GESIS Training Courses

Scientific Coordination

Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Loretta Langendörfer M.A.
Tel: +49 221 47694-143

Decomposition Methods in the Social Sciences

Course duration
Mo: 10:00-17:00 CET
Tu-Fr: 09:00-16:00 CET
General Topics:
Course Level:
Software used:
5 days
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Additional links
Lecturer(s): Johannes Giesecke, Ben Jann

About the lecturer - Johannes Giesecke

About the lecturer - Ben Jann

Course description

Is the difference in wages between men and women (the gender wage gap) due to less labor market experience of women compared to men, or is it due to discrimination against women, for example because labor market experience of women is valued less than labor market experience of men? How much of the gender wage gap can be "explained" by differences in endowments such as education, skill, or experience? How much do changes in educational attainment and general trends in earnings inequality contribute to the change in the wage gap over time? How would test scores of pupils with and without migration background compare if there would be no differences in average socio-economic status? How much did de-unionization and the decline in real minimum wages contribute to rising wage inequality? How high would the mortality rate in country A be if it had the demographic composition of country B? Decomposition methods can help find answers to such and other questions by providing insights into the mechanics of group differentials (such as earnings differences between men and women). Based on methodological developments mostly in labor economics (and some parallel developments in demography), these methods are increasingly popular in various fields of the social sciences. The seminar introduces the statistical concepts of decomposition methods, provides an overview of various approaches, and makes students familiar with the application of the methods and the interpretation of their results. Theoretical inputs and practical exercises (using Stata) will be alternated throughout the course.

Target group

Participants will find the course useful if:
  • they are applied quantitative social science researchers from universities, research units in government agencies, or other research institutions;
  • they are PhD students or Postdocs in quantitative social sciences;
  • they are advanced master students in quantitative social sciences.

  • Learning objectives

    By the end of the course participants will:
  • have an overview of the most common decomposition methods;
  • know the strengths and weaknesses of the different approaches;
  • can identify potential areas of application of the different approaches;
  • have a good understanding of how the methods work;
  • can apply the methods purposefully in the context of their own work;
  • can interpret the results correctly.
    Organizational structure of the course
    On each day, the course will start with about three hours of classroom instruction in the morning and then continue with about three hours of hands-on tutorials and exercises in the afternoon. The morning lectures will introduce and explain the theory and methods and discuss example applications. Students are strongly encouraged to actively participate in these sessions by asking questions or contributing to the discussions based on their own research experience. In the afternoon, students will work on assignments, individually or in small groups, to implement the presented methods in practice using statistical software (Stata). During these sessions, the lecturers will be available to provide help or discuss specific problems. The sessions will also include several inputs by the lecturers, in which they present example solutions to key parts of the assignments and discuss questions that came up during the exercises. Furthermore, the afternoon sessions will provide opportunity to discuss own research problems on an individual basis with the lecturers.


    Participants require:
  • solid basic statistical knowledge (including regression analysis);
  • practical experience in data analysis with a common statistical software (ideally Stata).
    Software and hardware requirements
    We will use Stata for the exercises. Stata short term licenses will be provided by GESIS for the duration of the course if needed. Participants who own a Stata license, should make sure that they have a recent version (14 or higher) of Stata installed. Throughout the course, participants will need to be able to install additional user packages on the fly (this requires an internet connection and appropriate writing rights on the local system).

    Recommended readings