a= CENTER for RETIREMENT RESEARCH at BOSTON COLLEGE RACIAL DISPARITIES IN COVID-19 EXPERIENCES AMONG OLDER ADULTS WITH DISABLING CONDITIONS Marisa Shenk, Amal Harrati, Bernadette Hicks, and Ana Quifiones CRR WP 2022-17 November 2022 Center for Retirement Research at Boston College Haley House 140 Commonwealth Avenue Chestnut Hill, MA 02467 Tel: 617-552-1762 Fax: 617-552-0191 https://crr.bc.edu Marisa Shenk, Amal Harrati, and Bernadette Hicks are researchers at Mathematica. Ana Quifiones is an associate professor at Oregon Health & Science University. The research reported herein was pursuant to a grant from the U.S. Social Security Administration (SSA) funded as part of the Retirement and Disability Research Consortium. The findings and conclusions expressed are solely those of the authors and do not represent the views of SSA, any agency of the federal government, Mathematica, Oregon Health & Science University, or Boston College. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the contents of this report. Reference herein to any specific commercial product, process or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply endorsement, recommendation or favoring by the United States Government or any agency thereof. The authors thank Jody Schimmel Hyde and Michael Anderson for their helpful feedback on early drafts. © 2022, Marisa Shenk, Amal Harrati, Bernadette Hicks, and Ana Quifiones. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit including © notice, is given to the source. About the Center for Retirement Research The Center for Retirement Research at Boston College, part of a consortium that includes parallel centers at the National Bureau of Economic Research, the University of Michigan, and the University of Wisconsin-Madison, was established in 1998 through a grant from the U.S. Social Security Administration. The Center's mission is to produce first-class research and forge a strong link between the academic community and decision makers in the public and private sectors around an issue of critical importance to the nation's future. To achieve this mission, the Center conducts a wide variety of research projects, transmits new findings to a broad audience, trains new scholars, and broadens access to valuable data sources. Center for Retirement Research at Boston College Haley House 140 Commonwealth Avenue Chestnut Hill, MA 02467 phone: 617-552-1762 Fax: 617-552-0191 https://crr.bc.edu Affiliated Institutions: The Brookings Institution Mathematica - Center for Studying Disability Policy Syracuse University Urban Institute Abstract This paper examines the experiences of older adults with disabling conditions during the COVID-19 pandemic using data from the 2020 Health and Retirement Study. It documents the negative health, work, and financial experiences of older adults by disability status, race, and ethnicity. It also explores the intersectionality of race and ethnicity with disability and the role of contextual factors affecting the environments in which people live (e.g., health and economic conditions) using county- and state-level data. The paper found that: Many older adults reported experiencing financial hardships (31 percent), delaying health care (31 percent), and experiencing effects on work (29 percent). Compared with older adults without disabilities, older adults with disabilities were more likely to report delaying many types of health care, such as surgeries and prescriptions, experiencing financial hardships since the start of the pandemic. Among older adults whose work was impacted by the pandemic, more older adults with disabilities reported stopping work than older adults without disabilities, and fewer moved to remote work. There were differences in experiences across races and ethnicities. Hispanic, Latino, and Black older adults, and older adults in other racial and ethnic groups, were more likely than White older adults to report that financial hardships and that the pandemic impacted their work. Differences across racial and ethnic groups were larger when examined across disability status than when examined among all older adults. For example, more Hispanic or Latino older adults with disabilities did not have enough money to buy food (22 percent) compared with Hispanic or Latino older adults without disabilities (11 percent) and with White older adults with disabilities (7 percent). Older adults with disabilities were more likely to live in counties with greater vulnerability to the pandemic. Although there were few associations between contextual factors and individual experiences, race, ethnicity and disability status remained important after accounting for these factors. The policy implications of the findings are: Developing a robust, disability-inclusive public health response for future public health emergencies could provide additional protection to this vulnerable population. Continuing to study intersecting identities is important to understanding the experiences of older adults and recognizing that multiple marginalized identities could exacerbate negative outcomes. Enacting policies that mitigate the financial impacts of public health emergencies might be universally beneficial for older adults. Introduction The COVID-19 pandemic has highlighted vulnerabilities in economic security and physical health among older adults, people with disabilities, and people of color. In this study, we explore the intersectionality of these identities, and examine how they are associated with the structural forces that contribute to and exacerbate these inequalities. We obtained the data for the study from the COVID-19 module of the Health and Retirement Study (HRS) 2020. This module asked a representative sample of older adults to self-report the effects of the pandemic on their work, finances, and physical health, including COVID diagnoses and receipt of health care. Our study is meant to inform the Social Security Administration's (SSA's) understanding of the disproportionality of negative COVID-19 outcomes among vulnerable populations, the intersectionality of disability with race and ethnicity among Americans ages 50 and older, and opportunities for providing supports to communities most impacted by COVID-19. This study addresses the following questions: e What percentage of older adults with disabling conditions had negative health, work, and financial experiences during the COVID-19 pandemic? e Did these percentages differ according to race, age, and disability? e Are contextual or social factors associated with intersectional disparities? We found that more older adults with disabilities reported negative health, work, and financial experiences than those without disabilities, and found differences by race and ethnicity overall and among older adults with disabilities. We also found that older adults with disabilities were more likely to live in counties with greater vulnerability to the pandemic, such as counties with dense populations and poor health infrastructure, indicating that people with disabilities might be particularly vulnerable to current and future pandemics. Individual race and disability remained significantly associated with negative COVID experiences after accounting for contextual factors. Background The impacts of the COVID-19 pandemic have been experienced unequally across both geography and the social structure of the United States. In particular, older adults, marginalized racial groups, and people living in communities with low resources have been among those hardest hit by the myriad health and economic effects of the pandemic. Historically marginalized racial and ethnic groups, particularly non-Hispanic Black adults, have had higher rates of infection, hospitalization, and death compared with non-Hispanic White adults (Atlantic Monthly Group 2021; Kirby 2020; Oppel et al. 2020; Price-Haywood et al. 2020). Observations of the disparate racial and ethnic burden of COVID-19 have been broadly documented across geographical regions (Polyakova 2021). Likewise, older Americans have been disproportionately impacted by the pandemic. According to the Centers for Disease Control and Prevention, more than 81 percent of COVID-19 deaths have been among people older than 65 (as of August 2021), and the number of deaths in that age group is 80 times higher than the number of deaths among those ages 18 to 29 (CDC 2021). These disparities in COVID outcomes have underscored the social and structural inequities that influence health and well-being, including the role of intersectionality. Intersectionality is a theory suggesting that multiple and coexisting dimensions of marginalization or inequality, such as race, age, gender, or disability