Social network structure and government provision crowding-out on voluntary contributions
Yao-Yu Chih
Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), 2016, vol. 63, issue C, 83-90
Abstract:
We propose a general equilibrium model of voluntary contributions in which people have an individual-specific level for social approval. This heterogeneous setting has evolved from the different degree of social interaction of individuals in the exogenously given network. By extending the techniques developed by Ghiglino and Goyal (2010), we show that, given a network, individuals who face higher standards of social norms contribute more to the public good and are simultaneously less sensitive to government provision crowding-out in relative value. When comparing different networks, we show that government provision is more effective in networks with higher average connectivity because of a lesser crowding-out effect.
Keywords: Social networks; Voluntary contributions; Norm-based motivation; Social approval; Crowding-out effect (search for similar items in EconPapers)
JEL-codes: D64 D85 H41 H42 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2214804316300520
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:soceco:v:63:y:2016:i:c:p:83-90
DOI: 10.1016/j.socec.2016.05.008
Access Statistics for this article
Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics) is currently edited by Pablo Brañas Garza
More articles in Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().