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<h1 itemprop="headline">Social Security Administration Disability Programs and Individuals Facing Homelessness</h1>
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<div id="hByline">by <span itemprop="author">Joyce Nicholas and Thomas W. Hale</span><br>Social Security Bulletin, <abbr title="Volume">Vol.</abbr> 81 <abbr title="Number">No.</abbr> 2, 2021 (released May 2021)</div>
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<div id="breadcrumbs" itemscope itemtype="http://schema.org/BreadcrumbList">You are here: <span itemprop="itemListElement" itemscope itemtype="http://schema.org/ListItem"><a href="/" itemprop="item"><span itemprop="name">Social Security Administration</span></a><meta itemprop="position" content="1" /></span> > <span itemprop="itemListElement" itemscope itemtype="http://schema.org/ListItem"><a href="/policy/index.html" itemprop="item"><span itemprop="name">Research, Statistics & Policy Analysis</span></a><meta itemprop="position" content="2" /></span> > <span itemprop="itemListElement" itemscope itemtype="http://schema.org/ListItem"><a href="/policy/docs/ssb/index.html" itemprop="item"><span itemprop="name">Social Security Bulletin</span></a><meta itemprop="position" content="3" /></span> > <span itemprop="itemListElement" itemscope itemtype="http://schema.org/ListItem"><a href="index.html" itemprop="item"><span itemprop="name"><abbr title="Volume">Vol.</abbr> 81 <abbr title="Number">No.</abbr> 2</span></a><meta itemprop="position" content="4" /></span></div>
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<p id="synopsis" itemprop="description">This article examines the geographic, demographic, socioeconomic, and program-participation characteristics of initial Supplemental Security Income (<abbr class="spell">SSI</abbr>) and Social Security Disability Insurance (<abbr class="spell">DI</abbr>) applicants who faced homelessness during <span class="nobr">2007–2017.</span> Using Social Security Administration data, we chart the distribution of homeless <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> applicants and beneficiaries across county-equivalent areas in the contiguous United States. We also use a text-mining method to identify 162,536 potentially homeless disability-program applicants, in addition to the 647,790 applicants identified using the standard homeless-status indicators in the administrative data. We find that homelessness among disability-program applicants was largely an urban phenomenon, with almost half (42.1 percent) of applicants living in one of 25 urban areas. Relative to their domiciled counterparts, homeless disability-program applicants were far more likely to be male, aged <span class="nobr">18–64,</span> and without a high school or general equivalency diploma. Allowance rates varied among studied applicants differentiated by program, mortality status, and primary impairment.</p>
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<hr />
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<div class="eightypercent">
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<p>Joyce Nicholas is a social science research analyst in the Office of Retirement and Disability Policy (<abbr class="spell">ORDP</abbr>), Social Security Administration (<abbr class="spell">SSA</abbr>). When this article was written, Thomas Hale was an economist with <abbr class="spell">ORDP</abbr>, <abbr class="spell">SSA</abbr>.</p>
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<p><i>Acknowledgments:</i> The authors thank Susan Wilschke, Jeffrey Hemmeter, Joyanne Cobb, Chris Tamborini, Angela Hood, Michael Compson, Hilary Waldron, Bert Kestenbaum, and Sherria Green for their support and thoughtful comments on drafts of this article.</p>
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<p>Contents of this publication are <a href="/policy/accessibility.html">not copyrighted</a>; any items may be reprinted, but citation of the <i>Social Security Bulletin</i> as the source is requested. The findings and conclusions presented in the <i>Bulletin</i> are those of the authors and do not necessarily represent the views of the Social Security Administration.</p>
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</div>
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</div>
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<h2>Introduction</h2>
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<div class="abbrtable">
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<table>
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<caption>Selected Abbreviations</caption>
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<colgroup span="1" style="width:25%"></colgroup>
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<colgroup span="1"></colgroup>
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<tbody>
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<tr>
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<td><abbr class="spell">DDS</abbr></td>
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<td>Disability Determination Service</td>
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</tr>
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<tr>
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<td><abbr class="spell">DI</abbr></td>
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<td>Disability Insurance</td>
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</tr>
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<tr>
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<td><abbr class="spell">EDCS</abbr></td>
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<td>Electronic Disability Collect System</td>
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</tr>
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<tr>
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<td><abbr>HUD</abbr></td>
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<td>Department of Housing and Urban Development</td>
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</tr>
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<tr>
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<td><abbr class="spell">ISM</abbr></td>
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<td>in-kind support and maintenance</td>
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</tr>
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<tr>
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<td><abbr class="spell">MSSICS</abbr></td>
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<td>Modernized Supplemental Security Income Claims System</td>
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</tr>
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<tr>
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<td><abbr class="spell">SSA</abbr></td>
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<td>Social Security Administration</td>
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</tr>
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<tr>
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<td><abbr class="spell">SSI</abbr></td>
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<td>Supplemental Security Income</td>
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</tr>
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<tr>
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<td><abbr class="spell">USICH</abbr></td>
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<td><abbr>U.S.</abbr> Interagency Council on Homelessness</td>
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</tr>
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</tbody>
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</table>
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</div>
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<p>This study provides new quantitative information about individuals who applied for Supplemental Security Income (<abbr class="spell">SSI</abbr>) and Social Security Disability Insurance (<abbr class="spell">DI</abbr>) disability benefits when they were experiencing or at risk of homelessness. The Social Security Administration (<abbr class="spell">SSA</abbr>) places great importance on identifying homeless disability-program applicants because their unmet housing needs, along with their health challenges, make it harder for them to navigate the application process. By understanding the geographic distribution of homeless disability-program applicants across <abbr class="spell">SSA</abbr>'s service areas, and their demographic, socioeconomic, and program-participation characteristics, <abbr class="spell">SSA</abbr> can improve its efforts to ensure that homeless applicants receive needed supports.</p>
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<p>For this article, we supplement structured data from <abbr class="spell">SSA</abbr> disability-benefit applicant intake forms with text mined from the “residential address” and “note” fields of those forms to identify individuals who were experiencing or at risk of homelessness.<sup><a href="#mn1" id="mt1">1</a></sup> Our primary purpose is to provide an overview of the prevalence of homelessness among <abbr class="spell">SSA</abbr>'s service population. We identify 810,326 individuals experiencing homelessness who submitted an initial <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> disability-benefit application during the years 2007 through 2017 and had a medical decision made by a state Disability Determination Service (<abbr class="spell">DDS</abbr>) after September 2007.<sup><a href="#mn2" id="mt2">2</a></sup> This study is the first to examine the distribution of homeless <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> applicants and beneficiaries across county-equivalent areas in the contiguous United States.<sup><a href="#mn3" id="mt3">3</a></sup></p>
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<p>To compile our count of homeless disability-program applicants, we began by identifying the individuals who were recorded as experiencing homelessness in one of two ways in the administrative records. The first is the “homeless flag,” which an <abbr class="spell">SSA</abbr> field officer activates in the <abbr class="spell">DI</abbr> or <abbr class="spell">SSI</abbr> applicant's file to alert other <abbr class="spell">SSA</abbr> and <abbr class="spell">DDS</abbr> staff to follow the special case-processing procedures required in cases involving homelessness. The second is the “transient indicator,” which is attached to an <abbr class="spell">SSI</abbr> applicant's file for the same purpose as the homeless flag but is also used in postentitlement <span class="nobr">in-kind</span> support and maintenance (<abbr class="spell">ISM</abbr>) evaluations.<sup><a href="#mn4" id="mt4">4</a></sup> To the count of individuals identified by the homeless flag and the transient indicator, we added applicants that we identified as experiencing homelessness by mining the text in “residential address” and “administrative note” fields in those application files. With the text-mining experiment, this article explores whether <abbr class="spell">SSA</abbr>'s processes and mechanisms for recording homeless and transient status potentially miss any disability-program applicants who face housing instability.</p>
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<h2>Background</h2>
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<p>In administering the <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr> programs, <abbr class="spell">SSA</abbr> provides income stability for individuals with disabilities who meet the program requirements and are experiencing homelessness. <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr>, in concert with other programs, can help individuals transition from homelessness toward stable and permanent housing. <abbr class="spell">SSA</abbr> promotes and seeks to improve collaboration with government and nonprofit stakeholders who serve individuals experiencing homelessness and can assist that population during both the initial <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> application and the medical determination process.<sup><a href="#mn5" id="mt5">5</a></sup> <abbr class="spell">SSA</abbr> is one of 19 agencies participating in the <abbr>U.S.</abbr> Interagency Council on Homelessness (<abbr class="spell">USICH</abbr>), which oversees and coordinates the federal response to homelessness.<sup><a href="#mn6" id="mt6">6</a></sup> In addition to this study, <abbr class="spell">SSA</abbr> has conducted various data analyses to inform <abbr class="spell">USICH</abbr> efforts. For example, in 2014, <abbr class="spell">SSA</abbr> evaluated the outcomes of Social Security disability applications submitted through the Benefits Entitlement Services Team (<abbr>BEST</abbr>) demonstration project to determine if the project successfully increased access to <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> benefits for individuals experiencing homelessness (Kennedy and King 2014).<sup><a href="#mn7" id="mt7">7</a></sup> <abbr class="spell">SSA</abbr> also conducted and evaluated the Homeless with Schizophrenia Presumptive Disability pilot. The evaluation found that providing support during the application process for homeless individuals with a serious mental illness led to higher allowance rates at the initial adjudication level, fewer requests for consultative examinations, and reduced time to allowance (Bailey, Engler, and Hemmeter 2016).</p>
