Why Beauty Matters
An Experimental Investigation
Markus Mobius (Harvard University)
Tanya Rosenblat (Wesleyan University)
November 2004
Is Beauty “in the Eye of the Beholder?”



Surprisingly psychologists say “No”
Strong agreement on what is considered
“beautiful” in facial photograph ratings across
genders and across cultures
Therefore beauty can be measured (objectively)!
Is Beauty in the Eye of the Employer?


Extensive research on beauty in social psychology
and human resource management
In economics, Hamermesh and Biddle (1994)
“Beauty Premium”
 Establish that
looks matter even after controlling for
many observable characteristics (actual labor market
experience, years of tenure in a firm, union status, firm
size, race, geographic location, fathers' occupation,
childhood background, immigrant status of respondents
and their parents and grandparents)
Psychology Literature
I. How are beautiful people perceived by others?

Attractiveness or Beauty-Is-Good Stereotype –
viewed superior along several dimensions:
personality traits (sociability, dominance, sexual
warmth, modesty, character), mental health,
intelligence and academic ability, and social skills
Psychology Literature
II. To what extent is this stereotype true?
Kernel of Truth Hypothesis
 Attractive people are treated better by others
throughout their life cycle.
 Physical attractiveness rating does not change
much throughout life cycle.
 A self-fulfilling prophecy? => Become more
confident and more persuasive
Experimental Literature
Physical attractiveness in Experiments:
 Ultimatum Game (Solnick and Schweitzer (1999))
 Prisoner’s Dilemma (Mulford, Orbell, Shatto and
Stockard (1998), Kahn, Hotes and Davis (1971))
 Public Goods (Andreoni and Petrie (2004))
 Trust Games (Eckel and Wilson (2004))
 Dictator Game
How does beauty affect wages?
Decompose the effects of beauty:



“Becker-type” discrimination (employers have a
taste for good-looking employees)
Ability Effect - more physically-attractive have
superior skills at performing a task
Stereotype, Confidence and Persuasion Effects
during wage negotiation process
How does beauty affect wages?
Wage Negotiation:

Worker forms a belief about his own ability
Channel – raises worker
confidence in his ability
 Confidence
How does beauty affect wages?
Wage Negotiation:

Employer forms a belief about worker’s
ability
Stereotype Channel – raises employer
belief about worker ability directly (because
beauty is good)
 Oral Stereotype Channel – raises employer
belief indirectly during verbal interaction
through characteristics correlated with beauty
 Visual
Wage Negotiation
Worker’s belief about his
ability
Employer’s belief about
worker’s ability
Visual Interaction
Worker’s Confidence
Oral Interaction
Wage Negotiation
Worker’s belief about his
ability
Employer’s belief about
worker’s ability
Visual Interaction
Worker’s Confidence
Oral Interaction
Wage Negotiation
Worker’s belief about his
ability
Employer’s belief about
worker’s ability
Visual Interaction
Worker’s Confidence
Oral Interaction
Wage Negotiation
Worker’s belief about his
ability
Employer’s belief about
worker’s ability
Visual Interaction
Worker’s Confidence
Oral Interaction
Final Wage
Wage Negotiation
Worker’s belief about his
ability
Beauty signals
higher ability
Employer’s belief about
worker’s ability
Visual Stereotype
(directly)
Confidence Channel –
Beautiful more likely to be
confident
Oral Stereotype
(indirectly)
Final Wage
Experimental Design
“Job Description”



Employees were “hired” to perform a skilled task
of solving Yahoo! mazes for 15 minutes.
Before interviews they had a chance to solve a
practice maze of level “Easy”
During employment period they solved mazes one
level of difficulty higher
Experimental Design
Experimental Design
Experimental Design
Why Mazes?


We would not expect beauty to be directly
productive for this task. We can therefore focus on
worker/employer interaction alone
The task requires true skill. Gneezy, Niederle and
Rustichini (2003) have shown that there is
considerable variation in skill and speed of
learning for performing this task.
Experimental Design


Neither worker nor employer have well defined
focal points for predicting future performance if
presented with the practice time.
There is a significant amount of learning possible
in performing this task during the allocated 15 min
time period.

This allows for overconfidence effects and also for
true persuasion: a confident worker might truly believe
that she can solve many mazes even though she did
poorly in the practice round, and possibly can
convince the employer to believe her.
Experimental Design

Playing the main game at the next level of difficulty
opens room for additional uncertainty and thus
further over-confidence and persuasion effects.
Experimental Design
Each worker enters her “resume”
information:


College major, name of the degree granting
institution, matriculation year, hobbies, team
sports, age, gender, dream job, the number of jobs
previously held, the number of job interviews they
have participated in, and whether they have
internet connection at home (income proxy)
Time it took to complete the practice round
Experimental Design
In addition,


