Research on Productivity, Trade, and Growth

July 8-12, 2019 in Amsterdam

This course provides a self-contained set of lectures to bring PhD students and practitioners up to speed in the area of empirical research using firm-level data. The course starts with an introduction to models of firm dynamics and innovation. Next, attention is paid to estimation of productivity, including methods to cope with sample selection, endogeneity of inputs, and lack of firm-level quality adjusted prices. The insights from production function estimation are used to develop a framework to estimate markups using the so-called production approach. This framework is used to discuss the recent debate around rising market power. Finally, the course discusses recent empirical work on structural modeling of productivity, trade and growth. Besides theory, the course will include a set of lectures on data handling, programming, and algorithms for empirical applications, as well as daily hands-on practical sessions. To enroll, students are expected to have finished first-year PhD economics and econometrics courses and have some experience in applied research.

Level

The summer school welcomes second year (research) master students, PhD students, post-docs, and professionals from a wide variety of fields (e.g. macro, applied micro, industrial organization, international trade, labor, empirical finance, regional and urban) who are interested in learning state-of-the art methods for (cross-country) firm-level panel data analysis.

The course serves as a training activity of the Competitiveness Research Network, CompNet, and is open to its members and participating institutions (https://www.comp-net.org/services/). 

Admission requirements

A formal background in statistics or econometrics, as well as macro and micro is required from students (at the level of first year courses in a graduate school PhD program in economics).

Preparation

Before each class, students are expected to have read the required papers in the syllabus. Each day will have two lectures and two tutorials. The tutorials will be address technical aspects (computer set-up, data cleaning and handling) as well as provide time for hands-on practice with empirical assignments.