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<h3><abbr class="spell">SSA</abbr> Disability Programs</h3>
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<p>The <abbr class="spell">SSI</abbr> program makes payments to individuals with a qualifying disability and limited income and resources; the <abbr class="spell">DI</abbr> program provides benefits to disabled workers who are insured (based on their earnings records) and, in some cases, to their eligible family members. Section 223 of the Social Security Act defines disability as “the inability to engage in any substantial gainful activity by reason of any medically determinable physical or mental impairment which can be expected to result in death or which has lasted or can be expected to last for a continuous period of not less than 12 months.” For both programs, individuals must meet that definition of disability. The <abbr class="spell">SSI</abbr> program is means-tested; qualifying applicants must have income and assets below certain levels. To qualify for <abbr class="spell">DI</abbr> benefits, individuals must have accrued sufficient work credits based on their earnings histories.</p>
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<p>The disability determination process begins when the individual applies for <abbr class="spell">SSI</abbr>, <abbr class="spell">DI</abbr>, or both and submits the <span class="nobr">application(s)</span> to an <abbr class="spell">SSA</abbr> field office, where a staff member first verifies nonmedical eligibility by determining whether the applicant is engaged in substantial gainful activity, as indicated by an annually adjusted earnings threshold.<sup><a href="#mn8" id="mt8">8</a></sup> If so, the field office denies the application; otherwise, the field office sends the case to a state <abbr class="spell">DDS</abbr> office.</p>
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<p>In both programs, the <abbr class="spell">DDS</abbr> determines disability based on vocational and medical evidence from the applicant's medical or behavioral care providers or from a consultative examination—that is, a physical or mental examination or test purchased by <abbr class="spell">SSA</abbr>. If the <abbr class="spell">DDS</abbr> determines that the applicant is not disabled, the applicant may request reconsideration, in which the <abbr class="spell">DDS</abbr> thoroughly reexamines all evidence used in the initial determination and any additional evidence or information submitted with the reconsideration appeal. If the <abbr class="spell">DDS</abbr> denies the application at the reconsideration level, the claimant may request an appeal hearing before an administrative law judge (<abbr class="spell">ALJ</abbr>). If the claim is denied at the <abbr class="spell">ALJ</abbr> level, the applicant can then bring the case to the <abbr class="spell">SSA</abbr> Appeals Council; if the Council denies the claim or decides not to review the case, the applicant can appeal to federal district court.<sup><a href="#mn9" id="mt9">9</a></sup></p>
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<h3><abbr class="spell">SSA</abbr> Definitions of Housing Instability</h3>
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<p><abbr class="spell">SSA</abbr> uses two definitions of housing instability in its disability programs. The first definition is the one that must be met to activate the homeless flag. It therefore applies to both the <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr> programs, and it has two components, one reflecting current status and the other reflecting prospective risk. <abbr class="spell">SSA</abbr> defines a disability-program applicant as “homeless” if he or she (1) does not have a fixed, regular, and adequate nighttime residence; or (2) is at risk of losing or is expected to lose his or her current accommodations within 14 days and will not have a fixed, regular, and adequate nighttime residence (<abbr class="spell">SSA</abbr> 2014a). <abbr class="spell">SSA</abbr> uses this definition to flag disability-program applications for special expedited processing so that individuals who face homelessness and meet the eligibility criteria can begin to receive stable income sooner. If an applicant meets this definition, <abbr class="spell">SSA</abbr> policy requires field office staff to activate the homeless flag manually in the agency's Electronic Disability Collect System (<abbr class="spell">EDCS</abbr>). Thus, we use the <abbr class="spell">EDCS</abbr> homeless flag to identify applicants meeting this first definition.</p>
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<p>The second definition applies only to the <abbr class="spell">SSI</abbr> program. <abbr class="spell">SSA</abbr> defines an applicant as “transient” if he or she has no permanent living arrangement or fixed place of residence. A member of a household or a resident of an institution is not considered transient; an individual who is homeless, or who stays with a succession of friends or relatives with no permanent arrangement, is considered transient (<abbr class="spell">SSA</abbr> 2005). <abbr class="spell">SSA</abbr> operational policy instructs field office staff to apply a “transient” indicator in the Modernized <abbr class="spell">SSI</abbr> Claims System (<abbr class="spell">MSSICS</abbr>) to record <abbr class="spell">SSI</abbr> applicants and recipients experiencing current housing instability.<sup><a href="#mn10" id="mt10">10</a></sup> <abbr class="spell">SSA</abbr> uses this information primarily to determine the applicant's living-arrangement category (which may affect <abbr class="spell">SSI</abbr> payment amounts) and to help account for <abbr class="spell">ISM</abbr> (such as food or shelter received from family or friends) at the time of application or, if <abbr class="spell">SSI</abbr> payments have begun, at the time of an <abbr class="spell">ISM</abbr> evaluation (Nicholas 2014). <abbr class="spell">SSA</abbr> operational policy also instructs field office staff to activate the <abbr class="spell">EDCS</abbr> homeless flag on any pending disability-program application for a claimant whose <abbr class="spell">SSI</abbr> living arrangement is flagged as transient. We use the <abbr class="spell">MSSICS</abbr> transient indicator to identify applicants meeting this second definition of homelessness.</p>
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<p>For this study, we also apply a third definition of homelessness, which more closely aligns with the Department of Housing and Urban Development (<abbr>HUD</abbr>) definition adopted by <abbr class="spell">USICH</abbr>. That definition identifies an individual as chronically homeless if he or she can be diagnosed with a physical or mental disability, is (or was) without a home, and experienced housing instability for at least 12 months either consecutively or during at least four separate occasions within the last 3 years (<abbr>HUD</abbr> 2015). For this study, we use the <abbr>HUD</abbr>/<abbr class="spell">USICH</abbr> homeless definition, which we identify in <abbr class="spell">SSA</abbr> records via text mining, to detect members of the <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> population who may be experiencing homelessness but do not have a homeless flag or transient indicator on their record.<sup><a href="#mn11" id="mt11">11</a></sup> Specifically, for applicants either filing an <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> claim or undergoing an <abbr class="spell">SSI</abbr> <abbr class="spell">ISM</abbr> evaluation, we search the content of the residential-address and administrative-note fields in their records for terms and phrases that reflect similarities with the <abbr class="spell">USICH</abbr> definition of homelessness.<sup><a href="#mn12" id="mt12">12</a></sup> This approach is broad, but it represents a first step toward understanding whether the homeless flag and transient indicator alone might undercount the homeless population.</p>
|
|
<h2>Data and Methods</h2>
|
|
<p>We used administrative data available from four <abbr class="spell">SSA</abbr> sources as of August 16, 2017. First, we used the Disability Analysis Support Hub (<abbr>DASH</abbr>) for programmatic information and <abbr>ZIP</abbr> Codes for all initial <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> applications transferred from an <abbr class="spell">SSA</abbr> field office to a state <abbr class="spell">DDS</abbr> where a medical decision occurred after September 2007. We also used the <abbr>DASH</abbr> to detect the use of the homeless flag and transient indicator, and to provide the address and note field contents needed to identify homeless applicants via text mining. Second, we used the 2017 release of the Disability Research File (<abbr class="spell">DRF</abbr>) to obtain a <span class="nobr">10-year</span> view of <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> application and payment information.<sup><a href="#mn13" id="mt13">13</a></sup> Third, we used the 2015 version of the Disability Analysis File to obtain 2015 earnings data and any additional or more current <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr> payment data. We concluded our analysis using death records available as of December 31, 2018 from the restricted-access Death Master File.</p>
|
|
<p>Several data limitations influenced the parameters of our study. First, the reference periods of available data sources permitted us to study only homeless individuals who submitted an initial application during calendar years 2007 through 2017 and had a medical decision rendered by a <abbr class="spell">DDS</abbr> after September 2007. Second, the limited availability of recent and accurate annual income data at the time of writing prevented us from examining earnings data for years since 2015.</p>
|
|
<h3>Identification of Study Group</h3>
|
|
<p>We applied the three methods of detecting homeless status to identify the subset of <span class="nobr">2007–2017</span> disability-program applicants we sought to include in our study. We selected <abbr class="spell">DI</abbr> applicants who had a homeless flag or text in the residential-address or administrative-note field indicating that they were homeless when they filed their application.<sup><a href="#mn14" id="mt14">14</a></sup> We chose <abbr class="spell">SSI</abbr> applicants who had a homeless flag, transient indicator, or text in the residential-address or administrative-note field specifying that they were homeless at the time of either an <abbr class="spell">SSI</abbr> application or a subsequent <abbr class="spell">ISM</abbr> evaluation.</p>
|
|
<p>Many applicants experiencing homelessness may not complete the <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> application process or may have their applications denied because they lack supporting documentation of medical impairments (Bailey, Engler, and Hemmeter 2016). As such, many individuals apply for benefits multiple times. To support a person-level analysis and to avoid double counting, we limited our study to the administrative records for only the <i>most recent</i> application of each homeless disability-program applicant whose <i>initial</i> application was received by <abbr class="spell">SSA</abbr> during <span class="nobr">2007–2017.</span> We examined data from the last application filed before an allowance or denial decision in which an <abbr class="spell">SSA</abbr> staff member identified the applicant as homeless.<sup><a href="#mn15" id="mt15">15</a>,<a href="#mn16" id="mt16">16</a></sup> Likewise, to avoid double counting members of our comparison group of domiciled disability-program applicants, we applied the same selection criteria and methodology.</p>
|
|
<p>The study group is composed of 810,326 individuals, hereafter referred to as “homeless disability applicants.” Of these, we identified 64,264 cases (7.9 percent) with an <abbr class="spell">EDCS</abbr> homeless flag but no transient indicator or text-mining results indicating homelessness; 339,697 cases (41.9 percent) with an <abbr class="spell">MSSICS</abbr> transient indicator but no homeless flag or text-mining results indicating homelessness; and 162,536 cases (20.1 percent) of homelessness indicated by only the text-mining method (Table 1 and Chart 1). We also identified 181,496 applicants (22.4 percent) by any combination of two of the three methods, as well as 62,333 applicants (7.7 percent) whose file met all three definitions. The 162,536 applicants who were identified by text mining alone—20.1 percent of the total group—would have been excluded from the study if we had used only the <abbr class="spell">SSI</abbr> transient indicator and <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> homeless flag to identify homeless disability applicants. This outcome confirms that our text-mining method, using the <abbr>HUD</abbr> definition of homelessness, greatly increases the number of disability applicants identified as experiencing homelessness.</p>
|
|
<div class="table" id="table1">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 1. </span>Disability-program applicants experiencing homelessness, by method of identifying homeless status, <span class="nobr">2007–2017</span></caption>
|
|
<colgroup span="1" style="width:27em"></colgroup>
|
|
<colgroup span="2" style="width:6em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th class="stubHeading" scope="col">Measure</th>
