Altruism, Reputation, and Noise in Linear Public Goods Experiments

Abstract

We report the results of a public goods experiment using a design that enables us to directly measure individual response functions in voluntary contributions games and estimate error rates. In addition, following Andreoni (1988), we employ two treatments in order to measure the extent to which voluntary contribution is due to reputation effects. The partners treatment involves a fixed group of subjects playing a repeated game. The strangers treatment approximates a one-shot game by randomly changing group assignments after each play. Our data shows that essentially the only difference between the two treatments is the amount of noise in the data, with the strangers treatment being the noisier of the two. This noise manifests itself in two distinct ways. First, there is more variation of decision rules across subjects in the strangers treatment. Second, individual behavior is, on average, less consistent with a cutpoint decision rule in the strangers treatment, which produces higher estimates of individual error rates. The differences between the strangers and partners data are virtually the same as differences between data from experienced and inexperienced subjects. This suggests an explanation for the finding in Andreoni (1988) that there was greater contribution under the strangers treatment in the standard homogeneous environment in which one direction of errors (under-contribution) are censored. Our results also support his conclusion that reputation effects do not appear to play a prominent role in repeated linear public goods voluntary contribution games. Many findings from past public goods experiments are consistent with our model of random variation in a population of subjects who are, on average, neither altruistic nor spiteful.

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