APPLIED MICROECONOMICS

APPLIED MICROECONOMICS

Fabiano Schivardi, Matteo Paradisi

Obiettivi formativi

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.

Risultati di apprendimento attesi

Capacità di analisi empirica rigorosa

Contenuti Del Corso

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.

Testi Di Riferimento

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

Metodologie Didattiche

Lezioni

Modalità di verifica dell'apprendimento

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.

Criteri per l’assegnazione dell’elaborato finale

Non è previsto elaborato finale

Settimana 1

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

Settimana 2

- Production function estimation: the control function approach;

Settimana 3

- Measuring productivity in imperfectly competitive markets; - Measuring misallocation

Settimana 4

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

Settimana 5

- Corporate control and performance; - Local development

Settimana 6

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

Settimana 7

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

Settimana 8

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

Settimana 9

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

Settimana 10

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

Settimana 11

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