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n. 549 November 2014
ISSN: 0870-8541
Economic Growth, Human Capital and Structural
Change: An Empirical Analysis
Anabela S. S. Queirós1
Aurora A. C. Teixeira1,2,3
1 FEP-UP, School of Economics and Management, University of Porto2 CEF.UP, Research Center in Economics and Finance, University of Porto
3 INESC TEC and OBEGEF
1
Economic growth, human capital and structural change: an empirical analysis
Anabela S.S. Queirós
Faculdade de Economia do Porto,
Universidade do Porto
Aurora A.C. Teixeira
CEF.UP, Faculdade de Economia,
Universidade do Porto; INESC TEC; OBEGEF
Abstract
Human capital is identified as one of the main determinants of economic growth and plays an
important role in the technological progress of countries. Nevertheless, existing studies have
to some extent neglected the importance of human capital on growth via the interaction it can
have with a country’s industrial specialization. Additionally, the emphasis is mainly placed on
supply-side determinants, being demand-side factors quite neglected, particularly the
relevance of the processes of structural change. Thus, using a growth model which integrates
variables from both the supply side and demand side, we assess the direct and indirect effects
of human capital on economic growth, including in the latter the interaction of human capital
with the industrial specialization of countries. Based on econometric panel data estimations
involving a set of OCDE countries over 1960-2011, we found that the countries’ productive
specialization dynamics is a crucial factor for economic growth. It is also shown that the
interaction between human capital and structural change towards high knowledge-intensive
industries impacts on the economic growth. However, the sign of this effect depends on the
type of country and length of the period of analysis. Specifically, in the long term and in
developed countries, where knowledge-intensive industries already account for a great share
of the economy, the impact of the interaction between human capital and structural change is
positive. In the case of less developed countries, and considering a shorter time period, the
effect of human capital via specialization in high-tech and knowledge-intensive activities
emerged as negative.
Keywords: Economic Growth; Human Capital; Structural Change; Panel Data
JEL Codes: J24, O3, O4, O47
2
1. Introduction
The analysis of the determinants of economic growth has been the subject of extensive
literature, especially since the start of the 1990s. Some authors (e.g., Barro, 1991; Mankiw et
al., 1992; Mauro, 1995) estimated the impact of some variables on economic growth through
cross-section analysis and concluded that human capital plays an important role in counties’
economic growth.
Neoclassical and endogenous growth theory identified and analyzed some determinants of
economic growth such as foreign trade, government consumption and geography, as well as
institutions, namely the case of political instability (Barro, 1991; Levine and Renelt, 1992;
Acemoglu et al., 2001; Moral-Benito, 2012). The most common determinants in the analysis
of economic growth are nevertheless, the initial GDP, population growth, investment in
physical capital, and human capital stock (e.g., Barro, 1991; Hanushek and Woessmann,
2012; Aisen and Veiga, 2013).
The concept of human capital can be interpreted as the set of intangible resources embedded
in the labor factor which have improved its productivity. These are associated to knowledge
and skills acquired through education, experience and health care (Schultz, 1961; Becker,
1962).
Human capital has a direct effect on economic growth because individuals with more
education are more productive and innovative leading to the creation of new products and
improving the productivity of factors (Romer, 1990; Benhabib and Spiegel, 1994; Teixeira
and Fortuna, 2011; Bodman and Le, 2013). On the other hand, human capital enhances
technology adoption from neighbor countries through the absorption of ideas and equipment
imports (Nelson and Phelps, 1966; Benhabib and Spiegel, 1994; Teixeira and Fortuna, 2011).
Human capital also has indirect effects namely via interaction with the productive structure of
countries. Concretely, the specialization of a country in technologically advanced activities
improves the impact (positive) of human capital on economic growth (Silva and Teixeira,
2012).
Theoretical approaches related to Economic Evolutionism reveal a need to add demand-side
factors to economic growth analysis (Witt, 2001; Metcalfe et al., 2006, Dietrich, 2012;
Teixeira, 2012). Certain changes in demand favoring more diverse and complex products lead
to structural changes, i.e., changes in sectoral composition and in the economic specialization
by boosting technological innovation and creating new products (Saviotti and Pyka, 2012;
3
Silva and Teixeira, 2012). In this line of thought, ‘high-tech’ industries have higher growth
rates of productivity and therefore contribute more than proportionally to economic growth
(Silva and Teixeira, 2012). This contribution tends to increase the more intense the absorption
capacity and innovation becomes, related to higher levels of human capital (Nelson and
Phelps, 1996; Teixeira and Fortuna, 2011).
This paper seeks to integrate in a single model supply-side variables linked to the endogenous
growth theory, and also demand-side variables linked to structuralism and evolutionism,
namely the specialization pattern of countries. More specifically, the aim of this paper is to
estimate the direct effects of human capital on economic growth as well as indirect effects
embodied in the interaction between human capital and the countries’ productive structure,
while controlling for other determinants reported in the literature. Our hypothesis is that a
country with a higher level of human capital will grow faster the higher the level of
specialization in high-tech and knowledge-intensive industries, in which skilled labor has an
important role.
In methodological terms, we use panel data econometric methods. We study a sample of
developed countries where industries that require qualified workers have increased their
relative importance in the economy.
The paper is organized as follows. The following section presents a literature review on the
relationship between economic growth and the three main variables of this study: human
capital stock, structural change and the interaction between the two. Section 3 presents
methodological considerations as well as a statistical description of the data for the relevant
variables. In section 4, we discuss the empirical results. The final section presents the main
contributions of this study, policy implications, limitations and paths for future research.
2. Literature review about determinants of economic growth
Many authors (e.g., Barro, 1991; Levine and Renelt, 1992; Mankiw et al., 1992; Mauro, 1995;
Rajan and Zingales, 1996; Easterly and Levine 1997; Hall and Jones, 1999; Hanushek and
Woessmann, 2008; Hanushek, 2013) analyze the impact of human capital on economic
growth (usually measured as the annual growth rate of gross domestic product (GDP), in real
terms).
Human capital is the set of intangible resources embedded in the labor factor which improve
its productivity. These are related to the knowledge and skills acquired through education,
experience and health care (Schultz, 1961; Becker, 1962).
