Student Publications

Author: Hector Cruz-Echevarria
U.S. Economic Growth Based on Education
Area: Marketing
Country :
Doctorate in Business Administration
Available for Download: Yes

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U.S. Economic Growth

The objective of this research is to discover advanced studies related with
the U.S. economic growth based on education and knowledge with the purpose
to determine a correlation between education and economic growth. The author
explains how the educational system of the United Sates has been for decades
a reflex in the nation's economy, and how this effect contributes positively to
the nation's economic growth and its influences on the evolution process. The
research demonstrates that knowledge is the most important determinant in a
society, and helps to prevent socio-economics problems, impacting direct and
positively the economic growth. Consequently, an increase in the U.S.
educational systems, means that this factor will generate more jobs and
experienced professionals in the industry, changing from a traditional
economy-based to an educational economy-based.

U.S. Economic Growth

This study investigates the U.S. economic growth using as a base the
educational system in correlation. The author uses statistical data to determine
the behavior of the US economic growth. The main objective of this
investigation is examining the activities of registered investment companies to
determine how these implications affect the U.S. economic growth. The author
uses the knowledge capital as a base in the US economic growth. He uses
statistical data to determine correlation with GDP growth. The research
demonstrates that the US economy totally depends of the society educational
level. His statistical data further indicates that when the economy grows, more
people would pursue graduate level degrees, and more Master's and Ph.D.


U.S. Economic Growth

It has been a great pleasure working with the faculty, staff, and students at the
Atlantic International University, during my tenure as a doctoral student. This
work would never have been possible if it were not for the freedom I was given
to pursue my own research interests. First, I would like to thank God, because
he was the one who give me support and show to believe in myself to achieve
this project. Thanks in large part to the kindness and considerable mentoring
provided and supporting advisory by Maria Salaman Bayron, marketing
professor at University of Phoenix.
I would especially like to thank my advisor, Dr. Franklin Valcin, for his
generous time and commitment. Throughout my doctoral work he encouraged
me to develop independent studying and research skills.
Finally, this dissertation is dedicated to my greatest blessing, my daughter
Pamela, the most innocent and loving person I've ever known. She and my
others sons, Hector Jr., Luismi, and Fernanda, they impart me the
commitment I have with the society and with the business Industry. They
served as an instrument to achieve my goals and show me to keep my eyes on
the right direction, focus in my career and my beliefs.



U.S. Economic Growth

Subjects: To conduct the investigation the author used three academic
libraries, three public libraries, three business magazines, and three peer
review articles.
Materials: A total of 24 online articles were used to conduct the research
process. The investigation is based on electronics articles such as: journals,
peer review articles, business magazines, and academic libraries. The materials
used in the investigation were texts books, magazines, electronics resources,
and a notebook computer with a high-speed Internet connection.
Procedure: The research was conducted to determine how the educational
system impacts the U.S. economic growth. The author uses as a references
online publications, and academic libraries. .



U.S. Economic Growth

ABSTRACT.......... ....................................................................

INTRODUCTION .......................................................................

ACKNOWLEDGMENTS .............................................................

METHODS......... ......................................................................

TABLE OF CONTENTS: (this page)......... ..................................

LIST OF FIGURES....................................................................

LIST OF TABLES......................................................................

CHAPTER ONE: ......... ...........................................................

CHAPTER TWO: ......... ............................................................

CHAPTER THREE: ......... .......................................................

CHAPTER FOUR: ......... .........................................................

CHAPTER FIVE: ......... ...........................................................

CONCLUSION........... ...............................................................

BIBLIOGRAPHY......... ..............................................................

CURRICULUM VITAE......... ......................................................



U.S. Economic Growth


1. Table 2.0 Percentage of bachelor's degrees by women .............. ......13
2. Table 2.1 Growth in gross domestic product .................................. .30



U.S. Economic Growth


1. Figure 4.1. Average net price, grants, loans, and total price ....... ......6
2. Figure 4.5. Children's reading and mathematics scale scores ........ .10
3. Figure 5.0. Percentage of public high school students.................... .19
4. Figure 5.1 Index of real output per hours of all persons .................. 24
5. Figure 5.2 Real GDP per worker in G-7 nations .............................. 30
6. Figure 5.3 Earnings for all wage and salary .................................... 39
7. Figure 5.4 Estimated returns to education among workers ............. 41
8. Figure 5.5 Median annual income for full-time workers .................. 42
9. Figure 5.6 Unemployment of adult labor force participants ............. 54
10. Figure 5.7 Average proficiencies of the unemployed ...................... 55
11. Figure 5.8 Mean weekly earnings of full-time workers .................. 56
12. Figure 5.9 Mean weekly earnings of full-time employed ................ 58
13. Figure 6.0 Weekly earnings of full-time black workers .................. 59
14. Figure 6.1 Percentage of labor force in each proficiency level ........ 60
15. Figure 6.2 Secondary school completion, by age: 1992 ................. 66
16. Figure 6.3 Completion of higher education, by age: 1992 .............. 67
17. Figure 6.4 Completion of postsecondary education ....................... 68
18. Figure 6.5 Mean mathematics achievement of students ................ 70
19. Figure 6.6 Mean science achievement of students......................... 71
20. Figure 6.7 Mean reading achievement of 14-year-old students ...... 71





U.S. Education Analysis
The 1990s brought rising tuition and fees but also expanded and restructured
financial aid programs to help students pay for college. At the federal level, the
1992 Reauthorization of the Higher Education Act broadened eligibility for
need-based aid, raised loan limits, and made unsubsidized loans available to
students regardless of need. States and institutions increased their grant aid
and put more emphasis on merit as a criterion for awards. As a result, the
overall picture of what and how students pay for college has changed
substantially since the early 1990s.
This special analysis uses data from the 1989­90 and 1999­2000
administrations of the National Postsecondary Student Aid Study to describe
some of these changes. It focuses on students who were enrolled full time and
were considered financially dependent on their parents for financial aid
purposes. All dollar amounts were adjusted for inflation.
Between 1990 and 2000, the average price of attending college (tuition and fees
plus an allowance for living expenses) increased at public 2-year institutions
(from $7,300 to $8,500), at public 4-year institutions (from $10,000 to
$12,400), and at private not-for-profit 4-year institutions (from $19,400 to
$24,400) (figure 4.1).


