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<h1 itemprop="headline">What Is the Relationship Between Socioeconomic Deprivation and Child Supplemental Security Income Participation?</h1>
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<div id="hByline">by <span itemprop="author">Michael Levere, David C. Wittenburg, and Jeffrey Hemmeter</span><br>Social Security Bulletin, <abbr title="Volume">Vol.</abbr> 82 <abbr title="Number">No.</abbr> 2, 2022 (released May 2022)</div>
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<p id="synopsis" itemprop="description">This article examines how socioeconomic deprivation relates to child Supplemental Security Income (<abbr class="spell">SSI</abbr>) participation in local areas. We construct a deprivation index that reflects a range of socioeconomic factors. We find that local areas with higher deprivation generally have higher levels of child <abbr class="spell">SSI</abbr> participation, but we also see substantial geographic variation. To explore this variation, we assess the demographic and economic factors associated with the deviation between observed child <abbr class="spell">SSI</abbr> participation and a level of participation predicted by the deprivation index. Local areas in which child <abbr class="spell">SSI</abbr> participation is substantially lower than the deprivation index predicts might be promising targets for outreach to better inform families about the <abbr class="spell">SSI</abbr> program. By measuring the deviation between predicted and actual <abbr class="spell">SSI</abbr> participation at the census tract level, outreach efforts can pinpoint the precise locations where they might plausibly have the greatest effect.</p>
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<div class="eightypercent">
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<p>Michael Levere is a visiting assistant professor of economics at Haverford College and a senior researcher at Mathematica. David Wittenburg is a senior fellow at Mathematica. Jeffrey Hemmeter is the Acting Deputy Associate Commissioner for the Office of Research, Demonstration, and Employment Support, Office of Retirement and Disability Policy, Social Security Administration.</p>
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<p><i>Acknowledgments:</i> We are grateful to Manasi Deshpande, Özlen Luznar, Rachel Edmonds, Robert Weathers, Susan Wilschke, and participants at the 2021 Retirement and Disability Research Consortium Annual Meeting for valuable feedback. We also wish to thank Ijun Lai and Addison Larson for their important contributions.</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">ACS</abbr></td>
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<td>American Community Survey</td>
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</tr>
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<tr>
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<td><abbr class="spell">ADI</abbr></td>
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<td>Area Deprivation Index</td>
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</tr>
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<tr>
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<td><abbr class="spell">CDR</abbr></td>
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<td>continuing disability review</td>
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</tr>
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<tr>
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<td><abbr class="spell">MSA</abbr></td>
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<td>metropolitan statistical area</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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</tbody>
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</table>
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</div>
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<p>Recent reductions in the number of children receiving Supplemental Security Income (<abbr class="spell">SSI</abbr>) raise questions of how well the program currently reaches those who need it. Administered by the Social Security Administration (<abbr class="spell">SSA</abbr>), <abbr class="spell">SSI</abbr> provides cash payments to families that have children with significant disabilities and meet certain income and asset criteria. The number of children participating in <abbr class="spell">SSI</abbr> peaked in 2013 but has gradually declined since then, for reasons that are not yet fully understood. In addition, child applications for <abbr class="spell">SSI</abbr> have dropped sharply during the <span class="nobr"><abbr>COVID</abbr>-19</span> pandemic, resulting in far fewer awards than <abbr class="spell">SSA</abbr> had projected (<abbr class="spell">SSA</abbr> 2021b).</p>
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<p><abbr class="spell">SSI</abbr> participation varies by county and state. Understanding the drivers of these geographic differences could help identify local areas where children and families not currently receiving <abbr class="spell">SSI</abbr> are likely to benefit from <abbr class="spell">SSI</abbr> receipt. Factors that may have driven the growth in child <abbr class="spell">SSI</abbr> participation through 2013 include a tightening of eligibility requirements for other state programs as well as increases in the numbers of children living in low-income families, identification of mental disorders by special education services, frequency of childhood mental disorder diagnoses, and awareness of childhood disability prevalence (Government Accountability Office 2012; Schmidt and Sevak 2017). Additional factors may relate directly to administrative processes in <abbr class="spell">SSA</abbr>. For example, changes in the frequency of continuing disability reviews (<abbr class="spell">CDR</abbr>s) likely play an important role in driving patterns of payment receipt among <abbr class="spell">SSI</abbr> children (Hemmeter and others 2021).</p>
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<p>The decline in child <abbr class="spell">SSI</abbr> participation since 2013 is directly relevant to <abbr class="spell">SSA</abbr>'s responsibility under the Social Security Act to provide outreach to potentially eligible populations.<sup><a href="#mn1" id="mt1">1</a></sup> The act authorizes <abbr class="spell">SSA</abbr> to partner with federal, state, private, and nonprofit entities to support outreach efforts. The agency received increased funding beginning in fiscal year 2021 to identify and reach out to potential child <abbr class="spell">SSI</abbr> applicants in response to the sharp decline in applications during the pandemic (<abbr class="spell">SSA</abbr> 2021b). In June 2021, <abbr class="spell">SSA</abbr> designated certain claims officers as Vulnerable Population Liaisons to support and advise over 1,100 external organizations that take in and submit <abbr class="spell">SSI</abbr> applications on behalf of targeted groups.</p>
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<p>Geographic variation, especially at local levels, represents an important consideration for outreach efforts and for understanding <abbr class="spell">SSI</abbr> program dynamics more broadly. In 2013, per capita child <abbr class="spell">SSI</abbr> participation was relatively higher in northeastern and southern states, although considerable variation existed within states at the county level (Schmidt and Sevak 2017). The large variation reflects how <abbr class="spell">SSI</abbr> operates alongside varying local and state systems that serve children with disabilities in different socioeconomic and political environments (Shogren and Wittenburg 2020). Outreach and other initiatives that attempt to influence program participation must take these factors into account to make the most efficient use of available resources. In turn, by targeting outreach to highly localized areas, <abbr class="spell">SSA</abbr> and its partners can try to address the underlying geographic variations in <abbr class="spell">SSI</abbr> participation.</p>
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<p>One likely driver of child <abbr class="spell">SSI</abbr> participation is the local area's socioeconomic deprivation, which reflects a variety of factors such as income, education, employment, and housing quality. Our analysis uses a measure that we developed by adapting the methodology used to create the Area Deprivation Index (<abbr class="spell">ADI</abbr>), a data set used by researchers and policymakers to study health care delivery and inform policy.<sup><a href="#mn2" id="mt2">2</a></sup> Our measure captures deprivation at the census tract level, allowing us to examine variation in <abbr class="spell">SSI</abbr> participation within highly localized areas. To qualify for payments under <abbr class="spell">SSI</abbr>'s stringent asset and income limits, families must have sufficiently low resources. Almost half of child <abbr class="spell">SSI</abbr> recipients come from families with income below the poverty level, and median liquid family assets in 2001 were less than $100 (Rupp and others 2005/2006). Levels of deprivation vary widely across the United States (Kind and others 2014), which may explain the geographic variations in <abbr class="spell">SSI</abbr> participation.</p>
