Download - Looks and Labor - Do Attractive People Work More?I · 2013-01-29 · Looks and Labor - Do Attractive People Work More?I Markus Gehrsitza,b, aThe City University of New York, 365 5th

Looks and Labor - Do Attractive People Work More?I

Markus Gehrsitza,b,∗

aThe City University of New York, 365 5th Avenue, New York, NY, 10016, USAbCologne Graduate School in Management, Economics and Social Sciences, Richard-Strauss-Str. 2,

50931 Cologne, Germany

Abstract

Using the German General Social Survey (ALLBUS) 2008, I investigate how looks affectan individual’s labor supply decision. My results are, by and large, in line with pre-dictions derived from the neoclassical model of labor supply. Applying regular probit,bivariate probit and craggit regression models, I find that good looks go hand in handwith higher employment probabilities and more hours of market work. Furthermore,physical attractiveness is positively associated with spousal income and spousal employ-ment. Hence, beauty appears to affect labor supply decisions both directly and throughthe marriage market.

Keywords: Beauty, Labor Force Participation, Working Hours, Gender Differences

1. Introduction

Social and evolutionary psychologists have long known that beauty matters. Theyargue that physical appearance is one of the most important determinants of humanmating preferences (Buss, 1989). Economists have also discovered the subject and shedsome light on the economics of beauty. They have found that physically attractivepersons are perceived as more trustworthy (Wilson and Eckel, 2006) and are grantedloans on better terms (Ravina, 2008). They are also more likely to receive an invitationto a job interview (Rooth, 2009), are regarded as more competent by employers (Mobiusand Rosenblat, 2006), and consequently enjoy higher incomes (Judge et al., 2009, amongothers). All their relative economic advantages appear to culminate in a relatively higherlife satisfaction (Hamermesh and Abrevaya, 2011). The homely, on the other hand, lackpreferential treatment. They have lower incomes and tend to marry spouses with lesseducational attainment if they find a mate in the first place (Harper, 2000).

In the labor market, it has been shown that physically attractive men and women onaverage earn higher wages in the United States (Hamermesh and Biddle, 1994), Australia

II am deeply indebted to Dan S. Hamermesh who provided the impetus for this paper and gave greatadvice throughout the writing process. I would also like to thank David A. Jaeger for valuable commentsand suggestions. I wish to thank Philipp Doerrenberg, Christian Weyand, and the participants of theCGS dissertation seminar for comments on an earlier version of this paper.

∗Corresponding authorEmail address: [email protected] or [email protected] (Markus Gehrsitz)

Preprint submitted to Elsevier July 24, 2012

(Leigh and Borland, 2007), and the UK (Harper, 2000) among other countries. Two ma-jor explanations have been provided for this wage premium. First, employers mightsimply have a taste for beautiful workers (Becker, 1971). That is, an employer might de-rive more utility from a good-looking employee than from an equally productive but plainemployee. Such taste-based employer discrimination will result in a wage gap betweenattractive and unattractive workers. In a perfectly competitive market such differencescannot prevail. Companies employing homely, but equally productive workers should beable to squeeze rivals that are staffed with attractive employees out of the market. Hence,it is likely that beauty has a productivity-enhancing effect that justifies pay differentials.This is obvious in occupations, such as modeling, where beauty is an integral part ofthe product or service itself. If customers prefer to interact with physically attractiveemployees, this also explains part of the earnings differential. For instance, a good-looking salesperson might be more convincing. The fact that good-looking professorsreceive higher evaluation ratings by students could also reflect customer discrimination(Hamermesh and Parker, 2005). In a similar vein, coworkers might prefer to interactwith pretty colleagues and handsome supervisors. For example, Pfann et al. (2000) findthat revenues of Dutch advertising firms rise commensurate with the attractiveness oftheir CEOs.

Discrimination, be it statistical, taste-based, or based on customers’ and coworkers’preferences, is a phenomenon that predominantly affects labor demand. There has beenremarkably little research, however, with respect to the role of beauty in labor supplydecisions. This paper intends to fill this gap in the literature. Economic theory predictsthat physical attractiveness is associated with higher employment rates, that attractiveworkers supply more hours of work to the labor market, and that beauty should affectmen and women, and married and single individuals, in a different manner. My empiricalanalysis is based on the German General Social Survey (ALLBUS) of 2008 (for detailssee Wasmer et al., 2010). The survey provides the first nationally- representative dataset that contains beauty measures for all respondents since the 1981 Canadian Qualityof Life study. Using this unique data source, I find that physical attractiveness is indeedassociated with higher employment probabilities and a larger labor supply, especially formen. Beauty also appears to affect labor supply via the marriage market. Husbands ofphysically attractive women tend to be more likely to work fulltime jobs, and earn higherincomes. Spousal income is generally negatively associated with women’s labor supply.Yet, this negative income effect is offset by better employment prospects for physicallyattractive women. Overall, beauty is positively associated with labor supply in manydifferent ways.

