Assessing a Provision Game for Two Units of a Public Good, With Different Group Arrangements, Marginal Benefits, and Rebate Rules: Experimental Evidence

The problem of public good provision remains an active area of economic research and one of the several areas that massively apply experimental methods in deriving analytical data. In such problems, aggregated individual utility maximization behaviors would not necessarily coincide with a socially best outcome. Thus, a possible solution shall reconcile this individual and social divergence, which encourages us to search for a set of mechanisms that enable individuals to act according to their own best interests while simultaneously maximize the total welfare of society. When providing public good through private fund, people tend to rest on the contributions of others to cover some cost of the goods, which is often referred a “free riding” problem. The efficient allocation of a public good happens when the sum of marginal benefits across people (or the sum of the heights of people’s demand curves) equals the marginal cost of public good provision. If individual each pays his/her marginal benefit, these individualized price levels would constitute the necessary condition for Lindahl equilibrium. This Lindahl pricing system would establish a Pareto optimal provision of the public good, however this system is rather unattainable even in carefully controlled experimental settings (R. Mark Isaac and James M. Walker, 1988, R. Mark Isaac et al., 1985): people quickly decrease their contribution in a voluntary environment as experience grows. This paper compare several elements (including alternative rebate rules) that are often seen in the public good game, in hope of finding a better way to raise individual contribution substantially compared to traditional volunteer contribution.


Subject(s):
Issue Date:
2011-12
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/148293
Total Pages:
39
Series Statement:
Working Papers
005




 Record created 2017-04-01, last modified 2017-08-27

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