Identifying Social Effects in Economic Field Experiments
By Marco Castillo and Michael Carter*
Abstract: We present an experimental and survey design that consistently identiÞes the existence of social interactions. We implement this design in a crosssection of 31 small Hondurean communities and show evidence of the existence of
social effects. Our results shows the importance of experimental methods in the
study of social norms.
JEL Codes: C93, Z13
1. Introduction
A growing body of research suggests that social context affects individual decision
making and economic outcomes. The characteristics and behavior of the group
one belongs to can affect one’s actions. This literature is motivated by observations that variation in behavior within groups is smaller than variation in behavior
across groups. For example, variation in crime levels across neighborhoods have
been found to be too large relative to the variation in their socio-economic composition (Glaeser, Sacerdote, and Scheinkman, 1996), and academic performance
of college students has been found to be inßuenced by the academic performance
of roomates (Sacerdote, 2001).
As noted by Manski (1993), identiÞcation of models with social interactions is
extremely difficult in the absence of strong assumptions. The behavior of people
belonging to the same group might resemble each other because they face the
same conditions and share similar backgrounds and not because group’s behavior
or characteristic affects individual behavior. Manski concluded that richer data
collection and controlled experiments might improve the prospect of identifying
social effects.
0
* Carter: Department of Agricultural and Applied Economics, University of WisconsinMadison; Castillo: Environmental Policy Program, Georgia State University, Atlanta, GA 30303.
We thank Paul Ferraro, Peter Matthews, and Ragan Petrie and for helpful comments.
This paper presents an experimental and survey design that collects richer
data to identify the existence of social interactions.1 We exploit the variation in
behavior and socioeconomic characteristics across individuals and across groups,
and we control for the potential existence of unobservable correlates. To generate
exogenous variation in socioeconomic characteristics at the level of experimental
sessions, we randomly assigned individuals to experimental sessions in a crosssection of communities of interest. We conducted repeated experimental sessions
in each community to control for the existence of correlated unobservables that
otherwise would render parameter estimation inconsistent. The differential effect
of socioeconomic variables at the individual level and at the group level allows
the identiÞcation of social effects. Our estimation strategy is related to the recent
approach developed by Graham and Hahn (2003), which shows how panel data
methods can be used to identify social effects.
Our approach shows that experimental methods combined with survey data
provide a cleaner test for social interactions. The data were collected in a sample
of 31 small Hondurean communities. We Þnd evidence that social interactions
are important in economic decision-making. We cannot, however, distinguish the
source of social interactions precisely without making strong exclusion restrictions.
Our analysis thus suggests that richer data on the background of participants
might be needed in the future in order to identify endogenous social effects. This
highlights the importance of experimental economics in the study of social norms
beyond the college lab.
The next section outlines the identiÞcation and estimation strategy of the
paper. The third section describes the experimental procedures. A fourth section
presents the analysis of the estimation results. We then conclude.
1
There exist a growing body of research in experimental economics suggesting that social
context affects individual decision making. For instance, Roth, Prasnikar, Okuno-Fujiwara,
and Zamir (1991) showed that subjects in the US, Israel, Japan and Yugoslavia reach different
bargaining outcomes. The authors suggest that culture might explain this variation in behavior
across countries. Henrich (2000) observed differences in behavior between Machiguenga in the
Peruvian Amazon and American college students. Henrich, Boyd, Bowles, Camerer, Gintis,
McElreath, and Fehr (2001) found that a measure of market integration is positively correlated
with the amounts passed in the Trust game for a sample of small communities around the world.
Fershtman and Gneezy (2001) showed evidence of discrimation in the Trust game based on ethnic
background in Israel. Barr (2003) showed that people in resettled Zimbabwean communities
tended to send less in the Trust game than people in old communities, and observed that in
communities where people returned more as trustees more money was sent by trustors.
2
2. Econometric Approach
This section discusses the problem of identiÞcation of preferences and social interactions in Þeld experiments within the framework of the linear model of social
interactions. Our discussion adapts Manski (1993, 1996) and Graham and Hahn
(2003) as applied to proposal-response games. In this model, social interactions
are represented as the expectation of others’ behavior. These assumptions are
quite restrictive, but they help us organize the analysis in a simple way.
The object of interest is the decisions made in the Dictator and Trust game in
Þeld experiments. In the Dictator game, a proposer is endowed with an amount
of money that he can keep or share with another individual. In the Trust game,
a proposer is endowed with an amount of money that he can keep or share with
another individual. Any amount sent is multiplied before reaching the responder.
