The Retirement and Disability Research Consortium (RDRC) is an extramural research program that was established by the U.S. Social Security Administration (SSA) in 2018. Mathematica’s Center for Studying Disability Policy has partnered with the Center for Retirement Research (CRR) at Boston College in this effort, along with CRR’s other partner organizations: Syracuse University, the Urban Institute, and the Brookings Institution.
Prior to 2018, SSA funded separate extramural research programs on retirement and disability. Mathematica’s Center for Studying Disability Policy was one of the centers participating in SSA’s Disability Research Consortium (DRC); more information about our work under the DRC can be accessed here.
The mission of the RDRC is to:
- Research and evaluate a wide array of topics related to Social Security's Old-Age, Survivors, and Disability Insurance and Supplemental Security Income programs and related federal policies;
- Disseminate information on these topics to policymakers, researchers, stakeholder organizations, and the general public; and
- Provide training and education to scholars and practitioners in research areas relevant to these topics.
The latest round of RDRC projects has been selected for FY2023. Our researchers were funded to conduct the following studies:
Mathematica and our partners have received RDRC funding to conduct 21 studies between 2018 and 2023 that add to the evidence base needed for policy changes to the disability support system. Learn more about these studies below.
RDRC publications by topic area
Disability Program Application, Participation, and Exit
Employment and Work-Related Overpayments
COVID-19
Disability Program Application, Participation, and Exit
Principal Investigators: Michael Levere, Haverford College and Mathematica; and David Wittenburg, Mathematica
With recent substantive declines in SSI applications, there are concerns that children who might be eligible for SSI are not able to access it. During the pandemic, applications declined dramatically. Yet this decline was not uniform across regions, consistent with long-standing geographical variation in child SSI participation. SSA has sought to reach vulnerable populations to support more equitable access, though it is difficult to determine how to target those efforts most efficiently. One critical step to make this determination is more fully understanding the number of potentially eligible children in an area, as well as their characteristics.
This project will estimate the number of child Medicaid recipients who might be eligible but are not currently receiving SSI using CMS administrative Medicaid data. Our analysis will use Medicaid claims data to identify children with similar health issues to those who are on SSI but who do not themselves receive SSI. Using longitudinal claims data, we will identify health patterns that are common among children receiving SSI: things like medications prescribed, reasons for office visits, and diagnosis codes. Our analysis will also identify areas with high concentrations of potentially eligible children and highlight characteristics of children eligible for but not yet receiving SSI benefits. By understanding the people who might be eligible for benefits, SSA can more effectively tailor its outreach efforts to encourage participation.
“How Do Continuing Disability Reviews Affect Child SSI Recipients?”
Principal Investigators: Michael Levere, Haverford College and Mathematica; David Wittenburg, Mathematica; and Jeffrey Hemmeter, Social Security Administration
The number of child SSI recipients has declined since 2013, falling by over 25 percent through February 2022. The number of continuing disability reviews (CDRs) during this period increased substantially to address a growing backlog, which had grown to nearly 350,000 cases by the start of 2015. These CDRs might therefore play a role in the aggregate decline in participation.
This project will explore how changes in CDRs since 2003 (when a backlog of reviews started to build) influenced the total SSI caseload over this period. We will summarize characteristics of those with benefits ceased over time and identify the share that returned to benefits within five years. Of particular interest is how these patterns have changed for recent “cessation” cohorts that did not face timely CDRs because of budgetary limitations. Using these patterns, we will conduct simulations to show how the SSI caseload would have evolved if CDRs had remained stable with no building backlog. The results could be used to inform discussion on the special program integrity funding SSA receives, as well as how CDRs contributed to recent caseload declines.
