TY - JOUR
T1 - A network approach to public goods
AU - Elliott, Matthew
AU - Golub, Benjamin
N1 - Funding Information:
We gratefully acknowledge the research support and hospitality of Microsoft Research New England. Elliott acknowledges financial support from National Science Foundation grant 1518941. Kevin He provided exceptional research assistance. The guidance of Nageeb Ali, Abhijit Banerjee, Matthew O. Jackson, Andy Skrzypacz, and Bob Wilson has been essential. For detailed comments on previous drafts, we thank Gabriel Carroll, Arun Chandrasekhar, Sylvain Chassang, Anil Jain, Juuso Toikka, Xiao Yu Wang, and Ariel Zucker. For helpful suggestions, we thank Daron Acemoglu, Yann Bramoullé, Federico Echenique, Glenn Ellison, Maryam Farboodi, Alex Frankel, Drew Fudenberg, Andrea Galeotti, Jerry Green, Hari Govindan, Sanjeev Goyal, Johannes Hörner, Scott Kominers, David Kreps, John Ledyard, Jacob
Publisher Copyright:
© 2019 by The University of Chicago. All rights reserved.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Suppose that 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 so-lutions—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.
AB - Suppose that 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 so-lutions—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.
UR - http://www.scopus.com/inward/record.url?scp=85064047638&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064047638&partnerID=8YFLogxK
U2 - 10.1086/701032
DO - 10.1086/701032
M3 - Article
AN - SCOPUS:85064047638
VL - 127
SP - 730
EP - 776
JO - Journal of Political Economy
JF - Journal of Political Economy
SN - 0022-3808
IS - 2
ER -