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Educação e Desenvolvimento
Económico
Pedro Telhado Pereira
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A evidência micro – económica mostra que o salário dum
indivíduo aumenta quando a sua educação aumenta.
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Podemos perguntar:
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Será que a nível macro-económico, ou seja, na Economia
como um todo tal se verifica?
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Teriamos assim uma uma equação salários “Macro-Mincer”
agregada (Heckman and Kleenow (1997)), ln Y =  + b S +
e, onde Y é a média geométrica dos salários e S é o nível
médio de educação.
Heckman and Kleenow (1997)
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Comparando o coeficiente da educação
nas equações do PIB em amostras
cross-country com o coeficiente da
educação dos modelos micro Mincer
verificam que as estimativas macro e
micro são muito semelhantes o que
parece levar a concluir que não existem
externalidades do capital humano.
Krueger and Lindahl (1998 and
2000)
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Questionam os resultados da literatura
macro que sugeriam que não havia
ligação entre os aumentos de educação e
o crescimento económico.
Depois de corrigirem para os erros de
medida concluíram que os efeitos das
variações da educação no crescimento
económico são,pelo menos, da ordem de
magnitude das estimativas microeconómicas da rendibilidade da educação.
Também verificaram que a taxa de
crescimento não depende do nível inicial
de educação.
De la Fuente and Domenech
(2000)
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Mostram que o resultado contra-intuitivo
da educação não ter influência no
crescimento tinha a ver com deficiências
dos dados sobre o capital humano
utilizado nos outros estudos.
Depois de removeram as deficiências
dos dados da OCDE mostram que o
capital humano é um factor de produção
crucial.
Cohen and Soto (2001)
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Apresentam uma nova base de dados sobre o
capital humano. Esta base de dados tenta
incorporar toda a informação disponível de um
grande número de fontes:
base de dados da OCDE sobre educação,
Census nacionais,
surveys publicados pela UNESCO’s Statistical
Yearbook e
Census acessíveis nas páginas dos institutos
nacionais de estatística.
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Encontraram um valor estimado para a
rendibilidade da educação de 8,4% o qual está
… “fairly much in line with the average return
obtained from micro data”.
Para testarem a robustez do resultado fizeram
a regressão da taxa de crescimento do
rendimento per capita no aumento dos anos
de educação. O resultado obtido de 8% é
muito semelhante ao acima.
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Quando o nível educacional foi colocado como
variável explicativa na equação de crescimento,
aparece como não significativamente diferente de
zero. Isto leva-los a concluir:
“this settles, at least for these data [the data used in
this authors’ paper], the long standing opposition
between the effects of levels and the effects of the
increase of human capital on growth. We find quite
simply that levels are correlated to levels and growth
rates to growth rates”. Again in this paper we see that
human capital seems to have social returns that are
identical to the private ones.
Education and Economic Growth:
A Meta-Regression Analysis
Nikos Benos and Stefania Zotou
University of Ioannina
March 2013
Online at https://mpra.ub.uni-muenchen.de/46143/ MPRA
Paper No. 46143, posted 15 April 2013 08:48 UTC
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measures of education and economic growth used in the
empirical literature vary. Education is a broad term and as
a result, empirical studies face difficulties with its
measurement. The literature uses several proxies. Most
proxies concern measures of formal education and include
literacy rates, enrollment rates and years of schooling.
Literacy rates are typically defined as the proportion of
the population aged 15 and older who are able to read
and write a simple statement on his/her everyday life
(UNESCO, 1993). However, literacy rates are not
objectively and consistently defined across countries and
omit important components of human capital (Le et al.,
2005).
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Enrollment rates measure the number of students enrolled
at a given level of education relative to the population
that, according to legislation, should be attending school
at that level. Enrollment rates measure the current
investment in human capital that will be reflected in the
future stock of human capital. Nevertheless, they are poor
proxies for the present stock of human capital for many
reasons. For instance, enrollment rates can be at best
satisfactory proxies for human capital only in some
countries. Judson (2002) argues that secondary
enrollment rates will only be good indicators for human
capital accumulation in countries where secondary
education is expanding rapidly.
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The deficiencies of literacy and enrollment rates as
measures of human capital have motivated researchers to
look for a more powerful human capital proxy, namely
years of schooling of the workforce. Schooling years
quantify the accumulated educational investment in 11
the current workforce and assume that human capital
embodied in workers is proportional to the years of
schooling they have attained. With respect to literacy and
enrollment rates, schooling years take into account the
total amount of formal education acquired by the
workforce, that is, schooling years proxy more accurately
the existing stock of human capital in a country (Bassetti,
2007). In this context, some studies use the percentage
of the working age population with primary, secondary
and tertiary education. All these measures reflect the
quantity of human capital. So, the above proxies do not
give an indication of the skill level of the workforce.
