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 course aims to offer an introduction to the main tools used in data analysis and applied empirical research, with a focus on identifying causal relationships between socio-economic variables. The methods covered in the course will enable students to investigate some of the most important policy issues faced by governments and modern societies and to quantitatively evaluate the effects of policy interventions implemented to address them. During the course, examples of policy evaluation applications in various fields, including labor economics, public economics, and development economics, will be explored. This will be done through the study of scientific articles and practical labs using the statistical software Stata.

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
• apply empirical policy evaluation methods using the statistical software Stata

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 laboratories with Stata. 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

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)

GIANLUCA CERRUTI

MAURIZIO CONTI

ALESSANDRO SPIGANTI

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 evaluation will be based on a final written exam lasting 1.5 hours, consisting of three parts. One of these requires students to interpret Stata output and comment on tables showing estimates of empirical models. Each of the three parts is worth 10 points. The final grade is the sum of the points obtained in each of the three parts.

Attending students will have the opportunity to work on a group project. Each attending 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 include studying an article, replicating some of its results, writing a brief report, and presenting the results in class. Students will have time to work on the project during the semester. Presentations will take place in the last week of the lecture program. The written assignment must be submitted by December 30th. The grade for the report and presentation provides the opportunity to earn up to 3 extra points. Active participation in exercise sections may also be rewarded.

Exam schedule

FURTHER INFORMATION

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