The structure of this paper is as follows. Section 2 discusses whether universal beautystandards exist, what makes a beautiful person and how beauty is measured. Section 3derives theoretical predictions about the relationship between beauty and labor supply.After giving a brief overview on the dataset and the variables at hand, I will empiricallytest these predictions in Section 4. Section 5 is concerned with the robustness of theresults while the concluding Section 6 discusses implications of my findings.

2. Beauty Standards and Measures

Unless there are standards of beauty that are commonly agreed upon, any analysis ofthe economic effects of beauty is futile. Yet, as Hamermesh (2011) points out, the notion

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that beauty is “in the eye of the beholder” turns out to be both widespread and false.Admittedly, beauty standards differ across cultures. While Western celebrity magazinesglorify female slimness, force-feeding of young girls is still common in Polynesia and someArab countries (Harter, 2004). Within the Western hemisphere, beauty standards havechanged dramatically over time. The voluptuous beauties that were ever-present on thetheater stages of the 19th century would probably not stand a chance of landing the leadin most of today’s Hollywood productions (Stearns, 2002). However, beauty standardschange very slowly over time. Catherine Tramell, the seductive serial killer played bySharon Stone in the 1992 movie “Basic Instinct” is probably still deemed attractive today.

Table 1 - Distribution of Ratings...

...by Rating Method:Beginning of Interview End of Interview Self-Assessed

Above Average Looks 862 34.04% 921 36.42% 591 23.37%Average Looks 1382 54.65% 1348 53.30% 1514 59.87%Below Average Looks 285 11.27% 260 10.28% 424 16.77%

...by Sex of Interviewer:Male Interviewer Female Interviewer

Above Average Looks 466 31.59% 396 37.57%Average Looks 849 57.56% 533 50.57%Below Average Looks 160 10.85% 125 11.86%

...by Sex of Respondent:Male Respondent Female Respondent


...by Age of Respondent:Respondent < 40 Years Respondent ≥ 40 Years


All ratings had originally been submitted on a scale of 1 (=unattractive) to 11 (=attractive). Theaverage rating at the start of the interview is 7.71 with a standard deviation of 1.89. The categoryAbove Average Looks comprises all respondents with a beauty rating of 9 or higher. The categoryBelow Average Looks comprises all respondents with a beauty rating of 5 or lower. Consequently,the category Average Looks contains all respondents with a rating larger than 5 and smaller than 9.

Data support this claim. Patzer (1985) is among the first to find that individualstend to have similar judgments about what makes a beautiful person. Ritts et al. (1992)support this hypothesis in their analysis of student perception by teachers. Hamermeshand Biddle (1994) compare ratings of respondents’ attractiveness in different survey

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waves by different interviewers, and find that the interviewers mostly agree with eachother. In a different study, four different observers had to rate law students’ pictures onan attractiveness scale of 1 to 5 (Biddle and Hamermesh, 1998). Again, the interviewerratings were highly correlated. In their meta-analysis Langlois et al. (2000) concludethat raters agree on a universal beauty standard, not just within cultures, but alsobetween cultures. Although observers do not always exactly agree, there is rarely largedisagreement about looks. That is, interviewers have the same standards and it is mostlydifferences in looks that lead to differences in ratings, rather than differences across theraters.

This has important implications for this study. When standards of attractiveness aremore or less universal, and persistent over a sufficient time horizon, they can also affectlabor supply decisions. Hence, the mere existence of such standards is a prerequisite forphysical appearance to influence labor supply decisions. Of course, the context in whichbeauty is assessed, affects how beauty is rated by different beholders. In the ALLBUS2008, interviewers were asked to rate participants twice on a scale from 1 (unattractive)to 11 (attractive). I deem those who received a rating of 9 or higher to have above-average looks and categorize those with a beauty rating of 5 or lower as below-averagelooking.1 It is important to note that the interviewers were not asked to focus on facialattractiveness, but took the overall physical appearance into account. They had to rendertheir first rating based on their first impression, that is before they had even started theactual interview. At the end of the session, the interviewers had to submit another ratingwhich tended to be slightly more flattering (see Table 1).

