Harvard Kennedy School
API-202: Empirical Methods II
Description: Intended as a continuation of API-201 (Quantitative Analysis and Empirical Methods), this course equips students with an understanding of common tools of empirical analysis in policy applications. Much of the learning will take place through hands-on analysis of data sets. The course will cover regression analysis, including multiple regression, dummy variables, and binary dependent variables; as well as program evaluation, including selection effects; the advantages and disadvantages of experimental, quasi-experimental, and observational data; and instrumental variable techniques. The final part of the course includes an integrative exercise in which students will have the opportunity to assess empirical analysis in an open-ended and professionally realistic project. Prerequisite: API-201 or equivalent.
This course is designed with two objectives in mind. The first is to provide students with the ability to analyze critically the empirical analysis done by others at a level sufficient to make intelligent decisions about how to use that analysis in the design of public policy. The second is to provide students with the skills necessary to perform empirical policy analysis on their own or to participate on a team involved in such an empirical analysis. An important segment of the course focuses on program evaluation. This includes both the design and analysis of experiments that aim at measuring policy effectiveness and the use of non-experimental data to evaluate policy effectiveness.
Faculty: Armitabh Chandra (API-202A, Spring 2013); David Yanagizawa-Drott (API-202B, Spring 2013); Daniel Shoag (API-202C, Spring 2013); Rema Hanna (API-202D, Spring 2013).
Source: At http://http://www.hks.harvard.edu/degrees/teaching-courses/course-listing (accessed 13 February 2013)
Four-munute video of Armitabh Chandra teaching API-202: