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Scientific Coordination

Alisa Remizova

Administrative Coordination

Noemi Hartung

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Decomposition Methods in the Social Sciences

About
Location:
Mannheim, B6, 4-5
 
General Topics:
Data Analysis
Course Level:
Advanced
Format:
Workshop
Software used:
Stata
Duration:
4 days
Language:
English
Fees:
Students: 440 €
Academics: 660 €
Commercial: 1320 €
Keywords
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 the labor market experience of women is valued less than the 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 the test scores of pupils with and without migration background compare if there were 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 becoming increasingly popular in various fields of the social sciences. The workshop 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.
 
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.


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;
  • be able to identify potential areas of application of the different approaches;
  • have a good understanding of how the methods work;
  • be able to apply the methods purposefully in the context of their own work;
  • be able to interpret the results correctly.


    • Prerequisites

  • Solid basic statistical knowledge (including regression analysis).
  • Experience in applied data analysis with  common statistical software (ideally Stata).
    •  
    Software and hardware requirements
    Each student should have a computer with Stata running for the exercises.
     
    We will use Stata for the exercises. Any version that is not too old will do (say Stata 14 or newer). Throughout the course, the students 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). Stata short-term licenses will be provided by GESIS for the duration of the course if needed. Please contact us two weeks in advance of the course if you need a license.


    Schedule