4
As well as physical capital accumulation, acquiring skills and knowledge is a means of capital
formation by delaying consumption with the aim of increasing future income. Human capital
improves the quality of labor, increasing its productivity (Mankiw et al., 1992; Woessmann,
2003; Bodman and Le, 2013). It is usually considered that an additional school year will
increase the productivity and efficiency of workers, and consequently, their income (Hall and
Jones, 1998). In other words, different individual earnings are associated to different levels of
investment in education (Woessmann, 2003). Similarly, differences in the average schooling
of countries are related to different economic growth rates. For example, Easterly and Levine
(1997) found that the low economic growth observed in African countries is due, in part, to
low rates of schooling.
Human capital is the driver of Research and Development (R&D), which enhances innovation
and technological progress, thus leading to increased productivity and creation of new
products (Romer, 1990; Benhabib and Spiegel, 1994; Teixeira and Fortuna, 2011; Bodman
and Le, 2013). This means that the more educated the workforce of a country, the greater the
benefits of the R&D activities in terms of economic growth. Human capital promotes the
absorption of new ideas (absorption capacity) and products already created by other countries.
This fosters a faster convergence of economies by importing equipment and technologies
(Nelson and Phelps, 1966; Benhabib and Spiegel, 1994; Bodman and Le, 2013). Through the
mechanisms described above, human capital will encourage greater investment in physical
capital (Benhabib and Spiegel, 1994).
Finally, human capital also affects economic performance indirectly. According to Sianesi
and Reenen (2003), human capital tends to improve the health levels, environmental
conditions, criminal rates, social cohesion and civic participation. This variable also
stimulates the productivity of more educated workers. Therefore, investment in education has
an impact not only in individual returns, but also drives a spillover effect that produces social
benefits.
According to neoclassical theory, human capital contributes to a faster convergence of
countries to the steady-state levels of income per capita (Mauro, 1995). A positive result can
evidence a catching-up effect for poor countries compared to rich countries. If the former
present a high initial stock of human capital, they will grow faster and converge to the income
levels of the richest countries (Azariadis and Drazen, 1990; Benhabib and Spiegel, 1994;
Hanushek, 2013).
5
To assess the impact of human capital on economic growth, we need to measure the stock of
this variable in each country. Hence, researchers use several proxies for the human capital
stock such as: literacy rate (Azariadis and Drazen, 1990; Romer, 1990), primary and
secondary school enrolment rates (Barro, 1991; Levine and Renelt, 1992; Mauro, 1995;
Batten and Vo, 2009), and average schooling years of adults (Benhabib and Spiegel, 1994;
Rajan and Zingales, 1996; Easterly and Levine, 1997; Hall and Jones, 1999; Temple, 1999;
Lee and Barro, 2001; Temple and Woessmann, 2006; Moral-Benito, 2012; Bodman and Le,
2013). It is found that the average schooling years of the workforce is the most common
measure of the human capital stock. The literacy rates and enrollment rates are a flow
variable, reflecting only the investment in human capital, which affects this stock with a time
lag (Woessmann, 2003). The measures used in empirical studies reflect imperfectly the
human capital stock of a country. This is because there are some mistakes in databases,
inadequate measures are used (Woessmann, 2003), and factors like experience, informal
education, the depreciation of capital and the quality of education systems are not included in
the analysis (Sianesi and Reenen, 2003). On the other hand, average schooling does not
include the differences in skills learned across countries and it implies that an additional
school year increases human capital at a constant rate (Woessmann, 2003). Although they are
directly related, schooling only contributes to economic growth if it allows for the acquisition
of those skills. These can be learned outside the formal education system (e.g., family, peers,
culture) (Hanushek and Woessmann, 2008).
Most studies show a positive and significant relationship between human capital and
economic growth (Barro, 1991; Mankiw et al., 1992; Easterly and Levine, 1997; Hall and
Jones, 1999; Bodman and Le, 2013) regardless of the proxy used (e.g., the average schooling
of the working population and initial enrollment rate).
Nevertheless, Levine and Renelt (1992), Benhabib and Spiegel (1994), Mauro (1995), Rajan
and Zingales (1996) and Moral-Benito (2012) did not find any statistically significant effect
of human capital on economic growth. In particular, when enrollment rates in primary and
secondary education are used as control variables, the results are not usually very robust. This
could be due to the high correlation between human capital and the estimated variables in the
econometric regressions. The use of small and unrepresentative samples can also affect the
results (Temple, 1999).
Hanushek and Woessmann (2008, 2012) and Hanushek (2013) also show weak results when
average schooling years are used as a measure of human capital and these authors introduce
6
quality of education variables in the models. When average schooling years are used as a
measure of human capital, we assume that learning grows at the same rate in all countries
(Woessmann, 2003). Since growth is driven by skills acquired by individuals and not by
education itself, the quality of education measures gain importance in the literature. To bridge
this point, measures are introduced that differentiate the quality of education in countries.
These can be: the share of public and/or private expenditures on education in real GDP,
student-to-teacher ratio, wages of teachers and length of school year (Barro and Lee, 1996;
Hanushek, 1996; Woessmann, 2003). In order to capture the differences in the knowledge
learned across countries, many authors (e.g., Lee and Barro, 2001; Hanushek and
Woessmann, 2008; 2012, Hanushek, 2013) employ the results of international assessment
tests as a proxy of quality of education (e.g., PISA - Programme for International Student
Assessment - and TIMSS – Trends in International Mathematics and Science Study) and they
find a significant positive impact of these on economic growth. However, Breton (2011)
concludes that average schooling years is the most robust measure of the human capital stock
since the quality of education is already implicit and the correlation between average
schooling and the results of the assessment tests is very high.
From the above, we assume that:
H1: Countries with a higher stock of human capital tend to grow faster than others.
The productive structure of an economy and especially its dynamics is recognized as an
important determinant of economic growth. Thus, the concept of “structural change” emerges,
which refers to shifts in sectoral composition, where certain industries gain relative shares in
economy (Montobbio, 2002; Silva and Teixeira, 2012).
Orthodox literature assumes homogeneity assumptions in order to ensure the general
equilibrium (Peneder, 2003). With the aggregated effects of macroeconomics, we cannot see
the sectors’ different patterns in productivity (Metcalfe et al., 2006). Industries within an
economy have distinct characteristics such as: intensity of physical and human capital,
technical progress, economies of scale and exchange characteristics (Marelli, 2004). It is,
therefore, natural that each sector has its own dynamic and a different growth rate.
Transformation in sectoral composition is continuous, constantly observing an increase of the
importance of some industries in the economy as well as the decline of others.
Structural change can be caused by exogenous forces, such as changes in consumption
patterns in favor of goods with high income elasticity of demand. Thus, sectors that have
7
higher income elasticity of demand see their relative importance in the economy rise
(Peneder, 2003).
