




































Economy 
ISSN: 2313-8181 
Vol. 3, No. 1, 1-18, 2016 
www.asianonlinejournals.com/index.php/Economy 

 

  

 

 

 

 

1 

 

The Impact of Physical and Human Capital Development on 

Economic Growth in Developing and Developed Countries: 

A Comparative Panel Data Approach 

 
Olusola Olalekan Joshua1 

1
International Economics and Finance, Bournemouth 

University, UK 

 
Abstract 

This research used panel data of 74 developing and 47 developed countries over the period of seven 

years (2005 -2011) to investigate and compare the impacts of physical and human capital development 

on economic growth in these two regions. The results from fixed effects method employed revealed that 

physical capital such as investment proxy by gross fixed capital formation is not positively and 

significantly contributing to economic growth in developing regions until investment from other sources 

such as FDI and other growth determinants were taken into account. The reverse is the case for 

developed countries with this had positive and significant effect on GDP in almost all the model 

specifications. Infrastructure and innovation technology prove positive and significant in developing 

countries while they were not significant in developed countries which may be due to catching up effect 

through technology externalities and knowledge spillover. Human capital contributes to GDP in 

developed region through investment flow in primary and tertiary education. However, the result further 

showed that developing countries have neglected basic primary and secondary education with focus on 

tertiary education which may lack quality to contribute to GDP due to poor foundation as a result of poor 

attention to primary and secondary education.   
 

Keywords: Panel data, Fixed effect, Physical capital, Human capital, Economic growth, Knowledge spillover. 
 

Contents 
1. Introduction ........................................................................................................................................................................... 2 

2. Literature Review .................................................................................................................................................................. 2 

3. Data Sources and Research Methods .................................................................................................................................... 6 

4. Analysis and Interpretation of Research Findings ............................................................................................................... 8 

5. Conclusion and Recommendations ..................................................................................................................................... 14 

References ................................................................................................................................................................................ 15 

Bibliography ............................................................................................................................................................................ 17 

 
 

Citation | Olusola Olalekan Joshua (2016). The Impact of Physical and Human Capital Development on Economic Growth in Developing and Developed 

Countries: A Comparative Panel Data Approach. Economy, 3(1): 1-18. 

DOI: 10.20448/journal.502/2016.3.1/502.1.1.18       

ISSN(E) : 2313-8181   

ISSN(P) : 2518-0118 

Licensed:  This work is licensed under a Creative Commons Attribution 3.0 License  

Funding: This study received no specific financial support. 

Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this paper. 

Transparency: The author confirms that the manuscript is an honest, accurate and transparent account of the study was reported; that 

no vital features of the study have been omitted; and that any discrepancies from the study as planned have been 

explained. 

Ethical: 

History: 

This study follows all ethical practices during writing.   

Received: 26 November 2015/ Revised: 23 January 2016/ Accepted: 27 January 2016/ Published: 2 February 2016 

Publisher: Asian Online Journal Publishing Group 

 
 

 

 

 

 

 

 

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Economy, 2016, 3(1):1-18 

 

 

 

 

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1. Introduction 
1.1. Background of the Study 

The growth of any economy has been found in a great number of literatures to be influenced by its level of 

physical and human capital. There is no country that can achieve sustained economic development without 

substantial investment in human capital (Ogujiuba, 2013). Harbison (as cited in Adelakun (2011)) refers to human 

capital as the abilities and skills of human resources while human capital development refers to the process of 

acquiring and increasing the number of persons who have the skills, education and experience which are critical for 

the economic growth of any country. Hence, education and health are closely related components of human capital 

that work together and make individuals more vigorous and productive (Javed et al., 2013). 

The developing countries are arguably more endowed with human resources in the world compare to developed 

countries in term of population. However, these are not amounted the kind of human capital for economic growth 

because human resources required for economic growth need to possess some level of skills that will enhance their 

productivity. This has largely been attributed to poor schooling system, and poor health system resulting to low life 

expectancy and high mortality rate which are all antidotes to human capital development.  Moreover, the importance 

of physical capital in terms of capital formation to create enabling environment for growth cannot be over-

emphasized. Rostow (1960) argued that necessary investment needs to be made in three key economic sectors such 

as technology, infrastructure and transportation system for rapid economic growth to take place. However, the level 

of infrastructure in many developing countries does not encourage economic activities to thrive. The small scale 

business and enterprises which form the real sector of many of these countries may find it difficult to grow and 

contribute meaningful impacts to economic growth. Meanwhile, the essence of comparing the cross-country results 

of both developing and developed economies to empirically examine what the countries in both regions do 

differently in exploiting their physical and human capital for economic growth. This will provide guidelines for 

policy makers in both regions, the areas of the economy sectors that need more attention for sustainable economic 

growth. 

However, in a more specific terms, the findings from this study provide answers to the following research 

questions: 

1) To what extent has education and health system in developing and developed countries influence their 

economic growth? 

2) To what degree has physical capital in terms of investment and infrastructure influence economic growth in 

developing and developed countries? 

3) What level of influence does technological advancement and innovation has brought to economic growth in 

developing and developed countries?  

 

2. Literature Review 
2.1. Economic Growth and Determinants in Developed and Developing Regions- Facts And Figures 

Economic growth is not the same across the globe. It is believed that countries in Europe, America and other 

developed continents tend to have higher GDP compare to their counterparts in developing and emerging economies.  

This can be depicted Figure 2.1 below as countries in European Union have higher GDP put together over the 

periods of 14 years than their counterparts in East Asia, Latin America and Sub-Saharan Africa regions. 

 

 
Figure-2.1. GDP Trends in European Union and Developing Regions 2000-2013 

Source: Plotted by author using data from World Bank Development Indicator (2015) 

 

However, looking at the relationship between GDP and human capital index in 2013 from Figure 2.2 below, it is 

evident that higher GDP in developed countries as shown in Figure 2.1 above could be attributed to the commitment 

of these countries to human capital development (comprising education, health and wellness) compare to their 

developing counterparts.   

 



Economy, 2016, 3(1):1-18 

 

 

 

 

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Figure-2.2. Relationship between GDP per capita and Human Capital Index 

Source: World Economic Forum (2013) 

 

In the above figure for instance, Japan, United States, Finland, Switzerland, Singapore and Qatar have higher 

human capital index score between 0.9 and 1.5 in 2013 compare to their counterparts in developing countries which 

their HCI score were even negative except countries like Indonesia, Russia, and China which could be seen as 

emerging economies. It can also be observed that corresponding GDP per capita for the aforementioned high-income 

countries are hovering around USD$20,000 and above while developing countries were all below that figure. 

Moreover, physical capital in terms of gross fixed capital formation and foreign direct investment could be seen 

as avenue in which technology and other major infrastructure could be promoted and subsequently lead to economic 

growth through productivity as a result of enabling environment created by the factors mentioned above. 

 
Figure-2.3. Trends in GDP, Gross Fixed Capital Formation and FDI from Euro Area, Latin America, South Asia 

and Sub-Saharan Africa, 2010-2013 
Source: Plotted by author using data from WBDI (2015) 

 

In Figure 2.3 above, GDP and gross fixed capital formation in Euro Area initially increased in 2011 before 

started declining from 2012. This is contrary to other 3 developing regions as the level of GDP starts increases in 

2010 so also the gross fixed capital formation. The same goes with FDI relationship between GDP and FDI. 

Meanwhile, despite the decline, the level of GDP and the two form of capital formation are higher in Euro Area 

compare to other regions. However, the situation described for developing countries above could be attributed to the 

fact that even though there is investment in physical capital but there is insufficient one in human capital that will 

utilise the technology and infrastructure for economic growth. That is why Appleton and Teal (1999) argue that 

human capital and investment in physical capital such as machine that will match the skills acquired through human 

capital development are necessary as both can complementarily contribute to economic growth. 

 

2.2. Theoretical Literatures 
Investigating the impact of economic growth especially from physical and human capital perspectives cannot be 

completed without reviewing the general ideas, thoughts and contributions of early economics scholars as well as 

empirical findings. For this reason, the theoretical frameworks of this research are discussed under three categories 

which are classical, neoclassical and endogenous growth models. The empirical findings from related studies in order 

to confirm the validity or otherwise of the postulations in theories were also reviewed. 

 



Economy, 2016, 3(1):1-18 

 

 

 

 

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2.2.1. Classical Theories of Economic Growth 
The emphasis of classical school for economic growth to occur is basically investment and capital accumulation. 

Prominent economic models associated to this school include Harrod-Domar growth model of early 1940s with 

emphasis on savings and investment as main determinants of economic growth; Walt Rostows‟ linear-stage growth 

model of 1959 emphasized that economic growth need to pass through five standard stages. 

In addition, structural change growth model by Arthur Lewis in 1960 established the need for economy to move 

from subsistence agricultural-based to industrialised one. In the same period, Chenery et al. came up with the pattern 

of development which include a shift from agricultural to industrial production; the steady accumulation of physical 

and human capital; change in consumer demands with emphasis shifting from the production of food and basic 

necessities to desires for diverse manufactured goods and services. However, Itagaki (1963) described the sequential 

process of Rostow‟s theory as inevitable for all the countries as not all of them actually follow these sequential stages 

in growth attainment. Okwuosa (2015) describes United States, Canada, New Zealand and Australia as countries that 

did not pass through traditional society stage and even derived their preconditions from already advanced country 

like United Kingdom. Gallo (2002) also agreed with Harrod-Domar‟s theoretical strand that the amount of savings 

and investment play a significant role in the process of economic growth. He therefore describes economic growth as 

a rise in per capita income and national product as a result of rise in investment greater than the amount necessary to 

replace depreciated capital. 

Meanwhile, Ray (as cited in Gallo (2002))  argued that emphasis on savings and investment by Harrod-Domar 

holding other factors of production constant is not sufficient enough to increase growth, as there is always the 

possibility of increasing the output by using additional labour with improved and more intensified techniques. 

Todaro and Smith (2011) argue that savings and investment are necessary conditions for accelerated rates of 

economic growth but not sufficient conditions. According to them necessary conditions are well-integrated financial 

and capital markets, highly developed transport facilities, a well-trained and educated workforce, good and efficient 

governance capable of converting new capital effectively into higher level of output. One of the criticisms levelled 

against Lewis‟ model was that it neglects traditional agricultural sector by transferring labour to modern sector until 

there is no labour in the former which could lead to shortage of food (Wang and Piesse, 2010).  

Prados (2005) argued that in development pattern, there is no implication that single unique paths, through which 

all economies have to pass must exist. Another argument against Chenery‟s pattern of development is that the 

approach may run the risk of leading the practitioners to draw the wrong conclusions about the causality- in effect, to 

pursue wrong economic development policy (Todaro and Smith, 2011). 

