Beauty is in the Eye of the Beholder: How Corruption Distance Affects R&D Investment Flows

Prior research has demonstrated that corruption has largely negative effects on incoming international investments. What is less clear, however, is to what extent these negative effects are a product not of a host country’s absolute level of corruption, but of the relative distance to the home country’s degree of corruption. We define the Directional Corruption Distance (DCD) as the arithmetic difference between two countries’ corruption levels. Avoiding distorted FDI measures, we analyze Research & Development Inflows (RDIF), as they represent critical MNE activities. Our RDIF dataset portrays all published R&D centers of 500 technology-intensive MNCs taken from the Fortune 1000 list. In accordance with previous research employing FDI as a dependent variable, we observe that RDIF flows towards countries of lower corruption (Trading Up Hypothesis). We further show that developed countries with lower degrees of corruption tend to prefer investments in other low- corruption countries (Comfort Hypothesis). High-corruption countries, conversely, do not exhibit this behavior and appear more open towards RDIF in high-corruption countries (Familiarity Hypothesis). These findings underscore the influence of contextualizing variables in the origin of investments, and suggest extended research using firm-level field data to compensate for potential bias and flaws in FDI data.