Quantitative Methods for Policy Evaluation
Docente titolare del corso
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.
Basic probability and statistics. Undergraduate econometrics.
Angrist, J. D. and J.-S. Pischke (2008). Mostly harmless econometrics: An empiricist's companion. Princeton university press.
Burtless, G. (1995). "The case for randomized field trials in economic and policy research". In: Journal of economic perspectives 9.2, pp. 63-84.
Cunningham, S. (2018). Causal Inference: the mixtape (V. 1.7).
Deaton, A. (2010). \Instruments, randomization, and learning about development". In: Journal of economic literature 48.2, pp. 424-55.
Duo, E., R. Glennerster, and M. Kremer (2007). \Using randomization in development economics research: A toolkit". In: Handbook of development economics 4, pp. 3895-3962.
Heckman, J. J. and J. A. Smith (1995). \Assessing the case for social experiments". In: Journal of economic perspectives 9.2, pp. 85-110.
The objective of this course is to introduce the main approaches used in the causal evaluation of public policies.
Modalità di insegnamento
Lectures and problem sets
The final grade will consider participation (10%), problem set (40%) and the final exam (50%).
- Angrist, Joshua D., Pischke, Jörn-Steffen. Mostly harmless econometrics: an empiricist's companion. Princeton Oxford: Princeton University Press, 2009.
- Cunningham, Scott. Causal inference: the mixtape. New Haven, CT London: Yale University Press, 2021. (Available online at: https://mixtape.scunning.com)
- Master of Science in Economics, Lezione, 1° anno