DRAFT 8/10/05. Comments welcome.

Detecting Trustworthiness: Does Beauty Confound Intuition?

Catherine C. Eckel Department of Economics (0316) Virginia Polytec nic Institute and State University hBlacksburg, VA 24061 540-231-7707 eckelc@vt.edu

Rick K. Wilson Department of Political Science Rice Universit yrkw@rice.edu

Abstract We report results of a laboratory experiment that examines the impact of attractiveness on trust and reciprocity in a trust game. Subjects in the experiment interact with a counterpart in a lab at another location, and each observe the others’ photograph while making their decisions. The photographs are rated by a separate set of subjects drawn from the relevant population. We find that attractive people trust less than unattractive people. However, subjects expect greater trust from attractive people, and their expectations are disappointed, leading to lower amounts returned to attractive first movers. Thus we provide some evidence of a beauty penalty in this game that operates through the distorting effect of beauty on the expectations of the players. Thanks go to Sezi Anac, Narine Badasyan, Johanna Dunnaway Adam Ferguson, Selhan Garip, Phil Grossman, Scott Simkins, Jill Stipanov, and Bing Yaun for help in conducting the experiments. Support for this research comes from grants by the National Science Foundation (SES 98-19943) and the Russell Sage Foundation. Neither Foundation is responsible for the content or findings reported here.

Beauty is truth, truth beauty,—that is all Ye know on earth, and all ye need to know.

--John Keats (1795-1821), Ode on a Grecian Urn. Remember that the most beautiful things in the world are the most useless; peacocks and lilies, for example.

–John Ruskin Throughout the animal world, attractiveness certifies biological quality.

--Randy Thornhill University of New Mexico ecologist If you wanna be happy for the rest of your life Never make a pretty woman your wife So for my personal point of view Get an ugly girl to marry you

--Jimmy Soul, “If you wanna be happy.”

I. Introduction.

Labor markets reward beauty. As documented by Hamermesh and Biddle (1994),

relatively attractive people – both men and women – earn a “beauty premium” that is a

significant portion of earnings. The question naturally arises: Is beauty productive – that is, are

the returns to beauty associated with increased productivity?

Beauty can be productive in at least two ways. If customers prefer to deal with attractive

salespersons, for example, then hiring attractive workers can enhance sales. In addition, beauty

may signal higher quality. Evolutionary biologists argue that beauty is associated with

symmetry, which is an indication of quality in an organism (Buss, 1997). Symmetry is positively

associated with IQ and health (e.g., Thornhill and Gangestad, 1993; Thornhill, 1998). In a

related argument, social psychologists contend that the higher quality of attractive people is

developed rather than endowed. They provide evidence that attractive people are treated better,

and so develop superior attributes (Langlois, et al., 2000). If attractive workers are of higher

average quality for whatever reason, they are likely to be more productive.

Considerable evidence points to an association between beauty and perceptions of

positive attributes. Beauty confers an “aura” of quality (McNeil, p. 280) which affects

expectations about attractive people: observers associate beauty with goodness. Many studies

show that people attribute superior ability in a variety of tasks, as well as positive attributes

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including kindness, strength and power to the attractive (see Webster and Driskell, 1983; Jackson

et al., 1995; Langlois, 2000). In addition, beauty interferes with subjective judgment. There is

strong evidence that beautiful people receive higher evaluations for their work. This result is

robust over a broad range of environments, from the evaluation of children’s essays in

elementary school to the evaluation of applications for managerial positions (McNeil, p. 280;

Langlois, et al., 2000.)

Are these superior evaluations warranted? Is beauty related to objective measures of

ability (such as intelligence) or performance (such as success or advancement)? Kanazawa and

Kovar (2004) develop the earlier observation of Buss (1985), that assortative mating should

produce a positive correlation between beauty and ability. They argue that beauty and

intelligence must become positively related over time, since intelligent men achieve higher status

and marry beautiful women. In a meta analysis of 68 studies, however, Jackson, et al. (1995)

find a strong relationship between beauty and expectations about intelligence, but virtually none

between beauty and measured intelligence as indicated by test scores or grades.

On the other hand, if symmetry is an indication of organism quality, then beauty and

ability should both be related to symmetry and so to each other. In a study where facial

photographs of several hundred subjects of all ages were judged by a sample of university

students, Zebrowitz et al. (2002) provides evidence of a small positive correlation between

attractiveness and intelligence, but this association is not nearly as strong as the correlation

between beauty and perceived intelligence. People’s perceptions get the direction right, but

exaggerate underlying differences. In this and a subsequent study (Zebrowitz and Rhodes,

2004), she shows that people are able to infer information about intelligence from the faces of

people in the lower half of the attractiveness distribution, but not in the upper half. Beauty

masks intuition, and interferes with the rater’s ability to make inferences about intelligence.

Attractiveness also is associated with superior performance and professional success.

Attractive people are more likely to receive an offer of employment and to advance within an

organization (Stone et al. 1992). It is no surprise that beauty is prized for actors and actresses in

the film industry or for high profile models (though for the latter, see the cautionary points by

Bower, 2001; Bower and Landreth, 2001). But a beauty premium also is observed in the broader

labor market. As Hamermesh and Biddle (1994) point out, good looks generate a premium and

bad looks a penalty in the labor market. The difference in earnings between above and below-

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average attractiveness is robust across occupations, but is larger for men (14.4-16.8 percent) than

for women (9.2-11.7 percent). Likewise Biddle and Hamermesh (1998) show that attractive

attorneys earn more than their unattractive counterparts because they are better able to attract and

retain clients. Beauty carries a premium for male and female attorneys; however, while beauty

enhances the likelihood that a man will make partner early, it reduces the likelihood for women.

Outside the workplace, Sigelman et al. (1986) and Budesheim and DePaola (1994) find

that attractiveness has an impact on vote choice among political candidates, although Riggle et

al. (1992) note that this effect can be diminished or eliminated with additional information.