|
|
<th scope="col">Number</th>
|
|
<th scope="col">Percent</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub2" scope="rowgroup">Total</th>
|
|
<td>810,326</td>
|
|
<td>100.0</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">One method only</th>
|
|
<td>566,497</td>
|
|
<td>69.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row"><abbr class="spell">EDCS</abbr> homeless flag</th>
|
|
<td>64,264</td>
|
|
<td>7.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row"><abbr class="spell">MSSICS</abbr> transient indicator</th>
|
|
<td>339,697</td>
|
|
<td>41.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Text mining</th>
|
|
<td>162,536</td>
|
|
<td>20.1</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Two methods</th>
|
|
<td>181,496</td>
|
|
<td>22.4</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row"><abbr class="spell">EDCS</abbr> homeless flag and <abbr class="spell">MSSICS</abbr> transient indicator</th>
|
|
<td>73,422</td>
|
|
<td>9.1</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row"><abbr class="spell">EDCS</abbr> homeless flag and text mining</th>
|
|
<td>38,815</td>
|
|
<td>4.8</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row"><abbr class="spell">MSSICS</abbr> transient indicator and text mining</th>
|
|
<td>69,259</td>
|
|
<td>8.5</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">All three methods</th>
|
|
<td>62,333</td>
|
|
<td>7.7</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="onlyNote" colspan="3">SOURCE: Authors' calculations using administrative data from <abbr class="spell">SSA</abbr>.</td>
|
|
</tr>
|
|
</table>
|
|
</div>
|
|
<div class="svgChart chart320" id="chart1"> <img src="v81n2p1-chart01.svg" role="img" alt="Chart 1. Disability-program applicants experiencing homelessness, by method of identifying homeless status, 2007 through 2017. Venn diagram depicting set relationships and values listed in Table 1."> </div>
|
|
<p>Before conducting our geospatial analysis, we assessed how frequently field office staff applied the <abbr class="spell">EDCS</abbr> homeless flag and the <abbr class="spell">MSSICS</abbr> transient indicator. The activation of the <abbr class="spell">MSSICS</abbr> transient indicator requires the activation of the <abbr class="spell">EDCS</abbr> homeless flag but only at the time an active <abbr class="spell">SSI</abbr> application is available for expedited processing (<abbr class="spell">SSA</abbr> 2005, 2014b, 2014c). As a result, for <abbr class="spell">SSI</abbr> allowances, we were unable to determine whether <abbr class="spell">SSA</abbr> staff had applied the <abbr class="spell">MSSICS</abbr> transient indicator at the time of application or during a postentitlement <abbr class="spell">ISM</abbr> evaluation. Therefore, we assessed the use of the <abbr class="spell">EDCS</abbr> homeless flag and the <abbr class="spell">MSSICS</abbr> transient indicator by focusing on <abbr class="spell">SSI</abbr> denials because it is certain that field office staff applied the <abbr class="spell">MSSICS</abbr> transient indicator for this subgroup only at the time of application, and not for a postentitlement <abbr class="spell">ISM</abbr> evaluation.</p>
|
|
<p>About <span class="nobr">one-quarter</span> (25.6 percent) of denied <abbr class="spell">SSI</abbr> applications had neither an <abbr class="spell">EDCS</abbr> homeless flag nor an <abbr class="spell">MSSICS</abbr> transient indicator; we identified the applicants as homeless using text mining (Table 2). Another 13.7 percent of denied <abbr class="spell">SSI</abbr> applications were identified with only an <abbr class="spell">EDCS</abbr> homeless flag. The remaining 60.7 percent of <abbr class="spell">SSI</abbr> denials had an <abbr class="spell">MSSICS</abbr> transient indicator; and although this entire subgroup should have had an <abbr class="spell">EDCS</abbr> homeless flag activated as well, only about one out of four had one.</p>
|
|
<div class="table" id="table2">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 2. </span>Denied disability-benefit applications from individuals experiencing homelessness, by program and method of identifying homeless status, <span class="nobr">2007–2017</span></caption>
|
|
<colgroup span="1" style="width:20em"></colgroup>
|
|
<colgroup span="2" style="width:6em"></colgroup>
|
|
<colgroup span="2" style="width:6em"></colgroup>
|
|
<colgroup span="2" style="width:6em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th rowspan="2" class="stubHeading" scope="colgroup">Method</th>
|
|
<th colspan="2" class="spanner" scope="colgroup">Total</th>
|
|
<th colspan="2" class="spanner" scope="colgroup"><abbr class="spell">DI</abbr> only</th>
|
|
<th colspan="2" class="spanner" scope="colgroup"><abbr class="spell">SSI</abbr> <sup>a</sup></th>
|
|
</tr>
|
|
<tr>
|
|
<th scope="col">Number</th>
|
|
<th scope="col">Percent</th>
|
|
<th scope="col">Number</th>
|
|
<th scope="col">Percent</th>
|
|
<th scope="col">Number</th>
|
|
<th scope="col">Percent</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr class="shaded">
|
|
<th class="stub1" scope="row">All</th>
|
|
<td>439,422</td>
|
|
<td>100.0</td>
|
|
<td>27,507</td>
|
|
<td>100.0</td>
|
|
<td>411,915</td>
|
|
<td>100.0</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="row"><abbr class="spell">EDCS</abbr> homeless flag only</th>
|
|
<td>62,003</td>
|
|
<td>14.1</td>
|
|
<td>5,375</td>
|
|
<td>19.5</td>
|
|
<td>56,628</td>
|
|
<td>13.7</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row"><abbr class="spell">MSSICS</abbr> transient indicator only</th>
|
|
<td>194,326</td>
|
|
<td>44.2</td>
|
|
<td>6,022</td>
|
|
<td>21.9</td>
|
|
<td>188,304</td>
|
|
<td>45.7</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">Both homeless flag and transient indicator</th>
|
|
<td>62,361</td>
|
|
<td>14.2</td>
|
|
<td>586</td>
|
|
<td>2.1</td>
|
|
<td>61,775</td>
|
|
<td>15.0</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">Text mining only</th>
|
|
<td>120,732</td>
|
|
<td>27.5</td>
|
|
<td>15,524</td>
|
|
<td>56.5</td>
|
|
<td>105,208</td>
|
|
<td>25.6</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="7">SOURCE: Authors' calculations using administrative data from <abbr class="spell">SSA</abbr>.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="7">a. Includes individuals who applied for concurrent <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr> benefits.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<p>Among all 439,422 denied <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> applications, we found that only 28.3 percent had an <abbr class="spell">EDCS</abbr> homeless flag activated for them and received expedited processing of their disability claims because of homelessness; however, there are many other reasons for which <abbr class="spell">SSA</abbr> may flag claims for expedited processing.<sup><a href="#mn17" id="mt17">17</a></sup> Despite the limits of our study data, the analysis of denied applications begins to illuminate how frequently field office staff use the homeless flag and transient indicator for individuals facing disability and homelessness.</p>
|
|
<h3>Geospatial Analysis</h3>
|
|
<p>Although our study group consists of 810,326 homeless disability applicants in <abbr class="spell">SSA</abbr>'s entire domestic service area, we focused our geospatial analysis on applicants in the 48 contiguous states. We anchored our geospatial analysis on the <abbr>ZIP</abbr> Codes of homeless disability applicants with a mailing address. We used Public Use Microdata Areas, developed for the Census Bureau's American Community Survey, to provide the conversion factors needed to generate county-based statistics from <abbr>ZIP</abbr> Code–level data. Our study covers 2,274 county-equivalent areas across the lower 48 states.<sup><a href="#mn18" id="mt18">18</a>,<a href="#mn19" id="mt19">19</a></sup> Of the full study group, about 82.6 percent (669,298) had <abbr>ZIP</abbr> Code data indicating residence in the lower 48 states.<sup><a href="#mn20" id="mt20">20</a></sup> Another 7.6 percent had <abbr>ZIP</abbr> Code data indicating residence in Alaska, Hawaii, or <abbr>U.S.</abbr> territories such as Guam and Puerto Rico. The remaining 9.8 percent of studied homeless disability applicants had no recorded <abbr>ZIP</abbr> Code.</p>
|
|
<h2>Findings</h2>
|
|
<p>We present our findings from three perspectives. First, we examine the geographic distribution of the homeless disability applicants. Second, we consider their demographic and socioeconomic characteristics. Third, we look at the differences (or similarities) between <abbr class="spell">DI</abbr> and <abbr class="spell">SSI</abbr> homeless disability applicants.</p>
|
|
<h3>Geospatial Distribution of Homeless Disability Applicants</h3>
|
|
<p>Charts <span class="nobr">2–4</span> are maps of the contiguous United States respectively showing homeless disability applicants per 50,000 residents, homeless disability beneficiaries per 50,000 residents, and the 25 metropolitan areas<sup><a href="#mn21" id="mt21">21</a></sup> with the highest numbers of homeless disability applicants, all for the period <span class="nobr">2007–2017.</span> These maps provide four main takeaways. First, across the lower 48 states, the most prominent clusters of homeless disability applicants appeared along the West Coast and the northeastern Interstate 95 corridor, in the Great Lakes region, and in Florida (Chart 2). Second, the geographic distributions of homeless disability applicants and beneficiaries were similar, based on a visual comparison of Charts 2 and 3. Third, most clusters of homeless disability applicants occurred in urban counties with at least 50,000 residents; about 9.8 percent of homeless disability applicants lived in either the Los Angeles or the New York City metropolitan area and an additional 32.3 percent lived in 23 other urban areas (Chart 4 and Table 3). Fourth, less than 1 percent of homeless disability applicants resided in a band of counties in the central states running continuously from North Dakota through western Texas (Chart 2). Our geospatial analysis revealed that 98 percent of our study group in the lower 48 states resided in county-equivalent areas with at least 50,000 inhabitants and that homelessness among disability applicants is largely an urban phenomenon. This finding is consistent with <abbr>HUD</abbr>'s point-in-time estimates of the population experiencing homelessness, which indicate that California and New York have the largest numbers of homeless individuals (driven by Los Angeles and New York City), followed by Florida (<abbr>HUD</abbr> 2017). By contrast, the share of the entire <abbr>U.S.</abbr> population that lived in urban areas at the end of our study period was 80 percent (Census Bureau 2017).</p>
|
|
<div class="svgChart chart960" id="chart2"> <img src="v81n2p1-chart02.svg" role="img" alt="Chart 2. Homeless disability applicants per 50,000 residents, by county-equivalent area, 2007 through 2017. Population density map summarized in the narrative. SOURCE: Authors' calculations using administrative data from S S A."> </div>
|
|
<div class="svgChart chart960" id="chart3"> <img src="v81n2p1-chart03.svg" role="img" alt="Chart 3. Homeless disability beneficiaries per 50,000 residents, by county-equivalent area, 2007 through 2017. Population density map summarized in the narrative. SOURCE: Authors' calculations using administrative data from S S A."> </div>
|
|
<div class="svgChart chart700" id="chart4"> <img src="v81n2p1-chart04.svg" role="img" alt="Chart 4. Twenty-five core-based statistical areas with the most homeless disability applicants, 2007 through 2017. Bubble map depicting relative sizes of populations listed in Table 3."> </div>
|
|
<div class="table" id="table3">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 3. </span>Twenty-five core-based statistical areas ranked by largest homeless disability applicant population in the period <span class="nobr">2007–2017</span></caption>
|
|
<colgroup span="1" style="width:26em"></colgroup>
|
|
<colgroup span="1" style="width:6em"></colgroup>
|
|
<colgroup span="2" style="width:8em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th rowspan="2" class="stubHeading" scope="colgroup">Rank and core-based statistical area</th>
|
|
<th rowspan="2" class="spanner" scope="colgroup">Airport code identifier</th>
|
|
<th colspan="2" class="spanner" scope="colgroup">Homeless disability applicants</th>
|
|
</tr>
|
|
<tr>
|
|
<th scope="col">Number</th>
|
|
<th scope="col">As a percentage of study group <sup>a</sup></th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub0" scope="row"> 1. Los Angeles-Long Beach-Anaheim, <abbr title="California">CA</abbr></th>
|
|
<td class="center"><abbr class="spell">LAX</abbr></td>
|
|
<td>46,135</td>
|
|
<td>5.7</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row"> 2. New York-Newark-Jersey City, <abbr title="New York">NY</abbr>-<abbr title="New Jersey">NJ</abbr>-<abbr title="Pennsylvania">PA</abbr></th>
|
|
<td class="center"><abbr class="spell">NYC</abbr></td>
|
|
<td>33,525</td>
|
|
<td>4.1</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row"> 3. Boston-Cambridge-Newton, <abbr title="Massachusetts">MA</abbr>-<abbr title="New Hampshire">NH</abbr></th>
|
|
<td class="center"><abbr class="spell">BOS</abbr></td>
|
|
<td>22,446</td>
|
|
<td>2.8</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row"> 4. Miami-Fort Lauderdale-West Palm Beach, <abbr title="Florida">FL</abbr></th>
