NATIONAL CENTER FOR EDUCATION STATISTICS

U.S. Department of Education

Office of Educational Research and Improvement NCES 97-269

EDUCATION AND THE

ECONOMY:

An Indicators Report
NATIONAL CENTER FOR EDUCATION STATISTICS

U.S. Department of Education

Office of Educational Research and Improvement NCES 97-269

EDUCATION AND THE ECONOMY:

An Indicators Report

Paul T. Decker

Jennifer King Rice

Mary T. Moore

Mathematica Policy Research, Inc.

Mary R. Rollefson

Project Officer

National Center for Education Statistics

March 1997
U.S. DEPARTMENT OF EDUCATION

Richard W. Riley

Secretary

OFFICE OF EDUCATIONAL RESEARCH AND IMPROVEMENT

Marshall S. Smith

Acting Assistant Secretary

NATIONAL CENTER FOR EDUCATION STATISTICS

Pascal D. Forgione, Jr.

Commissioner

The National Center for Education Statistics (NCES) is the primary federal entity for collecting, analyzing,

and reporting data related to education in the United States and other nations. It fulfills a congressional mandate to collect, collate, analyze, and report full and complete statistics on the condition of education in the

United States; conduct and publish reports and specialized analyses of the meaning and significance of such

statistics; assist state and local education agencies in improving their statistical systems; and review and

report on education activities in foreign countries.

NCES activities are designed to address high priority education data needs; provide consistent, reliable,

complete, and accurate indicators of education status and trends; and report timely, useful, and high quality

data to the U.S. Department of Education, the Congress, the states, other education policymakers, practitioners, data users, and the general public.

We strive to make our products available in a variety of formats and in language that is appropriate to a

variety of audiences. You, as our customer, are the best judge of our success in communicating information effectively. If you have any comments or suggestions about this or any other NCES product or report,

we would like to hear from you. Please direct your comments to:

National Center for Education Statistics

Office of Educational Research and Improvement

U.S. Department of Education

555 New Jersey Avenue NW

Washington, DC 20208-5574

March 1997

The NCES World Wide Web Home Page is: http://www.ed.gov/NCES/

Suggested Citation:

U.S. Department of Education, National Center for Education Statistics, Education and the Economy: An

Indicators Report, NCES 97-269, by Paul T. Decker, Jennifer King Rice, Mary T. Moore, and Mary R.

Rollefson, project officer. Washington, DC: 1997.

Contact:

James D. Houser

(202) 219-1419

FAX: (202) 219-1575
TABLE OF CONTENTS

Page

Commissioner’s Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxv

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxvi

1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 WORKER PRODUCTIVITY AND EDUCATION . . . . . . . . . . . . . . . . . . . 3

INDICATOR 1: Trends in U.S. Worker Productivity . . . . . . . . . . . . . . . . . . . 4

INDICATOR 2: Productivity Trends in Industrialized Countries . . . . . . . . 7

DISCUSSION: Contribution of Education to Economic Productivity . . . 10

CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3 ECONOMIC CONSEQUENCES OF EDUCATIONAL ATTAINMENT . . 15

INDICATOR 3: Education Attainment and Unemployment . . . . . . . . . . . . . 16

INDICATOR 4: Education Attainment and Earnings . . . . . . . . . . . . . . . . . . 18

DISCUSSION: Determinants of the Increasing Return to Education . . . 21

DISCUSSION: Estimated Returns Based on Direct Measures

of Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

INDICATOR 5: The Supply of Youth with High School and

College Educations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

DISCUSSION: Using Education to Increase Earnings at the 

National Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4 ECONOMIC CONSEQUENCES OF EDUCATIONAL ACHIEVEMENT . 33

INDICATOR 6: Achievement Test Scores and Employment Status . . . . . . . 34

INDICATOR 7: Achievement Test Scores and Wage Rates . . . . . . . . . . . . . . 38

INDICATOR 8: Trends in Student Achievement Test Scores . . . . . . . . . . . . 42

CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Table of Contents xxiii
Page

5 ECONOMIC CONSEQUENCES OF ADULT LITERACY . . . . . . . . . . . . 49

INDICATOR 9: Literacy and Unemployment . . . . . . . . . . . . . . . . . . . . . . . . 50

INDICATOR 10: Literacy and Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

INDICATOR 11: Literacy Levels of the Labor Force and 

New Job Entrants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

6 THE ROLE OF OCCUPATIONS IN THE 

EDUCATION-EARNINGS LINK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

INDICATOR 12: Education and Literacy of Workers by Occupation . . . . . 60

DISCUSSION: Job Skills Mismatch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

INDICATOR 13: College Degrees and Employment in College Jobs . . . . . . 65

CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

7 TRAINING OF LABOR FORCE PARTICIPANTS . . . . . . . . . . . . . . . . . . 67

INDICATOR 14: Training and Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

DISCUSSION: Training and Productivity . . . . . . . . . . . . . . . . . . . . . . . . . 72

INDICATOR 15: Trends in Training Rates in the United States . . . . . . . . . . 74

CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

8 INTERNATIONAL TRENDS IN EDUCATION . . . . . . . . . . . . . . . . . . . . 81

INDICATOR 16: Educational Attainment in Industrialized Countries . . . . 82

INDICATOR 17: Educational Achievement in Industrialized Countries . . . 85

DISCUSSION: Implications of U.S. Educational Achievement 

for Productivity Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

INDICATOR 18: Adult Literacy in Industrialized Countries . . . . . . . . . . . . 91

INDICATOR 19: Training in Industrialized Countries . . . . . . . . . . . . . . . . . 93

CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

xxiv Education and the Economy
LIST OF TABLES

Table Page

A International distribution of academic achievement relative to the 

United States: 1991–92 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi

2.1 Growth in gross domestic product per hour worked (average 

annual growth rate) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4.1 Onset and end of the achievement decline as indicated by

selected tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.1 Average proficiency on literacy scales for young adults,

by race–ethnicity: 1985 and 1992 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

6.1 Percent of employed 22- to 65-year-olds holding professional, 

managerial, or technical jobs, by educational attainment and 

proficiency level on the prose scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

6.2 Percent of employed 22- to 65-year-olds holding service, laborer/

helper/cleaner, or farm/forestry/fishing occupations, by 

educational attainment and proficiency level on the prose scale . . . . 63

8.1 International distribution of academic achievement relative 

to the United States: 1991–92 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

8.2 Training activity, by country . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

List of Tables xxv
LIST OF FIGURES

Figure Page

EXECUTIVE SUMMARY

A Index of real output per hour of all persons, business sector: 

1947–94 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

B Real GDP per worker in G–7 nations: 1950–90 . . . . . . . . . . . . . . . . . . ix

C Unemployment rate of recent high school graduates and dropouts 

not enrolled in college: 1960–94 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

D Earnings for all wage and salary earners ages 25–34 years, 

by sex and educational attainment: 1993 . . . . . . . . . . . . . . . . . . . . . . . xi

E High school completion, college enrollment, and college 

completion rates: 1972–94 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

F Secondary school completion, by age: 1992 . . . . . . . . . . . . . . . . . . . . . xiii

G Completion of higher education, by age: 1992 . . . . . . . . . . . . . . . . . . xiii

H Unemployment rate of civilian workers age 28 years, 

by age-adjusted ASVAB score quartile: 1985–93 . . . . . . . . . . . . . . . . . xiv

I Mean hourly rate of pay for civilian workers age 28 years, 

by age-adjusted ASVAB score quartile: 1985–93 . . . . . . . . . . . . . . . . . xiv

J Trends in average achievement of 17-year-olds in science, 

mathematics, and reading: 1969–92 . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

K Unemployment of adult labor force participants, 

by proficiency level on three literacy scales: 1992 . . . . . . . . . . . . . . . . xvii

L Mean weekly earnings of full-time workers, by proficiency 

level on three literacy scales: 1992 . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii

xxvi Education and the Economy
Figure Page

M Percentage of labor force in each proficiency level on the 

three literacy scales: 1992 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii

N Estimated percentage of the population in each proficiency level 

on three adult literacy scales, by selected countries: 1994 . . . . . . . . . xix

O Median weekly earnings of full-time workers ages 16 years and older, 

by training status and educational attainment: January 1991 . . . . . . xx

P All workers ages 16 years and older who participated in skill 

improvement training while on their current jobs, by education 

and occupation: 1983 and 1991 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi

CHAPTER 2

2.1 Index of real output per hour of all persons, business sector: 

1947–94 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Real GDP per worker in G–7 nations: 1950–90 . . . . . . . . . . . . . . . . . . . 7

2.3 Convergence of real GDP per worker among the G–7 nations: 

1950–90 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

CHAPTER 3

3.1 Unemployment rate of recent high school graduates and dropouts 

not enrolled in college: 1960–94 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2 Unemployment rate of recent high school graduates not enrolled

in college, by race: 1973–94 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3 Earnings for all wage and salary workers ages 25–34 years, 

by sex and educational attainment: 1993 . . . . . . . . . . . . . . . . . . . . . . . 18

3.4 Estimated returns to education among workers with 1 to 5 years 

of work experience (percent increase over average wages of 

non-high school graduates): 1963–86 . . . . . . . . . . . . . . . . . . . . . . . . . . 19

List of Figures xxvii
Figure Page

3.5 Median annual income for full-time workers ages 25 years and older,

by sex and educational attainment: 1980 and 1990 . . . . . . . . . . . . . . . 20

3.6 High school completion rates for 18- to 24-year-olds not enrolled 

in high school, by race–ethnicity: 1972–94 . . . . . . . . . . . . . . . . . . . . . . 26

3.7 Percentage of high school graduates enrolled in college in 

October following graduation, by type of college: 1968–93 . . . . . . . . 27

3.8 Percentage of high school graduates enrolled in college in 

October following graduation, by race–ethnicity: 1973–92 . . . . . . . . . 27

3.9 Percentage of high school graduates ages 25–29 years who 

have completed four years or more of college, 

by race–ethnicity: 1965–94 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

CHAPTER 4

4.1 Unemployment rate of civilian workers ages 19–31 years, 

by age-adjusted ASVAB score quartile . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.2 Unemployment rate for civilian workers ages 19–31 years, by 

educational attainment and age-adjusted ASVAB score quartile . . . . 36

4.3 Estimated relationship between test scores and unemployment 

probability (as compared to being in the bottom quartile), for 

28-year-old workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.4 Mean hourly rate of pay for civilian workers ages 19–31 years, 

by age-adjusted ASVAB score quartile . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.5 Mean hourly rate of pay for civilian workers ages 19–31 years, by 

educational attainment and age-adjusted ASVAB score quartile . . . . 40

4.6 Estimated relationship between test scores and hourly wage rates 

(as compared to being in the bottom quartile), for 28-year-old 

workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

xxviii Education and the Economy
Figure Page

4.7 Trends in average U.S. achievement in science, mathematics, 

and reading: 1969–92 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.8 Difference in average NAEP proficiency scores for whites and 

blacks and whites and Hispanics age 17 years, by subject: 

1969–92 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.9 Trends in percent of students at or above four mathematics and 

four science proficiency levels: 1977–92 . . . . . . . . . . . . . . . . . . . . . . . . 46

CHAPTER 5

5.1 Unemployment of adult labor force participants, by proficiency 

level on three literacy scales: 1992 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.2 Average proficiencies of the unemployed and full-time employed 

with high school diplomas only, by literacy scale: 1992 . . . . . . . . . . . 51

5.3 Mean weekly earnings of full-time workers, by proficiency level 

on three literacy scales: 1992 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.4 Mean weekly earnings of full-time employed college graduates, 

by proficiency level on three literacy scales: 1992 . . . . . . . . . . . . . . . . 53

5.5 Weekly earnings of full-time black workers as a percentage of 

white workers’ earnings, by literacy proficiency level: 1992 . . . . . . . 54

5.6 Percentage of labor force in each proficiency level

on the three literacy scales: 1992 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

CHAPTER 6

6.1 Job growth and characteristics of workers, by occupation . . . . . . . . . 61

6.2 College graduates in the labor force and in jobs that do not 

require college degrees: 1967–90 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

List of Figures xxix
Figure Page

CHAPTER 7

7.1 Median weekly earnings of full-time workers ages 16 years and older,

by training status and educational attainment: January 1991 . . . . . . . 70

7.2a All workers ages 16 years and older who participated in skill 

improvement training while on their current jobs, by sex, race–

ethnicity, and educational attainment: 1983 and 1991 (percent 

of workers in each category) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

7.2b All workers ages 16 years and older who participated in skill improvement training while on their current jobs, by occupational group and 

training source: 1983 and 1991 (percent of workers in each category) . . 76

7.3 Workers ages 17 years and older who were enrolled in 

employment-related training in the previous 12 months, 

by sex, race–ethnicity, education, and occupation: 1991 

(percent of workers in each category) . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

7.4 Training participation, by training type: 1991 (percent of workers 

participating in each type of training) . . . . . . . . . . . . . . . . . . . . . . . . . 78

CHAPTER 8

8.1 Secondary school completion, by age: 1992 . . . . . . . . . . . . . . . . . . . . . 83

8.2 Completion of higher education, by age: 1992 . . . . . . . . . . . . . . . . . . . 84

8.3 Completion of postsecondary education, percent of population 

ages 25–64 years: 1991 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

8.4 Mean mathematics achievement of students in their last year of

secondary school in industrialized countries, by topic: 1980–82 . . . . 86

8.5 Mean science achievement of students in their last year of 

secondary school in industrialized countries, by topic: 1983–86 . . . . 86

8.6 Mean reading achievement of 14-year-old students in industrialized

countries, by topic: 1990–91 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

8.7 Estimated percentage of the population in each proficiency level 

on three adult literacy scales, by selected countries: 1994 . . . . . . . . . . 92

xxx Education and the Economy
COMMISSIONER’S STATEMENT

This report on Education and the Economy is the first in a series of indicator reports

recommended by the congressionally mandated Special Study Panel on

Education Indicators. In its report, Education Counts, the panel challenged the

National Center for Education Statistics (NCES) to make a major advance in indicator reporting by going “. . . far beyond the kinds of indicator information now

reported by the federal government . . . bringing multiple indicators from a variety of data sources to bear on the issue and linking the indicators together with

analytic commentary and interpretation.” The panel focused specifically on six

policy issue areas in which the public would benefit from more synthesis and

interpretation of the available data. In this time of concern about U.S. economic

competitiveness, we have chosen the link between education and economic productivity as the topic for this first indicator report.

Information about the contribution of education to productivity is important to

the Department of Education, since our mission includes equal access to education and the promotion of educational excellence throughout the nation.

Economic productivity depends on human as well as physical capital. Workers

can accumulate human capital, and thereby increase their productivity, by

advancing their skills through education and training. Worker productivity is a

critical determinant of workers’ pay and living standards; hence, high quality

education, by enhancing worker productivity, is a key contributor to the economic success of workers and the population as a whole. Any deficiencies in the

United States educational system could harm entrants to the labor force by jeopardizing the productivity of these entrants. 

In this report, we examine the link between education and economic productivity from different angles using a variety of data sources. First, we present indicators related to historical trends in worker productivity in the United States and

other countries and the contribution of education to these trends. We also consider the link between education and productivity at the individual level, focusing on the economic consequences of educational attainment, educational

achievement, and adult literacy. Since accumulation of human capital does not

end with formal schooling, we also explore the link between worker training and

productivity. Finally, we compare key measures of educational performance in

the United States with corresponding measures in other countries.

Commissioner’s Statement iii
The indicators in this report come from many data sources, including NCES as

well as other federal and international statistical agencies. In addition, we examine the existing research on the indicators and related issues, presenting arguments and conclusions from researchers on all sides of the issues. Our hope is that

the report will inform the public about the role of education in determining the

economic well-being both of the nation and of individuals, and that the report

will serve as a model for future indicator reports recommended by the indicators

panel.

Pascal D. Forgione, Jr.

Commissioner

March 1997

iv Education and the Economy
ACKNOWLEDGMENTS

The authors would like to thank Jeanne Griffith, John Ralph, Nabeel Alsalam,

Mary Frase, and Thomas Smith of the National Center for Education Statistics

(NCES), U.S. Department of Education, for their valuable guidance and advice

during the study. James Houser of NCES managed the production of this report

as it neared completion. We also received helpful comments from Kenneth

Voytek, Andrew Kolstad, Steve Broughman, Paul Barton, John Bishop, Ed Wolff,

Audrey Pendleton, Dave Boesel, Sue Berryman, George Psacharopoulos, and Jim

Stedman. At Mathematica Policy Research, Daryl Hall edited the report; Deborah

Patterson, Michelle Taylor, Connie Blango, and Melanie Lynch produced the

report; and Robert Howard and Michael Stavrianos helped collect the data for the

report.

The authors also wish to thank the Production group at MPR Associates, Inc. for

their help in producing the final report. Leslie Retallick designed and composed

the report and Mary Sukkestad and Don Eike developed the graphics. Barbara

Kridl managed the project and, with Karyn Madden, proofread the final copy.

Acknowledgments v
EXECUTIVE SUMMARY

The productivity of the U.S. work force is a primary determinant of the standard

of living of the U.S. population. Worker productivity is typically measured as output per worker or per hour worked. It is affected by many factors, including the

education and skills of the work force. Education and skills are important because

they expand a worker’s capacity to perform tasks or to use productive technologies. In addition, better educated workers can adapt more easily to new tasks or

to changes in old tasks. Education may also prepare workers to work more effectively in teams because it enhances their ability to communicate with and understand their co-workers.

Much of the recent concern about the productivity of U.S. workers has been

prompted by uncertainty about the ability of domestic firms and workers to compete in an increasingly international marketplace. As growth in U.S. productivity

has slowed over the past two decades and other countries achieve productivity

levels similar to those in the United States, concern about the competitiveness of

U.S. firms and workers has increased. Some attribute the loss of the nation’s productivity advantage to what they claim is the limited ability of the U.S. educational system to provide students with the skills necessary to succeed in today’s

labor market. However, factors other than education also affect productivity, and

these must be considered when comparing productivity trends across countries.

Variation in the quality and quantity of education across countries is only one

factor contributing to differences in worker productivity; capital investment,

technical innovation, foreign trade, and government regulation can also affect

productivity. Nevertheless, education remains an important contributor to productivity growth and has a major influence on the standard of living. This essay

highlights several measures of productivity and education, and addresses the

link between these two sets of measures. A better understanding of the relationship between worker productivity and the condition of education is essential to

understand how investment in education contributes to the U.S. economy.

Executive Summary vii
TRENDS IN WORKER PRODUCTIVITY AND THE CONTRIBUTION

OF EDUCATION

■ Worker productivity in the United States has increased almost continuously since

the end of World War II, but growth has slowed since 1973.

Worker productivity in the United

States has grown almost continuously

since the end of World War II, rising to

a level in 1994 that is approximately

three times that of 1947 (figure A). Postwar growth in productivity was slower

after 1973 than it was before 1973. From

1947 through 1973, output per hour

increased by nearly 3 percent per year,

compared to slightly more than 1 percent per year from 1973 through 1994. It

is unclear whether the slowdown in

productivity growth since 1973 merely

reflects fluctuation around the longterm growth rate, which is equal to

about 2 percent, or whether it signals

slower long-term growth.

■ Since World War II, worker productivity has grown more slowly in the

United States than in other industrialized

countries.

For several decades, productivity in other industrialized countries has been gradually catching up to that in the United States (figure B). However, the United States

remained the leader as of 1990, with a gross domestic product (GDP) per worker

that was slightly higher than that in Canada, and about 25 percent higher than that

in Italy, the country with the third highest GDP per worker.

According to one theory of productivity growth, referred to as the convergence

hypothesis,1 it is to be expected that productivity in lagging countries will converge

on that of the United States because these countries can exploit technologies transferred from the United States, thereby closing the gap in worker productivity. This

“catching-up” process suggests that the United States is inevitably at risk of losing

viii Education and the Economy

0

20

40

60

80

100

120

140

’47 ’54 ’62 ’70 ’78 ’86 ’94

Year

Output index

(1977=100)

1947–73 Trend

Output per hour

Figure A

Index of real output per hour of all persons,

business sector: 1947–94

NOTE: Figures for years after 1988 were originally based on 1982=100. They

were multiplied by a factor of 1.013 for use in the 1977=100 index. Hours of all

persons include hours of employees, proprietors, and unpaid family workers.

Output is the constant-dollar market value of final goods and services produced.

For the business sector, the index relates to gross domestic product (GDP) less

general government, output of nonprofit institutions, output of paid employees

of private households, and rental value of owner-occupied dwellings. Business output

was about 78 percent of GDP in 1992.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, Handbook of

Labor Statistics, Washington, DC: U.S. Government Printing Office, 1989; Monthly

Labor Review 18 (8) (August 1995): 175.

1Moses Abramovitz, “Catching Up, Forging Ahead, and Falling Behind,” Journal of Economic History

46 (June 1986): 385–406.

See

page

4
its lead in worker productivity as long as

other countries have the capabilities, including an adequately educated work force,

to exploit new productive technologies.

The ability of the United States to have

maintained a substantial lead in productivity for nearly a century is at least partly

attributable to the two world wars, which

destroyed the productive capacity of other

countries while spurring technological

innovation in U.S. manufacturing. However, the huge productivity advantage of

the United States has dissipated under the

more normal post-war economic conditions, which have allowed other countries

to rebuild their productive capacities and

expand their technological capabilities. It

now appears that the other industrialized

countries may eventually share the lead in

productivity with the United States.

But insofar as the “catching-up” process involves the transfer of technology from the

leader country to the lagging countries, the process should eventually slow down as

the lagging countries exhaust their opportunities to exploit new technologies from

the leader. Eventually, the countries sharing the lead in productivity would presumably be in a position to exploit technological advances from each other.

■ Growth in education has historically been an important source of growth in

worker productivity.

Increases in educational attainment were responsible for an estimated 11 to 20

percent of growth in worker productivity in the United States in recent decades.2

Growth in factors other than education have also contributed to growth in worker productivity. For example, increases in capital accounted for an estimated 40

percent of growth in worker productivity in the United States from 1948 to 1990.3

Executive Summary ix

Figure B

Real GDP per worker in G–7 nations: 1950–90

 (in thousands of dollars based on 1985 international prices)

$0

$10

$20

$30

$40

Real GDP

per worker

’50 ’54 ’58 ’62 ’66 ’70 ’74 ’78 ’82 ’86 ’90

Canada

U.K.

Italy

Japan

U.S.

France West

Germany

Year

SOURCE: Penn World Table (Mark 5.6), distributed by the National Bureau of

Economic Research. For a description, see Robert Summers and Alan

Heston, “The Penn World Table (Mark 5): An Expanded Set of International

Comparisons, 1950–1988,” Quarterly Journal of Economics (May 1991):

327–368.

2U.S. Department of Labor, Bureau of Labor Statistics, Labor Composition and U.S. Productivity Growth,

1948–90, Bulletin 2426 (Washington, D.C.: U.S. Government Printing Office, 1993); Dale W. Jorgenson,

“The Contribution of Education to U.S. Economic Growth, 1948–73,” in E. Dean, ed., Education and

Economic Productivity (Cambridge, MA: Ballinger, 1984); Edward F. Denison, Accounting for Slower

Economic Growth: The United States in the 1970s(Washington, D.C.: Brookings Institute, 1979); and JongIl Kim and Lawrence Lau, “Human Capital and Aggregate Productivity: Some Empirical Evidence

from the Group-of-Five Countries,” unpublished paper (Palo Alto, CA: Stanford University, 1992).

3U.S. Department of Labor, Bureau of Labor Statistics, Labor Composition and U.S. Productivity

Growth, 1948–90, Bulletin 2426 (Washington, D.C.: U.S. Government Printing Office, 1993).

See

page

7
In addition to capital, such factors as technical innovation, foreign trade, and government regulation can also affect productivity.

The historical contributions of these factors affecting productivity are not necessarily indicative of the relative returns to potential investments in the factors.

They simply reflect the linking of the relative growth rates of each factor over the

past several years to the productivity growth rate. The growth accounting methods used to determine these sources of productivity growth cannot be used to

identify future preferred input investments.

Education appears to play an important role in worker productivity in all industrialized countries. The industrialized countries with the highest productivity

levels tend to have highly educated work forces, and the convergence in productivity among these countries generally parallels that in educational attainment.

THE ECONOMIC CONSEQUENCES OF EDUCATION FOR

INDIVIDUALS

Ultimately, growth in a nation’s productivity results from growth in the productivity of individual workers. The best available measure of a worker’s productivity is

that worker’s wages, as employers generally pay wages equal to the marginal productivity of their workers. The impact of education on the productivity of workers

can be determined by estimating the impact of education on wages.

Education may also improve workers’ employment stability, enabling more educated workers to maintain their jobs or to quickly find new jobs in the face of

changing economic conditions. Therefore, the association between education and

unemployment can be a further indication of the effect of education on the productivity of workers. 