One reason for this pattern is the “halo effect”. That is, the perception of a respon-dent’s attractiveness might be influenced by other impressions the interviewer gains overthe course of an interview. For example, nice and polite participants might be rewardeda higher score at the end of an interview, while rude respondents are subject to penalties.In order to account for endogeneity bias arising from the halo effect, I use the initiallyassigned score for all analyses in this paper. Generally, the initial and second assessmentare highly correlated with a correlation coefficient of 0.838. Furthermore, people wereasked to rate their own looks during the course of the interview. On average, they weremuch harder on themselves than a third person, namely the interviewer (see Table 1). Ialso find differences in ratings depending on the interviewer’s and the respondent’s sexand age. Female interviewers tend to give slightly more generous ratings. While thisis consistent with Hamermesh (2011), I cannot confirm his finding that women’s looksare rated more extreme. Instead I find that women are attributed above-average looksmore frequently than men. Women also seem to receive low ratings less often, although asimple t-test provides only weak evidence for this hypothesis (p-value: 0.037). Generally,women appear to be rated more favorably than men. Moreover, I find evidence for theimpact of the ravages of time on beauty. Respondents who are more than 40 years old,are significantly more likely to be attributed below-average looks and significantly lesslikely to be categorized as above-average looking (see Table 1). Youth and beauty areclearly positively correlated.

Overall, it is safe to assume that there is a universal beauty standard that mostinterviewers agree on. These standards are very stable, even over long periods of time.

1The average rating is 7.71 with a standard deviation of 1.89 (see Table 1). Thus, a person whoserating comes under more than 1 standard deviation from the mean is categorized as unattractive.

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Variations in beauty assessment, for instance in terms of interviewer or respondent genderand age, can easily be accounted for by stratification and including appropriate controlvariables.

3. Theoretical Predictions

In a standard neoclassical model of labor supply (e.g. Bryant and Zick, 2006) individ-uals divide their time between market work, household work and leisure depending ontheir marginal productivity and the real hourly wage. Due to productivity or discrimi-nation, an attractive individual will be offered higher wages than an otherwise identicalpeer. This has been repeatedly shown (Fletcher, 2009, among others) and also holds truefor the data set at hand.2 As with any conventional wage increase, this will induce threeeffects. First, there will be a production-substitution effect. Holding the total workingtime constant, an increase in the wage rate provides an incentive to substitute housework for market work. This, of course, assumes that beauty does not affect the non-market productivity. Second, leisure becomes relatively more expensive. Therefore, theindividual is induced to substitute leisure time for work time in order to buy additionalgoods. This is the consumption-substitution effect. Both substitution effects lead to anincrease in market labor supply. Third, a wage raise for physically attractive individu-als will also raise their income. Since both leisure and consumption goods, are normalgoods, the demand for either will increase. Consequently, the income effect counteractsthe two substitution effects. Only if the substitution effects dominate the income effect,physically attractive people will supply more labor than unattractive individuals.

So, theoretically, higher wage rates, induced by an attractive physical appearance,can affect labor supply either way. Yet, empirical studies tend to find positive wageelasticities for men and even more for women (Heckman and Killingsworth, 1986). As aresult, I expect attractive individuals to have higher employment rates and - conditionalon employment - to supply more work hours. I also expect the effects of attractiveness andlabor supply to differ across married and single individuals, and between men and women.The literature in evolutionary psychology concurs that individuals prefer attractive matesand value good financial prospects as well as traits that signal earnings capability, suchas intelligence. The latter qualities appear to be of particular importance for femalemate selection while men attach more value to physical attractiveness (Fisman et al.,2006, Hitsch et al., 2010). Put differently, good looks can be traded for income in themarriage market. If attractive individuals are more likely to marry affluent spouses, thisinduces an additional (negative) income effect. As a result, I expect the effect of beautyon labor supply to be less pronounced for married individuals than for single individuals,and less pronounced for women than for men. In the following section, I will test thesepredictions empirically.

2An increase of two standard deviations in the beauty rating is associated with an increase in nethourly wages of about 14 percent for women in fulltime employment and about 11 percent for men.In line with Hamermesh and Biddle (1994), I find that it is particularly attractive women who arerewarded and unattractive men who are punished in terms of their hourly wage rate. The results of thewage regressions are available from the author on request and will be published in a different paper.

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Table 2 - Impact of Looks on Employment Probability: Any EmploymentWomen Men

(1) (2) (3) (4) (5) (6)

Above Average Looks 0.091** 0.015(0.039) (0.024)

Below Average Looks -0.119* -0.080(0.070) (0.051)

Attractiveness (Scale 1-11) 0.028*** 0.039*** 0.024*** 0.016**(0.009) (0.011) (0.007) (0.007)

Observations 1105 797 797 1096 733 733Interviewer-Effects Yes Yes Yes Yes Yes YesMarried Respondents Only No Yes Yes No Yes YesPartner’s Employment Status No Yes Yes No Yes YesPseudo-R-Squared 0.172 0.189 0.186 0.274 0.346 0.344

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

The dependent variable is a dummy variable taking on the value 1 if the person works positivehours and 0 otherwise. The marginal effects at the mean are displayed.I control for age and its square, city size, marital status, immigrant status, educational attain-ment, mother’s and father’s education, experience and its square, union membership, self reportedhealth, the number of children and their age. Regional dummies are also included.

