Data Set

Tracking Chinese Development Finance: An Application of AidData’s TUFF 3.0 Methodology

Authors

  • Samantha Custer
  • Axel Dreher
  • Thai-Binh Elston
  • Brooke Escobar
  • Rory Fedorochko
  • Andreas Fuchs
  • Siddhartha Ghose
  • Joyce Jiahui Lin
  • Ammar A. Malik
  • Bradley C. Parks
  • Kyra Solomon
  • Austin Strange
  • Michael J. Tierney
  • Lydia Vlasto
  • Katherine Walsh
  • Fei Wang
  • Lincoln Zaleski
  • and Sheng Zhang
Publication Date

This publication provides a detailed guide to the 3.0 version of the Tracking Underreported Financial Flows (TUFF) methodology, which AidData developed 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 & Mary. The 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that facilitate the collection of information about aid and credit from official sector donors and lenders who do not publish comprehensive or detailed information about their overseas activities.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 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. The authors of the publication explain how the 3.0 version of the TUFF methodology was used to construct the 3.0 version of AidData’s Global Chinese Development Finance (GCDF) dataset.

Kiel Institute Expert

Info