IMPACT OF POLITICAL STABILITY AND HUMAN CAPITAL ON FOREIGN DIRECT INVESTMENT IN EAST ASIA & PACIFIC AND SOUTH ASIAN COUNTRIES

Samina Sabir¹⁺ --- Ahsan Khan²

¹Assistant professor, Kashmir Institute of Economics, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan.

²M. Sc student, Kashmir Institute of Economics, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan.

Keywords:FDI Inflation GDP Trade Human capital Institutions.

ARTICLE HISTORY: Received:16 April 2018. Revised:7 May 2018. Accepted:11 May 2018. Published:15 May 2018.

1. INTRODUCTION

Some of growth models highlight the importance of savings as the most important determinant of economic growth. A high saving rate implies high investment which increases output and income that leads to high living standards in the long run (Solow, 1956). But the problem with developing countries is that they do not have appropriate level of savings and hence savings and investment gap occurs. Saving- investment gap is a core issue which has been discussed and debated in developing countries since long time. Therefore, capital inflows in the form of Foreign Direct Investment (FDI) are required to fill up this gap to catalyze economic growth and development process. Moreover FDI is a major source of transfer of technology from developed countries to technology deficient developing countries. Thus FDI facilitates economic development through creating employment opportunities, increasing capital inflow, raising productive capacity, improving managerial and management skills, enhancing factor’s productivity, enhancing infrastructure, improving macroeconomic stability and also increasing competition in the host country (Kobrin, 2005; Bannaga et al., 2013).  Due to globalization, FDI has increased across the countries. FDI has increased worldwide since the 1970s and has reached $ 1.76 trillion in 2015. It is worth mentioning that developing Asia is the largest recipient of FDI (United Nations Conference on Trade and Development, 2016). Particularly, Hong Kong was receiving $72.3 billion FDI from the world in 2010 and it has increased to $174.9 billion in 2015, which shows that FDI grew by 141.8 percent. Moreover, China is also another big recipient of FDI from world. FDI inflow for China was $114.7 billion in 2010 and increased to $135.6 billion. We can see the position of emerging economy of Singapore, where FDI has increased from $55.1 billion to $65.3 billion, which shows the rising trend in FDI. India also portrayed a rise from $27.4 billion to $44.2 billion in 2010 and 2015 respectively (ADB, 2014). Many other Asian countries such as Australia, Indonesia, Viet Nam, Malaysia, Pakistan and Philippines also witness the increase in FDI after global financial crisis. FDI inflow from the world is very high in East Asia and Pacific region compared to South Asian region. We can see the trend of FDI inflow in both regions in figure 1.

Graph-1. FDI inflow in East Asia & Pacific and South Asia

The motivation of this study is to find out the determinants of FDI in South Asia and East Asia and Pacific countries. There have been a lot of theories that explain the mechanism of FDI, its determinants and implications but Dunning’s electric paradigm theory has been considered to be important one in explaining FDI. The electric paradigm theory of Dunning (1988) proposes certain factors which influence FDI inflow under three advantages; ownership specific advantage, locational specific advantage and International specific advantage. According to this theory, the foreign Investors give considerable importance to various macroeconomic factors before making investment in any country such as quantitative and qualitative factors of production, costs of transport, telecommunications, market size, common and specific government policies that affect FDI flows, distance among countries, cultural diversity, attitude towards strangers and above all, rate of return on capital.

There are certain other factors that play an immense role in determining the inflows of FDI, for example politic factors and business facilitation together with economic factors (UNCTAD, 2016). The empirical evidence shows that; market size,  trade openness, political risk, infrastructure, economic growth, tax to GDP ratio and some other such variables are also important determinants of FDI (Kravis and Lipsey, 1982; Schneider and Frey, 1985; Culem, 1988; Grubert and Mutti, 1991; Hines and Rice, 1994; Loree and Guisinger, 1995; Cassou, 1997; Chakrabarti, 2001; Jordaan, 2004; Artige and Nicolini, 2005; Pärletun, 2008).

Strong democratic and inclusive institutions make sure the provision of a better and stable economic environment (Acemoglu et al., 2006). Poor institutions lead to poor legal protection of assets, malfunctioning of markets due to corruption which increases costs of doing business and poor infrastructure provision (Blonigen, 2005). All these factors hinder the inflows of FDI.

