A Big Data Approach to Understand Sub-national Determinants of FDI in Africa
This research addresses the gap in firm-level and regional data for FDI determinants in developing countries, providing insights for policymakers and investors, though it is incremental in applying existing methods to new data.
The paper tackled the problem of understanding sub-national determinants of foreign direct investment (FDI) in Africa by analyzing over 167,000 online news articles using text mining and social network analysis, finding that regional structural and institutional characteristics significantly influence foreign ownership.
Various macroeconomic and institutional factors hinder FDI inflows, including corruption, trade openness, access to finance, and political instability. Existing research mostly focuses on country-level data, with limited exploration of firm-level data, especially in developing countries. Recognizing this gap, recent calls for research emphasize the need for qualitative data analysis to delve into FDI determinants, particularly at the regional level. This paper proposes a novel methodology, based on text mining and social network analysis, to get information from more than 167,000 online news articles to quantify regional-level (sub-national) attributes affecting FDI ownership in African companies. Our analysis extends information on obstacles to industrial development as mapped by the World Bank Enterprise Surveys. Findings suggest that regional (sub-national) structural and institutional characteristics can play an important role in determining foreign ownership.