status, might have overlapping and cumulative effects beyond the sum of their individual parts. In other words, people at the intersection of more than one identity that has been disproportionately impacted by COVID might be particularly vulnerable to adverse outcomes (Bowleg 2020; Elnaiem 2020; Gonzales et al. 2021; Walubita et al. 2021). For example, emerging evidence indicates that older Black and Hispanic adults are more likely than their White counterparts to report food and housing insecurity and difficulty paying household expenses during the pandemic (Bui et al. 2021; Lopez, Rainie, and Budiman 2020). Evidence suggests that the risks of COVID infection are exacerbated by preexisting vulnerabilities in the social environments in which people live. For example, counties with larger non-White populations have experienced higher rates of COVID-19 deaths and hospitalizations (Schnake-Mahl and Bilal 2021). In addition, people with disabilities who are non-White or live in households with incomes below the poverty level are significantly overrepresented in counties with higher COVID-19 incidence compared with other people with disabilities (Chakraborty 2021). Similarly, a nationwide study showed that just a small increase in fine particulate matter (PM2.5), was associated with an 11 percent increase in the COVID-19 death rate for U.S. counties (Wu et al. 2020), and emerging evidence suggests socioeconomic characteristics of counties are associated with both the severity and the transmission of COVID- 19 infection (Andersen et al. 2021; Baum and Henry 2020; Hatef et al. 2020). Despite growing evidence about inequities in vulnerability across sociodemographic groups, surprisingly little is known about the experiences people with disabilities have had with COVID-19. Their risk factors include being more likely to have an underlying health problem (Stevens et al. 2014; Dixon-Ibarra and Hormer-Johnson 2014), live in a congregate care setting (McConkey et al. 2016), and rely on assistance with personal care and routine preventative measures (Armitage and Nellums 2020). Emerging evidence on this topic has been grim. A study of nearly 65 million patients across 547 health care centers in the United States found the mortality rate of people with intellectual and developmental disabilities was nearly eight times higher than the general population (Gleason et al. 2021), confirming smaller studies suggesting people with intellectual and developmental disabilities are at higher risk for COVID infection and mortality (Landes, Turk, and Wong 2021; Landes et al. 2020). Likewise, a similar study of more than 2 million health care patients in Canada found people with physical disabilities were more likely to die of COVID-19 than those without a disability, even after adjusting for a number of socioeconomic factors. (To the authors' knowledge, such a study has not been done in the United States.) A systematic review of the impacts of COVID-19 on people with physical disabilities suggests that the pandemic is associated with daily functioning, resulting in diminished access to health care, mood changes, and lower levels of physical activity (Lebrasseur et al. 2021). In particular, evidence shows that people with disabilities who are older, people of color, or both, might be particularly vulnerable to negative impacts of COVID-19. Therefore, it is important to examine disability and COVID-19 using an intersectional lens. Already, racial differences in disability prevalence exist: Native American persons have the highest rate of disability (3 in 10), followed by Black persons (1 in 4), White persons (1 in 4), Native Hawaiians/Pacific Islander and Hispanic persons (1 in 6), and Asian persons (1 in 10) (Courtney- Long et al. 2017). Moreover, the virus's impact on older adults with disabilities might go beyond a higher risk for serious infection and include limited access to care for all health conditions, financial implications, and effects on employment. Given the nature of the infection and the social mitigation strategies put into place for containment, COVID-19 can be considered a "social disease" (Trout and Kleinman 2020), as it impacts social and economic domains of everyday life in addition to individual health. For older adults with disabilities, the impacts of COVID-19 might exacerbate existing differences in health care, employment, and financial security. COVID-19, Disability, Race or Ethnicity, and Disparities in Health Care Historically marginalized ethnic and racial groups and people with disabilities face disparities in health care. Compared with White communities, communities of color have less access to health care services, are more likely to receive lower quality care, and have poorer health outcomes (Agency for Healthcare Research and Quality 2011; U.S. Department of Health and Human Services, Office of Minority Health 2015, 2020). All of these factors could make these groups more vulnerable to the negative outcomes associated with COVID-19 (Lund et al. 2020). Even with improved access, the average cost of health care for working-age adults with disabilities is five times higher than for those without disabilities; prescription drug costs were seven times higher (Kennedy, Wood, and Frieden 2017). COVID-19, Disability, Race or Ethnicity, and Disparities in Employment Even before the pandemic's economic losses and the reduction in the U.S. workforce, the Bureau of Labor Statistics' summary of labor force characteristic for persons with a disability noted that unemployment rates for people with disabilities were higher than those of people without a disability (Jashinky et al. 2021; BLS 2020c). The effects of the pandemic on the labor market have amplified this concern. Between March and August 2020, employment of people with disabilities declined by 20 percent, compared with a 14 percent decline for those without a disability (BLS 2020a). Jobless rates continue to be greater for those with disabilities compared with those without disabilities, and this margin is expanding (BLS 2021a). The COVID-19 pandemic also dealt a unique shock to older workers (Davis 2021). Roughly 3.7 million workers 55 and older became unemployed between March and April 2020. Although many were soon recalled to work, 35 percent of the older unemployed lost their jobs permanently in the fourth quarter of 2020. By October 2021, the employment-population ratio of workers 55 and older was down 2.2 percentage points from February 2020. Employment declines among older workers were greatest for low earners, women, people of color, and workers without a college degree (Davis 2021). Quinby, Rutledge, and Wettstein (2021) used the monthly Current Population Survey to study older workers' transitions out of employment and into retirement before and during the pandemic. The paper found that among workers age 55 and older, the likelihood of leaving work over the course of a year rose by 7.6 percentage points, a 50 percent increase over the pre-pandemic rate. They also found differences in socioeconomic and demographic subgroups. Women, adults without a college degree, Asian- American persons, and employees in occupations less amenable to remote work saw disproportionate impacts. In contrast, the likelihood of retiring increased by only | percentage point, with retirement concentrated among those older than 70. Accordingly, workers were not more likely to claim Old-Age, Survivors, and Disability Insurance benefits. Likewise, Gopi at al. (2022) found that employment for older workers dropped substantially more than would have been predicted before the pandemic. They also found declines in labor force exit due to disability (4-5 percent), and applications for disability insurance (15 percent). COVID-19, Disability, Race or Ethnicity, and Disparities in Financial Security The disruption of financial resources - from job loss, unpaid leave, decreased household income, and economic hardship - for older adults with disabilities or their family caregivers during a crisis can result in reduced access to other resources, such as food and preventive health care services (Abrams et al. 2021; Bui et al. 2021; Choi, Carr, and Namkung 2022; Gauthier et al. 2021; Taylor et al. 2021; Morris 2022). Therefore, older adults with disabilities and historically marginalized racial and ethnic groups are likely to face unique challenges because of financial disruptions (Garcia et al. 2021). For example, emerging evidence indicates that older Black and Hispanic adults are more likely than their White counterparts to report food and housing insecurity and difficulty paying household expenses during the pandemic (Bui et. al. 2021; Lopez et al. 2020). Working-age adults