Principal Investigators: Jody Schimmel Hyde and Dara Lee Luca, Mathematica; Jonathan Schwabish, Urban Institute, and Paul O’Leary, U.S. Social Security Administration
This project will document the local-area predictors of flows onto the DI and SSI programs, and flows out of those programs due to work using combined data from SSA’s Disability Analysis File (DAF) and other national sources. Numerous studies have documented that local-level factors contribute to the share of working-age adults reporting a disability, the employment rate of workers with disabilities, and disability benefit receipt (for example, Rupp 2012; Nichols et al. 2017; Sevak and Schmidt 2018; and Gettens, et al. 2018). Yet, that research to date has not comprehensively assessed how these factors predict flows into and out of DI and SSI. The results of this study will inform SSA’s understanding of trends in disability and the drivers of disability benefit application, benefit receipt prevalence, and beneficiary employment milestones. The project will also produce a public use file of local-area data to facilitate future research and policy analysis.
Principal Investigators: Jody Schimmel-Hyde and Anna Hill, Mathematica
Previous work has documented substantial geographic variation in DI and SSI receipt, while a separate strand of literature has considered trends in disabling conditions among beneficiaries. Less is known about the extent of geographic variation in disabling conditions among federal disability beneficiaries. The types of available jobs, health behaviors, food deserts, and local health provider practices may mean that the types of disabling conditions most common among disability beneficiaries may vary at the local level.
This project will examine whether there are geographic “hot spots” in the country where the prevalence of certain primary impairments among DI and SSI awardees are substantially above average. In particular, we will identifying disabling condition hot spots among new adult awardees for each of five primary impairment groups from 2005-2018, separately for DI and SSI. Data visualizations will allow interested researchers to explore trends over time in awards by disabling condition type. We will also produce a manuscript that explore the association between local demographic, economic, and health factors and hot spots among the three most prevalent impairments among 2018 awardees (separately by SSI and DI). The analysis will leverage data from the Social Security Administration’s Disability Analysis File (DAF) and other publicly available data sources including the American Community Survey and Area Health Resource File. The results of this study will inform SSA’s understanding of geographic variation and trends in program awards.
Principal Investigators: Jody Schimmel-Hyde and Amal Harrati, Mathematica
The Health and Retirement Study (HRS) is the preeminent data source for research on the financial decision-making of older adults and has been used extensively to document the onset of adverse health events in the late working years, the financial consequences of those events, and the timing of labor force exits and retirement benefit claiming. Yet, it has been underutilized for studying participation in disability policy programs. This study aims to document the extent to which self-reported data on participation in Social Security disability programs (DI and SSI) align to administrative records. Based on work from other national surveys, one possible reason for the DI and SSI data being underutilized is concern over the accuracy of self-reported program participation. Confusion between DI and SSI, the availability of Social Security retirement benefits at age 62, and the interplay between disability and retirement benefit determinations mean that self-reports may not be accurate.
This project will assess the accuracy of self-reports of applications to and receipt of DI and SSI using data from the Health and Retirement Study (HRS) linked to SSA administrative records. Specifically, we will compare self-reports of DI and SSI application and receipt to administrative records, both cross-sectionally and longitudinally and assess the implication of these alternate measures on health status, income, and wealth comparisons across groups. Our work will also yield a primer for other researchers interested in measuring disability benefit application and receipt with the HRS using linked SSA administrative data, and our findings will inform future research based on DI and SSI measures available in the HRS.
Principal Investigators: Michael Levere and David Wittenburg, Mathematica; Jeff Hemmeter, U.S. Social Security Administration
There is evidence that substantial geographic variation exists in child SSI participation, though the factors driving this variation are not well understood. The project will fill the existing gap in knowledge by mapping the relationship between child Supplemental Security Income (SSI) participation and measures of local community deprivation using administrative data from the Supplemental Security Record and the Health Resources and Services Administration’s Area Deprivation Index (ADI). We will analyze SSI and ADI correlations at the national level, as well as cross-state and cross-county comparisons. Next, we will descriptively examine differences in SSI child recipient characteristics by economic deprivation status. Additionally, we will identify areas where there are large deviations in 2019 county level SSI participation and economic deprivation. For example, areas that have low rates of SSI participation and high rates of deprivation are potential areas where SSA could fulfill its statutory responsibility of conducting outreach. Conversely, areas that have high rates of SSI participation and low rates of deprivation are candidates for better understanding why some areas rely on SSI more than others. We will also conduct case studies at the census tract-level, focusing on high deprivation and low SSI participation that are candidates for potential outreach efforts. We conclude with policy recommendations based on our findings.