The responder then decides whether to keep the money received or to return some
to the proposer. Decisions made by senders will be denoted by x, and decisions
made by responders will be denoted by y. Subscripts i and j will indicate a
particular individual. Finally, subjects will be associated with two reference
groups: their community and their experimental session. Communities will be
denoted by g and experimental sessions will be denoted by gk .
We will assume that the actions of senders and responders are determined by
personal characteristics and by the expectations of what other people in their reference group, gk , would do. For instance, a proposer might feel more generous if
surrounded by generous people, or less inclined to share if surrounded by stingy
people.2 People in Þeld experiments are asked to make decisions that may not resemble everyday life, it is reasonable to think that they would look for appropriate
ways to behave by guessing what others would do. Since Þnal payoffs depend on
proposers and responders decisions, we must also consider that proposers might be
inßuenced by the expectation of what responders would do. Finally, since norms
of behavior take shape at the interior of a community, we must consider that decisions might depend also on the characteristics of the community, g. Formally,
the decision made by sender i can be represented by the following expression,
xi = zi β + γEgk [x] + Eg [z]α + δEg [y] + cg + εi ,
where zi represent the socioeconomic characteristics of the sender and Egk [x]
represents his expectation of what other senders in the experimental session would
2
A person might feel less generous when surrounded by generous people if he think he can
free ride of others’ good hearts.
3
do.3 Manski refers to this effect as endogenous social effects, because it supposes
that behavior might be inßuenced by what others would do in similar circumstances. Eg [z] represents the expected characteristics of the community, say the
level of human capital in the community or the average standard of living. This
is referred as contextual social effects. Eg [y] captures the expectation of what
responders would do. It is likely that senders’ expectations of responders’ actions
vary greatly across groups and communities. The subscript on this expression
makes it clear that we are assuming that senders in the same community share
the same expectation and that, due to anonymity, they are not aware of the exact
characteristics of responders. Notice that, in games where responders are inactive,
the term Eg [y] plays no role. The term cg captures factors affecting all members of
g that are not observable to the researcher. These correlated unobservables might
reßect variations in cooperativeness or altruism across communities or the sorting
of people into communities. Finally, εi are individual-level variables unobservable
to the researcher that are not correlated with the terms mentioned above. We
assume that εi has mean zero.
The behavior of responders can be modeled in an analogous way to that of
proposers. In particular, we can write yi as follows,
b + cbg + b
yj = zj βb + γb Egm [y] + Eg [z]α
εjt
In this case we have excluded the expectation of what proposers would do,
Eg [x], since the game ends with the responder’s action. This is not necessarily a
good assumption if we think that reciprocity judgements are based on what the
responders think the proposers should do (Rabin 1993, Martin Dufwenberg et al,
1998).
Taking expectations at the session level, gk , we observe that proposer’s aggregate behavior can be expressed as,
Egk [x] = Egk [z]β + γEgk [x] + Eg [z]α + δEg [y] + cg
and that responder’s expected behavior at the community level, g, must follow,
3
b +γ
b Eg [y] + cbg
Eg [y] = Eg [z](βb + α)
Implicit to this expression is that all the participant in a session share the same information
and therefore expectations are not homogenous. While this assumption is not trivial, it is likely
to be less restrictive among members of close-knit communities like the ones we visited.
4
Putting these two equations together we Þnd that proposer’s expected behavior
can be summarized by,
δb
cg
)+δ(b
β +b
α)(1−γ)
β
Egk [x] = Egk [z] 1−γ
+ Eg [z] α(1−bγ(1−γ)(1−
+ 1−
+
γ)
γ
b
b
cg
1−γ
Notice that behavior at the group level tend to magnify differences across
individuals due to the existence of a multiplier effects. Indeed, the existence of
multiplier effects can be exploited to test for the existence of social interactions
(Glaeser et al, 1996; Glaeser et al, 1999; Graham et al, 2003). Notice that
under the null hypothesis that contextual effects occur at the community level
only, the last equation shows that the term Eg [z] can be treated as a correlated
unobservable. The reduced-form equation representing proposer’s decision would
then take the following form,
β
xi = zi β + Egk [z] 1−γ
+ ceg + εi ,
where ceg is a complicated function of the structural parameters and Eg [z].
β
If we had consistent estimates of β and 1−γ
, we could infer the existence of social interactions by the presence of multiplier effects. However, there are at
least two reasons that argue for a more conservative interpretation of parameters.