with disability have also been especially vulnerable to food insecurity during the pandemic because of both financial and physical barriers, including inability to or fear of going out to purchase food (Choi, Carr, and Namkung 2022; Friedman 2021). In light of this evidence, and the gaps therein, we sought to understand the COVID-19 pandemic's effects on the health, work, and financial experiences using nationally representative data for working-age older adults and adults age 50 and older with disabling conditions. We compared differences in outcomes between adults with and without disabling conditions. We emphasized intersectionality with racial and ethnic identity by examining differences across and within races and ethnicities. Finally, we examined the extent to which contextual factors are correlated with outcomes. Given the unique ways in which people with disabilities might interact physically and socially with their community, the role of contextual factors in COVID- related outcomes might be particular important for people with disabilities. Data and Methods Data Sources We used the HRS as our primary data source for our analysis. The HRS is a longitudinal survey that has been fielded every other year since 1992. The survey collects data on health, work, retirement, income, and other related topics from a nationally representative sample of the U.S. population age 50 and older (Sonnega et al. 2014). For our COVID outcomes, we used the 2020 HRS wave, which included a module that asked about the effects of the COVID-19 pandemic on older adults' ability to access health care, their finances, and their labor force participation (Health and Retirement Study 2021). We also used the 2018 RAND-HRS Longitudinal File, a cross-wave HRS file that has been cleaned and streamlined to track each HRS respondent from 1992 to 2018.! Finally, for our area-level analysis we used the HRS Cross- Wave Geographic Information (Detail) Restricted Data file, which contains geographical data, including the county and state name and Federal Information Processing Series (FIPS) code, for each HRS respondent through 2018. We linked this file to the 2020 HRS file to determine the county and state of residence for each respondent in our sample. For our area-level analysis, we drew from a number of publicly available data sources to characterize aspects of potential COVID vulnerability. We describe the measures further in the text (Table 1). We used the following data sources: e The American Community Survey (ACS), administered by the Census Bureau annually to track population and housing data in the United States. We used the 2016-2020 ACS five-year estimates for our analysis. e The Agency for Healthcare Research and Quality's database of Social Determinants of Health (SDOH) beta files, which aggregates data from existing sources related to social 1We merged the 2020 HRS data to the RAND files using the household and respondent identification numbers. context, economic context, education, and physical infrastructure. We used the SDOH file from 2018 for our analysis. e The 2021 County Health Rankings, which provides county-level data on health outcomes and health factors. These measures are weighted and aggregated to provide a ranking for each county. e The COVID-19 Pandemic Vulnerability Index (PVI) Model 11.2, which aggregates 18 components of pandemic vulnerability related to infection spread, population concentration, intervention measures, and the health environment of each county to estimate its vulnerability to the COVID-19 pandemic. The PVI is updated daily. e COVID Act Now, which tracks COVID-19 risk level, case numbers, and vaccination rates at the county level. This data is updated daily. e The National Conference of State Legislatures, which produces yearly statistics of state- level political party representation in state legislatures and in the governor's office. Sample Our final sample included 8,828 participants from the 2020 HRS.' Of these, 3,155 participants were surveyed as part of the early-release sample. Figure 1 displays the sample restrictions. We excluded from our analysis any participant who we could not match to the 2018 RAND file, because we use these data in our modeling to define respondent race, disabling condition, and covariates.? We also excluded participants younger than 50, because they did not meet our definition of older adult. Finally, we limited our sample to respondents who completed the COVID-19 module. 2 The 2020 HRS was fielded between March 2020 and June 202] and included 15,732 participants. The COVID-19 module was added in May 2020. Between June 11, 2020 and October 2020, a random 25 percent subset of the HRS sample was surveyed with the COVID-19 module. From September 2020 to May 2021, another random 25 percent subset of the HRS sample was surveyed with the COVID-19 module. In January 2022, the data from the 2020 HRS was released without weights. 3 We could not match 1,466 participants to the HRS 2018 data. These participants might have been newly added to the HRS (such as a new spouse of an existing participant) or included in prior years but did not complete the 2018 survey. We excluded these participants because we measured disability and work status through 2018 for everyone in the analysis. We measured these variables through 2018 rather than 2020 because of the potential relationship between COVID and disability and work in 2020. For example, respondents could have developed disabling conditions because of COVID, and we can't disentangle that effect. Our estimates are therefore likely undercounting the share of people with disabilities, which could make our effect sizes appear smaller than the actual differences in COVID impacts between people with and without these disabling conditions. The early-release sample is a random 25 percent subset of the HRS sample that was surveyed with the COVID-19 module. The data from this nationally representative subsample was released in November 2020 with preliminary weights. We used this early-release sample and its corresponding weights as part of our sensitivity analysis. Figure 1. Sample Size and Restrictions Exclude people missing HRS 2018 data n=1,466 In HRS 2020 Exclude people who were Sree under age 50 years es cine Exclude people who did In HRS 2018 not complete the COVID n=14,257 module ears Age 50 and older n=13,789 Analysis sample eg ote) COVID Outcome Measures We focused on three primary outcomes related to COVID-19: (1) health care delays, (2) financial hardship, and (3) effects on work. For each domain, we examined self-reported measures of whether the respondent experienced that outcome. We also examined changes in preparedness for retirement. For health care delays, respondents were asked, "Since March 2020, was there any time when you needed medical or dental care, but delayed getting it, or did not get it at all?" Respondents who answered yes were asked to select why their care was delayed and what type of care was delayed. Respondents were also asked whether they thought they ever had COVID- 19. They were then asked whether they had ever been tested for COVID-19. Those who answered yes were asked for their test results. To assess financial hardship, respondents were first asked whether their income increased, decreased, or stayed the same. They were then asked to select the types of financial hardships they had experienced, such as missing a rent payment or being unable to pay a medical bill. To assess work effects, respondents were asked, "Was your work affected because of the coronavirus pandemic?" Respondents who answered yes were asked whether they had to stop working entirely. If they were still working, they were asked whether they experienced effects, such as changes in hours and switching to remote work. Respondents could also indicate that they were not working at the time of the pandemic. Each wave of the HRS asks respondents whether they expect to retire by age 65 and how frequently they think about retiring. The HRS also asks their expected retirement age. For these outcomes, we limited our sample to those who were working in both 2018 and 2020, so we could examine responses from both the 2018 HRS and the 2020 HRS. Definitions of Disability For our primary analysis, we defined "disabling condition" as the presence of difficulty with an activity of daily living (ADL) or an instrumental activity of daily living (ADL). We examined whether respondents ever reported having difficulties with one or more ADLs or IADLs in any wave of the HRS through 2018. We also considered four alternative measures of self-reported disability status: (1) the presence of a work-limiting condition when the 2018 HRS survey was