Attractiveness also seems to affect outcomes in the judicial system. Castellow et al. (1990) note

an advantage to more attractive plaintiffs and defendants in sexual harassment cases, and Stewart

(1980, 1984) documents an advantage for attractive defendants in conviction and sentencing in

criminal cases. These results are consistent with the notion that attractive people may be

perceived as more trustworthy.

An important element of productivity in a workplace setting is trust: trusting and

trustworthy employees are more productive than others, and enhance the productivity of those

with whom they work. Two possibilities come to mind. First, since attractive people are treated

better than others in many aspects of life, they may perceive the world as a less risky place, and

so be more trusting and trustworthy. In contrast, if attractive people are accustomed to better

treatment, they may not have found trustworthiness to be as important an element of reputation

as others who are less attractive. Their feeling of entitlement may make them less trustworthy.

In this study we examine the relationship between attractiveness and trust and

trustworthiness in a laboratory setting. We also are able to asses the role of expectations in

trusting behavior, and the impact of beauty on expectations of others. Our study uses the

investment game, first examined by Berg, Dickhaut and McCabe (1995), in a setting where

subjects observe the photographs of their counterparts. Interpersonal trust is usually measured

anonymously in laboratory trust game. However, we match subjects with anonymous

counterparts at another site using photographs. This procedure maintains important elements of

anonymity, but allows subjects to observe their counterpart. In previous studies, we have shown

that interpersonal trust is affected by observed information about one’s counterpart. In this study

we evaluate the attractiveness of the players, and show that attractiveness has a significant

impact on the propensity to trust and reciprocate trust.

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II. Motivation: trust and beauty in the lab.

Trust involves a decision in which an individual puts his payoffs at the discretion of

another. A decision to trust another individual is based on an assessment of that person’s

trustworthiness. In commercial interactions, agents often know something about the

trustworthiness of the counterpart, such as whether the counterpart has acted in a trustworthy

manner in the past and can be expected to continue to be trustworthy. In the business arena, trust

is supported by a number of legal and social institutions from tort law to gossip that monitor the

actions of others and punish transgressions.

Trust as a characteristic of a society is studied in the context of ongoing relationships

among different actors in a pre-existing and very rich environment. In lab experiments much of

the usual social context can be removed. Nevertheless, subjects in most laboratory experiments

on trust are matched with others drawn from the same classes in the same university, and are

close in age and socioeconomic status. These subjects are likely to share expectations of how

others around them will behave.

In our study we examine trust and reciprocity among strangers. Although our subjects

are all drawn from the convenience sample of undergraduate students, each subject is paired over

the internet with someone from a different school in a different state, observing only each others’

photographs. Given the degree of anonymity, expectations cannot be based on experience or

knowledge about the group the particular individual belongs to (i.e., Virginia Tech

undergraduates), but rather must be inferred from their observable characteristics. Several

previous studies have examined the relationship between observable characteristics and trust and

reciprocity in games similar to ours. Croson and Buchan (1999) and Chaudhuri and

Gangadharan (2002) find systematic sex differences in trust and/or reciprocity, though subjects

do not know the sex of their counterparts. In studies where subjects observe photographs of

their counterparts, Scharlemann et al. (2001) and Eckel and Wilson (2003b) find evidence that

smiling invites trust. They also show that there are systematic differences in behavior by sex

pairings in this setting. Fershtman and Gneezy (2001) provide evidence of racial and ethnic

stereotypes using names as labels for subjects; using photographs, Eckel and Wilson (2003a),

Burns (2003), and Buchan, Croson and Solnick (2003) find that observed race and/or gender are

important for initiating and reciprocating trust.

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In an experimental labor market, Mobius and Rosenblat (2003) decompose the effects of

beauty. They infer that 80 percent of the beauty premium is due to stereotyping effects that

assign positive attributes to better looking subjects, while the remaining 20 percent is due to

greater confidence exhibited by those who are more beautiful. This result is consistent with the

observation from attribution studies that, while there may be some correlation between beauty

and performance, expectations exaggerate the relationship.

Other experiments have explored the effect of attractiveness on behavior in games with

low to modest financial stakes. One of the earliest studies is by Kahn et al. (1971) who used a

repeated prisoner’s dilemma game. Male and female students were paired by sex and level of

attractiveness. Men were found to be motivated by competition and were not influenced by

either the sex or attractiveness of their counterpart. Women, on the other hand, were more

cooperative with male than female partners, except for unattractive women paired with attractive

men, in which case the women behaved competitively. Mulford et al. (1998), also using a PD

game, found that subjects responded systematically to attractiveness. In their design subjects

chose their own partners. Prior to doing so, subjects rated one another, and themselves, for

attractiveness. This study found that subjects tended to choose the most attractive partner, even

though they predicted that they would be left financially worse off as a result. Subject’s

predictions were met, with the most attractive subjects being the least likely to cooperate in the

game. Attractive subjects earned more because others cooperated with them, and they defected.

In an ultimatum game Solnick and Schweitzer (1999) found no difference in the

decisions made by attractive and unattractive people, but they did find a difference in how these

groups were treated by others. Proposers offered more money to attractive responders, although

attractive responders did not have higher minimum acceptable offers. On the other hand, higher

minimum offers were demanded from attractive than unattractive proposers, a result that is

echoed in our own results below.

