Foreign direct investment performance drivers at the country level: a robust compromise multi-criteria decision-making approach

This paper focuses on the performance drivers of Foreign Direct Investment (FDI) at the country level, exploring the socio-demographic specifics of donor and receiver countries. To this end, a novel Robust Compromise (RoCo) Multi -Criteria Decision -Making (MCDM) model is proposed using non-linear programming solved by genetic algorithms. The model builds upon established traditional models for alternative ranking and criteria weighting. Subsequently, a stochastic robust regression is performed, building upon previously computed bootstrapped Tobit, Simplex, and Beta regressions to handle performance scores ranging between 0 and 1. The goal is to test FDI performance against a set of contextual variables. The findings suggest that the performance of FDI is relatively low, and relevant improvements should be made. Our second stage analysis reports that higher GDP per capita and good social welfare, including lower infant mortality and higher life expectancy, contribute to the improvement in FDI performance. Furthermore, it is found that a large percentage of women in the total population, wealth concentration in the destination country, as well as the degree of urbanization, are helpful to improve FDI performance. Finally, we find that FDI performance is mainly concentrated on industries that are high-tech and high value-added.