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PANOECONOMICUS, 2018, Vol. 65, Issue 2, pp. 163-181
Received: 02 October 2015; Accepted: 01 March 2016.
UDC 005.94/.96:330.341.1 (1-775)
https://doi.org/10.2298/PAN151002014L
Original scientific paper
Carmen López-
Pueyo
Corresponding author
University of Zaragoza,
Faculty of Economics and Business,
Spain
clopez@unizar.es
Sara Barcenilla
University of Zaragoza,
Faculty of Economics and Business,
Spain
sbarceni@unizar.es
Gregorio Giménez
University of Zaragoza,
Faculty of Economics and Business,
Spain
gregim@unizar.es
Acknowledgement: This research has
been supported by the European Social
Fund, the Government of Aragón
(Spain) and the University of Zaragoza
(research projects 269-165 and
269-190).
The Two Faces of Human Capital and
Their Effect on Technological Progress
Summary: The aim of the paper is to investigate the effect of a new international
estimate of human capital on the process of innovation and technology catch-up
in developed countries. The new human capital variable is a measure of average
human capital efficiency per hour worked that considers the role of both the
quantity and quality of education. Our methodology is based on the framework
proposed by Jess Benhabib and Mark A. Spiegel (2005) that uses a logistic func-
tion of technology diffusion. The analysis employs panel econometrics and tack-
les the endogeneity bias. Empirical results show robust evidence of the signifi-
cance of this human capital variable as a driver of innovation and diffusion. The
effects of cognitive skills on technological progress are higher the closer the
economies are to the technology frontier. Furthermore, as technological pro-
gress has been measured using the improved total factor productivity (TFP) var-
iables built in Penn World Table (PWT) 8.0, we confirm the existence of social
returns to human capital.
Key words: Human capital, Innovation, Technology diffusion, TFP growth.
JEL: C33, J24, O47.
“A central idea in the critique of early human capital ideas
was that human capital was inherently an elusive concept
that lacked any satisfactory measurement” (Eric A. Hanushek
2013, p. 205).
The purpose of this paper is to validate the usefulness of new human capital data to
explore the relationship between human capital and technical change. Two kinds of
human capital data are used: the new variable supplied by recent PWT 8.0 (khpwt) and
a new variable (khgls) previously designed in Gregorio Giménez, Carmen López-
Pueyo, and Jaime Sanau (2015). The theoretical relationship between human capital
and technical change is based on Benhabib and Spiegel (2005). They consider that
human capital has two faces: it drives domestic innovation as was firstly recognized at
a theoretical level in the endogenous growth model of Paul M. Romer (1990), and it
promotes a country’s capabilities to imitate taking advantage of its backwardness as
was recognized in the seminal paper of Richard R. Nelson and Edmund S. Phelps
(1966).
Our contribution differs from the previous work in the following dimensions.
First, we use a new human capital variable that incorporates education quality into the
164 Carmen López-Pueyo, Sara Barcenilla and Gregorio Giménez
PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
labor force and is measured per hour worked. Second, the impact of this variable on
technological progress is tested using the recent PWT 8.0 which incorporates two “so-
phisticated” TFP measures and a variable of human capital. This allows us to distin-
guish between private and social returns to education. Third, the estimation of the re-
lationship is carried out with adequate econometric techniques (system generalized
method of moments - GMM).
The results obtained allow us to ensure the existence of social returns to human
capital. They also reveal a higher social return to innovation than to diffusion when
human capital is measured by combining qualitative and quantitative components, as
it is done in the khgls variable. Furthermore, from a given initial proximity to the tech-
nology frontier, the human capital variable that includes cognitive skills seems to have
a greater net effect on technological progress than the variable that does not incorporate
them.
The remainder of the paper is organized as follows. In Section 1, we review
recent developments in the literature. In Section 2, we address the difficulties of meas-
uring human capital. In Section 3, we propose the theoretical framework. In Section 4,
we describe the data and endogeneity problems and estimate the proposed model. In
Section 5, an analysis of the results is carried out. Finally, the conclusions of the work
are presented in the last section.
1. The Effect of Human Capital on Economic Growth: Recent
Developments
Modern economic theories consider human capital as a key driver of endogenous eco-
nomic growth. One strand of the literature - the accumulationist - follows the proposal
of Robert E. Lucas Jr. (1988) to recognize the role of human capital as a direct factor
of production whose accumulation enhances output growth. On the other hand, the
Schumpeterian or assimilationist tradition emphasizes the role of human capital as a
key element in the generation of technological progress (or TFP growth) which, in the
long-term, drives equilibrium growth rates of the economy. The first class of models
emphasizes the accumulation of human capital as a source of growth. On the contrary,
the second approach considers growth as being promoted by the stock o f hu man cap ita l
which is measured by school attainment or, alternatively, by the flow of education
spending.
Following these two theoretical proposals, the empirical literature on human
capital and economic growth has adopted a double perspective. In the accumulationist
tradition, a huge strand of research follows the influential paper of Gregory N.
Mankiw, David Romer, and David N. Weil (1992) which studies the effect of the
growth of human capital on per capita output growth. The results of this literature -
reviewed in the work of Alan B. Krueger and Mikael Lindahl (2001) and recently in
Michael S. Delgado, Daniel J. Henderson, and Christopher F. Parmeter (2014) - are
somewhat puzzling: education is positively and significantly associated with growth
only in countries with the lowest level of education.
One of the possible explanations of this counterintuitive result is the existence
of an indirect effect of education on growth which operates through its impact on TFP
165
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PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
growth, an idea also validated by the assimilationist tradition. Within this line of re-
search, the work of Benhabib and Spiegel (2005) is the most influential.
As in their well-known paper of 1994 (Benhabib and Spiegel 1994), these au-
thors follow the proposal of Romer (1990) and model the effect of human capital in
boosting innovation by introducing it as a direct explanatory factor in the knowledge
production function. Additionally, as in Nelson and Phelps (1966), Benhabib and Spie-
gel (2005) consider the stock of human capital as a catalyst of technological diffusion.
