Data and Development

Campus: 
Vienna
Academic Year: 
2019-2020
Term: 
Winter
US Credits: 
2
ECTS Credits: 
4
Course Description: 

Mandatory/Elective course for 1YMAPP and MPA 1Y students (Students have to complete 2 credits of either this course or
Economics of Higher Education (2); or Political Economy of Reforms (2); or Institutional and Behavioral Economics (2))

Development specialization

The course is designed for students interested in using data to understand and solve social and economic development problems, and assessing the effectiveness of policies in developing countries. Broadly, we will explore issues of health, education, climate change, and economic development through an empirical perspective. This will require a basic understanding of empirical methods, such as Ordinary Least Squares (OLS), Instrumental Variables (IV), and Difference-in-Difference estimation and other causal identification methods which we will review and further build upon. The course will advance student training in econometric methods and the use of statistical software (STATA, R) for empirical work on development topics.

Learning Outcomes: 

Course goals and learning outcomes

The primary goal of this course is to provide an introduction to persistent and emerging issues in the economics of development with an emphasis on empirical identification.  The course will engage students with the main tools used in applied development microeconomics. We will briefly introduce key concepts and theories in development economics and detail them by empirically examining (recent) journal articles. At the end of the course, students should be able to (1) outline the main theories and concepts in development economics for specific topics (2) use economic concepts to critically evaluate development policies (3) analyze and synthesize empirical work for the purpose of designing policy in specific contexts (4) use insights from behavioral economics and psychology for designing development policies. 

Assessment: 

Assignments 30%
Short test or quiz 10%
Team presentation 20%
Paper outline 5%
Final Paper 35%

Prerequisites: 

A basic understanding of empirical methods, such as Ordinary Least Squares (OLS), Instrumental Variables (IV), and Difference-in-Difference estimation and other causal identification methods which we will review and further build upon.