 

2.2.2. Solow’s Neoclassical Growth Model 
The model was developed in 1940s and 1950s by Robert Solow and Trevor Swan respectively. The model 

introduced labour into already established model by classical school theorists like Harrod-Domar by augmenting 

Cobb Douglas production function:             . However, while this is a welcome development as labour is also 

one of the critical factors contributing to GDP in any economy, but productivity factor of labour (AL) were 

determined outside the model. This made Solow to argue that an economy will reach a steady state (i.e a diminishing 

return to capital in the long run) where per capita output, capital stock and consumption will grow at a common 

constant rate equalling the exogenously given rate of technological progress. This argument led to the emergence of 

endogenous growth theories by Romer (1989) and Mankiw et al. (1992). 

 

2.2.3. Endogenous Growth Theories 
There are two major proponents of these theories as mentioned in the above subsection. Their models directly 

responded to the weaknesses of Harrod-Domar and Solow models which both explained savings and technological 

knowledge in their models exogenously. Romer argued that diminishing returns to capital can be eliminated by the 

link between state of knowledge and the amount of investment (as cited in Rafael and De La (2000)). Basically, 

Romer explained in his model how investment in technological progress or knowledge (A) in Solow‟s model will 

enhance labour productivity and in turn increase output. 

However, Mankiw et al. (1992) eventually came up with their influential contribution, and present a simple 

extension to the Solow model by allowing human capital as a separate input into an otherwise standard Cobb-

Douglas production function with Harrod-neutral (i.e., labour augmenting) technological progress Schütt (2003). 

Mankiw et al. (1992) argue that there will be constant increasing return to scale of reproducible factor such as human 

capital through technology advancement augmented by innovation as a result of deliberate development of human 

capital via research and development. 

 

2.3. Empirical Literatures 

2.3.1. Impact of Education and Health  
Education and health had been described as two main components of human capital stressing that while 

education enhances the quality of human capital, health on the other hand improves the efficiency and effectiveness 

of human capital (Ada and Acaroğlu, 2014). The idea here is that labour productivity will affect economic growth 

positively through skilful and healthy labour. Barro (1992) found that countries with a higher level of educational 

attainments grow faster for a given level of initial per capital GDP and for values of policy –related variables. 

Similarly, average schooling years were found to yield a rise in GDP growth of about 0.5% points in a panel data 

investigation of human capital and economic growth in OECD countries by Middendorf (2005). 

Tiruneh and Radvansky (2007) carry out a panel data investigation of human capital contribution on European 

economic growth between the period of 1995-2009 and they found that secondary school enrolment, and labour force 

with primary, secondary, and tertiary education are all significantly and positively influence GDP per capital growth 

rates. Idrees and Siddiqi (2013) compare the impacts of public education expenditure on economic growth in 

developed (G-7) nations and developing countries using panel data cointegration method. They found that „‟the 



Economy, 2016, 3(1):1-18 

 

 

 

 

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impact of public education expenditures on economic growth is greater in the case of developing countries as 

compare to the developed countries, which they conclude as sign of  the “catching-up effect” in developing 

countries‟‟. 

Son et al. (2013) panel data investigation of education impact on economic growth for five groups of EU 

member states(both developing and developed) revealed that both quantitative (measured by average schooling year) 

and qualitative (scores on skill tests) features of human capital have positive and significant influence on economic 

growth in these countries. Bloom et al. (2004) found in a panel study of countries for the period between 1960 and 

1990 that health has a positive and statistically significant effect on economic growth. It suggests that a one-year 

improvement in a population‟s life expectancy contributes to an increase of 4% in output. Eggoh et al. (2015) 

examine the relationship between education, health and economic growth among 49 African countries between 1996 

and 2010. They found that public expenditures on education and health have a negative impact on economic growth, 

whereas human capital stock indicators proxies by primary and secondary enrolment have a slight positive effect. 

They also found that education and health expenditure could complementarily influence economic growth positively. 

Somayeh et al. (2013) investigate effect of health on economic growth in 16 developed and 14 developing countries 

using panel unit root and panel data approach. They found that found that capital stock and life expectancy have a 

statistically significant positive effect on economic growth in both groups of countries. 

 

2.2.2. Impact of Physical Capital 
(a) Investment 

This study examined other physical capital variables apart from traditional gross capital formation. These include 

foreign direct investment, infrastructure, and natural resources. Three of Chenery‟s pattern of development 

characteristic features such as international trade, urbanisation and resource use (as cited in Todaro and Smith 

(2011)) serve as one of the justifications for including the above mentioned physical capital. These forms of capital 

vary from one country to another. 

Kubík (2010) found that physical capital contribute significantly 50% to output on the average in 73 countries 

between 1960 and 1990 using panel data from six different sources. A linkage analysis in Bangladesh between 1986 

and 2008 also reveals that level of capital formation caused significant positive effects on changes in real GDP 

(Adhikary, 2011). Similar results were found in Pakistan using data for the period of 1972-73 to 2010-11 (Ali et al., 

2012). Meanwhile Lucas (as cited in Benhabib and Spiegel (1994)) suggests that one reason why physical capital 

does not flow to poor countries may be linked to the fact that these countries are poorly endowed with factors 

complementary to physical capital. This justification was eventually linked to the negative and significant 

relationship found between income-to-capital ratio and income level (Benhabib and Spiegel, 1994).  

(Roy and Mandal, 2012) was another empirical study which has panel observation of 27 Asia countries for the 

period of 1974 to 2010. They found negative and statistically significant relationship between gross fixed capital 

formation and economic growth. It was however, argued that „‟domestic investment proxy by gross domestic capital 

formation is not conducive to economic growth for Asian economies due to the mismatch between capital 

requirement and saving capacity‟‟ (Roy and Mandal, 2012). 

Similarly, Azat (2014) found that GDP per capital income had insignificant negative relationship with gross 

fixed capital formation in 8 Central European economies (CEE) between 2005 and 2010 but ignore on insignificant 

ground. However, Cu et al. (2013) found in their panel data investigation of 5 countries in ASEAN region that gross 

fixed capital formation has a positive and significant effect on gross domestic product. 

Ndambiri et al. (2012) examine the determinants of economic growth in 19 Sub-Saharan Africa countries using 

Generalised Method of Momentum panel technique between 1982 and 2000. Physical capital proxy by gross capital 

formation alongside human capital significantly contributes to the economic growth among these countries. 

Similarly, Fayissa and Nsiah (2010) found positive and significant relationship between GDP per capita and gross 

fixed capital formation in 18 Latin American countries between 1980 and 2005.  

Harrod-Domar growth model assume closed economy where investment require for economic growth is 

determined by the amount of savings. One of the model‟s weaknesses is that developing countries could borrow 

where domestic investment is insufficient to achieve economic growth. However, literature has criticised this 

assumption claiming that this has resulted to huge debt profile for many developing countries with repayment 

problems (Nyandat, 2014). 

(b) Foreign direct investment 

Meanwhile, several empirical literatures have found that encouraging FDI could complement domestic 

investment and then lead to economic growth. There are mixed empirical results on the above as some findings 

revealed inverse relationship between FDI and growth especially in developing countries. Also the simultaneity 

problem causing bidirectional relationship used to produce ambiguous results. 

Cu et al. (2013) found that FDI had a negative and significant effect on GDP in panel study of ASEAN-5 

countries. They argue that the negative effect may be due to indirect effect such that FDI does not generate 

employment good enough to boost the economy. 

Behname (2012) found that capital formation and foreign direct investment had positive and significant effect on 

gross domestic product (GDP) in South Asia countries. The study uses panel data between 1977 and 2009.  

Similarly, Fayissa and Nsiah (2010) found positive and significant relationship between GDP per capita and FDI in 

18 Latin American countries between 1980 and 2005. 

In another study of developing countries, Mallick and Moore (2006) found in a panel study of 60 developing 

countries that „‟FDI flows exert beneficial complementarity effects on the domestic capital formation across all 

income-group countries, thus suggesting that external finance does positively contribute to economic growth‟‟.  

Similarly, Borensztein et al. (1998) found FDI from industrial countries to 69 developing countries in their cross-

country study as vehicle for the transfer of technology, contributing relatively to growth than domestic investment. 



Economy, 2016, 3(1):1-18 

 

 

 

 

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Their findings further revealed that FDI would contribute to economic growth only when the host countries have a 

minimum threshold of human capital and sufficient absorptive advanced technology capability.  

Meanwhile, a panel data investigation of OECD and non-OECD countries carried out by Luiz and Mello (1999) 

reveals that FDI would only contribute to economic growth of the recipient‟s economy with upgraded technology 

and knowledge spillovers. 

Similarly, an increase in annual GDP growth between 0.3 and 0.71 percentage points had been attributed to an 

increase in FDI share of GDP in 43 Sub-Saharan Africa between 1980 and 2009 (Juma, 2012). 

(c) Infrastructure 

There is no infrastructural development whether in energy, transportation, aviation & port and/or power sector 

that can really thrive without the support of telecommunication facilities and its allied products. This accounts for the 

importance of telecommunication infrastructure as life wire upon which other infrastructural facilities contribute to 

economic growth.  The effect of this category of infrastructure on economic growth had been established by a 

number of literatures. 

Telecommunication developments were found statistically and positively correlated with the real GDP per capita 

of 24 countries from low income, middle income and income groups between the period of 1985 and 2003. These 

results were based on the empirical panel data investigation by Zahra et al. (2009). 

Similarly, Sahin et al. (2014) examine in their panel data investigation, the infrastructure effects on economic 

growth of three groups of European Union countries (EU 12, EU 15 and EU 27) between the period of 1980 and 

2010 using generalised momentum method (GMM). They found „‟that telecommunications investments have 

positive effects on growth in all groups, energy investments have positive effects in EU 15-EU 27 groups and 

investments on railway and road have positive effects only in EU 27group.‟‟ 

Mahyideen et al. (2012) in their 5 ASEAN‟s panel data investigation for the period between 1980 and 2010 also 

revealed that all the 4 infrastructural development proxies including number of subscriptions for both fixed line and 

mobile phone, number of telephone lines, the number of mobile cellular subscription are statistically and 

significantly correlated with economic growth proxy by GDP per capita. 

Evidence from developing countries in a study carried out by Sridhar and Sridhar (2007) shows that there are 

positive impacts of mobile and landline phones on national output, even when they control for the effects of capital 

and labour. Economic development had been linked with a critical mass telecommunication infrastructure. Röller and 

Waverman (2001) found that mass presence of telecommunication infrastructure using penetration rate of line per 

capita had significant effect on GDP in 21 OECD countries over 20 year periods. Similarly, in a dynamic fixed panel 

data investigation of 22 OECD countries for the period of 1980 to 1992 by Datta and Agarwal (2004) found that 

„telecommunication infrastructure is both statistically significant and positively correlated with growth in real GDP 

per capita growth for these countries even after controlling for the effects of investment, government consumption, 

population growth, openness, past levels of GDP, and lagged growth‟‟. However, it has been argued further that 

telecommunications investment is subject to diminishing returns, suggesting thereby that countries at an earlier stage 

of development are likely to gain the most from investing in telecom infrastructure (Datta and Agarwal, 2004).
1
 

(d) Innovation and technological advancement 

Innovation as a result of research and development had been theoretically linked to technological advancement 

which in turn enhances productivity of labour (Mankiw et al., 1992). In view of this, high-technology exports 

percentage of manufacturing has been described as products with high R&D intensity, such as in aerospace, 

computers, pharmaceuticals, scientific instruments, and electrical machinery (WBDI, 2015). However, a number of 

empirical studies have also established this endogenous technology progress position of growth model. Benhabib and 

Spiegel (1994) found that human capital can positively influence economic growth through two mechanisms. First, 

through the rate of domestically produced technological innovation; second, through the speed of adoption of 

technology from abroad, this can be through FDI in case of developing countries especially. Kilavuz and Altay 

(2012) also found that high-tech manufacturing industry export contribute positively and significantly to economic 

growth in 22 developing countries investigated. Hence, it is expected that this factor should have positive and 

significant effect on growth in developed countries being industry-oriented. 

(e) Other factors affecting economic growth 

Trade openness, natural resources endowment, population and tax policy cannot be overemphasized in economic 

growth literature. According to Chenery‟s patterns of development empirical exposition (as cited in Todaro and 

Smith (2011)) „‟in addition to accumulation of capital, both physical and human, a set of interrelated changes in the 

economic structure of a country are required for transition from a traditional economic system to a modern one‟‟  

The structural changes being referred to simply involve factors responsible for economic function such as 

production transformation, composition of consumer demand, international trade, natural resources, urbanisation  and 

population density. These are all macro and socio economic factors determining economic growth in every country 

and their impacts vary from country to country. 

 

3. Data Sources and Research Methods 
3.1. Data Collection and Sample Size Description 

The study uses secondary sources mainly from WDI to collect data. The explanatory variables of interest are 

broadly classified into four categories and these are: human capital development, physical capital including 

infrastructure, innovation & technology advancement and other control variables (including trade openness, FDI, 

population, tax policy, e.t.c).  
 

 

Table-3.1. Data description 

                                                             
1
 See also China case in Ding and Haynes (2006). 



Economy, 2016, 3(1):1-18 

 

 

 

 

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S/N Procxy Description Microeconomic indicators Sour

ce 

1 LnGDP GDP constant 2005 U$$/ WDI Economic growth WDI 

2 EDI NDX 

 

 

 
HEI NDX 

Mean years of schooling for adult aged 25 

years+based on educational attainment & Expected 

years of schooling based on enrolment by age at all 

levels of education (min. 15 and Max. 18 years 
Life expectancy at birth, ( Min. 35 and Max. 85 years) 

 

Human capital development (Education)   
 

Human capital development (Health)  

 

 

 

HDI 

PRYSE 
SWW 

TSE 

LEXP 

School enrollment, primary ( % gross) 
School enrollment, secondary ( % gross) 

School enrollment, tertiary ( % gross) 

Life expectancy at birth 

Human capital development (Education)  
 

Human capital development (Health) 

 
 

WDI 

HEX Health expenditure, total (% of GDP) Human capital (Health facilities) WDI 

3 GFC 

INFR 

HETECH 

Gross fixed capital formation (% of GDP) 

Fixed telephone subscriptions ( per 100 people) 

High-technology exports (% of mfg. exports) 

Domestic investment 

Infrastructural development 

Innovation & technology advancement 

 

WDI 

4 FDI 

LPOP 

TROP 
NTR 

TAX 

Foreign direct investment, net inflows (% of GDP) 

Population ages 15-64 ( % of total) 

Trade (% of GDP) 
Total natural resource rents (% of GDP) 

Total tax rate (% of commercial profits) 

Foreign investment inflow 

Availability of labour 

Trade openness 
Resource endowment 

Tax policy 

 

 

WDI 

Notes:   

        1 Natural log dependent variable                                                                     WDI- World Development Indicator 

        2 Human capital components                                                                            HDI- Human Development Index 

        3 Physical capital components 

       4 Control variables-other factors that can influence economic growth 

Source: Author‟s compilation from WBDI (2015) 

 

There had been mixed results using these proxies in a number of studies both in developed and developing 

countries (Barro, 1992; Benhabib and Spiegel, 1994; Appleton and Teal, 1999; Ranis et al., 2000; Tatoğlu, 2011; 

Son et al., 2013; Ada and Acaroğlu, 2014; Eggoh et al., 2015). This study therefore used both index and school 

enrolment and reconciles the differences as shown in the above table ****. Another human capital health proxy used 

is health expenditure (% of GDP). 

The sample size contain 74 developing countries made up of 49% Sub-Saharan Africa (SSA),  11% Middle East 

& North Africa (MENA), 18% Latin America & Caribbean (LAC), 5% East Asia & Pacific (EAP), 11% Europe & 

Central Asia (ECA) and 7% South Asia (SA). However, 47 developed countries were majorly sampled from high 

income countries. The nature of observed data is panel because it comprises many countries as explained above for 

the period of seven years 2005 to 2011. This is sometimes called cross-section time-series data because it combines 

both cross-sectional data and time series data in the observation. 

 

3.2. Research Methods 
This study employed quantitative research method which involves the generation of data in quantitative form and 

subjected to rigorous quantitative analysis in a formal rigid fashion (Kothari, 2004). The quantitative analysis 

techniques used are panel (fixed-effects & random-effects) and cross-sectional regression techniques.  

However, this study basically used fixed-effects and random-effects model in order to eliminate endogeneity 

problem in the panel observation which may have been caused by unobserved variable which might have correlated 

with regressor and residual i.e error term. For instance education quality which could enhance skills and performance 

for productivity and economic growth were not observed in this study‟s model. However, the use of pooled OLS 

model estimation may be inconsistent and bias as it assumes that the intercepts are the same for all the countries. 

Thus, deny the heterogeneity or individuality effect that may exist among the countries in the panel observation.  

Meanwhile, fixed-effects model allows for heterogeneity or individuality effects among countries in the panel 

observation and hence each has its own intercept value. It accounts for the fact that though intercept may differ 

across countries but does not vary over time. Random-effects model also identify time-invariant effects but with 

common intercept value which resulted to a very important assumption upon which random-effect approach could be 

consisted and free from bias estimation. 

 

3.3. Models Specifications 
This study empirically employed Mankiw, Romer and Weil human capital model as human capital was included 

in the aggregate neo-classical Cobb-Douglas production functions stated below (as cited in L‟Angevin and Laïb 

(2005)):  

Y =               

where Y, K, H, L respectively total output, physical capital, human capital and labour,   and    the partial elasticity 

of production with respect to the two forms of capital, A Harrod-augmenting technical progress and t time and  

     . 

The above model can be re-written as simple production function with technical progress A treated as 

endogenous variable (Dewan and Hussein, 2001): 

              

Hence, two separate econometric functions could be derived from equation 3.8 by substituting human capital 

variables of interest. The first econometric function by substituting first set of human capital proxy is as follows: 

                                        

The second econometric function after incorporating second set of human capital proxy (i.e school enrolment at 

all education level and life expectancy at birth): 

                                             

Panel 

A Panel 

B 



Economy, 2016, 3(1):1-18 

 

 

 

 

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Where GFC is gross fixed capital formation, (PRYSE, SSE, and TSE) are primary to tertiary school enrolments, 

INFR- infrastructure, HTECH- high-tech innovation, LEXP- life expectancy, HEX-health expenditure, EDINDX- 

education index and HEINDX– health index. 

Equation *** and *** can be represented in econometric models form respectively as follows: 

Panel a specification: 

                                                                         
Panel B specification: 

                                                                                

       
Where         represent natural log of gross domestic products for i individual country at specific t time period 

and     is error term. All other variables remain as described in previous page. 

However, the above two separate panel models (using fixed-effects and random-effects regression techniques) 

for both developing and developed countries were subjected to econometric analysis. The Hausman test then 

applied in order to compare and choose appropriate and efficient estimate between fixed-effects and random-effects 

in each of the model category (Dewan and Hussein, 2001; Tvartani, 2007).  

A cross section analysis was also carried out for both developing and developed countries for the last four years 

of the sample years (i.e 2008-2011) in order to examine the efficacy of cross section regression technique and panel 

regression technique. These two methods had been used in a number of literatures earlier cited in this study.  

 

4. Analysis and Interpretation of Research Findings 
4.1. Descriptive Statistics Results 

The descriptive statistics of both developing and developing countries are shown in Table 4.1 below. The 

number of panel observations (N) for both developing and developed countries are 239 and 243 respectively. The 

average GDP for both regions within the period observed (2005-2011) were USD$102.25 billion and USD$973.82 

billion respectively. The standard deviation represents the variability of each data within the sample. It is a measure 

that is used to quantify the amount of variation or dispersion of a set of data values (Wikipedia, 2015). It explains 

how widely the values in a data set are spread around the mean. Hence, the larger the standard deviation, the more 

spread out the observation as shown in Table 4.1 below. In 2011 and 2005 Ukraine and Niger recorded maximum 

and minimum education index scores of 0.79 and 0.15 respectively in developing region. These are lower than those 

recorded in developed region as Australia and Saudi Arabia recorded 0.92 and 0.62 in 2009 and 2005 respectively. 

 
Table-4.1. Descriptive statistics 

  Developing countries     Developed countries     

Variables  Mean  Std. Dev.  Max.  Min. N  Mean  Std. Dev. Max. Min. N 

GDP (USD$bn) 102.25 238.61 1326.24 0.82 239 973.92 2358.33 13816.14 6.81 243 

EDINDX 0.53 0.15 0.79 0.15 239 0.82 0.06 0.92 0.62 243 

HEINDX 0.71 0.13 0.91 0.40 239 0.90 0.05 0.97 0.79 243 

PRYSE 103.88 16.23 149.95 50.04 239 102.28 4.75 118.43 91.02 243 

SSE 64.37 26.90 101.32 10.10 239 104.05 10.34 147.62 84.71 243 

TSE 24.72 19.30 79.25 0.49 239 66.98 16.23 113.98 10.33 243 

LEXP 66.38 8.15 79.56 45.86 239 78.62 2.94 82.93 70.87 243 

HEX 6.22 1.95 12.49 2.79 239 8.67 2.17 17.10 2.22 243 

GFC 23.13 5.69 46.73 12.81 239 22.87 4.26 36.75 10.47 243 

INFR 11.04 10.13 37.00 0.16 239 41.44 13.02 69.51 15.28 243 

HTECH 6.71 10.44 70.79 0.00 239 14.56 9.36 60.66 0.26 243 

FDI 5.00 4.53 27.52 -1.77 239 7.17 28.55 430.64 -16.15 243 

TROP 80.46 32.84 203.83 33.11 239 92.98 46.75 349.85 24.77 243 

TAX 43.74 14.17 112.90 8.10 239 43.69 16.95 107.40 14.50 243 

NTR 10.44 12.63 68.36 0.00 239 3.84 9.92 64.77 0.00 243 

LPOP 61.02 7.01 72.28 47.49 239 67.37 2.17 72.83 62.12 243 
     Source: Author‟s research outputs via E-Views 7 using World Bank (WDI) and UNDP (HDI) data (2015) 

 

In developing region, the maximum GDP of USD$1.326.24 trillion was recorded by India in 2011 while the 

minimum of USD$0.820 billion was recorded by Burkina Faso in 2007. However, in developed countries, the 

maximum GDP value of USD$13.816.14 trillion was recorded by United States in 2011 while the minimum GDP of 

USD$6.8 billion was recorded by Argentina in 2005. 

