Beauty still matters: The role of attractiveness in labour market outcomes

Abstract

Spanish

French

The main objective of the study is threefold: first, to examine the role of attractiveness in the Czech labour market; second, to assess gender differences in returns of attractiveness; and third, to show that the positive association between attractiveness and earnings does not disappear even when cognitive skills, social background, occupational status and individual characteristics are controlled for. The study uses data from the first large-scale sociological survey focusing on attractiveness carried out in the Czech Republic. The results provide strong evidence for the hypothesis that, in general, more attractive people have a better chance of higher socioeconomic occupational status as well as higher incomes than less attractive individuals even when controlling for cognitive skills, social background, occupational status and personality. However, the analysis also shows that the relationships are different for men and women. The study finds that the income premium for attractiveness is markedly higher among prime-aged women than men. The authors conclude that there have been profound changes in the last 30–40 years in the Western world and that the importance of physical attractiveness and erotic capital has been increasing, especially for women.

Keywords

Attractiveness cognitive skills gender differences income personality

Introduction

For a long time, sociologists have systematically ignored the role of many ‘soft’ factors, such as personal characteristics, height, weight or physical attractiveness in individuals’ social and economic outcomes and quality of life. However, we live in a society where physical appearance matters. As Hakim (2010) argued, the value of physical attractiveness and erotic capital has increased significantly in the 21st century. Physical traits clearly play an important role in workplace interactions and labour market outcomes (e.g. DeGroot and Motowidlo, 1999; Hamermesh and Biddle, 1994; Liu and Sierminska, 2014). Attractiveness, weight and body image have palpable importance for social interaction and social mobility (Hakim, 2010) and they are important determinants of popularity and influence over other people (Webster and Driskell, 1983). As Buss et al. (2001) showed, both sexes attribute increasing importance to physical attractiveness in a mate. In this way, the authors confirmed the important role of attractiveness in marital success.

Hakim (2010, 2011) has already argued that attractiveness and erotic capital play an increasing role in prosperous modern society, namely in marriage markets, labour markets, the media, politics, advertising, sports and the arts. She suggested that this capital is just as important as economic, cultural and social capital for understanding social and economic processes. In this regard, the aim of this article is to explore the role of physical attractiveness as a specific form of capital. We show that its effects are comparable to human capital, or more precisely to other forms of capital that economics and sociology have been exploring for more than half a century. Thus, this article can be viewed as an empirical contribution to this emerging field of sociology which takes full account of the changing social reality of the 21st century.

In particular, we focus on the importance of attractiveness in labour market outcomes in the Czech Republic. Several studies (Borland and Leigh, 2014; Fletcher, 2009; French, 2002; Hamermesh, 2011; Hamermesh and Biddle, 1994; Mocan and Tekin, 2010) demonstrate that people who are assessed as more attractive earn more than unattractive people. These were cohort studies, laboratory experiments or empirical examinations of evidence of the beauty premium in young adulthood. However, most of these studies used survey data only from the United States, Canada, Australia or the United Kingdom. Furthermore, the data are often quite outdated (from the 1970s and 1980s). Yet, as Hakim (2010) emphasizes the increasing role of attractiveness in the last decades, there is a need for more recent empirical evidence. In addition, other authors argue that the social, psychological and even labour market effects associated with physical appearance are likely to be culturally specific (e.g. Adams, 1980; Liu and Sierminska, 2014). Except for one German study (Doorley and Sierminska, 2012), we have no knowledge about the role of attractiveness in the labour market from a Central European country.

Most previous work was also based on a limited set of control variables, and measured skills in a reduced way. Harper (2000) used British data and found that the beauty premium disappears once skills are controlled for. One of the latest studies (Kanazawa and Still, 2017) examined US data (1994 and 2008) and showed that a positive effect of beauty disappeared completely once individual differences such as health, intelligence and the Big Five personality traits were controlled for. Moreover, a Mincer type of human capital model of wage income is the primary method used in the literature to model the effect of beauty on labour market outcomes. However, multiple regression models cannot account for the relationship, e.g. between attractiveness, education and cognitive skills, but rather treat each as an independent factor. In addition, since the relationship between these variables is also affected by family background, it is necessary to include a latent variable on family background in the model and to incorporate both direct and indirect effects. Lastly, we take into account the intervening role of occupational status with respect to income. For these reasons, we employ a structural equation modelling approach to examine the relationship between family background, education, cognitive skills, occupation, attractiveness, individual characteristics and income. By using measurement models that define latent variables within the overall structural model, we can directly compare the effect of individual factors while also taking into account their structure of covariance.

To sum up, we have three objectives: (1) by applying structural modelling, the goal is to ascertain that in the Czech Republic attractiveness plays a significant role in success in the labour market, even when social background, education, cognitive skills, personality and other relevant variables are controlled for; (b) to show that attractiveness plays a different role for men and women; and (3) to examine whether the role of attractiveness in the labour market is similar to other countries, and thus to contribute to the discourse on its cultural and social dependency. In the opening part, we briefly outline the theoretical conceptualization of physical attractiveness and present the main hypotheses concerning the role of attractiveness in the labour market. Then we test the validity of these hypotheses using data from a unique Czech survey in which attractiveness was measured along with other relevant variables.

Theoretical background

The role of beauty in life success from a sociological perspective

The evidence that beauty and physical attractiveness are important social phenomena has been extensively provided by economists (e.g. Hamermesh, 2011), psychologists (e.g. Byrne et al., 1968; Clifford and Walster, 1973; Dion et al., 1972; Etcoff, 1999; Langlois et al., 2000) and sociologists (e.g. Hakim, 2010, 2011). The theory of status generalization (Berger and Zelditch, 1985; Webster and Driskell, 1978; Webster and Foschi, 1988) is the most popular explanation of the role of attractiveness in life success. Status generalization refers to the situation when external characteristics (gender, race, physical attractiveness, etc.) are used as a proxy for characteristics which are not evident at first sight and require closer examination and investigation (intelligence, skills, performance, reliability, occupational status, income, etc.). This theory explicitly includes physical beauty among the external status characteristics from which observers infer other important status characteristics. The empirical experiments (Clifford and Walster, 1973) supporting this hypothesis show that more attractive individuals are perceived as intellectually more capable, more competent and overall more successful (see also Jackson et al., 1995). Due to these higher expectations, those who possess such characteristics are more successful in the end.

A sociologically relevant answer to the question of whether beauty is capital can partly be found in the theory of various forms of capital by Bourdieu (1986). In Bourdieu’s definition, capital is in principle any ‘capacity’ enabling one to gain a certain advantage and to generate profit, directly or indirectly, in the economic sense. According to Bourdieu, this characteristic pertains not only to economic capital, but also to cultural and social capital. Each capital requires investment, and needs to be constantly maintained at a certain level, and these individual forms of capital are mutually convertible. Furthermore, all ‘non-economic’ forms of capital function effectively, primarily because they have economic capital as their common denominator.