|
|
<td class="center"><abbr class="spell">MIA</abbr></td>
|
|
<td>18,420</td>
|
|
<td>2.3</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row"> 5. Chicago-Naperville-Elgin, <abbr title="Illinois">IL</abbr>-<abbr title="Indiana">IN</abbr>-<abbr title="Wisconsin">WI</abbr></th>
|
|
<td class="center"><abbr class="spell">ORD</abbr></td>
|
|
<td>15,769</td>
|
|
<td>1.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row"> 6. San Francisco-Oakland-Hayward, <abbr title="California">CA</abbr></th>
|
|
<td class="center"><abbr class="spell">SFO</abbr></td>
|
|
<td>15,677</td>
|
|
<td>1.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row"> 7. Seattle-Tacoma-Bellevue, <abbr title="Washington">WA</abbr></th>
|
|
<td class="center"><abbr class="spell">SEA</abbr></td>
|
|
<td>15,228</td>
|
|
<td>1.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row"> 8. Baltimore-Columbia-Towson, <abbr title="Maryland">MD</abbr></th>
|
|
<td class="center"><abbr class="spell">BWI</abbr></td>
|
|
<td>14,905</td>
|
|
<td>1.8</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row"> 9. Washington-Arlington-Alexandria, <abbr class="spell">DC</abbr>-<abbr class="spell">VA</abbr>-<abbr title="Maryland">MD</abbr>-<abbr title="West Virginia">WV</abbr></th>
|
|
<td class="center"><abbr class="spell">WAS</abbr></td>
|
|
<td>14,489</td>
|
|
<td>1.8</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">10. Atlanta-Sandy Springs-Roswell, <abbr title="Georgia">GA</abbr></th>
|
|
<td class="center"><abbr class="spell">ATL</abbr></td>
|
|
<td>12,611</td>
|
|
<td>1.6</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">11. Dallas-Fort Worth-Arlington, <abbr title="Texas">TX</abbr></th>
|
|
<td class="center"><abbr class="spell">DFW</abbr></td>
|
|
<td>12,454</td>
|
|
<td>1.5</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">12. Philadelphia-Camden-Wilmington, <abbr title="Pennsylvania">PA</abbr>-<abbr title="New Jersey">NJ</abbr>-<abbr title="Delaware">DE</abbr>-<abbr title="Maryland">MD</abbr></th>
|
|
<td class="center"><abbr class="spell">PHL</abbr></td>
|
|
<td>11,906</td>
|
|
<td>1.5</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">13. Detroit-Warren-Dearborn, <abbr title="Michigan">MI</abbr></th>
|
|
<td class="center"><abbr class="spell">DTW</abbr></td>
|
|
<td>10,821</td>
|
|
<td>1.3</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">14. Sacramento-Roseville-Arden-Arcade, <abbr title="California">CA</abbr></th>
|
|
<td class="center"><abbr class="spell">SMF</abbr></td>
|
|
<td>9,765</td>
|
|
<td>1.2</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">15. Denver-Aurora-Lakewood, <abbr title="Colorado">CO</abbr></th>
|
|
<td class="center"><abbr class="spell">DEN</abbr></td>
|
|
<td>9,714</td>
|
|
<td>1.2</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">16. San Diego-Carlsbad, <abbr title="California">CA</abbr></th>
|
|
<td class="center"><abbr class="spell">SAN</abbr></td>
|
|
<td>9,083</td>
|
|
<td>1.1</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">17. Riverside-San Bernardino-Ontario, <abbr title="California">CA</abbr></th>
|
|
<td class="center"><abbr class="spell">RAL</abbr></td>
|
|
<td>8,772</td>
|
|
<td>1.1</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">18. Cincinnati, <abbr title="Ohio">OH</abbr>-<abbr title="Kentucky">KY</abbr>-<abbr title="Indiana">IN</abbr></th>
|
|
<td class="center"><abbr class="spell">LUK</abbr></td>
|
|
<td>8,352</td>
|
|
<td>1.0</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">19. Phoenix-Mesa-Scottsdale, <abbr title="Arizona">AZ</abbr></th>
|
|
<td class="center"><abbr class="spell">PHX</abbr></td>
|
|
<td>8,251</td>
|
|
<td>1.0</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">20. Providence-Warwick, <abbr title="Rhode Island">RI</abbr>-<abbr title="Massachusetts">MA</abbr></th>
|
|
<td class="center"><abbr class="spell">PVD</abbr></td>
|
|
<td>8,231</td>
|
|
<td>1.0</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">21. Tampa-<abbr title="Saint">St.</abbr> Petersburg-Clearwater, <abbr title="Florida">FL</abbr></th>
|
|
<td class="center"><abbr class="spell">TPA</abbr></td>
|
|
<td>8,036</td>
|
|
<td>1.0</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">22. Houston-The Woodlands-Sugar Land, <abbr title="Texas">TX</abbr></th>
|
|
<td class="center"><abbr class="spell">IAH</abbr></td>
|
|
<td>7,458</td>
|
|
<td>0.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">23. Portland-Vancouver-Hillsboro, <abbr title="Oregon">OR</abbr>-<abbr title="Washington">WA</abbr></th>
|
|
<td class="center"><abbr class="spell">PDX</abbr></td>
|
|
<td>7,145</td>
|
|
<td>0.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">24. Columbus, <abbr title="Ohio">OH</abbr></th>
|
|
<td class="center"><abbr class="spell">CMH</abbr></td>
|
|
<td>6,285</td>
|
|
<td>0.8</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">25. Minneapolis-<abbr title="Saint">St.</abbr> Paul-Bloomington, <abbr title="Minnesota">MN</abbr>-<abbr title="Wisconsin">WI</abbr></th>
|
|
<td class="center"><abbr class="spell">MSP</abbr></td>
|
|
<td>6,001</td>
|
|
<td>0.7</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub1" scope="row">Top 25 combined</th>
|
|
<td>. . .</td>
|
|
<td>341,479</td>
|
|
<td>42.1</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub2" scope="row">Total United States</th>
|
|
<td>. . .</td>
|
|
<td>810,326</td>
|
|
<td>100.0</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="4">SOURCE: Authors' calculations using administrative data from <abbr class="spell">SSA</abbr> and Office of Management and Budget.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="note" colspan="4">NOTE: . . . = not applicable.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="4">a. "Study group" comprises the total <abbr class="spell">SSA</abbr> domestic service area rather than only the contiguous United States.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<h3>Demographic and Socioeconomic Characteristics</h3>
|
|
<p>Relative to domiciled disability applicants (that is, those not identified as homeless), homeless disability applicants were more likely to be men, of working age <span class="nobr">(18–64),</span> and without a high school diploma or equivalent (Table 4). They were also more likely to have died as of December 31, 2018.</p>
|
|
<p>Among the homeless disability applicants, 47,178 (5.8 percent) worked during 2015.<sup><a href="#mn22" id="mt22">22</a></sup> Some earnings-related statistics, not shown in Table 4, provide interesting perspectives on the applicants we identify as homeless. For example, those who worked had median annual earnings of $3,261. Furthermore, those whose applications were denied had median earnings that nearly doubled those of applicants who were allowed benefits ($5,273 versus $2,724). Surprisingly, earners in our study sample had an allowance rate of 72.6 percent, while nonearners had an allowance rate of 44.1 percent. The reasons we see higher allowance rates for earners than for nonearners are unclear.</p>
|
|
<div class="table" id="table4">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 4. </span>Selected characteristics of homeless and domiciled individuals who applied for disability-program benefits during the period <span class="nobr">2007–2017</span></caption>
|
|
<colgroup span="1" style="width:20em"></colgroup>
|
|
<colgroup span="2" style="width:8em"></colgroup>
|
|
<colgroup span="2" style="width:8em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th rowspan="2" class="stubHeading" scope="colgroup">Characteristic</th>
|
|
<th colspan="2" class="spanner" scope="colgroup">Homeless</th>
|
|
<th colspan="2" class="spanner" scope="colgroup">Domiciled</th>
|
|
</tr>
|
|
<tr>
|
|
<th scope="col">Number</th>
|
|
<th scope="col">Percent</th>
|
|
<th scope="col">Number</th>
|
|
<th scope="col">Percent</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub2" scope="rowgroup">Total</th>
|
|
<td>810,326</td>
|
|
<td>100.0</td>
|
|
<td>21,648,926</td>
|
|
<td>100.0</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Sex</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Male</th>
|
|
<td>550,335</td>
|
|
<td>67.9</td>
|
|
<td>11,505,359</td>
|
|
<td>53.1</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Female</th>
|
|
<td>259,991</td>
|
|
<td>32.1</td>
|
|
<td>10,143,568</td>
|
|
<td>46.9</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Age</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row"><span class="nobr">0–17</span></th>
|
|
<td>13,775</td>
|
|
<td>1.7</td>
|
|
<td>2,879,307</td>
|
|
<td>13.3</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row"><span class="nobr">18–64</span></th>
|
|
<td>750,362</td>
|
|
<td>92.6</td>
|
|
<td>16,215,046</td>
|
|
<td>74.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">65 or older</th>
|
|
<td>46,189</td>
|
|
<td>5.7</td>
|
|
<td>2,554,573</td>
|
|
<td>11.8</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Educational attainment</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">No high school diploma or equivalent</th>
|
|
<td>280,065</td>
|
|
<td>34.6</td>
|
|
<td>4,816,913</td>
|
|
<td>22.3</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">High school diploma or equivalent</th>
|
|
<td>356,614</td>
|
|
<td>44.0</td>
|
|
<td>8,093,621</td>
|
|
<td>37.4</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Some college</th>
|
|
<td>112,010</td>
|
|
<td>13.8</td>
|
|
<td>3,225,254</td>
|
|
<td>14.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">College graduate</th>
|
|
<td>34,570</td>
|
|
<td>4.3</td>
|
|
<td>1,527,840</td>
|
|
<td>7.1</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Missing data</th>
|
|
<td>27,067</td>
|
|
<td>3.3</td>
|
|
<td>3,985,299</td>
|
|
<td>18.4</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Earnings status in 2015</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Yes</th>
|
|
<td>47,178</td>
|
|
<td>5.8</td>
|
|
<td>a</td>
|
|
<td>a</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">No</th>
|
|
<td>763,148</td>
|
|
<td>94.2</td>
|
|
<td>a</td>
|
|
<td>a</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Vital status on December 31, 2018</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Living</th>
|
|
<td>705,908</td>
|
|
<td>87.1</td>
|
|
<td>19,195,496</td>
|
|
<td>88.7</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Deceased</th>
|
|
<td>104,418</td>
|
|
<td>12.9</td>
|
|
<td>2,453,430</td>
|
|
<td>11.3</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="5">SOURCE: Authors' calculations using administrative data from <abbr class="spell">SSA</abbr>.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="note" colspan="5">NOTES: Rounded components of percentage distributions do not necessarily sum to 100.0.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="5">a. We did not obtain earnings data for domiciled disability applicants.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<p>As of December 31, 2018, the respective death rates of homeless and domiciled individuals who had applied for disability benefits in the period <span class="nobr">2007–2017</span> were 12.9 percent and 11.3 percent; this difference is statistically significant, with a <i>p</i>-value of less than 0.01. To account for age differences between the groups, we also analyzed death rates by age group. We found that the age-normalized death rates likewise were higher for homeless disability applicants than for their domiciled counterparts. These findings are consistent with those in social science and medical literature (O'Connell 2005).</p>
|
|
<p>Males and individuals with physical primary impairments were overrepresented among the homeless disability applicants who had died by <span class="nobr">year-end</span> 2018 (not shown). Males constituted 77.3 percent and 66.5 percent of deceased and living applicants, respectively. Yet the characteristic with the largest difference between the percentages of deceased and living disability applicants is the physical primary impairment (74.1 percent versus 55.6 percent). No statistically significant differences in educational attainment existed among homeless disability applicants, living or dead.</p>
|
|
<h3>Program Type</h3>
|
|
<p>In this section, we examine the <abbr class="spell">SSA</abbr> disability programs from which homeless applicants sought benefits (<abbr class="spell">SSI</abbr>, <abbr class="spell">DI</abbr>, or <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr> concurrently). Table 5 shows that more applicants sought only <abbr class="spell">SSI</abbr> payments (31.2 percent of all homeless disability applicants) than only <abbr class="spell">DI</abbr> benefits (5.2 percent). The remaining 63.6 percent of the study subjects claimed concurrent <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr> benefits on their application records.</p>
|
|
<div class="table" id="table5">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 5. </span>Selected characteristics of individuals experiencing homelessness who applied for disability-program benefits during the period <span class="nobr">2007–2017,</span> with distributions by program</caption>
|
|
<colgroup span="1" style="width:20em"></colgroup>
|
|
<colgroup span="8" style="width:6em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th rowspan="3" class="stubHeading" id="c1">Characteristic</th>
|
|
<th colspan="2" rowspan="2" class="spanner" id="c2">All</th>
|
|
<th colspan="6" class="spanner" id="c3">Program</th>
|
|
</tr>
|
|
<tr>
|
|
