0822 Economic Policy (Applied Track)
Dr. Matthias Schnetzer
Contact details
Weekly hours
Language of instruction
10/04/23 to 10/04/23
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Day Date Time Room
Monday 10/09/23 10:00 AM - 12:00 PM TC.3.10
Monday 10/16/23 10:00 AM - 12:00 PM TC.3.10
Monday 10/23/23 10:00 AM - 12:00 PM TC.3.10
Monday 10/30/23 10:00 AM - 12:00 PM Online-Einheit
Monday 11/06/23 10:00 AM - 12:00 PM TC.3.10
Monday 11/13/23 10:00 AM - 12:00 PM TC.3.10
Monday 11/20/23 10:00 AM - 12:00 PM TC.3.10
Monday 11/27/23 10:00 AM - 12:00 PM TC.3.10
Monday 12/04/23 10:00 AM - 12:00 PM TC.3.08
Monday 12/11/23 10:00 AM - 12:00 PM TC.3.06
Monday 12/18/23 10:00 AM - 12:00 PM TC.3.06

Visualizing Innovation

Course website:

“A picture is worth a thousand words”. This course focuses on data visualization techniques with reference to contemporary issues of economic policy. Based on selected illustrations, we discuss the underlying data, the theoretical background, and policy implications in various fields of empirical economics. There will be coding sessions in class where students assemble plots in R and study the basics of data visualization. Students are required to recreate figures at home to improve their coding skills.

As a special feature, we will have a collaboration with the Austrian Patent Office (Österreichisches Patentamt). Students will gain insights into the evolution and dissemination of innovation in the economic system. The agency will exclusively provide internal data to create data visualizations and illustrate the process of private innovation in Austria. 

The main task in this course is that students will design a comprehensive data visualization with the provided data and the ggplot-Package in R and draft a report around that figure in RMarkdown. The best visualizations will be awarded by the Austrian Patent Office and might be printed in their annual report. Students need to bring their laptop to class and should have prior knowledge of data handling in R (tidyr).

To sum up, students will gain:

  • an overview of contemporary debates in economic policy based on recent empirical research
  • a basic understanding of principles of data visualization and the use of figures in economic policy debates
  • an opportunity to work with exclusive data from the Austrian Patent Office
Learning outcomes

After completing this course, students will:

  • know key figures in various fields of economic policy
  • be aware of the potentials and limitations of available data for specific policy discussions
  • show improved programming skills in R and the ability to create a RMarkdown report
  • have basic knowledge of important principles of data visualization
  • be able to create 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. A part of each session is dedicated to the work with data, where the lecturer provides R-code to produce figures in class.

Studens are expected to recreate three charts at home during the semester and create a comprehensive data visualization with exclusive data from the Austrian Patent Office. 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. Moreover, the best charts are awarded by the Patent Office.

The sessions are 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.

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

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Last edited: 2023-08-24