This “imitation effect” is incorporated into the knowledge production function inter-
acted with the distance to the frontier. Benhabib and Spiegel (2005) introduce three
important novelties: the superiority of the logistic (versus the exponential) diffusion
function to model TFP growth, the possibility of economic divergence across nations
and the existence of a threshold value below which countries fall into the poverty trap.
The authors test this specification for 84 countries from 1960 to 1995 and obtain
robust evidence only of the imitation effect of human capital. This result is also cor-
roborated in, among many others, the work of Jakob B. Madsen, Md. Rabiul Islam,
and James B. Ang (2010) who obtain insignificant coefficients for the innovation ef-
fect of educational attainment on TFP growth whereas the imitation (interaction effect)
is positive for the overall sample of 55 developing and developed countries over the
period 1970-2004 but not for each of the subgroups when the sample is split into de-
veloped and developing countries. The authors attribute this result to the existence of
a small sample bias. Also, for 159 European regions in 1992-2005, Johanna Vogel
(2013) obtains evidence of an innovation effect of human capital on TFP growth only
when the interaction term is excluded to resolve multicollinearity problems.
The works of Philippe Aghion et al. (2005) and Jérôme Vandenbussche, Agh-
ion, and Costas Meghir (2006) introduce an important novelty in this line of research
by considering that imitation and innovation require skilled and unskilled workers in
different proportions. If this is so, different countries will require different composi-
tions of human capital in order to grow, depending on their distance from the frontier.
Papers like that of Islam (2010) and Ang, Madsen, and Islam (2011) provide support
for this proposal while others, like Fabio Manca (2011) and Marianna Papakonstan-
tinou (2014), offer evidence of the positive effect of skilled workers on productivity
growth whatever the distance to the frontier.
Most of the studies cited above measure human capital and its composition by
average years of education, by far the most commonly used proxy of the stock of hu-
man capital. This choice is justified by the availability of large databases like that of
Robert J. Barro and Jong-Wha Lee (2013). Nevertheless, recently, a second strand of
analysis has emphasized variables that represent the quality of human capital because
the use of average years of schooling assigns the same return of an additional year of
education to countries whose education systems are extremely different.
The multiple works of Eric Hanushek (see, for example, Hanushek and Ludger
Wößmann 2012) emphasize cognitive skills to explain the importance of human capi-
tal and conclude that human capital causes economic growth and when it is incorpo-
rated into growth regressions, school attainment loses its significance.
166 Carmen López-Pueyo, Sara Barcenilla and Gregorio Giménez
PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
Manca (2011) reexamine Benhabib and Spiegel (2005) covering 88 countries
(both developed and developing) for the period 1960-2000. First, he elaborates a com-
posite indicator, which adjusts Daniel Cohen and Marcelo Soto’s (2007) number of
years of schooling data for the differences in the quality of each country’s educational
system, based on internationally comparable test scores. Then, he regresses TFP
growth on this indicator and its interaction with the TFP gap and finds that education
plays a fundamental role in the explanation of economic growth at all stages of devel-
opment; but the magnitude of the effect is very heterogeneous, being much larger in
developing countries. George Messinis and Abdullahi D. Ahmed (2013) delve into this
line of research with a new latent indicator of cognitive skills for seventy nations in
1970-2003. They demonstrate that, when this indicator - that accounts for years of
schooling, cognitive skills used in scientific research, life expectancy, and the use of
modern IT equipment for educational purposes - is used, human capital drives both
domestic innovation and technology diffusion. Similarly, Garett Jones (2012) obtains
evidence of the relevance of the quality of human capital for different samples of de-
veloped and developing countries in 1960-1995. Using the national average IQ (intel-
lectual quotient) of Richard Lynn and Tatu Vanhanen (2002, 2006) - who take into
account data from hundreds of published intelligence studies performed in 113 coun-
tries over the last century - Jones (2012), demonstrates that IQ and its interaction term
are much more statistically significant than the quantity of education term. Islam, Ang,
and Madsen (2014) try to capture the quality of human capital through two schooling
inputs - the teacher-pupil ratio and the real public educational expenditure per stu-
dent/real per capita GDP - and five schooling output variables - the number of univer-
sities per million workers listed in the top 500 Academic Ranking of World Universi-
ties published by the Shanghai Jiao Tong University, the rates of non-repetition and
the results of international test scores in mathematics, science and reading. In their
study, for a panel of 60 countries in 1970-2010, they find a robust relationship between
the direct and the indirect effects of a quality-adjusted human capital variable on TFP
growth.
So, recent evidence is conclusive. It is possible to assert as Jones (2012, p. 452)
does that: “the horse-race results of most papers provide no support for the hypothesis
that the quantity of education is more important that the quality of education in pro-
ducing and adopting TFP growth”. Our work is framed in this strand of analysis in
which quantity human capital variables are adjusted for by some measure of quality of
education. The next section presents the novelties in detail.
2. The Difficulties of Measuring Human Capital and New
Contributions
In spite of the huge number of theoretical and empirical studies published in recent
decades, measuring human capital remains a challenging task. The indicators that are
usually employed by growth researchers are strongly conditioned by the availability
of the data. As schooling indicators have been traditionally the most easily available
variables, and because schooling is a key determinant of earnings many empirical stud-
ies use these variables as a measure of human capital. However, the traditional
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PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
indicators of formal education, such as the enrolment rates of the school-age popula-
tion or the average years of schooling, fail to collect the complex nuances of the con-
cept of human capital, which goes far beyond mere formal education. As Giménez
(2005) stresses, the concept includes many elements apart from formal education, in-
cluding innate capacities, non-formal education, on-the-job training, experience and
health. Furthermore, many empirical studies that use these traditional indicators take
into account the human capital of the school-age population or the whole population,
when the relevant variable should be the human capital of the workforce.
