2016
DOI: 10.1007/s13209-016-0152-7
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Influence networks and public goods

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/outdegree 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.

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Cited by 4 publications
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“…information; investigate issues of network formation; Bramoullé, Kranton, and D'Amours (2014) investigate private provision with linear best replies; Allouch (2015Allouch ( , 2017 extends BBV analysis to networks; and Elliott and Golub (2019) explore decentralised mechanisms for efficient provision. Other important related contributions have been made by Acemoglu, García-Jimeno, and Robinson (2015), Acemoglu, Malekian, and Ozdaglar (2016), López-Pintado (2017), Kinateder and Merlino (2017), and Sun (2017). 2 For more recent contributions on the existence and uniqueness of a Nash equilibrium for network games, including public goods in networks, see Ramachandran and Chaintreau (2015), Liu (2017, 2018), Bodwin (2017), Melo (2018), Ozdaglar (2018, 2019), and Chen, Zenou, and Zhou (2018).…”
mentioning
confidence: 99%
“…information; investigate issues of network formation; Bramoullé, Kranton, and D'Amours (2014) investigate private provision with linear best replies; Allouch (2015Allouch ( , 2017 extends BBV analysis to networks; and Elliott and Golub (2019) explore decentralised mechanisms for efficient provision. Other important related contributions have been made by Acemoglu, García-Jimeno, and Robinson (2015), Acemoglu, Malekian, and Ozdaglar (2016), López-Pintado (2017), Kinateder and Merlino (2017), and Sun (2017). 2 For more recent contributions on the existence and uniqueness of a Nash equilibrium for network games, including public goods in networks, see Ramachandran and Chaintreau (2015), Liu (2017, 2018), Bodwin (2017), Melo (2018), Ozdaglar (2018, 2019), and Chen, Zenou, and Zhou (2018).…”
mentioning
confidence: 99%