Abstract

We consider a model of local public goods in a random network context. The influence network determines (exogenously) who observes whom every period and comprises a wide array of options depending on the degree distribution and the in/out-degree correlations. We show that there exists a unique equilibrium level of public good provision and compare it with the efficient level. We derive further insights for this problem by performing a comparative statics analysis.

Highlights

  • The study of networks has been of signiÖcant importance in diverse academic Öelds such as sociology, physics and computer science

  • We show that if we allow for some heterogeneity regarding out-degrees the e¢cient and equilibrium outcomes provide opposite results; the probability of contributing to the public good increases with respect to out-degree in equilibrium, whereas it decreases in the e¢cient state

  • In this paper we propose a stylized model of public good provision in a random network context

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Summary

Introduction

The study of networks has been of signiÖcant importance in diverse academic Öelds such as sociology, physics and computer science (see, e.g., Wasserman and Faust 1994; Newman 2003, and the long list of references cited therein). There exists a unique globally stable outcome (fraction of individuals investing in the public good) of this dynamics characterized by the (out-)degree distribution and the in/out-degree correlation. We show that if we allow for some heterogeneity regarding out-degrees the e¢cient and equilibrium outcomes provide opposite results; the probability of contributing to the public good increases with respect to out-degree in equilibrium, whereas it decreases in the e¢cient state. BramoullÈ et al (2014) focus on games of strategic substitutes on networks with linear best-reply functions which has recently been extended to non-linear settings by Allouch (2015) We contribute to this vast literature by analyzing local public goods in a random network context.

The static model
The dynamic model
Comparative Statics
Discussion
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