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<p>This article examines the extent to which socioeconomic deprivation explains geographic variations in <abbr class="spell">SSI</abbr> participation among children. We calculate local <abbr class="spell">SSI</abbr> participation rates at the county and census tract levels. Census tract data can reveal the variations that exist within counties. Our measure of deprivation allows us to rank socioeconomic factors across census tracts. This measure is similar to the <abbr class="spell">ADI</abbr> measure used in Kind and others (2014), reflecting a given area's general income, education, employment, and housing quality at a precise local level. Using a simple linear regression, we develop a measure of predicted area child <abbr class="spell">SSI</abbr> participation based on local area deprivation, which we then compare against the area's actual participation. We define this measure as <i>deviation</i> to highlight the difference between predicted and actual <abbr class="spell">SSI</abbr> participation. We also analyze the characteristics of communities that have lower-than-predicted <abbr class="spell">SSI</abbr> participation, which might help us understand how various factors contribute to the geographic variation in child <abbr class="spell">SSI</abbr> participation. Finally, we explore the extent to which areas with higher (or lower) deprivation experienced greater declines in applications after the onset of the <span class="nobr"><abbr>COVID</abbr>-19</span> pandemic.</p>
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<p>These findings contribute to an understanding of broader trends in <abbr class="spell">SSI</abbr> participation, particularly in identifying the areas with the greatest unmet need for <abbr class="spell">SSI</abbr>, which might be best served by targeted outreach. We find that <abbr class="spell">SSI</abbr> participation often varies substantially within census tracts, even after controlling for measures of deprivation. As a caveat, deviations represent only one measure of <abbr class="spell">SSI</abbr> participation and do not fully capture other factors that might influence outcomes, such as systemic disparities in access to resources and opportunities, the availability of related programs, or the economic environment in the local area. Hence, a large deviation only reflects that the area's caseload is above or below the national average for locations with a similar level of deprivation. Even in areas where actual participation exceeds predicted participation, large populations of eligible children might not currently receive <abbr class="spell">SSI</abbr>. Nonetheless, our quantitative measures provide a way to categorize areas that potentially deviate from these averages, which can be especially useful as an initial step in considering options for targeted outreach.</p>
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<h2>Background</h2>
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<p><abbr class="spell">SSI</abbr> eligibility for applicants younger than age 18 is determined by disability, income, and asset criteria. To meet the disability criteria, a child must have “a medically determinable physical or mental impairment, which results in <i>marked and severe functional limitations,</i> and 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” (42 <abbr>U.S.C.</abbr> § <span class="nobr">1382c[C][i];</span> emphasis added). To meet the income and asset criteria, a child's own financial resources, as well as any parental resources “deemed” to the child, must be sufficiently low.<sup><a href="#mn3" id="mt3">3</a></sup> <abbr class="spell">SSA</abbr> excludes certain resources, such as the primary residential home or one vehicle (as long as it is used for transportation), in the calculation.<sup><a href="#mn4" id="mt4">4</a></sup> Local field offices handle the application process.<sup><a href="#mn5" id="mt5">5</a></sup> Recent research suggests that field office closures can affect local <abbr class="spell">SSI</abbr> participation by increasing the costs of application both for those who need to travel farther to access the office and for those affected by longer wait times (Deshpande and Li 2019).</p>
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<p>In 2021, the federal maximum <abbr class="spell">SSI</abbr> payment was $794 per month, and 23 states exercised their option to provide a supplementation payment to children with disabilities.<sup><a href="#mn6" id="mt6">6</a></sup> On average, among families that include a child <abbr class="spell">SSI</abbr> recipient, almost half of family income comes from <abbr class="spell">SSI</abbr> (Davies, Rupp, and Wittenburg 2009). Children who qualify for <abbr class="spell">SSI</abbr> may qualify for services from other programs as well. For example, most children who receive <abbr class="spell">SSI</abbr> are automatically enrolled in Medicaid. Because of their limited income, many also qualify for other means-tested supports, such as Supplemental Nutrition Assistance Program (food stamp) benefits (Romig 2017).</p>
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<p><abbr class="spell">SSA</abbr> periodically reassesses the medical eligibility of <abbr class="spell">SSI</abbr> recipients during medical <abbr class="spell">CDR</abbr>s, which often result in benefit cessations. For a child whose impairment is expected to improve, <abbr class="spell">SSA</abbr> generally conducts a <abbr class="spell">CDR</abbr> within 6 to 18 months of <abbr class="spell">SSI</abbr> award; for a child whose impairment is judged “probable” to improve, <abbr class="spell">SSA</abbr> is supposed to conduct <abbr class="spell">CDR</abbr>s every 3 years; for a child whose impairment is not expected to improve, <abbr class="spell">SSA</abbr> is supposed to conduct <abbr class="spell">CDR</abbr>s at least every 7 years.<sup><a href="#mn7" id="mt7">7</a></sup> However, the numbers of <abbr class="spell">CDR</abbr>s <abbr class="spell">SSA</abbr> conducts varies over time depending on caseload size, administrative priorities, and budgets. <abbr class="spell">SSA</abbr> also conducts an eligibility redetermination when a recipient reaches age 18, which entails both a review of nonmedical eligibility and a new disability determination using the adult disability criteria.<sup><a href="#mn8" id="mt8">8</a></sup> At all ages, to remain eligible for payments, recipients must continue to not exceed the asset and income limits (including deemed income and assets from a parent for <abbr class="spell">SSI</abbr> recipients younger than 18). The number of <abbr class="spell">CDR</abbr>s <abbr class="spell">SSA</abbr> conducts has increased substantially since 2015, which might be an important driver of the decrease in <abbr class="spell">SSI</abbr> participation during this time, as frequent <abbr class="spell">CDR</abbr>s contribute to shorter durations of payment receipt (Hemmeter and others 2021).</p>
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<h3><abbr class="spell">SSI</abbr> Caseload Trends</h3>
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<p>The number of child <abbr class="spell">SSI</abbr> recipients has fluctuated substantially since 1996 despite no significant changes in the rules for eligibility (Chart 1). Although the statutory definition of eligibility for children has not changed in that time, administrative processes have changed in ways that can influence who becomes and remains eligible for <abbr class="spell">SSI</abbr> payments, with the most notable example being the large increase in <abbr class="spell">CDR</abbr>s in recent years. In the first years after the current <abbr class="spell">SSI</abbr> eligibility rules were implemented as part of larger welfare reforms in 1996, <abbr class="spell">SSI</abbr> caseloads dipped.<sup><a href="#mn9" id="mt9">9</a></sup> Caseloads then increased from 2000 through 2013. The possible causes for rising caseloads were discussed in congressional hearings (for example, Wittenburg 2011), which drew particular interest because the increase coincided with contractions in other cash transfer programs such as Temporary Assistance for Needy Families (Schmidt and Sevak 2004, 2017). Since their 2013 peak of 1.3 million, <abbr class="spell">SSI</abbr> child-recipient caseloads have declined; 1.1 million children received <abbr class="spell">SSI</abbr> as of December 2020. Caseloads have declined further during the <span class="nobr"><abbr>COVID</abbr>-19</span> pandemic, with the closure of <abbr class="spell">SSA</abbr> field offices cited as an important driver (Emanuel 2021). Other factors, such as supplemental unemployment benefits, eviction embargoes, and stimulus payments—which increased income and reduced poverty (Wheaton and others 2021)—might also have contributed to declines in <abbr class="spell">SSI</abbr> participation.</p>
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<div class="svgChart chart400" id="chart1"> <img src="v82n2p1-chart01.svg" role="img" alt="Line chart with tabular version below.">
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<div class="table altTable"> <a class="altToggle" href="">Show as table</a>
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<table>
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<caption><span class="tableNumber">Table equivalent for Chart 1. </span>Number of child <abbr class="spell">SSI</abbr> recipients, <span class="nobr">1996–2020</span> (in millions)</caption>
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<colgroup span="1" style="width:8em"></colgroup>
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<colgroup span="1" style="width:8em"></colgroup>
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<thead>
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<th class="stubHeading" scope="col">Year</th>
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<th scope="col">Number</th>
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</thead>
