Syllabus

Title
0750 Economic Policy (Applied Track)
Instructors
Dr. Matthias Schnetzer
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
10/03/24 to 10/03/24
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Dates
Day Date Time Room
Monday 10/07/24 10:00 AM - 12:00 PM TC.4.02
Monday 10/14/24 10:30 AM - 12:00 PM TC.4.18
Monday 10/21/24 10:30 AM - 12:00 PM TC.4.18
Monday 10/28/24 10:00 AM - 12:00 PM TC.3.07
Monday 11/04/24 10:00 AM - 12:00 PM D2.0.392
Monday 11/11/24 10:00 AM - 12:00 PM D2.0.030
Monday 11/18/24 10:00 AM - 12:00 PM TC.5.12
Monday 11/25/24 10:00 AM - 12:00 PM TC.5.18
Monday 12/02/24 10:00 AM - 12:00 PM TC.4.14
Monday 12/09/24 10:00 AM - 12:00 PM TC.3.07
Monday 12/16/24 10:00 AM - 12:00 PM TC.4.14
Monday 01/13/25 10:00 AM - 12:00 PM TC.3.07
Contents

Course website

https://mschnetzer.github.io/econpol24/

Economic Policy Visualization 

“A picture is worth a thousand words”. This course approaches contemporary issues of economic policy by analyzing innovative and iconic data visualizations. Based on selected illustrations, we discuss the underlying data, the theoretical background and policy implications with a focus on the nexus between inequality and economic growth. In a second step, we will assemble plots in class and study the basics of data visualization for policy design and evaluation. Students are required to recreate figures at home to improve their skills in coding. The main task is that students will design a comprehensive data visualization in R and draft a report around that figure in RMarkdown. Students will need to bring their laptop to class and have at least rudimentary knowledge of data handling in R.

To sum up, students will gain:

  • an overview of contemporary debates in economic policy based on recent empirical research
  • an understanding of the basics of data visualization for policy design and evaluation
  • knowledge how to enrich academic journal publications with informative figures
Learning outcomes

After completing this course, students will:

  • know key figures in various fields of economic policy
  • be aware of potentials and limitations of available data in specific policy areas
  • show improved programming skills in R and the ability to create an RMarkdown report
  • have basic knowledge of important principles of data visualization
  • be able to create simple charts to enrich their academic articles
Attendance requirements

Attendance and active participation are mandatory. Missing two classes with prior notification is accepted.

Teaching/learning method(s)

The lecturer introduces into various fields of economic policy and provides examples of data visualizations that serve as starting point for discussions. As the graphic illustration of statistical data gains in importance in both the academic and the public discourse, students will learn best practices and gain insights into the visualization of data. Another take-away of the course is to strengthen the knowledge about the power and limitations of the underlying data. A part of each session is dedicated to the work with data. The lecturer provides R-code to produce figures in class. Studens are expected to recreate three charts at home. The main assignment is the production of a comprehensive data visualization on a selected topic of economic policy. Studens will present their chart in class and draft a report with a detailed description of the data, the code and the final visualization in RMarkdown.

The course offers a lot of room for discussion in order to permit students to assess various arguments and perspectives, form their own opinion, and argue in a group setting. The teaching is designed to encourage students to actively participate in the debates, raise questions, and gain experience in visualizing data for academic publications or the general debate.

Assessment
  • Assignments: 30% (0-10 points for each visualization)
  • Chart presentation: 30% (0-20 points for the quality of the presentation, 0-10 for the preliminary chart)
  • Written report: 40% (0-40 points for the report and the final chart)
Readings

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Last edited: 2024-08-09



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