Development Economics Summer School

Course Overview

Geospatial data is becoming ever more widely available, for example in the form of georeferenced internet traffic, satellite imagery, or through the digitalization of historical maps. Exploiting such data has become a key skill for empirical researchers in economics and beyond. This course seeks to introduce the wide range of different geospatial data sources available, to deliver the practical skills required for using this data, and to showcase applications in which those can be used for research in development economics.

The objectives of this course are threefold: First, we will learn about the different types and formats of geospatial data and introduce a set of basic tools that can be employed for the extraction and modification of this data. Second, we will review selected pieces of economic research that use geospatial data in the field of development economics. Third, through continuous hands-on exercises using specific computer programs, we will learn the necessary skills to replicate these approaches and harness the benefits of geospatial data for applied research in development.

The course is designed for students and practitioners of any field and discipline interested in geospatial data and policy analysis.

Course Outline

1. Introduction
   1. Types of spatial data
   2. Spatial objects
   3. Spatial reference and projection
2. GIS for Economists
   1. Program choice and language
   2. Visualization of spatial data
   3. Data manipulation
3. GIS: Basic tools
   1. Overlay/collapse
   2. Buffer/distance
   3. Elevation/least cost paths
4. Statistical inference I: Difference-in-difference design
   1. Approach and assumptions
   2. Examples
   3. Replication exercise
5. Statistical inference II: Spatial regression discontinuity design
   1. Approach and assumptions
   2. Examples
   3. Replication exercise
6. Statistical inference III: Spatial propagation
   1. Approach and assumptions
   2. Examples
   3. Replication exercise

Prerequisites

No mandatory prerequisite is needed but an intermediate knowledge of econometrics and causal inference as well as basic Python skills are an advantage.

List of References

• 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

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

About the Instructor

Andre Groeger is Assistant Professor of Economics and Serra Húnter Fellow at Universitat Autònoma de Barcelona (UAB), Affiliated Professor at the Barcelona School of Economics (BSE), and Fellow at Markets, Organizations and Votes in Economics (MOVE). He received his doctorate from Goethe University Frankfurt in 2017. His research interests are in Applied Microeconomics, Data Science, Development, Labor and Political Economy.

Course Overview

The course is organized around randomized design methods, a widely used identification strategy in development research. The main purpose of this course is to provide a deep dive into the design and then data analysis from RCTs. We cover development topics as examples of such strategy (e.g. microfinance, secondary schooling, and women's labor force participation) and discuss other methods as we compare RCT results to quasi-experimental results within topical case studies. This course provides students with tangible skills that they can take away.

The students of this course typically work in policy-oriented institutions and hubs and/or are conducting graduate studies at the Master's or PhD level.

Course Outline   

1.  The Randomization Revolution in Development Economics (DAY 1)
Recommended readings:

• Gerber and Green (2012): Field Experiments, chapters 1 and 2
• Fisher (1935): Design of Experiments, chapter II

Related readings:

• Glennerster and Takavarasha (2013): Running Randomized Evaluations, chapter 1 to 3
• Jamison (2019): "The Entry of Randomized Assignment into the Social Sciences," Journal of Causal Inference, 7 (1)

​2. Research Design for Randomistas (DAY 2)

Recommended readings:

• Bruhn and McKenzie (2009): “In Pursuit of Balance: Randomization in Practice in Development Field Experiments,” American Economic Journal: Applied Economics, 1(4): 200–232
• Duflo, Glennerster, and Kremer (2007): “Using Randomization in Development Economics Research: A Toolkit," Handbook of Development Economics, Volume 3, 2007, Chapter 61, pages 3895-3962 (available from Elsevier or MIT/CEPR)
• McKenzie (2012): “Beyond baseline and follow-up: The case for more T in experiments,” Journal of Development Economics, 99(2): 210–221

Related readings:

• Gerber and Green (2012): Field Experiments, chapters 3 and 4
• Glennerster and Takavarasha (2013): Running Randomized Evaluations, chapter 4 to 7

3. Analyzing Data from Randomized Experiments (DAY 3)

Recommended readings:

• Anderson (2008): “Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects,” Journal of the American Statistical Association, 103(484): 1481-1495.
• Lee (2009): "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," Review of Economic Studies, 76(3) 1071-1102.

