Currículo

Policy Evaluation POEV

Contextos

ECTS

6.0 (para cálculo da média)

Objectivos

Policy Evaluation introduces students to the foundations of public policy design. Students will learn methods that allow establishing causal inference between exposure to programs and public policies with their outcomes, as well as the importance of behavioural science in the promotion and design of public policies in different areas such as health, education, social security, taxation, and the environment. This discipline has two main objectives: 1) a broad understanding of various social and economic issues that policymakers face, and 2) obtaining essential tools for future public policy researchers, as well as for policymakers, program managers, consultants, and advisors so that public policies are designed, implemented, and evaluated efficiently and effectively. This course is designed to give students the opportunity to choose, according to their own academic and professional interests, the topics they want to delve into. For each topic/lecture, a more specialized complementary bibliography will be indicated. This bibliography can be read individually or in groups through the creation of "reading and discussion groups" that enhance understanding and discussion of the topics. This discipline uses recent empirical examples from studies published in the most prestigious international scientific journals to motivate the use of (quasi-) experimental frontier scientific methods in the evaluation of public policies. These real-world policy applications will be presented in a non-technical and analytical manner. In this context, the course will provide an introduction to basic methods in data science, including regression, causal inference, and machine learning. Without neglecting economic theory and in-depth knowledge of institutional details, the goal is to estimate the causal impact of events and choices on a specific outcome of interest. The increase in computing power, the advent of big data, and new statistical and econometric techniques have given a new impetus to our better understanding of the effects of public policies. This discipline will take into account the importance of behavioural science in the design, implementation, and evaluation of public policies. We will also familiarize ourselves with concepts such as nudges. Finally, we will address recent methods for calculating the costbenefit of different policies. The course will use Stata software to showcase the main methods, but R and Phyton are also accepted in the evaluation components.

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).

Método de Avaliação

The teaching approach will strive to integrate, whenever possible, the exposition of concepts and theories with methodological analysis based on real cases of implementation and evaluation of public policies. Lecture slides will be made available on the course page on FÉNIX. Attendance and active participation in the majority of classes are expected from students. To encourage participation in the assessment, the evaluation during the regular period will consist of: • Written final exam: 50% of the grade. In groups of 2-3 students, depending on class size, students’ evaluation will have 2 further components: • At the end of each class, there will be a presentation of a recently published research paper from a scientific journal (10min), followed by in-class discussion. The paper must be selected from a list provided by the instructor (examples at the end of this syllabus) or proposed by the student in the first weeks of classes and accepted by the instructor: 25%. • In-class presentation of a research design (10min), followed by discussion, in the last weeks of the term: 25%. This should include: (i) a relevant research question related to the evaluation of a real public policy, explaining why this policy is important and why it should be studied (including a brief review of the literature on other articles and political debates); (ii) an evaluation design, including the method (field, laboratory, natural experiment, etc.) and a description of the measurement (i.e., the necessary data – feasibility will be particularly valued!). It is important to discuss whether the results would constitute correlations or causal parameters; (iii) potential limitations of the proposed approach.

Carga Horária

Bibliografia

Principal

• Causal inference: The mixtape: Cunningham, Scott 2021 Yale university press

Secundária

Disciplinas de Execução

2024/2025 - 2 Semestre