4. Empirical Results

The descriptive statistics suggest that beauty matters a great deal when it comes tolabor supply decisions. Compared to their average-looking peers, physically attractiverespondents spend more hours on market labor. The difference amounts to about 10percent for men and 20 percent for women. Part of this difference is explained by thefact that physically attractive respondents are also significantly more likely to work full-time. The reverse appears to hold true for plain Germans. They are much less likelyto be in full-time employment and, on average, supply only three quarters (men) and87 percent (women) the amount of work hours that average looking individuals provide.However, these descriptive findings obviously provide no clear-cut evidence as they areunadjusted for individual characteristics. The following sections will remedy this issue.

4.1. Beauty and Employment

I run two different probit models to test whether attractiveness is in fact associatedwith a higher employment probability. In my first specification the dependent variable isa dummy variable indicating whether an individual works any positive hours in a regularweek. In my second specification, the outcome of interest is whether an individual is infull-time employment, i.e. whether an individual works 20 hours or more in a typicalweek. I stratify my analysis by respondent sex and control for a broad array of socio-economic and household-specific characteristics, such as age, educational attainment,and the number and age of children living in the household. Since health might havea confounding effect, a set of dummy variables containing information on self-reportedhealth assessments is included in all model specification. So are controls for interviewer

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Table 3 - Impact of Looks on Employment Probability: Full-Time EmploymentWomen Men

(1) (2) (3) (4) (5) (6)

Above Average Looks 0.072 0.038(0.044) (0.027)

Below Average Looks -0.051 -0.083(0.075) (0.054)

Attractiveness (Scale 1-11) 0.023** 0.029** 0.031*** 0.021***(0.010) (0.012) (0.007) (0.008)

Observations 1105 797 797 1096 733 733Interviewer-Effects Yes Yes Yes Yes Yes YesMarried Respondents Only No Yes Yes No Yes YesPartner’s Employment Status No Yes Yes No Yes YesPseudo-R-Squared 0.180 0.175 0.174 0.279 0.325 0.322


The dependent variable is a dummy variable taking on the value 1 if the person works more than20 hours in a typical week, and 0 otherwise. The marginal effects at the mean are displayed.I control for age and its square, city size, marital status, immigrant status, educational attain-ment, mother’s and father’s education, experience and its square, union membership, self re-ported health, the number of children and their age. Regional dummies are also included.

characteristics, namely interviewer age, gender and educational attainment. As for mar-ital status, I classify respondents who are living with their partner as married alongwith those who are legally married. This is appropriate given the trend in unmarriedcouples and same-sex partnerships in Germany (Blossfeld and Timm, 2003). A completelist of controls is provided below each table. My main explanatory variable is physicalattractiveness, measured on a scale from 1 (=unattractive) to 11 (=attractive). In orderto account for potential non-linearity, I also run each specification with two dummiesvariables indicating above and below-average looks respectively.

Physical attractiveness is positively associated with the likelihood of supplying at leastone hour of market labor (see Table 2). An increase in attractiveness by two standarddeviations is associated with an increase in the odds of supplying any labor of about 10percent. The effect is larger for women, in particular for married women, than for men.The specifications using dummies suggest that both very attractive and below-averagelooking persons drive these effects. A similar pattern emerges when I look at the effectof physical attractiveness on the odds of being in full-time employment (see Table 3).Here, the point estimates are slightly larger for men. A two standard deviation changein beauty rating is associated with a change of roughly 12 percent in the likelihood ofbeing in full-time employment. To put this into perspective, being a high school dropoutreduces those odds by about 18 percent, a college degree increases them by 17 percent.That is, the effect of beauty is not only statistically significant but also relatively largein size. For men who are married, the effect appears somewhat smaller.

My specification with dummies indicates that looks might affect labor supply in anon-linear manner.3 Therefore, I compare my linear model with the continuous measure

3Although the dummies in columns 3 and 6 of Table 3 are not statistically significant, I find large and

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of attractiveness with a semi-parametric, generalized additive model (GAM). Yet, alikelihood-ratio test fails to reject the null hypothesis that these models do not differ(p-value of 0.113). Therefore a linear functional form seems appropriate.

