Master

Cause and Effect: Topics in Empirical Economics of Education, Labor and Health

• Structure: Lecture + Exercise
• Cycle: Summer semester
• Credits: 6 CP
• Module code: 02-VWL/BWL:MSc-V10-1
• Language: English
• Form of examination: Exam

• Description:

This course is offered in the summer semester and is open to Master's students in Economics and Business Studies.

The course is based on the book "Causal Inference" by Scott Cunningham (accessible online Welcome | Causal Inference (scunning.com)), as well as on the books "Mostly Harmless Econometrics" and "Mastering Metrics" by Joshua Angrist and Jörn-Steffen Pischke (online course Mastering Econometrics with Joshua Angrist | Marginal Revolution University (mru.org)).

Many of the big questions in the Social Sciences (and Economics) deal with cause and effect. How does immigration affect pay and employment levels? How does a longer education affect someone's future income? These questions are difficult to answer because we have nothing to use as a comparison. We do not know what would have happened if there had been less immigration or if that person had not continued studying.

However, this year's Laureates of the Nobel Prize in Economics - David Card, Joshua Angrist and Guido Imbens - have shown that it is possible to answer these and similar questions using natural experiments. The key is to use situations in which chance events or policy changes result in groups of people being treated differently, in a way that resembles clinical trials in medicine.

If you are curious about how economists can draw plausible conclusions about cause and effect I invite you to join this course. The course covers core methods and seminal applications (discussed in the lecture and replicated in the exercise) dealing with causal inference.

Preliminary schedule:

1. Randomized Controlled Trials
2. Linear Regression and Matching
3. Instrumental Variables
4. Difference-in-Differences (+ Synthetic Control Method)
5. Regression Discontinuity Design

Tools in Empirical Research

• Structure: Lecture + Exercise
• Cycle: irregular
• Credits: 6 CP
• Module code: 02-VWL/BWL:MSc-V10-Extra1
• Language: German
• Form of examination: term paper (70%) + homework (20%) + presentation (10%)
• Required prior knowledge: Basic statistical knowledge
• Schedule: - 

 
This course is an in-person class.

•  Description:

The course is offered in the summer semester and is open to students from the Master's degree programs in Business Administration and Economics. The course offers an introduction to the typesetting system LaTex and the statistical software R. We focus on the practical use of LaTex and R using data sets.

The course covers the following topics:

• Introduction to LaTex
• Creating documents and presentations in LaTex
• Introduction to R and RStudio
• Loading data sets in RStudio
• Data Cleaning in RStudio
• Creating graphics in RStudio

Seminar

• Structur: Kick-off meeting, weekly meetings (introduction to academic work, working with data, introduction to working with statistical software Stata, starting/creating a data project, outline meetings, ...), block seminar with presentations
• Cycle: Summer semester + winter semester
• Credits: 6 CP
• Module code: 02-VWL:MSc-Seminar
• Language: German (written paper and presentation optional in English)
• Form of examination: Written paper + final presentation + active participation

• Description:

Data Economy - sourcing, processing and analyzing data from our daily lives.

This course if offered in the summer semester and is open to students from the Master's programme in Economics and Business Administration. Basic knowledge of statistics and econometrics is required.

Willingness to work with data (using Stata) is expected. Namely, working with the organisation, process, analysis and visualization of data will be part of this course; basic knowledge of it is not required.

Covered Topics will be from following areas, for example:

• Living in Germany (descriptions of different fields of life with the Socio-Economic panel SOEP)
• Health economics (e. g. risky health behavior such as smoking, unhealthy diet, lack of exercise)
• Economics of education (accumulation and investment in human capital, returns to education)
• Labor economics (wage differences, family planning, and female labor markt participation)
• Policy evaluation (consequences of a reform/legislative change)
• Causal inference (natural experiments such as Difference-in-Differences approach, instrumental variables)
• ...