According to Metcalfe et al. (2006), economic growth is the result of structural configurations
of the economy and its dynamics. More productive industries, that Baumol named
‘progressive’ (Hartwig, 2012), gain a greater relative share in the economies because they
offer better wages, attracting more qualified and skilled individuals. These phenomena cause
direct effects on growth by creating new ways of production that will result in a more efficient
reallocation of resources and greater earning capacity (Zagler, 2009; Noseleit, 2013).
Therefore, structural change in favor of a specialization in progressive sectors and/or
technologically more advanced ones leads to economic growth (Peneder, 2003; Silva and
Teixeira, 2012).
In this sense, structuralist theories stress that economic specialization by itself is not sufficient
to generate economic growth (Aditya and Acharyya, 2013), depending on the sector that
dominates the economy. According to these approaches, economic growth can be the result of
a shift in specialization towards industrial products (Aditya and Acharyya, 2013). In fact, it
appears that most industrialized regions observe greater economic growth than mainly
agricultural regions (Marelli, 2004). Furthermore, specialization in agricultural products
makes the country more vulnerable to external shocks, according to Aditya and Acharyya
(2013).
Change in structure is also present in evolutionism approaches. According to these, economic
growth depends on the capacity for self-transformation of an economy (Metcalfe et al., 2006).
More specifically, the capacity that an economy has to generate new goods and services,
creators of value added and, thus, new activity sectors, affects economic growth (Zagler,
2009; Saviotti and Pyka, 2012). It is important, however, that the appearance of new products
coexists with the demand for them, as emphasized by Saviotti (2001).
According to several authors (e.g., Witt, 2001; Metcalfe et al., 2006; Dietrich, 2009), the
demand-side has been relatively neglected in traditional economic growth analyses,
particularly in more orthodox theories, analyzing predominantly the supply-side. The
evolution of demand is an important driver of structural change to ensure the need to re-invent
and produce new goods and innovation is the underlining concept of this process (Saviotti and
Pyka, 2012). Changes in consumption patterns reflect changes in learning, knowledge and
specialization (Witt, 2001; Saviotti and Pika, 2012).
8
According to evolutionist theories, the market is the result of a continuous process of
transformation fruit of innovation (Justman and Teubal, 1991; Saviotti and Pika, 2012). Under
Schumpeterian conceptions, radical changes in innovation contribute significantly to
economic development (Saviotti, 2001). The presence of innovation affects a firm's behavior
and its earnings. Therefore, the incorporation of earnings from Research and Development
(R&D) also contributes to the definition of the heterogeneity of industries (Peneder, 2003).
Innovation is closely associated to creativity and development of knowledge. This can be
applied and acquired within firms and they are transmitted between industries through the
market (Metcalfe et al., 2006). Furthermore, the innovation that emerges in firms fosters the
creation of new goods and services, which reflects new growth opportunities. Innovation is a
characteristic of entrants and it is the result of the vision of entrepreneurs who recognize new
business opportunities. These innovative firms reveal great adaptability and promote a more
efficient reallocation of factors than incumbents, which has a positive direct effect on
economic growth (Noseleit, 2013). Innovation also produces an indirect effect on economic
growth through the creation of positive externalities. Spillovers are created with technological
progress, since knowledge is spread by industries belonging to the same frontier (Romer,
1990; Peneder, 2003).
The creation of goods and services is related to technological progress. Technological
progress is an important determinant of economic growth to allow accumulation of capital,
the existence of economies of scale, and greater efficiency in the allocation of resources and
productivity gains through innovation (Romer, 1990; Hartwig, 2012). Thus, countries with a
more technologically advanced productive specialization have higher growth rates.
Empirically, Nelson and Pack (1999) note the rapid catching-up of Asian countries associated
with increasing specialization in activities with high technological content, and argue that the
fast growth in these economies is the result of the assimilation of technological innovations
and structural changes. Linked to demand issues, Aditya and Acharyya (2013) find that, in
dynamic terms, export specialization in high-tech goods has a positive relationship with
economic growth.1
1Although structural change is, in general, a necessary condition for the occurrence of economic growth, it can
also be a byproduct of this process, i.e., economic growth also leads to structural change (Justman and Teubal,
1991; Dietrich, 2012). In this view, economic growth creates incomes that enable the evolution of consumption
patterns, increasing the demand for more sophisticated goods. This issue does not lie within the scope of this
paper and therefore it is not explored empirically.
9
Zagler (2009) focuses on the direction of structural change, emphasizing the idea that the
occurrence of structural change may involve costs. On the one hand, “technological
unemployment” can occur with the closing of incumbents and their replacement with new
activities (Zagler, 2009; Noseleit, 2013), i.e., what Schumpeter called the 'creative destruction'
process. On the other hand, structural change can have negative effects on economic growth if
there is a large increase in demand for low productive sectors (Dietrich, 2009). In this case,
most of the expenditure is channeled towards the 'non-progressive' sector, i.e., the sector that
is not becoming more productive. These observe a growth in wages at the same rate of
progressive sectors, unaccompanied by increases in productivity which leads to the
economy’s stagnation (Hartwig, 2012). This last phenomenon is called ‘Cost Disease’ by
Baumol. From the above it is conjectured that:
H2: Countries that experience changes in productive structures towards a greater share of
technology/knowledge-intensive activities will tend to observe higher economic growth.
According to Justman and Teubal (1991), the structuralist approach considers human capital
as a major determinant of economic growth since this factor enhances structural change.
Thus, human capital is critically important in the evolution of countries’ specialization
(Afonso, 2012). The productive specialization of economies depends on their endowment
factors, whereby technologically more advanced industries will tend to locate in countries
with a high stock of human capital.
Ciccone and Papaioannou (2009) also emphasize the positive relationship between education
and ‘virtuous’ structural changes, i.e., when technology-intensive activities gain a relative
share in the economy. Therefore, structural change and the virtuous specialization of
economies depend on new information, new skills and workers’ productivity, factors which
are closely related to the stock of human capital (Justman and Teubal, 1991; Afonso, 2012).
The accumulation of human capital enables new business opportunities and endows the agents
with management skills and technological knowledge (Justman and Teubal, 1991). Countries
with highly skilled workers tend to be more efficient in activities that incorporate more
advanced technologies. From a micro perspective, firms that have employees with a high
level of human capital adopt complementary technologies in order to achieve maximum
efficiency. It follows that the accumulation of human capital enhances the role of Research
and Development (R&D) in the economies by promoting the creation of new products
(Caselli and Coleman, 2006; Bodman and Le, 2013). Therefore, human capital affects the
10
technological progress of countries (Nelson and Phelps, 1966; Romer, 1990; Benhabib and
Spiegel, 1994; Vandenbussche et al., 2006; Coleman, 2006). On the other hand, it reduces the
costs of implementing these technologies (Kim and Lee, 2009).
