Data Driven Analysis of Trade, FDI And International Relations On Global Scale

Abstract

International politics and economics are not independent. Often, countries face economic sanctions or deteriorated economic prospects because of adverse political developments. Foreign trade (exports and imports of goods), capital flow in form of foreign direct investments (FDI) or cross-border capital investments have frequently been studied to understand political relationships between countries. On one hand, we have quantitative macroeconomic indicators, and on the other we face qualitative multilevel political relations and events. To better understand the intertwined nature of economics and politics, we use the digitized massive archival news data, the Global Database of Events, Language, and Tone (GDELT) to model and systematically quantify global political processes. We then apply statistical and machine learning methods to analyze these political events correlations with global economies and societies. We categorize countries in four groups, based on the World Bank’s income classification, and find that international relations have strong correlation with economic parameters, highly dependent on countries’ income levels.