Identifying In-Group Conformity in Prosocial
Behavior
Silvia Saccardo, Justin Valasek and
Roel van Veldhuizen
Identifying In-Group Conformity in Prosocial Behavior
Silvia Saccardo,1 Justin Valasek,2 and Roel van Veldhuizen2∗
October 19, 2015
Work in Progress
-please do not cite or distribute without author’s permission-
1
Motivation
As evidenced in the seminal work of Elinor Ostrom, social norms reinforce prosocial behavior. As such, there has been a resurgent effort to analyze the use of social norms to decrease
behavior such as tax evasion and corruption, and increase behavior such as charitable giving. Indeed, field studies in multiple settings have shown individuals respond to normative
statements and observations of other’s prosocial behavior (for charitable giving, Frey and
Meier (2004), for tax payment, List et al (2013) and Sausgruber and Traxler (2013), energy
conservation Schultz et al. (2007) and Alcott (2011) and for public good contributions, Jack
and Recalde (2013)).
However, it remains uncertain whether these peer effects are an indirect response to the
information contained in actions of others (social learning), or due to a direct response to the
behavior of their peers (in-group conformity).1 Additionally, existing experimental studies
on peer effects are inconclusive: Potters, Sefton and Vesterlund (2005) show that subjects
respond to peer behavior in a setting where subjects have imperfect information regarding
a payoff-relevant variable, while other articles have pointed to conformity and in-group reciprocity as explanations for peer effects (Bohnet and Zeckhauser (2004) and Falk, Fischbacher
and Gächter (2013)). Note that in-group conformity could stem from either a group-specific
social norm of conformity or from in-group reciprocity or distributional preferences – since
∗1
UCSD, 2 WZB Berlin. Contact e-mail: [email protected]
See Frey and Meier (2004) for a discussion of potential mechanisms driving peer effects in prosocial
behavior.
1
1
both mechanism imply peer effects that are due to a direct response to the group-member’s
behavior, for the purposes of this paper we label both as “in-group conformity” (see Gächter,
Nosenzo, and Sefton (2013) for an experiment that provides evidence that peer effects are
due to distributional preferences).
Instead, in this paper, we report the results of an experiment designed to unambiguously
identify in-group conformity as a mechanism that drives prosocial behavior. Separating ingroup conformity from social learning is essential to designing policies to that utilize norms to
increase prosocial behavior since, as we argue (formally) in this paper, the difference between
social learning and in-group conformity corresponds to a difference in the underlying norms
that enforce prosocial behavior, namely whether a norm is “exogenous,” implying a fixed
moral cost/benefit, or “endogenous,” in the sense that the strength of the prosocial norm is
a function of peer, or reference-group, behavior.
Importantly, the different classes of norms imply different policies for increasing prosocial
behavior: Endogenous norms imply that multiple equilibria may exist, since expected payoffs
from an action depend on expectations of aggregate group behavior. Therefore, from a
policy perspective, interventions at the group or institutional level may be successful in
increasing prosocial behavior. For example, the role of leaders may be particularly important
for promoting prosocial behavior in a setting with in-group conformity, since leaders may
influence expectations of aggregate group behavior. Additionally, with enough information,
it may be possible to strategically design groups to maximize prosocial behavior by, say,
strategically manipulating seating at a benefit dinner based on donor’s past behavior.2
In contrast, exogenous norms suggest that group-level interventions, and that efforts to
increase prosocial behavior require interventions to directly increase the individual moral
benefit of prosocial behavior, such as campaigns to increase the saliency of the benefits of
prosocial behavior. By studying norms in a controlled, laboratory environment, we provide
unique evidence for whether, and how, group-based interventions are likely to be effective in
increasing prosocial behavior.
Literature
Peer effects in “prosocial” behavior have been documented both in the field and in the
lab. Notably, Frey and Meier (AER 2004) show that students’ charitable contributions are
sensitive to whether they are given information that contributions in previous years have
2
Similar to the strategic group design discussed in the literature on peer effects in educational outcomes.
For example, see Bhattacharya (2009), Duflo, Dupas, and Kremer (2011), and Carrell, Sacerdote, and West
(2013).
2
been “high” or “low.” As they note, however, they cannot determine whether this effect is
based on social learning or conformity.