Principal Investigators: Michael Anderson and Denise Hoffman, Mathematica; Kai Filion, U.S. Social Security Administration
This project will use data from SSA’s Disability Analysis File (DAF) and Master Earnings File (MEF) to document trends in labor market outcomes and return to SSDI or SSI following termination of SSDI benefits. The results will be useful to SSA in: (1) informing potential interventions to promote self-sufficiency and reduce return to disability benefit entitlement; and (2) anticipating potential outcomes of proposed legislation changes. Prior research suggests that beneficiaries terminated for medical improvement have limited earnings following termination and may return to SSA disability programs. However, that research focused on benefit terminations through 2008 and did not include termination for employment. We will add to the existing literature by identifying SSDI beneficiaries whose benefits were terminated for medical improvement or employment in 2001 through 2018 and track the outcomes of those terminated by 2013 in the first 5 years following termination. The years after 2008 are of special interest because of the recovery following the Great Recession and because of changes in the composition of the SSDI caseload since that time. We will investigate the beneficiary, programmatic, and economic characteristics that are associated with successful return to work (measured alternatively with earnings or program participation) following termination.
“Trends in Opioid Use among Social Security Disability Insurance Applicants”
Principal Investigators: April Yanyuan Wu and Denise Hoffman, Mathematica and Paul O’Leary, U.S. Social Security Administration
This study will use data from SSA’s Structured Data Repository (SDR) to document trends in opioid use among applicants to disability insurance (DI) and the extent to which opioid use is predictive of initial award, an award within five years, and mortality. The findings will inform SSA’s projections about future program entry based on aggregate trends in opioid use. The rise in opioid use nationwide – coupled with the numerous DI applicants who have conditions associated with opioid use, such as musculoskeletal problems – suggests that opioid use may be common and increasing among DI applicants. Beneficiaries cannot qualify for DI on the basis of drug addiction, but opioid use may exacerbate the effects of other conditions that meet DI qualifications. This study will address a major data issue that limits what is known about opioid use among DI applicants. Applicants are required to report their medications, but medications are recorded as a combination of coded and open-ended text fields. This study will capitalize on a previously developed supervised machine-learning algorithm to identify opioids recorded in freeform text and combine that information with opioids identified in populated medication codes.
"Trends in Opioid Use Among Social Security Disability Insurance Applicants," April Yanyuan Wu, Denise Hoffman, and Paul O’Leary, August 2019. (2019 RDRC Meeting Research Summary and Slides).
Employment and Work-Related Overpayments
“WIOA’s Effects on VR, SSA, and Earnings Outcomes for Youth Receiving SSI”
Principal Investigators: Todd Honeycutt and Isabel Musse, Mathematica; Jeffrey Hemmeter, Social Security Administration
The Workforce Innovation and Opportunity Act of 2014 (WIOA) required vocational rehabilitation (VR) agencies to expand their service offerings to high school and college students with disabilities. Such increased exposure to VR agency services, particularly through pre-employment transition services (pre-ETS), could lead to improved outcomes for youth receiving SSI. In this project, we will use data from SSA and the Rehabilitation Service Administration to document state-level prevalence of VR application, VR service use, SSA work incentive use, and earnings for youth ages 14 to 24 receiving SSI from 2010 to 2021. We will then examine how these outcomes changed following the passage of WIOA and if these changes varied based on youth’s age and state-level exposure to pre-ETS. Our findings will provide SSA and other policymakers interested in youth transition with evidence on whether increased exposure to VR services during high school affected key transition incomes in ways that could result in decreased reliance on SSI in young adulthood.
