Social and Economic Networks: Theory, Econometrics and Policy Implications

Networks are important in shaping behavior in many social, business, and economic environments. For example, through networks of research and development (R&D) collaborations between firms, new technologies are discovered and diffuse in the industry. Similarly, co-authorship networks in academic research play an indispensable role in the process of scientific discovery and knowledge production. The many aspects that are governed by networks make it critical to understand how they impact behavior, which network structures are likely to emerge, and how they affect welfare in the society.

Course level
Master, PhD candidates and professionals from all disciplines
Session 2
11 January to 18 January 2020 
Coordinating lecturer                                     Michael König
Form(s) of instructionLecture and integrated exercises
ECTS2 credits
Contact hours20 contact hours
Tuition fee

€800 - non-VU students and staff

€500 - VU students and staff

This course is suitable for graduate, as well as advanced undergraduate students, researchers and practitioners of all areas who are interested in the analysis of social and economic networks. The course combines theoretical models with relevant and recent econometric methods in order to evaluate the efficiency of different policy instruments. Students and researchers will benefit from the policy relevance of the topics covered within the course, while practitioners and policy makers benefit from a sound economic rational that underpins all policy-relevant material covered in the course. All parts of the course (theory, empirics, and policy) will be presented in a consistent and interdependent framework, which will make clear that none of them should be studied in isolation. Students becoming more familiar with one particular area will benefit from insights gained through complementary views from other areas. The course should be accessible to advanced undergraduate and graduate students/PhD students as well as practitioners from all related fields. If you have doubts about your eligibility for the course, please contact us: graduatewinterschool@vu.nl.

1. Examples of Networks and Data
• Why Networks in Economics?
• Examples of Economic Networks and Data

2. Characterization of Networks
• Elements of Graph Theory
• Graphs and Matrices
• Bipartite Graphs
• Core-periphery Networks and Nested Split Graphs
• Network Statistics
   – Average Path Length
   – Clustering
   – Assortativity and Nearest Neighbor Connectivity
• Centrality in Networks
   – Degree Centrality
   – Eigenvector Centrality
   – Katz-Bonacich Centrality
   – Google's Page Rank
• Network Visualization: Force-directed, circular and layered layout
• Appendix: A recap of linear algebra - vectors, matrices and systems of linear equations

3. Network Algorithms and Software

4. Games on Networks
• Games with Strategic Substitutes   
   – “Best-Shot” Public Goods Games    
   – Public Goods with Continuous Actions 
• Games with Strategic Complements   
   – Binary Games: Coordination games; cohesiveness and contagion    
   – Linear Quadratic Games: The baseline model; ex ante heterogeneity; local complements and global substitutes; multiple activities; welfare and efficiency; key players

5. Econometrics of Interaction in Networks
• Spatial Autoregressive (SAR) Model 
• Linear Quadratic Utility 
• Endogeneity of the Spatial Lag 
• Two-Stage Least Squares (2SLS) 
• Maximum Likelihood Estimation (MLE) 
• Identification Issues    
   – Correlated Effects, Sorting and Selection    
   – Endogenous Link Formation 
• Multiple Spatial Weight Matrices 
• Spatial Panel Data

6. Econometrics of Network Formation
• Exponential Random Graph Model (ERGM) 
• Conditional Edge-Independence    
   – Erdös-Rényi Random Graph    
   – Variational Approximation    
   – Logistic Regression    
   – Unobservable Characteristics (beta-model)    
   – Tetrad Logit Estimator 
• Random Utility Model
• Maximum Likelihood Estimation (MLE) 
• Markov Chain Monte Carlo    
   – Gibbs Sampling    
   – Metropolis Hastings Algorithm 
• Degeneracy 
• Missing Data and Projectivity
• Stochastic Block Model (SBM) 
• Temporal ERGM

7. Coevolution of Networks and Behavior

7.1 Network Formation with Local Complements and Global Substitutes - An Application to R&D Collaboration Networks 
• Structural Model    
   – Utility and the Potential Game    
   – Coevolution of Networks and Behavior 
• Estimation    
   – Computational Problem and the Exchange Algorithm    
   – Double Metropolis-Hastings (DMH) Algorithm    
   – Unobserved Heterogeneity 
• Empirical Illustration    
   – A Simple Model of R&D Collaboration    
   – Data    
   – Estimation Results    
   – Key Player Analysis    
   – R&D Subsidies
 
7.2 Network Formation with Multiple Activities - An Application to Team Production and Coauthorship Networks  

• Team Production in Coauthorship Networks 
• Equilibrium Characterization and Line Graphs 
• Superstars, Key Players and Rankings 
• Research Funding and Planner’s Problem 
• Estimating the Production Function 
• Matching Process 
• Policy Implications    
   – Rankings    
   – Research Funding 

Upon successful completion of the course, students will:

• become acquainted with different methodologies for analyzing networks while learning how to see these different methodologies complementing each other.
• learn to model network problem situations mathematically, and adapt the methods learned to new situations at hand.
• be able to recognize, understand, and analyze societal and business problems in which networks are central.
• learn how networks affect demand and supply in markets, how this leads to market failures, and how government policies can address these.
All relevant material will be covered in the lecture slides. The slides will be made available to the students on the course website before the start of the course. The following literature is complementary to the course slides and covers some additional relevant material for further reading:
 
• Bramoulle, Yann, Andrea Galeotti, and Brian Rogers. The Oxford Handbook of the Economics of Networks. Oxford University Press, 2016. 
• Jackson, Matthew. O. Social and Economic Networks. Princeton University Press, 2010.
• Kolaczyk, Eric, Statistical Analysis of Network Data: Methods and Models, Springer, 2009. 
• LeSage, James, and Robert Kelley Pace. Introduction to Spatial Econometrics. Chapman and Hall/CRC, 2009. 
• Carrington, Peter, John Scott, and Stanley Wasserman. Models and Methods in Social Network Analysis, Cambridge University Press, 2005.

Michael D. König is Associate Professor at the Department of Spatial Economics at VU, Amsterdam. He is also a research fellow at the Tinbergen Institute, the Centre for Economic Policy Research (CEPR) in London, and the Swiss Economic Institute (KOF/ETH) in Zurich. Prior to joining the VU Amsterdam, he was a visiting scholar at the Stanford Institute for Economic Policy Research (SIEPR) and the Department of Economics at Stanford University. His research focuses on the economics of innovation and technical change, and how these affect and are being effected by networked relationships between various economic actors, ranging from individuals to firms, and from sectors to countries. His research combines both theoretical as well as empirical methods, and he uses these methods to evaluate real-world policy instruments. 

Konig