The process of innovation requires a certain level of human capital to actually occur
(Vandenbussche et al., 2006). The sectors that arise through structural change require the
acquisition of new skills by workers from the declining industries in order to be absorbed by
the first ones (Zagler, 2009).
As previously mentioned (Section 1.1), individuals acquire a set of important skills to perform
certain functions, particularly those involving adaptation to change, through formal education
(Nelson and Phelps, 1966). In addition, the productivity gains resulting from technological
progress depend on both the accumulation of physical capital and human capital, and these
gains are greater the higher the stocks of these factors (Ciccone and Papaioannou, 2009).
Physical capital and human capital are complementary since increases in the stock of physical
capital lead to an increase in the marginal productivity of human capital and vice-versa
(Caselli and Coleman, 2006). In this context, high-tech activities observe a great increase in
productivity which leads to the creation of value added and employment growth in industries
(Ciccone and Papaioannou, 2009).
The technological catching-up process and structural change related to the transfer of
technology from developed countries to developing ones may be enhanced (in economic
growth terms) when the country has a higher level of human capital, which increases its
absorption capacity (Nelson and Phelps, 1966; Benhabib and Spiegel, 1994). Through this
process, developing countries can have productive structures with more technological content
via imitation. However, a successful imitation needs a minimum threshold of human capital
(Vandenbussche et al., 2006; Teixeira and Fortuna 2011). A fortiori, creative and innovative
processes require greater stocks of human capital (Vandenbussche et al., 2006).
The interaction between human capital and structural change is clearly explaining by Gürbüz
(2011) who highlights the situation of North and South countries. Southern countries, during
their tertiarization, specialized in labor-intensive services and absorbed low-skilled workers
from rural migration; in contrast, northern countries specialized in high-tech, tradable
products, which require a high level of human capital. According to this author, the
accumulation of human capital is a necessary condition for a virtuous structural change, i.e.,
an increasing share of the most productive sectors.
11
Regarding the demand-side, human capital makes consumers more sophisticated. This means
that if consumers are more highly educated, they will be more likely to seek 'high-tech'
products, which positively contributes to virtuous structural change (Justman and Teubal,
1991).
Structural change is also enhanced by entrepreneurs (Justman and Teubal, 1991; Dias and
McDermott, 2006; Saviotti and Pyka, 2012), because they invest in more innovative and
modern sectors. Recognizing a business opportunity, entrepreneurs create new
technologically advanced and efficient firms, which contribute to structural change (Noseleit,
2013). Entrepreneurs are, in general, more ‘talented’ than other workers and they invest in
their human capital, via professional experience, in order to enhance their own talent. The
skills acquired by entrepreneurs allow them to create new ideas and generate new businesses
(Iyigun and Owen, 1999). Entrepreneurs also foster the reallocation of productive factors
(such as labor) among sectors (Noseleit, 2013). The latter look for more productive agents so
that they can integrate the most innovative and technologically advanced activities, which
require certain skills and knowledge. Therefore, entrepreneurs create an attractive
environment for the accumulation of human capital by workers through education, since the
expected return is higher (Dias and McDermott, 2006; Noseleit, 2013). This means that
economic growth will be higher the more numerous and talented the entrepreneurs and the
higher their stock of human capital (Iyigun and Owen, 1999; Dias and McDermott, 2006).
H3: The impact of human capital on the economic growth of a country is greater the more
specialized the economy is in high technology/knowledge-intensive activities.
There are a set of determinants of economic growth recurrently reported by several studies
regarding economic growth. Thus, we may consider factors such as: initial GDP per capita
(Barro, 1991; Easterly and Levine, 1997; Moral-Benito, 2012), population growth (Dreher,
2006; Aisen and Veiga, 2013), physical capital accumulation/investment (Barro, 1991;
Benhabib and Spiegel, 1994; Fabro and Aixalá, 2009; Aisen and Veiga, 2013), public
consumption (Barro, 1991; Levine and Renelt, 1992; Moral-Benito, 2012; Afonso and Jalles,
2014) and institutions (Mauro, 1995; Hall and Jones, 1999; Acemoglu et al., 2001; Dreher,
2006; Hanushek and Woessmann, 2008).
12
3. Methodology
3.1. Econometric specification and option for panel data estimation
Panel data estimations have a few advantages over the traditional cross-country models (cf.
Table 1). This technique serves to study the dynamics of adjustment and to estimate their
effects over a long period of time. On the one hand, panel data can analyze a set of variables
for a great number of countries, which provides more information. Panel data estimations also
assume that countries are heterogeneous, with specific and unobservable characteristics.
‘Cross-section’ and ‘times series’ estimations do not control for this heterogeneity, meaning
the results may be biased (Greene, 2011).
Therefore, to estimate the effects of relevant variables on economic growth, namely human
capital and its interaction with the productive structure, we opted for the estimation of panel
data, such as in more recent studies with similar aims (e.g., Dreher, 2006; Batten and Vo,
2009; Aisen and Veiga, 2013; Iqbal and Daly, 2014).
According to the literature review and the hypotheses to be tested, the econometric model
specification (à la MRW) to estimate is:
yit = 1 + 2 HCit +3 SCit + 4 (HC*SC)it + 5 Yi;0 + 6 Iit+7 Git+8CP+ 9 Instit +ui+εit
where i represents the countries’ index and t represents time.
y the logarithm of GDP per capita
HC a measure of the human capital stock
SC a measure of structural change
HC*SC the interaction between the measures of human capital stock and structural
change
Y0 the initial GDP per capita in each decade
I a measure of investment in physical capital
G a measure of the share of public consumption in GDP
CP a measure of population growth
Inst a measure of the institutional characteristics of the country
u the unobserved time-invariant fixed effect
ε the unobserved random coefficient
13
3.2. Description of the proxies and data sources
The present paper considers a sample of 30 countries (26 are European and the others are:
Japan, South Korea, United States and Australia). We started by analyzing a time period of at
least 50 years, from 1960-2011, and we considered 21 countries because of the availability of
data. Over a shorter period (1990-2011), 30 countries in total were included in the analysis.
