A Network Approach to Public Goods: A Short Summary

TL;DR

This paper models public goods creation as a network problem, showing Pareto efficiency relates to the network's largest eigenvalue and identifying key agents via eigenvector centrality.

Contribution

It introduces a novel eigenvalue-based framework for analyzing efficient public goods provision and characterizes Lindahl outcomes through network centralities.

Findings

Pareto efficient outcomes occur when the network's largest eigenvalue is 1.

Lindahl contributions are proportional to agents' eigenvector centralities.

The framework helps identify key agents for Pareto improvements.

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.