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"As Rare as a Panda": How Facial Attractiveness, Gender, and Occupation Affect Interview Callbacks at Chinese Firms

Author

Listed:
  • Maurer-Fazio, Margaret

    (Bates College)

  • Lei, Lei

    (University of Maryland)

Abstract

This study explores how both gender and facial attractiveness affect job candidates' chances of obtaining interviews in China's dynamic Internet job board labor market. It examines how discrimination based on these attributes varies over occupation, location, and firms' ownership type and size. We employ a resume (correspondence) audit methodology. We establish the facial attractiveness of candidate photos via an online survey. 24,192 applications are submitted to 12,096 job postings across four occupations in 6 Chinese cities. We find sizable differences in the interview callback rates of attractive and unattractive job candidates. Job candidates with unattractive faces need to put in 33% more applications than their attractive counterparts to obtain the same number of interview callbacks. Women are preferred to men in three of our four occupations. Women on average need to put in only 91% as many applications as men to obtain the same number of interview callbacks.

Suggested Citation

  • Maurer-Fazio, Margaret & Lei, Lei, 2014. ""As Rare as a Panda": How Facial Attractiveness, Gender, and Occupation Affect Interview Callbacks at Chinese Firms," IZA Discussion Papers 8605, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp8605
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    References listed on IDEAS

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    1. Stijn Baert & Bart Cockx & Niels Gheyle & Cora Vandamme, 2015. "Is There Less Discrimination in Occupations Where Recruitment Is Difficult?," ILR Review, Cornell University, ILR School, vol. 68(3), pages 467-500, May.
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    3. López Bóo, Florencia & Rossi, Martín A. & Urzúa, Sergio S., 2013. "The labor market return to an attractive face: Evidence from a field experiment," Economics Letters, Elsevier, vol. 118(1), pages 170-172.
    4. Riach Peter A & Rich Judith, 2006. "An Experimental Investigation of Sexual Discrimination in Hiring in the English Labor Market," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 6(2), pages 1-22, January.
    5. Marianne Bertrand & Sendhil Mullainathan, 2004. "Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination," American Economic Review, American Economic Association, vol. 94(4), pages 991-1013, September.
    6. Booth, Alison & Leigh, Andrew, 2010. "Do employers discriminate by gender? A field experiment in female-dominated occupations," Economics Letters, Elsevier, vol. 107(2), pages 236-238, May.
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    8. AfDB AfDB, . "Annual Report 2012," Annual Report, African Development Bank, number 461.
    9. Peter Kuhn & Kailing Shen, 2013. "Gender Discrimination in Job Ads: Evidence from China," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(1), pages 287-336.
    10. Margaret Maurer-Fazio, 2012. "Ethnic discrimination in China's internet job board labor market," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 1(1), pages 1-24, December.
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    12. repec:feb:natura:0058 is not listed on IDEAS
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    Cited by:

    1. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    2. Wang-Sheng Lee & Zhong Zhao, 2017. "Height, Weight and Well-Being for Rural, Urban and Migrant Workers in China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 132(1), pages 117-136, May.
    3. Lucia Kureková & Miroslav Beblavý & Anna Thum-Thysen, 2015. "Using online vacancies and web surveys to analyse the labour market: a methodological inquiry," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-20, December.

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    More about this item

    Keywords

    facial attractiveness; hiring; Chinese firms; discrimination; field experiments; gender; beauty; internet job boards; resume correspondence audit study;
    All these keywords.

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East

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