Educational Attainment

■ Workers with higher educational attainment are unemployed less and earn more

than workers with lower educational attainment.

Over the past 30 years, a substantial proportion of high school graduates and dropouts

were unemployed shortly after leaving high school, with dropouts generally facing a

higher unemployment rate than graduates (figure C). In 1994, 30 percent of recent

dropouts were unemployed, compared to 20 percent of recent graduates not enrolled

in college. The unemployment rates for both groups have increased since 1960.

x Education and the Economy
Median earnings are positively associated with educational attainment (figure

D). Among males ages 25–34 years in

1993, median earnings of those with a

college degree were equal to about

$33,000 per year, which was more than

50 percent greater than the median earnings of high school graduates and more

than twice those of high school

dropouts. The relationship between

education and earnings for females is

similar, although within each educational category, earnings are lower for

females than for males.

■ Educational attainment in the United

States has increased over the past 20

years. 

The proportion of 18- to 24-year-olds

who have completed high school has

increased slowly, rising from approximately 83 percent in 1972 to about 86

Executive Summary xi

Figure C

Unemployment rate of recent high school graduates

and dropouts not enrolled in college: 1960–94

NOTE: Recent high school graduates include individuals ages 16–24

years who graduated during the survey year and were not enrolled in

college. Recent high school dropouts include individuals ages 16–24

years who did not graduate and who were in school 12 months earlier,

but who were not enrolled in the survey month.

SOURCE: U.S. Department of Education, National Center for Education

Statistics, The Condition of Education, 1995, and special tabulations;

U.S. Department of Commerce, Bureau of the Census, October Current

Population Surveys.

High school graduates

Dropouts

trend

line

Year

Unemployment rate (percent)

trend

line

’60 ’66 ’70 ’74 ’78 ’82 ’86 ’90 ’94

0

10

20

30

40

50

Figure D

Earnings for all wage and salary earners ages 25–34 years,

by sex and educational attainment: 1993

Median annual earnings

(thousands of 1994 dollars)

SOURCE: U.S. Department of Education, National Center for Education Statistics, The Condition of Education, 1995; U.S.

Department of Commerce, Bureau of the Census, March Current Population Survey, 1994.

Grades 9

to 11

High school

diploma only

Bachelor’s degree

or more Somecollege

Highest level of education completed

$0

$5

$10

$15

$20

$25

$30

$35

14.0

20.9

23.4

32.7

7.7

13.1

17.2

26.0

Male Female

See

page

18

See

page

16
percent in 1994 (figure E). An increasing

number of students who have completed

high school also move on to college.

Among recent high school graduates, the

college enrollment rate increased from 49

percent in 1972 to 62 percent in 1994.

However, many students who enroll in

college do not complete four years there.

The completion rate of 27 percent in 1994

was only slightly higher than the rate of

20 years before.

■ Although the rate of college completion in the United States still far exceeds

that in most other countries, educational

attainment generally is increasing more

slowly in the United States than in other

industrialized countries.

In each of the G–7 countries, the rate of

secondary school completion is higher

among 25- to 34-year-olds than among

25- to 64-year-olds, indicating that the

rate of secondary school completion is

increasing in these countries (figure F). Moreover, the gap between the completion rates of younger and older workers is larger in other G–7 countries than in

the United States, suggesting that secondary school attainment is increasing at a

faster rate in the other countries. The high school completion rates for young

adults in Japan and Germany are now comparable to those of young adults in the

United States, while the rates for young adults in Canada and the United

Kingdom are approaching those of their counterparts in the United States.

Most G–7 countries still lag well behind the United States in higher education

attainment (figure G). The proportion of the population ages 25–64 years who

have completed a college education is by far the highest in the United States.

Although the U.S. lead is smaller for adults ages 25–34 years, only Japan has a

rate of higher education attainment among young adults comparable to that in

the United States. The rate of college completion among young American

adults has risen slightly during the past 20 years, while the rate for young

adults in Japan has risen dramatically; thus, Japan has nearly caught up to the

United States. The rate of higher education attainment in most other G–7 countries has increased more slowly than that in Japan, as indicated by the smaller

attainment gaps between younger and older adults in those countries.

xii Education and the Economy

SOURCE: U.S. Department of Education, National Center for Education

Statistics, Dropout Rates in the United States: 1994; U.S. Department of

Education, National Center for Education Statistics, The Condition of

Education, 1995 and The Condition of Education, 1996; U.S. Department

of Commerce, Bureau of the Census, October and March Current

Population Surveys.

0

20

40

60

80

100

’72 ’75 ’79 ’83 ’87 ’91 ’94

Percent

Year

Completion of four or more years of college among

high school graduates 25 to 29 years old

High school completion rates

among 18- to 24-year-olds

College enrollment among

recent high school graduates

Figure E

High school completion, college enrollment,

and college completion rates: 1972–94

See

pages

26–

28
Executive Summary xiii

Figure G

Completion of higher education, by age: 1992

Percent

NOTE: In the United States, completing higher education is defined as earning a bachelor’s degree.

SOURCE: U.S. Department of Education, National Center for Education Statistics, The Condition of Education, 1995; Organization for Economic Cooperation and Development, Indicators of Education’s Systems, Digest of

International Education Statistics, forthcoming.

0

5

10

15

20

25

30

0

20

40

60

80

100

United

States

Japan Germany United

Kingdom

France Italy Canada

86.5

90.6 88.6

80.9

67.1

42.4

80.8

84.0

69.7

81.9

68.1

52.2

28.4

71.3

Figure F

Secondary school completion, by age: 1992

Percent

NOTE: In the United States, completing secondary school is defined as graduating from high school or earning a GED.

SOURCE: U.S. Department of Education, National Center for Education Statistics, The Condition of Education, 1995; Organization for Economic Cooperation and Development, Indicators of Education’s Systems, Digest of

International Education Statistics, forthcoming.

Ages 25–64 Ages 25–34

United

States

Japan Germany United

Kingdom

France Italy Canada

Ages 25–64 Ages 25–34

23.6

13.3

11.6 10.7 10.2

6.4

15.0

23.2 22.9

11.8 12.5 12.3

6.8

16.1

See

page

83

See

page

84
Educational Achievement

■ Workers who have a record of high academic achievement, as measured by

achievement test scores, are unemployed less and earn more than workers with

lower scores.

xiv Education and the Economy

Figure H

Unemployment rate of civilian workers age 28 years,

by age-adjusted ASVAB score quartile: 1985–93

NOTE: ASVAB is the Armed Services Vocational Aptitude Battery. To control for differences in age at testing, individuals were

assigned to age-specific performance quartiles for each subject area based on their age at testing. Respondents who were

out of the labor force were excluded from the sample.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, National Longitudinal Survey of Youth, 1985–93.

0

3

6

9

15

12

Percent

Top

Quartile

Second

Quartile

Third

Quartile

Bottom

Quartile

Mathematics Science Paragraph comprehension

2.9

4.8

11.5

6.7

3.6

6.4

4.1

Figure I

Mean hourly rate of pay for civilian workers age 28 years,

by age-adjusted ASVAB score quartile: 1985–93

NOTE: ASVAB is the Armed Services Vocational Aptitude Battery. Respondents reporting hourly pay of less than $1.00 or

greater than $100.00 (1992 dollars) at any given age were excluded from the sample for that age. To control for differences

in age at testing, individuals were assigned to age-specific performance quartiles for each subject based on their age at

testing.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, National Longitudinal Survey of Youth, 1985–93.

13.50

9.53

13.10

9.95

12.43

11.13

8.76

11.46

8.62

11.41

8.89

10.37

Top

Quartile

Second

Quartile

Third

Quartile

Bottom

Quartile

1992 dollars

$0

$3

$6

$9

$15

$12

$18

Mathematics Science Paragraph comprehension

11.5 11.5

3.6

6.3

4.2

See

page

35

See

page

38
Workers who are 28 years old and who have previously scored in the top quartile

on the Armed Services Vocational Aptitude Battery (ASVAB) mathematics, science,

or paragraph comprehension tests have a lower unemployment rate than other

workers (figure H). For example, 2.9 percent of workers in the top quartile of the

mathematics test are unemployed, compared to 7.5 percent of workers in the other

three quartiles combined. Workers in the top quartile on the tests in each subject also

earn more, on average, than other workers (figure I). For example, workers in the top

quartile on the mathematics test earn an average of $13.50 per hour, compared to an

average of $9.84 per hour for workers in the other three quartiles combined.

■ Test scores of U.S. students generally increased in the 1980s and

1990s, offsetting declines that

occurred during the 1970s.

Among 17-year-old students, National Assessment of Educational

Progress (NAEP) test scores increased from 1982 through 1992

(figure J). Increases in mathematics

and science scores reversed a trend

of declining scores that existed

throughout the 1970s. By 1992, the

scores in these subjects had recovered to the 1973 levels. NAEP

reading scores of 17-year-old students have increased slowly and

steadily since the early 1970s. 

■ U.S. students trail students from

many other countries in mathematics and science achievement, but

U.S. students tend to lead in reading

achievement.

Most of the countries included in a 1991 international study of mathematics and

science achievement outperformed the United States in the mathematics achievement

of both 9-year-old and 13-year-old students (table A). With respect to science achievement, 9-year-old U.S. students performed as well as those in most other countries, but

13-year-old U.S. students scored below their counterparts in half of the other countries.

In a separate international study of reading achievement, the United States led 20 of

22 countries in reading scores for 9-year-olds and was equivalent to or led 21 of 22

countries for 14-year-olds.

Executive Summary xv

Scale score

Year

’69 ’72 ’76 ’80 ’84 ’88 ’92

’69 ’72 ’76 ’80 ’84 ’88 ’92

’69 ’72 ’76 ’80 ’84 ’88 ’92

Reading

Science

Mathematics

280

290

300

310

280

290

300

310

280

290

300

310

Figure J

Trends in average U.S. achievement of 17-year-olds in science,

mathematics, and reading: 1969–92

SOURCE: U.S. Department of Education, National Center for Education Statistics, The National

Assessment of Educational Progress: 1992 Trends in Academic Progress, 1994.

See

page

43
Adult Literacy

A 1992 study tested the performance of U.S. adults on three scales of literacy—

prose, document, and quantitative—and categorized adults into five literacy

levels according to their test scores, with level 1 being the lowest literacy level

and level 5 being the highest.

■ Workers with higher literacy scores are unemployed less and earn more than

workers with lower literacy scores.

Unemployment rates are especially high for workers in the two lowest levels

of literacy—levels 1 and 2—on each of the three literacy scales (figure K). For

these workers, the unemployment rate ranges from 12 percent for workers

with level 2 quantitative literacy to nearly 20 percent for those with level 1.

Unemployment rates for individuals in the two highest literacy levels—levels

4 and 5—are less than 6 percent.

Workers with high literacy scores earn more than other workers, on average

(figure L). On the prose scale, for example, full-time workers in level 3 earn a

mean weekly wage that is 50 percent higher than that of their counterparts in

level 1. Those in level 5 earn a weekly wage that is 71 percent higher than the

wage of those in level 3.

xvi Education and the Economy

Table A 

International distribution of academic achievement relative 

to the United States: 1991–92

Number of countries performing:

Not Number of

Significantly significantly Significantly countries

Subject higher than different lower than in the

and age the U.S. from the U.S. the U.S. study

MATHEMATICS

9-year-olds 7 2 0 10

13-year-olds 12 1 1 15

SCIENCE

9-year-olds 0 7 2 10

13-year-olds 7 6 1 15

READING

9-year-olds 1 1 20 23

14-year-olds 1 14 7 23

SOURCE: U.S. Department of Education, National Center for Education Statistics, The Condition of

Education, 1993; Warwick B. Elley, How in the World Do Students Read?, International Association for the

Evaluation of Educational Achievement, Study of Reading Literacy, 1992; and Educational Testing Service,

International Assessment of Educational Progress, 1992.

See

page

88
Executive Summary xvii

Figure K

Unemployment of adult labor force participants,

by proficiency level on three literacy scales: 1992

Prose Document Quantitative

0

5

10

15

20

25

Percent

unemployed

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National Adult Literacy Survey, forthcoming;

U.S. Department of Education, National Center for Education Statistics, National Adult Literacy Survey, 1992.

19.9

8.8

12.5

4.9

2.7

16.9

7.9

15.4

5.6

4.2

17.1

8.4

12.9

5.0 5.2

Level 1 Level 2 Level 3 Level 4 Level 5

Figure L

Mean weekly earnings of full-time workers,

by proficiency level on three literacy scales: 1992

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National Adult Literacy Survey, forthcoming; U.S.

Department of Education, National Center for Education Statistics, National Adult Literacy Survey, 1992.

Mean weekly

earnings

355 355 330

436 458 438

531 553 533

709 710 684

910

807

913

Prose Document Quantitative

$0

$200

$400

$600

$800

$1,000

Level 1 Level 2 Level 3 Level 4 Level 5

See

page

50

See

page

52
■ The literacy proficiency of a substantial proportion of the U.S. labor force is

limited, and only a small proportion of workers perform at a high literacy level. 

Forty percent or more of the adult labor force perform at the two lowest levels on

each of the literacy scales, suggesting that many workers lack the skills needed to

interpret, integrate, and compare or contrast information using written materials

common to the home or workplace (figure M). These workers appear to be unable

to perform the types of tasks typical of certain occupations that demand high

skills, such as professional, managerial, technical, high-level sales, skilled clerical,

or craft and precision production occupations. Five percent or fewer of U.S. labor

force participants score in the highest proficiency levels, demonstrating an ability to

perform well on a wide array of literacy tasks.

■ Literacy of the U.S. adult population is, on average, roughly similar to that of

populations in other industrialized countries, but the United States has a greater

proportion of adults at the lowest literacy levels.

On average, the proportion of the U.S. population in the highest literacy levels is

similar to that in the other countries included in an international study of adult

literacy (figure N). However, the United States has a higher concentration of

adults in the lowest literacy level than nearly all of the other countries. More than

20 percent of the U.S. sample scored at the lowest literacy level on each of the

three literacy scales, while the other countries (except Poland) had less than 20

percent of the sampled population scoring at the lowest level on each scale.

xviii Education and the Economy

Figure M

Percentage of labor force in each proficiency level on the three literacy scales: 1992

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National Adult Literacy Survey, forthcoming;

U.S. Department of Education, National Center for Education Statistics, National Adult Literacy Survey, 1992.

Percent

0

20

30

40

50

10

25.3

14.4

35.2

20.8

4.2

27.2

15.8

34.6

19.0

3.4

24.5

15.2

34.5

20.8

5.0

Prose Document Quantitative

Level 1 Level 2 Level 3 Level 4 Level 5

See

page

55
Executive Summary xix

SOURCE: Organization for Economic Cooperation and Development and Statistics Canada, Literacy, Economy and Society:

Results of the First International Adult Literacy Survey, 1995.

Figure N

Estimated percentage of the population in each proficiency level on

three adult literacy scales, by selected countries: 1994

Percent

QUANTITATIVE LITERACY

0 20 40 60 80 100

Switzerland (German)

Switzerland (French)

Sweden

Poland

Netherlands

Germany

Canada

United States

DOCUMENT LITERACY

0 20 40 60 80 100

Switzerland (German)

Switzerland (French)

Sweden

Poland

Netherlands

Germany

Canada

United States

PROSE LITERACY

0 20 40 60 80 100

Switzerland (German)

Switzerland (French)

Sweden

Poland

Netherlands

Germany

Canada

United States

Level 1 Level 2 Level 3 Level 4/5

Percent

Percent

See

page

92
TRAINING OF LABOR FORCE PARTICIPANTS

Workers usually complete their formal education before joining the labor force,

but investment in human capital does not necessarily end at that time. Through

training, many workers continue to improve their skills throughout their lives.

■ Workers who have participated in training while at their current job earn more

than workers who have not participated in training.

Within broad categories of educational attainment, median earnings

in 1991 were higher for workers

who participated in training to

improve their skills while at their

current job than for those workers

who did not (figure O). Median

weekly earnings were higher for

trainees than for nontrainees in each

of the educational attainment categories. Studies that examine direct

measures of productivity confirm

that formal training also has a positive effect on productivity.

■ Training participation has increased in recent years, and it is most prevalent

among more highly educated workers and workers in highly skilled occupations.

An estimated 41 percent of the U.S. work force in 1991 had received skill improvement training on their current job, up from 35 percent in 1983 (figure P). Training

is positively associated with education—61 percent of workers with a college

degree in 1991 had participated in training on their current job, compared with 29

percent of workers with a high school degree or less and 46 percent of workers

with some college. Training also appears to be more common among workers in

highly skilled occupations, including managerial, professional, and technical

workers. Workers in these occupations in 1991 had training rates of more than 50

percent. In contrast, no other occupation had a training rate of more than 40 percent at that time.

xx Education and the Economy

Figure O

Median weekly earnings of full-time workers ages 16 years and older,

by training status and educational attainment: January 1991

NOTE: Includes only workers who needed no qualifying training for their current job.

SOURCE: Alan Eck, “Job-Related Education and Training: Their Impact on Earnings,” Monthly

Labor Review (October 1993): 21–38; U.S. Department of Commerce, Bureau of the Census,

Current Population Survey, January 1991.

$0 $200 $400 $600 $800

263

314

353

461

601

471

396

333 Workers who participated in training since

start of job

Workers who did not

participate in training

since start of job

Fewer than

12 years

High school

graduate

1 to 3 years

of college

College

graduate

Highest education

level completed

Median weekly earnings

See

page

70
Executive Summary xxi

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, How Workers Get Their Training: A 1991 Update, 1992;

U.S. Department of Commerce, Bureau of the Census, Current Population Survey, January 1983 and 1991.

Figure P

All workers ages 16 years and older who participated in skill improvement training while on

their current jobs, by education and occupation: 1983 and 1991

(percentage of workers in each category)

0 20 40 60 80 100

OCCUPATION

HIGHEST GRADE COMPLETED

College graduate

Some college

High school or less

All workers

Handlers, cleaners,

and laborers

Transportation and

material moving

Precision production,

craft, and repair

Farming, forestry,

and fishing

Service

Private household

Administrative

 support

Sales

Technical

Professional

Executive,

administrative, managerial

41

35

6

29

21

38

25

15

59

67

53

46

61

35

40

29

25 Machine operators,

assemblers, and inspectors

1983

1991

Percent

See

pages

75–

76
SUMMARY

Workers in the United States are still more productive, on average, than workers

in any other country. However, worker productivity in several industrialized

countries is gradually catching up to that in the United States, and eventually

the United States is likely to share the lead in worker productivity. This convergence in productivity is attributable, in part, to the rapid expansion of education in other countries. The education of the work force, according to at least

some measures that contribute to economic success, is growing more rapidly in

other countries than in the United States. But education is not the only determinant of worker productivity, and other factors no doubt have also played

important roles in the rapid productivity growth in other countries.

Although the United States leads almost every other industrialized country in

college attainment, and the academic achievement of U.S. students has been

improving in recent years, U.S. students still tend to lag behind students in other

countries with respect to some measures of achievement. In particular, the mathematics and science scores of U.S. students, especially older students, are lower

than those of their counterparts in other industrialized countries. U.S. students

do, however, perform relatively well on reading tests. Adults in the United States

may not be as skilled in some areas as their counterparts in other countries.

Compared to other countries that have tested literacy, the United States has a

higher concentration of adults who score at the lowest literacy levels.

xxii Education and the Economy
CHAPTER 1

Introduction

Policymakers are concerned about the ability of American businesses and workers to compete in an increasingly international marketplace. Some observers

assert that the growth in worker productivity in the United States has slowed

significantly in the past two decades and that workers in other countries are

achieving productivity levels similar to or greater than those of American workers. They attribute this alleged loss of competitiveness to deficiencies in the U.S.

educational system in providing students with the skills necessary for success in

the modern labor market. Such deficiencies in education, they argue, would

harm entrants to the labor force by jeopardizing their ability to attain a relatively high standard of living. Employers also would suffer, as they would face

shortages of skilled labor, which would jeopardize their ability to sustain competitive productivity levels.

This report presents a series of indicators related to productivity and education.

The objective in developing these indicators is to shed some light on the debate

about worker productivity in the United States and its relationship with the current condition of education. The indicators provide information at both the

national and individual levels. At the national level, we examine labor force

trends in productivity, earnings, and education, and compare the trends in the

United States with trends in other countries. At the individual level, we examine

the link between education and individual labor market outcomes, including

employment, productivity, and earnings. In addition to the indicators, we discuss issues related to them. Our objective is to further interpret the indicators

and to evaluate the research findings related to them. Throughout this report,

the discussions are presented as separate sections that follow the indicator(s) to

which they correspond. The placement of the discussion sections depends on the

issues addressed in the indicators. Some indicators are followed by more than

one discussion section, while other indicators are followed by no discussion section if none of the discussion sections is related to those indicators. The data used

to create the figures presented in this report are available from the NCES World

Wide Web site (http://www.ed.gov/NCES/) or by request from NCES (see the

back of the title page for specific contact information).

The fundamental limitation in any study of worker productivity is that productivity is rarely observed directly. Measuring the link between education and worker

productivity, either at the national level or at the individual level, usually depends

Introduction 1
on using earnings to represent productivity. This approach is based on economic

theory, which asserts that employers pay workers a wage that is equal to their

marginal productivity. Many researchers have criticized the use of earnings to

represent productivity, arguing that, for a variety of reasons, workers may not

necessarily be paid an amount that reflects their marginal productivity at a given

time. If this is true, the estimated relationship between education and wages would

not accurately represent the relationship between education and productivity.

Like productivity, education can also be difficult to measure. Recognizing the

limitations of any single measure of education, this report explores the link

between economic productivity and three different measures of education: educational attainment, scores on academic achievement tests, and scores on adult

literacy tests. Not only can attainment approximate the knowledge or skills that

a person brings to the work force, but the academic degrees that reflect attainment may serve as requisite credentials for particular jobs. Consequently, there

is reason to expect a relationship between a worker’s educational attainment and

the wage that he or she is paid. However, it cannot necessarily be assumed that

attainment is an accurate indicator of the knowledge or skills that are brought to

a job. Workers with similar educational attainment may be paid different wages

because they have different skills or knowledge. 

Given this possibility, the analysis is extended to a second type of education

measure: scores on academic achievement tests. Admittedly, the use of test

scores to measure achievement is not ideal because of the limitations of assessment technology as well as the uncertainty associated with the relationship

between academic achievement and workplace skills. Nevertheless, test scores

do provide some indication of educational success enjoyed by individuals, both

in general and in specific subjects. Finally, recent research suggests that literacy

in reading, writing, and basic quantitative and computing skills may be among

the most important qualities that an individual brings to the workplace. This

report uses scores on tests of adult literacy as a third measure of education.

The limitations in measuring productivity and education, together with other limitations that are discussed in this report, dictate that the indicators cannot be used to

draw definitive conclusions about the competitiveness of firms hiring U.S. workers

and the link between productivity and education. Rather, the indicators represent an

initial effort to gather information about a broad set of outcomes and trends that are

related to the ways in which education translates into worker productivity. One goal

of this report is to demonstrate how the available data can be used to inform these

issues and to show how additional data may be useful for future efforts to study

these issues.

2 Education and the Economy
CHAPTER 2

Worker Productivity and Education

Worker productivity is affected by many factors, including the education and

skill level of the work force. Education and skills are important because they

expand a worker’s capacity to perform a task or to use productive technologies.

More educated workers are also usually better able to adapt to new tasks or to

changes in their old tasks. Furthermore, because education enhances workers’

ability to communicate with and understand their co-workers, it may prepare

people to work in teams more effectively.

Some observers fear that the American educational system has deteriorated in

comparison with the educational systems in other countries, and that this deterioration may soon cause the productivity of U.S. workers to lag behind that of

workers in other countries. These observers have agreed that lagging productivity jeopardizes the nation’s competitiveness in international markets and

would eventually translate into a lower standard of living relative to other

countries. But others argue that the relative economic performance and standards of living should not be the sole focus of studies of economic well-being in

the United States. Although economic trends outside the United States can be

used as a benchmark for gauging U.S. progress, continued and substantial

improvements in U.S. productivity and standard of living can be maintained

regardless of our position compared with other countries. This point does not,

however, discount the importance of education. If educational deterioration

causes productivity to slow or even to decline, it would have a negative impact

on our standard of living.

This chapter begins our investigation of education and worker productivity by

examining recent trends in U.S. worker productivity. We extend this analysis of

productivity by comparing it with the productivity in other industrialized

countries and examining the extent to which American economic leadership is

threatened by these other countries.