U.S. Economic Growth
These higher prices, combined with reduced expected family contributions for
low- and middle-income students and their families resulting from
restructuring of the aid programs, meant that the average student was eligible
for more need-based financial aid in 2000 than in 1990.
Reflecting this greater need, more students received aid in 2000 than in 1990
(71% vs. 54%), and the average aided student received more aid ($8,700 vs.
$6,200). Financial aid increased for all income groups and at all types of
Grant aid partly offset the price increases, with the percentage of students
receiving grants rising from 45 to 57 percent and the average amount received
by students with grants increasing from $4,200 to $5,400. However, the
average net price after taking rants into account (i.e., price minus grants)
increased at each type of institution. In other words, the growth in grant aid
was not enough to offset the price increases.
The average net price after taking grants into account increased for all income
groups, except those in the lowest income quarter attending public 2-year or
private for-profit less-than-4-year institutions. Reflecting greater need and
expanded eligibility for the Stafford loan program, the percentage of students
who borrowed increased from 30 to 45 percent. In 2000, about half of low-
income students and 35 percent of high-income students borrowed to help pay
for their education. In 1990, about 46 percent of low-income students and 13
percent of high-income students borrowed. Among those who took out loans,
the average amount borrowed increased from $3,900 to $6,100.



U.S. Economic Growth
After taking into account both grants and loans, the average net price of
attending increased for full-time dependent undergraduates at public 2-year
institutions, remained stable for those at public 4-year institutions, and
declined for those at private for-profit less-than-4-year institutions. The
apparent decline at private not-for-profit 4-year institutions was not
statistically significant.
The average net price after grants and loans declined for low-income students,
except at public 2-year institutions, and increased for high-income students at
public 2- and 4-year institutions.

Participation in Education
As the U.S. population increases, so does its enrollment at all levels of
education. At the elementary and secondary levels, growth is due largely to the
increase in the size of the school-age population. At the postsecondary level,
both population growth and increasing enrollment rates help explain rising
enrollments. Adult education is also increasing due to demographic shifts in
the age of the U.S. population and increasing rates of enrollment, as influenced
by changing employer requirements for skills. As enrollments have risen, the
cohorts of learners--of all ages--have become more diverse than ever before.
As enrollment of school-age children is compulsory, growth in elementary and
secondary schooling is primarily the result of the increasing size of the
population. At the postsecondary level, both population growth and increasing
enrollment rates help explain rising enrollments. Between 1970 and 2002, for



U.S. Economic Growth
example, the enrollment rate of 20- and 21-year-olds increased from 32 to 48
Thirty-five percent of public elementary schools had pre-kindergarten programs
in 2000­01, serving over 800,000 children. Schools in the Southeast were more
likely to have pre-kindergarten programs and full-day programs than schools
in other regions of the country. Public schools with large enrollments (700 or
more students) and schools in central cities were more likely than other
schools to offer pre-kindergarten classes.
Enrollment among 4- to 6-year-olds in kindergarten increased from 3.2 million
in 1977 to 4 million in 1992 before decreasing to 3.7 million in 2001. During
this period, the proportion of students enrolled in full-day programs increased,
and by 1995, it was larger than the proportion enrolled in half-day programs.
Rising immigration and a 25 percent increase in the number of annual births
that began in the 1970s and peaked in the mid-1970s have boosted school
enrollment. Public elementary and secondary enrollment reached an estimated
48.0 million in 2003 and is projected to increase to an all-time high of 49.7
million in 2013. The West will experience the largest increase in enrollment of
all regions in the country.
In 2003, Black and Hispanic 4th-graders were more likely than White 4th-
graders to be in high-poverty schools (measured by the percentage of students
eligible for a subsidized lunch) and less likely to be in low-poverty schools. The
same is also true by school location: Black and Hispanic students were more



U.S. Economic Growth
likely than White students to be concentrated in the highest poverty schools in
central city, urban fringe, and rural areas in 2003.
In the next 10 years, undergraduate enrollment is projected to increase.
Enrollment in 4-year institutions is projected to increase at a faster rate than
in 2-year institutions, and women's enrollment is expected to increase at a
faster rate than men's. The number of part- and full-time students, those
enrolled at 2- and 4-year institutions, and male and female undergraduates are
projected to reach a new high each year from 2004 to 2013.
Forty percent of the population age 16 and above participated in some work-
related adult education in 2002­03. The most common types of programs were
formal work-related courses percent) and college or university degree programs
for work-related reasons (9 percent). Educational attainment was positively
associated with participating in adult education for work-related reasons.