<th colspan="2" class="spanner" id="c4" headers="c3"><abbr class="spell">DI</abbr> only</th>
|
|
<th colspan="2" class="spanner" id="c5" headers="c3"><abbr class="spell">SSI</abbr> only</th>
|
|
<th colspan="2" class="spanner" id="c6" headers="c3">Concurrent <abbr class="spell">DI</abbr> and <abbr class="spell">SSI</abbr></th>
|
|
</tr>
|
|
<tr>
|
|
<th id="c7" headers="c2">Number</th>
|
|
<th id="c8" headers="c2">Percent</th>
|
|
<th id="c9" headers="c3 c4">Number</th>
|
|
<th id="c10" headers="c3 c4">Percent</th>
|
|
<th id="c11" headers="c3 c5">Number</th>
|
|
<th id="c12" headers="c3 c5">Percent</th>
|
|
<th id="c13" headers="c3 c6">Number</th>
|
|
<th id="c14" headers="c3 c6">Percent</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub2" id="r1" headers="c1">Total</th>
|
|
<td headers="r1 c2 c7">810,326</td>
|
|
<td headers="r1 c2 c8">100.0</td>
|
|
<td headers="r1 c3 c4 c9">41,698</td>
|
|
<td headers="r1 c3 c4 c10">5.2</td>
|
|
<td headers="r1 c3 c5 c11">252,855</td>
|
|
<td headers="r1 c3 c5 c12">31.2</td>
|
|
<td headers="r1 c3 c6 c13">515,773</td>
|
|
<td headers="r1 c3 c6 c14">63.6</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r2" headers="c1">Sex</th>
|
|
<td colspan="8"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r3" headers="r2 c1">Male</th>
|
|
<td headers="r2 r3 c2 c7">550,335</td>
|
|
<td headers="r2 r3 c2 c8">100.0</td>
|
|
<td headers="r2 r3 c3 c4 c9">25,764</td>
|
|
<td headers="r2 r3 c3 c4 c10">4.7</td>
|
|
<td headers="r2 r3 c3 c5 c11">174,862</td>
|
|
<td headers="r2 r3 c3 c5 c12">31.8</td>
|
|
<td headers="r2 r3 c3 c6 c13">349,709</td>
|
|
<td headers="r2 r3 c3 c6 c14">63.5</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r4" headers="r2 c1">Female</th>
|
|
<td headers="r2 r4 c2 c7">259,991</td>
|
|
<td headers="r2 r4 c2 c8">100.0</td>
|
|
<td headers="r2 r4 c3 c4 c9">15,934</td>
|
|
<td headers="r2 r4 c3 c4 c10">6.1</td>
|
|
<td headers="r2 r4 c3 c5 c11">77,993</td>
|
|
<td headers="r2 r4 c3 c5 c12">30.0</td>
|
|
<td headers="r2 r4 c3 c6 c13">166,064</td>
|
|
<td headers="r2 r4 c3 c6 c14">63.9</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r5" headers="c1">Educational attainment</th>
|
|
<td colspan="8"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r6" headers="r5 c1">No high school diploma or equivalent</th>
|
|
<td headers="r5 r6 c2 c7">280,065</td>
|
|
<td headers="r5 r6 c2 c8">100.0</td>
|
|
<td headers="r5 r6 c3 c4 c9">7,565</td>
|
|
<td headers="r5 r6 c3 c4 c10">2.7</td>
|
|
<td headers="r5 r6 c3 c5 c11">108,789</td>
|
|
<td headers="r5 r6 c3 c5 c12">38.8</td>
|
|
<td headers="r5 r6 c3 c6 c13">163,711</td>
|
|
<td headers="r5 r6 c3 c6 c14">58.5</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r7" headers="r5 c1">High school diploma or equivalent</th>
|
|
<td headers="r5 r7 c2 c7">356,614</td>
|
|
<td headers="r5 r7 c2 c8">100.0</td>
|
|
<td headers="r5 r7 c3 c4 c9">18,666</td>
|
|
<td headers="r5 r7 c3 c4 c10">5.2</td>
|
|
<td headers="r5 r7 c3 c5 c11">95,746</td>
|
|
<td headers="r5 r7 c3 c5 c12">26.8</td>
|
|
<td headers="r5 r7 c3 c6 c13">242,202</td>
|
|
<td headers="r5 r7 c3 c6 c14">67.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r8" headers="r5 c1">Some college</th>
|
|
<td headers="r5 r8 c2 c7">112,010</td>
|
|
<td headers="r5 r8 c2 c8">100.0</td>
|
|
<td headers="r5 r8 c3 c4 c9">9,974</td>
|
|
<td headers="r5 r8 c3 c4 c10">8.9</td>
|
|
<td headers="r5 r8 c3 c5 c11">21,917</td>
|
|
<td headers="r5 r8 c3 c5 c12">19.6</td>
|
|
<td headers="r5 r8 c3 c6 c13">80,119</td>
|
|
<td headers="r5 r8 c3 c6 c14">71.5</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r9" headers="r5 c1">College graduate</th>
|
|
<td headers="r5 r9 c2 c7">34,570</td>
|
|
<td headers="r5 r9 c2 c8">100.0</td>
|
|
<td headers="r5 r9 c3 c4 c9">4,819</td>
|
|
<td headers="r5 r9 c3 c4 c10">13.9</td>
|
|
<td headers="r5 r9 c3 c5 c11">5,563</td>
|
|
<td headers="r5 r9 c3 c5 c12">16.1</td>
|
|
<td headers="r5 r9 c3 c6 c13">24,188</td>
|
|
<td headers="r5 r9 c3 c6 c14">70.0</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r10" headers="r5 c1">Missing data</th>
|
|
<td headers="r5 r10 c2 c7">27,067</td>
|
|
<td headers="r5 r10 c2 c8">100.0</td>
|
|
<td headers="r5 r10 c3 c4 c9">674</td>
|
|
<td headers="r5 r10 c3 c4 c10">2.5</td>
|
|
<td headers="r5 r10 c3 c5 c11">20,840</td>
|
|
<td headers="r5 r10 c3 c5 c12">77.0</td>
|
|
<td headers="r5 r10 c3 c6 c13">5,553</td>
|
|
<td headers="r5 r10 c3 c6 c14">20.5</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r11" headers="c1">Earnings status in 2015</th>
|
|
<td colspan="8"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r12" headers="r11 c1">Yes</th>
|
|
<td headers="r11 r12 c2 c7">47,178</td>
|
|
<td headers="r11 r12 c2 c8">100.0</td>
|
|
<td headers="r11 r12 c3 c4 c9">2,638</td>
|
|
<td headers="r11 r12 c3 c4 c10">5.6</td>
|
|
<td headers="r11 r12 c3 c5 c11">12,424</td>
|
|
<td headers="r11 r12 c3 c5 c12">26.3</td>
|
|
<td headers="r11 r12 c3 c6 c13">32,116</td>
|
|
<td headers="r11 r12 c3 c6 c14">68.1</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r13" headers="r11 c1">No</th>
|
|
<td headers="r11 r13 c2 c7">763,148</td>
|
|
<td headers="r11 r13 c2 c8">100.0</td>
|
|
<td headers="r11 r13 c3 c4 c9">39,060</td>
|
|
<td headers="r11 r13 c3 c4 c10">5.1</td>
|
|
<td headers="r11 r13 c3 c5 c11">240,431</td>
|
|
<td headers="r11 r13 c3 c5 c12">31.5</td>
|
|
<td headers="r11 r13 c3 c6 c13">483,657</td>
|
|
<td headers="r11 r13 c3 c6 c14">63.4</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r14" headers="c1">Primary impairment type</th>
|
|
<td colspan="8"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r15" headers="r14 c1">Mental or cognitive</th>
|
|
<td headers="r14 r15 c2 c7">340,266</td>
|
|
<td headers="r14 r15 c2 c8">100.0</td>
|
|
<td headers="r14 r15 c3 c4 c9">14,058</td>
|
|
<td headers="r14 r15 c3 c4 c10">4.1</td>
|
|
<td headers="r14 r15 c3 c5 c11">116,503</td>
|
|
<td headers="r14 r15 c3 c5 c12">34.2</td>
|
|
<td headers="r14 r15 c3 c6 c13">209,705</td>
|
|
<td headers="r14 r15 c3 c6 c14">61.6</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r16" headers="r14 c1">Physical</th>
|
|
<td headers="r14 r16 c2 c7">470,060</td>
|
|
<td headers="r14 r16 c2 c8">100.0</td>
|
|
<td headers="r14 r16 c3 c4 c9">27,640</td>
|
|
<td headers="r14 r16 c3 c4 c10">5.9</td>
|
|
<td headers="r14 r16 c3 c5 c11">136,352</td>
|
|
<td headers="r14 r16 c3 c5 c12">29.0</td>
|
|
<td headers="r14 r16 c3 c6 c13">306,068</td>
|
|
<td headers="r14 r16 c3 c6 c14">65.1</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="9">SOURCE: Authors' calculations using administrative data from <abbr class="spell">SSA</abbr>.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="9">NOTES: Rounded components of percentage distributions do not necessarily sum to 100.0.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<p>Table 6 shows that homeless disability applicants were more likely to have a physical condition than a mental or cognitive one recorded as their primary impairment (58.0 percent versus 42.0 percent).<sup><a href="#mn23" id="mt23">23</a></sup> For applicants with a physical impairment, the death rate was slightly more than double that of applicants with a mental/cognitive impairment (16.4 percent versus 8.0 percent).</p>
|
|
<div class="table" id="table6">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 6. </span>Selected characteristics of individuals experiencing homelessness who applied for disability-program benefits during the period <span class="nobr">2007–2017,</span> with distributions by type of primary impairment</caption>
|
|
<colgroup span="1" style="width:20em"></colgroup>
|
|
<colgroup span="6" style="width:6em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th rowspan="3" class="stubHeading" id="c1">Characteristic</th>
|
|
<th colspan="2" rowspan="2" class="spanner" id="c2">All</th>
|
|
<th colspan="4" class="spanner" id="c3">Primary impairment type</th>
|
|
</tr>
|
|
<tr>
|
|
<th colspan="2" class="spanner" id="c4" headers="c3">Mental or cognitive</th>
|
|
<th colspan="2" class="spanner" id="c5" headers="c3">Physical</th>
|
|
</tr>
|
|
<tr>
|
|
<th id="c6" headers="c2">Number</th>
|
|
<th id="c7" headers="c2">Percent</th>
|
|
<th id="c8" headers="c3 c4">Number</th>
|
|
<th id="c9" headers="c3 c4">Percent</th>
|
|
<th id="c10" headers="c3 c5">Number</th>
|
|
<th id="c11" headers="c3 c5">Percent</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub2" id="r1" headers="c1">Total</th>
|
|
<td headers="r1 c2 c6">810,326</td>
|
|
<td headers="r1 c2 c7">100.0</td>
|
|
<td headers="r1 c3 c4 c8">340,266</td>
|
|
<td headers="r1 c3 c4 c9">42.0</td>
|
|
<td headers="r1 c3 c5 c10">470,060</td>
|
|
<td headers="r1 c3 c5 c11">58.0</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r2" headers="c1">Sex</th>
|
|
<td colspan="6"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r3" headers="r2 c1">Male</th>
|
|
<td headers="r2 r3 c2 c6">550,335</td>
|
|
<td headers="r2 r3 c2 c7">100.0</td>
|
|
<td headers="r2 r3 c3 c4 c8">218,378</td>
|
|
<td headers="r2 r3 c3 c4 c9">39.7</td>
|
|
<td headers="r2 r3 c3 c5 c10">331,957</td>
|
|
<td headers="r2 r3 c3 c5 c11">60.3</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r4" headers="r2 c1">Female</th>
|
|
<td headers="r2 r4 c2 c6">259,991</td>
|
|
<td headers="r2 r4 c2 c7">100.0</td>
|
|
<td headers="r2 r4 c3 c4 c8">121,888</td>
|
|
<td headers="r2 r4 c3 c4 c9">46.9</td>
|
|
<td headers="r2 r4 c3 c5 c10">138,103</td>
|
|
<td headers="r2 r4 c3 c5 c11">53.1</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r5" headers="c1">Educational attainment</th>
|
|
<td colspan="6"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r6" headers="r5 c1">No high school diploma or equivalent</th>
|
|
<td headers="r5 r6 c2 c6">280,065</td>
|
|
<td headers="r5 r6 c2 c7">100.0</td>
|
|
<td headers="r5 r6 c3 c4 c8">124,702</td>
|
|
<td headers="r5 r6 c3 c4 c9">44.5</td>
|
|
<td headers="r5 r6 c3 c5 c10">155,363</td>
|
|
<td headers="r5 r6 c3 c5 c11">55.5</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r7" headers="r5 c1">High school diploma or equivalent</th>
|
|
<td headers="r5 r7 c2 c6">356,614</td>
|
|
<td headers="r5 r7 c2 c7">100.0</td>
|
|
<td headers="r5 r7 c3 c4 c8">144,906</td>
|
|
<td headers="r5 r7 c3 c4 c9">40.6</td>
|
|
<td headers="r5 r7 c3 c5 c10">211,708</td>
|
|
<td headers="r5 r7 c3 c5 c11">59.4</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r8" headers="r5 c1">Some college</th>
|
|
<td headers="r5 r8 c2 c6">112,010</td>
|
|
<td headers="r5 r8 c2 c7">100.0</td>
|
|
<td headers="r5 r8 c3 c4 c8">42,411</td>
|
|
<td headers="r5 r8 c3 c4 c9">37.9</td>
|
|
<td headers="r5 r8 c3 c5 c10">69,599</td>
|
|
<td headers="r5 r8 c3 c5 c11">62.1</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r9" headers="r5 c1">College graduate</th>
|
|
<td headers="r5 r9 c2 c6">34,570</td>
|
|
<td headers="r5 r9 c2 c7">100.0</td>
|
|
<td headers="r5 r9 c3 c4 c8">14,450</td>
|
|
<td headers="r5 r9 c3 c4 c9">41.8</td>
|
|
<td headers="r5 r9 c3 c5 c10">20,120</td>
|
|
<td headers="r5 r9 c3 c5 c11">58.2</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r10" headers="r5 c1">Missing data</th>
|
|
<td headers="r5 r10 c2 c6">27,067</td>
|
|
<td headers="r5 r10 c2 c7">100.0</td>
|
|
<td headers="r5 r10 c3 c4 c8">13,797</td>
|
|
<td headers="r5 r10 c3 c4 c9">51.0</td>
|
|
<td headers="r5 r10 c3 c5 c10">13,270</td>
|
|
<td headers="r5 r10 c3 c5 c11">49.0</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r11" headers="c1">Program</th>
|
|
<td colspan="6"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r12" headers="r11 c1"><abbr class="spell">DI</abbr> only</th>
|
|
<td headers="r11 r12 c2 c6">41,698</td>
|
|
<td headers="r11 r12 c2 c7">100.0</td>
|
|
<td headers="r11 r12 c3 c4 c8">14,058</td>
|
|
<td headers="r11 r12 c3 c4 c9">33.7</td>
|
|
<td headers="r11 r12 c3 c5 c10">27,640</td>
|
|
<td headers="r11 r12 c3 c5 c11">66.3</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r13" headers="r11 c1"><abbr class="spell">SSI</abbr> only</th>
|
|
<td headers="r11 r13 c2 c6">252,855</td>
|
|
<td headers="r11 r13 c2 c7">100.0</td>
|
|
<td headers="r11 r13 c3 c4 c8">116,503</td>
|
|
<td headers="r11 r13 c3 c4 c9">46.1</td>
|
|
<td headers="r11 r13 c3 c5 c10">136,352</td>
|
|
<td headers="r11 r13 c3 c5 c11">53.9</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r14" headers="r11 c1">Concurrent <abbr class="spell">DI</abbr> and <abbr class="spell">SSI</abbr></th>
|
|
<td headers="r11 r14 c2 c6">515,773</td>
|
|
<td headers="r11 r14 c2 c7">100.0</td>
|
|
<td headers="r11 r14 c3 c4 c8">209,705</td>
|
|
<td headers="r11 r14 c3 c4 c9">40.7</td>
|
|
<td headers="r11 r14 c3 c5 c10">306,068</td>
|
|
<td headers="r11 r14 c3 c5 c11">59.3</td>
|
|
</tr>
|
|
<tr class="shaded topPad1">
|
|
<th class="stub0" id="r15" headers="c1">Died as of December 31, 2018</th>
|
|
<td colspan="6"></td>
|
|
</tr>
|
|
<tr class="shaded">
|
|
<th class="stub1" id="r16" headers="r15 c1">Number</th>
|
|
<td class="center" colspan="2" headers="r15 r16 c2">104,418</td>
|
|
<td class="center" colspan="2" headers="r15 r16 c3 c4">27,094</td>
|
|
<td class="center" colspan="2" headers="r15 r16 c3 c5">77,324</td>
|
|
</tr>
|
|
<tr class="shaded">
|
|
<th class="stub1" id="r17" headers="r15 c1">Death rate</th>
|
|
<td class="center" colspan="2" headers="r15 r17 c2">12.9</td>
|
|
<td class="center" colspan="2" headers="r15 r17 c3 c4">8.0</td>
|
|
