Optimizing development aid allocation: A data-driven approach using unsupervised machine learning and multidimensional indices

Abstract

Development aid, often termed foreign assistance, involves the voluntary transfer of resources like goods, services, or capital from governments and international aid agencies to support the development of recipient countries or their people. This practice holds significant importance in international relations and the national economy of many nations. It is also a heavily researched area in economics due to its potential to create a safer, more equitable, environmentally sustainable, and prosperous world. However, it's observed that donor interests often take precedence over recipient needs in aid allocation decisions. Since these allocations are determined by national policies, there is limited room for external input. Consequently, the success of Sustainable Development Goals (SDGs) relies heavily on donor interests. To address this challenge, we propose data-driven approaches to ensure recipient needs are considered and aligned with SDGs. This involves balancing the legitimate interests of donors, recipients, and disadvantaged individuals. In our study, we utilize multidimensional poverty measures to better understand the hardships people face. This approach offers a more accurate representation than traditional monetary indicators, which can miss important dimensions of deprivation. We employ unsupervised machine learning to analyze this data objectively and recommend countries most in need of aid. Such systems can assist donor countries and agencies in efficiently allocating aid, ensuring that the needs of beneficiaries and targeted development goals remain well-aligned.