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.