84557 - Labor Economics

Learning outcomes

At the end of the course student has knowledge of a detailed analysis of the main areas in labor economics, both from a theoretical and an empirical perspective. Topics include the analysis of labor supply by individual and households, labor demand by firms, equilibrium wage differentials and employment outcomes resulting from the interaction of such supply and demand, education and human capital, life-cycle profiles, job search models, and labor market institutions. At the end of the course student has an understanding of how labor markets work and possess the basic tools to undertake original research in the field.

Course contents

This is a graduate-level (Master and PhD), research-oriented course in labor economics. The goal is to introduce students to advanced analysis of labor markets and to provide them with tools to engage in independent research in labor economics. We will cover the following topics, intertwined with methodological problems and applications:

- Labor supply and demand
- Search and matching
- Employers' market power
- The Roy model and its applications

Readings/Bibliography

- Lecture notes.
- Labor Economics, Second Edition, by Pierre Cahuc, Stéphane Carcillo and André Zylberberg, MIT Press, 2014 (a useful, though not required, graduate-level textbook).
- Research articles assigned during the semester.

Teaching methods

Lectures will illustrate theory and empirical applications, and will guide students to independent study and critical thinking.

Assessment methods

Problem sets will be assigned during the semester, and solutions will be discussed in class. The final exam consists of a presentation and discussion of a research article related to the topics studied in class, assigned to each student by the instructor.

The use of AI tools is strongly encouraged as an aid to the learning process, much like having a very smart, personalized tutor. Of course, AI should not replace your own thinking and trial-and-error processes, as doing so may undermine your ability to address and solve problems independently. Use AI to assist you in the hard work, not to replace you in that work. Like in sports, learning requires hard work and costly effort. If you try to avoid it by delegating thinking, you may trade off a short-run benefit for a long-term deficit.

The final grade is computed as follows: problem set discussion 20%; final exam 80%.

The maximum possible grade is 30 cum laude. The grading scale is the following:

<18: Fail
18-23: Sufficient
24-27: Good
28-29: Very good
30: Excellent
30 cum laude: Outstanding (the instructor was impressed)

Teaching tools

The Virtuale platform will provide the following resources:

- Updated information and notices
- Class slides
- Research articles

Office hours

See the website of Giulio Zanella