Human capital is another important determinant of FDI inflows. It enhances the absorptive capacity of the host economy to handle large inflows of FDI (Afza and Nazir, 2007; Majeed and Ahmad, 2008). Human capital can be enhanced by making appropriate investment in education and professional training. Globalization has integrated world markets and increased an immense competition among nations. Skill development to handle the technologies brings through FDI has led countries to invest in human capital development through education and technical training (Balasubramanyam et al., 1996). Therefore it is necessary to estimate the impact of human capital on FDI inflows in selected Asian countries.

Despite of all these factors mentioned above, political instability creates an uncertain economic environment, increasing risks and reducing investment (Alesina and Perotti, 1996). Moreover socio-economic instability shortens the time span of governments, upsetting long term economic policies beneficial to a better economic performance (Aisen and Veiga, 2011).

The aim of this study is to estimate the impact of political stability and human capital on FDI, we use panel data of 18 East Asian and Pacific countries and South Asian countries for the time 1981-2015. To tackle endogeneity problem in GDP per capita, trade openness and human capital variables, we use System GMM (Arellano and Bover, 1995; Blundell and Bond, 1998).

This study contributes in the literature through following ways. Firstly, this study aims to look at how competitive are countries in both region in attracting FDI. Secondly, this study is unique in the sense that it has considered the macroeconomic variables, institutions and political factors as possible determinants of FDI. Political stability and human capital have been given less importance in the literature. Therefore this study has included both these variables to see its impact on FDI in the presence of other variables. 

The study has been organized as; section 2 constitutes theoretical framework. Section 3 describes methodology and data, section 4 consists of results and discussion whereas section 5 concludes main findings of the paper.

2. THEORETICAL FRAMEWORK

The principal focus is to trace out the rationale behind FDI. According to the general belief, foreign firms make investment in those countries where they expect higher rates of return on their investments. There are a number of factors that influence a particular firm’s decision to make investments in foreign countries. Macroeconomic variables are greatly influential in the determination of FDI in developing countries. We use three macroeconomics variables such as inflation, GDP per capita, and trade Openness. Besides these two other variables have also been used which are human capital and democratic institutions. We can specify relationship between FDI and macroeconomic variables, democratic institutions and human capital in the following equation:

 FDI is the Foreign Direct Investment inflow, GDP is Gross Domestic Product per capita, TO is the Trade Openness which is the sum of exports and imports as a percentage of GDP, INF is the Inflation which is percentage change in consumer price index (CPI), HC  is human Capital which is measured as the average years of primary and secondary schooling and PI is a measure of democratic institutions, a proxy for political stability.

GDP per capita is positively related to FDI. GDP is an indicator of the market size of an economy. Market size has been considered to play an important role in affecting FDI. If the market size of an economy is larger, then FDI inflow will also be larger because of increased absorptive capacity of the economy.

Trade openness is positively related to FDI. An increase in trade liberalization increases vertical FDI inflows because in case of resource seeking or vertical FDI investors set their production plants to utilize relatively cheaper resources of the host economy and exploit economies of scale through increased export opportunities.

Inflation is negatively related to FDI. An increase in inflation rate causes FDI to decrease because FDI acts as a tax on investments and investor has to bear extra costs in the production of goods and services.

FDI causes human capital development but Government intervention is also vital which causes appropriate technological diffusion, which leads to further development of human capital. More development of human capital will attract more FDI but that would be more value added (Michie, 2001).

Strong institutions have ability to attract and absorb FDI.  For example, political stability as a leads to higher FDI inflow but if institutions are weak in a country then foreign investors and even domestic investors hesitate to invest. Gastanaga et al. (1998) argued that Political instability and poor institutions are main reasons for low foreign direct investment. Investor in global economic and financial market is scared to invest in those countries where institutions are weak.

