Structural Change and Economic Dynamics 11 2000 433 – 472
Human capital investment and economic growth: exploring the cross-country evidence
Edward N. Wolff
Department of Economics, Faculty of Arts and Science, New York Uni6ersity,
7
th Floor,
269
Mercer Street, New York, NY
10003
-
6687
, USA Received 1 October 1999; received in revised form 1 July 2000; accepted 29 August 2000
Abstract
The paper investigates three models on the role of education in economic growth: human capital theory, a threshold effect, and interaction effects between education and technological
activity. Data for 24 OECD countries on GDP, employment, and investment from the Penn World Tables over the period 1950 to 1990 was used. Five sources are used for educational
data. The descriptive statistics suggest that the convergence in labor productivity levels among these nations appears to correspond to their convergence in schooling levels.
However, econometric results showing a positive and significant effect of formal education on productivity growth among OECD countries are spotty at best. With only one or two
exceptions, educational levels, the growth in educational attainment, and interaction effects between schooling and RD were not found to be significant determinants of country labor
productivity growth. © 2000 Elsevier Science B.V. All rights reserved.
Keywords
:
Productivity; Convergence; Education; Human capital www.elsevier.nllocatestrueco
1. Introduction
There are three paradigms which appear to dominate current discussions of the role of education in economic growth: the first has stemmed from human capital
theory; the second could be classified as catch-up models; and the third important approach has stressed the interactions between education and technological innova-
tion and change.
Tel.: + 1-212-9988900; fax: + 1-212-9954186. E-mail address
:
edward.wolffnyu.edu E.N. Wolff. 0954-349X00 - see front matter © 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 9 5 4 - 3 4 9 X 0 0 0 0 0 3 0 - 8
1
.
1
. The human capital approach Human capital theory views schooling as an investment in skills and hence as a
way of augmenting worker productivity see, for example, Schultz, 1960, 1961, 1971; Becker, 1975.
1
This line of reasoning leads to growth accounting models in which productivity or output growth is derived as a function of the change in
educational attainment. The early studies on this subject showed very powerful effects of educational
change on economic growth. Griliches 1970 estimated that the increased educa- tional attainment of the U.S. labor force accounted for one-third of the Solow
residual, the portion of the growth of output that could not be attributed to the growth in unadjusted labor hours or capital stock, between 1940 and 1967.
Denison 1979 estimated that about one-fifth of the growth in U.S. national income per person employed between 1948 and 1973 could be attributed to
increases in educational levels of the labor force.
2
Jorgenson and Fraumeni 1993 calculated that improvements in labor quality accounted for one fourth of U.S.
economic growth between 1948 and 1986. Maddison 1987, in a growth accounting study of six OECD countries, covering the years 1913 – 1984 generally found that
increases in educational attainment explained a significant proportion of productiv- ity growth, though the contributions varied by country and sub-period.
Yet, some anomalies have appeared in this line of inquiry. Denison 1983 in his analysis of the productivity slowdown in the U.S. between 1973 and 1981, reported
that the growth in national income per person employed NIPPE fell by 0.2 points whereas increases in educational attainment contributed a positi6e 0.6
percentage points to the growth in NIPPE. In other words, whereas educational attainment was increasing, labor productivity growth was falling. Maddison 1982
reported similar results for other OECD countries for the 1970 – 1979 period. Benhabib and Spiegel 1992, using the Kyriacou series on educational attainment
see below, found no statistically significant effect of the growth in mean years of schooling on the growth in GDP per capita among a sample of countries at all
levels of economic development, when a ‘catch-up’ term is included.
1
.
2
. Catch-up models The second strand views the role of education in the context of a productivity
‘catch-up’ or ‘convergence’ model. Previous explanations of the productivity con- vergence process almost all involve the so-called ‘advantages of backwardness’, by
which it is meant that much of the catch-up can be explained by the diffusion of technical knowledge from the leading economies to the more backward ones see
Gerschenkron, 1952; Kuznets, 1973, for example. Competitive pressures in the
1
Smith 1776, was, perhaps, the first to put forward this view.
