1. Why Beauty Matters An Experimental Investigation
Markus Mobius (Harvard University)
Tanya Rosenblat (Wesleyan University)
November 2004

2. 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)!

3. 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)

4. 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

5. 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

6. 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

7. 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

8. How does beauty affect wages?
Wage Negotiation:
Worker forms a belief about his own ability
Confidence Channel – raises worker confidence in his ability

9. How does beauty affect wages?
Wage Negotiation:
Employer forms a belief about worker’s ability
Visual 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

10. Wage Negotiation Worker’s belief about his ability
Employer’s belief about worker’s ability
Visual Interaction
Oral Interaction
Worker’s Confidence

11. Wage Negotiation Worker’s belief about his ability
Employer’s belief about worker’s ability
Visual Interaction
Oral Interaction
Worker’s Confidence

12. Wage Negotiation Worker’s belief about his ability
Employer’s belief about worker’s ability
Visual Interaction
Oral Interaction
Worker’s Confidence

13. Wage Negotiation Worker’s belief about his ability
Employer’s belief about worker’s ability
Visual Interaction
Oral Interaction
Worker’s Confidence
Final Wage

14. Beauty signals higher ability
Wage Negotiation
Worker’s belief about his ability
Employer’s belief about worker’s ability
Beauty signals higher ability
Visual Stereotype
(directly)
Oral Stereotype
(indirectly)
Confidence Channel –
Beautiful more likely to be confident
Final Wage

15. 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

16. Experimental Design

17. Experimental Design

18. 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.

19. 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.

20. 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.

21. 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

23. Experimental Design In addition,
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.

24. Experimental Design
Each worker participates in 5 treatments in random order:
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.

25. Wage Negotiation Worker’s belief about his ability
Employer’s belief about worker’s ability
Visual Stereotype
Treatments B, D, E
Oral Stereotype
Confidence Channel –
Treatments C, D, E
Treatments C, D, E
Final Wage

26. Timing: Stage 1: Workers enter resume info Workers solve practice maze
Workers form their confidence estimates

27. 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

28. Timing: Stage 2: Why is employer wage used only in 80% of the cases?
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

29. Timing: Stage 2: To distinguish between:
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

30. 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.

31. Timing: Stage 3: Workers participate in 15 min work period
Compensation is determined for workers and employers

32. Compensation of Workers:
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.
Workers get a piece rate of 100 points for each maze they solve during 15 min work period + +

33. Compensation of Workers:
How do employers set wages?
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.
Workers get a piece rate of 100 points for each maze they solve during 15 min work period + +
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.

34. Compensation of Employers:
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).
Employers get a fixed fee of 4000 points +

35. 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.

36. 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)

37. 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.

38. 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)

39. 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

40. 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).

41. 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

42. 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.

43. 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

44. 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!

45. 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).

46. 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

47. 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%.

48. 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

49. Pooled Regression:
Visual Stereotype Channel - 7.2% wage gain for each standard deviation in beauty
Oral Stereotype Channel % 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

50. Wage Negotiation Worker’s belief about his ability
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
Confidence Channel
3.6% increase in wage for 1 standard deviation increase in beauty

51. 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…

52. 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?