Andreoni and Petrie (2003) focused on a 5-person, repeated play, public goods game in

which subjects viewed the photographs of others in their group. An independent group of

evaluators rated the photographs for attractiveness and other characteristics. Like Hamermesh

and Biddle (1994) and Mulford, et al, they found some evidence of an initial reward to beauty

premium, with higher contributions occurring initially in groups with a larger proportion of

relatively attractive participants. However, unlike Mulford et al. (1998) they did not find

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attractive people to be more self-interested: attractive people contributed to the public good at the

same rate as less attractive people. When individual contribution information was provided, this

information had a strong impact on contributions, and the presence of attractive subjects in a

group resulted in decreased overall contributions to the public good. They conclude:

“In general, our results show that premiums to beauty and gender are information conditional. Indeed, when group members are kept in the dark about the performance of each group member, people tend to reward beauty and women. However, when group members know what others are contributing, the beauty premium disappears….” (p. 20)

Our study investigates the impact of beauty on trust and reciprocity in a two-person

sequential game. The structure of the trust game allows us to examine expectations of trust and

reciprocity and how subjects react to surprise and disappointment by others. We match subjects

over the internet to play a trust game, then measure attractiveness levels after the fact. This

allows us to make a more careful comparison between the attractiveness and other observable

characteristics of the players without calling the subjets’ attention to any particular

characteristics.

III. Research Design.

Subjects in our experiment participated in a series of tasks, including trust games, risk-

assessment instruments, and surveys. All tasks were computer mediated. Before beginning the

experiment we photographed each subject with two neutral and two smiling expressions. Once

seated at their computers, the subjects chose one from the four photos that they wanted their

counterparts to observe. Subjects did not know what decisions they would be making before

choosing the photo, only that their counterparts would see it. The same photo was used

throughout the experiment. For the trust game component of the experiment, each subject was

randomly paired with other individuals at a different laboratory, whose photos they observed.

One site was the Lab for the Study of Human Thought and Action at Virginia Tech in

Blacksburg, Virginia; the other was the Behavioral Research Lab at Rice University in Houston,

TX. Use of two sites allows us to show subjects each others’ photographs without compromising

anonymity.

The first task was a 40-question survey designed to measure attitudes toward risk, the

widely used Zuckerman Sensation-Seeking Scale (SSS), form V (Zuckerman (1994)). Subjects

earned 10 experimental laboratory dollars for completing the survey (the exchange rate was 2 lab

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dollars for each US dollar). This earned payment was used as the endowment for the second

task, the trust game.

Subjects completed a series of trust games similar to the one first conducted by Berg, et

al. (BDM hereafter). In each game, the first mover was given the opportunity to send any part of

the $10 earned from the risk surveys (in whole dollars) to a counterpart whose photo they

observed. The sent amount was tripled. The second mover then decided how much to return (if

any) to the sender. Subjects were randomly assigned to the role of first mover or second mover,

and they kept that role throughout the experiment. A given subject participated in as many as 10

trust games, each with a different counterpart.1 A subject played with a given counterpart only

once. As explained to the subjects, in fact only one of the pictured counterparts was playing the

game at the same time at the other site, and this is the choice subjects were paid for. The other

photographed participants had completed the experiment previously.

First movers were given no feedback from decision to decision, and only learned the

amount returned to them for the paid decision.2 Thus they never received feedback on the

“robots” players with whom they were matched. In our data set, we use only the decision-

maker’s decision of what to send; second-mover data for the robots is not included in the data

set. Second movers were shown the actual offer of their real counterpart, and for the other

decisions saw the offer that had been made by those counterparts in the photos when they played

the game. Second movers also were paid for only one decision, the one with the real-time

counterpart. The amount sent to second movers by the “robot” first-movers was the amount that

player had sent to a real player when they actually played the game. Thus for second movers, the

amount sent by the robot players is included in the data set because the second movers observed

those amounts. All of this was explained to the subjects.

This feature of the trust-game component accomplishes two goals. First it gives us a

within-subjects design, so that we can gauge whether and how a given subject reacts to

differences in their counterparts. Second, this approach allows us to boost the number of

1 In the first session subjects participated in six trust games; in the second session subjects participated in eight trust games; in the remaining sessions subjects participated in ten trust games. At first we were concerned with how much time it would take to run the additional trust games. A pilot test indicated it might take a considerable amount of time. However, once we began running the experiment it was clear that subjects could quickly complete ten games. 2 Anderhub et al. (2002)use a repeated trust game. They find that population dynamics take effect, with subjects learning about the behavior of others and adjusting their strategies accordingly. In our experiment there is no feedback for the first mover, so no opportunity for dynamics to emerge.

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observations for different racial and ethnic minorities by increasing the number of times a racial

or ethnic minority appears as a “robot” counterpart, compensating in part for the small number of

minority subjects in our sample.

The “real” counterpart was not revealed until the end of the trust game. Before they were

informed, the subjects were asked to guess which player was their real counterpart and told that

if they guessed correctly they would earn $1.00. First movers only managed this 11.5 percent of

the time and second movers did so 12.4 percent of the time, which is no better than random

guessing. The two on-line players were always paired in the very first period, although the

subjects did not know this. This allows us to compare the first period play, with a real player,

and subsequent plays with “robot” players.

Data also were collected on subjects’ expectations of each others’ decisions at each stage

of the trust game. The first mover was asked to predict the second mover’s choice. Likewise,

before being informed of whether any money was sent, the second mover was asked to predict

what the first mover would do. Elicitation of expectations was incentivized: subjects received an

extra dollar for guessing their counterpart’s action.

To summarize, the trust-game component of our experiment differs from what has

become the standard BDM setup in several ways. First, subjects played a series of trust games

instead of only one. Second, our experiment is computerized, while theirs is hand-run. Third,

subjects observed one another’s photographs when making their decisions, while in BDM care

was taken to keep the groups separated to ensure anonymity. After subjects were told who was

their on-line counterpart and how much they had earned, they then were asked to write out a

short description of what sort of situation the trust experiment reminded them of. This was an

attempt to find out if subjects framed the experience in the same way we, the experimenters, did.

In the third component of the experiment subjects completed another task designed to

elicit risk preferences, a computerized version of the procedure used in Holt and Laury (2002).

In the final component subjects responded to questions that collected demographic information

as well as survey measures of trust and altruism. Subjects were paid, one at a time and in

private, and given a debriefing statement.