Due to these conceptual limitations of the variables that are used as proxies of
the concept and also to the poor quality of the data available for international compar-
isons, measurement errors in human capital are very common. These limitations may
be behind the fact that many empirical research works struggle to find a clear link
between human capital and economic growth, as authors such as Krueger and Lindahl
(2001), Wößmann (2003), Angel De la Fuente and Rafael Domenech (2006), Cohen
and Soto (2007), Yousif Khalifa Al-Yousif (2008) and Hanushek (2013) have pointed
out. This problem is of particular importance when we work with growth models based
on innovation, because human capital is a cornerstone to understand research processes
and technology adoption.
Despite the recognized limitations of the traditional indicators, essays that use
conceptually richer indicators, which take into account a greater number of elements
in the concept of human capital, are few. In recent years, some researchers have made
valuable efforts to build more elaborate and accurate indicators. As we have seen in
the previous section, the new indicators are used in the hope of modelling the relation-
ship between human capital and technological progress more precisely.
Notable among these new attempts is the index of human capital included in
version 8.0 of the Penn World Table (PWT); see Robert C. Feenstra, Robert Inklaar,
and Marcel P. Timmer (2013) and Inklaar and Timmer (2013). This index is based on
a Mincerian transformation of the average years of schooling calculated by Barro and
Lee (2013). The indicator has the important advantage of being comparable across
countries and over time, and estimates the human capital hc of country i at time t as a
function of the average years of schooling s:
ℎ𝑐 =𝑒
(), (1)
where 𝜙(𝑠) are the Mincerian rates of return to education defined by George
Psacharopoulos (1994).
Another recent attempt to build a more accurate international indicator of hu-
man capital is that of Giménez, López-Pueyo, and Sanau (2015) (GLS indicator, from
now on). These authors make a methodological proposal to calculate international
stocks of human capital by taking into account a double dimension: quantitative and
qualitative. The indicator proposed reflects three factors: (1) educational levels
achieved; (2) differences in productivity and wages, based on the education possessed;
(3) differences in educational quality and knowledge. The information corresponding
to hours worked and salaries comes from the EU KLEMS Growth and Productivity
Accounts database, financially supported by the European Commission (see Timmer,
Mary O’Mahony, and Bart van Ark 2008) and the results on cognitive skills from
168 Carmen López-Pueyo, Sara Barcenilla and Gregorio Giménez
PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
Hanushek and Wößmann (2012), who use all available international tests datasets be-
tween 1964 and 2003 and put performance on a common scale in order to facilitate
comparisons.
With this data, the authors estimate differences in productivity among workers,
calculated from the differences in remuneration according to their levels of education.
The differences in productivity are used to weight the total hours worked in the econ-
omy. Finally, the stock of human capital, in terms of numeraire hours of work accord-
ing to the basic educational level, is corrected by internationally comparable test
scores.
The PWT and GLS indicators provide more accurate ways to measure human
capital. However, they differ in key aspects. The PWT indicator, available for a wide
set of economies from 1950 to 2011, is based on the average years of schooling trans-
formed according to Mincerian rates of return that are common to all countries. The
GLS indicator is constructed in terms of the stock of numeraire hours, based on differ-
ences in levels of education and productivity that are calculated for each economy and
weighted by the quality of education. Thus, unlike other indicators, it allows to take
into account the existence of differences in productivity among workers, countries and
years, regardless of whether the workers have the same level of education. Moreover,
using numeraire workers allows to exclude differences in productivity between coun-
tries resulting from factors that are not strictly human capital, such as differences in
the stock of physical capital or in technology. Nevertheless, as it needs much more
information to be constructed, it is only available for 15 OECD economics between
1980 and 2005.
Table 1 Country Rankings in Terms of Human Capital (1995-2005)
khpwt khgls
1995 2005 1995 2005
United States 3.508 United States 3.575 United States 1.733 United States 2.058
A
ustralia 3.289 Czech Rep. 3.536 Czech Rep. 1.637 Hungary 1.767
Czech Rep. 3.231
A
ustralia 3.333 United Kingdom 1.608 United Kingdom 1.716
Slovenia 3.148 Germany 3.325 Germany 1.567 Netherlands 1.705
Japan 3.049 Korea Rep. 3.255 Netherlands 1.553 Czech Rep. 1.693
Korea Rep. 3.048 Hungary 3.245 Hungary 1.538 Japan 1.600
Netherlands 3.029 Slovenia 3.244
A
ustria 1.520 Slovenia 1.572
Hungary 3.023 Japan 3.198 Japan 1.509 Germany 1.570
Belgium 2.910 Netherlands 3.099 Slovenia 1.479
A
ustria 1.542
Denmar
k
2.869 Belgium 3.029 Korea Rep. 1.400 Belgium 1.498
Germany 2.770 Denmar
k
2.907 Belgium 1.376 Finland 1.480
Finland 2.753 Finland 2.887 Denmar
k
1.353 Spain 1.443
A
ustria 2.663 Spain 2.878 Finland 1.336 Denmar
k
1.404
United Kingdom 2.636
A
ustria 2.792
A
ustralia 1.291
A
ustralia 1.348
Spain 2.597 United Kingdom 2.759 Spain 1.290 Korea Rep. 1.296
Media 2.968 3.137 1.479 1.579
St. deviation 0.261 0.257 0.134 0.191
Coef. variation 0.088 0.082 0.090 0.121
Notes: The ranking has been calculated for 1995 because Hungary, Slovenia and Czech Republic only have khgls data from
1995.
Source: Prepared by the authors.
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PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
Table 1 shows the ranking change of the countries in terms of the two human
capital variables: khpwt and khgls. Furthermore, it offers the traditional measures of
dispersion, showing greater dispersion in khpwt and lower dispersion in khgls between
the beginning and the end of the period. All the countries, with the exception of the
Republic of Korea in khgls, have experienced a growth in their human capital. First,
we present the results in which the two variables show similar behaviour. The United
States occupies the leader position in both variables with a great increase in khgls dur-
ing the period and Hungary reaches a better position in both cases, occupying second
place in khgls in 2005. Below, we discuss the results in which the two variables show
different results (the Republic of Korea, Germany and the United Kingdom). The six
countries with less khgls are, both in the initial and final year: Spain, Australia, Fin-
land, Denmark, Belgium and the Republic of Korea. The latter occupies the last posi-
tion in 2005 while it rises from the sixth to the fifth position when using the khpwt
indicator. Germany goes down to eighth position in khgls while it experiences a great
growth in khpwt in the same period. Finally, while the United Kingdom occupies the
third position in khgls, it falls to bottom place in khpwt at the end of the period ana-
lyzed.
