We study the role of social preferences and conformity in explaining herding behavior in anonymous risky environments. In an experiment similar to information cascade settings, but with no private information, we find no evidence for conformity. On the contrary, we observe a significant amount of non-conforming behavior, which cannot be attributed to errors.herding, information cascades, conformity, non-conformity, laboratory experiments

Ben Greiner

Luca Corazzini

English

Research Papers in Economics

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Department of Economics
University of Cologne 
Albertus-Magnus-Platz 
D-50923 Köln 
Germany 
 
http://www.wiso.uni-koeln.de 
 UNIVERSITY OF COLOGNE 
 
  WORKING PAPER SERIES IN ECONOMICS 
No. 21
HERDING, SOCIAL PREFERENCES AND (NON-)
CONFORMITY
LUCA CORAZZINI
BEN GREINERHerding, Social Preferences and (Non-)Conformity
Luca Corazzini∗and Ben Greiner†,‡
January 24, 2007
Abstract
We study the role of social preferences and conformity in explaining herd-
ing behavior in anonymous risky environments. In an experiment similar to
information cascade settings, but with no private information, we ﬁnd no ev-
idence for conformity. On the contrary, we observe a signiﬁcant amount of
non-conforming behavior, which cannot be attributed to errors.
Keywords: herding, information cascades, conformity, non-conformity, laboratory
experiments
JEL Classiﬁcation: C92, D31, D81
1. Introduction
Herding behavior has been explained by payoﬀ externalities, correlated eﬀects, infor-
mation externalities or social preferences. Payoﬀ externalities lead to herding when
each agent’s actions aﬀect the payoﬀs of other agents in such a way that everybody
choosing the same action constitutes an equilibrium (see, for example, Choi, 1997;
Diamond and Dybvig, 1983; Scharfstein and Stein, 1990). Correlated eﬀects are rel-
evant when agents behave similarly because they are exposed to the same exogenous
inﬂuences (Manski, 2000).
According to the third explanation, information inferred by observing predeces-
sors’ choices can induce an individual to ignore his private information in making
his decision. As a consequence, his decision becomes uninformative for subsequent
players. A cascade starts, as followers ﬁnd themselves in the same situation (Baner-
jee, 1992; Bikhchandani et al., 1992; Welch, 1992). Experimental studies (including
the seminal study by Anderson and Holt, 1997; and, among others, Drehmann
et al., 2005; Hung and Plott, 2001; K¨ ubler and Weizs¨ acker, 2004; Willinger and
Ziegelmeyer, 1998) ﬁnd support for rational herding behavior, but also observe cas-
cade breaks which cannot be explained by standard theories. Goeree et al. (forth-
coming) propose a Quantal Response error model to explain these breaks.
∗University of East Anglia, School of Economics and Social Studies, Norwich, UK and University
of Bocconi, Istituto di Economia Politica ”Ettore Bocconi”, Via Gobbi, 5, 20136, Milan, Italy.
†Harvard University, Harvard Business School, Boston, MA 02163.
‡Corresponding author. Tel: +1 617-495-6753, Fax: +1 617-495-5287.
E-Mail addresses: luca.corazzini@unibocconi.it (L. Corazzini), bgreiner@hbs.edu (B. Greiner).Finally, in the psychology literature, agents are assumed to have a preference
for conformity. Examples are the models of Jones (1984) and Bernheim (1994),
while Asch (1958), among others, provides experimental evidence. Goeree and Yariv
(2006) show in a similar setting to ours that choices of others matter to subjects,
independently of their statistical information. As herding reduces expected inequal-
ity among subjects, also other-regarding preferences like inequality aversion (Bolton
and Ockenfels, 2000; Fehr and Schmidt, 1999) might play a role.
In this paper, we experimentally investigate the role of conformity and other
social preferences on herding. Our setting diﬀers from the standard information
cascade setting (Anderson and Holt, 1997) only for the fact that no player has pri-
vate information. Therefore, while they might be socially relevant, the decisions of
predecessors are statistically uninformative. As conformity relies on the (expected)
choices of others, we explicitly measure expectations. To see whether these decisions
are correlated with other-regarding preferences, we conduct a dictator game subse-
quently with the same participants. Additionally, we vary between a ”gain” and a
”loss” frame, as preferences in risky environments often depend on the framing of
outcomes (Tversky and Kahneman, 1991).
In our experiment we do not observe herding at all: most of our subjects exhibit
non-conforming behavior, choosing the alternative which (they believe) the fewest
others have chosen. A robustness check on the basis of a treatment in which one
alternative had a higher chance to win shows that about a quarter of subjects are
willing to sacriﬁce expected payoﬀ in favor of non-conforming behavior. We do not
observe diﬀerent behavior between the ’gain’ and ’loss’ frame.
2. Experimental design and procedures
The experiment consists of three parts: the herding game, a dictator game and an
expectations task.1 In the ﬁrst part, 12 individuals chose one of two alternatives
(called ”doors” A and B) sequentially, knowing that at the end of the experiment