3. METHODOLOGY AND DATA

We have constructed the following empirical model on the basis of our above mentioned theoretical framework.

represents the time in years, Y represents Foreign Direct Investment as a percentage of GDP (FDI), X represents the macroeconomic variables of interest such as inflation that is measured as the percentage change in CPI, Gross Domestic Product per capita and trade openness which is the sum of exports and imports as a percent of GDP. I  is vector of democratic institution measured as Polity2 and human capital which is measured as the average year of primary schooling and average year of secondary school and u_it is the error term which is normally distributed with zero mean and constant variance. All variables are expressed in natural logarithm (ln).

Variables appear on the right hand side of equation (2) have endogeneity problem which results in biased estimators. To fix this problem, the System GMM estimator was proposed by Arellano and Bover (1995) and Blundell and Bond (1998). It is a hybrid of difference GMM and levels. It uses the lagged levels of the variables as instruments for the difference equation and lagged difference variables as instruments for the level series to estimate the system of the level and first difference equation.

We use Sargan test to check the null hypothesis that all over identifying restrictions are valid against alternative restrictions are invalid. If null hypothesis is accepted then instruments are valid and strong otherwise not. For SGMM, autocorrelation should exist at first order AR(1) but autocorrelation at second order AR(2) should not exist. If AR(2) is significant then it means that there is problem of autocorrelation.

4. DATA AND DATA SOURCE

The data for FDI inflow, Inflation, Trade Openness and GDP per capita have been taken from WDI whereas the data for human capital has been obtained from Barro and Lee database. However data of democratic institutions are collected from Polity IV database. We use the sample of 18 East Asian & Pacific countries and South Asian Countries1 over the period 1981-2015.

4.1. Foreign Direct Investment (FDI)

FDI is the inflow of capital from source country to the host country. Our study has used FDI percentage of GDP as a dependent variable.

4.2. GDP Per Capita

GDP per capita is the value of all goods and services in the economy divided by total number of people within one year. GDP per capita has been recognized to be an important determinant of FDI (Chakrabarti, 2001; Jordaan, 2004; Artige and Nicolini, 2005; Pärletun, 2008). We have used GDP per capita as constant 2010 US$. In this study, GDP per capita has been used as an indicator of Macroeconomic condition.

4.3. Inflation

Inflation is the rise in general price level of an economy. It has also been found influential in FDI inflows. Our study uses it as a component of macroeconomic conditions. It has been measured by percentage change in Consumer Price Index (CPI). Some studies have referred it as an inflation tax on investments (Sayek, 2009).

4.4. Trade Openness

Trade openness is the extent of liberalization towards trade which refers to the lowering of trade barriers. It is an indicator of access to international market and increased export opportunities. It is an important determinant of FDI according to several studies (Kravis and Lipsey, 1982; Culem, 1988). We use it as an indicator of macroeconomic condition and taken it as a percentage of GDP.

4.5. Democratic Institutions

Democratic institutions are an indicator of political stability and institutional soundness of a country. It has also been recognized to be an important determinant of FDI (Chaib and Siham, 2004; Benassy-Quere et al., 2007; Klimek, 2013). A politically and institutionally more stable economy offers a better environment for MNCs through lowering of taxes, better security services, improved hospitable conditions, certain market privileges and minimum intervention. Unlikely if a country has political instability, then investor will not make investment and risk their capital in an unstable environment. We have used polity2 as a proxy for democratic institutions. It ranges from +10 which indicate complete democracy, to -10 which indicates autocracy.

4.6. Human Capital

Human capital is the enhancement of mental and physical abilities of human beings. It has also been indicated by researchers to be a determinant of FDI (Afza and Nazir, 2007; Majeed and Ahmad, 2008)). We have used primary and secondary schooling as proxies for human capital. Primary and secondary schooling have been taken as average years of primary and secondary schooling2 .

5. RESULTS AND DISCUSSIONS

This section constitutes the discussions of our empirical findings based on panel data for 18 countries over the period of 1981 to 2015. The panel data model is estimated initially by using fixed effects model and then by System GMM to deal with the possible issue of endogeneity and heteroscedasticity. Further, the results are presented in two sub-sections; First section presents the results of full sample while the second section consists of region specified results.

