Tracking Underreported Financial Flows
China and other so-called “emerging” donors and creditors are fundamentally changing the international development finance landscape; however, many of these actors do not participate in existing global reporting systems, such as the OECD’s Creditor Reporting System and the International Aid Transparency Initiative (IATI). To address this challenge and help those who seek to understand the nature, distribution, and effects of development finance from emerging donors and creditors, AidData developed the Tracking Underreported Financial Flows (TUFF) 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 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that facilitate the collection of information about aid and credit from official sector donors and lenders who do not publish comprehensive or detailed information about their overseas activities.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.
AidData's Tracking Underreported Financial Flows (TUFF) methodology provides a systematic, transparent and replicable way of tracking aid and other forms of state financing from non-Western governments—such as China, Saudi Arabia, and Qatar—that do not publish comprehensive or detailed information about their overseas activities. It was was first introduced in April 2013 as a way of tracking Chinese government-financed development projects in Africa (Strange et al. 2013) and progressively revised and extended to track Chinese and other government-financed development projects worldwide, resulting in AidData’s Global Chinese Official Finance Dataset 1.0, AidData's Geocoded Global Chinese Official Finance Dataset, 2000-2012, Version 1.1.1, Saudi Arabia TUFF Donor Dataset, and Qatar TUFF Donor Dataset. Since 2018, the TUFF methodology has been further re-engineered to support the creation of AidData’s Chinese Global Development Finance Dataset, Version 2.0, published in September 2021.
The new TUFF 2.0 methodology incorporates three significant improvements:
- Increased use of official sources: Instead of relying on media sources to identify individual projects, AidData begins its search process by systematically reviewing tens of thousands of official sources. These sources include unredacted grant and loan agreements published in government registers and gazettes, official records extracted from the aid and debt information management systems of host countries, annual reports published by Chinese state-owned banks, Chinese Embassy and Ministry of Commerce (MOFCOM) websites, reports published by parliamentary oversight institutions in host countries, and AidData’s direct correspondence with finance ministry officials in developing countries. Official source retrieval is undertaken on a country-by-country basis in order to comprehensively track the full range of financial and in-kind transfers from official sector institutions in China or other donors. Then, as a supplement, AidData conducts a set of systematic search procedures in Factiva—a Dow Jones-owned media database that draws on approximately 33,000 media sources worldwide in 28 languages, including newspapers and radio and television transcripts—to identify non-official sources that also provide useful information about Chinese government-financed projects.
- Enhanced focus on project implementation: The TUFF 2.0 methodology involves the implementation of an enhanced set of data collection and quality assurance protocols to identify important project implementation details, such as calendar day-level commencement and completion dates, precise geographical locations and features of project activities, and the contractors and subcontractors responsible for implementation.
- More transactional details: The TUFF 2.0 methodology prioritizes the collection of more detailed information on the terms and conditions that govern the financing agreements issued by Chinese or other donors’ state-owned entities. This includes retrieving information on transaction amount, interest rate, maturity length, and grace period. We use these details to then accurately measure financial concessionality levels (i.e. grant element) according to the OECD-DAC guidelines for Official Development Assistance (ODA) and Other Official Flows (OOF), making the data collected using the TUFF methodology comparable to flows reported by other donors. The 2.0 methodology also includes five new variables that measure commitment fees, management fees, and the use of credit enhancements (including collateral, insurance, and third-party repayment guarantees.
- AidData TUFF Methodology, Version 2.0
- AidData TUFF Coder Instructions, Version 1.3
- AidData's TUFF Methodology, Version 1.3
- AidData's TUFF Methodology, Version 1.2
- AidData's TUFF Methodology, Version 1.1
- AidData's Media-Based Data Collection Methodology, Version 1.0
- China's Development Finance to Africa: A Media-Based Approach to Data Collection
AidData’s Global Chinese Development Finance Dataset, Version 2.0 pulls back the curtain on China’s overseas development program, capturing more than USD $843 billion in foreign aid and other forms of state financing that China committed to all low- and middle-income countries between 2000-2017. Covering more than 13,427 projects in 145 countries and territories, this is the most comprehensive and detailed source of information on China’s global development footprint ever assembled. For more insights on Chinese development finance, see the accompanying report, Banking on the Belt and Road, and an overview of the data by the numbers. For questions or concerns, please contact firstname.lastname@example.org.
How does the TUFF methodology work?
The TUFF methodology was designed to provide comprehensive, detailed, and accurate information about projects financed by donors and lenders that do not participate in global reporting systems, by rigorously standardizing and synthesizing information from thousands of available sources. For detailed information on the methodology, please refer to the full TUFF Methodology, Version 2.0 document. The methodology is divided into three stages:
Stage One: Finding New Projects and Sources
Potential projects are identified through extensive searching of official sources and media reports. Projects undertaken in a particular country and supported by a specific supplier of official finance—be it a sovereign government, multilateral institution, non-governmental organization, or private foundation—are identified through country-by-country searches facilitated by a catalogue of official sources that AidData faculty and staff have assembled. The catalogue of official sources include documents, reports, and websites relevant to each recipient country, including un-redacted grant and loan agreements published in government registers and gazettes, official records extracted from the aid and debt information management systems of host countries, annual reports published by Chinese state-owned banks, Chinese Embassy and Ministry of Commerce (MOFCOM) websites, reports published by parliamentary oversight institutions in host countries. Coders then create a unique record in AidData’s data management platform for each project with a unique identification number (‘AidData TUFF Project ID’) and populate as many fields as possible for those records with the information that is provided by the official sources. To supplement the information from official sources, coders review a fixed and pre-processed set of media articles from Factiva/DNA in order to identify (a) any additional projects that are supported by official sector institutions in China and consistent with OECD-DAC definitions of ODA and OOF; and (b) any additional details about the projects exclusively identified via Factiva/DNA and the projects jointly identified by official sources and Factiva/DNA.
Stage Two: Project Record Enhancement and Verification
In Stage 2, AidData coders seek to dive deep into each project, populating and enhancing the information available on each project activity, and verify important project details. Coders do so in three steps: (1) Coders review the project information collected during Stage 1 and conduct duplicate checks to ensure that newly-generated project records capture new/unique projects that are not already captured elsewhere in the data management platform. (2) Coders review and seek to verify the coding and categorization determinations that were made during Stage 1 with the same set of sources that were identified during Stage 1. They also review the “Staff Comments” field to identify key information gaps that need to be addressed. (3) Coders conduct targeted searches with English, Mandarin Chinese, and local language terms. For example coders will conduct searches using English and Mandarin Chinese terms, search Chinese government, recipient government, and implementing agency sources to verify the existence of the project, dates related to key variables, the flow type, the transaction amount, and the official project title in Mandarin Chinese.
Stage Three: Quality Control
The third stage consists of a series of rigorous and systematic QA procedures that are designed to identify and eliminate common mistakes, coding errors, biases, false assumptions, and information gaps. On a project-by-project basis, coders review the project record and ensure that there is sufficient evidence from official sources to confirm key project details. AidData staff quality assures projects for (a) countries receiving especially high volumes of Chinese ODA and OOF and (b) and countries with many complex transactions. AidData’s strongest and most experienced coders quality assure the remaining project records. Quality checks include checking for logical consistency of related fields, eliminating duplicates, clarifying assumptions, targeted review of high-value projects, and verifying any calculations made in the project record. Following the project-by-project quality checks, the dataset then undergoes a rigorous set of protocols on the dataset as a whole to remove any errors and biases in order to produce the most consistent, complete and replicable dataset possible.