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Microeconomics II
Part A: Development Economics - Experiments
Part B: Spatial Economics and Big Data

Module Code: EEC7002

  • ECTS Credit: 10
  • Mandatory/Optional: Mandatory
  • Module Coordinator: Prof Andrea Guariso; Prof Martina Kirchberger.

Aims of Module

(a) The first half of this module will focus on the selection problem that often arises in empirical studies, and will discuss ways in which properly designed experiments can help addressing it. We will also discuss alternative solutions, for settings in which experiments are not feasible and/or desirable. Examples will be taken from the development economics literature.

(b) The second half of this module will introduce students to spatial economics and big data. We will discuss how spatial data can be used to understand the distribution of economic activity across space, measure outcomes, and aid identification. We will also cover which types of research questions have become feasible to study by using spatial and big data.

Module Delivery

The module will be delivered through a combination of lectures (18 hours) and tutorials (9 hours). Problems are circulated each week and answers are submitted before the next week's tutorial, at which they are discussed.

Learning Outcomes

On completion of the module students should be able to use economic tools to solve applied microeconomic problems in a number of areas. Students will be familiar with set of tools available to address selection problems, and will have a full understanding of their application, both from a theoretical and a practical point of view. Students will be knowledgeable of possible sources of spatial and big data and their applications in economics.

Syllabus

Topics covered in this module include:

Part A: Development Economics - Experiments

  • The Selection Problem; Experiments in Development Economics
  • Alternatives I : Difference-in-Difference and Instrument Variable Approaches
  • Alternatives II: Regression Discontinuity and Event Study Approaches

Part B : Spatial Economics and Big Data

  • Spatial data and methods in spatial economics: using remotely sensed and other spatial data as outcomes, covariates, and instruments
  • Big data in economics: possibilities and limitations

Assessment

Assessment for the course is based on weekly problem sets and one 3-hour examination.