Determinants of Foreign Direct Investment in South Asia
Rahim M. Quazi College of Business Prairie View A&M University Prairie View, Texas
Munir Mahmud College of Business Pennsylvania State University Hazelton, Pennsylvania
September 30, 2004
Key Words: Foreign direct investment, South Asia. JEL Classifications: F21, O53
Abstract Foreign direct investment (FDI) creates employment opportunities, helps transfer managerial skills and technology, and provides the much-needed capital for domestic investment in developing countries, all of which contribute to their economic development. Recognizing the manifold benefits of FDI, developing countries around the world have significantly eased restrictions on foreign capital transfer since the early 1980s. This paper studies 5 countries in South Asia - Bangladesh, India, Nepal, Pakistan, and Sri Lanka, and investigates which factors, both economic and non-economic, drive the flow of FDI into these countries. Employing 19952000 panel data, this study finds that economic freedom, economic openness, economic prosperity, human capital, and incremental lagged changes in FDI significantly increase FDI inflow in South Asia, while political instability significantly depresses it. These results further our knowledge of the determinants of FDI, which is crucial for devising strategies to promote economic development --a course that holds much at stake not only for South Asia, but also for developing countries in general.
I. Introduction Foreign direct investment (FDI) not only provides the developing countries with much needed capital for domestic investment, but also creates employment opportunities, helps transfer managerial skills and technology, all of which contribute to economic development. Recognizing the manifold benefits of FDI, developing countries around the world have significantly eased restrictions on foreign capital transfer since the early 1980s. Furthermore, the sizable reduction in foreign aid programs since the end of the Cold War has forced countries hitherto heavily dependent on foreign public aid to seek out alternative sources of foreign private capital. As a result, the annual inflow of FDI to the developing countries has increased manifold from $24 billion (24% of total foreign investment) in 1990 to almost $178 billion (61% of total foreign investment) in 2000 (World Bank 2001). It has been also estimated that since the 1980s FDI in Europe & Central Asia has grown by a colossal 5,200%, East Asia & Pacific by 942%, South Asia by 740%, Latin America & Caribbean by 455%, Sub-Saharan Africa by 59% and all developing countries by 672% (World Bank 2000).
The FDI inflow differential among developing countries has created much research interest among economists. Consequently, a sizeable empirical literature has grown that focuses on the determinants of FDI to the developing nations. These studies have identified a number of variables, such as infrastructure, trade openness, return on capital, labor cost, political instability, domestic macro policies, etc. that attract or deter FDI. Another variable -- the domestic investment climate in the recipient countries, which perhaps is the most significant determinant of FDI, has been hitherto excluded in the empirical literature, as reliable and consistent set of quantitative data on investment climate is generally unavailable. This study seeks to fill that void by using data on economic freedom, an annual index published by The Heritage Foundation and The Wall Street Journal since 1995, as a proxy for domestic investment climate. This paper studies five countries in South Asia - Bangladesh, India, Nepal, Pakistan, and Sri Lanka, and quantifies the effects of factors that drive the flow of FDI into these countries. Employing 1995-2000 panel data, this study finds that economic freedom, economic openness, economic prosperity, human capital and incremental lagged changes in FDI significantly increase FDI inflow in the sample countries, while political instability significantly depresses it. These results further our knowledge of the determinants of FDI, which is crucial for devising strategies to promote economic development --a course that holds much at stake not only for South Asia, but also for developing countries in general. The rest of the paper is organized as follows. Section II presents a review of the empirical literature, section III describes the model, section IV discusses data, methodology and estimation, section V discusses the results and their policy implications, and section VI concludes the paper.
