)
B. NLS Mature Men
Schooling
0.767
6.104
-0.554
6.618
<12 years
-6.983
(3.720)
(11.073)
n.a.
(2.941)
(4.901)
(5.385)
Schooling
-5.976
8.708
-3.273
-1.184
12 years
-11.809 ##
(4.752)
(9.873)
n.a.
(4.136)
(5.989)
(4.931)
Schooling
-1.232
-6.039
-17.600 ###
-3.662
13-15 years
-11.630 ##
(4.967)
n.a.
(11.804)
(5.263)
(8.691)
(4.544)
Schooling
-4.346
-17.591
-15.266 ##
-3.528
16 years
-16.202 ##
(8.660)
(24.302)
n.a.
(7.075)
(11.015)
(6.962)
Schooling
32.111 ###
-16.564
-5.786
34.462 ###
17+ years
-11.501
(7.226)
(25.753)
n.a.
(6.792)
(8.138)
(7.520)
NOTES: Probit estimates are for 2-year intervals. Standard errors in parentheses.
# Significant, from zero, at 10 percent level.
## Significant, from zero, at 5 percent level.
### Significant, from zero, at 1 percent level.
Table 3.7
EFFECTS OF TECHNOLOGICAL CHANGE ON THE PROBABILITY OF TRAINING,
BY SOURCE AND TYPE: CAREER WOMEN IN THE NLS
Source of Training
Type of Training
Business
and
Educational
Company
Technical
Professional
Other
Attainment
Training
and
Schools
Sources
Clerical
Managerial
Technical
Training
Schooling
-15.968
-14.184
<12 years
-16.084 ##
(12.218)
-4.090
(34.298)
-31.436 ##
(7.314)
-1.266
(231.742)
(15.366)
(19.064)
Schooling
-26.475 ##
32.934 ##
12 years
-17.349 ###
(10.210)
-52.363 ###
(15.941)
-30.165 ***
(5.726)
1.996
(14.528)
(8.128)
(7.970)
Schooling
33 I
12.051
11.019
13-15 years
-11.984
(11.649)
28.358 #
(57.907)
-5.956
(8.478)
5.296
(17.170)
(9.096)
(14.461)
Schooling
60.916 ##
-0.144
-27.543
16 years
(30.968)
76.039
(117.136)
-19.942
(21.719)
-5.134
(56.766)
(21.828)
(90.961)
Schooling
80.704 ###
-66.865
17+ years
-97.568 #
(26.109)
175.313 ###
(1682.919)
-51.023
(55.354)
94.234
(14.685)
(43.448)
(76.023)
# Significant, from zero, at 10 percent level.
NOTES: Standard errors in parentheses. Probit estimates are for 1-year intervals.
## Significant, from zero; at 5 percent level.
### Significant, from zero, at 1 percent level.
- 34 -
Together, both these and previous results for regular school
training provide important insights into the skill requirements that are
associated with technological change. They make two points: first,
that industries experiencing rapid technological change rely
significantly more on training from in-house sources than on outside
training, possibly because skills specific to new technologies are not
readily available outside the firm; and second, that company training is
especially prevalent among the most educated workers in high-tech firms,
and training from outside sources the least common. Both results lend
strong empirical support for Tan's (1980, 1986) model of technology-
specific skills and Welch's (1970) hypothesis of the allocative
efficiency of schooling.
Additional information about types of training are reported in the
NLS surveys (see Tables 3.6 and 3.7). Two points stand out in comparing
the likelihood of managerial and of professional and technical training
across samples. First, high-tech firms are associated with a greater
probability of managerial training, especially for the most educated
workers. Second, professional and technical types of training are
generally less prevalent in high-tech industries, but the relationship
varies across different schooling groups in the three samples; it is
significantly negative for the most educated youth and for the least
educated women.
To what may we attribute these results? One intuitive explanation
is suggested by the sources of these types of training. Employers
provide just under half of all reported managerial training and only 15
to 30 percent of all professional and technical types of training. Not
implausibly, one may characterize managerial training as being more firm-
specific, and professional and technical skills as being more general in
nature, i.e., equally useful to a number of different employers. As
technological change grows apace, requirements for more firm-specific
6 Among young men, about 52 percent of managerial training and 30
percent of professional/technical training come from company sources.
The corresponding proportions for mature men and career women are 48 and
30 percent, and 45 and 15 percent, respectively. Thus, between 50 and
85 percent of professional/technical training is provided by sources
other than the employer.
- 35 -
(or technology-specific) managerial skills are also likely to rise
relative to the demand for other general types of skills.
Other insights into the relationship between skill-specificity and
technical change may be gleaned from questions in the CPS on the sources
of training thought to be important (or needed) to get the current job.
How transferable is training from prior jobs? Table 3.5 suggests that
with a few exceptions, transferability of training from most sources is
diminished when new jobs are found in industries characterized by high
rates of technical change. In sharp contrast to the earlier finding,
both men and women working in high-tech industries are significantly
less likely to report that previous company training and informal OJT
was important. Only among postgraduates--presumably the group most
involved in R&D--is previous OJT important, i.e., transferable.
Clearly, the kinds of training information that we have explored
here are capable of yielding important insights into the skill
requirements needed for technological change.
Race
How likely are nonwhites to get training as compared with whites?
We address this question in a limited way by including an indicator
(0,1) variable, NONWHITE, in each probit estimate of training by source
and type. Thus, for example, we do not explore race difference in
training by schooling attainment or across high-tech and low-tech firms.
That analysis would require estimating separate probit models for each
ethnic group, a task beyond the scope of this study. Table 3.8
summarizes the net effects of race on the likelihood of training in the
five surveys.
With few exceptions, nonwhite males are significantly less likely
than white to get most kinds of post-school training, even after
controlling for a comprehensive set of observable worker attributes,
labor market experience, and job characteristics. This result is
especially striking, and statistically significant, for company training
and "other" training sources, and for managerial and
professional/technical types of training. Interestingly, the race
difference in training probability is quantitatively less pronounced for
young men than for mature men in the NLS or, for that matter, males of
all ages in the CPS.
- 36 -
Table 3.8
EFFECTS OF NONWHITE RACE ON THE PROBABILITY OF TRAINING,
BY SOURCE AND TYPE
A. Source of Training
Business
and
Company
Informal
Technical
Other
Group
Training
OJT
Schools
Sources
CPS males
-0.250 ***
-0.012
n.a.
-0.226 ***
(0.056)
(0.048)
(0.085)
CPS females
-0.142 ***
0.017
n.a.
-0.270 ***
(0.054)
(0.045)
(0.081)
NLS young men
-0.168 ***
n.a.
0.041
-0.136***
(0.044)
(0.051)
(0.046)
NLS men
-0.223 ***
n.a.
0.054
-0.029
(0.069)
(0.183)
(0.052)
NLS career women
0.155
n.a.
0.250
0.138 *
(0.132)
(0.347)
(0.080)
B. Type of Training
Professional
and
Semiskilled
Group
Managerial
Technical
Manual
Clerical
NLS young men
-0.218 ***
-0.120 ***
0.037
n.a.
(0.068)
(0.046)
(0.046)
NLS men
-0.177 **
-0.181 **
n.a.
n.a.
(0.087)
(0.073)
NLS career women
0.205
-0.024
n.a.
-0.059
(0.191)
(0.107)
(0.159)
NOTE: Standard error of probit estimates in parentheses.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 37 -
Race differences are less apparent for females. Among CPS women,
nonwhites get significantly less company training, but the race
differential is smaller than that for CPS men or for NLS youth. Among
NLS career women, race is not an important factor in training. In fact,
nonwhite women are more likely to get more training from both the
employer and business and technical schools, as well as managerial
training. However, only training from "other" sources is statistically
significant at the 5 percent level.
These results should be of interest to researchers studying race
differences in earnings. Numerous studies--for example, Smith and Welch
(1984) and Smith (1978) -have documented the existence of earnings
differentials among white and nonwhite males, and the absence of similar
differences among females. Differences in training propensity of
nonwhite males and females (relative to whites) highlighted by this
analysis suggest one possible explanation for these empirical
regularities.
Local and National Economic Conditions
A final issue is how local and national economic conditions affect
the likelihood of post-school training. As noted earlier, we control
for economic conditions in two ways. For the CPS, we use the state
indices of labor market conditions developed by Lillard (1986). These
include the state's long-run level of unemployment (SHAT), and the
cyclic sensitivity of the state relative to national unemployment cycles
(RHAT), both measured at the time a person joins the firm. For the NLS,
no state information is available, so we use the national unemployment
rate (NUR) as an aggregate measure of labor market conditions. Table
3.9 summarizes the effects of these variables on training probability.
The likelihood of getting training drops in local labor markets
characterized by persistently high unemployment rates or greater
cyclical volatility relative to the nation as a whole. These effects
are generally significant for training from in-house sources, informal
OJT in particular, but never for training outside the current firm.
These results are what one might expect if employment in states with
high values of SHAT and RHAT is concentrated in declining industries, or
- 38 -
Table 3.9
EFFECTS OF ECONOMIC CONDITIONS ON THE PROBABILITY
OF TRAINING, BY SOURCE AND TYPE
A. Source of Training
Business
and
Unemployment
Company
Informal
Technical
Other
Variable
Training
OJT
Schools
Sources
CPS males
SHAT
-2.615
-5.977 **
n.a.
-4.001
(2.488)
(2.447)
(3.352)
RHAT
-0.234 ***
-0.136 ***
n.a.
-0.083
(0.042)
(0.039)
(0.059)
CPS females
SHAT
-0.629
-9.384 ***
n.a.
-4.183
(2.746)
(2.659)
(3.675)
RHAT
-0.199 ***
-0.089 **
n.a.
-0.067
(0.048)
(0.043)
(0.059)
NLS young men
NUR
-0.002
n.a.
0.011
0.007
(0.012)
(0.013)
(0.012)
NLS men
NUR
0.014 ***
n.a.
0.014
0.013 ***
(0.005)
(0.015)
(0.004)
NLS career women
NUR
0.111 *
n.a.
0.040
-0.083 **
(0.058)
(0.154)
(0.035)
B. Type of Training
Professional
and
Unemployment
Technical
Semiskilled
Variable
Managerial
Training
Manual
Clerical
NLS young men
NUR
-0.013
0.015
0.010
n.a.
(0.017)
(0.010)
0.013)
NLS men
NUR
0.008
0.023 ***
n.a.
n.a.
(0.006)
(0.005)
NLS career women
NUR
-0.231 ***
0.148 ***
n.a.
-0.078
(0.088)
(0.045)
(0.065)
NOTE: See text for definitions of SHAT, RHAT, and NUR.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 39 -
in firms with cyclically sensitive product demand. If layoff rates are
high as a consequence, both employers and workers have few incentives to
either provide or take company training in job-specific skills, since
there is little probability of recouping training costs.
In the NLS, where the national unemployment rate (NUR) is used, a
different pattern of training effects is observed. For mature men and
career women, periods of high national unemployment are associated with
a greater likelihood of training from company sources, especially
professional and technical types of training, while no significant
effects are found for young men. One possible interpretation is that
employers are more likely to retrain older workers during periods of
slack economic activity when the opportunity cost of their time is low.
The apparent contradiction between the two kinds of training effects is
more apparent than real. They measure different phenomena: SHAT and
RHAT, the effects of persistent local labor market conditions; NUR, the
marginal effects of changing economic condition on training, given
persistent differences in local labor market conditions.
TRAINING AMONG THE DISADVANTAGED
Up to now, we have focused on training taken by nationally
representative samples of the population (CPS men and women) and by
several distinct demographic groups (the three NLS cohorts). We now
turn to the analysis of training among the economically disadvantaged
EOPP sample.
We noted earlier that a significantly smaller proportion of the
EOPP sample reported getting training, especially OJT, as compared with
other groups in the CPS and NLS. Moreover, their training patterns
resembled that of women with intermittent labor force participation.
These observations suggest several relationships that may explain the
low likelihood of training among the economically disadvantaged: low
levels of schooling, racial discrimination, and weak labor force
attachment.
To address some of these hypotheses, we estimated separate probit
models for training from three sources: OJT, business and vocational
schools, and miscellaneous other sources including participation in
- 40 -
government sponsored programs. In each, we related the probability of
training to a set of covariates that included educational attainment,
years of work experience, time not working, and number of jobs held
since January 1979 (over an interval of about 18 months). In addition,
we included an indicator (0,1) variable to distinguish individuals
belonging to "low-income" households from the nonpoor control group, as
well as a race (NONWHITE) dummy variable. 7 Finally, we controlled for
union membership when it was known. These probit estimates are reported
in Table 3.10.
Several results are suggested by the probit estimates. First, they
confirm what was revealed by simple tabular analysis, namely, that
members of the low-income group are significantly less likely to get OJT
from employers than people in the control population. No differences
are found in the prevalence of training from other sources. Second, in
striking contrast to the results for other samples (see Table 3.8), the
coefficient of the nonwhite variable is not statistically significant.
Thus, being black per se is not associated with a lower likelihood of
training; being a member of the low-income group, and having traits
associated with a high incidence of unemployment or low income, is.
Finally, whatever these (unobserved) factors are, they also appear to
diminish the positive effects of schooling on training. Though the
likelihood of training rises with educational attainment, these effects
are very small as compared with other groups in the CPS and NLS.
Several other relationships between training and labor market
experience are suggested in Table 3.10. The likelihood of training
declines with years in the labor force, and with time not working since
January 1979 for OJT and training from other sources. The latter is
likely to be particularly important for the low-income group (and
presumably for women as well), who are more likely to experience spells
of unemployment or to exit and enter the labor market frequently.
Contrary to our priors, the number of job changes was associated not
with a lower likelihood of training, as might be expected if it
Participation in the EOPP was not random; to be eligible,
individuals had to meet the eligibility requirements of being unemployed
and being either below a given family income (twice the poverty level
for a given family size) or on AFDC or SSI.
- 41 -
Table 3.10
PREVALENCE OF TRAINING BY SOURCE AMONG DISADVANTAGED
WORKERS EOPP SAMPLE
Source of Training
Business
and
Vocational
Other
Variable
OJT
Schools
Sources
Intercept
0.037
0.071
0.054
(0.005)
(0.006)
(0.007)
Low-income group
-0.007 **
-0.005
-0.002
(0.003)
(0.004)
(0.005)
Nonwhite
0.0001
-0.004
-0.0010
(0.004)
(0.005)
(0.005)
Schooling <12 years
-0.009 **
-0.026 ***
-0.005
(0.004)
(0.005)
(0.005)
Schooling 13-15 years
0.017 ***
0.016 **
0.024 ***
(0.005)
(0.006)
(0.006)
Schooling 16 years
0.023 ****
-0.015 *:
0.036 ***
(0.006)
(0.008)
(0.008)
Schooling >17 years
-0.0003
-0.037 ***
0.057 ***
(0.007)
(0.008)
(0.009)
Years of work
-0.0005*
-0.001 ***
-0.0009
experience
(0.0001)
(0.0002)
(0.0002)
Time not working
-0.001 **
-0.0003
-0.002 **
(0.0005)
(0.0006)
(0.0007)
Missing time
-0.010
-0.012
-0.025 *
(0.010)
(0.012)
(0.013)
Union member
0.008 **
-0.006
-0.0005
(0.003)
(0.004)
(0.005)
Missing union
-0.012 *
-0.016 *:
0.010
(0.006)
(0.008)
(0.008)
No. jobs since 1/1/79
0.003
0.009 ***
0.008 ***
(0.002)
(0.002)
(0.002)
NOTE: Probit specification.
Standard errors reported in
parentheses.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 42 -
accurately proxied weak job attachment, but rather with significantly
higher training from business/vocational schools and from other sources.
In part, this may simply reflect quits or voluntary job changes not
associated with an unemployment spell (since we are controlling for time
not working) to pursue training from sources outside the firm.
SUMMARY
In this section, we investigated several determinants of training
to shed light on the reasons for observed differences in the amounts and
kinds of training received by various demographic groups. We identified
four important factors.
First, a higher level of educational attainment increases the
likelihood of training, though the quantitative effects on training vary
considerably across demographic groups and, within groups, across
different kinds of training. These schooling effects are smaller for
women than for men, and especially so for the disadvantaged group as a
whole.
Second, schooling is also related to training through its
interaction with the rate of technological change in the current job.
The likelihood of getting company and informal OJT is greater in
industries experiencing rapid technological change, especially for the
most educated workers. This result holds equally for both men and
women. Moreover, the demand for in-house and managerial training
increases relative to general types of training from external sources
when the pace of technological change is high.
Third, nonwhite males are significantly less likely than whites to
get most kinds of training. Interestingly, however, these racial
differences in training are not apparent among females, or among the
disadvantaged sample.
Finally, the evidence suggests that training is diminished in
states characterized by persistently high levels of unemployment or by
greater cyclical volatility in unemployment relative to the nation as a
whole.
- 43 -
IV. THE ECONOMIC CONSEQUENCES OF TRAINING
Up to now, we have painted a broad picture of the overall patterns
and determinants of training among various groups in the population. We
now turn to an empirical analysis of the effects of training on
earnings, earnings growth, and employment stability. In particular, we
are interested in identifying the sources and types of training that are
importantly related to the outcomes of interest, and in estimating the
duration of such effects.
While we are critically aware that the decision to offer or to
receive training may be endogenous and thus subject to "self-selection"
problems, 1 we do not treat the issue here econometrically, simply
because the issue is more difficult than the scope of this research
project permits us to consider. Many of the issues are discussed in
Lillard and Kumbhakar (1986). The primary difficulty is the treatment
of multiple occurrences of multiple training sources and types, and of
their locations in time. These issues have not been raised in the
literature, much less solved. The availability of panel data from the
NLS makes the empirical relevance of these issues obvious. Our approach
in this section is to document the importance of dynamic patterns in
training for use in the development of future econometric models, and to
treat these training sequences as if they were exogenous for the current
specifications.
In the analysis, we focus on men and women in the 1983 CPS and in
the NLS Young Men cohort. Of the surveys considered so far, these two
have the most complete information on previous training history, but
even they have limitations. 2 The CPS is a cross-sectional survey with
current annual and weekly earnings data and retrospective data on
Ashenfelter (1978).
¹For example, see Willis and Rosen (1979), Keifer (1979), and
taken prior to the first survey in 1966. The problem of incomplete
effects is severely limited by the absence of information on training
²For both women and mature men in the NLS, the analysis of training
training histories is particularly acute for mature men, all of whom had
been in the labor force for at least 25 years prior to the time when we
first observe them.
- 44 -
multiple sources of training both to get the current job and to improve
current job skills, but without any information on either the number of
such events or their timing. On the other hand, the NLS Young Men data
contain annual earnings most years of the panel, but information on
weekly earnings only in some selected years. Training information is
available for every survey period, but multiple sources or types of
training within a period are not known, only the longest is. These
caveats, and those pertinent to endogenous training choices noted above,
should be kept in mind in the analyses that follow.
We begin with an examination of the effects of training on earnings
of men and women in the CPS. Next, we estimate earnings models for the
NLS sample of young men, including dynamic training effects, which we
treat as predetermined. This is followed, again for the NLS sample, by
an analysis of the impact of training on the likelihood of unemployment
and, conditional on unemployment, on weeks unemployed. We conclude with
a summary of the main findings.
THE 1983 CURRENT POPULATION SURVEY (CPS)
The analysis of training outcomes is based on earnings and weeks
worked over the previous year reported in the March 1983 CPS. We are
able to do this by merging the January and March CPS files. The sample
is restricted to males who report positive earnings and weeks worked the
previous year. We exclude the self-employed, those currently unemployed
during the survey week (they are included in the subsequent analysis of
unemployment as a training outcome), and those with such low earnings
(weekly wages less than $10) that we presume their earnings are
incorrectly recorded. Finally, we exclude individuals who entered the
firm of current employment prior to 1960. This was motivated by our
concern over possible recall error regarding training taken some 23 or
more years earlier. This resulted in a sample with 11,202 observations.
Two specifications of a wage model are used to study the effects of
training on earnings. The first is the conventional wage model
typically estimated in the human capital literature, where the logarithm
of annual earnings is related to educational attainment, potential work
experience, job tenure, and controls for a variety of demographic and
locational variables and job and local labor market characteristics. In
- 45 -
the second specification, we include additional variables for reported
training. These wage models are estimated by ordinary least squares
(OLS), and the results reported in Table 4.1.³
Column one of Table 4.1 presents the estimates for the first model
specification. The results are broadly similar to those reported
elsewhere in the literature and will be summarized only briefly here.
The returns to schooling are on the order of about 11 percent. 4 The
effect of experience prior to joining the firm of current employment,
while significantly positive, is small (0.7 percent) compared with that
of job tenure (which has a linear effect of about 15 percent). The
control for low work experience is significantly negative, indicating
that the earnings profile rises quite steeply in the first five years in
the labor market. Nonwhites, people living outside the western United
States (the omitted region), and those working in nonunionized firms
receive lower earnings.
The results also indicate that the returns to schooling are higher
if the individual worked in a high-technology industry. This result is
indicated by the positive interactions between schooling categories and
the measure of industry rates of technical change. The parameters of
these interactions are larger and more statistically significant for the
higher schooling categories. To illustrate, note that the effect is two
to three times larger for those with advanced degrees than for high
school graduates. This finding provides support for the allocative
efficiency hypothesis that better educated workers are more adept at
responding to technological change (Welch, 1970; Tan, 1980).
The results of the second model specification suggest that several
reported measures of training are associated with higher earnings. In
3A similar set of estimates for weekly earnings is reported in the
Appendix tables. Differences between the annual and weekly earnings
estimates reflect labor supply effects, that is, the effects of
variations in weeks worked last year. Because the results appear to be
broadly figures. similar, the following discussion focuses on the annual earnings
A continuous measure of educational attainment is used because
years of schooling appeared to have a linear effect on earnings in an
variables. alternative specification where schooling was entered as categorical
- 46 -
Table 4.1
THE EARNINGS AND TRAINING EQUATIONS FOR CPS MEN
Model specification
Variable
(1)
(2)
(3)
Constant
7.598
7.871
7.858
(.062)
(0.065)
(0.067)
Years of schooling
0.111 ***
0.083 ***
0.079 ***
(0.003)
(0.003)
(0.004)
Nonwhite
-0.239 ***
-0.219 ***
-0.200 ***
(0.026)
(0:026)
(0.025)
South region
-0.022)
-0.023
-0.010
(0.021)
(0.021)
(0.021)
North East region
-0.142 ***
-0.133 ***
-0.117 ***
(0.025)
(0.024)
(0.024)
North Central region
-0.091 ***
-0.086 ***
-0.077 ***
(0.022)
(0.021)
(0.021)
SHAT (long-run state
-0.138
0.375
0.766
unemployment rate)
(1.571)
(1.550)
(0.534)
RHAT (cyclical sensitivity
0.126 ***
0.138 ***
0.127 ***
of state unemployment)
(0.029)
(0.029)
(0.028)
Union member
0.160 ***
0.184 ***
0.181 ***
(0.026)
(0.026)
(0.025)
Union missing
-0.003
0.008
0.006
(0.019)
(0.016)
(0.015)
First 5 years of work
-0.458 ***
-0.457 ***
-0.436 ***
(0.021)
(0.020)
(0.020)
Prior work experience
0.007 ***
0.007 ***
0.006 ***
(0.001)
(0.001)
(0.001)
Years of job tenure
0.152 ***
0.144 ***
0.142 ***
(0.004)
(0.004)
(0.004)
Tenure squared
-0.005 ***
-0.005 ***
-0.005 ***
(0.0002)
(0.000)
(0.0002)
Technological change interaction
Schooling <12 years
-0.377
-0.942
-0.705
(1.558)
(1.536)
(1.522)
- 47 -
Table 4.1--continued
Model specification
Variable
(1)
(2)
(3)
Schooling 12 years
3.635 ***
4.363 ***
4.746 ***
(1.088)
(1.074)
(1.063)
Schooling 13-15 years
2.352 *
3.035 **
3.131 **
(1.469)
(1.451)
(1.439)
Schooling 16 years
6.883 ***
6.411 ***
6.364 ***
(1.808)
(1.784)
(1.767)
Schooling 17+ years
9.161 ***
8.808 ***
8.164 ***
(2.332)
(2.304)
(2.281)
Training to improve skills
Regular school
0.021
0.014
(0.024)
(0.024)
Company
0.270 ***
0.224 ***
(0.021)
(0.021)
On-the-Job
0.056 %%
0.044 **
(0.020)
(0.020)
Other
0.135 ***
0.112 ***
(0.035)
Training to get job
(0.035)
Regular school * years
0.014 ***
0.021 ***
of schooling
(0.001)
(0.008)
Regular school
-0.099
(0.123)
Company
0.176 ***
(0.023)
On-the-Job
0.198 ***
(0.016)
Other
-0.012
(0.027)
R-square
.362
.380
.3934
SOURCE: 1983 CPS.
NOTE: Standard errors in parentheses.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 48 -
column two, which includes training to improve job skills from each of
the sources, company training has the largest effect on earning (27
percent), followed by training from "other" sources (13 percent).
Oddly, informal OJT has only a small (though significant) earnings
effect of about 5 percent. The effect of regular school training is
statistically insignificant. However, schooling returns are higher if
the individual reported that regular school training was important in
getting the current job. The coefficient of the interaction between
these two variables varies between 1.5 and 2.0 percent, or about one-
sixth to one-quarter the estimated returns to schooling.
When measures of training to get the current job are included
(column three), the estimated effects of training to improve fall, but
only marginally. Of the training reported as needed, several training
sources proved to be important: company training and OJT from previous
jobs had a statistically significant effect on earnings in the current
job. It thus appears that many respondents accurately perceive that
some of their prior skills are transferable to other jobs, and report
accordingly. For these individuals, at least, the results indicate that
previous company training and informal OJT are quite portable: Their
effects on earnings in the current job (between 17 and 20 percent) are
not too different from the returns to company training on the current
job.
The CPS results, while informative, nonetheless are limited by the
cross-sectional nature of the data and the kinds of training questions
asked. The reporting of only two training events in the CPS (when our
earlier results indicate that multiple training events are common), and
the lack of information on when training to improve skills was taken,
limits our ability to estimate appropriately the effects of training on
earnings. In particular, we are unable to identify the time path of
these effects on earnings. Do the earnings effects of training persist
or are they dissipated over time? Are there variations in these effects
among the different kinds of training?
- 49 -
THE NATIONAL LONGITUDINAL SURVEY OF YOUNG MEN (NLS)
The panel nature of the NLS Young Men data allows us to investigate
the effect of training in greater depth. In what follows we describe
the econometric specification of the earnings equation, including
dynamic training effects--treated as predetermined. We also describe in
detail the construction of the measured training variables which follow
from the econometric specification. Then we report the main findings.
First, consider the earnings and training information available for
the NLS Young Men. We created a data set that pooled data across five
years: 1969, 1973, 1975, 1978, and 1980. These survey years were
selected because (relatively) clean data were available on both wage and
salary income and weeks worked in the past year, from which the
dependent variables - the logarithm of annual earnings and weekly wages-
can be calculated. 5
The samples for each survey year included those who worked sometime
in the past year and reported positive earnings. Those currently
unemployed at the time of the survey were also excluded (they are
included in the next section, where training effects on weeks unemployed
are investigated). This yielded the following sample sizes for each of
the survey years:
1969 = 2663
1973 = 3067
1975 = 2978
1978 = 2693
1980 = 2605
for a total pooled sample of just over 14,000 observations.
The Earnings Equation
We begin with a fairly standard specification of the earnings
equation in the absence of training. It is similar to the equation used
for the CPS data in terms of the vector of explanatory variables, X.
However, we now introduce panel data in which individuals are observed
repeatedly over time. Denote annual earnings (log) or the weekly wage
The computed weeks-worked variable in the other years was not
considered reliable: The algorithm produced a large number of
respondents who worked more than 52 weeks in the past year.
- 50 -
(log) of individual i in period t by Y(i,t), and similarly the vector
of regressors, X(i,t). Implicitly, the regression function Y(i,t)
= X(i,t)*1 + U(i,t) involves individual life-cycle earning or wage paths,
including potential experience and tenure on the job, which depend on X.
For a fuller treatment of these notions, see Lillard (1985), and Lillard
and Willis (1978).
The point of departure here is that earnings in the current period
may be affected in a number of ways by training. First, training in the
current period may actually reduce earnings through reduced productivity
during the learning period if the training occurs on the job or requires
leaving the job, as in the case of going to school or training classes.
Even if training does not directly reduce earnings, earnings may be less
in the current period if the training is completed in the middle of the
earnings interval and only increases earnings upon completion.
Second, the occurrence of the event of current and past training
may simply shift the earnings function up (presumably) by some
proportion. We assume for simplicity that each occurrence cf training
enhances earnings by the same amount. Each source or type of training
may have a different effect on earnings.
Third, the occurrence of training may alter the wage or earnings
growth rate of the worker permanently. In this case the effect of a
training event will depend on how long ago it occurred, the duration
since training, since at that point in the past the growth path was
changed. Again each source or type of training may have a different
effect. One may presume that training will enhance the growth of
earnings or wages.
An alternative model of the effect of training may be couched in
these same terms. That is, training may enhance earnings only
temporarily, with an initial increase that deteriorates over time. Then
the effect on earnings growth would be negative in the immediate period
after the initial increase. This may be an appropriate interpretation
if the work environment is rapidly changing or if the worker is more and
more likely over time to change jobs, either within or between firms, so
that the training becomes less valuable.
- 51 -
In accordance with these notions, we define three training
variables for their effect on earnings in the current period: (1)
whether training was taken since the previous interview, termed
"current"; (2) the accumulated sum of all training events taken since
1967 or whenever the individual first began work as a major activity,
termed "events"; and (3) the duration of time between getting training
and the current period, accumulated over all training events, termed
"duration." These three measures provide a parsimonious way of
characterizing the effects of multiple episodes of training on earnings.
Separate training measures are calculated for each source and type of.
training.
We provide below summary descriptions of these training measures
for the first and last years in the pooled sample: 1969 and 1980. To
read these tables, consider the three measures of training in Panel A of
Table 4.2. The first row for current training--shows that 19.3 percent
of the 1969 sample got some kind of training in the previous year. The
second row--the cumulated number of training events--shows that 68.9
percent had not received any training by 1969, 22.2 percent reported one
training event, and almost 9 percent got two or more training events.
The third row shows the total duration of time since training. Since a
total of three events may be reported, one for each of the years 1967 to
1969, the maximum duration summed across all events is 6 years. In
Panels B and C, these measures are reported by source and type for
selected kinds of training.
The second set of training measures is for 1980, the last year of
the pooled sample. Compared with the first year, individuals in this
sample may report an additional 7 training events, for a maximum of 10.
From Panel A, note that the proportion without any training falls (from
69 percent in 1969) to about 27 percent by 1980. Almost half have
between 2 and 5 training events, and over 6 percent have 6 or more. For
these latter groups, the duration of time since training can be in
excess of 11 years (exceeding 50 years for some). When cumulated
training is disaggregated by source and type, repeated training (2 to 5
events) is most common from company training programs and other school
sources, and for professional, technical and semiskilled training types.
Table 4.2
SUMMARY MEASURES OF TRAINING TAKEN BY NLS YOUNG MEN BY 1969 AND 1980;
SELECTED SOURCES AND TYPES OF TRAINING
By 1969
By 1980
Distribution of Years (%)
Distribution of Years (%)
Training Measures
0
1
2-3
4-5
6
0
1
2-5
6-10
11+
A. Any training
Current
80.7 19.3
68.5 31.5
Events
68.9
22.2
8.9
26.9
21.0
46.0
6.1
Duration
68.9
12.0
14.9
2.1
2.1
26.9
4.5
9.6
13.3
45.7
B. Source of training
Company Training
Current
93.9
6.1
86.2
13.8
Events
88.6
9.4
2.0
63.7
19.9
15.5
0.9
Duration
88.6
4.5
6.0
0.6
0.3
63.7
5.5
7.8
8.2
14.8
Business and technical
Current
97.1
2.9
95.7
4.3
Events
95.1
3.8
1.1
78.3
15.1
6.5
0.1
52 I I
Duration
95.1
2.1
2.4
0.3
0.1
78.3
2.4
4.1
8.4
6.8
C.
Type of training
Managerial
Current
98.1
1.9
94.4
5.6
Events
96.6
2.9
0.5
83.4
11.3
5.1
0.2
Duration
96.6
1.6
1.6
0.1
0.1
83.4
3.1
4.5
3.3
5.7
Professional-technical
Current
93.0
7.0
85.5
14.5
Events
89.5
8.8
1.7
60.0
20.7
18.7
0.6
Duration
89.5
5.7
4.3
0.4
0.1
60.0
5.3
4.9
11.7
18.1
Note: Training from regular schools and "other" sources excluded, as are semi-
skilled manual, clerical and "other" types of training.
- 53 -
This characterization of training forms the basis of our analyses
of the effects of training on earnings. In the following section, we
estimate wage models, where we relate annual earnings and weekly wages
to the three training measures, controlling for personal and firm
attributes, location, and labor market conditions. In addition, we
include year dummy variables to capture shifts in earnings over time
relative to 1980, the omitted year. We also report the findings of
separate analyses of the effects of training by source and by type of
training. The variables used, their definition, and summary statistics
are reported in the Appendix.
Earnings Results for NLS Young Men
Table 4.3 presents the results of estimating two variants of the
model: column one for the conventional wage model, column two for the
specification with the training variables.
Many of the results are familiar and consistent with findings
reported elsewhere in the extensive human capital literature on
earnings. Earnings exhibit the familiar quadratic shape, rising rapidly
with both years of work experience and job tenure, but at a slower pace
at higher levels of experience. Because this quadratic form may not
capture the steep rise in initial earnings, we included an indicator
variable for being in the first 5 years in the labor force. Its effect,
however, is negative only for the weekly wage variant, and is never
statistically significant. Those in the South earn 9 to 12 percent less
than in other regions, as do blacks whose earnings are between 19 and 24
percent less than those of other racial groups.
Like the results for the CPS sample, schooling and its interactions
with technical change have the expected effects on earnings. Earnings
rise with level of schooling completed, especially for the most educated
individuals with college or advance degrees in industries experiencing
rapid technological change.
The estimates including the training variables are reported in the
second column of Table 4.3. The training variables have the postulated
effects on earnings. Training taken in the current period is associated
with an initial (and one-time) drop in earnings of 2.4 percent. In
=
- 54 -
2
Table 4.3
EARNINGS AND ANY-TRAINING EQUATIONS
Variable
(1)
(2)
Constant
8.735
8.708
(0.047)
(0.047)
1969 year dummy
-1.040
-1.012
(0.022)
(0.023)
1973 year dummy
-0.601
-0.592
(0.020)
(0.020)
1975 year dummy
-0.439
-0.429
(0.019)
(0.019)
1978 year dummy
-0.168
-0.173
(0.018)
(0.018)
Nonwhite
-0.245
-0.230
(0.014)
(0.014)
Schooling <12 years
-0.199
-0.159
(0.019)
(0.019)
Schooling 13-15 years
0.099
0.077
(0.016)
(0.016)
Schooling 16 years
0.296
0.273
(0.020)
(0.020)
Schooling 17+ years
0.392
0.369
(0.019)
(0.019)
Prior work experience
0.110
0.100
(0.006)
(0.006)
Experience squared
-0.004
-0.003
(0.000)
(0.000)
First 5 years of work
0.005
0.006
(0.024)
(0.024)
Years of job tenure
0.096
0.092
(0.004)
(0.004)
Tenure squared
-0.005
-0.004
(0.001)
(0.000)
Tenure missing
0.233
0.234
(0.038)
(0.033)
- 55 -
Table 4.3 continued
Variable
(1)
(2)
South dummy variable
-0.099
-0.095
(0.012)
(0.012)
Local unemployment rate
-0.002
-0.002
(0.003)
(0.003)
Unemployment rate missing
-0.047
-0.052
(0.022)
(0.022)
Technical change interaction
Schooling <12 years
-1.833
-2.092
(1.163)
(1.153)
Schooling 12 years
-0.817
-0.390
(0.843)
(0.836)
Schooling 13-15 years
-0.139
0.399
(1.023)
(1.015)
Schooling 16 years
6.382
6.352
(1.481)
(1.468)
Schooling 17+ years
8.414
8.277
(1.554)
(1.541)
Any training
Current
-0.024
(0.019)
Events
0.119
(0.012)
Duration
-0.011)
(0.002)
SOURCE: NLS Young Men.
NOTE: Standard errors in parentheses.
every subsequent year, however, training has two effects on earnings:
an 11.9 percent increase in earnings level (the coefficient of the
cumulated training variable), and a 1.1 percent decline with each year
since training (the duration coefficient). Thus, the net effect of
- 56 -
training is an increase of 9.5 percent (11.9 - 2.4) in the first year,
10.8 percent (11.9 - 1.1) in the second year, and a decline to zero by
the 11th year after training.