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<th class="stub0" scope="row">1996</th>
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<td>0.955</td>
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<th class="stub0" scope="row">1997</th>
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<td>0.880</td>
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<th class="stub0" scope="row">1998</th>
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<td>0.887</td>
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<th class="stub0" scope="row">1999</th>
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<td>0.847</td>
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<th class="stub0" scope="row">2000</th>
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<td>0.847</td>
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<th class="stub0" scope="row">2001</th>
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<td>0.882</td>
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<th class="stub0" scope="row">2002</th>
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<td>0.915</td>
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<th class="stub0" scope="row">2003</th>
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<td>0.959</td>
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</tr>
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<th class="stub0" scope="row">2004</th>
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<td>0.993</td>
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<th class="stub0" scope="row">2005</th>
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<td>1.036</td>
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<th class="stub0" scope="row">2006</th>
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<td>1.079</td>
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<th class="stub0" scope="row">2007</th>
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<td>1.121</td>
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</tr>
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<th class="stub0" scope="row">2008</th>
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<td>1.154</td>
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<th class="stub0" scope="row">2009</th>
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<td>1.200</td>
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</tr>
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<th class="stub0" scope="row">2010</th>
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<td>1.239</td>
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</tr>
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<th class="stub0" scope="row">2011</th>
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<td>1.277</td>
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</tr>
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<th class="stub0" scope="row">2012</th>
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<td>1.312</td>
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</tr>
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<th class="stub0" scope="row">2013</th>
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<td>1.322</td>
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</tr>
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<th class="stub0" scope="row">2014</th>
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<td>1.300</td>
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</tr>
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<th class="stub0" scope="row">2015</th>
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<td>1.267</td>
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</tr>
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<th class="stub0" scope="row">2016</th>
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<td>1.213</td>
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<th class="stub0" scope="row">2017</th>
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<td>1.183</td>
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<th class="stub0" scope="row">2018</th>
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<td>1.148</td>
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<th class="stub0" scope="row">2019</th>
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<td>1.132</td>
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</tr>
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<th class="stub0" scope="row">2020</th>
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<td>1.109</td>
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</tr>
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</tbody>
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<tfoot>
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<tr>
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<td class="onlyNote" colspan="2">SOURCE: <abbr class="spell">SSA</abbr> (2021a, Table 7.A9).</td>
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</tr>
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</tfoot>
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</table>
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</div>
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</div>
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<p>Prior literature highlights substantial geographic variation in caseload growth through 2013. Wittenburg and others (2015) showed that more than half of the growth in caseloads from 1998 to 2013 took place in four states (California, Florida, Pennsylvania, and Texas). The authors also showed that, more generally, <abbr class="spell">SSI</abbr> participation rates per capita were higher in southern and northeastern states. Schmidt and Sevak (2017) showed that regional and state differences in the number of people living in poverty and the availability of special education services, among other factors, contributed to differential growth in child <abbr class="spell">SSI</abbr> caseloads. Several studies identified other factors that could affect local caseload trends, such as availability of advocacy networks, proximity and access to <abbr class="spell">SSA</abbr> field offices, information about <abbr class="spell">SSI</abbr> that is tied to other programs, and cultural issues (for example, views of disability that vary by region) (Deshpande and Li 2019; Duggan, Kearney, and Rennane 2016; Government Accountability Office 2012).</p>
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<p>Understanding the drivers of recent geographic variation in child <abbr class="spell">SSI</abbr> participation is important to ensure equitable access to the program. <abbr class="spell">SSA</abbr> has prioritized outreach to vulnerable populations such as children. The agency set aside $96 million in its fiscal year 2022 budget to support outreach efforts designed to acknowledge and address recent program declines associated with the <span class="nobr"><abbr>COVID</abbr>-19</span> pandemic (<abbr class="spell">SSA</abbr> 2021b).</p>
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<p>To better inform outreach efforts, and to understand <abbr class="spell">SSI</abbr> program dynamics more generally, this article addresses three notable gaps in the existing literature. First, recent geographic variation in child <abbr class="spell">SSI</abbr> participation is not well understood. Most studies analyze the period of large program growth through 2013, but recent declines in child <abbr class="spell">SSI</abbr> participation necessitate another look at whether geographic patterns might have changed. Second, most studies focus on larger geographic units such as counties, whereas understanding even narrower geographic areas such as census tracts might enable a deeper understanding of local patterns.<sup><a href="#mn10" id="mt10">10</a></sup> Finally, quantitative information that could be used to identify promising targets for potential outreach (or to learn how child <abbr class="spell">SSI</abbr> participation in those areas is correlated with demographic and other characteristics) is not widely available.</p>
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<h3>Deprivation</h3>
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<p>We incorporate a measure of local area socioeconomic deprivation into our geographic analysis of child <abbr class="spell">SSI</abbr> participation rates. Our measure is based on the <abbr class="spell">ADI</abbr>, which was initially developed by the Health Resources and Services Administration. The <abbr class="spell">ADI</abbr>, and our deprivation measure, capture information about income, education, housing, and other local characteristics. A research team from the University of Wisconsin updates and maintains a data set on the <abbr class="spell">ADI</abbr>, which offers a relative ranking of socioeconomic disadvantage at the level of the census block group, a subunit of the census tract. Table 1 shows the correlations of child <abbr class="spell">SSI</abbr> participation with the full list of our deprivation input variables, which are based on data from the Census Bureau's American Community Survey (<abbr class="spell">ACS</abbr>).</p>
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<div class="table" id="table1">
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<table>
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<caption><span class="tableNumber">Table 1. </span>Correlation of deprivation input variables for the period <span class="nobr">2015–2019</span> with child <abbr class="spell">SSI</abbr> participation in 2019 at the county and census tract levels</caption>
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<colgroup span="1" style="width:32em"></colgroup>
