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A Network Approach to Public Goods

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  • Elliott, M.
  • Golub, B.

Abstract

Suppose agents can exert costly effort that creates nonrival, heterogeneous benefits for each other. At each possible outcome, a weighted, directed network describing marginal externalities is defined. We show that Pareto efficient outcomes are those at which the largest eigenvalue of the network is 1. An important set of efficient solutions - Lindahl outcomes - are characterized by contributions being proportional to agents' eigenvector centralities in the network. The outcomes we focus on are motivated by negotiations. We apply the results to identify who is essential for Pareto improvements, how to efficiently subdivide negotiations, and whom to optimally add to a team.

Suggested Citation

  • Elliott, M. & Golub, B., 2018. "A Network Approach to Public Goods," Cambridge Working Papers in Economics 1813, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1813
    Note: mle30
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    References listed on IDEAS

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    1. John William Hatfield & Scott Duke Kominers & Alexandru Nichifor & Michael Ostrovsky & Alexander Westkamp, 2013. "Stability and Competitive Equilibrium in Trading Networks," Journal of Political Economy, University of Chicago Press, vol. 121(5), pages 966-1005.
    2. Ying‐Ju Chen & Yves Zenou & Junjie Zhou, 2018. "Competitive pricing strategies in social networks," RAND Journal of Economics, RAND Corporation, vol. 49(3), pages 672-705, September.
    3. Dequiedt, Vianney & Zenou, Yves, 2014. "Local and Consistent Centrality Measures in Networks," CEPR Discussion Papers 10031, C.E.P.R. Discussion Papers.
    4. Penta, Antonio, 2011. "Multilateral bargaining and Walrasian equilibrium," Journal of Mathematical Economics, Elsevier, vol. 47(4-5), pages 417-424.
    5. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
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    Cited by:

    1. Nizar Allouch & Maia King, 2019. "Constrained public goods in networks," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 21(5), pages 895-902, October.
    2. repec:eee:dyncon:v:105:y:2019:i:c:p:250-264 is not listed on IDEAS
    3. Bayer, Péter & Herings, P. Jean-Jacques & Peeters, Ronald & Thuijsman, Frank, 2019. "Adaptive learning in weighted network games," Journal of Economic Dynamics and Control, Elsevier, vol. 105(C), pages 250-264.
    4. Janmaat, Johannus & Geleta, Solomon & Loomis, John, 2019. "Detecting social network effects on willingness to pay for environmental improvements using egocentric network measures," MPRA Paper 96675, University Library of Munich, Germany.
    5. Walsh, A. M., 2019. "Games on Multi-Layer Networks," Cambridge Working Papers in Economics 1954, Faculty of Economics, University of Cambridge.

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