Decision of the National Bank of the Republic of Kazakhstan: the impact of monetary policy on foreign direct investments

Abstract

FDI has been widely studied throughout history and the impact of FDI on the country's economic growth has been empirically proven. However, there are no studies that examining the impact of monetary policy on FDI through economic growth. In this paper, I test the hypothesis of the impact of monetary policy (namely, a tool for changing the base rate) on foreign direct investment through economic growth. In the study I used secondary data on indicators of economic growth in Kazakhstan - GDP, volume of lending, retail and wholesale trade, average monthly salary, unemployment rate, reinvested part of FDI, weighted average lending rates in the country, and as an external factor - the historical price of Brent. As a result of the analysis of the variables involved in the study, it was found that there are problems of endogeneity/exogeneity, as well as multicollinearity. The solution to the problem was to use certain models to study the relationship, exclude variables from the study & use of lags. To solve the above problems and explore the main issue in the study, I used three methods - mediator variable regression approach, two-stage residual regression and principal component analysis. As a result of the study, it was found that the impact of monetary policy on FDI through economic growth is statistically insignificant. However, as a result of testing the second hypothesis of the study, namely the impact of monetary policy on part of FDI reinvested income, it was found that the weighted average lending rate in the country affects part of reinvested income as part of FDI at a statistically significant level of 5%, meaning that a 1% increase in the weighted average rate in the previous quarter increases part of reinvested income in FDI by 15.68 billion USD. Principal component analysis method seems to be the most effective, since this method solves the problem of multicollinearity and focuses on the most important variables that explain the largest variance in the data, which allows to exclude unnecessary variables.