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<colgroup span="2" style="width:6em"></colgroup>
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<colgroup class="shaded" span="1" style="width:6em"></colgroup>
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<thead>
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<tr>
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<th rowspan="2" class="stubHeading" scope="colgroup">Variable</th>
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<th colspan="2" class="spanner" scope="colgroup">Coefficient</th>
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<th rowspan="2" scope="colgroup"><abbr class="spell">ACS</abbr> question</th>
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</tr>
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<tr>
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<th scope="col">County</th>
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<th scope="col">Census tract</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th class="stub0" scope="rowgroup">Educational attainment, adults aged 25 or older</th>
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<td colspan="2"></td>
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<td></td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Less than 9 years</th>
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<td>0.197</td>
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<td>0.250</td>
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<td>B15003</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">High school diploma/equivalent or more</th>
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<td>-0.390</td>
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<td>-0.409</td>
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<td>B15003</td>
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</tr>
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<tr class="topPad1">
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<th class="stub0" scope="rowgroup">Employment status, individuals aged 16 or older</th>
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<td colspan="2"></td>
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<td></td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Employed in white-collar job <sup>a</sup></th>
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<td>-0.377</td>
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<td>-0.491</td>
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<td>C24010</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Unemployed</th>
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<td>0.456</td>
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<td>0.419</td>
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<td>B23025</td>
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</tr>
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<tr class="topPad1">
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<th class="stub0" scope="rowgroup">Housing characteristics</th>
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<td colspan="2"></td>
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<td></td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Homeowners</th>
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<td>-0.326</td>
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<td>-0.442</td>
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<td>B25003</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">More than one person per room in household</th>
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<td>0.019</td>
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<td>0.135</td>
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<td>B25014</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Median monthly mortgage ($)</th>
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<td>-0.376</td>
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<td>-0.395</td>
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<td>B25088</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Median gross rent ($)</th>
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<td>-0.403</td>
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<td>-0.379</td>
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<td>B25064</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Median home value ($)</th>
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<td>-0.376</td>
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<td>-0.348</td>
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<td>B25077</td>
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</tr>
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<tr class="topPad1">
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<th class="stub0" scope="rowgroup">Income and poverty characteristics</th>
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<td colspan="2"></td>
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<td></td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Median family income ($)</th>
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<td>-0.624</td>
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<td>-0.553</td>
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<td>B19113</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Disparity ratio <sup>b</sup></th>
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<td>0.658</td>
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<td>0.355</td>
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<td>B19001</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Family poverty rate</th>
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<td>0.721</td>
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<td>0.600</td>
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<td>B17010</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Individuals with earnings under 150 percent of federal poverty limit</th>
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<td>0.703</td>
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<td>0.634</td>
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<td>C17002</td>
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</tr>
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<tr class="topPad1">
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<th class="stub0" scope="rowgroup">Households with—</th>
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<td colspan="2"></td>
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<td></td>
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</tr>
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<tr>
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<th class="stub1" scope="row">Single parent and <span class="nobr">child(ren)</span> under age 18</th>
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<td>0.713</td>
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<td>0.569</td>
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<td>B11003</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">No motor vehicle</th>
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<td>0.377</td>
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<td>0.450</td>
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<td>B25044</td>
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</tr>
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<tr>
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<th class="stub1" scope="row">No telephone</th>
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<td>0.288</td>
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<td>0.235</td>
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<td>B25043</td>
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</tr>
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<tr class="topPad1">
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<th class="stub0" scope="rowgroup">Occupied housing units without complete plumbing</th>
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<td>0.318</td>
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<td>0.277</td>
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<td>B25047</td>
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</tr>
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<tr class="topPad1">
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<th class="stub0" scope="rowgroup">Overall deprivation</th>
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<td>0.626</td>
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<td>0.634</td>
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<td>. . .</td>
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</tr>
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</tbody>
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<tfoot>
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<tr>
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<td class="firstNote" colspan="4">SOURCE: Authors' calculations using <abbr class="spell">SSA</abbr> program records and <abbr class="spell">ACS</abbr> data.</td>
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</tr>
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<tr>
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<td class="note" colspan="4">NOTES: In a linear regression of child <abbr class="spell">SSI</abbr> participation, the coefficient is statistically significant at the <span class="nobr">1-percent</span> level for all input variables and both area types except "more than one person per room in household" at the county level.
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<div class="newNote">. . . = not applicable.</div>
