Econometrics 1:

Microeconometric Methods of Impact Evaluation

Matthieu Chemin
Département des sciences économiques
École des sciences de la gestion
Université du Québec à Montréal

Winter 2010


Description

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.

 

Documents

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.

 

Content

 

Lecture 1: Program Evaluation

Reading list:
Wooldridge, chapter 18
Ravallion, M (2001), "The Mystery of the Vanishing Benefits: An Introduction to Impact Evaluation", World Bank Economic Review, 15(1), 115-40.
Blundell, R and Costa Dias, M (2000), "Evaluation Methods for Non-Experimental Data", Fiscal Studies, 21(4), 427-68.
Angrist, J. and Krueger, A. (1999), "Empirical strategies in Labor Economics", in Ashenfelter,O. and Card, D. Handbook of Labor Economics Volume III.
Abadie, A. and J. Gardeazabal (2003), "The Economic Costs of Conflict: a Case-Control Study for the Basque Country", American Economic Review 93(1): 113-132.
Exercise 1
merge1.dta
merge2.dta
Data NSLY

Lecture 2: Randomized experiment

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.
Questionnaire 2
Exercise 2
 

Lecture 3: Instrumental Variables

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.
Exercise 3
Data Cigarette
Questionnaire 3

Lecture 4: Difference-in-Differences

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. 249-275(27).
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.
Exercise 4
injury.dta
Questionnaire 4

Lecture 5: Matching
 

Blundell, R. and M. Costa Dias (2000), "Evaluation Methods for Non_Experimental Data", Fiscal Studies, V.21, N.4, pp. 427-468.
Heckman, J., Ichimura, H. and P. Todd (1997), "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme", The Review of Economic Studies, 64(4), Special Issue: Evaluation of Training and Other Social Programmes, 605-654.
Sianesi, Barbara (2001), "Implementing propensity Score Matching Estimators with Stata".
Exercise 5
microfinance.dta