As explained below, in a separate protocol, photographs of subjects in the trust game

were evaluated by an independent set of subjects drawn from the same population that they faced

in the trust game. These evaluations included assessments of the attractiveness of the

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photographed subject as well as measures of affective state, trustworthiness, cooperativeness,

etc.

Procedure. A total of 206 subjects, half from Virginia Tech, 42.2 percent from Rice University

and 7.8 percent from North Carolina A & T, participated in 8 experimental sessions. Subjects

were recruited from introductory classes in Principles of Economics at Virginia Tech and NCAT

and from dining halls at Rice University. They were told to report at a specific time to an off-

campus laboratory at their respective locations. The number of subjects in a session ranged from

10 to 32. Subjects were 55.8 percent male and just under 94 percent of subjects were between

the age of 18 and 22. Care was taken to recruit an ethnically-diverse subject pool.3 The ethnic

composition of the subjects was 62.6 percent Caucasian, 15.0 percent African-American, 12.6

percent Asian-American, 5.3 percent Hispanic and the remaining 4.4 percent self-identified

foreign nationals (most were from India or the Middle East.)

When subjects arrived at the lab they were asked to sign a consent form and given a card

assigning them to a specific computer. Subjects posed for four pictures, then were seated at

computers and told that they could browse the internet before the experiment began. Because

subjects arrived at different times and because we had to coordinate activity at two sites, we

allowed subjects to browse, rather than converse with one another once in the lab. Once both

sites had an equal number of subjects, then everyone was asked to stop browsing and open a

window on the machine pointing to the experiment. Brief oral instructions were read to subjects

before beginning, then subjects went through self-paced, computerized instructions. In a post-

experiment questionnaire a little over 90 percent very strongly or strongly agreed that the

instructions were clear. Once subjects began the experiment, no talking was allowed. Subjects

were asked to raise their hands if they had a question or problem, and the experimenter would

answer their questions privately.

In previous two-site experiments, we found that subjects doubted whether they were

paired with real counterparts: their decisions shifted from trusting the image they viewed to

trusting the experimenter. In order to get around this problem, a subject was randomly selected

at each site and asked to think up a 4-5 letter “secret word.” That word was communicated to the

other site, written on a large sheet of paper, and a digital photograph was taken of the word and

3 One session was conducted between Virginia Tech and North Carolina A&T. The latter was chosen because it is an Historically Black University with a heavy emphasis on engineering. This provided a useful match with subjects from Virginia Tech.

9

subjects in the background (their backs were turned to the camera). The pictures from both

locations were uploaded to the server and then displayed. Subjects could then view their own

secret word displayed at the other site as well as observing the other site’s secret word displayed

and photographed at their own site. This was a tedious, but effective way to build beliefs that the

counterparts were real (see the discussion by Eckel and Wilson (2003a)). At the end of the

experiment subjects were asked whether they though they were participating with a real

counterpart and 93 percent indicating that they thought they were.

Figure 1. Sample First-Mover Decision Screen.

Figure 2. Sample Second-Mover Decision Screen.

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IV. Photo evaluations

The second phase of the experiment involved an independent sample of subjects

evaluating the photograph displayed in the trust game. These subjects had not participated in a

trust experiment, were only asked to evaluate a set of images and were paid for their evaluations.

In this experiment subjects were asked to rate a series of photos. Subjects were recruited over

the internet and from large classrooms at all of the sites. Each subject was asked to rate between

15 and 24 photos on 15 word-pairs and was paid between $.25 and $.50 per photo. Photos and

their order were randomly assigned to each subject. Subjects evaluated photos from their paired

University: i.e. subjects at Rice and NCAT viewed photos from Virginia Tech, etc. A total of

296 subjects participated: 56.4% were male. Subjects spent an average of 80.2 seconds per

photo (with a standard deviation of 64.5 seconds). Because subjects only evaluated photos of

people from a different University, because photos were randomly assigned and because turnout

at each site varied, the number of raters per photo ranged from 9 to 42, with an interquartile

range between 18 and 27. There were 230 photos rated with a total of 5216 evaluations.

Figure 3: A Sample Screen and Subset of the Word Pairs Used in the Evaluation

Experiment

Each image was evaluated using 15 opposite word pairs. A sample of the word-pair

items and the screen used by subjects is shown in Figure 3, which reproduces a decision form.

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The left/right order of the word pairs was randomly fixed prior to the experiment. The order of

presentation of the word pairs was randomized across each photograph for each subject,

providing control over response-set bias in the word pairs.

Our interest for this paper is with the attractiveness rating, and as a consequence we do

discuss results for the other word pairs. Raters were presented with the opposite word-pair

unattractive-attractive and asked to rate the photo. The words were anchors on a six-point scale,

with the word fitting the person in the photograph either “Very Well,” “Well” or “Somewhat

Well.” All word pairs were scaled such that a low value reflects a negative evaluation.

We find, similar toAndreoni and Petrie (2003), that women give significantly higher

ratings of attractiveness on average than men. To deal with this problem, we mean-center the

attractiveness rating such that:4

Attractivenessi = ( )jij xx − where . ⎩⎨⎧

==

evaluatorjth photoith

ji

Regardless of whether the original values or the mean-centered values are used, subjects were in

substantial agreement concerning their evaluations. Using Cronbach’s alpha as a measure of

consistency we get values of .80 and .83 for the original values and the mean-centered values,

respectively.5 These evaluations were then averaged across all evaluators and a composite score

was then generated. Figure 4 gives the distribution of the composite score broken out by male

and female photos. As is clear, from the figure, differences remain between the two sexes.

Following Andreoni and Petrie (2003), the upper and lower quartiles for the composite score

were used to identify attractive and less attractive individuals.

4 This procedure removes heterogeneity in individual evaluators. Unadjusted evaluations do vary systematically by gender and ethnicity. This analysis is available on request. 5 Because there were different numbers of evaluators for each photo, a bootstrap method was used to calculate the values of Cronbach’s alpha. Sample evaluations of 9 subjects were used for each photo. A total of 50 runs were used to compute the average scores.