Much more importantly, Hakim (2010, 2011) follows up directly on Bourdieu and describes erotic capital as the fourth form of capital. She is the first sociologist to present a new theory of erotic capital as a complete theory of why attractiveness matters, in childhood as well as in adult life, in private relationships and marriage markets as well as the labour market and public life. She also defines the social and psychological processes that intertwine social and physical attractiveness, and that make beauty an independent causal factor for empirical analyses. Although erotic capital is not identical to mere beauty, because apart from physical attractiveness it contains a particular social energy, an ability to communicate subtly and pleasantly, charm, playfulness and erotic imagination, physical attractiveness is an essential condition for this. As Hakim (2010, 2011) added, attractiveness is about one-half due to the skills of self-presentation, the investment of time and effort and developing social skills, and one-half due to having good genes.

Hakim (2010) argues that erotic capital is increasingly important in modern society and can be extensively used to achieve economic and social benefits, especially for women. According to her there are the three specific reasons for the rising importance and value of erotic capital in the 21st century. First, digital photos and social media make everyone more visible. Second, changes in the workforce and the increasing importance of white-collar and service jobs in the knowledge economy make smooth social interaction skills more important than before. And third, increasing affluence makes people spend more on luxury goods.

Beauty as capital from an economic perspective

Many researchers have focused on estimating the relationships between non-cognitive skills, individual attributes and market outcomes. In particular, researchers have found associations between wages and attractiveness (Fletcher, 2009; Hamermesh and Biddle, 1994), obesity (Cawley, 2004), height (Judge and Cable, 2004; Persico et al., 2004) and leadership skills (Kuhn and Weinberger, 2005).

The pioneering work on the role of beauty in the labour market has been done in economics. The significant economic studies on the relationship between appearance and labour market outcomes were carried out around the turn of the century by Hamermesh and Biddle (Biddle and Hamermesh, 1998; Hamermesh and Biddle, 1994; see also Averett and Korenman, 1996) and later by others (Borland and Leigh, 2014; Fletcher, 2009; French, 2002; Hamermesh, 2011; Harper, 2000; Mocan and Tekin, 2010; Scholz and Sicinski, 2015). Most of the studies confirm a positive effect of attractiveness on labour market outcomes (so-called beauty premium or penalty) that may, however, vary with respect to gender.

One of the first studies (Hamermesh and Biddle, 1994) showed that physical beauty pays in the labour market, namely as far as income is concerned. In short, individuals of above-average attractiveness in the USA and Canada earn between 10% and 15% more than those of below-average attractiveness. However, the matter is not that simple, as relatively large differences between men and women lie hidden behind this general conclusion. Analyses showed that when the majority of relevant variables (namely education and age) are controlled for, men are penalized more for below-average attractiveness (a 10% lower income in comparison to those of average attractiveness) than they are rewarded for above-average attractiveness (a 5% higher income). For women, the differences were far smaller: in comparison to women of average attractiveness, those of below-average attractiveness earned roughly 7% less, while those of above-average attractiveness earned roughly 5% more, which represents a difference of around 12% of average income between women of above-average and below-average attractiveness (Hamermesh and Biddle, 1994).

Harper (2000) shows in his British cohort study that males who are rated as unattractive at both age 7 and 11 incur a large pay penalty of 15% later in life, compared to a 11% unattractiveness penalty for women. Borland and Leigh (2014) show that in the 1980s in Australia the difference in income between men of above-average and below-average attractiveness was around 22%. However, for women, no statistically significant attractiveness premium was found. They conclude that over time (up to 2009) the effects of beauty on a worker’s hourly wage are more-or-less constant. Mocan and Tekin (2010) used US data and also confirm a similar pattern using a sample of young adults. Frieze et al. (1991) also used US data and find that gender differences hold even within specific occupations. Nevertheless, other authors argue (based on British and American data) that the beauty premium disappears once individual differences (ability, health, personality) are controlled for (Harper, 2000; Kanazawa and Still, 2017). The authors of the most recently published paper, also based on US data, come to the conclusion that there are no gender differences in the attractiveness gradient of income and that grooming accounts for the entire attractiveness premium for women and half of the premium for men (Wong and Penner, 2016; see also Robins et al., 2011).

Possible explanations for the mechanism through which attractiveness can affect earnings mentioned in literature include particularly discrimination, occupational self-selection and individual differences (Hamermesh, 2011; Kanazawa and Still, 2017; Liu and Sierminska, 2014). The first of these explanations is that physically less attractive workers are discriminated against (and paid less accordingly). Discrimination based on physical attractiveness may come from the employers, co-workers, or customers (Kanazawa and Still, 2017). It is based on the assumption that in some occupations, more attractive individuals achieve better results because their work involves contact with people who are more likely to buy goods and services offered by a physically more attractive individual (customer discrimination). Or an employer is less willing to hire, promote or give a higher wage to a less attractive employee while able to remunerate an employee of above-average attractiveness more (employer discrimination) without this choice necessarily being connected to an expectation of higher productivity or actual ability (see also Mobius and Rosenblat, 2006).

Other explanations often cited in the literature go far beyond the discrimination effect and involve individual work choices influenced by one’s own physical attractiveness. One’s physical attractiveness can help to determine the occupational choice one makes as a worker and sort individuals into different occupations and industries with different levels of earnings (Hamermesh and Biddle, 1994). Physically more attractive individuals could be more likely to choose to enter occupations which involve extensive interactions with customers and in which the potential payoff to one’s appearance is much higher (occupational self-selection).

The third explanation for the existence of the beauty premium is that beauty may in fact be positively correlated with (or serve as a signal for) certain individual differences such as cognitive ability, communication skills, confidence and personality, which can affect productivity and are rewarded in the labour market. Physical attractiveness is significantly positively correlated with general intelligence (Jackson et al., 1995; Kanazawa, 2011). Good-looking children are given more attention by teachers and achieve higher formal education (Langlois et al., 2000). Physically attractive people are more likely to develop extraverted personality and emotional stability, have more self-esteem, and are more trustworthy, efficient, sociable and communicative (Hosoda et al., 2003; Judge et al., 2009; Luxen and Van de Vijver, 2006). Perceived attractiveness may be correlated with these individual characteristics, but they are usually not observed from survey data.

It is obvious that the effect of appearance may also vary across gender, therefore an important question which needs to be answered concerns differences in the role of attractiveness in the labour market between men and women. American studies (Hamermesh and Biddle, 1994) show that women benefit less from physical attractiveness in income than men. An analysis of several possible explanations (which include a methodological artefact arising from the measurement of attractiveness, selective participation of more attractive women in the labour market, and greater chances of highly attractive women to enter into an economically ‘advantageous’ marriage) showed that, in all probability, these factors are not relevant (Hamermesh and Biddle, 1994: 1188–1189). For the time being, we must concede that beauty-based labour market outcome premiums may vary across cultures.

At the same time, it can be assumed that analyses carried out on data which are more than 30 years old (or valid only for a special cohort) could not reflect the effects of the continuing emancipation of women, or more precisely the increase in the number of women in occupations which were previously unequivocally male-dominated, which is also due to, among other things, the growth in the proportion of women with a college degree. A gradually increasing representation of women in these occupations with higher incomes can lead not only to greater competition between men and women, but also among women themselves, where highly attractive women may be more successful than less attractive ones in terms of income. In this sense, we do not consider the results of the previous studies conducted in the USA, Canada and the UK to be transferrable to the present, and particularly to the present-day Czech Republic, where changes brought about by transformation, modernization and the individualization of preferences between a focus on the home and career (Hakim, 2000; Matějů et al., 2017) clearly affect women’s strategies for asserting themselves in the labour market using various forms of capital. These forms undoubtedly include physical attractiveness and erotic capital.

Hypotheses

Both theory and existing empirical results demonstrating the considerable role of attractiveness in success in the labour market have lead us to the formulation of the following four hypotheses:

H1: In accordance with predominantly economic and sociological theories, it is assumed that a significant role in gaining employment with higher socioeconomic status and achieving a higher income is played by the attractiveness of an individual, even when attained education, skills and socioeconomic background are controlled for.

H2: The mechanisms by which social background, education, cognitive skills, socioeconomic occupational status and attractiveness are transformed into income are not the same for men and women.

H3: In accordance with the continuing emancipation of women and their entry into occupational groups with higher incomes, and the expected greater effect of erotic capital of women, possibly as a result of the greater sexual deficit of men (see Hakim, 2015),² a greater benefit from attractiveness in the labour market is assumed for women than men.

H4: Positive association between attractiveness and earnings will not disappear even once individual characteristics such as body mass index, height, self-esteem, grooming and personality are statistically controlled for.

Data, variables and methods

Data and sample

The data analysed in this article were drawn from a nationally representative Czech follow-up questionnaire survey – Neglected Human Capital Dimensions (2015) – carried out on respondents interviewed for the first time between the years 2012 and 2013 in the cross-national representative comparative PIAAC Survey of Adult Skills (OECD Programme for the International Assessment of Adult Competencies).³ In 2015, we randomly sampled 2220 respondents between the ages of 16 and 66 from the original Czech PIAAC data file comprising 6081 respondents⁴ and carried out the survey (face-to-face interviewing), which focused mainly on attractiveness, personality traits, values, social capital and certain aspects of lifestyle. Only economically active respondents with non-zero earnings were involved in the analyses.

The survey provided a wider range of information about the respondents thanks to its link to the PIAAC project, allowing us to study attractiveness in a much wider context. The principal, and to a considerable extent unique, advantage of the interconnection of data from the follow-up survey with data from the initial survey is the possibility to work with an unusually extensive set of variables covering social background (education and occupation of parents), detailed data on educational attainment and occupational status, income, information about family and information on cognitive skills.

Variables

The key dependent variable in the analysis is average hourly earnings inclusive of all bonuses in a logarithmic form (INC). As mentioned above, the analyses include only respondents who report having positive earnings.

Attractiveness of an individual represents a key variable in the analysis. In the follow-up Czech survey, there were three kinds of evaluation of respondent attractiveness (see also Matějů and Anýžová, 2017). As part of the survey, the respondents evaluated themselves on an 11-point scale (0–10) of physical attractiveness.⁵ They were also evaluated by the interviewers using the same 11-point scale of attractiveness.⁶ The interviewers assessed the respondent’s attractiveness just after his/her face-to-face interview. Generally, people get higher scores at the end of interviews because they have come alive while answering the questions. In effect, this score stands for erotic capital, rather than just good looks narrowly defined. In those cases where respondents agreed to have their portrait photograph taken, they were evaluated by a panel of 32 evaluators using Stephenson’s (Stephenson, 1935, 1953) Q-methodology.⁷ Our analysis of the effect of attractiveness on success in the labour market could not work with an index which includes the evaluation of photographs by the group of evaluators because of a limited number of cases for which it was possible to calculate (982 cases). The number of cases for which the reduced latent variable (BEAUTY) composed of the self-evaluation and interviewers’ evaluation is available is much higher (2200).

In addition to the main variables of interest, a wide variety of controls were included in the model. Basic Model 1 adds controls for cognitive skills, sociocultural status of the parents, education and occupational status to account for additional background factors that could impact both attractiveness and earnings. Model 2 includes extra controls for other individual characteristics such as body mass index (BMI), height, self-esteem, healthy lifestyle, grooming and personality. Coefficients are estimated separately for men and women and for three age groups. The control variables used are defined as follows.

The latent variable indicating cognitive skills (SKILLS) is based on two dimensions measured in the PIAAC survey, namely numeracy (NUM) and literacy (LIT). Literacy is defined as the ability to comprehend written text and acquire information from it. Numeracy is defined as the ability to solve tasks which require operations with numbers or other mathematical elements (geometric, spatial, etc.). In descriptive and regression analyses, we use the mean value of the variables NUM and LIT [(NUM + LIT) ÷ 2].

The latent variable of parents’ sociocultural status (FAMSES) uses three indicators: education of father and mother and number of books in parents’ household. Education of mother and father was categorized into three levels (1 = at most vocational training completed without the General Certificate of Secondary Education [GCSE], 2 = secondary education with GCSE, 3 = tertiary education). The analysis also included as a control variable the education of the respondent measured by the number of years spent in formal education (EDYRS). A control for occupation was also included to see whether the attractiveness premium remains once attractive and unattractive individuals working in the same occupation are compared. For this control variable, values of the international index of socioeconomic status (ISEI) of respondent’s occupation, derived from the ISCO88 classification of occupations, were computed (see Ganzeboom et al., 1992).