2
Denison 1962 appears to be the first work to provide detailed estimates of the contribution of education to economic growth.
international economy ensure rapid dissemination of superior productive techniques from one country to another. Through the constant transfer of knowledge, coun-
tries learn about the latest technology from each other, but virtually by definition the followers have more to learn from the leaders than the leaders have to learn
from the laggards. One direct implication of this view is that countries which lag behind the leaders can be expected to increase their productivity performance
toward the level of the leading nations and, ceteris paribus, should experience higher rates of productivity growth.
However, being backward does not itself guarantee that a nation will catch up. Other factors must be present, such as strong investment, an educated and well
trained work force, research and development activity, developed trading relations with advanced countries, a receptive political structure, low population growth, and
the like. Indeed, Abramovitz, 1986 and Abramovitz, 1994 has summarized this group of characteristics under the rubric of social capability.
3
In this context, education is viewed as one index of the social capability of the labor force to borrow existing technology. One of the prime reasons for the
relatively weak growth performance of the less developed countries is their failure to keep up with, absorb and utilize new technological and product information, and
to benefit from the international dissemination of technology. One of the elements that can be expected to explain an economy’s ability to absorb information and
new technology is the education of its populace. In this context, education may be viewed as a threshold effect in that a certain level of education input might be
considered a necessary condition for the borrowing of advanced technology. Moreover, varying levels of schooling might be required to implement technologies
of varying sophistication. On an econometric level, the correct specification would then relate the rate of productivity growth to the level of educational attainment.
Baumol, Blackman and Wolff 1989, Chapter 9 were among the first to report an extremely strong effect of educational level on the growth in per capita income
among a cross-section of countries covering all levels of development the chapter was originally written and circulated in 1986. For our educational variable, we
used the gross enrollment rate, defined as the ratio of the number of persons enrolled in school to the population of the corresponding age group. Enrollment
rates were constructed for primary school, secondary school, and higher education. We also used various country samples and time periods — 66 countries for
1950 – 1981, 112 countries for 1960 – 1981, and 105 countries for 1965 – 1984. The first two datasets were based on the Summers – Heston sample described in Sum-
mers and Heston 1984 and the last was calculated from the World Bank’s World De6elopment Report World Bank, 1986.
Since that time, many other studies have reported similar results on educational enrollment rates using both more recent data, particularly the 1960 – 1985 Sum-
mers – Heston sample described in Summers and Heston 1988 and data from Penn World Table Mark V see Summers and Heston, 1991 covering the period
3
This process has also been referred to as ‘conditional convergence’ by Mankiw et al. 1992.
1960 – 1988, and a more varied assortment of country samples see, for example, Barro, 1991; Mankiw et al., 1992. In these two, as well as in most others, the
secondary school enrollment rate has been used as the measure of educational input. However, several cracks appear to have formed in this strand of research see
Wolff and Gittleman, 1993, for details. First, the introduction of a number of ‘auxiliary’ variables — most notably, investment — appears to have mitigated the
importance of education in the growth process. Second, whereas primary and secondary school enrollment rates both remain statistically significant as a factor in
explaining economic growth, the uni6ersity enrollment rate often appears statisti- cally insignificant.
Third, the use of enrollment rates in productivity growth regressions has been aptly criticized because they are not indices of the educational attainment of the
current labor force but of the future labor force.
4
Moreover, high enrollment rates may be a consequence of high productivity growth — that is, the causation may go
the other way. As a result, several studies have used educational attainment at a particular point in time instead of educational enrollment rates in cross-country
regressions in which growth in GDP per capita is the dependent variable. However, measures of the direct educational attainment of the labor force or of the adult
population often produce weaker results than the use of enrollment rates see Wolff and Gittleman, 1993, for details.
1
.
3
. Interactions with technical change A third strand emanates from the work of Arrow 1962 and Arrow introduced
the notion of learning-by-doing, which implies that experience in the application of a given technology or new technology in the production process leads to increased
efficiencies over time. As a result, measured labor productivity in an industry should increase over time, at least until diminishing returns set in. One implication
of this is that an educated labor force should ‘learn faster’ than a less educated group and thus increase efficiency faster.