Figure 4.1. Average net price, grants, loans, and total price (in 1999 constant dollars) for
full-time, full-year dependent undergraduates, by type of institution: 1989­90 and 1999­
Public 2-year
Net price



U.S. Economic Growth
Public 4-year
Net price

Private not for profit 4-year
Net price



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Private for profit less than 4-year
Net price

NOTE: Averages computed for all students, including those who did not receive financial aid. Detail may not sum to totals
because of rounding.
SOURCE: Wei, C.C., Li, X., and Berkner, L. (2004). A Decade of Undergraduate Student Aid: 1989­90 to 1999­2000
(NCES 2004­158), tables A-1.2, A-2.2, A-3.2, A-4.2, A-1.6, A-2.6, A-3.6, A-4.6, A-1.10, A-2.10, A-3.10, A-4.10. Data
from U.S. Department of Education, National Center for Education Statistics, 1989­90 and 1999­2000 National
Postsecondary Student Aid Study (NPSAS:90 and NPSAS:2000). (Originally published as figure 10 on p. 24 of the complete
report from which this article is excerpted.)

Learner Outcomes
How well does the American educational system--and its students--perform?
Data from national and international assessments can help answer this
question, as can data on adults' educational and work experiences, health, and
earnings later in life. In some areas, such as reading, mathematics, and
writing, the performance of elementary and secondary students has improved
over the past decade, but not in all grades assessed and not equally for all
students. Long-term effects of education, such as on the health and earnings of
adults, help underscore the importance of education and the outcomes of
different levels of educational attainment.



U.S. Economic Growth
According to data from the Early Childhood Longitudinal Study, children
without family risk factors, such as poverty, start kindergarten with higher
performance and experience a larger gain in reading and mathematics scale
scores through 3rd grade than students with 1 or more family risk factors.
From the beginning of kindergarten in fall 1998 through the end of 3rd grade
in spring 2002, children with no family risk factors had an average gain of 84
points in reading, compared with a 73-point gain among children with 2 or
more family risk factors; the respective gains in mathematics were 65 and 57
points figure 4.5.
The average reading scale scores of 8th-graders assessed by the National
Assessment of Educational Progress (NAEP) increased between 1992 and 2003,
while no difference was detected for 4th-graders. The percentages of 4th- and
8th-graders performing at or above the Proficient level, defined as "solid
academic performance for each grade assessed," were higher in 2003 than in
1992. Among 12th-graders, average scores were lower in 2002 than in 1992
and 1998. The average writing scale scores of 4th and 8th graders assessed by
NAEP improved between 1998 and 2002 was increased. Twenty-eight percent
of 4th-graders, 31 percent of 8th-graders, and 24 percent of 12th-graders
performed at or above the Proficient level in 2002.
The average mathematics scale scores of 4th- and 8th-graders assessed by
NAEP increased steadily from 1990 to 2003. For both grades, the average scale
scores in 2003 were higher than in all previous assessments, and the
percentages of students performing at or above the Proficient level and at the



U.S. Economic Growth
Advanced level, defined as "superior performance," were higher in 2003 than in
1990. Thirty-two percent of 4th-graders and 29 percent of 8th-graders were at
or above the Proficient level. In addition to indicators on students' academic
achievement, there are also some indicators on the long-term outcomes of
The better educated a person is, the more likely that person is to report being
in "excellent" or "very good" health, regardless of income. Among adults age 25
and above, 78 percent of those with a bachelor's degree or higher reported
being in excellent or very good health in 2001, compared with 66 percent of
those with some education beyond high school, 56 percent of high school
completers, and 39 percent of those with less than a high school education.
In 2003, 13 percent of all persons ages 16­24 were neither enrolled in school
nor working, a decrease from 16 percent in 1986. The gap between the
percentage of poor youth and others neither enrolled nor working decreased
over the period. The percentages of White and Asian/Pacific Islander youth
neither enrolled nor working in 2003 were lower than the percentages of
Hispanic, Black, and American Indian youth. In addition, the percentage of
Hispanic youth neither enrolled nor working was lower than the percentages of
Black and American Indian youth.
The earnings of young adults with at least a bachelor's degree increased over
the past 20 years relative to their counterparts with a high school diploma or
General Educational Development (GED) certificate. Among men, the difference



U.S. Economic Growth
in median earnings rose from 19 percent in 1980 to 65 percent in 2002, while
among women, the difference increased from 34 percent to 71 percent.
Figure 4.5. Children's reading and mathematics scale scores for fall 1998 first-time
kindergartners from kindergarten through 3rd grade, by family risk factors: Fall 1998,
spring 1999, spring 2000, and spring 20021
Fall Kindergarden
Spring 1st grade Spring 3rd grade

Fall Kindergarden
Spring 1st grade
Spring 3rd grade

1Family risk factors include living below the poverty level, primary home language was non-English, mother's highest
education was less than a high school diploma/GED, and living in a single-parent household, as measured in kindergarten.

NOTE: The findings are based on children who entered kindergarten for the first time in fall 1998 and were assessed in fall



U.S. Economic Growth
1998, spring 1999, spring 2000, and spring 2002. Estimates reflect the sample of children assessed in English in all
assessment years (approximately 19 percent of Asian children and approximately 30 percent of Hispanic children were not
assessed). The Early Childhood Longitudinal Study, Kindergarten Class of 1998­99 (ECLS-K) was not administered in
spring 2001, when most of the children were in 2nd grade. Although most of the sample was in 3rd grade in spring 2002,
10 percent were in 2nd grade and about 1 percent were enrolled in other grades.
SOURCE: Rathbun, A, and West, J. (2004). From Kindergarten Through Third Grade: Children's Beginning School
Experiences (NCES 2004­007), tables A-4 and A-5. Data from U.S. Department of Education, National Center for
Education Statistics, Early Childhood Longitudinal Study, Kindergarten Class of 1998­99 (ECLS-K), Longitudinal
Kindergarten­First Grade Public-Use data file and Third Grade Restricted-Use data file, fall 1998, spring 1999, spring 2000,
and spring 2002. (Originally published as the Early Reading and Mathematics Performance figure on p. 48 of the complete
report from which this article is excerpted.)

