EDUCATIONAL OUTCOMES IN OECD COUNTRIES
The vast literature of cross-country growth regressions has tended to find a significant
positive association between quantitative measures of schooling and economic growth.4
To give an idea of the robustness of this association, an extensive empirical analysis by
Sala-i-Martin, Doppelhofer, and Miller (2004) of 67 explanatory variables in growth
regressions on a sample of 88 countries found that primary schooling was the most
robust influence factor (after an East Asian dummy) on growth in GDP per capita in
1960-1996.
Nevertheless, we believe that these formulations introduce substantial bias into the
estimation. Average years of schooling is a particularly incomplete and potentially
misleading measure of education for comparing the impacts of human capital on the
economies of different countries. It implicitly assumes that a year of schooling delivers
the same increase in knowledge and skills regardless of the education system. For
example, a year of schooling in South Africa is assumed to create the same increase in
productive human capital as a year of schooling in Korea. Additionally, formulations
relying on this measure assume that formal schooling is the primary (sole) source of
education and that variations in non-school factors have negligible effects on education
outcomes and skills. This neglect of cross-country differences in the quality of
education and in the strength of family, health, and other influences is probably the
major drawback of such a quantitative measure of schooling.
To see this, consider a standard version of an education production function as
employed in a very extensive literature (see Hanushek (2002) for a review), where skills
are expressed as a function of a range of factors:
human capital = β1 family inputs + β 2 schooling inputs + (2)
β 3 individual ability + β 4 other factors + υ
In general, human capital combines both school attainment and its quality with the other
relevant factors including education in the family, labor market experience, health, and
so forth.
Thus, while school attainment has been convenient in empirical work because of its
ready availability across countries, its use as a proxy for human capital is very
restrictive. Not only does it ignore differences in school quality, but also any other
important determinant of people’s skills. Still, human capital is a latent variable that is
not directly observed. To be useful and verifiable, it is necessary to specify its
measurement.
A more satisfying alternative is to incorporate variations in cognitive skills, which can
be determined by results of international assessments of mathematics, science, and
reading achievement, as a direct measure of the human capital input into empirical
analyses of economic growth. The focus on cognitive skills has a number of potential
advantages. First, it captures variations in the knowledge and ability that schools strive
to produce and thus relates the putative outputs of schooling to subsequent economic
4 For extensive reviews of this literature, see, e.g., Topel (1999); Temple (2001); Krueger and Lindahl (2001); Sianesi and Van
Reenen (2003).