IDEAS home Printed from https://ideas.repec.org/p/zbw/i4rdps/256.html
   My bibliography  Save this paper

Replication: "Physical Disability and Labor Market Discrimination: Evidence from a Video Résumé Field Experiment"

Author

Listed:
  • Gallegos, Sebastian

Abstract

Bellemare et al. (2023b) examine discrimination against individuals with physical disabilities in the labor market in Quebec, Canada. Their findings indicate that callbacks from potential employers decrease by 25 percentage points if physical disability is (randomly) revealed. Callbacks increase by 10 percentage points if there is a video resume (randomly) sent to potential employers. In this document, we first conduct a computational reproduction using the replication package. Then, we test the robustness of the findings to the inclusion of different covariates, selecting them with a Double Selection Lasso approach. We complement the analysis estimating heterogeneous treatment effects using Causal Forests, which allow us to uncover data-driven subgroups and test their responses to the treatment. We find that Bellemare et al. (2023b)'s estimates are stable across these robustness checks.

Suggested Citation

  • Gallegos, Sebastian, 2025. "Replication: "Physical Disability and Labor Market Discrimination: Evidence from a Video Résumé Field Experiment"," I4R Discussion Paper Series 256, The Institute for Replication (I4R).
  • Handle: RePEc:zbw:i4rdps:256
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/324167/1/I4R-DP256.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls," Papers 1201.0224, arXiv.org, revised May 2012.
    2. Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
    3. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"," Papers 1305.6099, arXiv.org, revised Jun 2013.
    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. Yash Raj Shrestha & Vivianna Fang He & Phanish Puranam & Georg von Krogh, 2021. "Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?," Organization Science, INFORMS, vol. 32(3), pages 856-880, May.
    2. Chakravorty, Bhaskar & Arulampalam, Wiji & Bhatiya, Apurav Yash & Imbert, Clément & Rathelot, Roland, 2024. "Can information about jobs improve the effectiveness of vocational training? Experimental evidence from India," Journal of Development Economics, Elsevier, vol. 169(C).
    3. Ian W. McKeague & Min Qian, 2015. "An Adaptive Resampling Test for Detecting the Presence of Significant Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1422-1433, December.
    4. Alexandre Belloni & Victor Chernozhukov, 2015. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1449-1451, December.
    5. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    6. David Cheng & Abhishek Chakrabortty & Ashwin N. Ananthakrishnan & Tianxi Cai, 2020. "Estimating average treatment effects with a double‐index propensity score," Biometrics, The International Biometric Society, vol. 76(3), pages 767-777, September.
    7. Strittmatter, Anthony & Wunsch, Conny, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," Working papers 2021/05, Faculty of Business and Economics - University of Basel.
    8. Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
    9. Clarke, Damian, 2023. "The Economics of Abortion Policy," IZA Discussion Papers 16395, Institute of Labor Economics (IZA).
    10. Chakravorty, Bhaskar & Bhatiya, Apurav Yash & Imbert, Clément & Lohnert, Maximilian & Panda, Poonam & Rathelot, Roland, 2023. "Impact of the COVID-19 crisis on India’s rural youth: Evidence from a panel survey and an experiment," World Development, Elsevier, vol. 168(C).
    11. Bütikofer, Aline & Ginja, Rita & Landaud, Fanny & Løken, Katrine V., 2020. "School Selectivity, Peers, and Mental Health," Working Papers in Economics 5/20, University of Bergen, Department of Economics.
    12. Helmut Wasserbacher & Martin Spindler, 2024. "Credit Ratings: Heterogeneous Effect on Capital Structure," Papers 2406.18936, arXiv.org.
    13. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
    14. González, Felipe & Muñoz, Pablo & Prem, Mounu, 2021. "Lost in transition? The persistence of dictatorship mayors," Journal of Development Economics, Elsevier, vol. 151(C).
    15. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    16. D’Amour, Alexander & Ding, Peng & Feller, Avi & Lei, Lihua & Sekhon, Jasjeet, 2021. "Overlap in observational studies with high-dimensional covariates," Journal of Econometrics, Elsevier, vol. 221(2), pages 644-654.
    17. Do Nascimento Miguel, Jérémy, 2024. "Returns to quality in rural agricultural markets: Evidence from wheat markets in Ethiopia," Journal of Development Economics, Elsevier, vol. 171(C).
    18. Joseph Antonelli & Matthew Cefalu & Nathan Palmer & Denis Agniel, 2018. "Doubly robust matching estimators for high dimensional confounding adjustment," Biometrics, The International Biometric Society, vol. 74(4), pages 1171-1179, December.
    19. Alex Armand & Britta Augsburg & Antonella Bancalari, 2021. "Coordination and the poor maintenance trap: an experiment on public infrastructure in India," NOVAFRICA Working Paper Series wp2110, Universidade Nova de Lisboa, Nova School of Business and Economics, NOVAFRICA.
    20. John A. List & Ian Muir & Gregory Sun, 2024. "Using machine learning for efficient flexible regression adjustment in economic experiments," Econometric Reviews, Taylor & Francis Journals, vol. 44(1), pages 2-40, July.

    More about this item

    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:zbw:i4rdps:256. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://www.i4replication.org/ .

    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.