 

4.2. Correlation Results 
The correlation matrix in Table 4.2 A shows the level of correlation among the variables of interest for 

developing countries. It also helps to detect multicollinearity. 

 

 

 

 

 

 

 

 

 



Economy, 2016, 3(1):1-18 

 

 

 

 

9 

 

Table-4.2A. Correlation Matrix- Developing countries 

 
  Source: Author‟s research outputs via E-Views 7 using World Bank (WDI) and UNDP (HDI) data (2015) 

 

However, secondary school enrolment and life expectancy are highly correlated with education index and health 

index with score index of 0.910 and 0.928 respectively. This is expected and does not constitute any multicollinearity 

problem as these variables were used in different models. 

 
Table-4.2B. Correlation Matrix- Developed countries 

 
Source: Author‟s research outputs via E-Views 7 using World Bank (WDI) and UNDP (HDI) data (2015) 

As mentioned above, the same applicable to developed countries as life expectancy and health index are highly 

correlated with 0.991. Apart from these, there is no incident of other independent variables being highly correlated 

with another which to some extents, indicate no multicollinearity problem. 

 

4.3. Analysis of Panel Regression Results 
Table 4.3 shows the Panel A results where education index and health index (as described in Table 3.1) were 

used human capital proxy for both developing and developed regions. The pooled OLS regression results in column 

1 for both regions indicate that the model is not fit enough as a result of R square of 0.2299 and 0.3991 respectively. 

This means that only 23% and 40% variations in GDP can be explained by independent variables of both regions 

respectively. However, the F statistic test of 18.71 and 31.999 shows that all the predictors in the models jointly 

influenced GDP by 19% and 32% in both developing and developed regions respectively. Also, the linear 

relationship between GDP and all the predictors in the model are entirely significant (at P-value<0.01). Although, 

Durbin Watson test result was very low but this may be due to small sample years.  

 
Table-4.3. Panel A: Summary of Panel Regression (Using Human Capital Index) 

Dependent variable: lnGDP 

without control variables with control variables 

Model 1 Model 2 Model 3 

Pooled OLS Fixed-effects Fixed-effects 

Developing Developed Developing Developed Developing Developed 

Constant 

22.1311** 8.9954** 19.6848** 19.9087** 17.5823** 18.1505** 

(0.6751) (2.2314) (0.1655) (0.5043) (0.3505) (0.5616) 

Regressors:       

 

    

GFC 

-0.0239 0.0217 -0.0007 0.0090** 0.0027* 0.0079** 

(0.0136) (0.0188) (0.0012) (0.0010) (0.0012) (0.0009) 

INFR 

-0.0106 -0.0163 0.0125** 0.0002 0.0083** 0.0002 

(0.0137) (0.0091) (0.0023) (0.0009) (0.0022) (0.0008) 

HTECH 

0.0356** -0.0267** 0.0015 -0.0001 0.0013 -0.0005 

(0.0072) (0.0075) (0.0008) (0.0008) (0.0008) (0.0008) 

EDINDX 

1.9020* 0.3015 1.9222** 1.6554** 1.3324** 1.1816** 

(0.8396) (1.3447) (0.269) (0.2855) (0.2533) (0.2756) 

HEINDX 

3.2586** 16.6002** 4.2933** 5.4299** 3.6198** 4.9530** 

(0.8745) (2.3825) (0.3353) (0.6944) (0.3068) (0.6727) 

HEX 

-0.2088** 0.3037** -0.0156** -0.0268** -0.0198** -0.0209** 

(0.0434) (0.0451) (0.0062) (0.0065) (0.0057) (0.0064) 

      Continue 



Economy, 2016, 3(1):1-18 

 

 

 

 

10 

 

FDI 

      

 

-0.0018 -0.0001 

        (0.0013) (0.0001) 

TAX 

      

 

-0.0004 -0.0014 

        (0.0007) (0.0009) 

TROP 

      

 

-0.0008 0.0002 

      

 

(0.0005) (0.0003) 

NTR 

        -0.0002 0.0016 

        (0.0011) (0.0014) 

LPOP 

        0.0500** 0.0400** 

        (0.0063) (0.0047) 

R-squared 0.2299 0.3991 0.9988 0.9995 0.9990 0.9996 

Durbin-Watson Test 0.0279 0.0207 0.5298 0.6848 0.6056 0.8529 

Observation 383 296 383 296 377 282 

F-statistic 18.7132 31.9925 3808.402 9784.497 4311.123 10650.420 

Prob. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 

Hausman Test     14.2620 21.1045 67.3743 40.1130 

Prob.     0.0268 0.0018 0.0000 0.0000 
Notes: The standard errors are in parenthesis,**and* indicate significant at p<0.05 respectively; see appendix I for the results of random-effects as 

compared with fixed-effects using Hausman test above. 

       Source: Author‟s research outputs via E-Views 7 using World Bank (WDI) and UNDP (HDI) data (2015 

 

Meanwhile, there is positive relationship between human capital proxy by education and health index in both 

developing and developed regions as shown in Figure 4.1. However, the causal effects relationship results and level 

of significance are shown in Table 4.3. In developing region, the results shows that  for every 1% point increase in 

education and health performance index, GDP correspondingly increases by 19.0% and 32.6% points at (P-

value<0.05) and (P-value<0.01) respectively. Moreover, education index had positive effect on GDP in developed 

region but not significant. However, coefficient of health index in developed region is far higher than that of 

developing region with every 1% point increase in health performance index, the GDP increases by 16.60 point (at P-

value<0.01). 

 

 
Figure-4.1. Relationship between GDP and education index 

                          Source: Author‟s research outputs  from pooled cross-country regression via E-Views (2015) 

 

However, there is negative relationship between GDP and health expenditure in developing region within 7 years 

panel observation while this relationship was positive in developed region as shown in Figure 4.2 below. The same 

relationship exists when school enrolments and life expectancy were used as human capital proxy (See appendix III 

and IV).  

 

 
Figure-4.2. Relationship between GDP and Health expenditure (% of GDP) 

                           Source: Author‟s research outputs from pooled cross-country regression via E-Views (2015) 

 

However, Table 4.3 shows causal effect results and level of significance of the relationship depicted in Figure 

4.2 above. For developing region, GDP decreases by 20.8% point for every 1% point increase in health expenditure 



Economy, 2016, 3(1):1-18 

 

 

 

 

11 

 

(at P-value<0.01) while for developed region, GDP increases by 30.37% point for every 1% point increase in health 

expenditure (at P-value<0.01). The physical capital proxy by gross fixed capital formation and other allied physical 

capital such as telecommunication infrastructure had negative effects on GDP in developing region while only 

infrastructure exerts negative effects on GDP in developed region as shown in Table 4.3 above. However, these 

effects are not significant in both regions. Meanwhile, Innovation and high technology advancement proxy by high-

tech manufacturing exports had effect on GDP but the effect was positive in developing region while it was negative 

in developed region.  

Meanwhile, these pooled OLS regression results may not be consistent as it fails to recognise country specific 

effects by treating countries in the panel observation as same. Hence, having applied Hausman test, fixed efects 

results were found appropriate as shown in column 2 and 3 of Table 4.3 with R square of 0.99 indicating that 99% 

variations in GDP can be explained by predictors and confirm the fitness of the model. F- statistic also indicates that 

all predictors in the model are jointly influence GDP at 1% significance level.  Contrary to pooled OLS results, both 

education and health performance index positively and significantly influence GDP in developing region with every 

1% point increase in both human capital index GDP correspondingly increases by 19.2% point and 42.9% point (at 

P-value<0.05). The explanatory power of education performance index for developed region increase contrary to 

pooled OLS results, with every 1% point increase, GDP also increases by 16.6% point (at P-value<0.01). Health 

performance index still retained its positive and significant effects on GDP for both regions while health expenditure 

still having negative and significant effects on GDP in both regions.Gross fixed capital formation became significant  

(at P-value<0.01) in developed region from fixed effects model results and the effect is positive indicating that GDP 

increases by 0.9% point for every 1% point increase in gross fixed capital formation. However, in developing region, 

the relationship was not significant and the effect is negative.  

However, after controlling for FDI, tax, trade openness, natural resources and population with working age (15-

65 years), the effects of education index, health index, gross fixed capital formation on GDP remain positive and 

significant in both regions. Health expenditure‟s negative and significant effects remain the same in both regions. As 

expected tax exerts negative effect on GDP in both regions but not significant. Population of age 15-65 years also 

have positive and significant effects on GDP in both regions. 
Table 4.4 below shows results of Panel B where human capital is proxy by primary, secondary and tertiary 

school enrolment and life expectancy. It comprises of pooled OLS, random effects, and fixed effects models for both 

developing and developed regions. 
 

Table-4.4. Panel B: Summary of Panel Regression (Using school enrollment & life expectancy) 

Dependent variable: lnGDP 

 without control variables with control variables 

Model 1 Model 2 Model 3 

Pooled-OLS Random-effects Fixed-effects Fixed-effects 

Developing Developed Developing Developed Developing Developed 

Constant 

19.7950** -0.3244 18.0603** 21.701** 16.471** 19.986** 

(1.1361) (3.3637) (0.3913) (0.5014) (0.5163) (0.6361) 

Regressors:             

GFC 

0.0034 0.0500 0.0020 0.0137** 0.0047** 0.0114** 

(0.0154) (0.0210) (0.0014) (0.0013) (0.0015) (0.0012) 

INFR 

-0.0003 -0.0129 0.0107** 0.0009 0.0061* -0.0002 

(0.0154) (0.0101) (0.0030) (0.0009) (0.0029) (0.0009) 

HTECH 

0.0332** -0.0335** 0.0026** -0.0024** 0.0018* -0.0011 

(0.0082) (0.0091) (0.0009) (0.0008) (0.0009) (0.0008) 

HEX 

-0.3525** 0.3568** -0.0094 -0.0055 -0.0096 0.0085 

(0.0481) (0.0507) (0.0075) (0.0066) (0.0070) (0.0067) 

PRYSE 

0.00451** 0.0646** -0.0001 0.0081** 0.0008 0.0057** 

(0.0054) (0.0181) (0.0013) (0.0017) (0.0014) (0.0016) 

SSE 

-0.0162** -0.0147 0.0011 -0.0002 0.0009 -0.0006 

(0.0064) (0.0091) (0.0013) (0.0010) (0.0013) (0.0010) 

TSE 

0.0274** 0.0074 0.0070** 0.0054** 0.0025 0.0044** 

(0.0076) (0.0053) (0.0014) (0.0007) (0.0014) (0.0007) 