5.1. Full Sample Results

Table 1 presents the results of the full sample. We have used the fixed effect model instead of random effects model based on Hausman test statistics. The results from SGMM have been explained in detail as the results from fixed effects can be biased due to endogeneity problem. The results from fixed effects show that GDP per capita, democratic institutions, average years of primary schooling and average years of secondary schooling are positively related to FDI while inflation and trade openness are negatively related.

The results from SGMM show that inflation is negatively related to FDI at 1 percent level of significance. The negative relation of inflation with FDI is based on fact that it acts as a tax on coming investment because the costs of labor, capital and transportation increase due to inflation, which make the investment less profitable for the foreign investors (Sayek, 2009).

Table-1. Estimated Results for Full Sample

Variables	Fixed Effect	GMM
lnFDI Inflation trade	-0.016 (0.110) -0.025** (0.030)	0.759*** (0.00) -0.028*** (0.000) 0.032*** (0.014)
GDP per capita	0.121*** (0.000)	0.160*** (0.000)
Polity2	0.035*** (0.000)	0.047* (0.085)
Avg. years of primary schooling	0.250 (0.289)	0.457** (0.031)
Avg. years of secondary schooling	0.358*** (0.000)	0.456*** (0.000)
constant	10.309* (0.000)	13.005*** (0.000)
F value	100.63
R square	0.60
Observations	630
No. of countries	18
Sargan		(0.387)
AR(1)		(0.058)
AR(2)		(0.135)

Note: ***, ** and * denote significance at 1%, 5% and 10% level respectively. Values in parenthesis show P-values

GDP per capita has been found to be significant at 1 % level and is positively associated with FDI. These results are consistent with the findings of Asiedu (2002). Higher GDP per capita is an indicator of increased market size which enhances the absorptive capacity of the economy for incoming FDI. A large market will also ensure the efficient utilization of resources and exploitation of economies of scale, which provides greater incentives for higher levels of incoming FDI (Chakrabarti, 2001).

A positive relation of democratic institutions with FDI has been observed at 10 % level of significance. A politically and institutionally more stable economy offers a better environment for MNCs through lowering of taxes and better security services with less uncertainty in economic policies. The role of institutions in attracting FDI can be catalytic (Bisson, 2011). Our findings are consistent with previous literature (Chaib and Siham, 2004; Benassy-Quere et al., 2007; Klimek, 2013).

Trade openness is positively related to FDI at 1 percent level of significance.  Kravis and Lipsey (1982); Culem (1988) and Edwards (1990) have also found positive effect of openness on FDI. As the trade barriers decreases, the opportunities for MNCs to export their products increases and hence they acquiring greater economies of scales. MNCs with export oriented motive are inclined towards a more open economy because trade protections have issues like higher transaction costs with exports and also because it is difficult to import product to the country (Demirhan and Masca, 2008).

Average years of primary and secondary schooling are positively related to FDI and both are found significant at 1 % and 5 % level respectively. An increase in average years of primary and secondary schooling is an indicator of human capital that enhances the absorption capacity of FDI as well as ability to handle the spillover of technology and skill (Afza and Nazir, 2007; Majeed and Ahmad, 2008). The results also show that there is no second order serial correlation while Sargan test confirms that over identifying restrictions are valid.

5.2. Results for South Asian Region and East Asia and Pacific

The empirical findings for South Asian countries have been presented in table 2 while those for East Asia and Pacific countries, in table 3.

The results for South Asian countries obtained from SGMM show that all variables are significant but level of significance varies among variables. The reason may be the regional distribution of countries. Inflation has been found to be negatively related to FDI as in case of our full sample and significant at 5 % level. GDP per capita is positive and significant at 1 % level. Trade openness and average years of secondary schooling is positive and significant at 5 % level while Democratic institutions and average years of primary schooling have also been found positive and significant at 10 % level.

