A Kuhn-Tucker Model for Behaviour in Dictator Games

Peter Moffatt and Graciela Zevallos-Porles
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Graciela Zevallos-Porles: School of International Development and CBESS, University of East Angle, Norwich.

No 20-03, Working Paper series, University of East Anglia, Centre for Behavioural and Experimental Social Science (CBESS) from School of Economics, University of East Anglia, Norwich, UK.

Abstract: We consider data from a dictator game experiment in which each dictator is repeatedly exposed to two different treatments: a Giving treatment in which the amount given to the recipient is constrained to be non-negative; and a Taking treatment in which the amount taken from the recipient is constrained to be non-negative. Another key design feature is that the price of transferring is varied between tasks. The data is used to estimate the parameters of a Stone-Geary utility function over own-payoff and other’spayoff. Between-subject heterogeneity is assumed in one of the selfishness parameters. The econometric model incorporates zero observations (e.g. zero-giving or zero-taking) by applying a version of the Kuhn-Tucker theorem and treating zeros as corner solutions in the Dictator’s constrained optimisation problem. The method of maximum simulated likelihood (MSL) is used for estimation. We find that the average dictator exhibits a strong degree of selfishness in the sense of having a high subsistence level for own payoff. However, once this basic need is met, dictators appear willing to share the remaining endowment equally. Above all, we find that selfishness is lower in taking tasks than in giving tasks, and we attribute this difference to the "cold prickle of taking".

Keywords: Dictator games; Taking games; Kuhn-Tucker conditions; Experimetrics. (search for similar items in EconPapers)
JEL-codes: C57 C91 D64 D91 (search for similar items in EconPapers)
Date: 2020-03
New Economics Papers: this item is included in nep-exp and nep-gth
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Related works:
Journal Article: A Kuhn–Tucker model for behaviour in dictator games (2021)
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