No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Racial Disparities in Federal Disability Benefits
Abstract
"We estimate racial differences in the Social Security Administration's (SSA) decision to award federal disability benefits using newly available data, multivariate econometric models, and Oaxaca decomposition methods. We focus on the appellate level of SSA's disability decision-making process. We find that for claimants represented by attorneys""there is no statistically significant difference in benefit award rates between whites and African-Americans. However, for claimants without attorney representation, we find sizable and significant differences between whites and African-Americans." ("JEL" J15, H53) Copyright 2006 Western Economic Association International.
... Their results suggest that those who applied for SSDI were significantly more likely to have had doctor visits and community mental health provider visits; after controlling for severity of the mental health need, these authors concluded that the providers were a source of information about the SSI/SSDI process and facilitated the applicant's knowledge. Godtland and colleagues similarly found that external support in the SSDI process affects the outcome of disability decisions [37]. Specifically, these authors found that African-American applicants for SSA disability had similar likelihood of being awarded disability insurance as white applicants only when they were represented by an attorney at a disability hearing [37]. ...
... Godtland and colleagues similarly found that external support in the SSDI process affects the outcome of disability decisions [37]. Specifically, these authors found that African-American applicants for SSA disability had similar likelihood of being awarded disability insurance as white applicants only when they were represented by an attorney at a disability hearing [37]. When African-American applicants were not represented by an attorney, they had statistically lower likelihood than white applicants of being awarded [37]. ...
... Specifically, these authors found that African-American applicants for SSA disability had similar likelihood of being awarded disability insurance as white applicants only when they were represented by an attorney at a disability hearing [37]. When African-American applicants were not represented by an attorney, they had statistically lower likelihood than white applicants of being awarded [37]. Unfortunately, in that study, significant differences in attorney representation were found, with African-American applicants being represented 58% of the time, and white applicants being represented 71% of the time [37]. ...
Background
Research on children and youth on the autism spectrum reveal racial and ethnic disparities in access to healthcare and utilization, but there is less research to understand how disparities persist as autistic adults age. We need to understand racial-ethnic inequities in obtaining eligibility for Medicare and/or Medicaid coverage, as well as inequities in spending for autistic enrollees under these public programs.
Methods
We conducted a cross-sectional cohort study of U.S. publicly-insured adults on the autism spectrum using 2012 Medicare-Medicaid Linked Enrollee Analytic Data Source (n = 172,071). We evaluated differences in race-ethnicity by eligibility (Medicare-only, Medicaid-only, Dual-Eligible) and spending.
Findings
The majority of white adults (49.87%) were full-dual eligible for both Medicare and Medicaid. In contrast, only 37.53% of Black, 34.65% Asian/Pacific Islander, and 35.94% of Hispanic beneficiaries were full-dual eligible for Medicare and Medicare, with most only eligible for state-funded Medicaid. Adjusted logistic models controlling for gender, intellectual disability status, costly chronic condition, rural status, county median income, and geographic region of residence revealed that Black beneficiaries were significantly less likely than white beneficiaries to be dual-eligible across all ages. Across these three beneficiary types, total spending exceeded $10 billion. Annual total expenditures median expenditures for full-dual and Medicaid-only eligible beneficiaries were higher among white beneficiaries as compared with Black beneficiaries.
Conclusions
Public health insurance in the U.S. including Medicare and Medicaid aim to reduce inequities in access to healthcare that might exist due to disability, income, or old age. In contrast to these ideals, our study reveals that racial-ethnic minority autistic adults who were eligible for public insurance across all U.S. states in 2012 experience disparities in eligibility for specific programs and spending. We call for further evaluation of system supports that promote clear pathways to disability and public health insurance among those with lifelong developmental disabilities.
... Analysis of individual-level data can be used to identify persistent barriers to function across specific patient populations, informing changes in policy and practice aimed at reducing outcome disparities. Better information on function and its contributing factors can also inform financial decisions such as reimbursement for care or government disability benefits, which directly affect long-term functional outcomes, to reduce observed disparities by better addressing the specific needs and values of individuals [79,80]. However, as we discuss in the following section, these significant opportunities for positive impact can only be achieved through careful design and with a thorough awareness of the potential risks of inappropriately designed technologies. ...
People with disabilities disproportionately experience negative health outcomes. Purposeful analysis of information on all aspects of the experience of disability across individuals and populations can guide interventions to reduce health inequities in care and outcomes. Such an analysis requires more holistic information on individual function, precursors and predictors, and environmental and personal factors than is systematically collected in current practice. We identify 3 key information barriers to more equitable information: (1) a lack of information on contextual factors that affect a person’s experience of function; (2) underemphasis of the patient’s voice, perspective, and goals in the electronic health record; and (3) a lack of standardized locations in the electronic health record to record observations of function and context. Through analysis of rehabilitation data, we have identified ways to mitigate these barriers through the development of digital health technologies to better capture and analyze information about the experience of function. We propose 3 directions for future research on using digital health technologies, particularly natural language processing (NLP), to facilitate capturing a more holistic picture of a patient’s unique experience: (1) analyzing existing information on function in free text documentation; (2) developing new NLP-driven methods to collect information on contextual factors; and (3) collecting and analyzing patient-reported descriptions of personal perceptions and goals. Multidisciplinary collaboration between rehabilitation experts and data scientists to advance these research directions will yield practical technologies to help reduce inequities and improve care for all populations.
