Book contents
- Frontmatter
- Contents
- Contributors
- Editors' Note
- 1 Dynamic Mechanism Design: Robustness and Endogenous Types
- 2 Learning, Experimentation, and Information Design
- 3 Dynamic Selection and Reclassification Risk: Theory and Empirics
- 4 Discussion of “Agency Problems”
- 5 Recent Developments in Matching Theory and Their Practical Applications
- 6 What Really Matters in Designing School Choice Mechanisms
- 7 Networks and Markets
- 8 Econometrics of Network Models
- 9 Networks in Economics: Remarks
- Index
9 - Networks in Economics: Remarks
Published online by Cambridge University Press: 27 October 2017
- Frontmatter
- Contents
- Contributors
- Editors' Note
- 1 Dynamic Mechanism Design: Robustness and Endogenous Types
- 2 Learning, Experimentation, and Information Design
- 3 Dynamic Selection and Reclassification Risk: Theory and Empirics
- 4 Discussion of “Agency Problems”
- 5 Recent Developments in Matching Theory and Their Practical Applications
- 6 What Really Matters in Designing School Choice Mechanisms
- 7 Networks and Markets
- 8 Econometrics of Network Models
- 9 Networks in Economics: Remarks
- Index
Summary
The past fifteen years have seen a burst in research on the economics of networks. Researchers have been studying a wide range of economic settings, and in each case, links between individuals arguably play critical roles in individual and aggregate outcomes. The following are some examples, with specified settings and links: peer effects with friendship links, innovation/research and development with links between researchers and colleagues, local public goods with geographic and social links, oligopoly and firms’ interlinked markets, macroeconomic shocks and supply chain links, information transmission and people's social links, banking and links due to cross-holdings, and markets and links between buyers and sellers.
The mathematical structure of networks ties together all this research. In a network, agents have pairwise “links” that affect their dealings. These links collectively give the “adjacency matrix,” also called the “graph,” or the “network,” that impacts outcomes for all agents. At the individual level, an agent's payoffs depend directly on the actions of her “neighbors,” i.e., the agents to whom she is linked. Distant agents also shape payoffs and incentives to the extent that they are indirectly linked, by “paths” in the network.
These remarks give a bird's eye view of this research, providing a road map to bring together the detailed accounts of the empirical and theoretical research provided by the two papers in this session. Following the road map, these remarks give a whirlwind tour of research objectives and themes and present challenges for future studies.
Research papers in this area typically begin with documenting an economic outcome of interest and recognizing that a network underlies this outcome. Figure 1 gives a road map. Starting at the top, a theoretical study typically posits pairwise links between N agents that form a network G. An empirical study will also typically begin with data on the links that make up the network, i.e., who is linked to whom in the relevant population. There are then two black boxes to be filled in. The first box concerns how networks shape outcomes, i.e., the theory and empirics of how individual actions, given a network, lead to the economic outcome. The second black box concerns network formation, i.e., how links come about in the first place.
- Type
- Chapter
- Information
- Advances in Economics and EconometricsEleventh World Congress, pp. 324 - 328Publisher: Cambridge University PressPrint publication year: 2017