These differences in the rankings and in the evolution of the endowments are
logical, given that the two indicators measure human capital in different ways. While
khpwt is based on a quantitative dimension of the concept and is constructed using
average years of schooling and fixed rates of returns of education, khgls is based on
differences in productivity and in the quality of education. The stock of human capital
in the first case would increase if we increase the duration of formal education. In the
second case, increases in the stock may be the result of: (i) increases in the proportion
of workers who have received higher education; (ii) improvements in the quality of
education; (iii) improvements in the productivity of workers with higher educational
levels, in comparison with unskilled workers. In sum, the two indicators could evolve
in different ways; for example, an increase in the number of average years of schooling
is not necessarily accompanied by better educational outcomes or increases in the la-
bour productivity gap between workers with higher and lower educational levels.
Innovation and imitation processes have become increasingly complex. As the
PWT and GLS indicators measure the stock of human capital more completely, they
offer new possibilities of establishing, with more empirical precision, the ties between
human capital, innovation and imitation. In the following sections, we test the capacity
of these novel human capital indicators to explain innovation and technology diffusion.
We also present our results and those obtained by other research papers that use the
same theoretical framework but different indicators of human capital.
3. Empirical Model
The purpose of this section is to establish the equation to estimate the impact of human
capital on technological progress. Technological progress in a country is the result of
two components: the domestic innovation driven by human capital and the technology
diffusion from the leader country. Following Benhabib and Spiegel (2005), the speci-
fication of a logistic functional form of technology diffusion is used. In this functional
form, technology diffusion depends on the human capital of the recipient country and
170 Carmen López-Pueyo, Sara Barcenilla and Gregorio Giménez
PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
on the distance to the frontier interacted with an extra term. This extra term seeks to
capture the idea introduced by Susanto Basu and Weil (1998) that the frontier technol-
ogy may not be immediately “appropriated” by the follower when the differences in
the factor proportions between leader and follower are large. Likewise, this extra term
could also capture all other impediments to assimilating foreign technology such as
intellectual property rights, social values and incompatibilities with domestic technol-
ogy. In contrast to the exponential case of technology diffusion that does not incorpo-
rate this extra term, the logistic diffusion function implies a faster catch-up process
when the country is in the middle distance and slower when it is too near or too far
from the frontier.
Another feature of the logistic model is that convergence in productivity growth
rates depends on the relationship between the relative magnitude of the difference in
the growth rate due to innovation and the growth rate due to diffusion. Growth rates
will converge when, due to the human capital of a follower, the diffusion rate exceeds
the differential innovation growth rate between the leader and the follower. Growth
rates will diverge when the human capital of a follower is too low and, consequently,
the catch-up rate is smaller than the differential innovation growth rate between the
leader and the follower. For this reason, investing in human capital is one way of over-
coming the difficulties of adopting distant technologies for the follower countries and
of diminishing the distance to the leader.
Equation (2) captures these two faces of human capital. In this equation, ∆TFP
is the technological progress, H is the human capital, TFP is the total factor productiv-
ity, and subscripts i, max and t denote country, country leader and year, respectively:
∆𝑙𝑜𝑔𝑇𝐹𝑃= g log 𝐻+ c log 𝐻
; (2)
∆𝑙𝑜𝑔𝑇𝐹𝑃= (g + c) log 𝐻 - c log 𝐻
. (3)
As in Benhabib and Spiegel (2005), human capital is a measure of an economy’s
capacity for domestic innovation and technology adoption from abroad. These two
roles of human capital are captured, respectively, by the first and the second term of
Equation (2). Rearranging this equation, we obtain Equation (3), which will now be
estimated. The coefficients to be estimated are (g + c) and (-c). In this equation, the
net effect of human capital on technological progress depends on how far a country is
from the frontier and corresponds to the expression (𝑔 + c) − c
. Conse-
quently, the net effect of human capital on TFP for the leader is only the domestic
innovation effect (g).
4. Estimation and Results
The sample is made up of a panel of data from fifteen countries for the years 1979-
2005. The selection of the countries has been conditioned by the availability of data
about the khgls human capital indicator. The countries included are Australia, Austria,
Belgium, Czech Republic, Denmark, Finland, Japan, Germany, Hungary, Netherlands,
Korea Republic, Slovenia, Spain, United Kingdom, and United States. The variables
of TFP level and TFP growth are taken from Penn World Tables (PWT version 8.0).
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PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
Human capital is measured by two alternative variables: the first (khpwt) comes from
PWT 8.0 and the second (khgls) is that proposed by Giménez, López-Pueyo, and Sanau
(2015), in terms of human capital per hour worked.
For the first time, the PWT 8.0 offers data on TFP that can be used for compar-
ing TFP levels across countries and for comparing TFP growth over time. The new
TFP measures in PWT are a great improvement on the standard approach used by pre-
vious versions. Asset composition in capital input and labour income of the self-em-
ployed are taken into account; purchasing power parities are used to compare capital
levels across countries; and, labour input is adjusted by an index of human capital
based on the average years of schooling of the population aged 15 and over and the
assumed rate of return (Barro and Lee 2013); see Inklaar and Timmer (2013) for a
detailed and technical document about these improvements and Feenstra, Inklaar, and
Timmer (2013) for the underlying theory. As a result of these improvements, PWT 8.0
offers two sets of productivity measures: one suitable for cross-country comparison at
a point in time to measure a country’s proximity to the frontier, and the other suitable
for comparisons over time. Furthermore, as these new sophisticated measures of
productivity take into account differences in the educational attainment of the labour
force and, thus, private returns to education, it is possible to estimate the social returns
or externalities to human capital in the framework of Benhabib and Spiegel’s (2005)
model.
Our mo del is a dyna mic p anel data mod el in wh ich t here are a rbitrary distributed
fixed effects and current realizations of the dependent variable are influenced by past
ones, generating the “dynamic panel bias”. This means that at least one regressor - the
lagged endogenous variable - is correlated with the error, violating an assumption nec-
essary for the consistency of OLS. It inflates the coefficient estimated for the lagged
endogenous variable, by attributing predictive power to it that actually belongs to the
country’s fixed effect. Additionally, the relationship between human capital and TFP
growth is likely to be simultaneous and affected by reverse causality, so endogeneity
is a question that needs to be addressed in this context. As David M. Roodman (2009)
states, there are two ways to tackle this endogeneity. One, at the heart of difference
GMM, is by transforming all the regressors by differencing to remove fixed effects.
The other, the system generalized method of moments (GMM), developed by Manuel
Arellano and Olympia Bover (1995) and Richard Blundell and Stephen Bond (1998),
is to instrument endogenous regressors by variables thought to be uncorrelated with
the fixed effect. In our study, we take this second alternative because it allows the
introduction of more instruments (the first difference of instrument variables) and dra-
matically improves on the efficiency of the GMM estimator. The two-step system
GMM estimation has been applied using the STATA statistical software package de-
veloped by Roodman (2009). Specifically, we use the xtabond2 STATA routine. As
technological progress is conditioned by innovation in previous periods, regressors are
considered as predetermined but not strictly exogenous and lags 1 to 3 of them are
used as instruments; the collapse option is implemented, so only one instrument for
each variable and lag is created. All significance levels of the variables are based on
the t-statistic using Windmeijer’s finite-sample correction for the two-step covariance
matrix.
172 Carmen López-Pueyo, Sara Barcenilla and Gregorio Giménez
PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
The coefficients to be estimated in Equation (3) are (g + c) and (-c). Table 2
presents the system GMM panel estimation using the two alternative variables of hu-
man capital. Column (1) presents the results of using khpwt and column (2) shows
those of khgls. In both columns, the coefficient (g + c) of human capital is positive at
the 5 per cent confidence level. As predicted, the coefficient of the catch-up term (-c)
shows a negative sign at the 5 per cent confidence level in both columns. Therefore,
the results support the existence of two faces of human capital in the promotion of
economic growth: human capital promotes domestic innovation and also acts as a cat-
alyst of technological diffusion from the leader.
Table 2 System GMM Estimation Results
(1) (2)
H = khpwt H = khgls
log H 0.087**
(0.019)
0.080*
(0.034)
log 𝐻
-0.086*
(0.022)
-0.077*
(0.036)
Sample size 368 332
Number of countries 15 15
Number of instruments 5 6
AR(1) -2.02
(0.043)
-1.99
(0.046)
AR(2) -0.35
(0.728)
-1.08
(0.279)
Sargan test 2.70
(0.259)
2.64
(0.450)
Hansen-J test 1.16
(0.561)
2.65
(0.449)
Notes: In parentheses are the corrected standard errors of the coefficients where ** and * denote the 1% and 5% respectively
level of significance by the t-statistic. The parentheses of the other tests show the probability of their null hypothesis.
Source: Prepared by the authors.
The estimations are accompanied by the Arellano and Bond test to detect the
first- and second-order autocorrelation in first differences. As Table 2 shows, the test
AR(1) on the residuals in first differences do not allow us to accept the hypothesis of
no first-order serial correlation and confirms the expected AR(1) in first differences.
Nevertheless, the hypothesis of no second-order serial correlation in the perturbations
tested with AR(2) is accepted. Furthermore, the Sargan and Hansen tests for the joint
validity of the instruments allow the acceptance of the joint exogeneity of the instru-
ments and support the estimations. The Sargan test is based on the observation that the
residuals must be correlated with the set of exogenous variables if all the instruments
are truly exogenous. The Sargan test under the null hypothesis that all the instruments
are exogenous, is distributed as a chi-square of m - r degrees of liberty, where m - r is
the difference between the number of instruments and the number of endogenous
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PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
variables employed. Although the Sargan test is not robust to heteroskedasticity or
autocorrelation, it is calculated because the Hansen-J test, though robust, can be weak-
ened by instrument proliferation.
These results support the existence of productivity externalities from human
capital, whether we use the human capital variable khgls or khpwt. These externalities
mean that, human capital leads to productivity gains at the macroeconomic level
through different channels: part of an individual’s education can be captured by other
workers or by the owners of other factors of production. As Serge Coulombe and Jean-
Francois Tremblay (2009) state, individual human capital can increase the productivity
of co-workers or can have a positive impact on technological progress. This result has
implications in terms of economic policy: from an efficiency perspective, large public
investment in education may be easier to defend if macroeconomic returns to education
exist and are large.
5. Analysis of Results
In this section we offer a threefold analysis of the results obtained: an analysis of our
coefficients and those obtained in previous studies, a calculation of the threshold value
of human capital to catch-up, and an analysis of individual countries’ behaviour.
Based on the estimation results in Table 2, we have calculated the more relevant
economic magnitudes in Table 3. First, the effect on the growth of productivity of one
more unit of human capital for the leader is 0.003 or 0.001, depending on whether we
measure human capital with khpwt or khgls. Second, comparing our results with the
point estimates obtained in similar studies we can conclude that, given a country’s
proximity to the frontier, human capital measured with khgls has a higher relative
productivity in innovation versus diffusion (g/c = 0.003/0.077) than if it is measured
with khpwt (g/c = 0.001/0.086). As these two variables have been built in different
ways, the estimates may be underlining that an additional unit of human capital, meas-
ured considering both qualitative and quantitative items, has a greater relative innova-
tion versus diffusion effect than an additional unit of only “quantitative human capi-
tal”. Approximating human capital without taking the quality of education into account
could result in an under-estimation of the impact of human capital on innovation and
an over-estimation of its impact on imitation.
