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Data‐driven allocation of development aid toward sustainable development goals: Evidence from HIV/AIDS
Author(s) -
Jakubik Johannes,
Feuerriegel Stefan
Publication year - 2022
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.13714
Subject(s) - heuristics , sustainable development , human immunodeficiency virus (hiv) , decision aids , business , budget constraint , work (physics) , aid effectiveness , operations research , computer science , economics , risk analysis (engineering) , developing country , economic growth , medicine , microeconomics , political science , law , engineering , operating system , mechanical engineering , alternative medicine , family medicine , pathology
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.