As for individual characteristics, standard measures of BMI and height were used, among other things. Rosenberg’s Self-Esteem Scale was also included as a control variable. It contains 10 questions that are used to measure self-esteem as a favourable or unfavourable attitude towards oneself. A four-point Likert scale accompanies each question, with higher values representing greater levels of self-esteem. To control for grooming, a principal component analysis was performed with the five measures of evaluation of what is important for attractiveness maintenance (protection against sunburn, skin care, hair dyeing, choice of becoming clothing, plastic surgery). The principal component analysis produced a single factor with reasonably high loadings for all five indicators. Finally, research suggests that personality and perception of attractiveness are often interlinked, so that individuals with agreeable personalities are rated higher in physical attractiveness (Lewis, 2015). In addition, Mueller and Plug (2006) show that all Big Five traits have statistically significant effects on earnings. A set of composite variables was constructed to measure neuroticism, openness, extraversion, conscientiousness and agreeableness. All five measures were used in Model 2.⁸

Methods

The above-mentioned hypotheses were tested primarily using structural modelling, which – unlike usual regression analyses – enables us to estimate coefficients for the whole relationship concatenation, in which the observed characteristics may occur in the role of both dependent and independent variables. Furthermore, a structural model allows for the identification of not only direct effects but also indirect effects (the effect of one variable on another by means of other variables). An additional advantage of structural modelling is that it enables us to work not only with manifest (directly measured) variables, but also with latent variables, which are defined directly in the model similarly to factor analysis.⁹

The basic structural model tested is shown in Figure 1. Its theoretical assumptions can be summarized as follows:

- Education, cognitive skills, occupational status, income and attractiveness themselves are to a greater or lesser degree influenced by social background.

- Education attainment has a direct effect on skills, occupational status and income. Education also affects income indirectly, i.e. via occupational status.

- Skills affect occupational status and income; in the case of income, the model also differentiates between direct and indirect effects of skills (chances of a better position as well as higher salary in the same position).

- More attractive individuals have higher chances of gaining employment with higher socioeconomic status and of achieving higher income.

- The relationships between attractiveness, on the one hand, and skills and education, on the other, are left as reciprocal (like correlations). It is assumed that attractiveness affects education attainment, on the one hand, and people with higher education attainment are more aware of the role of attractiveness in life success and therefore focus more on their appearance and behaviour, which no doubt contributes to the resultant effect, on the other hand. With reference to other studies (Clifford and Walster, 1973; Judge et al., 2009), it is assumed that more attractive individuals have greater chances of higher education attainment, namely thanks to the fact that good-looking children attract more positive attention from teachers, which leads to better grades and higher aspirations. The relationship between attractiveness and cognitive skills is similar (see Kanazawa, 2011; Kanazawa and Kovar, 2004). However, at the same time, a higher level of skills can also contribute to the cultivation of attractiveness as economically valuable capital.

Figure 1.

Diagram of basic model of relations determining income.

The basic model is applied to three age groups (up to 30 years, 30–50 years, above 50 years) separately for men and women. The main reason for the gendered application of the model is to identify the assumed gender differences in relationships between key variables, which will enable possible cultural differences between American and Czech society to be revealed. The division of the population into three age groups¹⁰ is based on the assumption that the effect of attractiveness on success in the labour market is the greatest in the prime age, when economically active individuals approach the peak of their professional careers. The detailed results for the younger and older age groups are practically only used to illustrate the differences between the age groups. Another reason is the relatively small number of economically active individuals in the youngest and oldest age groups.

In the second step, other individual characteristics such as body mass index (BMI), self-esteem, grooming and the Big Five personality traits are added to the basic model separately, step by step, as control variables. We investigate whether the association between attractiveness and earnings disappears once these individual characteristics are statistically controlled for. Once again, the model is applied to three age groups separately for men and women, with special attention devoted to prime age.

For structural modelling, cases with missing values were deleted from the data (listwise deletion) and the maximum likelihood (ML) method was used to estimate the model parameters.¹¹ The parameters of the individual models were estimated by the AMOS program (version 21.0) from the original data. For the sake of correctness, it is necessary to mention that in order to make the measurement models for sociocultural background comparable between men and women and between age groups, we fixed the factor loadings for the overall level of skills and attractiveness between groups as identical (so-called metric model).¹²

Results

More detailed results of the analysis of the basic Model 1 are presented in Table 1. In the context of the stated hypotheses, it is evident from the results in Table 1 that the effect of attractiveness on success in the Czech labour market is not negligible, particularly for women. We can observe that women and men capitalize on their attractiveness in the labour market in different ways, which is particularly true of the prime age group of 30–50 years. This manner of capitalizing on attractiveness is present not only in financial remuneration but also in education attainment and occupational status.

Table 1.

Standardized regression coefficients of Model 1 explaining incomes of the economically active men and women in three age groups.

	Men			Women
	Up to 30years	30–50years	Above 50 years	Up to 30years	30–50years	Above 50 years
SKILLS ⇨ INC	−0.04	0.06	0.07	0.33^**	0.13	−0.02
EDYRS ⇨ INC	0.81	0.01	0.11	0.17	0.24^**	0.31^**
ISEI ⇨ INC	0.20	0.27^**	0.40^***	0.13	0.27^**	0.44^***
FAMSES ⇨ INC	0.27	0.24^**	0.11	−0.08	−0.05	0.009
BEAUTY ⇨ INC	−0.13	0.09	0.14	0.13	0.23^**	−0.24^*
SKILLS ⇨ ISEI	0.01	0.26^***	0.21^**	−0.02	0.08	−0.04
EDYRS ⇨ ISEI	0.41^***	0.54^***	0.50^***	0.47^***	0.50^***	0.81^***
FAMSES ⇨ ISEI	0.42	0.03	0.19^**	0.11	0.23^**	0.01
BEAUTY ⇨ ISEI	−0.21	0.003	0.07	0.05	0.22^**	−0.17
EDYRS ⇨ SKILLS	0.26^**	0.36^***	0.45^***	0.41^***	0.23^**	0.40^***
FAMSES⇨SKILLS	0.41^**	0.22^*	0.17	0.28	0.48^***	0.10
BEAUTY⇔ SKILLS	−0.29^*	0.13^*	0.17^*	0.42^**	−0.01	−0.003
FAMSES ⇔ EDYRS	0.51^***	0.55^***	0.41^***	0.40^**	0.62^***	0.48^***
BEAUTY ⇔ EDYRS	0.13	0.10	0.003	0.25	0.15	0.31^*
FAMSES ⇨ BEAUTY	0.56^**	0.15^*	0.03^***	−0.37	0.07	0.05^***

Source: Survey – Neglected Human Capital Dimensions (2015). The level of statistical significance: ***p < 0.001, **p < 0.05, *p < 0.1.

In the case of cognitive skills the levels of statistical significance are only approximate, because the coefficient estimates were made using only the first set of ‘plausible values’.

Note: Model fit (men): Chi-square = 83.98 (70 df), p = 0.122, GFI = 0.963, CFI = 0.988, RMSEA = 0.022, AIC = 273.98), Model fit (women): Chi-square = 81.13 (70 df), p = 0.171, GFI = 0.967, CFI = 0.992, RMSEA = 0.019, AIC = 271.13).

