Spring '26 Research Data Associate, Part Time

Position Summary

AidData at William & Mary is seeking part-time Research Data Associates to support three interconnected projects within the Tracking Underreported Financial Flows (TUFF) Unit:

1. Chinese Loans and Grants to Low- and Middle-Income Countries(LMIC).

2. Transition Minerals.

3. Loan Performance.

Research Data Associates will apply the TUFF open-source data collection methodology to gather, verify, and synthesize information on Chinese official financing to guaranteed entities, and projects linked to critical minerals andthe clean energy transition.

In this role, associates will systematically review and extract key information from government documents, NGO reports, academic literature, media articles, and other open-source materials; generate structured database records that meetTUFF standards; and draft clear narrative explanations of financial flows. They will conduct targeted online searches to resolve information gaps, support data validation and quality assurance, and contribute to ongoing enhancements ofTUFF’s LMIC, Transition Minerals, and Loan Performance datasets.

Research Data Associates will work closely with TUFF Associate Program Managers/ProgramManagers, as well as other researchers, through regular check-ins to report progress and discuss challenges. Workload will vary with project timelines and deliverables, but associates should expect to average approximately 25 hours per week.

Responsibilities and Qualifications:

‍Cross-Project Responsibilities

• Apply the TUFF open-source methodology to systematically extract, code, and validate information on Chinese official financing across LMIC, Transition Minerals, and Loan Performance projects.
• Collect and review data from diverse primary and secondary sources—including government publications, NGO reports, academic literature, media stories, and official Chinese documents—to identify key details on financial flows, project attributes, and institutional actors.
• Generate high-quality, standardized database records that meet TUFF’s methodological and documentation requirements, ensuring completeness, accuracy, and traceability.
• Produce clear narrative summaries that synthesize complex financial and project information, describe transaction histories, and explain the context of each financial flow.
• Conduct targeted online searches to fill information gaps, resolve discrepancies, verify facts, and strengthen the reliability of collected data.

Loan Performance–Specific Responsibilities

• Extract, code, and validate loan performance information (e.g., disbursements, repayments, arrears, restructurings) from official sources and country-level debt data.
• Review and enter loan amortization and repayment schedule information into AidData’s proprietary database, ensuring alignment with standardized protocols.
• Reconcile discrepancies in loan data by cross-checking multiple sources, verifying amounts and dates, and ensuring correct classification of events.
• Compile sovereign debt restructuring and arrears events into structured datasets for inclusion in TUFF’s Debt Restructuring and Loan Performance products.
• Support quality assurance (QA) by identifying anomalies, missing data, or inconsistencies and flagging them for senior review.
• Prepare data extracts and brief analytical summaries for broader debt performance assessments, publications, or internal reporting.

Required Qualifications

1) Loan Performance (LP) qualifications

• Bachelor’s degree in Economics, Social Sciences, Public Policy, Public Administration, Political Science, or a related field; or equivalent combination of education and experience.
• Working knowledge of international development policy research or Chinese development finance.
• Strong understanding of sovereign debt, public finance, and the policy environment gained through government, research, or academic experience.
• Advanced ability to develop evidence-based recommendations related to sovereign loan restructurings with significant fiscal, regulatory, or policy implications.
• Demonstrated ability to communicate clearly—both orally and in writing—with diverse technical and non-technical audiences across a geographically dispersed team.
• Proven capacity to work independently, manage time effectively, meet deadlines, and operate with strong organizational skills and self-initiative.
• Significant experience using R or comparable data analysis software.
• Proficiency with Google Workspace and Microsoft Office (Word, Excel, PowerPoint).

2) LMIC qualifications

• Ability to read and extract key information on Chinese official financing from government documents, NGO reports, academic publications, media articles, and other open sources.
• Capacity to learn and consistently apply an open-source data collection methodology.
• Ability to generate structured database records that meet defined quality and methodological standards.
• Strong synthesis skills to produce clear narrative summaries explaining financial flows and project context.
• Experience conducting targeted online searches to fill information gaps and verify sources.
• Ability to participate in regular check-ins and maintain effective communication with AidData staff and fellow researchers.

3) Transition Minerals qualifications

• Prior research experience related to critical minerals, mineral supply chains, and/or the clean energy transition.
• Ability to read and extract key information on Chinese official financing from government documents, NGO reports, academic publications, media articles, and other open sources.
• Capacity to learn and consistently apply an open-source data collection methodology.
• Ability to generate structured database records that meet defined quality and methodological standards.
• Strong synthesis skills to produce clear narrative summaries explaining financial flows and project context.
• Experience conducting targeted online searches to fill information gaps and verify sources.
• Ability to participate in regular check-ins and maintain effective communication with AidData staff and fellow researchers.

Cross-Project Preferred Qualifications

• Graduate-level training (Master’s, Postgraduate degree, or PhD) in Economics, Social Sciences, Public Policy, or a related field.
• Ability to conduct research in Mandarin Chinese, Spanish, French, Arabic, or another language commonly used in countries receiving Chinese development finance.
• Advanced experience using R, Stata, or similar quantitative tools for data analysis.
• Experience working with Google Workspace and/or Microsoft Excel for structured data collection or analysis.
• Demonstrated interest in international development, global finance, or Chinese official financing.