Women’s labor supply is also sensitive to looks. A difference in the rating of twostandard deviations - about 4 notches on the scale - is associated with a 8 percent higherchance of being full-time employed, for married women the effect is almost 11 percent.I cannot find any statistically significant effect for the rather small and heterogeneousgroup of women who are either divorced, widowed or were never married.

4.2. Beauty and Hours of Work

Assessing the effect of physical attractiveness on the hours of work supplied by anindividual, requires a more sophisticated approach. A considerable proportion of thesample population reported zero hours of work. Hence, a multivariate OLS regressionof hours of work on attractiveness (and controls) will lead to inconsistent estimators(Greene, 2003). The most common solution to this problem is using a tobit model asproposed by Tobin (1958). However, one key limitation of the tobit models is that itrequires all variables to have the same sign in the first “hurdle” stage and the second“estimation” stage (Burke, 2009). That is, physical attractiveness needs to be positivelyassociated both with the probability of being in employment and with the number ofhours supplied conditional on being in employment. The latter relationship might notbe true if the income effects dominate the substitution effects. Thus, a tobit-modelmight fail to capture the true relationship between hours of work and attractiveness.I therefore fit the data using Cragg’s double hurdle model (Cragg, 1971), henceforthreferred to as “craggit model”. This model allows the explanatory variables to havecountervailing effects at different stages of the estimation process. Again, I find thatphysical attractiveness has positive effects on labor supply (see Table 4). Unconditionalon employment, a difference in attractiveness of two standard deviations is associatedwith a difference in labor supply of more than 4 hours for men. This effect is by andlarge independent of whether an individual is married, and neither do controls for thespouse’s income change the effect.4 What is more, even conditional on employment,physical attractiveness still has a positive effect on male labor supply (see row 2 in Table4). However, the statistical significance of this effect is not beyond doubt and dependson the specification at hand.

For women, the unconditional effect of appearance on labor supply is similar to theeffect for men. But, conditional on employment, there is no effect for women. That is,physically attractive women are more likely to be in employment but once this effect isaccounted for, they do not work more hours than unattractive or average looking women.This is not true for men, for whom good looks appear to be associated with more hoursof work even accounting for the employment hurdle. One explanation for this pattern

statistically significant effects when I compute the average effects instead of the effects at the averagevalues of the explanatory variables. The original probit coefficients for the Below Average Looks categoryare also significant at the 5 percent level.

4The ALLBUS 2008 does not explicitly survey spousal income, however it does obtain householdincome. For reasons of clarity and comprehensibility, I refer to the difference between household incomeand the respondent’s own income as “spousal income”. In some cases the difference will stem fromsources other than spousal labor income, e.g. capital gains, remittances from relatives, etc..

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Table 4 - Effect of Physical Attractiveness on Hours WorkedWomen Men

First Hurdle: Any Employment First Hurdle: Any EmploymentDependent Variable (1) (2) (3) (4) (5) (6)

E[hours] 0.805*** 1.130*** 1.408*** 1.196*** 1.052** 0.914*(0.296) (0.311) (0.389) (0.335) (0.426) (0.513)

E[hours|employment] 0.129 0.059 0.268 0.377* 0.404 0.289(0.282) (0.367) (0.418) (0.219) (0.280) (0.408)

Pr[Employment] 0.023*** 0.041*** 0.041*** 0.021*** 0.016* 0.16*(0.008 ) (0.009) (0.009) (0.007) (0.008) (0.009)

Observations 1105 799 692 1096 735 617Interviewer Effects Yes Yes Yes Yes Yes YesMarried Respondents Only No Yes Yes No Yes YesControl for Spousal Income No No Yes No No Yes

Log-Likelihood -3526.716 -2540.237 -2151.261 -3679.404 -2484.181 -2041.254Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The main explanatory variable is physical attractiveness as measured on a scale from 1 to 11.I control for age and its square, city size, marital status, immigrant status, educational attainment,mother’s and father’s education, experience and its square, union membership, self reported health, thenumber of children and their age. Regional dummies are also included.

might be that for working women beauty is associated with higher productivity in bothmarket and household production. Then again, it is not plausible why men should notbe affected in the same manner. An alternative explanation is that women might valueleisure more than men. Indeed there is some evidence that an even work-life-balancematters more to women than to men (Fagan, 2001). This would result in larger incomeeffects for women that offset the substitution effects of a wage rate increase.

