DEDP Master's Program

Students should spend the majority of their time working on a substantive project during their internship. Projects could include working on data analysis, policy implementation, intervention design, program evaluation, or other relevant topics. Students will complete a capstone report based on their internship project. At the end of the internship, students will present their findings to the faculty and program directors and their fellow classmates.

Previous capstone host organizations include:

Wei Lu: For my capstone project, I worked with the Data and Evidence for Justice Reform (DE JURE) unit of the World Bank on a Peru legal training project. The project evaluates the impact of social-emotional learning (SEL) and class monitoring on judicial performance of judges and prosecutors. I was responsible for drafting the pre-analysis plan and developing an online platform for the SEL experiment using the oTree application framework. It was an interesting learning experience to participate in the early planning, development and deployment stages of an RCT. Through this project, I managed to apply both my software engineering skills and economics knowledge to make meaningful contributions.

José Pinilla: I spent the summer at the Center for Effective Global Action (CEGA), a hub for global development research, conducting a policy impact review on a sample of research projects funded or supported by CEGA. The objective was to gain a better understanding of the different channels through which projects can influence policy decisions and programming, and to give recommendations on how to: 1) identify projects with high potential for impact; 2) capture policy impacts that may take time to materialize, and; 3) support researchers and policymakers to improve the policy impact potential of their projects. To understand the pathways for impact, we conducted a document review and interviewed researchers and implementing partners. This was a great opportunity to learn about how research projects are implemented on the ground and the challenges in working with decision-makers to influence policy.

Fredric Kong: For my internship, I worked with two professors in the Economics Department at MIT.  My work with Professor Autor focused on understanding where new work comes from and the differential effect of innovations on new occupations. I classified patents as process or product innovation using natural language processing tools and machine learning techniques with the goal of uncovering the origins of new tasks, the skills they demand, and how they contribute to the evolution of the labor market. With Professor Banerjee, I explored a general equilibrium model to understand trade, inequality, and policy in the context of imperfect markets that are typical in development economics. Across the two projects, I used a variety of computational tools to tease out new insights. True to the degree’s name, the internships applied the mathematical and econometric skills taught during our classes.

Max Ghenis: I worked with the Policy Simulation Library to estimate the income elasticity of labor supply using their open source dynamic macroeconomic model of the US economy, OG-USA. The simulations revealed that OG-USA predicts larger labor responses than those found in most microeconometric studies. Over the course of the study, I also identified opportunities to calibrate the model and wrote an open source Python package that improves the imputation of taxpayer age using machine learning. By applying economic theory and evidence I learned in the DEDP program to a rich model of the US economy, I developed a deeper understanding of how different areas of the field interact to inform policy design. Since graduating, I have been using these lessons to study universal basic income policies.