12

Figure 4. Distribution of attractiveness scores using a mean-centered adjustment.

0.2

.4.6

kden

sity

cen

t_at

tract

-2 -1 0Mean-centered attractiveness ra

Male Photos

Consistent with findings from the stranger attribution

evaluators attribute particular characteristics to more attracti

models in which the evaluation of the other opposite word-p

regressed against variables controlling for the sex and race o

the photograph and the attractiveness rating. Table 1 indicat

scaled) were either positively or negatively correlated with a

derived from regressions in which the evaluation of the indiv

variable, and the sex, race or ethnicity of the photograph, the

attractiveness rating were on the right-hand side of the equat

attractiveness were significant at p=.01 or better. It is not su

friendly and happy are all positively associated with the attra

analyses by Eagly et al. (1991); Jackson et al. (1995) or Lan

trustworthy and trusting are positively correlated with attrac

guarantee that such individuals are always seen in a positive

negatively related to cooperativeness, honesty and generosit

attractive subjects in bargaining games that depend on both c

13

Female Photos

1 2ting

literature in social psychology,

ve subjects. We estimated statistical

air items by each subject was then

f the evaluator, the sex and race of

es which words (all positively

ttractiveness. Correlations are

idual word pair was the dependent

evaluator and the photo

ion. All coefficients for

rprising that terms like intelligent,

ctiveness rating (see the meta-

glois et al., 2000). Note that both

tiveness. Yet attractiveness does not

light. In particular attractiveness is

y. These items may work against

ooperation and generosity.

Table 1: Attributes positively and negatively correlated with attractiveness rating.

Positive Correlations Negative Correlations

Motivated Cooperative

Trustworthy Honest

Trusting Generous

Hardworking Calm

Intelligent

Respectful

Friendly

Happy

Accepting

Dependable

V. Analysis of the Trust Game

Because of the nature of our experiment, the data set has an unusual structure. First

movers observe a set of M second movers, only one of which they are actually matched with.

They only observe the amount returned for their real counterpart. Second movers see a set of M

first movers, only one of which they are actually matched with. In addition, some subjects were

over sampled in the analysis to increase data on minority counterparts. Thus the number of

observations that are responses to any particular photo vary in the data set. For first movers, the

data includes only the decisions made by the actual first movers in response to the photos of real

and robot counterparts. For second movers, the data include decisions made by second movers

in response to amounts sent by real and robot first movers. These amounts sent – both real and

robot – are also included in the data set as they were observed by the second movers.

Descriptive statistics for the overall levels of trust are shown in table 2. Overall, subjects

sent an average of $4.91 (compared with the $5.16 sent by subjects in BDM). While subjects

send slightly more to their on-line counterpart than to robot players the difference is not

significant (t=.267, p=.79, df=954). In the aggregate, trust pays. Subjects returned $6.43 on

average, while for BDM an average of $4.66 was returned. Although slightly more is returned to

robots than to an on-line counterpart, the difference is not statistically significant (t=.581, p=.56,

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df=900). For the remainder of the analysis we pool the data in the following way: first mover

data includes offers made by each (real) first mover to real and to robot counterparts; second

mover data includes amounts returned by (real) second movers to real and robot counterparts.

The relatively high standard deviations indicate considerable heterogeneity in what individuals

send and return.

TABLE 2: Average Amount Sent by First Movers and Average Amount Returned by Second

Movers (SD in parentheses)

Manipulation

Overall Average

Average for “Real”

Counterpart

Average for a “Robot”

Counterpart Average Amount Sent to Counterpart

$4.91 (3.23) n=954

$4.99 (3.06) n=103

$4.90 (3.25) n=851

Average Amount Returned

$6.43 (5.90) n=902

$6.10 (5.17) n=98

$6.47 (5.98) n=804

Percentage Returned

33.24% (22.56) n=902

38.73% (22.95) n=98

32.56% (22.21) n=804

Figure 5 below is a scatter plot of amounts sent and returned for all second-mover

decisions. Each circle indicates what was sent by the first mover as seen by the second mover,

and what was returned. In part because amounts only could be sent and returned in whole

dollars, many of these points coincide. They have been perturbed by a small value in order to

give a sense of the distribution. The solid line on the figure represents equal amounts sent and

returned. Circles above the solid line meant that trust was more than reciprocated. This figure

suggests that there is a positive relationship between the amount sent and the amount returned.

There also appears to be a relationship between the percent returned and the offer amount: the

more the first-mover sends, the higher fraction of the payoff is returned to her. It is also clear

that there is substantial heterogeneity across decisions. For any given amount offered, there are

many different responses, from zero to the full tripled amount.

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Figure 5. Amount Returned by Second Movers.

02

46

810

1214

1618

2022

2426

2830

Am

ount

Ret

urne

d (in

Dol

lars

)

0 1 2 3 4 5 6 7 8 9 10Amount Sent (in Dollars)

We find heterogeneity across subjects, and also for a given subject across decisions. One

feature of this design is that it allows us to examine the behavior of individuals across many

decisions. We find that 25 of 103 first movers (24.3 percent) never change their strategy during

the course of the experiment. They always do exactly the same thing. Almost half always sent

their full endowment (12 of 25), two subjects never sent anything, and five always sent half of

their endowment. The remaining 75.7 percent of first movers all exhibit some variation in what

they choose to send.

There also considerable heterogeneity among the second movers. Only 7 of the 103

reciprocators showed obvious rules in their strategy for returning (5 always returned nothing, 1

returned exactly what was sent and 1 returned everything). These data indicate that subjects are

conditioning their decisions on something they observe about their counterparts.