In the Nelson – Phelps model, it is argued that a more educated workforce may make it easier for a firm to adopt and implement new technologies. Firms value
workers with education because they are more able to evaluate and adapt innova- tions and to learn new functions and routines than less educated ones. Thus, by
implication, countries with more educated labor forces should be more successful in implementing new technologies.
5
The Arrow and Nelson – Phelps line of reasoning suggests that there may be interaction effects between the educational level of the work force and measures of
4
Mankiw et al. 1992 try to avoid this problem by using the ratio of secondary school enrollment to the working-age population in their regression analysis, a variable which they interpret as a proxy for
the human capital investment rate.
5
These ideas have been revived and reformulated in the new growth theory of Lucas 1988 and Romer 1990.
technological activity, such as the RD intensity of a country. Several studies provide some corroboration of this effect. Welch 1970 analyzed the returns to
education in U.S. farming in 1959 and concluded that a portion of the returns to schooling results from the greater ability of more educated workers to adapt to new
production technologies. Bartel and Lichtenburg 1987, using industry-level data for 61 U.S. manufacturing industries over the 1960 – 1980 period, found that the
relative demand for educated workers was greater in sectors with newer vintages of capital. They inferred from this that highly educated workers have a comparative
advantage with regard to the implementation of new technologies. A related finding is reported by Mincer and Higuchi 1988, using U.S. and Japanese employment
data, that returns to education are higher in sectors undergoing more rapid technical change. Another is from Gill 1989, who calculated on the basis of U.S.
Current Population Survey data for 1969 – 1984 that returns to education for highly schooled employees are greater in industries with higher rates of technological
change. Howell and Wolff 1992 and Wolff 1994, using industry level data for 43 industries covering the period 1970 – 1985, found that the growth of cognitive skill
levels as defined by the Dictionary of Occupational Titles of employees was positively related to indices of industry technological change, including computer
intensity, capital vintage, and RD activity.
1
.
4
. Methodological issues There are several methodological problems in the types of cross-country growth
regressions cited in the literature above see Levine and Renelt, 1992. First, there may be problems of comparability with cross-country measures of many of the
independent variables used in this type of analysis, particularly between countries at very different levels of development. Behrman and Rosenzweig 1994, for example,
stress the difficulties in comparing educational measures across countries, particu- larly in regard to the quality of schooling. Second, a related problem is that the
availability of educational attainment data is much more limited than that of enrollment data. This may bias the sample of countries and the regression results.
Also, imputations of missing educational data can also be misleading again, see, Behrman and Rosenzweig, 1994. Third, there may be specification problems in the
equations that relate education and other variables to productivity or output growth. Levine and Renelt 1992 report that econometric results for certain
exogenous variables can be very sensitive to the form in which they are entered into the equation.
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.
5
. Objecti6e The objective of this paper is to subject the three alternative models of the
relation of education to economic growth described above to empirical analysis. I will also try to account, at least in part, for any discrepancies in results and,
perhaps, to shed some new light on the role of education in economic growth.
In this study, I will confine the analysis to OECD countries. This has two methodological advantages. First, it will provide a relatively consistent sample of
countries to be used in testing a wide range of models though, in some cases, missing observations will force the exclusion of one or more of these countries.
Second, it will mitigate, to some extent, problems of comparability of educational data. However, it should be stressed even at this point that educational systems do
differ even among OECD countries. For example, as Maddison 1991 notes, standardized tests of cognitive achievement are usually much lower in the U.S. than
in other industrialized countries at the same grade level. Moreover, some countries, such as Germany, have an extensive system of apprentice training integrated with
part-time education, which is not reflected in the figures for formal schooling.
6
Thus, comparisons of standard measures of formal schooling even in this select sample of countries must be interpreted cautiously. We shall return to this point
again in the conclusion of the chapter. The remainder of this paper is organized as follows: The following section
Section 2 provides descriptive statistics on productivity levels and on educational enrollment and attainment for OECD countries. Section 3 reports econometric
results on the effects of educational enrollment and attainment levels on per capita income growth. Section 4 analyzes the role of the growth in educational capital on
productivity and output growth. Section 5 investigates evidence on interactive effects between education and RD. Implications are outlined in the concluding
part.
2. Comparative statistics among OECD countries
