Does Group Identification Matter? - Experimental Evidence Shamlesh Annah Sebastian Edwards Christian Freund Julian Schumacher Marcin Strzelczyk Course:

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Presentation transcript:

Does Group Identification Matter? - Experimental Evidence Shamlesh Annah Sebastian Edwards Christian Freund Julian Schumacher Marcin Strzelczyk Course: Experimental Methods in Economics Supervisors: Elke Renner, Chris Starmer 7 May 2010

Outline 1.Motivation 2.Literature Review 3.Research Question & Hypothesis 4.Experimental Design 5.Results 6.Conclusion

Motivation Most group-based experiments reported in the economic literature so far consider groups as being a homogeneous entity. Real-world social structures, however, often exhibit different layers of organization, and different scales on which groups exist. Examples: International Economics: Trade agreements, environmental agreements (Kyoto Protocol etc.): Individuals as a group make up a country, and countries then make up another group, e.g. as defined by an international treaty. Political Economy: Social identities and voter behaviour. At least three different scales: The individual, the social subgroup it counts itself towards – e.g. with respect to marital or social status - and the whole population. We devised a Public Good experiment in order to illuminate whether the introduction of an “intermediate social layer”, that is an artificial social structure between “individual” and “whole group”, affects individual contribution behaviour in a significant way. The added structure is not reflected in the payoff structure of the experiment, i.e. the game is purely psychological.

Literature Review 1/2 Individual behaviour in PG Games. There seem to be general pattern in public contribution games, depending on repetition (Ledyard 1995), punishment (Fehr and Gächter 2000), and other factors. (Isaac, Walker and Williams 1994,Gächter and Renner 2003 and Gächter, Thöni and Herrmann 2008) Groups in Public Good games. Tan and Bolle (2007) find an increase in contributions to local Public Goods if different groups compete. George A. Akerlof and Rachel E. Kranton (2000), suggest that simply being able to identify with a group is itself an important source of individual well-being. PG field experiments. Yoeli (2009) examines the behaviour of customers of an electricity utility. He finds an increase in contributions to the public good if contributions are revealed to local neighbours.

Literature Review 2/2 Psychological group effects. Group identification, Insider/Outsider-effects – while the former would presumably lead to higher contribution (achieve a socially optimal outcome), the latter might rather decrease average contributions. Hargreaves Heap and Daniel John Zizzo (2006) find decreasing trust in a group setting, due to negative discrimination against outsiders. The individual and the group. Social comparison theory emphasizes that people in group settings behave differently than in isolation. In particular, it assumes that people are motivated both to perceive and present themselves in a socially desirable way. To accomplish this, a person might react in a way that is closer to what he regards as the social norm than how he would act in isolation. (Levinger and Schneider, 1969) Groups and Social Capital. Putnam (1993, 2001). Social capital in the form of trust is most effectively build by experiencing interaction in social networks. Since the public good game requires trust in the action of others to achieve a social optimal outcome, this should increase average contributions.

Research Question & Hypothesis Research Question. Does the existence of an “intermediate social layer”, i.e. of additional group structure within the population, affect individual contribution behaviour in a Public Good game in a significant way? Hypothesis. H0: Additional structure does not change individual contribution behaviour. H1: Additional Structure does change individual behaviour (direction not determined)

Experimental Design

IG treatmentGI treatment PSYCHOLOGICAL GROUP EFFECT Individual decision Group decision Individual decision Repetition control / preference bias Payoffs Revelation Preference control / repetition bias Repetition bias Changing beliefs about other’s contributions Imperfect conditional cooperation Lack of enforcement/ punishment Declining contributions Preference bias Unequal distribution of free riding / conditional cooperation / social preferences between groups Confunding group effect with heterogenous preferences Mitigate by N → ∞ IG1 IG2 GI1 GI2

Results 1/3 μ IG1 = 3.78 > 2.33 = μ GI1 Strong clustering of group contributions, almost uniform contribution of individual contributions No significant difference in distributions (Mann-Whitney) Results of limited explanatory power due to sample size (preference bias vanishes for N → ∞

Results 2/3 μ IG1 = 3.78 > 2.33 = μ IG2 Almost uniform distribution of individual and group contributions No significant difference in distributions (Mann-Whitney)

Results 3/3 μ GI1 = 2.33 > 1.33 = μ GI2 Overall lower contributions than in IG treatment No significant difference in distributions (Mann-Whitney)

No significant differences between group/individual contributions However, the quantitative results are questionable due to limitations: –subject knew each other, were familiar with experimental design and game theoretic prediction –perfect stranger design “not so perfect” –given the time slot, it was not possible to pose control questions in order to make sure each participant fully understood the game, especially the lack of any connection between the group structure and the payoff function. –very small sample size: strong evidence of preference bias –Resources not sufficient to investigate further group effects Slight hint of repetition bias: across-treatment comparison more meaningful Possible future directions: –varying the group structure (larger subgroups / different subgroups) –varying the extent to which these groups are constituted. Here: lowest possible level, mere identification with an anonymous group. May be extended to groups with known members, communication, … Conclusions

Thank you for your attention!

Old slides – not part of presentation!

Literature Review Andreoni 1988 runs a PG game to try to solve the puzzle. Finds neither the learning hypothesis or a rational strategy can explain the puzzle. Fehr and Schmidt 1999 model Inequality aversion people resist inequitable outcomes meaning they are willing to give up some material payoff to move in a direction of more equitable outcomes. This has lead to beliefs that people get utility from the fact others are benefitting ‘warm glow’. References: 1.Fischbacher & Gächter – Social Preferences, Beliefs and the Dynamics of Free Riding in Public Good Experiments, forthcoming in AER 2.Fehr & Gächter – Cooperation and Punishment in Public Goods Experiments, AER Fehr & Schmidt – A theory of fairness, competition and cooperation, QJE Andreoni – Cooperation in Public-Goods Experiments: Kindness or Confusion?, AER 1988

Literature Review The social comparison theory emphasizes that people in group settings behave differently than in isolation. In particular, it assumes that people are motivated both to perceive and present themselves in a socially desirable way. To accomplish this, a person might react in a way that is closer to what he regards as the social norm than how he would act in isolation. (Levinger and Schneider, 1969) The persuasive argument theory, deliberation drives group decisions in a particular direction because it is more persuasive. A related explanation of group shifts is that people with certain preferences tend to be more persuasive than others (for example, more selfish individuals are also more aggressive in deliberation). (Burnstein et al., 1973; Brown, 1974)

Results (between treatments)

Results: Additional remarks Weakly significant mean-difference across treatment groups (t = -1.64)  heterogenous preference bias? Weakly significant difference of individual contribution across treatments in the individual setting (z = -1.81)  preference and repetition bias?

Results (stage 2) Group mean (3.44) higher individual mean (1.33) Strong clustering of individual contributions around 0, rather uniform group contributions Weakly significant (10 %-level) difference in distributions (Mann-Whitney)