LEXP 

0.0867** 0.2256** 0.0796** 0.0384** 0.0658** 0.0249** 

(0.0182) (0.0411) (0.0062) (0.0064) (0.0063) (0.0068) 

FDI 

        0.00006 -0.00003 

        (0.0016) (0.0001) 

TAX 

        -0.0011 -0.0029* 

        (0.0010) (0.0012) 

TROP 

        -0.0011 0.0013** 

        (0.0006) (0.0004) 

NTR 

        0.0008 0.0017 

        (0.0014) (0.0018) 

LPOP 

        0.0452** 0.0475** 

        (0.0079) (0.0065) 

R-squared 0.4115 0.4314 0.6598 0.9995 0.9992 0.9996 

Durbin-Watson Test 0.0579 0.0340 0.5537 0.9988 0.7915 1.0016 

Observation 244 253 244 253 239 243 

F-statistic 20.5359 23.1447 56.9601 9132.276 3329.835 9676.342 

Prob. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 

Hausman Test     14.7941 26.5473 41.8944 50.2677 

Prob.     0.0633 0.0008 0.0001 0.0000 

 Notes: The standard errors are in parenthesis; ** and * indicate significant at p≤0.01 and p≤0.05 respectively; see appendix II for the results of random-effects   

as compared with fixed-effects using Hausman test above.       
   Source: Author‟s research outputs via E-Views 7 using World Bank (WDI) and UNDP (HDI) data (2015) 



Economy, 2016, 3(1):1-18 

 

 

 

 

12 

 

The pooled OLS regression in column 1 revealed that physical capital proxy by gross fixed capital formation and 

telecommunication infrastructure does not have significant effect on GDP in both developing and developed regions. 

Meanwhile, life expectancy exerts significant and positive effect on GDP in both developing and developed regions. 

Innovation and technological advancement proxy by high-tech manufacturing exports have significant effects on 

GDP in both regions but the effect is negative for developed world while it proves positive in developing region. 

Only primary school enrolment was significant and had positive effect on GDP in developed region while enrolment 

from primary to tertiary school turn significant in developing region but secondary school enrolment influences GDP 

negatively. However, the panel regression results in column 2 and 3 having applied Hausman test revealed that 

random effect estimate is appropriate for developing region meaning that country specific effects associated with 

unobserved variables are not correlated with error terms. Meanwhile, fixed effect estimate proves appropriate in the 

case of developed region. Exactly like result in Panel A, gross fixed capital formation had positive and significant 

effect on GDP in developed region while it is not significant in developing region but infrastructure exerts positive 

effect in both regions. However, this effect was significant in developing region but not significant in developed 

region. Moreover, contrary to OLS results, primary and tertiary school enrolments in developed region were 

significant (at P<0.01) and influence GDP positively while only primary enrolment had positive and significant 

relationship with GDP in developing region. Meanwhile, having controlled for tax, FDI, trade openness, natural 

resources and population, none of the human capital development proxies had significant effects on GDP in 

developing region except life expectancy.  However, primary and tertiary school enrolments as well as life 

expectancy still exert positive and significant effect on GDP (at P<0.01) in developed region. The explanatory power 

of physical capital in developing region also increased as it influences GDP significantly and positively. As expected, 

tax exerts negative effects on GDP in both regions but significant (at P<0.05) in developed region. 

 

4.4. Analysis of Cross-Section Regression Results 

Table 4.5 below presents the results of single cross section regression of developing region from 2008 to 2011. 

Only health expenditure proves significant with negative effects in all the four years of analysis even when school 

enrolments and life expectancy were used as proxy for human capital development. 
 

Table-4.5. Single Cross-Section Regression Results: Developing Countries (2008 - 2011) 

 

 
Notes: The standard errors are in parenthesis; ** and * indicate significant at p≤0.01 and p≤0.05 respectively 
Source: Author‟s research outputs via E-Views 7 using World Bank (WDI) and UNDP (HDI) data (2015) 
 

 

 

 

 



Economy, 2016, 3(1):1-18 

 

 

 

 

13 

 

Table-4.6. Single Cross-Section Regression Results: Developed Countries (2008 - 2011) 

 

 

 Notes: The standard errors are in parenthesis; ** and * indicate significant at p≤0.01 and p≤0.05 respectively;  
 Source: Author‟s research outputs via E-Views 7 using World Bank (WDI) and UNDP (HDI) data (2015) 

 

However, Table 4.6 below presents cross section regression results of developed region where health 

performance index and primary school enrolment were found to be significant (at P<0.05) and exert positive effects 

on GDP in 2011. There is also positive and significant relationship between health expenditure and GDP in 2011. 

Meanwhile, different statistical tests were carried out in order to ensure that the above cross section regressions are 

free from error and bias. Hence Breusch Godfrey serial correlation LM test shows that no serial correlation exist (at 

p-value>0.05). Jarque-Bera normality test also confirmed that errors are normally distributed with (p-value>0.05). 

Heteroskedasticity test also informed that there is no standard errors bias which could render t-statistics or F-statistics 

useless. It is a situation whereby the variability of a variable is unequal across the range of values of a second 

variable that predicts it Taylor (2013). However, based on the above analysis, it can be concluded that human capital 

development in form of quality health status measured by life expectancy index and basic primary education had 

been the catalyst behind economic growth in developed region throughout the four years. Meanwhile, only health 

expenditure had significant effect on GDP in developing region and this effect was negative while it was positive in 

developed region but this could be due to corruption in health sector of developing region. 

 

4.5. Discussion, Comparison and Implication 

4.5.1. Physical Capital 
The discussion and comparison are based on panel results. It could be observed from Table 4.3 that the results of 

fixed effects estimates having acknowledged endogeneity problem shows that GDP increases by 0.91% point for 

every 1% point increase in gross fixed capital formation (at P-value<0.01) in developed region
2
. The cross-section 

regression result in Table 4.6 showed that gross fixed capital formation had positive and significant effects on GDP 

of developed countries in 2010 and 2011 (at P-value<0.05) when HDI was used as human capital proxy. 

However, it was negative effect in developing region with GDP declined by 0.07% point but not significant. This 

could be attributed to mismatch between capital requirement and saving capacity as well as low rates of investment 

in terms of physical capital in developing regions (Appleton and Teal, 1999; Roy and Mandal, 2012). Meanwhile, 

after controlling for FDI and other growth determinants as shown in Table 4.3 gross fixed capital formation in 

developing region turns positive (at P-value<0.05) while this remains positive and significant (at P-value<0.01) in 

developed region.  

However, when school enrolments were used as proxy for human capital in Table 4.4 gross fixed capital 

formation in developed region remains positive and significant even after controlling for other growth determinants 

variables while it was not significant in the case of developing region until when FDI and other determinants were 

introduced into the model.  The implication is that domestic investment is not sufficient enough for economic growth 

                                                             
2 See the findings from EU and OECD countries in Son, Noja, Ritivoiu and Tolteanu (2013). and Queirós and Teixeira (2014). respectively. Murthy and  Chien 

(1997). Also found that physical capital measured by real investment ratio to real GDP plays a significant role in economic growth of OECD countries if 

complements with technology know-how. 



Economy, 2016, 3(1):1-18 

 

 

 

 

14 

 

in developing region compare to developed region due to poor savings unless other growth factors are taken into 

account. This means there is need to mobilise capital from other source such as FDI as Mallick and Moore (2006) 

found positive complementary effects of FDI and domestic capital formation on GDP per capita in 18 Latin 

American countries regardless of income group. 

 

4.5.2. Telecommunication Infrastructure and High-Tech Exports 
The results in column 2 and 3 from Table 4.3 and 4.4 revealed that infrastructure development in terms of ICT 

facilities and innovative technology advancement proxy by high technology manufacturing exports were positively 

and significantly influence GDP in developing region. This could be attributed to the speed of adopting technology 

from abroad through FDI (Benhabib and Spiegel, 1994). Technology externalities and knowledge spillover through 

FDI had made infrastructure and innovation technology development contribute immensely to economic growth in 

developing region (Luiz and Mello, 1999). 

However, in developed region, results in column 2 and 3 from Table 4.3 and 4.4 revealed that telecommunication 

infrastructure and high technology manufacturing had negative effects on GDP in developed region but not 

significant. The implication is infrastructure and technology advancement are not driving force behind economic 

growth in this region within the period observed even after controlling for other growth determinants. This could be 

attributed to the concept of diminishing return to capital going by most neo-classical theories indicating that 

developed countries tends to benefit at earlier stage of infrastructure development in terms of contribution to 

economic growth. Datta and Agarwal (2004) argued that this benefit becomes less significant as telecommunications 

infrastructure is more developed. 

 

4.5.3. Human Capital Development (Education and Health Capital) 
The results in column 2 and 3 from Table 4.3 shows that education and health index as proxy for human capital 

development exhibit positive and significant effects on GDP in both regions though the beta value of health index for 

developed region is higher than developing  region by average 1.2 points. This implies that there is more 

commitment to health and wellness of human capital in developed region compare to developing region. This 

consistent with the findings of number of growth literatures in developed countries (Gyimah-Brempong and Wilson, 

2004; Ecevit, 2013; Şen et al., 2015). 

Meanwhile, health expenditure exhibits significant inverse relationship with GDP in both regions and from the 

entire panel results (both Table 4.3 and 4.4). This could be attributed to corruption in health sector in developing 

region especially Africa (Agbenorku, 2012). However, the situation is different in developed countries as several 

literatures have attributed this adverse effect to higher proportion of national income as health expenditure on ageing 

labour force who might not be productive as the younger ones (Isabe and Poças, 2012; Churchill et al., 2015) 

Moreover, the results in column 2 and 3 from Table 4.4 confirm that human capital development through 

education contribute to economic growth in developed countries than developing countries. For instance, the results 

of model 2 from Table 4.4 show that commitments to primary and tertiary education in developed countries 

contribute about 0.8% and 0.5% points to GDP respectively for every 1% point increase in the enrolment rate at 

these two levels of education. Meanwhile, only tertiary school enrolment had positive and significant effect on GDP 

in developing countries with 0.7% point increase in GDP for every 1% point increase in number of potential human 

capital in tertiary institution. Similar results were again found in model 3 from Table 4.4 for both regions.  

This study also identified concern about the choice of proxy for human capital in terms of education like other 

growth studies. For instance, Barro (1992) used school enrolment as flow of investment in human capital while 

student-teacher ratio was used as quality of education. Zaman (2012) criticised the use of school attainment and 

school enrolments as perfect proxy while he supports education quality measured by mathematics and science test 

score. Meanwhile, Wo ßmann (2003) supports the use of education attainment and average year of schooling while 

describing school enrolment as imperfect.  

