Causal Inference with Spatial Data
Intensive PhD course at Department of Economics, University of Oslo
August 27-31, 2018

Lecturer: Masayuki Kudamatsu (OSIPP at Osaka University)

The purpose of this course is two-fold. First, we expose ourselves to various pieces of empirical economics research that use spatial datasets. This will help you come up with original research ideas by taking advantage of spatial datasets. Second, we have hands-on experiences of using ArcGIS 10 and of programing in Python by replicating the spatial datasets for the actual pieces of empirical economics research. This way, we will be able to find out how to process the spatial datasets needed for our own research.

This course has been taught since 2010, and the lecture slides are available at the lecturer’s website.

Grading Policy

This course welcomes those who just want to audit the course. For obtaining credit, however, you have to submit a research proposal. 

Your grade is based on three criteria: Importance, Originality, and Feasibility of your research proposal. I want you to use this opportunity to practice coming up with a good research question. 

The submission deadline is 23:59 on 19th December, 2018. Email a PDF copy of your research proposal to the lecturer at m.kudamatsu@gmail.com. 

I will return feedback to each student’s proposal by the end of December.

The guideline for writing a research proposal is attached at the end of this syllabus.  

Course Schedule and Reading List

Lecture 1: Introduction to Spatial Data
August 27, 9:00-11:30

Donaldson, Dave, and Adam Storeygard. 2016. "The View from Above: Applications of Satellite Data in Economics." Journal of Economic Perspectives, 30(4): 171–198.

Read this article for how satellite image data has recently been used by economists. In the lecture, we will learn the basics of spatial datasets: polygons, raster, map projection, etc.


Lecture 2: Spatial Join 
August 28, 9:00-12:00

Alsan, Marcella. 2015. "The Effect of the TseTse Fly on African Development." American Economic Review, 105(1): 382–410.

We will replicate how this paper spatially merges ethnic group level data with the grid of weather data, a great source of exogenous variation.


Lecture 3: Buffer
August 28, 13:00-16:00

Conley, Timothy G., and Christopher R. Udry. 2010. "Learning about a New Technology: Pineapple in Ghana." American Economic Review, 100(1): 35–69.

We will replicate how this paper matches each agricultural plot with its neighboring plots within a radius of 1km, to control for common shocks in the peer effect estimation.


Lecture 4: Distance
August 29, 9:00-12:00