Another curious finding is that beauty affects married women in a different way thanunmarried women. Admittedly, it is hard to compare married with single, divorced orwidowed women as for those groups only small subsamples are available. But, the effectof beauty on labor supply is significantly larger for married women than for the overallfemale population. One explanation for this unexpected result, could be a negativeeffect of beauty on spousal income. But, in the next section I will show that physicalattractiveness is associated with larger spousal income, especially for women. What ismore, I control for spousal income in columns 3 and 6 of Table 4. As hypothesized earlier,husband’s income is negatively associated with female labor supply (p-value=0.002), butspousal income does not affect men’s hours of work. Another explanation would be thatunmarried women have been working more hours than married women in the first place.Given a backward-bending labor supply curve, the income effect could then dominate thesubstitution effect for husbandless women whereas the substitution effect prevails for themarried. Yet, in my sample the average difference in working time between unmarriedand married women is less than 1.5 hours. Finally, beauty might improve the bargainingposition of married women. The next section will show that husbands of attractivewomen are more likely to be in employment. In the same vein, these very husbandsmight also put more effort into home production. Their (attractive) wives would thenhave more time at their disposal, part of it is put to work in the labor market. Single

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women, on the other hand, cannot share the home production with their husbands.

4.3. Beauty and the Interplay of the Labor and Marriage Market

The previous paragraph has already hinted that labor and marriage market outcomesare related with each other. It is reasonable to assume that physical attractivenessaffects spousal income as both looks and earnings capabilities are traded in the marriagemarket. For instance, Hamermesh and Biddle (1994) show that physical attractivenessis positively associated with spousal education; Judge et al. (2009) find that looks andhousehold income go together for their sample from the Boston area. My findings areconsistent with this literature. Physical attractiveness is strongly associated with higherspousal income for women (see Table 5). The difference in spousal income betweenattractive and unattractive women amounts to more than 23 percent. This is strongevidence for the hypothesis that looks are traded for income in the marriage market. Itshould be noted, that this analysis is limited to married women only. That is, the factthat homely women might have a harder time finding a partner in the first place (andthus might not have any spousal income) cannot explain this difference.5

Table 5 - Impact of Looks on Net Monthly Spousal IncomeWomen Men

(1) (2) (3) (4) (5) (6)

Above Average Looks 0.089* -0.008(0.047) (0.079)

Below Average Looks -0.148* -0.083(0.088) (0.120)

Attractiveness (Scale 1-11) 0.036** 0.037*** 0.008 0.010(0.014) (0.014) (0.022) (0.023)

Observations 677 667 667 511 502 502R-squared 0.291 0.331 0.331 0.111 0.166 0.167Interviewer-Effects Yes Yes Yes Yes Yes YesControl Own Income No Yes Yes No Yes YesControl Partner Characteristics No Yes Yes No Yes Yes


The dependent variable is the natural logarithm of spousal income which is defined as the dif-ference between the respondent’s household income and her own, personal income.I control for age and its square, city size, immigrant status, educational attainment, mother’sand father’s education, experience and its square, union membership, self reported health, thenumber of children and their age. Partner charactersitics refer to the partner’s age (and itssquare) and educational attainment. Regional dummies are also included.

My results are also robust to including partner characteristics such as the partner’sage and education. Neither does controlling for the respondent’s own personal incomesignificantly change the results. What is more, I fail to find a similar pattern for men.

5I also find that attractiveness is positively associated with marriage probabilities. Hence, my es-timates can be interpreted as a lower limit. The results of the regressions of marriage probability onattractiveness are available from the author on request and will be published in a different paper.

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Husbands’ looks appear to have no effect whatsoever on the income of their wives. Thesegender differences are consistent with predictions made by evolutionary psychologists whofind that men value looks in their mate selection whereas women put their focus on theirpartner’s earnings capabilities (Buss, 1989). As a result, women can trade their looks fora higher spousal income, men cannot. It is also plausible to assume that men try harderto provide for an attractive spouse. This results in higher spousal income which, in turn,is negatively associated with a married woman’s market labor supply, as has been shownin the previous section.

If looks are associated with higher spousal income, they will probably also affectspousal labor supply. To test this hypothesis, one would ideally run a craggit regressionof spousal hours of work on the respondent’s physical attractiveness, controlling - amongother things - for the person’s own labor supply and spousal looks. Unfortunately, I onlyhave information on the physical attractiveness of one person in the household, namelythe respondent. What is more, I only know the spouse’s labor market status (full-timeemployment, part-time employment or not employed), but not her exact hours of work.This still allows me to run a probit model where spousal labor market status is thedependent variable and the respondent’s physical attractiveness the main explanatoryvariable. But, the labor supply decisions of married couples could be interrelated. Itherefore apply a seemingly unrelated regression equation (SURE) model as suggested byZellner (1963). The model consists of two equations, one for the employment probabilityof the respondent, one for the employment probability of the spouse. The model assumesa joint bivariate distribution of the error terms which allows them to be correlated.The model will then be fitted using a maximum-likelihood two-equation bivariate probitmodel.6