How do these time effects of training vary across training from
different sources? Panel A of Table 4.4 summarizes the earnings effects
of training, disaggregated by training taken in a company program, in
business and vocational schools, in regular schools, and from other
sources. The regression results on which these estimates are based are
reported in the Appendix. Several striking differences become apparent
in comparing the effects of the different kinds of training in the
earnings regressions. First, company training has the largest effect on
earnings (16 percent), followed by training from business and vocational
schools (11 percent), and over 8 percent from regular schools and other
sources. Second, company training is characterized by almost no decline
in initial period earnings as compared with a 10 percent fall for those
getting training in business and vocational schools. Finally, the rate
of decay in training effects on earnings is very similar across kinds of
training, varying between 1 and 1.3 percent per year. 6
Panel B of Table 4.4 reports the results of estimating the wage
models including type of training. Similar kinds of training effects
are found here. Taking training in the current period has negligible
effects on earnings for managerial and for professional and technical
training, and somewhat larger negative effects on semiskilled manual
training. The initial effects of the former types of training are
larger (14 to 16 percent) than the 9.5 percent for semiskilled training.
However, because the rate of decay of semiskilled training is lower, the
earnings effects of this type of training persist over a longer period
(15 years) as compared to 12 and 11 years for managerial and
professional/technical training, respectively. The earnings effects of
other types of training are short-lived, lasting no more than 7 years.
The time paths of the earnings effects of different sources and
types of training are summarized in Table 4.5. Because of the points
noted above, the positive effects of company training on earnings
6A similar, but more pronounced, pattern of training effects is
found in the weekly wage set of estimates. (See Appendix.)
- 57 -
Table 4.4
EFFECTS OF TRAINING ON ANNUAL EARNINGS OF NLS YOUNG MEN
BY SOURCE AND TYPE OF TRAINING
A. Source of Training
Business
and
Training
Regular
Technical
Company
Other
Variable
School
School
Training
Sources
Current
-0.046
-0.109 **
0.000
-0.008
(0.039)
(0.038)
(0.029)
(0.028)
Events
0.082 **
0.119 ***
0.169 ***
0.088 ***
(0.031)
(0.029)
(0.020)
(0.020)
Duration
-0.010 **
-0.013 ***
-0.013 ***
-0.009 ***
(0.004)
(0.004)
(0.003)
(0.003)
B. Type of Training
Professional
Training
and
Semi-
Other
Variable
Managerial
Technical
skilled
Types
Current
0.016
-0.011
-0.061 *
-0.025
(0.046)
(0.028)
(0.031)
(0.033)
Events
0.166 ***
0.142 ***
0.096 ***
0.101 ***
(0.035)
(0.021)
(0.020)
(0.026)
Duration
-0.014 ***
-0.013 ***
-0.007 **
-0.015 ***
(0.005)
(0.003)
(0.003)
(0.004)
SOURCE: The estimates are reported in full in the Appendix.
NOTE: Standard errors in parentheses.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
***
Significant, from zero, at 1 percent level.
- 58 -
Table 4.5
TIME PATH OF TRAINING EFFECTS ON ANNUAL EARNINGS
OF NLS YOUNG MEN
A. Source of Training and Percent Increase in Earnings
Business
Years Since
Company
and
Regular
Other
Taking Training
Training
Technical
School
Sources
Current period
16.8
1.1
3.5
8.0
1
year
15.5
10.6
7.1
7.8
3
years
12.8
7.9
5.0
6.0
6
years
8.8
3.9
1.9
3.2
9
years
4.8
0.5
12
years
0.8
Duration of positive
training effects
13 yrs
9 yrs
8 yrs
10 yrs
B. Type of Training and Percent Increase in Earnings
Professional
Years Since
and
Semiskilled
Other
Taking Training
Managerial
Technical
Manual
Types
Current period
18.2
13.1
3.5
7.6
1 year
15.2
12.9
8.9
8.6
3
years
12.4
10.3
7.5
5.6
6 years
8.2
6.4
5.4
1.1
9
years
4.0
2.5
3.3
12
years
1.2
Duration of positive
training effects
12 yrs
11 yrs
15 yrs
7 yrs
SOURCE: Table 4.4.
persist for 13 years, as compared with 8 to 10 years for the other
sources of training. Also note the markedly lower initial earnings when
training is taken from regular schools or business and vocational
schools. This may simply reflect earnings forgone in diverting work
time to attending school. However, these results hold even when we
control for weeks worked in the past year. Are employers providing
workers with these external sources of general training, and having them
- 59 -
pay for it through lower weekly wages? If so, then this view also
suggests that employers pay for training taken within the company, which
(presumably) is firm-specific.
THE EFFECTS OF TRAINING ON UNEMPLOYMENT
Here we investigate a second training outcome, employment
stability, using the NLS Young Men sample. The issue is whether
training reduces the likelihood of unemployment. Several other research
questions are also of interest here. Are some kinds of training, such
as company training, more effective than others in cementing worker-
firm job attachment, as suggested by the literature on firm-specific
human capital? Just as trained workers earn more, are they also
persistently less likely to be unemployed, or do training effects change
over time? Finally, how important are the labor displacement effects of
technological change?
For the analysis, we use the 1969 and 1980 NLS surveys, in which
NLS respondents are asked directly about weeks unemployed in the
previous year. In other years, this variable was thought to be less
reliable, because it was constructed from information on previous work
history (a similar problem was encountered in the earnings analyses).
For the analysis of unemployment probability, only individuals without
missing data on the weeks-unemployed question were included in the
analysis samples. This selection criterion resulted in a total pooled
sample of 6114 observations: 2954 in 1969 and 3160 in 1980.
The Probability of Unemployment
The effects of training on unemployment are investigated using a
probit model. The dependent variable is a dichotomous variable with a
value of 1 if an individual is either currently unemployed or has been
unemployed sometime over the past year, and 0 otherwise. This variable
is related to the vector of explanatory variables used in the previous
analysis of earnings. As before, these include the three measures of
training: training in the "current" period, accumulated training
"events," and "duration" since training.
- 60 -
Table 4.6 presents the maximum likelihood probit estimates of the
determinants of unemployment propensity. The first column, which
reports the estimates for the basic model, provides a benchmark for
comparison with the other models which include training variables.
Column two reports the results for having taken any vocational training.
Several results suggested by Table 4.6 resemble findings reported
by other research on the determinants of unemployment. First, the
likelihood of unemployment is lower for the more educated, and for
whites than for other racial groups. Second, those with less than five
years of labor market experience are more likely to experience
unemployment, but this declines with increasing years of job tenure (see
Mincer and Jovanovic, 1981). Third, as might be expected, individual
probabilities of unemployment rise with the national unemployment rate.
A final result, which is novel, is the absence of any relationship
between technological change and the likelihood of unemployment. Unlike
the previous results for earnings, the interactions between
technological change and schooling are not statistically significant
except for the negative interaction for high school graduates. We
interpret this finding to mean that a higher industry rate of
technological change in the current or last job is not associated with a
greater incidence of unemployment, or, in other words, that concerns
over the labor displacement effects of technical change are not
warranted.
The effects of training on the probability of unemployment mirror
the effects of training on annual earnings. The estimates in column
two suggest that vocational training is associated with a decline in the
likelihood of unemployment, an effect that persists for approximately 12
years. As before, the period over which training effects attenuate can
be calculated from the parameters of the cumulated training and training
duration variables, i.e., -0.240/0.019.
The unemployment effects of training vary systematically by source
(Panel A) and type of training (Panel B), as is evident in Table 4.7.
The probit results on which these estimates are based are reported in
full- in the Appendix. They suggest that of all the different sources of
training, company training has the most enduring effect on reducing the
- 61 -
Table 4.6
EFFECTS OF ANY TRAINING ON UNEMPLOYMENT PROBABILITY:
NLS YOUNG MEN SAMPLE
Variable
(1)
(2)
Constant
-1.119 ***
-1.097 ***
(0.099)
(0.102)
Schooling <12 years
0.176 ***
0.137 **
(0.065)
(0.066)
Schooling 13-15 years
-0.126 **
-0.097 ÷
(0.058)
(0.058)
Schooling 16 years
-0.506 ***
-0.479 ***
(0.088)
(0.089)
Schooling 1.7+ years
-0.601 ***
-0.581 ***
(0.078)
(0.079)
Technological change interaction
Schooling <12 years
-1.496
-1.264
(4.167)
(4.186)
Schooling 12 years
-6.084 to
-6.065 to
(3.141)
(3.142)
Schooling 13-15 years
-3.674
-4.920
(4.081)
(4.052)
Schooling 16 years
2.322
1.720
(7.838)
(7.943)
Schooling 17+ years
-10.098
-10.145
(7.066)
(7.238)
Nonwhite
0.328 ***
0.318 ***
(0.051)
(0.051)
South region dummy
-0.201 ***
-0.210 ***
(0.047)
(0.048)
Prior work experience
-0.001
-0.002
(0.006)
(0.006)
First 5 years of work
0.207 ***
0.148 **
(0.066)
(0.068)
Years of job tenure
-0.117 ***
-0.116 ***
(0.006)
(0.006)
- 62 -
Table 4.6--continued
Variable
(1)
(2)
Tenure missing
1.575 ***
1.563 ***
(0.081)
(0.081)
National unemployment rate
0.079 ***
0.099 ***
(0.017)
(0.018)
Any training
Current
0.198 **
(0.082)
Events
-0.240 ***
(0.056)
Duration
0.019 ***
(0.007)
SOURCE: NLS Young Men 1969, 1980.
NOTE: Probit Specification. Standard errors in
parentheses.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
likelihood of unemployment (12.8 years), followed closely by training
from "other" sources (12.3 years). In contrast, the effects of training
from regular school sources disappear within 7 years. Training from
business and vocational schools was not significant in inhibiting
unemployment. The effects of training on unemployment also vary by type
of training (Panel B). Professional and technical training reduces the
likelihood of unemployment the most, but because this effect attenuates
rapidly over time, training effects only persist over 11.8 years. The
effects of semiskilled manual and other types of training last between
12.2 and 10.7 years, respectively. Managerial training did not have a
significant effect in reducing the likelihood of unemployment.
- 63 -
Table 4.7
EFFECTS OF TRAINING ON UNEMPLOYMENT PROBABILITY:
NLS YOUNG MEN SAMPLE
A. Source of Training
Business
and
Training
Regular
Technical
Company
Other
Variable
School
School
Training
Sources
Current
-0.203
-0.056
0.256 **
0.412 ***
(0.200)
(0.178)
(0.129)
(0.123)
Events
-0.128
0.071
-0.332 ***
-0.357 ***
(0.149)
(0.124)
(0.099)
(0.087)
Duration
0.018
-0.011
0.026 **
0.029 ***
(0.019)
(0.016)
(0.012)
(0.010)
B. Type of Training
Professional
Semi-
Training
and
skilled
Other
Variable
Managerial
Technical
Manual
Types
Current
-0.133
0.226
0.305 **
0.252 *
(0.247)
(0.154)
(0.127)
(0.147)
Events
-0.120
-0.459 ***
-0.159 *
-0.224 **
(0.208)
(0.124)
(0.081)
(0.106)
Duration
0.001
0.039 ***
0.013
0.021
(0.029)
(0.013)
(0.009)
(0.014)
SOURCE: NLS Young Men 1969, 1980.
NOTE: The probit results on which this table is based
are reported in full in the appendix. Standard Errors of
probit estimates in parentheses.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 64 -
We also analyzed the effects of training on the duration of
unemployment for those currently unemployed or who had experienced a
spell of unemployment over the past year. These results, reported in
the Appendix, suggest that weeks unemployed are longer for those
unemployed workers with less than a high school degree, are shorter for
workers in the South, are shorter for workers with more job tenure, and
are longer when the national monthly unemployment rate (averaged over 12
months) is high. However, the effects of training are very imprecisely
estimated, largely because of the small sample of unemployed workers in
any given period, so that any statements of relationships would be
correspondingly tentative. Therefore, we simply conclude that we cannot
find any significant effect.
- 65 -
V. SUMMARY AND CONCLUSIONS
We have used reported training measures taken from five surveys--
the CPS, three cohorts from the NLS, and the EOPP--to paint a broad
picture of post-school training in the United States. We have sought to
answer the question of who gets training, how much, and why, and the
effect of training on earnings and employment. In this section, we
summarize the main findings on the determinants of training and its
effects, highlight questions they raise, and discuss areas where future
research will be most useful in guiding training policy.
Our use of reported training measures represents an important
departure from the traditional reliance in the literature on alternative
proxies for training, such as work experience and job tenure. The focus
on these latter proxy variables, though important in testing different
theories, has nonetheless precluded a better understanding of the
empirical correlates of training and its effects, which can come only
from actual measures of training received. Our report confirms that
only the more formal kinds of training tend to be reported, but that
they appear to be reported consistently. In fact, our results using
these measures are remarkably consistent, despite the different types of
information covered by the data sources, the different time intervals
that they reflect, and the different groups of workers that they
include.
What they reveal about training deserves special emphasis. On the
most general level, they suggest that highly aggregated descriptions of
training miss important differences among various sources and types of
training, their determinants, and their consequences for earnings and
employment. There is not one kind of training, but various kinds for
different purposes. Some kinds of training are relevant in the context
of technological change, and some are not. Some actively complement
formal schooling, and some do not: The various kinds of training also
have different effects on earnings and the likelihood of unemployment,
and some effects persist longer than others.
- 66 -
The gross data in Sec. II revealed several aggregate differences
across the various groups in the amounts and kinds of training received.
First, both young men and career women in the NLS get more training than
mature men, but women receive proportionately less company training than
either of the other two male cohorts. In contrast, employed men and
women in the CPS report get roughly similar amounts and kinds of
training. The absence of any sex differences here may simply reflect
sample selection (employed individuals), the age composition of the
different samples, or other variables that we have not controlled for.
Finally, compared with these populations, the economically disadvantaged
EOPP sample get the least training, especially from company sources.
Their training experience most closely resembles that of women with weak
attachment to the labor force.
In Sec. III, we inquired into these differences by analyzing the
determinants of training in each group. We estimated probit models
relating training by source and by type to a comprehensive set of
covariates. These included controls for variables such as educational
attainment, worker characteristics, labor market experience, the
industry rate of technical change, and local and national labor market
conditions.
This analysis confirmed the importance of formal schooling as a
determinant of post-school training. For both men and women, and for
the economically disadvantaged group as well, the likelihood of getting
most kinds of training rises with the level of educational attainment,
except for the most highly educated group (those with postgraduate
degrees). This suggests that both sources of "training" are strongly
complementary. However, the quantitative importance of schooling
varies. Compared with men, increased schooling among women is
associated with a relatively smaller increase in company training and a
larger increase in training from other sources. For the disadvantaged
sample, the effects of schooling on the likelihood of training is
smaller in all cases when compared with other groups in the population.
Formal schooling also plays an important role in getting the
current job. The postgraduate group, in particular, is significantly
more likely to report formal schooling as important over other
- 67 -
alternative sources of training. Possibly because the job-related
content of their schooling is already high, postgraduates tend to get
less of other kinds of training while working. The exceptions are those
employed in high-tech industries, where formal school training may be
less pertinent to the requirements of new technologies. In those firms,
this group receives significantly more company and informal training,
and are more likely to report OJT from prior jobs as being needed to get
the current job.
These results are part of an overall pattern of skill requirements
accompanying technological change found throughout the report. As the
rate of technological change quickens, the probability increases of
getting managerial training and training from in-house sources such as
company programs or informal on-the-job training (OJT), especially for
employees with more education. In contrast, the likelihood of getting
professional, technical, and semiskilled manual training, or training
from external sources such as business, technical, and traditional
schools, falls as the rate of technological change quickens. These
results confirm what has hitherto only been speculation, namely, that
rapid technological change leads to increased reliance on in-house
training, possibly because technology-specific skills are not readily
available elsewhere, and to greater demand for highly skilled and
educated employees, who may adapt more readily to new technologies.
These results provide insights into a related issue of how
transferable skills are across jobs. We find that prior work skills are
less important when new jobs are found in industries characterized by
high rates of technological change. In contrast to the earlier finding,
both men and women working in high-tech industries are significantly
less likely to report that previous company training and OJT was
important in getting the current (or last) job. An exception, as we
have already noted, is postgraduates, for whom previous informal OJT is
important. Their OJT may embody many noncodified kinds of technical and
managerial skills not taught in traditional schools but important,
nonetheless, for technological progress in new jobs.
The probability of getting training is affected by economic
conditions, but in ways that differ depending upon whether cross-
sectional or panel data are used. First, in the cross-section, the
- 68 -
likelihood of getting most kinds of training is depressed in local labor
markets characterized by persistently high unemployment rates or greater
cyclical volatility relative to the nation as a whole. We suspect that
layoffs are more common in such states, possibly because employment is
overrepresented in declining industries or in firms with volatile
product demand. Employers and workers therefore have few incentives to
provide or get training, since the likelihood of recouping training
costs is low. Second, using time-series data, we find that periods of
high national unemployment are associated with a greater likelihood of
training from company sources, especially professional and technical
types of training, for career women and mature men but not for youth.
One possible interpretation is that employers retrain older workers
during periods of slack economic activity when the opportunity cost of
their time is low.
Training propensity might be expected to vary over the life-cycle,
and it does. The likelihood of getting most kinds of training is low in
the first five years in the labor market, coinciding with an initial
period of job search. In the absence of job attachment, this likelihood
continues to fall over time but at a lower pace; however, the likelihood
of training rises with time on the job. The implication of these
results is that those who work intermittently or change jobs frequently
receive less training over the life-cycle.
Interestingly, when we control for observable worker attributes,
nonwhite males are significantly less likely to get most kinds of post-
school training, while race differences are not apparent among females.
In fact, nonwhite females tend to get more training, though the
differences are generally not statistically significant. These
differences in training offer one possible explanation for observed
earnings differentials among white and nonwhite males, and the absence
of race differences among females noted in the literature.
In Sec. IV we investigated the effects of training on subsequent
labor market outcomes of men. We estimated earnings models both with
and without measures of reported training, and probit specifications for
the likelihood of unemployment. Large differences in the effects of
training on earnings, earnings growth, and unemployment were found,
depending upon the source or type of training. Many of these results
- 69 -
are consistent with, and may explain, the determinants of training found
earlier to be important.
For example, earnings are observed to rise with the level of
schooling completed in industries experiencing rapid technical change,
especially for people with bachelor or postgraduate degrees. Earlier,
we had hypothesized that better educated workers are more adept at
responding to technical change, and therefore are more productive in
high-tech firms. Supporting evidence for this "allocative efficiency of
schooling" hypothesis was found in the greater likelihood of in-house
training among the most educated workers in high-tech industries and,
here, in their higher productivity and earnings.
The effects of training on earnings and earnings growth vary by
source and by type. Among the different sources of training, company
training has the greatest quantitative effect on increasing earnings,
persisting for over 13 years. The effects of training from other
sources are much smaller and persist for 8 to 10 years. When types of
training are considered, managerial training increases earnings the
most, but its effects are less enduring (12 years) than the effects of
semiskilled manual training (15 years).
The effects of training on reducing the likelihood of unemployment
mirror the earnings-augmenting effect of training. On average, training
is associated with a subsequent decline in the likelihood of
unemployment lasting approximately 12 years. Of all the sources of
training, company training is most enduring (12.8 years), while the
effects of training from regular school sources disappear within 7
years. Variations in these effects are also found across training
types. Professional and technical training reduces the likelihood of
unemployment the most, but because this attenuates rapidly over time,
its effects persist only over 11.8 years. In contrast, managerial
training did not have a significant effect on unemployment. The absence
of any effect may simply reflect different (and offsetting) influences
of training on increasing the trained manager's value both to the firm
and to other employers, but this is speculation.
Finally, unlike the previous result for earnings, the industry rate
of technological change in the current (or last) job is not
statistically correlated with the probability of experiencing an
- 70 -
unemployment spell in the past year. In fact, for the sample of male
youth studied, a higher rate of technological change is typically
associated with a lower probability of unemployment, and for high school
graduates, this relationship is statistically significant. At least for
this group of youth, the results suggest that concern over the labor
displacement effects of technological change may be misplaced. Whether
or not this finding holds for other groups is a subject for future
research.
In this report, we have developed rough estimates of the amounts of
training received by different groups, and taken an important first step
in identifying the major determinants of training and its effects on
labor market outcomes. However, many important questions are raised by
several research findings:
The complementarity between formal schooling and post-school
training. What kinds of training acquired in traditional
schools are most likely to be useful in subsequent work? Do
school curricula--vocational versus college preparatory
coursework--matter for the kinds of training received on the
job?
The relationships between technological change, schooling, and
skill requirements. What are the training needs (skill
shortages) of different industries, and can policies be
developed to encourage greater investments by employers and
other providers in skills required for technological change?
The relationship between training and local labor market
conditions. Which states have persistently high unemployment
rates or high cyclic volatility in unemployment relative to the
nation, and why?
Race differences in training propensity among males, and the
absence of any differences between males and females. What
factors account for these differences? To what extent has
increased training contributed to the narrowing of the earnings
gap between the ethnic groups, and between white and nonwhite
women? How have trends over time in the training received by
racial minorities changed?
- 71 -
The relationship between training and labor force attachment.
To what extent do women's skills obsolesce when they withdraw
from the labor force? What kinds of training or retraining
would facilitate their reentry?
- 73 -
APPENDIX
Table A.1
DETERMINANTS OF TRAINING TO GET CURRENT JOB AND IMPROVE SKILLS:
CPS MEN
Training to Get Current Job
Training to Improve Job Skills
Variable
Company
OJT
Other
Company
OJT
Other
CONSTANT
-0.722 ***
-0.167 **
-1.052 ***
-0.969 ***
-0.863 ***
(0.089)
-2.193 ***
(0.074)
(0.099)
(0.071)
(0.067)
(0.113)
SCHLT12
-0.447 ***
-0.239 ***
-0.304 ***
-0.483 ***
-0.076 #
(0.053)
(0.037)
-0.392 ***
(0.053)
(0.059)
(0.046)
(0.090)
SCH1315
0.186 ***
0.143 ***
0.105 **
0.229 ***
0.102 ###
(0.038)
0.317 ###
(0.032)
(0.041)
(0.040)
(0.038)
(0.059)
SCH16
0.245 ###
0.134 ***
-0.120 **
0.478 ***
0.116 ***
(0.044)
(0.037)
0.552 ###
(0.052)
(0.043)
(0.043)
(0.062)
SCH17P
-0.095 #
-0.123 ***
-0.291 ***
0.308 ***
-0.051
(0.049)
0.832 ***
(0.038)
(0.058)
(0.045)
(0.046)
(0.057)
TCHLT12
-5.106
-4.213
-9.854 ###
1.925
-1.200
(4.469)
2.822
(2.695)
(3.798)
(4.539)
(3.131)
(7.712)
TCH12
-4.980 ##
-7.058 ***
-3.202
0.408
5.525 ***
(1.955)
-9.097 **
(1.725)
(2.347)
(2.323)
(2.124)
(3.800)
TCH1315
-5.512 ##
-2.596
-7.850 ##
2.878
-0.809
(2.738)
-0.306
(2.337)
(3.124)
(2.736)
(2.823)
(4.151)
TCH16
-6.472 ##
0.520
3.005
3.561
-0.561
(2.981)
-3.261
(2.874)
(4.067)
(2.984)
(3.358)
(4.311)
TCH17P
-0.311
14.338 ***
-2.083
15.322 ***
(4.894)
6.632
-8.307
(3.632)
(5.734)
(3.951)
(4.487)
(5.411)
NONWHT
-0.103 ##
-0.266 ***
-0.203 ***
-0.250 ***
-0.012
(0.053)
(0.041)
-0.226 ***
(0.062)
(0.056)
(0.048)
(0.085)
SOUTH
-0.041
-0.171 ***
-0.118 ***
-0.018
-0.074 #
(0.041)
(0.033)
0.021
(0.045)
(0.042)
(0.039)
(0.058)
NE
-0.149 ###
-0.181 ***
-0.180 ***
-0.127 **
-0.123 ***
(0.048)
(0.038)
0.019
(0.055)
(0.049)
(0.047)
(0.066)
NC
-0.047
-0.180 ***
-0.090 ##
-0.054
-0.072 #
(0.042)
(0.034)
0.182 ***
(0.045)
(0.042)
(0.040)
(0.056)
UNION
0.235 ***
-0.061
-0.041
-0.090 #
(0.050)
-0.022
(0.044)
-0.243 ***
(0.062)
(0.053)
(0.050)
(0.088)
E1ST5
-0.153 ***
-0.290 ***
-0.236 ***
-0.132 ***
(0.041)
0.016
(0.033)
-0.094 #
(0.046)
(0.041)
(0.039)
(0.056)
POTEXP
0.000
0.001
0.003
-0.008 ***
(0.002)
-0.010 ***
(0.001)
0.002
(0.002)
(0.002)
(0.002)
(0.003)
TENURE
0.034 ***
0.011 ***
0.026 ***
(0.003)
(0.002)
(0.003)
SELFEMP
-0.108 **
0.108 ***
0.238 ***
(0.049)
-0.296 ***
(0.038)
-0.288 ***
(0.047)
0.208 ***
(0.052)
(0.050)
(0.056)
SHAT
3.417
-1.039
-3.249
(2.567)
-2.615
-5.977 **
(2.067)
(2.599)
-4.001
(2.488)
(2.447)
(3.352)
RHAT
0.030
-0.022
-0.147 ***
(0.042)
-0.234 ***
(0.034)
-0.136 ***
(0.047)
-0.083
(0.042)
(0.039)
(0.059)
NUR
-0.055 ***
-0.011 *
-0.008
(0.008)
(0.006)
(0.009)
# Significant, from zero, at 10 percent level.
Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 74 -
Table A.2
DETERMINANTS OF TRAINING TO GET CURRENT JOB AND IMPROVE SKILLS:
CPS WOMEN
Training to Get Current Job
Training to Improve Job Skills
Variable
Company
OJT
Other
Company
OJT
Other
CONSTANT
-1.114 ***
-0.447 ###
-2.075 ***
-1.086 ***
-0.961 ###
-1.698 ***
(0.110)
(0.080)
(0.160)
(0.080)
(0.073)
(0.105)
SCHLT12
-0.228 ***
-0.287 ***
-0.092
-0.414 ###
-0.095 #
-0.568 ###
(0.070)
(0.048)
(0.096)
(0.076)
(0.055)
(0.129)
SCH1315
0.119 ***
0.131 ***
0.236 ###
0.242 ***
0.081 ##
0.103 #
(0.043)
(0.032)
(0.062)
(0.041)
(0.037)
(0.056)
SCH16
0.055
0.057
0.252 ***
0.373 ***
0.099 ##
0.410 ###
(0.057)
(0.042)
(0.078)
(0.051)
(0.047)
(0.063)
SCH17P
-0.119
-0.020
0.174 #
0.301 ###
-0.116 #
0.265 ###
(0.074)
(0.051)
(0.096)
(0.061)
(0.062)
(0.098)
TCHLT12
-24.843 ***
6.782 #
-3.786
-23.692 ###
2.337
-46.897 ##
(6.595)
(3.853)
(8.168)
(8.936)
(4.620)
(20.172)
TCH12
-16.008 ###
-3.469 #
-4.424
-14.787 ###
-2.191
-23.260 ###
(3.140)
(2.055)
(4.149)
(2.824)
(2.487)
(4.239)
TCH1315
-9.553 ##
-5.639 #
-0.331
-5.505
-2.988
-13.579 ***
(3.875)
(3.132)
(5.649)
(3.628)
(3.636)
(4.838)
TCH16
6.569
6.514
7.717
0.091
2.282
4.687
(5.558)
(4.920)
(8.901)
(4.900)
(5.283)
(6.625)
TCH17P
-4.699
25.745 ***
13.223
18.435 ##
7.627
-27.000
(10.039)
(7.046)
(11.348)
(8.170)
(9.927)
(18.085)
NONWHT
-0.088
-0.076 *
-0.080
-0.142 ###
0.017
-0.270 ###
(0.058)
(0.040)
(0.084)
(0.054)
(0.045)
(0.081)
SOUTH
0.007
-0.189 ***
-0.048
-0.073
-0.076 #
-0.173 ***
(0.050)
(0.036)
(0.070)
(0.046)
(0.041)
(0.062)
NE
-0.156 ###
-0.190 ###
0.099
-0.176 ###
-0.073
-0.300 ***
(0.058)
(0.042)
(0.083)
(0.056)
(0.050)
(0.072)
NC
-0.102 *#
-0.166 ***
-0.105
-0.217 ***
-0.047
-0.019
(0.052)
(0.037)
(0.075)
(0.048)
(0.043)
(0.058)
UNION
0.030
-0.110 ##
0.012
-0.053
0.095
-0.175 #
(0.075)
(0.054)
(0.113)
(0.068)
(0.060)
(0.100)
E1ST5
-0.122 ##
-0.235 ***
-0.074
-0.110 ##
-0.081 *
-0.181 ###
(0.053)
(0.038)
(0.081)
(0.050)
(0.044)
(0.064)
POTEXP
-0.007 ###
-0.000
0.006 #
-0.004
-0.008 ###
-0.003
(0.002)
(0.002)
(0.003)
(0.002)
(0.002)
(0.003)
TENURE
0.032 ***
0.012 ***
0.025 ###
(0.003)
(0.003)
(0.004)
SELFEMP
0.225 ***
-0.091
0.420 ***
-0.176 ##
-0.212 ***
0.295 ###
(0.070)
(0.056)
(0.081)
(0.073)
(0.069)
(0.075)
SHAT
3.533
-3.773 #
-6.226
-0.629
-9.384 ***
-4.183
(3.054)
(2.235)
(4.045)
(2.746)
(2.659)
(3.675)
RHAT
0.114 ##
-0.018
-0.324 ***
-0.199 ###
-0.089 ##
-0.067
(0.050)
(0.037)
(0.066)
(0.048)
(0.043)
(0.059)
NUR
-0.027 ##
0.010
0.020
(0.010)
(0.007)
(0.013)
# Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 75 -
Table A.3
DETERMINANTS OF TRAINING FOR NLS YOUNG MEN, BY SOURCE AND TYPE
Source of Training
Type of Training
Business/
Variable
Professional/
Company
Technical
Other
Managerial
Technical
Semiskilled
Other
CONSTANT
-1.410 ###
-1.468 ***
-1.323 ***
-2.059 ###
-1,470 ***
-1.132 ***
(0.108)
(0.210)
-1.599 ***
(0.107)
(0.157)
(0.103)
(0.114)
(0.121)
SCHLT12
-0.437 ###
-0.522 ###
-0.385 ###
-0.449 ###
-0.697 ***
(0.068)
-0.451 ***
(0.084)
-0.245 ***
(0.068)
(0.122)
(0.095)
(0.064)
(0.069)
SCH1315
0.301 ***
0.047
0.186 ***
0.344 *##
0.380 ***
-0.114 **
(0.046)
(0.054)
0.272 ***
(0.047)
(0.069)
(0.049)
(0.047)
(0.050)
SCH16
0.454 ###
-0.229 ###
0.203 ###
0.620 ***
0.705 ***
-0.776 ***
(0.054)
(0.072)
0.237 ***
(0.057)
(0.076)
(0.054)
(0.077)
(0.061)
SCH17P
0.261 ###
-0.066
0.360 ###
0.501 ###
0.952 ###
-1.047 ***
(0.054)
(0.062)
0.211 ***
(0.052)
(0.074)
(0.051)
(0.085)
(0.059)
TCHLT12
4.250
18.005 ***
7.035
9.938
9.914
(4.621)
15.638 ***
-3.021
(5.364)
(5.431)
(10.018)
(7.361)
(4.486)
(4.267)
TCH12
1.250
-4.796 #
-5.062 *
-1.498
-2.545
(2.465)
-4.675 **
(2.715)
-0.987
(2.620)
(4.524)
(2.699)
(2.221)
(2.843)
TCH1315
0.283
-3.219
-7.542 ##
5.064
-3.153
-9.867 ***
(2.583)
(3.124)
-1.972
(2.956)
(3.709)
(2.746)
(2.909)
(2.807)
TCH16
9.866 ***
-6.554
-8.612 *
10.435 ##
-6.851 ##
(3.351)
4.132
(4.820)
-0.867
(4.624)
(4.931)
(3.353)
(4.850)
(4.424)
TCH17P
16.877 ***
0.302
-13.354 ***
20.738 ###
-10.974 ###
(3.462)
-0.018
(4.550)
-4.315
(4.305)
(4.235)
(3.428)
(6.630)
(4.121)
NONWHT
-0.168 ***
0.041
-0.136 ***
-0.218 ***
-0.120 ###
(0.044)
0.037
(0.051)
-0.176 ###
(0.046)
(0.068)
(0.046)
(0.046)
(0.048)
SOUTH
-0.002
-0.003
-0.049
-0.012
-0.051
(0.034)
-0.052
(0.043)
-0.014
(0.036)
(0.046)
(0.035)
(0.040)
(0.037)
UNION
-0.064
-0.125 #
-0.049
-0.224 ###
-0.120 **
(0.056)
0.030
(0.071)
-0.030
(0.058)
(0.085)
(0.061)
(0.059)
(0.061)
POTEXP
0.009 **
-0.018 ###
-0.005
-0.013
-0.003
(0.004)
-0.019 ###
(0.005)
-0.000
(0.004)
(0.077)
(0.005)
(0.005)
(0.005)
E1ST5
0.002
0.023
-0.045
0.021 ###
0.021
(0.055)
0.067
(0.067)
-0.003
(0.056)
(0.006)
(0.054)
(0.064)
(0.059)
CHGJOB
-0.184 ***
0.155 ***
0.023
-0.196 ***
-0.022
(0.037)
0.029
(0.044)
-0.020
(0.037)
(0.051)
(0.037)
(0.042)
(0.039)
SCHWK
0.089 ##
0.056
-0.048
-0.013
0.021
(0.039)
0.062
(0.050)
0.071 #
(0.040)
(0.017)
(0.040)
(0.044)
(0.042)
SCHWKT
-0.044
0.139 **
0.141 ##
0.072
0.155 ***
(0.060)
0.115 #
(0.068)
0.057
(0.058)
(0.056)
(0.055)
(0.068)
(0.062)
NUR
-0.002
0.011
0.007
0.042
0.015
(0.012)
0.010
(0.013)
0.007
(0.012)
(0.081)
(0.010)
(0.013)
(0.013)
SOURCE: NLS Young Men, 2-year intervals.
NOTE: Standard errors of probit specifications in parentheses.
# Significant, from zero, at 10 percent level.
## Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 76 -
Table A.4
DETERMINANTS OF TRAINING FOR NLS MATURE MEN, BY SOURCE AND TYPE
Source of Training
Type of Training
Business/
Professional/
Variable
Company
Technical
Other
Manager
Technical
Other
CONSTANT
-0.599 **
-1.499 ###
-0.229
-1.371 #**
-0.595 ##
-0.884 ***
(0.260)
(0.497)
(0.229)
(0.512)
(0.262)
(0.301)
SCHLT12
-0.331 ***
-0.154
-0.285 ***
-0.363 ***
-0.409 ***
-0.289 ***
(0.070)
(0.172)
(0.056)
(0.091)
(0.070)
(0.076)
SCH1315
0.190 **
0.177
0.279 ###
0.136
0.373 ###
0.174 #
(0.079)
(0.191)
(0.069)
(0.100)
(0.078)
(0.090)
SCH16
0.106
0.547 ###
0.401 ###
0.242 #
0.565 ### #
0.057
(0.113)
(0.191)
(0.087)
(0.125)
(0.094)
(0.127)
SCH17P
-0.076
0.047
0.625 ###
0.038
0.808 *** #
-0.088
(0.108)
(0.285)
(0.079)
(0.126)
(0.086)
(0.126)
TCHLT12
0.767
6.104
-0.554
6.618
-6.983
-4.835
(3.720)
(11.073)
(2.941)
(4.901)
(5.385)
(3.877)
TCH12
-5.976
8.708
-3.273
-1.184
-11.809 ##
-5.161
(4.752)
(9.873)
(4.136)
(5.989)
(4.931)
(5.592)
TCH1315
-1.232
-6.039
-17.600 ###
-3.662
-11.630 ##
-20.667 **
(4.967)
(11.804)
(5.263)
(8.691)
(4.544)
(9.378)
TCH16
-4.346
-17.591
-15.266 **
-3.528
-16.202 ##
-6.894
(8.660)
(24.302)
(7.075)
(11.015)
(6.962)
(10.507)
TCH17P
32.111 ***
-16.564
-5.786
34.462 ###
-11.501
9.319
(7.226)
(25.753)
(6.792)
(8.138)
(7.520)
(10.053)
NONWHT
-0.223 ###
0.054
-0.029
-0:177 ##
-0.181 ##
0.022
(0.069)
(0.183)
(0.052)
(0.087)
(0.073)
(0.073)
SOUTH
-0.031
-0.007
0.036
0.069
0.044
-0.094
(0.055)
(0.147)
(0.045)
(0.065)
(0.053)
(0.066)
UNION
-0.589 ***
-0.178
-0.239
0.216
-0.367 ##
-0.506 ##
(0.168)
(0.195)
(0.160)
(0.431)
(0.173)
(0.208)
POTEXP
-0.016 ###
-0.027 ##
-0.025 ###
-0.028 ###
-0.021 ###
-0.013 ##
(0.005)
(0.013)
(0.004)
(0.007)
(0.005)
(0.006)
NUR
0.014 ***
0.014
0.013 ***
0.008
0.023 ***
-0.001
(0.005)
(0.015)
(0.004)
(0.006)
(0.005)
(0.006)
TENURE
0.004 #
-0.015 ###
-0.007 ###
0.007 ##
-0.005 ##
-0.006 ###
(0.002)
(0.006)
(0.002)
(0.003)
(0.002)
(0.002)
SOURCE: NLS Mature Men, 2-year intervals.