<td class="center" colspan="2" headers="r15 r17 c3 c5">16.4</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="onlyNote" colspan="7">SOURCE: Authors' calculations using administrative data from <abbr class="spell">SSA</abbr>.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<p>The allowance rate for homeless disability applicants overall was 45.8 percent (Table 7). Of the applicant subgroups, <abbr class="spell">DI</abbr>-only applicants had the lowest allowance rate of 34.0 percent, while those filing only an <abbr class="spell">SSI</abbr> claim had an allowance rate of 46.8 percent. Applicants with a physical primary impairment had an allowance rate of 41.4 percent while those with a mental or cognitive primary impairment had an allowance rate of 51.8 percent.</p>
|
|
<div class="table" id="table7">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 7. </span>Selected characteristics of individuals experiencing homelessness who applied for disability-program benefits during the period <span class="nobr">2007–2017,</span> with distributions by application outcome</caption>
|
|
<colgroup span="1" style="width:20em"></colgroup>
|
|
<colgroup span="6" style="width:6em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th rowspan="3" class="stubHeading" id="c1">Characteristic</th>
|
|
<th colspan="2" rowspan="2" class="spanner" id="c2">All</th>
|
|
<th colspan="4" class="spanner" id="c3">Application decision</th>
|
|
</tr>
|
|
<tr>
|
|
<th colspan="2" class="spanner" id="c4" headers="c3">Not allowed <sup>a</sup></th>
|
|
<th colspan="2" class="spanner" id="c5" headers="c3">Allowed</th>
|
|
</tr>
|
|
<tr>
|
|
<th id="c6" headers="c2">Number</th>
|
|
<th id="c7" headers="c2">Percent</th>
|
|
<th id="c8" headers="c3 c4">Number</th>
|
|
<th id="c9" headers="c3 c4">Percent</th>
|
|
<th id="c10" headers="c3 c5">Number</th>
|
|
<th id="c11" headers="c3 c5">Percent</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub2" id="r1" headers="c1">Total</th>
|
|
<td headers="r1 c2 c6">810,326</td>
|
|
<td headers="r1 c2 c7">100.0</td>
|
|
<td headers="r1 c3 c4 c8">439,422</td>
|
|
<td headers="r1 c3 c4 c9">54.2</td>
|
|
<td headers="r1 c3 c5 c10">370,904</td>
|
|
<td headers="r1 c3 c5 c11">45.8</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r2" headers="c1">Sex</th>
|
|
<td colspan="6"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r3" headers="r2 c1">Male</th>
|
|
<td headers="r2 r3 c2 c6">550,335</td>
|
|
<td headers="r2 r3 c2 c7">100.0</td>
|
|
<td headers="r2 r3 c3 c4 c8">290,684</td>
|
|
<td headers="r2 r3 c3 c4 c9">52.8</td>
|
|
<td headers="r2 r3 c3 c5 c10">259,651</td>
|
|
<td headers="r2 r3 c3 c5 c11">47.2</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r4" headers="r2 c1">Female</th>
|
|
<td headers="r2 r4 c2 c6">259,991</td>
|
|
<td headers="r2 r4 c2 c7">100.0</td>
|
|
<td headers="r2 r4 c3 c4 c8">148,738</td>
|
|
<td headers="r2 r4 c3 c4 c9">57.2</td>
|
|
<td headers="r2 r4 c3 c5 c10">111,253</td>
|
|
<td headers="r2 r4 c3 c5 c11">42.8</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r5" headers="c1">Educational attainment</th>
|
|
<td colspan="6"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r6" headers="r5 c1">No high school diploma or equivalent</th>
|
|
<td headers="r5 r6 c2 c6">280,065</td>
|
|
<td headers="r5 r6 c2 c7">100.0</td>
|
|
<td headers="r5 r6 c3 c4 c8">149,989</td>
|
|
<td headers="r5 r6 c3 c4 c9">53.6</td>
|
|
<td headers="r5 r6 c3 c5 c10">130,076</td>
|
|
<td headers="r5 r6 c3 c5 c11">46.4</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r7" headers="r5 c1">High school diploma or equivalent</th>
|
|
<td headers="r5 r7 c2 c6">356,614</td>
|
|
<td headers="r5 r7 c2 c7">100.0</td>
|
|
<td headers="r5 r7 c3 c4 c8">198,025</td>
|
|
<td headers="r5 r7 c3 c4 c9">55.5</td>
|
|
<td headers="r5 r7 c3 c5 c10">158,589</td>
|
|
<td headers="r5 r7 c3 c5 c11">44.5</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r8" headers="r5 c1">Some college</th>
|
|
<td headers="r5 r8 c2 c6">112,010</td>
|
|
<td headers="r5 r8 c2 c7">100.0</td>
|
|
<td headers="r5 r8 c3 c4 c8">61,784</td>
|
|
<td headers="r5 r8 c3 c4 c9">55.2</td>
|
|
<td headers="r5 r8 c3 c5 c10">50,226</td>
|
|
<td headers="r5 r8 c3 c5 c11">44.8</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r9" headers="r5 c1">College graduate</th>
|
|
<td headers="r5 r9 c2 c6">34,570</td>
|
|
<td headers="r5 r9 c2 c7">100.0</td>
|
|
<td headers="r5 r9 c3 c4 c8">17,389</td>
|
|
<td headers="r5 r9 c3 c4 c9">50.3</td>
|
|
<td headers="r5 r9 c3 c5 c10">17,181</td>
|
|
<td headers="r5 r9 c3 c5 c11">49.7</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r10" headers="r5 c1">Missing data</th>
|
|
<td headers="r5 r10 c2 c6">27,067</td>
|
|
<td headers="r5 r10 c2 c7">100.0</td>
|
|
<td headers="r5 r10 c3 c4 c8">12,235</td>
|
|
<td headers="r5 r10 c3 c4 c9">45.2</td>
|
|
<td headers="r5 r10 c3 c5 c10">14,832</td>
|
|
<td headers="r5 r10 c3 c5 c11">54.8</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r11" headers="c1">Program</th>
|
|
<td colspan="6"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r12" headers="r11 c1"><abbr class="spell">DI</abbr> only</th>
|
|
<td headers="r11 r12 c2 c6">41,698</td>
|
|
<td headers="r11 r12 c2 c7">100.0</td>
|
|
<td headers="r11 r12 c3 c4 c8">27,511</td>
|
|
<td headers="r11 r12 c3 c4 c9">66.0</td>
|
|
<td headers="r11 r12 c3 c5 c10">14,187</td>
|
|
<td headers="r11 r12 c3 c5 c11">34.0</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r13" headers="r11 c1"><abbr class="spell">SSI</abbr> only</th>
|
|
<td headers="r11 r13 c2 c6">252,855</td>
|
|
<td headers="r11 r13 c2 c7">100.0</td>
|
|
<td headers="r11 r13 c3 c4 c8">134,643</td>
|
|
<td headers="r11 r13 c3 c4 c9">53.2</td>
|
|
<td headers="r11 r13 c3 c5 c10">118,212</td>
|
|
<td headers="r11 r13 c3 c5 c11">46.8</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r14" headers="r11 c1">Concurrent <abbr class="spell">DI</abbr> and <abbr class="spell">SSI</abbr></th>
|
|
<td headers="r11 r14 c2 c6">515,773</td>
|
|
<td headers="r11 r14 c2 c7">100.0</td>
|
|
<td headers="r11 r14 c3 c4 c8">277,268</td>
|
|
<td headers="r11 r14 c3 c4 c9">53.8</td>
|
|
<td headers="r11 r14 c3 c5 c10">238,505</td>
|
|
<td headers="r11 r14 c3 c5 c11">46.2</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r15" headers="c1">Earnings status in 2015</th>
|
|
<td colspan="6"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r16" headers="r15 c1">Yes</th>
|
|
<td headers="r15 r16 c2 c6">47,178</td>
|
|
<td headers="r15 r16 c2 c7">100.0</td>
|
|
<td headers="r15 r16 c3 c4 c8">12,906</td>
|
|
<td headers="r15 r16 c3 c4 c9">27.4</td>
|
|
<td headers="r15 r16 c3 c5 c10">34,272</td>
|
|
<td headers="r15 r16 c3 c5 c11">72.6</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r17" headers="r15 c1">No</th>
|
|
<td headers="r15 r17 c2 c6">763,148</td>
|
|
<td headers="r15 r17 c2 c7">100.0</td>
|
|
<td headers="r15 r17 c3 c4 c8">426,516</td>
|
|
<td headers="r15 r17 c3 c4 c9">55.9</td>
|
|
<td headers="r15 r17 c3 c5 c10">336,632</td>
|
|
<td headers="r15 r17 c3 c5 c11">44.1</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r18" headers="c1">Vital status on December 31, 2018</th>
|
|
<td colspan="6"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r19" headers="r18 c1">Living</th>
|
|
<td headers="r18 r19 c2 c6">705,908</td>
|
|
<td headers="r18 r19 c2 c7">100.0</td>
|
|
<td headers="r18 r19 c3 c4 c8">402,036</td>
|
|
<td headers="r18 r19 c3 c4 c9">55.8</td>
|
|
<td headers="r18 r19 c3 c5 c10">303,872</td>
|
|
<td headers="r18 r19 c3 c5 c11">42.2</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r20" headers="r18 c1">Deceased</th>
|
|
<td headers="r18 r20 c2 c6">104,418</td>
|
|
<td headers="r18 r20 c2 c7">100.0</td>
|
|
<td headers="r18 r20 c3 c4 c8">37,386</td>
|
|
<td headers="r18 r20 c3 c4 c9">35.8</td>
|
|
<td headers="r18 r20 c3 c5 c10">67,032</td>
|
|
<td headers="r18 r20 c3 c5 c11">64.2</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" id="r21" headers="c1">Primary impairment type</th>
|
|
<td colspan="6"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r22" headers="r21 c1">Mental or cognitive</th>
|
|
<td headers="r21 r22 c2 c6">340,266</td>
|
|
<td headers="r21 r22 c2 c7">100.0</td>
|
|
<td headers="r21 r22 c3 c4 c8">164,128</td>
|
|
<td headers="r21 r22 c3 c4 c9">48.2</td>
|
|
<td headers="r21 r22 c3 c5 c10">176,138</td>
|
|
<td headers="r21 r22 c3 c5 c11">51.8</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" id="r23" headers="r21 c1">Physical</th>
|
|
<td headers="r21 r23 c2 c6">470,060</td>
|
|
<td headers="r21 r23 c2 c7">100.0</td>
|
|
<td headers="r21 r23 c3 c4 c8">275,294</td>
|
|
<td headers="r21 r23 c3 c4 c9">58.6</td>
|
|
<td headers="r21 r23 c3 c5 c10">194,766</td>
|
|
<td headers="r21 r23 c3 c5 c11">41.4</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="7">SOURCE: Authors' calculations using administrative data from <abbr class="spell">SSA</abbr>.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="7">a. Denied or decision pending.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<p>Among the homeless disability applicant subgroups, one of the highest allowance rates was for those who died after their <abbr class="spell">DDS</abbr> decision (64.2 percent). This outcome might be attributed to an <abbr class="spell">SSA</abbr> initiative to expedite processing for certain applications by flagging them as terminal illness (or <abbr>TERI</abbr>) cases. <abbr class="spell">SSA</abbr> and <abbr class="spell">DDS</abbr> staff expedite the <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> claims of homeless disability applicants who have a terminal illness at the initial step of the disability determination process (Rajnes 2012). In our study, the highest observed allowance rates were for those who died after they began receiving benefits and who belonged to diagnostic groups involving many of the descriptors used by <abbr class="spell">SSA</abbr> and <abbr class="spell">DDS</abbr> staff to identify a potential <abbr>TERI</abbr> case. Examples of diagnostic groups common in <abbr>TERI</abbr> cases include various types of malignant neoplasms such as cancers of the esophagus or liver. Subsequently deceased homeless disability applicants with primary impairments involving neoplasms or diseases of the digestive system had allowance rates of 95.4 percent and 76.6 percent, respectively (not shown). However, given the high number of homeless disability applicants with a mental/cognitive or musculoskeletal impairment, not all who died were <abbr>TERI</abbr> cases.</p>
|
|
<p>Many homeless disability beneficiaries may be unable to manage their <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr> payments. <abbr class="spell">SSA</abbr> appointed a representative payee to manage the <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> payments received by 24.4 percent of homeless disability beneficiaries in our study (not shown).<sup><a href="#mn24" id="mt24">24</a></sup> In comparison, during December 2019, an estimated 18.6 percent of all working-age <abbr class="spell">DI</abbr>/<abbr class="spell">SSI</abbr> disability beneficiaries had a representative payee who helped them manage their program payments (<abbr class="spell">SSA</abbr> 2020a, 2020b). Of the homeless disability beneficiaries we identified as having a representative payee, half had their <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr> benefits managed by a natural or adoptive parent or an authorized social service agency or custodial institution. Finally, the majority (69.1 percent) of homeless disability beneficiaries with a payee at any point during program participation had a mental or cognitive condition rather than a physical one recorded as their primary impairment, consistent with needing assistance in managing one's benefits.</p>
|
|
<h2>Summary and Conclusions</h2>
|
|
<p>This study provides new insights about the <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr> programs in the context of homelessness as well as new statistics about the geographic, demographic, socioeconomic, and program-participation characteristics of homeless disability-program applicants. Some highlights of our findings follow.</p>
|
|
<p class="noindent"><span class="h4">Homelessness among disability-program applicants was largely an urban phenomenon, involving individuals living within concentrated areas in the contiguous United States</span>. At least 98 percent of homeless <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> applicants in the lower 48 states resided in urban counties, in contrast with 80 percent of the general population (Census Bureau 2017). About 42.1 percent of homeless disability applicants lived in one of 25 urban areas (<a href="#chart4">Chart 4</a> and <a href="#table3">Table 3</a>).</p>
|
|
<p class="noindent"><span class="h4">Several demographic subgroups were overrepresented among the study group</span>. Relative to their domiciled counterparts, homeless disability applicants were far more likely to be male, aged 18 to 64, and without a high school diploma or equivalent (<a href="#table4">Table 4</a>).</p>
|
|
<p class="noindent"><span class="h4">Allowance rates varied by program, postdecision mortality rate, and primary impairment</span>. The overall allowance rate of homeless disability applicants was 45.8 percent (<a href="#table7">Table 7</a>). Those who applied for only <abbr class="spell">DI</abbr> had one of the lowest allowance rates (34.0 percent) of any applicant subgroup while those applying for only <abbr class="spell">SSI</abbr> had an allowance rate of 46.8 percent. Applicants who subsequently died had one of the highest allowance rates, at 64.2 percent. <abbr class="spell">SSA</abbr> was more likely to allow <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> benefits for applicants with a mental or cognitive primary impairment than for those with a physical condition (51.8 percent versus 41.4 percent).</p>
