Programa

Policy Evaluation

Mestrado Bolonha em Economia

Programa

1. Why are Big Data and administrative micro-data (from both the public and private sectors) transforming the evaluation of public policies? Stata revision. • Main applied paper: Chetty, R., Grusky, D., Hell, M., Hendren, N., Manduca, R., & Narang, J. (2017). The fading American dream: Trends in absolute income mobility since 1940. Science, 356(6336), 398-406. • Course book Chapters 1-2. 2. Introduction to Experimental Methodology. Basic concepts in experimental design. Procedures to consider in the implementation of experiments. Potential outcomes model and Directed Acyclic Graphs. • Main applied paper: Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review, 94(4), 991-1013. • Method: Randomized Control Trials • Course book Chapters 3-4. 3. The importance of geographical context (Place-based policies). Short-term and longterm evaluation. What to do when randomization is not possible or ethically acceptable? • Main applied paper: Chetty, R., Hendren, N., & Katz, L. F. (2016). The effects of exposure to better neighborhoods on children: New evidence from the moving to opportunity experiment. American Economic Review, 106(4), 855- 902. • Method: Matching algorithms and difference-in-differences (Course book Chapters 5, 8-9) 4. Public policies and political support. • Main applied paper: Manacorda, Marco, Edward Miguel, and Andrea Vigorito. 2011. “Government Transfers and Political Support.” American Economic Journal: Applied Economics 3 (3): 1–28. • Method: Regression Discontinuity Designs (Course book Chapter 6) 5. Taxes on the consumption of goods and services: saliency, enforcement, and incidence issues. The invention of VAT. Asymmetry when tax rates decrease or increase. • Main applied papers: Chetty, R., Looney, A., & Kroft, K. (2009). Salience and taxation: Theory and evidence. American Economic Review, 99(4), 1145-1177 and Benzarti, Y., Carloni, D., Harju, J., & Kosonen, T. (2020). What goes up may not come down: asymmetric incidence of value-added taxes. Journal of Political Economy, 128(12), 4438-4474. • Method: more modern difference-in-differences methods (Roth, J., Sant’Anna, P. H., Bilinski, A., & Poe, J. (2023). What’s trending in differencein-differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235(2), 2218-2244). 6. How can taxes (and other public policies) be designed and implemented to discourage socially harmful behaviours and negative externalities such as pollution? Pigouvian taxes and sin taxes. • Main applied paper: Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493-505. • Method: Synthetic control (Course book Chapter 10) 7. Taxes on labor and behavioral responses: tax evasion, migration. • Main applied papers: Kleven, H. J., Knudsen, M. B., Kreiner, C. T., Pedersen, S., & Saez, E. (2011). Unwilling or unable to cheat? Evidence from a tax audit experiment in Denmark. Econometrica, 79(3), 651-692.; Kleven, H. J., Landais, C., & Saez, E. (2013). Taxation and international migration of superstars: Evidence from the European football market. American Economic Review, 103(5), 1892-1924 and Saez, E. (2010). Do taxpayers bunch at kink points?. American Economic Journal: Economic Policy, 2(3), 180-212. • Method: Bunching (see Kleven, H. (2016). Bunching. Annual Review of Economics, 8, 435-464). 8. Introduction to machine learning using an example: Can computer assistance improve judicial decisions? Will these decisions be fairer, or do they raise technical and ethical issues? • Main applied paper: Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018). Human decisions and machine predictions. The Quarterly Journal of Economics, 133(1), 237-293. • Method: Machine learning (see Athey, Susan and Imbens, Guido W (2019). Machine Learning Methods That Economists Should Know About. Annual Review of Economics, 11(1), 685-725). 9. The importance of behavioural economics in understanding the decision-making process. Cognitive and emotional biases. Social and moral preferences. Incentive policies and crowding-out effects. • Main applied paper: Stantcheva, S. (2021). Understanding tax policy: How do people reason?. The Quarterly Journal of Economics, 136(4), 2309-2369. • Method: Surveys to measure feelings, perceptions, convictions (see Stantcheva, S. (2023). How to run surveys: A guide to creating your own identifying variation and revealing the invisible. Annual Review of Economics, 15, 205-234). 10. Essay presentations (research design) (see evaluation).