Institutional Quality and Foreign Aid Effectiveness: The Future of Corruption and Foreign Aid Flows

Abstract

This study examines the relationship between institutional quality and the effectiveness of foreign aid, using corruption as a key indicator. Institutional quality, often emphasized in policy discussions, plays a pivotal role in determining the outcomes of aid initiatives. Previous literature has emphasized the dependency of foreign aid effectiveness on good institutional quality. To evaluate this relationship, a Corruption Index is used as a representative of institutional quality; a factor most talked about in policy making. Using machine learning techniques to analyze whether corruption hinders economic growth and development by undermining the foreign aid effectiveness. Through a panel data approach, aid, foreign direct investment, gross domestic product per capita, and the Corruption Index, are used to predict the future direction of corruption and GDP per capita, which serves as a proxy for the efficiency of foreign aid. The analysis employs multiple machine learning models, with Random Forest and XGBoost demonstrating the highest accuracy. These models suggest that in the future, corruption is likely to decline while GDP per capita is projected to rise, indicating that foreign aid flows may become more effective as corruption diminishes.