OVERVIEW

This course provides an introduction to methods for program and policy evaluation. The lecture covers the most important econometric methods that are frequently used for the evaluation of public policies. The course introduces students to the counterfactual causality model and provides them with a unified framework for answering questions of cause and effect. The course emphasizes the intuition behind the methodology rather than formal proofs. It is based on a textbook and frontal lessons 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 on modern econometric methods for program evaluation and causal inference. After completing this module, students will be able to

• build a counterfactual model
• explain the assumptions required to identify a causal effect in experimental and non-experimental designs
• know when to use different causal inference methods
• perform causal inference with existing data sets 
• interpret the results of causal inference

PREREQUISITES

The course is designed for Master students who want to become familiar with modern econometric methods for program evaluation and causal inference. Participants are expected to have attended an introductory Econometrics course at the Bachelor level and to have knowledge of statistics and regression models at the level of e.g. Stock and Watson (2019) or Wooldridge (2012).

TEACHING METHODS

Frontal lessons with space for discussions and personal work during practical exercises. The course will be held in presence in Genoa.

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
7. Combining methods: The causal effect of schooling on wages

RECOMMENDED READING/BIBLIOGRAPHY

Lecture notes, problem sets, and additional materials (e.g. dofiles and data sets) will be posted on AulaWeb. The lecture notes summarize the key points covered in this course. To gain a deeper understanding of each topic we recommend you to also study the main background reading. The main background reading is the textbook by Angrist and Pischke (2015). The video course by Angrist (n.d.) gives a good nontechnical overview of the methods. Additional references will be given in class. For instance, we will cover applications from original research articles. 

• Angrist, J. D. (n.d.): Mastering Econometrics, Marginal Revolution University: https://mru.org/mastering-econometrics (accessed June 9, 2022).
• Angrist, J.D. and Pischke, J.-S. (2015) Mastering ’Metrics: The Path from Cause to Effect. Princeton: Princeton University Press.
• Stock, J. H., and Watson, M. W. (2019). Introduction to Econometrics (4th ed.). New York: Pearson.
• Wooldridge, J. M. (2012) Introductory Econometrics: A Modern Approach (5th ed.). Mason: South-Western Cengage Learning.

TEACHERS AND EXAM BOARD

Exam Board

ELENA LAGOMARSINO (President)

MAURIZIO CONTI

LESSONS

LESSONS START

Frontal lectures take place in presence during the SPRING  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

Written final exam. Parts of the exam may require you to interpret STATA-output and to comment on tables showing empirical results. In case of special requirements, students with disabilities and/or learning disabilities are requested to contact – at the beginning of the semester – the Student Disability Service and the lecturer.

ASSESSMENT METHODS

Grading will be based on a written final exam covering topics discussed in the lecture and applications. Parts of the exam may require students to interpret STATA-output and to comment on tables showing empirical results. Students attending the course will have the opportunity to prepare an homework assignment in groups (replication study). Each student participating in the homework is required to sign up for a group at the beginning of the semester (details will be published and communicated via AulaWeb). The in-group homework assignment includes studying a scientific research article, replicating selected results of it, writing a seminar paper about the (replication) study and presenting it in class. Students will have time to work on the assignment over the semester. Presentations will be held in the last week of the lecture schedule. Seminar papers need to be handed-in by December, 30. The oral grade and homework assignment contributes 50% of your overall grade for the course. Joint with the homework assignment, we reward active participation in the exercise sessions. This rule applies only if the final exam is taken during one of the three exam periods of this academic year. 

Exam schedule

FURTHER INFORMATION

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