fielded, (2) receipt of Supplemental Security Income (SSD or Disability Insurance (DI) benefits in any wave of the HRS through 2018, (3) the presence of two or more chronic health conditions, and (4) the presence of five or more chronic health conditions. However, we could define only the first two measures for working age adults, because "work-limiting" conditions are directly tied to working, and SSI is converted to retirement benefits after age 65 regardless of disability status. Because the sample population includes adults age 50 and older, many respondents were not working or were retired by 2018, regardless of disability status. Our measure using ADLs and IADLs reflects disability status among people of all ages or work statuses in our sample. This approach of combining ADLs and IADLs is often used to measure functional disability for older adults (Spector and Fleishman 1998; Millan-Calenti et al. 2010). Results for the alternative measures of disability are available in the appendix. Other Individual-Level Measures We considered four mutually exclusive categories of race and ethnicity: (1) non-Hispanic White, (2) non-Hispanic Black, (3) non-Hispanic other race, and (4) Hispanic. Our other covariates included gender, birth year, education, marital status, HRS cohort, and number of long-term health conditions.* Area-Level Measures We considered nine area-level contextual factors (Table 1). These factors were in five domains: (1) COVID-risk and vulnerability; (2) socioeconomic characteristics; (3) medical infrastructure and health care access; (4) employment opportunities and income; and (5) governance. Based on prior evidence, we hypothesized that these five domains would have the biggest influence on individual-level COVID outcomes. Within the first four domains, we chose two factors that were measurable and publicly available. For example, we considered hospital bed capacity and the ratio of primary care physicians to the population as measures of health care access. Because the spike in COVID-19 hospitalizations highlighted a shortage in hospital capacity, we prioritized this measure. Eight of our measures are reported at the county level; governance is collected at the state level. Table 1. List of Area-Level Contextual Data Area-level factor | Source | Time period | Definition COVID-19 risk and vulnerability Cases per COVID March 2020- | This measure represents the average number 100,000 Act Now May 2021 of COVID-19 cases in a given county per 100,000 people. A higher number implies that COVID-19 was more prevalent in that county than others. We calculated monthly means by averaging daily data on cases per 100,000 people for each month. We then averaged the monthly means to get a mean cases per 100,000 metric across our time period. Pandemic PVI March 2020- | This measure aggregates components of Vulnerability May 2021 pandemic vulnerability to create a PVI score Index (PVI) score of 0 to 1. Scores closer to 1 imply higher vulnerability to COVID-19. We used the PVI score for the first day of each month as the month-specific PVI. We then averaged 4 This measure comes from the RAND file. The eight long-term health conditions included high blood pressure, diabetes, cancer, lung disease, heart disease, stroke, psychiatric problems, and arthritis. 10 the score across all months to get a mean PVI score across our time period. Socioeconomic characteristics Social Agency for | 2018 This measure ranks factors such as Vulnerability Healthcare socioeconomic status, housing, Index score Research transportation, and racial breakdown of a and Quality county to index its vulnerability to natural disasters and other emergencies. Values range from 0 to 1, where values closer to 1 imply higher social vulnerability. Racial County 2015-2019 This measure indexes the degree to which segregation, Health White and non-White residents are White versus Rankings geographically segregated from one another non-White within a county. Values range from 0 to 100, where values closer to 100 imply a higher level of segregation. Medical infrastructure and health care access Hospital bed capacity PVI subscore June 2020 This is a measure of the number of hospital beds divided by the population of the county. Values range from 0 to 1, with values closer to 1 implying that the county has a higher hospital capacity. We used the PVI hospital bed capacity subscore for the first day of each month as the month-specific capacity. We then averaged the score across all months to get a mean hospital bed capacity across our time period. Years of potential life lost, all races County Health Rankings 2017-2019 This measure is the age-adjusted rate of potential life lost from deaths of people under age 75 per 100,000 people in a given county. A higher value suggests a higher rate of premature death. Employment opportunities and income Unemployment American | 2016-2020 | This measure is the percentage of the rate, all races Community population older than 16 who are Survey unemployed. Values range from 0 to 100, with values closer to 100 implying a higher rate of unemployment. Percentage of American | 2016-2020 This measure is the percentage of the total people receiving | Community population that receive cash public assistance government Survey or participate in the Supplemental Nutrition assistance Assistance Program. Values range from 0 to 100, with values closer to 100 implying a higher rate of people receiving government assistance. To create this measure, we divided the number of people receiving 11 assistance by the total population of a given county. Governance National 2020 These data detail partisan composition of a Conference state based on (1) the majority political party of State in control of the state legislature and (2) the Legislature party affiliation of the governor. States are defined as republican, democrat, or divided if the legislative and governor parties are different. We code 1 for republican and 0 otherwise. We linked these data to the individual-level HRS data through the county and state FIPS code available in the HRS Cross-Wave Geographic Information file. Weights Our primary results are regression-adjusted for race and ethnicity, disability status, and personal characteristics (gender, birth year, education, marital status, and number of long-term health conditions). They are unweighted. The weights for the full HRS 2020 sample had not been released at the time of our analysis. Because the HRS weights are based primarily on descriptive characteristics similar to the characteristics we adjusted for, our regression-adjusted results would be very similar to the results from a weighted analysis (Heeringa, West, and Berglund 2017). Although we do not have weights for the full sample, the HRS released preliminary weights for the nationally representative early-release sample. We applied these weights in sensitivity analyses. Results remain unchanged with the addition of weights. Methods Individual-Level Analysis We present descriptive statistics summarizing self-reported COVID-19 effects on work (for example, lost job, changes in income, and effects on workplace), financial issues, receipt of first round of stimulus payment, and health experiences (for example, self-reported COVID diagnosis, test results, effects on receipt of or delays in health care, and hospitalizations) for older adults by race or ethnicity and disabling conditions. We also examined retirement preparedness (for example, plans to retire, and frequency of thoughts about retirement) among working-age older adults. 