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

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

I. How are beautiful people perceived by others? I. How are beautiful people perceived by others?

Psychology LiteratureII. To what extent is this stereotype true?II. To what extent is this stereotype true?

Kernel of Truth HypothesisAttractive 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?

“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

Decompose the effects of beauty:Decompose the effects of beauty:

How does beauty affect wages?

Worker forms a belief about his own abilityConfidence Channel – raises worker

confidence in his ability

Wage Negotiation:Wage Negotiation:

How does beauty affect wages?

Employer forms a belief about worker’s abilityVisual 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

Wage Negotiation:Wage Negotiation:

Wage Negotiation

Employer’s belief about worker’s ability

Visual Interaction

Oral Interaction

Worker’s belief about his ability

Worker’s Confidence

Wage Negotiation

Employer’s belief about worker’s ability

Visual Interaction

Oral Interaction

Worker’s belief about his ability

Worker’s Confidence

Wage Negotiation

Employer’s belief about worker’s ability

Visual Interaction

Oral Interaction

Worker’s belief about his ability

Worker’s Confidence

Wage Negotiation

Employer’s belief about worker’s ability

Visual Interaction

Oral Interaction

Worker’s belief about his ability

Worker’s Confidence

Final Wage

Wage Negotiation

Employer’s belief about worker’s ability

Visual Stereotype

(directly)

Oral Stereotype

(indirectly)

Worker’s belief about his ability

Confidence Channel –

Beautiful more likely to be confident

Final Wage

Beauty signals higher ability

Experimental Design

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

“Job Description”“Job Description”

Experimental Design

Experimental Design

Experimental Design

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.

Why Mazes?Why Mazes?

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

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

Each worker enters her “resume” information:Each worker enters her “resume” information:

Experimental Design

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.

In addition, In addition,

Experimental Design

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.

Each worker participates in 5 treatments in random order:Each worker participates in 5 treatments in random order:

Wage Negotiation

Employer’s belief about worker’s ability

Visual Stereotype

Oral Stereotype

Worker’s belief about his ability

Confidence Channel –

Final Wage

Treatments C, D, E

Treatments B, D, E

Treatments C, D, E

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:

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

Why is employer wage used only in 80% of the cases?Why is employer wage used only in 80% of the cases?

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

Why is employer wage used only in 80% of the cases?Why is employer wage used only in 80% of the cases?

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:

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.

+ +

How do employers set wages?How do employers set wages?

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

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.

Beauty Ratings:Beauty Ratings:

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 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 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:

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:

Estimated number of rounds (ln)

Confidence Measure:Confidence Measure:

Actual number of rounds (ln)

Ability Measure:Ability Measure:

Prediction based on extrapolation from the practice round:Prediction based on extrapolation from the practice round: Ln (15*60/Practice)

Becker DiscriminationBecker Discrimination

SETWAGE=1 if employer estimate was used to determine worker’s wage

Beauty Measure

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.

Detrend beauty ratings to get rid of measurement error:Detrend beauty ratings to get rid of measurement error:

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 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

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 Confidence

Channel

Gross Beauty Premium

Oral 2.4% 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

Employer’s belief about worker’s ability

Visual Stereotype

Oral Stereotype

Worker’s belief about his ability

Confidence Channel

3.6% increase in wage for 1 standard deviation increase in beauty

10.4% gain for 1 standard deviation increase in beauty

7.2% gain for 1 standard deviation increase in beauty

Policy Implications Job interviews are currently the most common method of

employee selection. Direct discrimination can be minimized by reducing face-to-

face 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 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 self-confidence of workers is payoff-relevant for employers?

Is underperformance of females in part due to different responses to goal-setting?