Prime-aged Czech women capitalize on attractiveness far more than men. Not only do more attractive women achieve better job positions than less attractive women (a regression coefficient of 0.22 for women vs 0.003 for men), but they also earn more (0.23 vs 0.09). Prime-aged women also capitalize on their attractiveness both directly, in the remuneration for their work, and indirectly, through education and better-paid job positions. In this age group, women also capitalize on the social status of the family of origin more than men, in terms of both education attainment (0.62 vs 0.55) and occupational socioeconomic status (0.23 vs 0.03), which is in keeping with the findings of other studies (DiPrete and Buchmann, 2013), according to which more educated and therefore less conservative parents support their daughters more in attaining higher education, in gaining better employment and in developing their skills. The correlation between attractiveness and cognitive skills cannot be overlooked, especially in the younger age of up to 30 years, which is more than twice as strong for women than for men (0.42 vs −0.29).

These results are further highlighted by the analysis of the total, direct and indirect effects of attractiveness on income, which shows a marked difference between men and women in the total effect of attractiveness on income (0.29 vs 0.09) in favour of prime-aged women (see Table 2).

Table 2.

Standardized total, direct and indirect effects of attractiveness on income of Model 1 (by sex in three age groups).

		Attractiveness→Income
		Total effect	Direct effect	Indirect effect
Men	Up to 30 years	−0.17	−0.13	−0.04
	30–50 years	0.09	0.09	0.001
	Above 50 years	0.16	0.14	0.03
Women	Up to 30 years	0.13	0.13	0.01
	30–50 years	0.29	0.23	0.06
	Above 50 years	−0.32	−0.24	−0.06

Source: Survey – Neglected Human Capital Dimensions (2015). Authors’ own calculations.

Table 3 shows the results of the same analysis after controlling for the set of individual characteristics in Model 2. For the sake of clarity, only changes in standardized regression coefficients of the relationship between attractiveness and earnings are shown in the table. In the first row of the table, the original values of the regression coefficients from Model 1 (relationship between attractiveness and earnings controlled for cognitive skills) are presented. In the following rows, regression coefficients of the same relationship are presented, only after controlling for particular individual characteristics which were included in the Model 2 separately, step by step.¹³ This analysis confirms that their inclusion did not affect the substance or significance of the coefficients for attractiveness, and that prime-aged Czech women still capitalize on attractiveness far more than men. It is a different outcome than Wong and Penner (2016) or Kanazawa and Still (2017) presented in their British and American studies because they argued that the beauty premium disappears once individual differences (ability, health, personality, grooming) are controlled for.

Table 3.

Changes in standardized regression coefficients of the relationship between attractiveness and earnings of Model 2 after controlling individual characteristics (for economically active men and women in three age groups).

Controlling for	Attractiveness→Income
	Men			Women
	Up to 30years	30–50years	Above 50 years	Up to 30years	30–50years	Above 50 years
Skills (Model 1)	−0.13	0.05	0.11	0.13	0.23^**	−0.24^*
Height	−0.15	0.05	0.11	0.07	0.22^**	−0.24
BMI	−0.14	0.08	0.16	0.11	0.26^**	−0.31
Self-esteem	−0.31	0.06	0.11	0.08	0.22^**	−0.31^*
Grooming	–	0.08	0.19	0.13	0.24^**	−0.30^*
Neuroticism	–	0.06	0.09	−0.002	0.19^**	−0.35
Extraversion	–	0.08	0.17	0.03	0.22^**	−0.27^*
Openness	–	0.08	0.15	0.11	0.24^**	−0.29^*
Conscientiousness	–	0.08	0.13	0.12	0.26^**	−0.27^*
Agreeableness	–	0.08	0.15	0.09	0.25^**	−0.27^*

Source: Survey – Neglected Human Capital Dimensions (2015). The level of statistical significance: **p < 0.05, *p < 0.1.

It is also apparent from Table 4 that many individual characteristics are not significant predictors of income when controlled for skills, socioeconomic status of family, education, occupational status and attractiveness. This confirms Hakim’s assumption (2010) that physical attractiveness or erotic capital are independent causal factors (especially for economically productive women) and that other individual characteristics such as height, BMI, self-esteem, grooming and personality have different effects on labour market outcomes. Other results confirm the generally known facts that height is positively associated with earnings, significantly so for economically active men – perhaps due to the close linkage between height and power in society (see also Judge and Cable, 2004). Body mass index seems to be important for higher income for men above 50 years. Self-esteem is positively associated with income also only for men in the prime age group and for older men above 50 years. As for personality, workers – especially men – who are more conscientious, more extraverted and less neurotic on average earn more. On the other hand, women who are more agreeable earn less.

Table 4.

Selected standardized regression coefficients of Model 2 for individual characteristics explaining income (for economically active men and women in three age groups).

	Men			Women
	Up to 30years	30–50years	Above 50 years	Up to 30years	30–50years	Above 50 years
HEIGHT ⇨ INC	0.08	0.19^**	0.17^**	0.07	0.08	0.06
BMI ⇨ INC	−0.07	−0.05	0.15^*	0.01	0.03	−0.08
SELF-ESTEEM ⇨ INC	−0.10	0.17^**	0.15^*	0.01	0.01	0.12
GROOMING ⇨ INC	–	−0.10	−0.05	0.09	−0.01	0.13
NEUROTICISM ⇨ INC	–	−0.33^**	−0.13	−0.13	−0.02	−0.20
EXTRAVERSION ⇨ INC	–	0.14^**	0.10	0.14	0.01	0.03
OPENNESS ⇨ INC	–	−0.04	−0.01	−0.04	−0.08	0.08
CONSCIENTIOUSNESS ⇨ INC	–	0.08	0.13^*	−0.08	−0.08	0.08
AGREEABLENESS ⇨ INC	–	0.02	−0.03	0.03	−0.12^**	−0.09

Source: Survey – Neglected Human Capital Dimensions (2015). The level of statistical significance: **p < 0.05, *p < 0.1.

In order to meet the requirements for the application of structural modelling, we hitherto needed to treat attractiveness as though its impact was ‘continual’ or ‘linear’. However, as previous studies and life experience show, attractiveness probably has a different impact. While an individual of average attractiveness does not attract much attention, people who are either of highly above-average attractiveness or, on the contrary, below-average, do attract attention. This is why the majority of studies to date have worked with categorical variables based on five- or seven-point attractiveness scales. This is one of the reasons why the results of analyses of the effects of attractiveness on employment and earnings are more contrasting than if continuous variables had been used.

