With limited public resources, determining which programs, reforms, policies are efficient, and at what cost, is crucial, and allows public policy to be driven by evidence. However, evaluating programs is made difficult by the “counterfactual problem”: one cannot observe the outcomes or behavior of a participant, had (s)he not participated. This course will present the state-of-the-art empirical techniques used by economists to address this concern (randomized experiments, instrumental variables, difference-in-differences, matching).
For each of these approaches, we will give the basic intuition, discuss the necessary assumptions, present the strengths and weaknesses, analyze applications drawn from the literature. Moreover, each technique will be implemented by the participants in hands-on Stata sessions.
The main manual is Greene (2000) Econometric Analysis (4th Edition). Alternative options are Wooldridge (2000) Econometric Analysis of Cross Section and Panel Data and Davidson, MacKinnon (1993) Estimation and Inference in Econometrics. Research articles discussed in the course, exercises, and databases, will be available on this website.
Wooldridge, chapter 18
Stock, James and Mark Watson, "Introduction to Econometrics", Chapter 11.
Gertler, Paul and Simon Boyce, "An Experiment in Incentive-based Welfare: The Impact of PROGRESA on Health in Mexico", 2001.
Miguel, Edward and Michael Kremer, "Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities", Econometrica, Vol. 72, No. 1 (January, 2004), 159–217.
Wooldridge, chapter 5.
Wooldridge (introductory Econometrics), chapitre 15.
Angrist, J. and A. Kruger (1991), "Does Compulsory School Attendance Affect Schooling and Earnings?", The Quarterly Journal of Economics, 106(4), 979-1014.
Bound, Jaeger, and Baker (1995), "Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogeneous Explanatory Variable is Weak", Journal of the American Statistical Association, Vol. 90, No. 430. (Jun., 1995), pp. 443-450.
Bertrand, M., Duflo, E. and S. Mullainathan (2004),
"How Much Should We Trust Differences-In-Differences Estimates?",
The Quarterly Journal of Economics, V.119, N.1, 1 February 2004, pp.
Besley Timothy and Anne Case (2000), "Unnatural Experiments? Estimating the Incidence of Endogenous Policies", The Economic Journal, Vol. 110, No. 467, Features. (Nov., 2000), pp. F672-F694.
Blundell, R. and M. Costa Dias (2000), "Evaluation Methods for Non_Experimental Data", Fiscal Studies, V.21, N.4, pp. 427-468.
Duflo, E. (2001), "Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment", American Economic Review, 91(4), 795-813.
Meyer, B., Viscusi, K., and D. Durbin (1995), "Workers' Compensation and Injury Duration: Evidence from a Natural Experiment", American Economic Review, 85(3), 322-340.
Blundell, R. and M. Costa Dias (2000),
for Non_Experimental Data", Fiscal Studies, V.21, N.4, pp.
Heckman, J., Ichimura, H. and P. Todd (1997), "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme",