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Beauty and job accessibility: new evidence from a field experiment

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Abstract

This study uses a field experiment to resolve the difficulties of quantifying personal appearance and identify a direct causal relationship between appearance and employment in China. The experiment reveals that taste-based pure appearance discrimination exists at the pre-interview stage. There are significant gender-specific heterogeneous effects of education on appearance discrimination: having better educational credentials reduces appearance discrimination among men but not among women. Moreover, attributes of the labor market, companies, and vacancies matter. Beauty premiums are larger in big cities with higher concentrations of women and in male-focused research positions. Similarly, the beauty premium is larger for vacancies with higher remuneration.

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Notes

  1. 1.

    Facial appearance refers to how the combination of eyes, nose, mouth, ears, brow, and facial outline looks. Outward appearance refers to the external image of a human being, including height, figure, and the presence of any disability.

  2. 2.

    Hamermesh et al. (2002) also find that beauty raises women’s earnings, even after including a wide range of controls.

  3. 3.

    For example, in Guo et al. (2017), the rating of looks is made by the investigator, which may not match public perception. Additionally, their estimations omit some important influencing factors, such as vacancy requirements.

  4. 4.

    Two methods are mainly adopted in previous studies: descriptive statistics (Judge and Cable 2004; D'Hombres and Brunello 2005; Garcia and Quintana-Domeque 2006; Lundborg et al., 2007) and the metering method (Blaker et al. 2013; Biddle and Hamermesh, 2011). Descriptive statistics use statistical technology to identify the current situation of appearance discrimination in the labor market through site surveys or questionnaires. The metering method uses linear regression through the Oaxaca–Blinder decomposition method or sets dummy variables to analyze appearance discrimination in the labor market.

  5. 5.

    Heilman and Saruwatari (1979) design a lab experiment with 23/22 male/female undergraduates to evaluate applicants for insurance company vacancies.

  6. 6.

    An audit study general uses “live” job applicants with identical-quality resumes. A correspondence study usually just submits paper (or web-based) applications with equal qualifications.

  7. 7.

    Except for the picture, the two CVs are different only in some “necessary and negligible” features such as fonts, content order, and names of the large companies in which the candidates acquired working experience.

  8. 8.

    López Bóo et al. (2013) investigate these occupations: sales–commercial; administrative–accountancy; marketing–advertisement; secretaries–receptionists–customer service; gastronomy; or general unskilled positions. Patacchini et al. (2015) explore seven occupations: administrative clerk, bookkeeper, call center operator, receptionist, sales clerk, secretary, and shop assistant. Ruffle and Shtudiner (2015) study ten different types of jobs: banking, budgeting, chartered accountancy, finance, accounts management, industrial engineering, computer programming, senior sales, junior sales, and customer service.

  9. 9.

    Due to confidentiality agreements, we cannot provide the name of this platform.

  10. 10.

    The original resume information cannot be released because of personal privacy and confidentiality agreements. We chose the financial industry because the financial sector is a rapidly growing service sector with attractive work opportunities for a wide range of professionals. It has attracted graduates of economics, management, history, social science, science, and engineering. Graduates of economics and management account for approximately 80% of applicants, while engineering, science, and law graduates account for 19%. Therefore, applications in the financial sector are comprehensive and representative.

  11. 11.

    The real and adjusted ID photos cannot be released to protect personal privacy and due to confidentiality agreements.

  12. 12.

    See Appendix 3: example of artificially synthesized ID Photos.

  13. 13.

    Ruffle and Shtudiner (2015) hired eight judges (four men and four women) to rate the submitted students’ pictures; this sample is too small to satisfy the basic requirement for valid estimations.

  14. 14.

    We consulted several human resource experts, who provided us with relevant facts and basic statistics of recruitment processes in the labor market.

  15. 15.

    “Project 985” started on May 4, 1998, and this group includes 38 universities. This project’s aim is to establish first-class universities. “Project 211” started in November 1995 and this group contains more schools, including all Project 985 schools. To face the twenty-first century and meet the challenges of the world’s new technological revolution, the Chinese government concentrates resources around construction of 100 world-class universities.

  16. 16.

    In China, master’s degrees from non-high-quality universities are usually no more popular than high-quality bachelor’s degrees in the labor market. Thus, we only consider high-quality master’s degrees in our study.

  17. 17.

    This platform is one of the biggest online recruitment websites. At present, the number of valid registered users exceeds 100 million, more than 5 million valid vacancies are posted on the website daily, and more than 40 million resumes a week are sent to enterprises via this recruitment platform.

  18. 18.

    Because some vacancies only accept applicants of one type of gender, only when a vacancy accepts applicants of both types of gender did we deliver at most four resumes. According to the relevant results of regressions and tests, which are available upon request, our results are not sensitive to this delivery strategy.

  19. 19.

    We consulted some human-resource experts of financial companies. According to them, one vacancy posted by big (small) companies receives about 200 (50) resumes. We only submitted two or four resumes to one vacancy. Thus, the signal-to-noise ratio is relatively high. We cannot completely rule out the possibility that some companies realized that we were delivering fake resumes, but the probability should be relatively small.

  20. 20.

    We also estimated the ordinary least squares (OLS) regressions without the pair fixed effects but with robust standard errors clustered at the individual level. The results of these OLS regressions are presented in the appendices (e.g., Tables 10, 11, 12, 13, 14, 21, 22, 23, 24, and 25).

  21. 21.

    However, the differences in beauty premium by employer features and vacancy features all are insignificant. The relevant test results are available upon request.

  22. 22.

    The OLS estimate of Look_Male in Table 10 is statistically significant, in contrast.

  23. 23.

    “Better education” in this context indicates having higher educational degrees or graduating from better universities.

  24. 24.

    The OLS estimates of Look_Private in Table 12 show that private firms are less likely to discriminate with respect to ordinary men, but are more likely to prefer good-looking women, while other firms (i.e., foreign firms and SOEs) are more likely to discriminate with respect to ordinary-looking women.

  25. 25.

    Results of the OLS estimations in panel D of Table 12 suggest that the coefficient of the interaction term Look_Internet is significantly positive (negative) for male (female subsample) which infers that compared with other financial firms, Internet-based firms are more (less) likely to discriminate against ordinary-looking men (women).

  26. 26.

    The OLS results do indicate that males (females) are less (more) likely to suffer appearance discrimination when applying for sales-related jobs.

  27. 27.

    Research positions generally require better education and provide higher payment than sales. Thus, these two dummies reflecting job features are highly correlated.

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Acknowledgments

We are grateful to the editors, Junsen Zhang and Madeline Zavodny, two anonymous referees, as well as Pinghan Liang, Ruixin Wang, Xianghong Wang, Erte Xiao, and various seminar participants at the First China Labor Economists Forum Annual Conference, the 6th Xiamen University International Workshop on Experimental Economics, for useful comments and helpful suggestions. We also thank the anonymous reviewers for the insightful comments and suggestions.

Funding

Deng has received research grants from the National Natural Science Fund [Project No. 71874051], Li has received research grants from the National Social Science Fund Youth Project [Project No. 16CSH072], and Zhou has received research grants from the National Natural Science Fund Youth Project [Project No. 71703100].

Author information

Correspondence to Dong Zhou.

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The authors declare that they have no conflict of interest.

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Appendices

Appendix 1

Table 8 The basic layout of resume template

Appendix 2

Table 9 Description of information type in resume

Appendix 3. ID photo

Fig. 1
figure1

a Attractive woman. b Ordinary woman

Fig. 2
figure2

a Attractive man. b Ordinary man

Appendix 4. Tables

Table 10 Look and callback by gender: OLS estimation
Table 11 Look and callback by education: OLS estimation
Table 12 Look and callback by employers’ attributes: OLS estimation
Table 13 Look and callback by job features: OLS estimation
Table 14 Look and callback: OLS estimation including all interaction terms
Table 15 Look and callback: probit estimation
Table 16 Beauty score and callback by gender: fixed effect estimation
Table 17 Beauty score and callback by education: fixed effect estimation
Table 18 Beauty score and callback by employers attributes: fixed effect estimation
Table 19 Beauty score and callback by job features: fixed effect estimation
Table 20 Beauty score and callback: fixed effect estimation including all interaction terms
Table 21 Beauty score and callback by gender: OLS estimation
Table 22 Beauty score and callback by education: OLS estimation
Table 23 Beauty score and callback by employers attributes: OLS estimation
Table 24 Beauty score and callback by job features: OLS estimation
Table 25 Beauty score and callback: OLS estimation including all interaction terms

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Deng, W., Li, D. & Zhou, D. Beauty and job accessibility: new evidence from a field experiment. J Popul Econ (2019) doi:10.1007/s00148-019-00744-7

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Keywords

  • Appearance discrimination
  • Beauty premium
  • Pre-interview stage
  • Field experiment

JEL classifications

  • C93
  • I21
  • J71