|
|
<p class="noindent"><span class="h4">Not all homeless disability applicants had an <abbr class="spell">EDCS</abbr> homeless flag on their files to prompt expedited processing of their applications</span>. We examined the activation of the <abbr class="spell">EDCS</abbr> homeless flag (along with the <abbr class="spell">MSSICS</abbr> transient indicator) by focusing on <abbr class="spell">SSI</abbr> disability applicants facing housing instability whose claims were denied. Under <abbr class="spell">SSA</abbr> operational policy, field office staff can activate the <abbr class="spell">MSSICS</abbr> transient indicator only at the time of application and are required to activate the <abbr class="spell">EDCS</abbr> homeless flag for every applicant with an activated <abbr class="spell">MSSICS</abbr> transient indicator. Only 28.3 percent of files for denied <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> applications had an <abbr class="spell">EDCS</abbr> homeless flag activated (with or without an <abbr class="spell">MSSICS</abbr> transient indicator) and thereby received expedited processing of their disability claim (<a href="#table2">Table 2</a>). Future studies should explore the specific situations of these cases to determine whether certain circumstances that we did not observe precluded the need for the homeless flag.</p>
|
|
<p class="noindent"><span class="h4">Finally, a significant share of our study sample would not have been identified as homeless if we had relied on only the <abbr class="spell">EDCS</abbr> homeless flag and the <abbr class="spell">MSSICS</abbr> transient indicator</span>. About 20 percent of our study group (162,536 claimants) would not have been included in this research if we had used only the homeless flag and transient indicator to identify those experiencing or at risk of homelessness (<a href="#chart1">Chart 1</a>). The application of a text-mining approach, informed by the <abbr>HUD</abbr> definition of homelessness, provides additional insight about the subset of disability-program applicants who may be experiencing or at risk of homelessness. Although additional research is needed to validate the current analysis or improve the methods used here, text mining could be a way to identify individuals facing disability and housing instability to ensure that they receive appropriate supports and assistance during the application process.</p>
|
|
<h2 id="appA">Appendix A: Text-Mining Search Terms and Phrases</h2>
|
|
<p>Listed below are the text-mining search terms and phrases we used to identify <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> applicants experiencing homelessness. We searched the residential-address and administrative-note fields of the claimants' files (including those for <abbr class="spell">SSI</abbr> <abbr class="spell">ISM</abbr> evaluations) to detect any of the listed terms, which we selected because they align with the <abbr>HUD</abbr>/<abbr class="spell">USICH</abbr> definition of homelessness. We began building the list with a set of search terms and phrases generated by <abbr class="spell">SSA</abbr> researchers who attempted to identify <span class="nobr">2009–2011</span> disability-program claimants who were experiencing homelessness. Then, we checked and augmented the initial list of search terms and phrases by comparing them with those appearing in the residential-address and administrative-note fields of the files for 6,941 individuals belonging to the treatment group of the Homeless Outreach Projects and Evaluation (<abbr>HOPE</abbr>) demonstration from January 2005 through April 2007 (McCoy and others 2007). The <abbr>HOPE</abbr> demonstration had targeted chronically homeless individuals who applied for <abbr class="spell">DI</abbr> and <abbr class="spell">SSI</abbr> benefits to participate in the project. Validating our terms against the <abbr>HOPE</abbr> list further assured the appropriateness of the terms we included; however, we acknowledge that further validation—including checking for false positives—would be necessary prior to any operationalization of this method to inform new policy or service delivery practices.</p>
|
|
<div class="textBox2">
|
|
<div class="title" style="text-align:left">Search terms and phrases <span class="float-right"><i>(70 items)</i></span></div>
|
|
<div class="float-left" style="padding:.5em;width:23%">
|
|
<p class="noindent">abandoned</p>
|
|
<p class="noindent">airport</p>
|
|
<p class="noindent">angels</p>
|
|
<p class="noindent">angels watch</p>
|
|
<p class="noindent">angel's watch</p>
|
|
<p class="noindent">bench</p>
|
|
<p class="noindent">bus</p>
|
|
<p class="noindent">cardboard box</p>
|
|
<p class="noindent">camping</p>
|
|
<p class="noindent">car</p>
|
|
<p class="noindent nobr">catholic charities</p>
|
|
<p class="noindent">clinic</p>
|
|
<p class="noindent">coalition</p>
|
|
<p class="noindent">corr fac</p>
|
|
<p class="noindent">corr facility</p>
|
|
<p class="noindent">correc</p>
|
|
<p class="noindent">correction</p>
|
|
<p class="noindent">correctional</p>
|
|
</div>
|
|
<div class="float-left" style="padding:.5em;width:23%">
|
|
<p class="noindent">couch</p>
|
|
<p class="noindent nobr">double(d) (up)</p>
|
|
<p class="noindent">empty</p>
|
|
<p class="noindent">field office</p>
|
|
<p class="noindent">forest</p>
|
|
<p class="noindent">garage</p>
|
|
<p class="noindent">general delivery</p>
|
|
<p class="noindent">homeless</p>
|
|
<p class="noindent">hotel</p>
|
|
<p class="noindent">inn</p>
|
|
<p class="noindent"><span class="nobr">live(s)</span> with (friend or parent or relative or <span class="nobr">neighbor…)</span></p>
|
|
<p class="noindent">metro</p>
|
|
<p class="noindent">mission</p>
|
|
<p class="noindent">motel</p>
|
|
<p class="noindent">motor lodge</p>
|
|
<p class="noindent">no address</p>
|
|
</div>
|
|
<div class="float-left" style="padding:.5em;width:23%">
|
|
<p class="noindent">no permanent</p>
|
|
<p class="noindent">no place to live</p>
|
|
<p class="noindent">park</p>
|
|
<p class="noindent">park bench</p>
|
|
<p class="noindent">pathfinder</p>
|
|
<p class="noindent">rescue</p>
|
|
<p class="noindent">residing with</p>
|
|
<p class="noindent">salvation army</p>
|
|
<p class="noindent">shelter</p>
|
|
<p class="noindent">skid row</p>
|
|
<p class="noindent">sofa</p>
|
|
<p class="noindent"><abbr class="spell">SSA</abbr> <abbr class="spell">FO</abbr></p>
|
|
<p class="noindent">station</p>
|
|
<p class="noindent">stay with</p>
|
|
<p class="noindent">staying with</p>
|
|
<p class="noindent">staying with friends</p>
|
|
<p class="noindent">street</p>
|
|
<p class="noindent"><span class="nobr">temp(orary)</span> housing</p>
|
|
</div>
|
|
<div class="float-left" style="padding:.5em;width:23%">
|
|
<p class="noindent">tent</p>
|
|
<p class="noindent">tent off</p>
|
|
<p class="noindent">train</p>
|
|
<p class="noindent">transcient</p>
|
|
<p class="noindent">transient</p>
|
|
<p class="noindent">transition housing</p>
|
|
<p class="noindent">truck</p>
|
|
<p class="noindent">under the bridge</p>
|
|
<p class="noindent">undomicile</p>
|
|
<p class="noindent">undomiciled</p>
|
|
<p class="noindent">undomociled</p>
|
|
<p class="noindent">vacant</p>
|
|
<p class="noindent">vacant home</p>
|
|
<p class="noindent">van</p>
|
|
<p class="noindent">vehicle</p>
|
|
<p class="noindent">woods</p>
|
|
<p class="noindent"><abbr class="spell">YMCA</abbr></p>
|
|
<p class="noindent"><abbr class="spell">YWCA</abbr></p>
|
|
</div>
|
|
</div>
|
|
<h2 id="appB">Appendix B: Mapping Methods</h2>
|
|
<p>We executed seven steps before creating the density maps (<a href="#chart2">Charts 2 and 3</a>) of the contiguous United States. First, we assumed that homeless disability applicants who had a recorded <abbr>ZIP</abbr> Code were dispersed among the component counties by the same proportions with which the <abbr>ZIP</abbr> Code's land area fell within those counties. (The majority of homeless disability applicants in the lower 48 states had a recorded <abbr>ZIP</abbr> Code that was contained within a single county.) Second, we summed the number of homeless disability applicants living within each county-equivalent area of the lower 48 states. Third, we extracted Census county-resident counts and divided them by 50,000, the minimum number of residents living in an urban county (Missouri Census Data Center 2016). Fourth, we divided county homeless disability-applicant counts by the factor resulting from our third step to compute homeless disability applicants per 50,000 county residents. Fifth, we sorted county-level records in ascending order of homeless disability applicants per 50,000 residents. Sixth, we divided the records into quintiles and identified the minimum and maximum values for each quintile. Finally, we used those values to assign each county-equivalent into a quintile or density category, shown in <a href="#chart2">Chart 2</a>. We then replicated this procedure for homeless disability beneficiaries, shown in <a href="#chart3">Chart 3</a>.</p>
|
|
<div id="notes">
|
|
<h2>Notes</h2>
|
|
<p> <a href="#mt1" id="mn1">1</a> Hereafter, our use of the term “experiencing homelessness” should be taken to include individuals at risk of, but not necessarily currently experiencing, homelessness.</p>
|
|
<p> <a href="#mt2" id="mn2">2</a> We use the acronym “<abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr>” to refer collectively to three types of disability-program participation: (1) <abbr class="spell">SSI</abbr> only, (2) <abbr class="spell">DI</abbr> only, and (3) concurrent <abbr class="spell">SSI</abbr> and <abbr class="spell">DI</abbr>.</p>
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<p> <a href="#mt3" id="mn3">3</a> The contiguous United States includes the lower 48 continental states, and excludes Alaska, Hawaii, and <abbr>U.S.</abbr> territories (Census Bureau 2013).</p>
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<p> <a href="#mt4" id="mn4">4</a> For information on how <abbr class="spell">SSA</abbr> uses the homeless flag and the transient indicator, see <abbr class="spell">SSA</abbr> (2014a) and <abbr class="spell">SSA</abbr> (2005), respectively.</p>
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<p> <a href="#mt5" id="mn5">5</a> Homeless-service stakeholders include providers of health care, behavioral health, and social services, as well as faith- and community-based organizations and partners. One example of collaboration is <abbr class="spell">SSA</abbr>'s participation in the Substance Abuse and Mental Health Services Administration's <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> Outreach, Access, and Recovery (<abbr>SOAR</abbr>) program. <abbr>SOAR</abbr> aims to increase access to <abbr class="spell">SSA</abbr> disability-program benefits for eligible children and adults who are experiencing or at risk of homelessness and have a serious mental illness, medical impairment, and/or substance-use disorder (see <a href="https://www.samhsa.gov/communities/homelessness-programs-resources/grants/soar">https://www.samhsa.gov/homelessness-programs-resources/grant-programs-services/soar</a>).</p>
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<p> <a href="#mt6" id="mn6">6</a> The McKinney-Vento Homeless Assistance Act of 1987 established <abbr class="spell">USICH</abbr>.</p>
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<p> <a href="#mt7" id="mn7">7</a> Kennedy and King found that <abbr>BEST</abbr> contributed to increased access to disability benefits for applicants. Relative to other disability cases, the <abbr>BEST</abbr> cases had high allowance rates and short processing times.</p>
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<p> <a href="#mt8" id="mn8">8</a> In 2015, for example, <abbr class="spell">SSA</abbr> considered substantial gainful activity to be indicated by monthly earnings of at least $1,090 for a nonblind individual and at least $1,820 for a blind individual.</p>
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<p> <a href="#mt9" id="mn9">9</a> For detailed information on <abbr class="spell">SSA</abbr>'s sequential disability determination process, see Wixon and Strand (2013).</p>
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<p><a href="#mt10" id="mn10">10</a> When using non-<abbr class="spell">MSSICS</abbr> paper records, <abbr class="spell">SSA</abbr> field office staff note transience in the remarks field.</p>
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<p><a href="#mt11" id="mn11">11</a> <abbr class="spell">SSA</abbr> field office staff record homeless status only at the time of submission of a disability-program application or, in the case of <abbr class="spell">SSI</abbr>, a recipient's most recent <abbr class="spell">ISM</abbr> evaluation. Because the <abbr class="spell">SSA</abbr> definition of “homeless” focuses on housing status at the time of application, disability-program staff are not required to follow up with applicants recorded as homeless or transient to determine the severity or duration of their housing instability (or to check whether domiciled applicants later become homeless).</p>
