The MIDP curriculum is built around quantitative and analytical methods — microeconomics, econometrics, and causal inference — and the Capstone is where students apply that training to a real problem for a real client. The students have strong technical skills in empirical analysis, particularly causal inference and impact evaluation. I supervise three to four teams each year and meet with each team weekly. We are also open to projects that support evaluation design or data collection activities rather than only ex-post analysis. If you have an interesting idea that does not fit these criteria perfectly, email me anyway.

How the year is typically structured:

• Fall semester. Teams build context on the policy problem, review relevant literature, and refine the research question with the client. For ex-post analysis projects: develop an analysis plan and conduct basic descriptive analysis. For research-support projects: support survey design and data collection activities, or design an impact evaluation.
• Spring semester. Teams carry out the analysis, interpret findings, and produce a technical report, a short policy brief, and a final presentation to the client.

Teams present interim work through the year, depending on client needs. Final deliverables belong to the client. Typical clients are international organizations, research centers, and non-governmental or civil society organizations working on development policy.

A strong MIDP Capstone project has three things: a clear policy or research question, accessible quantitative data (or planned data collection that will wrap up by January of the project year), and a scope that fits an academic year.

Types of projects we take on:

• Regression analysis of randomized controlled trials
• Quasi-experimental analysis (difference-in-differences, regression discontinuity, instrumental variables)
• Machine learning to predict social phenomena
• Analysis of survey or administrative data to inform policy or future field experiments
• Support in designing an impact evaluation, developing survey instruments, or running data quality checks on ongoing data collection

What “feasible data access” looks like:

• An existing dataset the client can share securely with the team, or
• Publicly available data (World Bank, DHS, government administrative data, etc.) that students can use to answer the question, or
• Data the client is in the process of collecting, with a realistic handover timeline during the fall semester (by the end of December).

What is not a good fit:

• Projects requiring extensive primary data collection or in-country fieldwork by students
• Purely descriptive work or literature reviews with no data analysis
• Projects that need rapid turnaround (weeks rather than an academic year)
• Marketing, communications, or advocacy products
• Work that cannot be shared with me as the faculty advisor or with peer student teams for feedback

If you’re not sure whether your idea fits, email me before submitting — that conversation usually saves time on both sides.

There is no cost to participate. In return, I ask clients to commit to the following over the academic year:

• A designated point of contact who is knowledgeable about the policy context and can respond to the team.
• A one-hour kick-off meeting at the start of the fall semester to align on the research question and deliverables.
• At least two check-ins per semester to review progress and provide guidance.
• Regular email communication, roughly bi-weekly, between check-ins.
• Attendance at the end-of-semester presentations (one hour each, in December and April/May; remote attendance is fine).
• Organizational sign-off before submitting a proposal, so that if the project is selected the team can start in September without delay.