IDEAS home Printed from https://ideas.repec.org/p/sef/csefwp/681.html
   My bibliography  Save this paper

Gender-science Implicit Association and Employment Decisions

Author

Listed:

Abstract

In this paper, we document that implicit associations, measured by the gender-science implicit association test, explain employment decisions, both in terms of access to the labour market and in terms of career advancement. In both cases, when choosing between a female and a male worker with the same ex-ante ability, the higher the male-science implicit association of the employer, the higher her/his likelihood of hiring/promoting a male intentionally and the lower her/his likelihood of leaving the decision to chance. Increasing the incentives to employers does not vary the effect of implicit gender-science association which is also not heterogeneous by gender, age or income earned.

Suggested Citation

  • Francesca Gioia & Giovanni Immordino, 2023. "Gender-science Implicit Association and Employment Decisions," CSEF Working Papers 681, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
  • Handle: RePEc:sef:csefwp:681
    as

    Download full text from publisher

    File URL: https://www.csef.it/WP/wp681.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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. 5(2), pages 1-22, January.
    2. Lucia Corno & Eliana La Ferrara & Justine Burns, 2022. "Interaction, Stereotypes, and Performance: Evidence from South Africa," American Economic Review, American Economic Association, vol. 112(12), pages 3848-3875, December.
    3. Katherine B. Coffman & Christine L. Exley & Muriel Niederle, 2021. "The Role of Beliefs in Driving Gender Discrimination," Management Science, INFORMS, vol. 67(6), pages 3551-3569, June.
    4. Michela Carlana, 2019. "Implicit Stereotypes: Evidence from Teachers’ Gender Bias," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1163-1224.
    5. Eric Luis Uhlmann & Anthony Greenwald & Andrew Poehlmann & Mahzarin Banaji, 2009. "Understanding and Using the Implicit Association Test: III. Meta-Analysis of Predictive Validity," Post-Print hal-00516146, HAL.
    6. Alberto Alesina & Michela Carlana & Eliana La Ferrara & Paolo Pinotti, 2018. "Revealing Stereotypes: Evidence from Immigrants in Schools," NBER Working Papers 25333, National Bureau of Economic Research, Inc.
    7. Azmat, Ghazala & Petrongolo, Barbara, 2014. "Gender and the labor market: What have we learned from field and lab experiments?," Labour Economics, Elsevier, vol. 30(C), pages 32-40.
    8. Della Giusta, Marina & Bosworth, Steven J., 2020. "Bias and Discrimination: What Do We Know?," IZA Discussion Papers 13983, Institute of Labor Economics (IZA).
    9. Rooth, Dan-Olof, 2010. "Automatic associations and discrimination in hiring: Real world evidence," Labour Economics, Elsevier, vol. 17(3), pages 523-534, June.
    10. Phelps, Edmund S, 1972. "The Statistical Theory of Racism and Sexism," American Economic Review, American Economic Association, vol. 62(4), pages 659-661, September.
    11. Isabelle Régner & Catherine Thinus-Blanc & Agnès Netter & Toni Schmader & Pascal Huguet, 2019. "Committees with implicit biases promote fewer women when they do not believe gender bias exists," Nature Human Behaviour, Nature, vol. 3(11), pages 1171-1179, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sevilla, Almudena, 2020. "Gender Economics: An Assessment," IZA Discussion Papers 13877, Institute of Labor Economics (IZA).
    2. J. Michelle Brock & Ralph De Haas, 2023. "Discriminatory Lending: Evidence from Bankers in the Lab," American Economic Journal: Applied Economics, American Economic Association, vol. 15(2), pages 31-68, April.
    3. Barron, Kai & Ditlmann, Ruth & Gehrig, Stefan & Schweighofer-Kodritsch, Sebastian, 2020. "Explicit and implicit belief-based gender discrimination: A hiring experiment," Discussion Papers, Research Unit: Economics of Change SP II 2020-306, WZB Berlin Social Science Center.
