Abstract
We experimentally investigate the impact of recognizing contributors on public good contributions. We vary recognizing all, highest or lowest contributors. Consistent with previous studies, recognizing all contributors significantly increases contributions relative to the baseline. Recognizing only the highest contributors does not increase contributions compared to not recognizing contributors, while recognizing only the lowest contributors is as effective as recognizing all contributors. These findings support our conjecture that aversion from shame is a more powerful motivator for giving than anticipation of prestige.
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Notes
One laboratory experiment that does not find a positive effect of recognition is the experiment of Dufwenberg and Muren (2006). However, as the authors point out, in their setting the reduction of anonymity may come with additional confounding factors.
As one reviewer pointed out, anonymity is a hallmark of laboratory experiments. Making group members known to one another moves us closer to a field setting.
We decided to employ a fixed matching design for several reasons. First, by using fixed matching we amplify the effect of prestige and shame, which we argue are one of the main motivating factors for contributions, and hence create the most conducive environment wherein our conjectures could be tested. Second, individuals repeatedly participate in social groups and online communities, and thus the fixed matching design better represents these environments. Finally, a fixed matching design gives us an opportunity to investigate group dynamics over periods and to maintain independence among the matched groups. However, one potential drawback of our fixed matching design is that some prestige may come from not being identified as a low contributor in the BOTTOM treatment, and some shame may occur from not being identified as a high contributor in the TOP treatment. It would be interesting to see if our results hold when employing a random matching design (which would have controlled of the latter drawback).
While social groups of 5 are rarely observed in practice, the choice of small group allows us to assume that all 5 photos are easily viewed by participants when they are displayed by default (e.g., no time cost to view).
An additional behavioral motivator that has been cited as important is signaling wealth (Glazer and Konrad 1996).
Several additional theories of the link between social image concerns and prosocial behavior have been advanced. Rege (2004) propose a model that includes “contributor” or “non-contributor” types.
Similarly, guilt arises when an individual realizes that she has hurt someone with her behavior and perceives herself as a bad person, independent of being recognized (Lewis 1971; Baumeister et al. 1994). Psychologists have found that priming individuals with feelings of guilt, but not shame, increased cooperativeness in a social dilemma game with anonymous participants (Ketelaar and Au 2003; de Hooge et al. 2007). Guilt was only effective for selfish individuals and did not increase the contributions of prosocial individuals who were already contributing (de Hooge et al. 2007). While guilt is expected to result in an increase in prosocial behavior (to make up for wrongdoing), according to psychologists, shame is expected to result in hiding or withdrawing from the situation and from others (Tangney and Fischer 1995). In our experiment, hiding from the situation comes from increasing contributions to avoid being recognized again as a low contributor.
One difference between our work and Andreoni and Petrie (2004), is that we use a fixed matching while they use a random matching design. Another difference is that we also display the first name of each individual. Other experimental design aspects, such as ranking of the individuals and overall endowment, are also different.
Since individual contribution information is viewed in all treatments, individuals would always know whether they are in the top or bottom, and can adjust their behavior accordingly.
Andreoni and Petrie (2004) use a similar approach of classifying leaders who contributed 15 or more tokens out of 20 and as laggards as those who contributed 5 or fewer tokens out of 20. However, the difference is that we use only one set of 20 periods while in Andreoni and Petrie (2004), individuals complete 5 sequences of contributions with different group members. In that case, they use the measure for “leaders” as those who contributed 15 or more in 4 out of 5 sequences, and as “laggards” as those who contributed 5 or fewer tokens in 4 out of 5 sequences.
A χ 2 goodness of fit test has a p-value of 0.04 when comparing leaders, and a p-value of 0.01 when comparing laggards.
Interestingly, there are more laggards in the TOP treatment even compared to the NONE treatment. This may be because highlighting only the top contributors implicitly emphasizes that the rest of individuals are laggards and thus they should not contribute as much. It is also possible that highlighting only top contributors may implicitly deemphasize the guilt effect, and thus cause more laggards in the TOP treatment relative to the NONE treatment.
This finding also may be due to the fact that prestige is relative in this setting, and depends heavily on participants’ expectations. If participants do not expect the highest contributors to give over 75 % of the endowment, then we may not find a high proportion of leaders.
A χ 2 goodness of fit test has a p-value of 0.08 when comparing laggards.
The proportion of leaders and laggards in the Bottom is not significantly different from the All (p-values are 0.37 and 0.33).
This difference is not significant, however. A χ 2 goodness of fit test has a p-value of 0.25 when comparing leaders.
The loss aversion argument is also in line with findings in the sanction and reward literature. Sefton et al. (2007) find that monetary rewards by themselves cannot sustain cooperation the way that monetary sanctions can.
Related work on monetary sanctions and incentive schemes has similarly identified a preference for bonuses over fines. For example, Sutter et al. (2010) found that while punishment points are more effective than reward points in a public goods game, groups prefer to use reward option when given a choice. In principal-agent settings with financial incentives, the principal prefers to use a bonus contract for the agent, and this is more effective than combining a bonus with a fine (Fehr and Schmidt 2007).
Of course, these lists could be prohibitively long for larger universities, which is why these lists could be published online.
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Acknowledgements
Financial support was provided through the Network for Computational Nanotechnology, which operated nanoHUB.org with funding by the National Science Foundation under grant numbers EEC-0228390, EEC-0634750, OCI-0438246, and OCI-0721680. We thank Gerhard Klimeck and George Adams III for sharing with us the challenges of online community participation on nanoHUB.org, which gave us the idea to pursue this line of research. We thank the editor Jacob Goeree as well as two anonymous referees for their helpful comments. We thank Kory Garner for help in conducting experiments. We also thank Jim Andreoni, Timothy Cason, Shakun Datta Mago, Peter DeScioli, Dirk Engelmann, Laura Gee, Justin Krieg, Rob Kurzban, John List, Lise Vesterlund, Stefano DellaVigna, Judd Kessler, participants at the International Economic Science Association meetings, the North America Economic Science Association meetings, and the University of Michigan, University of California-Santa Barbara, and Southern Methodist University for helpful discussions and comments. Any remaining errors are ours.
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Savikhin Samek, A., Sheremeta, R.M. Recognizing contributors: an experiment on public goods. Exp Econ 17, 673–690 (2014). https://doi.org/10.1007/s10683-013-9389-1
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DOI: https://doi.org/10.1007/s10683-013-9389-1