First, proposers might use the composition of their experimental session to predict responder’s characteristics, this would imply that we cannot discern between
endogenous social effects and the expectation of responders’ behavior. Second,
if the composition of the experimental session evokes certain feelings or norms
of behavior, the composition of the group might act as a contextual effect. For
instance, men in the company of men might feel compelled to set an example
regardless of what they expect other men would do. A more appropriate description of group behavior would then merge all sources of social interactions.4
Model uncertainty argues for a reduce-form equation of the form,
e + ceg + εi
xi = zi β + Eg [z]α
e and ceg are both complicated function of the underlying structural
where α
parameters of the model. This equation makes clear that multiplier effects are
difficult to identify because aggregate behavior might reßect endogenous effects or
4
The equation representing group behavior would be,
δb
c
cg
γ )+δ(b
β +b
α)(1−γ)
Egk [x] = Egk [z] (β+α)(1−b
+ g + 1−γ
(1−γ)(1−b
γ)
1−b
γ
5
contextual effects at the session level or game equilibrium effects. IdentiÞcation of
the endogenous social interactions in games of proposal-response will therefore be
difficult. This will hold true even in the Dictator game, where we can assume that
parameter δ is equal to zero, because possible contextual effects might still exist.
Because Egk [x] and Eg [y] are endogenous variables, we cannot estimate equations
(1) and (2) directly. We will then concentrate on the estimation of the above
reduced-form equation. Evidence of social effects, endogenous or contextual, will
e is different from zero.5
be present if parameter α
e is difficult due to the existence of correlated unobConsistent estimation of α
b
servables (variables cg and cg ). To consistently estimate parameters, we need to
break the perfect collinearity between community level effects, cg , and the effects
taking place at the experimental session level. One way to do this is to implement
independent experimental sessions in the same population of interest.6 Random
assignment of subjects to different experimental sessions generates exogenous variation in session-level composition, which then allows the consistent estimation of
social effects.
In particular, consider two experimental sessions implemented independently
on a random sample of community members (x1i , yi1 )i=1...n1 and (x2i , yi2 )i=1...n2 . We
could then estimate the reduced-form equations,
e + ceg + c1 + ε1i , and
x1i = zi1 β + z g1 α
2
2
e + ceg + c2 + ε2i
xi = zi β + z g2 α
where z g1 and z g2 are the sample analogues of Eg1 [z] and Eg2 [z]. With repeated
sampling we can use standard panel data methods to control for ceg and obtain
e (Hahn et al (2003) provide a detailed explanation
consistent estimates of β and α
of this approach). We include parameters c1 and c2 to represent the possibility
of session level effects.
5
Durlauf (2002) shows that identiÞcation of the structural parameters requires the existence
of variables that affect behavior at the individual level but not at the group level. These are
variables that do not affect the context in which decisions are made. This condition will be
violated, for instance, if subjects discriminate against others according to their gender or status.
Intuitively, we will need as many exclusion restrictions as structural equations, and we will need
that data varies enough within communities and across communities.
6
Due to the likelihood of information spillovers, independence would require simultaneous
implementation also.
6
2.1. Estimation
The approach followed in this paper uses the individual-level reduced-form equae . This can be done because
tion to consistently estimate parameters β and α
the variation in session-level socioeconomic variables permits us to control for the
presence of correlated unobservables ceg . If, in addition, we can conÞdently claim
that some individual characteristic are not contextual variables, we could identify
structural parameter also. We consider that we do not have strong instruments
b
b , and β.
as to recover structural level parameters α, β, α
An additional estimation issue is the presence of censoring in the experimental
data. Censoring occurs because experimenters impose budget constraints on subjects. Ignoring this issue could lead to inconsistent estimates even in the absence
of the identiÞcation issues mentioned above. Under censoring, the individual-level
reduced-form equation can be written as,
e + ceg + c1 + ε1i }, 1}
x1i = min{max{0, zi1 β + z g1 α
where x1i is expressed in shares.
