The assessment of corruption impact on the inflow of foreign direct investment

Abstract

The aim of this paper is to investigate the impact of corruption on the inflow of foreign direct investment (FDI). The data, taken from official sources, Transparency International and the Heritage Foundation, have been treated in a special program “Deductor Studio Academic” by the method of Machine Learning (cluster analysis using Kohonen Self-Organizing Maps). There was composed a Kohonen map, in which the countries were divided into 4 clusters: countries with low levels of corruption and high level of FDI inflow, countries with low level of corruption and FDI above average, countries with average level of corruption and the average level of FDI, and countries with high level of corruption and low level of FDI. The research has shown that corruption influences the investment attractiveness of the host country. This means that in countries where the level of corruption is low and economic environment is attractive, the level of foreign direct investment is high, and in those countries where the level of corruption is high and and economic attractiveness is low – the level of investment is low. However, the study identified countries which have high level of corruption and high FDI inflow - China, India, Brazil and Russia (BRIC countries). These countries are the exception from the rule due to the wide domestic market, cheap labour, the wealth of natural resources - all these factors increase the investment attractiveness of these countries. It was found that corruption in BRIC countries has similarity being a controlled and predictable phenomenon. This allows calculating the cost of corruption for accounting it in business projects.