... In our bivariate analyses, rent burden was not strongly associated with control variables including demographic characteristics, HIV status, having a mental health condition, and being convicted of a felony, though there was a trend toward African Americans being overrepresented in the no rent burden group. Since participants in the no rent burden group had no legitimate income source, this trend could reflect racial disparities in accessing disability benefits as well as employment (Godtland, Grgich, Petersen, Sloane, & Walker, 2007). It is somewhat surprising that felon status was not associated with higher rent burden, as people with felony convictions often face barriers to gaining income and accessing services such as housing subsidy programs (Lichtenstein, 2009;Millstein, 2011). ...
A growing body of health determinants research recognizes that housing and health are intimately linked. This study explores the relationship between rent burden (the ratio of rent to income) and health risk behaviors among a sample of single room occupancy (SRO) building residents. Cross-sectional data were gathered from a sample of 162 residents living in privately owned, for-profit SROs in Chicago. Findings indicated that participants who had full rental subsidies and thus were designated in a no-rent-burden category were more likely to engage in risk behaviors including illicit drug use, having multiple sexual partners, and having sex without a condom, in comparison to participants with moderate or high-rent burdens. These findings suggest that interventions to increase housing stability and affordability and bolster reliable income sources (in addition to rental subsidies) may be key in reducing risk behaviors and improving health for vulnerably housed populations such as SRO residents.
In this ambitious collection, Zofia Bednarz and Monika Zalnieriute bring together leading experts to shed light on how artificial intelligence (AI) and automated decision-making (ADM) create new sources of profits and power for financial firms and governments. Chapter authors—which include public and private lawyers, social scientists, and public officials working on various aspects of AI and automation across jurisdictions—identify mechanisms, motivations, and actors behind technology used by Automated Banks and Automated States, and argue for new rules, frameworks, and approaches to prevent harms that result from the increasingly common deployment of AI and ADM tools. Responding to the opacity of financial firms and governments enabled by AI, Money, Power and AI advances the debate on scrutiny of power and accountability of actors who use this technology. This title is available as Open Access on Cambridge Core.
We develop methodology to identify medical eligibility for Social Security’s Disability programs using publicly available data. Using a structural model of Social Security’s disability determination process estimated on a sample of applicants, we make out-of-sample predictions of medical eligibility for non-beneficiaries in the general population aged 18–64. This methodology defines an observable,
publicly-available indicator of disability consistent with Social Security’s definition. This would be useful
in a wide number of applications and may be of particular interest to empirical labor economists. We
find that 2.9% of this population were medically eligible but not receiving disability benefits early in
1992. Our methodology permits use of publicly available survey data to analyze medical criteria that
determine who receives disability benefits. We consider effects of sample selection adjustments, sample
restrictions, and several methods of estimating eligibility from continuous probabilities. Our preferred
measure outperforms the conventional single variable model based on the “prevented” measure.
We provide an empirical analysis of the Social Security disability application, award, and appeal process using the Health and Retirement Survey (HRS). We show that the appeal option increases the award probability from 46% to 73%. However, this comes at the cost of significant delays: the duration between application and award is over three times longer for those who are awarded benefits after one or more stages of appeal. Our results reveal the importance of self-selection in application and appeal decisions. In particular, an individual's self-assessed disability status emerges as one of the most powerful predictors of application, appeal, and award decisions.
We estimate a multistage sequential logit model reflecting the structure of the disability determination process of the Social Security Administration (SSA). The model is estimated using household survey information exact-matched to SSA records on disability adjudications from 1989 to 1993. Under program provisions, different criteria dictate outcomes at different steps of the determination process. We find that, without the multistaged structural approach, effects of many important health, disability, and vocational factors are not readily discernible. As a result, split-sample predictions of overall allowance rates from the sequential model perform considerably better than do those for the conventional allowed/denied logit regression. © 2001 by the President and Fellows of Harvard College and the Massachusetts Institute of Technolog
This study analyzes gender differences in Social Security disability awards. Logit analysis is used to identify significant determinants of receiving a Social Security Disability Insurance (SSDI) or Supplemental Security Income (SSI) award. The results show that, even after controlling for applicant characteristics and nature of impairment, women receive Social Security disability awards at a significantly lower rate than do men. An analysis of the process whereby eligibility for SSDI is determined shows that men and women are equally likely to satisfy medical criteria for awards, but women, particularly women over age 55, are more likely to be rejected on the basis of vocational criteria.
This study investigates the determinants of applications for U.S. disability benefits between 1986 and 1993 using a semiparametric discrete factor procedure separately for men and women. Approximating a dynamic optimization model, the estimation accounts for a variety of potential biases that were unaddressed in prior studies. Our results indicate different responses of men and women to variations in policy measures. Past labor earnings and fringe benefits as well as benefit eligibility and benefit amounts clearly affect application behavior.
———. Social Security Disability: Most of Gender Differ-ence Explained. GAO/HEHS-94-94
- Washington
- Dc
Washington, DC, April 21, 1992. ———. Social Security Disability: Most of Gender Differ-ence Explained. GAO/HEHS-94-94. Washington, DC, May 1994. ———. SSA Disability Decision Making: Additional Steps Needed to Ensure Accuracy and Fairness at the Hear-ings Level.