This course approaches contemporary issues of economic policy by analyzing innovative or iconic data visualizations. We analyse selected illustrations, discuss the underlying data, the theoretical background and policy implications. We will assemble plots in class and study the basics of data visualization.
You will gain:
an overview of contemporary debates in economic policy
a basic understanding of principles of data visualization
knowledge how to enrich academic publications with informative graphs
Who are you?
What do you expect of this course? Do you already have some coding experience in R?
Schedule
Date
Time
Room
Content
Assignment
Oct 07, 2024
10:00-12:00
TC.4.02
Introduction
Oct 14, 2024
10.30-12.00
TC.4.18
Data
Oct 21, 2024
10.30-12.00
TC.4.18
Visualization
Oct 28, 2024
10:00-12:00
TC.3.07
Income · Geometries
Nov 04, 2024
10:00-12:00
D2.0.392
Wealth · Scales
Nov 11, 2024
10:00-12:00
D2.0.030
Mobility · Colors
Nov 18, 2024
10:00-12:00
TC.5.12
Growth · Labels
Nov 25, 2024
10:00-12:00
TC.5.18
Redistribution · Maps
Dec 02, 2024
10:00-12:00
TC.4.14
Policy · Themes
Dec 09, 2024
10:00-12:00
TC.3.07
Student presentations
Dec 16, 2024
10:00-12:00
TC.4.14
Student presentations
Jan 13, 2025
10:00-12:00
TC.3.07
Round-up
Assignments
Assignment 1 provides the setup of the R infrastructure that is required in this course. There are no points for this assignment.
Assignments 2 to 4 are recreations of examplary figures. These examples are related to figures that are discussed in class. Students should then try to reproduce the plots at home and improve their individual coding skills.
The raw data for the figures are available as CSV files. The charts should then be uploaded to the learning platformbefore 9 a.m. on the day of the deadline.
Assignments
Chart & Report
Chart presentation
Research question Which economic policy question do you want to answer with your chart?
Data
Chart
Conclusion What can we learn from the data visualization for economic policy?
Deadline: December 9, 2024
RMarkdown (or Quarto) Report
Title
Author
Introduction
Research question
Data
Result
Conclusion
Code
Deadline: January 31, 2025
Grading
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)
Visualizations by students
Feedback, cooperation and help
Let me know your feedback on the course anytime. If possible, I will try to incorporate your feedback immediately. At least, I will consider it for future courses.
As some of you might already have advanced coding skills in R, please support each other and collaborate. This does not mean that one person does all the coding and shares with all colleagues. Students should have an intrinsic motivation to improve their coding skills but cooperate to learn from each other.
There is a forum on the learning platform for exchange among students. Please also consult support platforms like Stack Overflow or take a look at the cheatsheets:
Recommended Literature
Jonathan Schwabish Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks Columbia University Press ISBN-13: 9780231193115
David Spiegelhalter The Art of Statistics: Learning from Data Penguin Books UK ISBN-13: 9780241258767
