Quantitative Methods for Policy Evaluation

The main challenge for policy evaluation is to establish a causal link between interventions and outcomes. The objective of this course is to introduce the main approaches used in the evaluation of public policies: randomized evaluations, natural experiments, the regression discontinuity design, selection on observables and difference-in-differences. The course presents strengths and weaknesses of each approach in terms of internal and external validity. During the theory sessions, each approach will be presented and illustrated with specific policies in the areas of labor, health, education and development economics. In the practical sessions, students themselves (with help from the instructor) replicate the results of a widely-cited published study for each evaluation approach.

Course prerequisites
Basic probability and statistics. Undergraduate econometrics.

References
Duflo, E., Glennerster R. and M. Kremer, 2007, “Using Randomization in Development Economics Research: A Toolkit,” CEPR Discussion Paper No. 6059.
Katz L. F., J. R. Kling and J. B. Liebman, 2007, “Experimental Analysis of Neighborhood Effects,” Econometrica, 75(1): 83-119.
Miguel E. and M. Kremer 2004, “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities,” Econometrica, 72(1): 159-217.
Krueger, A. B., 1999, “Experimental Estimates of Education Production Functions,” Quarterly Journal of Economics, 14(2): 497-562.

John Snow, 1855, On the Mode of Communication of Cholera, Churchill, London. Reprinted by Hafner, New York (1965).
Stock, J. H., J. Wright and M. Yogo, 2002, “A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments,” Journal of Business and Economic Statistics, 20: 518 – 529.

Stock, J. H. and M. Yogo, 2005, “Testing for Weak Instruments in Linear IV Regression,” Ch. 5 in D. W. K. Andrews (ed.), Identification and Inference for Econometric Models, New York, Cambridge University Press, 109-120.
Angrist J. D. and W. N. Evans, 1998, “Children and Their Parents’ Labor Supply: Evidence from Exogenous Variation in Family size,” American Economic Review, 88: 450-477.

Lee, D. S., and T. Lemieux, 2009, “Regression Discontinuity Designs in Economics,” NBER Working Paper 14723.
Angrist, J. D. and V. Lavy, 1999, “Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement,” Quarterly Journal of Economics, 114(2): 533-775.

Fredriksson, P., Öckert B. and H. Oosterbeek, 2013, “Long-Term Effects of Class Size,” Quarterly Journal of Economics, 249-285.

Practical session:
Jens Ludwig and Douglas L. Miller, 2007, “Does Head Start Improve Children’s Life Chances? Evidence from a Regression Discontinuity Design,” Quarterly Journal of Economics, 122(1): 159-208.

J. D. Angrist and J.-S. Pischke, 2009, Mostly Harmless Econometrics: An Empiricist´s Companion, Princeton University Press.

Dehejia, R. and S. Wahba, 2002, “Propensity Score Matching Methods for Non-experimental Causal Studies,” Review of Economics and Statistics 84(1), 151-161.

Hastings, J., 2004, “Vertical Relationships and Competition in Retail Gasoline Markets: Empirical Evidence from Contract Changes in Southern California,” American Economic Review, 94(1): 317-328. 

Duflo E., 2001, “Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment,” American Economic Review, 91(4): 795-913.