Table 3 also offers a comparison between the estimated values obtained in this
work and those of previous papers, and gives additional information about their re-
spective samples. First, we have to consider the different human capital and total factor
productivity variables, periods, number and kinds of countries, and estimation methods
they use. Benhabib and Spiegel (2005) used the PWT 6.1 to calculate TFP and the
updated Barro and Lee human capital variable (both the initial and the average period
of the years of schooling in the population over 25 years of age). On the other hand,
Messinis and Ahmed (2013) used PWT 6.2 to calculate TFP and estimated a composite
index of the cognitive skills employed by the adult population. They also used Barro
and Lee (2013) human capital variable, but they do not obtained significative values
of the estimator. We observe larger coefficients in absolute value for the more recent
estimations.
174 Carmen López-Pueyo, Sara Barcenilla and Gregorio Giménez
PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
Table 3 Innovation and Imitation Coefficients in Logistic Diffusion Models
g + c -c (g + c) - c
leade
r
Period n Estimation
khpwt 0.087 0.086 0.001 1979-2005 15 System GMM panel
khgl
s
0.080 0.077 0.003 1979-2005 15 System GMM panel
Messinis and Ahmed (2013) with skills 0.084 0.075 0.009 1970-2003 70 System GMM panel
Manca (2011) with cognitive skills 0.018 0.015 0.003 1960-2000 88 System GMM panel
Benhabib and Spiegel (2005) with
years of education (period average) 0.016 0.012 0.004 1960-1995 84
Maximum likelihood
cross-section
Benhabib and Spiegel (2005) with
years of education (initial values) 0.013 0.007 0.006 1960-1995 84
Maximum likelihood
cross-section
Source: Prepared by the authors.
Based on these results and the predictions of the theoretical model, we now try
to find out if there are any countries that, as a consequence of their low level of human
capital, will experience no catch-up with the technology frontier. As Benhabib and
Spiegel (2005) underline, the logistic diffusion model implies that the steady state
growth relationship will depend on the relative magnitude of the difference in the
growth rate due to innovation between the leader and the follower and the catch-up
rate. If this relative magnitude is only the consequence of human capital differences
between the leader and the follower, then the TFP growth rate of the follower will
converge to the growth rate of the leader when the catch-up rate exceeds the differen-
tial growth in innovation. This condition of convergence can be expressed in terms of
a human capital threshold value below which a country will fall farther and farther
behind the leader nation over time. This critical value is:
𝑙𝑜𝑔𝐻
∗=
,
. (4)
Comparing the human capital coefficient estimated (g + c) with that of the in-
teraction term (-c) in Table 2, we can appreciate that the estimate for g is positive in
both columns. This positive value allows us to calculate a positive critical human cap-
ital value below which catch-up in TFP is not possible. The threshold value H* in the
initial year for variable khpwt is 1.0081 while it is 1.0099 for variable khgls (see Table
4). Because all the countries in our sample are developed countries, they all surpass
this critical value and could converge to the leader under the conditions previously
explained. In 2005, the countries also have their human capital values greater than the
critical value, as Table 4 shows: 1.0085 for khpwt and 1.0232 for khgls. These values
are greater than those of the initial year because the human capital of the leader has
grown during the period and, consequently, as there will be innovation at a higher rate,
followers will need to growth at a higher rate to experience higher total factor produc-
tivity growth than the leader.
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Table 4 Critical Values of Human Capital
khpwt khgls
g
0.0006 0.0025
g
+ c 0.0865 0.0797
h*1981 1.0081 1.0099
h*2005 1.0085 1.0232
Source: Prepared by the authors.
With these results, if the ranking of the countries in terms of human capital did
not change, all the countries would exhibit faster growth in total factor productivity in
the future than the leader. Another way to predict the same is to calculate the total
annual effect of human capital on technological progress. Table 5 shows the ranking
of countries according to their estimated net effect of human capital on technological
progress, considering the initial distance to the leader [(g + c) - c
]. Due to the
specification chosen, the country position is the same as that derived from its initial
proximity to the frontier country. Logically, then, the ranking is the same but the values
are different when they have been estimated using the alternative variables of human
capital khpwt and khgls, respectively. As we can see in Table 5, all the countries have
a greater net effect than the leader and, consequently, if the ranking of their respective
human capital does not change over the period, the follower countries will have faster
total factor productivity growths than the leader.
Table 5 Total Annual Effect of Human Capital on Technological Progress
List of countries Calculated with khpwt List of countries Calculated with khgls
United States 0.0006 United States 0.0025
Austria 0.0011 Austria 0.0030
Spain 0.0011 Spain 0.0030
Germany 0.0025 Germany 0.0043
United Kingdom 0.0026 United Kingdom 0.0044
Belgium 0.0033 Belgium 0.0050
Netherlands 0.0060 Netherlands 0.0074
Australia 0.0101 Australia 0.0111
Japan 0.0162 Japan 0.0166
Denmark 0.0185 Denmark 0.0186
Czech Republic 0.0202 Czech Republic 0.0201
Finland 0.0221 Finland 0.0219
Slovenia 0.0278 Slovenia 0.0269
Hungary 0.0394 Hungary 0.0374
Korea Republic 0.0407 Korea Republic 0.0386
Source: Prepared by the authors.
Table 5 shows that, in countries with a proximity to the frontier in the initial
year of less than 0.78 (Czech Republic, Finland, Slovenia, Hungary and Korea
176 Carmen López-Pueyo, Sara Barcenilla and Gregorio Giménez
PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
Republic), one more unit of khgls leads to less technological progress than an addi-
tional unit of khpwt. In other words, in countries near the frontier, one more unit of
khgls results in greater technological progress than an additional unit of khpwt. This is
consistent with our previous analysis of Table 2 and could be a sign of the higher effect
of cognitive skills on technological progress for a certain level of proximity to the
technology frontier.
From these results, it can be inferred that an innovation-effective education pol-
icy should thus focus not only on increasing school attainment, but also on enhancing
skills. This goal is especially important for countries that have reached high levels of
development and have little scope for increasing their average years of schooling.