Related readings:

• Glennerster and Takavarasha (2013): Running Randomized Evaluations, chapter 8
• Leaver, Ozier, Serneels, and Zeitlin (2019): "Recruitment, Effort, and Retention Effects of Performance Contracts for Civil Servants: Experimental Evidence from Rwandan Primary Schools," working paper.
• Young (2018): “Channelling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results,” Quarterly Journal of Economics, 134 (2): 557-598.

4. Replication and Pre-Analysis Plans (DAY 4)

• Ozier (2019): “Replication Redux: The Reproducibility Crisis and the Case of Deworming,” Policy Research working paper; no. WPS 8835. Washington, D.C.: World Bank Group. Forthcoming, World Bank Research Observer
• Leaver, Ozier, Serneels, and Zeitlin (2018): "Power to the Plan" Development Impact Blog. Available Online.

Related readings:

• Brodeur, Lé, Sangnier, and Zylberberg (2016): “Star Wars: The Empirics Strike Back,” American Economic Journal: Applied Economics, 8(1): 1-32
• Christensen and Miguel (2018): “Transparency, Reproducibility, and the Credibility of Economics Research,” Journal of Economic Literature, 56(3): 920-980
• Coffman and Niederle (2015): “Pre-analysis Plans Have Limited Upside, Especially Where Replications Are Feasible,” Journal of Economic Perspectives, 29(3): 81-98
• Olken (2015): “Promises and Perils of Pre-Analysis Plans,” Journal of Economic Perspectives, 29(3): 61-80

5. What Have We Learned (and What Haven’t We Learned) from RCTs? 

5. a) Case study 1: Microfinance

• Banerjee, Karlan, and Zinman (2015): "Six Randomized Evaluations of Microcredit: Introduction and Further Steps,"; American Economic Journal: Applied Economics 7(1): 1-21.
• Banerjee, Duflo, Glennerster and Kinnan (2015): “The Miracle of Microfinance? Evidence from a Randomized Evaluation,” American Economic Journal: Applied Economics, 7(1): 22-53
• Breza and Kinnan (2018): “Measuring the Equilibrium Impacts of Credit: Evidence from the Indian Microfinance Crisis,” NBER Working Paper No. 24329
• Meager (2019): “Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments,” American Economic Journal: Applied Economics, forthcoming
• Pitt and Khandker (1998): “The impact of group-based credit programs on poor households in Bangladesh: Does the gender of participants matter?” Journal of Political Economy, 106(5): 958-996

5. b) Case study 2: Education - and secondary school in particular

• Evans and Popova (2016): “What Really Works to Improve Learning in Developing Countries? An Analysis of Divergent Findings in Systematic Reviews,” World Bank Research Observer, 31(2): 242–270

Related readings:

• Brudevold-Newman (2018): “The Impacts of Free Secondary Education: Evidence from Kenya,” working paper
• Duflo, Dupas, and Kremer (2017): “The Impact of Free Secondary Education: Experimental Evidence from Ghana,” working paper
• Lucas and Mbiti (2012): “Access, Sorting, and Achievement: The Short-Run Effects of Free Primary Education in Kenya,” American Economic Journal: Applied Economics, 4(4): 226-253.
• Mbiti, Muralidharan, Romero, Schipper, Manda, and Rajani, forthcoming: “Inputs, Incentives, and Complementarities in Education: Experimental Evidence from Tanzania,” Quarterly Journal of Economics
• Muralidharan, Singh, and Ganimian (2019): “Disrupting Education? Experimental Evidence on Technology-Aided Instruction in India.” American Economic Review, 109(4): 1426-60
• Ozier (2018): “The Impact of Secondary Schooling in Kenya: a Regression Discontinuity Analysis,” Journal of Human Resources, 53 (1): 157-188

Software / Hardware

Every participant taking this course will receive a time-limited personal free license of STATA several days before the start of the Summer School. Participants should install the STATA software on their laptops for use during the practical sessions.

About the Instructors

Pamela Jakiela is an associate professor of economics at Williams College, where she studies gender issues, behavioral development economics, survey design and measurement, and impact evaluation. She is also a Non-Resident Fellow at the Center for Global Development, and an Affiliate of BREAD, IZA, and J-PAL. Her research has been published in leading academic journals including Science and the Review of Economic Studies, and has been featured in media outlets including the New York Times and NPR. Her current work includes research on women’s labor force participation and occupational choice, the gender dynamics of investments in early childhood, and active labor market interventions in low-income countries. She received her PhD from UC Berkeley in 2008, an MSc in Development Studies from the London School of Economics, and a BA in Sustainable Development from the University of Michigan. 