NOTE: Standard errors of probit specification in parentheses.
# Significant, from zero, at 10 percent level.
## Significant, from zero, at 5 percent level.
### Significant, from zero, at 1 percent level.
- 77 -
Table A.5
DETERMINANTS OF TRAINING FOR NLS CAREER WOMEN, BY SOURCE AND TYPE
Source of Training
Type of Training
Business/
Professional/
Variable
Company
Technical
Other
Managerial
Technical
Clerical
Other
CONSTANT
-1.794 ###
-1.974 ***
-0.921 ***
-2.423 ***
-1.628 ***
-1.181 ###
-1.428 ***
(0.354)
(0.701)
(0.213)
(0.509)
(0.276)
(0.374)
(0.272)
SCHLT12
-0.232
0.418
-0.270 ##
-2.510
-0.233
-0.628 ***
-0.052
(0.179)
(0.378)
(0.106)
(285.02)
(0.167)
(0.205)
(0.135)
SCH1315
0.236 *
0.248
0.376 ***
0.086
0.654 ***
-0.186
0.238
*
(0.135)
(0.369)
(0.091)
(0.188)
(0.110)
(0.151)
(0.125)
SCH16
0.058
0.312
0.893 ***
0.211
1.053 ***
-0.383
0.595 ***
(0.209)
(0.622)
(0.140)
(0.273)
(0.156)
(0.383)
(0.173)
SCH17P
-0.067
-0.224
1.105 ***
0.631 ##
1.227 ***
-0.973
-0.693
(0.245)
(7.612)
(0.265)
(0.252)
(0.231)
(1.400)
(1.261)
TCHLT12
-15.968
-14.184
-16.084 **
-4.090
-31.436 **
-2.038
-10.864
(12.218)
(34.298)
(7.314)
(231.742)
(15.366)
(17.723)
(8.241)
TCH12
-26.475 **
32.934 ##
-17.349 ***
-52.363 ***
-30.165 ***
1.921
-19.201 *
(10.210)
(15.941)
(5.726)
(14.528)
(8.128)
(7.677)
(9.976)
TCH1315
12.051
11.019
-11.984
28.358 #
-5.956
5.806
-9.776
(11.649)
(57.907)
(8.478)
(17.170)
(9.096)
(14.017)
(12.378)
TCH16
60.916 **
-0.144
-27.543
76.039
-19.942
-0.569
12.956
(30.968)
117.136)
(21.719)
(56.766)
(21.828)
(92.703)
(28.447)
TCH17P
80.704 ***
-66.865
-97.568
#
175.313 ***
-51.023
103.240
-332.356
(26.109)
(682.919)
(55.354)
(14.685)
(43.448)
(81.936)
278.987)
NONWHT
0.155
0.250
0.138 #
0.205
-0.024
-0.040
0.394 ***
(0.132)
(0.347)
(0.080)
(0.191)
(0.107)
(0.147)
(0.102)
SOUTH
-0.059
0.197
-0.075
-0.215
-0.183 #
-0.001
0.095
(0.115)
(0.253)
(0.078)
(0.171)
(0.101)
(0.124)
(0.102)
UNION
0.044
-0.014
-0.170
0.299
0.086
-0.206
-0.498 ##
(0.213)
(0.593)
(0.135)
(0.268)
(0.149)
(0.273)
(0.223)
POTEXP
-0.029 ##
-0.042
0.008
0.060 ###
-0.028 ###
-0.008
0.013
(0.013)
(0.033)
(0.008)
(0.021)
(0.010)
(0.013)
(0.011)
TENURE
0.024 **
0.002
-0.005
-0.006
-0.001
0.001
0.007
(0.009)
(0.016)
(0.006)
(0.014)
(0.007)
(0.010)
(0.007)
NUR
0.111 *
0.040
-0.083 **
-0.231 ***
0.148 ***
-0.069
-0.163 ###
(0.058)
(0.154)
(0.035)
(0.088)
(0.045)
(0.061)
(0.047)
SOURCE: NLS Mature Women, 1-year intervals.
NOTE: Standard errors of probit specification in parentheses.
# Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 78 -
Table A.6
SUMMARY STATISTICS FOR THE NLS YOUNG MEN SAMPLE
Variable
Variable Description
Mean
Std. Dev.
LOGY
Logarithm annual earnings
9.0628
0.8858
LOGWW
Logarithm of weekly wages
5.2166
0.7753
YR69
Year dummy 1969
0.1901
0.3924
YR73
Year dummy 1973
0.2189
0.4135
YR75
Year dummy 1975
0.2126
0.4091
YR78
Year dummy 1978
0.1922
0.3941
NONWHT
Black
0.2324
0.4223
SCHLT12
Schooling < 12 years
0.1587
0.3654
SCH1315
Schooling 13-15 years
0.2479
0.4318
SCH16
Schooling 16 years
0.1200
0.3249
SCH17P
Schooling > 16 years
0.1550
0.3619
POTEXP
Potential work experience
9.6115
5.5028
EXPSQ
Experience squared
122.6614
125.1492
E1ST5
First 5 years in labor market
0.2688
0.4433
TENURE
Years of job tenure
3.4448
3.8712
TENSQ
Tenure squared
26.8520
50.1881
TENMISS
Tenure missing
0.0312
0.1740
SOUTH
South
0.3980
0.4895
UNRATE
Local unemployment rate
5.1896
3.4692
URMISS
Unemployment rate missing
0.1735
0.3787
Technical change interactions
TCHLT12
Schooling < 12 years
0.0004
0.0047
TCH12
Schooling 12 years
0.0013
0.0067
TCH1315
schooling 13-15 years
0.0008
0.0055
TCH16
Schooling 16 years
0.0003
0.0037
TCH17P
Schooling > 16 years
0.0000
0.0035
OCCTRN
Any training current period
0.2903
0.4539
SOCCTR
Cumulated any training
1.3015
1.5799
DOCCTR
Duration any training
6.4756
10.4335
Current period training
COTRN
Company training
0.1038
0.3051
BTVTRN
Business/vocational
0.0478
0.2134
RSTRN
Regular school
0.0464
0.2103
OTHSCH
Other sources
0.0891
0.2850
MANTRN
Managerial
0.0378
0.1908
PROTRN
Professional/technical
0.1053
0.3069
SSMTRN
Semiskilled manual
0.0667
0.2496
OTHTYP
Other types
0.0657
0.2478
Cumulated training
SCOTR
Company training
0.4309
0.9299
SBVTR
Business/vocational
0.2099
0.5750
SRSTR
Regular school
0.2041
0.5342
SOTHS
Other sources,
0.4461
0.8150
SMAN
Managerial
0.1497
0.5225
SPROTR
Professional/technical
0.4664
0.9033
SSSMTR
Semiskilled manual
0.3782
0.8602
SOTYP
Other types
0.2746
0.6081
Duration of training
DCOTR
Company training
2.0987
5.7908
DBVTR
Business/vocational
1.0430
3.6320
DRSTR
Regular school
0.9826
3.2163
DOTHS
Other sources
2.3098
5.3080
DMAN
Managerial
0.6967
3.0628
DPROTR
Professional/technical
2.3177
5.6222
DSMTR
Semiskilled manual
2.0423
5.7468
DOTYP
Other types
1.3379
3.7549
- 79 -
Table A.7
RESULTS FOR EARNINGS AND TRAINING
BY SOURCE AND TYPE: NLS
YOUNG MEN SAMPLE
Source of Training
Type of Training
Annual
Weekly
Annual
Weekly
Variable
Earnings
Wages
Earnings
Wages
INTERCEPT
8.728
5.146
8.730
5.142
(0.047)
(.042)
(0.047)
(0.042)
YR69
-1.022 ***
-0.975 ***
-1.021 ***
-0.974 ***
(0.023)
(0.020)
(0.023)
(0.020)
YR73
-0.594 ***
-0.593 ***
-0.599 ***
-0.598 ***
(0.020)
(0.018)
(0.020)
(0.018)
YR75
-0.429 ***
-0.417 ***
-0.437 ***
-0.424 ***
(0.019)
(0.017)
(0.019)
(0.017)
YR78
-0.172 ***
-0.179 ***
-0.170 ***
-0.178 ***
(0.018)
(0.016)
(0.018)
(0.016)
NONWHT
-0.228 ***
-0.175 ***
-0.228 ***
-0.176 ***
(0.014)
(0.012)
(0.014)
(0.012)
SCHLT12
-0.166 ***
-0.167 ***
-0.161 ***
-0.161 ***
(0.019)
(0.017)
(0.019)
(0.017)
SCH1315
0.074 ***
0.055 ***
0.076 ***
0.058 ***
(0.016)
(0.014)
(0.016)
(0.014)
SCH16
0.259 ***
0.218 ***
0.255 ***
0.217 ***
(0.020)
(0.018)
(0.021)
(0.018)
SCH17P
0.373 ***
0.333 ***
0.349 ***
0.314 ***
(0.019)
(0.017)
(0.020)
(0.017)
POTEXP
0.100 ***
0.067 ***
0.099 ***
0.066 ***
(0.006)
(0.005)
(0.006)
(0.005)
EXPSQ
-0.003 ***
-0.002 ***
-0.003 ***
-0.002 ***
(0.000)
(0.0002)
(0.0002)
(0.0002)
E1ST5
0.003
-0.027
0.005
-0.025
(0.024)
(0.021)
(0.024)
(0.021)
TENURE
0.091 ***
0.052 ***
0.092 ***
0.053 ***
(0.004)
(0.003)
(0.004)
(0.003)
TENSQ
-0.004 ***
-0.002 ***
-0.004 ***
-0.002 ***
(0.000)
(0.0003)
(0.0003)
(0.0003)
TENMISS
0.232 ***
0.115 ***
0.230 ***
0.113 ***
(0.032)
(0.029)
(0.032)
(0.029)
SOUTH
-0.096 ***
-0.117 ***
-0.096 ***
-0.117 ***
(0.012)
(0.011)
(0.012)
(0.011)
UNRATE
-0.002
0.0007
-0.002
0.001
(0.003)
(0.002)
(0.003)
(0.002)
URMISS
-0.054 **
-0.041 **
-0.052 **
-0.039 *
(0.022)
(0.020)
(0.022)
(0.020)
TCHLT12
-1.970
-2.114 *
-1.978
-2.124 *
(1.150)
(1.016)
(1.152)
(1.018)
TCH12
-0.803
-2.143 ***
-0.456
-1.803 **
(0.836)
(0.739)
(0.835)
(0.738)
- 80 -
Table A. 7 - continued
Source of Training
Type of Training
Annual
Weekly
Annual
Weekly
Variable
Earnings
Wages
Earnings
Wages
TCH1315
0.018
-0.945
0.178
-0.792
(1.013)
(0.895)
(1.014)
(0.896)
TCH16
5.762 ***
4.648 ***
6.234 ***
5.088 ***
(1.466)
(1.295)
(1.468)
(1.297)
TCH17P
7.118 ***
6.135 ***
7.967 ***
6.895 ***
(1.543)
(1.364)
(1.542)
(1.362)
Regular School
Managerial
Current
-0.046
-0.032
0.016
-0.006
(0.039)
(0.034)
(0.046)
(0.040)
Events
0.082 **
0.061 **
0.166 ***
0.151 ***
(0.031)
(0.027)
(0.035)
(0.031)
Duration
-0.010 **
-0.007 *
-0.014 **
-0.012 ***
(0.004)
(0.004)
(0.005)
(0.004)
Company Training
Prof/Technical
Current
0.000
-0.006
-0.011
-0.010
(0.029)
(0.025)
(0.028)
(0.025)
Events
0.169 ***
0.147 ***
0.142 ***
0.110 ***
(0.020)
(0.018)
(0.021)
(0.018)
Duration
-0.013 ***
-0.011 ***
-0.013 ***
-0.008 **
(0.003)
(0.003)
(0.003)
(0.003)
Business/Vocational
Semiskilled Manual
Current
-0.109 **
-0.089 **
-0.061 *
-0.040 *
(0.038)
(0.034)
(0.031)
(0.028)
Events
0.119 ***
0.080 ***
0.096 ***
0.071 ***
(0.029)
(0.025)
(0.020)
(0.018)
Duration
-0.013 ***
-0.008 **
-0.007 ***
-0.003
(0.004)
(0.004)
(0.003)
(0.002)
Other Sources
Other Types
Current
-0.008
0.0008
-0.025
-0.010
(0.028)
(0.025)
(0.033)
(0.029)
Events
0.088 ***
0.059 ***
0.101 ***
0.069 ***
(0.020)
(0.017)
(0.026)
(0.023)
Duration
-0.009 ***
-0.005 **
-0.015 ***
-0.012 ***
(0.003)
(0.002)
(0.004)
(0.003)
SOURCE: NLS Young Men.
NOTE: Standard errors in parentheses.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent leve.
- 81 -
Table A.8
PROBABILITY OF UNEMPLOYMENT AND WEEKS
UNEMPLOYED EQUATIONS BY SOURCE
AND TYPE OF NLS YOUNG MEN
Unemployment Probability
Weeks Unemployed
Variable
Source
Type
Source
Type
INTERCEPT
-1.088 ***
-1.122 ***
-0.124
-0.085
(0.103)
(0.103)
(0.193)
(0.192)
SCHLT12
0.143 **
0.141 **
0.209 *
0.211 *
(0.067)
(0.067)
(0.113)
(0.112)
SCH1315
-0.101 *
-0.081
0.017
0.028
(0.059)
(0.059)
(0.117)
(0.116)
SCH16
-0.469 ***
-0.423 ***
-0.261
-0.236
(0.091)
(0.092)
(0.187)
(0.189)
SCH17P
-0.578 ***
-0.500 ***
-0.163
-0.171
(0.081)
(0.083)
(0.164)
(0.164)
TCHLT12
-1.461
-1.370
3.329
2.306
(4.191)
(4.187)
(6.778)
(6.749)
TCH12
-6.085 *
-5.517 *
-7.241
-8.710
(3.220)
(3.189)
(5.924)
(5.881)
TCH1315
-4.400
-4.726
-9.799
-10.255
(4.152)
(4.180)
(8.188)
(8.308)
TCH16
2.093
1.741
11.576
14.005
(7.981)
(7.934)
(20.413)
(21.088)
TCH17P
-9.958
-11.152
-14.486
-11.644
(7.375)
(7.492)
(19.221)
(19.275)
NONWHT
0.312 ***
0.312 ***
0.159 *
0.145
(0.051)
(0.051)
(0.093)
(0.094)
SOUTH
-0.211 ***
-0.207 ***
-0.280 ***
-0.266 ***
(0.048)
(0.048)
(0.093)
(0.093)
POTEXP
-0.002
-0.002
-0.005
-0.007
(0.007)
(0.007)
(0.012)
(0.011)
E1ST5
0.147 **
0.151 **
0.082
0.053
(0.069)
(0.068)
(0.134)
(0.133)
TENURE
-0.117 ***
-0.116 ***
-0.030 *
-0.031 *
(0.007)
(0.007)
(0.016)
(0.016)
TENMISS
1.580 ***
1.563 ***
1.775 ***
1.749 ***
(0.081)
(0.081)
(0.124)
(0.123)
NUR
0.096 ***
0.099 ***
0.193 ***
0.193 ***
(0.018)
(0.018)
(0.033)
(0.033)
- 82 -
Table A. 8--continued
Unemployment Probability
Weeks Unemployed
Variable
Source
Type
Source
Type
Reg. School
Managerial
Reg. School
Managerial
Current
-0.203
-0.133
-1.027 **
0.072
(0.200)
(0.247)
(0.402)
(0.778)
Events
-0.128
-0.120
0.473
-0.535
(0.149)
(0.208)
(0.322)
(0.701)
Duration
0.018
0.001
-0.051
0.092
(0.019)
(0.029)
(0.038)
(0.114)
Reg. School
Managerial
Reg. School
Managerial
Current
-0.056
0.226
-0.170
-0.395
(0.178)
(0.154)
(0.365)
(0.367)
Events
0.071
-0.459 ***
0.221
0.335
(0.124)
(0.124)
(0.258)
(0.344)
Duration
-0.011
0.039 ***
-0.056 *
-0.046
(0.016)
(0.013)
(0.031)
(0.040)
Reg. School
Managerial
Reg. School
Managerial
Current
0.256 **
0.305 **
-0.090
-0.258
(0.129)
(0.127)
(0.285)
(0.221)
Events
-0.332 ***
-0.159 *
0.035
0.213
(0.099)
(0.081)
(0.239)
(0.161)
Duration
0.026 **
0.013
-0.007
-0.033 *
(0.012)
(0.009)
(0.026)
(0.018)
Reg. School
Managerial
Reg. School
Managerial
Current
0.412 ***
0.252 +
-0.405 *
-0.250
(0.123)
(0.147)
(0.245)
(0.352)
Events
-0.357 ***
-0.224 **
0.253
-0.028
(0.087)
(0.106)
(0.207)
(0.296)
Duration
0.029 ***
0.021
-0.028
0.006
(0.010)
(0.014)
(0.026)
(0.041)
TAU 2
0.593 ***
0.590 ***
(0.045)
(0.045)
TAU 3
1.093 ***
1.088 ***
(0.056)
(0.055)
TAU 4
1.558 ***
1.554 ***
(0.065)
(0.065)
TAU 5
1.952 ***
1.949 ***
(0.076)
(0.075)
SOURCE: NLS Young Men 1969, 1980
NOTE: Binomial or multinomial probit specifications.
Standard errors in parentheses.
* Significant, from zero, at 10 percent level.
** Significant, from zero, at 5 percent level.
*** Significant, from zero, at 1 percent level.
- 83 -
REFERENCES
Ashenfelter, Orley, "Estimating the Effect of Training Programs on
Earnings, Review of Economics and Statistics, Vol. 60, No. 1,
February 1978, pp. 47-57.
Ayres, R., and S. Miller, Robotics Applications and Social Implications,
Cambridge, Ballinger Publishing, 1983.
Becker, Gary, Human Capital, 2d ed., Columbia University Press, New
York, 1975.
Carnevale, Anthony, and Harold Goldstein, Employee Training: Its
Changing Role and An Analysis of New Data, American Society for
Training and Development, Washington, D.C., 1985.
Choate, P., Retooling the American Workforce: Towards a National
Strategy, Northeast-Midwest Institute, Washington, D.C., 1982.
Enos, John, "Invention and Innovation in the Petroleum Refining
Industry," in Kenneth Arrow (ed.), The Rate and Direction of Inventive
Activity, Princeton University Press, 1962.
Freeman, Richard, and David Wise, The Youth Employment Problem: Its
Nature, Causes and Consequences, National Bureau of Economic Research,
Chicago, 1982.
Goldstein, Harold, Training and Education by Industry, National
Institute for Work and Learning, Washington, D.C., 1980.
Gollop, Frank M., and Dale W. Jorgenson, "U.S. Productivity Growth by
Industry: 1947-1973," in John W. Kendrick and Beatrice N. Vaccara
(eds.), New Developments in Productivity Measurement and Analysis,
NBER Studies in Income and Wealth, Vol. 44, 1980.
Grasso, John, and John Shea, Vocational Education and Training: Impact
on Youth, Carnegie Foundation, Washington, D.C., 1979.
Gustman, Alan, and Thomas Steinmeier, The Relation between Vocational
Training in High School and Economic Outcomes, Technical Paper
prepared for Office of Assistant Secretary for Planning and Budget,
U.S. Department of Labor, 1980.
Hollander, Samuel, The Sources of Increased Efficiency, Boston, M.I.T.
University Press, 1965.
Keifer, Nicholas, "Population Heterogeneity and Inference from Panel
Data on the Effects of Vocational Training and Education,' Journal of
Political Economy, Vol. 87, October 1979, pp. S213-S226.
- 84 -
Lillard, Lee, Wage Expectations in Labor Supply and the Time-Series and
Cross-Section Effects of State Unemployment, The Rand Corporation,
R-2844-DOL, September 1981.
Lillard, Lee, Local Labor Market Cycles and Their Consequences: Wages,
Hours of Work, and Unemployment, The Rand Corporation, N-2276-DOL,
January 1986.
Lillard, Lee, and Subal Kumbhakar, "The Earnings Effects of Ceta-Type
Program Participation: Some Issues Behind the Scenes," Journal of
Human Resources (forthcoming), 1986.
Lillard, Lee, and Robert Willis, "Dynamic Aspects of Earnings Mobility,"
Econometrica, Vol. 46, 1978, pp. 985-1012.
McDowell, John, "Obsolescence of Knowledge and Career Publication
Profiles: Some Evidence of Differences among Fields in Costs of
Interrupted Careers," American Economic Review, Vol. 72, No. 4, 1982,
pp. 752-768.
Meyer, Robert, and David Wise, "High School Preparation and Early Labor
Force Experience," in Richard Freeman and David Wise (eds.), The Youth
Employment Problem: Its Nature, Causes and Consequences, National
Bureau of Economic Research, Chicago, 1982.
Mincer, Jacob, Schooling, Experience and Earnings, Columbia University
Press, New York, 1974.
Mincer, Jacob, and Bryan Jovanovic, "Labor Mobility and Wages," in
Sherwin Rosen (ed.), Studies in Labor Markets, NBER, University of
Chicago Press, 1981.
Mincer, Jacob, and Haim Ofek, "Interrupted Work Careers: Depreciation
and Restoration of Human Capital," Journal of Human Resources, Vol.
17, Winter 1982.
National Commission for Employment Policy, The Federal Role in
Vocational Education, Washington, D.C., 1981.
Setzer, Francis, "Technical Change Over the Life of a Product: Changes
in Skilled Inputs and Production Processes," Ph.D. Dissertation, Yale
University, 1974.
Smith, James P., Convergence to Racial Equality in Women's Wages, The
Rand Corporation, P-6026-NSF, March 1978.
Smith, James P., and Finis Welch, "Affirmative Action and Labor
Markets," Journal of Labor Economics, Vol. 2, No. 21, April 1984, pp.
269-300.
- 85 -
Somers, Gerald (ed.), Retraining the Unemployed, University of Wisconsin
Press, Madison, 1968.
Tan, Hong W., "Human Capital and Technological Change: A Study of Wage
Differentials in Japanese Manufacturing," Ph.D. Dissertation, Yale
University, 1980.
Tan, Hong W., and Michael Ward, Forecasting the Wages of Youth, The Rand
Corporation, R-3115-MIL, May 1985.
Tan, Hong W., "Technical Change, Earnings and Long-Term Jobs," Review of
Economics and Statistics (forthcoming).
Tannen, Michael, "Vocational Education and Earnings for White Males:
New Evidence from Longitudinal Data," Southern Economic Journal, 1984.
Welch, Finis, "Education in Production," Journal of Political Economy,
January-February 1970, pp. 350-366.
Willis, Robert, and Sherwin Rosen, "Education and Self-Selection,"
Journal of Poiitical Economy, Vol. 87, No. 2, October 1979, pp.
S7-S36.
United States
Department
of Labor
Bureau of Labor Statistics
Washington, D.C. 20212
Technical Information: (202) 272-5381
USDL 87-258
Media Contact
: (202) 523-1913
FOR RELEASE: IMMEDIATE
THURSDAY, JUNE 25, 1987
BLS PREVIEWS THE ECONOMY OF THE YEAR 2000
The U.S. Department of Labor's Bureau of Labor Statistics today provided a preview of its
economic and employment projections to the year 2000. The projections, the Bureau's 16th since
1957, are widely used in studying long-range economic growth and are the basis for the Bureau's
occupational outlook program.
The projections offer three alternative scenarios of the labor force, economic growth, and
employment by industry and occupation to the year 2000 in the United States.
All three scenarios project substantial employment increases between 1986 and 2000 ranging
from 15 million for the low growth scenario, 21 million for the moderate, and 26 million for the
high growth scenario.
Here are highlights of the moderate growth projections:
The labor force will grow more slowly than in the past and will reach 139 million in the
year 2000. The workforce of the future will be older and made up of more women and
minority workers than in the past. (See table 1.)
Productivity growth will contribute more to future economic growth than it has during the
previous 14 years. The rate of economic growth will be similar to that experienced
since 1972, despite slower labor force growth. (See table 2.)
Service-producing industries will add nearly 21 million jobs. (See table 3.)
Employment in manufacturing will decline as manufacturing's share of total employment
goes to 14 percent in 2000. The output share of the nation's factories, however, is
projected to hold steady.
Some industries will grow very rapidly, including most business services--especially
computer and data processing and personnel supply--and health services--in particular,
offices of health practitioners, nursing and personal care facilities, and outpatient
care and other health services. (See table 4.)
Employment in the broad occupational groups that require the most educational
preparation--such as executive, administrative, and managerial workers; professional
workers; and technicians and related support workers--will grow faster than the average.
The occupational groups requiring the least educational preparation are expected to grow
slowly or decline, except for the rapidly growing service workers. (See table 5.)
In developing the projections, BLS worked with a series of. models used in the Economic
Growth and Employment Projections program, which produces a comprehensive set of alternative
projections every other year. Each projection scenario employs a specific set of assumptions,
including factors such as fiscal policy, monetary policy, demographic changes in the population,
exchange rates, and unemployment.
Following is a summary of the projections and some of their more significant implications.
More detail will be published in a series of articles in the September 1987 issue of the Monthly
Labor Review.
Table 1. Civilian labor force by sex, age, race, and origin, 1972,
1986 and projected 2000 low, moderate, and high alternatives
Labor force (in thousands)
Projected 2000
Group
alternatives
1972
1986
Low
Moderate
High
Total, 16 and over
87,037
117,837
134,517
138,775
141,107
Men, 16 and over
53,556
65,423
71,729
73,136
74,464
16 to 24 years
11,243
12,251
11,261
11,506
11,811
25 to 54 years
33,133
44,406
52,043
53,024
54,009
55 years and over
9,180
8,766
8,425
8,606
8,644
Women, 16 and over
33,481
52,414
62,788
65,639
66,643
16 to 24 years
8,943
11,117
10,898
11,125
11,365
25 to 54 years
19,192
35,159
45,007
47,756
48,487
55 years and over
5,346
6,138
6,883
6,758
6,791
White
77,275
101,801
112,918
116,701
118,474
Black
8,748
12,684
16,031
16,334
16,518
Asian and other
N.A.
3,352
5,568
5,740
6,115
Hispanic 2/
N.A.
8,076
12,675
14,086
14,122
Changes, 1972-86 and 1986-2000 moderate alternative
Number
Percent
Annual rate
(in thousands)
change
of change
1972-86
1986-2000
1972-86
1986-2000
1972-86
1986-2000
Total, 16 and over
30,800
20,938
21.6
17.8
2.2
1.2
Men, 16 and over
11,867
7,713
18.1
11.8
1.4
.8
16 to 24 years
1,008
-745
8.2
-6.1
.6
-.4
25 to 54 years
11,273
8,618
25.4
19.4
2.1
1.3
55 years and over
-414
-160
-4.7
-1.8
-.3
-.1
Women, 16 and over
18,933
13,225
36.1
25.2
3.3
1.6
16 to 24 years
2,174
8
19.6
0.1
1.6
.0
25 to 54 years
15,967
12,597
45.4
35.8
4.4
2.2
55 years and over
792
620
12.9
10.1
1.0
.7
White
24,526
14,900
24.1
14.6
2.0
1.0
Black
3,936
3,650
31.1
28.8
2.7
1.8
Asian and other
2,388
--
71.2
--
3.9
--
Hispanic 2/
6,010
74.4
--
4.1
--
1/ The "Asian and other" group includes
2/ Persons of Hispanic origin
American Indians, Alaskan Natives, Asians,
may be of any race. Labor force
and Pacific Islanders. Labor force data for
data for Hispanics are not available
Asians and other are not available for 1972.
before 1976.
Table 2. Selected major aggregate economic variables, 1972, 1986, and projected 2000
low, moderate, and high alternatives
Annual rate of change
Projected 2000
1972
1986
alternatives
1986-2000
Economic variable
1972-86
Low
Moderate
High
Low
Moderate
High
Demand GNP, billions in
1982 dollars
Gross National Product
2,608.5
3,678.5
4,617.5
5,161.4
5,552.4
2.5
1.6
2.4
3.0
Personal consumption
1,621.9
2,418.7
3,101.2
3,429.4
3,659.7
2.9
1.8
2.5
3.0
Durables
200.4
368.9
472.9
527.2
589.0
4.5
1.8
2.6
3.4
Nondurables
665.5
872.4
1,038.3
1,116.4
1,204.6
2.0
1.2
1.8
2.3
Services
756.0
1,177.4
1,590.0
1,785.9
1,866.1
3.2
2.2
3.0
3.3
Gross investment
465.4
659.7
767.8
932.1
1,103.2
2.5
1.1
2.5
3.7
Equipment
167.5
320.3
424.8
504.2
560.8
4.7
2.0
3.3
4.1
Structures
109.5
134.7
146.5
198.8
224.6
1.5
0.6
2.8
3.7
Residential
166.5
193.9
190.9
202.1
279.6
1.3
-0.1
0.4
2.6
Exports
195.2
371.3
516.8
634.5
712.0
4.7
2.4
3.9
4.8
Imports
244.6
521.0
555.9
733.0
862.2
5.5
0.5
2.5
3.7
Federal government
246.0
333.4
319.8
354.4
371.2
2.2
-0.3
0.4
0.8
Defense
185.3
251.0
222.5
251.0
263.0
2.2
-0.9
0.0
0.3
Nondefense
60.7
82.4
97.3
103.4
108.2
2.2
1.2
1.6
2.0
State and Local government
324.7
414.5
467.8
544.01
568.6
1.8
0.9
2.0
2.3
Labor force and GNP
Civilian labor force, millions
87.0
117.8
134.5
138.8
141.1
2.2
1.0
1.2
1.3
Unemployment rate
5.6
7.0
7.7
6.0
4.5
GNP per employee, in 1982 dollars
30.25
33.55
37.20
39.57
41.20
0.7
0.7
1.2
1.5
Income
Gross National Product,
in billions
1,212.8
4,208.5
.7,312.4
9,455.0
12,637.5
9.3
4.0
5.9
8.1
Personal Income, billions
981.6
3,487.0
6,384.0
7,752.1
10,433.3
9.5
3.8
5.8
8.1
Disposable Personal Income
(DPI), billions
839.6
2,973.7
5,601.7
6,705.6
8,908.2
9.5
4.0
5.9
8.1
DPI, billions in 1982 dollars
1,794.4
2,603.7
3,481.6
3,626.1
3,938.1
2.7
1.5
2.4
3.0
DPI per capita, in 1982 dollars
8,562
10,780
12,886
13,421
14,050
1.7
0.7
1.6
1.9
SOURCE: Historical data are from the Bureau of Labor
Department of Commerce. Projected data are from
Statistics and the Bureau of Economic Analysis, U.S.
the Bureau of Labor Statistics.
Table 3. Employment by major sector and selected industries, 1972, 1986, and projected 2000 Inw,
moderate, and high alternatives
Employment (in thousands)
Industry
1972
1986
Projected 2000 alternatives
Low
Moderate
High
Total
84,549
111,623
126,432
133,030
137,533
Nonfarm wage and salary
73,514
99,044
113,554
119,156
123,013
Goods-producing
23,668
24,681
23,148
24,678
25,906
Mining
628
783
672
724
779
Construction
3,889
4,904
5,643
5,794
6,077
Manufacturing
19,151
18,994
16,833
18,160
19,050
Durable goods
11,050
11,244
9,654
10,731
11,193
Primary metal industries
1,173
753
489
574
646
Fabricated metal products
1,547
1,431
1,172
1,313
1,361
Machinery, except electrical
1,889
2,060
1,951
2,129
2,171
Electronic computing equipment
182
418
466
503
510
Electrical and electronic equipment
1,813
2,123
1,927
2,128
2,222
Radio and TV communication equipment
299
505
472
542
585
Electronic components and accessories
354
630
649
675
666
Transportation equipment
1,790
2,015
1,516
1,697
1,742
Motor vehicles
875
865
679
749
770
Nondurable goods
8,101
7,750
7,179
7,429
7,857
Food and kindred products
1,745
1,617
1,421
1,456
1,512
Apparel and other textile products
1,382
1,106
903
924
965
Printing and publishing
1,094
1,457
1,643
1,706
1,798
Chemicals and allied products
1,009
1,023
912
950
1,017
Rubber and plastics
631
790
825
861
913
Service-producing
49,846
74,363
90,406
94,478
97,107
Transportation and public utilities
4,541
5,244
5,410
5,719
5,903
Transportation
2,678
3,041
3,315
3,500
3,568
Communications
1,152
1,279
1,130
1,222
1,320
Public utilities
711
924
965
998
1,015
Wholesale trade
4,113
5,735
7,015
7,266
7,361
Retail trade
11,835
17,845
21,795
22,702
23,079
Eating and drinking places
2,860
5,879
8,084
8,365
8,501
Finance, insurance, and real estate
3,907
6,297
7,508
7,917
8,159
Services
12,117
22,531
30,778
32,545
33,708
Hotels
813
1,401
1,848
1,971
2,061
Personal services
912
1,104
1,298
1,357
1,391
Business services
1,790
4,781
7,593
8,121
8,533
Services to dwellings
336
681
995
1,020
1,046
Personnel supply
221
1,017
1,730
1,851
1,908
Computer and data processing
107
591
1,090
1,203
1,281
Research, management, and public relations
NA
788
1,186
1,301
1,394
Health services
3,412
6,551
9,369
9,774
10,039
Offices of health practitioners
694
1,672
2,901
3,061
3,137
Nursing and personal care facilities
591
1,250
1,992
2,097
2,124
Hospitals
1,980
3,038
3,438
3,513
3,611
Outpatient care and other health services
146
591
1,038
1,103
1,167
Educational services
958
1,428
1,532
1,620
1,666
Government
13,333
16,711
17,900
18,329
18,897
Federal
2,684
2,899
2,900
3,000
3,093
State and local
10,649
13,812
15,000
15,329
15,804
Agriculture
3,523
3,252
2,784
2,917
3,009
Private households
1,693
1,241
1,122
1,215
1,234
Nonfarm self-employed and unpaid family workers
5,819
8,086
8,972
9,742
10,277
Excludes SIC 074,5,8, (agricultural services) and
NA = Not available.
99 (nonclassifiable establishments), therefore not
exactly comparable with data from the Current Employment
Survey.
Table 4. Employment change for major sectors and selected industries, 1972-86 and projected 1986-2000
Change, 1972-86 and 1986-2000 moderate alternative
Number
(In thousands)
Percent change
Annual rate of change
Industry
1972-86
1986-2000
1972-86
1986-2000
1972-86
1986-2000
Total
27,074
21,407
32.0
19.2
2.0
1.3
Nonfarm wage and salary
25,530
20,112
34.7
20.3
2.2
1.3
Goods-producing
1,013
-3
4.2
0.0
0.3
0.0
Mining
155
-59
24.7
-7.5
1.6
-0.6
Construction
1,015
890
26.1
18.1
1.7
1.2
Manufacturing
-157
-834
-0.8
-4.4
-0.1
-0.3
Durable goods
194
-513
1.8
-4.6
0.1
-0.3
Primary metal industries
-420
-179
-35.8
-23.8
-3.1
-1.9
Fabricated metal products
-116
-118
-7.5
-8.2
-0.5
-0.6
Machinery, except electrical
171
69
9.1
3.3
0.6
0.2
Electronic computing equipment
236
85
129.7
20.3
6.1
1.3
Electrical and electronic equipment
310
5
17.1
0.2
1.1
0.0
Radio and TV communication equipment
206
37
68.9
7.4
3.8
0.5
Electronic components and accessories
276
45
78.0
7.1
4.2
0.5
Transportation equipment
225
-318
12.6
-15.8
0.9
-1.2
Motor vehicles
-10
-116
-1.1
-13.4
-0.1
-1.0
Nondurable goods
-351
-321
-4.3
-4.1
-0.3
-0.3
Food and kindred products
-128
-161
-7.3
-9.9
-0.5
-0.7
Apparel and other textile products
-276
-182
-20.0
-16.5
-1.6
-1.3
Printing and publishing
363
249
33.2
17.1
2.1
1.1
Chemicals and allied products
14
-73
1.4
-7.1
0.1
-0.5
Rubber and plastics
159
71
25.2
9.0
1.6
0.6
Service-producing
24,517
20,115
49.2
27.0
2.9
1.7
Transportation and public utilities
703
475
15.5
9.1
1.0
0.6
Transportation
363
459
13.6
15.1
0.9
1.0
Communications
127
-57
11.0
-4.5
0.7
-0.3
Public utilities
213
74
30.0
8.0
1.9
0.6
Wholesale trade
1,622
1,531
39.4
26.7
2.4
1.7
Retail trade
6,010
4,857
50.8
27.2
3.0
1.7
Eating and drinking places
3,019
2,486
105.6
42.3
5.3
2.6
Finance, insurance, and real estate
2,390
1,620
61.2
25.7
3.5
1.7
Services
10,414
10,014
85.9
44.4
4.5
2.7
Hotels
588
570
72.3
40.7
4.0
2.5
Personal services
192
253
21.1
22.9
1.4
1.5
Business services
2,991
3,340
167.1
69.9
7.3
3.9
Services to dwellings
345
339
102.7
49.8
5.2
2.9
Personnel supply
796
834
360.2
82.0
11.5
4.4
Computer and data processing
484
612
452.3
103.5
13.0
5.2
Research, management, and public relations
NA
513
NA
65.1
NA
3.6
Health services
3,139
3,223
92.0
49.2
4.8
2.9
Offices of health practitioners
978
1,389
140.9
83.1
6.5
4.4
Nursing and personal care facilities
659
847
111.5
67.8
5.5
3.8
Hospitals
1,058
475
53.4
15.6
3.1
1.0
Outpatient care and other health services
445
512
304.8
86.6
10.5
4.6
Educational services
470
192
49.1
13.4
2.9
0.9
Government
3,378
1,618
25.3
9.7
1.6
0.7
Federal
215
101
8.0
3.5
0.6
0.2
State and local
3,163
1,517
29.7
11.0
1.9
0.7
Agriculture
-271
-335
-7.7
-10.3
-0.6
-0.8
Private households
-452
-26
-26.7
-2.1
-2.2
-0.1
Nonfarm self-employed and unpaid family workers
2,267
1,656
39.0
20.5
2.4
1.3
Excludes SIC 074,5,8, (agricultural services) and
NA - Not available.
