In this seminar we try to answer apparently simple but intriguing questions from daily life. For example:
Can beauty affect employment chances and income level?
How does unemployment affect happiness?
Does owning a pet makes you live longer?
Can working from home increase employee's work effort?
Why can't money buy happiness?
What is the impact of a vegetarian diet?
Are lefthander different from righthander?
A wordcloud of 25 students answers (2020) on the question: "What do you expect to learn in this seminar?" Also check Course Evaluations.
The Seminar in Applied Economics: Happiness and Discrimination is about empirical analysis of questionnaire data (SOEP) to answer questions from the field of happiness research and labour market discrimination. Students are given the opportunity to work on a personal term project under intense supervision.
The analysis is done with help of the statistical package R and results are reported in an R Markdown (html document). In this course you learn and practice critical, statistical thinking based on complex rectangular panel data. You will become data fluent and learn major skills that you can use in your academic and business career.
The seminar also serves as a preparation for an (empirical) thesis.
Quick Start
Online Mode
The seminar is given in full online mode for the third time. It takes place during the entire semester (starting from 15.04.2021 to 15.07.2021). Attendance is compulsory (you need audio + video to join group and individual sessions).
Language
The working language is English (data reports can be German).
Grading
Your grade is composed of weekly assignments and a final data analysis report.
Target Group
Students with different study programs or levels and diverse backgrounds are welcome.
Bachelor Students (IBA, IBWL, WiR, BWL) with core area European Economics or Quantitative Methods. Master Students from IBA (with Finance & International Economics track) or MES (with Economics Core Area).
Is this the right seminar for me?
All students are welcome! The majority of successful former students did not had any prior programming experience.
Explore the course material carefully. Check whether you are interested in some of the topics and what former students tell you about the seminar, see course evaluations.
