Development economics is an interdisciplinary field that draws on insights from economics, political science, sociology, and other social sciences. It involves theoretical analysis, empirical research, and policy formulation aimed at promoting inclusive and sustainable development in the world's poorest regions.
The BSE Development Economics Summer School explores some of the most recent and exciting work in the field of economic development with particular attention to the evaluation of development policy.
The courses at the BSE Development Economics Summer School will provide students with specific knowledge of a wide set of tools for policy evaluation such as the design and implementation of Randomized Control Trials (RCTs), Geospatial Data and Inference, Complex Network Analysis, and Regression Discontinuity Designs.
In all courses, students will learn through case studies relevant to development policy (i.e. assessing development policies previously conducted in the literature). Further, students will also learn by doing in afternoon practical sessions in which students will put into practice the tools taught in the morning lectures.
The set of courses at the BSE Development Summer School is largely complementary in the sense that each course is geared to provide students with knowledge of a specific and different set of tools of the same level of complexity. At the same time, all courses share that they are designed to address questions regarding development topics and the evaluation of development policy.
Course list for 2024
Week of June 17-June 21, 2024 (Online)
- Geospatial Tools for Development: Data and Inference
Instructor: Andre Groeger
Week of July 8 - July 12, 2024 (Face-to-face)
- Complex Network Analysis: Tools for Economic Development
Instructor: Pau Milan - Regression Discontinuity Designs in Development Economics: Theory and Practice
Instructor: Bruno Conte
Program director
See you in Summer 2025!
Courses for the 2025 edition of the BSE Summer Schools will be announced later this year.
Apply to Summer School 2024
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Apply to Summer School courses
10% Early-bird discount deadline: April 14, 2023
Last day to apply: June 1, 2023
Fees and discounts
Fees vary by course. You may be eligible for one or more available Summer School discounts. Our staff can provide a personalized quote for you.
Applications will open soon!
Very soon you'll be able to apply to the 2023 edition of the BSE Summer Schools.
See you in Summer 2023!
Courses for the 2023 edition of the BSE Summer Schools will be announced later this year. We look forward to meeting you here in Barcelona!
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Complex Network Analysis: Tools for Economic Development
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
- 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 - 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 - Segregation and Economic Opportunity (Day 3)
a) Measuring Upward Mobility
b) Segregation and Integration
c) Social Capital - 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 - 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.
- Introduction to Networks and Random Graphs (Day 1)
Geospatial Tools for Development: Data and Inference
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
- 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
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.
- Introduction
Regression Discontinuity Designs in Development Economics: Theory and Practice
Course Overview
Causal inference is a key tool for development economics and policy evaluation. One challenge for its practical implementation is the ideal quasi-experimental empirical setting: policies usually target specific groups or regions, based often on arbitrary rules. In this course, you will learn how to overcome this challenge with a prominent method: regression discontinuity designs (RDD).
More specifically, we will learn how to use ad-hoc - discontinuous - implementation rules for designing the ideal empirical setting for causal inference. Examples include, among others, policies targeting specific age groups, income levels, and geographical regions. These discontinuities, combined with the right regression framework, permit inferring causal effects of policies in developing economies.
The course will cover both conceptual and practical aspects of RDD. Theoretical lectures will cover the formal assumptions (and their interpretation) for state-of-the-art RDD settings (e.g., sharp, fuzzy, or spatial RDDs), followed by practical sessions that will expose students to hands-on activities of real-world implementation of these tools. By the end of the course, students will command a powerful toolbox for applied empirical work and policy evaluation in developing settings.
An advantage of the course is its open programming environment: practical sessions will cover frontier computational RDD tools from the rd packages toolbox in both Stata and R. Hence, students can either follow the hands-on sessions of the course with their preferred software or practice with both.
The course targets researchers and professionals interested in broadening their causal inference toolbox, as well as graduate students aiming at deepening their theoretical and practical knowledge of RDD methods.
Course Outline
Day 1. Introduction to RDD, applications in development economics, and brush up
- Revision of causal inference, selection into observables, and policy (causal) evaluation
- Overview of RDD methods and applications in (development) economics
- Practical session: brush up on econometrics with R and Stata
Day 2. Sharp RDD
- Overview and assumptions
- The local nature of estimates and graphical evidence of discontinuities
- Estimation, validation, and testable implications of RD designs
- Practical session: replication of Britto (2022) and/or Dell (2010)
Day 3. Fuzzy RDD
- Overview and assumptions: RD as an instrument
- Additional assumptions and tests, (distinct) estimation nature and interpretation (LATE)
- Practical session: replication of Alix-Garica (2013) and/or Lodoño-Vélez et al. (2020)
Day 4. Spatial RDD
- Overview and assumptions: spatial boundaries as discontinuities
- Additional (practical) aspects of spatial RDDs (e.g., geographical distances, buffers)
- Practical session: replication of Burgess et al. (2019) and/or Hsiao (2023)
Day 5. Additional RDD methods
- Conceptual overview: RDD with discrete running variables, multiple cutoffs, kink RD designs, local randomization approach to RDDs.
- Practical session: covering these tools with artificial data and/or applications in research
Prerequisites
Students are expected to have basic skills in econometrics, causal inference, and computation coding with Stata and/or R. Practical sessions will require students to bring their own computer to classes.