Principal Investigators: Purvi Sevak and Dara Lee Luca, Mathematica
This project will use data from the 1997 National Longitudinal Survey of Youth to examine the implications of the proliferation of gig employment and jobs with irregular scheduling on workers ages 18-36 who have disabilities. Trends in labor force participation for young adults with disabilities may signal future DI benefit claiming patterns. Changes in the labor market including alternative work arrangements in the gig economy and “just-in-time” scheduling” have introduced significant volatility in household incomes by creating more uncertainty in schedules, hours worked, and earnings, particularly for the working poor and young adults (Hannagan and Morduch 2015; Lambert, Fugiel, and Henly 2014). Although the flexibility of the gig economy and the challenges created by unpredictable schedules and earnings have been documented by the media, little is known about how these changes affect people with disabilities. For some young people with disabilities, such changes could make working less attractive than receiving DI benefits. But for others, the availability of a job with more flexible hours could have the opposite effect.
"Racial Inequities in BOND Impacts by Community-Level Factors"
Principal Investigators: Amal Harrati and Denise Hoffman, Mathematica; John Jones, Social Security Administration
This study re-examines impacts from the Benefit Offset National Demonstration (BOND) to explore racial differences. First, we examine any differences in the impact of BOND on employment-related outcomes and SSDI benefits by race. Then, we estimate the extent to which race differences in program impact are associated with community-level racial inequities in economic conditions. We use addresses information to match participants to publicly-available county-level data on measures of economic racial inequities, including disparities in wage rates, economic mobility, and residential segregation. This analysis will identify characteristics of participants’ communities that may hinder opportunities to employment unequally across racial groups. Racial disparities in local labor markets could limit the potential reach of the BOND intervention for some participants. This research can help highlight racial disparities in return to work and inform future SSA efforts to tailor implementation of programs in light of the context in which they take place.
Principal Investigators: Denise Hoffman and Michael Anderson, Mathematica; John Jones, U.S. Social Security Administration
Work-related overpayments are known to be prevalent among SSDI beneficiaries who engage in substantial gainful activity after exhausting work incentives that allow SSDI cash benefit payments to continue despite work activity. However, little is known about the trajectory of events pre-dating the overpayments. This project will explore trajectories of beneficiaries awarded benefits in 2008 who were ever at risk of an overpayment, following them through 2018. We will document the order and timing of events and examine the differences in trajectories among beneficiaries who are at risk of overpayments who are and are not overpaid. Understanding common pathways to overpayments may help identify points of intervention that could prevent overpayments.
Principal Investigators: Marisa Shenk and Gina Livermore, Mathematica
This study will use data from the 2017 National Beneficiary Survey (NBS) to examine the extent to which a representative sample of beneficiaries who return to work at the substantial gainful activity (SGA) level understand the rules well enough to anticipate benefit suspensions or are caught by surprise, and how they react. Several SSI and DI program rules allow beneficiaries to keep cash benefits while they test their ability to return to work and allow them to maintain Medicare and Medicaid coverage even if they lose eligibility for SSI or DI because of earnings. Despite these work incentives, only about 5 percent of SSI and DI beneficiaries work and leave the rolls during their first 15 years of participating in the programs (SSA 2020). Avoidance of overpayments and the need to repay large sums is often cited as a reason why some working beneficiaries choose to keep earnings below an amount that would trigger benefit suspension. Research to date has provided little information about the extent to which beneficiaries who experience overpayments and benefit suspensions are knowledgeable about the relevant SSA work incentive provisions and whether they anticipate these events; the reasons why individuals who work enough for their benefits to be suspended do not sustain their earnings; and why many of those who leave the disability rolls because of earnings subsequently return. This study will produce the first statistics about overpayment and benefit suspension experiences for a representative sample of beneficiaries.
Principal Investigators: Denise Hoffman and Jonah Deutsch, Mathematica
This project will draw on insights from behavioral science to diagnose process problems and highlight best practices that may reduce overpayments from Social Security Disability Insurance (DI). Overpayments are prevalent among DI beneficiaries engaged in substantial gainful activity and are undesirable for both program administrators and beneficiaries. The Social Security Administration (SSA) has made several efforts to reduce work-related overpayments, but those efforts have not addressed a root cause of overpayments: beneficiaries’ failure to promptly and accurately report their earnings. This study will help SSA understand how communicating reporting requirements to beneficiaries can affect their compliance.