99 (nonclassifiable establishments), therefore not
exactly comparable with data from the Current Employment
Survey.
Table 5. Employment by broad occupational group and selected occupations, 1986 and projected 2000 low, moderate, and niyn
alternatives
(Numbers in thousands)
Change, 1986-2000
moderate alternative
Projected 2000 alternatives
Annual
Number
Percent
rate of
Occupation
1986
Low
Moderate
High
change
change
change
Total employment
111,623
126,432
133,030
137,533
21,407
19.2
1.3
Executive, administrative, and managerial workers
10,583
12,900
13,616
14,105
3,033
28.7
1.8
Education administrators
288
316
325
336
37
12.9
0.9
Financial managers
638
747
792
824
154
24.1
1.6
General managers and top executives
2,383
2,820
2,965
3,052
582
24.4
1.6
Marketing, advertising, and public relations managers
323
402
427
444
105
32.5
2.0
Accountants and auditors
945
1,251
1,322
1,371
376
39.8
2.4
Personnel, training, and labor relations specialists
230
264
278
288
49
21.2
1.4
Professional workers
13,538
16,438
17,192
17,793
3,654
27.0
1.7
Electrical and electronics engineers
401
544
592
616
192
47.8
2.8
Computer systems analysts
331
544
582
607
251
75.6
4.1
Lawyers
527
676
718
748
191
36.3
2.2
Teachers, preschool
176
233
240
248
64
36.3
2.2
Teachers, kindergarten and elementary
1,527
1,778
1,826
1,883
299
19.6
1.3
Teachers, secondary school
1,128
1,246
1,280
1,320
152
13.4
0.9
College and university faculty
754
703
722
745
-32
-4.2
-0.3
Dentists
151
184
196
203
45
29.6
1.9
Physicians and surgeons
491
645
679
700
188
38.2
2.3
Registered nurses
1,406
1,951
2,018
2,077
612
43.6
2.6
Technicians and related support workers
3,726
4,884
5,151
5,325
1,424
38.2
2.3
Licensed practical nurses
631
835
869
891
238
37.7
2.3
Drafters
348
331
354
366
5
1.6
0.1
Computer programmers
479
758
813
850
335
69.9
3.9
Sales workers
12,606
15,522
16,334
16,760
3,728
29.6
1.9
Cashiers
2,165
2,616
2,740
2,798
575
26.5
1.7
Sales agents, real. estate
313
422
451
468
138
43.9
2.6
Salespersons, retail
3,579
4,563
4,780
4,871
1,201
33.5
2.1
Administrative support workers, including clerical
19,851
21,028
22,109
22,885
2,258
11.4
0.8
Switchboard operators
279
313
330
343
51
18.3
1.2
Computer operators, except peripheral equipment operators
263
364
387
403
124
47.2
2.8
Bookkeeping, accounting, and auditing clerks
2,116
2,085
2,208
2,291
92
4.3
0.3
Payroll and timekeeping clerks
204
171
180
186
-25
-12.0
-0.9
General office clerks
2,361
2,688
2,824
2,916
462
19.6
1.3
Receptionists and information clerks
682
913
964
997
282
41.4
2.5
Secretaries
3,234
3,470
3,658
3,789
424
13.1
0.9
Typists and word processors
1,002
820
862
892
-140
-13.9
-1.1
Private household workers
981
883
955
970
-26
-2.6
-0.2
Service workers, except private household workers
16,555
21,051
21,962
22,562
5,407
32.7
2.0
Janitors and cleaners, including maids/housekeepers
2,676
3,144
3,280
3,382
604
22.6
1.5
Waiters and waitresses
1,702
2,360
2,454
2,503
752
44.2
2.6
Nursing aides, orderlies, and attendants
1,224
1,584
1,658
1,691
433
35.4
2.2
Hairdressers, hairstylists, and cosmetologists
562
627
662
683
99
17.7
1.2
Police patrol officers
349
400
409
422
61
17.4
1.2
Guards
794
1,104
1,177
1,241
383
48.3
2.9
Precision production, craft, and repair workers
13,923
14,722
15,590
16,225
1,666
12.0
0.8
Carpenters
1,010
1,134
1,192
1,252
182
18.1
1.2
Electricians
556
617
644
676
89
15.9
1.1
Painters and paperhangers, construction and maintenance
412
475
502
526
90
21.9
1.4
Plumbers, pipefitters, and steamfitters
402
452
471
493
69
17.2
1.1
Aircraft mechanics and engine specialists
107
122
129
130
22
20.1
1.3
Automotive mechanics
748
758
808
830
60
8.0
0.6
Machinists
378
345
373
385
-5
-1.5
-0.1
Operators, fabricators, and laborers
16,300
15,774
16,724
17,411
424
2.6
0.2
Sewing machine operators, garment
633
526
541
567
-92
-14.5
-1.1
Electrical and electronics assemblers
249
105
116
119
-134
-53.7
-5.3
Welders and cutters
287
284
307
320
19
6.7
0.5
Bus drivers
478
541
555
572
77
16.2
1.1
Truck drivers
2,463
2,821
2,968
3,050
505
20.5
1.3
Industrial truck and tractor operators
426
265
283
296
-143
-33.6
-2.9
Farming, forestry, and fishing workers
3,556
3,229
3,393
3,497
-163
-4.6
-0.3
Gardeners and groundskeepers, except farming
767
964
1,005
1,033
238
31.1
2.0
Farm workers
940
705
750
779
-190
-20.3
-1.6
Farm operators and managers
1,336
1,001
1,051
1,078.
-285
-21.3
-1.7
Note: Data do not add to totals because of rounding.
Projections 2000: SUMMARY
This report summarizes the Bureau of Labor Statistics'
projections of labor force, economic growth, and industry and
occupational employment between 1986 and 2000 and their implications
for the future of the U.S. economy. Five articles presenting changes
projected over the period in more detail will be published in the
September issue of the Monthly Labor Review.
OVERVIEW
Labor force. The labor force is projected to expand by nearly 21
million over the 1986-2000 period based on the moderate projections
scenario. This projected increase--nearly 18 percent represents a
slowing both. in the number to be added to the labor force and in the
rate of labor force growth compared with the previous 14 years. For
example, the labor force increased by almost 31 million between 1972
and 1985--a 35 percent expansion. The slower labor force growth
projected for the 1986-2000 period continues a trend that started in
the late 1970s. Slower growth will result primarily from fewer births
1960-76, the age group entering the labor force at least through the
mid-1990s. The labor force projections are based on forthcoming
national population projections from the U.S. Bureau of the Census
which incorporate revised fertility, mortality, and immigration
assumptions.
The labor force also is expected to become increasingly minority
and female. Blacks, Hispanics, and the Asian and other race groups
combined will account. for roughly 57 percent1 of labor force growth
1986-2000. If non-Hispanic white women are included, the minority and
female share of labor force growth exceeds 90 percent.
White labor force expansion is projected at less than 15 percent,
1986-2000, while black labor force growth, nearly 29 percent, accounts
for 3.6 million of the total increase--over 17 percent of total labor
force growth. The Asian and other races group (American Indians,
Alaskan Natives, Asians, and Pacific Islanders) is projected to grow
by nearly 2.4 million and to account for over 11 percent of labor
force growth. Hispanics, growing by about 6 million 1986-2000,
will constitute nearly 29 percent of labor force growth over this
period. Women will account for 63 percent of net labor force
expansion--slightly greater than their share of the 1972-86 growth.
Consequently, women, who have increased their share of the labor force
from 38.5 percent in 1972 to 44.5 percent in 1986, are projected to
continue to expand that share to over 47 percent by 2000.
1
Nonwhite Hispanics are double counted in this total (the sum of
black, 17 percent; Asians and other races, 11 percent; and Hispanics,
29 percent). However, data indicates that less than 1 percent of the
growth of the Hispanic labor force will be nonwhite.
- 2 -
The age composition of the projected work force also is expected
to change. Current trends in age of the labor force are projected to
continue through the mid-1990s, when some trend reversal will begin.
After the baby boom generation (usually defined as those born between
1946-early 1960s) a period of significantly lower births prevailed
until the late 1970s. Births increased 1978 to date (even while the
birth rate was stable or declining), as women of the baby boom
generation began having children. Because of the smaller numbers born
during the 1965-78 period, the number of 16 year olds in the
population and the labor force began to decline about 1976, a decline
expected to continue fairly steadily until about 1992. This pattern
will continue in consecutive years.
Differing birth cohorts moving into older age groups has two
important consequences for the age composition of the labor force:
1. By the year 2000, the share of the labor force who are 16-34
years old is projected to decline; and the share of the labor
force 35-54 years old is expected to increase.
2. For some age groups, sharp changes in direction are projected
to occur during the 1986-2000 period. For example:
16 to 24 year olds are projected to decline into the
mid-1990s and then begin to increase.
25 to 34 year olds are projected to increase through the
early 1990s and then decline sharply.
The number of 55 to 64 year olds is projected to decline
through the mid-1990s and then increase very rapidly.
Economic growth. BLS has developed three alternative projections
of the U.S. economy. In the moderate alternative, the rate of
economic growth as measured by real gross national product (GNP) is
projected to increase by 40 percent--an annual rate of 2.4 percent
1986-2000. While this growth is only slightly slower than the 2.5
percent yearly rate in growth of real GNP 1972-86, it represents a
slightly faster rate of growth than the economy achieved 1979-86--a
period marked by two recessions. The faster rate of economic growth
projected through the year 2000 results from a projected acceleration
in productivity even as the rate of labor force growth slows.
Changes also are projected in the demand structure of GNP. Among
the most important of these changes are: (1) The share of consumer
durables, which increased considerably 1972-86, is not expected to
change from 1986-2000; (2) an increase is projected in the share of
GNP devoted to exports--a reversal in the trend of the 1979-86 period;
- 3 -
(3) the share of GNP going to imports will not change 1936-2000, even
though the import share of GNP has increased by nearly 5 percentage
points 1972-86; and (4) the share of GNP devoted to defense
expenditures will decline--again a reversal of the 1979-86 trend when
defense expenditures took an increasing share of GNP.
Employment. The U.S. economy is projected to add more than 21
million jobs during the 1986-2000 period. Of this increase, 20.1
million are projected to be nonagricultural wage and salary jobs and
1.7 million to be nonagricultural, self-employed and unpaid family
workers. These gains will be somewhat offset by a projected decline
in agricultural employment. The projected employment increase--over
19 percent for the period and 1.3 percent a year--represents a slowing
of employment growth due in large part to the slowing of labor force
growth. In absolute terms, nonagricultural wage and salary workers
increased by nearly 26 million 1972-86--an expansion of nearly 35
percent or 2.2 percent a year. When compared with the more recent
period, 1979-86, the projected slowdown in employment growth is not
quite as dramatic; over that time span, nonagricultural wage and
salary jobs grew only 1.5 percent a year.
Goods-producing industries will show almost no aggregate
employment change over the projections' period. Service-producing
industries will, therefore, account for nearly all of the projected
growth. Only construction is expected to increase among the
goods-producing industries, adding almost 900,000 jobs. Although
agriculture will show growth in wage and salary jobs, that increase
should be more than offset by a decline in selfemployed.
Manufacturing employment is projected to fall by over 800,000 jobs
over the period, although output growth in that sector is projected to
grow 2.4 percent annually. But productivity growth in manufacturing
is expected to be even faster.
Within manufacturing, many industries are projected to experience
employment growth--some quite rapid. The sector will provide over 18
million wage and salary jobs--employment for 15 percent of the wage
and salaried workforce. In general, the manufacturing industries
expected to decline in employment are those that have experienced job
loss for years, such as basic steel, leather goods, shoes, tobacco,
some of the textile and basic metal processing industries, and many of
the food processing industries. Projected employment gains in
manufacturing are in printing and publishing, drugs and pharmaceutical
products, computers, and the instruments industries.
Large growth in jobs is projected for wholesale and retail
trade. The expected expansion of over 6 million wage and salary jobs
is in line with the long-term trend of these industries, which are
growing at the same or a slightly faster pace than the economy.
Finance, insurance and real estate also is projected to grow, adding
- 4 -
over 1.6 million jobs over the projection period. Over 10 million
jobs are projected to be added to service industries, with health and
business services important contributors. Government is expected to
expand by about 1.6 million jobs, with virtually all of the increase
at the State and local level.
Occupations. The projected growth in employment can be viewed
not only by industry but also by occupation. Five occupational groups
are projected to exceed the average growth in employment (technicians
and related support; service except private household; professional;
sales; and executive, administrative, and managerial workers).
Only two occupational groups are projected to show absolute
declines over the period--farming, forestry, and fishing workers and
private household workers. In addition, below-average growth is
projected for administrative support workers, including clerical, and
precision production, craft, and repair workers. Virtually no change
is projected for operators, fabricators, and laborers.
When employment by major occupational group is distributed in
1986 and in 2000 by the most prevalent 1986 educational level, the
projections show growth in the share of jobs requiring most workers to
have at least one year of college. There is a slight decline in the
share of jobs where high school completion is the predominant
education level. However, there is a sharp decline in the share of
jobs from 1986 to 2000 where less than a high school education is
currently the prevalent pattern.
Comparing projected employment growth by major occupational group
with those jobs currently held by blacks and Hispanics shows that
neither group is well represented in the fast-growing occupations;
both are over-represented in slow-growing or declining occupations. A
similar analysis for women yields comparable results, although the
disparities are not nearly as great as for blacks and Hispanics.
Alternatives. The high and low alternatives show a relatively
broad band around the moderate. The high labor force alternative is
2.3 million more than the moderate; the low, 4.3 million less than the
moderate. The annual growth rate of overall real GNP ranges from 1.6
percent in the low alternative to 3.0 percent in the high. The
unemployment rate in the low is 7.7 percent in 2000; 6.0 percent in
the moderate; and 4.5 percent in the high growth alternative. The
level of employment in 2000 is projected to be 6.6 million lower in
the low alternative than in the moderate. The high alternative, on
the other hand, is 4.5 million higher than the moderate.
- 5 -
IMPLICATIONS
Labor force. The projected continued decline of job seekers
16-19 years old through the early 1990s offers an opportunity to lower
the unemployment rate for a labor force group that historically has
had a high rate. This is particularly true because the projections
show large job increases in eating and drinking places, retail sales,
and in service industries--slots typically filled by first-time job
seekers. As noted, the share of labor force growth among blacks and
Hispanics also is projected to increase. These groups traditionally
have had higher unemployment rates than whites, which may make
lowering the overall unemployment rate more difficult, unless past
problems of jobs for minorities can be dealt with, including education
and geographic location of work.
Other important implications can be drawn from declines projected
for 20 to 24 year olds, whose numbers will shrink until the late
1990s. Many who have a primary interest in this age group--for
example, community and 4-year colleges and the military--will see the
population from whom they seek students and recruits shrink throughout
most of the 1986-2000 period. Also, producers of goods or services
focused chiefly on 16 to 24 year olds can expect their market base to
continue to decline.
Considerable attention has been focused on a potential shortage
of workers. Often this reflects not an overall lack of workers, but
the declining numbers in the younger age groups in the labor force.
This has caused employers to turn their attention to alternative
sources of workers, such as immigrants or the recently retired.
Several important considerations with regard to the large
projected immigrant share of labor force growth are: (1) To the
extent immigrants are not English speaking, their integration into the
workforce is more difficult, (2) given the skill shifts implied by the
occupational projections, many immigrants may not possess the job
skills which are in high demand in the U.S. economy, and (3) the
geographic distribution of immigrants is more concentrated than that
for the total labor force, a factor that may complicate immigrants'
search for jobs.
The baby-boom generation has been written about extensively, and
the implications of this large population group for society have been
widely discussed. Less well-known and, consequently, less written
about is the younger so-called "birth dearth" group, i.e., those born
between. about 1960 to 1978. That population group already has caused
a decline, first in 16 to 19 year olds in the population and in the
labor force and then among 20 to 24 year olds. In the late 1980s,
that decline will extend to older groups, and other implications are
likely to result.
- 6 -
The growing labor force share of blacks and Hispanics projected
over the remainder of this century poses two important
considerations. First, both groups historically have had higher
unemployment rates than whites. Thus, the opportunity for a lower
overall unemployment rate with the decline in the youth cohort could
be negated if inroads cannot be made into the historically high
unemployment rate among blacks and Hispanics. The second factor
raised by the faster labor force growth for blacks and Hispanics is
the disparity between their current employment by occupation and the
projected growth in occupational employment. Increased attention will
need to be focused on insuring that all youth, and particularly
minorities, have sufficient education to insure their entry into the
job market and to provide the education and labor market skills needed
for advancement to better jobs. While education alone is not the
solution to all labor market problems, clearly it is an important or
core element in any solution.
The increasingly larger role that women are projected to play
in the labor force of the future also has some important
implications. For unemployment, however, the gap between male and
female unemployment rates has narrowed considerably--more from an
increase in the male rate than from any significant lowering of the
female unemployment rate. While in the 1960s and 1970s women's
unemployment rates were typically 1 to 2 percentage points higher than
men's, that difference has narrowed in the 1980s. For 1984-86,
women's unemployment rates have ranged from only 0.2 to 0.4 percentage
points higher than men's. The distribution of men's and women's jobs
by major occupational group shows some disparities despite progress in
narrowing the differences over the last decade. An opportunity exists
for further improvement in that a key source of job growth, 1986-2000,
is professional, technical, and managerial jobs, and women are
projected to constitute over three-fifth of net additions to the labor
force.
Economic. Several implications can be drawn from the projected
overall economic growth and the changes expected in the structure of
demand, 1986-2000. These projections show an increase in the rate of
productivity growth. Because greater uncertainty is attached to
projected productivity growth than to projected labor force growth,
the projected rate of economic growth can be viewed as having a higher
degree of uncertainty. Consequently, users should examine the
alternative projections and their implications carefully. As an
example, the low projection alternative, which has a rate of real GNP
growth of 1.6 percent per year, 1986-2000, results from a rate of
productivity growth consistent with the 1972-86 trend. One important
implication of this is that real disposable income per capita (one
measure of well-being in the economy) only increases under the low
- 7 -
alternative by 0.7 percent per year, or much slower than the 1.7
percent growth, 1972-86, and at less than one-half the rate of
increase projected in the moderate alternative. If a faster rate of
productivity increase should prevail, it would be more favorable for
the economy, since that is the primary factor leading to gains in
future living standards of the population.
Perhaps the most significant trend change projected in the
composition of demand for the 1986-2000 period is in foreign trade.
As a result of changes in the exchange rates, exports are projected to
increase at a faster rate than imports. This is important both to
many exporting industries and to other industries which have been
pressed by the very rapid growth of imports over the last decade.
However, the extent of the slowdown in the growth of imports and the
increase in the growth of exports varies considerably from industry to
industry. World conditions make the projections of exports and
imports uncertain, with trends more volatile than most other demand
categories. Another difficulty in developing projections of foreign
trade is estimating the capacity of some U.S. industries to recover
their export markets once they have been lost--even though a
significant turnaround in the value of the dollar has taken place.
Several other important implications follow from the projected
shifts in the structure of demand. The projected slowing of the
younger age groups in the- population and the resulting slowdown in
household and family formation are expected to impact expenditure
patterns. This is most noticable in consumer durables, particularly
in automobile purchases and in new housing construction. Another
demand impact related to demographic changes in the population is
health care expenditures for the older age groups, particularly the
very rapid growth expected in the over 85 population between now and
2000. Not only is this older age group expected to keep health care
expenditures among the most rapidly growing demand categories, but
coupled with cost pressures the distribution of health care purchases
also is projected to be shifting toward nursing homes and home health
care.
Industry employment. Most of the projected employment growth is
found among the service-producing industries of the economy. Further
declines in employment are projected for many industries, including
agriculture, many of the mining industries, a significant number of
manufacturing industries, and a few service industries. Consequently,
despite the projected overall employment growth, workers will continue
to be displaced as employment in these industries declines. Because
of the geographic concentration of many of the declining industries,
some localities will be harder hit from these displacements. Although
some displaced workers should be able to obtain related jobs and
maintain their standard of living, others may require further training
and/or education or may have to relocate geographically.
- 8 -
Some of those displaced from their jobs may not find employment
of a like nature given the occupational shifts that are projected to
occur between now and 2000, particularly if they lack the education
and training required for the emerging jobs. Jobs for displaced
workers are a nettlesome problem. While much geographic and
occupational mobility exists in our economy, it is heavily
concentrated among the young. While it is important that entry level
workers be provided with as high a level of education as possible,
this doesn't help displaced workers who are past 40 and have
relatively low educational attainment. 2
The expected continuation of the movement of employment to the
service-producing industries has several important implications.
Firms in some of these industries are more likely to be small.
Because small firms generally have a higher turnover rate, they may
be less likely to provide workers a life-time employment opportunity.
Consequently, workers will need to be prepared through education and
training for more frequent changes of employers and occupations. This
is best accomplished by a broad-based education at least through the
secondary school level. Many smaller firms also are less able than
larger employers to provide other benefits such as health care.
Another trend is developing that will in all likelihood require
adjustments in the future. As younger age groups in the work force
decline and women increasingly seek full-time work, there is conflict
between those industries. which traditionally demand a high proportion
of part-time workers and the economy's ability to supply those
workers. Resolution of this conflict could take the form of a
movement back toward providing a larger share of full-time jobs in
these industries, expansion of self-service stores, or drawing into
the work force some older workers who currently are not employed.
The alternative is that some seekers of full-time work might be able
to find only part-time employment. Another implication is a slowing,
or possibly a reversal, in the decline of average hours in the
economy. This seems likely since the increase in part-time employment
was. the primary factor behind past declines in average hours.
Occupations. Industry employment shifts and changes in staffing
patterns are expected to alter the occupational structure of
employment. In general, occupations in which current participants
have the most education are projected to grow the most rapidly
--although many jobs should continue to bei available for those with
2 Displaced Workers, 1979-83 Bureau of Labor Statistics, July 1985
BLS Bulletin 2240; also June 1987 Monthly Labor Reivew for 1986
survey.
3
See Employee Benefit Research Institute tabulations of the May 1983
Current Population Survey.
- 9 -
only a high school education despite their relatively slower growth.
However, those with less than a high school education face increasing
difficulty in their job search and less opportunity for good pay and
advancement. These labor market problems frequently stem from a lack
of education or training needed to adapt to the employment effects of
changes in technology and the structure of demand. The continuing
large number of high school dropouts clearly. signals that an
important problem remains.⁴ Given that blacks and Hispanics are
disproportionately represented among those with less education, and
are projected to account for an increasing share of workers, the
recent decline in college enrollment of blacks reported in a
Department of Education release is unfortunate.⁵
Despite the faster than average growth in employment for
occupations requiring a bachelor's or higher degree, the surplus of
college graduates that began in the early 1970s is expected to
continue through the end of the century. However, the gap between
supply and demand for new college graduates is expected to narrow
considerably as we move into the 1990s, in part because of the decline
in number of college graduates stemming from the population decline of
individuals of college age. 6
Occupations generally filled by young workers, such as food
service, retail sales, and construction labor, are projected to
continue to generate many jobs; and the declining numbers of young
workers could improve the youth labor market situation. At the same
time, given the expected sharp decline in the number of youth,
employment opportunities may arise for others seldom employed in those
jobs, such as the recently retired who desire some work. This
development also could increase the labor market participation of some
groups, such as black males, who currently have much lower
participation rates than white males of the same age.
Women, blacks, and Hispanics have traditionally been highly
concentrated by occupation. Although this occupational segregation
has lessened in the past decade, the future offers a chance for
further improvement due to rapid growth in many occupations not
traditionally filled by Hispanics, blacks, women.
4
Elementary and Secondary Education Indicators in Brief, Office of
Educational Research and Improvement 1987.
5 Ibid.
6 Trends in Education 1975-76--1995-96, U.S. Department of Education,
Center for Education Statistics.
News
United States
Department
of Labor
Bureau of Labor Statistics
Washington, D.C. 20212
Technical information: (202) 523-1371
USDL 89-225
523-1944
523-1959
TRANSMISSION OF MATERIAL IN THIS
Media contact:
523-1913
RELEASE IS EMBARGOED UNTIL
8:30 A.M. (EDT), FRIDAY,
MAY 5, 1989
THE EMPLOYMENT SITUATION: APRIL 1989
Unemployment rose in April and payroll employment showed little
growth, the Bureau of Labor Statistics of the U.S. Department of Labor
reported today. The overall jobless rate was 5.2 percent and the civilian
worker rate was 5.3 percent, each three-tenths of a point above March
levels.
Nonagricultural payroll employment, as measured by the survey of
business establishments, rose by 115,000 in April, the second straight
month that the payroll survey has shown relatively small job gains. Total
civilian employment, as measured by the survey of households, was about
unchanged over the month.
Unemployment (Household Survey Data)
Both the number of unemployed persons and the civilian worker
unemployment rate increased in April, after seasonal adjustment, offsetting
much of the improvement that had occurred in February and March. The
number of unemployed persons increased by 420,000 to a seasonally adjusted
level of 6.5 million, and the civilian worker unemployment rate rose by 0.3
percentage point to 5.3 percent. The increase returned both figures to the
levels that prevailed in the last quarter of 1988. (See table A-2.)
The unemployment rate for adult men rose four-tenths of a percentage
point in April to 4.6 percent. The rate for whites also rose to 4.6
percent; the rate for Hispanics was up sharply over the month to 8.3
percent, reversing a decline of a similar magnitude in February. Jobless
rates for adult women (4.7 percent), teenagers (14.4 percent), and blacks
(10.8 percent) were little changed in April. (See tables A-2 and A-3.)
The median duration of unemployment, at 5.4 weeks, was unchanged from
the previous month. The number of persons working part time for economic
reasons--often referred to as the partially unemployed--edged up by 175,000
over the month to a seasonally adjusted level of 5.1 million. (See tables
A-7 and A-4.)
Civilian Employment and the Labor Force (Household Survey Data)
Total civilian employment was unchanged in April, after seasonal
adjustment, at 117.1 million, and the employment-population ratio--the
proportion of the population that is employed--held steady at 63.0 percent,
the record high reached in March. (See table A-2.)
-2-
-
The civilian labor force rose by 400,000 over the month to 123.7
million. The labor force participation rate rose to 66.5 percent,
returning to the high reached in January. Over the year, the civilian
labor force has grown by 2.3 million, three-fifths of which occurred among
adult women. (See table A-2.)
Table A. Major indicators of labor market activity, seasonally adjusted
Quarterly
Monthly data
averages
Mar.-
Category
1988
1989
1989
Apr.
IV
I
Feb.
Mar.
Apr.
change
HOUSEHOLD DATA
Thousands of persons
Labor force 1/
124,084
124,979
124,865
124,948
125,343
395
Total employment 1/.
117,539
118,588
118,537
118,820
118,797
-23
Civilian labor force
122,388
123,291
123,181
123,264
123,659
395
Civilian employment
115,843
116,900
116,853
117,136
117,113
-23
Unemployment
6,545
6,391
6,328
6,128
6,546
418
Not in labor force
62,865
62,482
62,596
62,633
62,365
-268
Discouraged workers.
951
855
N.A.
N.A.
N.A.
N.A.
Percent of labor force
Unemployment rates:
All workers 1/
5.3
5.1
5.1
4.9
5.2
0.3
All civilian workers
5.3
5.2
5.1
5.0
5.3
.3
Adult men
4.7
4.5
4.5
4.2
4.6
.4
Adult women
4.7
4.6
4.5
4.6
4.7
.1
Teenagers
14.6
15.0
14.8
13.7
14.4
.7
White
4.6
4.4
4.3
4.2
4.6
.4
Black
11.3;
11.6
11.9
10.9
10.8
-.1
Hispanic origin
7.8
7.2
6.8
6.5
8.3
1.8
ESTABLISHMENT DATA
Thousands of jobs
Nonfarm employment
107,344
p108,306
108,341
p108,512
p108,629
p117
Goods-producing
25,827
p26,015
26,011
p25,986
p25,991
p5
Service-producing
81,517
p82,291
82,330
p82,526
p82,638
p112
Hours of work
Average weekly hours:
Total private
34.8
p34.7
34.6
p34.6
p35.0
p0.4
Manufacturing
41.1
p41.1
41.1
p41.0
p41.3
p.3
Overtime
3.9
p3.9
3.9
p3.9
p4.0
p.1
1/ Includes the resident Armed Forces.
N.A. =not available.
p=preliminary
-3-
Industry Payroll Employment (Establishment Survey Data)
Employment growth in nonagricultural establishments continued to slow,
as payroll jobs increased by 115,000 in April to a seasonally adjusted
level of 108.6 million. Payroll employment gains have averaged only
145,000 for the last 2 months, compared to 300,000 per month in the prior
12 months. In addition to being relatively weak, employment growth in
April was very narrowly concentrated; the services industry alone accounted
for 100,000 of the over-the-month gain.
The number of jobs in the goods-producing sector was unchanged in
April, following 2 months of decline. Manufacturing employment was flat
for the third consecutive month, in contrast to the October-to-January
period when it added some 250,000 jobs. Employment in machinery, which has
accounted for a quarter of manufacturing's growth in the last 2 years, has
shown little change over the last 2 months. The number of jobs in
electrical equipment has fallen by 25,000 in the last 5 months. The lumber
and wood products industry has also declined recently, as employment was
down by about 15,000 since January, largely a reflection of recent weakness
in the construction industry.
Construction employment was unchanged in April, seasonally adjusted,
following back-to-back declines in February and March. Employment in
mining rose for the second consecutive month, as oil and gas extraction
added 10,000 jobs in the last 3 months, following 7 months of job losses.
In the service-producing sector, the only significant employment
growth took place in the services industry. Employment in that industry
grew by 100,000 in April, even though health services was not as strong as
usual (up 35,000) and business services, following an erratic pattern
recently, was also weak (up about 15,000). Above-average growth was
reported in several other services industries. After rising rapidly in the
first quarter, employment in retail trade was unchanged over the month.
Wholesale trade added 10,000 jobs in April, much less than its average pace
of more than 25,000 per month since the end of 1987. Except for a slight
decline in the real estate component, employment in the finance, insurance,
and real estate industry was about unchanged.
Weekly Hours (Establishment Survey Data)
The average workweek for production or nonsupervisory workers on
private nonagricultural payrolls showed an increase of 0.4 hour in April,
seasonally adjusted, to 35.0 hours. Similarly, the manufacturing workweek
increased 0.3 hour to 41.3 hours, while manufacturing overtime edged up 0.1
hour to 4.0 hours. These seasonally adjusted gains in weekly hours are
overstated, however, because of the way the seasonal adjustment process is
affected by the timing of the Easter week; historically, large April
movements in hours (both increases and decreases) have been reversed in
May. (See table B-2.)
-4-
The index of aggregate weekly hours of production or nonsupervisory
workers on private nonagricultural payrolls, at 129.5 (1977=100), climbed
1.1 percent in April, after seasonal adjustment. The manufacturing index
rose 0.6 percent to 97.7. These increases were also affected by the
overstatement in hours discussed above. (See table B-5.)
Hourly and Weekly Earnings (Establishment Survey Data)
Average hourly earnings of private production or nonsupervisory
workers increased 0.7 percent in April, seasonally adjusted, following
increases totaling only 0.3 percent over the prior 2 months. Average weekly
earnings climbed by 1.9 percent, largely reflecting the movement in the
hours series. Before seasonal adjustment, average hourly earnings rose by
5 cents to $9.60, and average weekly earnings jumped $5.56 to $334.08.
Over the past year, hourly earnings have risen by 4.0 percent and weekly
earnings were up 4.3 percent. (See tables B-3 and B-4.)
Revisions in the Establishment Survey Data
The Employment Situation news release of data for May will introduce
revisions in the establishment-based series on nonagricultural payroll
employment, hours, and earnings to reflect the regular annual benchmark
adjustments and updated seasonal adjustment factors.
The Employment Situation for May 1989 will be released on Friday, June
2, at 8:30 A.M. (EDT).
Explanatory Note
This news release presents statistics from two major surveys,
that time; and they made specific efforts to find employment
the Current Population Survey (household survey) and the
sometime during the prior 4 weeks. Persons laid off from their
Current Employment Statistics Survey (establishment survey).
former jobs and awaiting recall and those expecting to report
The household survey provides the information on the labor
to a job within 30 days need not be looking for work to be
force, total employment, and unemployment that appears in
counted as unemployed.
the A tables, marked HOUSEHOLD DATA. It is a sample
The labor force equals the sum of the number employed and
survey of about 55,800 households that is conducted by the
the number unemployed. The unemployment rate is the
Bureau of the Census with most of the findings analyzed and
percentage of unemployed people in the labor force (civilian
published by the Bureau of Labor Statistics (BLS).
plus the resident Armed Forces). Table A-5 presents a special
The establishment survey provides the information on the
grouping of seven measures of unemployment based on vary-
employment, hours, and earnings of workers on
ing definitions of unemployment and the labor force. The
nonagricultural payrolls that appears in the B tables, marked
definitions are provided in the table. The most restrictive
ESTABLISHMENT DATA. This information is collected
definition yields U-1 and the most comprehensive yields U-7.
from payroll records by BLS in cooperation with State agencies.
The overall unemployment rate is U-5a, while U-5b represents
The sample includes over 300,000 establishments employing
the same measure with a civilian labor force base.
over 38 million people.
Unlike the household survey, the establishment survey only
For both surveys, the data for a given month are actually
counts wage and salary employees whose names appear on the
collected for and relate to a particular week. In the household
payroll records of nonagricultural firms. As a result, there are
survey, unless otherwise indicated, it is the calendar week that
many differences between the two surveys, among which are
contains the 12th day of the month, which is called the survey
the following:
week. In the establishment survey, the reference week is the
- The household survey, although based on a smaller sample, reflects a
pay period including the 12th, which may or may not corres-
larger segment of the population; the establishment survey excludes agriculture,
pond directly to the calendar week.
the self-employed, unpaid family workers, private household workers, and
The data in this release are affected by a number of technical
members of the resident Armed Forces;
factors, including definitions, survey differences, seasonal ad-
- The household survey includes people on unpaid leave among the
justments, and the inevitable variance in results between a
employed; the establishment survey does not;
survey of a sample and a census of the entire population. Each
- The household survey is limited to those 16 years of age and older; the
of these factors is explained below.
establishment survey is not limited by age;
Coverage, definitions, and differences
- The household survey has no duplication of individuals, because each in-
between surveys
dividual is counted only once; in the establishment survey, employees working at
more than one job or otherwise appearing on more than one payroll would be
The sample households in the household survey are selected
counted separately for each appearance.
so as to reflect the entire civilian noninstitutional population
16 years of age and older. Each person in a household is
Other differences between the two surveys are described in
classified as employed, unemployed, or not in the labor force.
"Comparing Employment Estimates from Household and
Those who hold more than one job are classified according to
Payroll Surveys," which may be obtained from the BLS upon
the job at which they worked the most hours.
request.
People are classified as employed if they did any work at all
as paid civilians; worked in their own business or profession or
Seasonal adjustment
on their own farm; or worked 15 hours or more in an enter-
Over the course of a year, the size of the Nation's labor
prise operated by a member of their family, whether they were
force and the levels of employment and unemployment
paid or not. People are also counted as employed if they were
undergo sharp fluctuations due to such seasonal events as
on unpaid leave because of illness, bad weather, disputes be-
changes in weather, reduced or expanded production, har-
tween labor and management, or personal reasons. Members
vests, major holidays, and the opening and closing of schools.
of the Armed Forces stationed in the United States are also in-
For example, the labor force increases by a large number each
cluded in the employed total.
June, when schools close and many young people enter the job
People are classified as unemployed, regardless of their
market. The effect of such seasonal variation can be very
eligibility for unemployment benefits or public assistance, if
large; over the course of a year, for example, seasonality may
they meet all of the following criteria: They had no employ-
account for as much as 95 percent of the month-to-month
ment during the survey week; they were available for work at
changes in unemployment.