II. Literature Review Most empirical studies in the FDI literature have identified a number of variables, such as GDP per capita, trade openness, return on capital, labor cost, quality of infrastructure, political stability, domestic macro policies, etc., as factors that influence the flow of FDI. However, there is no general consensus in the literature as to the direction of influence of some of these variables. For example, Schneider & Frey (1985), Tsai (1994), and Lipsey (1999) found real GDP per capita to have a positive effect on FDI, while Edwards (1990) and Jaspersen et al (2000) found it to have a negative effect. Wheeler & Mody (1992) found labor cost to have a significant and positive effect on FDI, while Schneider & Frey (1985) found the opposite. Some studies found results that suggest that even if all the factors that have influences on the FDI are accounted for, there still exits a regional bias in the FDI inflow. For example, Schneider & Frey (1985), Edwards (1990), Gastanaga et al (1998), Jaspersen et al (2000), Asiedu (2002), etc., have found that there exists a regional bias in the FDI inflow against Sub-Saharan Africa (SSA). These studies, however, could not agree on the factors that are responsible for SSA’s apparent failure in attracting FDI. When the notion of this regional bias was tested with yet another model (Quazi & Rashid 2004), where economic freedom (used as a proxy of domestic investment climate) was included as one of the explanatory variable, this variable proved to be very significant and remained robust under different model specifications. The incorporation of economic freedom also showed that there was no inherent bias against SSA visà-vis Asia and North Africa, but there is indeed a regional bias in favor of countries located in Latin America and Caribbean vis-à-vis other regions, which is perhaps due to the geographical proximity of this region to the United States and Japan – the two most significant source countries of FDI.
Hanson (1996), Root & Ahmed (1979) and Schneider & Frey (1985) found that the level of human capital, which is a good indicator of the availability of a skilled work force, is a significant determinant of the locational advantage of a host country. Noorbakhsh et al (2001) recognized the importance of investment attractiveness as a factor in attracting FDI. In their model, they used the level of human capital as a proxy for investment attractiveness. Barro (1991) and Corbo & Schmidt-Hebbel (1991) have argued that by creating an uncertain economic environment detrimental to long-term planning and by reducing economic growth and investment opportunities, political turmoil seriously erodes the investors' confidence in the local investment climate, which likely repels foreign investors away. Leavell et al (2004) addressed the importance of political structure, level of political corruption, efficient markets, enforceable contracts and property rights in attracting FDI. Focusing on African countries, they tried to show how national pride may lead inappropriately to an opposition of FDI and emphasized the need for social, political and economic reforms in many African countries as a precondition for attracting more FDI. Asidieu (2002) and Haque et al (2000) contended that SSA countries are perceived as inherently risky, and that can be a factor which likely keeps away FDI from the region. In the backdrop of this empirical literature, this study makes two contributions to the empirical FDI literature. First, it adds South Asia to the empirical regional studies of FDI. Second, and more importantly, it explicitly treats domestic investment climate, as captured by the index of economic freedom, as a determinant of FDI.
III. The Model Most empirical models in the FDI literature have included various subsets of the following variables as exogenous variables: lagged changes in FDI, economic openness, economic prosperity, political instability, human capital, quality of infrastructure, rate of return, financial liberalization, etc. In the absence of a consistent theoretical framework in the FDI literature that incorporates economic freedom to guide our empirical work, this study formulates a general-to-specific model comprising all these exogenous variables in the initial version and upon statistical testing retain only the significant variables in the final version. Accordingly, the following initial version is specified. Since the model is estimated with panel data, subscript i refers to countries and t refers to time.
FDIi,t = α + β1∆FDI i,t–1 + β2 Economic Freedom i,t + β3 Economic Openness i,t + β4 Per Capita Income i,t + β5 Political Instability i,t + β6 Human Capital i,t + β7 Quality of Infrastructure i,t + β8 Rate of Return i,t + β9 Inflation i,t + β10 Financial Liberalization i,t + ε
Selection of the explanatory variables has been guided by the empirical literature. The lagged change in dependent variable (∆FDI i,t–1) has been added following Noorbakhsh et al (2001); economic openness has been added following Edwards (1990), Gastanaga et al (1998) and Ryckeghem (1998); per capita income has been added following Edwards (1990), Jaspersen et al (2000), Lipsey (1999), Loree & Guisinger (1995), Schneider & Frey (1985), Tsai (1994), and Wei (2000); infrastructure quality has been added following Kumar (1994), Loree & Guisinger (1995), and Wheeler & Mody (1992); political instability has been added following
Edwards (1990), Hanson (1996), Jaspersen et al (2000), Loree & Guisinger (1995), and Schneider & Frey (1985); human capital has been added following Hanson (1996), Noorbakhsh et al (2001), Root & Ahmed (1979), and Schneider & Frey (1985); and financial liberalization has been added following Arguelles (1986) and Root & Ahmed (1979).