Worker Productivity and Education 3
INDICATOR 1

Trends in U.S. Worker Productivity

Research on the productivity of U.S. workers has focused on trends in the growth

of productivity in the post-World War II period. Worker productivity is typically

measured by dividing output by the number of workers or the number of hours

worked. Figure 2.1 shows the postwar trend in worker productivity as measured

by business sector output per hour worked. Output per hour has increased nearly continuously over the postwar period. Decreases were generally confined to

single-year fluctuations. Output per hour in 1994 was about three times the output per hour in 1947. The average annual rate of productivity growth from 1947

through 1994 was 2.1 percent. 

Concern about the trend in U.S. productivity is based primarily on the

lower rate of productivity growth

since 1973 as compared with the period from 1947 through 1973. It is clear

that the growth in output per hour

worked since 1973 has lagged behind

the 1947–73 trend, as shown in figure

2.1. From 1947 through 1973, output

per hour worked increased by an

average of nearly 3 percent per year,

compared with slightly more than 1

percent per year from 1973 through

1994. In recent years, slow productivity growth has especially been a

problem in nonmanufacturing sectors of the economy, which represent

an increasing share of total U.S.

employment.1 Because of the slowdown in labor productivity, growth

in worker compensation (earnings plus

4 Education and the Economy

0

20

40

60

80

100

120

140

’47 ’54 ’62 ’70 ’78 ’86 ’94

Year

Output index

(1977=100)

1947–73 Trend

Output per hour

Figure 2.1

Index of real output per hour of all persons,

business sector: 1947–94

NOTE: Figures for years after 1988 were originally based on 1982=100. They

were multiplied by a factor of 1.013 for use in the 1977=100 index. Hours of all

persons include hours of employees, priorietors, and unpaid family workers.

Output is the constant-dollar market value of final goods and services produced.

For the businesssector, the index relates to gross domestic produce (GDP) less

general government,output of nonprofit institutions, output of paid employees

of private housholds, andrental value of owner-occupied dwellings. Business output

was about 78 percent of GDP in 1992.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, Handbook of

Labor Statistics, Washington, DC: U.S. Government Printing Office, 1989; Monthly

Labor Review 18 (8) (August 1995): 175.

1Baily and Gordon (1988) show that output per hour of work increased by 2.52 percent per year in

manufacturing from 1973 through 1987, compared with an increase of only 0.25 percent per year in

nonmanufacturing. However, measuring productivity in nonmanufacturing can be difficult

because changes in the quality of goods and services can be difficult to track in this sector. Problems

in measuring productivity are discussed in detail later in this chapter.
benefits) has slowed by a similar magnitude (Bosworth and Perry 1994). Given

the strong connection between productivity and compensation, the productivity

slowdown has been described by Baily and Gordon (1988) as “America’s greatest

economic problem.” 

Despite a vast amount of research on trends in productivity, economists remain

perplexed about the nature of the post-1973 productivity slowdown. Various

researchers attribute the slowdown to sectoral shifts in the labor force, inadequate

accumulation of physical capital, inadequate work force training, or overemphasis on short-term goals in business management.2 However, none of the studies

has isolated the specific determinants of the post-1973 productivity slowdown.

Bishop (1989) argues that declines in education achievement, as measured by test

scores, play an important role in the slowdown of productivity growth since 1973.

On the basis of estimated returns to test scores and the historical trends in test

scores and economic productivity, Bishop claims that declines in test scores since

1967 reduced the contribution of education to productivity by 0.05 to 0.12 percentage points per year from 1973 through 1987.3 Although this estimated impact

appears to be small, Bishop argues that it translates into substantial social costs.

He sets the social cost in terms of foregone national product at $86 billion in 1987,

and he projects that it will double from 1987 through 2004.

Although low academic achievement may inhibit the growth in productivity, it

cannot account for the majority of the slowdown in U.S. productivity since 1973.

First, the decline in productivity growth occurred all at once—too quickly to be

attributed to slow-moving changes in work force quality. Second, the magnitude

of the slowdown is much larger than the impact of dropping test scores cited by

Bishop. Bishop’s estimate would explain less than 10 percent of the overall productivity slowdown. Third, as is shown later in this chapter, productivity grew

more slowly after 1973 in all industrialized countries, not just in the United States.

It would be difficult to believe that the quality of education declined simultaneously in all industrialized countries beginning in 1973. Finally, Bishop’s argument

applies exclusively to the cohort of students educated in the late 1960s and 1970s.

As is shown in chapter 4, the levels of achievement of U.S. students in the late

1980s were restored to the levels of the early 1970s. 

Worker Productivity and Education 5

2Baily, Burtless, and Litan (1993) discuss each of these possibilities.

3Bishop (1989) estimates the impact of academic achievement on individual productivity by estimating the relationship between earnings as a proxy for productivity and test scores as a proxy for

achievement. The achievement proxy is constructed based on the responses to the 13 questions from

the Lorge-Thorndike intelligence test, part of the Panel Study of Income Dynamics survey. Bishop

then measures trends in academic achievement over time on the basis of scores on the Iowa Test of

Educational Development. These trends are translated into changes in labor quality and linked to

productivity in a growth-accounting framework.
Another possible explanation for the productivity slowdown is that measurement

errors have caused observers to overestimate the magnitude of the slowdown.

Researchers have paid particular attention to the accuracy of the price indices that

are used in the calculation of real output. In the U.S. economy, there is a general

trend that shifts away from standardized commodities with easily definable

characteristics that change little over time toward goods and services for which

issues of quality are of primary importance. Some argue that the complexity in

defining quality as it pertains to modern goods and services makes it extremely

difficult to disentangle pure increases in the price paid for the same quality goods

from price increases that reflect changes in quality. If the trend in prices is mismeasured, trends in output and productivity will also be mismeasured. While

this argument is appealing, a detailed study (Baily and Gordon 1988) of the

empirical evidence suggests that errors in measuring output fail to explain the

majority of the observed post-1973 productivity slowdown.4

A final possible explanation for the slowdown is that the lower rate of growth in

productivity after 1973 may simply represent a return to the long-run trend in

productivity, and that the high growth rate from 1947 through 1973 was a historical aberration (Baumol, Blackman, and Wolff 1989). The annual growth rate in

output per hour for the entire period shown in figure 2.1, 1947–94, is approximately equal to the long-run productivity growth rate of 2 percent that has prevailed in the United States since 1870.5 While these findings do not guarantee that

the U.S. economy will return to and sustain 2 percent productivity growth in the

future, they still do not conclusively show that productivity in the United States

has already declined to a slower long-run growth rate. Rather, recent trends may

be attributable to a short-run variation around an unchanged long-run trend.6

6 Education and the Economy

4Baily and Gordon (1988) estimate that errors in measurement explain, at most, 0.5 percentage

points of the 1.5 point slowdown in productivity growth between 1948 and 1973 and 1973 and 1987.

The majority, 0.3 percentage points, of this errors-in-measurement estimate is attributed to declines

in the quality of labor, such as the decline in test scores documented by Bishop (1989), rather than

to previously overlooked or mismeasured increases in the quality of goods and services. However,

Baily and Gordon (1988) also present estimates from other studies, which suggest that the actual

contribution of the decline in labor quality may be smaller than 0.3 percentage points. 

5Maddison (1982) presents statistics showing that GDP per man-hour grew by an annual average

compound rate of 2.3 percent from 1870 through 1979.

6Darby (1984) supports the argument that the statistics do not provide evidence of a long-run

decline in productivity growth in the United States. Nordhaus (1982) points out that two similar

periods of stagnancy in U.S. productivity occurred in this century. He presents statistics showing

that U.S. productivity did not grow from 1901 through 1917 and grew slowly from 1924 through

1937. 
INDICATOR 2

Productivity Trends in Industrialized Countries

Alarm about the recent slowdown in productivity in the United States is driven by

the fear that other countries will surpass the United States in productivity, thereby

achieving a higher standard of living at the expense of U.S. workers. While the available evidence is unclear as to whether the post-1973 U.S. productivity slowdown

represents a long-term slowdown, it is clear that productivity in other countries is

catching up to that of the United States. Figure 2.2 shows real gross domestic product (GDP) per worker for the “group of seven” (G–7) industrialized countries. The

United States has clearly been the world leader in productivity for many years.

During the postwar period, however, the other industrialized countries are catching

up to the United States because they have increased productivity at a faster rate than

the United States. 

Despite the fact that other countries are gaining on the United States in productivity,

the United States is still the world leader in

productivity, and the trends do not necessarily signal a significant decline in U.S.

economic capabilities. As of 1990, the

United States was still the leader in productivity among the G–7 countries. GDP

per worker was slightly higher than in

Canada and about 25 percent higher than

in Italy (the country with the third highest

GDP per worker) (figure 2.2). Furthermore,

other countries are not positioned to surpass the United States in the next few

years; rather, they have been slowly catching up over many decades. Alternative

data on productivity presented in table 2.1

show that this phenomenon, which is not

new, actually began shortly after the end of

World War II as other countries experienced higher growth rates than the United States from 1950 through 1973.7

Worker Productivity and Education 7

Figure 2.2

Real GDP per worker in G–7 nations: 1950–90

 (in thousands of dollars based on

1985 international prices)

$0

$10

$20

$30

$40

Real GDP

per worker

’50 ’54 ’58 ’62 ’66 ’70 ’74 ’78 ’82 ’86 ’90

Canada

U.K.

Italy

Japan

U.S.

France West

Germany

Year

SOURCE: Penn World Table (Mark 5.6), distributed by the National Bureau of

Economic Research. For a description, see Robert Summers and Alan

Heston, “The Penn World Table (Mark 5): An Expanded Set of International

Comparisons, 1950–1988,” Quarterly Journal of Economics (May 1991):

327–368.

7The data presented in table 2.1 and figure 2.2 are from different sources and are based on different

productivity measures—productivity is measured as GDP per worker in figure 2.2 and GDP per

hour worked in table 2.1.
Table 2.1 

Growth in gross domestic product per hour worked 

(average annual growth rate)

I II III Acceleration Slowdown

1913–1950 1950–1973 1973–1984 from I to II from II to III

France 2.0 5.1 3.4 +3.1 -1.7

Germany 1.0 6.0 3.0 +5.0 -3.0

Japan 1.7 7.7 3.2 +6.0 -4.5

Netherlands 1.7 4.4 1.9 +2.7 -2.5

United Kingdom 1.6 3.2 2.4 +1.6 -0.8

United States 2.4 2.5 1.0 +0.1 -1.5

SOURCE: Angus Maddison, “Growth and Slowdown in Advanced Capitalist Economies: Techniques of

Quantitative Assessment,” Journal of Economic Literature (June 1987): 649–698.

The data also indicate that the pattern of growth in productivity that occurred in

the United States in the post-World War II period also occurred in the other

industrialized countries. As shown in table 2.1, productivity grew at an accelerated rate in these countries from 1950 through 1973, compared with the period

from 1913 through 1950. The slowdown in the growth of productivity that

occurred in the United States in the early 1970s appears also to have occurred in

the other industrialized countries. The percentage-point magnitude of the decline

was largest in Japan (4.5 percentage points) and smallest in the United Kingdom

(0.8 percentage points). The decline in productivity in the United States (1.5 percentage points) was between these two extremes.

The productivity trends in figure 2.2 and table 2.1 appear to be consistent with the

economic hypothesis that productivity levels in countries with broadly similar

labor resources will converge over time.8 When the productivity of one country

is superior to that of a number of other countries, largely as a result of differences

in technical knowledge, the “follower” countries can catch up to the leader by

acquiring new technical knowledge from the leader. Productivity converges

because countries eventually learn these new productive techniques through

trade, technology transfer, and their own research and development efforts.

Figure 2.3, which shows that the coefficient of variation in productivity in the G–7

countries has declined steadily since 1950, demonstrates that productivity in

these countries is, in fact, converging.9

8 Education and the Economy

8Baumol, Blackman, and Wolff (1989) provide a detailed description and analysis of this hypothesis.

9The coefficient of variation is equal to the standard deviation of productivity divided by the mean.
According to the convergence hypothesis, followers can eventually turn their

technological disadvantage into faster

growth. Although all countries benefit

from technology transfer, the follower

countries have more to learn from the

leader than the leader has to learn from

them. For the follower countries, this

greater learning opportunity speeds up

the increases in productivity, and these

“stepped-up” increases cause the followers to begin to catch up to the

leader. The process continues as long as

the follower countries have a lot to learn

from the leader. As the gap in technical

knowledge between the countries narrows, the relative growth benefits of

technological disadvantage tend to disappear and convergence may slow.

Evidence presented in Van Ark and Pilat (1993) is consistent with this theory.

They show that prior to 1980, Germany and Japan converged rapidly on the

United States, and by the early 1980s, Germany and Japan had achieved an

overall productivity level in manufacturing close to that of the United States.

But from 1980 through 1990, the rate at which Germany and Japan gained on

the United States slowed substantially.

Productivity convergence can be a slow process. The convergence among

industrialized countries appears to have begun more than 100 years ago

(Baumol, Blackman, and Wolff 1989; Maddison 1987). The ability of the United

States to maintain a substantial lead in productivity for so long is partly attributable to the two world wars, which destroyed the productive capacities of

other countries while spurring technical innovation in U.S. manufacturing.

Barring similar catastrophes, it is likely that the United States will eventually

share the lead in worker productivity with other industrialized countries.

Because technology can readily be transferred back to the United States, it is

unlikely that the United States will fall far behind the other countries unless it

loses the capability to utilize new technologies efficiently.

While the transfer of production technology from leader to followers is the primary factor that underlies the convergence hypothesis, other factors also play

major roles in determining productivity. Increases in the educational attainment

of the work force, which we discuss in the following section, is one important factor. Investment in physical capital is another. The exact relationship between

Worker Productivity and Education 9

Figure 2.3

Convergence of real GDP per worker among the G–7

nations: 1950–90

Year

’50 ’58 ’62 ’86

0

0.1

0.2

0.3

0.4

0.5

0.6

’54 ’66 ’70 ’74 ’78 ’82 ’90

Coefficient of variation of

real GDP per worker

NOTE: The coefficient of variation is equal to the standard deviation of

productivity divided by the mean.

SOURCE: Penn World Table (Mark 5.6), distributed by the National Bureau

of Economic Research. For a description, see Robert Summers and Alan

Heston, “The Penn World Table (Mark 5): An Expanded Set of International

Comparisons, 1950–1988,” Quarterly Journal of Economics (May 1991):

327–368.
investment in capital and the convergence hypothesis is unclear, but the former

is highly correlated with productivity. A study of productivity growth by the

Bureau of Labor Statistics (1993) found that increases in capital intensity accounted for about 40 percent of productivity growth in the United States from 1948

through 1990.10 Countries with the newest and the least obsolete capital tend to

have higher productivity growth rates than other countries. The dwindling leadership position of the United States in the world economy is coincident with low

growth in the rate of capital accumulation and the relative aging of its stock of

capital (Wolff 1991). In the early 1960s when the United States was the dominant

economy in the world, the nation had a much higher capital-labor ratio than other

industrialized countries. Since then, the other countries that converged with the

United States in terms of productivity have accumulated capital at a faster rate

than the United States. By 1979, the U.S. capital stock was 15 percent older than

the average for four other industrialized countries11 and 73 percent older than

Japan’s. The rate at which other countries have caught up to the United States

with respect to productivity is positively associated with the rate of growth of the

capital-labor ratios in those countries.

DISCUSSION: CONTRIBUTION OF EDUCATION TO ECONOMIC

PRODUCTIVITY

Economic research based on growth-accounting methods has shown that education

has made a major contribution to growth in U.S. economic productivity.12 Denison

(1979) estimated that education contributed about 20 percent of the growth in national income per person from 1948 through 1973. Using similar methods and data for the

same period, Jorgenson (1984) estimated that education accounted for 38 percent of

the total labor contribution to U.S. output growth, or about 17 percent of growth overall. Recent estimates for the period from 1973 through 1984 (Sturm 1993) suggest that

education accounted for about 15 percent of the growth in output per hour worked

over this period. A more comprehensive study of productivity from 1948 through

1990 using growth accounting (U.S. Department of Labor 1993) showed that during

this period, rising levels of educational attainment were responsible for about 14 percent of the growth in output per hour worked in the private sector.

10 Education and the Economy

10According to the U.S. Department of Labor (1993), increases in capital intensity accounted for an

estimated 0.9 percent points of the 2.2 percentage point per year increase in output per hour in private nonfarm business from 1948 through 1990.

11This group of countries includes Germany, Italy, Japan, and the United Kingdom.

12In growth accounting, researchers attribute growth in output to changes in factor inputs, such as

capital and labor. The relative value of different levels of education attainment in the growth

accounts is determined by the earnings of workers with different levels of attainment.
The growth-accounting methods used in these studies have been frequently criticized. First, they use variation in earnings to represent variation in productivity,

which cannot be observed directly. The relative productivity contributions of different levels of educational attainment are set according to earnings differentials

among educational groups. If earnings are not closely correlated with productivity,

this approach is inappropriate. The use of earnings differentials to measure the effect

of educational attainment on productivity is discussed in chapter 3.

Second, growth-accounting methods are used to “capture” the direct effect of different growth factors, but they do not account for interaction among the factors.

Many researchers have discussed the importance of interaction, such as that

between education and new technology. For example, a country’s ability to

exploit new technologies may depend on workers who have the education necessary to use the new technologies effectively. Third, growth-accounting methods

focus exclusively on changes in years of formal education to measure the contribution of education. They do not control either for changes in the quality of

education or for the contribution of informal education or training.

Despite the weaknesses of growth-accounting methods, they have provided the best

available estimates of the contribution of education to productivity growth. Although

the exact magnitude of the contribution may be unclear, studies consistently show that

education makes a substantial contribution to productivity growth. Recent attempts

to generate estimates that are not subject to the traditional criticisms of growthaccounting methods support this conclusion. Kim and Lau (1992) use a new methodology called the “meta-production function” approach to estimate the relationship

between aggregate output and inputs.13 They estimate that education accounted for

11 percent of the growth in aggregate real output from 1948 through 1985.

International evidence suggests that education plays a similarly important role in

influencing productivity in other countries as it does in the United States. Sturm

(1993) demonstrates that among a select group of industrialized countries, the contribution of education to economic growth from 1973 through 1984 was the highest

in France (22 percent) and the lowest in Germany (4 percent).14 Using methods as

well as a sample period different from those of Sturm (1993), Kim and Lau (1992)

show that the contribution of education to growth in five industrialized countries

from 1957 through 1985 was between 11 percent and 27 percent, depending on the

Worker Productivity and Education 11

13The assumption of a meta-production function implies that the same production function can be

used to characterize productivity in different countries. Kim and Lau use the meta-production function to perform an alternative growth accounting that dispenses with the traditional assumptions of

constant returns to scale, neutrality of technical progress, and profit maximization. (Boskin and Lau

(1990) describe the alternative growth-accounting procedure.)

14According to the estimates presented in Sturm (1993), the percentage of the growth rate explained

by education is 15.5 percent in the United States, 22.0 percent in France, 20.9 percent in the

Netherlands, 18.9 percent in the United Kingdom, and 10.8 percent in Japan.
country. The lowest impact was 11 percent in the United States and Japan, and the

highest impact was 27 percent in West Germany.15 Overall, the estimates suggest

that the extent to which education has contributed to productivity growth in the

United States is generally the same as in other industrialized countries. Therefore,

the data provide no indication that the contribution of education to growth in the

United States lags behind the contribution of education to growth in other countries.

Evidence related to the convergence hypothesis also suggests that education plays

an important role in productivity. Baumol, Blackman, and Wolff (1989) find that different groups of countries are converging to different productivity levels according

to their educational levels. The industrialized countries with the highest educational levels are converging to the highest productivity levels. Other countries are converging to lower levels—countries with roughly comparable educational levels are

converging to a similar level, but they are not closing the gap with countries at higher educational levels.16 Supporting evidence about the importance of education in

productivity convergence is presented in Barro (1991), who shows that countries

with low per capita GDP but relatively high levels of schooling tend to catch up to

the GDP leaders.17

These findings suggest that countries that lag in productivity must have some

minimum level of education to be able to catch up to the leaders in productivity.18

Regression estimates presented in Baumol, Blackman, and Wolff (1989), which are

based on a broad cross-section of countries, suggest that high school education is

especially important in helping a country absorb and use new production technologies. Based on these estimates, Baumol, Blackman, and Wolff (1989) argue that

primary education alone may not prepare the work force to adopt and implement

new technologies. At the same time, findings on higher education appear to indicate that it may be less important than high school education in the productivity

“catch-up” process for the broad cross-section of countries. However, higher education may still be a critical determinant of the relative productivity levels among

the most industrialized countries.

12 Education and the Economy

15The impact of education on growth in the other two countries was 19 percent in France and 24 percent in the United Kingdom. The difference between the Kim and Lau (1992) estimate and the Sturm

(1993) estimate for Germany is striking. Because the two studies use different data, different estimation

methods, and different (though overlapping) time periods, it is difficult to determine what causes this

difference.

16Baumol, Blackman, and Wolff (1989) estimate the impact of education on productivity using cross-section data from the Penn World Table (Summers and Heston 1991). Their findings show that enrollment

rates for primary, secondary, and higher education have significant positive impacts on productivity

growth. Controlling for enrollment rates, countries tend to converge on the productivity leader over time.

17Barro (1991) examines data on a cross-section of 98 countries from 1960 through 1985. Based on these

data, Barro shows that the growth rate of real per capita GDP over the observation period is positively

related to the school enrollment rates in 1960 and negatively related to the 1960 level of real per capita

GDP.

18Kyriacou (1991) also presents findings that suggest productivity convergence occurs only if suf- ficient levels of schooling among the labor force have been accumulated.
CONCLUSIONS

Indicators on productivity, together with the existing research, lead to the following conclusions:

■ Worker productivity in the United States has grown nearly continuously since the

end of World War II. Decreases in productivity have been limited to single-year fluctuations. 

■ Postwar growth in U.S. productivity was slower after 1973 than it was before

1973. Researchers have not yet discovered an adequate explanation for this slowdown in productivity growth. But the growth rate prior to 1973 was high by historical

standards, so an eventual decline in the growth rate might be expected. The historical data therefore suggest that the post-1973 decline may just represent fluctuation

around a long-run trend.

■ Worker productivity in other industrialized countries has increased at a faster rate

than in the United States; therefore, these countries are slowly catching up to the

United States. This process is not a new phenomenon. The latest phase of convergence began just after World War II, many years prior to the slowdown in productivity that occurred in the United States after 1973. Similar to the United States, the

other industrialized countries experienced slower growth in productivity after 1973

than before 1973. The convergence hypothesis together with the trends in productivity growth suggest that the United States is likely to eventually share the lead in productivity with other industrialized countries. However, the convergence hypothesis

also implies that the United States is unlikely to fall far behind any other country in

terms of worker productivity.

■ Growth in education appears to be a substantial contributor to productivity growth,

accounting for an estimated 10 to 20 percent of growth in U.S. productivity in recent

decades. For other countries, convergence to the U.S. productivity level appears to

depend on a minimum level of education, especially secondary education, prevailing

among the work force.

■ Factors other than education are important to productivity. The most prominent

example is investment in physical capital. One study suggests that increases in capital intensity accounted for about 40 percent of U.S. productivity growth in recent

decades. Other countries that have increased worker productivity faster than the

United States have also invested in physical capital at a faster rate than the United

States. Those countries with the highest capital-labor ratios have been catching up to

the United States at the fastest rate.

Worker Productivity and Education 13
CHAPTER 3

Economic Consequences of 

Educational Attainment

While it is possible to link trends in worker productivity at the national level to

changes in education at the national level, increases in worker productivity at the

national level occur as conditions in the economy, including education, change to

make individual workers more productive. In this chapter, we focus on the economic consequences of education at the individual level in an attempt to measure

the economic value of educational attainment and the incentive for individuals to

invest in education.

As discussed in the previous chapter, estimates of the contribution of educational

attainment to worker productivity at the national level are based on the observed

average earnings of workers at different education levels. Researchers characterize

the differences in earnings by level of education as the return to the investment in

human capital that is inherent in the acquisition of more education.19 The returns as

measured by earnings differences are used to represent the impact of education on

worker productivity. This approach is based on economic theory, which states that

in a competitive labor market, a worker’s wage rate will be equal to his or her marginal productivity. Education may also improve a worker’s long-term productivity

because it increases his or her employment stability, thereby minimizing periods of

unemployment in which the worker is not productive.

In this chapter, we consider the trends in the economic returns to education as

measured by differences in unemployment and earnings. We acknowledge the possibility that differences among workers in unemployment and earnings may not

closely mirror differences in productivity.20 But even if the estimated returns are not

an accurate representation of the effects of education on productivity, the estimates

still provide measures of the economic incentives for further education, and we

can examine how these incentives have changed over time.

