This paper addresses itself first to the argument against the quantitative approach in political risk analysis. Using both regression analysis and correlation analysis on nine different countries, low correlation, if not, lack of statistical relationship between the economic and market indicators and foreign direct investment was found. This is followed by the discussion on major factors ignored in a quantitative framework of political risk analysis. Finally, a general non-market framework for political risk ast is presented. Efforts are made here to expand on the societal and governmental actions that can adversely affect foreign direct investment. This is followed by discussion on strategic management of political risk.
Since the mid 1980's, the emphasis in political risk analysis has shifted from conceptually-oriented studies to a more quantitative orientation. Euromoney (a monthly magazine) uses internal debt, gross domestic product, balance of payments, official and private interest rates, and debt service payments to develop index for political risk evaluations. Business International (BI) also uses credit and market indicators to develop indexes of environmental risk (currently known as country assessment) and Business Environment Risk (BERI) indexes.
Critics argue that quantitative approaches suffer from their inability to include certain conceptual variables. Consequently, they are regarded as potentially ineffective as reliable tools in political risk analysis.
This paper thus addresses itself first to the theoretical issues in political risk assessment, focusing on the statistical interrelationships between economic and market variables and foreign direct investment. This will be followed by discussion of the impact of different environments on the formation of political risk. The intent is to find out whether or not relationships exist between these economic and market variables and foreign direct investment.
To measure the statistical relationship both regression analysis and correlation analysis were conducted. The purpose of regression analysis is to measure the degree to which two or more variables are linearly related. The correlation coefficient measures the strength of the linear relationship between the dependent variable, Foreign Direct Investment (FDI), and the independent variables (economic and market factors).
For this study nine countries classified as lower income and middle income by the World Bank and U.N. were of much interest. Also we wanted a cross section of the various regions of the world to be represented in our sample. In this connection the following countries were selected for the study: Brazil, Poland, Indonesia, Bolivia, Mexico, Nigeria, Portugal, Haiti, and China. According to both the World Bank and the United Nations, China, Haiti, Indonesia, and Nigeria are classified as lower income countries, while Bolivia, Brazil, Mexico, Poland, and Portugal are classified as middle income countries. It is clear that these countries also represent cross sections of various regions of the world.
The variables used in this study are among the variables used by the Euromoney Magazine in developing its political risk index. These variables are Foreign Direct Investment (FDI), Gross Domestic Product (GDP), Balance of Payment (BP), External Debt (ED), Debt Service (DS), Private Interest Rate (PIR), and Official Interest Rate (OIR). The data for these variables were collected from various World Bank and the United Nations reports from the period 1983 to 1991.
The results of the regression analysis are presented in Table 1:
Generally speaking, a reasonable assumption is that there is low correlation between the economic and market credit indicators and foreign direct investment. By the same token an inverse relationship is observed between official interest rate and direct foreign investment in all of the countries included in the study. …