The above shows that there is no consensus yet on human capital measurement in economic growth literature and 

that is why this study uses different proxies. However,  education index measured by mean years of schooling for 

adult aged 25 years plus (education attainment) and expected years of schooling  showed positive and significant 

effects on GDP in both regions (see model 2 and 3 in Table 4.3). Although, the results of model 2 and 3 in Table 4.4 

were quite different when school enrolments were used as proxy for human capital in terms of education. Only 

tertiary school enrolment had positive and significant impacts on GDP in developing countries while both primary 

and tertiary enrolment positively and significantly influence GDP in developed countries. The implication here going 

by school enrolment proxy‟s results, is that developing countries neglect primary education which is the foundation 

for any form of human capital development and that is why the qualities of university products in this region are not 

contributing enough to the economic growth because educational foundation in terms of primary school education is 

poor. 

Meanwhile, when health index proxy and life expectancy were compared, the effect was positive and level of 

significant on GDP was same for both regions except the big coefficient exhibits by health index proxy (see Table 

3.1 for the description of health index and life expectancy). 

 

5. Conclusion and Recommendations 
5.1. Conclusion 

The objective of this paper is to examine how physical and human capital development had impacted the 

economic growth in developing and developed countries and what policy makers in both regions could do in order to 

make best use of these two resources (physical and human capital) for the economic growth. 

However, the overall empirical results of this paper based on fixed effects outcome show that physical capital in 

terms of investment measured by gross fixed capital formation does not contribute to economic growth in developing 

countries unless other growth determinants such as FDI, trade openness, e.t.c are taken into account. However, gross 



Economy, 2016, 3(1):1-18 

 

 

 

 

15 

 

fixed capital formation contributes positively and significantly to GDP in developed countries in all the models 

specified. 

Meanwhile, Infrastructure and innovative technology prove positive and significant in developing countries than 

developed ones which had been attributed technology externalities and knowledge spillover through the speed of 

adopting technology from abroad via FDI (Benhabib and Spiegel, 1994; Luiz and Mello, 1999). This can also be 

related to catching up effect of convergence hypothesis of neo-classical Solow growth model of 1956. 

It was also discovered that developing countries have only concentrated on developing human capital through 

tertiary education neglecting primary education which is the foundation for all levels of education. However, 

investment flow in basic and higher education proxy by primary and tertiary school enrolments were found positively 

and significantly contributing to GDP in developed countries. Meanwhile, health expenditure exerts negative and 

significant effects on GDP in both regions although a number of growth literatures have attributed these effects to the 

high proportion of government expenditure on ageing work force which manifest in high life expectancy especially 

in developed countries. This category of workforce might not be productive or resist innovation and change (Isabe 

and Poças, 2012; Churchill et al., 2015). 

 

5.2. Recommendations 
In light of the findings of this research, efforts of the policy makers in developing countries should be geared 

towards improving primary and secondary education as these are foundations for higher education. There is 

possibility that poor primary education system could affect the products of secondary and later graduates from 

university making them not relevant or productive in the economy. 

There is over reliance on investment from abroad in developing countries as this made gross fixed capital 

formation not significant until other factors like FDI and trade openness were taken into account. This investment 

from abroad in form of FDI and trade openness does not themselves impact GDP positively as shown in Table 4.3 

and 4.4 (model 3). However, they does contribute indirectly by serving as conduit pipe through which technology 

flown into developing region coupled with improved infrastructure. 

Meanwhile, it may look as if everything is well with the developed countries but this is far from the truth as there 

is need for developed countries to encourage knowledge and skills development among their nationals. More than 

half of the educated persons in developed countries like UK and USA acquired their doctorate and professional 

qualification in these countries and remain important part of labour force contributing to the economic growth of 

these countries (Dodani and LaPorte, 2012). 

However, the argument above is that this has constituted brain drain as majority of these skilled and professional 

workers are foreigners from developing countries. This could also lead to brain gain in the long run as these foreign 

professionals might decide to return to their home country with all skills and technology know-how acquired to 

develop their countries. This again brings us back to convergence hypothesis through catching up effect of 

technology externalities and knowledge spillover as demonstrated by endogenous growth literatures (Romer, 1989; 

Romer, 1990; Benhabib and Spiegel, 1994; Luiz and Mello, 1999).  

 

5.3. Limitations of the Study for Further Research 
This research had faced data availability limitations like any other quantitative studies. The researcher however, 

still collect the available data that reasonably serve as alternative ones without grossly affected the overall research 

results. 

 Data on important proxy for infrastructure like electricity (power) in developing countries are not available 

for huge number of countries. This forced researcher to use alternative one i.e telecommunication line (per 

100 people) which has been widely used in growth literature (Datta and Agarwal, 2004; Mahyideen et al., 

2012; Sahin et al., 2014) 

 Data for research and development expenditure is very problematic to source for developing countries. This 

forced the researcher to use high-technology exports to measure innovation & technology advancement. This 

proxy has been described as export with high R&D intensity (WBDI, 2015). It has also been used in 

empirical literature (Kilavuz and Altay, 2012). 

 This research employed cross-section regression to complement the results of within transformation fixed 

effects and random effects methods as Least Square Dummy Variable (LSDV) is practically difficult to use 

due to large number of countries involve in the panel observation. Though the degree of freedom is 

exhausted in within transformation method due to elimination of intercept but the results had been argued to 

be same with LSDV (Dougherty, 2012). 

This research does not investigate convergence issue in either developing or developed regions as many growth 

studies have done in the past. It will be good if future research can take a look at the situation especially for the 

recent periods like the ones covered by this study. 

Finally, quite numbers of recent growth literatures are now shifting attention from quantitative human capital 

proxy such as school enrolment to qualitative measure like mathematics and science test score (Zaman, 2012). Even 

old literature like Barro (1992) used student-teacher ratio as proxy for education quality. Hence, future growth 

research should include this proxy as measure of human capital quality. 

 

References 
Ada, A. and H. Acaroğlu, 2014. Human capital and economic growth: A panel data analysis with health and education for MENA region. 

Advances in Management & Applied Economics, 4(4): 59-71. 
Adelakun, J., 2011. Human capital development and economic growth in Nigeria. European Journal of Business and Management, 3(9): 29-38. 

Adhikary, B., 2011. FDI, trade openness, capital formation, and economic growth in Bangladesh: A linkage analysis. International Journal of 

Business and Management, 6(1): 16-28. 



Economy, 2016, 3(1):1-18 

 

 

 

 

16 

 

Agbenorku, P., 2012. Corruption in Ghanaian healthcare system: the consequences. Journal of Medicine and Medical Science-International 
Research Journal,, 3(10): 622-630. S., I. Chaudhry and F. Farooq, 2012. Human capital formation and economic growth in Pakistan. 

Pakistan Journal of Social Sciences, 2032(2011): 2229-2240. 

Ali, S., I. Chaudhry and F. Farooq, 2012. Human capital formation and economic growth in Pakistan. Pakistan Journal of Social Sciences, 

32(1): 229-240. 
Appleton, S. and F. Teal, 1999. Human capital and economic development. Available from 

http://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/00157612-FR-ERP-39.PDF [Accessed 6 Jun. 2015]. 

Azat, D., 2014. GMM estimation and Shapiro-Francia normality test: A case study of CEE economies. International Journal of Economic 

Sciences, 3(1): 12-26. 
Barro, R., 1992. Human capital and economic growth. Available from https://www.kansascityfed.org/publicat/sympos/1992/s92barro.pdf 

[Accessed 17 Aug. 2015]. 

Behname, M., 2012. Foreign direct investment and economic growth: Evidence from Southern Asia. Atlantic Review of Economics, 2(1). 

Available from http://www.unagaliciamoderna.com/eawp/coldata/upload/14_Foreign_Direct_Investment.pdf [Accessed 25 Jun. 
2015]. 

Benhabib, J. and 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. 

Bloom, D., D. Canning and J. Sevilla, 2004. The effect of health on economic growth: A production function approach. World Development, 
32(1): 1-13. 

Borensztein, E., J.D. Gregorio and J. Lee, 1998. How does foreign direct investment affect economic growth? Journal of International 

Economics, 45(1): 115-135. 

Churchill, S., S. Yew and M. Ugur, 2015. Effects of government education and health expenditures on economic growth: A meta-analysis. 
Monash Business School Discussion Paper, 40(15). Available from 

http://econstor.eu/bitstream/10419/110901/1/Human_Capital_Paper.pdf [Accessed 20 Aug. 2015]. 

Cu, G.O., S.H. Kim and P.A. Naga, 2013. ASEAN-5 countries: Panel data analysis on the impact of foreign direct investment, openness, and 

fixed capital formation on the economic growth. Academia. Available from https://www.academia.edu/4408795/ASEAN-
5_countries_Panel_Data_Analysis_on_the_Impact_of_Foreign_Direct_Investment_Openness_and_Fixed_Capital_Formation_on_th

e_Economic_Growth. 

Datta, A. and S. Agarwal, 2004. Telecommunications and economic growth: A panel data approach. Applied Economics, 36(15): 1649-1654. 

Dewan, E. and S. Hussein, 2001. Determinants of economic growth panel data approach. Working Paper, 1(4): 1-51. 
Ding, L. and K. Haynes, 2006. The role of telecommunications infrastructure in regional economic growth in China. Australian Journal of 

Regional Studies, 12(3): 281-302. 

Dodani, S. and R. LaPorte, 2012. Brain drain from developing countries: How can brain drain be converted into wisdom gain? Journal of the 

Royal Society of Medicine, 98. Available from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1275994/pdf/487.pdf [Accessed 20 
Aug. 2015]. 

Dougherty, C., 2012. Fixed effects regressions: LSDV method. London School of Economics and Political Science Learning Resources. 

Available from http://learningresources.lse.ac.uk/140/ [Accessed 20 Aug. 2015]. 

Ecevit, E., 2013. The impact of life expectancy on economic growth: Panel cointegration and causality analyses for OECD countries. 
International Journal of Social Sciences, 16(1): 1-14. 

Eggoh, J., H. Houeninvo and G. Sossou, 2015. Education, health and economic growth in African countries. Journal of Economic 

Development, 40(1): 93-111. 

Fayissa, B. and C. Nsiah, 2010. Can remittances spur economic growth and development? Evidence from Latin American countries (LACs). 
Department of Economics and Finance Working Paper Series. Available from 

http://econpapers.repec.org/paper/mtswpaper/201006.htm. 

Gallo, C., 2002. Economic growth and income inequality: Theoretical background and empirical evidence. 9-13. Available from 

https://www.ucl.ac.uk/silva/bartlett/dpu/publications/.../WP119.pdf [Accessed 19 Jun. 2015]. 
Gyimah-Brempong, K. and M. Wilson, 2004. Health human capital and economic growth in Sub-Saharan African and OECD countries. 

Quarterly Review of Economics and Finance, 44(2): 296-320. 

Idrees, A. and M. Siddiqi, 2013. Does public education expenditure cause economic growth? Comparison of developed and developing 

countries. Pakistan Journal of Commerce and Social Sciences, 7(1): 174-183. 
Isabe, D.S.A. and A. Poças, 2012. The interrelations between health, human capital and economic growth. Empirical Evidence from the OECD 

Countries and Portugal. Tese De Doutoramento Em Economia, Apresentada à Faculdade De. Available from 

https://estudogeral.sib.uc.pt/bitstream/10316/23323/1/Tese%20Doutoramento%20Ana%20Pocas.pdf [Accessed 19 Aug. 2015]. 