Because these seasonal events follow a more or less regular
from the results of a complete census. The chances are approx-
pattern each year, their influence on statistical trends can be
imately 90 out of 100 that an estimate based on the sample will
eliminated by adjusting the statistics from month to month.
differ by no more than 1.6 times the standard error from the
These adjustments make nonseasonal developments, such as
results of a complete census. At approximately the 90-percent
declines in economic activity or increases in the participation
level of confidence-the confidence limits used by BLS in its
of women in the labor force, easier to spot. To return to the
analyses-the error for the monthly change in total employ-
school's-out example, the large number of people entering the
ment is on the order of plus or minus 358,000; for total
labor force each June is likely to obscure any other changes
unemployment it is 224,000; and, for the overall unemploy-
that have taken place since May, making it difficult to deter-
ment rate, it is 0.19 percentage point. These figures do not
mine if the level of economic activity has risen or declined.
mean that the sample results are off by these magnitudes but,
However, because the effect of students finishing school in
rather, that the chances are approximately 90 out of 100 that
previous years is known, the statistics for the current year can
the "true" level or rate would not be expected to differ from
be adjusted to allow for a. comparable change. Insofar as the
the estimates by more than these amounts.
seasonal adjustment is made correctly, the adjusted figure pro-
Sampling errors for monthly surveys are reduced when the
vides a more useful tool with which to analyze changes in
data are cumulated for several months, such as quarterly or
economic activity.
annually. Also, as a general rule, the smaller the estimate, the
Measures of labor force, employment, and unemployment
larger the sampling error. Therefore, relatively speaking, the
contain components such as age and sex. Statistics for all
estimate of the size of the labor force is subject to less error
employees, production workers, average weekly hours, and
than is the estimate of the number unemployed. And, among
average hourly earnings include components based on the
the unemployed, the sampling error for the jobless rate of
employer's industry. All these statistics can be seasonally ad-
adult men, for example, is much smaller than is the error for
justed either by adjusting the total or by adjusting each of the
the jobless rate of teenagers. Specifically, the error on monthly
components and combining them. The second procedure
change in the jobless rate for men is .25 percentage point; for
usually yields more accurate information and is therefore
teenagers, it is 1.29 percentage points.
followed by BLS. For example, the seasonally adjusted figure
In the establishment survey, estimates for the 2 most current
for the labor force is the sum of eight seasonally adjusted
months are based on incomplete returns; for this reason, these
civilian employment components, plus the resident Armed
estimates are labeled preliminary in the tables. When all the
Forces total (not adjusted for seasonality), and four seasonally
returns in the sample have been received, the estimates are
adjusted unemployment components; the total for unemploy-
revised. In other words, data for the month of September are
ment is the sum of the four unemployment components; and
published in preliminary form in October and November and
the overall unemployment rate is derived by dividing the
in final form in December. To remove errors that build up
resulting estimate of total unemployment by the estimate of
over time, a comprehensive count of the employed is con-
the labor force.
ducted each year. The results of this survey are used to
The numerical factors used to make the seasonal ad-
establish new benchmarks-comprehensive counts of
justments are recalculated regularly. For the household
employment-against which month-to-month changes can be
survey, the factors are calculated for the January-June period
measured. The new benchmarks also incorporate changes in
and again for the July-December period. The January revision
the classification of industries and allow for the formation of
is applied to data that have been published over the previous 5
new establishments.
years. For the establishment survey, updated factors for
seasonal adjustment are calculated only once a year, along
Additional statistics and other information
with the introduction of new benchmarks which are discussed
at the end of the next section.
In order to provide a broad view of the Nation's employ-
ment situation, BLS regularly publishes a wide variety of data
in this news release. More comprehensive statistics are contain-
Sampling variability
ed in Employment and Earnings, published each month by
Statistics based on the household and establishment surveys
BLS. It is available for $8.50 per issue or $25.00 per year from
are subject to sampling error, that is, the estimate of the
the U.S. Government Printing Office, Washington, DC
number of people employed and the other estimates drawn
20204. A check or money order made out to the Superinten-
from these surveys probably differ from the figures that would
dent of Documents must accompany all orders.
be obtained from a complete census, even if the same question-
Employment and Earnings also provides approximations of
naires and procedures were used. In the household survey, the
the standard errors for the household survey data published in
amount of the differences can be expressed in terms of stand-
this release. For unemployment and other labor force
ard errors. The numerical value of a standard error depends
categories, the standard errors appear in tables B through J of
upon the size of the sample, the results of the survey, and other
its "Explanatory Notes." Measures of the reliability of the
factors. However, the numerical value is always such that the
data drawn from the establishment survey and the actual
chances are approximately 68 out of 100 that an estimate based
amounts of revision due to benchmark adjustments are pro-
on the sample will differ by no more than the standard error
vided in tables M, O, P, and Q of that publication.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-1. Employment status of the population, including Armed Forces In the United States, by sex
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Employment status and sex
Apr.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989
1988
1988
1989
1989
1989
1989
TOTAL
Noninstitutional population²
185,964
187,581
187,708
185,964
187,098
187,340
187,461
187,581
187,708
Labor force2
121,996
123,907
124,260
123,060
124,259
125,124
124,865
124,948
125,343
Participation rate³
65.6
66.1
66.2
66.2
66.4
66.8
66.6
66.6
66.8
Total employed²
115,637
117,528
118,031
116,392
117,705
118,407
118,537
118,820
118,797
Employment-population ratio⁴
62.2
62.7
62.9
62.6
62.9
63.2
63.2
63.3
63.3
Resident Armed Forces
1,732
1,684
1,684
1,732
1,696
1,696
1,684
1,684
1,684
Civilian employed
113,905
115,844
116,347
114,660
116,009
116,711
116,853
117,136
117,113
Agriculture
3,193
2,934
3,116
3,187
3,193
3,300
3,223
3,206
3,104
Nonagricultural industries
110,712
112,911
113,231
111,473
112,816
113,411
113,630
113,930
114,009
Unemployed
6,359
6,378
6,229
6,668
6,554
6,716
6,328
6,128
6,546
Unemployment rate5
5.2
5.1
5.0
5.4
5.3
5.4
5.1
4.9
5.2
Not in labor force
63,968
63,674
63,448
62,904
62,839
62,216
62,596
62,633
62,365
Men, 16 years and over
Noninstitutional population²
89,225
90,032
90,094
89,225
89,792
89,914
89,973
90,032
90,094
Labor force²
67,798
68,472
68,684
68,462
68,638
69,032
69,113
69,190
69,360
Participation rate³
76.0
76.1
76.2
76.7
76.4
76.8
76,8
76.9
77.0
Total employed²
64,288
64,875
65,185
64,866
65,055
65,322
65,572
65,920
65,767
Employment-population ratio⁴
72.1
72.1
72.4
72.7
72.5
72.6
72.9
73.2
73.0
Resident Armed Forces
1,569
1,521
1,521
1,569
1,534
1,532
1,521
1,521
1,521
Civilian employed
62,719
63,354
63,664
63,297
63,521
63,790
64,051
64,399
64,246
Unemployed
3,510
3,597
3,499
3,596
3,583
3,710
3,540
3,270
3,593
Unemployment rate⁵
5.2
5.3
5.1
5.3
5.2
5.4
5.1
4.7
5.2
Women, 16 years and over
Noninstitutional population²
96,739
97,550
97,614
96,739
97,306
97,427
97,488
97,550
97,614
Labor force²
54,198
55,435
55,576
54,598
55,621
56,091
55,752
55,758
55,983
Participation rate³
56.0
56.8
56.9
56.4
57.2
57.6
57.2
57.2
57.4
Total employed²
51,349
52,654
52,846
51,526
52,650
53,085
52,965
52,900
53,029
Employment-population ratio⁴
53.1
54.0
54.1
53.3
54.1
54.5
54.3
54.2
54.3
Resident Armed Forces
163
163
163
163
162
164
163
163
163
Civilian employed
51,186
52,491
52,683
51,363
52,488
52,921
52,802
52,737
52,866
Unemployed
2,849
2,781
2,730
3,072
2,971
3,006
2,787
2,858
2,953
Unemployment rate5
5.3
5.0
4.9
5.6
5.3
5.4
5.0
5.1
5.3
1 The population and Armed Forces figures are not adjusted for
3 Labor force as a percent of the noninstitutional population.
seasonal variation; therefore, identical numbers appear in the unadjusted
4 Total employment as a percent of the noninstitutional population,
and seasonally adjusted columns.
5
Unemployment as a percent of the labor force (including the resident
2 Includes members of the Armed Forces stationed in the United
Armed Forces).
States.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-2. Employment status of the civilian population by sex and age
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Employment status, sex, and age
Apr.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989
1988
1988
1989
1989
1989
1989
TOTAL
Civilian noninstitutional population
184,232
185,897
186,024
184,232
185,402
185,644
185,777
185,897
186,024
Civilian labor force
120,264
122,223
122,576
121,328
122,563
123,428
123,181
123,264
123,659
Participation rate
65.3
65.7
65.9
65.9
66.1
66.5
66.3
66.3
66.5
Employed
113,905
115,844
116,347
114,660
116,009
116,711
116,853
117,136
117,113
Employment-population ratio²
61.8
62.3
62.5
62.2
62.6
62.9
62.9
63.0
63.0
Unemployed
6,359
6,378
6,229
6,668
6,554
6,716
6,328
6,128
6,546
Unemployment rate
5.3
5.2
5.1
5.5
5.3
5.4
5.1
5.0
5.3
Men, 20 years and over
Civilian noninstitutional population
80,326
81,333
81,413
80,326
81,001
81,162
81,256
81,333
81,413
Civilian labor force
62,442
63,210
63,370
62,774
63,002
63,358
63,490
63,557
63,709
Participation rate
77.7
77.7
77.8
78.1
77.8
78.1
78.1
78.1
78.3
Employed
59,504
60,191
60,430
59,833
60,049
60,420
60,636
60,869
60,757
Employment-population ratio²
74.1
74.0
74.2
74.5
74.1
74.4
74.6
74.8
74.6
Agriculture
2,280
2,166
2,277
2,259
2,292
2,277
2,320
2,317
2,252
Nonagricultural industries
57,224
58,025
58,154
57,574
57,757
58,143
58,316
58,552
58,505
Unemployed
2,938
3,019
2,940
2,941
2,953
2,938
2,853
2,688
2,952
Unemployment rate
4.7
4.8
4.6
4.7
4.7
4.6
4.5
4.2
4.6
Women, 20 years and over
Civilian noninstitutional population
89,307
90,242
90,318
89,307
89,954
90,072
90,153
90,242
90,318
Civilian labor force
50,465
51,803
51,855
50,591
51,587
51,998
51,821
51,851
51,992
Participation rate
56.5
57.4
57.4
56.6
57.3
57.7
57.5
57.5
57.6
Employed
48,162
49,462
49,578
48,120
49,165
49,543
49,514
49,484
49,544
Employment-population ratio²
53.9
54.8
54.9
53.9
54.7
55.0
54.9
54.8
54.9
Agriculture
637
594.
600
653
646
715
666
664
615
Nonagricultural industries
47,525
48,868
48,978
47,467
48,519
48,827
48,849
48,819
48,929
Unemployed
2,303
2,341
2,277
2,471
2,422
2,455
2,306
2,367
2,448
Unemployment rate
4.6
4.5
4.4
4.9
4.7
4.7
4.5
4.6
4.7
Both sexes, 16 to 19 years
Civilian noninstitutional population
14,598
14,323
14,293
14,598
14,447
14,410
14,367
14,323
14,293
Civilian labor force
7,357
7,210
7,350
7,963
7,974
8,071
7,871
7,856
7,958
Participation rate
50.4
50.3
51.4
54.5
55.2
56.0
54.8
54.9
55.7
Employed
6,239
6,192
6,338
6,707
6,795
6,748
6,703
6,783
6,812
Employment-population ratio²
42.7
43.2
44.3
45.9
47.0
46.8
46.7
47.4
47.7
Agriculture
276
174
240
275
255
307
237
224
237
Nonagricultural industries
5,962
6,018
6,098
6,432
6,540
6,441
6,466
6,559
6,575
Unemployed
1,118
1,018
1,012
1,256
1,179
1,323
1,168
1,073
1,146
Unemployment rate
15.2
14.1
13.8
15.8
14.8
16.4
14.8
13.7
14.4
1
The population figures are not adjusted for seasonal variation;
2
Civilian employment as a percent of the civilian noninstitutional
therefore, identical numbers appear in the unadjusted and seasonally
population.
adjusted columns.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-3. Employment status of the civillan population by race, sex, age, and Hispanic origin
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Employment status, race, sex, age, and
Hispanic origin
Apr.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989
1988
1988
1989
1989
1989
1989
WHITE
Civilian noninstitutional population
157,943
159,020
159,098
157,943
158,705
158,865
158,947
159,020
159,098
Civilian labor force
103,758
105,100
105,542
104,517
105,411
106,106
105,798
105,988
106,312
Participation rate
65.7
66.1
66.3
66.2
66.4
66.8
66.6
66.7
66.8
Employed
99,141
100,435
100,941
99,663
100,567
101,183
101,278
101,554
101,458
Employment-population ratio²
62.8
63.2
63.4
63.1
63.4
63.7
63.7
63.9
63.8
Unemployed
4,617
4,664
4,601
4,854
4,844
4,923
4,521
4,434
4,854
Unemployment rate
4.5
4.4
4.4
4.6
4.6
4.6
4.3
4.2
4.6
Men, 20 years and over
Civilian labor force
54,430
55,070
55,207
54,653
54,898
55,213
55,308
55,382
55,448
Participation rate
78.1
78.2
78.3
78.4
78.2
78.5
78.6
78.6
78.7
Employed
52,275
52,800
53,033
52,478
52,636
53,007
53,197
53,387
53,246
Employment-population ratio²
75.0
75.0
75.2
75.3
75.0
75.4
75.6
75.8
75.5
Unemployed
2,155
2,270
2,173
2,175
2,262
2,205
2,111
1,995
2,202
Unemployment rate
4.0
4.1
3.9
4.0
4.1
4.0
3.8
3.6
4.0
Women, 20 years and over
Civilian labor force
42,882
43,767
43,954
42,955
43,644
43,936
43,770
43,780
44,016
Participation rate
56.2
56.9
57.1
56.3
56,9
57.2
56.9
56.9
57.2
Employed
41,297
42,115
42,291
41,233
41,930
42,201
42,177
42,115
42,207
Employment-population ratio²
54.1
54.7
54.9
54.0
54,6
54.9
54.8
54.7
54.8
Unemployed
1,586
1,652
1,663
1,722
1,714
1,734
1,593
1,665
1,810
Unemployment rate
3.7
3.8
3.8
4.0
3.9
3.9
3.6
3.8
4.1
Both sexes, 16 to 19 years
Civilian labor force
6,445
6,262
6,382
6,909
6,869
6,958
6,720
6,826
6,848
Participation rate
54.2
53.9
55.0
58.1
58.6
59.6
57.7
58.7
59.0
Employed
5,569
5,520
5,617
5,952
6,001
5,975
5,904
6,052
6,005
Employment-population ratio²
46.9
47.5
48.4
50.1
51.2
51.1
50.7
52.1
51.8
Unemployed
876
742
765
957
868
983
816
774
843
Unemployment rate
13.6
11.9
12.0
13.9
12.6
14.1
12.1
11.3
12,3
Men
14.1
13.8
12.7
14.4
13.4
16.4
14.0
12.3
13.1
Women
13.1
9.8
11.2
13.3
11.8
11.7
10.2
10.2
11.5
BLACK
Civilian noninstitutional population
20,622
20,930
20,956
20,622
20,842
20,877
20,905
20,930
20,956
Civilian labor force
12,941
13,243
13,121
13,101
13,405
13,477
13,476
13,425
13,287
Participation rate
62.8
63.3
62.6
63.5
64.3
64.6
64.5
64.1
63.4
Employed
11,394
11,761
11,699
11,534
11,856
11,860
11,873
11,961
11,846
Employment-population ratio²
55.3
56.2
55.8
55.9
56.9
56.8
56.8
57.1
56.5
Unemployed
1,547
1,483
1,422
1,567
1,549
1,617
1,603
1,464
1,442
Unemployment rate
12.0
11.2
10.8
12.0
11.6
12.0
11.9
10.9
10.8
Men, 20 years and over
Civilian labor force
6,142
6,187
6,165
6,151
6,179
6,226
6,199
6,230
6,171
Participation rate
75.1
74.3
73.9
75.2
74.6
75.0
74.6
74.8
74.0
Employed
5,467
5,541
5,515
5,510
5,561
5,576
5,549
5,620
5,554
Employment-population ratio²
66.8
66.6
66.1
67.3
67.1
67.2
66.7
67.5
66.6
Unemployed
675
646
650
641
618
650
650
611
617
Unemployment rate
11.0
10.4
10.5
10.4
10.0
10.4
10.5
9.8
10.0
Women, 20 years and over
Civilian labor force
6,062
6,281
6,174
6,112
6,316
6,369
6,349
6,315
6,227
Participation rate
59.1
60.2
59.1
59.6
60.9
61.2
61.0
60.5
59.6
Employed
5,412
5,699
5,637
5,444
5,654
5,706
5,697
5,739
5,677
Employment-population ratio²
52.7
54.6
54.0
53.1
54.5
54.9
54.7
55.0
54.3
Unemployed
650
582
536
668
662
663
651
576
550
Unemployment rate
10.7
9.3
8.7
10.9
10.5
10.4
10.3
9.1
8.8
Both sexes, 16 to 19 years
Civilian labor force
737
775
783
838
910
881
928
880
889
Participation rate
33.8
35.6
36.0
38.5
41.7
40.5
42.7
40.5
40.9
Employed
516
521
546
580
641
577
627
602
615
Employment-population ratio²
23.7
24.0
25.1
26.6
29.4
26.5
28.8
27.7
28.3
Unemployed
221
255
236
258
269
304
301
278
274
Unemployment rate
30.0
32.8
30.2
30.8
29.6
34.5
32.4
31.6
30.8
Men
24.8
29.3
33.6
27.9
29.8
36.7
33.1
28.6
35.5
Women
35.8
36.4
26.8
33.9
29.3
32.0
31.6
34.8
26.2
See footnotes at end of table.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-3. Employment status of the civilian population by race, sex, age, and Hispanic origin-Continued
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted'
Employment status, race, sex, age, and
Hispanic origin
Apr.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989
1988
1988
1989
1989
1989
1989
HISPANIC ORIGIN
Civilian noninstitutional population
13,230
13,649
13,690
13,230
13,533
13,564
13,606
13,649
13,690
Civilian labor force
8,773
9,109
9,210
8,823
9,133
9,205
9,219
9,210
9,262
Participation rate
66.3
66.7
67.3
66.7
67.5
67.9
67.8
67.5
67.7
Employed
8,002
8,504
8,461
8,030
8,441
8,434
8,596
8,607
8,495
Employment-population ratio²
60.5
62.3
61.8
60.7
62.4
62.2
63.2
63.1
62.1
Unemployed
771
605
749
793
692
771
624
603
767
Unemployment rate
8.8
6.6
8.1
9.0
7.6
8.4
6.8
6.5
8.3
1
The population figures are not adjusted for seasonal variation;
population.
therefore, identical numbers appear in the unadjusted and seasonally
NOTE: Detail for the above race and Hispanic-origin groups will not
adjusted columns.
sum to totals because data for the "other races" group are not presented
2
Civilian employment as a percent of the civilian noninstitutional
and Hispanics are included in both the white and black population groups.
Table A-4. Selected employment indicators
(In thousands)
Not seasonally adjusted
Seasonally adjusted
Category
Mar.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
Apr.
Apr.
1988
1988
1989
1989
1989
1988
1989
1989
1989
CHARACTERISTIC
Civilian employed, 16 years and over
113,905
115,844
116,347
114,660
116,009
116,711
116,853
117,136
117,113
Married men, spouse present
40,338
40,754
40,726
40,494
40,483
40,925
40,928
41,083
40,890
Married women, spouse present
28,888
29,628
29,804
28,772
29,053
29,589
29,412
29,569
29,656
Women who maintain families
6,109
6,275
6,255
6,091
6,399
6,416
6,385
6,256
6,243
MAJOR INDUSTRY AND CLASS OF WORKER
Agriculture:
Wage and salary workers
1,688
1,517
1,608
1,632
1,698
1,684
1,645
1,656
1,554
Self-employed workers
1,356
1,298
1,385
1,390
1,349
1,387
1,419
1,403
1,419
Unpaid family workers
149
119
123
152
149
189
150
138
124
Nonagricultural industries:
Wage and salary workers
101,897
104,143
104,301
102,562
103,904
104,510
104,797
104,982
104,985
Government
17,236
17,625
17,403
17,012
17,423
17,393
17,311
17,382
17,180
Private industries
84,660
86,518
86,898
85,550
86,481
87,117
87,486
87,600
87,806
Private households
1,087
1,084
1,091
1,114
1,210
1,196
1,135
1,163
1,117
Other industries
83,573
85,434
85,807
84,436
85,271
85,921
86,350
86,437
86,689
Self-employed workers
8,533
8,420
8,636
8,567
8,602
8,718
8,517
8,645
8,671
Unpaid family workers
283
347
293
272
266
298
285
332
281
PERSONS AT WORK PART TIME'
All industries:
Part time for economic reasons
4,851
4,784
4,783
5,212
5,321
5,097
4,981
4,968
5,143
Slack work
2,167
2,306
2,266
2,264
2,549
2,302
2,303
2,232
2,373
Could only find part-time work
2,287
2,204
2,204
2,519
2,410
2,352
2,333
2,393
2,425
Voluntary part time
16,082
16,510
16,676
14,949
15,363
15,401
15,126
15,561
15,498
Nonagricultural industries:
Part time for economic reasons
4,624
4,572
4,600
4,953
5,033
4,837
4,697
4,709
4,930
Slack work
2,053
2,148
2,158
2,131
2,377
2,144
2,105
2,048
2,243
Could only find part-time work
2,196
2,155
2,146
2,426
2,307
2,283
2,272
2,317
2,369
Voluntary part time
15,540
16,095
16,205
14,441
14,928
14,970
14,688
15,127
15,060
1
Excludes persons "with a job but not at work" during the survey
period for such reasons as vacation, illness, or industrial dispute.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-5. Range of unemployment measures based on varying definitions of unemployment and the labor force, seasonally adjusted
(Percent)
Quarterly averages
Monthly data
Measure
1988
1989
1989
II
III
IV
Feb.
Mar.
Apr.
U-1 Persons unemployed 15 weeks or longer as a percent of the
civilian labor force
1.4
1.3
1.3
1.2
1.1
1.1
1.1
1.2
U-2 Job losers as a percent of the civilian labor force
2.6
2.5
2.5
2.5
2.4
2.3
2.3
2.4
U-3 Unemployed persons 25 years and over as a percent of the
civilian labor force
4.4
4.2
4.2
4.1
4.0
4.0
3.9
4.1
U-4 Unemployed full-time jobseekers as a percent of the
full-time civilian labor force
5.3
5.1
5.1
5.0
4.9
4.8
4.8
5.0
U-5a Total unemployed as a percent of the labor force,
Including the resident Armed Forces
5.6
5.4
5.4
5.3
5.1
5.1
4.9
5.2
U-5b Total unemployed as a percent of the civilian labor force
5.7
5.5
5.5
5.3
5.2
5.1
5.0
5.3
U-6 Total full-time jobseekers plus 1/2 part-time jobseekers plus
1/2 total on part time for economic reasons as a percent of
the civilian labor force less 1/2 of the part-time labor force
7.9
7.6
7.6
7.5
7.2
7.2
7.1
7.4
U-7 Total full-time jobseekers plus 1/2 part-time jobseekers
plus 1/2 total on part time for economic reasons plus discouraged
workers as a percent of the civilian labor force plus
discouraged workers less 1/2 of the part-time labor force
8.7
8.3
8.4
8.2
7.9
N.A.
N.A.
N.A.
N.A. = not available.
Table A-6. Selected unemployment indicators, seasonally adjusted
Number of
unemployed persons
Unemployment rates'
(in thousands)
Category
Apr.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989
1988
1988
1989
1989
1989
1989
CHARACTERISTIC
Total, 16 years and over
6,668
6,128
6,546
5.5
5.3
5.4
5.1
5.0
5.3
Men, 16 years and over
3,596
3,270
3,593
5.4
5.3
5.5
5.2
4.8
5.3
Men, 20 years and over
2,941
2,688
2,952
4.7
4.7
4.6
4.5
4.2
4.6
Women, 16 years and over
3,072
2,858
2,953
5.6
5.4
5.4
5.0
5.1
5.3
Women, 20 years and over
2,471
2,367
2,448
4.9
4.7
4.7
4.5
4.6
4.7
Both sexes, 16 to 19 years
1,256
1,073
1,146
15.8
14.8
16.4
14.8
13.7
14.4
Married men, spouse present
1,294
1,209
1,347
3.1
3.1
3.1
3.1
2.9
3.2
Married women, spouse present
1,143
1,074
1,247
3.8
3.7
3.6
3.4
3.5
4.0
Women who maintain families
566
533
513
8.5
8.2
8.0
8.0
7.9
7.6
Full-time workers
5,338
5,028
5,247
5.1
5.1
5.0
4.8
4.8
5.0
Part-time workers
1,311
1,120
1,295
7.5
7.0
7.9
7.3
6.2
7.2
Labor force time lost2
--
--
--
6.2
6.3
6.2
5.9
5.8
6.0
INDUSTRY
Nonagricultural private wage and salary workers
4,848
4,636
5,003
5.4
5.4
5.6
5.1
5.0
5.4
Goods-producing industries
1,895
1,718
1,753
6.5
6.4
6.4
6.1
5.8
6.0
Mining
67
51
42
8.1
7.7
6.1
8.0
7.0
5.6
Construction
674
610
616
10.6
10.4
10.4
10.0
9.4
9.7
Manufacturing
1,154
1,058
1,095
5.3
5.2
5.3
4.9
4.8
4.9
Durable goods
628
608
614
4.8
5.0
5.0
4.4
4.7
4.7
Nondurable goods
526
450
481
5.9
5.5
5.7
5.5
4.9
5.2
Service-producing Industries
2,953
2,918
3,250
4.8
4.9
5.2
4.7
4.6
5.1
Transportation and public utitities
246
254
265
3.8
3.8
3.8
3.9
3.9
4.0
Wholesale and retail trade
1,335
1,294
1,381
5.9
6.3
6.3
5.6
5.6
5.9
Finance and service industries
1,372
1,371
1,604
4.3
4.1
4.7
4.3
4.1
4.8
Government workers
521
466
485
3.0
2.7
2.7
2.7
2.6
2.7
Agricultural wage and salary workers
202
161
183
11.0
8.8
9.5
8.9
8.9
10.5
1
Unemployment as a percent of the civilian labor force.
economic reasons as a percent of potentially available labor force hours.
2
Aggregate hours lost by the unemployed and persons on part time for
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A=7, Duration of unemployment
(Numbers in thousands)
Net seasonally adjusted
Boasenally adjusted
Weeks of unemployment
Apr.
Mar:
Apr.
Apr:
Dee.
Jan.
Feb.
Mar.
Apr:
1988
1000
1989
1988
1000
1000
1989
1989
1989
DURATION
Less than 0 weeks
2,701
2,756
2,778
0,000
3,020
0,181
0,247
3,055
3,000
0 to 14 weeks
1,751
2,072
1,804
1,000
2,039
2,081
1,885
1,621
2,034
18 weeks and over
1,827
1,850
1,647
1,582
1,495
1,912
1,304
1,310
1,428
15 to 20 weeks
963
851
878
756
758
757
685
848
689
27 weeks and ever
864
690
760
826
737
755
630
663
737
Average (mean) duration, in weeks
14.4
12.9
13.8
13.5
12.8
12.7
12.1
12.4
12.7
Median duration, in weeks
6.8
6.8
6.3
5.8
5.8
5.7
5.3
5.4
5.4
PERCENT DISTRIBUTION
Total unemployed
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Less than 5 weeks
43.7
43.2
44,6
46.6
46.2
47.0
50.6
49.4
47.2
5 to 14 weeks
27.5
32.5
29.0
29.6
31.1
30.7
29.1
29.4
31.1
15 weeks and over
28.7
24.3
26.4
23.8
22.8
22.3
20.3
21.2
21.8
15 to 26 weeks
15.1
13.3
14.1
11.4
11.5
11.2
10.4
10.5
10.5
27 weeks and over
13.6
11.0
12.3
12.4
11.2
11.1
10.0
10.7
11.3
Table A-8. Reason for unemployment
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Reasons
Apr.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989
1988
1988
1989
1989
1989
1989
NUMBER OF UNEMPLOYED
Job losers
2,977
3,176
2,990
2,968
3,066
3,121
2,876
2,831
2,984
On layoff
785
996
787
844
819
827
774
808
847
Other job losers
2,192
2,180
2,203
2,124
2,247
2,294
2,102
2,023
2,137
Job leavers
895
850
889
985
998
985
985
885
978
Reentrants
1,643
1,721
1,720
1,804
1,725
1,835
1,740
1,730
1,894
New entrants
843
631
630
886
799
780
765
713
671
PERCENT DISTRIBUTION
Total unemployed
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Job losers
46.8
49.8
48.0
44.7
46.5
46.4
45.2
46.0
45.7
On layoff
12.3
15.6
12.6
12.7
12.4
12.3
12.2
13.1
13.0
Other job losers
34.5
34.2
35.4
32.0
34.1
34.1
33.0
32.8
32.7
Job leavers
14.1
13.3
14.3
14.8
15.1
14.7
15.5
14.4
15.0
Reentrants
25.8
27.0
27.6
27.2
26.2
27.3
27.3
28.1
29.0
New entrants
13.3
9.9
10.1
13.3
12.1
11.6
12.0
11.6
10.3
UNEMPLOYED AS A PERCENT OF THE
CIVILIAN LABOR FORCE
Job losers
2.5
2.6
2.4
2.4
2.5
2.5
2.3
2.3
2.4
Job leavers
.7
.7
.7
.8
.8
.8
.8
.7
.8
Reentrants
1.4
1.4
1.4
1.5
1.4
1.5
1.4
1.4
1.5
New entrants
.7
.5
.5
.7
.7
.6
.6
.6
.5
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-9. Unemployed persons by sex and age, seasonally adjusted
Number of
unemployed persons
Unemployment rates'
(in thousands)
Sex and age
Apr.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989
1988
1988
1989
1989
1989
1989
Total, 16 years and over
6,668
6,128
6,546
5.5
5.3
5.4
5.1
5.0
5.3
16 to 24 years
2,518
2,182
2,344
11.2
10.9
11.9
10.5
9.8
10.5
16 to 19 years
1,256
1,073
1,146
15.8
14.8
16.4
14.8
13.7
14.4
16 to 17 years
580
477
463
17.7
16.6
18.3
18.2
15.3
14.9
18 to 19 years
658
597
667
14.1
13.3
15.4
12.7
12.5
13.8
20 to 24 years
1,262
1,109
1,198
8,7
8.7
9.3
8.1
7.7
8.4
25 years and over
4,144
3,921
4,191
4.2
4.1
4.1
4.0
3.9
4.1
25 to 54 years
3,696
3,542
3,761
4.4
4.3
4.2
4.2
4.1
4.4
55 years and over
460
396
451
3.0
3.0
3.1
3.1
2.6
2.9
Men, 16 years and over
3,596
3,270
3,593
5.4
5.3
5.5
5.2
4.8
5.3
16 to 24 years
1,321
1,128
1,238
11.2
11.1
12.8
11.1
9.7
10.7
16 to 19 years
655
582
641
15.9
15.4
18.6
16.7
14.2
15.5
16 to 17 years
300
258
274
17.6
17.3
20.6
19.6
15.8
17.0
18 to 19 years
355
330
368
14.7
13.5
17.9
15.1
13.2
14.6
20 to 24 years
666
546
597
8.7
8.7
9.6
8.1
7.2
8.0
25 years and over
2,270
2,136
2,344
4.1
4.1
4.0
4.0
3.8
4.2
25 to 54 years
1,994
1,890
2,076
4.3
4.3
4.2
4.1
4.0
4.4
55 years and over
281
246
283
3.2
3.3
3.0
3.4
2.8
3.2
Women, 16 years and over
3,072
2,858
2,953
5.6
5.4
5.4
5.0
5.1
5.3
16 to 24 years
1,197
1,054
1,106
11.1
10.7
10.9
9.7
10.0
10.4
16 to 19 years
601
491
505
15.6
14.2
14.0
12.8
13.1
13.2
16 to 17 years
280
219
189
17.7
15.8
15.9
16.8
14.8
12.7
18 to 19 years
303
267
299
13.5
13.1
12.7
10.0
11.7
12.8
20 to 24 years
596
563
601
8.6
8.7
9.1
8.0
8.3
8.9
25 years and over
1,874
1,784
1,847
4.3
4.1
4.1
3.9
4.0
4.1
25 to 54 years
1,702
1,652
1,685
4.6
4.4
4.3
4.2
4.3
4.4
55 years and over
179
151
169
2.8
2.6
3.1
2.5
2.3
2.6
1
Unemployment as a percent of the civilian labor force.
Table A-10. Employment status of black and other workers
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Employment status
Apr.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989
1988
1988
1989
1989
1989
1989
Civilian noninstitutional population
26,289
26,877
26,926
26,289
26,697
26,779
26,830
26,877
26,926
Civilian labor force
16,506
17,123
17,034
16,777
17,172
17,283
17,386
17,347
17,319
Participation rate
62.8
63.7
63.3
63.8
64.3
64.5
64.8
64.5
64.3
Employed
14,764
15,409
15,406
14,998
15,457
15,449
15,540
15,651
15,656
Employment-population ratio²
56.2
57.3
57.2
57.1
57.9
57.7
57.9
58.2
58.1
Unemployed
1,742
1,714
1,628
1,779
1,715
1,833
1,846
1,696
1,664
Unemployment rate
10.6
10.0
9.6
10.6
10.0
10.6
10.6
9.8
9.6
Not in labor force
9,783
9,754
9,892
9,512
9,525
9,496
9,444
9,530
9,607
1
The population figures are not adjusted for seasonal variation;
2 Civilian employment as a percent of the civilian noninstitutional
therefore, identical numbers appear in the unadjusted and seasonally
population.
adjusted columns.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-11. Occupational status of the employed and unemployed, not seasonally adjusted
(Numbers in thousands)
Civilian employed
Unemployed
Unemployment rate
Occupation
Apr.
Apr.
Apr.
Apr.
Apr.
Apr.
1988
1989
1988
1989
1988
1989
Total, 16 years and over1
113,905
116,347
6,359
6,229
5.3
5.1
Managerial and professional specialty
29,238
30,568
511
556
1.7
1.8
Executive, administrative, and managerial
14,152
14,777
278
296
1.9
2.0
Professional specialty
15,086
15,791
233
259
1.5
1.6
Technical, sales, and administrative support
35,401
35,837
1,301
1,347
3.5
3.6
Technicians and related support
3,476
3,575
94
86
2.6
2.3
Sales occupations
13,617
13,820
586
600
4.1
4.2
Administrative support, including clerical
18,308
18,441
620
661
3.3
3.5
Service occupations
15,114
15,204
1,032
1,149
6.4
7.0
Private household
832
840
56
66
6.3
7.3
Protective service
1,838
1,918
64
80
3.4
4.0
Service, except private household and protective
12,444
12,446
911
1,003
6.8
7.5
Precision production, craft, and repair
13,552
13,560
762
797
5.3
5.6
Mechanics and repairers
4,522
4,555
153
205
3.3
4.3
Construction trades
4,972
4,905
416
439
7.7
8.2
Other precision production, craft, and repair
4,058
4,099
193
153
4.5
3.6
Operators, fabricators, and laborers
17,196
17,886
1,621
1,503
8.6
7.8
Machine operators, assemblers, and inspectors
7,855
8,257
678
650
7.9
7.3
Transportation and material moving occupations
4,627
4,770
283
302
5.8
5.9
Handlers, equipment cleaners, helpers, and laborers
4,714
4,859
659
552
12.3
10.2
Construction laborers
739
755
208
157
22.0
17.2
Other handlers, equipment cleaners, helpers, and laborers
3,975
4,104
451
394
10.2
8.8
Farming, forestry, and fishing
3,404
3,292
230
221
6.3
6.3
1
Persons with no previous work experience and those whose last job was
in the Armed Forces are included in the unemployed total.
Table A-12. Employment status of male Vietnam-era veterans and nonveterans by age, not seasonally adjusted
(Numbers in thousands)
Civilian labor force
Civilian
noninstitutional
population
Unemployed
Veteran status
and age
Total
Employed
Number
Percent of
labor force
Apr.
Apr.
Apr.
Apr.
Apr.
Apr.
Apr.
Apr.
Apr.
Apr.