Table 4 presents the amount sent and the percentage returned by the characteristics of the

first mover. For example, the first row of the table shows the average amount that male first

movers trust to all counterparts, and the trustworthiness or percent returned on average to male

first movers by second-mover decision makers. Each line indicates trust by and reciprocity

16

toward persons with the relevant characteristic. The table includes data sorted by the sex of the

first mover, the race/ethnicity of the first-mover, and the evaluators’ ratings of whether the

subject was smiling in the photo. Although men who were first movers sent slightly more than

women, the difference is not statistically significant (t=-1.277, p=.20, df=952). The amount sent

differs by race and ethnicity. Caucasians are likely to send more than African-Americans,

Hispanics and Asians. However, only the difference between Asians and Whites statistically

significant in pairwise comparisons. (This is different from results reported by Eckel and Wilson

(2003) for a slightly different game in which African American and Hispanic subjects are trusted

less than white subjects.)

Table 4. Average Amount Sent and Percentage Returned by characteristics of the first mover. (Standard deviation, Frequency in parentheses)

First mover Amount Sent

by first-mover Percent Returned

to first mover Male 5.03

(3.19, n=516) 34.97

(21.16, n=482) Female 4.76

(3.28, n=438) 31.23

(23.52, n=420) Caucasian 5.26

(3.35, n=572) 31.87

(22.86, n=488) African-American 4.28

(3.07, n=166) 38.53

(20.09, n=143) Asian 4.25

(2.74, n=134) 29.07

(23.32, n=162) Hispanic 4.47

(3.03, n=36) 37.33

(16.68, n=44) Other 5.02

(3.16, n=46) 39.31

(20.93, n=65) Smiling 4.96

(3.27, n=584) 33.38

(23.02, n=591) Not Smiling 4.84

(3.18, n=370) 32.93

(21.08, n=311)

In a previous published study, we found evidence that smiling invites trust (Scharleman

et al., 2002). Subjects in the face-evaluation phase were asked to judge whether the person in the

photograph was smiling. A score ranging from 0 to 1.00 was derived from the judgments of

subjects and a value of .75 was used as a cut-off between those smiling and those not. Given

17

these data this meant that 65 percent of the second movers were perceived as smiling. We find

no difference in the amount sent by smiling and nonsmiling first movers

The second column shows the percent of the tripled sent amount that was returned to first

movers who were male, female, etc. More was returned to male than female first movers

(t=2.51, p=.01, df=900). The race/ethnicity of the first-mover is related to what was returned to

them. We also observe significant variations in the percent returned, with less returned to whites

and Asians than to other ethnic groups. For example, the paired difference between blacks and

whites is significant (t=3.14, p=.002, df=629); the difference between the amounts returned to

Asians and whites is not significant (t=-1.34, p=.18, df=648). Using the same measure as above,

smiles have no effect on the percentage returned (t=-.29, p=.77, df=900).

Table 5: Amount Sent and Percentage Returned by the Characteristics of Second Mover (Standard Deviations, Frequencies in parentheses)

Second-Mover Characteristics

Sent to the second mover

Percent Returned by the second mover

Male 4.77 (3.20, n=555)

31.74 (21.86, n=504)

Female 5.10 (3.27, n=438)

35.11 (22.85, n=398)

Caucasian 4.95 (3.22, n=485)

32.76 (23.85, n=585)

African-American 4.86 (3.46, n=162)

33.45 (16.07, n=124)

Asian 5.02 (3.21, n=180)

35.39 (22.24, n=107)

Hispanic 4.58 (3.01, n=71)

29.90 (19.43, n=52)

Foreign 4.75 (3.11, n=56)

38.73 (19.07, n=34)

Smiling 4.93 (3.22, n=618)

32.94 (22.91, n=627)

Not Smiling 4.86 (3.26, n=336)

33.89 (21.06, n=275)

Table 5 contains the amount sent and percent returned broken down by the characteristics

of the second mover. We see that more is sent to second movers that are women, although that

difference is not statistically significant (t=-1.523, p=.13, df=952). We also observe little

variation by the race or ethnicity of the counterpart: the difference between black and white

18

counterparts is not significant (t=-.311, p=.75, df=645), nor is the difference between white and

Asian counterparts (t=.27, p=.78, df=663). Slightly more is sent to smiling than nonsmiling

counterparts though the difference is not statistically significant ((t=.308, p=.76, df=952).

Turning to the second-movers, women return a significantly higher percentage than men

(t=-2.256, p=.024, df=900). Referring back to Table 4, we see that women are sent more than

men. This pair of results is consistent with women being more trustworthy, and perceived as

such by their counterparts. There are no significant differences across race and ethnicity; while

Asians return the most and Hispanics the least, the difference between those groups is not

significant (t = -1.51, p=.13, df=157).

Table 6 contains average amounts sent by sex pairing. Men send more on average than

women do. While both men and women send more to female second movers, the difference is

only statistically significant in pairwise t-test for M1F2 compared with F1M2: that is, men send

more to women than women send to men. More is returned on average by both men and women

to male than to female counterparts (t = 2.4458, p=.015).

Table 6: Amounts sent and returned by sex pairing

(standard errors and number of observations in parentheses)

Sex Pairing Amount Sent Percent Returned Male 1 with Male 2 4.95

(3.22, n=251) 33.40

(20.18, n=255) Male 1 with Female 2 5.25

(3.11, n=177) 35.85

(21.63, n=176) Female 1 with Male 2 4.45

(3.08, n=234) 29.90

(24.60, n=185) Female 1 with Female 2 4.97

(3.45, n=152) 29.88

(23.58, n=141)

Is There a Beauty Premium?

The simplest measure of a beauty premium is the difference in overall earnings by

attractive and unattractive subjects. If we compare the earnings in the trust game for the most

attractive and least attractive trusters and trustees we find no evidence for a beauty premium.

