Syllabus

The course is co-taught by Pauline Morault.

Syllabus

Prerequisites:

This course is open to 3rd undergraduate students of any field. However, a good level of spoken and written English is required (B1 level), as well as Statistics I. 

Course description:

This course provides an introduction to the analysis of inequalities in income, wealth, access to education, gender, and ethnicity. Three sets of core questions will be tackled: 1) How has inequality evolved over time? 2) What are the theories that can explain the level of inequalities and its dynamics? 3) How do policies affect inequalities? 

This course will also show how data can be used to understand and address these important social and economic problems, by providing an introduction to basic methods in data science (Python), including regression, causal inference. Students will be assigned empirical projects that will give them hands-on experience in working with data from the World Inequality Database, the OECD, the IPUMS International, and the Opportunity Insights. 

The objectives of the course are the following: 

• Will be able to analyze the social mobility and social class relations in modern industrial and/or post-industrial societies. 

• Identify the reasons for social inequalities in industrial and/or post-industrial societies. 

• Compare different forms of social inequalities such as social class, gender, and ethnicity

• Develop a methodological framework for analyzing social inequalities

Required readings:

There is no textbook that covers all the material that we will see in class, so to succeed in this course it is essential to attend all the lectures. Lecture notes will be posted on the course website, but these notes are not comprehensive, so it is critical to be attentive and to take notes during lectures. Lectures are key to doing well in this course. 

Python references: 

1. Python Programming for Economics and Finance

2. Wealth distribution dynamics and other inequality measures in Python

Course structure:

The course is organized into two parts: 

1. class 1 & 2: introduction and core concepts, empirical tools (Python)

2. invited lecturers: 

• Nov 14th - Stefan Buciuc (BCR Social Finance IFN SA/ERSTE Group) - with a presentation on Financial inclusion

• Nov 21st - Marius-Ionuț UNGUREANU (Department of Public Health, UBB) - with a presentation on Health inequalities

• Nov 28th - Caroline Coly (University of Barcelona and IEB) - with a presentation on Gender inequalities

Grading:

• Project to be completed in group (3-4 students at most): 80%. The project will be presented to the rest of the class, and a podcast will be created and published online. 

• Participation: 20%

Syllabus