Itagaki, Y., 1963. Criticism of rostow's stage approach: The concept of stage, system and type. Developing Economies, 1(1): 1-17. 
Javed, M., S. Abbas, A. Fatima, M. Azeem and S. Zafar, 2013. Impact of human capital development on economic growth of Pakistan: A 

public expenditure approach. World Applied Sciences Journal, 24(3): 408-413. 

Juma, M., 2012. The effect of foreign direct investment on growth in Sub-Saharan Africa. A Degree Thesis Submitted to Department of 

Economics, Amherst College, United States. Available from https://www.amherst.edu/media/view/434456/original/Juma,%20Mary-
Ann%20Thesis.pdf. 

Kilavuz, E. and T.B. Altay, 2012. Export and economic growth in the case of the manufacturing industry: Panel data analysis of developing 

countries. International Journal of Economics and Financial Issues, 2(2): 201-215. 
Kothari, C., 2004. Research methodology: Methods and techniques. 2nd Edn., New Delhi: New Age International (P) Limited. 

Kubík, R., 2010. Looking for the right human capital proxy. Review of Economic Perspectives, 10(2): 61-70. 

L‟Angevin, C. and N. Laïb, 2005. Education and growth in a panel of 21 OECD countries. Available from 

http://www.mtk.ut.ee/sites/default/files/mtk/dokumendid/9757fae6d1a6f3fd343b28526b50fe1f.pdf [Accessed 17 Aug. 2015]. 
Luiz, R. and D. Mello, 1999. Foreign direct investment-led growth: Evidence from time series and panel data. Oxford Economic Papers, 51: 

133-151. Available from http://dept.ku.edu/~empirics/Courses/Econ915/papers/FDI_panel.pdf [Accessed 26 Jun. 2015]. 

Mahyideen, J., N. Ismail and L. Hook, 2012. A pooled mean group estimation on ICT infrastructure and economic growth in ASEAN-5 

countries. International Journal of Economics and Management, 6(2): 360-378. 
Mallick, S. and T. Moore, 2006. Foreign capital in a growth model. Review of Development Economics, 12(1): 1-23. 

Mankiw, N., D. Romer and D. Weil, 1992. A contribution to the emprics of economic growth. National Bureau of Economic Research Series, 

3541: 1-44. Available from http://eml.berkeley.edu/~dromer/papers/MRW_QJE1992.pdf [Accessed 24 Apr. 2015]. 

Middendorf, T., 2005. Human capital and economic growth in OECD countries. SSRN Journal. Available from http://repec.rwi-
essen.de/files/DP_05_030.pdf [Accessed 20 Jun. 2015]. 

Murthy, V.N.R. and I.S. Chien, 1997. The empirics of economic growth for OECD countries: Some new findings. Economics Letters, 55(3): 

425-429. 

Ndambiri, H., C. Ritho, S. Ng‟ang‟a, P. Kubowon, F. Mairura, P. Nyangweso, E. Muiruri and F. Cherotwo, 2012. Determinants of economic 
growth in Sub-Saharan Africa: A panel data approach. International Journal of Economics and Management Sciences, 2(2): 18-24. 

Nyandat, C., 2014. Models of growth and development. GAKSU: 1-4. Available from http://www.gaksu.com/allpdf/960_notes.pdf [Accessed 

18 Jul. 2015]. 

Ogujiuba, K., 2013. The impact of human capital formation on economic growth in Nigeria. J Economics, 4(2): 121-132. 
Okwuosa, O., 2015. A critical analysis of rostow's model of development. Academia: 20-23. Available from 

http://www.academia.edu/7468777/A_CRITICAL_ANALYSIS_OF_ROSTOWS_MODEL [Accessed 21 Jun. 2015]. 

Prados, D.L.E.L., 2005. European patterns of development in historical perspective? Economic History and Institutions Series, 10(10). 

Available from http://orff.uc3m.es/bitstream/handle/10016/419/wh057910.pdf?sequence=1 [Accessed 23 Jun. 2015]. 

http://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/00157612-FR-ERP-39.PDF
http://www.kansascityfed.org/publicat/sympos/1992/s92barro.pdf
http://www.unagaliciamoderna.com/eawp/coldata/upload/14_Foreign_Direct_Investment.pdf
http://econstor.eu/bitstream/10419/110901/1/Human_Capital_Paper.pdf
http://www.academia.edu/4408795/ASEAN-5_countries_Panel_Data_Analysis_on_the_Impact_of_Foreign_Direct_Investment_Openness_and_Fixed_Capital_Formation_on_the_Economic_Growth
http://www.academia.edu/4408795/ASEAN-5_countries_Panel_Data_Analysis_on_the_Impact_of_Foreign_Direct_Investment_Openness_and_Fixed_Capital_Formation_on_the_Economic_Growth
http://www.academia.edu/4408795/ASEAN-5_countries_Panel_Data_Analysis_on_the_Impact_of_Foreign_Direct_Investment_Openness_and_Fixed_Capital_Formation_on_the_Economic_Growth
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1275994/pdf/487.pdf
http://learningresources.lse.ac.uk/140/
http://econpapers.repec.org/paper/mtswpaper/201006.htm
http://www.ucl.ac.uk/silva/bartlett/dpu/publications/.../WP119.pdf
http://www.amherst.edu/media/view/434456/original/Juma,%20Mary-Ann%20Thesis.pdf
http://www.amherst.edu/media/view/434456/original/Juma,%20Mary-Ann%20Thesis.pdf
http://www.mtk.ut.ee/sites/default/files/mtk/dokumendid/9757fae6d1a6f3fd343b28526b50fe1f.pdf
http://dept.ku.edu/~empirics/Courses/Econ915/papers/FDI_panel.pdf
http://eml.berkeley.edu/~dromer/papers/MRW_QJE1992.pdf
http://repec.rwi-essen.de/files/DP_05_030.pdf
http://repec.rwi-essen.de/files/DP_05_030.pdf
http://www.gaksu.com/allpdf/960_notes.pdf
http://www.academia.edu/7468777/A_CRITICAL_ANALYSIS_OF_ROSTOWS_MODEL
http://orff.uc3m.es/bitstream/handle/10016/419/wh057910.pdf?sequence=1


Economy, 2016, 3(1):1-18 

 

 

 

 

17 

 

Queirós, A. and A. Teixeira, 2014. Economic growth, human capital and structural change: An empirical analysis. FEP Economics and 
Management: 1-27. Available from http://wps.fep.up.pt/wps/wp549.pdf [Accessed 24 Jun. 2015]. 

Rafael, A. and T.B. De La, 2000. Endogenous growth, R&D and labour variety. Available from http://eprints.ucm.es/6702/1/0005.pdf 

[Accessed 20 Jul. 2015]. 

Ranis, G., F. Stewart and A. Ramirez, 2000. Economic growth and human development. World Development, 28(2): 197-219. 
Röller, L. and L. Waverman, 2001. Telecommunications infrastructure and economic development: A simultaneous approach. American 

Economic Review, 91(4): 909-923. 

Romer, P., 1989. Endogenous technological change. National Bureau of Economic Research Working Paper, 3210. Available from 

http://www.nber.org/papers/w3210.pdf [Accessed 20 Aug. 2015]. 
Romer, P., 1990. Endogenous technological change. Journal of Political Economy, 98(5). Available from 

http://www.jstor.org/stable/pdf/2937632.pdf?acceptTC=true [Accessed 20 Aug. 2015]. 

Rostow, W., 1960. The stages of economic growth: A non-communist manifesto. 3rd Edn., New York: Cambridge University Press. pp: 1-49. 

Roy, S. and K. Mandal, 2012. Foreign direct investment and economic growth: A cross-country exploration in Asia using panel cointegration 
technique. Department of Economics, University of Calcutta, India: 1-30. Available from 

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiIt4eAwL3KAhWFPQ8KHVjKAY8QFg

ghMAA&url=https%3A%2F%2Feditorialexpress.com%2Fcgi-

bin%2Fconference%2Fdownload.cgi%3Fdb_name%3DFEMES12%26paper_id%3D381&usg=AFQjCNFfcEEoxjq2Ua4lKNyyH_Iq
3LPS4Q&sig2=5BuwWwuJka1uB8NwyG3XhA&bvm=bv.112454388,d.ZWU&cad=rja. 

Sahin, O., N. Can and E. Demirbas, 2014. The effects of infrastructure determinants on economic growth: European union sample. Eurasian 

Journal of Business and Economics, 7(13): 11-27. 

Schütt, F., 2003. The importance of human capital for economic growth. Institute for World Economics and International Management: 1-63. 
Available from http://www.iwim.uni-bremen.de/publikationen/pdf/W027.pdf [Accessed 24 Jun. 2015]. 

Şen, H., A. Kaya and B. Alpaslan, 2015. Education, health, and economic growth Nexus: A bootstrap panel granger causality ana lysis for 

developing countries. Economics Discussion Paper. School of Social Sciences, The University of Manchester. Available from 

http://www.socialsciences.manchester.ac.uk/medialibrary/economics/discussionpapers/EDP-1502.pdf. 
Somayeh, H., M. Teymoor and S. Mina, 2013. Effect of health on economic growth: A panel data study of developed and developing 

countries. European Online Journal of Natural and Social Sciences, 2(3): 1273-1278. 

Son, L., G. Noja, M. Ritivoiu and R. Tolteanu, 2013. Education and economic growth: An empirical analysis of interdependencies and impacts 

based on panel data. Timisoara Journal of Economics and Business, 6(19): 40-54. 
Sridhar, K. and V. Sridhar, 2007. Telecommunications infrastructure and economic growth: Evidence from developing countries. Applied 

Econometrics and International Development, 7(2): 38-61. 

Tatoğlu, F., 2011. The relationships between human capital investment and economic growth: A panel error correction model. Journal of 

Economic and Social Research, 13(1): 77-90. 
Taylor, J., 2013. Confusing stats terms explained: Heteroscedasticity (Heteroskedasticity). Stats Make Me Cry. Available from 

http://www.statsmakemecry.com/smmctheblog/confusing-stats-terms-explained-heteroscedasticity-heteroske.html [Accessed 12 

Aug. 2015]. 

Tiruneh, M. and M. Radvansky, 2007. The contribution of human capital to European economic growth: An empirical exploration from a panel 
data. Institute of Economic Research: 1-8. Available from http://ecomod.net/system/files/Tiruneh_Radvansky_Ecomod2011_1.pdf 

[Accessed 24 Jun. 2015]. 

Todaro, M. and S. Smith, 2011. Economic development. 11th Edn., Essex: Pearson Education Limited. 

Tvartani, 2007. Fixed effects models and neighborhood studies vartanian: SW 541. Available from 
http://www.learningace.com/doc/345927/e46d32be0dfca8512c9b3b0db6c0e36e/fixed-effects-models1 [Accessed 15 Jun. 2015]. 