Table 7 presents the mean earnings for subjects by their position in the game. The table takes

subjects who are in the lower and upper quartiles of attractiveness based on the overall

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distribution of the composite measure of attractiveness described above. Using this measure,

attractive first-movers earn slightly less than unattractive first-movers. Attractive second

movers earn 15.8 percent less than unattractive second movers, but this is insignificantly

different from zero.

Table 7. Earnings by real players in lower and upper quartile of attractiveness rating (standard deviation in parentheses).

Earnings First mover

Unattractive(Lower Quartile)

$11.27 (4.18)

Attractive(Upper Quartile)

$11.11 (4.34)

t-test t=.15, df=66, p=.44 Second mover

Unattractive(Lower Quartile)

$9.00 (7.88)

Attractive(Upper Quartile)

$7.58 (5.72)

t-test t=-.80, df=61, p=.21

To better understand the components of the earnings difference, Table 8 compares the

amount sent, conditional on the attractiveness of the first and second movers. Relatively

attractive people are less trusting than relatively unattractive people. Both attractive and

unattractive first-movers send more to attractive than to less attractive second-movers. There is

an overall beauty premium of 11.6 percent in the amount sent. Despite the fact that they receive

less, unattractive second movers send back a higher percentage. Both attractive and unattractive

second-movers send back a higher percentage to unattractive than attractive first movers, perhaps

because of the greater trust exhibited by the unattractive first movers. This discrimination is

especially evident for attractive second movers.

Examining only the upper and lower quartiles of the counterpart may be masking more

complicated relationships. After all, what is returned to the subject is sensitive to what was sent

in the first place. Moreover, the relative rather than the absolute attractiveness of subjects may

be an important factor. In addition, interactions by gender and race/ethnicity may affect trust and

reciprocity. The next section presents multivariate analysis.

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Table 8. Average amounts sent to and percentage returned by players, contingent on the attractiveness ratings of the decision makers and counterparts

(standard deviation in parentheses).

Unattractive Decision Maker

Attractive Decision Maker

Amount Sent to counterpart who is: Unattractive

(Lower Quartile)$5.29 (3.06)

$3.79 (2.71)

Attractive(Upper Quartile)

$6.12 (3.19)

$4.46 (3.07)

t-test t=1.33, df=100, p=.09 t=1.22, df=112, p=.11Percent Returned to counterpart who is:

Unattractive(Lower Quartile)

35.03 (21.20)

40.11 (19.94)

Attractive(Upper Quartile)

28.62 (22.20)

30.91 (19.21)

t-test t=.89, df=98, p=.19 t=2.24, df=95, p=.01 Multivariate Models. Table 9 contains an analysis of the first-mover’s decision of how much to

send to the second mover. The dependent variable in this regression is the amount in lab dollars

that the first-mover sent to the second-mover. Because each first-mover made between 6 and 10

decisions, we estimate a random-effects model. In addition, decisions were censored: subjects

could not send more than 10 or less than 0. To account for this we use Tobit regression.

The model in Table 9 includes control variables for sex, race/ethnicity, self-reported

trustworthiness, and expected return, as well as attractiveness measures. Other regressions (not

reported) tested for differences in behavior by sex-pairing, and we could not reject the null

hypothesis that subjects were not conditioning on the sex of their counterpart. The

trustworthiness survey scale is calculated from seven questions included on the final

questionnaire, which had a 6-point range (1-6), which ask the subject about their own degree of

trustworthiness. These questions were taken from a survey instrument reported in Wrightsman

(1991). The scores on this survey scale averaged 3.47 and ranged from 1.71 (low

trustworthiness) to 5.29 (high trustworthiness). The truster’s expectations for what would be

returned are included; recall that first-movers are asked after deciding what to send the amount

they expect to be returned, and are paid for correct forecasts. On average trusters expected $8.88

to be returned. Two attractiveness variables are included. First, we include the attractiveness

rating of the first mover to test for the effect of absolute levels of beauty. The second variable is

21

the difference between the rated attractiveness of the first mover minus the rated attractiveness of

the second mover. This variable picks up any additional variation in the amount sent that is due

to relative attractiveness.

Table 9: Determinants of the Amount Sent by First Movers Dependent variable = amount sent in lab dollars.

(Standard error in parentheses)

Model 1: Attractiveness difference ( 1st – 2nd mover)

-0.277 (0.084) p=0.001

Female to Male 0.273 (0.201) p=0.173

Female to Female 0.240 (0.229) p=0.293

Male to Female 0.215 (0.189) p=0.254

Black 1 -1.403 (0.201) p<0.001

Asian 1 0.044 (0.218) p=.839

Hispanic 1 -1.181 (0.385) p=0.002

Black 2 (photo rating) 0.477 (0.192) p=0.013

Asian 2 (photo rating) 0.222 (0.214) p=0.300

Hispanic 2 (photo rating) 0.128 (0.225) p=0.571

Trustworthiness 1 (self-report survey)

0.703 (0.131) p<0.001

Expected amount returned 0.176 (0.013) p<0.001

Constant 1.755 (0.512) p=0.001

Log Likelihood -1597.7 Uncensored 641 Left-censored 86 Right censored 170

The coefficients on the control variables are consistent with what others have found. We

find a strong positive coefficient on the trustworthiness variable, a result similar to that found by

Glaeser et al. (2000), who noted that an individual’s own sense of trustworthiness affects trusting

22

behavior. Other researchers have shown that the sex of the counterpart is significantly related to

trust and reciprocity. For example Croson and Buchan (1999) and Chaudhuri and Gangadharan

(2002) find systematic sex differences in behavior. Scharlemann et al. (2001) find differences

specifically due to different male/female pairings. Our data do not replicate these results: we

find no significant difference in sex pairing once the attractiveness difference is included.

Likewise, Fershtman and Gneezy (2001), Eckel and Wilson (2003a) and Burns (2003) all find

racial and ethnic effects across subject pairings. These features, like the sex of the counterpart,

can be observed in the photos. Since evaluators sometimes made errors in judging the race or

ethnicity of their counterpart, we classified the second-mover photos by the modal response of

the evaluators. We think this most closely approximates how the average subject viewed the

photograph. Conditional on the control variables, we see a statistically significant, negative

effect of the difference in attractiveness. People who are more attractive than their counterparts

tend to send less.