Principal Investigators: April Yanyuan Wu and Denise Hoffman, Mathematica and Paul O’Leary, U.S. Social Security Administration
This project will use SSA administrative data to examine the relationship between opioid use and employment outcomes among SSDI applicants. Although recent studies suggest that opioid use may have negative consequences for economic outcomes (Krueger 2017; Franklin et al. 2014; Savych et al. 2018), little is known about this relationship among SSDI applicants. This study will build on the results of an ongoing RDRC project that uses a supervised machine learning method to classify medication information and identify opioids among SSDI applicants. The study will provide SSA with information about the work capacity and post-adjudication economic well-being of SSDI applicants who use opioids, a notable share of the pool of SSDI applicants. The results of this study will be indicative of the extent to which this population would be receptive to services that could facilitate return to work after entry, as well as the extent to which services delivered to similar workers prior to SSDI entry would keep them in the labor force instead.
Principal Investigators: Duncan Chaplin and Denise Hoffman, Mathematica and John Jones, U.S. Social Security Administration
This project will use data from the evaluation of the Benefit Offset National Demonstration (BOND) to assess the efficacy of comparative regression discontinuity (CRD) and regression discontinuity (RD) relative to each other and to randomized controlled trials (RCTs). RD is known as a relatively rigorous non-experimental method but produces imprecise results that apply to small populations. CRD addresses these issues. We will estimate CRD and RD models using simulated assignment to the BOND treatment group based on cut-points on duration of benefit receipt at the start of the BOND program. We will compare those estimates to each other and to RCT estimates for the treated group, for CRD, and to RCT estimates for those near to the cut-point, for RD. The findings will support interpreting CRD and RD studies on DI and related programs. They might also help SSA decide whether to consider these methods for testing impacts of proposed rules, using criteria such as duration of benefit receipt and beneficiary age to define those eligible and ineligible for the new rules. The CRD and RD methods are potentially attractive because they can avoid many of the challenges and costs of an RCT, but only if their validity is high.
COVID-19
“Effects of Suspending In-Person Services at SSA Field Offices on Disability Applications and Allowances”
Principal Investigators: Michael Anderson, Monica Farid and Gina Freeman, Mathematica; Christopher Earles, Social Security Administration
The suspension of in-person services at SSA field offices during the COVID-19 pandemic likely had an impact on disability insurance applications. Because there are many other reasons why the pandemic may have affected applications, little is currently known about the impacts of in-person service suspension on applications and how they vary with applicant characteristics. This project will study the causal impacts of suspending in-person applications to Disability Insurance and Supplemental Security Income during the COVID-19 pandemic on the volume of applications, composition of applicants, and initial acceptance rates. We will use difference-in-differences methods to compare areas with high field office coverage to those with low field office coverage (which are less affected by office service suspensions) pre- and post-March 2020. This approach will allow us to isolate the impacts of suspending in-person services at SSA field offices from other aspects of the pandemic. Our results will inform SSA’s understanding of the effects of in-person service suspensions and whether certain populations’ access to services was impacted more than others. We will document whether there are differences in education, age, disabling condition (physical versus mental) and work history between applicants based on mode of application, examine whether the likelihood of an initial allowance varies by mode of application after controlling for applicant characteristics, and investigate whether field office closures to in-person services during the pandemic disproportionately reduced applications from more vulnerable subgroups.
Principal Investigators: Amal Harrati and Marisa Shenk, Mathematica
Evidence from early in the pandemic suggests that people with disabilities experienced worse health, financial, and work impacts compared to people without disabilities. Yet, the pandemic has varied in intensity over time, and little is known about the experiences of people with disabilities as the pandemic has stretched over time. This project will examine monthly patterns of the experiences with, behaviors during, and attitudes about COVID-19 among people with disabilities for the entire period of the pandemic.