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Each worker is asked to form an estimate of how
many mazes she will be able to solve in 15
minutes
This information is only provided for the
experimenter and is not revealed to the employers.
Compensation is structured in an incentive
compatible manner to induce workers to truthfully
reveal their estimates.
Workers and employers complete a control
questionnaire to make sure they understand how
payments are calculated.
Experimental Design
Each worker participates in 5 treatments in
random order:

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Treatment A: Resume only without a facial
photograph.
Treatment B: Resume and facial photograph.
Treatment C: Resume without a photograph and
oral telephone communication.
Treatment D: Resume with a facial photograph
and oral telephone communication.
Treatment E: Resume with a facial photograph and
face-to-face interview.
Wage Negotiation
Worker’s belief about his
ability
Employer’s belief about
worker’s ability
Visual Stereotype
Treatments B, D, E
Confidence Channel –
Treatments C, D, E
Oral Stereotype
Treatments C, D, E
Final Wage
Timing:
Stage 1:
Workers enter
resume info
Workers solve
practice maze
Workers form their
confidence estimates
Timing:
Stage 2:
Workers interact
with employers
(C,D,E) or
employers
review worker’s
files (A, B); each
employer sees 5
candidates
Employers find out
whether their
productivity
estimate will be
used to pay
workers (80% of
the time)
Employers set their
estimates of
workers’ productivity
(“wages”) after
having seen all 5
candidates
Timing:
Why is employer wage used
only in 80% of the cases?
Stage 2:
Workers interact
with employers
(C,D,E) or
employers
review worker’s
files (A, B); each
employer sees 5
candidates
Employers find out
whether their
productivity
estimate will be
used to pay
workers (80% of
the time)
Employers set their
estimates of
workers’ productivity
(“wages”) after
having seen all 5
candidates
Timing:
Why is employer wage used
only in 80% of the cases?
Stage 2:
Workers interact
with employers
(C,D,E) or
employers
review worker’s
files (A, B); each
employer sees 5
candidates
Employers find out
whether their
productivity
estimate will be
used to pay
workers (80% of
the time)
Employers set their
estimates of
workers’ productivity
(“wages”) after
having seen all 5
candidates
To distinguish between:
(a) Employers choosing to transfer some money to workers
independent of their skill and (b) Compensation for perceived
skill
Experimental Design




Use this to check for direct taste-based discrimination
Also tests whether subjects are playing a larger supergame.
Note that all workers are “hired”, but get different
compensation.
“Wages” are paid by the experimenter. The job of
employers is to determine productivity.
Timing:
Stage 3:
Workers participate
in 15 min work period
Compensation is
determined for workers
and employers
Compensation of Workers:
Workers get a
piece rate of 100
points for each
maze they solve
during 15 min
work period
+
Workers get a “wage”
determined by each
employer. (used 80% of
the time; 20% of the
time the wage is set by
the experimenter)
all wages are paid by
the experimenter.
+
40 points are subtracted
from worker’s compensation
for each maze they
mispredict (above or below
their estimate).
This provides a marginal
incentive of 60 points per
maze to continue solving
mazes even after they hit
their estimate.
Compensation of Workers:
How do employers set wages?
Workers get a
piece rate of 100
points for each
maze they solve
during 15 min
work period
+
Workers get a “wage”
determined by each
employer. (used 80% of
the time; 20% of the
time the wage is set by
the experimenter)
all wages are paid by
the experimenter.
+
40 points are subtracted
from worker’s compensation
for each maze they
mispredict (above or below
their estimate).
This provides a marginal
incentive of 60 points per
maze to continue solving
mazes even after they hit
their estimate.
During the interview and resume review they form an estimate of how
many mazes each candidate can solve. This number times 100 points
becomes employee wage in 80% of cases; experimenter sets an average
wage.
Compensation of Employers:
Employers get a
fixed fee of 4000
points
+
Regardless of whether employer wage is
used or not 40 points are subtracted from
employer’s compensation for each maze
they mispredict (above or below their
estimate for each employee).
Experimental Design
Beauty Ratings:

By a panel of 50 independent evaluators on a scale
from 1 to 5
1 - homely, far below average in attractiveness; 2 plain, below average in attractiveness; 3 - of
average beauty; 4 - above-average; and 5 strikingly handsome or beautiful.
 Standard passport-type photographs were presented
to evaluators in random order via a website.

Subjects

Undergraduate and masters students from
Tucuman University, Argentina
 instructions in Spanish delivered orally and via
a computer
 subjects completed a control questionnaire to
ensure understanding of compensation
schemes
 33 sessions of 5 workers and 5 employers each
worker being reviewed by 5 employers (825
observations)
Subjects

Subjects were paid 12 pesos for participation +
additional earnings described above
 Average earnings 25 pesos for an experiment
of up to one and a half hours in length.
 Made sure subjects did not know each other
prior to the experiment.
Employee Subject Pool Description

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Subjects from 3 university campuses, 85%
from UNT
56% male
Average age 22.9; more graduate students
Majors: business and economics (21%);
science, medicine, and information technology
(46%); humanities and arts (33%)
51% have internet access at home (80% from
private; 41% from public)
Employee Subject Pool Description