However, it is not an easy goal to achieve because raising spending on education has
little consistent impact on improving cognitive skills if it is not accompanied by other
instruments. The policies that appear most effective in the long-run take an integrated
approach that comprises all levels of education, from preschool to college. These pol-
icies focus on: (a) ensuring access to quality preschool education as a way to encourage
early cognitive and social skills; (b) expanding the autonomy of the educational cen-
ters; (c) promoting grant programs that ensure that the best students have access to the
highest educational levels; (d) developing systems to evaluate the performance of stu-
dents and institutions; (e) encouraging competition among the latter. And above all,
the evidence suggests that the most effective way to increase educational quality is by
improving teacher quality. This requires an effective design in the way teachers are
hired, trained, motivated and paid. Finally, it should be noted that the efforts to equip
the workforce with the necessary skills should not focus only on the years of formal
education, that is, before entering the labour market. Investment in education is a life-
long process, and on-the-job training has become crucial. Surveys such as the Organ-
isation for Economic Co-operation and Development (OECD) Programme for the In-
ternational Assessment of Adult Competencies (PIAAC) have pointed out the big dif-
ferences that exist in labour force skills in developed countries. It is critical to under-
score that, as advanced skills play a central role in innovation processes, improving
workforce skills is the only way to excel in an increasingly competitive global envi-
ronment.
6. Conclusions
This paper presents a new variable of human capital and applies it to explain techno-
logical progress, using the new total factor productivity variables from PWT 8.0. This
new variable (khgls) is a measure of human capital efficiency per hour worked that
considers the role of both education quantity and quality. Moreover, the improved total
factor productivity variables already discount the private returns to education. So, us-
ing the logistic model of technology diffusion proposed by Benhabib and Spiegel
(2005) and applying dynamic panel estimations, the results capture the social returns
of education. Furthermore, our results have shown the two faces of human capital, that
is, its role in the innovation process and, above a threshold value, its role in the imita-
tion process for countries far from the technology frontier.
In our attempt to con tribut e to the debate on the me asur emen t of the externalities
of education, these findings encourage us to future extensions. First, with the aim of
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providing a more solid basis for policy initiatives, the disaggregation of this human
capital variable by levels of education, or a differentiation between its quantity and
quality components, would be necessary. Second, it would be desirable to expand the
sample to detect possible differences between developed and developing countries.
Finally, the addition of institutional variables which have a direct relationship in the
appearance of education externalities would enable a better understanding of the catch-
up process.
178 Carmen López-Pueyo, Sara Barcenilla and Gregorio Giménez
PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
References
Aghion, Philippe, Leah Boustan, Caroline Hoxby, and Jérôme Vandenbussche. 2005.
“Exploiting States Mistakes to Identify the Causal Impact of Higher Education on
Growth.”
https://pdfs.semanticscholar.org/ab27/496b0f9653f0926631a09f0f7e8c93a79e72.pdf.
Al-Yousif, Yousif Khalifa. 2008. “Education Expenditure and Economic Growth: Some
Empirical Evidence from the GCC Countries.” Journal of Developing Areas, 42(1):
69-80. http://dx.doi.org/10.1353/jda.0.0025
Ang, James B., Jakob B. Madsen, and Md. Rabiul Islam. 2011. “The Effects of Human
Capital Composition on Technological Convergence.” Journal of Macroeconomics,
33(3): 465-476. http://dx.doi.org/10.1016/j.jmacro.2011.03.001
Arellano, Manuel, and Olympia Bover. 1995. “Another Look at the Instrumental Variable
Estimation of Error-Components Models.” Journal of Econometrics, 68(1): 29-52.
http://dx.doi.org/10.1016/j.jmacro.2011.03.001
Barro, Robert J., and Jong-Wha Lee. 2013. “A New Data Set of Educational Attainment in
the World, 1950-2010.” Journal of Development Economics, 104(C): 184-198.
http://dx.doi.org/10.1016/j.jdeveco.2012.10.001
Basu, Susanto, and David N. Weil. 1998. “Appropriate Technology and Growth.” The
Quarterly Journal of Economics, 113(4): 1025-1054.
http://dx.doi.org/10.1162/003355398555829
Benhabib, Jess, and Mark M. Spiegel. 1994. “The Role of Human Capital in Economic
Development Evidence from Aggregate Cross-Country Data.” Journal of Monetary
Economics, 34(2): 143-173. http://dx.doi.org/10.1016/0304-3932(94)90047-7
Benhabib, Jess, and Mark M. Spiegel. 2005. “Human Capital and Technology Diffusion.”
In Handbook of Economic Growth - Volume 1A, ed. Philippe Aghion and Steven N.
Durlauf, 935-966. North-Holland: Elsevier.
http://dx.doi.org/10.1016/s1574-0684(05)01013-0
Blundell, Richard, and Stephen Bond. 1998. “Initial Conditions and Moment Restrictions in
Dynamic Panel Data Models.” Journal of Econometric, 87(1): 115-143.
http://dx.doi.org/10.1016/s0304-4076(98)00009-8
Cohen, Daniel, and Marcelo Soto. 2007. “Growth and Human Capital: Good Data, Good
Results.” Journal of Economic Growth, 12(1): 51-76.
http://dx.doi.org/10.1007/s10887-007-9011-5
Coulombe, Serge, and Jean-Francois Tremblay. 2009. “Education, Productivity and
Economic Growth: A Selective Review of the Evidence.” International Productivity
Monitor, 18: 3-24.
De la Fuente, Angel, and Rafael Domenech. 2006. “Human Capital in Growth Regressions:
How Much Difference Does Data Quality Make?” Journal of the European Economic
Association, 1(3): 1-36. http://dx.doi.org/10.1162/jeea.2006.4.1.1
Delgado, Michael S., Daniel J. Henderson, and Christopher F. Parmeter. 2014. “Does
Education Matter for Economic Growth?” Oxford Bulletin of Economics Statistics,
76(3): 334-359. http://dx.doi.org/10.1111/obes.12025
Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer. 2013. “The Next Generation
of the Penn World Table.” National Bureau of Economic Research Working Paper
19255. http://dx.doi.org/10.1257/aer.20130954
179
The Two Faces of Human Capital and Their Effect on Technological Progress
PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
Giménez, Gregorio. 2005. “The Human Capital Endowment of Latin America and the
Caribbean.” Naciones Unidas Comisión Económica para América Latina y el Caribe
Review LC/G.2282-P.