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</td>
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</tr>
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<tr>
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<td class="note" colspan="4">a. Management, business, science, and arts occupations.</td>
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</tr>
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<tr>
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<td class="lastNote" colspan="4">b. Ratio of individuals with income below $15,000 to individuals with income above $75,000.</td>
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</tr>
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</tfoot>
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</table>
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</div>
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<p>Researchers and policymakers use measures of deprivation such as the <abbr class="spell">ADI</abbr> to examine health care delivery and inform policy. Not limited to measuring poverty or income alone, socioeconomic deprivation provides a more holistic view of the ways that a local area might be disadvantaged. We use this measure to rank neighborhoods by socioeconomic disadvantage relative to the national average. Research has linked areas with greater deprivation to worse health outcomes, such as higher rates of obesity and hospital readmission (Kind and others 2014; Hu, Kind, and Nerenz 2018). Areas with higher deprivation also have higher rates of infant mortality (Singh and Kogan 2007) and shorter life expectancies at birth (Singh and Siahpush 2006).</p>
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<p>Socioeconomic deprivation is not the only way to measure local needs, which is a noteworthy consideration when interpreting findings. Kim and Loh (2020) identified eight measures developed by federal agencies or nonprofit research groups that capture different dimensions of local needs, including the <abbr class="spell">ADI</abbr>.<sup><a href="#mn11" id="mt11">11</a></sup> All eight measures included poverty rate as one of their input metrics, although some of the measures diverged notably from others in their regional results. The authors also showed that all <span class="nobr">high-need</span> communities fare worse than other communities on a range of alternative measures, such as greater prevalence of employment in <span class="nobr">low-wage</span> occupations. Although our <abbr class="spell">ADI</abbr>-based measure of deprivation captures an essential component of need, Kim and Loh showed that of the eight measures, <abbr class="spell">ADI</abbr> identified relatively few <span class="nobr">high-need</span> areas in the West and Midwest. Hence, using this measure of deprivation might lead to characterizations of <span class="nobr">high-need</span> local areas that differ from those of other measures, a notable caveat when targeting localized outreach efforts.</p>
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<p>Nevertheless, the families of children with disabilities in areas with high deprivation are likely to have greater need for services or income support such as <abbr class="spell">SSI</abbr>. Local areas with low child <abbr class="spell">SSI</abbr> participation relative to the level of participation one might expect based on a deprivation measure could therefore be suitable targets for outreach.</p>
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<h2>Data and Methods</h2>
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<p>We used administrative data from the Supplemental Security Record, <abbr class="spell">SSA</abbr>'s main system of records for the <abbr class="spell">SSI</abbr> program, to measure the number of children receiving <abbr class="spell">SSI</abbr> payments at the census tract and county levels in 2019. We also measured the number of child <abbr class="spell">SSI</abbr> applicants at the county level for 2019 and 2020. The administrative data contain the recipient's address, including the county. To assign a census tract, we geocoded the addresses of all child <abbr class="spell">SSI</abbr> recipients.<sup><a href="#mn12" id="mt12">12</a></sup> We were able to assign a census tract with geocoding for 95 percent of the records. About 3.5 percent of the records had an unusable address, and 1.5 percent had an address that could not be geocoded for various reasons and thus could not be placed in a particular census tract. We dropped those records from the analysis.</p>
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<p>Our primary outcome measure is the number of child <abbr class="spell">SSI</abbr> recipients per 1,000 children in the geographic unit. We gathered data on the population of individuals aged <span class="nobr">0–17</span> from <abbr class="spell">ACS</abbr> <span class="nobr">5-year</span> estimates for the period <span class="nobr">2015–2019.</span> These data were available at the census tract and county levels.</p>
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<p>We also explored the characteristics of child <abbr class="spell">SSI</abbr> recipients in each local area. Specifically, we measured the percentage distributions of child <abbr class="spell">SSI</abbr> recipients in each local area by sex, age <span class="nobr">(0–4,</span> <span class="nobr">5–13,</span> <span class="nobr">14–17),</span> and primary diagnosis. For the latter, we used the standard list of primary diagnoses presented in the <i><abbr class="spell">SSI</abbr> Annual Statistical Report</i> (<abbr class="spell">SSA</abbr> 2021c).</p>
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<p>Chart 2 shows the prevalence of child <abbr class="spell">SSI</abbr> participation in 2019 by county. As previous studies have shown, we find heavier concentrations of children receiving <abbr class="spell">SSI</abbr> in the southern and northeastern states.</p>
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<div class="svgChart chart960" id="chart2"> <img src="v82n2p1-chart02.svg" role="img" alt="Chart 2. Child S S I participation rate relative to the national mean, by county, 2019. Population density map summarized in the narrative. SOURCE: Authors' calculations using S S A program records and A C S data. NOTES: Participation rate is calculated relative to child population size. 'Average' participation is within one standard deviation of the national mean. Respectively, 'low' and 'high' participation are more than one standard deviation below and above the national mean."> </div>
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<p>We measured deprivation using <abbr class="spell">ACS</abbr> <span class="nobr">5-year</span> estimates for the period <span class="nobr">2015–2019.</span> We calculated deprivation at the county and census tract levels by following the process described in Singh (2003).<sup><a href="#mn13" id="mt13">13</a></sup> Specifically, we gathered data on the components of the <abbr class="spell">ADI</abbr>.<sup><a href="#mn14" id="mt14">14</a></sup> We conducted a factor analysis to assign weights to each of the components, then created a raw index measure using those weights. We express the resulting value as a percentile so that the final index indicates the level of deprivation in the local area relative to the rest of the country.</p>
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<p>Chart 3 shows the geographic variation in relative deprivation across the United States at the county level. Deprivation is relatively high in southern states such as Arkansas, Kentucky, and Louisiana, many of which also have high levels of <abbr class="spell">SSI</abbr> participation (shown in Chart 2). However, many counties with relatively high deprivation also have lower levels of <abbr class="spell">SSI</abbr> participation, for example in North Dakota and South Dakota, and some counties combine lower deprivation with higher <abbr class="spell">SSI</abbr> participation.</p>
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<div class="svgChart chart960" id="chart3"> <img src="v82n2p1-chart03.svg" role="img" alt="Chart 3. Socioeconomic deprivation relative to the national mean in the period 2015-2019, by county. Population density map summarized in the narrative. SOURCE: Authors' calculations using A C S data. NOTE: 'Average' deprivation is within one standard deviation of the national mean. Respectively, 'low' and 'high' deprivation are more than one standard deviation below and above the national mean."> </div>
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<p>To better understand the relationship between deprivation and child <abbr class="spell">SSI</abbr> participation, we developed a regression framework to examine correlations between the two measures. We first estimated a simple linear regression of child <abbr class="spell">SSI</abbr> participation on deprivation as shown in equation 1:</p>
|
|
<div class="equation">
|
|
<div class="float-right">(1)</div>
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|
<div class="scrollMath"><math display='inline'><mrow> <msub> <mrow> <mtext>SSI</mtext></mrow> <mi>g</mi> </msub> <mo>=</mo><mi>α</mi><mo>+</mo><mi>β</mi><msub> <mrow> <mtext>Deprivation</mtext></mrow> <mi>g</mi> </msub> <mo>+</mo><msub> <mi>ε</mi> <mi>g</mi> </msub> </mrow></math> .</div>
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</div>
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|
<p>We weighted this regression by the child population in the geographic unit. Using the coefficient <math display='inline'><mi>β</mi></math> from the regression, we created a predicted value of child <abbr class="spell">SSI</abbr> participation based on the local level of deprivation. As discussed above, we conducted separate analyses for census tracts and counties (both designated with the geographic variable <math display='inline'><mi>g</mi></math>).</p>