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61% participated in team sports
43% had no previous work experience (out of
them 63% never interviewed for a job)
Those with work experience worked in
education, information technology, retail sales,
business, public sector, arts, food production
and service, and industry.
Intensity of interpersonal interaction on a job
Hobbies in computers, recreation (listening to
music, reading), creative tasks (writing,
drawing, composing music), sports
Average Performance:
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The mean number of mazes solved was 9.5 (10.9
for men; 7.8 for women)
The average maze during 15 minute trial took 94
sec; the average practice time was 127 sec
Subjects systematically underestimated their own
productivity by 24% on average.
Employers underestimated workers’ productivity
in a similar manner (20% on average).
Variable Transformations:
Confidence Measure:
 Estimated number of rounds (ln)
Ability Measure:
 Actual number of rounds (ln)
Prediction based on extrapolation from the
practice round:
 Ln (15*60/Practice)
Becker Discrimination
 SETWAGE=1 if employer estimate was used to
determine worker’s wage
Beauty Measure
Detrend beauty ratings to get rid of
measurement error:
 Measurement error arises because each rater
has a distinct definition of “baseline” beauty
 Formally, for each rater we take her average
beauty rating and subtract it from each raw
rating for subject in order to define the
centered rating
 The measure BEAUTY for subject is then
defined as the mean over all raters’ centered
ratings.
Procedure for Data Analysis:
1. Relationship between beauty and ability
 2. Relationship between beauty and
confidence
 3. Wage regressions without controlling for
confidence
 4. Wage regressions with a control for
confidence
 5. Pooled Regression

Beauty and Ability

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After controlling for all labor market
characteristics, find no evidence of a relationship
between actual ability during 15 min work period
and physical attractiveness.
There is also no evidence of a relationship
between projected ability using practice time and
physical attractiveness.
Therefore, a beauty premium in this setting is NOT a
(maze-solving) skill premium!
Confidence and Beauty
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There is a strongly significant (at the 1 percent level) effect of
physical attractiveness on confidence. Raising beauty by one
standard deviation increases confidence about 13%.
This effect is very large: if we define a ‘beautiful’ person to be
one standard deviation above the mean and a ‘plain’ person to
be one standard deviation below then the plain subject is about
26% less confident than the beautiful subject.
Interestingly, there is no difference in confidence between men
and women in this setting (once we control for actual ability).
Wage regressions (w/o confidence)

Regressions of wages on workers’ characteristics
including BEAUTY but excluding
CONFIDENCE. (Separate regression for each
treatment).
 First of all, there is a beauty premium in our
experiment in all treatments except A ranging
from 12 to 17% with CV controls.
 There is no evidence for direct taste-based
Becker-type discrimination
Wage Regressions (w/ Confidence Controls)

Same as regressions before but with an additional control
for confidence.
 As expected, confident subjects only do better in
treatments with oral communication.
 A 1% increase in confidence raises wages by about
0.18 to 0.33%.
 The beauty effects in treatments C to E are smaller by
about 2 to 4%. This decline is consistent because we
know that one standard deviation in beauty increases
confidence by about 13%.
Confidence channel
Treatment
Oral
Confidence
Channel
2.4%
Gross Beauty
Premium
12.8%
Oral+Visual
2.1%
12.3%
FTF
4.0%
16.6%
Wage increases for one standard deviation increase in beauty
Pooled Regression:
Visual Stereotype Channel - 7.2% wage
gain for each standard deviation in beauty
 Oral Stereotype Channel - 10.4% wage
gain for each standard deviation in beauty
 Confidence Channel - raises wage by .3%
for each 1% increase in confidence. This
translates into 3.6% increase in wage for
one standard deviation increase in beauty

Wage Negotiation
Worker’s belief about his
ability
Confidence Channel
3.6% increase in wage for
1 standard deviation
increase in beauty
Employer’s belief about
worker’s ability
7.2% gain for 1
standard deviation
increase in beauty
Visual Stereotype
10.4% gain for 1
standard deviation
increase in beauty
Oral Stereotype
Policy Implications
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Job interviews are currently the most common method of
employee selection.
Direct discrimination can be minimized by reducing face-toface interactions and relying on telephone interviews instead
or hard data like test scores.
For example, Goldin and Rouse (2000) have found that blind
auditions reduce gender discrimination in hiring women
musicians.
We find that blind interview procedures (like telephone
interviews) can reduce beauty premium by 40% (due to
elimination of direct stereotype effects).
Elimination of oral interaction can eliminate beauty premium
completely. Too drastic…
What We Don’t Know
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Is taste based discrimination present in repeated
relationships?
Do students care more about physical attractiveness
than older human resource officers?
Are employers over-interpreting visual and audio
stimuli because those can be productive in most
other environments?
Can we design an experiment in which selfconfidence of workers is payoff-relevant for
employers?
Is underperformance of females in part due to
different responses to goal-setting?