Consequences of Educational Attainment 15

19Alternative theories to human capital theory assert that additional education does not increase

productivity, but rather that it is valuable for sorting out individuals with inherently low or high

abilities or aptitudes. Even in this theory, additional education represents a potentially valuable

investment from the worker’s perspective because education is a signal that the worker has high

ability and the potential to be highly productive.

20Several theories of employment contracts have attempted to explain why workers or employers

may prefer contracts that offer only modest adjustments of wages in response to differences in

worker productivity. For example, employment contracts that limit wage adjustments may appeal

to risk-averse workers who prefer a steady income. Use of such contracts implies that a worker’s

wage rate at a given point in time may not be equal to that worker’s marginal productivity.
INDICATOR 3

Education Attainment and Unemployment

Students often have trouble moving from school to work. Of particular concern

in the United States is the transition from high school to work. This transition

may entail lost productivity if students are unable to find employment soon

after they graduate from or drop out of school. The unemployment rate among

those who have recently left school indicates just how difficult the transition

can be.21

Over the past 30 years, a large proportion

of school dropouts and high school

graduates not enrolled in college were

unemployed shortly after leaving school

(figure 3.1). The transition from school to

work appears to be tougher for dropouts

than for graduates, as dropouts tend to

have had a higher unemployment rate

throughout most of the years shown in

figure 3.1. For example, in 1994, 30 percent

of dropouts compared with 20 percent

of graduates were unemployed. This

difference is an indication that there is a

substantial economic penalty for not

graduating from high school. The transition to work also appears to have become

more difficult over time for some

dropouts and high school graduates. The

unemployment rates for both dropouts

and graduates have generally increased

since 1960, as shown by the trend lines in

figure 3.1.

16 Education and the Economy

21Time spent unemployed following school can also have potentially positive implications for labor

force entrants if they use the time to find the best possible match between their own skills and the

needs of prospective employers.

Figure 3.1

Unemployment rate of recent high school graduates

and dropouts not enrolled in college: 1960–94

NOTE: Recent high school graduates include individuals ages 16–24

years who graduated during the survey year and were not enrolled in

college. Recent high school dropouts include individuals ages 16–24

years who did not graduate and who were in school 12 months earlier,

but who were not enrolled in the survey month.

SOURCE: U.S. Department of Education, National Center for Education

Statistics, The Condition of Education, 1995, and special tabulations;

U.S. Department of Commerce, Bureau of the Census, October Current

Population Surveys.

High school graduates

Dropouts

trend

line

Year

Unemployment rate (percent)

trend

line

’60 ’66 ’70 ’74 ’78 ’82 ’86 ’90 ’94

0

10

20

30

40

50
The unemployment rate for recent high

school graduates and dropouts fluctuates in the short run in response to the

business cycle, with recessions generating higher unemployment rates. For

example, the most recent economic

recession, beginning in 1990, had a substantial adverse impact on high school

graduates and dropouts as they made

the transition from school to work.

Consequently, unemployment rates at

least for graduates increased substantially between 1989 and 1991. Similar

fluctuations occurred in response to

previous recessions.

Blacks appear to face a much more difficult transition from high school to

work than whites. Among graduates,

the unemployment rate from 1973

through 1994 was generally higher for blacks than for whites (figure 3.2).

Blacks were also affected significantly by economic conditions. In response to

the latest recession, for example, the unemployment rate among black high

school graduates doubled, from about 25 percent in 1989 to about 50 percent in

1991. While the unemployment rate for white graduates increased over the

same period, the unemployment rate for whites remained substantially below

the rate for blacks.

Consequences of Educational Attainment 17

Figure 3.2

Unemployment rate of recent high school graduates not

enrolled in college, by race: 1973–94

NOTE: Recent high school graduates include individuals ages 16–24 years

who graduated during the survey year and were not enrolled in college.

SOURCE: U.S. Department of Education, National Center for Education Statistics,

The Condition of Education, 1995, and special tabulations; U.S. Department

of Commerce, Bureau of the Census, October Current Population Surveys.

0

10

20

30

40

50

60

’73 ’76 ’78 ’80 ’82 ’74 ’84 ’86 ’88 ’90 ’92 ’94

Unemployment

rate (percent)

Year

Black

White
INDICATOR 4

Education Attainment and Earnings

Most of the research on the effect of education on economic outcomes has focused

on how education affects earnings. The effect of education on earnings represents

the private economic return to the investment in education. Education probably

also generates social benefits that are not reflected in earnings. For example,

increased education may reduce crime rates or the use of government assistance

programs, thereby benefiting other members of society. While these effects may

be important, they do not relate directly to economic productivity and are beyond

the scope of this chapter. 

Data from the Current Population

Survey demonstrate that median

earnings increase with the level of

schooling. Among males 25–34

years old in 1993, the median earnings of those with a college degree

were approximately $33,000 per

year, which was more than 50 percent greater than the median earnings of high school graduates and

more than twice the earnings of

high school dropouts (figure 3.3). A

similar relationship between education and earnings held for females

25–34 years old, although for each

educational category, the median

earnings were lower for females

than for males.

Estimates of the returns to education, holding other factors constant, also demonstrate the positive returns to education for young workers. According to these

estimates, the returns to a college degree increased dramatically in the first half of

the 1980s. Figure 3.4 shows that the earnings advantage for college graduates

compared with that for high school dropouts increased from 56 percent from 1975

through 1980 to 84 percent from 1981 through 1986.22 The returns to a high school

18 Education and the Economy

Figure 3.3

Earnings for all wage and salary earners ages 25–34 years,

by sex and educational attainment: 1993

Median annual earnings

(thousands of 1994 dollars)

SOURCE: U.S. Department of Education, National Center for Education Statistics,

The Condition of Education, 1995; U.S. Department of Commerce, Bureau of the

Census, March Current Population Survey, 1994.

Grades 9

to 11

High school

diploma only

Bachelor’s

degree or more

Some

college

Highest level of education completed

$0

$5

$10

$15

$20

$25

$30

$35

14.0

20.9

23.4

32.7

7.7

13.1

17.2

26.0

Male Female

22These comparisons, which are from Murphy and Welch (1989), control for differences in race, sex,

and age.
diploma also increased during the 1980s, but more

modestly than for a college

degree. Other things being

equal, high school graduates earned 19 percent more

than dropouts from 1981

through 1986, compared

with 17 percent more from

1975 through 1980. 

Increases in the returns to

education were most dramatic at the highest levels

of education. The increase

in the returns to a high

school diploma brought the

rate of return back to the 19

percent that prevailed from

1963 through 1968, as

shown in figure 3.4. But for

higher levels of education, the rates of return in the 1980s exceeded those in earlier

periods. The earnings advantage for each level of additional education compared

with high school increased in the 1980s. For example, the returns shown in figure

3.4 for high school and college graduates imply that, compared with high school

graduates, the earnings advantage for college graduates increased in the early

1980s, from 33 percent to 55 percent.23

The increase in returns to a college degree occurred as real wages increased

among college graduates while real wages declined for high school graduates and

dropouts.24 Figure 3.5 shows that between 1980 and 1990 real income increased

for men with four or more years of college and for women with one or more years

of college. Real income decreased or remained approximately constant for groups

with less education.

Consequences of Educational Attainment 19

0

20

40

60

80

100

120

High school Some college College Graduate school

19

14 17 19

34

24 27

37

70

62

56

84

88

83

75

107

Figure 3.4

Estimated returns to education among workers with 1 to 5 years

of work experience (percent increase over average wages of non-high school graduates): 1963–86

Percent

increase

NOTE: Estimates of the returns to education control for differences between educational groups in

race–ethnicity, age, and sex.

SOURCE: Kevin Murphy and Finis Welch, “Wage Premiums for College Graduates: Recent Growth and

Possible Explanations,” Educational Researcher (May 1989): 17–26; U.S. Department of Commerce,

Bureau of the Census, March Current Population Surveys.

Highest level of education completed

1963–68 1969–74 1975–80 1981–86

23These estimates are based on dividing one plus the return to college shown in figure 3.4 by one

plus the return to high school.

24See Murphy and Welch (1989), Eck (1993), and Katz and Murphy (1992) for detailed discussions

of the earnings trends by level of educational attainment.
20 Education and the Economy

Figure 3.5

Median annual income for full-time workers ages 25 years and older,

by sex and educational attainment: 1980 and 1990

24.4

32.2

34.6

20.3

27.6

32.9

42.8

44.3

Male

SOURCE: Alan Eck, “Job-Related Education and Training: Their Impact on Earnings,” Monthly Labor

Review (October 1993): 21–38; U.S. Department of Commerce, Bureau of the Census, March

Current Population Surveys.

Median annual income

(thousands of 1991 dollars)

Educational attainment

Fewer than 4 years

of high school 4 years of

high school

1 to 3 years

of college

4 or more

years of college

Median annual income

(thousands of 1991 dollars)

Educational attainment

Fewer than 4 years

of high school 4 years of

high school

1 to 3 years

of college

4 or more

years of college

15.1

21.4

27.1

14.3

19.1

23.2

31.7

19.1

Female

1980 1990

$50

$40

$30

$20

$10

$0

$50

$40

$30

$20

$10

$0

1980 1990
DISCUSSION: DETERMINANTS OF THE INCREASING RETURN

TO EDUCATION

Recent research has attempted to identify the factors that influence the increase

in the returns to education. Several labor demand and supply factors have been

cited as important. On the demand side, there appears to have been a rise in

technological factors favoring more highly educated workers with greater problem-solving skills, driving up their relative wages (Katz and Murphy 1992).25

Recent research (Berman, Bound, and Griliches 1994) attributes much of the

change in the wage structure of manufacturing to increased demand for highskilled labor.26 The introduction of new production labor-saving technology

has also decreased the demand for lower-skilled workers in manufacturing,

depriving them of traditionally high-paying jobs. An important factor in the

increased demand for high-skilled labor may be the expansion of computer use.

Estimates presented by Krueger (1993) suggest that between one-third and onehalf of the increase in the rate of return to education can be attributed to

expanded computer use.27

A number of supply-side factors have also contributed to the increased returns

to education. First, the educational attainment of new labor force entrants leveled off after a period of rapid growth. For males, there was even a slight drop

in the proportion of labor force entrants with education beyond high school.

This decrease in the rate of growth of college graduates, combined with the

demand changes discussed above, put upward pressure on wages paid to those

who did graduate. At the same time, the influx of new immigrants, both legal

Consequences of Educational Attainment 21

25Using data from the Current Population Survey, Katz and Murphy (1992) show that the majority

of the shift in relative demand for more highly educated workers occurred within industrial and

occupational sectors. They conclude that these within-sector shifts are likely to reflect skill-biased

technological changes. 

26Berman, Bound, and Griliches (1994) use data for the period 1959 to 1989 from the Annual Survey

of Manufactures, the Census of Manufactures, and the National Bureau of Economic Research trade

data set. They base their conclusion about the importance of technological changes on three findings: (1) the shift in relative demand for more educated workers is due to increased use of nonproduction workers within 450 manufacturing industries rather than to a reallocation of employment

among the industries; (2) international trade generated only minor shifts in employment away from

production-labor-intensive industries; and (3) within-industry increases in the use of nonproduction workers are strongly correlated with investment in computers and research and development. 

27Based on data from the Current Population Survey, Krueger (1993) estimates that workers who

use a computer at their job earn 10 to 15 percent higher wages than other workers. Because of the

high rate of computer use among highly educated workers and the expansion of computer use in

the 1980s, the wage premium for computer use accounts for a substantial proportion of the increase

in returns to education. 
and illegal, increased the supply of less-educated workers. From 1975 through

1985, the percentage of high school dropouts who were immigrants increased

from 17 to 31 percent (Borjas, Freeman, and Katz 1992). The impact of immigrants

on average wages is further exacerbated if, as seems likely, the immigrants with

less education face even greater barriers to employment than other Americans

with a similar level of education. As a result of these barriers, immigrant workers

are paid relatively low wages, which pulls down the average wage for the lesseducated group even before accounting for the supply effect of the immigrants on

relative wages.

Increased imports may also contribute to the effect of foreign labor supply

because they create an indirect increase in the supply of less-educated labor

from abroad. Economists disagree about the extent to which increased imports

have affected wage differentials. Borjas, Freeman, and Katz (1992) estimate that

growth in the U.S. trade deficit accounted for 15 to 25 percent of the rise in the

college-high school wage differential from 1980 through 1985. Karoly and

Klerman (1994) and Wood (1994) also argue that international trade has played

a significant role in pushing down the relative wages of less-educated workers.

In contrast, a recent detailed study of this effect (Lawrence and Slaughter 1993)

argues that imports did not make an important contribution to changes in U.S.

relative wages in the 1980s. They conclude, as have other researchers, that technological change rather than trade has been the primary factor driving down

relative wages for production workers.

Another potential supply factor that may contribute to increasing returns to

education is a change in the skill of labor force entrants with a given level of

educational attainment. Specifically, researchers have pointed to a decline in the

quality of U.S. elementary and secondary education as a contributor to the

increase in returns for college education. According to this argument, high

school graduates are paid less, both in real terms and compared with college

graduates, because they are less skilled than high school graduates of previous

years. There is evidence, however, to dispute this argument. Older high school

graduates, who received their formal education before the alleged decline in

U.S. education, suffered real wage declines similar to those of younger high

school graduates (Blackburn, Bloom, and Freeman 1990).

The rising returns to education are not simply the product of increased earnings

for better-educated workers. Rather, the data imply that less-educated workers

are at greater risk of having difficulty in the labor market now than in the past,

and that the increase in returns to education is caused in part by the decrease in

real earnings for those with less schooling. Nevertheless, it is difficult to evaluate changes in average wages for separate education groups. Part of the change

in wages by level of education is caused by the changing composition of the

22 Education and the Economy
groups. Hence, tracking the wages of an education group, like high school

graduates, over time can be misleading because characteristics of high school

graduates have changed over time. 

Part of the change in the composition of the educational groups occurs naturally

as the educational attainment of the population increases. For example, the number of college graduates increases as students who in previous years would have

entered the labor market directly now go on to college instead. These students, on

average, are likely to be the most able of the students who in the past did not

attend college, but they are also likely to be less able than the students who would

have previously gone to college.28 Consequently, the movement of this group

between educational categories can cause a decrease in average wages in both categories. The wages of high school graduates will decrease as the best students

from the group move into the college group. At the same time, these students

bring down the average wage of the college group if they are less able, on average, than the traditional college group. Of course, average wages for the entire

population will still increase if the new college attendees earn more than if they

had not attended college.

Trends in immigration have probably contributed to the compositional changes

in the education groups. As immigrants become a larger proportion of the loweducation groups, they probably drag down the average wage for these groups

because they face significant obstacles to employment, such as language barriers

or unfamiliarity with the U.S. labor market, and are forced to accept lower-paying

jobs. But this wage decrease is not evidence of a decline in the economic standing

of a particular educational group. Rather, the groups themselves have changed in

significant ways that affect the group-specific average wages.

The increase in earnings inequality by education level has been accompanied by

a general increase in income inequality in the United States. Income inequality

may be harmful to the overall economy regardless of its source. Recent empirical

research by Persson and Tabellini (1994) provides evidence that greater income

inequality causes slower economic growth. Findings based on pooled historical

data from a cross-section of nine developed countries demonstrate that differences in income distribution explain about one-fifth of the variance in growth

rates across countries and over time.29 None of the other variables tested by

Persson and Tabellini (1994) explains more than one-tenth of the variation. 

Consequences of Educational Attainment 23

28Baker and Smith (1994) report (based on data from the High School and Beyond Survey and the

1992 National Educational Longitudinal Study) that the percentage of high school seniors in the bottom quartile of academic performance who plan to go to college doubled from 1982 through 1992.

29The growth measure used by Persson and Tabellini (1994) is annual average growth rate of gross

domestic product per capita.
The mechanism by which inequality slows economic growth is unclear. Persson

and Tabellini (1994) argue that it is political. According to their theory, greater

inequality leads to policies that increase tax rates on investment and other productive activities in order to redistribute income. As tax rates are increased,

investment declines, which eventually causes productivity to slow down. Other

researchers have argued that inequality slows growth through an economic

mechanism. In this theory, increased income inequality makes it difficult for

those at the bottom of the income distribution to acquire the skills necessary to

succeed in the labor market. This may occur because poor families cannot borrow

money to educate their children or because poor communities cannot effectively

educate their children or provide them with role models. Employers, therefore,

may face shortages of qualified workers, which can negatively affect production

efficiency. Eventually, the overall economy is harmed by the lack of skills among

the poor as companies become less productive and economic growth suffers. 

DISCUSSION: ESTIMATED RETURNS BASED ON DIRECT

MEASURES OF PRODUCTIVITY

The effect of education on earnings is typically used as a proxy for the effects of

education on productivity. However, recent research has attempted to identify

direct measures of productivity as a means of estimating the impact of education

on productivity. Hellerstein, Neumark, and Troske (1994) estimate the impact of

education on productivity using data from the Worker-Establishment

Characteristics Database (WECD), which matches employer data from the

Longitudinal Research Database with employee data from the 1990 Decennial

Census. By combining individual worker data with data on the plants where

workers are employed, the researchers compare relative wages of workers with

their relative marginal productivities.

Findings based on the WECD data demonstrate that college attendance has a substantial impact on productivity. Moreover, the estimated impact of college attendance on productivity presented in Hellerstein, Neumark, and Troske (1994) is at

least 23 percent higher than its estimated impact on wages. College-educated

workers are paid 26 to 36 percent more than workers who have not attended college, but college-educated workers are also 51 to 75 percent more productive.

These findings imply that college-educated workers are only partially compensated for their increased productivity. 

24 Education and the Economy
A recent study based on new company-level data also suggests that education has

a significant impact on worker productivity. The National Center on the

Educational Quality of the Workforce (EQW) recently conducted a survey of

3,000 establishments employing 20 workers or more. An EQW study of these data

shows that a 10 percent increase in schooling is associated with an 8.6 percent

increase in output. This estimated effect is large, and it is used to highlight the

importance of education as a determinant of productivity. In fact, the EQW study

argues that the effect of education on productivity is greater than the effect of a

comparable percent increase in capital stock—a 10 percent increase in capital

stock is associated with an increase in output of about 3.4 percent. This finding

does not, however, demonstrate that increased education is necessarily a superior investment to increased capital because the EQW study makes no reference to

the costs of a 10 percent increase in education compared with a 10 percent

increase in capital. To identify the superior investment, one should determine

whether a dollar spent on increased education generates a higher return than a

dollar spent on increased capital.

Consequences of Educational Attainment 25
INDICATOR 5

The Supply of Youth with

High School and College Educations

The positive economic outcomes that ensue from additional years of schooling

raise the issue of the extent to which youth who will enter the labor force have

pursued additional years of education. The high school completion rate for

young adults between the ages of 18 and 24 increased from 83 percent in 1972

to 86 percent in 1994 (figure 3.6). This slow increase, however, masks larger

increases for particular racial–ethnic subgroups. The completion rate for white

18- to 24-year-olds is only two to three percentage points higher than it was 20

years ago. In contrast, the completion rate for black 18- to 24-year-olds has

climbed substantially over the past 20 years (83.3 percent in 1994 compared

with 72.1 percent in 1972). The completion rate for black 18- to 24-year-olds,

however, still lags behind the completion rate for white 18- to 24-year-olds. The

completion rate for Hispanic 18- to 24-year-olds is even lower than for blacks.

Although the rate for Hispanics has fluctuated from year to year, it did not

change significantly from 1972 through 1994.

The percentage of the adult population

that completes high school is noticeably

higher for older adults than for younger

adults. For example, 86 percent of 25- to

34-year-olds in 1991 had completed high

school, compared with 81 percent of 19-

to 20-year-olds. The higher proportion

in the older age groups reflects the completion of high school by older students

and the tendency for many who drop out

of high school to eventually finish high

school or acquire a General Education

Development (GED) equivalency degree.

The issue of whether equivalency degrees have value equal to a high school

diploma in the workplace has been extensively debated. One recent study has

shown that once the number of years of

schooling completed is taken into account, individuals with equivalency degrees

do not enjoy the same earnings or outcomes in the workplace as individuals with

26 Education and the Economy

SOURCE: U.S. Department of Education, National Center for Education

Statistics, Dropout Rates in the United States: 1994; U.S. Department of

Commerce, Bureau of the Census, October Current Population Surveys.

Figure 3.6

 High school completion rates for 18- to 24-year-olds

 not enrolled in high school, by race–ethnicity: 1972–94

Year

0

20

40

60

80

100

Total

White

Black

Hispanic

’72 ’74 ’76 ’78 ’80 ’82 ’84 ’86 ’88 ’90 ’92 ’94

Percent
a high school diploma (Cameron and

Heckman 1993). However, there can be a

payoff in terms of earnings if people

with equivalency degrees complete

school before the age of 20 or if they use

the degree as a means to access additional education and training.

In summary, the rate of high school

completion in the United States has

increased slightly over the past 20 years.

In light of the bleak economic prospects

that confront those who do not complete

high school, one might have expected a

greater increase in high school completion. Some researchers have observed,

however, that youth may not fully perceive the benefits of completing high

school until several years later when differences in earnings and job stability

become apparent.

In the postsecondary arena, a trend to

pursue additional education exists.

Young people leaving high school have

increasingly gone on to two-year or

four-year colleges (figure 3.7). More than

three-fifths of high school graduates enrolled in college shortly after graduating,

compared with about one-half who did

so 20 years ago. The increase is fairly

similar for high school graduates who

go to two-year and four-year colleges.

However, the differential participation

of subgroups within the college-going

population is much more uneven. White

students have demonstrated a consistent

pattern of growth in college enrollment,

whereas the rate of enrollment for black

high school graduates increased in the

1970s, decreased in the early 1980s, and

climbed back to the higher level in the late 1980s and early 1990s (figure 3.8).

Patterns for Hispanic students have been similarly inconsistent over the past 20

years. Part of the volatility in the college enrollment rates for black and

Consequences of Educational Attainment 27

Year

Percent

SOURCE: U.S. Department of Education, National Center for Education

Statistics, The Condition of Education, 1996 and The Condition of Education,

1991; U.S. Department of Commerce, Bureau of the Census, October

Current Population Surveys.

Figure 3.7

Percentage of high school graduates enrolled in college

in October following graduation, by type of

college: 1968–93

0

20

40

60

80

100

’68 ’71 ’74 ’77 ’80 ’83 ’86 ’89 ’92 ’94

2-Year

4-Year

Total

Figure 3.8

 Percentage of high school graduates enrolled in college in

October following graduation, by race–ethnicity: 1973–92

Year

’73’74 ’76 ’78 ’80 ’82 ’84 ’86 ’88 ’90 ’92

Percent

Hispanic

Black

White

0

20

40

60

80

100

NOTE: Data shown are three-year averages. The three-year average for

1992 is the average percentage of graduates enrolled in college in 1991,

1992, and 1993.

SOURCE: U.S. Department of Education, National Center for Education

Statistics, The Condition of Education, 1995; U.S. Department of Commerce,

Bureau of the Census, October Current Population Surveys.
Hispanic students that is reflected in figure 3.8 is due to the relatively small

samples used to calculate these rates.

Although approximately 40 percent of

high school graduates immediately

enroll in a four-year college, considerably fewer earn a diploma. In 1994,

approximately one-quarter of high

school graduates ages 25–29 years had

completed four or more years of college

(figure 3.9). Black and Hispanic adults of

this age were less likely to have completed this level of education (16 and 13

percent, respectively, in 1994) than

white adults (30 percent).

The rate of college completion has not

changed much over the past 20 years.

The completion rate of 27 percent in

1994 was only slightly higher than the

rate from 20 years before. This slight

increase, which might seem puzzling given the increased economic advantages

associated with more years of education, can be explained by several factors.

First, the increase in the returns to education occurred largely in the 1980s. The

youngest high school graduate represented in figure 3.9 would have entered

college in the mid-1980s, before much of the increase in the returns to education

was widely recognized. Second, the increase in financial and other costs associated with investing in education over this period may have reduced the number of

students who chose to pursue a college degree (U.S. Department of Education

1995). Finally, changes in the composition of the population of youth and young

adults may also have affected college completion rates. Throughout most of the

period represented in figure 3.9, the college completion rate for the overall population of high school graduates closely tracked the completion rate for whites

because whites dominated the population of high school graduates. But in recent

years, the completion rate for the population has gradually begun to diverge from

the completion rate for whites as blacks and Hispanics become a larger proportion of the population. This trend will have a negative impact on the total college

completion rate because the latter two groups, which have relatively low completion rates, will be weighted more heavily in the total population. 

28 Education and the Economy

SOURCE: U.S. Department of Education, National Center for Education

Statistics, The Condition of Education, 1995; The Condition of Education,

1991; U.S. Department of Commerce, Bureau of the Census, March Current

Population Surveys.