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|
<p>Based on this regression, we then calculated deviation, which captures the gap between actual <abbr class="spell">SSI</abbr> participation and a prediction based on deprivation. In short, the deviation is the residual from the regression (<math display='inline'><msub><mi>ε</mi><mi>g</mi></msub></math>).</p>
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|
<p>Deviation can be negative or positive. A negative deviation indicates that actual child <abbr class="spell">SSI</abbr> participation was lower than predicted participation. Conversely, a positive deviation indicates that actual <abbr class="spell">SSI</abbr> participation was higher than predicted participation. In the maps that follow, we consider a geographic unit to have less-than-predicted participation if deviation in that unit is lower than the 25<sup>th</sup> percentile of the deviation distribution. Similarly, we consider a geographic unit to have greater-than-predicted participation if deviation in that unit is greater than the 75<sup>th</sup> percentile of the deviation distribution.<sup><a href="#mn15" id="mt15">15</a></sup> All metrics, even those presented for specific local areas, are based on the national distribution of deviation.</p>
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|
<p>We next explore how characteristics of local areas, listed in Box 1, are associated with larger or smaller deviations to help identify the types of places that would most likely benefit from outreach. These measures capture a range of local and regional characteristics in publicly available data. Our analysis includes information on demographic characteristics, disability prevalence, and other features of the local areas (such as population density, availability of social capital, and presence of Opportunity Zones) that might be correlated with deviations. We regress deviation (<math display='inline'><msub><mi>ε</mi><mi>g</mi></msub></math> in equation 1) on the list of measures from Box 1, signified as <math display='inline'><msub><mi>X</mi><mi>g</mi></msub></math> in equation 2:<sup><a href="#mn16" id="mt16">16</a></sup></p>
|
|
<div class="equation">
|
|
<div class="float-right">(2)</div>
|
|
<div class="scrollMath"><math display='inline'><mrow> <msub> <mrow> <mtext>Deviation</mtext></mrow> <mi>g</mi> </msub> <mo>=</mo><mi>γ</mi><mo>+</mo><mi>δ</mi><msub> <mi>X</mi> <mi>g</mi> </msub> <mo>+</mo><msub> <mi>ω</mi> <mi>g</mi> </msub> </mrow></math> .</div>
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</div>
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|
<div class="textBox2 no-right-padding no-left-padding no-bottom-padding" id="box1">
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<div class="title align-left no-right-margin no-left-margin" style="line-height: 1.1;padding-bottom:.6em"><span class="tableNumber">Box 1. </span>Selected sociodemographic characteristics of local areas with which deviations between predicted and observed child <abbr class="spell">SSI</abbr> participation can be associated</div>
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<div class="table no-margin">
|
|
<table class="textTable">
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|
<thead>
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|
<tr>
|
|
<th class="stubHeading" scope="col">Characteristic</th>
|
|
<th scope="col">Description and/or data source</th>
|
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</tr>
|
|
</thead>
|
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<tbody>
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<tr>
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|
<th class="stub0" scope="row">Percentage of population that is non-White</th>
|
|
<td>Based on <abbr class="spell">ACS</abbr> <span class="nobr">2015–2019</span> <span class="nobr">5-year</span> estimates.</td>
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</tr>
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<tr class="topPad1">
|
|
<th class="stub0" scope="row">Percentage of population that has a disability</th>
|
|
<td>Based on <abbr class="spell">ACS</abbr> <span class="nobr">2015–2019</span> <span class="nobr">5-year</span> estimates.</td>
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</tr>
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<tr class="topPad1">
|
|
<th class="stub0" scope="row">Region</th>
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|
<td>Northeast, South, Midwest, and West, defined at Census Bureau (2021a).</td>
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</tr>
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<tr class="topPad1">
|
|
<th class="stub0" scope="row">Urbanicity</th>
|
|
<td>Metropolitan, suburban, and rural, based on categories adapted from Economic Research Service (2020).</td>
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</tr>
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|
<tr class="topPad1">
|
|
<th class="stub0" scope="row">Population density (counties only)</th>
|
|
<td>Population from <abbr class="spell">ACS</abbr> <span class="nobr">2015–2019</span> <span class="nobr">5-year</span> estimates; land area from Census Bureau (2021b).</td>
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</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="row">Social capital (counties only)</th>
|
|
<td>Measures of participation in civic, religious, and sports organizations, defined in Rupasingha, Goetz, and Freshwater (2006).</td>
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</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="row">Opportunity Zone (census tracts only)</th>
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|
<td>Economically distressed areas nominated by governors and certified by the Secretary of the Treasury. <br>Opportunity Zones are listed at <a href="https://www.irs.gov/pub/irs-drop/n-18-48.pdf">https://www.irs.gov<wbr>/pub<wbr>/irs<wbr>-drop<wbr><span class="nobr">/n-18-48.pdf</span></a>.</td>
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</tr>
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|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="onlyNote" colspan="2">SOURCES: Cited above.</td>
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</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
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|
</div>
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|
<p>We estimated multivariate regressions, including all control variables, and weighted the regressions by population size. Because deviation does not have a readily intuitive cardinal interpretation, we present only standardized coefficients and <i>p</i>-values. This enables us to identify measures that have relatively higher and lower correlations with deviation. Because this estimation requires two steps, we bootstrap the entire process to calculate standard errors.</p>
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|
<p>Finally, we explore how the <span class="nobr"><abbr>COVID</abbr>-19</span> pandemic has affected the underlying relationship between deprivation and child <abbr class="spell">SSI</abbr> participation. Specifically, we assess whether the change in <abbr class="spell">SSI</abbr> applications from 2019 to 2020 was associated with deprivation and deviation. <abbr class="spell">SSI</abbr> applications for children declined by 17 percent in 2020 (<abbr class="spell">SSA</abbr> 2021c, Table 57), with substantial geographic variation in the decline. Because census tract–level data were not available, we focused on counties for this aspect of the analysis.</p>
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<h2>Results</h2>
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|
<p>There is a strong positive relationship between deprivation and child <abbr class="spell">SSI</abbr> participation (Chart 4), which is expected because <abbr class="spell">SSI</abbr> serves low-income populations. For each successive decile of deprivation (for example, the 20<sup>th</sup> percentile relative to the 10<sup>th</sup>), child <abbr class="spell">SSI</abbr> participation increases by 3.5 per 1,000, on average. Relative to the 17.3 child recipients per 1,000 child residents in the average census tract, this represents an increase of nearly 20 percent. Table 2 presents the results of this regression at both the county and census tract levels, weighted and not weighted for the area's child population size. Results are statistically significant at both geographic levels, although the magnitude of the relationship is substantially stronger in the census tract analysis.<sup><a href="#mn17" id="mt17">17</a></sup> The <i>R</i><sup>2</sup> from the simple linear regression in equation 1 (weighted for child population) is 0.392 at the county level and 0.402 at the census tract level. This indicates that although there is a strong correlation between deprivation and <abbr class="spell">SSI</abbr> receipt, much variation remains in predicting local area <abbr class="spell">SSI</abbr> participation.</p>
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|
<div class="svgChart chart700" id="chart4"> <img src="v82n2p1-chart04.svg" role="img" alt="Line chart with tabular version below.">
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|
<div class="table altTable"> <a class="altToggle" href="">Show as table</a>
|
|
<table>
|
|
