EDUCATIONAL OUTCOMES IN OECD COUNTRIES
models, a given level of education can lead to a continuing stream of new ideas, thus
making it possible for education to affect growth even when no new education is added
to the economy. The common way to estimate these models is to relate growth rates in
GDP per worker (or per capita) to the level of education.
A final view of education in production and growth centers on the diffusion of
technologies. If new technologies increase firm productivity, countries can grow by
adopting these new technologies more broadly. Theories of technological diffusion such
as Nelson and Phelps (1966), Welch (1970), and Benhabib and Spiegel (2005) stress that
education may facilitate the transmission of knowledge needed to implement new
technologies. In tests involving cross-country comparisons, Benhabib and Spiegel
(1994) find a role for education in both the generation of ideas and in the diffusion of
technology.
Our empirical analysis adopts a very simple growth model that combines elements of a
general “endogenous growth” framework and a basic “augmented neoclassical”
approach. Specifically, we model a country’s growth rate as a function of the skills of
workers and other factors that include initial levels of income and technology, economic
institutions, and other systematic factors. Skills are frequently referred to simply as the
workers’ human capital stock.
growth = α1 human capital + α2 other factors + ε (1)
In this formulation, nations with more human capital tend to continue to make greater
productivity gains than nations with less human capital. The fact that the rate of
technological change and productivity improvement is directly related to the stock of
human capital of the nation makes it an endogenous growth model. The relationship
between cognitive skills on the one hand and innovations and technology on the other
seems to be a natural view of the role of education. At the same time, by including the
initial level of income among the control variables, our model does allow for conditional
convergence, a leading feature of the “augmented neoclassical” approach, the commonly
suggested alternative view.
The empirical macroeconomic literature focusing on cross-country differences in
economic growth has overwhelmingly employed measures related to school attainment,
or years of schooling, to test the human capital aspects of growth models.3 Initial
analyses employed school enrollment ratios (e.g., Barro (1991); Mankiw, Romer, and
Weil (1992)) as proxies for the human capital of an economy. An important extension
by Barro and Lee (1993, 2010) was the development of internationally comparable data
on average years of schooling for a large sample of countries and years, based on a
combination of census and survey data.
3 The earliest studies used adult literacy rates (e.g., Azariadis and Drazen (1990); Romer (1990b)) but these data cover a
limited number of countries and are error prone.