    4. Kondylis,Florence & Legovini,Arianna & Vyborny,Kate & Zwager,Astrid Maria Theresia & Cardoso De Andrade,Luiza, 2020. "Demand for Safe Spaces : Avoiding Harassment and Stigma," Policy Research Working Paper Series 9269, The World Bank.
    5. Michela Carlana, 2019. "Implicit Stereotypes: Evidence from Teachers’ Gender Bias," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1163-1224.
    6. Dylan Glover & Amanda Pallais & William Pariente, 2017. "Discrimination as a Self-Fulfilling Prophecy: Evidence from French Grocery Stores," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(3), pages 1219-1260.
    7. Mari, Gabriele & Luijkx, Ruud, 2020. "Gender, Parenthood, and Hiring Intentions in Sex-Typical Jobs: A Survey Experiment," SocArXiv kwdyp, Center for Open Science.
    8. Finseraas, Henning & Johnsen, Åshild A. & Kotsadam, Andreas & Torsvik, Gaute, 2016. "Exposure to female colleagues breaks the glass ceiling—Evidence from a combined vignette and field experiment," European Economic Review, Elsevier, vol. 90(C), pages 363-374.
    9. Hipp, Lena, 2018. "Do hiring practices penalize women and benefit men for having children? Experimental evidence from Germany," SocArXiv 4a68p, Center for Open Science.
    10. Hipp, Lena, 2020. "Do hiring practices penalize women and benefit men for having children? Experimental evidence from Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 36(2), pages 250-264.
    11. Igor Asanov & Maria Mavlikeeva, 2023. "Can group identity explain the gender gap in the recruitment process?," Industrial Relations Journal, Wiley Blackwell, vol. 54(1), pages 95-113, January.
    12. Miguel A. Fonseca & Ashley McCrea, 2023. "The role of shortlisting in shifting gender beliefs on performance: experimental evidence," Discussion Papers 2315, University of Exeter, Department of Economics.
    13. Daniel J. Lee, 2016. "Racial bias and the validity of the Implicit Association Test," WIDER Working Paper Series wp-2016-53, World Institute for Development Economic Research (UNU-WIDER).
    14. Ash, Elliott & Chen, Daniel L. & Ornaghi, Arianna, 2020. "Gender Attitudes in the Judiciary:Evidence from U.S. Circuit Courts," CAGE Online Working Paper Series 462, Competitive Advantage in the Global Economy (CAGE).
    15. Daniel J. Lee, 2016. "Racial bias and the validity of the Implicit Association Test," WIDER Working Paper Series 053, World Institute for Development Economic Research (UNU-WIDER).
    16. Kahori Ishibashi & Ryo Takahashi, 2024. "Too“hot”to recognize her rights: The impact of climate change on attitude toward gender equality," Working Papers 2310, Waseda University, Faculty of Political Science and Economics.
    17. Birkelund, Gunn Elisabeth & Lancee, Bram & Larsen, Edvard Nergård & Polavieja, Javier G. & Radl, Jonas & Yemane, Ruta, 2022. "Gender Discrimination in Hiring: Evidence from a Cross-National Harmonized Field Experiment," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 38(3), pages 337-354.
    18. Clara Cortina & Jorge Rodríguez & M. José González, 2021. "Mind the Job: The Role of Occupational Characteristics in Explaining Gender Discrimination," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(1), pages 91-110, July.
    19. Fenske, James & Castagnetti, Alessandro & Sharma, Karmini, 2020. "Attribution Bias by Gender : Evidence from a Laboratory Experiment," CAGE Online Working Paper Series 452, Competitive Advantage in the Global Economy (CAGE).
    20. Button, Patrick & Walker, Brigham, 2020. "Employment discrimination against Indigenous Peoples in the United States: Evidence from a field experiment," Labour Economics, Elsevier, vol. 65(C).

    More about this item

    Keywords

    Gender; Labor discrimination; Implicit Association.;
    All these keywords.

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sef:csefwp:681. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Maria Carannante (email available below). General contact details of provider: https://edirc.repec.org/data/cssalit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.