My findings suggest that physically attractive individuals are more likely to live indual income households. This is especially true for women. For them, good looks areassociated with a 13 percent higher chance that both spouses are in full-time employment(see Table 6). For men the effects are smaller and less significant, especially in thespecification that uses dummies instead of the continuous beauty measure. Good looksgenerally do not only improve a person’s own employment prospects, they also appearto induce a person’s spouse to spend more time on market work. Figure 1 furtherillustrates this point. The better a respondent’s looks, the higher are the odds thatboth, the respondent and his or her spouse, are in employment. Likewise, the chancesthat neither of them supplies any market labor decrease with the respondent’s physicalattractiveness.

One explanation for this pattern, which is consistent with my findings in the previoussection, is that spouses, in particular husbands, will try harder to provide for a goodlooking partner. This is all the more plausible as characteristics of family members havebeen shown to influence male labor supply decisions. For example, married men increasetheir hours of work more in response to the birth of a son than to the birth of a daughter(Lundberg and Rose, 2002). I find that the husbands of women with above-average looksare particularly eager to work more hours in the labor market. This, further supports

6While such a model allows for the errors to be correlated, it does not guard against misspecificationand omitted variable bias. For example, I cannot observe spousal health, which might be a determinantof labor supply in either equation. Due to these data limitation the following results should be interpretedcarefully.

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Table 6 - Impact of Looks on Joint Employment Probability - Full-time EmploymentWomen Men

(1) (2) (3) (4) (5) (6)

Above Average Looks 0.123*** 0.040(0.046) (0.051)

Below Average Looks -0.149** -0.037(0.071) (0.067)

Attractiveness (Scale 1-11) 0.049*** 0.048*** 0.022** 0.020(0.011) (0.012) (0.011) (0.012)

Observations 784 682 682 720 606 606Interviewer-Effects Yes Yes Yes Yes Yes YesMarried Respondents Only Yes Yes Yes Yes Yes YesControl for Spousal Income No Yes Yes No Yes YesControl for Partner Characteristics No Yes Yes No Yes Yes

Log-Likelihood -701.701 -598.257 -599.160 -654.502 -549.426 -550.966Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable is a dummy taking on the value 1 if both husband and wife report are in fulltimeemployment.For both, the respondent and his spouse I control for age and its square, city size, educational attain-ment, the number of family members, the number of children and their age. For the respondent (but notthe spouse) I add information on immigrant status, mother’s and father’s education, experience and itssquare, union membership, and self reported health. Regional dummies are also included

the hypothesis that men will try to trade income for their spouse’s looks and vice versa.Hence, my results indicate that looks do not only affect a person’s own labor supply, butalso have spill-over effects on his or her partner’s labor supply decisions.

5. Discussion and Limitations

This paper provides evidence for an effect of physical appearance on labor supplydecisions. Several robustness checks, e.g. controls for interviewer effects, or spousalcharacteristics, have been performed. Still, some potential limitations should be dis-cussed.

Section 2 has cleared up doubt about the representativeness of interviewer ratings.The evidence for the existence of universal beauty standards is overwhelming. Relativeto other persons of the same age, a beautiful person is perceived as beautiful for virtuallyall her working life. The effect of age can easily be captured. However, beauty-enhancingmeasures, such as clothing or plastic surgery, challenge the notion of inherent attractive-ness. They possibly erode the importance of beauty for long-term labor and marriagemarket decisions. Yet, the relative costs of beauty enhancing action suggest that thisis unlikely. Lee and Ryu (2009) analyze the returns on plastic surgery and come to theresult that the economic benefits from such procedures rarely outweigh the monetarycosts, and much less if the physical and psychological pain are also taken into account.Moreover, there appears to be no “dress for success”. Expenses for cosmetics and cloth-ing yield small and strongly diminishing returns to beauty (Hamermesh et al., 2002).Put differently, beauty cannot be bought. This also mitigates concerns about reversecausality running from labor supply to beauty.

