The Age Structure of Human Capital and Economic Growth
| DOI | http://doi.org/10.1111/obes.12274 |
| Author | Amparo Castelló‐Climent |
| Published date | 01 April 2019 |
| Date | 01 April 2019 |
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©2018 The Department of Economics, University of Oxford and JohnWiley & Sons Ltd.
OXFORD BULLETIN OF ECONOMICSAND STATISTICS, 81, 2 (2019) 0305–9049
doi: 10.1111/obes.12274
The Age Structure of Human Capital and Economic
Growth*
Amparo Castell ´
o-Climent†
†Institute for International Economics, Facultad de Economia, University of Valencia,
Campus dels Tarongers s/n 46022, Valencia Spain(e-mail: amparo.castello@uv.es.)
Abstract
This paper shows that the age structure of human capital is a relevant characteristic to take
into account when analysing the role of human capital in economic growth. The effect of
an increase in the education of the population aged 40–49 years is found to be an order
of magnitude larger than an increase in the education attained by any other age cohort.
The results are unlikely to be driven by the age structure of the population, as we find that
the effects on growth of the age structure of education and the age structure of population
are distinct. The findings are robust across specifications and remain unchanged when we
control for long-delayed effects in human capital or for the experience of the workforce.
I. Introduction
Human capital has been considered one of the fundamental determinants of the differ-
ences in growth rates observed between different countries (e.g. Lucas, 1988).A common
approach in the empirical literature has been to proxy the human capital of the working
age population using the average years of schooling of the population aged 15years and
over or 25years and over. These measurements, however, include the years of schooling of
the retired section of the population, and mask whether the effect of education on growth
varies across age groups. The goal of this paper is to break down total years of schooling
into its different components in order to analyse whether its effect on growth depends on
the age structure of human capital. The results of the paper suggest that this is in fact the
case.
We estimate a growth accounting model that incorporates human capital both as a
regular factor in the production function (e.g. Lucas, 1988; Mankiw, Romer and Weil, 1992)
and as a promoter of productivity through the facilitation of innovation and the adoption
of new technologies (e.g. Nelson and Phelps, 1966; Romer, 1990). In the econometric
JEL Classification numbers: I25, J11, O11, O50.
*I am very grateful to the editor, Jonathan Temple, and to an anonymous referee for their helpful comments
and suggestions. I also thank Rafael Dom´enech, Abhiroop Mukhopadhyay, and the participants at the 2018 Royal
Economic Society Conference and at the 33rd Annual Congress of the European Economic Association for fruitful
discussions.The financial support from the Spanish Ministry of Economy and Competitiveness through the ECO2015-
65263-P project is also gratefully acknowledged.
Age structure and economic growth 395
specification, the first channel is captured by the increments in the years of schooling and
the second by the initial level of education. In this model, we evaluate the effect of the
age structure of human capital on economic growth, measured by the ratio of the average
years of schooling of a given age group to the average years of schooling of the working
age population. The results show that whereas the education of the youngest generations
relative to that of the labour force does not have a clear effect on the growth rates, the
education of the middle-aged section of the population has the largest impact, with positive
but decreasing effects in older age groups. The largest estimates are found for the human
capital of the population aged 40–49 years.
We show our results are robust to an ar ray of sensitivity tests. In the first place, we
show the results are unlikely to be driven by the effect of the age structure of the pop-
ulation on growth rates. Recent evidence has studied the influence of population ageing
on productivity and growth (e.g. Maestas, Mullen and Powell, 2016; B¨orsch-Supan and
Weiss, 2016; Acemoglu and Restrepo, 2017). Feyrer´s (2007) findings reveal an inverted
U-shape between workers’ age and total factor productivity (TFP), with the segment of
the workforce aged 40–49 being the most productive. Our findings could therefore be
the result of the direct effect of the age structure of the population on productivity. This
does not seem to be the case, however. When we control for the age structure of the
population, we find the largest impact of human capital in the 40–49 age bracket holds.
Interestingly, in line with Feyrer (2007), we also find an inverted U-shape relationship
between the age structure of the population and economic growth rates, suggesting that
the age structure of education and the age structure of the population have distinct effects
on growth.
The fact that middle-aged workers have more experience than the younger and better
educated age group could also explain the findings. Cook (2004) estimates a standard
Cobb–Douglas production function and shows that average experience has a positive ef-
fect on growth rates. We control for the average experience of the workforce to check
whether the larger coefficients of human capital for the middle age group are picking
up the effect of experience. The results on the age structure of human capital change
only slightly. The evidence indicates that average years of schooling among the popu-
lation aged 15–29 contributes less to growth than the education in older age groups. It
is the human capital of the middle-aged population that is more relevant for economic
growth.
Wealso analyse whether the larger effect of the schooling of the population aged 40–49
years could reflect the delayed effectof human capital, since it takes time for human capital
to influence growth rates. We find that controlling for earlier expansion in education does
not change the main results; the inverted U-shape association between the age structure of
human capital and the growth rate of per capita GDP holds. The largest estimates are also
found in the human capital of the 40–49 age group.
Finally, results could be subject to omitted variable and endogeneity problems that are
typical in cross-section growth regressions. We estimate a dynamic panel data model with
the system GMM estimator, which controls for time-invariant omitted variables and takes
into account the endogeneity of the regressors using instrumental variables. Again, the
education of the middle-aged section of the population has the largest impact on growth,
with positive but decreasing effects in older age groups.
©2018 The Department of Economics, University of Oxford and JohnWiley & Sons Ltd
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