1988
1989
1988
1989
1988
1989
1988
1989
1988
1989
VIETNAM-ERA VETERANS
Total, 30 years and over
7,891
7,918
7,290
7,212
6,981
6,939
309
273
4.2
3.8
30 to 44 years
5,984
5,590
5,712
5,270
5,452
5,048
260
222
4.6
4.2
30 to 34 years
750
529
707
482
648
448
59
34
8.3
7.1
35 to 39 years
2,256
1,840
2,152
1,731
2,071
1,639
81
92
3.8
5.3
40 to 44 years
2,978
3,221
2,853
3,057
2,733
2,961
120
96
4.2
3.1
45 years and over
1,907
2,328
1,578
1,942
1,529
1,891
49
51
3.1
2.6
NONVETERANS
Total, 30 to 44 years
20,206
21,259
19,025
20,100
18,221
19,239
804
861
4.2
4.3
30 to 34 years
8,993
9,303
8,495
8,840
8,114
8,438
381
402
4.5
4.5
35 to 39 years
6,718
7,302
6,351
6,924
6,114
6,624
237
300
3.7
4.3
40 to 44 years
4,495
4,654
4,179
4,336
3,993
4,177
186
159
4.5
3.7
NOTE: Male Vietnam-era veterans are men who served in the Armed
those 30 to 44 years of age, the group that most closely corresponds to
Forces between August 5, 1964 and May 7, 1975. Nonveterans are men
the bulk of the Vietnam-era veteran population.
who have never served in the Armed Forces; published data are limited to
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-13. Employment status of the civilian population for eleven large States
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted²
State and employment status
Apr.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989
1988
1988
1989
1989
1989
1989
California
Civilian noninstitutional population
20,777
21,037
21,059
20,777
20,973
20,994
21,016
21,037
21,059
Civilian labor force
13,959
14,092
14,051
14,002
14,198
14,220
14,117
14,120
14,096
Employed
13,263
13,434
13,328
13,278
13,524
13,505
13,405
13,480
13,339
Unemployed
695
657
724
724
674
715
712
640
757
Unemployment rate
5.0
4.7
5.1
5.2
4.7
5.0
5.0
4.5
5.4
Florida
Civilian noninstitutional population
9,643
9,881
9,902
9,643
9,819
9,839
9,860
9,881
9,902
Civilian labor force
6,045
6,161
6,197
6,095
6,085
6,155
6,086
6,179
6,245
Employed
5,740
5,871
5,880
5,782
5,755
5,793
5,762
5,880
5,922
Unemployed
304
290
316
313
330
362
324
299
323
Unemployment rate
5.0
4.7
5.1
5.1
5.4
5.9
5.3
4.8
5.2
Illinois
Civilian noninstitutional population
8,729
8,702
8,699
8,729
8,712
8,709
8,706
8,702
8,699
Civilian labor force
5,656
5,894
5,880
5,731
5,817
5,837
5,976
5,983
5,960
Employed
5,237
5,531
5,544
5,327
5,429
5,491
5,663
5,648
5,640
Unemployed
419
363
337
404
388
346
313
335
320
Unemployment rate
7.4
6.2
5.7
7.0
6.7
5.9
5.2
5.6
5.4
Massachusetts
Civilian noninstitutional population
4,595
4,598
4,598
4,595
4,598
4,598
4,598
4,598
4,598
Civilian labor force
3,133
3,156
3,178
3,151
3,150
3,166
3,205
3,160
3,197
Employed
3,041
3,028
3,061
3,058
3,043
3,063
3,094
3,051
3,077
Unemployed
91
128
118
93
107
103
111
109
120
Unemployment rate
2.9
4.1
3.7
3.0
3.4
3.3
3.5
3.4
3.8
Michigan
Civilian noninstitutional population
7,007
7,081
7,087
7,007
7,063
7,069
7,075
7,081
7,087
Civilian labor force
4,528
4,568
4,537
4,561
4,648
4,687
4,668
4,620
4,573
Employed
4,187
4,243
4,259
4,221
4,306
4,364
4,382
4,316
4,296
Unemployed
341
324
278
340
342
323
286
304
277
Unemployment rate
7.5
7.1
6.1
7.5
7.4
6.9
6.1
6.6
6.1
New Jersey
Civilian noninstitutional population
6,031
6,055
6,057
6,031
6,050
6,051
6,053
6,055
6,057
Civilian labor force
3,953
4,003
3,960
3,969
4,043
4,046
4,043
4,010
3,977
Employed
3,828
3,867
3,818
3,826
3,875
3,888
3,884
3,890
3,816
Unemployed
125
136
142
143
168
158
159
120
161
Unemployment rate
3.2
3.4
3.6
3.6
4.2
3.9
3.9
3.0
4.0
New York
Civilian noninstitutional population
13,792
13,806
13,807
13,792
13,807
13,806
13,807
13,806
13,807
Civilian labor force
8,238
8,491
8,647
8,426
8,580
8,621
8,701
8,540
8,841
Employed
7,955
8,099
8,166
8,113
8,177
8,198
8,258
8,173
8,328
Unemployed
283
392
480
313
403
423
443
367
513
Unemployment rate
3.4
4.6
5.6
3.7
4.7
4.9
5.1
4.3
5.8
North Carolina
Civilian noninstitutional population
4,890
4,983-
4,991
4,890
4,959
4,967
4,975
4,983
4,991
Civilian labor force
3,266
3,379
3,424
3,320
3,371
3,435
3,390
3,415
3,478
Employed
3,156
3,269
3,288
3,197
3,254
3,302
3,283
3,311
3,330
Unemployed
110
110
136
123
117
133
107
104
148
Unemployment rate
3.4
3.2
4.0
3.7
3.5
3.9
3.2
3.0
4.3
Ohio
Civilian noninstitutional population
8,228
8,298
8,303
8,228
8,281
8,286
8,292
8,298
8,303
Civilian labor force
5,281
5,375
5,357
5,301
5,355
5,426
5,432
5,428
5,381
Employed
4,964
5,068
5,085
4,970
5,060
5,094
5,152
5,144
5,093
Unemployed
317
307
273
331
295
332
280
284
288
Unemployment rate
6.0
5.7
5.1
6.2
5.5
6.1
5.2
5.2
5.4
See footnotes at end of table.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-13. Employment status of the civilian population for eleven large States-Continued
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted²
State and employment status
Apr.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989
1988
1988
1989
1989
1989
1989
Pennsylvania
Civilian noninstitutional population
9,355
9,413
9,418
9,355
9,400
9,404
9,409
9,413
9,418
Civilian labor force
5,680
5,892
5,840
5,778
5,816
5,947
5,932
6,012
5,940
Employed
5,419
5,642
5,606
5,490
5,543
5,689
5,679
5,778
5,677
Unemployed
261
250
234
288
273
258
253
234
263
Unemployment rate
4.6
4.2
4.0
5.0
4.7
4.3
4.3
3.9
4.4
Texas
Civilian noninstitutional population
12,013
11,991
11,988
12,013
12,000
11,997
11,994
11,991
11,988
Civilian labor force
8,204
8,160
8,242
8,305
8,284
8,303
8,254
8,283
8,350
Employed
7,629
7,642
7,666
7,686
7,693
7,713
7,703
7,788
7,729
Unemployed
575
518
576
619
591
590
551
495
621
Unemployment rate
7.0
6.3
7.0
7.5
7.1
7.1
6.7
6.0
7.4
1
These are the official Bureau of Labor Statistics' estimates used in the
identical numbers appear in the unadjusted and the seasonally adjusted
administration of Federal fund allocation programs.
columns.
2
The population figures are not adjusted for seasonal variation; therefore,
ESTABLISHMENT DATA
ESTABLISHMENT DATA
Table B-1. Employees on nonagricultural payrolls by industry
(In thousands)
Not seasonally adjusted
Seasonally adjusted
Industry
Apr.
Feb.
Mar
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989p/
1989p/
1988
1988
1989
1989
1989p/
1989p/
Total
105,159
106,937
107,606
108,496
105,281
107,641
108,065
108,341
108,512
108,629
Total private
87,505
89,041
89,635
90,544
87,973
90,100
90,506
90,725
90,898
91,029
Goods_producing industries
25,180
25,314
25,444
25,726
25,435
25,889
26,048
26,011
25,986
25,991
Mining
729
705
711
720
737
719
718
716
720
728
Oil and gas extraction
414.9
400.2
401.8
404.3
421
402
400
401
406
410
Construction
5,081
4,957
5,052
5,320
5,238
5,430
5,537
5,514
5,479
5,485
General building contractors
1,348.0
1,317.8
1,321.1
1,354.6
1,400
1,414
1,444
1,437
1,414
1,407
Manufacturing
19,370
19,652
19,681
19,686
19,460
19,740
19,793
19,781
19,787
19,778
Production workers
13,213
13,398
13,425
13,431
13,280
13,481
13,518
13,510
13,512
13,501
Durable goods
11,433
11,610
11,619
11,619
11,459
11,651
11,686
11,667
11,653
11,646
Production workers
7,618
7,734
7,744
7,746
7,632
7,776
7,799
7,781
7,766
7,760
Lumber and wood products
743.7
744.0
744.5
744.3
758
771
775
769
765
759
Furniture and fixtures
534.4
542.6
543.0
544.9
535
540
540
542
544
545
Stone, clay, and glass products
583.8
569.6
576.8
587.4
587
592
593
593
591
590
Primary metal industries
775.7
795.6
798.4
798.3
773
794
796
794
795
796
Blast furnaces and basic steel products
280.9
280.6
281.2
280.7
281
280
281
281
281
281
Fabricated metal products
1,439.2
1,479.1
1,479.3
1,477.2
1,444
1,479
1,487
1,487
1,485
1,482
Machinery, except electrical
2,115.1
2,206.0
2,210.9
2,210.1
2,111
2,190
2,198
2,204
2,204
2,206
Electrical and electronic equipment
2,108.4
2,109.5
2,102.4
2,095.3
2,117
2,123
2,118
2,114
2,109
2,104
Transportation equipment
2,044.8
2,053.9
2,048.6
2,045.5
2,045
2,051
2,066
2,048
2,042
2,046
Motor vehicles and equipment
848.4
859.3
854.0
853.1
848
858
872
858
849
852
Instruments and related products
705.7
727.7
729.9
730.2
706
726
727
728
731
731
Miscellaneous manufacturing
381.9
381.6
384.9
385.7
383
385
386
388
387
387
Nondurable goods
7,937
8,042
8,062
8,067
8,001
8,089
8,107
8,114
8,134
8,132
Production workers
5,595
5,664
5,681
5,685
5,648
5,705
5,719
5,729
5,746
5,741
Food and kindred products
1,590.8
1,605.0
1,604.4
1,608.0
1,648
1,656
1,663
1,660
1,663
1,666
Tobacco manufactures
50.5
52.9
51.3
48.2
54
53
52
53
53
51
Textile mill products
726.5
723.8
724.3
725.2
727
722
727
726
726
726
Apparel and other textile products
1,101.8
1,101.5
1,107.2
1,105.3
1,100
1,096
1,097
1,103
1,108
1,103
Paper and allied products
684.0
687.1
688.4
688.1
687
692
692
691
692
692
Printing and publishing
1,555.5
1,596.4
1,600.7
1,604.3
1,554
1,592
1,598
1,596
1,601
1,603
Chemicals and allied products
1,052.7
1,077.0
1,080.9
1,082.5
1,056
1,076
1,080
1,082
1,083
1,086
Petroleum and coal products
164.1
163.5
164.8
167.4
165
168
166
167
167
168
Rubber and misc. plastics products
865.6
891.2
895.7
894.8
864
890
887
891
895
893
Leather and leather products
145.1
143.8
144.2
143.4
146
144
145
145
146
144
Service-producing industries
79,979
81,623
82,162
82,770
79,846
81,752
82,017
82,330
82,526
82,638
Transportation and public utilities
5,511
5,635
5,642
5,685
5,543
5,670
5,692
5,705
5,701
5,718
Transportation.
3,275
3,396
3,401
3,439
3,298
3,422
3,441
3,455
3,449
3,463
Communication and public utilities
2,236
2,239
2,241
2,246
2,245
2,248
2,251
2,250
2,252
2,255
Wholesale trade
6,065
6,305
6,337
6,373
6,089
6,301
6,332
6,361
6,388
6,399
Durable goods
3,603
3,794
3,815
3,828
3,610
3,779
3,796
3,817
3,838
3,836
Nondurable goods
2,462
2,511
2;522
2,545
2,479
2,522
2,536
2,544
2,550
2,563
Retail trade
18,883
19,089
19,236
19,477
19,093
19,429
19,556
19,619
19,689
19,694
General merchandise stores
2,448.9
2,487.5
2,483.3
2,500.0
2,546
2,544
2,563
2,570
2,592
2,599
Food stores
3,015.1
3,166.9
3,178.5
3,185.8
3,049
3,177
3,195
3,202
3,224
3,221
Automotive dealers and service stations
2,055.4
2,085.2
2,094.5
2,112.0
2,064
2,106
2,109
2,115
2,116
2,120
Eating and drinking places
6,313.3
6,213.5
6,338.0
6,514.6
6,326
6,449
6,466
6,493
6,514
6,528
Finance, insurance, and real estate
6,628
6,689
6,708
6,732
6,650
6,741
6,733
6,757
6,761
6,755
Finance
3,292
3,312
3,318
3,320
3,302
3,325
3,320
3,329
3,331
3,330
Insurance
2,063
2,101
2,101
2,101
2,065
2,101
2,096
2,103
2,103
2,103
Real estate
1,273
1,276
1,289
1,311
1,283
1,315
1,317
1,325
1,327
1,322
Services
25,238
26,009
26,268
26,551
25,163
26,070
26,145
26,272
26,373
26,472
Business services
5,381.9
5,519.5
5,554.8
5,590.6
5,420
5,605
5,583
5,621
5,617
5,630
Health services
7,112.1
7,524.8
7,580.3
7,614.3
7,126
7,466
7,494
7,547
7,596
7,630
Government
17,654
17,896
17,971
17,952
17,308
17,541
17,559
17,616
17,614
17,600
Federal
2,963
2,969
2,973
2,974
2,963
2,990
2,981
2,987
2,979
2,974
State
4,150
4,177
4,194
4,197
4,041
4,071
4,063
4,079
4,084
4,087
Local
10,541
10,750
10,804
10,781
10,304
10,480
10,515
10,550
10,551
10,539
P = preliminary.
ESTABLISHMENT DATA
ESTABLISHMENT DATA
Table B-2. Average weekly hours of production or nonsupervisory workers1/ on private nonagricultural payrolls by industry
Not seasonally adjusted
Seasonally adjusted
Industry
Apr.
Feb.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
1988
1989
1989p/
1989p/
1988
1988
1989
1989
1989p/
1989p/
Total private
34.7
34.3
34.4
34.8
34.9
34.7
34.8
34.6
34.6
35.0-
Mining
42.8
41.7
41.9
43.0
(2)
(2)
(2)
(2)
(2)
(2)
Construction
37.9
36.1
37.3
37.7
(2)
(2)
(2)
(2)
(2)
(2)
Manufacturing
41.0
40.8
40.9
41.0
41.2
40.8
41.1
41.1
41.0
41.3
Overtime hours
3.7
3.8
3.8
3.7
3.9
3.9
3.9
3.9
3.9
4.0
Durable goods
41.7
41.5
41.7
41.7
42.0
41.5
41.8
41.7
41.6
42.0
Overtime hours
4.0
4.0
4.0
3.9
4.2
4.1
4.1
4.1
4.0
4.1
Lumber and wood products
40.6
39.0
39.8
40.3
40.6
40.3
40.3
39.5
40.0
40.3
Furniture and fixtures
39.1
39.1
39.6
39.4
39.5
39.2
40.1
39.7
39.9
39.8
Stone, clay, and glass products
42.5
41.1
42.0
42.8
42.5
42.4
42.6
42.1
42.3
42.8
Primary metal industries
43.5
43.4
43.5
43.4
43.5
43.4
43.6
43.3
43.4
43.4
Blast furnaces and basic steel products
44.0
43.7
44.0
44.2
43.8
43.7
44.0
43.7
44.1
44.0
Fabricated metal products
41.7
41.5
41.6
41.6
42.0
41.7
41.9
41.8
41.6
41.9
Machinery, except electrical
42.6
42.4
42.5
42.4
42.8
42.3
42.5
42.5
42.3
42.6
Electrical and electronic equipment
40.8
40.6
40.5
40.8
41.2
40.7
40.8
40.9
40.5
41.2
Transportation equipment
42.9
43.0
43.3
43.2
43.0
42.4
42.6
43.0
42.9
43.3
Motor vehicles and equipment
44.1
43.8
44.2
44.0
44.1
43.0
43.3
43.7
43.5
44.0
Instruments and related products
41.5
41.5
41.1
41.1
41.8
41.0
41.6
41.6
40.9
41.4
Miscellaneous manufacturing
39.1
39.1
39.3
39.5
39.4
38.9
39.4
39.5
39.3
39.8
Nondurable goods
39.9
39.8
39.9
39.9
40.3
39.9
40.1
40.2
40.1
40.3
Overtime hours
3.4
3.5
3.6
3.5
3.6
3.6
3.6
3.7
3.8
3.8
Food and kindred products
39.5
39.6
39.9
39.9
40.1
40.3
40.1
40.3
40.4
40.5
Tobacco manufactures
38.5
37.8
36.3
39.1
(2)
(2)
(2)
(2)
(2)
(2)
Textile mill products
41.0
40.5
41.0
41.0
41.6
40.5
40.9
40.7
41.2
41.6
Apparel and other textile products
36.8
36.8
36.9
36.9
37.4
36.6
37.0
37.1
36.9
37.5
Paper and allied products
43.0
42.9
43.0
43.0
43.3
43.1
43.1
43.2
43.3
43.3
Printing and publishing
38.0
37.7
37.9
37.7
38.2
37.7
38.0
38.0
37.9
37.9
Chemicals and allied products
42.1
42.4
42.3
42.4
42.1
42.3
42.4
42.5
42.3
42.4
Petroleum and coal products
44.4
44.0
43.0
43.5
(2)
(2)
(2)
(2)
(2)
(2)
Rubber and misc. plastics products
41.8
41.5
41.5
41.4
42.0
41.2
41.7
41.7
41.5
41.6
Leather and leather products
37.0
37.8
37.4
37.7
37.3
37.7
38.3
38.8
37.9
38.0
Transportation and public utilities
39.2
39.1
39.3
39.5
39.5
39.4
39.7
39.3
39.5
39.8
Wholesale trade
38.2
37.7
37.8
38.1
38.3
38.0
38.1
38.0
38.0
38.2
Retail trade
28.9
28.3
28.5
28.9
29.2
29.2
29.1
28.9
28.9
29.2
Finance, insurance, and real estate
36.2
35.8
35.8
36.4'
(2)
(2)
(2)
(2)
(2)
(2)
Services
32.6
32.4
32.4
32.8
32.7
32.6
32.8
32.5
32.5
32.9
1/ Data relate to production workers in mining and
2/ These series are not published seasonally
manufacturing; construction workers in construction;
adjusted since the seasonal component is small
and nonsupervisory workers in transportation and
relative to the trend-cycle and/or irregular
public utilities; wholesale and retail trade; finance;,
components and consequently cannot be sepa-
insurance, and real estate; and services. These groups
rated with sufficent precision.
account for approximatly four-fifths of the total
P = preliminary.
employees on private nonagricultural payrolls.
ESTABLISHMENT DATA
ESTABLISHMENT DATA
Table B-3. Average hourly and weekly earnings of production or nonsupervisory workers1/ on private
nonagricultural payrolls by industry
Average hourly earnings
Average weekly earnings
Industry
Apr.
Feb.
Mar.
Apr.
Apr.
Feb.
Mar.
Apr.
1988
1989
1989p/
1989p/
1988
1989
1989p/
1989p/
Total private
$9.23
$9.54
$9.55
$9.60
$320.28
$327.22
$328.52
$334.08
Seasonally adjusted
9.23
9.50
9.52
9.59
322.13
328.70
329.39
335.65
Mining
12.60
13.16
13.09
13.05
539.28
548.77
548.47
561.15
Construction
12.88
13.17
13.22
13.29
488.15
475.44
493.11
501.03
Manufacturing
10.12
10.37
10.39
10.40
414.92
423.10
424.95
426.40
Durable goods
10.65
10.90
10.92
10.94
444.11
452.35
455.36
456.20
Lumber and wood products
8.50
8.68
8.66
8.76
345.10
338.52
344.67
353.03
Furniture and fixtures
7.81
8.06
8.10
8.09
305.37
315.15
320.76
318.75
Stone, clay, and glass products
10.41
10.63
10.62
10.72
442.43
436.89
446.04
458.82
Primary metal industries
12.11
12.28
12.28
12.37
526.79
532.95
534.18
536.86
Blast furnaces and basic steel products
13.94
14.13
14.14
14.26
613.36
617.48
622.16
630.29
Fabricated metal products
10.22
10.44
10.45
10.49
426.17
433.26
434.72
436.38
Machinery, except electrical
10.88
11.19
11.21
11.21
463.49
474.46
476.43
475.30
Electrical and electronic equipment
10.09
10.25
10.29
10.29
411.67
416.15
416.75
419.83
Transportation equipment
13.28
13.64
13.69
13.63
569.71
586.52
592.78
588.82
Motor vehicles and equipment
14.09
14.27
14.34
14.25
621.37
625.03
633.83
627.00
Instruments and related products
9.89
10.11
10.15
10.23
410.44
419.57
417.17
420.45
Miscellaneous manufacturing
7.92
8.20
8.19
8.19
309.67
320.62
321.87
323.51
Nondurable goods
9.37
9.62
9.65
9.64
373.86
382.88
385.04
384.64
Food and kindred products
9.14
9.27
9.34
9.31
361.03
367.09
372.67
371.47
Tobacco manufactures
14.98
14.62
15.18
15.56
576.73
552.64
551.03
608.40
Textile mill products
7.35
7.59
7.59
7.62
301.35
307.40
311.19
312.42
Apparel and other textile products
6.04
6.29
6.31
6.31
222.27
231.47
232.84
232.84
Paper and allied products
11.60
11.79
11.82
11.78
498.80
505.79
508.26
506.54
Printing and publishing
10.40
10.75
10.80
10.76
395.20
405.28
409.32
405.65
Chemicals and allied products
12.57
12.89
12.92
12.87
529.20
546.54
546.52
545.69
Petroleum and coal products
15.00
15.52
15.54
15.49
666.00
682.88
668.22
673.82
Rubber and misc. plastics products
9.04
9.27
9.29
9.32
377.87
384.71
385.54
385.85
Leather and leather products
6.29
6.51
6.55
6.54
232.73
246.08
244.97
246.56
Transportation and public utilities
12.27
12.51
12.48
12.56
480.98
489.14
490.46
496.12
Wholesale trade
9.88
10.21
10.19
10.32
377.42
384.92
385.18
393.19
Retail trade
6.26
6.46
6.46
6.48
180.91
182.82
184.11
187.27
Finance, insurance, and real estate
9.03
9.47
9.43
9.55
326.89
339.03
337.59
347.62
Services
8.82
9.26
9.26
9.30
287.53
300.02
300.02
305.04
1/ See footnote 1, table B-2.
p = preliminary.
Table B-4. Average hourly earnings of production or nonsupervisory workers]/ on private
nonagricultural payrolls by industry, seasonally adjusted
Percent
change
Industry
Apr.
Dec.
Jan.
Feb.
Mar.
Apr.
from:
1988
1988
1989
1989
1989p/
1989p/
Mar. 1989-
Apr. 1989
Total private?/:
Current dollars
$9.23
$9.45
$9.49
$9.50
$9.52
$9.59
0:7
Constant (1977) dollars3/
4.85
4.82
4.81
4.80
4.79
N.A.
(4)
Construction
12.93
13.09
13.14
13.18
$13.25
$13.34
.7
Manufacturing
10.11
10.31
10.32
10.35
10.37
10.39
.2
Excluding overtime5/
9.65
9.84
9.86
9.88
9.90
9.92
.2
Transportation and public utilities
12.29
12.36
12.46
12.46
12.51
12.59
.6
Wholesale trade
9.88
10.08
10.18
10.15
10.17
10.32
1.5
Retail trade
6.25
6.42
6.43
6.43
6.44
6.47
.5
Finance, insurance, and real estate
8.99
9.37
9.41
9.35
9.36
9.50
1.5
Services
8.81
9.09
9.14
9.17
9.20
9.29
1.0
1/ See footnote 1, table B-2.
4/ Change was -0.2 percent from February to March 1989,
2/ Includes mining, not shown separately, because its seasonal
the latest month available.
component is too small to be separated out with sufficient precision.
5/ Derived by assuming that overtime hours are paid at the rate of
3/ The Consumer Price Index for Urban Wage Earners and Clerical
time and one-half.
Workers (CPI-W) is used to deflate this series.
N.A. = not available.
p/ = preliminary.
ESTABLISHMENT DATA
ESTABLISHMENT DATA
Table B-5. Indexes of aggregate weekly hours of production or nongupervisory workers1, on private nonagricultural
payrolls by industry
(1977=100)
Not seasonally adjusted
Seasonally adjusted
Industry
Apr.
Feb.
Mar.
Apr.
Apr.
Dec.
Jan.
Feb.
Mar
Apr.
1988
1989
1989₽/
1989g/
1988
1988
1989
1989
1989g/
1989p/
Total private
123.6
123.9
125.4
128.0
125.1
127.2
128.3
127.8
128.1
129.5
Goods-producing industries
100.8
99.4
101.1
102.9
102.7
103.5
104.4
104.2
104.1
104.8
Mining
83.8
78.4
79.7
83.1
85.9
81.2
80.4
80.7
81.8
85.1
Construction
135.4
123.2
130.4
140.3
141.1
144.6
146.3
145.4
145.8
146.0
Manufacturing
94.9
95.8
96.4
96.5
96.1
96.6
97.4
97.3
97.1
97.7
Durable goods
93.4
94.2
94.8
94.9
94.0
94.8
95.7
95.3
94.8
95.6
Lumber and wood products
102.5
97.9
100.2
101.4
104.7
105.2
106.0
103.0
103.8
103.6
Furniture and fixtures
111.9
114.0
115.7
114.9
113.2
113.9
116.2
115.3
116.5
116.2
Stone, clay, and glass products
87.7
82.3
85.4
88.9
88.3
88.9
89.5
88.4
88.5
89.5
Primary metal industries
67.8
69.4
70.0
69.7
67.6
69.6
69.8
69.2
69.3
69.5
Blast furnaces and basic steel products
54.7
54.2
54.8
54.7
54.8
54.1
54.8
54.4
54.9
54.8
Fabricated metal products
91.0
93.1
93.3
93.1
91.8
93.7
94.7
94.3
93.6
94.0
Machinery, except electrical
91.4
95.5
96.1
95.6
91.5
94.3
95.1
95.6
95.1
95.8
Electrical and electronic equipment
101.4
101.3
100.4
100.6
102.8
102.3
102.2
102.1
100.6
102.1
Transportation equipment
100.0
100.6
101.0
100.8
100.0
98.7
99.9
99.8
99.4
100.6
Motor vehicles and equipment
90.3
90.7
91.0
90.5
89.8
89.0
91.0
90.1
88.6
90.1
Instruments and related products
105.8
109.3
108.7
109.0
106.5
108.3
109.6
109.6
108.0
109.6
Miscellaneous manufacturing
84.0
83.3
84.5
85.4
85.0
83.6
85.3
85.8
85.4
86.5
Nondurable goods
97.2
98.2
98.8
98.9
99.1
99.2
99.9
100.2
100.3
100.8
Food and kindred products
95.0
96.7
97.4
97.6
101.0
102.1
102.3
102.9
103.4
103.8
Tobacco manufactures
66.7
69.0
63.8
63.0
73.8
73.2
67.8
70.5
67.3
71.1
Textile mill products
80.9
79.3
80.3
80.5
82.2
79.1
80.6
80.0
80.9
81.8
Apparel and other textile products
85.0
85.3
86.0
85.8
86.2
84.2
85.4
86.0
86.1
86.9
Paper and allied products
100.2
100.0
100.5
100.5
101.4
101.3
101.1
101.1
101.7
101.7
Printing and publishing
136.3
137.3
138.7
138.3
136.5
137.5
138.7
138.7
138.8
138:5
Chemicals and allied products
97.2
99.9
100.1
100.8
97.1
99.5
100.3
100.7
100.2
100.9
Petroleum and coal products
84.0
82.1
81,2
84.0
84.9
86.7
84,1
85.9
83.0
84.7
Rubber and misc. plastics products
122.7
126.4
127.1
126.8
122.9
125.1
126.2
126.9
126.9
127.0
Leather and leather products
54.8
56.0
55.6
55.7
55.5
55.6
57.0
58.2
56.9
56.6
Service-producing industries
136.1
137.5
138.8
141.9
137.4
140.4
141.5
140.9
141.4
143.2
Transportation and public utilities
111.9
114.5
115.0
116.3
113.5
116.2
117.4
116.5
117.0
118.0
Wholesale trade
123.8
127.1
128.1
129.8
124.8
128.1
129.1
129.3
129.8
130.7
Retail trade
123.2
121.4
123.1
126.3
126.0
127.8
128.2
127.7
128.2
129.5
Finance, insurance, and real estate
140.6
139.4
139.6
142.5
141.1
140.0
142.1
140.7
141.0
143.3
Services
158.8
162.5
164.2
168.3
159.0
164.1
165.6
164.9
165.6
168.1
See footnote 1, table B-2.
P = preliminary.
ESTABLISHMENT DATA
ESTABLISHMENT DATA
Table B-6. Diffusion indexes of employment change, seasonally adjusted
(Percent)
Time span
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept
Oct.
Nov.
Dec.
Private nonagricultural payrolls, 349 industries]
Over 1-month span:
1987
57.4
58.3
59.9
64.6
61.3
61.6
68.6
60.6
62.3
67.6
63.9
65.0
1988
60.3
64.6
64.0
63.0
58.9
66.6
62.3
56.2
54.0
62.5
68.9
61.7
1989
65.0
57.9
p/59.0
p/53.0
Over 3-month span:
1987
61.3
62.2
67.3
68.9
69.3
69.8
71.5
72.5
72.1
73.4
74.5
68.2
1988
70.6
68.8
68.3
67.2
69.1
69.8
68.8
61.9
62.6
68.3
71.9
73.4
1989
68.5
p/67.0
p/60.2
Over 6-month span:
1987
69.2
66.3
66.3
70.1
72.5
75.2
76.9
77.4
78.5
74.2
74.4
75.6
1988
72.2
71.5
70.8
74.2
72.2
69.1
68.8
74.5
71.1
72.3
72.5
p/73.6
1989
p/72.9
Over 12-month span:
1987
68.1
70.3
71.1
74.1
76.6
77.2
77.4
77.8
79.1
78.7
77.8
80.5
1988
77.2
78.1
74.2
73.9
75.6
75.6
77.8
76.5
p/75.2
p/75.5
1989
Manufacturing payrolls, 143 industries],
Over 1-month span:
1987
46.8
52.5
53.9
56.4
58.9
55.7
67.7
56.0
64.2
64.2
64.2
61.0
1988
58.2
55.7
55.7
60.6
57.4
61.3
60.3
44.0
46.8
61.7
68.1
57.4
1989
61.0
51.4
p/53.5
p/46.8
Over 3-month span:
1987
50.7
50.7
58.5
63.8
63.5
68.4
69.5
73.8
70.2
74.1
74.5
67.0
1988
66.0
61.0
62.8
64.5
66.7
68.8
61.3
52.1
53.5
65.6
70.9
69.5
1989
62.1
p/61.3
p/51.8
Over 6-month span:
1987
58.5
57.1
57.1
66.7
69.1
74.5
75.5
76.6
79.4
74.1
72.7
72.3
1988
68.4
67.0
66.0
70.9
66.0
63.8
62.1
68.8
66.0
66.0
67.7
b/71.6
1989
p/66.7
Over 12-month spant
1987
59.6
63.5
64.5
68.8
73.0
73.8
75.2
75.2
75.9
1988
75.9
75.2
79.1
74.1
72.3
68.8
70.6
72.0
70.9
72.3
71.3
1989
p/69.5
p/69.5
1/ Based on seasonally adjusted data for 1-, 3-, and 6-month spans and
NOTE: Figures are the percent of Industries with employment increasing plus
unadjusted data for the 12-month span. Data are centered within the span.
one-half of the Industries with unchanged employment, where 50 percent
- preliminary.
indicates an equal balance between Industries with increasing and decreasing
employment.
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U.S. Department of Labor
Bureau of Labor Statistics
First Class Mail
Washington. D.C. 20212
Postage and Fees Paid
U.S. Department of Labor
Official Business
Permit No. G-59
Penalty for Private Use. $300
Bureau of Labor Statistics
NEWS
RELEASES
I
ONLINE
20503
20503BROWNOEB1AA J365
MAUREEN BROWN
WHITEHOUSE RES OFC R1111/2
OEB/17 & PA NW
WASH DC
This and many other BLS news releases are available electronically at the time of
their release to the press.
The Bureau of Labor Statistics makes its principal releases available online
through a commercial computer center. There is no charge for the data. Users
may access all or parts of the releases, paying only for the actual computer time
used, at a rate of about $7.50 per hour for local access and about $20 per hour
for access anywhere in the country.
For more information, clip and send this page to Bureau of Labor Statistics,
Electronic News Release Service, 44I G St. N.W., Room 2029, Washington, D.C.
20212. If your mailing label is not on this page, or needs correction, please
provide your current mailing address.
ESTABLISHMENT DATA
ESTABLISHMENT DATA
Table B-6. Diffusion indexes of employment change, seasonally adjusted
(Percent)
Time span
Jan.
Feb.
Mar,
Apr.
May
June
July
Aug.
Sept
Oct.
Nov.
Dec.
Private nonagricultural payrolls, 349 industries]
Over 1-month span
1987
57.4
58.3
59.9
64.6
61.3
61.6
68.6
60.6
62.3
67.6
63.9
65.0
1988
60.3
64.6
64.0
63.0
58.9
66.6
62.3
56.2
54.0
62.5
68.9
61.7
1989
5/64.8
p/57.3
Over 3-month span!
1987
61.3
62.2
67.3
68.9
69.3
69.8
71.5
72.5
72.1
73.4
74.5
68.2
1988
70.6
68.8
68.3
67.2
69.1
69.8
68.8
61.9
62.6
68.3
71.9
74.4
1989
/69.1
Over 6-month span:
1987
69.2
66.3
66.3
70.1
72.5
75.2
76.9
77.4
78.5
74.2
74.4
75.6
1988
72.2
71.5
70.8
74.2
72.2
69.1
68.8
74.5
71.1
p/72.6
p/72.6
1989
Over 12-month span:
1987
68.1
70.3
71.1
74.1
76.6
77.2
77.4
77.8
79.1
78.7
77.8
80.5
1988
77.2
78.1
74.2
73.9
75.6
75.6
p/78.4
p/76.5
1989
Manufacturing payrolls, 143 industries]/
Over 1-month span:
1987
46.8
52.5
53.9
56.4
58.9
55.7
67.7
56.0
64.2
64.2
64.2
61.0
1988
58.2
55.7
55.7
60.6
57.4
61.3
60.3
44.0
46.8
61.7
68.1
57.4
1989
p/61.0
p/51.8
Over 3-month spar:
1987
50.7
50.7
58.5
63.8
63.5
68.4
69.5
73.8
70.2
74.1
74.5
67.0
1988
66.0
61.0
62.8
64.5
66.7
68.8
61.3
52.1
53.5
65.6
70.9
70.9
1989
g/62.1
Over 6-month span:
1987
58.5
57.1
57.1
66.7
69.1
74.5
75.5
76.6
79.4
74.1
72.7
72.3
1988
68.4
67.0
66.0
70.9
66.0
63.8
62.1
68.8
66.0
p/66.7
p/69.9
1989
Over 12-month span:
1987
59.6
63.5
64.5
68.8
73.0
73.8
75.2
75.2
1988
75.9
75.9
74.1
75.2
79.1
72.3
68.8
70.6
72.0
70.9
p/72.3
1989
p/69.9
Based on seasonally adjusted data for 1-, 3-, and 6-month spans and
NOTE: Figures are the percent of Industries with employment increasing plus
unadjusted data for the 12-month span. Data are centered within the span.
one-half of the industries with unchanged employment, where 50 percent
- preliminary.
indicates an equal balance between industries with increasing and decreasing
employment.
ESTABLISHMENT DATA
ESTABLISHMENT DATA
Table B-5. Indexes of aggregate weekly hours of production or nonsupervisory workers1/ on private nonagricultural
payrolls by industry
(1977=100)
Not seasonally adjusted
Seasonally adjusted
Industry
Feb.
