APPLIED MICROECONOMICS

APPLIED MICROECONOMICS

Fabiano Schivardi, Matteo Paradisi

Instructional goals

The main goal of the course is to give endow students with both state-of-the art tools and expose them to the current debates in the study of firms. The course can be useful both to continue for a PhD and to pursue a professional careers in private or public research centers dealing with firm analysis and regulation.

Intended learning outcomes

Acquisition of empirical analysis skills

Course Contents

The class will focus on industry dynamics and productivity. In the first part of the class we will learn to estimate production functions. We will then cover some applications of the techniques learned: productivity in imperfectly competitive markets, misallocation, measuring markups with production data, IT and productivity, matched employer-employee data, corporate governance, local development.

Reference Books

“Microeconometrics: Methods and Applications”, A. Colin Cameron, Pravin K. Trivedi “Mostly Harmless Econometrics”, J. D. Angrist and J.-S. Pischke

Teaching Methods

Classes

Assessment Method

The grade will be based on class participation (including the presentation of a paper) and a final exam. I will also assign two problem sets during the class.

Thesis assignment criteria

No final thesis is required

Week 1 Contenuto sessioni on line e on campus

- Introduction. Basic facts about industry dynamics and productivity; - A basic framework to study industry dynamics

Week 2 Contenuto sessioni on line e on campus

- Production function estimation: the control function approach;

Week 3 Contenuto sessioni on line e on campus

- Measuring productivity in imperfectly competitive markets; - Measuring misallocation

Week 4 Contenuto sessioni on line e on campus

- Matched employer-employee data; - Management, ICT and productivity

Week 5 Contenuto sessioni on line e on campus

- Corporate control and performance; - Local development

Week 6 Contenuto sessioni on line e on campus

Introduction to panel data analysis. Motivation and Identification. First-difference estimator Materials: scientific papers, lecture notes

Week 7 Contenuto sessioni on line e on campus

Fixed-effects models. Within and dummy estimators. Materials: scientific papers, lecture notes

Week 8 Contenuto sessioni on line e on campus

Random-effects models. An application of fixed-effects: the AKM model Materials: scientific papers, lecture notes

Week 9 Contenuto sessioni on line e on campus

Dynamic panel models. Standard errors in panel data analysis. Introduction to clustering. Identification in panel data. Materials: scientific papers, lecture notes

Week 10 Contenuto sessioni on line e on campus

Difference-in-differences models. Intuition, applications and identification assumptions.

Week 11 Contenuto sessioni on line e on campus

New advancements in difference-in-differences estimation. The use of event studies in economics. Advantages, assumptions and potential threats to the identification of causal effects. Materials: scientific papers, lecture notes