Data‐driven allocation of development aid toward sustainable development goals: Evidence from HIV/AIDS
针对HIV/AIDS援助分配效率低的问题，结合数据分析和数学优化提出新决策模型，用OECD数据验证可降低感染率，帮助资助机构管理者实现成本有效的援助分配。
Ending the HIV/AIDS epidemic is an important target of the United Nations Sustainable Development Goals (SDGs). To achieve it, countries worldwide donate large amounts of development aid (USD 15.18 billion annually). However, current practice in allocating development aid is largely based on decision heuristics and thus subject to inefficiencies. To address this problem, we aim to support managers of funding bodies in identifying cost‐effective allocations of development aid and thus develop a new decision model. We combine data analytics with mathematical optimization, whereby the former estimates the country‐specific effectiveness of aid, and the latter suggests an allocation under budget constraints. We evaluate our decision model using aid data obtained from the SDG Financing Lab of the OECD, demonstrating that our decision model could reduce the infection rate over current practice. Our work directly benefits managers of funding bodies tasked with financing development activities and helps them achieve cost‐effective progress toward ending the HIV/AIDS epidemic.