Giménez, Gregorio, Carmen López-Pueyo, and Jaime Sanau. 2015. “Human Capital
Measurement in OECD Countries and Its Relation to Economic Growth and
Technological Indicators.” Revista de Economia Mundial, 39: 77-108.
Hanushek, Eric A., and Ludger Wößmann. 2012. “Do Better Schools Lead to More
Growth? Cognitive Skills, Economic Outcomes, and Causation.” Journal of Economic
Growth, 17(4): 267-321. http://dx.doi.org/10.1007/s10887-012-9081-x
Hanushek, Eric A. 2013. “Economic Growth in Developing Countries: The Role of Human
Capital.” Economics of Education Review, 37(C): 204-212.
http://dx.doi.org/10.1016/j.econedurev.2013.04.005
Inklaar, Robert, and Marcel P. Timmer. 2013. “Capital, Labour and TFP in PWT8.0.”
http://piketty.pse.ens.fr/files/InklaarTimmer13.pdf.
Islam, Md. Rabiul. 2010. “Human Capital Composition, Proximity to Technology Frontier
and Productivity Growth.” Monash University, Department of Economics Working
Paper 23-10.
Islam, Md. Rabiul, James B. Ang, and Jakob B. Madsen. 2014. “Quality-Adjusted Human
Capital and Productivity Growth.” Economic Inquiry, 52(2): 757-777.
http://dx.doi.org/10.1111/ecin.12052
Jones, Garett. 2012. “Cognitive Skill and Technology Diffusion: An Empirical Test.”
Economic Systems, 36(3): 444-460. http://dx.doi.org/10.1016/j.ecosys.2011.10.003
Krueger, Alan B., and Mikael Lindahl. 2001. “Education for Growth: Why and for
Whom?” Journal of Economic Literature, 39(4): 1101-1136.
http://dx.doi.org/10.1257/jel.39.4.1101
Lucas Jr., Robert E. 1988. “On the Mechanics of Economic Development.” Journal of
Monetary Economics, 22(1): 3-4. http://dx.doi.org/10.1016/0304-3932(88)90168-7
Lynn, Richard, and Tatu Vanhanen. 2002. IQ and the Wealth of Nations. Westport, CT:
Praeger.
Lynn, Richard, and Tatu Vanhanen. 2006. IQ and Global Inequality. Augusta, GA:
Washington Summit Publishers.
Madsen, Jakob B., Md. Rabiul Islam, and James B. Ang. 2010. “Catching Up to the
Technology Frontier: The Dichotomy between Innovation and Imitation.” Canadian
Journal of Economics, 43(4): 1389-1411.
http://dx.doi.org/10.1111/j.1540-5982.2010.01618.x
Manca, Fabio. 2011. “The Farthest Need the Best. Human Capital Composition and
Development-Specific Economic Growth.” University of Barcelona, Research Institute
of Applied Economics Working Paper IR11/017.
Mankiw, Gregory N., David Romer, and David N. Weil. 1992. “A Contribution to the
Empirics of Economic Growth.” The Quarterly Journal of Economics, 107(2): 407-
437. http://dx.doi.org/10.2307/2118477
Messinis, George, and Abdullahi D. Ahmed. 2013. “Cognitive Skills, Innovation and
Technology Diffusion.” Economic Modelling, 30(1): 565-578.
http://dx.doi.org/10.2307/2118477
Nelson, Richard R., and Edmund S. Phelps. 1966. “Investment in Humans, Technological
Diffusion, and Economic Growth.” American Economic Review, 56(1/2): 69-75.
180 Carmen López-Pueyo, Sara Barcenilla and Gregorio Giménez
PANOECONOMICUS, 2018, Vol. 65, Iss ue 2, pp. 163-181
Papakonstantinou, Marianna. 2014. “Composition of Human Capital, Distance to the
Frontier and Productivity.” Paper presented at the 33rd General Conference of the
International Association for Research in Income and Wealth, Rotterdam.
Psacharopoulos, George. 1994. “Returns to Investment in Education: A Global Update.”
World Development, 22(9): 1325-1343.
http://dx.doi.org/10.1016/0305-750x(94)90007-8
Romer, Paul M. 1990. “Endogenous Technological Change.” Journal of Political Economy,
98(5): 71-102. http://dx.doi.org/10.1086/261725
Roodman, David M. 2009. “How to Do xtabond2: An Introduction to Difference and System
GMM in Stata.” The Stata Journal, 9(1): 86-136.
Timmer, Marcel P., Mary O’Mahony, and Bart van Ark. 2008. “EU KLEMS Growth and
Productivity Accounts: An Overview.”
http://www.euklems.net/data/overview_07i.pdf.
Vandenbussche, Jérôme, Philippe Aghion, and Costas Meghir. 2006. “Growth, Distance to
Frontier and Composition of Human Capital.” Journal of Economic Growth, 11(2):
97-127. http://dx.doi.org/10.1007/s10887-006-9002-y
Vogel, Johanna. 2013. “The Two Faces of R&D and Human Capital: Evidence from Western
European Regions.” Regional Science, 94(3): 525-551.
http://dx.doi.org/10.1111/pirs.12084
Wößmann, Ludger. 2003. “Specifying Human Capital.” Journal of Economic Surveys,
17(3): 239-270. http://dx.doi.org/10.1111/1467-6419.00195
181
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Appendix
Table 6 Variables, Definitions and Data
∆TFP TFP at constant national prices are used to implement system GMM panel estimation.
Variable rtfpna in PWT 8.0 (2005 = 1).
TFP TFP level at current PPPs (USA = 1).
Variable ctfp in PWT 8.0.
khpwt PWT 8.0. index of human capital per person, based on years of schooling (Barro and Lee 2013) and returns to
education (Psacharopoulos 1994); see Inklaar and Timmer (2013).
khgl
s
Giménez, López-Pueyo, and Sanau (2015) index of human capital.
Source: Prepared by the authors.
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