Data Science in International Economics Research

DSIER [/dɪˈzaɪər/]

Fields in Kazakhstan.

Summary

This course provides a snapshot of state-of-the-art research in the field of international economics that makes use of big, often unconventional datasets, and novel methods.

Topics include issues in development studies (e.g. using satellite imagery), international trade and migration (large semi-structured administrative data and cellphone trace data) and international finance (data from social networks).

The course combines a weekly lecture that introduces a research project, its data and methods, as well as an application session in which students are tasked with handling similar datasets and methods.

Coursework includes short assignments along the semester, as well as a final project.

Topics

1. Getting started: Course outlook and good research practice

2. The toolkit: R and the shell

3. Conventional large data

4. New sources of economic data: Web scraping and APIs

5. Networks

6. Satellite imagery

7. Spatial data

8. Text as data

9. Event and sensor data

10. Social media data

Preliminary schedule

The course takes place on Wednesdays from 10 – 12h and 16 – 18h, sometimes in person, sometimes online.

Date	Time	Topic	Teacher	Location
April 20	10 – 12	Course outlook	Irene & Julian	U2-228
	16 – 18	Good research practice	Julian	U5-133
April 27	10 – 12	The shell	Julian	U2-228
	16 – 18	R	Julian	U5-133
May 4	10 – 12	Conventional large data	Irene	U2-228
	16 – 18		Julian	jhi.nz/dsier
May 11	10 – 12	Web scraping and APIs	Irene	U2-228
	16 – 18		Julian	jhi.nz/dsier
May 18	10 – 12	Networks	Irene	jhi.nz/dsier
	16 – 18		Julian	jhi.nz/dsier
May 25	10 – 12	Satellite imagery	Irene	U2-228
	16 – 18		Julian	jhi.nz/dsier
June 1	10 – 12	Spatial data	Irene	jhi.nz/dsier
	16 – 18		Julian	jhi.nz/dsier
June 8	10 – 12	Text as data	Irene	U2-228
	16 – 18		Julian	jhi.nz/dsier
June 15	10 – 12	Event and sensor data	Irene	jhi.nz/dsier
	16 – 18		Julian	jhi.nz/dsier
June 22	10 – 12	Social media data	Irene	U2-228
	16 – 18		Julian	jhi.nz/dsier
June 29	10 – 12	to be determined	Irene	jhi.nz/dsier
	16 – 18		Julian	jhi.nz/dsier
July 6	10 – 12
	16 – 18	Presentation of projects	Irene & Julian	U5-133
July 13	10 – 12
	16 – 18	Presentation of projects	Irene & Julian	U5-133

References and additional resources

The course material, both for the lectures and coding examples, is often inspired by fantastic work from other educators and researchers. Here are some references that sometimes go beyond what we do in class:

• Kieran Healy’s Plain Text Guide to Social Science

• Hadley Wickham and Garrett Grolemund’s R for Data Science

• Grant McDermott’s Data Science for Economists

• Jenny Bryan’s Stat 545

• and a bit more towards computer science, The Missing Semester of Your CS Education

Questions?

Any general questions? Post them in the dedicated #general Slack Channel in case you think this is a question of general interest. Of course, you can also contact us privately

• Julian Hinz: Slack DM or E-Mail
• Irene Iodice: Slack DM or E-Mail
• Philipp Brockhaus: Slack DM