Unraveling the Determinants of U.S. Foreign Aid: A Machine Learning Approach

• In Event: Flows of Aid, Trade, and Finance

Abstract

This study investigates the determinants of U.S. foreign aid across nations, focusing on the period from 2001 to 2024 using machine learning techniques. It integrates insights from international relations, socioeconomics, democratic theory, and public opinion to address a central question: what factors shape U.S. foreign aid generosity and contribute to variations in aid allocation among nations? While recognizing diverse perspectives on foreign aid practices and outcomes, the study acknowledges the altruistic intentions of certain aid subcategories aimed at uplifting impoverished communities. However, it also notes that peace and security aid are primarily influenced by the United States' geopolitical interests. This finding is reflected in the determinants of aid amount, as the major amount of U.S. foreign aid is influenced by the security category of aid in specific countries with geopolitical importance to the U.S. Through synthesizing existing literature, analyzing a USAID dataset using machine learning, and formulating two hypotheses, this research enhances theoretical frameworks and deepens our understanding of foreign aid dynamics.

Author

• Alireza Raisi, Northeastern University