In order to achieve partial comparability with previous studies, we also analysed the effect of attractiveness by classifying individuals as being of above-average attractiveness (the top 25% on the attractiveness scale), average and below-average attractiveness (the 25% least attractive). For the sake of clarity, the index of cognitive skills was categorized in the same way, i.e. the top 25% on the competence scale versus the 25% least competent respondents. Since the structural modelling results showed that neither attractiveness nor cognitive skills have a consistent labour market effect until prime age, a time when people are near their career peaks, the regression analyses were carried out for this age group only.

In the analysis, we used multiple regression analyses in which the dependent variable was average hourly earnings including all bonuses in logarithmic form (INC), which enabled the results to be expressed as percentages of average income. Attractiveness and cognitive skills entered into the analyses as sets of so-called dummy variables: ATT_TOP (the individual is of above-average attractiveness), ATT_BOT (the individual is of below-average attractiveness), SKILLS_TOP (the individual is of above-average cognitive skills) and SKILLS_BOT (the individual is of below-average cognitive skills). Those of average attractiveness and competencies were excluded from the analysis as they served as reference group. Apart from attractiveness and cognitive skills, the regression analyses also controlled for respondent education.

The results of the regression analysis presented in Table 5, showing attractiveness expressed in such contrasting categories as used in everyday life (beautiful versus unattractive), do correspond roughly to the results of structural modelling, although they are somewhat clearer. Above all, they show that education attainment is key to gaining higher income. On average, every year of education brings a 6% income increase in men and 7% in women. As anticipated, the effect of attractiveness is statistically significant. Among men, there is no premium for above-average attractiveness, but the penalty for below-average attractiveness is 9% of income (education and cognitive skills controlled for).

Table 5.

Regression analysis of income (30–50 years of age).

		Non-standardized coefficients		Standardized coefficients	t	Sig.
		B	SE	Beta	t	Sig.
Men	Constant	4.146	0.138		30.105	0.000
	Education	0.059	0.010	0.335	5.643	0.000
	ATT_TOP	0.000	0.071	0.000	0.007	0.995
	ATT_BOT	−0.091	0.051	−0.102	−1.803	0.072
	SKILLS_TOP	0.092	0.053	0.106	1.758	0.080
	SKILLS_BOT	−0.023	0.070	−0.019	−0.327	0.744
Women	Constant	3.638	0.162		22.514	0.000
	Education	0.069	0.012	0.365	5.782	0.000
	ATT_TOP	0.152	0.056	0.161	2.732	0.007
	ATT_BOT	−0.064	0.067	−0.056	−0.953	0.342
	SKILLS_TOP	0.258	0.064	0.251	4.002	0.000
	SKILLS_BOT	−0.047	0.060	−0.048	−0.789	0.431

Dependent variable: (ln)INC.

Source: Survey – Neglected Human Capital Dimensions (2015). Authors’ own calculations.

Among prime-aged women, the differences are far greater. The premium for those of above-average attractiveness is 15% of average income and the penalty is 6%, which means that there is a 21% difference in income between women of above-average and below-average attractiveness. In the case of cognitive skills, the results are similar. The income difference between those of above-average and below-average skills is around 11% for men and 30% for women. In the competition for higher income, both attractiveness and cognitive skills put women in a more favourable position than men. Czech women earn approximately 20% less than men, yet the greatest difference in average income for men is among those of below-average attractiveness and skills. Women of above-average attractiveness and competence have the same income as men of above-average attractiveness and competence.

Discussion

We live in a society where physical appearance matters. Attractiveness clearly plays an important role both in our social lives and in workplace outcomes. The main objective of the present study was to ascertain whether attractiveness can be considered as a form of capital which affects labour market outcomes. Above all, it appears evident that more attractive people have greater chances of gaining a higher socioeconomic occupational status and a higher income than less attractive people. It is significant that the visible effect of attractiveness does not disappear even when education attainment, cognitive skills, social background and individual characteristics are taken into consideration.

Another significant finding is that attractiveness has the greatest effect in prime age, when people are gradually approaching or have already reached career peak. The evidence suggests that women’s and men’s attractiveness might be treated differently in the labour market just in this age group. As for capitalization on attractiveness in income, men do not have any premium for above-average attractiveness and they are penalized more than women for below-average attractiveness (the drop in income caused by below-average attractiveness is 9% for men, compared to 6% for women).

However, the principal difference is in premiums for above-average attractiveness and cognitive skills. For women, the premium for above-average attractiveness is 15% of average income, which leads to an overall income difference of 21% between attractive and unattractive women. The situation is similar in the case of cognitive skills. The difference in income between women of above-average and below-average competence is 30% of average income, even when education and attractiveness are controlled for. As has already been mentioned, these effects are noticeably weaker for men. The difference in income between prime-aged men of above-average and below-average attractiveness is 9%. Neither do men benefit significantly from above-average competence in comparison to women. Its pure effect on income is half that of women.

From these analyses, it is evident that capital associated with attractiveness pays off more for women than for men in the Czech Republic, while the beauty penalty is paradoxically greater for men than women. These results thus do not corroborate the findings of research carried out in the United States and Canada (Hamermesh and Biddle, 1994), according to which men benefit more than women from exceptional physical attractiveness in terms of income. In the Czech Republic, the opposite is true, and the differences in favour of women are considerable.

One of the explanations of the gender differences in the effect of attractiveness on earnings can be cultural universality of the effect of attractiveness on income combined with cultural specificity of the gender differences in this effect (see also Adams, 1980). This does not seem plausible because we cannot find the reason why female attractiveness or erotic capital should be more valued in the Czech Republic or why Czech women should be more skilled at exploiting good looks. Moreover, similarly high beauty premiums for women appear in Germany, Luxembourg and China as well (Doorley and Sierminska, 2012; Hamermesh et al., 2002).

But these studies (just as this one) used updated datasets (the Czech survey was carried out in 2014/15), whereas the North American datasets were collected in the 1970s. There have been profound changes in the last 30–40 years in the Western world. Hakim (2010) has already mentioned the increasing importance of erotic capital in the 21st century, especially for women, due to digital photography, social media, rising incomes, and other developments in the labour market. She argues that women nowadays have more erotic capital than men in most societies because they work harder at personal presentation and performance of gender and sexuality. But this sex differential is not fixed, and can vary over time due to social and economic changes. Therefore, the second explanation that emphasized time changes in this phenomenon seems to be more plausible.

In their upcoming American study, Kanazawa and Still (2017) propose three explanations for the effect of physical attractiveness on earnings – discrimination, self-selection and individual differences. All the explanations are supported by previous studies to some extent. Our results suggest that discrimination (on the part of the employers, co-workers and clients) is a plausible explanation in the Czech Republic because the association between attractiveness and earnings does not disappear once respondents’ occupations, cognitive skills, and even personalities are statistically controlled for. On the contrary, descriptive analyses (not shown) confirm a rather monotonically positive association between attractiveness and earnings.

