OVERVIEW

This course aims to provide an introduction to policy evaluation methods. Specifically, the main econometric techniques used for the evaluation of public policies will be discussed during the lectures, introducing students to the counterfactual causality model and providing them with a unified framework for addressing questions about the estimation of causal effects. The course focuses on the econometric intuition underlying the methodologies rather than their formal demonstrations. The course is based on a textbook and lectures, as well as practical exercises using the statistical software Stata.

AIMS AND CONTENT

LEARNING OUTCOMES

The aim of this course is to provide students with the necessary tools to analyse and solve economic questions empirically using microeconomic data. Students will learn to handle questions of causality and how to interpret empirical results of economic policy evaluation. The course is based on the statistical software for data science STATA. During the course we will conduct several empirical applications in the field of labor economics and economic policy evaluation such as the gender wage gap or the evaluation of specific job market programs.

AIMS AND LEARNING OUTCOMES

The course aims to provide both theoretical and applied skills in modern econometric methods for impact policy evaluation. Upon completion of the course, students will be able to:

• adopt approaches inspired by the counterfactual model

• discuss the assumptions required to identify causal effects in both experimental and non-experimental settings

• choose appropriate models based on the specific features of the context and the research question

• interpret the results of causal inference

• apply empirical methods of impact policy evaluation using the statistical software Stata

PREREQUISITES

The course is intended for Master's students who wish to become familiar with modern econometric methods for impact policy evaluation. Participants are expected to have completed an introductory econometrics course at the undergraduate level and to possess a solid background in statistics and regression models. The required level of preparation corresponds, for example, to that of Stock and Watson (2019) or Wooldridge (2012).

TEACHING METHODS

The course includes in-person lectures with opportunities for discussion of scientific articles related to the topics covered. It also features hands-on Stata lab sessions and individual work during practical exercises. The course will be held in person.

SYLLABUS/CONTENT

1. Introduction to STATA
2. Randomized trials
3. Regression and matching
4. Instrumental variables
5. Regression discontinuity designs
6. Difference-in-differences design

RECOMMENDED READING/BIBLIOGRAPHY

Lecture slides, scientific articles, and additional materials (e.g., scripts and datasets) will be made available on AulaWeb. The slides summarize the key points covered in the course. To gain a deeper understanding of each topic, students are encouraged to study Causal Inference: The Mixtape by Prof. Scott Cunningham, which will serve as the main textbook for the course.
Angrist’s video course (Mastering Econometrics, n.d.) offers a useful non-technical overview of the methods. Additional references—such as selected scientific articles—will be provided during the course.

References

• Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. https://doi.org/10.2307/j.ctv1c29t27

• Angrist, J. D. (n.d.). Mastering Econometrics, Marginal Revolution University. https://mru.org/mastering-econometrics (accessed June 9, 2022).

• Angrist, J. D., & Pischke, J.-S. (2015). Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press.

• Stock, J. H., & Watson, M. W. (2019). Introduction to Econometrics (4th ed.). Pearson.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Frontal lectures take place in presence during the autumn term in Genoa. See AulaWeb for details on the course schedule.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Final written exam. Parts of the exam may require interpreting Stata output and commenting on tables showing empirical results. In case of special needs, students with disabilities and/or learning disorders are kindly requested to contact the Student Disability Service and the instructors at the beginning of the semester.

ASSESSMENT METHODS

The final grade will be based on a written exam (1.5 hours) consisting of three sections. One of these requires students to interpret Stata output and comment on tables displaying empirical model estimates. Each section is worth 10 points. The final grade is the sum of the points earned across the three sections (maximum: 30 points).

Students attending the course will have the opportunity to work on a group project. Each student will be assigned to a group at the beginning of the semester (details will be published and communicated via AulaWeb). The group work may involve the analysis of a scientific article, the preparation of Stata code for data analysis, the writing of a short report, and an in-class presentation. Students will have time to work on the project—also during class sessions—throughout the semester. Presentations will take place during the last week of the course, and the report along with the code must be submitted before the first available exam session. The project can contribute up to 4 extra points to the final exam grade.
Active participation in class exercises may also be rewarded.

FURTHER INFORMATION

Attendance recommended. Students may bring their own laptop or work with the university computer.