In order to measure the dependent variable, economic growth, we use the logarithm of GDP
per capita as a proxy. To this end, we resorted to the dataset Penn World Table (version 8.0)
(Feenstra et al., 2013) which reports the values for real GDP at constant 2005 prices (in mil.
US$).
The average years of schooling of adults is the most widely used proxy of human capital (e.g.,
Rajan and Zingales, 1996; Easterly and Levine, 1997; Hall and Jones, 1999; Temple and
Woessmann, 2006; Moral-Benito, 2012; Bodman and Le, 2013). Thus, according to the
available literature, we use educational attainment for the population aged 25 and over to
measure the stock of human capital from the Barro and Lee (2010) dataset. The Barro and Lee
dataset is widely used in the literature on economic growth, and it has been updated over the
last few years. Barro and Lee’s (2010) data cover the period from 1950 to 2010 and are
related to 146 countries. These are disaggregated for periods of 5 years and come from
UNESCO, Eurostat, national statistical agencies, etc.. For the present study, we consider the
average attainment value for each five-year period for all years within the same timeframe.
As mentioned previously, structural change refers to long-term shifts in the sectors of an
economy. To measure these shifts, we use employment composition data and its evolution
over the period under review. These values come from the EU KLEMS (O'Mahony and
Timmer, 2009) dataset, available at www.euklems.net, which gives us (among other
variables) the number of employees in each industry. The several economic activities are
divided according to the ISIC REV.3 classification (‘International Standard Industrial
Classification’). We use these values as a share of the total employment in each economy.
Subsequently, we grouped the data depending on the intensity of knowledge that each activity
requires. This classification is based on Peneder (2007) who divides each industry into: ‘very
low’, ‘low’, ‘medium-low’, ‘intermediate’, ‘medium-high’, ‘high’ and ‘very high’. Since EU
KLEMS only provides data from the period between 1972 and 2007, we estimated the values
for the remaining missing years. To this end, we assume that the share of each industry during
the period between 1960 and 1971 evolves at the same average growth rate of the next 10
14
years. Similarly, the values of the subsequent years up to 2007 are calculated assuming the
average growth rate of the previous 10 years.
In line with the many empirical studies on economic growth (Barro, 1991; Levine and Renelt,
1992; Mauro, 1995; Moral-Benito, 2012), the physical capital accumulation (I) will be
measured by investment share to GDP. Similarly, public expenditures (G) will be measured
by public consumption to GDP ratio. There are many authors that resort to the Penn World
Table dataset to determine economic growth (e.g., Easterly and Levine (1997) and Moral-
Benito (2012) used the Penn World Table, Version 6.2). Therefore, besides GDP per capita,
we used the Penn World Table (Version 8.0) (Feenstra et al., 2013) to obtain investment in
physical capital and public consumption data.
Regarding institutional measures, this study uses the Civil Liberties Index and Political Rights
Index from ‘Freedom House’, as in Moral-Benito (2012). These indexes are based on the
evaluation of human rights specialists, scholars, journalists and politicians. They are measured
using a scale of 1 to 7, being 1 the highest freedom level and 7 the lowest. Through these
measures, we can analyze the impact of the quality of institutions and governability of a
country on economic growth. The hypothesis to be tested is that freer and fairer countries
have higher economic growth rates.
4. Empirical Results
We used 2 different panel data sets in order to estimate the specified model. The first one
includes 21 OCDE countries, of which we have a balanced panel (the entire cross-section is
observed every year), during the period from 1960 to 2011. The second panel adds 9 countries
to the first, mostly belonging to Eastern Europe (Cyprus, Slovakia, Slovenia, Estonia,
Hungary, Lithuania, Malta, Poland and Czech Republic). However, due to data unavailability,
the period is shorter (1990-2011).
In the second panel, the economic growth analysis is to some extent impaired because the
time horizon is too short (about 20 years), since the dynamics analyzed change very slowly
over time. Nevertheless, the Eastern European countries included in the study have
experienced great structural change due to the establishment of new industries and industrial
relocation resulted from FDI (Foreign Direct Investment) since the 1990s (Janicki and
Wunnava, 2004). Thus, the motivation to include these countries in our study lies in this last
argument, despite the limited data available.
15
Ta
ble 1
: Em
pir
ical stu
die
s on
eco
no
mic g
row
th
Au
thors (y
ear)
Perio
d
Cou
ntrie
s (#)
Estim
atio
n M
etho
d
Pro
xy o
f the d
ep
en
den
t
varia
ble (eco
nom
ic gro
wth
) P
rox
ies of relev
an
t ind
ep
en
den
t varia
bles
Barro
(199
1)
19
60
-198
5
98
cou
ntries (S
um
mers
and
Hesto
n, 1
988
) ‘C
ross-sectio
n A
pp
roa
ch’
GD
P
per
cap
ita
gro
wth
(ann
ual, %
)
Hu
ma
n C
apita
l: Prim
ary an
d S
econ
dary
enro
llmen
t rates (19
60
)
Initia
l GD
P: R
eal GD
P p
er cap
ita real in
196
0
Ph
ysical C
ap
ital: A
verag
e investm
ent in
ph
ysical cap
ital betw
een 1
960
-
19
85
(%G
DP
)
Pu
blic E
xpen
ditu
res: Averag
e real pu
blic co
nsu
mp
tion
betw
een 1
970
-
19
85
(% G
DP
)
Institu
tion
s: Nu
mb
er of rev
olu
tion
s and
coup
s per y
ear (19
60
-19
85);
Nu
mb
er of assassin
ation
s per m
illion p
opu
lation
Lev
ine an
d R
enelt
(199
2)
19
60
-198
9
11
9 co
un
tries exclu
din
g
oil ex
po
rters (BM
; FM
I;
Su
mm
ers and
Hesto
n,
19
88
)
‘EB
A: E
xtrem
e Bo
und
s
An
alysis’
GD
P
per
cap
ita
gro
wth
(ann
ual, %
)
Hu
ma
n C
apita
l: Prim
ary an
d S
econ
dary
enro
llmen
t rates (19
60
)
Hu
ma
n C
apita
l: Prim
ary an
d S
econ
dary
enro
llmen
t rates (19
60
)
Ph
ysical
Ca
pita
l: In
vestm
ent
in
ph
ysical
capital
by
‘Wo
rld
Ban
k
Natio
nal A
ccou
nts’ (%
GD
P)
Pu
blic E
xpen
ditu
res: Real p
ub
lic con
sum
ptio
n (%
GD
P)) b
y ‘W
orld
Ban
k N
ation
al Acco
un
ts’ In
stitutio
ns: N
um
ber o
f revo
lutio
ns an
d co
up
s ;
Man
kiw
et al.