Dec.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1988
1989p/
1989p/
1988
1988
1988
1988
1989E/
1989p/
Total private
120.1
128.9
124.5
124.1
123.9
127.1
127.1
127.2
128.3
127.9
Goods-producing industries
96.6
104.7
100.5
99.3
101.1
104.0
104.5
103.5
104.3
104.1
Mining
80.5
83.2
79.6
77.7
82.5
83.5
80.9
81.2
79.9
80.0
Construction
115.0
141.0
127.4
123.4
136.0
145.3
147.5
144.6
146.2
145.6
Manufacturing
93.7
98.6
96.3
95.7
95.2
96.9
97.2
96.6
97.4
97.2
Durable goods
91.6
97.2
94.9
94.2
92.7
95.2
95.6
94.8
95.7
95.3
Lumber and wood products
98.5
103.9
100.4
98.3
103.6
104.8
104.7
105.2
106.0
103.2
Furniture and fixtures
111.6
119.1
114.3
113.8
113.2
114.2
114.5
113.9
115.9
115.3
Stone, clay, and glass products
81.1
87.2
83.6
82:0
87.3
88.3
88.9
88.9
89.7
83.0
Primary metal industries
66.7
70.8
70.2
69.6
66.4
70.1
70.0
69.6
69.9
69,3
Blast furnaces and basic steel products
54.0
54.9
54.6
54.3
53.9
55.1
54.8
54.1
54.6
54.4
Fabricated metal products
89:5
96.3
94.2
93.1
90.8
93.6
94.6
93.7
94.5
94.4
Machinery, except electrical
90.0
96.9
95.3
95.5
90.2
93.7
94.3
94.3
95.0
95.7
Electrical and electronic equipment
101.1
105.9
102.8
101.3
101.8
103.4
103.7
102.3
102.1
102.2
Transportation equipment
98.1
103.0
100.9
100.1
97.3
100.7
100.8
98.7
100.3
99.3
Motor vehicles and equipment
85.4
94.2
91.0
90.3
85.7
91.9
92.6
89.0
91.2
89.7
Instruments and related products
104.8
111.2
110.2
110.0
105.0
109.5
109.0
108.3
110.4
110.4
Miscellaneous manufacturing
81.9
84.9
82.2
83.6
84.8
83.1
83.6
83.6
85.5
86.3
Nondurable goods
96.9
100.7
98.2
97.9
99.0
99.4
99.7
99.2
99.8
99.9
Food and kindred products
95.7
102.9
98.2
96.2
101.7
102.7
103.3
102.1
102.4
102.1
Tobacco manufactures
74.1
78.6
71.1
67.2
75.8
69.7
72.7
73.2
67.6
69.9
Textile mill products
81.8
80.4
79.4
79.3
82.7
80.2
80.2
79.1
80.1
79.9
Apparel and other textile products
84.6
85.3
83.8
84.9
85.5
83.9
84.9
84.2
85.4
85.7
Paper and allied products
100.2
103.5
100.9
100.2
101.5
101.3
101.3
101.3
101.1
101.5
Printing and publishing
134.3
141.0
137.3
137.4
135.5
137.6
137.2
137.5
138.7
138.7
Chemicals and allied products
96.4
100.7
99.5
99.5
97.1
99.7
99.4
99.5
100.5
100.2
Petroleum and coal products
80.4
84.9
81.2
81.7
84.5
87.3
86.3
86.7
83.7
86.1
Rubber and misc. plastics products
120.5
127.7
125.9
126.4
121.0
124.7
126.0
125.1
125.9
126.8
Leather and leather products
55.0
56.8
55.8
55.3
57.2
56.4
55.1
55.6
57.0
57.4
Service-producing industries
133.2
142.3
137.8
137.7
136.4
139.9
139.6
140.4
141.6
141.2
Transportation and public utilities
109.7
117.5
114.5
114.8
111.8
115.0
115.2
116.2
117.2
117.0
Wholesale trade
120.9
129.0
127.3
127.1
123.1
127.4
127.7
128.1
129.3
129.5
Retail trade
119.0
134.2
123.5
122.1
125.2
127.2
126.7
127.8
128.7
128.6
Finance, insurance, and real estate
140.2
140.5
140.7
139.0
141.6
141.2
140.4
140.0
142.4
140.3
Services
155.9
163.0
161.2
162.7
158.0
163.5
163.2
164.1
165.4
164.7
See footnote 1, table B-2.
P = preliminary.
ESTABLISHMENT DATA
ESTABLISHMENT DATA
Table B-3. Average hourly and weekly earnings of production or nonsupervisory workers1/ on private
nonagricultural payrolls by industry
Average hourly earnings
Average weekly earnings
Industry
Feb.
Dac.
Jan.
Feb.
Feb.
Dec.
Jan,
Feb.
1988
1988
1989
1989p/
1988
1988
1989p/
1989n/
Total private
$9.17
$9.46
$9.54
$9.54
$316.37
$330.15
$329.13
$327.22
Seasonally adjusted
9.13
9.45
9.50
9.51
317.72
327.92
330.60
330.00
Mining
12.71
12.97
13.11
13.03
531.28
553.82
549.31
542.05
Construction
12.82
13.16
13.21
13.16
462.80
489.55
480.84
476.39
Manufacturing
10.05
10.37
10.37
10.37
409.04
431.39
425.17
422.06
Durable goods
10.58
10.90
10.90
10.90
436.95
462.16
454.53
451.26
Lumber and wood products
8.53
8.75
8.70
8.69
339.49
353.50
344.52
339.78
Furniture and fixtures
7.74
8.04
8.07
8.06
301.09
325.62
316.34
314.34
Stone, clay, and glass products
10.33
10.58
10.60
10.60
426.63
446.48
439.90
432.48
Primary metal industries
12.03
12.27
12.27
12.23
519.70
541.11
537.43
530.78
Blast furnaces and basic steel products
13.89
14.07
13.99
13.96
609.77
621.89
614.16
610.05
Fabricated metal products
10.13
10.43
10.44
10.44
418.37
445.36
436.39
432.22
Machinery, except electrical
10.82
11.20
11.16
11.20
459.85
486.08
474.30
473.76
Electrical and electronic equipment
10.02
10.29
10.27
10.25
406.81
430.12
420.04
414.10
Transportation equipment
13.17
13.65
13.63
13.61
553.14
595.14
586.09
582.51
Motor vehicles and equipment
13.85
14.31
14.29
14.26
587.24
636.80
625.90
623.16
Instruments and related products
9.92
10.10
10.17
10.24
408.70
424.20
424.09
425.98
Miscellaneous manufacturing
7.90
8.17
8.22
8.20
307.31
324.35
323.05
321.44
Nondurable goods
9.31
9.60
9.62
9.62
370.54
388.80
383.84
381.91
Food and kindred products
9.06
9.26
9.28
9.30
358.78
378.73
371.20
367.35
Tobacco manufactures
14.01
14.18
14.33
14.71
540.79
565.78
543.11
551.63
Textile mill products
7.30
7.52
7.59
7.60
301.49
309.07
308.15
307.80
Apparel and other textile products
6.02
6.27
6.29
6.28
220.93
232.62
230.84
230.48
Paper and allied products
11.50
11.79
11.77
11.80
494.50
518.76
508.46
507.40
Printing and publishing
10.40
10.71
10.73
10.69
393.12
411.26
404.52
401.94
Chemicals and allied products
12.55
12.91
12.84
12.92
530.87
553.84
545.70
547.81
Petroleum and coal products
14.96
15.28
15.30
15.34
647.77
676.90
662.49
664.22
Rubber and misc. plastics products
9.00
9.27
9.32
9.29
372.60
389.34
388.64
384.61
Leather and leather products
6.19
6.45
6.49
6.53
227.79
247.04
245.97
245.53
Transportation and public utilities
12.23
12.43
12.51
12.48
475.75
490.99
489.14
487.97
Wholesale trade
9.78
10.12
10.22
10.22
370.66
386.58
388.36
386.32
Retail trade
6.23
6.42
6.47
6.49
177.56
190.03
184.40
184.97
Finance, insurance, and real estate
9.02
9.32
9.48
9.45
328.33
333.66
343.18
338.31
Services
8.81
9.15
9.24
9.27
287.21
297.38
301.22
300.35
1/ See footnote 1, table B-2.
P = preliminary.
Table B-4. Average hourly earnings of production or nonsupervisory workers1/ on private
nonagricultural payrolls by industry, seasonally adjusted
Percent
change
Industry
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
from:
1988
1988
1988
1988
1989p/
1989p/
Jan. 1989-
Feb. 1989
Total private?/
Current dollars
$9.13
$9.43
$9.42
$9.45
$9.50
$9.51
0.1
Constant (1977) dollars3/
4.84
4.84
4.82
4.82
4.32
N.A.
(4)
Construction
12.82
13.03
13.01
13.09
13.13
$13.17
.3
Manufacturing
10.03
10.28
10.29
10.31
10.32
10.35
.3
Excluding overtime5/
9.59
9.81
9.83
9.84
9.86
9.88
.2
Transportation and public utilities
12.19
12.43
12.37
12.36
12.50
12.43
-.6
Wholesale trade
9.72
10.13
10.04
10.08
10.19
10.16
-.3
Retail trade
6.20
6.37
6.42
6.42
6.43
6.46
.5
Finance, insurance, and real estate
8.91
9.36
9.26
9.37
9.43
9.33
-1.1
Services
8.72
9.06
9.04
9.09
9.14
9.18
.4
1/ See footnote 1, table B-2.
4/ Real earnings were unchanged from December 1988 to January
2/ Includes mining, not shown separately, because its seasonal
1989, the latest month available.
component is too small to be separated out with sufficient
5/ Derived by assuming that overtime hours are paid at the rate
precision.
of time and one-half.
3/ The Consumer Price Index for Urban Wage Earners and Clerical
N.A. . not available.
Workers (CPI-W) is used to deflate this series. The seasonally
p/ preliminary.
adjusted CPI-W has been revised to reflect the experience through
December 1988. Constant-dollar earnings series have been revised
back to 1984.
ESTABLISHMENT DATA
ESTABLISHMENT DATA
Table B-2. Average weekly hours of production or nonsupervisory workers]/ on private nonagricultural payrolls by industry
Not seasonally adjusted
Seasonally adjusted
Industry
Feb.
Dec.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1988
1989p/
1989p/
1938
1988
1988
1988
1989p/
1989p/
Total private
34.5
34.9
34.5
34.3
34.8
34.9
34.8
34.7
34.8
34.7
Mining
41.8
42.7
41.7
41.6
(2)
(2)
(2)
(2)
(2)
(2)
Construction
36.1
37.2
36.4
36.2
(2)
(2)
(2)
(2)
(2)
(2)
Manufacturing
40.7
41.6
41.0
40.7
41.0
41.2
41.2
40.8
41.0
41.0
Overtime hours
3.6
4.2
3.8
3.7
3.7
4.0
3.9
3.9
3.9
3.9
Durable goods
41.3
42.4
41.7
41.4
41.5
41.9
41.9
41.5
41.8
41.6
Overtime hours
3.7
4.5
4.0
3.9
3.8
4.2
4.2
4.1
4.1
4.0
Lumber and wood products
39.8
40.4
39.6
39.1
40.3
40.7
40.3
40.3
40.3
39.6
Furniture and fixtures
38.9
40.5
39.2
39.0
39.5
39.4
39.4
39.2
40.0
39.6
Stone, clay, and glass products
41.3
42.2
41.5
40.8
42.3
42.5
42.6
42.4
42.6
41.8
Primary metal industries
43.2
44.1
43.8
43.4
43.1
43.8
43.7
43.4
43.7
43.3
Blast furnaces and basic steel products
43.9
44.2
43.9
43.7
43.8
44.3
44.0
43.7
43.9
43.7
Fabricated metal products
41.3
42.7
41.8
41.4
41.6
41.9
42.2
41.7
41.8
41.7
Machinery, except electrical
42.5
43.4
42.5
42.3
42.6
42.6
42.5
42.3
42.4
42.4
Electrical and electronic equipment
40.6
41.8
40.9
40.4
40.9
41.0
41.0
40.7
40.7
40.7
Transportation equipment
42.0
43.6
43.0
42.8
42.0
43.3
43.3
42.4
42.7
42.8
Motor vehicles and equipment
42.4
44.5
43.8
43.7
42.3
44.2
44.6
43.0
43.4
43.6
Instruments and related products
41.2
42.0
41.7
41.6
41.3
41.9
41.6
41.0
41.7
41.7
Miscellaneous manufacturing
38.9
39.7
39.3
39.2
39.3
39.1
39.2
38.9
39.5
39.6
Nondurable goods
39.8
40.5
39.9
39.7
40.2
40.2
40.2
39.9
40.1
40.1
Overtime hours
3.4
3.8
3.5
3.5
3.6
3.8
3.6
3.6
3.6
3.7
Food and kindred products
39.6
40.9
40.0
39.5
40.3
40.6
40.6
40.3
40.1
40.2
Tobacco manufactures
38.6
39.9
37.9
37.5
(2)
(2)
(2)
(2)
(2)
(2)
Textile mill products
41.3
41.1
40.6
40.5
41.6
41.0
41.0
40.5
40.8
40.7
Apparel and other textile products
36.7
37.1
36.7
36.7
37.0
36.8
37.0
36.6
37.0
37.0
Paper and allied products
43.0
44.0
43.2
43.0
43.3
43.2
43.1
43.1
43.1
43.3
Printing and publishing
37.8
38.4
37.7
37.6
38.1
38.0
37.8
37.7
38.0
37.9
Chemicals and allied products
42.3
42.9
42.5
42.4
42.4
42.5
42.4
42.3
42.5
42.5
Petroleum and coal products
43.3
44.3
43.3
43.3
(2)
(2)
(2)
(2)
(2)
(2)
Rubber and misc. plastics products
41.4
42.0
41.7
41.4
41.6
41.5
41.7
41.2
41.6
41.6
Leather and leather products
36.8
38.3
37.9
37.6
37.8
37.9
37.3
37.7
38.3
38.6
Transportation and public utilities
38.9
39.5
39.1
39.1
39.1
39.4
39.2
39.4
39.5
39.3
Wholesale trade
37.9
38.2
38.0
37.8
38.2
38.1
38.0
38.0
38.2
38.1
Retail trade
28.5
29.6
28.5
28.5
29.1
29.2
29.0
29.2
29.2
29.1
Finance, insurance, and real estate
36.4
35.8
36.2
35.8
(2)
(2)
(2)
(2)
(2)
(2)
Services
32.6
32.5
32.6
32.4
32.7
32.8
32.6
32.6
32.8
32.5
Data relate to production workers in mining and
2/ These series are not published seasonally
manufacturing; construction workers in construction;
adjusted since the seasonal component is small
and nonsupervisory workers in transportation and
relative to the trend-cycle and/or irregular
public utilities; wholesale and retail trade; finance;
components and consequently cannot be sepa-
insurance, and real estate; and services. These groups
rated with sufficent precision.
account for approximatly four-fifths of the total
P = preliminary.
employees on private nonagricultural payrolls.
ESTABLISHMENT DATA
ESTABLISHMENT DATA
Table B-1. Employees on nonagricultural payrolls by industry
(In thousands)
Not sensonally adjusted
Seasonally adjusted
Industry
Feb.
Dac.
Jan.
Fab.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1988
1989₽/
1989p/
1988
1988
1988
1988
1989p/
1989p/
Total
103,373
108,491
106,523
106,939
104,729
106,973
107,419
107,641
108,056
108,345
Total private
85,844
90,654
88,989
89,053
87,475
89,481
89,855
90,100
90,515
90,739
Goods_producing industries
24,609
25,869
25,419
25,313
25,271
25,743
25,849
25,889
26,044
26,012
Mining
720
724
710
703
731
729
722
719
716
714
Oil and gas extraction
413.9
410.0
402.7
396.9
415
413
406
402
399
398
Construction
4,628
5,376
5,053
4,956
5,150
5,366
5,413
5,430
5,535
5,513
General building contractors
1,260.9
1,415.6
1,348.3
1,316.2
1,377
1,393
1,406
1,414
1,440
1,435
Manufacturing
19,261
19,769
19,656
19,654
19,390
19,648
19,714
19,740
19,793
19,785
Production workers
13,136
13,507
13,403
13,414
13,249
13,412
13,465
13,481
13,524
13,524
Durable goods
11,348
11,631
11,626
11,617
11,404
11,595
11,637
11,651
11,688
11,674
Production workers
7,552
7,803
7,756
7,756
7,599
7,733
7,765
7,776
7,806
7,801
Lumber and wood products
731.4
759.8
750.2
745.1
756
760
767
771
776
770
Furniture and fixtures
535.4
545.9
541.8
542.7
535
540
541
540
540
542
Stone, clay, and glass products
560.8
585.7
571.7
569.5
584
588
590
592
592
593
Primary metal industries
771.0
795.2
796.3
795.4
770
794
796
794
796
794
Blast furnaces and basic steel products
279.7
280.4
280.8
281.0
280
282
282
280
281
281
Fabricated metal products
1,430.7
1,484.5
1,481.0
1,482.1
1,438
1,469
1,474
1,479
1,487
1,490
Machinery, except electrical
2,093.1
2,193.9
2,196.4
2,204.8
2,091
2,173
2,185
2,190
2,196
2,203
Electrical and electronic equipment
2,108.7
2,131.8
2,122.3
2,110.0
2,112
2,126
2,130
2,123
2,120
2,115
Transportation equipment
2,036.6
2,071.6
2,061.6
2,055.9
2,031
2,045
2,050
2,051
2,066
2,050
Motor vehicles and equipment
837.8
872.8
862.0
858.9
837
859
860
858
871
857
Instruments and related products
704.3
728.0
728.1
728.2
705
719
721
726
729
729
Miscellaneous manufacturing
375.6
384.3
376.6
382.2
382
381
383
385
386
388
Nondurable goods
7,913
8,088
8,030
8,037
7,986
8,053
8,077
8,089
8,105
8,111
Production workers
5,584
5,704
5,647
5,658
5,650
5,679
5,700
5,705
5,718
5,723
Food and kindred products
1,594.0
1,646.5
1,614.4
1,603.0
1,649
1,654
1,661
1,656
1,664
1,658
Tobacco manufactures
54.4
56.0
54.3
52.3
54
52
53
53
52
52
Textile mill products
729.3
722.9
721.7
721.5
732
722
723
722
725
724
Apparel and other textile products
1,103.4
1,095.6
1,088.4
1,099.3
1,104
1,086
1,093
1,096
1,096
1,100
Paper and allied products
682.2
693.4
687.9
687.2
686
691
691
692
691
691
Printing and publishing
1,543.1
1,598.5
1,595.3
1,598.8
1,544
1,581
1,583
1,592
1,597
1,599
Chemicals and allied products
1,043.8
1,074.0
1,074.3
1,076.2
1,049
1,071
1,073
1,076
1,081
1,082
Petroleum and coal products
161.5
166.1
163.9
164.1
165
169
169
168
167
168
Rubber and misc. plastics products
856.2
890.4
885.7
891.5
856
882
887
890
887
892
Leather and leather products
145.5
144.7
143.8
143.2
147
145
144
144
145
145
Service-producing industries
78,764
82,622
81,104
81,626
79,458
81,230
81,570
81,752
82,012
82,333
Transportation and public utilities
5,446
5,716
5,648
5,653
5,513
5,631
5,658
5,670
5,711
5,723
Transportation
3,217
3,470
3,401
3,406
3,272
3,380
3,407
3,422
3,453
3,465
Communication and public utilities
2,229
2,246
2,247
2,247
2,241
2,251
2,251
2,248
2,258
2,258
Wholesale trade
5,979
6,313
6,285
6,306
6,035
6,246
6,275
6,301
6,332
6,362
Durable goods
3,550
3,783
3,777
3,792
3,573
3,736
3,758
3,779
3,796
3,815
Nondurable goods
2,429
2,530
2,508
2,514
2,462
2,510
2,517
2,522
2,536
2,547
Retail trade
18,521
20,070
19,264
19,101
19,045
19,327
19,401
19,429
19,557
19,631
General merchandise stores
2,479.1
2,857.2
2,644.5
2,517.2
2,561
2,520
2,533
2,544
2,580
2,600
Food stores
2,994.7
3,243.3
3,176.1
3,166.5
3,029
3,143
3,157
3,177
3,195
3,202
Automotive dealers and service stations
2,018.0
2,095.1
2,085.2
2,085.4
2,047
2,103
2,106
2,106
2,108
2,115
Eating and drinking places
6,018.4
6,390.5
6,168.7
6,213.5
6,291
6,415
6,440
6,449
6,466
6,493
Finance, insurance, and real estate
6,571
6,720
6,678
6,675
6,636
6,708
6,725
6,741
6,732
6,743
Finance
3,289
3,318
3,313
3,308
3,305
3,308
3,314
3,325
3,320
3,325
Insurance
2,051
2,099
2,093
2,097
2,053
2,089
2,092
2,101
2,095
2,099
Real estate
1,231
1,303
1,272
1,270
1,278
1,311
1,319
1,315
1,317
1,319
Services
24,718
25,966
25,695
26,005
24,975
25,826
25,947
26,070
26,139
26,268
Business services
5,287.9
5,627.3
5,494.8
5,517.6
5,385
5,553
5,563
5,605
5,578
5,619
Health services
7,037.3
7,451.1
7,481.7
7,521.1
7,056
7,365
7,414
7,466
7,497
7,544
Government
17,529
17,837
17,534
17,886
17,254
17,492
17,564
17,541
17,541
17,606
Federal
2,955
2,981
2,952
2,957
2,972
2,989
2,989
2,990
2,973
2,975
State
4,109
4,156
4,033
4,177
4,014
4,070
4,074
4,071
4,061
4,079
Local
10,465
10,700
10,549
10,752
10,268
10,433
10,501
10,480
10,507
10,552
P = preliminary.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-13. Employment status of the civilian population for eleven large States-Continued
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted²
State and employment status
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
Pennsylvania
Civilian noninstitutional population
9,343
9,404
9,409
9,343
9,390
9,396
9,400
9,404
9,409
Civilian labor force
5,672
5,884
5,814
5,793
5,744
5,779
5,816
5,947
5,932
Employed
5,337
5,592
5,533
5,487
5,436
5,510
5,543
5,689
5,679
Unemployed
335
292
281
306
308
269
273
258
253
Unemployment rate
5.9
5.0
4.8
5.3
5.4
4.7
4.7
4.3
4.3
Texas
Civilian noninstitutional population
12,015
11,997
11,994
12,015
12,005
12,003
12,000
11,997
11,994
Civilian labor force
8,184
8,188
8,150
8,289
8,309
8,308
8,284
8,303
8,254
Employed
7,469
7,566
7,556
7,616
7,708
7,725
7,693
7,713
7,703
Unemployed
715
622
594
673
601
583
591
590
551
Unemployment rate
8.7
7.6
7.3
8.1
7.2
7.0
7.1
7.1
6.7
1 These are the official Bureau of Labor Statistics' estimates used in the
identical numbers appear in the unadjusted and the seasonally adjusted
administration of Federal fund allocation programs.
columns.
2
The population figures are not adjusted for seasonal variation; therefore,
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-13. Employment status of the civilian population for eleven large States
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted²
State and employment status
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
California
Civilian noninstitutional population
20,726
20,994
21,016
20,726
20,927
20,951
20,973
20,994
21,016
Civilian labor force
13,910
14,168
14,083
13,947
14,063
14,186
14,198
14,220
14,117
Employed
13,102
13,407
13,309
13,199
13,363
13,451
13,524
13,505
13,405
Unemployed
808
761
774
748
700
735
674
715
712
Unemployment rate
5.8
5.4
5.5
5.4
5.0
5.2
4.7
5.0
5.0
Florida
Civilian noninstitutional population
9,598
9,839
9,860
9,598
9,777
9,798
9,819
9,839
9,860
Civilian labor force
5,966
6,052
6,013
6,034
6,170
6,144
6,085
6,155
6,086
Employed
5,667
5,693
5,702
5,722
5,862
5,823
5,755
5,793
5,762
Unemployed
298
358
312
312
308
321
330
362
324
Unemployment rate
5.0
5.9
5.2
5.2
5.0
5.2
5.4
5.9
5.3
Illinois
Civilian noninstitutional population
8,733
8,709
8,706
8,733
8,718
8,716
8,712
8,709
8,706
Civilian labor force
5,717
5,791
5,903
5,793
5,771
5,844
5,817
5,837
5,976
Employed
5,249
5,419
5,543
5,369
5,388
5,433
5,429
5,491
5,663
Unemployed
467
372
359
424
383
411
388
346
313
Unemployment rate
8.2
6.4
6.1
7.3
6.6
7.0
6.7
5.9
5.2
Massachusetts
Civilian noninstitutional population
4,593
4,598
4,598
4,593
4,598
4,598
4,598
4,598
4,598
Civilian labor force
3,098
3,139
3,162
3,141
3,151
3,153
3,150
3,166
3,205
Employed
2,982
3,020
3,038
3,038
3,047
3,032
3,043
3,063
3,094
Unemployed
116-
119
124
103
104
121
107
103
111
Unemployment rate
3.8
3.8
3.9
3.3
3.3
3.8
3.4
3.3
3.5
Michigan
Civilian noninstitutional population
6,992
7,069
7,075
6,992
7,050
7,057
7,063
7,069
7,075
Civilian labor force
4,482
4,589
4,612
4,535
4,615
4,652
4,648
4,687
4,668
Employed
4,083
4,230
4,300
4,161
4,282
4,310
4,306
4,364
4,382
Unemployed
399
358
312
374
333
342
342
323
286
Unemployment rate
8.9
7.8
6.8
8.2
7.2
7.4
7.4
6.9
6.1
New Jersey
Civilian noninstitutional population
6,025
6,051
6,053
6,025
6,046
6,048
6,050
6,051
6,053
Civilian labor force
3,969
4,009
4,031
3,981
3,963
3,978
4,043
4,046
4,043
Employed
3,808
3,825
3,851
3,841
3,810
3,821
3,875
3,888
3,884
Unemployed
161
184
180
140
153
157
168
158
159
Unemployment rate
4.0
4.6
4.5
3.5
3.9
3.9
4.2
3.9
3.9
New York
Civilian noninstitutional population
13,787
13,806
13,807
13,787
13,805
13,807
13,807
13,806
13,807
Civilian labor force
8,437
8,652
8,624
8,517
8,533
8,560
8,580
8,621
8,701
Employed
8,065
8,170
8,152
8,176
8,174
8,177
8,177
8,198
8,258
Unemployed
372
482
473
341
359
383.
403
423
443
Unemployment rate
4.4
5.6
5.5
4.0
4.2
4.5'
4.7
4.9
5.1
North Carolina
Civilian noninstitutional population
4,872
4,967
4,975
4,872
4,943
4,951
4,959
4,967
4,975
Civilian labor force
3,294
3,381
3,381
3,306
3,387
3,386
3,371
3,435
3,390
Employed
3,156
3,231
3,255
3,185
3,254
3,266
3,254
3,302
3,283
Unemployed
138
150
125
121
133
120
117
133
107
Unemployment rate
4.2
4.4
3.7
3.7
3.9
3.5
3.5
3.9
3.2
Ohio
Civilian noninstitutional population
8,214
8,286
8,292
8,214
8,269
8,276
8,281
8,286
8,292
Civilian labor force
5,298
5,384
5,380
5,355
5,349
5,366
5,355
5,426
5,432
Employed
4,922
5,015
5,063
5,014
5,049
5,059
5,060
5,094
5,152
Unemployed
376
369
317
341
300
307
295
332
280
Unemployment rate
7.1
6.9
5.9
6.4
5.6
5.7
5.5
6.1
5.2
See footnotes at end of table.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-11. Occupational status of the employed and unemployed, not seasonally adjusted
(Numbers in thousands)
Civilian employed
Unemployed
Unemployment rate
Occupation
Feb.
Feb.
Feb.
Feb.
Feb.
Feb.
1988
1989
1988
1989
1988
1989
Total, 16 years and over'
112,460
115,023
7,482
6,883
6.2
5.6
Managerial and professional specialty
28,621
30,106
586
602
2.0
2.0
Executive, administrative, and managerial
13,759
14,592
348
378
2.5
2.5
Professional specialty
14,862
15,514
238
224
1.6
1.4
Technical, sales, and administrative support
35,209
35,400
1,502
1,478
4.1
4.0
Technicians and related support
3,381
3,569
113
102
3.2
2.8
Sales occupations
13,376
13,600
648
623
4.6
4.4
Administrative support, including clerical
18,453
18,231
741
752
3.9
4.0
Service occupations
15,170
15,537
1,311
1,043
8.0
6.3
Private household
885
910
67
35
7.0
3.7
Protective service
1,897
1,950
85
80
4.3
3.9
Service, except private household and protective
12,388
12,678
1,158
928
8.6
6.8
Precision production, craft, and repair
13,373
13,466
993
985
6.9
6.8
Mechanics and repairers
4,558
4,596
197
169
4.1
3.6
Construction trades
4,728
4,705
572
608
10.8
11.4
Other precision production, craft, and repair
4,086
4,165
223
208
5.2
4.8
Operators, fabricators, and laborers
17,237
17,655
1,977
1,785
10.3
9.2
Machine operators, assemblers, and inspectors
7,914
8,169
756
658
8.7
7.5
Transportation and material moving occupations
4,696
4,683
451
373
8.8
7.4
Handlers, equipment cleaners, helpers, and laborers
4,627
4,803
770
755
14.3
13.6
Construction laborers
684
719
254
205
27.1
22.2
Other handlers, equipment cleaners, helpers, and laborers
3,943
4,084
515
549
11.6
11.8
Farming, forestry, and fishing
2,849
2,858
299
265
9.5
8.5
1
Persons with no previous work experience and those whose last job was
in the Armed Forces are included in the unemployed total.
Table A-12. Employment status of male Vietnam-era veterans and nonveterans by age, not seasonally adjusted
(Numbers in thousands)
Civilian labor force
Civilian
noninstitutional
population
Unemployed
Veteran status
and age
Total
Employed
Number
Percent of
labor force
Feb.
Feb.
Feb.
Feb.
Feb.
Feb.
Feb.
Feb.
Feb.
Feb.
1988
1989
1988
1989
1988
1989
1988
1989
1988
1989
VIETNAM-ERA VETERANS
Total, 30 years and over
7,877
7,914
7,243
7,226
6,881
6,936
362
290
5.0
4.0
30 to 44 years
6,033
5,664
5,724
5,371
5,433
5,142
291
229
5.1
4.3
30 to 34 years
781
564
732
515
688
487
44
28
6.0
5.4
35 to 39 years
2,329
1,905
2,223
1,808
2,082
1,701
141
107
6.3
5.9
40 to 44 years
2,923
3,195
2,769
3,048
2,663
2,954
106
94
3.8
3.1
45 years and over
1,844
2,250
1,519
1,855
1,448
1,794
71
61
4.7
3.3
NONVETERANS
Total, 30 to 44 years
20,071
21,081
18,873
19,870
17,905
18,971
968
899
5.1
4.5
30 to 34 years
9,001
9,255
8,529
8,740
8,027
8,337
502
403
5.9
4.6
35 to 39 years
6,637
7,190
6,223
6,786
5,901
6,491
322
295
5.2
4.3
40 to 44 years
4,433
4,636
4,121
4,344
3,977
4,143
144
201
3.5
4.6
NOTE: Male Vietnam-era veterans are men who served in the Armed
those 30 to 44 years of age, the group that most closely corresponds to
Forces between August 5, 1964 and May 7, 1975. Nonveterans are men
the bulk of the Vietnam-era veteran population.
who have never served in the Armed Forces; published data are limited to
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-9. Unemployed persons by sex and age, seasonally adjusted
Number of
unemployed persons
Unemployment rates'
(in thousands)
Sex and age
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
Total, 16 years and over
6,892
6,716
6,328
5.7
5.3
5.4
5.3
5.4
5.1
16 to 24 years
2,531
2,663
2,316
11.1
10.9
10.6
10.9
11.9
10.5
16 to 19 years
1,243
1,323
1,168
15.5
15.0
14.1
14.8
16.4
14.8
16 to 17 years
588
581
572
17.7
17.2
15.8
16.6
18.3
18.2
18 to 19 years
665
751
605
14.1
13.3
12.9
13.3
15.4
12.7
20 to 24 years
1,288
1,340
1,148
8.7
8.6
8.7
8.7
9.3
8.1
25 years and over
4,377
4,101
4,026
4.4
4.1
4.2
4.1
4.1
4.0
25 to 54 years
3,887
3,632
3,559
4.7
4.3
4.4
4.3
4.2
4.2
55 years and over
485
474
466
3.2
2.8
2.8
3.0
3.1
3.1
Men, 16 years and over
3,702
3,710
3,540
5.5
5.4
5.4
5.3
5.5
5.2
16 to 24 years
1,340
1,494
1,302
11.4
11.8
10.9
11.1
12.8
11.1
16 to 19 years
649
772
687
15.8
16.5
14.8
15.4
18.6
16.7
16 to 17 years
300
330
317
17.6
18.5
17.3
17.3
20.6
19.6
18 to 19 years
360
455
379
14.9
15.0
13.0
13.5
17.9
15.1
20 to 24 years
691
722
615
9.0
9.2
8.8
8.7
9.6
8.1
25 years and over
2,369
2,245
2,246
4.3
4.0
4.2
4.1
4.0
4.0
25 to 54 years
2,071
1,986
1,943
4.5
4.2
4.4
4.3
4.2
4.1
55 years and over
297
269
303
3.4
3.0
3.2
3.3
3.0
3.4
Women, 16 years and over
3,190
3,006
2,787
5.9
5.3
5.3
5.4
5.4
5.0
16 to 24 years
1,191
1,169
1,014
10.9
9.9
10.3
10.7
10.9
9.7
16 to 19 years
594
551
481
15.1
13.3
13.3
14.2
14.0
12.8
16 to 17 years
288
251
255
17.7
15.8
14.1
15.8
15.9
16.8
18 to 19 years
305
296
226
13.3
11.6
12.8
13.1
12.7
10.0
20 to 24 years
597
618
533
8.5
7.9
8.6
8.7
9.1
8.0
25 years and over
2,008
1,856
1,780
4.6
4.2
4.2
4.1
4.1
3.9
25 to 54 years
1,816
1,646
1,616
4.9
4.5
4.4
4.4
4.3
4.2
55 years and over
188
205
164
3.0
2.4
2.4
2.6
3.1
2.5
Unemployment as a percent of the civilian labor force.
Table A-10. Employment status of black and other workers
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Employment status
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
Civilian noninstitutional population
26,196
26,779
26,830
26,196
26,590
26,641
26,697
26,779
26,830
Civilian labor force
16,544
17,075
17,147
16,777
17,070
17,079
17,172
17,283
17,386
Participation rate
63.2
63.8
63.9
64.0
64.2
64.1
64.3
64.5
64.8
Employed
14,641
15,279
15,276
14,897
15,394
15,365
15,457
15,449
15,540
Employment-population ratio²
55.9
57.1
56.9
56.9
57.9
57.7
57.9
57.7
57.9
Unemployed
1,904
1,795
1,871
1,880
1,676
1,714
1,715
1,833
1,846
Unemployment rate
11.5
10.5
10.9
11.2
9.8
10.0
10.0
10.6
10.6
Not in labor force
9,652
9,704
9,682
9,419
9,520
9,562
9,525
9,496
9,444
1
The population figures are not adjusted for seasonal variation;
2
Civilian employment as a percent of the civilian noninstitutional
therefore, identical numbers appear in the unadjusted and seasonally
population.
adjusted columns.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-7. Duration of unemployment
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Weeks of unemployment
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
DURATION
Less than 5 weeks
2,973
3,464
3,117
3,097
3,059
3,117
3,029
3,181
3,247
5 to 14 weeks
2,602
2,258
2,329
2,093
1,835
1,935
2,039
2,081
1,865
15 weeks and over
1,907
1,586
1,436
1,732
1,554
1,502
1,495
1,512
1,304
15 to 26 weeks
977
817
768
842
788
787
758
757
665
27 weeks and over
930
770
668
890
766
715
737
755
639
Average (mean) duration, in weeks
14.3
12.3
12.3
14.1
13.4
12.6
12.8
12.7
12.1
Median duration, in weeks
7.1
5.6
6.0
6.3
5.7
5.6
5.8
5.7
5.3
PERCENT DISTRIBUTION
Total unemployed
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Less than 5 weeks
39.7
47.4
45.3
44.7
47.4
47.6
46.2
47.0
50.6
5 to 14 weeks
34.8
30.9
33.8
30.2
28.5
29.5
31.1
30.7
29.1
15 weeks and over
25.5
21.7
20.9
25.0
24.1
22.9
22.8
22.3
20.3
15 to 26 weeks
13.1
11.2
11.2
12.2
12.2
12.0
11.5
11.2
10.4
27 weeks and over
12.4
10.5
9.7
12.9
11.9
10.9
11.2
11.1
10.0
Table A-8. Reason for unemployment
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Reasons
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
NUMBER OF UNEMPLOYED
Job losers
3,739
3,701
3,382
3,182
2,951
3,031
3,066
3,121
2,876
On layoff
1,181
1,210
1,042
877
844
814
819
827
774
Other job losers
2,558
2,491
2,340
2,305
2,107
2,217
2,247
2,294
2,102
Job leavers
988
1,067
1,005
969
984
963
998
985
985
Reentrants
1,974
1,866
1,799
1,916
1,747
1,766
1,725
1,835
1,740
New entrants
782
675
696
855
747
799
799
780
765
PERCENT DISTRIBUTION
Total unemployed
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Job losers
50.0
50.7
49.1
46.0
45.9
46.2
46.5
46.4
45.2
On layoff
15.8
16.6
15.1
12.7
13.1
12.4
12.4
12.3
12.2
Other job losers
34.2
34.1
34.0
33.3
32.8
33.8
34.1
34.1
33.0
Job leavers
13.2
14.6
14.6
14.0
15.3
14.7
15.1
14.7
15.5
Reentrants
26.4
25.5
26.1
27.7
27.2
26.9
26.2
27.3
27.3
New entrants
10.4
9.2
10.1
12.4
11.6
12.2
12.1
11.6
12.0
UNEMPLOYED AS A PERCENT OF THE
CIVILIAN LABOR FORCE
Job losers
3.1
3.0
2.8
2.6
2.4
2.5
2.5
2.5
2.3
Job leavers
.8
.9
.8
.8
.8
.8
.8
.8
.8
Reentrants
1.6
1.5
1.5
1.6
1.4
1.4
1.4
1.5
1.4
New entrants
.7
.6
.6
.7
.6
.7
.7
.6
.6
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-5. Range of unemployment measures based on varying definitions of unemployment and the labor force, seasonally adjusted
(Percent)
Quarterly averages
Monthly data
Measure
1987
1988
1988
1989
IV
I
II
III
IV
Dec.
Jan.