In the laboratory, Falk, Fischbacher and Gächter (Econ. Inq. 2013) show that subjects’ contributions in a repeated public-goods game are sensitive to the behavior of their
“neighbors.” Each participant is matched to two groups in each round (the matching is held
constant over the 20 rounds), and observes aggregate contributions of each group at the end
of each round. They show that, within-subject, contributions are sensitive to the aggregate
contributions of the group. Gächter, Nosenzo and Sefton (JEEA 2013) show that contributions in a gift-exchange experiment are sensitive to information about the (non-payoff
relevant) contribution of another subject.
On social and group norms, Goette, Huffman and Meier (AER 2006) show that “group
membership” matters, even when these groups are formed exogenously and randomly. They
use random assignment to groups during a four-week officer training in the Swiss Army, and
show that cooperation and expected cooperation in a public-goods contribution game (with
punishment) are higher between in-group members. Additionally, they show that there is
no evidence that punishments vary depending on whether the “transgressor” is in-group or
out-group, but that punishment is higher if the “victim” is in-group.
Krupka and Weber (JEEA 2013) propose a measure of social norms by eliciting beliefs
of the average response of other subjects regarding appropriate behavior in a dictator game.
Since all subject are paid for matching the average response, elicitation is a pure coordination
game; arguably, this implies that the measure captures a focal point. However, under the
assumption that the focal point is the social norm, the method works.
Perhaps most relatedly, Charness and Schram (Working Paper 2012) attempt to experimentally distinguish between Moral and Social norms, where moral implies intrinsic enforcement and social implies “soft” extrinsic enforcement (such as shame). They argue that a
social norm is only relevant if behavior is observable, and use a dictator game with variance
in both observability and advice from a neutral third party. The distinction between a moral
and social norm is separated by comparing a baseline of no advice and no observability to
“advice-private” and “advice-public.” Only advice-public has a significant effect, implying
that only the social norm is relevant (social learning through advice was not statistically
important). Note, this argument might be flawed, since the advice given by the neutral
third party is not incentivized. Therefore, a moral norm can be driving baseline behavior,
and social learning could be absent simply because advice is an “injunctive” norm (what one
ought to do) rather than a “descriptive” norm (what individuals actually do).
3
2
A model of endogenous and exogenous norms
Here we illustrate how peer effects in prosocial behavior can result from both endogenous
and exogenous norms using an illustrative framework of non-pecuniary motivations. In the
model, agents choose from a set of actions, a, which affect both their pecuniary and nonpecuniary payoffs. Specifically, agents have utilities represented by the following equation:
Ui (ai , n, x) = Mi (ai , n, x) + Wi (ai ),
where n represents the arguments of the endogenous norm, and xi the arguments of the
exogenous norm. More specifically, n consists of the actions of all other agents, n = {aj }j=i .
This does not imply that the endogenous norm is a function of all other agent’s action; e.g.
a conformity norm might entail that only the actions of a subset of j, the “reference group,”
are relevant for the endogenous norm. For the exogenous norm, we consider xi to be a set
of “moral” costs or benefits associated with each action ai . Importantly, xi is not a function
of the actions of other agents (n).3
For expositional clarity, and to illustrate a setting that corresponds to our experiment,
we put a bit more structure on the model: First, take ai ∈ [0, ae ] to indicate the amount
of effort or money that i allocates to a certain prosocial goal, say, a charity (ae is the
endowment of time or budget). Accordingly, Mi is strictly increasing in ai and Wi is strictly
decreasing in ai . Second, take ni ⊂ n to be i’s reference group, i.e. the set of j that are
relevant for i’s endogenous norm, and take āi equal to the average aj in the reference group
ni
(āi =
aj /|ni |). For simplicity, we assume that all agents other than i take actions
prior to i. Following the logic of a conformity norm, we assume that the marginal nonpecuniary benefit of ai is increasing in the average of the reference group (∂Mi /∂ai ∂āi > 0).
Additionally, the marginal non-pecuniary benefit of ai is increasing in its moral benefit
(∂Mi /∂ai ∂xi > 0).
First, we detail the result that peer effects can occur due to in-group conformity.
Result 1 (Peer Effects due to In-Group Conformity)
If Mi (ai , n, x) is a direct function of i’s reference group, ni , then i’s action, ai , is a function
of information on the reference group’s actions, {aj } for j ∈ ni .
This result is trivial: since i’s non-pecuniary payoff, Mi (ai , n, x), depends directly on the
actions of i’s reference group, i’s optimal action will respond to the reference group’s actions.
3
xi might be a function of, say, the level of scrutiny of a decision. For the analysis, as for the experiment,
we consider factors such the level of scrutiny to be constant.
