Determinants of Foreign Direct Investment in Middle Income and High Income Countries

Introduction

The process of globalization has led to increased international transfers. Foreign direct investment (FDI) is one of the most important factors that contribute to economic integration between countries. Henrik Hansen and John Rand (2005) pointed out that FDI inflows play a vital role for the development of a country, it fosters economic growth and rises productivity and efficiency of the recipient country. However income inequality could be increased in case that host countries cannot impose proper policies to extract the benefits from FDI inflows (Andrew Sumner 2005). In general the majority of countries recognize the importance of FDI inflows and compete each other to attract a higher share of investment, focusing on economic liberalization (Boros-Torstila, 1999).

According to Figure 1 world FDI inflows have increased from 2000 to 2017, while high income countries receive higher FDI inflows compared to middle income countries. It is crucial to understand if the differences in FDI inflows are attributed to alternative policies (corporate tax, institutions quality, trade openness, etc.) or to inherent heterogeneous characteristics (market size, total population, location, etc.) of each country. This study will focus on high income and middle income countries, which receive the higher share of FDI, and analyse separately those 2 distinct groups of countries, having a variety of different characteristics (Table 1). This paper may be a helpful tool for governments in order to impose policies that attract higher FDI inflows.

The objective and the contribution of the present study to the current literature is that it will analyse the determinants of FDI, taking into consideration a wide range of potential factors, economic, political and institutional. By looking carefully the current literature it has not been conducted by any other study earlier the comparison of high income and middle income countries, using a total sample of 40 countries.

 To sum up this paper intends to give answer to the following questions:

• Which factors determine FDI inflows in high and middle income countries?
• Why high income countries have higher FDI inflows than middle income countries?
• What measures should be taken from each group in order to attract FDI inflows?

Literature Review

Current studies have tried to capture the determinants of FDI, emphasizing either in macroeconomic variables such as technology and gross domestic product or in variables regarding the economic structures of a country such as economic freedom and global competitiveness. In this paper I will analyse the impact of market size, trade openness, policy measures, measures of institutions quality, corporate tax, measures of infrastructure quality and human capital on FDI inflows.

Abdullah Alam and Syed Zulfiqar Ali Shah (2013) analysed the determinants of FDI in OECD member countries. They pointed out that market size (gross domestic product was taken as a proxy) has a positive and significant coefficient, the larger the economy the more inflows a country receives. Infrastructure quality (proxied by the mobile cellular subscriptions per 100 people) has a positive impact on FDI inflows and shows that countries which are equipped with good infrastructure facilities are able to attract higher FDI flows.

Sufian Eltayeb Mohamed and Moise G. Sidiropoulos (2010) investigated the determinants of FDI in developing and Mena countries. Regarding the developing countries it was proved that FDI inflows are positively correlated with the quality of institutions (corruption was taken as a proxy). In addition stable government policies, a representative variable is the government size which is measured by the government expenditure, and friendly trade policies can attract foreign capital investors. Market size and infrastructure have also a positive impact in FDI inflows. It is worth noting that for Mena countries the infrastructure quality was proved insignificant.

Omar G. Aziz and Anil V. Mishra (2015) examined the determinants of FDI in Arab countries. In their analysis they found that countries with high skilled employees can receive higher share of FDI. This result was also confirmed by Reenu Kumari, Anil Kumar Sharma, (2017), who examined the determinants of FDI in developing countries. Human capital was proxied by the enrolment in the secondary school. Both studies proved that market size and trade openness have a positive impact on FDI inflows.

Paweł Folfas (2011) analysed the FDI flows between the EU member states. It was concluded that countries with low corporate tax rates, which can increase the profitability of an investment, and high market size receive greater FDI inflows.

Data and Methodology

The dataset contains information for 20 high income countries and 20 middle income countries (Table 2) for a period of 18 years (2000-2017). The source of the data is World Bank and International Country Risk Guide. The variables, used in this paper, are described in Table 3.

First of all it is important to analyse the correlation between the explanatory variables (Table 4). It is noticed that there are no signs of multicollinearity for both groups of countries. As expected countries with well-educated population and controlled levels of corruption have a higher Gross Domestic Product. In addition the quality of infrastructure has a positive impact on GDP, but only for middle income countries. A high corporate tax leads to lower development of infrastructure. In high income countries when the level of corruption is controlled then the government size is reduced, while in middle income countries high levels of corruption lead to higher government size. Moreover it is important to determine if there is stationarity between the variables, unit root tests will be used for this purpose.

To analyse the determinants of FDI, it will be used panel data techniques which include pooled OLS, random effects and fixed effects approaches. The general form of panel data is the following:

Yit = β0 + β1Xit + εit, where εit = αi + uit 

The error (εi)term has 2 components, the unobserved heterogeneity (αi), which is time invariant and the idiosyncratic error (uit), which varies over time and different countries.

In case pooled OLS estimator fail because of the existence of unobserved heterogeneity or correlation between the unobserved heterogeneity and the regressors, then it could be used either random effects or fixed effects methods. Fixed effects method does not take into account the time-invariant characteristics, while random effects method assumes that variation across countries is uncorrelated with the regressors. In order to decide which of the 2 models is appropriate for this study I will use Hausman’s specification test.

Limitations

In this study I will use some proxy variables (according to the current literature) in order to measure the institution quality, the infrastructure quality and human capital. Those proxy variables may not be precisely representative. Also lack of some data on variables such as tax rate, corruption and school enrolment may be considered as a limitation.

References

• Andrew Sumner (2005). “Is Foreign Direct Investment Good for the Poor? A Review and Stocktake.” Development in Practice.
• Abdullah Alam and Syed Zulfiqar Ali Shah (2013). “Determinants of foreign direct investment in OECD member countries.” Journal of Economic Studies.
• Boros-Torstila, J. (1999). “The determinants of foreign direct investment operations of finnish MNCs in transition economies.” The Research Institute of Finnish Economy.
• Henrik Hansen and John Rand (2005). “On the Causal Links between FDI and Growth in Developing Countries.” Development Economics Research Group
• Omar G. Aziz and Anil V. Mishra (2015). “Determinants of FDI inflows to Arab economies.” The Journal of International Trade & Economic Development.
• Pawel Folfas (2011). “FDI between EU member states: Gravity model and taxes.”
• Reenu Kumari, Anil Kumar Sharma, (2017). "Determinants of Foreign Direct Investment in developing countries: a panel data study.” International Journal of Emerging Markets.
• Sufian Eltayeb Mohamed and Moise G. Sidiropoulos (2010). “Another look at the determinants of Foreign Direct Investment in Mena Countries.” Journal of Economic Development.
• William H. Greene (2003). “Econometrics Analysis, 5th Edition.” Prentice Hall
• World Bank. World Development Indicators

Appendix

Figure 1

FDI inflows, world and by group of economies, 2000–2017.

Source: World Bank Indicators

Table 1

Synopsis of the main characteristics between high income and middle income countries.                                        Source: World Bank Indicators

Table 2

Sample of countries used in the study. The above countries have the highest share of FDI inflows.                                               Source: World Bank Indicators

Table 3

Definitions, data source and expected signs of the examined variables

Table 4

Correlation analysis between the regressors used in this study for high income and low income countries.