Consistent estimation of this equation requires controlling for Þxed-effects at
the community level as well as the experimental session level. One approach
is to impose a parametric form on the distribution of errors and use maximumlikelihood methods where ce1g , ce2g , c1 and c2 are treated as nuisance parameters. This
approach is likely to suffer from the incidental parameter problem (see Lancaster,
2002) making maximum-likelihood estimates inconsistent. Our approach follows
Honore’s (1993) semiparametric approach to censored panel data models. In
e and β will be based on the fact that the two following
particular, our estimates of α
e and β if the distribution of errors
moment conditions hold at the true parameter α
is symmetric,
E[(min{1 + 4zij1 β, max{x1i , 4zij1 β}} − min{1 − 4zij1 β, max{x1j , 4zij1 β}}−
4zij1 β) 4 zij1 ] = 0,
e max{x1 , 4z 12 β}}
e
e max{x2 , 4z 12 β}}−
e
− min{1 − 4zij1 β,
E[(min{1 + 4zij12 β,
i
ij
j
ij
e 4 z 12 ] = 0,
4zij12 β)
ij
where 4zij1 = (zi1 − zj1 ) represent differences across individuals in the same
experimental session, and 4zij12 = (zi1 − zj2 , z g1 − z g2 ) represents difference across
e
individuals in different sessions but in the same community. Finally βe = (β, α).
7
The Þrst equation says that observations must be distributed symmetrically
around 4zij1 β in each experimental session, and the second equation says that
observations must be distributed symmetrically around 4zij12 βe across experimental
session in the same community. While the estimators have the downside that
they cannot identify the parameter associated with time-invariant variables, they
e and the effect of
provide consistent estimates of the reduced-form parameters α
individual characteristics on behavior.7
3. Experimental Procedures
Experiments were based on modiÞed versions of the Dictator game (Forsythe et al.,
1995) and the Trust game (Berg, Dickhaut, and McCabe, 1996). In the Dictator
game, the proposer was endowed with an amount of cash that he had to decide
to keep or share with an individual without an endowment. Each unit passed to
the other was tripled before reaching the other person. In the Trust game, the
proposer was also endowed with an amount of cash that he had to decide to keep
or share with an individual without an endowment. The receiver of the tripled
amount in the Trust game had the opportunity to send back none, part or all the
amount received.
These experiments were implemented in 31 separate Hondurean communities.
These communities were originally selected at random from a sample of communities studied in 1994 and 2001. One in seven of our experimental subjects were
recruited from the respondents to these surveys, while the others were selected
from other families in the same communities. Not more than one participant per
household was allowed. All the participants were of 18 years of age or more and
they were not told about experimental payments at the time of recruitment. The
average age of participants was 41 years old, with 3 out of 5 being male. 25%
of the sample was at least 50 years of age and 25% was at most 31 years of age.
Twenty Þve percent of participants had at most 5 years of education and 25%
of them had at least 6 years of schooling. On average, there were 24 subjects
per session. Two sessions were smaller (16 participants), and three sessions were
larger (32 participants). All participants in each session belonged to the same
community or neighborhood. On average, participants knew 88% of the people
in the session by name. The average payment to a participant in the experiment
7
The estimators are obtained as the minimization of convex problem to avoid non-optimal
solutions. The estimator is identical to Honore’s except that it allows for lower and upper
censoring.
8
was 90 Lempira (or around $5), which amounts to two-days wage in rural areas.
Recruitment of participants was made with the help of local leaders. In particular, school principals, and occasionally, the president of the patronato. They were
asked to recruit adults among families of different backgrounds.
Before the experiment, participants were given numbers at random. These
numbers were used to divide people into groups and to assign experimental treatments at random. Subjects were divided into 3 rooms. In most cases, we had
three school classrooms at our disposal, and only in two occasions did we need to
accommodate people in different areas. Two rooms contained a quarter of participants each and a third contained the remaining half of participants. The third
group was further divided into two.
The rooms containing a quarter of participants each were labeled room A and
room B. Room C contained the remaining 50% of subjects. In room A, people
played the Dictator game Þrst and the Trust game second. In room B, people
played the trust game Þrst and the dictator game second. The endowment for
the Dictator game was 40 Lempiras ($2.5) and the endowment for the Trust game
was 50 Lempiras ($3.1). Each Lempira sent to the other room was tripled in
both games. In all rooms, instructions were read out loud, and then a series
of questions were asked to make sure that the games were clear. In room B,
participants were asked to make their decisions as dictators before the trustees’
decisions were revealed. In this way, we avoided inßuencing choices as dictator
by trustees’ actions. In rooms A and B, subjects were told that two different
persons in room C were going to receive the Þrst and second envelope sent by
them. Moreover, it was made clear that a participant could be either a sender
or a receiver, but not both. To make sure that different receivers did not receive
envelopes from the same person, room C was divided in two sections. One section
received the dictator’s envelopes sent by room A and the trustor’s envelopes sent
by room B, and the other section received the dictator’s envelopes sent by room B
and the trustor’s envelopes sent by room A. Sessions were run simultaneously. As
mentioned above, in the Dictator game money sent by a dictator was tripled. This
was done to make intra-personal comparison easier. Finally, the post experiment
questionnaire in Honduras concentrated on background questions and detailed
information on social organizations and networks.
