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

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? 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:

8. How does beauty affect wages? Employer forms a belief about worker’s ability  Direct Stereotype Channel – raises employer belief about worker ability directly (because beauty is good)  Indirect Stereotype Channel – raises employer belief indirectly during verbal interaction through characteristics correlated with beauty Wage Negotiation:

9. How does beauty affect wages? Worker forms a belief about his own ability  Confidence Channel – raises worker confidence in his ability Employer decides on the wage based on his prior and worker’s confidence  Persuasion Channel – raises wage by increasing weight on worker’s confidence Wage Negotiation:

11. 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”

12. Experimental Design

14. 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?

15. 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.

16. 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.

17. 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:

19. 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,

20. 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:

21. Treatments C, D, E (especially C - speech only!) Treatments B, D, E (especially B - picture only!) Only matters in treatments C, D, E Does interaction of beauty and confidence matter in Treatments C, D, E?

22. Experimental Design Workers enter their resume information and confidence estimates. Workers interact with employers (treatments C, D, E) or employers see workers’ files (treatments A, B). Employers find out whether their estimates of worker productivity will be used to compensate employees (80% of the time). Timing:

23. Experimental Design Employers decide on their estimates of worker productivity and submit their choices to the experimenter after they have been the audience to all 5 candidates. Note, that all workers are “hired”, but get different compensation. Workers participate in 15 minute work period. Total compensation is determined for workers and employers.

24. Experimental Design  To distinguish between: Employers choosing to transfer some money to workers independent of their skill and Compensation for perceived skill Use this to check for direct taste-based discrimination. Why is employer wage used only in 80% of the cases?

25. Experimental Design  Workers get a piece rate of 100 points for each maze they solve during the work period.  Workers get a wage determined by each employer. This wage is 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:

26. Experimental Design  Employers get a fixed fee of 4000 points.  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.  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). Compensation of Employers:

27. 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:

28. 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)

29. 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.

30. 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)

31. 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

32. 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).

33. Variable Transformations: Estimated number of rounds (ln) Confidence Measure: Actual number of rounds (ln) Ability Measure: 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

34. 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 rating. Detrend beauty ratings to get rid of measurement error:

35. 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. Persuasion Effect 6. Pooled Regression

36. Beauty and Ability Regression of actual ability during 15 min work period measured by LNACTUAL on age, sex, family wealth (approximated by INTERNET), and physical attractiveness (with and w/o decision variables).  MALE is significant – men are 30% better at solving mazes in 15 minutes (can be also seen from summary statistics – 10.9 vs 7.8)  Beauty is NOT statistically significant!

38. Beauty is NOT statistically significant

39. Practice time doesn’t fully predict actual ability

40. Beauty is NOT statistically significant Practice time doesn’t fully predict actual ability Men have better skills

41. Beauty and Ability Regression of predicted ability extrapolated from the practice round PREDICT on age, sex, family wealth, and physical attractiveness  Again men do better in the practice round  In addition older subjects do better (with decreasing returns to age)  Beauty is not significant Both regressions show that there is no relationship between beauty and ability! Note that we run two specifications: with and without major and hobby choices.

43. Beauty is NOT statistically significant

44. Men are more skilled

45. Beauty is NOT statistically significant Men are more skilled Older subjects do better, but with decreasing returns

46. Confidence Regression of confidence on beauty, true ability and worker characteristics.

48. Actual ability weakly raises confidence (but coef. is not 1)

49. Rely on practice performance; more so than is justified based on ability regression before

50. Actual ability weakly raises confidence (but coef. is not 1) Rely on practice performance Men are not more confident if we control for actual ability

51. Raising Beauty by 1 standard deviation raises confidence by 13%

52. Beauty raises confidence equally for men and women.

53. Confidence Regression of confidence on beauty, true ability and worker characteristics.  Actual ability weakly raises confidence. A 1% increase in actual ability increases confidence by about 0.15%.  Note that if confidence were ‘truthful’ and based only on self- knowledge about true abilities then we would expect a coefficient close to 1 on LNACTUAL and all other variables to be not significant.  The biggest boost of confidence is performance in the practice round: a 1% increase in predicted performance raises confidence by at least.4%.

54. Confidence  Male subjects are not more confident once we control for their higher average ability in solving mazes.  Physical attractiveness raises confidence equally for men and women since coefficient on interaction term beauty*male is not significant.  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.

55. Confidence  Confidence is by no means a truthful reflection of actual ability. The large coefficient on PREDICT suggests that subjects have a hard time evaluating their own ability and tend to over-extrapolate from their practice performance.  Interestingly, physical attractiveness has a very large confidence- enhancing effect while gender has none. Although men in our sample are more confident, they actually perform better at the task.

56. Wage Regressions (w/o Confidence Controls) Fixed effects regressions of wages on workers’ characteristics including BEAUTY but excluding CONFIDENCE. (Separate regression for each treatment). y is wage of worker j set by employer i is employer fixed effect B is worker jth beauty S is SETWAGE=1 if employer determines worker’s wage directly X vector of CV characteristics

57. Beauty Premium Dep. Var: LNWage (w/o CV Controls)

58. Beauty Premium Dep. Var: LNWage (w/ CV Controls)

59. Beauty Premium Dep. Var: LNWage (w/o CV Controls) No Evidence for direct taste-based discrimination Practice Performance Matters a lot!

60. Beauty Premium Dep. Var: LNWage (w/ CV Controls) Not much evidence for direct taste-based discrimination Practice Performance Matters a lot!

61. Wage Regressions (w/o Confidence Controls) 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 9.4 to 12.7% without CV controls and from 12 to 17% with CV controls.  SETWAGE*BEAUTY is not significant – there is no evidence for direct taste-based Becker-type discrimination  1% increase in practice performance increases wages by.4% (from coefficient on PREDICT)  MALE is significant in treatments C and D only.

62. Wage Regressions (w/ Confidence Controls) Fixed effects regressions of wages on workers’ characteristics including BEAUTY and CONFIDENCE. (Separate regression for each treatment). C is worker j’s confidence

63. Confidence matters only in treatments with verbal interaction Dep. Var: LNWage (w/ CV Controls)

64. Confidence matters only in treatments with verbal interaction Beauty Premium declines in those treatments Dep. Var: LNWage (w/ CV Controls)

65. Confidence matters only in treatments with verbal interaction Beauty Premium declines in those treatments As before, actual performance doesn’t matter and practice performance does. Dep. Var: LNWage (w/ CV Controls)

66. Wage Regressions (w/ Confidence Controls) Same as regressions before but with an additional control for confidence.  As expected, confident subjects do better in treatments with verbal interaction.  A 1% increase in confidence raises wages by about 0.18 to 0.33%.  The beauty effects in treatments B 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%.

67. Wage Regression w/ Confidence Controls The coefficient on MALE is the same as before LNACTUAL is still not significant SETWAGE*LNESTIMATED and SETWAGE*BEAUTY are also not significant

68. Confidence channel

69. Other Covariates: One percent increase in practice performance raises wages by about.4 percent in treatments A and B and.3 percent in treatments C, D, and E => Employers put less emphasis on practice performance when they can interact verbally with the worker Actual Ability is NOT statistically significant in any treatment. Gender effects in treatments C and D only Age effects in treatments D and E only Team sports and internet are not significant.

70. Testing for Persuasion Channel Fixed effects regressions of wages on workers’ characteristics including BEAUTY, CONFIDENCE, and BEAUTY*CONFIDENCE. (Separate regression for each treatment).

71. Testing for Persuasion Channel: Coefficient on the interaction term is not significant => no evidence for persuasion channel

72. Pooled Regression: AUDIO =1 if worker and employer can talk to each other (treatments C, D, and E) VISUAL=1 if employer can see worker’s picture (treatments B, D, and E) FTF=1 if there is face-to-face communication (treatment E) Interact PREDICT, BEAUTY and LNESTIMATED with the dummies above and include CV controls.

74. Direct Stereotype

75. Indirect Stereotype

76. Direct Stereotype Indirect Stereotype Confidence

77. Pooled Regression: Direct Stereotype Channel identified by coefficient on BEAUTY*VISUAL (7.2% wage gain for each standard deviation in beauty) Indirect Stereotype Channel is captured by coefficient on BEAUTY*AUDIO (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

78. 10.4% gain for 1 standard deviation increase in beauty 7.2% gain for 1 standard deviation increase in beauty 3.6% increase in wage for 1 standard deviation increase in beauty No Evidence

79. 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 verbal interaction can eliminate beauty premium completely. Too drastic…

80. 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?