Are There “Hot Spots” of Primary Impairments among New SSDI Awardees – and Do We Know Why?



This paper examines local-level variation in the primary disabling conditions of new awardees for Social Security Disability Insurance (SSDI) from 2005 through 2018. It uses data from the Social Security Administration’s Disability Analysis File data linked to other publicly available information from the American Community Survey and Area Health Resource File. The analysis is at the level of U.S. Census Bureau Public Use Microdata Areas (PUMAs). The paper documents the share of awards in each PUMA and year in one of five impairment group categories, selected to align with areas of strong policy interest. In each impairment group, the paper identifies “hot spots” as PUMAs in which the share of awards for that condition is in the top 10 percent relative to other PUMAs in the same year. The paper documents the geographic variation in award shares and hot spots using maps and uses regression analysis to explore relationships between SSDI award shares by impairment group and a range of PUMA-level factors. The findings are descriptive and should not be interpreted causally.

We find that:

  • SSDI awards by impairment groups have geographic patterns that show important variation across state lines as well as within state borders. The share of awards for musculoskeletal disorders is higher in PUMAs in Appalachia and into the southeast, while award shares for mental disorders are highest in New England – persistently so in in Vermont, and New Hampshire. Award shares for circulatory and respiratory disorders are higher in the Mississippi Delta region and northward along the Mississippi River to and through Illinois and Indiana. There are no obvious patterns for the neoplasms, infectious diseases, injuries category nor the systems diseases category.
  • The general geographic patterns that show cross- and within-state differences are stable over time, but PUMA “hot spots” are not always the same in each year. In most cases, this simply reflects PUMAs that are just above the 90th percentile in some years and just below in others, rather than large swings in award shares within PUMAs over time. Nonetheless, areas with high award shares in particular impairment groups do persist over time.
  • Regression analysis shows that demographic and socioeconomic factors explain part of the observed variation in award shares, but the estimated effects are small, suggesting that other factors may be critical determinants of local-level variation in award shares.

The policy implications of the findings are:

  • When considering future caseload patterns, it may be important to consider local-level predictors of new awards, overall and by impairment type. Some of the factors that might affect award shares for certain disabling conditions include environmental exposures and features of the disability determination process (such as distance to a field office and examiner stringency). Because of concerns about multicollinearity, it is difficult to incorporate the full range of observable factors that might affect awards by impairment category. Additionally, other factors such as the efficacy of legal representation when filing claims or social networks and attitudes toward benefit receipt may be important determinants of SSDI award patterns but more difficult to measure in a way that would allow for them to be incorporated into a regression model.
  • Qualitative research on geographic patterns in new benefit awards may help SSA better understand program trends, as well as the supports that new beneficiaries might need if they want to consider leaving the program rolls.

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