<caption><span class="tableNumber">Table equivalent for Chart 4. </span>Relationship between census tract socioeconomic deprivation in the period <span class="nobr">2015–2019</span> and child <abbr class="spell">SSI</abbr> participation rate in 2019</caption>
|
|
<colgroup span="1" style="width:8em"></colgroup>
|
|
<colgroup span="1" style="width:8em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th class="stubHeading" scope="col">Deprivation index percentile</th>
|
|
<th scope="col">Child <abbr class="spell">SSI</abbr> recipients per 1,000 children</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub0" scope="row">5</th>
|
|
<td>1.103</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">10</th>
|
|
<td>2.267</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">15</th>
|
|
<td>3.578</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">20</th>
|
|
<td>4.758</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">25</th>
|
|
<td>6.028</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">30</th>
|
|
<td>7.521</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">35</th>
|
|
<td>9.001</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">40</th>
|
|
<td>10.108</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">45</th>
|
|
<td>11.686</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">50</th>
|
|
<td>12.922</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">55</th>
|
|
<td>14.080</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">60</th>
|
|
<td>15.549</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">65</th>
|
|
<td>17.095</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">70</th>
|
|
<td>18.387</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">75</th>
|
|
<td>20.750</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">80</th>
|
|
<td>23.034</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">85</th>
|
|
<td>25.398</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">90</th>
|
|
<td>29.374</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">95</th>
|
|
<td>33.438</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">100</th>
|
|
<td>37.981</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="2">SOURCE: Authors' calculations using <abbr class="spell">SSA</abbr> program records and <abbr class="spell">ACS</abbr> data.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="2">NOTE: Plotted points represent the average participation rate in all census tracts within a given ventile (5th-percentile interval). For example, the point plotted for the 5th percentile represents the average participation rate among all census tracts in the 1st through 5th percentiles of socioeconomic deprivation.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
</div>
|
|
<div class="table" id="table2">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 2. </span>Results of separate linear regressions on the relationship between child <abbr class="spell">SSI</abbr> participation and each of two measures of local area socioeconomic conditions in 2019 (weighted and not weighted for child population)</caption>
|
|
<colgroup span="1" style="width:15em"></colgroup>
|
|
<colgroup span="2" style="width:6em"></colgroup>
|
|
<colgroup span="2" style="width:6em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th rowspan="2" class="stubHeading" scope="colgroup">Measure</th>
|
|
<th colspan="2" class="spanner" scope="colgroup">County level</th>
|
|
<th colspan="2" class="spanner" scope="colgroup">Census tract level</th>
|
|
</tr>
|
|
<tr>
|
|
<th scope="col">Weighted</th>
|
|
<th scope="col">Not weighted</th>
|
|
<th scope="col">Weighted</th>
|
|
<th scope="col">Not weighted</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr class="shaded">
|
|
<th class="stub0" scope="rowgroup">Number of observations</th>
|
|
<td>3,130</td>
|
|
<td>3,130</td>
|
|
<td>71,976</td>
|
|
<td>71,976</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Regression 1: Deprivation <sup>a</sup></th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Coefficient</th>
|
|
<td>0.209</td>
|
|
<td>0.197</td>
|
|
<td>0.349</td>
|
|
<td>0.407</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Standard error</th>
|
|
<td>0.014</td>
|
|
<td>0.006</td>
|
|
<td>0.002</td>
|
|
<td>0.004</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row"><i>R</i><sup>2</sup></th>
|
|
<td>0.392</td>
|
|
<td>0.331</td>
|
|
<td>0.402</td>
|
|
<td>0.089</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Regression 2: Poverty rate <sup>b</sup></th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Coefficient</th>
|
|
<td>0.229</td>
|
|
<td>0.206</td>
|
|
<td>0.335</td>
|
|
<td>0.392</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Standard error</th>
|
|
<td>0.013</td>
|
|
<td>0.005</td>
|
|
<td>0.002</td>
|
|
<td>0.007</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row"><i>R</i><sup>2</sup></th>
|
|
<td>0.487</td>
|
|
<td>0.378</td>
|
|
<td>0.375</td>
|
|
<td>0.084</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="5">SOURCE: Authors' calculations using <abbr class="spell">SSA</abbr> program records and <abbr class="spell">ACS</abbr> data.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="note" colspan="5">a. Local area percentile on a deprivation index. The deprivation measure is based on data for the period <span class="nobr">2015–2019.</span></td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="5">b. Percentage of population with family income below 150 percent of the federal poverty level. The percentage is converted to a percentile for consistency with the deprivation measure.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<p>The distribution of child <abbr class="spell">SSI</abbr> recipients by primary diagnosis<sup><a href="#mn18" id="mt18">18</a></sup> varies depending on the level of deprivation (Chart 5), while distributions by sex and age do not (Table 3). Using descriptive data on the average characteristics of child <abbr class="spell">SSI</abbr> recipients in each census tract, we find that communities with higher levels of deprivation have a lower percentage of child <abbr class="spell">SSI</abbr> recipients with autistic disorders as their primary diagnosis.<sup><a href="#mn19" id="mt19">19</a></sup> This is consistent with evidence that autism diagnosis rates are higher in places with higher socioeconomic status (Thomas and others 2012). By contrast, children in communities with higher deprivation have greater incidence of developmental disorders or other childhood and adolescent disorders as their primary diagnosis.<sup><a href="#mn20" id="mt20">20</a></sup> The age and sex distributions of child <abbr class="spell">SSI</abbr> recipients are mostly constant across communities regardless of the level of deprivation.</p>
|
|
<div class="svgChart chart700" id="chart5"> <img src="v82n2p1-chart05.svg" role="img" alt="Line chart with tabular version below.">
|
|
<div class="table altTable"> <a class="altToggle" href="">Show as table</a>
|
|
<table>
|
|
<caption><span class="tableNumber">Table equivalent for Chart 5. </span>Percentage of child <abbr class="spell">SSI</abbr> recipients with selected primary diagnoses in 2019, by census tract deprivation index for the period <span class="nobr">2015–2019</span></caption>
|
|
<colgroup span="1" style="width:8em"></colgroup>
|
|
<colgroup span="3" style="width:10em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th class="stubHeading" scope="col">Deprivation index percentile</th>
|
|
<th scope="col">Autistic disorders</th>
|
|
<th scope="col">Developmental disorders</th>
|
|
<th scope="col">Other childhood and adolescent disorders</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub0" scope="row">5</th>
|
|
<td>28.38</td>
|
|
<td>11.77</td>
|
|
<td>11.20</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">10</th>
|
|
<td>27.05</td>
|
|
<td>14.18</td>
|
|
<td>12.39</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">15</th>
|
|
<td>26.09</td>
|
|
<td>13.69</td>
|
|
<td>12.76</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">20</th>
|
|
<td>26.05</td>
|
|
<td>14.33</td>
|
|
<td>12.75</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">25</th>
|
|
<td>24.89</td>
|
|
<td>14.21</td>
|
|
<td>13.88</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">30</th>
|
|
<td>24.51</td>
|
|
<td>15.17</td>
|
|
<td>14.52</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">35</th>
|
|
<td>23.50</td>
|
|
<td>15.86</td>
|
|
<td>14.63</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">40</th>
|
|
<td>23.14</td>
|
|
<td>15.76</td>
|
|
<td>14.43</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">45</th>
|
|
<td>22.87</td>
|
|
<td>15.66</td>
|
|
<td>15.33</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">50</th>
|
|
<td>22.59</td>
|
|
<td>16.17</td>
|
|
<td>15.61</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">55</th>
|
|
<td>21.29</td>
|
|
<td>16.62</td>
|
|
<td>16.03</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">60</th>
|
|
<td>21.85</td>
|
|
<td>16.46</td>
|
|
<td>16.20</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">65</th>
|
|
<td>20.70</td>
|
|
<td>17.08</td>
|
|
<td>16.43</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">70</th>
|
|
<td>20.46</td>
|
|
<td>17.05</td>
|
|
<td>16.63</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">75</th>
|
|
<td>20.28</td>
|
|
<td>17.58</td>
|
|
<td>16.50</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">80</th>
|
|
<td>20.07</td>
|
|
<td>18.06</td>
|
|
<td>16.87</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">85</th>
|
|
<td>18.62</td>
|
|
<td>18.52</td>
|
|
<td>17.36</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">90</th>
|
|
<td>17.48</td>
|
|
<td>19.68</td>
|
|
<td>17.93</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">95</th>
|
|
<td>16.72</td>
|
|
<td>20.36</td>
|
|