one of the two alternatives would be randomly selected. Before deciding, each
individual was informed about the frequency of choices of her predecessors over the
two alternatives. We conducted three diﬀerent treatments. In the ”gain” and ”loss”
treatment, the two doors had equal probability to pay out. In the ”gain” frame,
each individual who chose the alternative randomly drawn won a prize of 8 Euros
while the others won nothing. In the ”loss” frame, individuals were initially endowed
with an income of 8 Euros, and the alternative randomly selected yielded a loss of 8
Euros for those who chose it. The ”robustness” treatment diﬀered from the ”gain”
treatment only in the probabilities attached to the doors, being 55% for door A and
45% for door B.
The second part consisted of a dictator game played in strategy method. In
the dictator game, one player decides freely about the split of a ﬁxed monetary
amount between herself and another player. Subjects were randomly and anony-
mously matched to groups and roles, but before roles were revealed, decisions were
made by everybody in the role of the dictator. Individuals knew that only part one
1Instructions can be found at http://www.people.hbs.edu/bgreiner/supplements/.
2or part two would be randomly selected for payment by tossing a coin at the end of
the session.
In the third part, which was not announced before, we asked for subjects’ ex-
pectations of the ﬁnal number of choices for door A and door B in part one, and the
average amount given by the other individuals in part two. Each question was paid
with one Euro extra when subjects got the right numbers (or the diﬀerence was at
most 50 cents, respectively).
The experimental sessions took place in September and October 2005 in the
Cologne Laboratory for Economic Research and lasted about 30 minutes on average.
Participants were recruited using an online recruitment system (Greiner, 2004).
Altogether 266 subjects, mostly students in economics and business administration,
participated in 9, 9 and 5 sessions, respectively. The experiment was computerized
using the zTree software (Fischbacher, forthcoming). To ensure public knowledge,
all instructions were additionally displayed publicly and read aloud. No payoﬀ
feedback was given between the parts. At the end of the experiment, random draws
were taken publicly and subjects were paid out privately. Average payoﬀs were
about 7 Euros, including a show-up fee of 2.50 Euros.
Table 1: Choices and expectations in gain and loss frame
Gain sessions Loss sessions
Decisions A EA Decisions A EA
Session 1 BAAABBBBAAAA 7 5.92 AABBAAAAABBA 8 7.33
Session 2 BABABABAAABA 7 6.17 BABAABABABBB 5 5.92
Session 3 ABAAABBABBAB 6 6.75 AABBABBAAABA 7 6.58
Session 4 BBBBAAABBBAA 5 6.83 AABABABBABAA 7 6.92
Session 5 BABABBAAAABB 6 5.83 BAABBAAABABA 7 6.25
Session 6 AABAABAABBAA 8 7.42 ABBABBAABAAB 6 6.00
Session 7 AABBBAAABAAB 7 6.67 ABBAABAAABAA 8 6.50
Session 8 ABBABBBAABAA 6 5.92 ABABBBABAABB 5 6.08
Session 9 ABBAAAABBBAA 7 6.58 BABAABABABAA 7 6.25
∅ 6.56 6.45 6.67 6.43
Note that B = 12 − A and EB = 12 − EA, respectively.
3. Experimental results
Table 1 lists for each frame and session the sequence of individual choices and the
average of individuals’ expectations on the length of the ﬁnal queue A, EA. We do
not ﬁnd herding at all. Using a Sign test taking sessions as independent observations,
we cannot ﬁnd a signiﬁcant predominance of any door. On the other hand, individual
expectations reveal that door A is expected to be chosen more often than door B
(2-sided Sign test based on individual expectation statements, p=.000). Since our
data do not exhibit any signiﬁcant diﬀerence between the two frames (including the
analysis below), in the following we report statistics only for the pooled data.2
2Note that conclusions would not have been diﬀerent with separate analysis of the two frames.
3Using individual data, we question whether subjects either chose randomly or
they showed preferences for the shorter/longer queue.3 Table 2 reports the choices
for the shorter or longer queue with respect to subjects’ actual observed queue sizes
and to subjects’ stated expectations about ﬁnal queue sizes.
Table 2: Correlations and tests on decisions with respect to observed and
expected ﬁnal queues
with respect to
observed
queue sizes
expected
queue sizes
Two-sided Spearman-Rho Correlations of Decision (A=0, B=1) with
longer queue (A=0, B=1) Coeﬀ -.264*** -.203***
N 148 214
p .001 .003
queue size A – queue size B Coeﬀ .235*** .212***
N 214 214
p .001 .002
Distribution of individual choices NqA6=qB 150 214
long queue 59 88
short queue 91 126
Binomial test p-Value .011** .011**
Sign test on session aggregates p-Value .006*** .011**
Robustness Treatment
Distribution of individual choices NqA6=qB 54 60
long queue 40 46
short queue 14 14
Binomial test p-Value .001*** .000***
Sign test on session aggregates p-Value .188 .036**
All tests are two-sided. NqA =qB denotes the number of choices where corresponding ob-
served/expected queue sizes were diﬀerent. *,**, and *** denote signiﬁcance at a level of
10%, 5%, and 1%, respectively.
The correlation statistics show that subjects were more likely to choose the
shorter queue. Also, the larger the diﬀerence in length between the queues, the