The equation in Table 10 focuses on what is reciprocated. Because the amount of money

that subjects have to deal with varies with how much is sent, the dependent variable is calculated

as the percentage of the amount that was passed (and tripled) and returned to the truster. This

allows comparisons across all subjects. Once again a random effects model is used to estimate

the coefficients. The distribution of percent return is also censored at zero and 100%, so Tobit us

used.

The equation includes control variables for the sex and race/ethnicity of the first and

second movers. As before, variables for the race/ethnicity of the first mover were based on the

evaluations of the photos. Because these subjects received some information about their

counterpart’s offers in each round of the game, it is possible for subjects to learn something

about the distribution of offers in the population. To account for this, we include a variable for

the period in which they were making the decision. A variable was also included measuring the

difference between what the second mover expected to receive and what was actually sent, a

measure of disappointment. Along the lines of Rabin (1993) this measure captures dashed

expectations. If what was sent was less than what was expected, then the measure is negative,

indicating disappointment. If expectations were exceeded, then the measure is positive. Finally,

a survey measure of altruism, again based on Wrightsman (1991), is included. Like the measure

of trustworthiness, it is constructed from seven questions, it is an additive scale and it has been

23

widely used. Higher values of the variable indicate a greater commitment to altruism. Again we

use the modal response for the race or ethnicity of the photo. Attractiveness is measured as in the

first equation, the difference between the attractiveness ratings of the first mover and the second

mover. Again a positive value reflects the fact that the first mover was rated more attractive than

the second mover.

The results indicate that there are no important main effects for the sex, race or ethnicity

of the second mover. There is a large effect for the measure of altruism: subjects with a greater

commitment to altruism return a larger percentage of what was sent. There is also a period

effect, with a smaller percentage being returned over time. Expectations play an important role

in the decision of how much to return. More is returned to the first mover when his trusting

move is more generous than expected. Those who fail to meet expectations are given less (the

difference between what was sent and what was expected turns negative, thereby decreasing

what is returned).

Conditional on the control variables, the coefficient on the attractiveness variable is

negative and significant. This means that for positive values of the variable (when the first

mover is more attractive) a lower percentage is returned. This result is the opposite of a beauty

premium – a beauty penalty.

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Table 10: Second mover decision to reciprocate Dependent variable = percent of tripled amount that is returned to the sender.

(Standard error in parentheses)

Model 1: Attractiveness difference ( 1 – 2)

-2.225 (0.783) p=0.005

Female 1 x Male 2 0.413 (1.661) p=0.804

Female 1 x Female 2 -2.145 (2.306) p=0.352

Male 1 x Female 2 -0.352 (2.237) p=0.875

Black 2 -2.969 (5.030) p=0.555

Asian 2 3.565 (5.420) p=.511

Hispanic 2 3.114 (6.853) p=0.649

Black 1 (photo rating) 2.155 (1.710) p=0.207

Asian 1 (photo rating) -1.010 (1.737) p=0.561

Hispanic 1 (rating) 1.465 (2.061) p=0.477

Altruism 2 (self-report survey)

5.720 (2.858) p=0.045

Expected – received amount

0.999 (0.143) p<0.001

Period -0.433 (0.189) p=0.022

Constant 17.593 (9.335) p=0.0059

R2 Within 0.133 Between 0.012 Overall 0.055

Disappointment, the difference between the expected and received amount trusted,

appears to play an important role in determining the decision of the second mover. This variable

is a measure of bias in expectations. A disappointed person sends back less, while a pleasantly

surprised person sends back more as a fraction of the amount sent. To further examine when

people are disappointed, we present a regression analysis of the correlates of disappointment.

25

This regression shows the direction and magnitude of biases in expectations by players by gender

and ethnicity, as well as biases that are induced by the observable characteristics of the first-

mover. These results are shown in table 11.

Table 11: Determinants of disappointment, random effects. Dependent variable is

disappointment: Amount Expected – Amount Received from the first mover.

Model 1: Attractiveness of first mover

-0.940 (.477) p= 0.049

Attractiveness of second mover

0.276 (0.169) p=0.104

Female first mover 0.793 (0.704) p=0.260

Female second mover -0.296 (0.223) p=0.263

Black 2 0.121 (0.321_ p=0.707

Black 1 (rating) 2.170 (0.865) p=.012

Asian 2 -.349 (0.330) p=0.290

Asian 1 (rating) -.386 (.928) p=0.677

Hispanic 2 -0.214 (.456) p=0.638

Hispanic 1 (rating) 2.314 (1.198) p=0.053

Constant -0.373 (0.555) p=0.502

R2 Within 0.007 Between 0.053 Overall 0.114

The significant negative coefficient on the attractiveness of the first mover indicates that

second movers systematically overestimate the generosity of first movers, and are disappointed

by them. However, second movers are systematically surprised in a positive way by black and

Hispanic first movers. Since disappointment leads second movers to return a smaller percentage

of what they were sent, relationship between disappointment and attractiveness may partially

explain why there is no beauty premium in this trust game experiment, as well as why observable

26

minorities are not disadvantaged.

Discussion and Conclusion Beauty has a substantial impact on behavior in two ways. It affects people’s expectations

of others’ behavior, but it also has an independent effect over and above the effect on

expectation. Second-mover subjects expect attractive first movers to be more generous than

others. Since attractive persons are not more generous, this leads to disappointment, which is

then punished in the second stage of the of this game. We also find, similar to the results of

Mulford et al. (1998) and Solnick and Schweitzer (1999), that more is sent to attractive second

movers. We also provide evidence that attractive second movers are not more trustworthy. This

in this context we do not find evidence of a beauty premium.

27

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