Feb
U-1 Persons unemployed 15 weeks or longer as a percent of the
civilian labor force
1.5
1.4
1.3
1.3
1.2
1.2
1.2
1.1
U-2 Job losers as a percent of the civilian labor force
2.7
2.6
2.5
2.5
2.5
2.5
2.5
2.3
U-3 Unemployed persons 25 years and over as a percent of the
civilian labor force
4.5
4.4
4.2
4.2
4.1
4.1
4.1
4.0
U-4 Unemployed full-time jobseekers as a percent of the
full-time civilian labor force
5.5
5.3
5.1
5.1.
5.0
5.1
5.0
4.8
U-5a Total unemployed as a percent of the labor force,
including the resident Armed Forces
5.8
5.6
5.4
5.4
5.3
5.3
5.4
5.1
U-5b Total unemployed as a percent of the civilian labor force
5.9
5.7
5.5
5.5
5.3
5.3
5.4
5.1
U-6 Total full-time jobseekers plus 1/2 part-time jobseekers plus
1/2 total on part time for economic reasons as a percent of
the civilian labor force less 1/2 of the part-time labor force
8.1
7.9
7.6
7.6
7.5
7.6
7.5
7.2
U-7 Total full-time jobseekers plus 1/2 part-time jobseekers
plus 1/2 total on part time for economic reasons plus discouraged
workers as a percent of the civilian labor force plus
discouraged workers less 1/2 of the part-time labor force
8.9
8.7
8.3
8.4
8.2
N.A.
N.A.
N.A.
N.A. = not available.
Table A-6. Selected unemployment indicators, seasonally adjusted
Number of
unemployed persons
Unemployment rates'
(in thousands)
Category
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
CHARACTERISTIC
Total, 16 years and over
6,892
6,716
6,328
5.7
5.3
5.4
5.3
5.4
5.1
Men, 16 years and over
3,702
3,710
3,540
5.5
5.4
5.4
5.3
5.5
5.2
Men, 20 years and over
3,053
2,938
2,853
4.9
4.6
4.8
4.7
4.6
4.5
Women, 16 years and over
3,190
3,006
2,787
5.9
5.3
5.3
5.4
5.4
5.0
Women, 20 years and over
2,596
2,455
2,306
5:1
4.7
4.7
4.7
4.7
4.5
Both sexes, 16 to 19 years
1,243
1,323
1,168
15.5
15.0
14.1
14.8
16.4
14.8
Married men, spouse present
1,416
1,304
1,289
3.4
3.1
3.3
3.1
3.1
3.1
Married women, spouse present
1,205
1,115
1,028
4.0
3.7
3.8
3.7
3.6
3.4
Women who maintain families
557
557
558
8.3
7.9
7.7
8.2
80
8.0
Full-time workers
5,526
5,295
5,024
5.3
5.0
5.0
5.1
5.0
4.8
Part-time workers
1,379
1,445
1,314
7.9
7.4
7.1
7.0
7.9
7.3
Labor force time lost2
--
--
6.6
6.1
6.2
6.3
6.2
5.9
INDUSTRY
Nonagricultural private wage and salary workers
5,149
5,177
4,749
5.7
5.4
5.5
5.4
5.6
5.1
Goods-producing industries
1,965
1,894
1,784
6.8
6.4
6.4
6.4
6.4
6.1
Mining
66
43
57
7.8
8.8
8.9
7.7
6.1
8.0
Construction
688
663
648
10.9
10.0
10.6
10.4
10.4
10.0
Manufacturing
1,211
1,189
1,079
5.6
5.3
5.1
5.2
5.3
4.9.
Durable goods
734
661
576
5.7
5.0
4.9
5.0
5.0
4.4
Nondurable goods
477
528
503
5.4
5.7
5.3
5.5
5.7
5.5
Service-producing industries
3,184
3,283
2,965
5.2
4.9
5.1
4.9
5.2
4.7
Transportation and public utitities
247
245
244
3.8
3.5
4.0
3.8
3.8
3.9
Wholesale and retail trade
1,460
1,489
1,284
6.3
6.0
6.2
6.3
6.3
5.6
Finance and service industries
1,477
1,550
1,437
4.6
4.5
4.6
4.1
4.7
4.3
Government workers
501
486
477
2.9
2.6
2.5
2.7
2.7
2.7
Agricultural wage and salary workers
192
176
160
10.5
10.2
9.31
8.8
9.5
8.9
1 Unemployment as a percent of the civilian labor force.
economic reasons as a percent of potentially available labor force hours.
2
Aggregate hours lost by the unemployed and persons on part time for
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-3. Employment status of the civilian population by race, sex, age. and Hispanic origin-Continued
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Employment status, race, sex, age, and
Hispanic origin
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
HISPANIC ORIGIN
Civilian noninstitutional population
13,153
13,564
13,606
13,153
13,458
13,495
13,533
13,564
13,606
Civilian labor force
8,905
9,110
9,129
8,987
9,075
9,148
9,133
9,205
9,219
Participation rate
67.7
67.2
67.1
68.3
67.4
67.8
67.5
67.9
67.8
Employed
8,086
8,274
8,441
8,241
8,368
8,419
8,441
8,434
8,596
Employment-population ratio²
61.5
61.0
62.0
62.7
62.2
62.4
62.4
62.2
63.2
Unemployed
820
836
688
746
707
729
692
771
624
Unemployment rate
9.2
9.2
7.5
8.3
7.8
8.0
7.6
8.4
6.8
1
The population figures are not adjusted for seasonal variation;
population.
therefore, identical numbers appear in the unadjusted and seasonally
NOTE: Detail for the above race and Hispanic-origin groups will not
adjusted columns.
sum to totals because data for the "other races" group are not presented
2
Civilian employment as a percent of the civilian noninstitutional
and Hispanics are included in both the white and black population groups.
Table A-4. Selected employment indicators
(In thousands)
Not seasonally adjusted
Seasonally adjusted
Category
Feb.
Feb.
Oct.
Jan.
Feb.
Nov.
Dec.
Jan.
Feb.
1988
1988
1988
1989
1989
1988
1988
1989
1989
CHARACTERISTIC
Civilian employed, 16 years and over
112,460
114,786
115,023
114,273
115,573
115,947
116,009
116,711
116,853
Married men, spouse present
39,868
40,475
40,314
40,488
40,504
40,407
40,483
40,925
40,928
Married women, spouse present
28,477
29,323
29,265
28,620
28,890
28,995
29,053
29,589
29,412
Women who maintain families
6,157
6,435
6,391
6,151
6,344
6,375
6,399
6,416
6,385
MAJOR INDUSTRY AND CLASS OF WORKER
Agriculture:
Wage and salary workers
1,407
1,420
1,416
1,640
1,661
1,672
1,698
1,684
1,645
Self-employed workers
1,274
1,287
1,284
1,410
1,405
1,450
1,349
1,387
1,419
Unpaid family workers
79
124
95
123
177
125
149
189
150
Nonagricultural industries:
Wage and salary workers
101,341
103,158
103,644
102,498
103,733
103,770
103,904
104,510
104,797
Government
17,270
17,532
17,623
16,961
17,240
17,387
17,423
17,393
17,311
Private industries
84,071
85,626
86,021
85,537
86,493
86,383
86,481
87,117
87,486
Private households
1,087
1,116
1,056
1,167
1,152
1,209
1,210
1,196
1,135
Other industries
82,984
84,510
84,965
84,370
85,341
85,174
85,271
85,921
86,350
Self-employed workers
8,146
8,517
8,321
8,338
8,479
8,619
8,602
8,718
8,517
Unpaid family workers
213
280
262
232
232
300
266
298
285
PERSONS AT WORK PART TIME'
All industries:
Part time for economic reasons
5,377
5,138
4,996
5,369
4,963
5,061
5,321
5,097
4,981
Slack work
2,661
2,634
-2,554
2,408
2,220
2,279
2,549
2,302
2,303
Could only find part-time work
2,390
2,150
2,153
2,591
2,399
2,375
2,410
2,352
2,333
Voluntary part time
15,446
15,750
15,958
14,619
15,161
15,446
15,363
15,401
15,126
Nonagricultural industries:
Part time for economic reasons
5,117
4,914
4,725
5,101
4,727
4,819
5,033
4,837
4,697
Slack work
2,504
2,455
2,343
2,258
2,095
2,116
2,377
2,144
2,105
Could only find part-time work
2,292
2,112
2,102
2,477
2,319
2,288
2,307
2,283
2,272
Voluntary part time
15,055
15,374
15,584
14,172
14,679
14,986
14,928
14,970
14,688
1
Excludes persons "with a ich but not at work" during the survey
period for suchreasons as vacation, illness, or industrial dispute.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-3. Employment status of the civillan population by race, sex, age, and Hispanic origin
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Employment status, race, sex, age, and
Hispanic origin
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
WHITE
Civilian noninstitutional population
157,773
158,865
158,947
157,773
158,524
158,603
158,705
158,865
158,947
Civilian labor force
103,398
105,020
104,758
104,404
105,051
105,395
105,411
106,106
105,798
Participation rate
65.5
66.1
65.9
66.2
66.3
66.5
66.4
66.8
66.6
Employed
97,819
99,506
99,747
99,350
100,199
100,543
100,567
101.183
101,278
Employment-population ratio²
62.0
62.6
62.8
63.0
63.2
63.4
63.4
3.7
63.7
Unemployed
5,579
5,514
5,012
5,054
4,852
4,852
4,844
923
4,521
Unemployment rate
5.4
5.3
4.8
4.8
4.6
4.6
4.6
4.6
4.3
Men, 20 years and over
Civilian labor force
54,268.
54,854
54,920
54,627
54,861
54,922
54,898
55,213
55,308
Participation rate
77.9
78.0
78.0
78.4
78.3
78.3
78.2
78.5
78.6
Employed
51,551
52,159
52,399
52,348
52,612
52,624
52,636
53,007
53,197
Employment-population ratio²
74.0
74.2
74.4
75.2
75.1
75.0
75.0
75.4
75.6
Unemployed
2,717
2,695
2,521
2,279
2,249
2,298
2,262
2,205
2,111
Unemployment rate
5.0
4.9
4.6
4.2
4.1
4.2
4.1
4.0
3.8
Women, 20 years and over
Civilian labor force
42,748
43;803
43,657
42,848
43,298
43,625
43,644
43,936
43,770
Participation rate
56.1
57.0
56.8
56.2
56.5
56.9
56.9
57.2
56.9
Employed
40,780
41,948
42,008
40,942
41,583
41,889
41,930
42,201
42,177
Employment-population ratio²
53.5
54.6
54.6
53.7
54.2
54.6
54.6
54.9
54.8
Unemployed
1,969
1,854
1,649
1,906
1,715
1,736
1,714
1,734
1,593
Unemployment rate
4.6
4.2
3.8
4.4
4.0
4.0
3.9
3.9
3.6
Both sexes, 16 to 19 years
Civilian labor force
6,381
6,363
6,182
6,929
6,892
6,848
6,869
6,958
6,720
Participation rate
53.7
54.5
53.0
58.3
58.5
58.3
58.6
59.6
57.7
Employed
5,488
5,399
5,340
6,060
6,004
6,030
6,001
5,975
5,904
Employment-population ratio²
46.2
46.2
45.8
51.0
51.0
51.3
51.2
51.1
50.7
Unemployed
893
964
841
869
888
818
868
983
816
Unemployment rate
14.0
15.2
13.6
12.5
12.9
11.9
12.6
14.1
12.1
Men
14.8
18.5
16.4
12.5
14.4
12.6
13.4
16.4
14.0
Women
13.2
11.7
10.6
12.6
11.3
11.3
11.8
11.7
10.2
BLACK
Civilian noninstitutional population
20,569
20,877
20,905
20,569
20,786
20,811
20,842
20,877
20,905
Civilian labor force
12,965
13,275
13,303
13,138
13,290
13,330
13,405
13,477
13,476
Participation rate
63.0
63.6
63.6
63.9
63.9
64.1
64.3
64.6
64.5
Employed
11,288
11,705
11,655
11,504
11,807
11,831
11,856
11,860
11,873
Employment-population ratio²
54.9
56.1
55.8
55.9
56.8
56.8
56.9
56.8
56.8
Unemployed
1,678
1,570
1,648
1,634
1,483
1,499
1,549
1,617
1,603
Unemployment rate
12.9
11.8
12.4
12.4
11.2
11.2
11.6
12.0
11.9
Men, 20 years and over
Civilian labor force
6,094
6,163
6,153
6,140
6,157
6,146
6,179
6,226
6,199
Participation rate
74.7
74.3
74.0
75.2
74.6
74.3
74.6
75.0
74.6
Employed
5,352
5,504
5,432
5,469
5,566
5,545
5,561
5,576
5,549
Employment-population ratio²
65.6
66.3
65.3
67.0
67.4
67.1
67.1
67.2
66.7
Unemployed
742
659
721
671
591
601
618
650
650
Unemployment rate
12,2
10.7
11.7
10.9
9.6
9.8
10.0
10.4
10.5
Women, 20 years and over
Civilian labor force
6,114
6,357
6,327
6,135
6,234
6,280
6,316
6,369
6,349
Participation rate
59.7
61.1
60.7
59.9
60.2
60.6
60.9
61.2
61.0
Employed
5,462
5,712
5,669
5,490
5,620
5,663
5,654
5,706
5,697
Employment-population ratio²
53.4
54.9
54.4
53.6
54.3
54.6
54.5
54.9
54.7
Unemployed
652
645
658
645
614
617
662
663
651
Unemployment rate
10.7
10.1
10.4
10.5
9.8
9.8
10.5
10.4
10.3
Both sexes, 16 to 19 years
Civilian labor force
757
755
822
863
899
904
910
881
928
Participation.rate
34.8
34.7
37.8
39.7
41.2
41.5
41.7
40.5
42.7
Employed
473
490
553
545
621
623
641
577
627
Employment-population ratio²
21.8
22.5
25.4
25.1
28.5
28.6
29.4
26.5
28.8
Unemployed
284
265
269
318
278
281
269
304
301
Unemployment rate
37.5
35.1
32.7
36.8
30.9
31.1
29.6
34.5
32.4
Men
42.9
37.8
35.2
39.9
32.8
32.1
29.8
36.7
33.1
Women
32.5
32.3
30.0
33.8
28.6
29.9
29.3
32.0
31.6
See footnotes at end of table.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-2. Employment status of the civilian population by sex and age
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted
Employment status, sex, and age
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
TOTAL
Civilian noninstitutional population
183,969
185,644
185,777
183,969
185,114
185,244
185,402
185,644
185,777
Civilian labor force
119,942
122,095
121,906
121,165
122,091
122,510
122,563
123,428
123,181
Participation rate
65.2
65.8
65.6
65.9
66.0
66.1
66.1
66.5
66.3
Employed
112,460
114,786
115,023
114,273
115,573
115,947
116,009
116,711
116,853
Employment-population ratio²
61.1
61.8
61.9
62.1
62.4
62.6
62.6
62.9
62.9
Unemployed
7,482
7,309
6,883
6,892
6,518
6,563
6,554
6,716
6,328
Unemployment rate
6.2
6,0
5.6
5.7
5.3
5.4
5.3
5.4
5.1
Men, 20 years and over
Civilian noninstitutional population
80,203
81,162
81,256
80,203
80,851
80,924
81,001
81,162
81,256
Civilian labor force
62,205
62,926
63,031
62,614
62,915
62,995
63,002
63,358
63,490
Participation rate
77.6
77.5
77.6
78.1
77.8
77.8
77.8
78.1
78.1
Employed
58,626
59,442
59,681
59,561
60,004
59,999
60,049
60,420
60,636
Employment-population ratio²
73.1
73.2
73.4
74.3
74.2
74.1
74.1
74.4
74.6
Agriculture
2,027
2,054
2,065
2,279
2,315
2,313
2,292
2,277
2,320
Nonagricultural industries
56,599
57,387
57,616
57,282
57,689
57,686
57,757
58,143
58,316
Unemployed
3,578
3,485
3,350
3,053
2,911
2,996
2,953
2,938
2,853
Unemployment rate
5.8
5.5
5.3
4.9
4.6
4.8
4.7
4.6
4.5
Women, 20 years and over
Civilian noninstitutional population
89,178
90,072
90,153
89,178
89,807
89,887
89,954
90,072
90,153
Civilian labor force
50,407
51,850
51,675
50,530
51,201
51,558
51,587
51,998
51,821
Participation rate
56.5
57.6
57.3
56.7
57.0
57.4
57.3
57.7
57.5
Employed
47,714
49,287
49,279
47,934
48,788
49,113
49,165
49,543
49,514
Employment-population ratio²
53.5
54.7
54.7
53.8
54.3
54.6
54.7
55.0
54.9
Agriculture
552
606
578
638
640
640
646
715
666
Nonagricultural industries
47,162
48,681
48,702
47,296
48,148
48,473
48,519
48,827
48,849
Unemployed
2,693
2,563
2,396
2,596
2,413
2,445
2,422
2,455
2,306
Unemployment rate
5.3
4.9
4.6
5.1
4.7
4.7
4.7
4.7
4.5
Both sexes, 16 to 19 years
Civilian noninstitutional population
14,588
14,410
14,367
14,588
14,456
14,433
14,447
14,410
14,367
Civilian labor force
7,331
7,319
7,199
8,021
7,975
7,957
7,974
8,071
7,871
Participation rate
50.2
50.8
50.1
55.0
55.2
55.1
55.2
56.0
54.8
Employed
6,120
6,057
6,062
6,778
6,781
6,835
6,795
6,748
6,703
Employment-population ratio²
42.0
42.0
42.2
46.5
46.9
47.4
47.0
46.8
46.7
Agriculture
181
171
152
283
283
285
255
307
237
Nonagricultural industries
5,939
5,886
5,910
6,495
6,498
6,550
6,540
6,441
6,466
Unemployed
1,211
1,261
1,137
1,243
1,194
1,122
1,179
1,323
1,168
Unemployment rate
16.5
17.2
15.8
15.5
15.0
14.1
14.8
16.4
14.8
1
The population figures are not adjusted for seasonal variation;
2 Civilian employment as a percent of the civilian noninstitutional
therefore, identical numbers appear in the unadjusted and seasonally
population.
adjusted columns.
HOUSEHOLD DATA
HOUSEHOLD DATA
Table A-1. Employment status of the population, including Armed Forces in the United States, by sex
(Numbers in thousands)
Not seasonally adjusted
Seasonally adjusted'
Employment status and sex
Feb.
Jan.
Feb.
Feb.
Oct.
Nov.
Dec.
Jan.
Feb.
1988
1989
1989
1988
1988
1988
1988
1989
1989
TOTAL
Noninstitutional population²
185,705
187,340
187,461
185,705
186,801
186,949
187,098
187,340
187,461
Labor force²
121,678
123,791
123,590
122,901
123,778
124,215
124,259
125,124
124,865
Participation rate3
65.5
66.1
65.9
66.2
66.3
66.4
66.4
66.8
66.6
Total employed²
114,196
116,482
116,707
116,009
117,260
117,652
117,705
118,407
118,537
Employment-population ratio⁴
61.5
62.2
62.3
62.5
62.8
62.9
62.9
63.2
63.2
Resident Armed Forces
1,736
1,696
1,684
1,736
1,687
1,705
1,696
1,696
1,684
Civilian employed
112,460
114,786
115,023
114,273
115,573
115,947
116,009
116,711
116,853
Agriculture
2,760
2,831
2,795
3,200
3,238
3,238
3,193
3,300
3,223
Nonagricultural industries
109,700
111,955
112,228
111,073
112,335
112,709
112,816
113,411
113,630
Unemployed
7,482
7,309
6,883
6,892
6,518
6,563
6,554
6,716
6,328
Unemployment rate⁵
6.1
5.9
5.6
5.6
5.3
5.3
5.3
5.4
5.1
Not in labor force
64,026
63,549
63,871
62,804
63,023
62,734
62,839
62,216
62,596
Men, 16 years and over
Noninstitutional population²
89,099
89,914
89,973
89,099
89,637
89,716
89,792
89,914
89,973
Labor force²
67,484
68,197
68,273
68,289
68,569
68,686
68,638
69,032
69,113
Participation rate³
75.7
75.8
75.9
76.6
76.5
76.6
76.4
76.8
76.8
Total employed²
63,252
63,944
64,233
64,587
64,976
65,074
65,055
65,322
65,572
Employment-population ratio⁴
71.0
71.1
71.4
72.5
72.5
72.5
72.5
72.6
72.9
Resident Armed Forces
1,577
1,532
1,521
1,577
1,526
1,542
1,534
1,532
1,521
Civilian employed
61,675
62,412
62,712
63,010
63,450
63,532
63,521
63,790
64,051
Unemployed
4,232
4,252
4,040
3,702
3,593
3,612
3,583
3,710
3,540
Unemployment rate⁵
6.3
6.2
5.9
5.4
5.2
5.3
5.2
5.4
5.1
Women, 16 years and over
Noninstitutional population²
96,606
97,427
97,488
96,606
97,164
97,234
97,306
97,427
97,488
Labor force2
54,195
55,594
55,317
54,612
55,209
55,529
55,621
56,091
55,752
Participation rate³
56.1
57.1
56.7
56.5
56.8
57.1
57.2
57.6
57.2
Total employed²
50,944
52,538
52,474
51,422
52,284
52,578
52,650
53,085
52,965
Employment-population ratio⁴
52.7
53.9
53.8
53.2
53.8
54.1
54.1
54.5
54.3
Resident Armed Forces
159
164
163
159
161
163
162
164
163
Civilian employed
50,785
52,374
52,311
51,263
52,123
52,415
52,488
52,921
52,802
Unemployed
3,250
3,057
2,843
3,190
2,925
2,951
2,971
3,006
2,787
Unemployment rate⁵
6.0
5.5
5.1
5.8
5.3
5.3
5.3
5.4
5.0
1 The population and Armed Forces figures are not adjusted for
3
Labor force as a percent of the noninstitutional population.
seasonal variation; therefore, identical numbers appear in the unadjusted
4 Total employment as a percent of the noninstitutional population.
and seasonally adjusted columns.
5 Unemployment as a percent of the labor force (including the resident
2 Includes members of the Armed Forces stationed in the United
Armed Forces).
States.
Because these seasonal events follow a more or less regular
from the results of a complete census. The chances are approx-
pattern each year, their influence on statistical trends can be
imately 90 out of 100 that an estimate based on the sample will
eliminated by adjusting the statistics from month to month.
differ by no more than 1.6 times the standard error from the
These adjustments make nonseasonal developments, such as
results of a complete census. At approximately the 90-percent
declines in economic activity or increases in the participation
level of confidence-the confidence limits used by BLS in its
of women in the labor force, easier to spot. To return to the
analyses-the error for the monthly change in total employ-
school's-out example, the large number of people entering the
ment is on the order of plus or minus 358,000; for total
labor force each June is likely to obscure any other changes
unemployment it is 224,000; and, for the overall unemploy-
that have taken place since May, making it difficult to deter-
ment rate, it is 0.19 percentage point. These figures do not
mine if the level of economic activity has risen or declined.
mean that the sample results are off by these magnitudes but,
However, because the effect of students finishing school in
rather, that the chances are approximately 90 out of 100 that
previous years is known, the statistics for the current year can
the "true" level or rate would not be expected to differ from
be adjusted to allow for a comparable change. Insofar as the
the estimates by more than these amounts.
seasonal adjustment is made correctly, the adjusted figure pro-
Sampling errors for monthly surveys are reduced when the
vides a more useful tool with which to analyze changes in
data are cumulated for several months, such as quarterly or
economic activity.
annually. Also, as a general rule, the smaller the estimate, the
Measures of labor force, employment, and unemployment
larger the sampling error. Therefore, relatively speaking, the
contain components such as age and sex. Statistics for all
estimate of the size of the labor force is subject to less error
employees, production workers, average weekly hours, and
than is the estimate of the number unemployed. And, among
average hourly earnings include components based on the
'the unemployed, the sampling error for the jobless rate of
employer's industry. All these statistics can be seasonally ad-
adult men, for example, is much smaller than is the error for
justed either by adjusting the total or by adjusting each of the
the jobless rate of teenagers. Specifically, the error on monthly
components and combining them. The second procedure
change in the jobless rate for men is .25 percentage point; for
usually yields more accurate information and is therefore
teenagers, it is 1.29 percentage points.
followed by BLS. For example, the seasonally adjusted figure
In the establishment survey, estimates for the 2 most current
for the labor force is the sum of eight seasonally adjusted
months are based on incomplete returns; for this reason, these
civilian employment components, plus the resident Armed
estimates are labeled preliminary in the tables. When all the
Forces total (not adjusted for seasonality), and four seasonally
returns in the sample have been received, the estimates are
adjusted unemployment components; the total for unemploy-
revised. In other words, data for the month of September are
ment is the sum of the four unemployment components; and
published in preliminary form in October and November and
the overall unemployment rate is derived by dividing the
in final form in December. To remove errors that build up
resulting estimate of total unemployment by the estimate of
over time, a comprehensive count of the employed is con-
the labor force.
ducted each year. The results of this survey are used to
The numerical factors used to make the seasonal ad-
establish new benchmarks-comprehensive counts of
justments are recalculated regularly. For the household
employment-against which month-to-month changes can be
survey, the factors are calculated for the January-June period
measured. The new benchmarks also incorporate changes in
and again for the July-December period. The January revision
the classification of industries and allow for the formation of
is applied to data that have been published over the previous 5
new establishments.
years. For the establishment survey, updated factors for
seasonal adjustment are calculated only once a year, along
Additional statistics and other information
with the introduction of new benchmarks which are discussed
In order to provide a broad view of the Nation's employ-
at the end of the next section.
ment situation, BLS regularly publishes a wide variety of data
in this news release. More comprehensive statistics are contain-
Sampling variability
ed in Employment and Earnings, published each month by
Statistics based on the household and establishment surveys
BLS. It is available for $8.50 per issue or $25.00 per year from
are subject to sampling error, that is, the estimate of the
the U.S. Government Printing Office, Washington, DC
number of people employed and the other estimates drawn
20204. A check or money order made out to the Superinten-
from these surveys probably differ from the figures that would
dent of Documents must accompany all orders.
be obtained from a complete census, even if the same question-
Employment and Earnings also provides approximations of
naires and procedures were used. In the household survey, the
the standard errors for the household survey data published in
amount of the differences can be expressed in terms of stand-
this release. For unemployment and other labor force
ard errors. The numerical value of a standard error depends
categories, the standard errors appear in tables B through J of
upon the size of the sample, the results of the survey, and other
its "Explanatory Notes." Measures of the reliability of the
factors. However, the numerical value is always such that the
data drawn from the establishment survey and the actual
chances are approximately 68 out of 100 that an estimate based
amounts of revision due to benchmark adjustments are pro-
on the sample will differ by no more than the standard error
vided in tables M, O, P, and Q of that publication:
Explanatory Note
- 3
Industry Payroll Employment (Establishment Survey Data)
Total nonagricultural employment increased by 290,000 in February,
after seasonal adjustment, to a level of 108.3 million. This followed an
increase of 415,000 in January. The February gain was confined to the
service-producing sector; employment in the goods sector decreased
slightly, largely because of a weather-related decline in construction.
This news release presents statistics from two major surveys,
that time; and they made specific efforts to find employment
(See table B-1.)
the Current Population Survey (household survey) and the
sometime during the prior'4 weeks. Persons laid off from their
Current Employment Statistics Survey (establishment survey).
former jobs and awaiting recall and those expecting to report
In the service-producing sector, the services industry led the over-
The household survey provides the information on the labor
to a job within 30 days need not be looking for work to be
the-month gains with an employment increase of 130,000. Within services,
force, total employment, and unemployment that appears in
counted as unemployed.
employment in the health services component rose by 45,000, and business
the A tables, marked HOUSEHOLD DATA. It is a sample
The labor force equals the sum of the number employed and
services, which had declined in January, rebounded by 40,000. Elsewhere in
survey of about 55,800 households that is conducted by the
the number unemployed. The unemployment rate is the
the sector, retail trade added 75,000 jobs, and wholesale trade, with an
Bureau of the Census with most of the findings analyzed and
percentage of unemployed people in the labor force (civilian
increase of 30,000, continued its pattern of strong job growth.
published by the Bureau of Labor Statistics (BLS).
plus the resident Armed Forces). Table A-5 presents a special
The establishment survey provides the information on the
grouping of seven measures of unemployment based on vary-
In the goods-producing sector, the construction industry, which posted
employment, hours, and earnings of workers on
ing definitions of unemployment and the labor force. The
a very large increase in January, lost 20,000 jobs in February. This swing
nonagricultural payrolls that appears in the B tables, marked
definitions are provided in the table. The most restrictive
in construction employment probably reflects the shift in weather
ESTABLISHMENT DATA. This information is collected
definition yields U-1 and the most comprehensive yields U-7.
conditions from unusually mild to harsh over the 2 months. Employment in
from payroll records by BLS in cooperation with State agencies.
The overall unemployment rate is U-5a, while U-5b represents
manufacturing, which had been increasing since September, showed little
The sample includes over 300,000 establishments employing
the same measure with a civilian labor force base.
movement in February. The only sizable change was a decline of 15,000 in
over 38 million people.
Unlike the household survey, the establishment survey only
auto employment; this followed a similar increase in the prior month. In
For both surveys, the data for a given month are actually
counts wage and salary employees whose names appear on the
mining, employment was also about unchanged over the month.
collected for and relate to a particular week. In the household
payroll records of nonagricultural firms. As a result, there are
survey, unless otherwise indicated, it is the calendar week that
many differences between the two surveys, among which are
Weekly Hours (Establishment Survey Data)
contains the 12th day of the month, which is called the survey
the following:
week. In the establishment survey, the reference week is the
The average workweek for production or nonsupervisory workers on
pay period including the 12th, which may or may not corres-
- The household survey, although based on a smaller sample, reflects a
private nonagricultural payrolls edged down by 0.1 hour to 34.7 hours in
larger segment of the population; the establishment survey excludes agriculture,
pond directly to the calendar week.
the self-employed, unpaid family workers, private household workers, and
February, after seasonal adjustment, while both the factory workweek and
The data in this release are affected by a number of technical
members of the resident Armed Forces;
overtime were unchanged at 41.0 and 3.9 hours, respectively. (See table B-
factors, including definitions, survey differences, seasonal ad-
2.)
- The household survey includes people on unpaid leave among the
justments, and the inevitable variance in results between a
employed; the establishment survey does not;
survey of a sample and a census of the entire population. Each
The index of aggregate weekly hours of production or nonsupervisory
- The household survey is limited to those 16 years of age and older; the
of these factors is explained below.
workers on private nonagricultural payrolls, at 127.9 (1977=100), declined
establishment survey is not limited by age;
by 0.3 percent, seasonally adjusted. The index for manufacturing, at 97.2,
Coverage, definitions, and differences
- The household survey has no duplication of individuals, because each in-
showed little change. (See table B-5.)
between surveys
dividual is counted only once; in the establishment survey, employees working at
more than one jób or otherwise appearing on more than one payroll would be
The sample households in the household survey are selected
counted separately for each appearance.
Hourly and Weekly Earnings (Establishment Survey Data)
so as to reflect the entire civilian noninstitutional population
16 years of age and older. Each person in a household is
Other differences between the two surveys are described in
Both average hourly and average weekly earnings of private production
classified as employed, unemployed, or not in the labor force.
"Comparing Employment Estimates from Household and
or nonsupervisory workers were little changed in February, after seasonal
Those who hold more than one job are classified according to
Payroll Surveys," which may be obtained from the BLS upon
adjustment, following large increases in January. Prior to seasonal
the job at which they worked the most hours.
request.
adjustment, average hourly earnings remained at $9.54, and average weekly
People are classified as employed if they did any work at all
earnings declined by $1.91 to $327.22.
Hourly earnings rose by 4.0
as paid civilians; worked in their own business or profession or
Seasonal adjustment
percent over the past year, and weekly earnings were up 3.4 percent. (See
on their own farm; or worked 15 hours or more in an enter-
Over the course of a year, the size of the Nation's labor
tables B-3 and B-4.)
prise operated by a member of their family, whether they were
force and the levels of employment and unemployment
paid or not. People are also counted as employed if they were
undergo sharp fluctuations due to such seasonal events as
on unpaid leave because of illness, bad weather, disputes be-
changes in weather, reduced or expanded production, har-
tween labor and management, or personal reasons. Members
vests, major holidays, and the opening and closing of schools.
of the Armed Forces stationed in the United States are also in-
For example, the labor force increases by a large number each
cluded in the employed total.
June, when schools close and many young people enter the job
People are classified as unemployed, regardless of their
market. The effect of such seasonal variation can be very
The Employment Situation for March 1989 will be released on Friday,
eligibility for unemployment benefits or public assistance, if
large; over the course of a year, for example, seasonality may
April 7, at 8:30 A.M. (EST).
they meet all of the following criteria: They had no employ-
account for as much as 95 percent of the month-to-month
ment during the survey week; they were available for work at
changes in unemployment.
- 2 -
News
United States
The civilian labor force, which had also increased markedly in
January, showed a small decline in February. As a result, the labor force
Department
participation rate edged down to 66.3 percent. Over the year, the labor
of Labor
force expanded by about 2.0 million. (See table A-2.)
Bureau of Labor Statistics
Washington, D.C. 20212
Table A. Major indicators of labor market activity, seasonally adjusted
Technical information: (202) 523-1371
USDL 89-113
Quarterly
Monthly data
523-1944
averages
523-1959
TRANSMISSION OF MATERIAL IN THIS
Category
Jan.-
Media contact:
523-1913
RELEASE IS EMBARGOED UNTIL
1988
1988
1989
Feb.
8:30 A.M. (EST), FRIDAY,
change
MARCH 10, 1989
III
IV
Dec.
Jan.
Feb.
HOUSEHOLD DATA
THE EMPLOYMENT SITUATION: FEBRUARY 1989
Thousands of persons
Labor force
123,570
124,084
124,259
125,124
124,865
-259
Employment continued to increase in February and unemployment
Total employment
116,892
117,539
117,705
118,407
118,537
130
declined, the Bureau of Labor Statistics of the U.S. Department of Labor
Civilian labor force
121,881
122,388
122,563
123,428
123,181
-247
reported today. Both the overall and the civilian worker unemployment
Civilian employment
115,202
115,843
116,009
116,711
116,853
142
rates were 5.1 percent, down from 5.4 percent in January.
Unemployment
6,678
6,545
6,554
6,716
6,328
-388
Not in labor force
62,959
62,865
62,839
62,216
62,596
380
Nonagricultural payroll jobs, as measured by the survey of business
Discouraged workers
941
951
N.A.
N.A.
N.A.
N.A.
establishments, rose by 290,000 in February, with the gains confined to the
service-producing industries. Total civilian employment, as measured by
the household survey, rose only slightly, following a very large gain in
Percent of labor force
January.
Unemployment rates:
All workers
5.4
5.3
5.3
5.4
5.1
-0.3
Unemployment (Household Survey Data)
All civilian workers
5.5
5.3
5.3
5.4
5.1
-.3
Adult men
4.7
4.7
4.7
4.6
4.5
-.1
The number of unemployed persons dropped to a seasonally adjusted
Adult women
4.9
4.7
4.7
4.7
4.5
-.2
level of 6.3 million in February. As a result, the civilian worker
Teenagers
15.3
14.6
14.8
16.4
14.8
-1.6
unemployment rate fell to 5.1 percent, the lowest since May 1974. The rate
White
4.8
4.6
4.6
4.6
4.3
-.3
was 5.3 or 5.4 percent in the prior 5 months. (See table A-2.)
Black
11.2
11.3
11.6
12.0
11.9
-.1
Hispanic origin
8.0
7.8
7.6
8.4
6.8
-1.6
The February decline in unemployment was limited essentially to youth
16-24 years of age. The rate for teenagers dropped by 1.6 percentage
ESTABLISHMENT DATA
points to 14.8 percent, after rising by the same magnitude in January, and
Thousands of jobs
the 20-24 young adult rate fell 1.2 points to 8.1 percent. There was
Nonfarm employment
106,478
107,344
107,641
p108,056
p108,345
p289
little change among adults 25 years and over. The unemployment rate for
Goods-producing
25,650
25,827
25,889
p26,044
p26,012
p-32
Hispanics, which often fluctuates from month to month, fell by 1.6
Service-producing
80,828
81,517
81,752
p82,012
p82,333
p321
percentage points to 6.8 percent. The rate for white workers (4.3 percent)
also declined, while that for blacks (11.9 percent) was about unchanged.
(See tables A-2, A-3, and A-9.)
Hours of work
Average weekly hours:
The unemployment decrease in February occurred among persons jobless
Total private
34.7
34.8
34.7
p34.8
p34.7
p-0.1
for more than 5 weeks. The proportion jobless for 27 weeks and over fell
Manufacturing
41.1
41.1
40.8
p41.0
p41.0
p0
to 10 percent of the unemployed, the lowest in nearly 9 years. Both the
Overtime
3.9
3.9
3.9
p3.9
p3.9
p0
mean (average) and median duration of unemployment declined--to 12.1 and
5.3 weeks, respectively. The number of unemployed persons who had lost
Includes the resident Armed Forces.
N.A.=not available.
their jobs also dropped over the month to 2.9 million. (See tables A-7
p=preliminary.
and A-8.)
Civilian Employment and the Labor Force (Household Survey Data)
Following a large increase in January, civilian employment rose only
slightly in February, to a seasonally adjusted level of 116.9 million. The
proportion of the population with jobs (the employment-population ratio)
held at the record high level of 62.9 percent attained in the previous
month. (See table A-2.)