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Alisa Remizova
Administrative Coordination
Noemi Hartung
Tel: +49 621 1246-211
Tel: +49 621 1246-211
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Decomposition Methods in the Social Sciences
About
Location:
Mannheim, B6, 4-5
Mannheim, B6, 4-5
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 440 €
Academics: 660 €
Commercial: 1320 €
Keywords
Additional links
Lecturer(s): Johannes Giesecke, 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:
Learning objectives
By the end of the course participants will:
Prerequisites
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.
