Microeconometric Evaluation of Public Policies

Objectif

This course presents an overview of econometric methods used for causal inference, i.e., methods designed to estimate the impact of a potential cause (usually a policy intervention or other institutional change) on an outcome of interest. Selection effects can impede attempts to infer causality. Causes and consequences are discussed relying on the counterfactual framework used in the program evaluation approach. The course will be based on critical reading of empirical articles, putting emphasis on the identification issues. 

• The course covers a variety of identification designs, including randomized experiments, instrumental variables, matching and difference-in-differences. In addition, it presents recent advanced statistical methods such as the synthetic control method. It also discusses the appropriateness of the underlying assumptions of these estimators, as well as the interpretation of the results obtained by those methods. 

By the end of the course, students should be able to:

-    explain the counterfactual framework, and use it to interpret the concept of selection. 
-    understand the leading quantitative methods for causal inference, and apply them to a variety of policy designs and available data
-    compare the strengths and weaknesses of these estimators in a given research context
-    recognize and interpret the conditions under which these estimators possess desirable statistical properties
-    explain the consequences of the violation of their identifying assumptions

Prerequisites

The course assumes a good knowledge of basic statistics, linear econometrics (linear regression model, estimation and testing) and causal identification (Neyman-Rubin framework)
 

Plan

•    The Rubin Causal Model 
•    Randomized Experimentation
•    Standard errors, Multiple Hypothesis Testing, and Permutation Tests
•    Estimation under the 
•    Advanced difference-in-differences methods 
•    Synthetic control method

Références

Related books 

• Imbens Guido and Donald Rubin: Causal Inference for Statistics Social and Biomedical Sciences, Cambridge University Press
• Angrist, Joshua and Jörn-Steffen Pischke: Mastering Metrics, Princeton University Press.
• Angrist, Joshua and Jörn-Steffen Pischke: Mostly Harmless Econometrics: An Empiricist's Companion, Princeton University Press. 
• Glennerster, R., Takavarasha K. Running Randomized Evaluations:  A Practical Guide, Princeton University Press
• Wager, Stefan. 2024. “Causal Inference: A Statistical Learning Approach.” Technical report, Stanford University.

Main references

General (theory)

• Imbens, Guido W. and Jeffrey M. Wooldridge (2009): “Recent Developments in the Econometrics of Program Evaluation,” Journal of Economic Literature 47(1), pp. 5--86. 
• Duflo, Esther, Rachel Glennerster and Michael Kremer (2008): "Using Randomization in Development Economics Research: A Toolkit," in: Handbook of Development Economics.

Applications using RCTs

• Special issue of American Economic Journal : Applied devoted to microcredit
• Banerjee AV, Duflo E, Glennerster R, Kothari D.  2010  Improving immunisation coverage in rural India: clustered randomised controlled evaluation of immunisation campaigns with and without incentives., BMJ
• Meyer, B. D. (1995): “Lessons from the US unemployment insurance experiments,” Journal of economic literature, 91–131. 

Applications using IV

• Angrist, Joshua and Alan Krueger (2001) "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments", Journal of Economic Perspectives, 15(4), 69-85. 
• Angrist, Joshua and William Evans (1998) "Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size", American Economic Review, 88(3), 450-477.
• Behaghel, L., B. Crepon, and M. Gurgand (2014): “Private and Public Provision of Counseling to Job Seekers: Evidence from a Large Controlled Experiment,” American Economic Journal: Applied Economics, 142–74. 

Applications with Targeting

• Bhattacharya, D. and P. Dupas (2012): “Inferring welfare maximizing treatment as- signment under budget constraints,” Journal of Econometrics, 167, 168–196. 

Applications taking into account equilibrium effects

• Crepon, B., E. Duflo, M. Gurgand, R. Rathelot, and P. Zamora (2013): “Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment,” The Quarterly Journal of Economics, 128, 531–580. 
• Angellucci, M. and V. Di Maro (2015): “Program Evaluation and Spillover Effects,” Working Paper 9033, IZA. 
• Ferracci, M., G. Jolivet, and G. J. van den Berg (2014): “Evidence of treatment spillovers within markets,” Review of Economics and Statistics, 96, 812–823. 
• Miguel, Edward and Michael Kremer (2004): “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities,” Econometrica 72(1), pp. 159—217.

Matching (theory and applications)

• Imbens, Guido W. 2015. “Matching Methods in Practice: Three Examples.” Journal of Human Resources 50 (2): 373–419.
• Imbens, Guido W, Donald B Rubin, and Bruce I Sacerdote. 2001. “Estimating the Effect of Unearned Income on Labor Earnings, Savings, and Consumption: Evidence from a Survey of Lottery Players.” American Economic Review 91 (4): 778–94.

Difference in Differences / event studies

• Callaway, Brantly, and Pedro HC Sant’Anna. 2021. “Difference-in-Differences with Multiple Time Periods.” Journal of Econometrics 225 (2): 200–230.
• Chaisemartin, Clement de, and Xavier D’Haultfœuille. 2023. “Credible Answers to Hard Questions: Differences-in-Differences for Natural Experiments.” Available at SSRN 4487202.
• Chaisemartin, Clement de, and Xavier D’Haultfœuille. 2023. “Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey.” The Econometrics Journal 26 (3): C1–30.
• Sun, Liyang, and Sarah Abraham. 2021. “Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects.” Journal of Econometrics 225 (2): 175–99.

Synthetic control method

• Abadie, Alberto. 2021. “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects.” Journal of Economic Literature 59 (2): 391–425.