Figure 3.9

 Percentage of high school graduates ages 25–29 years

 who have completed four years or more of college,

by race–ethnicity: 1965–94

Percent

Total

White

Black

Hispanic

Year

’65 ’68 ’70 ’72 ’74 ’76 ’78 ’80 ’82 ’84 ’86 ’88 ’90 ’92 ’94

0

5

10

15

20

25

30

35
DISCUSSION: USING EDUCATION TO INCREASE EARNINGS AT

THE NATIONAL LEVEL

The strong link between education and earnings at the individual level implies

that the education of the work force as a whole also plays a role in determining

the productive capacity of the work force and the average earnings among all

workers. As individuals increase their earnings by acquiring additional education, they also expand the productive capacity of the economy as a whole. This

description of the link between education and productivity is consistent with the

findings from the growth-accounting studies discussed in chapter 2, which estimated the statistical link between increases in education of the work force and

increases in labor productivity. Productivity growth, therefore, can be supported

by encouraging students to pursue additional schooling. For example, policies

such as the provision of loans and educational grants that increase access to college

can have a positive impact on productivity and on average earnings. 

The ability to increase the earnings of the work force in general through increased

education is limited however, both because of market responses and because

individuals vary in their capabilities. We cannot, for example, ensure that everyone will have a salary equal to the average salary for attorneys simply by putting

everyone through college and law school. First, not all people are prepared to be

attorneys, regardless of the training received, and most of the new entrants would

probably be less capable than the average student who becomes an attorney on

his or her own. In addition, flooding the market with attorneys would inevitably

decrease the salaries for all attorneys (and the price of legal services) due to excess

supply. Hence, average pay in general and the earnings differentials between

occupations and educational levels are sensitive to a dynamic labor market.

The link between education and earnings of the work force is also somewhat tenuous because many other factors affect the productivity of workers and their

earnings. For example, as discussed in chapter 2, the availability of capital is an

important determinant of labor productivity. Labor productivity is also affected

by changes in production technology and the ways in which work is organized. 

Some researchers have questioned the degree to which estimates of the impact of

education on individual earnings levels are useful for evaluating the social benefit of increases in education. Levin and Kelley (1994), for example, argue that the

estimated returns to education overstate the actual social benefits of education,

claiming that changes in aggregate educational attainment do not bring about the

increases in earnings that estimates of individual returns to education imply. As

evidence, Levin and Kelley point out that the increases in education from 1968

Consequences of Educational Attainment 29
through 1987 were accompanied by a decline in median earnings rather than an

increase as implied by positive returns to education. But estimates of the returns

to education are based on the assumption that other factors remain constant, and,

as pointed out above, this is not the case in a dynamic market. The drop in earnings from 1968 through 1987 was not caused by the increase in education over the

same period. Rather, it is likely to have been caused by factors beyond the

changes in education, such as increased competition from foreign producers or

decreased power of labor unions. The increase in education may have kept median earnings from dropping even lower than it did. 

CONCLUSIONS

■ The transition from high school directly into the labor market can be difficult, and

unemployment rates are high for recent high school dropouts and graduates not

enrolled in college. These rates have increased over the past 30 years. They are higher among dropouts than among graduates, and higher among black graduates than

among white graduates. 

■ The differences in earnings between college graduates, high school graduates,

and high school dropouts have increased over time, suggesting that the economic

returns to education have also increased. Recent estimates confirm this trend—economic returns to a college education have increased dramatically, while the economic

returns to a high school education have increased modestly. These changes have

occurred as the average real wages of college graduates have increased and the real

wages of high school graduates and dropouts have declined.

■ The returns to education have increased over time, partly in response to the use

of new production technologies, which increase the demand for highly educated workers and decrease the demand for production workers. Supply factors also play a role,

as the proportion of labor force entrants with college degrees stopped growing in the

1980s, following rapid increases in the 1970s. Meanwhile, competition from immigrants and imports increased the effective supply of less-educated workers.

■ It is not likely that increases in the returns to postsecondary education result primarily from alleged declines in the quality of high school and primary education in the

United States. The real wage decreases experienced by high school graduates

occurred among older workers as well as young workers, and the older workers

attended school before the alleged decline in the quality of education. 

30 Education and the Economy
■ The factors that caused the returns to education to increase also contributed to

greater income inequality in the economy overall. Researchers have argued that

growing income inequality may constrain productivity growth, and this argument has

recently received some empirical support.

■ The ability of increases in education to increase aggregate earnings is somewhat

limited. First, large changes in education will generate market responses that will limit

the earnings impact. Second, many other factors affect earnings, limiting the degree

to which education alone can dictate earnings levels.

■ Over the past 20 years, high school and college completion rates in the United

States have risen slightly while college enrollment rates have increased substantially.

More than three-fifths of high school graduates enroll in college shortly after graduating, compared with about half who did so 20 years ago. The expansion in college

enrollment reflects positive enrollment trends at both two-year and four-year colleges.

Consequences of Educational Attainment 31
CHAPTER 4

Economic Consequences of 

Educational Achievement

Students who complete higher levels of schooling realize greater economic

returns, as measured by employment status and wage rates. But attainment is

only one way to measure the educational experience and the degree to which

education prepares students for work. Students within broad attainment categories may vary significantly in the extent and content of their knowledge and in

their ability to think, learn, and communicate. This knowledge and these skills are

likely to affect a student’s ability to eventually succeed in the labor market, even

after controlling for attainment. The ability of students to succeed is determined

not only by the degrees they earn and the number of years that they attend

school, but also by what they learn. Presumably, achievement in an academic

environment is a reflection of skills and knowledge, and test scores act as indicators of academic achievement. 

In this chapter, we investigate the economic consequences of educational achievement as measured by test scores. We begin by exploring the degree to which labor

market outcomes (employment status and wage rates) of labor force participants

are related to measures of individual educational achievement. Our indicators are

derived from longitudinal data on a sample of young workers from the National

Longitudinal Study of Youth (NLSY). We then consider trends in achievement of

U.S. students over the past 25 years, as reflected in performance trends on the

National Assessment of Educational Progress.

Consequences of Educational Achievement 33
INDICATOR 6

Achievement Test Scores and Employment Status

One approach to assessing the importance of academic skills in the labor market is

to examine the link between achievement test scores and labor market outcomes.

Although test scores may not fully measure all facets of academic achievement,

they form a quantitative basis from which to begin investigating the economic payoffs to achievement. The first set of indicators, which address the unemployment

experiences of workers according to their performance on achievement tests, are

based on data from the NLSY. Part of this database was constructed from responses of NLSY sample members to the Armed Services Vocational Aptitude Battery

(ASVAB).30 The ASVAB consists of tests to measure knowledge in 10 areas.31 Using

the longitudinal data from the NLSY from 1979 through 1993, we examine economic outcomes for sample members ages 19–31 years according to the scores in

three of these areas: mathematics knowledge, general science, and paragraph comprehension.32

Workers who have higher academic skills are better able than workers with lower

academic skills to avoid unemployment. Figure 4.1 shows that workers in the

NLSY with lower ASVAB test scores in mathematics, science, and paragraph comprehension have a higher probability of being unemployed than workers with

higher scores.33 For example, among 28-year-olds, 2.9 percent of workers in the top

quartile of the mathematics test are unemployed, compared with 7.5 percent of

workers in the other three quartiles combined. The patterns are similar for the

unemployment rates for workers by science and paragraph comprehension scores.

In all subjects, workers ages 19–22 years in the bottom two quartiles have higher

unemployment rates than those in the top two quartiles. For example, workers ages

19–22 years with mathematics scores in the third quartile have unemployment rates

between 14 and 21 percent, and those in the bottom quartile have unemployment

34 Education and the Economy

30The administration of the ASVAB to the NLSY sample members is referred to as the Profile of

American Youth (U.S. Department of Defense 1982).

31These 10 areas include general science, arithmetic reasoning, word knowledge, paragraph comprehension, numerical operations, coding speed, auto and shop information, mathematics knowledge, mechanical comprehension, and electronics information.

32Because all sample members completed the ASVAB in the summer of 1980, the age of respondents at the time of testing ranged from 16 through 23 years. To control for differences in age at testing, we assigned individuals to age-specific performance quartiles for each subject area based on

their age at testing.

33We also examined the results for arithmetic and vocabulary scores, but the findings are similar to

those for mathematics and paragraph comprehension, respectively. We therefore excluded the

arithmetic and vocabulary results from our presentation.
rates of 21 to 29 percent. Differences exist even for older workers. The 31-year-old

workers in the bottom quartile of any test have an unemployment rate of about 10

percent, compared with 3 percent for workers in the top quartile.

Some differences in unemployment rates by test scores exist even after controlling for broad differences in educational attainment. Figure 4.2 shows that at least

for workers ages 19–22 years who are high school graduates or have 1–3 years of

college, unemployment rates tend to be lower for workers with scores in the top

Consequences of Educational Achievement 35

Paragraph Comprehension

Figure 4.1

 Unemployment rate of civilian workers ages 19–31 years, by age-adjusted ASVAB score quartile

Mathematics

Science

Percent

0

10

20

30

Percent

0

10

20

30

Percent

0

10

20

30

Age

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

19 20 21 22 23 24 25 26 27 28 29 30 31

Top Quartile Second Quartile Third Quartile Bottom Quartile

NOTE: ASVAB is the Armed Services Vocational Aptitude Battery. To control for differences in age at testing, individuals were assigned to

age-specific performance quartiles for each subject area based on their age at testing. Respondents who were out of the labor force

at any age were excluded from the sample for that age.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, National Longitudinal Survey of Youth, 1979–93.
36 Education and the Economy

NOTE: ASVAB is the Armed Services Vocational Aptitude Battery. To control for differences in age at testing, individuals were assigned to age-specific

performance quartiles for each subject area based on their age at testing. Respondents who were out of the labor force at any age were excluded

from the sample for that age. Data points based on fewer than 30 observations are excluded from the figure.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, National Longitudinal Survey of Youth, 1979–93.

Figure 4.2

Unemployment rate of civilian workers ages 19–31 years,

 by educational attainment and age-adjusted ASVAB score quartile

Top quartile 2nd quartile 3rd quartile Bottom quartile

25

20

15

10

5

0

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

Unemployment rate (percent)

1 TO 3 YEARS

OF COLLEGE

25

20

15

10

5

0

19 20 21 22 23 24 25 26 27 28 29 30 31

Mathematics

Age

Unemployment rate (percent)

HIGH SCHOOL

GRADUATES

25

20

15

10

5

0

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

Unemployment rate (percent)

4 TO 5 YEARS

OF COLLEGE

25

20

15

10

5

0

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

Unemployment rate (percent)

25

20

15

10

5

0

19 20 21 22 23 24 25 26 27 28 29 30 31

Paragraph Comprehension

Age

Unemployment rate (percent)

25

20

15

10

5

0

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

Unemployment rate (percent)

25

20

15

10

5

0

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

Unemployment rate (percent)

25

20

15

10

5

0

19 20 21 22 23 24 25 26 27 28 29 30 31

Science

Age

Unemployment rate (percent)

25

20

15

10

5

0

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

Unemployment rate (percent)
quartile than workers in the bottom

quartile. For 20-year-old high school

graduates, for example, the unemployment rate for workers with mathematics scores in the bottom quartile is 23

percent, compared with less than 10

percent for workers in the top quartile.

In contrast, for workers with four to five

years of college, there do not appear to

be consistent substantial differences in

unemployment rates by test scores

(third column of graphs in figure 4.2). 

Regression estimates also demonstrate

that the probability of unemployment

is negatively related to test scores.

These estimates, which are based on

the sample of 28-year-olds from the

NLSY, largely support the findings

shown in the preceding two figures.34

A composite of the three test scores

shows that 28-year-old workers in the

top three quartiles of test scores have a

roughly 3 to 4 percent lower probability of being unemployed than workers

in the bottom quartile (figure 4.3). The differences in unemployment probability between the top three quartiles, however, are statistically insignificant.

Consequences of Educational Achievement 37

-2.1*

-2.1

-2.0*

-0.0 -1.0 -2.0 -3.0 -4.0 -5.0

-1.7

-0.8

-1.6

Paragraph

Comprehension

Figure 4.3

Estimated relationship between test scores and

unemployment probability (as compared to being in the

bottom quartile), for 28-year-old workers

Science

Mathematics

Percent

Top quartile

Second quartile

Third quartile

-1.3

-3.3*

-4.1*

-1.5

-1.5

-3.5*

Top quartile

Second quartile

Third quartile

Top quartile

Second quartile

Third quartile

Top quartile

Second quartile

Third quartile

Composite

*Significantly less than zero at the 95 percent confidence level in a two-tailed test.

NOTE: The number shown is the percentage impact on the unemployment probability

of being in the specified quartile compared to being in the bottom quartile.

SOURCE: Authors’ calculations based on the National Longitudinal Survey of Youth,

1979–93 data.

34The regression equations include as explanatory variables a set of quartile indicators for each

ASVAB subject area in addition to sets of indicator variables to control for education, father’s

education, mother’s education, local unemployment rates, gender, race–ethnicity, and year of

interview. The regression was run on 28-year-olds in an effort to look at the latest age possible

and still use most of the sample.
INDICATOR 7

Achievement Test Scores and Wage Rates

Workers with higher achievement test scores earn higher hourly wages than

workers with low test scores. Figure 4.4 shows mean hourly wage rates for workers by score quartile for the mathematics, science, and paragraph comprehension

tests. For workers 23 years and older, those with higher scores in each of the

38 Education and the Economy

NOTE: ASVAB is the Armed Services Vocational Aptitude Battery. Respondents reporting hourly pay of less than $1.00 or greater

than $100.00 (1992 dollars) at any given age were excluded from the sample for that age. To control for differences in age at

testing, individuals were assigned to age-specific performance quartiles for each subject based on their age at testing.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, National Longitudinal Survey of Youth, 1979–93.

Figure 4.4

Mean hourly rate of pay for civilian workers ages 19–31 years, by age-adjusted ASVAB score quartile

Paragraph Comprehension

Science

1992 dollars Mathematics

0

4

8

12

16

Age

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

19 20 21 22 23 24 25 26 27 28 29 30 31

1992 dollars

1992 dollars

0

4

8

12

16

0

4

8

12

16

Top Quartile Second Quartile Third Quartile Bottom Quartile
three subjects earn higher hourly wages than workers with lower scores. For

example, 28-year-old workers with mathematics scores in the top quartile earn a

mean hourly wage of $13.50 (in 1992 dollars) compared with a mean hourly wage

of $9.84 earned by workers in the other three quartiles combined—a difference of

37 percent.

Wage differences by test scores may grow larger as workers get older.

Murnane, Willett, and Levy (1995) showed that test scores are a much stronger

predictor of wages six years after high school graduation than two years after

graduation.35 Bishop (1991) suggests two reasons for this age effect. First, academic achievement may improve access to jobs that offer more training opportunities, therefore creating a greater potential for wage growth. Second,

employers may be unable to evaluate the skills of new hires, because they learn

about a particular worker’s productivity only over time. In this case, workers

with higher academic skills are rewarded with higher pay only after the

delayed realization of their greater productivity. 

The effect of test scores on wages also appears to have expanded in recent

years. Murnane, Willett, and Levy (1995) find that the relationship between

scores on basic skills tests conducted in grade 12 and subsequent wages of

young workers was stronger in the mid-1980s than in the late 1970s. They conclude that a high school senior’s mastery of basic skills is an increasingly

important determinant of subsequent wages.

The wage differences by test scores shown in figure 4.4 are not simply attributable to differences in educational attainment. Some differences still exist

even after controlling for broad measures of attainment. For example, workers

in the categories of attainment shown in figure 4.5 who scored in the top two

quartiles of mathematics and science generally earn more at ages 28–31 years

than those who scored in the bottom two quartiles.

Regression estimates based on these data also demonstrate that workers with

higher test scores earn higher wages, even after controlling for other factors.

These findings, which are presented in figure 4.6, show that overall performance on the achievement tests is strongly and positively correlated with

wages for 28-year-olds. Workers with composite test scores in the top two

quartiles earn about 20 percent more than workers in the bottom quartile and

about 10 percent more than workers in the third quartile (figure 4.6).

Consequences of Educational Achievement 39

35Murnane, Willett, and Levy (1995) base their findings on data from the National Longitudinal

Study of the High School Class of 1972 and the High School and Beyond Survey, both of which contain data on academic test scores.
40 Education and the Economy

NOTE: ASVAB is the Armed Services Vocational Aptitude Battery. Respondents reporting hourly pay of less than $1.00 or greater than $100.00 (1992

dollars) at any given age were excluded from the sample for that age. Data points based on fewer than 30 observations are excluded from the figure.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, National Longitudinal Survey of Youth, 1979–93.

Figure 4.5

Mean hourly rate of pay for civilian workers ages 19–31 years,

by educational attainment and age-adjusted ASVAB score quartile

Top quartile 2nd quartile 3rd quartile Bottom quartile

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

1992 dollars

18

16

10

6

2

0

14

12

8

4

1 TO 3 YEARS

OF COLLEGE

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

1992 dollars

18

16

10

6

2

0

14

12

8

4

Science

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

1992 dollars

18

16

10

6

2

0

14

12

8

4

Mathematics

HIGH SCHOOL

GRADUATES

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

1992 dollars

18

16

10

6

2

0

14

12

8

4

Paragraph Comprehension

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

1992 dollars

18

16

10

6

2

0

14

12

8

4

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

1992 dollars

18

16

10

6

2

0

14

12

8

4

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

1992 dollars

18

16

10

6

2

0

14

12

8

4

4 TO 5 YEARS

OF COLLEGE

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

1992 dollars

18

16

10

6

2

0

14

12

8

4

19 20 21 22 23 24 25 26 27 28 29 30 31

Age

1992 dollars

18

16

10

6

2

0

14

12

8

4
On the surface, these findings appear to

contradict the findings of Bishop (1991;

1992), which suggest that achievement

test scores are not strong predictors of

employment and earnings outcomes

for the NLSY sample respondents. But

the differences between Bishop’s findings and our findings may be due to the

use of later data in this report. At the

time of Bishop’s studies, longitudinal

data on employment and wages were

available only for a relatively young

sample of workers.36 This study reflects

the addition of seven years of longitudinal

data to the time frame used by Bishop,

so it is based on substantial samples of

workers up to about 31 years old. The

additional data allow for the examination

of wage differences among samples of

relatively older workers, which, as was

discussed earlier, tend to be larger than

the wage differences among younger

workers.

Consequences of Educational Achievement 41

2.6

15.0*

6.2*

10.6*

Paragraph

Comprehension

Figure 4.6

Estimated relationship between test scores and hourly

wage rates (as compared to being in the bottom quartile),

for 28-year-old workers

Science

Mathematics

Top quartile

Second quartile

Third quartile

10.1*

4.6*

19.5*

Top quartile

Second quartile

Third quartile

Top quartile

Second quartile

Third quartile

Top quartile

Second quartile

Third quartile

Composite

*Significantly greater than zero at the 95 percent confidence level in a two-tailed

test.

NOTE: The number shown is the percentage impact on the hourly wage of being in

the specified quartile compared to being in the bottom quartile.

SOURCE: Authors’ calculations based on the National Longitudinal Survey of Youth,

1979–93 data.

4.6*

4.8*

18.2*

3.9*

4.2

0.0 5.0 10.0 15.0 20.0 25.0

Percent

36Bishop (1991; 1992) used data through 1986.
INDICATOR 8

Trends in Student Achievement Test Scores

Trends in student achievement have recently received a great deal of attention.

This interest has been fueled, in part, by the growing availability of published

aggregate achievement test scores that have been collected to monitor student

academic progress over time. Drawing upon data from numerous different

achievement tests, the Congressional Budget Office (1986) found that most

indicators of student achievement show a constant or an upward trend in the

aggregate scores, which was interrupted by a period of decline beginning in

the mid-1960s and ending in the mid- to late 1970s. Table 4.1 shows the years

marking the decline by different sources of achievement test data. Since the

end of this decline, test scores have generally increased over time and, according

to several measures of academic achievement, most have reached the levels of

average scores that existed before the period of decline.

Table 4.1 

Onset and end of the achievement decline as indicated by selected tests

Test Decline onset Decline end

SAT 1963 1979

ACT composite 1966 1975

ITBS, grade 5 1966 1974

ITBS, grade 8 1966 1976

ITED, grade 12 1968 1979

SAT = Scholastic Assessment Test

ACT = American College Testing

ITBS = Iowa Test of Basic Skills

ITED = Iowa Test of Educational Development

SOURCE: Congressional Budget Office, Trends in Educational Achievement, 1986.

One way to study trends in student achievement is to draw upon cross-sectional data to consider changes in the level of academic performance of typical students over time. The most widely referenced source of cross-sectional achievement data is the National Assessment of Educational Progress (NAEP), which

has provided mathematics, science, and reading achievement test scores for

representative samples of the nation’s 9-, 13-, and 17-year-olds periodically over

42 Education and the Economy
the past 20–25 years.37 The NAEP data

show that while reading test scores

have slowly increased for 13- and 17-

year-olds during the past two

decades, science achievement test

scores declined for 13- and 17-yearolds through the 1970s and early

1980s, and mathematics test scores

declined for 17-year-olds (see figure

4.7). On average, achievement test

scores of 17-year-olds fell by 6 points

in mathematics and 13 points in science from 1973 through 1982.

This declining trend in mathematics

and science achievement for 17-yearolds reversed in the early 1980s. Figure

4.7 shows that achievement in mathematics and science increased throughout the 1980s and into the 1990s for all

ages. For all ages, it is generally the

case that the 1992 NAEP achievement

test scores are at least as high as those

from the late 1960s/early 1970s. The

only exception is 17-year-olds in science. The mean science test score of

these students in 1992 was lower than

their achievement status in 1969.

There are at least two possible explanations for the apparent reversal in the

trend for achievement in mathematics and science. First, the negative public

attention to the decline in test scores in the late 1970s and early 1980s could have

stimulated a more aggressive and ultimately successful effort to generate higher

achievement. Much of the concern over student performance was reflected in A

Nation at Risk (U.S. Department of Education 1983). This document denounced

the falling achievement levels of American youth and encouraged a more rigorous mathematics and science education program for all students. Subsequent

reform efforts may have contributed to the observed improvement in achievement test scores from 1982 through 1992. 

Consequences of Educational Achievement 43

37In addition, NAEP writing test scores have been collected and reported for 4th, 8th, and 12th

graders periodically since 1984.

Figure 4.7

Trends in average U.S. achievement in science, mathematics,

and reading: 1969–92

SOURCE: U.S. Department of Education, National Center for Education

Statistics, The National Assessment of Educational Progress: 1992 Trends in

Academic Progress, 1994.

’69 ’72 ’74 ’76 ’78 ’80 ’82 ’84 ’86 ’88 ’90 ’92

Year

Age 9

Age 13

Age 17

Scale score

Reading

Mathematics

Mathematics

Mathematics

Science

Science

Reading

Reading

Science

290

300

310

320

260

270

240

230

220

200

210

250

280
Second, it could be that as the use and importance of standardized tests have

grown over the past two decades, students’ ability to succeed at these tests has

concomitantly increased, regardless of true gains in achievement. There are several possible sources of test score corruption. The growing reliance on standardized tests as a tool for making schools and teachers accountable is likely to have

inadvertently encouraged coaching or inappropriate teaching to the tests (Koretz

1994). Some practitioners contend that it is easier to teach to the test in mathematics and science than in reading. This view is consistent with the pattern of

improvement in NAEP scores. In addition, it could be that students have simply

benefited from additional experience with this type of test. Their familiarity with

standardized tests could account for a large portion of the observed improvement

in scores over the past decade. 

Trends in achievement by race–ethnicity

are an indication of how the educational

enterprise has performed in terms of

equity. Much attention has focused on

equalizing educational resources among

racial–ethnic groups over the past two

decades, and a trail of litigation—ranging from Brown v. Board of Education in

1957 to cases in over half the states in the

early 1990s—documents this movement.

Trends in achievement by race–ethnicity

are especially salient given the relationship between performance in school and

economic outcomes as measured by

employment and earnings.

The NAEP data show that over the past

20 years, the average achievement of

black and Hispanic 17-year-olds has

generally improved more over time than

that of white 17-year-olds. Figure 4.8

shows that while gaps have consistently

separated the average scores of white

and minority students, the magnitude of

these gaps generally has declined over

time in mathematics and reading. The achievement gap between white and black

students decreased by 35 percent in mathematics from 1973 through 1992, and by

30 percent in reading from 1971 through 1992. This narrowing of the mathematics and reading achievement gaps occurred during the 1970s and 1980s; since

then, the gap has increased in reading and has remained steady in mathematics.

44 Education and the Economy

SOURCE: U.S. Department of Education, National Center for Education Statistics,

The National Assessment of Educational Progress: 1992 Trends in Academic

Progress, 1994.