IV. Data, Methodology and Estimation This study uses panel data over 1995-2000 from a sample of five South Asian countries -Bangladesh, India, Nepal, Pakistan, and Sri Lanka. Data on the dependent variable (FDI) and several of the explanatory variables -- quality of physical infrastructure, per capita income, literacy rate, GDP growth rate, inflation rate, and money supply, are collected from the World Tables (World Bank 2003). FDI is measured by the net foreign direct investment inflow as a percentage of GDP; the natural log of the number of telephones per 1,000 people is used as a proxy for the quality of physical infrastructure; per capita income is measured by the natural log of per capita GDP; human capital is measured by the literacy rate; financial depth is measured by the share of broad money (M2) in GDP; and the inverse of the real GDP per capita is used a proxy for return on capital1. In line with the standard practice in the economic literature, this study measures economic openness by the ratio of trade (exports plus imports) to GDP. In addition, this study uses two alternative measures of trade openness, which are developed by Frankel and Romer (1996)2. The first one of these alternative measures, coined as the “pure geography” approach, considers only the geographical components of trade: countries’ sizes, distances from each other, whether they share common borders, and whether they are land locked. The second measure,
coined as the “factor accumulation” approach, considers geographical variables as well as countries’ capital accumulation and population growth rates as additional factors. Both these measures are constructed by first estimating bilateral trade equations and then aggregating the fitted values to compute overall openness indices. This study uses the alternative openness indices as computed and reported by Frankel and Romer (1996). The FDI regression equation is therefore estimated in three different versions, each one incorporating an alternative measure of economic openness. It is well accepted in the FDI literature that by creating an uncertain economic environment detrimental to long-term planning, political instability reduces domestic investment opportunities and thus repels foreign investors away. The brutal civil war in Sri Lanka, which during the sample period had practically rendered the economic environment there anything but congenial to foreign investment, provides a test-case for probing this hypothesis3. Although the political landscape in other countries in the sample is far from placid, for example, Bangladesh, Pakistan and Nepal are often jolted by bouts of political turmoil, the severity of the decade long brutal civil war in Sri Lanka has, however, exacted a far heavier toll on the FDI inflow. Consequently, the other countries in the sample are considered, notwithstanding their occasional episodes of turmoil and uncertainty, politically stable compared to Sri Lanka. The model uses a dummy variable as a proxy for political instability, i.e. the dummy takes on the value of “1” for Sri Lanka and “0” for other countries in the sample. Data on economic freedom are collected from the 2003 Index of Economic Freedom (Heritage Foundation/Wall Street Journal 2003). The publication defines economic freedom as “the absence of government coercion or constraint on the production, distribution, or consumption of goods and services beyond the extent necessary for citizens to protect and
maintain liberty itself” (p. 50). The index is constructed by studying 50 independent variables that fall into 10 broad categories: trade policy, fiscal burden of government, government intervention in the economy, monetary policy, capital flows and foreign investment, banking and finance, wages and prices, property rights, regulation and black market activity. These factors are weighted equally in determining a country’s overall score, which broadly reflects the institutional setting for economic activities in a country. The index is constructed on a scale of 1 to 5, where a score of 1 signifies a consistent set of policies most conducive to economic freedom, while a score of 5 signifies a set of policies least conducive to economic freedom. Therefore, countries with lower scores on the Economic Freedom Index are likely to attract more FDI vis-à-vis countries with higher scores on the index. Three panel regression equations, incorporating three alternative measures of economic openness, are estimated with the GLS method with corrections for heteroscedasticity and autocorrelation. The estimated coefficients and t stats from the three regression equations are presented in the following table. Equation 1 incorporates the standard measure of economic openness (trade/GDP), while equations 2 and 3 incorporate openness as computed by the “pure geography” and “factor accumulations” approaches, respectively. All estimated coefficients turn out individually highly significant with correct signs, while the overall equations turn out highly significant as well. These equations find that lagged changes in FDI, economic freedom, economic openness, per capita income, human capital and political instability are statistically significant determinants of FDI4.
