Data Analysis for Policy Research Using

This course develops the skills necessary to prepare, analyze, and present data for policy analysis and program evaluation using R. Building on the foundations from Quant I and II—probability, statistics, regression analysis, and causal inference—this course emphasizes the practical application of microeconometric methods to real-world policy questions. (Note: macroeconomic topics and forecasting methods are not covered.) The central objective is to train students to be effective analysts and policy researchers. Key questions include: Given the available data, what analysis best informs the policy question? How should we design research, prepare data, and implement statistical methods using R? How can we assess causal effects of policies rather than mere correlations? What ethical considerations arise when working with data on marginalized populations? Students will learn through hands-on analysis of datasets tied to a range of policy issues, including: caste-based expenditure gaps in India, racial disparities in NYPD fare evasion enforcement, water shutoffs in Detroit, Village Fund grants in Indonesia, public health insurance and child mortality, and Stand Your Ground laws and gun violence. The course culminates in a student-led project on a policy topic of their choosing.