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The Use of Field Experiments for Studies of Employment Discrimination: Contributions, Critiques, and Directions for the Future

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  • Devah Pager

    (Princeton University)

Abstract

Have we conquered the problems of racial discrimination? Or have acts of discrimination become too subtle and covert for detection? This discussion serves to situate current debates about discrimination within the context of available measurement techniques. In this article, the author (1) considers the arguments from recent debates over the contemporary relevance of labor market discrimination; (2) provides a detailed introduction to experimental field methods for studying discrimination (also called audit studies), including an overview of the findings of recent audit studies of employment; (3) addresses the primary critiques of the audit methodology and the potential threats to the validity of studies of this kind; and (4) considers how we might reconcile evidence from field experiments with those from analyses of large-scale survey data, each of which points to markedly different conclusions. Only by gathering rigorous empirical evidence can we begin to understand the nature of race and racial discrimination in labor markets today.

Suggested Citation

  • Devah Pager, 2007. "The Use of Field Experiments for Studies of Employment Discrimination: Contributions, Critiques, and Directions for the Future," The ANNALS of the American Academy of Political and Social Science, , vol. 609(1), pages 104-133, January.
    • Handle: RePEc:sae:anname:v:609:y:2007:i:1:p:104-133
    DOI: 10.1177/0002716206294796
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    References listed on IDEAS

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    2. Nicolás Ajzenman & Bruno Ferman & Sant’Anna Pedro C., 2023. "Discrimination in the Formation of Academic Networks: A Field Experiment on #EconTwitter," Working Papers 235, Red Nacional de Investigadores en Economía (RedNIE).
    3. Cai, Guowei & Deng, Weiguang & Li, Xue, 2023. "Student leadership experience and job accessibility: An experiment from China," China Economic Review, Elsevier, vol. 78(C).
    4. Christopher Rauh & Aranu Valladares-Esteban, 2023. "On the black-white gaps in labor supply and earnings over the lifecycle in the US," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 424-449, December.
    5. Christopher T. Bennett, 2023. "Labor Market Returns to MBAs From Less‐Selective Universities: Evidence From a Field Experiment During COVID‐19," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(2), pages 525-551, March.
    6. Rebecca Tunstall & Anne Green & Ruth Lupton & Simon Watmough & Katie Bates, 2014. "Does Poor Neighbourhood Reputation Create a Neighbourhood Effect on Employment? The Results of a Field Experiment in the UK," Urban Studies, Urban Studies Journal Limited, vol. 51(4), pages 763-780, March.
    7. Ahmad, Husnain F & Banuri, Sheheryar & Bokhari, Farasat, 2024. "Discrimination in healthcare: A field experiment with Pakistan's transgender community," Labour Economics, Elsevier, vol. 87(C).
    8. Mladen Adamovic & Andreas Leibbrandt, 2023. "A large‐scale field experiment on occupational gender segregation and hiring discrimination," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 62(1), pages 34-59, January.
    9. Fausto Hernández-Trillo & Ana Laura Martínez-Gutiérrez, 2022. "The Dark Road to Credit Applications: The Small-Business Case of Mexico," Journal of Financial Services Research, Springer;Western Finance Association, vol. 62(1), pages 1-25, October.
    10. Haoran He & Sherry Xin Li & Yuling Han, 2023. "Labor Market Discrimination against Family Responsibilities: A Correspondence Study with Policy Change in China," Journal of Labor Economics, University of Chicago Press, vol. 41(2), pages 361-387.
    11. Carrasco, Raquel & Nuevo-Chiquero, Ana, 2024. "Women's Sexual Orientation and Occupational Tasks: Partners, Prejudice, and Motherhood," IZA Discussion Papers 17318, Institute of Labor Economics (IZA).
    12. Thu T. Nguyen & Shaniece Criss & Amani M. Allen & M. Maria Glymour & Lynn Phan & Ryan Trevino & Shrikha Dasari & Quynh C. Nguyen, 2019. "Pride, Love, and Twitter Rants: Combining Machine Learning and Qualitative Techniques to Understand What Our Tweets Reveal about Race in the US," IJERPH, MDPI, vol. 16(10), pages 1-19, May.

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