Determinants of FDI in South Asia (1995-2000): GLS Estimators Variable Eq. 1 Eq. 2 Eq. 3 ∆FDI –1
Wald Chi Square (6)
0.49*** (5.72) -0.50** (-2.05) 0.02** (2.10) 1.01*** (3.68) -2.08*** (-2.60) 0.02* (1.68) 78.36***
0.50*** (5.82) -0.73*** (-3.25) 0.06** (2.30) 1.40*** (4.05) -2.53*** (-2.94) 0.02** (2.25) 91.85***
0.48*** (5.75) -0.94*** (-3.86) 0.09** (2.14) 1.30*** (3.85) -2.88*** (-2.83) 0.02** (2.07) 100.58***
Economic Freedom Index Economic Openness Per Capita Income Political Instability Literacy Rate
*significant at 10% level
**significant at 5% level
***significant at 1% level
V. Policy Implications The estimated results are noteworthy for several reasons. First, in addition to the usual determinants of FDI found in the literature, such as economic openness, human capital, etc., this study finds that economic freedom, which is used as a proxy for domestic investment climate, is also a significant determinant of FDI in South Asia. The statistical significance of economic freedom as an explanatory variable to FDI is found to be very robust to different model specifications, which suggests that excluding domestic investment climate, or its proxy, from the FDI equation may very well render the equation mis-specified, which may in turn render policy recommendations based on the mis-specified function misleading.
These results generally suggest that in order to attract more FDI, South Asian countries, and more importantly developing countries across the third world, need to improve their domestic investment climate. Improving domestic investment climate is however an arduous process, which cannot be achieved overnight. A closer look at how the Economic Freedom Index is computed suggests that host country governments can improve their domestic investment climate by lowering average tariff rate and non-tariff barriers, reducing tax rates and government expenditures, reducing government ownership of businesses and industries, curbing the inflation rate, lifting restrictions on foreign ownership of resources, liberalizing the banking and financial sectors, allowing market wages and prices, securing private property rights and an independent judicial system, reducing excessive regulatory burden and reining in black market activities. Adopting these policies maybe politically difficult in the short run, but the economic performances of countries that have already achieved “economic freedom” in these policy yardsticks, demonstrate convincingly that these policies yield long-run economic benefits that far outweigh any short-run political costs. Economic openness, in all three alternative measures, is also found to be a significant determinant of FDI. The point estimates however suggest that, compared with the two alternative measures of openness developed by Frankel and Romer (1996), the traditional measure of economic openness (trade/GDP) yields smaller impact on the FDI inflow. These results generally suggest that more open economies by and large host economic regimes that instill greater confidence in foreign investors and hence are able to attract more FDI. It is noteworthy that increased economic integration among South Asian nations, caused by SAFTA (South Asian Free Trade Agreement), has contributed to economic openness in the region in recent years, which likely has helped attract more FDI inflow.