Owen Ozier is an Associate Professor of Economics at Williams College. He is also an Affiliate of BREAD, IZA, and J-PAL. His research focuses on health, education, and economic decisions in Sub-Saharan Africa. Prior to joining Williams College, he was a Senior Economist in the Development Research Group at the World Bank. He received his M.Eng. and B.S. degrees in Electrical Engineering and Computer Science and in Brain and Cognitive Sciences from the Massachusetts Institute of Technology, and his Ph.D. in Economics from the University of California at Berkeley.

Course Overview

Network Analysis teaches you how to make sense of a complex world teeming with big systems of interconnected agents. You will learn how to model and predict social behavior at a large scale, without compromising the detailed interaction structures that are increasingly observed in growing datasets. Understanding these complex systems is crucial to construct effective policies that promote economic development in poor and developing regions.

More specifically, we will analyze how social and economic outcomes rely on the shape and structure of networks, and we will design policy recommendations that feed on rich network data to describe the best course of action. Complex Network analysis can be applied to study military alliances, the evolution of pandemics, sexual behaviour among adolescents, insurance transfers within villages, road networks and congestion, or Facebook networks of (mis)information, among many other examples that pertain to the field of development economics.

The course combines theoretical/mathematical modelling and manipulation/analysis of social network data, focusing primarily on R as the preferred programming environment. Students will learn innovative concepts, models and algorithms that can be widely applied to different contexts and that provide a comprehensive toolbox to sustain and enrich applied empirical work.

Focusing on specific data sets, students will learn, among other things, how to build and plot graphs from raw data, compute centrality measures, simulate random graph models, and apply and interpret the results of community detection methods. Once these tools have been trained, we will learn how to apply them to recent topics in economic development, paying special attention to the latest field experiments and interventions in this topic. We will also learn about optimal seeding techniques that target policy effectively on complex systems such as real-world networks.

The course targets graduate students and/or professionals with an interest in network data and how it can be used to inform development policy.

Course Outline

1. Introduction to Networks and Random Graphs (Day 1)
   a) What are networks? Graphs, Centralities, Paths, etc...
   b) E-R Random Graphs, Giant Components
   c) Configuration Model
   d) The Friendship Paradox
2. Diffusion, Adoption, and Optimal Targeting (Day 2)
   a) Simple and Complex Diffusion
   b) Models of Information Aggregation
   c) Broadcasting Vs. Targeting
   d) Random Vs. Targeted Seeding
3.  Segregation and Economic Opportunity (Day 3)
   a) Measuring Upward Mobility
   b) Segregation and Integration
   c) Social Capital
4. Risk Sharing and Financial Transfers (Day 4)
   a)The Full Insurance Benchmark
   b) Risk Sharing Networks as Collateral
   c) Local Insurance
   d) Interaction with Formal Institutions: Crowding out
5. Routing and Migration (Day 5)
   a. Optimal Routing Problems
   b. Trafficking Networks
   c. Migration and Risk Sharing

Prerequisites

Basic knowledge of programming (in R) is an advantage but is not required. 

List of References

Textbooks:

• Easley, David, and Jon Kleinberg. Networks, crowds, and markets. Vol. 8. Cambridge: Cambridge university press, 2010.
• Jackson, Matthew O. Social and economic networks. Princeton university press, 2010.
• Barabási, Albert-László. "Network science." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371.1987 (2013): 20120375.
• Surveys:
• Banerjee, Abhijit V., and Esther Duflo. “The economic lives of the poor.” The Journal of Economic Perspectives 21.1 (2007): 141.
• Breza, E., Chandrasekhar, A., Golub, B., & Parvathaneni, A. (2019). Networks in economic development. Oxford Review of Economic Policy, 35(4), 678-721.
• Breza, E. (2016). Field experiments, social networks, and development. Oxford, UK: Oxford University Press.

A detailed Reading List for each session will be provided before the start of the course.

Software / Hardware

R Studio

About the Instructor

Pau Milan is Research Fellow at MOVE, Assistant Professor at Universitat Autònoma de Barcelona and Affiliated Professor at the BSE. He received his PhD in 2016 from Universitat Pompeu Fabra and is also a member of Insight on Immigration and Development (INSIDE). His research interests include applied theory and development economics, particularly as it relates to the theory of social and economic networks. He is currently working on questions related to informal insurance in village economies, the role of local information in strategic decisions, and developing new formal measures of social integration across communities.