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<p><a href="#mt12" id="mn12">12</a> <a href="#appA">Appendix A</a> lists all search terms and phrases used to inform our text-mining method for selecting study members. The residential-address and administrative-note fields are associated with application forms <span class="nobr"><abbr class="spell">SSA</abbr>-3368</span> (for <abbr class="spell">DI</abbr>) and <span class="nobr"><abbr class="spell">SSA</abbr>-8000<abbr class="spell">BK</abbr></span> (for <abbr class="spell">SSI</abbr>).</p>
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<p><a href="#mt13" id="mn13">13</a> <abbr class="spell">SSA</abbr> restricts <abbr class="spell">DRF</abbr> adjudicative data to the first three levels of the <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> disability determination process (initial <abbr class="spell">DDS</abbr> decision, <abbr class="spell">DDS</abbr> reconsideration, and administrative law judge hearing) because of data-reporting issues associated with the higher adjudicative levels.</p>
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<p><a href="#mt14" id="mn14">14</a> Following guidelines in <abbr class="spell">SSA</abbr> (2006), we included individuals who faced housing instability and met the requirements for <span class="nobr">Old-Age</span> and Survivor's Insurance (<abbr class="spell">OASI</abbr>) benefits under the assumption that they had converted from <abbr class="spell">DI</abbr> to <abbr class="spell">OASI</abbr> on reaching their full retirement age (or age 55, if they were blind).</p>
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<p><a href="#mt15" id="mn15">15</a> Examining the last application indicating homelessness may bias allowance rates upward because the likelihood of being allowed benefits increases with the number of applications submitted. However, we used the most recent application because it is more likely to reflect current information for homeless <abbr class="spell">SSI</abbr>/<abbr class="spell">DI</abbr> disability applicants.</p>
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<p><a href="#mt16" id="mn16">16</a> Nearly 28 percent of studied homeless disability applicants submitted multiple disability-program applications and had homelessness indicated on at least one.</p>
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<p><a href="#mt17" id="mn17">17</a> Quick Disability Determination, Compassionate Allowance, Terminal Illness, Wounded Warrior, and other flags may likewise expedite handling. </p>
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<p><a href="#mt18" id="mn18">18</a> <a href="#appB">Appendix B</a> details the methodology of our geospatial analysis.</p>
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<p><a href="#mt19" id="mn19">19</a> The federal government describes noncounty administrative or statistical areas that are comparable to counties as “county equivalents” (Census Bureau 2013). Louisiana parishes; the organized boroughs of Alaska and New York City; the District of Columbia; and the independent cities of the states of Virginia, Maryland, Missouri, and Nevada are equivalent to counties for administrative purposes.</p>
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<p><a href="#mt20" id="mn20">20</a> Among the 2,274 county equivalents in the lower 48 states with homeless disability applicants, about 34.7 percent had no more than one applicant per 50,000 residents and 28.3 percent had at least 50 applicants per 50,000 residents. About 40.9 percent of county equivalents with homeless disability applicants had no more than one beneficiary per 50,000 residents and 13.5 percent had at least 50 beneficiaries per 50,000 residents.</p>
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<p><a href="#mt21" id="mn21">21</a> We used the Office of Management and Budget core-based statistical areas to define the metropolitan areas.</p>
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<p><a href="#mt22" id="mn22">22</a> For this study, we did not access the earnings data of the 21,648,926 individuals who were domiciled and who submitted at least one disability application from calendar year 2007 through 2017.</p>
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<p><a href="#mt23" id="mn23">23</a> <abbr class="spell">SSA</abbr> statistical publications provide statistics by diagnostic group for beneficiaries but not for applicants. The rate of mental/cognitive primary impairments we found among our sample of homeless disability-program beneficiaries (47 percent; not shown) was greater than that of all <abbr class="spell">DI</abbr> beneficiaries (29 percent) but less than that of all <abbr class="spell">SSI</abbr> recipients (57 percent; <abbr class="spell">SSA</abbr> 2017a, 2017c).</p>
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<p><a href="#mt24" id="mn24">24</a> <abbr class="spell">SSA</abbr> appoints a representative payee for an adult beneficiary who is physically or mentally incapable of managing his or her own funds. In addition, <abbr class="spell">SSA</abbr> usually appoints a payee to receive benefits on behalf of a child younger than 18 (<abbr class="spell">SSA</abbr> 2017b).</p>
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</div>
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<div id="references">
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<h2>References</h2>
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<p>Bailey, Michelle Stegman, Debra Goetz Engler, and Jeffrey Hemmeter. 2016. “<a href="/policy/docs/ssb/v76n1/v76n1p1.html">Homeless with Schizophrenia Presumptive Disability Pilot Evaluation</a>.” <i>Social Security Bulletin</i> 76(1): <span class="nobr">1–25.</span></p>
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<p>Census Bureau. 2013. “County Population Totals: <span class="nobr">2010–2012;</span> Datasets. Population, Population Change, and Estimated Components of Population Change: April 1, 2010 to July 1, 2012.” <a href="https://www.census.gov/data/tables/time-series/demo/popest/2010s-counties-total.html">https://www.census.gov/data/tables/time-series/demo/popest/2010s-counties-total.html</a>.</p>
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<p>———. 2017. “One in Five Americans Live in Rural Areas.” <a href="https://www.census.gov/library/stories/2017/08/rural-america.html">https://www.census.gov/library/stories/2017/08/rural-america.html</a>.</p>
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<p>[<abbr>HUD</abbr>] Department of Housing and Urban Development. 2015. “Homeless Emergency Assistance and Rapid Transition to Housing: Defining 'Chronically Homeless.'” <i>Federal Register</i> 80(233): <span class="nobr">75791–75806</span> (December 4). <a href="https://www.govinfo.gov/content/pkg/FR-2015-12-04/pdf/2015-30473.pdf">https://www.gpo.gov/fdsys/pkg/FR-2015-12-04/pdf/2015-30473.pdf</a>.</p>
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<p>———. 2017. <i>The 2016 Annual Homeless Assessment Report (<abbr class="spell">AHAR</abbr>) to Congress. Part 1: Point-in-Time Estimates of Homelessness.</i> <a href="https://www.hudexchange.info/homelessness-assistance/ahar/#2024-reports">https://www.hudexchange.info/resources/documents/2016-AHAR-Part-1.pdf</a>.</p>
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<p>Kennedy, Elizabeth, and Laura King. 2014. “<a href="/policy/docs/ssb/v74n4/v74n4p45.html">Improving Access to Benefits for Persons with Disabilities Who Were Experiencing Homelessness: An Evaluation of the Benefits Entitlement Services Team Demonstration Project</a>.” <i>Social Security Bulletin</i> 74(4): <span class="nobr">45–55.</span></p>
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<p>McCoy, Marion L., Cynthia S. Robins, James Bethel, Carina Tornow, and William D. Frey. 2007. <i>Evaluation of Homeless Outreach Projects and Evaluation (<abbr>HOPE</abbr>). Task 6: Final Evaluation Report.</i> Rockville, <abbr title="Maryland">MD</abbr>: Westat.</p>
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<p>Missouri Census Data Center. 2016. “Geocorr 2014: Geographic Correspondence Engine.” <a href="https://mcdc.missouri.edu/applications/geocorr2014.html">https://mcdc.missouri.edu/applications/geocorr2014.html</a>.</p>
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<p>Nicholas, Joyce. 2014. “<a href="/policy/docs/ssb/v74n3/v74n3p39.html">Source, Form, and Amount of <span class="nobr">In-kind</span> Support and Maintenance Received by Supplemental Security Income Applicants and Recipients</a>.” <i>Social Security Bulletin</i> 74(3): <span class="nobr">39–54.</span></p>
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<p>O'Connell, J. J. 2005. “Premature Mortality in Homeless Populations: A Review of the Literature.” Nashville, <abbr title="Tennessee">TN</abbr>: National Health Care for the Homeless Council.</p>
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<p>Rajnes, David. 2012. “<a href="/policy/docs/ssb/v72n1/v72n1p79.html">'Fast-Track' Strategies in <span class="nobr">Long-Term</span> Public Disability Programs Around the World</a>.” <i>Social Security Bulletin</i> 72(1): <span class="nobr">79–108.</span></p>
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<p>[<abbr class="spell">SSA</abbr>] Social Security Administration. 2005. “Program Operations Manual System (<abbr>POMS</abbr>) <abbr class="spell">SI</abbr> 00835.060: Transients, Homeless Individuals, and <abbr class="spell">LA</abbr>/<abbr class="spell">ISM</abbr> Determinations.” <a href="https://secure.ssa.gov/apps10/poms.nsf/lnx/0500835060">https://secure.ssa.gov/apps10/poms.nsf/lnx/0500835060</a>.</p>
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<p>———. 2006. “Program Operations Manual System (<abbr>POMS</abbr>) <abbr class="spell">DI</abbr> 10105.080: Age Requirement.” <a href="https://secure.ssa.gov/apps10/poms.nsf/lnx/0410105080">https://secure.ssa.gov/apps10/poms.nsf/lnx/0410105080</a>.</p>
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<p>———. 2014a. “Program Operations Manual System (<abbr>POMS</abbr>) <abbr class="spell">DI</abbr> 11005.004: Identifying and Flagging Homeless Cases.” <a href="https://secure.ssa.gov/apps10/poms.nsf/lnx/0411005004">https://secure.ssa.gov/apps10/poms.nsf/lnx/0411005004</a>.</p>
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<p>———. 2014b. “Program Operations Manual System (<abbr>POMS</abbr>) <abbr class="spell">DI</abbr> 81010.080: Alerts, Flags, and Messages in the Electronic Disability Collect System (<abbr class="spell">EDCS</abbr>).” <a href="https://secure.ssa.gov/apps10/poms.nsf/lnx/0481010080">https://secure.ssa.gov/apps10/poms.nsf/lnx/0481010080</a>.</p>
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<p>———. 2014c. “Program Operations Manual System (<abbr>POMS</abbr>) <abbr class="spell">DI</abbr> 81020.085: Certified Electronic Folder (<abbr class="spell">CEF</abbr>) Flags.” <a href="https://secure.ssa.gov/apps10/poms.nsf/lnx/0481020085">https://secure.ssa.gov/apps10/poms.nsf/lnx/0481020085</a>.</p>
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<p>———. 2017a. <i>Annual Statistical Report on the Social Security Disability Insurance Program, 2016.</i> Publication <abbr title="Number">No.</abbr> <span class="nobr">13-11826.</span> Washington, <abbr class="spell">DC</abbr>: <abbr class="spell">SSA</abbr>. <a href="/policy/docs/statcomps/di_asr/2016/index.html">https://www.ssa.gov/policy/docs/statcomps/di_asr/2016/index.html</a>.</p>
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<p>———. 2017b. <i>Annual Statistical Supplement to the Social Security Bulletin, 2016.</i> Publication <abbr title="Number">No.</abbr> <span class="nobr">13-11700.</span> Washington, <abbr class="spell">DC</abbr>: <abbr class="spell">SSA</abbr>. <a href="/policy/docs/statcomps/supplement/2016/index.html">https://www.ssa.gov/policy/docs/statcomps/supplement/2016/index.html</a>.</p>
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<p>———. 2017c. <i><abbr class="spell">SSI</abbr> Annual Statistical Report, 2016.</i> Publication <abbr title="Number">No.</abbr> <span class="nobr">13-11827.</span> Washington, <abbr class="spell">DC</abbr>: <abbr class="spell">SSA</abbr>. <a href="/policy/docs/statcomps/ssi_asr/2016/index.html">https://www.ssa.gov/policy/docs/statcomps/ssi_asr/2016/index.html</a>.</p>
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<p>———. 2020a. <i>Annual Statistical Report on the Social Security Disability Insurance Program, 2019.</i> Publication <abbr title="Number">No.</abbr> <span class="nobr">13-11826.</span> Washington, <abbr class="spell">DC</abbr>: <abbr class="spell">SSA</abbr>. <a href="/policy/docs/statcomps/di_asr/2019/index.html">https://www.ssa.gov/policy/docs/statcomps/di_asr/2019/index.html</a>.</p>
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<p>———. 2020b. <i><abbr class="spell">SSI</abbr> Annual Statistical Report, 2019.</i> Publication <abbr title="Number">No.</abbr> <span class="nobr">13-11827.</span> Washington, <abbr class="spell">DC</abbr>: <abbr class="spell">SSA</abbr>. <a href="/policy/docs/statcomps/ssi_asr/2019/index.html">https://www.ssa.gov/policy/docs/statcomps/ssi_asr/2019/index.html</a>.</p>
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<p>Wixon, Bernard, and Alexander Strand. 2013. “<a href="/policy/docs/rsnotes/rsn2013-01.html">Identifying <abbr class="spell">SSA</abbr>'s Sequential Disability Determination Steps Using Administrative Data</a>.” Research and Statistics Note <abbr title="Number">No.</abbr> <span class="nobr">2013-01.</span> Washington, <abbr class="spell">DC</abbr>: <abbr class="spell">SSA</abbr>.</p>
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