higher the probability for an individual to choose the door with the shorter queue.
Binomial tests corroborate these statistics: we can reject the hypothesis that deci-
sions stem from a distribution of random choices (with a probability of .5 for either
the long or the short queue). Additional sign tests based on single session outcomes
show that we observe more choices for the shorter queue than for the longer queue
in our sessions.
Distributions of dictator giving in the second part of the experiment are shown in
3The expectations of two subjects in the gain treatment were not consistent with the queue sizes
they observed and their own decisions. We excluded these subjects from the analysis.
4Figure 1: Dictator giving and expectations
 0
 10
 20
 30
 40
 50
 60
 70
 80
0 1 2 3 4 5 6 7 8
N
GIVE
GIVING
 0
 10
 20
 30
 40
 50
 60
 70
 80
0 1 2 3 4 5 6 7 8
N
AVG. GIVE
EXPECTATIONS
Figure 1. We ﬁnd no correlations between actual dictator giving and behavior in the
herding game. Also non-parametrical tests on diﬀerences in giving behavior between
conforming and non-conforming subjects do not yield signiﬁcance at any level. When
considering participants who give zero in the dictator game and participants who
give a positive amount separately, the results of binomial tests on herding decisions
do not change for both subgroups, although – due to a lower number of observations
– they are only signiﬁcant at the 10%-level. Correspondingly, a Chi-Square test does
not yield diﬀerences in decision distributions between these groups.
In order to test for the robustness of the observed non-conforming behavior
we conducted 5 sessions on a ”robustness” treatment in which door A had an 80
cents higher expected payoﬀ than door B. The lower part of Table 2 reports the
results. While most of the subjects go with the crowd and choose door A, about
one quarter of our subjects choose door B, showing robust non-conform behavior.
Also, subjects who behaved non-conforming gave signiﬁcantly more in the dictator
game (on average 3.61 Euros) than those who conformed (on average 2.24 Euros;
two-sided Mann-Whitney-U test, P = 0.049).
4. Discussion and Conclusions
In a study carried out independently of this paper, Goeree and Yariv (2006) let
subjects choose between observing a statistically informative signal or the history of
play of other uninformed subjects.4 The latter option was chosen by up to 51% of
their subjects, thereby forgoing expected payoﬀ in the form of better information.
Of those who chose the history of play, about 77% behaved conforming and followed
the current majority. In a treatment where all subjects are paid according to the
majority decision Goeree and Yariv (2006) rule out inequality aversion as a possible
motivation for history of play selections.
Our results partially contradict the ﬁndings of Goeree and Yariv (2006). While
we also ﬁnd that inequality aversion plays no role in cascade settings, we do not
observe conformity at all. On the contrary, we observe a signiﬁcant number of non-
conforming choices. Even in the treatment with asymmetric probabilities assigned
to the doors we observe that about 25% of our subjects chose the door with the
4Or, to put it diﬀerently: Subjects choose between participating in our experiment rather than
in an individual risky choice task.
5smaller queue and lower expected payoﬀ. These results cannot be explained by
symmetrically distributed errors around payoﬀ maximizing choices (see, for example,
the model of Goeree et al., forthcoming).5
We did not observe signiﬁcant diﬀerences between the gain and the loss frame.
The lack of correlation between dictator giving and choices in the baseline treat-
ments suggests that although two-thirds of subjects might entertain other-regarding
preferences, this does not straightforwardly translate to (non-)conformity. On the
other hand, strongly non-conforming subjects in the robustness treatment give more
in the dictator game than others, suggesting that expected payoﬀ maximizers in the
herding game put more weight on their own payoﬀ in the dictator game.
Our results suggest that social preferences do not explain herding in information
cascade settings, but that it is more than just errors that makes these cascades
break. One can think of several sources of non-conforming behavior. Monetary
outcomes of lotteries can be ”positional goods” (Frank, 1985), such that the lower the
number of winners, the higher their satisfaction. People can exhibit a preference for
symmetry, as observed in a number of biological and psychological studies.6 Another
explanation could be that people take over the perspective of a risk-averse social
planner such that an equal distribution of individuals between doors maximizes
expected social utility.
Acknowledgements
We thank Andrew Clark, Dorothea K¨ ubler, Axel Ockenfels, Robert Sudgen, An-
thony Ziegelmeyer, Daniel Zizzo, participants at the 2006 ESA Regional Meeting
in Nottingham, and seminar participants in Cologne and Padova for helpful com-
ments on earlier drafts of this paper. Financial support from the GEW Foundation
Cologne and the German Science Foundation (DFG) is gratefully acknowledged.
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7