12 We first compared the characteristics of our sample by disabling condition and race or ethnicity. For our primary analysis, we estimated logistic regression models for binary and categorical outcomes and linear regression models for continuous outcomes to assess the differences in outcomes between those with and without disabling conditions. These models controlled for personal characteristics (gender, birth year, education, marital status, HRS cohort, and number of long-term health conditions). We estimated models among all older adults and separately within each race or ethnicity category. To compare characteristics of older adults with and without disabling conditions, we report the results from t-tests for binary and continuous outcomes and Chi-squared tests for categorical outcomes. To compare characteristics of older adults by race and ethnicity, we report the results from Chi-squared tests for binary outcomes and F-tests for continuous outcomes. In addition, we estimated overall differences in our outcomes across race within disabling conditions. We used a Chi-squared test for our binary outcomes, and an F-test for our continuous outcomes. We conducted a variety of sensitivity analyses to determine how our results would change if we altered our chosen disability definition, weights, or covariates. We present our primary analysis using alternative definitions of disability: presence of a work-limiting condition, receipt of SSI or DI, and presence of two or more or five or more chronic health conditions. We also present unadjusted regression results. Finally, we repeated our analysis by restricting our sample to the nationally representative, early-release sample and using the corresponding early- release weights. Area-Level Analysis We used multilevel modeling techniques to assess whether and how contextual factors might be associated with the differences between self-reported COVID-19 effects among those with disabling conditions and those without disabling conditions and by race and ethnicity. Drawing on standard multilevel approaches in which individuals are nested within counties, multilevel models enabled us to simultaneously consider individual-level and community-level contextual factors-and the interdependencies therein-in these experiences. We first compared the area-level contextual factors of our sample by race or ethnicity and disabling condition. We used t-tests to compare characteristics of older adults with and without disabling conditions. We then focused our area-level analysis on the three primary COVID-19- 13 related outcomes in our previous analysis: (1) health care delays, (2) financial hardship, and (3) effects on work. We used the full analytical sample in which geocoded information was available. We fit all models in STATA using the melogit command and standardized all area- level factors and control variables. We assessed the model's goodness-of-fit with the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) statistics and likelihood- ratio tests. To test the potential association of area-level factors with individual COVID-19 experience, we employed three models. Our first model was an unadjusted multilevel model with a random slope for county. Our second model built on this first model by adjusting for individual-level control variables. These models enabled us to examine individual-level and area-level factors in conjunction and in relation to each other. First, we examined whether there is an association between area-level contextual factors and individuals' financial, health care and work-related experiences related to COVID-19 as we have hypothesized. Second, the models enabled us to determine whether disabling conditions and race or ethnicity remain significant predictors of differences in COVID outcomes after taking into account contextual factors. Our second model follows the specification in Equation 1: (1) Yi =a t+ BS; + Bor, + B3dj + yw; + dyia + &; b; ~ N(0,t?), where, for individual i in county j, yj; = COVID outcomes for individual i (financial hardship, health care delays, or effects on work because of COVID-19), 1; denotes the race or ethnicity category of individual i, d; denotes the disability status of individual i, w;;;, denotes the county or state-level factor of interest, and s; represents a vector of individual-level covariates that include gender, birth year, education, marital status, HRS cohort, and number of long-term health conditions. Finally, our third model added interaction terms between the county- or state-level factor and race and disability status. The addition of the interaction terms enabled us to ascertain whether there are differential impacts of area-level contextual factors across race or ethnicity and across disabling conditions. In other words, we could ask whether area level factors are 14 associated with individual COVID outcomes differently for individuals of different racial and ethnic identities and for adults with or without disabling conditions. Model 3 follows the specification in Equation 1, with variables defined the same as above: (2) ¥i = at Bis; + Bor; + B3d; + iw; + Pinw; + b3d;w; + bye + & b; ~ N(0,t7) For each of our three COVID outcomes, we ran a separate set of models testing each of our nine area-level measures separately. We did not include a model with multiple contextual factors for two reasons. First, we wanted to simplify interpretation of our models by identifying the unique effects of the identified contextual factor. Second, there were high levels of collinearity between contextual factors. As such, we ran 27 multilevel models: 3 models for each of the 8 county-level factors, and 3 models for the state-level measure. This resulted in a total of 81 models. All models are unweighted, because the HRS does not produce weights for the geocoded subsample that are nationally representative. For models with county-level measures, we also include sensitivity analyses subsetting our models to counties in which there are at least 10 respondents, following guidelines in the literature for minimum thresholds for sample sizes (Ali et al. 2019). All models use our primary definition of disability as the presence ADL or IADLS. Models using the three alternative definitions of disability are available upon request; model results are very similar across all disability definitions. Results Almost 40 percent of the older adults in our sample required assistance with an ADL or IADL (Table 2). About 60 percent of the respondents were married or living with a partner, and 40 percent were male. The average birth year was 1951, meaning the average age was about 69 in 2020. On average, respondents had 13 years of education and 2.3 health conditions out of the 8 conditions we examined. Slightly more than one-third of respondents were working for pay in 2020 when they were surveyed. There were differences in demographic characteristics across race. Overall, about one- half of respondents were non-Hispanic White, one-quarter were non-Hispanic Black, and 18 percent were Hispanic. Hispanic and Black respondents had higher rates of disability and were also younger, on average, than White respondents by about five years. White respondents were 15 the oldest on average and were least likely to be working. The distributions of each of these characteristics across race and ethnicity were statistically significant. What Negative Health Experiences Did Older Adults Experience During the Pandemic? Overall, 4 percent of respondents reported that they had received a positive COVID diagnosis (Table 3). Higher shares of Hispanic or Latino older adults or older adults of another race (about 6 percent each) reported a positive diagnosis, compared with Black or White older adults (about 3 percent each). About one-third (32 percent) of respondents reported that they had been tested for COVID. This percentage was higher among older adults with disabling conditions (35 percent compared with 30 percent) and, across race and ethnicity, it was highest among Black older adults (41 percent). About 10 percent of those tested reported that they received a positive test result. Among those with a positive COVID test result, higher shares of Black or Hispanic older adults reported a hospitalization because of the virus (24 percent and 21 percent), compared with White older adults (11 percent). About one-third (31 percent) of respondents reported that they needed medical or dental care but delayed getting it, or did not get it at all, since March 2020. More people with disabilities reported delaying needed health care (36 percent compared with 27 percent). Respondents were asked why they delayed or did not get health care and could indicate multiple responses. More than half (52 percent) of older adults who delayed care reported that the clinic, hospital, or doctor's office canceled, closed, or suggested rescheduling. About a third (33 percent) reported that they decided to wait. Other less common responses included not being able to afford it, not being able to get an appointment, and fear of going. Reported reasons differed according to respondents' disability status. Older adults with disabilities were more likely than those without disabilities to report that they delayed care because they could not afford it (17 percent versus 12 percent, respectively), and less likely to report that the doctor's office canceled their care (47 percent versus 56 percent) or that they decided to wait (31 percent versus 35 percent). Respondents who delayed receiving needed health care were asked what type of care they delayed and could indicate multiple responses. There were differences in many of the types of care delayed between those with and without disabilities and in all of the types of care delayed by race and ethnicity. Among respondents who delayed any health care, we found the