(199
2)
19
60
-198
5
98
cou
ntries ex
clud
ing
oil ex
po
rters (Su
mm
ers
and
Hesto
n, 1
988
)
‘Cro
ss-section
App
roa
ch’
Lo
garith
m o
f GD
P p
er
wo
rkin
g ag
e po
pulatio
n
19
60
-198
5
Hu
ma
n C
apita
l: Wo
rkin
g ag
e pop
ulatio
n w
ith seco
nd
ary (%
) P
hysica
l Cap
ital: In
vestm
ent in
ph
ysical cap
ital (% G
DP
)
Mau
ro (1
99
5)
19
60
-198
5
58
cou
ntries ex
clud
ing
oil ex
po
rters ‘C
ross-sectio
n A
pp
roa
ch’
GD
P p
er cap
ita g
row
th
(196
0-1
98
5)
Hu
ma
n C
ap
ital:
Prim
ary an
d S
econ
dary
en
rollm
ent
rates (1
96
0)P
IB
Initia
l GD
P: R
eal GD
P p
er cap
ita real in
196
0
Ph
ysical C
ap
ital: In
vestm
ent in
ph
ysical cap
ital (% G
DP
)
Pu
blic E
xpen
ditu
res: Real p
ublic co
nsu
mp
tion (%
GD
P)
Institu
tion
s: Co
rrup
tion
Ind
ex; B
ureau
cratic Efficien
cy R
atio; P
olitical
Instab
ility In
dex
Easterly
and
Lev
ine
(199
7)
60
’s, 70
’s e
80
’s 1
27 co
un
tries ‘C
ross-sectio
n A
pp
roa
ch’
Real G
DP
per ca
pita
gro
wth
cap
ita real
Hu
ma
n C
apita
l: Lo
garith
m o
f averag
e attainm
ent
Initia
l GD
P: L
ogarith
m o
f initial in
com
e by d
ecade (6
0’,7
0’8
0’)
Institu
tion
s: Ind
ex o
f po
litical instab
ility; E
thn
ic Div
ersity.
Acem
oglu
et al.
(200
1)
19
85
-199
5
64
cou
ntries
OL
S R
egressio
n
Lo
garith
m o
f GD
P p
er cap
ita
in 1
995
(PP
C)
Institu
tion
s: Averag
e Pro
tection A
gain
st Exp
rop
riation
Risk
; Co
nstrain
t
on
Execu
tive ; D
emo
cracy in
dex
Tem
ple
and
Wo
essman
n (2
006
) 1
960
-19
96
76 co
untries
‘‘Cro
ss-section
App
roa
ch’
Lo
garith
m
GD
P
per
cap
ita
(196
0-1
99
6)
Hu
ma
n C
apita
l: Lo
garith
m o
f averag
e attainm
ent
Stru
ctura
l cha
ng
e: Ch
anges in
emp
loyem
ent co
mp
ositio
n p
er sector
(…)
16
Au
thors (y
ear)
Perio
d
Cou
ntrie
s (#)
Estim
atio
n M
etho
d
Pro
xy o
f the d
ep
en
den
t
varia
ble (eco
nom
ic gro
wth
) P
rox
ies of relev
an
t ind
ep
en
den
t varia
bles
Dreh
er (200
6)
19
70
-200
0
123
coun
tries ‘F
ixed
Effects P
anel D
ata’ G
DP
gro
wth
rate per ca
pita
Hu
ma
n C
ap
ital: S
econ
dary
enro
llmen
t rate and
logarith
m o
f averag
e
life exp
ectancy
by W
orld
Ba
nk
Initia
l GD
P: L
ogarith
m o
f GD
P p
er cap
ita b
y W
orld
Ban
k
Ph
ysical C
apita
l: Intern
Investm
ent (%
GD
P) b
y G
lob
al D
evelop
men
t
Netw
ork G
row
th.
Pu
blic E
xpen
ditu
res: Real p
ublic co
nsu
mp
tion
(% G
DP
) by W
orld
Ba
nk
Po
pu
lation
Gro
wth
: Lo
garith
m o
f fertility rate b
y W
orld
Ban
k
Institu
tion
s: Ru
le of L
aw in
dex
by G
wartn
ey an
d L
awso
n (2
002
)
Batten
and
Vo
(200
9)
19
80
-200
3
79 co
untries
‘Fix
ed E
ffects Pan
el Data’
GD
P g
row
th rate p
er cap
ita
Hu
ma
n C
apita
l: Seco
nd
ary en
rollm
ent rate b
y W
orld
Ba
nk (2
00
4)
Ph
ysical C
ap
ital: G
ross cap
ital form
ation
(% o
f GD
P) b
y W
orld
Ba
nk
(20
04
) P
opu
latio
n G
row
th: D
ifference b
etween
the lo
garith
ms in
the n
um
ber
of in
hab
itants in
each y
ear by W
orld
Ban
k (20
04
)
Hartw
ig ( 2
012
) 1
970
-200
5
18
OC
DE
coun
tries ‘G
rang
er Cau
sality M
etho
d’
Real G
DP
per ca
pita
Hu
ma
n C
apita
l: Exp
end
iture o
n ed
ucatio
n an
d h
ealth
Stru
ctura
l C
han
ge:
Ch
ange
in
the
com
po
sition
o
f ex
pen
ditu
res b
y
sector
Fab
ro an
d A
ixalá
(201
2)
19
76
-200
5
79
coun
tries ‘P
anel D
ata estimatio
n’
GD
P g
row
th rate p
er cap
ita
Hu
ma
n C
apita
l: Prim
ary an
d S
econ
dary
enro
llmen
t rate
Ph
ysical C
ap
ital: In
vestm
ent in
ph
ysical cap
ital (% G
DP
) In
stitutio
ns:
Eco
no
mic
Freed
om
in
dex
, P
olitical
Rig
hts
and
Civ
il
Lib
erties ind
ex.
Mo
ral-Ben
ito(2
012
) 1
960
-200
0
73
coun
tries ‘B
ayesian
Pan
el Data
Ap
pro
ach’
Real G
DP
gro
wth
rate per
cap
ita (b
ase year 2
00
0 U
S$
)
Hu
ma
n
Ca
pita
l: A
verag
e attain
men
t o
f w
ork
ing
age
po
pu
lation
b
y
Barro
and
Lee
Initia
l GD
P: L
ogarith
m o
f GD
P p
er cap
ita in
196
0
Pu
blic E
xpen
ditu
res: Real p
ublic co
nsu
mp
tion (%
GD
P)
Institu
tion
s: Po
litical Rig
hts an
d C
ivil L
iberties in
dex
.