Student Effort and Educational Progress
Many factors are associated with school success, persistence, and progress
toward high school graduation or a college degree. These include student
motivation and effort, the expectations of students, encouragement from
others, and learning opportunities, as well as various student characteristics,
such as sex and family income. Monitoring these factors in relation to the
progress of different groups of students through the educational system and
tracking students' attainment are important for knowing how well we are doing
as a nation in education. The proportion of 10th-graders who expected to
complete a bachelor's as their highest degree nearly doubled between 1980 and
2002, and the proportion who intended to earn a graduate degree more than
doubled. Rising aspirations were also notable among students from families
with low socioeconomic status: about 13 percent of such students intended to
earn a bachelor's degree in 1980, but this figure had tripled by 2002.
During the 1970s and 1980s, "event dropout rates," which measure the
proportion of students who drop out of high school each year, declined.
However, event dropout rates remained unchanged during the 1990s on
average and for students from low-, middle-, and high-income families.



U.S. Economic Growth
First-time entry rates into programs that lead to a bachelor's or higher degree
increased from 1998 to 2001 in many countries that were members of the
Organization for Economic Cooperation and Development (OECD). In 2001, the
U.S. rate was lower than the OECD country average. Despite assistance offered
through remediation, students enrolled in remediation are less likely to earn a
postsecondary degree or certificate. The need for remedial reading appears to
be the most serious barrier to degree completion: 12th-graders in 1992 who
took remedial reading at the postsecondary level were about half as likely as
those who took no remedial courses to have earned a degree or certificate by
While bachelor's degree completion rates have been steady over time, the
likelihood of still being enrolled with no degree at the end of 5 years has
increased. When comparing students who enrolled in a 4-year college or
university for the first time in 1989­90 with those who began in 1995­96, 53
percent of both cohorts had completed a bachelor's degree within 5 years;
however, the later cohort was more likely to have no degree but still be enrolled
and also less likely to have left college without a degree.
Women have earned more than half of all bachelor's degrees every year since
1981­82. They still trail men in certain fields but have made substantial gains
since 1970­71 at both the undergraduate (table 2.0) and graduate levels.
Table 2.0 Percentage of bachelor's degrees earned by women and change in the percentage
earned by women from 1970­71 to 2001­02, by field of study: 1970­71, 1984­85, and
Field of study
1970-71 1984-85 2001-02 Change in percentage points



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1970-71 1984-85 1970-71
1984-85 2001-02 2001-02
Health professions related
English language &
Visual & performing arts
Social sciences & history
Biological sciences/life sciences 29.1
Physical sciences
Computer & information sciences 13.6
Agriculture & natural resources 4.2
1Includes others fields not shown separately

Contexts of Elementary and Secondary Education
The school environment is shaped by many factors, including the courses
offered in the school and taken by students, the instructional methods used by
teachers, students' opportunities to attend a "chosen" public school, the role of
school staff in providing various support services to students, the extent to
which teachers are teaching in their field, and the characteristics of school
principals and their influence over school governance. Monitoring these and

1 NOTE: Based on data from all degree-granting institutions.
SOURCE: U.S. Department of Education, National Center for Education Statistics. (2003). Digest of Education statistics
2002 (NCES 2003­060), tables 246, 276­297, and (forthcoming) Digest of Education Statistics 2003 (NCES 2004­024),
tables 265, 268, and 271. Data from U.S. Department of Education, National Center for Education Statistics, 1969­86
Higher Education General Information Survey (HEGIS), "Degrees and Other Formal Awards Conferred" and 1987­2002
Integrated Postsecondary Education Data System, "Completions Survey" (IPEDS-C:87­02), fall 2002. (Originally published
as the Bachelor's Degrees table on p. 65 of the complete report from which this article is excerpted.)



U.S. Economic Growth
other factors provides a better understanding of the conditions in schools that
influence education.
Since the early 1980s, the percentage of high school graduates completing
advanced coursework in science and mathematics has increased. Between 1982
and 2000, the percentage that had completed advanced courses in science
increased from 35 to 63 percent, and the percentage that had completed
advanced courses in mathematics increased from 26 to 45 percent.
Among high school graduates in 2000, Asian/Pacific Islander and private
school graduates completed advanced levels of science and mathematics
coursework at higher rates than their peers. Females were more likely than
males to have completed some advanced science coursework and to have
completed level II advanced academic mathematics courses (i.e., pre-calculus
or an introduction to analysis).
According to findings from the 1999 Third International Mathematics and
Science Study (TIMSS) Video Study--which examined 8th-grade science
lessons in Australia, the Czech Republic, Japan, the Netherlands, and the
United States--46 percent of U.S. 8th-grade science lessons had students
conduct experiments or other practical activities, while 31 percent had
students collect and report data from those activities.
In 1999­2000, high school students in high-minority schools and high-poverty
schools (measured by the percentage of students eligible for a subsidized
lunch) were more often taught English, science, and mathematics by "out-of-
field" teachers (i.e., teachers who have neither a major nor certification in the



