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Development Economics

Geospatial Tools for Development: Data and Inference

Leveraging Geospatial Data for Applied Research in Development Economics

17.5h (5 days)

€600 - €1,000

Online

English

Program date: June 23 - 27, 2025

Early bird deadline: April 15, 2025

Learn more

Geospatial Tools for Development: Data and Inference

Applications for 2025 Summer School programs are now open!

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Summer school

Development Economics

Geospatial Tools for Development: Data and Inference

This course seeks to introduce the wide range of different geospatial data sources available, deliver the practical skills required for using this data, and showcase applications for research in development economics and beyond.

Teaching Faculty

Exploiting Geospatial data is a key skill for empirical researchers in economics and beyond

This course would suit individuals with the following background:

Any students and practitioners interested in geospatial data and policy analysis.

Understand Geospatial Data Sources

Upon completion of the course, participants will be able to:

Identify various types and formats of geospatial data available for research.
Use basic tools for extracting and modifying geospatial data.
Apply Geospatial Data in Research.
Have hands-on experience in replicating research approaches.
Leverage geospatial data for empirical research in development economics.

Program Syllabus for Geospatial Tools for Development: Data and Inference

Here is a list of themes and topics you will cover during the course:

Introduction

Types of spatial data.
Spatial objects.
Spatial reference and projection.

GIS for Economists

Program choice and language.
Visualization of spatial data.
Data manipulation.

GIS: Basic tools

Overlay/collapse.
Buffer/distance.
Elevation/least cost paths.

Statistical inference I: Difference-in-difference design

Approach and assumptions.
Examples.
Replication exercise.

Statistical inference II: Spatial regression discontinuity design

Approach and assumptions.
Examples.
Replication exercise.

Statistical inference III: Spatial propagation

Approach and assumptions.
Examples.
Replication exercise.

List of References

The following texts will help you prepare for the course:

Articles and books

Brodeur, A., Lekfuangfu, W. N. and Zylberberg, Y.: 2017, War, migration and the origins of the Thai sex industry, Journal of the European Economic Association 16(5), 1540–1576.
Burgess, R., Hansen, M., Olken, B. A., Potapov, P. and Sieber, S.: 2012, The political economy of deforestation in the tropics, The Quarterly Journal of Economics 127(4), 1707–1754.
Dell, M.: 2015, Trafficking networks and the mexican drug war, The American Economic Review 105(6), 1738–1779.
Dell, M.: 2010, The persistent effects of peru’s mining mita, Econometrica 78(6), 1863–1903.
Dinkelman, T.: 2011, The effects of rural electrification on employment: New evidence from south africa, The American Economic Review 101(7), 3078–3108.
Donaldson, D.: 2018, Railroads of the raj: Estimating the impact of transportation infrastructure, The American Economic Review 108(4-5).
Gonzalez, R. M.: 2016, Social monitoring and electoral fraud: Evidence from a spatial regression discontinuity design in afghanistan, Technical report.
Gröger, A. and Zylberberg, Y.: 2016, Internal labor migration as a shock coping strategy: Evidence from a typhoon, American Economic Journal: Applied Economics 8(2), 123–153.
Heblich, S., Trew, A. and Yanos, Z.: 2021, East side story: Historical pollution and persistent neighborhood sorting, Journal of Political Economy 129(5).
Henderson, J. V., Storeygard, A. and Weil, D. N.: 2012, Measuring economic growth from outer space, The American Economic Review 102(2), 994–1028.
Hodler, R. and Raschky, P.: 2014, Regional favoritism, The Quarterly Journal of Economics 129(2), 995–1033.
Imbens, G. and Zajonc, T.: 2011, Regression discontinuity design with multiple forcing variables, Report, Harvard University.[972] .
Kudamatsu, Masayuki, GIS for Credible Identification Strategies in Economics Research, CESifo Economic Studies 64(2), 327–338.
Lipscomb, M., Mobarak, M. A. and Barham, T.: 2013, Development effects of electrification: Evidence from the topographic placement of hydropower plants in brazil, American Economic Journal: Applied Economics 5(2), 200–231.
Michalopoulos, S. and Papaioannou, E.: 2014, National institutions and subnational development in africa, The Quarterly Journal of Economics 129(1), 151–213.
Miguel, E. and Kremer, M.: 2004, Worms: identifying impacts on education and health in the presence of treatment externalities, Econometrica 72(1), 159–217.
Olken, B. A.: 2009, Do television and radio destroy social capital? evidence from indonesian villages, American Economic Journal: Applied Economics 1(4), 1–33.
Pascali, L.: 2017, The wind of change: Maritime technology, trade and economic development, American Economic Review 107(9), 2821-54.
Qian, N.: 2008, Missing women and the price of tea in china: The effect of sex-specific earnings on sex imbalance, The Quarterly Journal of Economics 123(3), 1251–85.
Skovron, C. and Titiunik, R.: 2015, A practical guide to regression discontinuity designs in political science.
Yanagizawa-Drott, D.: 2014, Propaganda and conflict: Evidence from the Rwandan genocide, The Quarterly Journal of Economics 129(4), 1947–1994.

Software / Hardware

Participants will make use of the QGIS program throughout the course.

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers.

2

Study in vibrant Barcelona.

3

Learn from world-renowned faculty.

Admissions and requirements

Participants to BSE Summer School must check they are eligible to apply.

Program date: June 23 - 27, 2025

Early bird deadline: April 15, 2025

Requirements

Summer School applicants normally demonstrate one or more of the following:

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.).
Postgraduate degree or current Master’s/PhD studies related to the course topic.
Relevant professional experience.

Requirements for Geospatial Tools for Development: Data and Inference

There are no mandatory requirements, however, intermediate knowledge of econometrics and causal inference as well as basic Python skills are an advantage.

Apply now

Schedule

Here is your schedule for this edition of BSE Geospatial Tools for Development: Data and Inference course.

Time

23

mon

24

tue

25

wed

26

thu

27

fri

13:00 - 15:00

Lecture

17:45 - 19:15

Practical

Credit transfers (ECTS)

To be eligible for credit transfer, students must complete a final project. The proposal should be 3-4 pages, 1.5 spaced, font size 11, and include:

A clear research question.
Motivation, including the question’s policy relevance.
A discussion of existing literature and how your paper relates.
An explanation of your approach, detailing the empirical strategy and/or theoretical model.

Consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants not interested in credit transfer will instead receive a Certificate of Attendance free of charge. These Participants will not be graded or assessed during the course.