4
Next, we formally illustrate how peer effects can occur in a setting where there is no
endogenous norm and ex-ante uncertainty regarding moral benefits. Specifically, suppose
that ni = ∅, or Mi (ai , n, x) = Mi (ai , xi ) and xi is imperfectly observed ex ante (moral benefits
realize at a future date a la Benabou and Tirole (2011)). That is, each agent observes x̃i ,
where xi = x̃i + i and i is unobserved and drawn from some symmetric distribution with
mean zero. Moreover, suppose that agents’ norms are positively correlated, σxi ,xj > 0.
Result 2 (Peer Effects due to Social Learning)
If i’s non-pecuniary payoffs, Mi (ai , xi ), are independent of i’s reference groups’ actions, but
xi is imperfectly observed ex ante, then i’s action, ai , is a function of information regarding
the reference group’s actions, {aj } for j ∈ ni . Moreover, i’s optimal response to aj is a
function of σxi ,xj .
Note that in this case, peer effects occur indirectly due to the information contained in
j’s action, rather than a direct payoff pertaining to aj . That is, agent i will increase ai if
they observe a high aj , since j’s behavior is informative of x̃j , which is in turn informative
of xi .
Since peer effects in prosocial behavior can be due to either social learning or conformity,
distinguishing between exogenous and endogenous norms in a field setting has proved elusive
(see Frey and Meier (2004)). Even a finding that i responds more to information on the
behavior of some subset of the population, ni , is not sufficient to identify conformity: this
disproportionate response could occur because ni is a reference group for i’s conformity norm,
or it could occur simply because σxi ,xj is higher for j ∈ ni . For example, if individuals respond
more to a statement regarding the tax behavior of their neighbors than to a statement
regarding the general population, the differential effect could be due to a local conformity
norm, or because their moral norm is more highly correlated with that of their neighbors.
This conflation of exogenous and endogenous norms, or between social learning and conformity, can be overcome in a laboratory setting, allowing us to identify whether endogenous
norms are relevant in promoting prosocial behavior. Specifically, our approach will be to
(partially) control {n}i , while holding σxi ,xj constant. In a laboratory setting, σxi ,xj can be
held constant through randomization, while ni can be manipulated by inducing reference
groups in the lab and limiting ex post observability to {n}i (both intrinsic and extrinsic
motivations might play a role in a conformity norm). This strategy allows for a partial
identification of endogenous norms; since we control for σxi ,xj , if subjects respond disproportionately to information about the induced reference group’s behavior, then we can conclude
that this disproportionate response is due to an endogenous norm.
Moreover, this design allows us to directly examine whether Mi , and hence average proso5
cial behavior, can be increased by manipulating ni . Similar to the literature on peer effects
in educational outcomes (see for example Bhattacharya (2009), Duflo, Dupas, and Kremer
(2011), and Carrell, Sacerdote, and West (2013)), if subjects exhibit a differential response
to information about their reference group, then the reference groups can be manipulated
to maximize average prosocial behavior. Moreover, if these effects are driven by an intrinsic
motivation to conform, these effects should persist past the initial intervention.
3
Experimental Design
As illustrated in the previous section, the main challenge of distinguishing between peer
effects due to social learning and peer effects due to conformity is to control for correlation
in exogenous norms by inducing an endogenous norms between randomly matched pairs. We
achieve this through a group task, in which teams work together in an environment where
there is limited opportunity for subjects to learn about their teammate’s underlying type.
This approach is similar to the minimal group treatment approach (see Chen and Li (2009)
for an overview); however, we avoid any approach that divides subjects into teams based on
subject choice, since subjects may perceive a correlation between subjects’ choice and moral
values.
Group Task
The group task consisted of 18 sub-tasks, each of which had two parts: First, each teammates
has ten seconds to individually solve a four-letter ”CAPTCHA” (short for “Completely
Automated Public Turing test to tell Computers and Humans Apart”), a common task
used by websites to distinguish between humans and automated “bots,” that consists of
entering a set of letters that are visually distorted. Second, the eight letters entered by the
subjects in the captcha task are shown, simultaneously, to both members of the team, and
the team has 60 sec to unscramble the letters into a dictionary word. The teammates cannot
communicate, as this may give them the opportunity to learn about the other’s moral type,
but enter answers separately. If either teammate enters the unscrambled word correctly, the
team is considered to have successfully completed the task.
To give an example, teammate one would have to solve the CAPTCHA in Figure 1, while
teammate two would have to solve the CAPTCHA in Figure 2. If entered correctly, both
teammates would then observe the letters “peotscih,” and each have a chance to enter the
german word formed by the eight letters (“poetisch”).