9
4. Results
4.1. Overview of the Data
Table 1 reports the summary statistics of the experiments in Honduras. Subjects
sent around 40% of their endowment in the Dictator game, 50% in the Trust
game, and returned around 40% as trustees. Average amounts passed in the Dictator game are higher than commonly found in experiments with college students
(Forsythe et al, 1994; Eckel et al, 1996). This is remarkable given that amounts
sent by dictators were tripled. However, results from both games are consistent
with previous results with non-college students (see Camilo Cardenas et al (2003)
for a survey). All decisions presented a high degree of variability. This variability is present at the community level also; the lowest average share passed in the
Dictator game was 22% and the highest was 69%, and the lowest average share
passed in the Trust game was 26% and the highest was 67%.
Decisions across games are correlated at the individual level as well at the
community level. For instance, the correlation between decisions made as dictator and as trustor equals 53%. Interestingly, this correlation is strongest in
the Dictator-Trust sessions (63%) than in the Trust-Dictator sessions (44%). As
found by Haurbaugh et al. (2000), the order in which decisions are made seems
to matter. The fact that decisions are highly correlated across games points to
the importance of the identiÞcation issues.8
More importantly, we also Þnd signiÞcative correlations between decisions at
the community level. Decisions as dictator and as trustor, and as dictator and
trustee, are positively and signiÞcantly correlated at the community level. The
latter fact is particularly important. Dictator and trustees decisions were made independently and in separate rooms. Moreover, decisions by dictators and trustees
are non-strategic, they face no responders and they cannot affect the behavior of
future game partners. Since we Þnd that behavior at the community level is correlated even when no interactions are permitted, this shows that experimental data
must be capturing factors other than social or game equilibrium.
8
Sessions starting with the Dictator game sent on average 40% of their endowment, which
amounted to a Þnal distribution in this game of 24 Lempiras for the dictator and 54 Lempiras
for the receiver. As Cox (2002) has suggested, any measure of trust must consider the fact
that people are willing to reach very unequal distributions even without expecting any money
in return.
10
Table 1
Descriptive Statistics for Shares Sent and Returned
Dictator Trustor Trustee
389
389
3699
N
42%
49%
42%
Mean
29%
29%
30%
St. Deviation
Correlations at the Individual Level
0.53*
Dictator v. Trustor
Correlations at the Community Level
0.77*
Dictator v. Trustor
0.37*
Dictator v. Trustee
0.30
Trustor v. Trustee
* signiÞcant at 10% level
4.2. Regression Analysis and Evidence on Social Interactions
This section discusses the results of the estimation as proposed in section 2. Table 2 presents estimates of the reduced-form equations for the decisions made by
dictators and trustors.10 Table 2 present consistent estimates of the impact of
individual characteristics and group characteristics on individual behavior. All
the regressions are on the shares of the endowment passed to others to make comparisons straightforward. The regression includes age, sex, education level,11 and
religion of the responder. Additional indicators of economic status are subjects’
family size and the proportion of the family’s diet that is purchased.12 Number of
siblings attempts to capture subjects’ economic background. As a measure of social connectedness, we included subjects’ familiarity with other participants and
time living in the community. Finally, as a measure of personal apprehension
9
Some data on trustees’ decisions was lost due to miscoding.
We concentrate on decisions by dictators and trustors because they were two separate rooms
in which the experiment was run simultaneously. Trustees were grouped in a third room and
therefore we cannot claim we have two independent samples of the game.
11
Zero equals no education, one equals incomplete grade education, two equals complete grade
education, three equals incomplete high school, four equals complete high school, and Þve equals
post-secondary education.
12
In most developing countries, there is a high negative correlation between self-sufficiency
and income.
10
11
towards others we included the answer to the question: “In matters of money,
how much do you trust others?
As mentioned in section 3, the regression parameters associated with sessionlevel variables are a complicated function of the social effects and game equilibrium
structural equations. In the Dictator game, however, the regression parameters
associated with session level variables only combine endogenous social effects and
contextual effects since proposers cannot inßuence responders’ decisions. This
will make their interpretation easier.
