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One bad apple? Heterogeneity and information in public good provision

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One bad apple don’t spoil the whole bunch, girl. Donny Osmond.

Abstract

Previous research demonstrates that individuals vary in their social preferences. Less well-understood is how group composition affects the behavior of different social preference types. Does one bad apple really spoil the bunch? This paper exogenously identifies experimental participants’ social preferences, then systematically assigns individuals to homogeneous or heterogeneous groups to examine the impact of ‘bad apples’ on cooperation and efficiency. Consistent with previous research, we find that groups with more selfish types achieve lower levels of efficiency. We identify two mechanisms for the effect. First, the selfish players contribute less. Second, selfish players induce lower contributions from the conditional cooperators, and this effect increases in the number of selfish players. These results are not sensitive to information about the distribution of types in the group.

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Notes

  1. A notable exception is Burlando and Guala (2005), though their focus is on homogeneous groups.

  2. In a replication of FGF using several locations in Russia, Herrmann and Thöni (2009) find the share of conditional cooperators varies between 47.7 and 60.0 % and that the share of Nash/selfish types varies between 1.9 and 11.4 %. Kocher (2006) finds approximately 35 % of the population to be conditional cooperators and that, given the option, subjects choose to condition on the responses of others 56 % of the time. Martinsson et al. (2009) find conditional cooperators randing from 54.2 to 62.5 % of their sample while free riders range from 4.2 to 25.0 %. For Kurzban and Houser (2005) type elicitation mechanism and population, the respective percentages are 20 % Nash/selfish and 63 % conditional cooperators. For Burlando and Guala (2005), these percentages are 32 and 35 % respectively.

  3. As stated by the authors: “It was made clear that the composition of the groups differed from that of the first session” (p. 44). However, there is no mention of whether this meant subjects knew the group composition was homogeneous, or just that these were different groups than those in the first session.

  4. To avoid feedback effects, for subjects participating in the lab-study, earnings are picked up or delivered on the day of their participation, after their session is completed. For those not participating in the lab study, earnings are picked up or delivered (via the university debit card) after the lab study is complete.

  5. Specifically, we classify subjects who never give more than five as selfish. In the 21 decisions of the type elicitation task, over 90 % of the decisions for our selfish subjects are zeroes, and over 98 % are either zero or one.

  6. There are no significant differences in demographics by information treatment at p < 0.05, except the following: Asian (p = 0.01). Due to difficulty recruiting the required number of the various types of subjects, a limited number of experienced subjects (19) were recruited.

  7. This was done to confirm that there was no impact of telling an individual their own type, which there was not (p = 0.80, from a t test of means comparing the one-shot game and the first round of the repeated-play game in the No Information treatment). This result further hold when considering only selfish (p = 0.45) or conditional cooperator (p = 0.75) types.

  8. Several other types of decision rules which might readily come to the subjects’ mind are listed, even though they are not of interest for this study. This was done to minimize demand effects.

  9. Estimated as a random effects regression where the dependent variable is the amount of tokens sent, as a function of the round, treatment, an interaction between the round and treatment, and the constant.

  10. Modeled as a random effects Tobit regression using only the conditional cooperators in the sample, allowing for censoring at zero and twenty. The dependent variable is the amount of tokens sent to the group account, modeled as a function of the round, information treatment, dummy variables for the (C, C, S) and (C, S, S) groupings, plus a constant. The (C, C, C) grouping is the omitted dummy variable. See Appendix Table 5.

  11. We thank an anonymous reviewer for raising this issue and suggesting some of these important ideas.

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Acknowledgments

We would like to thank Sunny Kukreja and Thomas Sires for programming this experiment and Elizabeth Pickett for her managerial magic. Abdul Kidwai provided research support. Additionally, we would like to thank Daniel Arce, Nathan Berg, Chetan Dave, Sherry Xin Li, James Murdoch, Todd Sandler and participants at the Negotiation Center at the University of Texas at Dallas, Social Dilemmas Conference at Rice University, International Association for the Study of the Commons Annual Meeting 2010, the Economic Science Association North American Regional Meetings, Southern Economic Association Annual Meetings, Allied Social Science Association Annual Meetings, and SITE 2009 Segment 7 for helpful comments. Funding provided by the Center for Behavioral and Experimental Economic Science at the University of Texas at Dallas (now closed). Any errors remain our own.

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Authors and Affiliations

  1. University of Massachusetts Amherst, Department of Resource Economics, 203 Stockbridge Hall, 80 Campus Center Way, Amherst, MA, 01003, USA

    Angela C. M. de Oliveira

  2. University of Texas, Arlington Dean, College of Business, 701 S. West Street, Room 334, Box 19377, Arlington, TX, 76019, USA

    Rachel T. A. Croson

  3. Texas A&M University, Department of Economics 4228 TAMU, College Station, TX, 77843-4228, USA

    Catherine Eckel

Authors
  1. Angela C. M. de Oliveira
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  2. Rachel T. A. Croson
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  3. Catherine Eckel
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Corresponding author

Correspondence to Angela C. M. de Oliveira.

Appendix A

Appendix A

See Appendix Tables 3, 4 and 5.

Table 3 Description of sample, by treatment
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Table 4 Percent of possible group contributions, random effects panel
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Table 5 Individual contributions: random effects panel tobit for the amount sent to the group account, dummy variable specification
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de Oliveira, A.C.M., Croson, R.T.A. & Eckel, C. One bad apple? Heterogeneity and information in public good provision. Exp Econ 18, 116–135 (2015). https://doi.org/10.1007/s10683-014-9412-1

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