Determinants of foreign direct investment in developing countries: a panel data study

Abstract

Purpose The purpose of this paper is to identify key determinants of foreign direct investment (FDI) inflows in developing countries by using unbalanced panel data set pertaining to the years 1990-2012. This study considers 20 developing countries from the whole of South, East and South-East Asia. Design/methodology/approach Using seven explanatory variables (market size, trade openness, infrastructure, inflation, interest rate, research and development and human capital), the authors have tried to find the best fit model from the two models considered (fixed effect model and random effect model) with the help of Hausman test. Findings Fixed effect estimation indicates that market size, trade openness, interest rate and human capital yield significant coefficients in relation to FDI inflow for the panel of developing countries under study. The findings reveal that market size is the most significant determinant of FDI inflow. Research limitations/implications Like any other study, this work also has some limitations. Lack of data on key determinants such as labor cost, exchange rate, corruption, natural resources, effectiveness of rule of law and political risk may be considered one such limitation. Further, controlling for variables such as exchange rate, corruption, labor cost and political risk could make significant improvements to this study. Practical implications This study has significant implications for policy makers, mangers and investors. Policy makers would be able to understand the importance of the major determinants of FDI mentioned in the paper, and take steps to formulate policies that encourage FDI. Such measures could include developing market size, making regulations more international trade friendly and investing in the nation’s human capital. Further, steps could be taken to keep interest rates and inflation rates under control as these factors have been found to influence FDI. Originality/value The sample of 20 developing nations chosen for this study has not been considered by any study earlier. This is a unique contribution to existing body of research, and highlights the originality value of this paper.

Foreign direct investment into BRICS: an empirical analysis

“…Grohand and Wich (2012) examined the FDI inflows to emerging countries and used the composite index to summarise a number of socio-economic variables and found that the FDI inflows were concentrated in developed countries while in the emerging economies FDI is less attracted due to the poor political, legal system and meagre infrastructure. Some studies have digged-in deeper by undertaking the influence of macro-economic variables on FDI inflows (Bevan & Saul, 2000;Banik, 2003;Cevis & Camurdan, 2007;Khachoo & Khan, 2012;Kumari & Sharma, 2017) and suggested market size, total reserves, infrastructure, trade openness, interest rate human capital yield, and labour costs are the main variables responsible for attracting inflows of FDI in emerging economies. Chakraborty and Basu (2002) found unidirectional causality between FDI and economic growth and bi-directional causality from economic growth to FDI only for India.…”

Natural resource or market seeking motive of China’s FDI in asia? New evidence at income and sub-regional level

“…Theory of economics purports that human capital in the shape of education is one of the potent determinants for the influx of FDI (Dunning, 1977). Most of the studies considered various types of measures as proxies for human capital, of which school enrolment ratios and literacy rate are consistently used (Asiedu, 2002;Azemar & Desbordes, 2009;World Bank, 2018a;Kinda, 2013;Kumari & Sharma, 2017;Okafor et al, 2017). The significance of efficiency entails the following hypotheses:…”

Determinants of Foreign Direct Investment in Gulf Cooperation Council (Gcc) Region

“…The data is analyzed through statistical models (using STATA 15 software) as fixedeffects model and random-effect model (Kumari & Sharma, 2017). The regression can be formulated as: FDI = α + β1 GDP + β2 GDPP + β3 INFLATION + β4 UNEMPLOYMENT + β5 CCORRUPTION + β6 PSTABILITY + β7 RLAW + β8 VACCOUNTABILITY + β9 GFC + β10 ASPRING + ɛ…………………..(1) Where, FDI is the natural logarithm of FDI; GDP represents the natural logarithm of GDP; GDDP denotes the natural logarithm of GDP per capita; INFLATION is the percentage of interest rates; UNEMPLOYMENT describes the percentage of unemployment; CCORRUPTION is control of corruption percentage; PSTABILITY is the rate of political stability; RLAW explains rule of law percentage; VACCOUNTABILITY denotes the rate of voice and accountability; GFC is the dummy of global financial crisis period; ASPRING is the dummy of Arab Spring period.…”

“…The statistical methods to examine the relationship between FDI and its determinants are fixed-effects model (FEM) and random effects-model (REM) following Bengoa and Sanchez-Robles (2003) and Kumari and Sharma (2017). The paper is organized as follows: part 2 presents the literature review.…”