Using Administrative Data to Validate HRS Survey Responses on Application for DI and SSI Disability Benefits
In this paper, we use administrative data from the Social Security Administration to validate survey responses for the Health and Retirement Study (HRS) regarding the application for disability benefits from Social Security Disability Insurance (DI) or Supplemental Security Income (SSI), focusing on applications that occurred after individuals entered the HRS. In our samples, amongst those that the administrative data identifies as having applied for DI or SSI, over 40% either do not report having applied or inaccurately identify whether or not the application was successful. We find some evidence that the less well educated, those with cognitive limitations, and those experiencing a health limitation on their capacity for work are more likely to misreport applications. We also explore the effect that reporting errors have on parameter estimates in a simple model of the application for DI benefits. Parameter estimates are qualitatively similar regardless of whether we use survey or administrative data to identify the application for DI benefits in our model.
- There is some discrepancy between survey responses and administrative records, even for salient experiences such as DI/SSI applications and awards.
- People who are less well educated, have some degree of cognitive limitations, or experience health-related limitations on their capacity to work are more likely to misreport.
- Although a researcher can identify DI/SSI applications using either the HRS or Social Security administrative data, in an empirical setting, the two sources of data yield qualitatively similar results in estimations of factors that drive initial DI/SSI applications in a simple model of the application process.
Brown, Charles, John Bound, and Chichun Fang. 2023. “Using Administrative Data to Validate HRS Survey Responses on Application for DI and SSI Disability Benefits.” Ann Arbor, MI. University of Michigan Retirement and Disability Research Center (MRDRC) Working Paper; MRDRC WP 2023-462. https://mrdrc.isr.umich.edu/publications/papers/pdf/wp462.pdf
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Paper IDWP 2023-462