Tracking Chinese Development Finance: An Application of AidData’s TUFF 2.0 Methodology

Many of these actors do not participate in existing global reporting systems, such as the OECD's Creditor Reporting System (CRS) and the International Aid Transparency Initiative (IATI). To address this challenge, AidData developed the Tracking Underreported Financial Flows (TUFF) methodology in collaboration with an international network of researchers from Harvard University, Heidelberg University, the University of Göttingen, the University of Cape Town, Brigham Young University, and William and Mary. The TUFF methodology codifies a systematic, transparent, and replicable set of procedures that facilitate the collection of information about aid and credit from official sector donors and lenders who do not publish comprehensive or detailed information about their overseas activities. It synthesizes and standardizes vast amounts of unstructured, open-source, project-level information published by governments, intergovernmental organizations, companies, nongovernmental organizations, journalists, and research institutions. The methodology was first introduced in April 2013 as a way of tracking Chinese government-financed development projects in Africa. The TUFF 2.0 methodology involved three major improvements. First, AidData began its search process by systematically reviewing tens of thousands of official sources, including unredacted grant and loan agreements, official records, annual reports, and direct correspondence with finance ministry officials in developing countries. Official source retrieval was undertaken on a country-by-country basis to comprehensively track the full range of financial and in-kind transfers. Second, the methodology improved the accuracy of project-level data by incorporating more detailed information from official sources. Third, the TUFF 2.0 methodology enhanced the geographic coverage of the dataset by including more countries and regions. The TUFF 2.0 methodology was used to create AidData's Chinese Global Development Finance Dataset, Version 2.0, which was published in September 2021. The dataset includes information on Chinese official development assistance (ODA) and other official flows (OOF) to developing countries. The dataset covers a wide range of financial and in-kind transfers, including grants, loans, and investments. The data is organized into a systematic and transparent framework that facilitates analysis and comparison. The AidData team, led by Samantha Custer, Axel Dreher, Thai-Binh Elston, and other researchers, played a crucial role in refining and implementing the TUFF 2.0 methodology. The team worked closely with an international network of researchers and collaborators to pilot coding procedures, recommend new sources and methods, and scrutinize preliminary project records. The team also received financial support from various organizations, including the William and Flora Hewlett Foundation, the Ford Foundation, and the Smith Richardson Foundation. The TUFF 2.0 methodology has been widely used to track Chinese development finance and has contributed significantly to the understanding of the nature, distribution, and effects of development finance from emerging donors and creditors. The methodology has been applied to various regions, including Africa, Asia, Latin America and the Caribbean, the Middle East, Oceania, and Eastern and Central Europe. The dataset has been used by researchers, policymakers, and practitioners to inform decision-making and policy development. The AidData team continues to refine and expand the TUFF methodology, incorporating new sources and methods to improve the accuracy and comprehensiveness of the dataset. The team also works closely with stakeholders to ensure that the methodology meets the needs of users and contributes to the advancement of knowledge in the field of international development finance.