Higher level of per capita income, which is a proxy for domestic market potential, is found to attract more FDI inflow in South Asia. Since per capita income is generally affected by economic growth, government strategies to promote higher FDI should comprise pro-growth economic policies. Although higher economic growth per se is a desirable outcome, under certain circumstances, such as during IMF prescribed austerity programs, the government may be forced to temporarily embrace policies that slow down economic growth and consequently lower per capita income, which will adversely affect FDI inflow. Policymakers should remain wary of the linkages between reduced economic growth and a lower FDI inflow, which in turn reduces future economic growth potential and thus sets a vicious cycle in motion. Political instability is found to significantly depress FDI inflow in South Asia. The point estimates suggest that the occurrence of civil war is in fact the most damaging hurdle to attracting FDI inflow in Sri Lanka vis-à-vis other nations in the region. Although this study does not explicitly investigate the effects of other politically destabilizing events, such as military coups, high profile political assassinations, recurring strikes and shutdowns, etc., it is quite conceivable that these events also severely erode the foreign investors’ confidence in the host country economy and consequently reduce FDI inflow. Developing nations should therefore try their utmost to prevent a politically destabilizing climate and instead promote a stable economic environment that is conducive to long-term planning and investment opportunities, which in turn will attract more FDI inflow. Literacy rate, which is a proxy for available human capital, is found to be a significant determinant of FDI inflow in South Asia. Evidently, the presence of a skilled work force capable of functioning effectively with modern production techniques improves the locational advantages of a host country, which induces more FDI inflow. Developing countries aspiring to attract more
FDI inflow should therefore pursue educational policies that can raise both quantity and quality of educated labor to assure the foreign investors of availability of adequate human capital. Finally, incremental lagged changes in FDI, which is a proxy variable for foreign investors’ incremental knowledge about investment opportunities in a host country, is found to significantly increase current level of FDI. This result suggests that if a host country is able to successfully attract incremental FDI, that will boost foreign investors’ confidence in an already familiar host country, which in turn will open the door to additional FDI inflow, thus setting a favorable cycle in motion. Since the level of FDI is not a policy instrument for host country governments, they should utilize the available pro-FDI policy instruments, which are discussed in the preceding paragraphs, to dispel the generally risk-averse foreign investors’ fear of investing in an unknown territory, which will help attract additional FDI inflow.
VI. Conclusion It is well accepted in development economics literature that FDI plays an important role in the growth dynamics of developing countries. Available data however suggest that there is wide divergence in FDI inflow among the third world host countries. This study seeks to investigate the factors that drive the inflow of FDI to a sample of developing countries in South Asia. This study makes significant contributions to the FDI literature, as it adds South Asia to the regional studies of FDI, and more importantly, it explicitly treats domestic investment climate, which has been hitherto excluded from the FDI literature due to non-availability of reliable data, as a determinant of FDI.
The estimated results, obtained from a GLS regression model based on 1995-2000 panel data, suggest that greater economic freedom, which is a proxy for better domestic investment climate, higher economic openness, greater economic prosperity, higher literacy rate and incremental lagged changes in FDI significantly boost the FDI inflow, while political instability causes the contrary. While these results are generally consistent with the current FDI literature, however the result that domestic investment climate is a statistically significant and robust determinant of FDI is a noteworthy improvement over the current literature, which by and large focuses on the other commonly used determinants. This study finds that a domestic investment climate that is not conducive to economic freedom will likely negate the stimulating effects of other positive determinants of FDI, such as greater human capital, political stability, etc. Therefore, strategies should be formulated to promote long-term economic freedom in the developing countries, which will likely foster a healthy economic environment that is not only ready to attract more FDI inflow, but also prepared to nurture the economic ingredients necessary for economic development. The research focus of this study is worthwhile as it seeks to further our knowledge of the factors that affect FDI inflows to South Asia. Needless to say that a better knowledge of the determinants of FDI is crucial for devising strategies to promote long-term economic development -- a course that holds much at stake not only for South Asia, but also for developing countries in general.
End Notes 1
Edwards (1990), Jaspersen et al (2000), and Asiedu (2002) also use the same proxy for return on capital.
Frankel and Romer (1996) draw on the literature on the gravity model of trade, originally developed by Linneman (1966), to develop their alternative measures of trade openness.
The severity of this civil war has apparently ebbed. Although the current political landscape is far form normal, it is however expected to return to normalcy, albeit very gradually. 4
The other explanatory variables included in the regression equation -- quality of infrastructure, return on capital, inflation, financial depth, and GDP growth, turn out statistically insignificant, and hence are dropped from further analysis.
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