Figure 4.8

Difference in average NAEP proficiency scores for whites

and blacks and whites and Hispanics age 17 years, by

subject: 1969–92

Year

Average score for white students minus

average score for black students

Science

Reading

Mathematics

0

10

20

30

40

50

60

’69 ’72 ’74 ’76 ’78 ’80 ’82 ’84 ’86 ’88 ’90 ’92

Year

Average score for white students minus

average score for Hispanic students

Science

Reading

Mathematics

0

10

20

30

40

50

60

’69 ’72 ’74 ’76 ’78 ’80 ’82 ’84 ’86 ’88 ’90 ’92
Similarly, the achievement gap between white and Hispanic students closed by

39 percent in mathematics between 1973 and 1992, and by 35 percent in reading between 1975 and 1992. Less progress has occurred in the area of science—

the apparent convergence of white and minority test scores for 17-year-olds is

not significant. Furthermore, despite the progress realized in narrowing the

achievement gaps, noteworthy differences in test scores between white and

minority students still remain.

The NAEP achievement scores have also been reported in terms of proficiency

levels.38 When the scores are considered in terms of proficiency levels, there is

some indication that among 17-year-olds, improvements have been realized in

the middle levels of proficiency, while little progress has been made at the top

level. Student proficiency in the lower levels of mathematics and science has

remained relatively constant over time. Figure 4.9 shows that in mathematics,

some progress was made among 17-year-olds in beginning problem solving

(level two) and reasoning (level three) from 1978 through 1992, but the proportion of 17-year-olds that have mastered multi-step problem solving and

algebra (level four) did not change over the same period. In science, the percentage of 17-year-old students able to analyze scientific procedures and data

(level three) increased by 10 points from 1982 through 1992, but by only five

percentage points from 1977 through 1992. Furthermore, no increase is apparent in the percentage of students able to integrate specialized scientific information (level four). There is still work to be done in linking these proficiency

levels with educational productivity in terms of economic returns. It may be

that different levels of proficiency are more relevant to different types of jobs.

Nonetheless, limited progress at the upper proficiency levels in mathematics

and science may be a source of concern.

Consequences of Educational Achievement 45

38For each subject area in the NAEP, experts anchored five levels of proficiency at 50–point intervals on the subject-specific trend scale and identified the types of knowledge and skills associated

with each level (see U.S. Department of Education 1994c for a more thorough description of the various proficiency levels). In this study, we present results for 17-year-old students and include information only on the top four levels of mathematics and science proficiency (scores of 200, 250, 300,

and 350), since 100 percent of 17-year-olds consistently reach at least the lowest proficiency level (a

score of 100) in these subjects.
46 Education and the Economy

Figure 4.9

Trends in percent of students at or above four mathematics

and four science proficiency levels: 1977–92

Percentage of 17-year-olds at or above mathematics proficiency levels

1978 1982 1986 1990 1992

100

80

60

40

20

0

Beginning skills

and understanding

(level 1)

Numeric operations

and beginning

problem solving

(level 2)

Moderately complex

procedures and

reasoning

(level 3)

Multi-step solving

and algebra

(level 4)

SOURCE: U.S. Department of Education, National Center for Education Statistics, The National Assessment of Educational

Progress: 1992 Trends in Academic Progress, 1994.

Percentage of 17-year-olds at or above science proficiency levels

Understands simple

scientific principles

(level 1)

Applies general

scientific information

(level 2)

Analyzes scientific

procedures and data

(level 3)

Integrates specialized

scientific information

(level 4)

100

80

60

40

20

0

1977 1982 1986 1990 1992
CONCLUSIONS

■ Workers with high academic achievement, as measured by achievement test

scores, earn more and are unemployed less than workers with lower scores. Some differences in wages and unemployment tend to exist even within broad categories of

educational attainment. For high school graduates with higher test scores, there is

generally both a wage payoff for older workers and lower unemployment for younger

workers. For workers with four to five years of college, there is no clear unemployment payoff, but there is a wage payoff associated with mathematics and science

scores. Regression estimates further support the positive link between test scores and

labor market outcomes.

■ Numerous achievement tests show constant or upward trends in the aggregate

scores of students, interrupted by a period of decline beginning in the mid-1960s and

ending in the mid- to late 1970s. According to NAEP scores, there generally has been

no overall decline in math, science, or reading achievement over the past 20 years.

In general, scores have increased through the 1980s and early 1990s, offsetting

some prior declines in achievement in the 1970s. However, little progress has been

realized in the number of students mastering the top levels of proficiency in mathematics and science.

■ The achievement gap that has historically separated white and minority students

has decreased over the past 20 years in math and reading, although large gaps still

remain. The convergence of scores between the races is due to increases in the

scores of minority students coupled with stability or smaller increases in scores of

white students. This suggests that the largest racial group (white students) has made

less progress, on average.

Consequences of Educational Achievement 47
Consequences of Adult Literacy 49

CHAPTER 5

Economic Consequences of Adult Literacy

The previous chapter linked academic achievement of individuals as students to

their eventual performance in the labor market. Alternative measures based on adult

literacy can be used to evaluate adults once they are in the labor market. The term

literacy in this context refers to the skills individuals need to use printed and written

information, including quantitative information, to function successfully in their

work and personal lives. Some observers are concerned that there is a mismatch

between the supply of and the demand for literacy skills in the labor force in the

United States. Although not all skills required in the workplace can be characterized

as literacy skills, they are likely to play an important role in the workplace. They may

even be more important than academic achievement for the population at large

because they are likely to be important in all types of tasks and settings.

Information about adult literacy was provided recently by the 1992 National Adult

Literacy Survey (NALS) sponsored by the National Center for Education Statistics.

The survey was initiated to fill the need for accurate and detailed information on the

English literacy skills of America’s adults. For the purpose of the survey, a national

panel of experts defined literacy as “using printed and written information to

function in society, to achieve one’s goals, and to develop one’s knowledge and

potential” (U.S. Department of Education 1993a). To investigate and measure literacy, the survey contained a series of exercises that required respondents to read and

interpret written material, compare and contrast findings, complete various forms,

make arithmetic calculations, and write short letters. Respondents’ proficiencies

were measured on prose, document, and quantitative scales, ranging from 0 to 500.

To capture the progression of information-processing skills, each scale was divided

into five levels: level one (0 to 225), level two (226 to 275), level three (276 to 325),

level four (326 to 375), and level five (376 to 500). A low score (level one) indicates

that an individual has very limited skills in processing information from tables,

charts, graphs, and maps, even those that are brief and uncomplicated. On the other

hand, a high score (level five) indicates advanced skills in performing a variety of

tasks that involve the use of complex documents.39

In this chapter, we examine the relationship between literacy scores and labor market outcomes to identify the literacy skills that pay off in the labor market. The data

necessary to examine the impact of literacy on worker productivity is unavailable.

Therefore, in examining the link between literacy and productivity, unemployment

and earnings are used as indicators of productivity. 

39A detailed description of the prose, document, and quantitative literacy levels is contained in U.S. Department of Education (1993a).
INDICATOR 9

Literacy and Unemployment

Unemployment tends to be correlated with low literacy. Figure 5.1 shows that the

unemployment rate is generally higher for individuals with lower literacy levels

on all three of the scales used in the NALS. Unemployment rates are especially

high for workers in the two lowest literacy levels on each scale (levels one and

two). For instance, the unemployment rate for these workers ranges from 12 percent for those with level two quantitative skills up to 20 percent for those with

level one quantitative skills. The unemployment rate for workers in the top three

literacy levels in each scale (levels three through five) is 9 percent or less. 

50 Education and the Economy

Figure 5.1

Unemployment of adult labor force participants,

by proficiency level on three literacy scales: 1992

Prose Document Quantitative

0

5

10

15

20

25

Percent

unemployed

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National Adult Literacy Survey, forthcoming;

U.S. Department of Education, National Center for Education Statistics, National Adult Literacy Survey, 1992.

19.9

8.8

12.5

4.9

2.7

16.9

7.9

15.4

5.6

4.2

17.1

8.4

12.9

5.0 5.2

Level 1 Level 2 Level 3 Level 4 Level 5
Literacy affects unemployment

even beyond the degree to which it

is correlated with educational

attainment. Within most categories

of attainment, such as high school

diploma only (figure 5.2), the average proficiency of the unemployed

is less than that of the employed.

Even after controlling for levels of

educational attainment, workers

with higher literacy levels are still

less likely to be unemployed. This

finding is supported by regression

analysis of these literacy data in

Sum (forthcoming), which shows

that a 60-point increase in prose,

document, or quantitative literacy

reduces the probability of unemployment by about 2 percentage points.

The connection between literacy and unemployment may exist for many reasons.

First, if individuals with low literacy levels are less productive, they may be at

greater risk of being laid off than workers with higher levels of literacy. This

would translate into greater layoff frequency and more unemployment. Once

unemployed, low-literacy workers may also have more trouble finding a new

job than workers with higher literacy. Low-literacy workers who lose their jobs

would therefore probably face longer unemployment spells than high-literacy

workers who lose their jobs. This could happen either because low literacy

tends to make workers less attractive to employers or because they cannot

search for work as effectively as other job seekers. Finally, those with low literacy levels may make unwise labor market decisions that negatively affect

their job stability. For example, low-literacy workers may not be able to accurately evaluate alternative job prospects; therefore, they quit jobs on the basis

of flawed evaluations of their prospects.

Consequences of Adult Literacy 51

0

50

100

150

200

250

300

350

Prose Document Quantitative

Unemployed Full-time employed

Figure 5.2

Average proficiencies of the unemployed and full-time employed

with high school diplomas only, by literacy scale: 1992

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National

Adult Literacy Survey, forthcoming; U.S. Department of Education, National

Center for Education Statistics, National Adult Literacy Survey, 1992.

259

275

256 256

272 278

Proficiency

score
INDICATOR 10

Literacy and Earnings

Overall, full-time workers with high literacy skills earn more, on average, than fulltime workers with low literacy skills. The earnings advantage for high-literacy

workers is evident for each of the three literacy scales. On the prose scale, for example, full-time workers at level three earn a mean weekly wage that is 50 percent

higher than the wage for their counterparts at level one, and those at level five earn

a weekly wage that is 71 percent higher than the average wage of those at level

three (figure 5.3).

52 Education and the Economy

Figure 5.3

Mean weekly earnings of full-time workers,

by proficiency level on three literacy scales: 1992

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National Adult Literacy Survey, forthcoming; U.S.

Department of Education, National Center for Education Statistics, National Adult Literacy Survey, 1992.

Mean weekly

earnings

355 355 330

436 458 438

531 553 533

709 710 684

910

807

913

Prose Document Quantitative

$0

$200

$400

$600

$800

$1,000

Level 1 Level 2 Level 3 Level 4 Level 5
The effect of literacy on earnings, however, is not simply the result of variations in education. Some differences in average earnings by literacy level exist

even within categories of educational attainment. For example, college graduates with a level five proficiency on any scale have greater earnings than college graduates with a level two proficiency on the same scale (figure 5.4). For

the prose scale, college graduates in level five earn $993 per week compared

with $677 per week for college graduates in level two—a difference of 47 percent. Sum (forthcoming) also conducted extensive regression analysis of the

impact of literacy on employment and earnings outcomes. His findings

demonstrate that literacy has both positive direct and positive indirect effects

on employment and earnings. The indirect effect occurs because individuals

with higher literacy tend to acquire higher education, which leads to more stable employment and higher earnings. But individuals with higher literacy also

have more favorable employment and earnings outcomes even after controlling for their education level. 

Consequences of Adult Literacy 53

NOTE: No figure is available for quantitative literacy level 1 because there are too few college graduates in level 1

on the quantitative scale to generate reliable estimates.

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National Adult Literacy Survey, forthcoming;

U.S. Department of Education, National Center for Education Statistics, National Adult Literacy Survey, 1992.

Mean weekly

earnings

Prose Document Quantitative

Figure 5.4

Mean weekly earnings of full-time employed college graduates,

by proficiency level on three literacy scales: 1992

$0

$200

$400

$600

$800

$1,000

$1,200

586

739

677

866

993

632

787

717

878 904

731

610

865

1,031

Level 1 Level 2 Level 3 Level 4 Level 5
Enhanced prose and quantitative literacy could be an important ingredient

in any prospective improvement in the

economic condition of black workers

compared with white workers. In the

aggregate, black workers earn significantly less than white workers. Mean

weekly earnings among black workers

in the 1992 NALS sample were $425 or

73 percent of the $582 earned by white

workers. But the differences in earnings were smaller among individuals

at the same quantitative and prose

proficiency levels. For instance, in

terms of quantitative literacy, the

mean weekly earnings of black workers ranged from 92 to 98 percent of

those of whites, depending on the proficiency level (figure 5.5).

54 Education and the Economy

Figure 5.5

Weekly earnings of full-time black workers as a

percentage of white workers’ earnings,

by literacy proficiency level: 1992

NOTE: No figures are available for literacy level 5 because there are too few

cases to provide reliable estimates.

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National

Adult Literacy Survey, forthcoming; U.S. Department of Education, National

Center for Education Statistics, National Adult Literacy Survey, 1992.

0

20

40

60

80

100

Black workers’ earnings as a percentage

of white workers’ earnings

Level 1 Level 2 Level 3 Level 4

Prose Quantitative

82

98

93

87

95 93 92

97
INDICATOR 11

Literacy Levels of the Labor Force and 

New Job Entrants

Indicators 9 and 10 clearly demonstrate that literacy is strongly related to individual

success in the labor market. Given this relationship, it is disappointing to find that

the literacy proficiency of a substantial proportion of the U.S. labor force is limited.

Approximately 40 percent or more of the adult labor force perform at the two lowest levels on each of the literacy scales (figure 5.6). For example, 43 percent of labor

force participants perform at the two lowest document literacy levels—15.8 percent

at level one and 27.2 percent at level two. This finding suggests that a substantial

fraction of U.S. workers lack the skills needed to interpret, integrate, and compare or

contrast information using written materials common to the home or workplace.

These workers appear to be unable to perform the types of tasks typical of certain

occupations that demand high skills, such as professional, managerial, technical,

high-level sales, skilled clerical, or craft and precision production occupations.

While a large proportion of the U.S. labor force has limited literacy skills, only a

small proportion of the labor force performs at the highest literacy levels. For each

literacy scale, 5 percent or fewer of labor force participants score in the highest proficiency level, demonstrating an ability to perform well on a wide array of literacy

tasks.

Consequences of Adult Literacy 55

Figure 5.6

Percentage of labor force in each proficiency level on the three literacy scales: 1992

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National Adult Literacy Survey, forthcoming; U.S.

Department of Education, National Center for Education Statistics, National Adult Literacy Survey, 1992.

Percent

0

20

30

40

50

10

25.3

14.4

35.2

20.8

4.2

27.2

15.8

34.6

19.0

3.4

24.5

15.2

34.5

20.8

5.0

Prose Document Quantitative

Level 1 Level 2 Level 3 Level 4 Level 5
Given the positive relationship between literacy and success in the labor market,

increases in literacy should contribute to the productivity of U.S. workers. To

examine recent trends in literacy among U.S. workers, we compared the literacy

scores of respondents in the 1992 NALS and the 1985 NAEP Young Adult

Literacy Survey (YALS).

Findings from these studies suggest that the literacy levels of young adults from

ages of 21–25 years may have declined in recent years. Adults in this age range, most

of whom are current or soon-to-be job entrants, performed less well in 1992 than the

comparable group in 1985 (table 5.1). In addition, the cohort of adults who were ages

21–25 years in 1985 appear to have had lower test scores in 1992, when they were

ages 28–32 years, than they did in 1985.

Table 5.1 

Average proficiency on literacy scales for young adults, 

by race–ethnicity: 1985 and 1992

PROSE

Young Adult Literacy National Adult Literacy Survey - 1992 Survey - 1985

(ages 21–25) (ages 21–25) (ages 28–32)

Total 293 281 283

White 305 296 301

Black 248 256 251

Hispanic 251 231 223

DOCUMENT

Young Adult Literacy National Adult Literacy Survey - 1992 Survey - 1985

(ages 21–25) (ages 21–25) (ages 28–32)

Total 292 281 281

White 305 296 300

Black 248 254 245

Hispanic 243 233 225

QUANTITATIVE

Young Adult Literacy National Adult Literacy Survey - 1992 Survey - 1985

(ages 21–25) (ages 21–25) (ages 28–32)

Total 293 279 282

White 304 295 301

Black 252 244 240

Hispanic 253 229 223

SOURCE: U.S. Department of Education, National Center for Education Statistics, Adult Literacy in America:

A First Look at the Results of the National Adult Literacy Survey, 1993a; U.S. Department of Education,

National Center for Education Statistics, National Adult Literacy Survey, 1992; NAEP Young Adult Literacy

Survey, 1985.

The influx of new immigrants in the late 1980s may have contributed to these patterns. Recent immigrants have much lower average scores as measured on the literacy scales than either the native-born population or immigrants who have lived

56 Education and the Economy
in the United States for more than 10 years. Among employed respondents to the

1992 NALS, the mean scores of recent immigrants were 25–30 points below the

scores of other foreign-born respondents and 89–92 points below the scores of

native-born respondents. The influx of immigrants from 1985 through 1992 is

reflected in the increase in the proportion of the population that is Hispanic.

These findings on the apparent decline in literacy should be interpreted cautiously for at least two reasons. First, the data provide only two observation

points. Further observation points are necessary to establish a trend in literacy.

Second, the procedural differences in the application of the NALS and YALS may

make comparisons difficult. A reevaluation of the NALS and YALS data is currently being conducted by NCES and the Educational Testing Service.

Preliminary estimates suggest that after controlling for procedural differences

between the NALS and YALS, the estimated decline in literacy may be smaller

than originally indicated and possibly insignificant.

CONCLUSIONS

■ Workers with higher literacy earn more and experience less unemployment than

workers with lower literacy. Differences in unemployment and earnings by literacy

level exist even within broad categories of educational attainment.

■ Literacy appears to account for a large proportion of the earnings differences

between black and white workers. Within the same proficiency level, black workers

earn nearly as much as white workers. 

■ The literacy proficiency of a substantial proportion of the U.S. labor force is limited, and only a small proportion of workers perform at a high literacy level. These

findings suggest that many U.S. workers are unable to perform the types of tasks that

are typical of occupations that demand high skills.

■ Literacy levels of young adults may have declined from 1985 through 1992.

However, because we have data for only two points in time and the data may not be

comparable, we cannot conclude definitively that literacy is declining.

Consequences of Adult Literacy 57
CHAPTER 6

The Role of Occupations in the 

Education-Earnings Link

Workers with higher levels of educational attainment and literacy may have higher earnings and greater employment stability in part because their educational

advantage gives them access to occupations in which they can maximize their

productivity and receive more generous compensation. Workers with low levels

of education and literacy, for the most part, are shut out of these occupations. In

this chapter, we examine trends in earnings, literacy levels, and educational

attainment by occupation. Our findings show that occupations which are expanding most rapidly and in which earnings are highest also tend to have the most

educated and literate workers. Although high education and high literacy may

not be essential for a worker to move into these occupations, our findings suggest

that entrants into these occupations must compete against a highly educated,

highly literate group. 

Education-Earnings Link 59
INDICATOR 12

Education and Literacy of Workers 

by Occupation

Employment generally is growing fastest in the occupations that have workers

with relatively high education levels and high literacy skills. Of the 11 occupational groups shown in figure 6.1, the three groups with the highest proportion

of college graduates are professional workers, technical workers, and managers

and executives. Workers in these same three occupational groups also have the

highest average literacy scores. Employment growth in these high-education,

high-literacy occupations was also strong. Employment growth in each was 40

percent or greater from 1979 through 1992. Relatively strong employment

growth is expected to continue in these occupations, with projected increases in

each of at least 25 percent from 1992 through 2005 according to the Bureau of

Labor Statistics. In some occupations with less-educated, less-literate workers—

farming, forestry, and fishing, and the combined occupations of transportation,

laborers, and operators—employment declined from 1979 through 1992 and is

projected to grow by less than 10 percent from 1992 through 2005.

Occupations dominated by workers with substantial education and literacy

also tend to be high-paying occupations. Median weekly earnings in the professional and managerial categories, for instance, are nearly twice the median

for occupations with less than 10 percent college graduates and the lowest

average literacy levels, with the exception of craft production. Services, the one

fast-growing occupation dominated by less-educated, less-literate workers, has

the lowest median earnings of all the occupations shown in figure 6.1. 

The relationship between literacy, education, and access to occupations is illustrated by the education and proficiency characteristics of workers in the highest- and

lowest-paying occupations. The characteristics of workers in the highest-paying

occupations (professional, managerial, and technical) are reflected in table 6.1. The

table shows the proportion of workers at each literacy and education level who hold

a job in one of the highest-paying occupations. Workers in the highest-paying occupations tend to have both high literacy and high education levels. Nearly 83 percent

of workers with a college degree and the highest level of prose literacy have a job in

one of the highest-paying occupations. The proportion of workers in these occupations drops to 46 percent for college graduates at the lowest level of prose literacy.

Literacy is therefore associated with access to these jobs, even holding educational

60 Education and the Economy
Education-Earnings Link 61

0 100 200 300 400

329

321

326

319

310

321

309

308

308

296

290

293

267

267

275

290

285

291

258

260

266

262

259

258

245

242

248

247

245

249

248

247

254

Professional

Technical

Service

Farming and

forestry

Assemblers

and operators*

Laborers and

cleaners*

Transport

operatives*

Craft

production

Sales

Administrative

support

Managers and

executives

Mean literacy proficiency scores

596.0 43.0 37.4 71.5 9.0

652.0 50.4 25.9 47.2 24.9

488.0 57.6 32.2 28.9 25.7

341.0 15.0 13.7 13.1 49.6

346.0 30.7 20.6 22.0 45.4

470.0 4.3 13.3 6.3 67.8

232.0 24.6 33.4 6.4 67.8

331.0 -10.3 9.5 3.4 80.2

258.0 -5.2 3.4 7.7 74.9

Quantitative

Document

Prose

Figure 6.1

Job growth and characteristics of workers, by occupation

*Separate data are not available for transport operatives, laborers and cleaners, and assemblers and operators. The data for these categories are shown as a

group.

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National Adult Literacy Survey, forthcoming; George T. Silvestri, “Occupational Employment:

Wide Variations in Growth,” Monthly Labor Review (November 1993): 58–86; U.S. Department of Education, National Center of Education Statistics, National Adult

Literacy Survey, 1992; U.S. Department of Commerce, Bureau of the Census, Current Population Survey, 1992.

Percent

High

Median Percent Percent Percent School

Weekly Job Projected College Graduates

Earnings Growth Growth Graduates or Less,

($), 1992 1979–92 1992–2005 1992 1992
attainment constant.40 A similar point is true of variations in education: workers

with higher educational attainment are more likely to be in the highest-paying

occupations, even holding literacy constant. These findings suggest that both literacy and education contribute to an individual’s ability to obtain a job in the highestpaying occupations.

Table 6.1

Percent of employed 22- to 65-year-olds holding professional, 

managerial, or technical jobs, by educational attainment 

and proficiency level on the prose scale

Proficiency level All

Educational proficiency 

attainment 1 2 3 4 5 levels

0 to 8 years 2.0 5.5 5.5 38.7 * 3.0

9 to 12 years,

no diploma or GED 2.0 4.7 6.2 11.0 * 4.2

High school diploma or

GED 5.6 8.5 9.7 12.4 15.2 9.1

Some college, 

no degree 8.8 16.8 21.3 29.0 43.9 22.1

Two-year degree 27.6 29.1 36.8 43.2 40.0 37.7

Four-year degree or higher 46.1 56.2 64.3 74.9 82.7 71.0

All educational 

attainment levels 5.3 13.5 26.1 49.9 71.8 27.4

*Too few cases to provide reliable estimates.

NOTE: The number shown in each cell is the proportion of workers with the indicated proficiency

and education levels who hold professional, managerial, or technical jobs. For example, 2 percent

of workers with 8 years or less of schooling and at prose proficiency level one hold professional,

management, or technical jobs.

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National Adult Literacy Survey,

forthcoming; U.S. Department of Education, National Center for Education Statistics, National Adult

Literacy Survey, 1992.

62 Education and the Economy

40The relationship between jobs and literacy may also exist, in part, because jobs affect literacy; that

is, workers who stay in certain occupations may increase their level of prose proficiency over time.
Increased literacy and education also decrease the probability that workers

hold jobs in the lowest-paying occupations (service, laborers/helpers/cleaners, and farming/fishing/forestry). Only 2.3 percent of college graduates with

the highest proficiency in prose hold jobs in the lowest-paying occupations

(table 6.2). In contrast, nearly 50 percent of those individuals with the lowest

proficiency and education levels work in these lowest-paying occupations. A

worker who has not completed high school and has a level two proficiency or

less in the prose category has at least a 40 percent chance of being in one of the

lowest-paying occupations. This is nearly double the percentage of the full

population that is in these occupations. 

Table 6.2 

Percent of employed 22- to 65-year-olds holding service, laborer/helper/cleaner,

or farm/forestry/fishing occupations, by educational attainment and proficiency

level on the prose scale

Proficiency level All

Educational proficiency 

attainment 1 2 3 4 5 levels

0 to 8 years 49.0 41.5 38.4 23.7 * 46.6

9 to 12 years,

no diploma or GED 42.5 39.9 34.4 28.1 28.5 39.3

High school diploma or 

GED 44.4 30.5 25.1 19.3 8.2 28.9

Some college, 

no degree 39.6 24.6 19.1 15.0 9.1 20.2

Two-year degree 19.9 14.2 13.5 11.6 10.5 12.9

Four-year degree or higher 11.2 7.3 7.5 4.6 2.3 5.5

All educational

attainment levels 43.5 29.4 19.5 10.5 4.0 22.6

*Too few cases to provide reliable estimates.

NOTE: The number shown in each cell is the proportion of workers with the indicated proficiency

and education levels who hold service, laborer/helper/cleaner, or farm/ forestry/fishing jobs. For

example, 49 percent of workers with 8 years or less of schooling and at prose proficiency level

one hold jobs of this kind.

SOURCE: Andrew Sum, Literacy and the Labor Force: Results of the National Adult Literacy Survey,

forthcoming; U.S. Department of Education, National Center for Education Statistics, National Adult

Literacy Survey, 1992.

Education-Earnings Link 63
DISCUSSION: JOB SKILLS MISMATCH

The issue of a mismatch between the skills demanded by U.S. employers and those

offered by workers has been hotly debated. The Workforce 2000 study sponsored by

the U.S. Department of Labor popularized the idea that, as we approach the year

2000, U.S. producers will encounter an insufficient supply of professional, technical,

and managerial workers and an oversupply of less-skilled workers. The primary

cause of this trend is technological change, which researchers and policymakers

argue requires more highly skilled labor than was needed in the past. This trend is

reflected in the statistics presented in the previous section, which demonstrated that

occupations with the greatest growth are those in which workers are most highly

educated. Based on this prediction, researchers and policymakers have argued that

the current and future work force will need to be better educated and more highly

skilled if U.S. producers are to have access to the labor needed to exploit new

technologies, maintain productivity and wage growth, and compete effectively in

international markets. Bishop (1992) argues that based on current projections of skill

shortages, public policy should be used to stimulate a substantial increase in the

supply of college graduates. Cappelli (1996) presents findings based on the EQW

establishment survey suggesting that changes in the workplace and adoption of new

technologies are increasing skill requirements even among production workers.

Not all researchers are convinced by the arguments in Workforce 2000 and the projections of shortages of skilled labor. Mishel and Teixeira (1991), for example, argue that

contrary to the Workforce 2000 projections of increased skill requirements, the trend

toward higher-skilled occupations will slow down in the future. In addition, they

argue that “up-skilling” within occupations due to technological change does not

appear to be widespread. Mishel and Teixeira (1991) conclude that the prediction of

a skills shortage has tended to overstate the growth in professional and technical jobs,

and led to an overemphasis on college education. They assert that the more important challenge is to improve the jobs, pay, and skills of the noncollege-educated work

force. 

Regardless of the changes in the aggregate demand for skills, the issue faced by

workers is whether, given the wage structure, it is to their advantage to seek greater

skills. The increased wage premium paid to college graduates discussed in chapter 3

clearly implies that the economic returns to college education are increasing. Such an

increase in returns is a direct indication that demand for college graduates has

increased more rapidly than supply in recent years. The increase in returns also

suggests that expansion of college education is probably still a valuable investment,

despite the argument made by Mishel and Teixeira (1991) that there is already an

overemphasis on college education. Even if low-skill jobs are readily available, the

average worker is likely to continue to need higher skills and more education to

increase productivity, compete for desirable jobs, and expand his or her earnings

potential. 

64 Education and the Economy
INDICATOR 13

College Degrees and Employment in College Jobs

Despite the increase in earnings for college graduates relative to other workers,

some researchers have expressed concern about the types of jobs being taken by

college graduates. The proportion of college graduates holding jobs that traditionally have been held by workers without a college degree has increased over

the past two or three decades (figure 6.2).41 From 1967 through 1990, the rate at

which college graduates held “noncollege” jobs increased from 10 to 20 percent.

This trend has been interpreted by some as an indication that there is an excess

supply of college graduates and that they are being forced to take jobs that do not

require a college education (Hecker 1992).

The increase in the number of college

graduates in traditionally noncollege

jobs does not imply, however, that students are unwisely investing in a college education. The phenomenon is

probably inevitable given the large

increase in the proportion of workers

with college degrees over the past two

or three decades. The share of the labor

force that graduated from college

increased from 12 percent to 23 percent

over the period shown in figure 6.2. For

all the new college graduates to have

been employed in jobs traditionally

held by college graduates, the number

of these jobs would have needed to

grow at a similar rate and represent a

much larger proportion of all jobs. Such

changes in employment did not occur.

However, the increase in the number of college graduates indicates that employers found ways to use these graduates productively in “noncollege” jobs. At the

Education-Earnings Link 65

NOTE: Unemployed graduates are counted as college graduates in nondegree

jobs. Data refer to the noninstitutionalized population ages 16 years and

older.

SOURCE: Daniel H. Hecker, “Reconciling Conflicting Data on Jobs for College

Graduates,” Monthly Labor Review (July 1992): 3–12; U.S. Department of

Commerce, Bureau of the Census, Current Population Survey.

Figure 6.2

College graduates in the labor force and in jobs

that do not require college degrees: 1967–90

Year

’67’68 ’70 ’72 ’74 ’76 ’78 ’80 ’82 ’84 ’86 ’88 ’90

Percent

25

20

15

10

5

0

Percent of college graduates

in nondegree jobs

Percent of labor force members

with college degrees

41Our treatment of jobs follows Hecker (1992). Jobs traditionally held by college graduates include

those in managerial, professional, sales representative, or technician occupations. They are also traditionally employed as police officers, blue-collar worker supervisors, farm managers, or seniorlevel administrative support workers. Jobs traditionally held by nongraduates include those in

retail sales, administrative support, service, precision production, farming, and craft and repair.

They are also traditionally employed as operators, fabricators, and laborers. 
same time, the increasing returns to education suggest that college graduates

continued to be rewarded for their educational investment, even as more of

them moved into these “noncollege” occupations, which suggests that these

occupations may have changed in such a way as to use college-educated workers

efficiently.

The rapid increase in college graduates may have also diluted, to some extent, the

average “quality” of college graduates as more students of lesser ability are

drawn into college. This would be likely to contribute to an increase in the proportion of college graduates holding traditionally noncollege jobs. Students with

limited abilities even after having completed a college education may not qualify

for jobs that traditionally have been held by workers with a college degree.

Therefore, more graduates taking noncollege jobs is not necessarily a negative

indicator of the market for college graduates. Rather, the critical question in terms

of productivity is whether college graduates who take noncollege jobs are more

productive having attended college than they would have been had they not

attended college, regardless of their occupation. Findings of wage differentials

between college graduates and other workers within the same occupation suggest

that this may be the case.42

CONCLUSIONS

■ Occupations that have the greatest recent job growth and highest earnings are

those in which employees have the most education.

■ High-paying, high-growth occupations also are filled with high-literacy workers.

Workers with limited education or limited literacy are generally shut out of the bestpaying occupations.

■ An increasing number of college graduates now hold jobs traditionally held by

workers without college degrees. But this is not an indication that students are making unwise investments in higher education. On the contrary, the returns to college

education have increased over time, even as more graduates take jobs in these other

occupations.

66 Education and the Economy

42This argument is made by Alsalam (1993) and is supported by data presented in Tyler, Murnane,

and Levy (1995).
CHAPTER 7

Training of Labor Force Participants

Education clearly has a role in building human capital by making students

more productive workers. As a general investment in human capital, education tends to raise an individual’s ability to succeed in the labor market in general, as opposed to raising productivity in a specific job or occupation.

Although most workers complete their formal education before joining the

labor force, the investment in human capital does not end at that time. Through

training, many workers continue to improve their skills throughout their lives.

Training can be formal or informal. Formal training would include school programs, such as those at a high school or postsecondary institution, or special

company programs to improve a specific set of skills. Informal training would

include on-the-job training that often occurs at the beginning of a job or work

assignment. This may involve self-study or learning through developmental

assignments. Compared with education, training tends to represent a more

specific investment in human capital—it is usually intended to develop particular job skills or increase productivity in a particular task, job, or occupation.

It has been argued that U.S. workers are less skilled than European and

Japanese workers, partly because U.S. workers do not have the same access to

training as workers do in other countries.43 On the basis of this alleged deficiency in U.S. training, researchers have argued that the United States must

expand work force training in order to compete internationally (U.S.

Department of Labor 1989) or to reverse a perceived slowdown in the growth

of worker productivity in the United States since the early 1970s (Goldstein

1980). But an expansion of training can only have an impact on productivity

growth if training has a positive impact on the productivity of individual

workers. One objective of this chapter is to investigate the relationship

between training and individual productivity. The other objective is to examine the extent of training of U.S. workers and the characteristics of those who

receive training.

Two indicators are presented in this chapter. The first addresses the relationship between training and the productivity and earnings of individual workers.

As in previous chapters, we focus on earnings of individual workers as a proxy

Training of Labor Force Participants 67

43 U.S. Congress (1990) and Lynch (1993) discuss the training of U.S. workers compared with workers in other countries.
for productivity. The data used for this indicator are drawn from the 1991

Current Population Survey (CPS) training supplement, which was used to

gather information on the training experiences of workers across the United

States. We also discuss estimates from the most recent econometric studies of

the impact of training on earnings and productivity. 

The second indicator measures the prevalence of employment-related training

in the United States. Using data from the 1991 CPS and the 1991 National

Household Education Survey (NHES), we measure rates of participation in

training among U.S. workers in 1991. We also use CPS data from 1983 and 1991

to examine the trends in training participation in the United States between

these two years. Finally, we examine the training rates by education level and

occupation.

68 Education and the Economy
INDICATOR 14

Training and Earnings

The economic theory of the accumulation of human capital argues that individual workers can use training to increase their productivity, which in turn will

increase their earnings as they become more valuable to employers. This section

compares average earnings of two groups of workers—those who participated in

training on their current job and those who did not.

Data on average earnings from the 1991 CPS training supplement suggest that

participation in skill improvement training is positively related to the earnings of

workers (Eck 1993). Figure 7.1 shows results for two groups—one group that

needed qualifying training for their current job and the other group that needed

no qualifying training. Within these two groups and within four broad education

categories, the figure shows the median weekly earnings for workers who participated in training since they were hired in their current job and for workers who

did not participate in training since they were hired. Within each of the education

classifications, earnings in 1991 were higher for workers who participated in skill

improvement training while on their current job than for those who did not. In

percentage terms, the lowest difference in earnings in figure 7.1 is 11 percent

($683 per week for trainees compared with $616 for nontrainees) for college

graduates in jobs with qualifying training. The largest difference in earnings is 33

percent ($471 for trainees compared with $353 for nontrainees) for workers with

one to three years of college who are in jobs not requiring qualifying training.

The earnings differences between trainees and nontrainees presented in figure 7.1

are probably biased estimates of the effect of training on earnings because

trainees and nontrainees differ in ways that affect their relative earnings but that

are not controlled for in the calculations of figure 7.1. For example, trainees may

earn more simply because they are in higher-paying occupations than nontrainees (a similar point is addressed in chapter 3 in the discussion of Indicator 4

and the impacts of education on earnings). In addition, the phrasing of the training question in the CPS, which refers to any training since the start of the current

job, implies that trainees are likely to have longer average tenure than nontrainees

with their current employer. This is true because workers with longer tenure have

had more time to participate in training since the start of their current job, so the

probability of having participated in training at the current job is greater for

workers with longer tenure. Since tenure has been shown to be positively related

to earnings, the tenure differences may explain why trainees earn more than

Training of Labor Force Participants 69
nontrainees. Of course, observable differences between trainees and nontrainees,

such as differences in average tenure, could be controlled for in a regression

analysis. But some differences between trainees and nontrainees may not be

observable. For example, trainees may be more “motivated” than nontrainees.

70 Education and the Economy

$200 $400 $600 $800

Median weekly earnings

405

465

515

683

319

380

422

616

Workers who needed qualifying training for current job

Fewer than 12 years

High school graduate

1 to 3 years of college

College graduate

Highest education

level completed

Figure 7.1

Median weekly earnings of full-time workers ages 16 years and older,

by training status and educational attainment: January 1991

SOURCE: Alan Eck, “Job-Related Education and Training: Their Impact on Earnings,” Monthly Labor Review (October

1993): 21–38; U.S. Department of Commerce, Bureau of the Census, Current Population Survey, January 1991.

$0 $200 $400 $600 $800

Median weekly earnings

333

263

396

471

601

314

353

461

Workers who needed no qualifying training for current job

Workers who participated

in training since start of

job

Workers who did not

participate in training

since start of job

Fewer than 12 years

High school graduate

1 to 3 years of college

College graduate

Highest education

level completed

$0
Such differences might suggest that the trainees would earn more than nontrainees even without having participated in training. It would be a mistake in

this case to attribute all of the earnings difference to the impact of training. 

More sophisticated estimates of the impact of training on earnings, which

attempt to control for the observed and unobserved differences between

trainees and nontrainees, tend to show that training on the current job does

have a positive impact on wages. Using longitudinal data on men from the

NLSY, Lillard and Tan (1992) show that in the first year after training, training

is associated with an 11.9 percent increase in earnings. In subsequent years,

this effect decreases by 1.1 percentage points per year so that the net effect of

training drops to zero by the eleventh year after training. In terms of source,

training from the company had the largest effect on post-training earnings (16

percent), followed by training from business and vocational schools (11 percent), and regular school and other sources (8 percent).44 The rates at which

these effects decreased over time is similar across sources.45

Blanchflower and Lynch (1994) also present evidence that formal training has a

positive impact on earnings. Using a sample of 25-year-old noncollege graduates

(from the NLSY), they show that training provided by the current employer

increases hourly earnings by an estimated 8 percent, while training provided by

a previous employer has little effect on earnings.46 Participation in off-the-job

training increases hourly earnings by 4 percent, while participation in an apprenticeship increases hourly earnings by 19 percent. These findings support those of

Lynch (1992), which also show that training provided by the current employer

has a positive effect on earnings. Lynch (1994) cites two other studies (Mincer

1988; Holzer 1989) that found positive effects of formal training on hourly earnings of about 4 or 5 percent. Another recent study conducted by Bartel (1995)

shows that workers at a large manufacturing company who participated in formal training at the company had much higher earnings than nontrainees. Bartel

(1995) estimates a rate of return to training of 26 to 58 percent, depending on the

assumed rate of depreciation. Finally, findings from both Lynch (1992) and

Bishop (1994) confirm that training from a previous employer has little effect on

current earnings.

Training of Labor Force Participants 71

44The NLSY does not explicitly ask about informal on-the-job training.

45Lillard and Tan (1992) also find that training is associated with a decrease in the likelihood of

unemployment, and that this effect persists for approximately 12 years after training. Company

training is associated with the greatest reduction in the likelihood of unemployment.

46These findings are based on regression estimates of hourly earnings equations. Blanchflower and

Lynch (1994) also report the results for estimated wage difference equations, which imply somewhat higher effects of training on hourly earnings.
Evidence about particular types of training indicates that basic skills training may

be positively associated with earnings. Using data from the NHES, Hollenbeck

(1993) finds that participation in basic skills training in the 12 months prior to the

interview generates a 19 percent increase in annual earnings for men. However,

the same training had no significant impact on women according to the NHES

data. The findings for men and women are reversed when Hollenbeck examines

data from the CPS. Based on these data, participation in basic skills training since

the start of the current job increases weekly earnings by 14 percent for women but

has no significant impact on men. Hollenbeck provides several potential explanations for the discrepancy in findings from the two data sources. For example,

the two data sources use different earnings and training measures—NHES data

are based on annual earnings and training occurring in the 12 months prior to the

interview, while the CPS data are based on weekly earnings and training occurring since the start of the current job. 

DISCUSSION: TRAINING AND PRODUCTIVITY

Because changes in earnings may not accurately reflect changes in productivity, estimates of the impact of training on earnings may either overstate or

understate the impact of training on productivity. Researchers therefore

recently have attempted to estimate the impact of training on productivity

using data from company-based surveys, which can be used to derive direct

measures of productivity.

Estimates generated from company-based data suggest that formal training

has a substantial impact on productivity. Bartel (1994) and Bishop (1994) both

find that formal training increases productivity. Bartel’s study, which is based

on a survey of U.S. manufacturing companies in 1986,47 shows that businesses

that implemented training programs from 1983 through 1986 experienced an

estimated gain in productivity of nearly 19 percent over the three-year period.48

The companies in the survey that implemented new training programs previously tended to lag behind comparable companies in terms of labor productivity in 1983. The impact of training allowed these companies to catch up to

the productivity level of the other companies by 1986. Bishop’s study, based on

data from the Employment Opportunities Pilot Projects (EOPP), shows that

72 Education and the Economy

47Bartel (1994) used data from a 1986 Columbia Business School survey that covered 495

Compustat II business lines. The businesses surveyed are not a random sample of all U.S. businesses; no such random sample exists. Bartel, therefore, cautions that her findings may not be generalizable to all U.S. businesses.

48Bartel (1994) measures labor productivity at a business unit as net sales per employee in the unit.
company-sponsored training received on the job raises the productivity of

newly hired employees by 16 percent.49

The effect of informal training, such as learning-by-doing, appears to be more

limited than the effect of more formal training. Weiss (1994) used data on

workers at three facilities operated by a telecommunications manufacturing

company to evaluate productivity improvements among newly hired workers.

His findings suggest that learning-by-doing does generate rapid growth in

productivity among workers during the first month of employment—the

median increase in productivity was between 11 and 45 percent at the three

facilities.50 However, this growth slows rapidly over the next few months and

declines to zero by the sixth month on the job. He concludes that the overall

effect of learning-by-doing on productivity is relatively small in the long run. 

Training of Labor Force Participants 73

49The productivity measure used by Bishop (1994) is based on the employer or supervisor rating

(on a scale from 0 to 100) of an employee’s productivity.

50In the Weiss (1994) data, productivity is measured according to an individual’s physical output.
INDICATOR 15

Trends in Training Rates in the United States

Given that formal training appears to increase worker productivity and earnings,

it is useful to consider the rates of training participation among U.S. workers and

the trends in these rates. To measure training rates, we reexamine the data on skill

improvement training from the 1983 and 1991 CPS training supplements.

Training rates for different subgroups of workers and by source of training are

shown in figures 7.2a and 7.2b. We also examine training rates by subgroups of

workers (figure 7.3) based on data drawn from the 1991 NHES. These data differ

from the CPS data in at least two important ways. First, the NHES asks about any

employment-related training that occurred in the 12 months prior to the interview, while the CPS supplement asks about training at any time on the current

job. Second, the NHES data refer to enrollment in formal training courses only,

while the CPS data include participation in more informal on-the-job training. 

The CPS data suggest that the rate of training participation among U.S. workers

increased between 1983 and 1991. In 1991, 41 percent of the work force had

received skill improvement training on their current job, up from 35 percent in

1983 (figure 7.2a). Training rates increased for all of the gender and race–ethnicity

subgroups shown in the figure. As shown in figure 7.3, NHES data reveal a lower

training participation rate for 1991 (33 percent) than that found in the CPS, but this

is to be expected because the NHES data do not include on-the-job training and

reflect only training in the previous 12 months. Both the CPS and NHES data show

that in 1991, training rates were similar for male and female workers, while white

workers tended to receive more training than black or Hispanic workers.

Training is more prevalent among more highly educated workers than among

other workers. According to the CPS data, 61 percent of workers in 1991 with a

college degree participated in training on their current job, compared with 29 percent of workers with a high school education or less and 46 percent of workers

with some college (figure 7.2a).51 Data from the NHES also show that the probability of participating in training is correlated with education. Fifty-three percent

of workers with a college degree participated in some job-related training in the

previous year, compared with 38 percent of those with some postsecondary education, 22 percent of those with a high school diploma, and 9 percent of those who

did not finish high school (figure 7.3).

74 Education and the Economy

51Lillard and Tan (1992) present empirical evidence confirming that the likelihood of getting most

kinds of training rises with the level of educational attainment.
These findings either suggest

that companies target training

resources to their most educated

workers or that more highly educated workers are more eager or

willing to participate in training,

possibly because they expect to

realize a relatively high return

from training. Recent research

conducted by the Bureau of Labor

Statistics suggests that workers

with more education and greater

aptitudes hold more complex

jobs and are less likely to perform their duties adequately

when they begin their jobs than

workers with less education and

lower aptitudes. Therefore, more

educated workers appear to be

in greater need of additional

training to perform their duties,

which may explain why receipt

of training is positively correlated with educational attainment

and test scores (U.S. Department

of Labor 1996). Regardless of the

reason for the positive correlation

between education and training,

it is clear that investments in

training appear to complement

investments in schooling. Training

is not generally used to remedy

the skill deficiencies of the less

educated as compared with the

more educated; rather, training tends to exacerbate the skill differences that

already exist between workers with different educational backgrounds. 

Training also appears to be more common among workers in highly skilled occupations and is most common among professional, technical, and managerial

occupations. According to the CPS data, workers in 1991 in all of these occupations had training rates in their current job of more than 50 percent (figure 7.2b).

In contrast, no other occupation had training rates of more than 40 percent. The

NHES data yield similar findings. About 56 percent of those working in a manTraining of Labor Force Participants 75

All workers

0 20 40 60 80 100

Male

Female

White

Black

All other races

Hispanic

High school or less

Some college

College graduate

35

41

35

40

34

41

36

42

28

34

32

38

23

28

26

29

41

46

54

61

RACE–ETHNICITY

SEX

HIGHEST GRADE

COMPLETED

1983

1991

Percent

Figure 7.2a

All workers ages 16 years and older who participated in skill

improvement training while on their current jobs, by sex,

race–ethnicity, and educational attainment: 1983 and 1991

(percent of workers in each category)

NOTE: Hispanics may be of any race, and each of the racial categories includes Hispanics.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, How Workers Get Their

Training: A 1991 Update, 1992; U.S. Department of Commerce, Bureau of the Census,

Current Population Survey, January 1983 and 1991.
76 Education and the Economy

Figure 7.2b

All workers ages 16 years and older who participated in skill improvement training while on

their current jobs, by occupational group and training source: 1983 and 1991

(percent of workers in each category)

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, How Workers Get Their Training: A 1991 Update, 1992; U.S.

Department of Commerce, Bureau of the Census, Current Population Survey, January 1983 and 1991.

0 20 40 60 80 100

Percent

Executive,

administrative,

and managerial

Farming, forestry,

and fishing

Precision production,

craft, and repair

Machine operators,

assemblers, and

inspectors

Transportation and

material moving

Handlers, cleaners,

and laborers

Other source

Professional

Technical

Sales

Administrative support

Private household

Service

School

Formal company

programs

Informal

on-the-job

training

OCCUPATIONAL

GROUP

TRAINING SOURCE

11

16

14

15

4

7

12

13

14

15

18

25

22

25

35

38

16

21

25

29

3

6

32

40

32

35

52

59

61

67

47

53

1983

1991
agerial or professional job had participated in training in the past 12

months, compared with substantially lower proportions of workers

in the other four occupational categories shown in figure 7.3.

The relatively low rates of training

for less-educated workers and for

workers in less-skilled occupations

have been used to argue that companies in the United States underinvest in their front-line workers

(Barton 1993). According to this

argument, recent changes in the

economy have generated a greater

demand for problem-solving skills,

but front-line workers tend to lack

these skills and have no obvious

way to acquire them. Lillard and

Tan (1992) present estimates suggesting that as the rate of technological change accelerates, there is

likely to be even greater targeting

of in-house training to more highly

skilled, highly educated workers.

Alternatively, if U.S. companies

use a different skill mix or organize

work differently than companies in

other countries, the current training levels may be optimal. 

School programs, formal company training, and informal on-the-job training

are all important sources of training according to reports from workers in the

CPS training supplements. The incidence of training is about evenly divided

among these three training sources (figure 7.2b). The rates of school training

and formal company training are above average for workers in managerial,

professional, and technical occupations. The rate of less formal, on-the-job

training is evenly distributed across occupations.