Some interesting patterns emerge from the comparison of results at the individual level. Age is correlated with amounts passed in both games, but only
decisions as dictators are explained by the level of education. An increase in age
of 10 years increases the share passed in the Dictator game by 3% and increases
the share passed in the Trust game by 2% . Completing grade school, or obtaining
a secondary education is associated with an increase of almost 6 percentage points
more in the share passed in the Dictator game. Both, age and education, are likely
to be correlated with income, which would indicate that decisions in both games
are partially explained by economic status. Since giving in the Dictator game
is unconditional, a possible explanation of these results is that Dictator giving
reßects socioeconomic status more readily than the Trust game does. These results warn us that experiments are partially a reßection of the prevalent economic
conditions.
Familiarity measures the percentage of people in the session a subject knows.13
Table 2 indicates that knowing an additional 10% of the people in the session
increases the amount passed in the Dictator game by 1.6%. This increase would
almost be doubled in the Trust game. Familiarity can affect giving in the Dictator
game only if people tend to favor those they know better. This result is consistent
with the effect of social distance in Dictator games (Eckel et al, 1996; Bohnet et
al, 2000; Glaeser et al, 2000). Interestingly, the magnitude of this impact doubles
in the Trust game, which suggests that either uncertainty or expected reciprocity
might play a role in the Trust game.14 Similarly, the variable Trust in Others has
four times the impact on the Trust game than in the Dictator game. All this
indicates that the Dictator game and the Trust game, despite its similarities, might
be able to capture different aspects of the normative environment of communities.
Decisions in the Trust game are relatively more inßuenced by expectations, and
13
Familiarity varies across communities. At the community level, the minimum average was
53% and the maximum was 100%.
14
Results are similar if the variable trust on others is dropped.
12
decisions in the Dictator game seem to be more inßuenced by economic status.
Table 2
Semiparametric Estimation of Social Interaction Models
Decision as:
Dictator
Trustor
Individual Level Variables
Socio-Economic Status
Age
1=Men,0=Woman
Education Level
Family Size
Number of Siblings
Market Dependency
1=Evangelical,0=Other
Social Capital
Familiarity With Participants
Trust in Others
Time in Residence
Session Level Variables
Age
1=Men,0=Woman
Education Level
Family Size
Number of Siblings
Market Dependency
1=Evangelical,0=Other
Social Capital
Familiarity With Participants
Trust in Others
Time in Residence
Experimental Treatment
1=Dictator Game 1st, 0=otherwise
0.00324 (0.01673)
0.02690 (0.27365)
0.05842 (0.02236)
0.00495 (0.23232)
-0.01174 (0.97609)
-0.04232 (0.96871)
-0.06220 (0.87792)
0.00212 (0.06060)
-0.03423 (0.79464)
0.01461 (0.31169)
-0.00162 (0.60721)
-0.00866 (0.95363)
-0.00854 (0.67557)
0.03864 (0.22692)
0.15915 (0.03530)
0.02097 (0.13770)
-0.04754 (0.78174 )
0.28757 (0.00012)
0.08233 (0.00003)
-0.16677 (0.99318)
0.00335 (0.32131)
0.28891 (0.04597)
0.16367 (0.06027)
-0.06640 (0.99085)
0.06318 (0.00447)
-0.07187 (0.91069)
-0.12030 (0.71454)
0.00943 (0.07083)
0.24812 (0.04529)
0.18626 (0.06136)
-0.02349 (0.78668)
0.08662 (0.00004)
-0.14956 (0.99875)
0.14945 (0.22033)
0.45641 (0.06432)
-0.22816 (0.99501)
-0.26860 (0.88277)
0.21106 (0.25455)
-0.27691 (0.99937)
0.30476 (0.07236)
0.11319 (0.02023)
0.04038 (0.20553)
p-values in parentheses
When expectations on the actions of responders do not inßuence proposers’
decisions, contextual effects can be recognized if there is a variable that affects
13
decisions at the session level but does not affect behavior at the individual level.
The Dictator game provide evidence of contextual effects. Proposer’s sex does
not affect decisions by dictators but sessions composed solely of men pass 29%
more than a session composed solely of women. This can be because own session’s gender composition affects proposers’ expectations of the receiver’s gender
or because own session’s gender composition affects proposer’s expectation of what
others would do as dictators. In the Þrst case, this would be consistent with discrimination. In the second case, this would be evidence of misplaced expectations.
People expect men to contribute more even when men themselves do not do so.
In the same manner, we observe that communities where members tend to come
from larger families tend to share more. It could be that people from large families learned to share more or that people coming from larger families tend to be
of a poorer background.
