Economics of Networks - GEO3-3805 

Course description

Economists use concepts and methods from network science to understand a complex, global, and interconnected world. In network thinking, the fundamental unit of analysis consists of the relationships among interacting units rather than the individual characteristics of these units. The ability to analyze the particular way relationships are organized - i.e. the network structure - is crucial in understanding complex phenomena in nature and society. In this course, we discuss how a network perspective can help us to re-think key issues in economics. More specifically, this course will introduce concepts and methods to map and measure relationships and flows between people, firms, cities, economic sectors, communities, or any other element of a complex economic system. We will discuss how the structure of a system influences its overall performance, why the relative position in a network conditions the access to critical resources, and how relationships are created and dissolved over time. This course consists of lectures combined with computer exercises and online tutorials.


What you will learn

1. What a network-based paradigm is, and how it can be applied to economics
2. How to identify, describe, and analyze the structure of networks 
3. How the structure of economic systems influence their performance
4. How networks evolve in space and over time
5. Basic programming skills and advanced network analysis techniques


Meet the instructors 

Pierre-Alexandre Balland -

Teresa Farinha -

Ron Boschma -

Computer Lab

No prior knowledge in quantitative methods or statistical computing is required to follow this course.  We will use the R statistical software (open source). Students are strongly encouraged to bring a notebook computer to class. 



The overall grade for the class will be based on an individual mid-term exam (50%) and a report of a collective research project (50%).


Research project

The report of the research project involves examination and analysis of economic network data, a short essay reviewing empirical and theoretical arguments, and an oral presentation in class. Groups of 2-3 students will focus on a specific economic question and use network thinking and network analysis tools to answer it. Preliminary ideas will be discussed during each lecture, presented by students and discussed with the full class, and further discussed in group meetings. The final project will be presented at the end of the class. The paper should be 2,000-word long maximum (excluding references) and contain network graphs. It should be submitted to this Dropbox folder as a PDF by November 14. 


Individual meetings

One of the most important parts of this class is your network project (report + presentation). You will learn many new skills, that can lead to more advanced research or even a business opportunity. But this project is also challenging, from applying network thinking and collecting the data, to presenting your finding in a professional way. These individuals meetings are milestones to make sure that you stay on schedule and do not get lost in the project complexity. 

Oral presentation

Knowing how to present your big idea is key in today’s world. Startups do not fund themselves, they pitch their idea to raise money from investors. Make sure to prepare well the presentation of your project. Tell us why it matters, what you are doing, and how. Make it simple, and practice a lot. Two weeks after the presentation, you’ll have to hand in the final report so this presentation is also your last chance to fine-tune your project. 



There is no class reader. The weekly readings are provided on this web-page and slides/videos will be regularly uploaded here. All articles listed should be considered mandatory reading. Additional online materials might be assigned throughout the quarter.


Course Schedule

Week  |        Day        |   Date  |        Time        |          Location        |        Activity          |  Lecturer  


   37     |    Thursday   |  12/09  |  13:15-15:00  |  ICU - Spinoza 109  |         Lecture 1       |  Balland

   37     |    Thursday   |  12/09  |  15:15-17:00  |  ICU - Spinoza 109  |            Lab 1           |  Balland    

   38     |    Thursday   |  19/09  |  13:15-15:00  |  ICU - Spinoza 109  |         Lecture 2       |  Balland     

   38     |                                                   online                                           |            Lab 2           |  Balland    

   38     |    Thursday   |  19/09  |  15:15-17:00  |  ICU - Spinoza 109  |         Lecture 3       |  Balland   

   38     |                                                   online                                           |            Lab 3           |  Balland     

   38     |    Thursday   |  19/09  |  17:15-19:00  |  ICU - Spinoza 109  |             Ideas          |  Balland     

   39     |    Thursday   |  26/09  |  15:15-17:00  |  ICU - Spinoza 109  |         Lecture 4       |  Steijn 

   39     |                                                   online                                           |            Lab 4           |  Balland       

   40     |    Thursday   |  03/10  |  13:15-15:00  |  ICU - Spinoza 109  |           Exam            |  Balland

   41     |    Thursday   |  10/10  |  13:15-17:00*|  ICU - Spinoza 109  |  Group meetings  |  Farinha    

   42     |                                                                     work on paper                                                        

   43     |                        to be arranged with the lecturer                           |  Group meetings  |  Balland    

   44     |    Thursday   |  31/10  |  13:15-19:00  |  ICU - Spinoza 109  |     Presentations    |  Balland

Lecture 1: Introduction to organizational network analysis [PDF]


Topics covered
- Overview of class
- Introduction to network thinking 
- Networks in natural sciences, social sciences, and business
- Economics and networks

- Barabási, A. L. (2012) The network takeover, Nature Physics 8 (1), 14-16 [PDF]
- Ter Wal, A. L., and Boschma, R. A. (2009) Applying social network analysis in economic geography: framing some key analytic issues. The Annals of Regional Science 43 (3): 739-756 [PDF]
- Hanneman, R.A. and Riddle, M. (2005) Introduction to social network methods. Riverside, CA:  University of California, Riverside - Chapter 1 [PDF]