Training of Labor Force Participants 77

Figure 7.3

Workers ages 17 years and older who were enrolled in

employment-related training in the previous 12 months,

by sex, race–ethnicity, education, and occupation: 1991

(percent of workers in each category)

All workers

0 20 40 60 80 100

33

33

34

35

24

27

9

22

38

53

56

32

21

26

17

Male

Female

SEX

White

Black

Hispanic

RACE–ETHNICITY

Less than high school

High school diploma

Some postsecondary

education

College graduation

EDUCATION

Managerial/professional

Technical/sales/

administrative support

Service

Precision production

and repair

Operations/

fabrications/laborers

OCCUPATION

Percent

NOTE: Employment status of individuals was determined by status during the week of

the survey.

SOURCE: U.S. Department of Education, National Center for Education Statistics, Adult

Education: Employment-Related Training, 1994; U.S. Department of Education, National

Center for Education Statistics, National Household Education Survey, 1991.
Both the CPS and NHES include

information on types, as well as

sources, of training. The most common type cited in the CPS (figure

7.4) is occupation-specific technical

training (26 percent), followed by

computer-related training (13 percent), managerial or supervisory

training (11 percent), and reading,

writing, or math training (6 percent). The NHES categories are not

the same as the CPS categories,

although some are close. According

to the NHES data (figure 7.4), in

1991, the most common type of

training in the past 12 months for

workers was professional development (25 percent), followed by

technical or skilled job training (20

percent), executive or managerial

development (13 percent), supervisory skills (12 percent), job health

and safety (12 percent), computer

and quality or statistical process

control (both 10 percent), sales and

marketing (8 percent), and new

employee training (7 percent). 

78 Education and the Economy

FROM CPS DATA

Technical

Computer-related

Managerial/supervisory

Reading/writing/math

Other

Professional development

Technical or skilled

Executive or management

development

Supervisory skills

Job health and safety

Computer

Sales and marketing

New employee

Quality or statistical

process control

FROM NHES DATA

Figure 7.4

Training participation, by training type: 1991

(percent of workers participating in each type of training)

NOTE: CPS data include all employed individuals ages 16 years and older. NHES data

include all employed individuals ages 17 years and older. CPS data refer to any training

on the current job and NHES data to training in the last 12 months.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, How Workers Get Their

Training: A 1991 Update, 1992; U.S. Department of Education, National Center for

Education Statistics, Adult Education: Employment-Related Training, 1994; U.S.

Department of Commerce, Bureau of the Census, Current Population Survey, January

1991; U.S. Department of Education, National Center for Education Statistics, National

Household Education Survey, 1991.

26

13

11

6

7

25

20

13

12

12

10

10

8

7

0 10 20 30

Percent
CONCLUSIONS

■ Participation in training is positively associated with earnings. Studies of the

returns to training consistently show that formal employment-based training on the

current job increases earnings. Studies that examine direct measures of productivity

find that formal training also has a positive effect on productivity. However, more

informal on-the-job training has a relatively small effect on productivity, according to

at least one study.

■ A substantial proportion of U.S. workers reported participating in some type of

training. Approximately 30 to 40 percent of workers reported in 1991 either that they

participated in formal employment-related training in the previous 12 months or that

they participated in formal or informal training since the start of their current job.

Training rates increased between 1983 and 1991. 

■ Training is most prevalent among the most highly educated workers and among

workers in managerial, professional, and technical occupations, which require a high

level of skills. Employment-related training in the United States therefore tends to

contribute to the earnings advantage already enjoyed by highly educated, highly

skilled workers. 

Training of Labor Force Participants 79
CHAPTER 8

International Trends in Education

According to the findings presented in chapter 2, worker productivity in other

industrialized countries is increasing at a faster rate than in the United States,

and these countries are therefore slowly catching up to the United States.

Furthermore, although factors other than education (for example, physical capital) are important to economic productivity, education appears to play a substantial role in determining productivity. In fact, throughout this report, we

have shown a link between economic productivity and various measures of

education, including attainment, achievement, literacy, and training. The next

step in our examination of education and economic productivity is to explore

how the United States compares with other countries in these specific measures of education. 

This chapter presents four sets of indicators to compare education and skill

training in the United States and other industrialized countries: measures of

educational attainment in industrialized countries, the international distribution of educational achievement, adult literacy in industrialized countries, and

training rates in industrialized countries.

International Trends in Education 81
INDICATOR 16

Educational Attainment in Industrialized Countries

Evidence on productivity convergence brings to light two considerations central

to an examination of the level of education in and between nations. First, it is necessary for countries to have a level of education that is roughly comparable to that

in the leader country in order to benefit from the leader country’s technical

knowledge (see discussion in chapter 2). Second, analysis of productivity in a

broad sample of countries suggests that a high rate of secondary education is

especially important in enabling countries to be among the world leaders in

worker productivity. A large proportion of the population in countries with productivity converging on that of the United States has completed or is enrolled in

secondary education (Barro 1991; Baumol, Blackman, and Wolff 1989). There is

less evidence about the importance of college education for determining relative

productivity among countries. However, substantial evidence of the connection

between college education and productivity at the individual level (chapter 3)

suggests that rates of college education may also be important determinants of

cross-country differences in worker productivity. These considerations raise the

issue of how levels of attainment among the industrialized countries known as

the G–7 (United States, Japan, Germany, United Kingdom, France, Italy, and

Canada) compare to one another.

Although the percentage of the adult population ages 25–64 years that has completed secondary school varies across countries, the evidence shows that nations

are closing the gap with the United States at the secondary level. More than 80

percent of the adult population ages 25–64 years in both Germany and the United

States have finished the equivalent of a high school education (figure 8.1). The

trend among the youngest workers, however, is for the other countries to converge on—and in some cases overtake—the leader’s level in secondary attainment. Japan, Germany, the United States, the United Kingdom, and Canada all

educate between 80 and 90 percent of their young adults ages 25–34 years

through high school completion. Furthermore, in countries other than the United

States, the attainment gap between the oldest and youngest age groups is larger

than in the United States, indicating that attainment is increasing more rapidly in

the other countries. This is due, in part, to the fact that older workers in most of

these countries have a much lower level of attainment than older workers in the

United States. The convergence of secondary education completion rates in G–7

countries is likely to be one of the factors contributing to the convergence of

worker productivity in these countries. 

82 Education and the Economy
Most G–7 countries still lag well behind the United States in postsecondary attainment. The United States has by far the highest proportion of the population ages

25–64 years that has completed a college education, as shown in figure 8.2. But the

rate of college completion among young adults in the United States has risen very

slowly over the past 20 years, and according to the data in figure 8.2, the rate of

college completion among adults ages 25–34 years is slightly lower than for

adults ages 25–64 years. The rate of college completion for the youngest cohort of

adults in most of the other countries is only slightly higher than for all adults ages

25–64 years. The one exception is Japan, in which the rate of college completion

among the adult population is rapidly increasing. By 1992, approximately 23 percent of Japanese adults ages 25–34 years had completed a college-level education,

the same as U.S. adults in the same age range. These findings suggest that, to

date, G–7 countries other than Japan have placed less emphasis on increasing the

share of their population with this high level of education. This finding generally holds true even when college completions are combined with completions in

nonuniversity postsecondary programs (figure 8.3).

International Trends in Education 83

0

20

40

60

80

100

United

States

Japan Germany United

Kingdom

France Italy Canada

86.5

90.6 88.6

80.9

67.1

42.4

80.8

84.0

69.7

81.9

68.1

52.2

28.4

71.3

Figure 8.1

Secondary school completion, by age: 1992

Percent

NOTE: In the United States, completing secondary school is defined as graduating from high school or earning a GED.

SOURCE: U.S. Department of Education, National Center for Education Statistics, The Condition of Education, 1995;

Organization for Economic Cooperation and Development, Indicators of Education’s Systems, Digest of

International Education Statistics, forthcoming.

Ages 25–64 Ages 25–34
84 Education and the Economy

Figure 8.3

Completion of postsecondary education, percent of population ages 25–64 years: 1991

0

10

20

30

40

50

Percent

NOTE: Postsecondary education includes university and nonuniversity education above the secondary level. Completion of postsecondary education is defined according to the International Standard Classification of Education, which is used as a means of

compiling internationally comparable statistics on education. Postsecondary completion includes education at the postsecondary level which leads to an award or degree. The classification is described in detail in Annex 4 of Organization for

Economic Cooperation and Development (1993). In the United States, completion of postsecondary education refers to high

school graduates who complete programs at a technical or vocational institution, a two-year college, or a four-year college

or university.

SOURCE: Organization for Economic Cooperation and Development, Education at a Glance, 1993.

United

States

Germany United

Kingdom

France Italy Canada

22

16

36

15

6

40

Figure 8.2

Completion of higher education, by age: 1992

Percent

United

States

Japan Germany United

Kingdom

France Italy Canada

23.6

13.3

11.6 10.7 10.2

6.4

15.0

23.2 22.9

11.8 12.5 12.3

6.8

16.1

NOTE: In the United States, completing higher education is defined as earning a bachelor’s degree.

SOURCE: U.S. Department of Education, National Center for Education Statistics, The Condition of Education, 1995;

Organization for Economic Cooperation and Development, Indicators of Education’s Systems, Digest of

International Education Statistics, forthcoming.

0

5

10

15

20

25

30

Ages 25–64 Ages 25–34
INDICATOR 17

Educational Achievement in 

Industrialized Countries

Education attainment levels are merely an indication of the mix of skills and

knowledge shared by populations in different countries. Consequently, many

observers question whether the increase in the level of attainment in the United

States over the past 30 to 40 years represents an increase in people with the skills

and knowledge necessary to sustain economic productivity. Unfortunately,

addressing this concern is difficult for a number of reasons. Among the most

important is the dual problem of determining the kinds of skills and knowledge

that lead a country to higher levels of productivity, and obtaining agreement on

the mix of skills that should be measured across similar populations in different

countries. Furthermore, because of differences in the selective educational tracks

in different nations, identifying comparable groups of students is also a challenge. This issue has created a tendency in international assessments of student

performance to concentrate on younger populations that have not been subjected

to selective educational practices. But skills at these younger ages are far from the

point at which they would influence productivity in the workplace. In addition,

most international studies compare students at a single point in time, and when

the assessments are repeated over time, they tend to include a changing cast of

countries. Consequently, it is problematic to make comparisons that indicate

whether U.S. students have changed their performance relative to students in

other countries over time.

Despite these limitations, it is clear from the existing data that the United States

is typically not the leader nation in average student achievement among G–7

countries in mathematics and science. In the early to mid-1980s, the average mathematics and science scores of U.S. students in their last year of secondary school

were generally lower than those of students at a similar level of education in other

G–7 countries (figures 8.4 and 8.5). The mean scores of students in Japan and the

United Kingdom were consistently higher than those of U.S. students in the various mathematics and science areas presented in the figures. International reading

achievement data for 14-year-old students, on the other hand, show that the mean

scores of students in the United States are closer to the top of the international distribution. Among the five G–7 countries presented in figure 8.6, the United States

consistently trails only France in the three measures of reading achievement.52

International Trends in Education 85

52The changing cast of countries included in figures 8.4 through 8.6 reflects the inconsistency with

which nations participate in the various studies of international achievement.
86 Education and the Economy

Figure 8.4

Mean mathematics achievement of students in their last year of secondary school in

industrialized countries, by topic: 1980–82

Mean mathematics achievement

0

20

40

60

80

100

United States Japan United Kingdom Canada (B.C.)

43

29

78*

66* 66*

58*

47

21*

40

31

68*

60* 59*

51*

43

30

*Mean score is significantly different from that of U.S. students.

SOURCE: U.S. Department of Education, National Center for Education Statistics, International Mathematics and Science

Assessments: What Have We Learned?, 1992, based on data from the Second International Mathematics Study (SIMS).

Number Systems Algebra Geometry Functions and Calculus

Figure 8.5

Mean science achievement of students in their last year of

secondary school in industrialized countries, by topic: 1983–86

*Mean score is significantly different from that of U.S. students.

SOURCE: U.S. Department of Education, National Center for Education Statistics, International Mathematics and Science

Assessments: What Have We Learned?, 1992, based on data from the Second International Science Study (SISS).

United

States

Japan United

Kingdom

Italy Canada

38

46 46*

42*

28*

46*

40* 38 38 37

56*

63*

58*

52*

70*

Mean science achievement

0

20

40

60

80

Biology Chemistry Physics
Data from the most recent international studies confirm the finding that the

average mathematics and science performance of U.S. students is below that of

students from other countries. On the mathematics test, the mean U.S. scores

for both 9-year-olds and 13-year-olds were below those of most other countries

(table 8.1). No country scored below the United States for 9-year-olds, and only

Jordan scored below the United States for 13-year-olds. On the science test,

U.S. 9-year-olds scored above their counterparts in two other countries and

similar to their counterparts in the rest of the countries. But U.S. 13-year-olds

trailed their counterparts in many of the other countries and surpassed only

the 13-year-olds in Jordan. 

The relative success of U.S. students on reading tests is also reflected in table

8.1. U.S. 9-year-olds scored higher than their counterparts in 20 of the other 22

countries included in the study. U.S. 14-year-olds also scored high in reading,

equaling or surpassing their counterparts in most of the other countries. Only

14-year-olds in Finland had higher reading scores than 14-year-olds in the

United States.

International Trends in Education 87

Figure 8.6

Mean reading achievement of 14-year-old students in industrialized countries,

by topic: 1990–91

*Mean score is significantly different from that of U.S. students.

SOURCE: Warwick B. Elley, How in the World Do Students Read?, 1992, based on data from the International Association

for the Evaluation of Educational Achievement Reading Literacy Study.

539 539

528

514*

532

556*

544*

520*

501*

526* 521* 522

546

524*

516*

400

450

500

550

600

Mean reading achievement

United States Germany

(West)

France Italy Canada

(B.C.)

Narrative Expository Documents
Table 8.1 

International distribution of academic achievement relative 

to the United States: 1991–92

Number of countries performing:

Not Number of

Significantly significantly Significantly countries

Subject higher than different lower than in the

and age the U.S. from the U.S. the U.S. study

MATHEMATICS

9-year-olds 7 2 0 10

13-year-olds 12 1 1 15

SCIENCE

9-year-olds 0 7 2 10

13-year-olds 7 6 1 15

READING

9-year-olds 1 1 20 23

14-year-olds 1 14 7 23

SOURCE: U.S. Department of Education, National Center for Education Statistics, The Condition of Education,

1993; Warwick B. Elley, How in the World Do Students Read?, International Association for the Evaluation of

Educational Achievement, Study of Reading Literacy, 1992; and Educational Testing Service, International

Assessment of Educational Progress, 1992.

These results reveal that the ranking of countries by level of student achievement varies across broad areas of the curriculum (for example, mathematics,

science, and reading), as well as more specific subjects within these areas (for

example, arithmetic, statistics, and measurement). Furthermore, younger U.S.

students tend to rank higher than older students, relative to those of the same

age in other countries. Overall, the position of the United States in the international distribution of student achievement varies depending on the subject matter, age of the students, and countries in the study. The U.S. is rarely a leader

among industrialized countries in mathematics and science, and is often ranked

near the bottom of the international distribution of student mathematics and

science achievement.

The variability in the ranking of the United States has fueled the ongoing debate

over the degree to which definitive conclusions can be drawn from the data on

international student achievement. Several factors are central to this debate (see

review in Stedman 1994). First, critics argue that the sampling methods in the

studies impede meaningful comparisons across countries. A report produced by

NCES recognizes four technical weaknesses associated with sampling in the

assessments: poor response rates, lack of comparable populations, variability in

88 Education and the Economy
sample quality and target population, and frequent small sample sizes (U.S.

Department of Education 1992). However, Stedman (1994) argues that critics

have exaggerated the impact of sample-related issues such as variations in high

school enrollment rates, and that the problems tend to be the extreme cases.

A second criticism of the international achievement studies is that there is test

bias resulting from exams that favor the curricular content of some countries

over that of others. For instance, some have argued that the U.S. students are

at a disadvantage in mathematics because algebra is not taught until high

school and calculus until college. However, U.S. students perform relatively

poorly in most mathematics subjects. Furthermore, the curricular content of

the test used in the Second International Mathematics Study has been reported

to be satisfactory for the United States (Travers 1987). The educational quality

of the tests has been challenged on the grounds of the limitations of the multiple choice format, the short time frame for the exam, international differences

in student motivation to do well, and variability in student experience with

standardized testing. Stedman (1994) argues that these concerns are largely

anecdotal and unsubstantiated, and that they explain little of the difference in

student performance across industrialized countries.

As the debate over the validity of international assessments of student achievement continues, these potential shortcomings of the data remain unresolved,

limiting our ability to report conclusively on international achievement rankings.

Nevertheless, one consistent finding across the international studies is that the

United States is rarely a leader nation in student achievement in mathematics

and science. 

DISCUSSION: IMPLICATIONS OF U.S. EDUCATIONAL

ACHIEVEMENT FOR PRODUCTIVITY GROWTH

Experts differ in the significance they attach to comparisons of achievement outcomes for school subjects across countries. Bishop (1992) uses international test

data to argue that the low mathematics and science performance of U.S. 17- and

18-year-olds relative to students in other countries is a major problem for the

nation’s future productivity.53 Basing his results on both the growing importance

of the technical elements in these fields for the changing economy and the connection between test performance and productivity, Bishop sees a threat to U.S.

competitiveness. Levin and Kelley (1994) view the situation differently. Although

International Trends in Education 89

53Bishop’s (1992) cross-country comparisons of test scores are based on results from the Second

International Math and Science Studies.
they do not take issue with the relative position of U.S. students in international

assessments as critics often do, they assert that the achievement tests behind

such comparisons do not reflect the kinds of skills that are likely to increase

productivity. They base their conclusions on a different body of research than

that used by Bishop, citing instead teamwork and work habits as being more

influential than academic skills on worker productivity.54 Levin and Kelley agree

that a threshold of basic skill levels among U.S. students will be a key element in

future productivity—particularly among those at risk of not achieving these minimums in the areas of computation, communication, and reading. They see little

evidence, however, that the U.S. performance on international achievement tests

reveals a threat to the country’s international economic competitiveness.

90 Education and the Economy

54Levin and Kelley (1994) cite two types of studies on the link between test scores and productivity.

First, they argue that studies of earnings have failed to find a strong link between test scores and earnings. Second, they argue that research has found that there is only a modest connection between test

scores and productivity ratings by supervisors.
INDICATOR 18

Adult Literacy in 

Industrialized Countries

The previous indicator compares the skills and knowledge of students in the U.S.

with students in other countries. Alternative comparisons can be made based on

the literacy skills of adults in different countries. A recent study (OECD and

Statistics Canada 1995) based on the International Adult Literacy Survey (IALS)

addresses this issue. The IALS was conducted in several countries in Europe and

North America in 1994. The survey tested the performance of respondents on the

same three scales—prose, document, and quantitative—that were used in the

National Adult Literacy Survey (NALS) in the United States, as is described in

chapter 5.

The literacy of the U.S. population is roughly similar to that of populations in

other industrialized countries. The statistics represented in figure 8.7 show that

the proportion of the U.S. population in the two highest literacy levels (levels

four and five) is generally comparable to other countries included in the OECD

and Statistics Canada study (1995). The one exception to this finding occurs on

the prose scale, where the United States has a greater proportion of high scorers than four of the other six countries included in the study. At the other end

of the performance scale, the United States tends to have a higher concentration

of adults in the lowest literacy level than the other countries. More than 20 percent of the U.S. sample scored at the lowest literacy level on each of the three literacy scales, while the other countries (except Poland) had less than 20 percent

of the sample scoring at the lowest level on each scale.

International Trends in Education 91
92 Education and the Economy

Figure 8.7

Estimated percentage of the population in each proficiency level

on three adult literacy scales, by selected countries: 1994

SOURCE: Organization for Economic Cooperation and Development and Statistics Canada, Literacy, Economy and Society:

Results of the First International Adult Literacy Survey, 1995.

PROSE LITERACY

Level 1 Level 2 Level 3 Level 4/5

0 20 40 60 80 100

Switzerland (German)

Switzerland (French)

Sweden

Poland

Netherlands

Germany

Canada

United States

QUANTITATIVE LITERACY

Percent

0 20 40 60 80 100

Switzerland (German)

Switzerland (French)

Sweden

Poland

Netherlands

Germany

Canada

United States

DOCUMENT LITERACY

0 20 40 60 80 100

Switzerland (German)

Switzerland (French)

Sweden

Poland

Netherlands

Germany

Canada

United States
INDICATOR 19

Training in Industrialized Countries

Some researchers have argued that the United States lags behind other industrialized countries in work force skills. Much of this alleged deficiency has

been attributed to the relative scarcity of employer-provided training in the

United States, especially for front-line workers (Barton 1993). According to this

view, U.S. producers rely too heavily on informal learning-by-doing as their

primary method of skills development, and they underinvest in more rigorous

training. This view has recently prompted policymakers to consider policy

changes, such as instituting minimum employer training investments, to

encourage the expansion of employer-provided training in the United States.

Despite the widespread belief that U.S. workers are undertrained, the available

training data are not really adequate to determine whether this belief is accurate. In fact, the data on training rates and training expenditures in industrialized countries (table 8.2) fail to indicate a substantial training deficiency in the

United States. According to these data, the United States has neither the highest nor the lowest rate of training participation among industrialized countries.

Findings for training expenditures are similar: the United States and Germany

appear to be about equal, second only to France among the countries for which

there are data. The data in table 8.2, however, come from a variety of sources

and are based on different worker populations, different periods, and different

definitions of training; therefore, cross-country comparisons of these data may

be inappropriate.

Lynch (1994) suggests that, even if comparisons of the data in table 8.2 are

appropriate, the similarities across countries may mask two important types of

underinvestment in training in the United States. First, U.S. companies in certain sectors spend substantially less on training overall and provide their nontechnical, nonmanagerial employees with more limited training than competitors in other countries provide to comparable workers. For example, Berg

(1994) estimates that German auto firms devote from 1.5 to 10 times more time

than comparable U.S. companies to technical training. Second, in some sectors,

the level of expenditures or number of training hours may be the same in the

United States as in other countries, but because of lower initial skills of U.S.

workers, training in the United States is not sufficient to bring trainees up to

the proficiency levels of workers in other countries. For example, Mason (1990)

compared the training of workers in the nuclear power industries in the United

International Trends in Education 93
States and Europe. He found that half of all technician training in the U.S.

nuclear industry is fundamental, including remedial education, whereas training for European technicians covers more advanced study of nuclear engineering

and plant administration. Mason (1990) asserts that the difference reflects the

superior educational preparation of European workers entering the industry as

compared with their American counterparts.

Table 8.2

Training activity, by country

Workers receiving Average training expenditure

formal traininga (percent of 

(percent) total wage bill)

United States 16.8 1.8c

Canada 6.7 0.9

West Germany 12.7 1.8

United Kingdom 14.4 1.3

France 32.0 2.5

Netherlands 25.0 1.5

Sweden 25.4 NA

Japan 36.7 0.4d

Australia 34.9b 1.7

Norway 33.1 NA

NA = not available.

aPeriod over which training incidence is measured varies by country. For example, the figure for the

United States refers to the percentage that ever received formal training from the current employer,

while the figure for Japan refers to training in the past two years (see Lynch 1994 for further discussion of these data).

bReceived in-house training.

cBased on a survey of large firms conducted by Training Magazine.

dTraining expenditures as a percentage of monthly labor costs, but excludes trainees’ wages.

SOURCE: Lisa M. Lynch, “Introduction,” in L. Lynch, ed., Training and the Private Sector: International

Comparisons, 1994.

94 Education and the Economy
CONCLUSIONS

■ Educational attainment levels among the group of industrialized countries are

becoming more similar over time. The United States maintains a strong lead over all

countries but Japan in college completion, but secondary school attainment in most

other countries is converging with that of the United States.

■ Although the population in the United States has higher educational attainment than

that in most other industrialized countries, students in the United States do not have

higher achievement levels than students in other industrialized countries. In particular,

elementary and secondary students in the United States have far to go in mathematics

and science before their test scores assume the lead.

■ Although existing data are insufficient to determine the adequacy of worker training

in the United States, the data do show that the United States ranks neither highest nor

lowest in rates of participation in and expenditures on training among industrialized

countries. Despite the similarity in training rates and expenditures, U.S. employers in

certain sectors may spend substantially less on training than similar employers in other

countries, especially on their nontechnical, nonmanagerial employees. In addition, U.S.

workers may not receive as sophisticated training as comparable workers in other

countries because they are not as well educated as their foreign counterparts and are

not as well prepared for higher-level training. 

■ Overall, the picture of education among developed nations suggests that the

trends in education do play a role in the trends in worker productivity. The convergence in worker productivity among nations parallels the convergence in educational

attainment and reflects, at least partly, the relatively high educational performance of

students outside the United States.

International Trends in Education 95
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