Moreover, in the present study, we tried to develop a theoretical model of the relationship between attractiveness and other indicators of career success as a framework to direct future research in this area. We conducted structural modelling to test some of the general implications of the theoretical model. The results showed that it is methodologically more appropriate to measure given relationships in the prime-aged population where physical attractiveness is no longer primarily an attribute of youth but requires a certain degree of, usually systematic, grooming. Furthermore, people of this age are approaching their career peak and it is therefore logical that they capitalize the most, whether consciously or unconsciously, on their physical attractiveness and erotic capital.

The research reported here has several limitations that should be noted. First, even if structural modelling was used as analytical tool, our goal was not a causal explanation of social phenomena on the basis of static (cross-sectional) data because the anticipated causes and consequences cannot be unequivocally placed on a timeline without longitudinal data. Because attractiveness was measured at the same time as earnings, the direction of causality is not clear. It is obvious that people with higher wages are not only more attractive, but they can also choose to invest more in their own attractiveness. Therefore, we have tried to avoid a causal interpretation and to model the relationship between variables so that they are not only independent factors in a wage equation, but we also take into account their structure of covariance.

But even if we had longitudinal data, Hakim (2011) quotes studies showing strong correlations between measures of attractiveness across the lifecycle. Attractiveness is always assessed relative to the age group, so it remains constant across life, even if young people are more attractive than old people generally. The crucial issue for our purposes would be whether standards of attractiveness change slowly enough to allow labour-market decisions related to beauty to be planned for a horizon as long as a person’s expected working life (Hamermesh and Biddle, 1994).

The other limitation of our study was the quite low number of respondents in every group tested, because we had to select only economically active respondents who provided information about their income and occupational status and then we tested the model of relationships separately for men and women in three age groups. Unfortunately, this is the reason why the regression coefficients in our outcomes have such low levels of significance and some effects could not be calculated for some groups (especially for the younger age group).

Conclusions

In empirical analyses using Czech survey data (2015) we find some evidence of a positive impact of workers’ attractiveness on their earnings. The evidence in each group is suggestive but not very strong. Other things equal, the wages of people with below-average attractiveness are lower than those of above-average attractiveness workers. For prime-aged women, there is a beauty premium in wages for above-average attractiveness that is much higher than the penalty. Even when attractiveness is a relatively weak predictor of men’s income, the beauty penalty is higher for prime-aged men. This significant effect of attractiveness does not disappear when educational attainment, cognitive skills, social background, occupational status and individual characteristics are taken into consideration. Our results suggest that discrimination (on the part of the employers, co-workers and clients) is a plausible explanation for this phenomenon in the Czech Republic.

Footnotes

Appendix

Table A1.

Descriptive statistics of selected variables.

	Mean	SD	N	Minimum	Maximum
Father’s education	1.992	0.0106	2157	1	3
Mother’s education	1.781	0.0116	2197	1	3
Number of books	3.953	0.0261	2206	1	6
Interviewer’s evaluation of attractiveness	6.874	0.0352	2219	1	10
Respondent’s self-evaluation	5.800	0.0370	2201	1	10
Education (number of years spent in formal education)	12.859	0.0551	2220	5	21
Index of socioeconomic status	42.873	0.5185	1444	16	90
Monthly income	21,891.7	408.13	1031	8366	159,537
Reading skills	275.966	0.8539	2220	118.97	445.15
Numeric skills	275.756	0.9105	2220	124.99	425.31
Age	40.531	0.3063	2220	16	65
BMI index	26.228	0.1035	2139	15.9	54.1
Height	172.84	9.5	2205	148	200
Importance of protection against sunburn	3.19	1.12	2212	1	5
Importance of skin care	3.68	0.96	2214	1	5
Importance of hair dyeing	2.83	1.26	2215	1	5
Importance of choice of becoming clothing	4.03	0.81	2217	1	5
Importance of plastic surgery	1.99	1.00	2202	1	5
Neuroticism	21.07	7.13	2174	0	45
Openness	26.01	5.87	2159	3	44
Extraversion	30.44	6.76	2164	3	48
Conscientiousness	33.04	6.38	2167	9	48
Agreeableness	30.88	5.14	2177	12	48
	Percentages (%)
Men/women	50.2/49.8

Source: Survey – Neglected Human Capital Dimensions (2015). Authors’ own calculations.

Acknowledgements

The authors express thanks to anonymous reviewers for their inspiring critical comments.

Funding

Research for this article has been made possible by financial support from the Czech Science Foundation ‘Neglected Dimensions of Human Capital: The Role of Intangible Assets and Attractiveness in the Labour and Marriage Market and Lifestyle Preferences’ (Grant No. 14-06264S).

Notes

Author biographies

Petra Anýžová completed her doctoral studies in sociology at the Faculty of Arts, Charles University in Prague, in 2014. She works as postdoc at the Institute of Sociology of the Czech Academy Science. Her research interests are comparative research methodology, educational inequalities, human capital, and human values.

Petr Matějů (deceased) was Professor of Sociology at the University of Finance and Administration. In 2002 he established the Institute for Economic and Social Analysis, an independent think-tank addressing and promoting reform processes and policies in the Czech Republic. His research interests were social and educational inequality, social stratification, social mobility and human capital.

References

Adams

(1980) Social psychology of beauty: Effects of age, height, and weight on self-reported personality traits and social behavior. The Journal of Social Psychology 112(2): 287–293.

Averett

Korenman

(1996) The economic reality of the beauty myth. Journal of Human Resources 31(2): 304–330.

Berger

Zelditch

(1985) Status, Rewards, and Influence. San Francisco, CA: Jossey-Bass.

Biddle

Hamermesh

(1998) Beauty, productivity, and discrimination: Lawyers’ looks and lucre. Journal of Labor Economics 16(1): 172–201.

Borland

Leigh

(2014) Unpacking the beauty premium: What channels does it operate through, and has it changed over time? Economic Record 90(288):17–32.

Bourdieu

(1986) The forms of capital. In: Richardson

(ed.) Handbook of Theory and Research for the Sociology of Education. New York: Greenwood, pp. 241–258.

Buss

Shackelford

Kirkpatrick

Larsen

(2001) A half century of mate preferences: The cultural evolution of values. Journal of Marriage and Family 63(2): 491–503.

Byrne

(2010) Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, 2nd edn. New York: Taylor and Francis.

Byrne

London

Reeves

(1968) The effects of physical attractiveness, sex, and attitude similarity on interpersonal attraction. Journal of Personality 36(2): 259–271.

10.

