1860 Specialization Course - Economics of Distribution
Franziska Disslbacher, PhD, MSc, BSc, Jan Gromadzki, Ph.D.
Weekly hours
Language of instruction
09/13/23 to 10/05/23
Registration via LPIS
Notes to the course
Day Date Time Room
Friday 10/13/23 09:30 AM - 01:00 PM TC.5.16
Friday 10/20/23 09:30 AM - 01:00 PM D1.1.078
Friday 10/27/23 09:30 AM - 01:00 PM TC.5.16
Friday 11/03/23 09:30 AM - 01:00 PM TC.5.16
Friday 11/10/23 09:30 AM - 01:00 PM TC.5.16
Friday 11/24/23 09:30 AM - 01:00 PM TC.5.16
Friday 12/01/23 03:00 PM - 07:00 PM D4.0.133
Friday 12/15/23 03:00 PM - 07:00 PM TC.4.14
Friday 12/22/23 09:30 AM - 01:00 PM D4.0.133
Friday 01/12/24 09:30 AM - 12:00 PM TC.5.16
Friday 01/19/24 09:30 AM - 12:00 PM TC.5.16
Friday 01/26/24 09:30 AM - 04:00 PM TC.5.16
Saturday 01/27/24 09:00 AM - 02:00 PM D4.0.144

First, this course introduces the economics of distribution and inequality, and important research methods of this field. Second, it helps students improve their research skills by guiding them through a small research project based on the topics covered in the course.

Throughout the semester, we are introducing grand empirical trends and debates around income and wealth inequality and confront theories on the economis of distribution with empirical evidence. We ask, among others: What is income, how can we measure it, how is it different from wealth? Has income inequality increased, and if so, where? How can we explain trends in income and wealth inequality? How is inequality different from poverty, economic insecurity, and social mobility? What are the economic consequences of increasing inequality? Does inequality really matter, and if so, for whom? How do the welfare state and public sector relate to inequality? Can taxes curb rising inequality? What is the role of labor market institutions, including minimum wages, collective bargaining, and firm-level wage setting, for wage and income inequality?

We also prepare students for the work on their research project by discussing practical aspects around the measurement of inequality. We introduce classical measures of inequality and explain and show how to estimate them using complex survey data using the software R. In addition, we prepare students for the work on the projects by explaining and implementing important research methods used in the field economies of distribution, such as decomposition methods.

Learning outcomes
  • Students will acquire the ability to proficiently use the statistical software R for conducting empirical research with survey data.
  • Students will be able to assess the strengths and limitations of various data types, including survey data and tax data, within the context of research (especially on the economics of distribution).
  • Students will achieve a comprehensive understanding of major empirical trends of income and wealth inequality and theories aimed at explaining these trends.
  • Students will develop a solid understanding of current debates in the field of economics of distribution and inequality.
  • Students will improve their ability to comprehend research articles and critically evaluate research designs.
  • Students will enhance their academic writing and presentation skills.
Attendance requirements

Attendance is strictly required. Expectational absence in one unit is tolerated. 

Teaching/learning method(s)

The course mixes lectures, weekly readings, practical sessions where students work with data and statistical software packages, as well as student presentations during the final workshop at the end of the semester.


  • We introduce major themes and topics in a series of lectures.
  • Students are required to read and prepare one academic article for each lecture.
  • Occasional quizzes on these articles to assess the understanding of the content.

Practical Sessions (Tutorials):

  • Practical sessions focus on using data from the Survey of Income and Living Conditions (SILC) for empirical reserach on wage and income inequality and poverty.
  • Students will gain hands-on experience with the software R, as used in the tutorials.

Student Research Projects:

  • Students explore their own interests and work in groups of up to three students on a small research project.
  • While SILC data and R software are recommended, students are free to propose the use of other micro-datasets for their projects. The topic of the project has to relate to the content of the lectures.
  • We offer additional voluntary tutoring to support students in their project work.
  • In a short presentation, students introduce their research idea and proposed methodology.
  • A final workshop, held at the end of the semester, provides a platform for students to present and discuss their projects and findings.
  • The course includes a structured timeline for project reports, with a draft due one week before the final workshop and the final report due one week after the workshop.



35% Final exam

45% Group project, including two presentations

10% Quizzes

10%  Active participation in discussions and attendance

Prerequisites for participation and waiting lists

Enrollment is based on the usual ‘first come – first serve’ principle. If you are registered but not able to participate, de-register via LPIS during the registration period so your place is available to students on the waiting list.


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Availability of lecturer(s)

Office hours upon appointment!



Additional (blank) field

Students are required to bring their own laptop (with R or R Studio installed) to all R tutorials; students can rent a notebook at the Library and Learning Center.  

Deadlines are strict; no extensions will be granted.

Last edited: 2023-09-15