1.
Empirical approaches, potential outcomes, and causality
1.1.Structural and treatment effect
approaches.
1.2.Descriptive analysis vs. causal
inference.
1.3.Potential outcomes and causality.
2.
Social experiments
2.1.Experimental testing of welfare
programs in the US.
2.2.Employment effects of job and
earnings subsidies.
2.3.Experimental evaluation of labor
histories.
2.4.Econometric models of labor
histories.
3.
Matching
3.1.Exogeneity, matching, and multiple
regression.
3.2.Methods based on the propensity
score.
3.3.The common support condition.
3.4.Monetary incentives and schooling in
the UK.
4.
Instrumental variables
4.1.Instrumental variable estimation
using natural experiments.
4.2.Interpreting IV estimates when
effects are heterogeneous.
4.3.Local average treatment effects and
marginal treatment effects.
4.4.Estimating the distributions of
potential outcomes.
4.5.The econometric selection model.
5.
Regression-discontinuity
5.1.Identification from discontinuities
in assignment rules.
5.2.Parametric ans semiparametric
estimation methods.
5.3.Financial aid offers and college enrollment
decisions.
6.
Differences in differences
6.1.Comparisons based on policy changes.
6.2.Identifying the average treatment
effect for the treated.
6.3.Changes in the distribution of
effects vs. changes in means.
6.4.Minimum wages and employment.
7.
Further topics
7.1.Continuous treatments.
7.2.Treatment effects in duration
analysis.
· General
1)
Angrist, J. and A. Krueger (2000): “Empirical Strategies in Labor
Economics”, Handbook of Labor Economics, O. Ashenfelter and D. Card
(eds.), North Holland, 1277-1366.
2)
Heckman, J. J. (2001): “Micro Data, Heterogeneity, and the Evaluation of
Public policy: Nobel Lecture”, Journal of Political Economy, 109,
673-748.
3)
Meyer, B. (1995): “Natural and Quasi-experiments in Economics”, Journal
of Business and Economic Statistics, 13, 151-161.
· Potential outcomes and causality
1)
Holland, P. W. (1986): “Statistics and Causal Inference”, Journal of
the American Statistical Association, 81, 945-970.
2)
Rubin, D. B. (1974): “Estimating
Causal Effects of Treatments in Randomized and Nonrandomized Studies”, Journal
of Educational Psychology, 66, 688-701.
1)
Banerjee, A. and E. Duflo (2009): “The
Experimental Approach to Development Economics”, Annual Review of
Economics, 1, 151-178.
2)
Duflo, E., R. Glennerster, and M. Kremer (2008): “Using
Randomization in Development Economics Research: A Toolkit”. In T.P.
Schultz and J. Strauss (eds.): Handbook of Development Economics, Vol.
4, 3895-3962.
3)
Card, D. and D. R. Hyslop (2005): “Estimating the Effects of a
Time-Limited Earnings Subsidy for Welfare-Leavers”, Econometrica, 73,
1723-1770.
4)
Ham, J. C. and R. J. LaLonde (1996): “The Effect of Sample Selection and
Initial Conditions in Duration Models: Evidence from Experimental Data on
Training”, Econometrica, 64, 175-205.
5)
Hesselius, P., P. Johansson, and L. Larsson (2005): “Monitoring Sickness
Insurance Claimants: Evidence from a Social Experiment”, IFAU, Uppsala.
6)
Holla, A. and M. Kremer (2008): “Pricing
and Access: Lessons from Randomized Evaluations in Education and Health”.
In W. Easterly and J. Cohen (eds.): What Works in Development? Thinking Big
vs. Thinking Small, Brookings Institution Press, forthcoming.
7)
LaLonde, R. J. (1995): “Evaluating the Econometric Evaluations of
Training Programs with Experimental Data”, American Economic Review, 76,
604-620.
8)
Miguel, E. and M. Kremer (2004): “Worms: Identifying Impacts on
Education and Health in the Presence of Treatment Externalities”, Econometrica,
72, 159-217.
9)
Moffitt, R. A. (2004): “The Role of
Randomized Field Trials in Social Science Research: A Perspective from Evaluations
of Reforms of Social Welfare Programs”, American Behavioral
Scientist, 47(5),
506-540.
1)
Dearden, L., C. Emmerson, C. Frayne, and C. Meghir (2004): “Can
Education Subsidies Stop School Drop-outs? An Evaluation of Education
Maintenance in England”, Institute for Fiscal Studies, London.
2)
Heckman, J. J., H. Ichimura, and C. Taber (1997): “Matching as an Econometric
Evaluation Estimator: Evidence from Evaluating a Job Training Programme”, Review
of Economic Studies, 64, 605-654.
3)
Imbens, G. (2004): “Nonparametric Estimation of Average Treatment
Effects Under Exogeneity: A Review”, Review of Economics and Statistics,
86, 4-29.