following: 16 e About three-quarters (72 percent) of older adults delayed dental care. There was no difference by disability. Older adults of another race were most likely to report delaying dental care (77 percent). e More than half (57 percent) of older adults delayed doctor visits, including delaying telemedicine and in-person care. There was no difference by disability. Hispanic or Latino older adults were most likely to report delaying a doctor visit (63 percent). e About 13 percent delayed surgeries. More older adults with disabilities delayed surgeries (16 percent compared with 11 percent). There were small differences by race and ethnicity, with rates varying from 12 percent (Hispanic or Latino) to 14 percent (White and another race). e About 7 percent delayed filling a prescription. More older adults with disabilities delayed prescriptions (9 percent compared with 5 percent). There were differences by race and ethnicity, with rates varying from 5 percent (White) to 12 percent (Hispanic or Latino). e About one-quarter (23 percent) reported delaying another type of health care, with more older adults with disabilities (27 percent) and older adults of another race (27 percent) delaying other health care. What Negative Work Experiences Did Older Adults Experience During the Pandemic? More than one-quarter (29 percent) of older adults reported that their work was affected during the pandemic. Because many older adults were not working at the start of the pandemic, we also examined the share whose work was affected among those who were working. About 40 percent of older adults who were working when the pandemic started reported that their work was affected. Even among those working, a smaller share of older adults with disabilities reported effects on their work (35 percent compared with 40 percent). This percentage was highest among Hispanic or Latino older adults (46 percent) and older adults of another race (49 percent). Among those whose work was affected, almost half (44 percent) reported that they stopped work entirely. This percentage was higher among people with disabilities (51 percent) and Hispanic or Latino (59 percent) or Black (53 percent) older adults. About half of those who stopped work reported that it was because of a furlough or temporary layoff (49 percent). Among those whose work was affected, but who did not stop working, many older adults reported switching to remote work (47 percent), changing their work days or hours (28 percent), 17 their work becoming more risky or dangerous (21 percent), or their work becoming harder (21 percent). White adults and non-Hispanic older adults of another race were most likely to have moved to remote work (51 and 56 percent, respectively). What Negative Financial Experiences Did Older Adults Experience During the Pandemic? Respondents were asked which types of financial hardships they experienced since the start of the pandemic. Many older adults reported no hardships (69 percent). However, older adults with disabilities were less likely to report no financial hardships (61 percent compared with 74 percent)-that is, they were more likely to experience hardships. Similarly, Hispanic or Latino, Black, and other older adults were less likely to report no financial hardships than White older adults. Among the most common hardships reported were not having enough money to buy food (9 percent) and having trouble buying food even if they had the money (16 percent). About 1 in 15 older adults reported missing credit card or other debt payments, missing other payments, or other material hardships (7 percent each), or missing rent or mortgage payments (6 percent). For each of these hardships, older adults with disabilities were more likely to report hardships than older adults without disabilities. Black older adults were the most likely to report missing payments. For example, 10 percent of Black older adults missed a rent or mortgage payment compared with 2 percent of White older adults. Non-Hispanic older adults of another race were the most likely to report not having enough money to buy food (17 percent) or having trouble buying food even though they had money (19 percent), compared with White older adults (5 and 14 percent, respectively). Most older adults reported that their income stayed the same (77 percent), but 18 percent reported their income declined, and 5 percent reported their income increased. Though there were no differences between those with and without disabilities, there were differences across race. Hispanic or Latino older adults were the most likely to see a decrease in income (29 percent), compared with 13 percent of White older adults. Most older adults reported that they received a stimulus payment in late 2020 or early 2021 (80 percent). There were no differences by disability. There were some differences by race, with rates varying from 77 percent (Hispanic or Latino) to 82 percent (White). 18 What Was the Role of Intersectional Identities on These Outcomes? We examined COVID outcomes both by disability within racial and ethnic categories and by race and ethnicity among those with disabilities to understand the intersectionality of these characteristics among Americans age 50 and older. Although many patterns of health, work, and financial impacts were similar, intersectional findings emerged in several areas: e COVID testing. Although there were differences across race and ethnicity in COVID testing, diagnosis, and hospitalization rates (Table 3), there were few intersectional findings with disability. Among White older adults, those with disabilities were more likely to receive a COVID diagnoses or to be tested than those without disabilities (Table 4). e Health care delays. Within each race and ethnicity group, disabled older adults were more likely to delay health care. The difference was largest for older adults of another race, among whom 43 percent of those with disabilities delayed health care compared with 28 percent of those without disabilities. Among those with disabilities, there were also statistically significant differences across race and ethnicity. We compared the same 43 percent of older adults of another race with a disability who delayed health care to 38 percent of White older adults with a disability and 35 percent of older adults with disabilities who were Black or Hispanic or Latino. e Delaying prescriptions. Many of the same patterns in types of health care delays by disability persisted or were exacerbated when we consider separate racial and ethnic groups. For example, 17 percent of Hispanic or Latino older adults with a disabling condition reported delaying prescriptions compared with 6 percent of those without disabilities. Among all older adults, the difference by disability was 4 percentage points. e Stopping work. Among those whose work was affected, there were differences in those who stopped working by disability and exacerbations by race or ethnicity. For example, 62 percent of Black older adults with disabilities reported stopping work, compared with 44 percent of all older adults, 52 percent of older adults with disabilities of any race, and 53 percent of Black older adults regardless of disability status. e Moving to remote work. Among all older adults whose work was affected, there was no difference by disability in those who moved to remote work (Table 3). However, 19 Hispanic or Latino older adults with disabilities were more likely to move to remote work compared with those without disabilities (48 percent compared with 28 percent). Conversely, Black older adults with disabilities were less likely to move to remote work compared with those without disabilities (23 percent compared with 48 percent). Financial hardships. The differences in older adults' financial hardships by disability status persisted across White, Black, and Hispanic or Latino older adults. Some of the largest differences were in having enough money to buy food. Twenty-two percent of Hispanic or Latino older adults with disabilities did not have enough money to buy food, compared with 11 percent without disabilities. For Black older adults, 18 percent with disabilities did not have enough money to buy food compared with 8 percent without disabilities. There were similar patterns of older adults reporting that they had trouble buying food even if they had the money. Stimulus payments. There were no differences in stimulus receipt by disability, overall or within racial and ethnic groups. There were some differences by race or ethnicity that persisted among those with disabling conditions. Seventy-one percent of disabled older adults of another race received the stimulus payment, compared with about 80 percent of disabled older adults who were Black (78 percent), Hispanic or Latino (80 percent), or White (81 percent). Our results were similar when examining different definitions of disabling conditions, including having a work-limiting condition and ever receiving SSI or DI benefits (Appendix Table 1). We saw a similar pattern for older adults with two or more chronic conditions compared with those with one or fewer, and larger negative experiences for older adults with five or more chronic conditions (Appendix Table 2). We also found similar results when examining non-regression adjusted outcomes (Appendix Table 3) and when examining a weighted, nationally representative subset of respondents (Appendix Table 4). Retirement Preparedness Among respondents who were working at the time of the surveys in both 2018 and 2020, most respondents expected to work full-time past age 65. The average self-reported probability 20 of working after age 65 was about 45 percent in 2018 and 43 percent in 2020. There were differences across disability, race, and ethnicity groups. Working older adults with disabilities predicted lower probabilities of working past age 65 (39 percent in both years). Black older adults had the lowest predicted probability of working past age 65 (about 37 percent), but there were no differences between older Black workers with and without disabilities in 2020. Role of Contextual and Social Factors Next, we focus on the contextual factors associated with COVID experiences. We link respondent level HRS to a series of data, described earlier, for their respective counties and states of residence in 2020. Data security restrictions prevent us from reporting some specifics on the county of HRS geocoded respondents' residence. In general terms, the HRS respondents in our sample represent a large number but less than half of the U.S counties with geographic variation in terms of U.S. region and urbanicity. There is an average of 11 HRS respondents per county, with a minimum of | respondent and a maximum of 306 respondents residing in a county. What Were the Unadjusted Differences in County Characteristics Across Disabling Conditions and Race or Ethnicity? As with our individual-level analysis, we explored differences in county characteristics between older adults with and without disabling condition, across our whole sample and within racial and ethnic categories (Table 5). We found statistically significant differences in county- level characteristics for people with and without disabilities, across the full sample and within racial and ethnic categories. Across the full sample, adults with disabling conditions were more likely to live in counties characterized by higher levels of COVID vulnerability, and lower levels of economic opportunity, socioeconomic vulnerability, and health care access, as measured by our county-level factors. Specifically, adults with disabling conditions were more highly concentrated in counties with a higher average case counts, higher scores on the Pandemic Vulnerability and Social Vulnerability Indexes, more years of potential life lost (YPLL), and higher rates of unemployment and populations receiving government assistance. This pattern also remained consistent within race and ethnicity. Non-Hispanic Black adults with disabling conditions live in counties that have poor performance on measures across 21 the eight county characteristics we examined.> However, for residential racial segregation, non- Hispanic Black adults with disabling conditions were more likely to live in counties that are less racially segregated. Likewise, Hispanic adults with disabling conditions were more likely to live in counties with higher scores on the Pandemic Vulnerability and Social Vulnerability indexes, and higher levels of unemployment and percentages of county residents receiving public assistance relative to their counterparts without a disability. However, those individuals were more likely to live in counties with more hospital beds per capita. When examining differences within the subgroup of respondents with disabling conditions, the observed racial patterns persisted. Older adults with disabilities who are non- White were more clustered in counties with less favorable county characteristics, relative to people with disabilities who identify as non-Hispanic White. Are Contextual Factors Associated with COVID Outcomes for Individuals with Disabling Conditions? Next, we examined the association of contextual factors on individual experiences with financial hardship, delaying health care, and whether one's work was affected. We started by examining the intraclass coefficient for all three COVID outcomes separately. In the context of multilevel models, the intraclass correlation coefficient (ICC) ranges from 0 to 1 and reveals the correlation between two observations (individuals) within the same cluster (county or state). Higher correlations between individuals means there is clustering in our data and, as such, a higher ICC suggests that the use of a multilevel model is preferred to a linear model. Although there is no uniform benchmark for what constitutes a value of an ICC that is "large enough" to suggest the use of a multilevel model, a commonly used rule of thumb is that a value of an ICC greater than 0.05 merits the use of a multilevel model. The intraclass coefficient for the unadjusted model of financial hardship with county measures is 0.07, suggesting that clustering at the county level can explain 7 percent of the individual-level variation in financial hardship. However, the ICC for work being affected is 0.0388 and the ICC for delaying health care is 0.0046. These small values suggest that there might not be clustering of work effects and health care delays at the county level. As such, we report findings on financial hardship in this text and > We did not measure differences in state-level governance because the sample size was too small for inference. 22 reserve the results for models related to whether work was affected or delays in health care for the appendix. Tables 6 to 14 display the results of the set of multilevel models estimating associations of area-level factors with financial hardship, health care, and work, using odds ratios. We found only two significant county-level factors on the likelihood of declaring financial hardship because of COVID-19: (1) YPLL (Table 10) and (2) state political party affiliation (Table 14). We did, however, observe statistically significant positive associations with financial hardship for both disability and with race or ethnicity across all models for all county-level factors, suggesting that an individual's disability status and race or ethnicity remain positive predictors of financial hardship because of COVID-19 even after accounting for contextual factors. Focusing on the set of models with statistically significant relationships, we first describe the set of models examining the role of YPLL. Model | suggests that for every one standard deviation increase in an individual's county mean value of YPLL, that individual is 5 percent more likely to report a financial hardship. YPLL measures premature mortality in a county by counting the years of life lost before age 75, thus focusing on preventable deaths. This measure captures elements associated with SDOH and area-level health behaviors such as smoking and accidents. In Model 1, the estimated odds ratios corresponding to whether an individual has a disabling condition and corresponding to an individual's race or ethnicity are also greater than one and highly significant. Including individual-level covariates in Model 2, the odds ratio corresponding to YPLL attenuates slightly but remains statistically significant and greater than one. In Model 2, there is no statistically significant association between the YPLL measure at the county level and disability, implying that there is no differential effect of a county's YPLL on the likelihood of financial hardship for people with or without a disabling condition. There is a Statistically significant association for the interaction between YPLL and both non-Hispanic Black persons and persons of non-Hispanic other races. The main effects on YPLL, disability, and race or ethnicity all remain greater than one and statistically significant. Focusing on the state-level model of political party (Table 12), we found that older adults were more likely to report a financial hardship in a state with a republican-controlled government (OR = 1.2). As with the county-level models, the main effects on disability and race or ethnicity remain positive and statistically significant. In addition, we observed an association between 23 delayed health