Table-2. South Asian Countries

Variable	Fixed effect	GMM
lnFDI(t-1)		0.544***
		(0.000)
Inflation	0.0180	-0.0484**
	(0.378)	(0.028)
GDP per capita	2.112***	0.574***
	(0.000)	(0.000)
Trade openness	0.033***	0.745**
	(0.000)	(0.005)
Polity	0.300*	0.002*
	(0.109)	(0.070)
Avg. years of primary schooling	0.715**	1.991*
	(0.037)	(0.085)
Avg. years of secondary schooling	0.302	0.668**
	(0.307)	(0.013)
constant	-38.295*** (0.003)	4.727*** (0.000)
F value	105.56
R square	0.794
Observations	175	135
No. of countries	5	5
Sargan		0.986
AR(1)		0.070
AR(2)		0.407

Note: ***, ** and * denote significance at 1%, 5% and 10% level respectively. P-values I are given in parenthesis

The reason for low level of significance of democratic institutions is lack of political stability in South Asian countries. The results for East Asia and Pacific region show that inflation, trade openness and average years of secondary schooling are significant at 5 % level while GDP per capita and democratic institutions are significant at 10 % level. However, an average year of primary schooling is found to be insignificant. All the variables have positive signs except inflation.

Table-3. East Asian and Pacific Countries

Variable	Fixed effect	GMM
lnFDI		0.420**
		(0.040)
Inflation	-0.017	-0.031**
	(0.148)	(0.018)
GDP per capita	2.158***	1.531*
	(0.000)	(0.082)
Trade openness	-0.005*	0.093**
	(0.079)	(0.036)
Polity	0.068***	0.529*
	(0.001)	(0.087)
Avg. years of primary schooling	-0.250	7.442
	(0.224)	(0.321)
Avg. years of secondary schooling	0.725***	6.284**
	(0.000)	(0.048)
constant	2.189 (0.185)	-9.826 (0.000)
F value	75.53
R square	0.552
Observations	385	110
No. of countries	11	11
Sargan		1.000
AR(1)		0.0041
AR(2)		0.371

Note: ***, ** and * denote significance at 1%, 5% and 10% level respectively. P-values are given in parenthesis.

The reason for the insignificance of primary schooling for East Asian region may be that the region is better developed compared to South Asia and is having high levels of human capital. That is why primary schooling is not much influential in attracting FDI in that region while secondary schooling is found to be very influential.

A comparative analysis of two regions show mixed responses of FDI towards different variables. Variables like; Inflation, GDP per capita and democratic institutions have been found to have greater effects on FDI in East Asia and Pacific region while Trade openness, average year of primary schooling and average year of secondary schooling are found to be much more effective in case of South Asian region.

FDI is more responsive towards inflation in East Asia and Pacific as compared to South Asian region. An increase of 1 percent change in inflation causes FDI to reduce by 4.84 % in South Asian countries while a similar change in inflation causes FDI to go down by 0.31 % in case of East Asian and Pacific countries. The reason is that East Asia and Pacific region is much more developed as compared to South Asia and the Inflation is also low in East Asia and Pacific and comparatively stable also. That is why a rise in inflation rate has greater effects on FDI inflows in East Asia and Pacific as compared to South Asia. 

Similarly, FDI is found more responsive to GDP per capita in East Asian and Pacific countries as compared to South Asian countries. A rise of 1 % in GDP per capita results in FDI to go up by 0.57 % in South Asian countries while the same change in case of East Asia and Pacific region causes FDI to go up by 1.53 %.

Trade openness has been found very effective towards FDI in South Asia as compared to East Asia and Pacific region.  An increase of 1 unit in trade openness causes FDI to increase by 7.4 % in case of South Asian countries while a similar change causes an increase of 0.93 % in East Asia and Pacific countries. It means that FDI is highly responsive to increase in trade openness in South Asia.

The reason is that East Asia have employed export oriented trade approach since 1970s while  South Asians have been employing import oriented trade policies (Hossain, 2015) therefore increase in trade liberalizations can work out a lot for South Asian countries in terms of FDI.

FDI has been found much more responsive towards democratic institutions in East Asia and Pacific as compared to South Asian countries. If democratic institutions improve by 1 unit then FDI goes up by 5.3 % in case of East Asian and Pacific region while the same improvement in case of South Asian region causes an enhancement of only 0.7 % in FDI. The democratic institutions are much stronger in East Asia and Pacific as compared to South Asia.