- An application of ONA by Deloitte [PDF]


Lab. 1: Network analysis in R - An introduction to R 
Topics covered
- Network data collection (research projects)
- Software for network analysis 
- Introduction to R and RStudio
- Basic programming skills




Lecture 2: Graph theory and Complex networks 


Topics covered
- Graphs and matrices 
- Key concepts: nodes, links, structure
- Random networks 
- Small worlds
- Growing networks   
- Key structural patterns of real-world networks

- Principles to keep in mind when working with your own network data

- Scope and milestones of the project


- Barabási, A. L. (2016) Network Science. Cambridge, England: Cambridge University Press - Chapter 2 [PDF]
- Hanneman, R.A. and Riddle, M. (2005) Introduction to social network methods.  Riverside, CA:  University of California, Riverside - Chapters 2 [PDF], 3 [PDF], 5 [PDF] & 7 [PDF]
- Barabási, A. L., and Albert, R. (1999) Emergence of scaling in random networks, Science 286 (5439): 509-512 [PDF]
- Watts, D. J., and Strogatz, S. H. (1998) Collective dynamics of ‘small-world’ networks, Nature 393 (6684): 440-442 [PDF]

Lab. 2: Network analysis in R - Network Data 


Topics covered
- Basic matrix algebra [VIDEO 2.1]

- Network data management [VIDEO 2.2]

- Creating an igraph object (from raw data) [VIDEO 2.3]


- Balland, P.A. (2017) Economic Geography in R: Introduction to the EconGeo Package, Papers in Evolutionary Economic Geography, 17 (09): 1-75 [PDF]
- Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006 [PDF]


Lecture 3: Centrality and power 


Topics covered
Why positions of actors/nodes in a network matter
Degree, betweenness, and closeness centrality 
Strength of weak ties, structural holes, and network closure    


- Granovetter, M. (1985) Economic action and social structure: The problem of embeddedness, American journal of sociology 91 (3): 481-510 [PDF]
- Burt, R. S. (2004) Structural holes and good ideas. American journal of sociology 110 (2): 349-399 [PDF] 
- Hanneman, R.A. and Riddle, M. (2005) Introduction to social network methods.  Riverside, CA:  University of California, Riverside - Chapters 9 [PDF] 10 [PDF]

Lab. 3: Network analysis in R - Computing global & local indicators


Topics covered
Structural analysis of global networks [VIDEO 3.1]
Computing different forms of centrality [VIDEO 3.2]
Mastering the igraph R package

Lecture 4: The economy as a complex system


Topics covered
Endogenous mechanisms of growth in economic systems
Mapping economic systems as 2-mode networks
Relatedness and evolution 
Predicting changes in economic systems
An application to European innovation policy  



- Hidalgo, C., Balland, P.A., Boschma, R., Delgado, M., Feldman, M., Frenken, K., Glaeser, E., He, C., Kogler, D., Morrison, A.,  Neffke, F., Rigby, D., Stern, S., Zheng, S., and Zhu, S. (2018)  The Principle of Relatedness,  Proceedings of the 20th International Conference on Complex Systems, forthcoming [PDF]

- Hidalgo, C. A., Klinger, B., Barabási, A. L., & Hausmann, R. (2007). The product space conditions the development of nations. Science, 317(5837), 482-487 [PDF]

- Farinha, T., Balland, P.A., Morrison, A., and Boschma, R. (2018) What drives the geography of jobs in the US? Unpacking relatedness, Papers in Evolutionary Economic Geography, 18 (13): 1-24 [PDF]

- Balland, P.A., Boschma, R., Crespo, J. and Rigby, D. (2018)  Smart Specialization policy in the EU: Relatedness, Knowledge Complexity and Regional Diversification, Regional Studies, forthcoming [PDF]

- INET Webinar "How Regions Can Re-invent Themselves" by Pierre-Alexandre Balland

Lab. 4: Mapping the structure of economic systems in R 


Topics covered
Computing relatedness between (economic) activities [VIDEO 4.1]

Relatedness density and predicting entry (diversification) [VIDEO 4.2]
Visualization of complex networks [VIDEO 4.3]

Additional reading 

Students are not required to purchase any books to follow this course. If you are interested in additional reading, these three books make an excellent introduction to the world of network science and network analysis:
- Barabási, A.L. (2002) Linked: The New Science of Networks. Cambridge, MA: Perseus.
- Newman, M.E.J. (2010) Networks: An Introduction. Oxford, England: Oxford University Press.
- Wasserman, S., and Faust, K. (1994) Social Network Analysis: Methods and Applications. Cambridge, England: Cambridge University Press.

Useful websites

- Full introductory online textbook on social network analysis by Robert Hanneman and Mark Riddle: 
- Full introductory online textbook on network science by Albert-László Barabási: 
- A simple introduction to R by Robert Kabacoff: 
- The website of Tom Snijders on dynamic network analysis: