Olle Folke is a researcher at the department of government at Uppsala University. He has previously been an assistant professor at SIPA, Columbia University. He has held visiting appointments at Harvard, MIT and Berkeley. He has published in, for example, the American Political Science Review, American Journal of Political Science, Journal of Politics, and the American Economic Review.

Johanna Rickne is an associate professor at SOFI, Stockholm University. She has previously been a lecturer in quantitative methods at SIPA, Columbia University and a researcher at Research Institute for Industrial Economics. She has held visiting appointments at Harvard and Berkeley. Her research has been published in the American Political Science Review, American Economic Review, Comparative Political Studies, and the Economic Journal.

Course Content
Policy evaluation is important across academic fields. In economics, policies may address levels and distributions of taxes and subsidies. In political science, policies may address the design of the electoral system, for example by the introduction of gender quotas. This course teaches common econometric tools for evaluating policy impacts. A strong emphasis is placed on the logic of causal inference: How can we design our evaluation so that we can estimate actual causal effect of the policy?
Half the time will be devoted to lectures and the other half to practical work in STATA. Replicating and discussing previous research will be an important tool for highlighting and reinforcing learning of the lecture content.

Course Objectives
1. Students will be able to design advanced policy evaluations with quantitative methods. In particular, they will be able to correctly apply i) regression discontinuity designs, ii) instrumental variable designs, and iii) difference-in-difference designs.
2. Students should be able to critically evaluate the empirical soundness of existing policy evaluations.

Course Prerequisites

Students should be familiar with the basic principles of policy evaluation. This includes concepts such as randomization, treatment and control groups, and “exogeneity”. The course will devote considerable time to practical applications of advanced econometric methods. Familiarity with application of basic quantitative methods in STATA is therefore an important pre-requisite.

Representative Background Reading
Washington, Ebonya L. 2008. Female Socialization: How Daughters Affect Their Legislator Fathers. American Economic Review 98(1).

Required texts
The following books will be provided on arrival to the Summer School as part of the course material for this course.
Angrist, Joshua D., and Jörn-Steffen Pischke. Mastering Metrics: The path from cause to effect. Princeton University Press, 2014.

Dunning, Thad. Natural experiments in the social sciences: A design-based approach. Cambridge University Press, 2012.

Background knowledge required
OLS = m

Computer Background
Stata = m

e = elementary, m = moderate, s = strong

Day 1: Course introduction and the experimental ideal
Mastering ‘Metrics Chapter 1

Paluck, Elizabeth Levy, Paul Lagunes, Donald P. Green, Lynn Vavreck, Limor Peer, and Robin Gomila. 2015. Does Product Placement Change Television Viewers’ Social Behavior?. PloS one 10, no. 9

Stata Guide

Day 2: Multivariate regression and control variables
Craig Volden, Alan E. Wiseman, and Dana Wittmer, “When are Women More Effective Lawmakers than Men?” American Journal of Political Science 57, no. 2 (2013): 326-341

Research summary: http://www.scholarsstrategynetwork.org/brief/who-are-most-effective-lawmakers-congress
Lenz, Gabriel, and Alexander Sahn. 2017. Achieving Statistical Significance with Covariates. Mimeo U.C. Berkeley. [https://drive.google.com/file/d/0B13GrSdju4CpbDlQLUtRS2dNMlU/view]

Day 3: Standard natural experiments
Natural Experiments in the Social Science, Chapter 2.

Washington, Ebonya L. 2008. Female Socialization: How Daughters Affect Their Legislator Fathers. American Economic Review 98(1).

Day 4: Instrumental variable designs
Mastering ‘Metrics, Chapter 3

Acemoglu, Daron, Simon Johnson, and James A. Robinson. 2002. The Colonial Origins of Comparative Development: An Empirical Analysis. American Economic Review. 91(5): 1369—1401.

Day 5: Regression Discontinuity Design
Mastering ‘Metrics, Chapter 4

Lee, David S., Enrico Moretti, and Matthew J. Butler. Do voters affect or elect policies? Evidence from the US House. The Quarterly Journal of Economics 119(3): 807—859.

Day 6: Advanced topics in Regression Discontinuity Design
Cattaneo, Matias D., Nicolás Idrobo, and Rocıo Titiunik. 2017. A Practical Introduction to Regression Discontinuity Designs.

de la Cuesta, Brandon and Kosuke Imai. 2016. Misunderstandings about the Regression Discontinuity Design in the Study of Close Elections. Annual Review of Political Science. 19: 375-396.

Day 7: Differences-in-Differences design
Mastering ‘Metrics, Chapter 5

Duflo, Esther. 2001. Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment. American Economic Review, 91(4): 795—813.

Day 8: Validating identifying assumptions with quantitative and qualitative methods
Natural Experiments in the Social Science, Chapter 7 and Chapter 8.

Day 9: External validity and mechanisms
Natural Experiments in the Social Science, Chapter 10.
Mastering ‘Metrics, p. 205-208.

Alan Gerber and Neil Malhotra (forthcoming) Can the American Political Science Review and the American Journal of Political Science be Believed? A Study of Publication Bias in Leading Political Science Journals. Quarterly Journal of Political Science.

Day 10: Course conclusion
Natural Experiments in the Social Science, Chapter 11.
Brainstorming about students’ own research des