5In an experiment involving private information and market prices for alternatives,

6See, for example, Møller and Swaddle

A Simple Model of Herd Behavior,

A Theory of Conformity,

A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades,

A Theory of Fairness, Competition, and Cooperation,

An Online Recruitment System for Economic Experiments, in: Kurt Kremer, Volker Macho, eds, Forschung und wissenschaftliches Rechnen

Are More Informed Agents Able to Shatter Information Cascades in the Lab?, in:

Bank Runs, Deposit Insurance and Liquidity,

Conformity in the Lab, Working Paper, California Institute of Technology, Division of the Humanities and Social

Economic Analysis of Social Interactions,

Eﬀects of Group Pressure upon the Modiﬁcation and Distortion of Judgements,

ERC - A Theory of Equity,

Herd Behavior, the ’Penguin Eﬀect’, and the Suppression of Informational Diﬀusion: An Analysis of Information Externalities and Payoﬀ Interdependency, The RAND

Herding and Contrarian Behavior in Financial Markets: An Internet Experiment,

Information Cascades: Replication and an Extension to Majority Rule and Conformity Rewarding Institutions,

Loss Aversion in Riskless Choice: A ReferenceDependent Model,

Sequential Sales, Learning and Cascades,

The Demand of Unobservable and

The Economics of Conformism (Blackwell,

Herding, Social Preferences and (Non-) Conformity

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