Economic Policy Visualization
Introduction
October 7, 2024
“A picture is worth a thousand words”
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:
What do you expect of this course? Do you already have some coding experience in R?
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 | Taxation · 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 |
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 platform before 9 a.m. on the day of the deadline.
Deadline: December 9, 2024
Deadline: January 31, 2025
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)
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:
Jonathan Schwabish Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks Columbia University Press ISBN-13: 9780231193115 |
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David Spiegelhalter The Art of Statistics: Learning from Data Penguin Books UK ISBN-13: 9780241258767 |
PI 0750 Economic Policy (Applied track) | Winter term 2024