care and political party wherein individuals in republican-controlled states are less likely to report delaying health care (OR = 0.7). We do not see a consistent pattern of association between county-level contextual factors and individual experiences with delayed health care or work being affected. Discussion Summary of Key Findings We found evidence of negative COVID-19 effects on health, work, and financial experiences for older adults with disabilities. This group was more negatively impacted on several measures compared with those without disabilities. In addition, older adults with disabilities who were Black or Hispanic often had disparate impacts compared with either those without disabilities or White older adults with disabilities. Most notably, older adults with disabilities were more likely to report experiencing financial hardships than older adults without disabilities. Overall, about one-third of older adults reported financial hardships since the start of the pandemic. Although there was no difference by disabling condition in the receipt of stimulus payments, older adults with disabling conditions were more likely to experience each of the financial hardships examined. There were also large differences at the intersection of race or ethnicity and disabling condition. For example, twice as many Hispanic older adults with disabilities did not have enough money to buy food compared with those without disabilities. Older adults with disabilities also reported negative impacts on health care delays and work. They were also more likely than older adults without disabilities to report delaying many types of health care, such as surgeries and prescriptions, since March 2020. Among older adults whose work was impacted by the pandemic, more older adults with disabilities reported stopping work than older adults without disabilities, and fewer moved to remote work. Although few contextual factors were associated with these negative COVID outcomes, we found evidence that the contextual factors mattered. First, there were important differences in county-level characteristics for people with and without disabilities, across the full sample and within race or ethnicity. Moreover, older adults with disabilities who are Hispanic, Black, or another race other than White tend to live in counties that performed poorly on measures of several county-level factors relative to people with disabilities who identify as non-Hispanic 24 White. We did not see a consistent pattern of association between county-level contextual factors and individual experiences with delayed health care or work being affected. For financial outcomes, only YPLL is significant and in the expected direction. These findings reveal that people with disabilities tend to be more highly concentrated in areas that are more susceptible to COVID and other socioeconomic inequalities. However, these contexts do not appear to influence their COVID outcomes beyond respondents' individual identities and experiences. Study Limitations The results of this analysis are similar to the results we examined on a nationally representative, weighted subset of adults age 50 and older. However, the data and findings have two key limitations. First, the negative COVID experiences are self-reported and subject to potential biases. For example, because the survey was fielded over about a year, some respondents were answering questions about the impacts of COVID very early during shutdowns while others had a longer period of time to experience negative impacts. Thus, early respondents might not yet have experienced the negative effects that they would eventually experience, and later respondents might have forgotten or misreported experiences from nearly a year earlier. However, the timing of interviews is plausibly random with respect to whether someone has a disabling condition (measured in 2018) and with respect to their race or ethnicity. In addition, some older adults might have experienced different delays in care given variation in the duration and timing of lockdowns and temporary clinic closures. Second, we cannot determine a causal link between disability, race, or ethnicity on pandemic-related outcomes. Some questions were phrased to inquire about the "effects" of COVID-19, while others focused on experiences since March 2020. However, it is likely that older adults with disabilities had more financial hardships, barriers to health care, and difficulty accessing work before the pandemic. Indeed, there is a great deal of literature documenting many such difficulties. This study provides descriptive evidence of the disparities experienced in approximately the first year of the pandemic, but causation cannot be determined. Implications of the Findings The findings highlight key takeaways about the importance of: 1) a robust disability- inclusive public health response; 2) the intersectionality lens; and 3) financial policies. First, the 25 pandemic had disparate impacts on people with disabilities, and the vulnerability of this community suggests future public health events, such as pandemics or natural disasters, might impact them similarly. Before such events occur again, resources could be put into understanding particular needs to better support the well-being of people with disabilities. Second, it is important to examine this population using an intersectionality lens. Older adults with intersecting identities of disability and historically marginalized race or ethnicity were more likely to have been negatively impacted by the pandemic. When considering impacts separately by race or ethnicity, or by disability, the extent of hardships experienced at the intersection of these identities is hidden. Although the effects we examined are self-reported and do not measure the extent of the hardships experienced, the finding that more people with multiple marginalized identities faced difficulties highlights that continued study of this intersection should lead to policies better aimed at alleviating hardship. Finally, policies mitigating financial impacts could be universally beneficial. Financial impacts were widespread across every demographic group. We found evidence that intersectional disparate effects were felt on many types of financial hardships. Although most older adults received a stimulus payment, about a third still experienced financial hardships. Although many benefits were expanded during the pandemic, such as extended eligibility for the Supplemental Nutrition Assistance Program, 13 percent of older adults with disabilities reported not having enough money to buy food. 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'ICVI syupe ponyea oruedsty Jo oruedsty oruedsiy | ,onfeA 10 [qV TdavVI =' JOpyjo JSI]-] -UON oluedsiy -UON, -UON -d ON JO 7Tq0V IIV RECENT WORKING PAPERS FROM THE CENTER FOR RETIREMENT RESEARCH AT BOSTON COLLEGE What Is the Risk to OASI Benefits from Unpaid Student Loans? Gal Wettstein and Siyan Liu, November 2022 How Does Local Cost-Of-Living Affect Retirement for Low and Moderate Earners? Laura D. Quinby and Gal Wettstein, November 2022 Did the Stimulus Checks Improve Household Balance Sheets? Andrew G. Biggs, Angi Chen, and Alicia H. Munnell, November 2022 Technology and Disability: The Relationship Between Broadband Access and Disability Insurance Awards Barbara A. Butrica and Jonathan Schwabish, October 2022 How Does COVID-Induced Early Retirement Compare to the Great Recession? Angi Chen, Siyan Liu, and Alicia H. Munnell, October 2022 Outcomes Following Termination of Social Security Disability Insurance Michael T. Anderson, Monica Farid, Serge Lukashanets, Denise Hoffman, and Kai Filion, September 2022 Will Survivors of the First Year of the COVID-19 Pandemic Have Lower Mortality? Gal Wettstein, Nilufer Gok, Angi Chen, and Alicia H. Munnell, August 2022 Understanding the Increased Financial Hardship Experienced by Older Adults with Disabilities during the COVID-19 Pandemic Zachary A. Morris, August 2022 A Framework for Evaluating the Adequacy of Disability Benefit Programs and Its Application to U.S. Social Security Disability Zachary A. Morris, August 2022 Comparative Regression Discontinuity and Regression Discontinuity as Alternatives to RCT for Estimating Average Treatment Effects Duncan Chaplin, Charles Tilley, Denise Hoffman, and John T. Jones, August 2022 Work Overpayments Among New Social Security Disability Insurance Beneficiaries Denise Hoffman, Monica Farid, Serge Lukashanets, Michael T. Anderson, and John T. Jones, July 2022 All working papers are available on the Center for Retirement Research website (https://crr.bc.edu) and can be requested by e-mail (crr@bc.edu) or phone (617-552-1762). 64