The results for level of education and familiarity are intriguing. Indeed, the
parameters associated with variables at the session level are about 3 times the size
of the parameters associated with the same variables at the individual level. If we
were to assume that contextual effects are unimportant, this would be consistent
with a parameter of endogenous social effect of size .75. However, the results
discussed above would suggest that contextual effects might be of importance in
this case also. Among other reasons, education and familiarity might affect group
decisions because they make it clear the beneÞts of mutual help. Interestingly,
the existence of endogenous social interactions would imply that the parameters
associated with gender and number of siblings on dictators’ decisions are indeed
smaller in magnitude, since they are susceptible to multiplier effects.
Interpretation of results in the Trust game is intrinsically harder. Decisions by
trustors might reßect endogenous social interactions as well as game equilibrium
behavior. At Þrst sight, we do not observe a multiplication of parameters from
the individual level to the session level as we did in the Dictator game, except for
the variable age. In particular, education at the individual level is not signiÞcant
in trustor’s decision in any of the speciÞcations of the reduced-form equations we
have estimated. However, education at the session level is signiÞcant in trustor’s
decisions. There are two possible explanations: education might be a contextual
variable and/or might be a determinant of trustees’ decisions. Indeed, regressions
on trustees’ decisions (not presented here) show that every additional level of
education increases the share passed back to trustors by 9%. Gender, however,
seems to play a role in trustor decisions at the session level as it does in the
Dictator game. It is entirely possible that endogenous social effects appear more
14
easily in one kind of interactions than in others. However, the results related to
age in trustors’ decisions are consistent with the existence of endogenous social
effects. Since we did not Þnd any relationship between age and trustee’s decisions
this implies that this results must be due to social effects.
To summarize, the results are consistent with the existence of social effects.
But, without stronger identiÞcation assumptions, we cannot distinguish whether
the social effects are endogenous or contextual. Table 2 also provides evidence
that decisions in the Dictator and Trust games, while correlated, are perceived as
different. Average age and time in residence at the session level are both positively
related to the amount passed in the Trust game but no so in the Dictator game.
Finally, our experimental design allows us to test whether presenting the Dictator game Þrst or second had an impact on subjects’ decisions. Table 2 shows
that order effects are important in the Dictator game. Subjects tended to pass
around 11% more in sessions starting with the Dictator game than in sessions
starting with the Trust game. We do not have a theory to explain this results,
only a cautionary note.
5. Conclusions
Experimental economics has been successful in improving our understanding of
the deep motivations behind economic decisions. These advances have been
made with controlled experiments with college students. The external validity of
experimental economics, however, relies in its usefulness to understand economic
interactions of non-college students also. While there is a growing literature on
how social context affects economic behavior, little is known of how much can be
learned from experiments with real economic agents. This paper focuses on how
social interactions can affect decisions made by experimental subjects and how
simple experiments can be used to uncover its impact.
Economic experiments with heterogenous populations faces some challenges.
The heterogeneity of the subject population makes it difficult to determine whether
variation in behavior across populations are due to preferences, social interactions,
background or context. Standard games used in experiments may not capture a
single norm or aspect of behavior either, making it difficult to draw conclusions
from the data. Despite these limitations, we show that simple games like the
Dictator game and the Trust game can be used to capture different aspects of
behavior. More importantly, we show that variations in behavior and socioeconomic characteristics across individuals and across populations can be exploited
15
to identify whether subjects’ decisions are consistent with the existence of social interactions. Our approach is a novel way to interpret experimental data
and highlights the richness of behavioral data that experiments with real people
contain.
This approach was tested on a sample of 31 small Hondurean communities. As
with college-student population and as reported on a series of experiments with
traditional societies, we found evidence that people behave in ways not predicted
by the paradigm of selÞsh economic behavior. We found evidence consistent with
altruism, trust and reciprocity. Interestingly, patterns of behavior show great
variation across individuals and groups. We implemented simultaneous experimental sessions as a way of sampling equilibrium behavior repeatedly and control
for correlated unobservables. This is important because regressions of individual decisions on individual and group characteristics do not produce consistent
estimates in general. Our approach does.
In general, we found evidence that social interactions are important in the
Dictator and Trust game. This is intriguing since it suggests a possible mechanism through which social norms might be enforced (i.e., imitation or conformity).
Overall, the behavior in Dictator and Trust game seems to capture different aspects of the social environment of the communities despite the fact that they are
highly correlated with each other. We Þnd that socioeconomic status (e.g., sex,
age, and education) matters in all economic decisions, but familiarity and the age
of the community are relatively more important in time-sensitive transactions.