Iqb
al and
Daly
(201
4)
19
86
–2
01
0
52
mid
dle-in
com
e
cou
ntries
‘Dyn
amic P
anel D
ata
Mo
dels’
GD
P g
row
th rate p
er cap
ita
(ann
ual %
)
Hu
ma
n
Cap
ital:
Hu
man
D
evelo
pm
ent
Ind
ex
by
U
nited
N
ation
s
Dev
elop
men
t Pro
ject (UN
DP
) In
itial G
DP
: GD
P p
er cap
ita
Ph
ysical C
ap
ital: G
ross cap
ital form
ation
(% o
f GD
P)
Institu
tion
s: Co
rrup
tion
ind
ex; F
reedo
m fro
m co
rrup
tion; D
emo
cratic
force
17
The model specified previously served to test the impact of the three main variables of
economic growth: human capital measured by the average educational attainment of
adults, productive specialization in ‘high-level’ industries measured by the share of
employment in knowledge/technology-intensive industries (e.g., Financial
Intermediation, Research and Development (R&D), Education), and the moderating
effect of human capital on the productive structure. The estimation still includes a set of
control variables related to investment, public consumption, population growth, and
institutional environment, which the literature identifies as determinant factors of
economic growth.
We use the panel data estimation as an econometric estimation technique. Within this
framework, it is important to take into account the existence of two models: ‘random
effects model’ and ‘fixed effects model’. The first assumes that the observations have
unobserved individual effects that are constant over time and are correlated with the
explanatory variables. Contrariwise, the fixed effects model considers that countries
have specific effects, correlated with explanatory and non-random variables (Dreher,
2006; Batten and Vo, 2009).
The Hausman Test serves to objectively assess which of the two models mentioned
above is the most suitable considering the available data. The null hypothesis (H0)
underlying the test is that the random effects model is more efficient than the fixed
effects model. For our data, the Hausman Test has a p-value of 0.000 which means the
null hypothesis (H0) is rejected at a significance level of 1%. Thus, we opt for the fixed
effects model, since it is the most efficient.
Table 2 presents the estimation for coefficients corresponding to each variable for both
considered panels.
Both models present a good quality of adjustment reflected in a higher adjusted
coefficient of determination (�̅�2). Concretely, about 90% (96.7% in the second model)
of the dependent variable around its sample mean is explained by the variables of the
specified model. By observing the F statistics and respective p-values, we can conclude
that the models are globally significant, i.e., their explanatory variables relate
statistically and significantly with economic growth.
Since we are working with fixed effects panel data, we cannot use time invariant
variables. Thus, the estimation of the fixed effects model does not allow for the use of
18
the variable “Initial GDP”, measured by GDP per capita observed in 1960. Since the
catching-up phenomenon is more relevant in the case of the poorest countries and we
are considering countries with high levels of development, the estimation of Models I
and II does not include this variable.
The human capital presents a significant positive impact in both models, which supports
H1 (“Countries with a higher stock of human capital tend to grow faster than others”)
and suggests, as postulated in the literature, that countries with a higher educational
attainment of adults grow faster in the considered periods (1960-2011/1990-2011). This
result meets the expectation that a higher stock of human capital improves the
workforce’s skills, which has a positive impact on its productivity (Bodman and Le,
2013).
In addition, it is noted that the countries where structural change contributes to
increasing the share of knowledge-intensive activities that require high skills, (e.g.,
Financial Intermediation, Computers, Research and Development and Education), tend,
on average, to grow faster. Thus, H2 (“Countries that experience changes in productive
structures towards a greater share of technology/knowledge-intensive activities will
tend to observe higher economic growth.”) is also confirmed. Knowledge-intensive
activities employ individuals with higher skills and knowledge because they are more
productive (Hartwig, 2012) and capable of enhancing the emergence of new products
and processes (Zagler, 2009). Therefore, the growth rates of countries that observe an
increase in specialization in high-level industries tend to be higher.
Human capital also has a moderating effect, i.e., it has an indirect impact via productive
specialization. However, H3 (The impact of human capital on the economic growth of a
country is greater the more specialized the economy is in high technology/knowledge-
intensive activities.”) is only confirmed in a much broader timeframe (1960-2011)
(Model I). In this case particularly, the more educated the adult population, i.e., the
higher their level of human capital, the higher the impact of a specialization in high-
level industries on economic growth. The combination of a structural change in favor of
more progressive, knowledge-intensive activities, with high levels of human capital,
results in higher innovation in product and progress, which tends to generate increases
in GDP per capita (cf. Justman and Teubal, 1991; Ciccone and Papaioannou, 2009).
19
Table 2: Estimation of the relationship between economic growth, human capital and productive
structure (dependent variable: GDP per capita (in log), 1960-2011)
Variable Proxy Model I Model II
Human Capital Number of years of schooling of the
population aged 25 or more (in log)
0.540***
(0.059)
1.000***
(0.127)
Structural
Change
Share of ‘high-level’ industries in
total employment
1.606**
(0.637)
9.388***
(0.999)
Interaction
between Human
Capital and
Productive
Structure
Share of ‘high-level’ industries *
Human Capital
0.342***
(0.051)
-0.375***
(0.086)
Investment Investment rate 1.366
***
(0.127)
0.334***
(0.121)
Public
Expenditures Public Consumption in GDP
-2.160***
(0.224)
-2.369***
(-2.369)
Population
Growth Population growth rate
-9.162***
(-6.645)
6.435***
(1.219)
Institutions
Political Rights index (1: the highest
level … 7: the lowest level)
-0.248***
(0.024)
-0.144***
(0.022)
Civil Liberties index (1: the highest
level … 7: the lowest level)
0.050**
(0.023)
-0.085***
(0.016)
Constant 8.213
***
(0.121)
7.193***
(0.301)
Global Effect of
Human Capital
∂PIB
∂CH= β2 + β4 High 0.589 0.936
𝑅2 Adjusted 0.900 0.967
Number of observations 1071 630
F statistic (p-value) 343.865
(0.000)
506.093
(0.000)
Note: Dependent variable: Logarithm of real GDP per capita, in constant prices; ‘High-level’ activities include: Financial Intermediation, Computers, Research and Development and Education; Standard deviations are in parentheses; ***
(**)[*]Statically significant at 1% (5%) [10%].