4)
Rosenbaum, P. R. and D. B. Rubin (1983): “The Central Role of the
Propensity Score in Observational Studies for Causal Effects”, Biometrika,
70, 41-55.
5)
Rubin, D. B. (2001): “Using Propensity Scores to Help Design
Observational Studies: Application to the Tobacco Litigation”, Health
Services & Outcomes Research Methodology, 2, 169-188.
· Methods
1)
Imbens, G. W. and J. Angrist (1994): “Identification and Estimation of
Local Average Treatment Effects”, Econometrica, 62, 467-475.
2)
Imbens, G. W. and D. B. Rubin (1997): “Estimating Outcome Distributions
for Compliers in Instrumental Variable Models”, Review of Economic Studies,
64, 555-574.
3)
Abadie, A. (2002): “Bootstrap Tests for Distributional Treatment Effects
in Instrumental Variable Models”, Journal of the American Statistical
Association, 97, 284-292.
4)
Vytlacil, E. (2002): “Independence, Monotonicity, and Latent Index
Models: An Equivalence Results” Econometrica, 70, 331-341.
5)
Heckman, J. J. and E. Vytlacil (2005): “Structural Equations, Treatment
Effects, and Econometric Policy Evaluation”, Econometrica, 73, 669-738.
6)
Angrist, J., G. Imbens, and K. Graddy (2000): “The Interpretation of
Instrumental Variable Estimators in Simultaneous Equations Models with an Application
to the Demand for Fish”, Review of Economic Studies, 67, 499-528.
· Examples
1)
Gentzkow, M. and J. M. Shapiro (2008): “Preschool Television Viewing and
Adolescent Test Scores: Historical Evidence from the Coleman Study”, Quarterly Journal of Economics, 123,
279-323.
2)
Banerjee, A., S. Cole, E. Duflo, and L. Linden (2007): “Remedying
Education: Evidence from Two Randomized Experiments in India”, Quarterly
Journal of Economics, 122, 1235-1264.
3)
Moffitt, R. (2008): “Estimating
Marginal Treatment Effects in Heterogeneous Populations”, unpublished.
1)
Card, D., R. Chetty, and A. Weber (2007): “Cash-on-Hand and Competing
Models of Intertemporal Behavior: New Evidence from the Labor Market”, Quarterly Journal of Economics, 122,
1511-1560.
2)
Hahn, J., P. Todd, and W. van der Klaauw (2001): “Estimation of
Treatment Effects with a Quasi-Experimental Regression-Discontinuity Design”, Econometrica,
69, 201-209.
3)
Van der Klaauw, W. (2002): “Estimating the Effect of Financial Aid
Offers on College Enrollment: A Regression-Discontinuity Approach”, International
Economic Review, 43, 1249-1287.
4)
Angrist, J. and V. Lavy (1999): “Using Maimonides’ Rule to Estimate the
Effect of Class Size on Scholastic Achievement”, Quarterly Journal of
Economics, 114, 533-575.
1)
Card, D. and A. Krueger (1994): “Minimum Wages and Employment: A Case
Study of the Fast Food Industry”, American Economic Review, 84, 772-793.
2)
Meyer, B., K. Viscusi and D. Durbin (1995): “Workers’ Compensation and
Injury Duration: Evidence from a Natural Experiment”, American Economic
Review, 85, 322-340.
3)
Athey, S. and G. W. Imbens (2006): “Identification and Inference in
Nonlinear Difference-in-differences Models”, Econometrica, 74, 431-497.
4)
Bertrand, M., E. Duflo, and S. Mullainathan (2004): “How Much Should We
Trust Differences-in-Differences Estimates?”, Quarterly Journal of Economics,
119, 249-75.
5)
Montalvo, J. G. (2011): “Voting After the Bombing: A Natural Experiment
on the Effect of Terrorist Attacks on Democratic Elections”, Review of Economics
and Statistics, forthcoming.
· Continuous treatments
1)
Hirano, K. and G. W. Imbens (2004): “The Propensity Score
with Continuous Treatments”. Draft chapter for Missing Data and Bayesian
Methods in Practice: Contributions by Donald Rubin’s Statistical Family,
Wiley, forthcoming.
2)
Florens, J. P., J. Heckman, C. Meghir, and E. Vytlacil (2004): “Identification
of Treatment Effects Using Control Functions in Models with Continuous,
Endogenous Treatment and Heterogeneous Effects”, Econometrica,
76, 1191-1206.
· Treatment effects in duration
analysis
1)
Abbring, J. H. and G. J. van den Berg (2003): “The nonparametric
identification of treatment effects in duration models”, Econometrica,
71, 1491-1517.
2)
Abbring, J. H. and G. J. van den Berg (2005): “Social Experiments and
Instrumental Variables with Duration Outcomes”, unpublished.
Econometric
Methods of Program Evaluation, lecture at Instituto de Estudios Fiscales, Madrid,
14 December 2010.
http://www.cemfi.es/~arellano/Pol_Eval_PROGRAM.htm
(pdf file)