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0.2

.4.6

.81

0 5 10Attractiveness (Scale 1−11)

Fitted Values(Both Spouses Work)Lowess Pr(1,1)

Women

0.2

.4.6

.81


Fitted Values (Both Spouses Work)Lowess Pr(1,1)

Men0

.2.4

.6.8

1


Fitted Values (Neither Spouse Works)Lowess Pr(0,0)

Women

0.2

.4.6

.81


Fitted Values (Neither Spouse Works)Lowess Pr(0,0)

Men

Figure 1: Effect of Attractiveness on Joint Employment Probability

Another worry relates to a potential relationship between beauty and unobservabletraits, in particular intelligence and self-esteem. The dataset does not contain exactmeasures for these characteristics. Neither can I account for time-invariant unobservableheterogeneity by taking individual fixed effects as the ALLBUS 2008 is a cross-sectionalsurvey. If beauty and intelligence are positively correlated, this will lead to omitted vari-able bias. Some theories indeed predict that beauty and intelligence come together. Asmentioned earlier, men prefer attractive partners while female mate selection tends tofocus on intelligence and earnings capabilities. Kanazawa (2004), among others, arguesthat if both intelligence and beauty are heritable, this will lead to a unification of brainsand beauty. However, imperfections in assortative mating are obvious. In particular,attractive men do not disproportionally tend to marry high income spouses. In neithermodel, do controls for parental education, which is a rough proxy for inherited intel-ligence, lead to any significant changes in the coefficients. Other studies come to thesame conclusion. In their empirical analysis, Hamermesh and Biddle (1994) find thatcontrolling for intelligence alters the effects of beauty on wages by less than 1 percent.

Similarly, attractiveness and self-esteem might be positively correlated. Mocan andTekin (2011) find that obesity negatively affects the self-esteem of young adults. Thisleads to an upward bias of the obesity penalty on wages. In a similar vein, unattractivepersons’ self-confidence might have been sapped by teasing and stereotypes. This wouldexplain part of the effect of physical attractiveness on wage rates and hours of work.As mentioned above, the effect of self-esteem is hard to account for as the ALLBUSquestionnaire lacks questions that were specifically designed to measure self-esteem. I

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use a proxy instead, namely the difference in interviewer-rated and self-rated beauty.A negative difference indicates high self-esteem; such a person is more convinced ofher attractiveness than objectively justified. A positive difference between these twomeasures, on the other hand, points to low self-esteem.7 Including these controls doesnot considerably change the results.8 If anything the point-estimates tend to increase. Infact, the craggit-coefficients for male hours of work conditional on employment increasein terms of both their size and their statistical significance. My, admittedly crude proxyfor low self-esteem, is also associated with lower employment probabilities for women.The overall robustness of my results with respect to self-esteem is in line with previousstudies (Mobius and Rosenblat, 2006, Hamermesh and Biddle, 1994) which used moreprecise controls for self-esteem and also found that these barely influenced the impact oflooks on labor market outcomes.

6. Conlcusion

I find that physical attractiveness is an important determinant of labor supply. Myempirical analysis is based on the German General Social Survey (ALLBUS) 2008, anationally representative cross-sectional survey that contains interviewer-ratings of thephysical attractiveness of all its respondents. It is the first nationally representativesample containing beauty measures in almost 3 decades in any country. Theoretical pre-dictions derived from the neoclassical model of labor supply are, by and large, confirmedby my data. Physically attractive men and women are more likely to work positive hoursthan their average looking peers. They are also more likely to be in full-time employ-ment. Consequently above-average looks are associated with more hours of market work.My craggit model indicates that this holds true for men even after accounting for thepositive employment effect of looks.

Beauty also affects labor market outcomes through the marriage market. Good look-ing women tend to be married to men who have higher incomes and who are more likelyto be in full employment. Higher spousal income in turn negatively affects attractivewives’ labor supply. However, the positive employment effect of beauty on female laborsupply predominates. As a result, good looks are associated with an increased laborsupply of both spouses.

Although my results are robust with respect to different specifications and functionalforms, it is hard to determine how exactly beauty effects the amount of labor an individualsupplies. Good looks could be associated with higher wages because attractive workersare more productive. On the other hand, demand side-factors could also play a role.After all, labor market outcomes result from the interaction of supply with demand.Wage differences between physically attractive and unattractive individuals could bedue to statistical, taste-based or customer discrimination. These wage differences then

7More specifically I generate two dummies to test for the effects of self-esteem. One dummy forlow self-esteem equals one when the difference between the two ratings is larger than 2. This groupcompromises about 15 percent of the respondents. Another dummy (for high self-esteem) equals 1 whenthe difference between the two ratings is smaller than -1, which is the case for about another 15 percentof respondents.

8The results of these specifications are not displayed in this paper, but are available from the authoron request.

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translate into differences in labor supply. In fact, the evidence on whether beauty actsproductivity enhancing is mixed (Pfann et al., 2000, Hamermesh and Biddle, 1994). Anobvious avenue for future research will be to disentangle these effects, although this willbe hard, given the lack of data sets containing measures of attractiveness. My paper laysthe foundation for these future investigations, as I show that beauty matters - for laborsupply decisions as much as in other areas of life.

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