Research Data Associate

AidData is currently seeking a Research Data Associate to support the Tracking Underreported Financial Flows (TUFF) Unit. They will be responsible for conducting quantitative and qualitative analysis on loans issued by Chinese official sector institutions to government and government-guaranteed borrowing institutions in low- and middle-income countries.

Position Term: 6 Months with a potential 6 month extension.

Full-time (works remotely full-time and has no assigned university office) for $25 per hour.

Applications accepted on a rolling basis. Preliminary review will begin on Monday, April 7th.

• This is a non-exempt position, eligible to receive overtime in accordance with the Fair Labor Standards Act. This position may be subject to overtime during high peak times, etc. as directed and approved by the supervisor.
• This position is a temporary (hourly) position not to exceed 12 months. All hourly employees are subject to the limitations of the Virginia Manpower Control Program and total hours worked may not exceed on average 29 hours per week, over the course of a twelve month period beginning May 1 each year.

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Responsibilities:

• This position will work closely with the Associate Director of TUFF and a TUFF Program Manager Analyst on four types of activities:
  • Design a data collection methodology to systematically capture disbursements, repayments, rescheduling, and amounts outstanding for all PPG loans from Chinese official sources. The methodology will incorporate debt rescheduling activities, including niche loan contract terms, such as cross-defaults, DSSI rescheduling activities, and sovereign debt restructuring agreements.
  • Investigate and capture observed loan amortization data points from primary source documents in low- and-middle income country debt registries and mapping the observed information to existing loan records captured in TUFF data management software.
  • Design and implement a methodology for creating synthetic loan amortization tables across all sovereign debt loans from China’s official sector institutions. This includes high discretion detailed financial modeling that incorporates observed loan amortization financial flows and sovereign debt stocks with estimated loan flows and debt stocks over the life cycle of the loan while incorporating relevant debt events, arrears, and debt rescheduling agreement terms.
  • Conduct quantitative and qualitative analysis at the country and regional level on sovereign debt events and demonstrating the use cases for the final Loan Performance dataset, including drafting original short or long-form content for publication.

Required Qualifications:

• Bachelor’s Degree in Economics, Social Sciences, Public Policy, Public Administration, Political Science, or related fields, or comparable education and experience.
• Working knowledge of policy research in the field of international development or Chinese development finance.
• Deep knowledge of the policy environment through experience in government, policy research, or academia.
• Advanced skills in developing recommendations on sovereign loan debt involving restructurings which might have a significant fiscal, budgetary, programmatic, regulatory, planning, performance, and policy impact.
• Demonstrated ability to communicate effectively, orally and in writing, with a diverse population of technical and non-technical staff; and to maintain good communication with a geographically dispersed team.
• Demonstrated ability to function independently, meet project timelines and schedules, utilize exceptional organizational skills to meet deadlines, and work under your own initiative.
• Considerable experience working with R or similar data analysis software.
• Proficient user of Google Suite of applications and Microsoft Office programs (Word, Excel, PowerPoint, etc.).

Preferred Qualifications:

• Graduate or Post graduate studies in Economics, Social Sciences, Public Policy, or Related Fields.
• Ability to conduct research in Mandarin Chinese, Spanish, French, Arabic, or another language commonly used in countries that receive Chinese development financing.