U.S. Economic Growth
subject they teach) than their peers in low-minority and low-poverty schools
(figure 5.0).
The percentage of students in grades 1­12 whose parents enrolled them in a
"chosen" public school (i.e., a public school other than their assigned public
school) increased from 11 to 15 percent between 1993 and 2003. In the same
period, the percentage of children attending private schools also increased (.9
percentage points for private, church-related schools and .8 percentage points
for private, non-church-related schools). In addition, in 2003, parents of 24
percent of students reported that they moved to a neighborhood so that their
children could attend a particular school.
Principals' perceptions of their own influence over a number of school
governance functions vary by the control of the school. In 1999­2000, private
elementary and secondary school principals were more likely than their public
school counterparts to report a high degree of influence over establishing
curriculum, setting disciplinary policies, and setting performance standards for
The goals that guidance programs in public high schools emphasize vary
according to the size and location of the school. For example, in 2002, the
smallest schools were more likely than larger schools to report that their
primary emphasis was on helping students prepare for postsecondary
schooling, while the largest schools were more likely to emphasize helping
students with their high school academic achievement. Schools located in a



U.S. Economic Growth
central city or an urban fringe area were more likely than rural schools to make
helping students with their academic achievement the primary emphasis.
At the elementary and secondary school levels, most schools have staff that
provides various support services directly to students (e.g., counselors, social
workers, speech therapists, and instructional and non instructional aides). In
1999­2000, the most common student support staff in public elementary and
secondary schools were school counselors, speech therapists, school nurses,
and special education aides, each of which were found in 79 percent or more of

Contexts of Postsecondary Education
The postsecondary education system encompasses various types of
institutions, both public and private. Although issues of student access,
persistence, and attainment have been predominant concerns in postsecondary
education, the contexts in which postsecondary education takes place matter
as well. The diversity of the undergraduate and graduate populations, the
various educational missions and learning environments of colleges and
universities, the courses that students take, the modes of learning that are
employed, and the ways in which colleges and universities attract and use
faculty and other resources all are important aspects of the contexts of
postsecondary education.
Students age 24 and above represented 43 percent of all undergraduates in
1999­2000, and 82 percent of these students worked while enrolled. Many



U.S. Economic Growth
older undergraduates were employees first, focusing primarily on their jobs,
and students second. Those whose primary focus was on their employment
were less likely to complete their postsecondary programs than were older
students who worked primarily to meet their educational expenses.
The list of the top 30 postsecondary courses, which reports the subjects that
students study the most in college (and which is referred to as the "empirical
core curriculum"), has remained relatively stable over the past three decades.
Among bachelor's degree recipients who graduated from high school in 1972,
1982, and 1992, each cohort earned about one-third of its credits from the top
30 postsecondary courses for the cohort. For the 1992 cohort, the top 30 list
for students attending highly selective institutions included a concentration of
engineering and humanities courses and courses with an international theme,
a pattern not present for students in selective and non-selective institutions.
Postsecondary institutions provided remedial coursework for 28 percent of
entering freshmen in fall 2000 (22 percent undertook remediation in
mathematics, 14 percent in writing, and 11 percent in reading). Public 2-year
colleges provided such coursework for 42 percent of their entering students.
In 2000­01, 56 percent of all postsecondary institutions offered distance
education courses, up from 34 percent 3 years earlier. The number of course
enrollments in distance education also increased, nearly doubling between
1997­98 and 2000­01; by 2000­01, about half of these enrollments were at
public 2-year institutions.



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Figure 5.0 Percentage of public high school students taught selected subjects by teachers
without certification or a major in the field they teach, by minority concentration and school
poverty: 1999­2000

Low minority
High minority
Low poverty
High poverty
Social Studies

NOTE: "Major" refers to a teacher's primary fields of study for a bachelor's, master's, doctorate, first-professional, or
education specialist degree. "Major field" can be an academic or education major. "High minority" refers to schools in which
75 percent or more of their enrollments are minority students; "low minority" refers to schools with a minority enrollment
of less than 10 percent. "High poverty" refers to a school in which 75 percent or more of students are eligible to participate
in the federal free or reduced-price lunch program, a common proxy measure of poverty; "low poverty" refers to schools in
which less than 10 percent of students are eligible to participate in this program.
SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS),
1999­2000, "Public School Survey" and "Public Charter School Survey." (Originally published as the Out-of-Field Teachers
figure on p. 73 of the complete report from which this article is excerpted.)

Societal Support for Learning
Society and its members--families, individuals, employers, and governmental
and private organizations--provide support for education in various ways. This
support includes learning activities that take place outside schools and colleges
as well as the financial support for learning inside schools and colleges.
Parents contribute to the education of their children in the home through
reading with young children, setting aside a time and place for schoolwork, and



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seeing that assignments are completed. Communities impart learning and
values through various modes, both formal and informal. Financial
investments in education are made both by individuals in the form of income
spent on their own education (or the education of their children) and by the
public in the form of public appropriations for education. These investments in
education are made at all levels of the education system. Other collective
entities, such as employers and other kinds of organizations, also invest in
various forms of education for their members.
In 2001, 50 percent of children in kindergarten through 8th grade were
enrolled in a variety of non-parental care arrangements after school,
most commonly center- or school-based programs, relative care, and self-
care. Black children were more likely than White and Hispanic children
to participate in non-parental care.
Thirty-eight percent of children in kindergarten through 8th grade
participated in one or more organized activities after school in 2001.
Children in 3rd through 5th grade and 6th through 8th grade were more
likely to participate than children in kindergarten through 2nd grade.
Parents of 19 percent of these children reported using activities to cover
hours when adult supervision was needed for their children.
Total expenditures per public elementary and secondary school student,
adjusted for inflation, increased by 25 percent between 1991­92 and
2000­01. The largest increases occurred in midsize cities and rural
In 2000, expenditures per student for the OECD member countries
averaged $5,162 at the combined elementary/secondary level and $9,509
at the post-secondary level. The United States and Switzerland, two of
the world's wealthiest nations, ranked highest in expenditures per
student at the elementary/secondary and postsecondary levels. Wealthy



