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Disability Insurance: Error Rates and Gender Differences

Author

Listed:
  • Hamish low

    (University of Oxford and Institute for Fiscal Studies)

  • Luigi Pistaferri

    (Stanford University, SIEPR, NBER and CEPR)

Abstract
We show the extent of errors made in the award of disability insurance using matched survey-administrative data. False rejections (Type I errors) are widespread, and there are large gender differences in these type I error rates. Women with a severe, work-limiting, permanent impairment are 20 percentage points more likely to be rejected than men, controlling for the type of health condition, occupation, and a host of demographic characteristics. We investigate whether these gender differences in Type I errors are due to women being in better health than men, to women having lower pain thresholds, or to women applying more readily for disability insurance. None of these explanations are consistent with the data. We use evidence from disability vignettes to suggest that there are different acceptance thresholds for men and women. The differences by gender arise because women are more likely to be assessed as being able to find other work than observationally equivalent men. Despite this, after rejection, women with a self-reported work limitation do not return to work, compared to rejected women without a work limitation.

Suggested Citation

  • Hamish low & Luigi Pistaferri, 2019. "Disability Insurance: Error Rates and Gender Differences," Economics Papers 2019-W09, Economics Group, Nuffield College, University of Oxford.
    • Handle: RePEc:nuf:econwp:1909
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    File URL: https://www.nuffield.ox.ac.uk/economics/Papers/2019/2019W09_HL_LP_disability_gender.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. Naoki Aizawa & Soojin Kim & Serena Rhee, 2020. "Labor Market Screening and the Design of Social Insurance: An Equilibrium Analysis of the Labor Market for the Disabled," NBER Working Papers 27478, National Bureau of Economic Research, Inc.
    2. Ding Liu & Daniel L. Millimet, 2021. "Bounding the joint distribution of disability and employment with misclassification," Health Economics, John Wiley & Sons, Ltd., vol. 30(7), pages 1628-1647, July.
    3. Ashesh Rambachan, 2022. "Identifying Prediction Mistakes in Observational Data," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    Disability Insurance; Gender Differences.;

    JEL classification:

    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination

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