Using monthly data from the Understanding America Study COVID-19 Longitudinal Study (UAS), we describe trends over time for people with disabilities in four domains: 1) SSA program participation; 2) COVID-related measures of economic security; 3) work and financial security; and 4) personal experiences with COVID-19. We also document changes in self-reported disability status over time and describe the characteristics of individuals with new disability onset. Finally, we examine the timing and predictors of plans to apply for SSA benefits as a result of the pandemic. The results of this work will inform SSA’s understanding of how the work-related and benefits-related impacts of the COVID-19 pandemic have evolved over time for people with disabilities and shed light on the potential future demand for SSA programs.
Principal Investigators: R. Vincent Pohl and David Mann, Mathematica; Kai Filion, Social Security Administration
The COVID-19 pandemic affected many with chronic health conditions who were at risk of qualifying for disability benefits. In addition, the economic consequences of the pandemic, including business closures and spikes in unemployment, may have led to labor market exits among people qualifying for disability or early retirement benefits. However, it is unknow to what extent the pandemic affected rates of long-term disability and applications for disability-related or early retirement benefits.
This project will assess how the COVID-19 pandemic and its economic consequences affected Social Security Disability Insurance (SSDI) applications and awards and early retirement claims. Using a newly constructed county-month level data set, we will document changes in SSDI applications, SSI awards, and early retirement claims during the pandemic and how these changes varied across regions. The project will use geographic differences in infection rates and economic activity to describe the association between severity of the pandemic—both in terms of infections and economic consequences—and new SSDI applications and initial awards and early retirement claims in 2020 and 2021. This analysis will shed light on an important consequence of the pandemic for Social Security benefits and contribute to a broader understanding of the factors that determine SSDI take-up and early retirement claims.
"Child SSI Applications and Awards During the COVID-19 Pandemic"
Principal Investigators: Michael Levere, Haverford College and Mathematica; David Wittenburg, Mathematica; Jeffrey Hemmeter, Social Security Administration
Nationwide, child applications and awards for Supplemental Security Income (SSI) declined dramatically during the COVID-19 pandemic. Applications fell by nearly 20 percent and awards fell by more than 30 percent. Exploiting variation in local-level pandemic experiences, demographics, and economic characteristics, we will explore the factors driving this decline. We will link administrative data on applications and awards with publicly available information on a variety of local-level characteristics. It is critical for SSA to understand patterns in the local decline in child SSI participation to identify areas with the strongest future need to conduct outreach and encourage equitable program growth.
There are myriad reasons why the decline in SSI applications and awards likely varied regionally, signaling that as the pandemic wanes, there may be areas in more intense need of SSI benefits or SSA outreach (or both). District-level school closings differed widely in both level and duration, which may have made it more difficult for families in some areas to apply for and access government programs such as child SSI. Some areas of the country were more likely to close businesses and institutions, which may contribute to local variation in rates of job loss. Local area demographics may be an especially important factor, as African Americans and people with low-income had high COVID-19 positivity and mortality rates, as well as particularly high rates of job loss during the pandemic. Finally, though all field offices closed, the effects of closures might differ depending on distance to the nearest field office.
"Racial Disparities in COVID-19 Experiences among Older Adults with Disabling Conditions"
Principal Investigators: Amal Harrati and Marisa Shenk, Mathematica
This study will use data from the COVID-19 module of the Health and Retirement Study (HRS) 2020 to examine disparities in the experiences of older adults during the COVID-19 pandemic. The pandemic has highlighted vulnerabilities in both economic security and physical health among older adults, people with disabilities, racial minorities, and people living in communities experiencing low incomes. These disparities have also spotlighted the force of pre-existing structural and geographic factors in contributing to and exacerbating existing economic and health inequalities. This study will explore the effects of the intersectionality of age, disability, and race on experiences of COVID-19 as well as examine how the role of county-level factors may contribute to these inequities. Using a representative sample of older adults which includes information on self-reported effects of COVID on work, financial issues, and physical health, including COVID diagnoses and receipt of health care, this study will document the percentage of older adults with disabling conditions who had negative work, financial, and health experiences during the COVID-19 pandemic; explore the intersectionality of race, age, and disability in these experiences; and examine the role of contextual/social factors in explaining these intersectional disparities.