U.S. Economic Growth
countries such as the United States spent more on education, and a
larger share of their gross domestic product (GDP) per capita on
education, than less wealthy nations.
The percentage of full-time undergraduates receiving institutional aid
and the average amount awarded increased at 4-year institutions during
the 1990s. In 1992­93, some 17 percent of full-time undergraduates at
public institutions and 47 percent at private not-for-profit institutions
received institutional aid; by 1999­2000, the respective proportions had
increased to 23 and 58 percent. During this period, the average award
increased from $2,200 to $2,700 at public institutions and from $5,900
to $7,000 at private not-for-profit institutions.
Those who had received bachelor's degrees in 1999­2000 were more
likely than their 1992­93 counterparts to have borrowed to pay for their
undergraduate education (65 vs. 49 percent), and if they had done so, to
have borrowed larger amounts, on average ($19,300 vs. $12,100 in
constant 1999 dollars). However, the median "debt burden" (monthly
payment as a percentage of monthly salary) a year later did not change.

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



U.S. Economic Growth
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.



U.S. Economic Growth

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 5.1 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.

Figure 5.1 Index of real outputper hours of all persons, busines ssector
FY1947 FY1954 FY1952 FY1970 FY1973 FY1974 FY1978 FY1986 FY1994

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 business sector, the index relates to gross



U.S. Economic Growth
domestic produce (GDP) less general government, output of nonprofit institutions, output of paid employees of private
households, 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.

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 1947­73 trend, as shown in figure 5.1. 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 non-manufacturing sector of the economy, which represents
an increasing share of total U.S. employment.1 Because of the slowdown in
labor productivity, growth in worker compensation (earnings plus benefits) has
slowed by a similar magnitude (Bosworth and Perry 1994). Given the strong
connection between productivity and compensation, the productivity slow down
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

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



U.S. Economic Growth
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

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



U.S. Economic Growth
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.
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 mis-measured, trends in
output and productivity will also be mis-measured. 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

4 Baily 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



U.S. Economic Growth
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 5.1, 1947­94, 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

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

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.
5 Maddison (1982) presents statistics showing that GDP per man-hour grew by an annual average compound rate of 2.3
percent from 1870 through 1979.
6 Darby (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.



U.S. Economic Growth
productivity in other countries is catching up to that of the United States.
Figure 5.2 shows real gross domestic product (GDP) per worker for the group
of seven (G­7) 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
between the G­7countries. 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 5.2). 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 5.1 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

7 The data presented in table 5.1 and figure 5.2, are from different sources and are based on different productivity
measures--productivity is measured as GDP per worker in figure 5.2 and GDP per hour worked in table 2.1.



U.S. Economic Growth
Figure 5.2 Real GDP per worker in G-7 nations: 1950-90 (in thousands od
dol ars based on 1985 internationals prices)
FY1950 FY1954 FY1958 FY1962 FY1966 FY1970 FY1974 FY1978 FY1982 FY1986 FY1990
E: Penn World Table (Mark 5.6), distributed by the National Bureau of Economic Research. For a description, see Robert
Summers and Alan Heuston, The Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950­
1988, Quarterly Journal of Economics (May 1991):327­368.

Worker Productivity and Education

Table 2.1 Growth in gross domestic product per hour worked

(average annual growth rate)


from I to II
from II to III

United Kingdom
United States

SOURCE: Angus Maddison, Growth and Slowdown in Advanced Capitalist Economies: Techniques of Quantitative
Assessment, Journal of Economic Literature (June 1987): 649­698

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



U.S. Economic Growth
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 5.2 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 G­7 countries has declined steadily since
1950, demonstrates that productivity in these countries is, in fact, converging.9

8 Baumol, Blackman, and Wolff (1989) provide a detailed description and analysis of this hypothesis.
9 The coefficient of variation is equal to the standard deviation of productivity divided by the mean.



U.S. Economic Growth
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.10 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.
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

10 In 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.



U.S. Economic Growth
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 growth accounting 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.11 They
estimate that education accounted for 11 percent of the growth in aggregate
real output from 1948 through 1985.

11 The 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.)



U.S. Economic Growth
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).12 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 country. The lowest impact was
11 percent in the United States and Japan, and the highest impact was 27
percent in West Germany.13 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

12 According 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.
13 The 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.



U.S. Economic Growth
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.14 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.15
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.16 Regressions 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

14 Baumol, 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.
15 Barro (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.
16 Kyriacou (1991) also presents findings that suggest productivity convergence occurs only if sufficient levels of schooling
among the labor force have been accumulated.



U.S. Economic Growth
critical determinant of the relative productivity levels among the most
industrialized countries.



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



U.S. Economic Growth
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.17 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.18 But even if

17 Alternative 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.
18 Several 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



U.S. Economic Growth
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.

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 25­34 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 5.3). A similar relationship between education and earnings
held for females 25­34 years old, although for each educational category, the
median earnings were lower for females than for males.



U.S. Economic Growth
Figure 5.3 Earnings for al wage and salary earners ages 25­34 years, by sex
and educational attainment: 1993
Highest Level Education Completed
Grades 9 to 11
High School Diploma
Some College
Bachelor' degree or
E: 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.