Human capital has been found to have large positive effects on FDI for both regions. An increase of 1 year in Average years of primary and secondary schooling causes FDI to go up by 1.99 % and 0.66 % respectively, in case of South Asian region while in case of East Asia and Pacific region, a similar increase in Average years of secondary schooling causes FDI to go up by 6.28 %. The reason for much higher effects of human capital on FDI inflows in East Asia is that the skill level, managerial skills and abilities to handle technology is quite higher in East Asian countries as compared to South Asia. Hence, a better developed human capital is more receptive towards FDI because of enhanced capabilities for handling advanced technology (Afza and Nazir, 2007; Majeed and Ahmad, 2008). Another reason for a more effective relation of FDI and Human capital in East Asian countries is that FDI further enhances skill development in countries where it is already enough (particularly secondary and tertiary enrollment) to start with (Te Velde and Xenogiani, 2007). As the skill level is already higher in East Asian economies, so FDI spill overs are also greater.

6. CONCLUSIONS

This study investigates the impact of macroeconomic factors, democratic institutions and human capital on FDI inflows in 18 South Asian and East Asia and Pacific countries over the period 1981 to 2015. To consider the role of stages of development, we divided countries into South Asian region and East Asia and Pacific region. We used static panel data technique and dynamic panel data technique to fix the problem of endogeneity to carry out our empirical analysis. We estimated the impact of macroeconomic variables, democratic institutions and human capital on inwarded FDI for full sample of Asian countries. We found that inflation has negative impact on inwarded FDI and all other macroeconomic indicators, democratic institution and human capital have positive and significant impact on FDI inflows.

We have found that in our full sample of developing countries, trade openness and market size have significant positive effects on FDI. Trade openness through opportunities like economies of scale and larger markets enhance FDI while market size expansion enhances the absorptive capacity of the economy for larger inflows of FDI. Inflation on the other hand has been found to be negatively related to FDI as it becomes a hindrance to FDI inflows by playing the role of a tax on investments.

The region wise comparative analysis show that variables such as; Inflation, GDP per capita and democratic institutions have been found to have greater effects on FDI in East Asia and Pacific region while Trade openness, average year of primary schooling and average year of secondary schooling are found to be much more effective in case of South Asian region because East Asia and Pacific region has large market size and hence offering greater absorptive capacity, comparatively stronger democratic institutions and comparatively lower inflation rate whereas, south Asia has been found to be comparatively poor and institutionally less stable. So, FDI spill overs are greater in East Asia and Pacific region as compared to South Asian region due to higher macroeconomic stability.

The full sample as well as region wise analysis concludes that macroeconomic factors, democratic institutions and human capital are important determinants of FDI. If macroeconomic indicators are stable, then it would ultimately results in higher inwarded FDI. Human capital development and political stability in the form are democratic institution are most desirable factor which attract the foreign investors to invest. 

First; developing countries should take policy measures to bring higher macroeconomic stability for greater FDI inflows. Second; Labor intensive developing countries from South Asian region like Pakistan, Bangladesh, and Sri-Lanka etc. have greater opportunities to attract vertical FDI because of the availability of cheap labor and abundance of natural resources conditioned on human capital development and political stability. These countries should take radical steps to improve their democratic institutions and human capital to attract more FDI and to gain from existing FDI inflows.

Funding: This study received no specific financial support.

Competing Interests: The authors declare that they have no competing interests.

Contributors/Acknowledgement: Both authors contributed equally to the conception and design of the study.

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Appendix A

Graph-A1

Graph-A2

Source: WDI

Graph-A3

Source: WDI

Appendix B

Table B-1. Summary Statistics

Variables	Observations	Mean	Std. Dev	Minimum	Maximum
LnFDI	630	20.085	3.155	9.134	26.396
TO	630	82.573	77.714	12.009	439.657
INF	630	5.938	5.254	-1.408	58.387
DINST	630	2.785	6.270	-10	10
PRI	630	4.436	1.608	0.64	7.03
SEC	630	2.454	1.326	0.31	4.85
GDP	630	13.248	7.396	5.89	28.462

Table-B2. List of countries

East Asia and Pacific Countries	South Asian countries
China Fiji Malaysia Papua new Guinea Philippines Singapore Thailand Japan Tonga Korea Republic Indonesia Australia New Zeeland	Pakistan India Sri Lanka Nepal Bangladesh

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Footnote