List of References
Course textbook
- Angrist, J.D. and Pischke, J.S., 2009. Mostly harmless econometrics: An empiricist's companion. Princeton university press.
- Cattaneo, M.D., Idrobo, N. and Titiunik, R., 2019. A practical introduction to regression discontinuity designs: Foundations. Cambridge University Press.
- Cattaneo, M.D., Idrobo, N. and Titiunik, R., 2023. A practical introduction to regression discontinuity designs: Extensions. arXiv preprint arXiv:2301.08958.
References (TBU)
- Alix-Garcia, J., McIntosh, C., Sims, K.R. and Welch, J.R., 2013. The ecological footprint of poverty alleviation: evidence from Mexico's Oportunidades program. Review of Economics and Statistics, 95(2), pp.417-435.
- Britto, D.G., 2022. The employment effects of lump-sum and contingent job insurance policies: Evidence from Brazil. Review of Economics and Statistics, 104(3), pp.465-482.
- Burgess, R., Costa, F. and Olken, B.A., 2019. The Brazilian Amazon’s double reversal of fortune. Working paper.
- Dell, M., 2010. The persistent effects of Peru's mining mita. Econometrica, 78(6), pp.1863-1903.
- Hsiao, A., 2022. Sea Level Rise and Urban Adaptation in Jakarta. Working paper.
Software / Hardware
Practical sessions require students bringing their own computer with either Stata or R Studio (or both).
About the Instructor
Bruno Conte is an Assistant Professor of Economics at Universitat Pompeu Fabra (UPF) and a Barcelona School of Economics Affiliated Professor. He is also a CESifo research fellow.
He has been an Assistant Professor at the University of Bologna and held consulting positions at The World Bank, the United Nations Commission for Africa (UNECA), and LSE's International Growth Centre.
In his research, Professor Conte studies questions on Environmental and Development Economics using tools from Spatial Economics and International Trade. He teaches Environmental Economics, Data Science tools, and how to implement them in economic research.
Who will benefit from this program?
The courses should be of interest to graduate students (or young academics) as well as members of policy-driven institutions who want to expand their knowledge and to acquire solid and practical tools for the analysis of development policy. Students will learn tools such as RCTs, Geospatial Analysis, Complex Network tools, Regression Discontinuity Designs, etc. During the course, faculty are available to discuss the assessment of policy interventions and also other research ideas and projects with program participants.
Entry requirements
Applicants to all Summer School programs should meet the basic entry requirements.
Credit transfers (ECTS)
In the courses "Complex Network Analysis: Tools for Development Economics", and "Regression Discontinuity Designs", students will be evaluated with problem sets.
In the course "Geospatial Tools for Development: Data and Inference”, students will deliver a short research proposal one week after the summer school finishes. It will consist of 3-4 pages, 1.5 spaced, font size 11, with a clear research question, a motivation (introduce the question and its policy relevance), a discussion of the existing literature and on how your paper relates to that, and an explanation on how you would approach the questions (empirical strategy and/or theoretical model).
Consult the Credit Transfer page for more information about this option.
Certificate of attendance
Participants not interested in credit transfer will instead receive a Certificate of Attendance, stating the courses and number of hours completed. These students will be neither evaluated nor graded. There is no fee for the certificate.
Fees
Multiple course discounts are available. Fees for courses in other Summer School programs may vary.
Course | Modality | Lecture Hours | Practical Hours | ECTS | Regular Fee | Reduced Fee* |
---|---|---|---|---|---|---|
Complex Network Analysis: Tools for Economic Development | Face-to-face | 10 | 7.5 | 1 | 1375€ | 800€ |
Geospatial Tools for Development: Data and Inference | Online | 10 | 7.5 | 1 | 1000€ | 600€ |
Regression Discontinuity Designs in Development Economics: Theory and Practice | Face-to-face | 10 | 7.5 | 1 | 1375€ | 800€ |
* Reduced Fee applies for PhD or Master's students, Alumni of BSE Master's programs, and participants who are unemployed.
** Flexible cancelation policy: view the BSE Summer School Policies
See more information about available discounts or request a personalized discount quote by email.
Course schedule
The schedule is designed to allow students to participate in all Development Economics courses. Courses can also be taken individually or in combination with courses in other BSE Summer School programs, schedule permitting.
Day / Time | Mon | Tue | Wed | Thu | Fri |
---|---|---|---|---|---|
13:00 - 15:00 | Geospatial Tools for Development (Lectures) | ||||
17:45 - 19:15 | Geospatial Tools for Development (Practical) |
Day / Time | Mon | Tue | Wed | Thu | Fri |
---|---|---|---|---|---|
9:00 - 11:00 |
Complex Network Analysis: Tools for Economic Development (Lectures) |
||||
11:30 - 13:30 | Regression Discontinuity Designs in Development Economics: Theory and Practice (Lectures) | ||||
14:30 - 16:00 | Complex Network Analysis: Tools for Economic Development (Practical) | ||||
16:15 - 17:45 | Regression Discontinuity Designs in Development Economics: Theory and Practice (Practical) |
Mix and match your summer courses!
Remember that you can combine Development Economics courses with courses in any of the other BSE Summer School programs (schedule permitting).