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 5.4 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 1986.19 The
returns to a high school 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.

19 These comparisons, which are from Murphy and Welch (1989), control for differences in race, sex, and age.



U.S. Economic Growth

Figure 5.4 Estimated returns to education among workers with 1 to 5 years of
work experience (percent increase over average wages of non-high school
graduates): 1963­86
Estimates of the returns to education control for differences between educational groups in race­ethnicity, age, and sex.
SOURCE: Kevin Murphy and Finis Welch, Wage Premiums for College Graduates: Recent Growth and Possible
Explanations, Educational Researcher (May 1989): 17­26; U.S. Department of Commerce, Bureau of the Census, March
Current Population Surveys.

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 5.4. 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 5.4 for high school and college
graduates imply that, compared with high school graduates, the earnings



U.S. Economic Growth
advantage for college graduates increased in the early 1980s, from 33 percent
to 55 percent.20
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.21 Figure 5.5 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
Figure 5.5 Median annual income for full-time workers ages 25 years and
older, by sex and educational attainment: 1980 and 1990
Educational Attainment

Fewer than 4 years
4 years of high
1 to 3 of college
4 or more years of
of high school

20 These estimates are based on dividing one plus the return to college shown in figure 3.4 by one plus the return to high
21 See Murphy and Welch (1989), Eck (1993), and Katz and Murphy (1992) for detailed discussions of the earnings trends
by level of educational attainment.



U.S. Economic Growth
Educational Attainment

Fewer than 4 years
4 years of high
1 to 3 of college
4 or more years of
of high school
E: 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.

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).22Recent research (Berman, Bound, and Griliches 1994) attributes much
of the change in the wage structure of manufacturing to increased demand for

22 Using 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.



U.S. Economic Growth
high skilled labor.23 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 one half of the increase in the rate of return to education can be
attributed to expanded computer use.24
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 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

23 Berman, 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 non production 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 non production workers are strongly
correlated with investment in computers and research and development.
24 Based 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.



U.S. Economic Growth
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 less educated 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



U.S. Economic Growth
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 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.25 Consequently, the

25 Baker and Smith (1994) report (based on data from the High School and Beyond Survey and the 1992 National



U.S. Economic Growth
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 low
education 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

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.



U.S. Economic Growth
countries demonstrate that differences in income distribution explain about
one-fifth of the variance in growth rates across countries and over time.26 None
of the other variables tested by Persson and Tabellini (1994) explains more
than one-tenth of the variation.
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.

26 The growth measure used by Persson and Tabellini (1994) is annual average growth rate of gross domestic product per



U.S. Economic Growth
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.



U.S. Economic Growth
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 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



U.S. Economic Growth
decreased power of labor unions. The increase in education may have kept
median earnings from dropping even lower than it did.



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



U.S. Economic Growth
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 1993). 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



U.S. Economic Growth
hand, a high score (level five) indicates advanced skills in performing a variety
of tasks that involve the use of complex documents.27
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.
Literacy and Unemployment
Unemployment tends to be correlated with low literacy. Figure 5.6 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.

27 A detailed description of the prose, document, and quantitative literacy levels is contained in U.S. Department of
Education (1993).



U.S. Economic Growth
Figure 5.6 Unemployment of adult labor force participants, by proficiency level on
three literacy scales: 1992
E: 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.

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 5.7), 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.



U.S. Economic Growth
Figure 5.7 Average proficiencies of the unemployed and full-time employed with
high school diplomas only, by literacy scale: 1992
Full time employed
RCE: 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.

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



U.S. Economic Growth
workers may not be able to accurately evaluate alternative job prospects;
therefore, they quit jobs on the basis of flawed evaluations of their prospects.

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 5.8).
Figure 5.8 Mean w eekly earnings of ful -time w orkers, by proficiency level on three literacy
scales: 1992
Level 1
Level 2
Level 3
Level 4
Level 5
CE: 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.

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



U.S. Economic Growth
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.
Figure 5.9 Mean weekly earnings of full-time employed college graduates,
by proficiency level on three literacy scales: 1992
Level 1
Level 2
Level 3
Level 4
Level 5
E: 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.



U.S. Economic Growth
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.

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 6.0).



U.S. Economic Growth
Figure 6.0 Weekly earnings of full-time black workers as a
percentage of white workers' earnings, by literacy proficiency level: 1992
Level 1
Level 2
Level 3
Level 4
TE: 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.

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 dis-
appointing 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 6.1).
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



U.S. Economic Growth
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.
Figure 6.1 Percentage of labor force in each proficiency level on the three
literacy scales: 1992
Level 1
Level 2
Level 3
Level 4
Level 5
RCE: 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.

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



U.S. Economic Growth
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 21­25 years may have declined in recent years. Adults in this age
range, most of who are current or soon-to-be job entrants, performed less well
in 1992 than the comparable group in 1985. In addition, the cohort of adults
who were ages 21­25 years in 1985 appear to have had lower test scores in
1992, when they were ages 28­32 years, than they did in 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 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 89­92 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. NCES and the Educational Testing
Service are currently conducting a reevaluation of the NALS and YALS data.



U.S. Economic Growth
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.



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.



U.S. Economic Growth
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.

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



U.S. Economic Growth
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 G­7 (United
States, Japan, Germany, United Kingdom, France, Italy, and Canada) compare
to one another.
Although the percentage of the adult population ages 25­64 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 25­64 years in both Germany and
the United States have finished the equivalent of a high school education
(figure 6.2). 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 25­34 years
through high school completion. Furthermore, in countries other than the
United States, the attainment gap between the oldest and youngest age groups



U.S. Economic Growth
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 G­7 countries are likely to be one of the factors
contributing to the convergence of worker productivity in these countries.