What do these results tell us about the utility of experiments with nonstandard subjects? First, simple comparisons of experimental results across groups
or populations are problematic. For instance, Japanese students might be more
generous in public good games than American students because they are indeed
more cooperative or because they have a relatively higher status or income. Second, even if these comparisons were possible, behavior in experiments might be
distorted by the existence of social interactions. IdentiÞcation of what is due to
individual or group characteristics and what is due to reinforced behavior requires
laying out a model of norms and preferences formation. Third, despite these identiÞcation problems, appropriately designed experiments can help us to tease apart
the relative importance of these components. That is, economic experiments can
help us discover the process through which preferences and norms come about.
Fourth, decisions in the trust game might combine social interactions with expectations on others’ behavior, suggesting that people might care about the act of
trusting and not only possible gains from reciprocity.
16
References
Barr, A. “Trust and Expected Trustworthiness: Experimental Evidence from Zimbabwean Villages,” Economic Journal, vol. 113, pp. 614-630, 2003.
Berg, J., J. Dickhaut, and K. McCabe, “Trust, Reciprocity, and Social History,"
Games and Economic Behavior, 10, 122-142, 1995.
Bohnet, I, and B. Frey, “Social Distance and Other-Regarding Behavior in Dictator
Games: Comment," American Economic Review, 89(1, pp. 335-39, 1999.
Cox, “How to Identify Trust,” Games and Economic Behavior, forthcoming 2003.
Dufwenberg, M. and G. Kirchsteiger, Theory of Sequential Reciprocity, Tilburg CentER for Economic Research Discussion Paper, 1998.
Durlauf, S. “On the Empirics of Social Capital," Economic Journal, 112, pp. 459-479,
2002.
Eckel, C. and P. Grossman, “Altruism in Anonymous Dictator Games," Games and
Economic Behavior, 16, 181-191, 1996.
Eckel, C. and P. Grossman, “Are Women Less SelÞsh Than Men? Evidence from
Dictator Experiments," Economic Journal, 108, 726-35,1998.
Fershtman, C., and U. Gneezy, “Discrimination in a Segmented Society: An Experimental Approach,” Quarterly Journal of Economics, 116(1), pp. 351-77, 2001.
Forsythe, R., Horowitz, J., Savin, N. and M. Sefton, “Fairness in Simple Bargaining
Experiments," Games and Economic Behavior, 6(3), pp. 347-69 , 1994.
Glaeser, E. and J. Scheinkman, Measuring Social Interactions, mimeo, 1999.
Glaeser, E., Sacerdote, B. and J. Scheinkman “Crime and Social Interactions," Quarterly Journal of Economics 101(2): 507-548, 1996.
Glaeser, E., D. Laibson, J. Scheinkman and C. Soutter, “Measuring Trust," Quarterly
Journal of Economics, 105, pp. 811-846, 2000.
Graham, B., and Y. Hahn, “IdentiÞcation and Estimation of the Linear-in-Means
Model of Social Interactions,” mimeo, UCLA, 2003.
Henrich, J., Does Culture Matter in Economic Behavior? “Ultimatum Game Bargaining Among the Machiguenga," American Economic Review, 90(4), pp. 973979, 2000.
Henrich, J., Boyd, R., Bowles, S., Camerer, C., Gintis, R., McElreath, R. and E.
Fehr, “In Search of Homo Economicus: Experiments in 15 Small-Scale Societies,"
American Economic Review, 91(2), pp. 73-79, 2001.
Honore, B. “Trimmed LAD and Least Squares Estimation of Truncated and Censored
17
Regression Models with Fixed Effects,"Econometrica, 60(3), pp. 533-65, 1992.
Karlan, D., “Using Experimental Economics to Measure Social Capital and Predict
Financial Decisions,"Working Paper, Princeton University, 2003.
Manski, C. “IdentiÞcation of Endogenous Social Effects: The Reßection Problem,"
Review of Economic Studies, 60 (3) pp. 531-42.
Manski, C. “Economic Analysis of Social Interactions," Journal of Economic Perspectives, 14 (3) pp. 115-136.
Rabin, M., “Incorporating Fairness into Economics and Game Theory,” American
Economic Review, 83(5), pp. 1281-1302, 1993.
Roth, A., Prasnikar, V., Okuno-Fujiwara, M., and S. Zamir, “Bargaining and Market Behavior in Jerusalem, Ljubljana, Pittsburgh, and Tokyo: An Experimental
Study,” American Economic Review, 81, pp. 1068-95, 1991.
Sacerdote, B., “Peer Effects with Random Assignment: Results for Dartmouth
Roommates,” Quarterly Journal of Economics, 116, pp. 681-704, 2001.
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