In Model II, covering a shorter period (1990-2011) and more countries (added to Model
I: Cyprus, Slovakia, Slovenia, Estonia, Hungary, Lithuania, Malta, Poland and Czech
Republic), H3 is refuted. The estimation of the moderating effect of human capital on
structural change is significantly negative. Thus, regardless of human capital having a
positive direct (and significant) impact on economic growth, its indirect impact via the
productive structure in favor of high-level industries is negative. We conclude that for
the period in study (1990-2011) and the set of countries in this model, which includes a
considerable number of Eastern European countries, economic growth is explained by
the dynamics of human capital (education) and by the declining share of knowledge-
intensive activities (‘high-level’). It seems evident that in the sample with a shorter time
horizon and transition economies, the matching between adult population with high
20
educational attainment and structural change towards a specialization in
knowledge/technology-intensive activities does not contribute to higher economic
growth. Nevertheless, the evidence shows that the overall effect (direct + indirect) of
human capital on economic growth is higher in Model II than Model I.
Thus, we found that the effects of the interaction between structural change and human
capital only appear in the long term, yielding opposite results in shorter periods that
involve samples with transition economies.
Regarding the control variables, we found that high investment rates are related to
higher economic growth, since high physical capital formation contributes positively to
the productivity of production factors (Barro, 1991). In brief, economies with a high
level of investment tend to grow faster than others. We also verified that investment/
physical capital formation plays an important role in economic growth, both for the
more developed, Western countries (Model I) and the more heterogeneous sample with
Eastern European emergent economies (Model II). This evidence is in line with the
results of Dreher (2006), Batten and Vo (2009) and Fabro and Aixalá (2009).
Our results confirm the idea that high public consumption can create market distortions,
inefficiencies and crowding-out, that negatively affect economic growth Barro, 1991;
Dreher, 2006; Moral-Benito, 2012; Afonso and Jalles, 2014); in both models – the
estimate associated to the public consumption variable is significant (significance level
of 1%). These results are in line with seminal studies (e.g., Barro, 1991) as well as more
recent studies (e.g., Batten and Vo, 2009; Dreher, 2006; Afonso and Jalles, 2014).
Model I also confirms a negative relationship between population growth and economic
growth, as suggested in the literature. Higher population growth reduces the
capital/labor ratio, which affects negatively the GDP per capita (Mankiw et al., 1992).
However, in Model II, based on a more heterogeneous sample that includes less
developed countries, we find that higher population growth leads to higher economic
growth.
The institutional and political measures present a statistically significant estimate. The
‘Political Rights’ index combines the degree of freedom in the electoral process,
political participation and a decentralized political system (Moral-Benito, 2012), where
lower values represent a higher level of democracy. Thus, since the result for the
‘Political Rights’ index is negative, we can conclude that more democratic, freer
21
countries have, on average, higher economic growth (in both models). The ‘Civil
Liberties’ index includes freedom of belief and expression, associative freedom, respect
for individual rights and laws that protect them. Similarly to the ‘Political Rights’ Index,
lower values in this index mean a greater level of freedom. In Model II, the result for
the ‘Civil Liberties’ index is negative (with a significance level of 1%), which means
that countries with greater expression of individual freedoms tend on average to have
higher economic growth. Thus, according to Fabro and Aixalá (2012), the institutional
variables play an important role in boosting economic growth, by encouraging
investment in physical and human capital and an efficient allocation of resources.
In Model I, we found the opposite result to the one expected. This may be due to the
fact that for some countries included in this panel, the first years of the period studied
(essentially the 1960s), in which they reveal very high economic growth rates,
correspond to periods of dictatorial regimes, thus having also higher ‘Civil Liberties’
indexes.
Finally, our results support the relevance of institutional variables for economic growth.
This is in line with the results obtained by Moral-Benito (2012), who estimated a 73-
country panel over 40 years (1960-2000), and obtained a positive result in the ‘Political
Right’ index and a negative result in the ‘Civil Liberties’ index.
5. Conclusions
Based on panel data covering 30 OCDE developed countries, this paper highlights the
crucial role of structural change processes and their interaction with human capital on
economic growth in the last few decades.
Our results suggest that, as supported in the literature, human capital plays a
determinant role in economies growth, since a more educated adult population boosts
the economic growth of countries. Secondly, it is shown that structural change is also a
determinant of economic growth. Increasing specialization in knowledge/technology-
intensive industries accelerates the economic growth of countries. Lastly, our results
suggest that the contribution of human capital to economic growth is greater when the
economy is more specialized in industries that require that same human capital. This
means that the impact of human capital on GDP growth is amplified when knowledge-
intensive industries, producing value-added goods and services, grow in importance in
the composition of the economy. The interaction between human capital and structural
22
change towards 'high-level' industries is especially relevant in the richest and most
developed countries, where these productive activities already represent a significant
share of the economy. Moreover, the indirect effects of human capital via productive
specialization in high-technology and/or knowledge-intensive activities are only visible
over extended time horizons.
Thus, the present paper highlights the importance of the demand-side on economic
growth, which has been neglected in the literature. More specifically, it confirms the
crucial role that the productive specialization of countries plays in generating economic
growth. It also evidences the indirect effect of human capital, when knowledge-
intensive industries gain relative weight in the economies.
Following these conclusions, we put forward that the promotion of economic growth
should not contemplate only investment in human capital through education, but also
investment in technology/knowledge-intensive activities, generating high value added to
the economies (e.g., Financial Intermediation, Computers, Research and Development
and Education). Training and education as an investment in human capital should
consider areas of knowledge and skills required by the dominant 'progressive' industries
in the economy, i.e., those that experience increases in productivity and accelerate the
rates of economic growth. Therefore, it makes sense to rethink the educational offer and
system, and wager on vocational courses that meet market demands. The aim will be to
promote the matching between skilled labor and economic activities that require these
same qualifications. This implication is more relevant and a priority in richer countries.
This study has some limitations with respect to sample selection, including the small
number of observations. Due to the unavailability of data and the aim of performing a
long-term analysis, the cross-section is very small, containing only developed countries.
This empirical analysis thus suffers from low heterogeneity with respect to economic
development. Thus, the findings of this study cannot be extrapolated to the poorest
economies, with different trends of structural change. Taking as a starting point the low
heterogeneity of the countries belonging to the sample considered in this study, the
analysis of the role of structural change and its interaction with human capital in
economic growth could be extended to the least developed countries. Additionally,
different taxonomies can be used to classify the industries so as to measure structural
change phenomena. The application of a different taxonomy may also test the
robustness of the results we obtained.
23
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