Does China’s Flow of FDI and Institutional Quality Matter for Poverty? Evidence from Africa
Description
The study examined several alternative hypotheses. The first hypothesis (H1) posited that Chinese foreign direct investment (FDI) promotes poverty alleviation. The second hypothesis (H2) suggested that institutional quality plays a role in reducing poverty. The third hypothesis (H3) proposed that institutional quality and Chinese FDI work together synergistically to reduce poverty. To test these hypotheses, macro-level data spanning 20 years (2000-2020) for 36 African countries was utilized. The Human Development Index (HDI) was employed as a proxy for poverty reduction, while the poverty gap at the threshold of US$1.90/day was used as a robustness check. Data for the HDI was sourced from the Human Development Report Office (HDRO), while poverty gap data was obtained from the World Development Indicators (WDI) of the World Bank. In Table 1, Chinese FDI inflows were sourced from China's Ministry of Commerce database, given their significant contribution to overall FDI inflows in Africa. An index of institutions and governance, which includes measures such as the rule of law, regulatory quality, government effectiveness, corruption control, voice and accountability, and political stability, was employed. Separate regressions were conducted using individual indicators to assess their impact on poverty reduction. Data on governance and institutions were sourced from the World Governance Indicator (WGI). Control variables such as GDP per capita, trade openness, inflation, population, domestic capital, private credit, and government expenditure were included in the analysis. These control variables were sourced from the World Development Indicators (WDI). The results of the pooled ordinary least squares (OLS) analysis indicated that Chinese FDI alone does not reduce poverty unless there are strong institutions and good governance. Moreover, the findings demonstrated that strong institutions and good governance have a poverty-reducing effect in Africa.
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Steps to reproduce
In Step 1, the author imported the excel dataset into STATA 17 and organized it into a data panel format. Moving to Step 2, the authors performed data interpolation to minimize the presence of gaps in the dataset. In Step 3, certain variables with high values were logarithmically transformed. Additionally, an institutional quality index was generated, and the Human Development Index (HDI) was converted into a percentage by multiplying it by 100. Proceeding to Step 4, basic preliminary analyses were conducted, including descriptive statistics, correlation matrix examination, and a test for multicollinearity. In Step 5, the model was estimated using various methods, including pooled OLS and fixed effects. Two different measures of poverty reduction, namely the poverty gap and HDI, were employed. Please note that the authors have provided a do file in the attached zip file, which contains detailed instructions for replicating these steps.