Wang, X. and J. Piesse, 2010. The microfoundations of dual economy models. Economics Discussion Paper Series, 10(10): 1-25. 

WBDI, 2015. World bank. Available from http://data.worldbank.org/indicator/TX.VAL.TECH.MF.ZS. 

Wikipedia, 2015. Definition of standard deviation. Wikipedia. Available from https://en.wikipedia.org/wiki/Standard_deviation. 
Wo ßmann, L., 2003. Specifying human capital. Journal of Economic Survey, 17(3): 239-270. 

World Bank Development Indicator, 2015. World bank. Available from http://data.worldbank.org/indicator/TX.VAL.TECH.MF.ZS. 

World Economic Forum, 2013. The human capital report. Insight Report. Cologn/Geneva: World Economic Forum. 

Zahra, K., P. Azim and A. Mahmood, 2009. Telecommunication infrastructure development and economic growth: A panel data approach. 1-
22. Available from http://www.pide.org.pk/psde24/pdf/18.pdf [Accessed 22 Jun. 2015]. 

Zaman, M., 2012. Human capital and economic growth: An empirical investigation using panel data. Journal of Humanities and Social 

Science, 4(6): 43-56. 

 

Bibliography 
Alfaro, L., A. Chanda, S. Kalemli-Ozcan and S. Sayek, 2003. FDI and economic growth: The role of local financial markets. SSRN Journal. 

Available from http://econweb.umd.edu/~kalemli/jiefinal.pdf [Accessed 21 Jun. 2015]. 

Barberá, D.L.T.R., 2000. Endogenous growth, R&D and labour variety: 1-3. Available from http://eprints.ucm.es/6702/1/0005.pdf [Accessed 
19 Jul. 2015]. 

Bhattacherjee, A., 2012. Social science research: Principles, methods, and practices. 1st Edn., Florida: Creative Commons Attribution-

NonCommercial-ShareAlike. 

Dawson, C., 2002. Practical research methods: A user-friendly guide to mastering research. 1st Edn., Oxford, United Kingdom: Baseline Arts 
Limited. 

Domar, E., 1947. Expansion and employment. American Economic Review, 37(1): 34-55. 

Dritsakis, N., E. Varelas and A. Adamopoulos, 2006. The main determinants of economic growth: An empirical investigation with Granger 

causality analysis for Greece. European Research Studies, 9(3-4). Available from http://www.ersj.eu/repec/ers/papers/06_34_p4.pdf 
[Accessed 27 Jul. 2015]. 

Emmanuel, O., V. Wujung and S. Emmanuel, 2014. Mechanisms of the influence of human capital on economic growth: A panel data analysis 

of the CEMAC region. Asian Economic and Financial Review, 4(5): 625-640. 

Fields, G., 2004. Dualism in the labor market: A perspective on the lewis model after half a century. Manchester School, 72(6): 1-18. 
Garrido, L., 2014. Pattern of development. A review of the literature for an empirical update. 1-3. Available from 

https://www.gtap.agecon.purdue.edu/resources/download/7030.pdf [Accessed 22 Jun. 2015]. 

Greener, S., 2008. Business research methods. 1st ed. [ebook] Bookboon. Available from http://bookboon.com/en/introduction-to-research-

methods-ebook [Accessed 27 Jun. 2015]. 
Gyimah-Brempong, K. and M. Wilson, 2004. Health human capital and economic growth in Sub-Saharan African and OECD countries. 

Quarterly Review of Economics and Finance, 44(2): 296-320. 

Harrod, R., 1939. An essay in dynamic theory. Economic Journal, 49(193): 14-33. 

Hirota, Y., 2002. Reconsidering of the lewis model: Growth in a dual economy. Okayama Universiry Economics Society Magazine, 34(1): 49-
55. 

Hox, J. and H. Boeije, 2005. Data collection, primary vs. Secondary. Encyclopedia of Social Measurement, 1. Available from 

http://www.joophox.net/publist/ESM_DCOL05.pdf [Accessed 30 Jun. 2015]. 

Ickes, B., 1996. Endogenous growth models. 1-26. Available from http://grizzly.la.psu.edu/~bickes/endogrow.pdf [Accessed 23 Jun. 2015]. 
Mankiw, N., D. Romer and D. Weil, 1990. A contribution to the emprics of economic growth. National Bureau of Economic Research Series, 

3541: 1-44. Available from http://www.nber.org/papers/w3541.pdf [Accessed 24 Apr. 2015]. 

http://wps.fep.up.pt/wps/wp549.pdf
http://eprints.ucm.es/6702/1/0005.pdf
http://www.nber.org/papers/w3210.pdf
http://www.jstor.org/stable/pdf/2937632.pdf?acceptTC=true
http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiIt4eAwL3KAhWFPQ8KHVjKAY8QFgghMAA&url=https%3A%2F%2Feditorialexpress.com%2Fcgi-bin%2Fconference%2Fdownload.cgi%3Fdb_name%3DFEMES12%26paper_id%3D381&usg=AFQjCNFfcEEoxjq2Ua4lKNyyH_Iq3LPS4Q&sig2=5BuwWwuJka1uB8NwyG3XhA&bvm=bv.112454388,d.ZWU&cad=rja
http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiIt4eAwL3KAhWFPQ8KHVjKAY8QFgghMAA&url=https%3A%2F%2Feditorialexpress.com%2Fcgi-bin%2Fconference%2Fdownload.cgi%3Fdb_name%3DFEMES12%26paper_id%3D381&usg=AFQjCNFfcEEoxjq2Ua4lKNyyH_Iq3LPS4Q&sig2=5BuwWwuJka1uB8NwyG3XhA&bvm=bv.112454388,d.ZWU&cad=rja
http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiIt4eAwL3KAhWFPQ8KHVjKAY8QFgghMAA&url=https%3A%2F%2Feditorialexpress.com%2Fcgi-bin%2Fconference%2Fdownload.cgi%3Fdb_name%3DFEMES12%26paper_id%3D381&usg=AFQjCNFfcEEoxjq2Ua4lKNyyH_Iq3LPS4Q&sig2=5BuwWwuJka1uB8NwyG3XhA&bvm=bv.112454388,d.ZWU&cad=rja
http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiIt4eAwL3KAhWFPQ8KHVjKAY8QFgghMAA&url=https%3A%2F%2Feditorialexpress.com%2Fcgi-bin%2Fconference%2Fdownload.cgi%3Fdb_name%3DFEMES12%26paper_id%3D381&usg=AFQjCNFfcEEoxjq2Ua4lKNyyH_Iq3LPS4Q&sig2=5BuwWwuJka1uB8NwyG3XhA&bvm=bv.112454388,d.ZWU&cad=rja
http://www.iwim.uni-bremen.de/publikationen/pdf/W027.pdf
http://www.socialsciences.manchester.ac.uk/medialibrary/economics/discussionpapers/EDP-1502.pdf
http://www.statsmakemecry.com/smmctheblog/confusing-stats-terms-explained-heteroscedasticity-heteroske.html
http://ecomod.net/system/files/Tiruneh_Radvansky_Ecomod2011_1.pdf
http://www.learningace.com/doc/345927/e46d32be0dfca8512c9b3b0db6c0e36e/fixed-effects-models1
http://data.worldbank.org/indicator/TX.VAL.TECH.MF.ZS
http://data.worldbank.org/indicator/TX.VAL.TECH.MF.ZS
http://www.pide.org.pk/psde24/pdf/18.pdf
http://econweb.umd.edu/~kalemli/jiefinal.pdf
http://eprints.ucm.es/6702/1/0005.pdf
http://www.ersj.eu/repec/ers/papers/06_34_p4.pdf
http://www.gtap.agecon.purdue.edu/resources/download/7030.pdf
http://bookboon.com/en/introduction-to-research-methods-ebook
http://bookboon.com/en/introduction-to-research-methods-ebook
http://www.joophox.net/publist/ESM_DCOL05.pdf
http://grizzly.la.psu.edu/~bickes/endogrow.pdf
http://www.nber.org/papers/w3541.pdf


Economy, 2016, 3(1):1-18 

 

 

 

 

18 

 

McCallum, B., 1996. Neoclassical vs. Endogenous growth analysis: An overview. 41-79. Available from 
https://www.richmondfed.org/~/media/richmondfedorg/publications/research/economic_quarterly/1996/fall/pdf/mccallum.pdf 

[Accessed 23 Jun. 2015]. 

Parr, J., 2001. On the regional dimensions of rostow's theory of growth. Review of Urban & Regional Development Studies, 13(1): 2-19. 

Qian, J., 2014. Dealing with endogeneity. Available from http://jhqian.org/ec310/06endogeneity.pdf [Accessed 15 Jun. 2015]. 
Rajasekar, S., P. Philominathan and V. Chinnathambi, 2013. Research methodology. 2, 9. Available from 

http://arxiv.org/pdf/physics/0601009.pdf [Accessed 24 Jun. 2015]. 

Rostow, W., 1959. The stages of economic growth. Economic History Review, 12(1): 1-16. 

Saunders, M., P. Lewis and A. Thornhill, 2009. Research methods for business students. 5th Edn., Edinburgh Gate, England: Pearson 
Education Limited. pp: 3,10, 34, 60, 108, 113, 137-138, 256. 

Schmidheiny, K., 2014. Panel data: Fixed and random effects. Short guides to microeconometrics fall 2014, Unversity at Basel. Available from 

http://www.schmidheiny.name/teaching/panel2up.pdf [Accessed 15 Jul. 2015]. 

Schwab, K. and X. Sala-i-Martín, 2014. The global competitiveness report 2014–2015. Insight Report Geneva: World Economic Forum. 
Söderbom, M., 2011. Econometric II lecture 6: Panel sata part I. Department of Economics, University of Gothenburg. Available from 

http://www.soderbom.net/metrix2/lec6_7.pdf [Accessed 15 Jun. 2015]. 

Vanm, D.B.H., 2013. Growth theory after keynes, part I: the unfortunate suppression of the harrod-domar model. Journal of Philosophical 

Economics, 7(1): 1-23. 
Wallace, M. and A. Wray, 2011. Critical reading and writing for postgraduates. 2nd Edn., London: SAGE Publication Limited. pp: 178, 199. 

Walliman, N., 2011. Research methods: The basics. 1st Edn., New York: Routledge. 

 

 
 

 

 

 
 

 

 

 
 

 

 

 
 

 

 

 
 

 

 

 
 

 

 

 
 

 

 

 
 

 

 

 
 

 

 

 
 

 

 
 

 

 

 
 

 

 

 
 

 

 

 
 

 

 

 
 

 

 
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http://www.richmondfed.org/~/media/richmondfedorg/publications/research/economic_quarterly/1996/fall/pdf/mccallum.pdf
http://jhqian.org/ec310/06endogeneity.pdf
http://arxiv.org/pdf/physics/0601009.pdf
http://www.schmidheiny.name/teaching/panel2up.pdf
http://www.soderbom.net/metrix2/lec6_7.pdf