The purpose of this design is to induce a group or team through a joint production
6
Figure 1: CAPTCHA a.
Figure 2: CAPTCHA b.
process – neither teammate can compete the task without their teammate correctly solving
their CAPTCHA – while minimizing direct interaction, so that individuals do not learn
about their teammate’s moral type.
Subjects were first given detailed instructions and examples of the CAPTCHA/unscramble
task, including an illustration of how errors in the input process would distort the sequence
of letters to be unscrambled. They were informed that the group task would be successfully
completed if the team correctly solved 12 of 18 CAPTCHA/unscramble tasks. Next, subjects were informed that they had been matched with a randomly selected subject, and then
proceeded to complete the group task. After each CAPTCHA/unscramble task, the subjects
were shown the correct answer, and informed whether the team successfully unscrambled the
word.
At the end of the group task, both members of the team were informed as to whether
the team successfully completed the group task. We attempted to calibrate the group task
so that reaching the threshold of 12 correct sub-task would not be trivial (and hence require
effort), but also not be overly difficult. The data suggest that group task was reasonably
well-calibrated to meet these goals: all but one of the 72 groups reached the threshold, and
41 of the groups (57%) solved only 12-14 of the CAPTCHA/unscramble tasks.
Social Preference Measure
After the group task, subjects are informed that they will have an opportunity to donate
a portion of their saving to a charity. Here, the subjects are divided into three different
treatments. The first set of subjects are immediately shown the donation screen, and are
informed that their teammate and another subject will observe their chosen donation. The
7
donation decision of the first treatment is not of direct interest; rather, the first treatment
is required to generate a reference donation for the two main treatments of interest.
Individuals in the two main treatment groups are shown a waiting screen, and informed
that they will be given the opportunity to observe another subject’s donation before making
their choice. After all subjects in the first treatment make their donation decision, individuals
in both main treatments are shown the donation decision of one individual in the reference
group.
The treatments vary as follows:
Random Information Treatment: The individual is reminded how much money they
earned in the group task, and is show (translated from German):
Another participant, who earned 14 euros, chose to donate X euros. How much
would you like to donate?
Group Information Treatment: The individual is reminded how much money they
earned in the group task, and is show (translated from German):
Your teammate chose to donate X euros. How much would you like to donate?
That is, the Random Info and Group Info treatment only vary whether the provided information on another subject’s donation decision originates from the subject’s teammate, or
from a randomly selected participant.
Since subjects are made aware that the teams were randomly selected, another subject’s
donation decision should be equally informative of an underlying exogenous social norm,
regardless of whether information originates from their teammate or not (i.e. expected σxi ,xj
is constant due to the random assignment). Therefore, the donation decision of the teammate
and the non-teammate is equally informative from the perspective of social learning:
H1 (Social Learning): The correlation between the donation decision and the
reference donation is the same for the Random Info treatment and the Group
Info treatment.
However, to the extent that our experimental procedure creates groups that are relevant for
an endogenous norm, the donation decision of the subject’s teammate will be more relevant
for conformity:
H2 (Conformity): The correlation between the donation decision and the
reference donation is smaller for the Random Info treatment than for the Group
Info treatment.
8
80
60
count of donation
40
20
0
0
1
2
3
4
5
6
7
8
9
10
14
Figure 3: Notes: Graph shows the total number of donations in bins of [0, 1), ect.
Note that the group intervention we choose for our experimental procedure is exceedingly
minimal, since we do not wish to give subjects the opportunity to learn about the other’s
moral type. In this sense, any endogenous norm generated by the group task can be seen as
“minimal.” However, there is one channel for subjects to learn about their teammates ability, which could conceivably be correlated with moral type: if a subject fails to unscramble
a word, but observes that the team unscrambled the word correctly, then they can ascertain
that their teammate successfully unscrambled the word; and if a subject fails to unscramble
a word, and observes that the team failed to unscrambled the word, then they can ascertain
that their teammate failed to unscramble the word (if a subject successfully unscrambles a
word, then they cannot ascertain their teammates performance). However, we find that perceptions of their teammates ability (elicited in an ex-post questionnaire) is not a significant
factor explaining the correlation between the donation decision and the reference donation,
which suggests that this channel is not driving our results.
4
Results
-Preliminary- Figure 4 shows the distribution of donations across all treatments. Note that
a plurality of subjects chose a donation of zero, which may be a function of the fact that
9
Own Donation
5
10
0
0
Own Donation
5
10
15
Group Info Treatment
15
Random Info Treatment
0
2
4
6
Reference Donation
donation
8
10
0
2
4
6
Reference Donation
Fitted values
donation
8
10
Fitted values
Figure 4: Random Info Treatment, full data. Figure 5: Group Info Treatment, full data
3
Own Donation
2
1
0
0
1
Own Donation
2
3
4
Group Info Treatment
4
Random Info Treatment
0
1
2
Reference Donation
donation
3
4
0
Fitted values
1
2
Reference Donation
donation
Figure 6: Random Info Treatment, [0, 4].
3
4
Fitted values
Figure 7: Group Info Treatment, [0, 4].
subjects “earned” their endowments by participating in a real effort task. However, a five
subjects chose to donate their full endowment, which suggests a slight bimodal distribution
in donations.
The following figures (Figures 4 and 5) plot subjects’ donations relative to their reference
donation for, respectively, the Random Info and Group Info treatments. Perhaps unsurprisingly, given the number of outliers, the full data show little systematic relationship between
the donations and reference donations for the Group Info treatment, and a slightly positive
relationship for the Random Info treatment.
However, if we limit the graphs to donations in the range of zero to four, which captures
a large bulk of the data (88% of observations), a different picture emerges. Looking at the
truncated data (Figures 6 and 7), the relationship flips and the correlation for the Random
Info treatment is essentially zero, while it is positive for the Group Info treatment.
10
Figure 8: Analysis on full data OLS and Quantile (median) regressions by treatment.
Figure 9: Analysis on data minus observations greater than three s.d. from mean; OLS and
Quantile (median) regressions by treatment.
This suggests that our experiment suffers from the familiar problem of outliers, which in
the linear-regression framework are given disproportionate weight. In fact, by treatment, six
observed donations fall outside of three standard deviations of the mean, which implies that
donations are unlikely to be drawn from a truncated-normal distribution (given a normal
distribution, in expectation we should see less than 0.5 observations that fall outside of three
standard deviations of the mean).
The same pattern is observed in the regression analysis (figure 8): OLS estimates show
no statistically significant relationship between donations and reference donations for either
treatment. However, a quantile regression shows that the median effect of the reference
donation is positive and statistically significant at the 10 percent level for the Group Info
treatment, and is smaller non-significant for the Random Info treatment. Moreover, if we
exclude the six observation greater than three s.d. from mean (outliers), we find the same
11
result in the OLS regressions as with the quantile regressions on the full data. This suggests that in-group conformity is a relevant factor in determining subject’s level of prosocial
behavior.
References
Bénabou, Roland and Jean Tirole (2011), “Identity, Morals, and Taboos: Beliefs as Assets.”
The Quarterly Journal of Economics, 126, 805–855.
Bernheim, B D (1994), “A theory of conformity.” Journal of Political Economy, 102, 841–
877.
Charness, G and A Schram (2012), “Social and Moral Norms in the Laboratory.” Working
Paper.
Dal Bó, Ernesto and Marko Terviö (2013), “Self-Esteem, Moral Capital, and Wrongdoing.”
Journal of the European Economic Association, 11, 599–633.
Falk, Armin, Urs Fischbacher, and Simon Gächter (2010), “Living In Two NeighborhoodsSocial Interaction Effects In The Laboratory.” Economic Inquiry, 51, 563–578.
Frey, B S and S Meier (2004), “Social Comparisons and Pro-Social Behavior: Testing ”Conditional Cooperation” in a Field Experiment.” The American Economic Review, 94, 1717–
1722.
Gächter, Simon, Daniele Nosenzo, and Martin Sefton (2013), “Peer Effects in Pro-Social
Behavior: Social Norms or Social Preferences?” Journal of the European Economic Association, 11, 548–573.
Goette, L, D Huffman, and S Meier (2006), “The Impact of Group Membership on Cooperation and Norm Enforcement: Evidence Using Random Assignment to Real Social
Groups.” The American Economic Review, 96, 212–216.
Krupka, Erin L and Roberto A Weber (2013), “Identifying Social Norms Using Coordination
Games: Why does dictator game sharing vary?” Journal of the European Economic
Association, 11, 495–524.
Ostrom, Elinor (2000), “Collective Action and the Evolution of Social Norms.” The Journal
of Economic Perspectives, 14, 137–158.
12
Sliwka, Dirk (2007), “Trust as a Signal of a Social Norm and the Hidden Costs of Incentive
Schemes.” The American Economic Review, 97, 999–1012.
13