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Monitoring Harassment in Organizations

Author

Listed:
  • Laura Boudreau

    (Columbia University)

  • Sylvain Chassang

    (Princeton University)

  • Ada González-Torre

    (Ben Gurion University)

  • Rachel Heath

    (University of Washington)

Abstract

We evaluate secure survey methods designed for the ongoing monitoring of harassment in organizations. We use the resulting data to answer policy relevant questions about the nature of harassment: How prevalent is it? What share of managers is responsible for the misbehavior? How isolated are its victims? To do so, we partner with a large Bangladeshi garment manufacturer to experiment with different designs of phone-based worker surveys. Garbling responses to sensitive questions by automatically recording a random subset as complaints increases reporting of physical harassment by 288%, sexual harassment by 269%, and threatening behavior by 46%. A rapport-building treatment has an insignificant aggregate effect, but may affect men and women differently. Removing team identifiers from survey responses does not significantly increase reporting and prevents the computation of policy-relevant team-level statistics. The resulting data shows that harassment is widespread, that the problem is not restricted to a minority of managers, and that victims are often isolated in teams.

Suggested Citation

  • Laura Boudreau & Sylvain Chassang & Ada González-Torre & Rachel Heath, 2023. "Monitoring Harassment in Organizations," Working Papers 2022-19, Princeton University. Economics Department..
  • Handle: RePEc:pri:econom:2022-19
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    File URL: https://www.sylvainchassang.org/assets/papers/sensitive_stats.pdf
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    References listed on IDEAS

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    1. Ing-Haw Cheng & Alice Hsiaw, 2022. "Reporting Sexual Misconduct in the #MeToo Era," American Economic Journal: Microeconomics, American Economic Association, vol. 14(4), pages 761-803, November.
    2. Makowsky, Michael D. & Wang, Siyu, 2018. "Embezzlement, whistleblowing, and organizational architecture: An experimental investigation," Journal of Economic Behavior & Organization, Elsevier, vol. 147(C), pages 58-75.
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    6. Sylvain Chassang & Christian Zehnder, 2019. "Secure Survey Design in Organizations: Theory and Experiments," NBER Working Papers 25918, National Bureau of Economic Research, Inc.
    7. Sylvain Chassang & Juan Ortner, 2019. "Collusion in Auctions with Constrained Bids: Theory and Evidence from Public Procurement," Journal of Political Economy, University of Chicago Press, vol. 127(5), pages 2269-2300.
    8. Olle Folke & Johanna Rickne, 2022. "Sexual Harassment and Gender Inequality in the Labor Market [High Wage Workers and High Wage Firms]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(4), pages 2163-2212.
    9. Dahl, Gordon B. & Knepper, Matthew, 2021. "Why Is Workplace Sexual Harassment Underreported? The Value of outside Options amid the Threat of Retaliation," IZA Discussion Papers 14740, Institute of Labor Economics (IZA).
    10. Mehmet Bac, 2009. "An economic rationale for firing whistleblowers," European Journal of Law and Economics, Springer, vol. 27(3), pages 233-256, June.
    11. Naila Kabeer & Lopita Huq & Munshi Sulaiman, 2020. "Paradigm Shift or Business as Usual? Workers’ Views on Multi‐stakeholder Initiatives in Bangladesh," Development and Change, International Institute of Social Studies, vol. 51(5), pages 1360-1398, September.
    12. Abhijit V. Banerjee & Sylvain Chassang & Sergio Montero & Erik Snowberg, 2020. "A Theory of Experimenters: Robustness, Randomization, and Balance," American Economic Review, American Economic Association, vol. 110(4), pages 1206-1230, April.
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    Cited by:

    1. Nugent, Jeffrey B., 2023. "A new institutional perspective on business associations: Filling a gap between firms and states in the dynamic analysis of Richard Day," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 49-59.

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    More about this item

    Keywords

    Harassment; Garbled Survey Method; Direct Survey Method; Bangladesh;
    All these keywords.

    JEL classification:

    • D79 - Microeconomics - - Analysis of Collective Decision-Making - - - Other
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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