Here comes the issue of human capital quality. The lack of human
capital quality data in many studies considering the relationship between
education and growth may be the biggest challenge in this area of
research. The quantity of education is an inadequate measure of human
capital differences, since school systems vary across countries in terms
of resources, organization and duration. One solution in order to account
for qualitative differences across education systems, is to focus on
human capital quality measures, such as educational expenditure,
student/teacher ratios and test scores. These indicators can be
measured at different levels of education. However, using such quality
measures as proxies of human capital, it is very difficult to get a measure
that can be reliably extrapolated for the entire workforce. As a result, any
possible measure of education has advantages and disadvantages, and
they must be taken into account when the effect of education on
economic growth is estimated.
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Moreover, the output measure used varies across studies,
being Gross Domestic Product (GDP), GDP per-capita or
GDP per worker in real terms. 2 The respective output
growth measures used as dependent variables are real
GDP growth, real GDP per capita growth or real GDP per
worker growth. From the previous discussion, we can
argue that the coefficients estimating the relationship
between education and economic growth may differ
between studies partly due to differences in the type of
the education and output variables used.
Main conclusion
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Thus, it seems safe to conclude that the educationeconomic growth empirical research, exhibits substantial
publication selection toward positive growth effects of
education, while the economic growth impact of education
after taking into account publication bias depends critically
on the specific features of the study. These findings do
not necessarily imply that the positive impact of education
on growth postulated by theory does not exist. It may well
be the case that the problems characterizing empirical
research on this question are so severe that they make it
impossible to uncover this effect. In any case, our paper
provides important information for future empirical studies
evaluating the role of education in the process of
economic growth.
Education and Growth: Where All the Education Went
Theodore R. Breton* and Andrew Siegel Breton
Universidad EAFIT
February 1, 2016
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VI. Conclusions
For over 20 years researchers have tried without success
to find an effect on GDP from increases in schooling over
five-year periods. After performing one of these analyses
and finding only negative correlations, Pritchett [2001]
famously asked, “Where has all the education gone?” In
this paper we provide an answer to this question.
The existing analyses that fail to find any effect assume
that the entire effect of schooling is immediate. We
examine whether the effect of schooling on GDP may be
substantially delayed to determine whether this may
explain the failure to find any effect.
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We first present data showing that increases in schooling
affect workers’ earnings differently depending on their
level of schooling. We also show that workers’ earnings in
middle income countries only increase with experience if
they have prior schooling.
We conclude that increases in worker productivity and in
earnings on the job are a delayed effect of their prior
schooling. We then examine whether a pattern with a
delayed effect similar to the one observed for workers´
earnings may characterize the relationship between
increased schooling and GDP.
 We find that this pattern can explain changes in GDP.
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But we find that a pattern in which the initial effect of
schooling on GDP is slightly lower than in the earnings
studies (30% of its eventual effect) provides results that
are more statistically significant. The clear implication is
that the increase in GDP during a five-year period is due
to the increases in schooling during the prior 40 years.
We find that an additional year of schooling in the
population age 25-64 raises GDP by 7% on average over
a 40-year period, but the effect associated with this
additional year in the initial five-year period is only 3%.
Since average schooling typically increases by less than a
year over a five-year period, the initial effect of increased
adult schooling on GDP is very small.
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So this is where the education went. It had a small initial
positive effect on GDP and then contributed steadily to
workers’ productivity as they gained experience over their
working lives. Even though their improvement in
productivity occurred on the job, it was not independent
of their prior schooling. As a consequence, it is
appropriate to consider the productivity improvements on
the job as a delayed effect of the workers’ earlier
schooling.
The results in this article highlight the reality that
increasing a country’s productivity through education is a
very long-term process. It begins by investing in
children’s pre-schooling and continues through primary,
secondary, and post-secondary schooling. If there is any
effect on GDP during the schooling period, it is negative
since students forego work to attend school.
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Even if the students entering the work force have more
schooling than the existing workers, the positive effect of
this additional schooling materializes slowly. In this study
the initial effect did not occur until after age 25 and then
it continued to increase until age 65.
But the positive effects of additional adult schooling are
not limited to this 40-year period. There is considerable
evidence that student achievement in school is positively
affected by their parents’ level of education [Fuchs and
Woessmann, 2007]. In addition, students with educated
parents stay in school longer and eventually are more
productive on the job [Hanushek and Woessmann, 2008 ].
This continuing positive effect of additional schooling
beyond an adult’s working life may explain why the effect
of schooling on GDP in crosssectional analyses is greater
than the effect found in this study.