Figure 6.2 Secondary school completion, by age: 1992
Ages 25-64
Ages 25-34
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.

Most G­7 countries still lag well behind the United States in postsecondary
attainment. The United States has by far the highest proportion of the
population ages 25­64 years that has completed a college education, as shown



U.S. Economic Growth
in figure 6.3. 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 6.3, the rate of college completion among adult ages 25­34 years
is slightly lower than for adult ages 25­64 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 adult ages 25­64 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 25­34
years had completed a college-level education, the same as U.S. adults in the
same age range. These findings suggest that, to date, G­7 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 non-university
postsecondary programs (figure 6.4).



U.S. Economic Growth
Figure 6.3 Completion of higher education, by age: 1992
Ages 25-64
Ages 25-34
6.4 6.8
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.



U.S. Economic Growth
Figure 6.4 Completion of postsecondary education, percent of population
ages 25­64 years: 1991
United States
Postsecondary education includes university and non-university 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.

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



U.S. Economic Growth
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 G­7 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 G­7 countries (figures 6.5 and 6.6). 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



U.S. Economic Growth
the five G­7 countries presented in figure 6.3, the United States consistently
trails only France in the three measures of reading achievement.28

Figure 6.5 Mean mathematics achievement of students in their last year of
secondary school in industrialized countries, by topic: 1980­82
Number System
United States
United Kingdom
E: 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).

28 The 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.



U.S. Economic Growth
Figure 6.6 Mean science achievement of students in their last year of
secondary school in industrialized countries, by topic: 1983­86
38 38
United States
United Kingdom
E: 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).

Figure 6.7 Mean reading achievement of 14-year-old students in industrialized
countries, by topic: 1990­91
470 United States
E: 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.



U.S. Economic Growth
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.
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.



U.S. Economic Growth

This research clearly demonstrate that the US educational system has a
potentially effect in the economy growth. Research has found that higher
education is associated with substantial earnings premiums in the job market.
The rate of return on education, however, varies with such factors as family
Students of ability from economically disadvantaged backgrounds might
decline to invest in higher education because of financial risk. The state should
give such students grants and tuition subsidies.
During the next century, higher education will become increasingly important
for landing high-paying jobs. But for the foreseeable future, many jobs will
require no formal schooling beyond high school.



U.S. Economic Growth

Bialy, Martin Neil, Gary Burtless, and Robert E. Litan. Growth and quity:
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Baumol, William, Sue Ann Blackman, and Edward Wolff. Productivity
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U.S. Economic Growth

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Evanston, IL: Northwestern University, 1994.



U.S. Economic Growth
Hollenbeck, Kevin. The Economic Payoffs to Workplace Literacy. Staff
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Employment Research, October 1993.

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U.S. Economic Growth
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U.S. Economic Growth


Mr. Hector Cruz Echevarría
Address: 609 Tito Castro Ave. Suite #102
PMB- 362 Ponce, P.R. 00716
Phones: (787) 585-1000 (787) 585-1018
Career Profile:
Over ten (12) years experience working in the Marketing industry with strong
interpersonal skills.

Strong Inter-Personal skills:
Structured/ Analytical thinking,
Systematic and informed (data based) problem solving



U.S. Economic Growth
Strong communication skills
Leadership and ownership.

Academic Background
10/2004- in progress ­ North central University ­ On line Campus
Doctor of Business Administration (DBA)
Major: International Business

05/2001 to 07/2003 - University of Phoenix - Puerto Rico Campus
Master in Business Administration (MBA)
Major: Marketing
08/1997 to 12/2000 - Pontifical Catholic University of Puerto Rico - Ponce Campus
Bachelor of Science (BS)
Major: Liberal Studies

Job Experience
07/2003 PCR Marketing Consultants
CEO & Marketing Consultant

PCR Marketing Consultants a full service marketing and small business development
provider. We work with our clients to define, clarify, and achieve their marketing and
communications goals. At PCR Marketing Consultants, we understand that each client
is unique therefore we provide customized solutions based on their goals. This
approach has helped us to develop our reputation as a company that consistently
exceeds our customer's expectations. Our current and former clients include small,
medium, and industrial businesses. All of our clients benefit from our knowledge of
effective marketing and strategies techniques as well as our commitment to prompt,
exceptional service.

03/2005 to 12/2005 Centennial of PR
Business Account Executive
As a business account executive I was in charge to identify corporate accounts and
individual prospective to bring them to our company. In order to select and identify my
target market I have to prepare an effective marketing plan. I base my target market
by identifying and analyzing the segment in a product-market, deciding which
segment to target and designing and implementing a positioning strategy for each

10/1994 to 11/2003 Cingular Wireless
Business Account Executive



U.S. Economic Growth
As a business account executive, also I was in charge to identify corporate accounts
and individual prospective to bring them to our company. This includes Identification,
research and education of potential new clients through to close of sale and
Maintenance of good client relationships in order to keep the 100% satisfied.

Extracurricular Activities
Member of the American Marketing Association (AMA)
Member of the Phi-Alpha-Delta Law Fraternity, International
Honorary Award Recognition by The National Dean's List (1998)


Bilingual: Spanish & English

Computer Literacy:

Microsoft Publisher®, Microsoft FrontPage®, Microsoft Visio®, Microsoft Excel®,
Microsoft PowerPoint®, Microsoft Word®, Internet Explorer, Microsoft Access® &
Microsoft Outlook®.



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