"Tracking Loans and Grants from China to Low-, Middle-, and High-Income Countries" - by CWP alum Austin Strange
Over the last thirteen years, AidData has refined the Tracking Underreported Financial Flows (TUFF) methodology to track grant and loan commitments from official sector entities that do not report to international monitoring organizations, such as the OECD’s Development Assistance Committee. During this period of time, AidData has primarily used the TUFF methodology to capture official financial flows from Chinese government and state-owned entities to low- and middle-income countries. Yet Beijing’s official sector donors and creditors are not exclusively focused on the Global South; they also extend aid and credit to high-income countries. The purpose of this methodological guidance note is to document the standards and procedures by which we have tracked China’s official sector grant and loan commitments to countries of all income levels, including high-income countries, between 2000 and 2023 (Parks et al. 2025). To help those who seek to understand the nature, distribution, and effects of official sector financial flows from emerging donors and creditors, AidData developed the TUFF methodology in collaboration with an international network of researchers from Harvard University, Heidelberg University, the University of Göttingen, the University of Cape Town, the University of Hong Kong, Georgetown University, Brigham Young University, the Center for Global Development, the Peterson Institute for International Economics, and the Kiel Institute for the World Economy (Strange et al. 2013, 2017; Muchapondwa et al. 2016; Dreher et al. 2018, 2019, 2021, 2022; Custer et al. 2021; Malik et al. 2021; Gelpern et al. 2023, 2025a, 2025b, forthcoming; Horn et al. 2023a, 2023b; Parks et al. 2022, 2023; Asmus-Bluhm et al. 2024; Franz et al. 2024; Goodman et al. 2024; Wellner et al. 2025; Bluhm et al. 2025). The methodology codifies a systematic, transparent, and replicable set of procedures that facilitate the collection of information about grants and loans from official sector donors and lenders who do not publish comprehensive or detailed information about their overseas activities. It does so by synthesizing and standardizing vast amounts of unstructured, open-source, project-level information published by governments, intergovernmental organizations, companies, nongovernmental organizations, journalists, and research institutions.
Austin Strange is Associate Professor of International Relations in the Department of Politics and Public Administration. He researches and teaches Chinese foreign policy, international political economy, and international development. Austin’s research mainly focuses on China’s historical and contemporary roles in the world economy.
Austin is a Public Intellectuals Program Fellow with the National Committee on US-China Relations from 2023–2025. Previously he was a Wilson China Fellow at the Wilson Center and a fellow with the Columbia-Harvard China and the World Program. He received a Ph.D. in Government from Harvard University, M.A. from Zhejiang University, and B.A. from William & Mary.
Austin received HKU’s Early Career Teaching Award in 2022, Research Output Prize in 2023, and Outstanding Young Researcher Award in 2024. With colleagues he was awarded the Best New Dataset Award from the International Political Economy Society.
Photo Credit: https://docs.aiddata.org/reports/chasing-china/Tracking_Chinese_Loans_and_Grants_TUFF_4_Methodology.pdf