Cawley

(2004) The impact of obesity on wages. Journal of Human Resources 39(2): 451–474.

11.

Clifford

Walster

(1973) The effect of physical attractiveness on teacher expectations. Sociology of Education 46(2): 248–258. .

12.

DeGroot

Motowidlo

(1999) Why visual and vocal interview cues can affect interviewers’ judgments and predict job performance. Journal of Applied Psychology 84(6): 986–993.

13.

Desjardins

Thorn

Schleicher

et al . (2013) OECD Skills Outlook 2013: First Results from the Survey of Adult Skills. Paris: OECD Publishing.

14.

Dion

Berscheid

Walster

(1972) What is beautiful is good. Journal of Personality and Social Psychology 24(3): 285–290.

15.

DiPrete

Buchmann

(2013) The Rise of Women: The Growing Gender Gap in Education and What it Means for American Schools. New York: Russell Sage Foundation.

16.

Doorley

Sierminska

(2012) Myth or fact? The beauty premium across the wage distribution. IZA Discussion Papers 6674, Institute for the Study of Labour.

17.

Etcoff

(1999) Survival of the Prettiest: The Science of Beauty. New York: Anchor.

18.

Fletcher

(2009) Beauty vs. brains: Early labor market outcomes of high school graduates. Economics Letters 105(3): 321–325.

19.

French

(2002) Physical appearance and earnings: Further evidence. Applied Economics 34(5): 569–572.

20.

Frieze

Olson

Russell

(1991) Attractiveness and income for men and women in management. Journal of Applied Social Psychology 21(13): 1039–1057.

21.

Ganzeboom

De Graaf

Treiman

(1992) A standard international socio-economic index of occupational status. Social Science Research 21(1): 1–56.

22.

Hakim

(2000) Work-Lifestyle Choices in the 21st Century: Preference Theory. Oxford: Oxford University Press.

23.

Hakim

(2010) Erotic capital. European Sociological Review 26(5): 499–518.

24.

Hakim

(2011) Erotic Capital: The Power of Attraction in the Boardroom and the Bedroom. New York: Basic Books.

25.

Hakim

(2015) The male sexual deficit: A social fact of the 21st century. International Sociology 30(3): 314–335.

26.

Hamermesh

(2011) Beauty Pays: Why Attractive People Are More Successful. Princeton, NJ: Princeton University Press.

27.

Hamermesh

Biddle

(1994) Beauty and the labor market. American Economic Review 84(5): 1174–1194.

28.

Hamermesh

Meng

Zhang (2002) Dress for success – does primping pay? Labour Economics 9(3): 361–373.

29.

Harper

(2000) Beauty, stature and the labour market: A British cohort study. Oxford Bulletin of Economics and Statistics 62(S1): 771–800.

30.

Hosoda

Stone-Romero

Coats

(2003) The effects of physical attractiveness on job-related outcomes: A meta-analysis of experimental studies. Personnel Psychology 56(2): 431–462.

31.

Jackson

Hunter

Hodge

(1995) Physical attractiveness and intellectual competence: A meta-analytic review. Social Psychology Quarterly 58(2): 108–122.

32.

Judge

Cable

(2004) The effect of physical height on workplace success and income: Preliminary test of a theoretical model. Journal of Applied Psychology 89(3): 428–441.

33.

Judge

Hurst

Simon

(2009) Does it pay to be smart, attractive, or confident (or all three)? Relationships among general mental ability, physical attractiveness, core self-evaluations, and income. Journal of Applied Psychology 94(3): 742–755.

34.

Kanazawa

(2011) Intelligence and physical attractiveness. Intelligence 39(1): 7–14.

35.

Kanazawa

Kovar

(2004) Why beautiful people are more intelligent. Intelligence 32(3): 227–243.

36.

Kanazawa

Still

(2017) Is there really a beauty premium or an ugliness penalty on earnings? Journal of Business and Psychology. Epub ahead of print 16 February 2017. DOI: 10.1007/s10869–017–9489–6

37.

Kuhn

Weinberger

(2005) Leadership skills and wages. Journal of Labor Economics 23(3): 395–436.

38.

Langlois

Kalakanis

Rubenstein

et al . (2000) Maxims or myths of beauty? A meta-analytic and theoretical review. Psychological Bulletin 126(3): 390–423.

39.

Lewis

DMG

(2015) Evolved individual differences: Advancing a condition-dependent model of personality. Personality and Individual Differences 84: 63–72.

40.

Liu

Sierminska

(2014) Evaluating the effect of beauty on labor market outcomes: A review of the literature (No. 2014–11). LISER.

41.

Luxen

Van de Vijver

(2006) Facial attractiveness, sexual selection, and personnel selection: When evolved preferences matter. Journal of Organizational Behavior 27(2): 241–255.

42.

Matějů

Anýžová

(2017) Krása jako kapitál: Role atraktivity v úspěchu na trhu práce. Sociologický časopis/Czech Sociological Review 53(4): 503–531.

43.

Matějů

Smith

Weidnerová

Anýžová

(2017) The role of basic values and education on women’s work and family preferences in Europe. International Journal of Sociology and Social Policy 37(9/10): 494–514.

44.

Mobius

Rosenblat

(2006) Why beauty matters. The American Economic Review 96(1): 222–235.

45.

Mocan

Tekin

(2010) Ugly criminals. The Review of Economics and Statistics 92(1): 15–30.

46.

Mueller

Plug

(2006) Estimating the effect of personality on male and female earnings. ILR Review 60(1): 3–22.

47.

Persico

Postlewaite

Silverman

(2004) The effect of adolescent experience on labor market outcomes: The case of height. Journal of Political Economy 112(5): 1019–1053.

48.

Robins

Homer

French

(2011) Beauty and the labor market: Accounting for the additional effects of personality and grooming. Labour 25(2): 228–251.

49.

Scholz

Sicinski

(2015) Facial attractiveness and lifetime earnings: Evidence from a cohort study. Review of Economics and Statistics 97(1): 14–28.

50.

Stephenson

(1935) Correlating persons instead of tests. Character and Personality; A Quarterly for Psychodiagnostic and Allied Studies 4(1): 17–24.

51.

Stephenson

(1953) The Study of Behavior: Q-Technique and its Methodology. Chicago: University of Chicago Press.

52.

Webster

Driskell

(1978) Status generalization: A review and some new data. American Sociological Review 43(2): 220–236.

53.

Webster

Driskell

(1983) Beauty as status. American Journal of Sociology 89(1): 140–165.

54.

Webster

Foschi

(1988) Status Generalization: New Theory and Research. Stanford, CA: Stanford University Press.

55.

Wong

Penner

(2016) Gender and the returns to attractiveness. Research in Social Stratification and Mobility 44: 113–123.