<td>18.57</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">100</th>
|
|
<td>15.24</td>
|
|
<td>21.17</td>
|
|
<td>19.87</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="4">SOURCE: Authors' calculations using <abbr class="spell">SSA</abbr> program records and <abbr class="spell">ACS</abbr> data.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="note" colspan="4">NOTES: For each primary diagnosis, the plotted points represent the average percentage of child <abbr class="spell">SSI</abbr> recipients with that diagnosis in all census tracts within a given deprivation ventile (5th-percentile interval). For example, the point plotted for the 5th percentile represents the average percentage among all census tracts in the 1st through 5th percentiles of socioeconomic deprivation.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="4">Percentages for other primary diagnoses are available on request from the authors.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
</div>
|
|
<div class="table" id="table3">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 3. </span>Percentage distributions of child <abbr class="spell">SSI</abbr> recipients in 2019 by age and sex, by local area deprivation index percentile for the period <span class="nobr">2015–2019</span></caption>
|
|
<colgroup span="1" style="width:6em"></colgroup>
|
|
<colgroup span="3" style="width:6em"></colgroup>
|
|
<colgroup span="2" style="width:6em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th rowspan="2" class="stubHeading" scope="colgroup">Deprivation index percentile</th>
|
|
<th colspan="3" class="spanner" scope="colgroup">Age</th>
|
|
<th colspan="2" class="spanner" scope="colgroup">Sex</th>
|
|
</tr>
|
|
<tr>
|
|
<th scope="col"><span class="nobr">0–4</span></th>
|
|
<th scope="col"><span class="nobr">5–12</span></th>
|
|
<th scope="col"><span class="nobr">13–17</span></th>
|
|
<th scope="col">Female</th>
|
|
<th scope="col">Male</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub0" scope="row">5</th>
|
|
<td>15.76</td>
|
|
<td>50.17</td>
|
|
<td>34.08</td>
|
|
<td>31.90</td>
|
|
<td>68.10</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">10</th>
|
|
<td>15.22</td>
|
|
<td>48.50</td>
|
|
<td>36.29</td>
|
|
<td>32.55</td>
|
|
<td>67.45</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">15</th>
|
|
<td>14.98</td>
|
|
<td>48.76</td>
|
|
<td>36.27</td>
|
|
<td>34.27</td>
|
|
<td>65.73</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">20</th>
|
|
<td>15.04</td>
|
|
<td>49.98</td>
|
|
<td>34.98</td>
|
|
<td>33.59</td>
|
|
<td>66.41</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">25</th>
|
|
<td>14.80</td>
|
|
<td>50.05</td>
|
|
<td>35.15</td>
|
|
<td>32.81</td>
|
|
<td>67.19</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="row">30</th>
|
|
<td>14.38</td>
|
|
<td>50.94</td>
|
|
<td>34.69</td>
|
|
<td>32.83</td>
|
|
<td>67.17</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">35</th>
|
|
<td>14.63</td>
|
|
<td>50.14</td>
|
|
<td>35.23</td>
|
|
<td>32.93</td>
|
|
<td>67.07</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">40</th>
|
|
<td>14.45</td>
|
|
<td>50.61</td>
|
|
<td>34.94</td>
|
|
<td>32.73</td>
|
|
<td>67.27</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">45</th>
|
|
<td>14.20</td>
|
|
<td>50.45</td>
|
|
<td>35.35</td>
|
|
<td>32.96</td>
|
|
<td>67.04</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">50</th>
|
|
<td>14.09</td>
|
|
<td>50.55</td>
|
|
<td>35.36</td>
|
|
<td>32.49</td>
|
|
<td>67.51</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="row">55</th>
|
|
<td>13.95</td>
|
|
<td>50.62</td>
|
|
<td>35.43</td>
|
|
<td>32.28</td>
|
|
<td>67.72</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">60</th>
|
|
<td>13.74</td>
|
|
<td>50.63</td>
|
|
<td>35.63</td>
|
|
<td>32.44</td>
|
|
<td>67.56</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">65</th>
|
|
<td>13.38</td>
|
|
<td>50.71</td>
|
|
<td>35.91</td>
|
|
<td>32.52</td>
|
|
<td>67.48</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">70</th>
|
|
<td>13.76</td>
|
|
<td>51.05</td>
|
|
<td>35.19</td>
|
|
<td>32.40</td>
|
|
<td>67.60</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">75</th>
|
|
<td>13.66</td>
|
|
<td>50.73</td>
|
|
<td>35.61</td>
|
|
<td>32.57</td>
|
|
<td>67.43</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="row">80</th>
|
|
<td>13.67</td>
|
|
<td>50.98</td>
|
|
<td>35.35</td>
|
|
<td>32.26</td>
|
|
<td>67.74</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">85</th>
|
|
<td>13.30</td>
|
|
<td>51.15</td>
|
|
<td>35.55</td>
|
|
<td>32.41</td>
|
|
<td>67.59</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">90</th>
|
|
<td>13.79</td>
|
|
<td>51.17</td>
|
|
<td>35.04</td>
|
|
<td>32.76</td>
|
|
<td>67.24</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">95</th>
|
|
<td>13.05</td>
|
|
<td>51.30</td>
|
|
<td>35.65</td>
|
|
<td>32.33</td>
|
|
<td>67.67</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">100</th>
|
|
<td>13.11</td>
|
|
<td>51.82</td>
|
|
<td>35.07</td>
|
|
<td>32.44</td>
|
|
<td>67.56</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="onlyNote" colspan="6">SOURCE: Authors' calculations using <abbr class="spell">SSA</abbr> program records and <abbr class="spell">ACS</abbr> data.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<h3>Geographic Heterogeneity and Deviation Between Predicted and Actual Child <abbr class="spell">SSI</abbr> Participation</h3>
|
|
<p>We next examine the geographic dispersion of deviation (Chart 6). We show that most census tracts have deviation values close to zero, although some can be very high (or low). Note that Chart 6 <span class="nobr">top-codes</span> values at 65, representing the 99<sup>th</sup> percentile of deviation, to simplify the presentation.</p>
|
|
<div class="svgChart chart700" id="chart6"> <img src="v82n2p1-chart06.svg" role="img" alt="Histogram with tabular version below.">
|
|
<div class="table altTable"> <a class="altToggle" href="">Show as table</a>
|
|
<table>
|
|
<caption><span class="tableNumber">Table equivalent for Chart 6. </span>Percentage distribution of census tracts by deviation between actual child <abbr class="spell">SSI</abbr> participation and the level of participation predicted by deprivation index in 2019</caption>
|
|
<colgroup span="1" style="width:10em"></colgroup>
|
|
<colgroup span="1" style="width:10em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th class="stubHeading" scope="col">Deviation value</th>
|
|
<th scope="col">Percent</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub0" scope="row">-35</th>
|
|
<td>0.03</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">-30</th>
|
|
<td>0.16</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">-25</th>
|
|
<td>0.44</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">-20</th>
|
|
<td>1.86</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">-15</th>
|
|
<td>5.55</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">-10</th>
|
|
<td>11.71</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">-5</th>
|
|
<td>19.81</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">0</th>
|
|
<td>29.08</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">5</th>
|
|
<td>13.34</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">10</th>
|
|
<td>5.91</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">15</th>
|
|
<td>3.44</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">20</th>
|
|
<td>2.26</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">25</th>
|
|
<td>1.55</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">30</th>
|
|
<td>1.09</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">35</th>
|
|
<td>0.80</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">40</th>
|
|
<td>0.62</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">45</th>
|
|
<td>0.44</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">50</th>
|
|
<td>0.33</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">55</th>
|
|
<td>0.28</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">60</th>
|
|
<td>0.20</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="row">65</th>
|
|
<td>1.09</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="2">SOURCE: Authors' calculations using <abbr class="spell">SSA</abbr> program records and <abbr class="spell">ACS</abbr> data.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="note" colspan="2">NOTES: Deviations are top-coded at 65.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="2">Each bar shows the percent of tracts that have deviations in a bucket centered at the number shown. For example, the bucket around 0 shows tracts with deviations between −2.5 and 2.5.</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
</div>
|
|
<p>Chart 7 shows that child <abbr class="spell">SSI</abbr> participation in many areas is notably higher—or lower—than predicted. Recall that we define an area to have higher-than-predicted participation if the deviation measure is greater than the 75<sup>th</sup> percentile, and lower-than-predicted participation if the measure is below the 25<sup>th</sup> percentile, of the deviation distribution.<sup><a href="#mn21" id="mt21">21</a></sup></p>
|
|
<div class="svgChart chart960" id="chart7"> <img src="v82n2p1-chart07.svg" role="img" alt="Chart 7. Deviation between actual child S S I participation and the level of participation predicted by deprivation index, by county, 2019. Population density map summarized in the narrative. SOURCE: Authors' calculations using S S A program records and A C S data. NOTE: Counties are characterized as having actual participation greater (or less) than predicted participation if deviation is greater than the 75th percentile (or less than the 25th percentile)."> </div>
|
|
<p>Areas with lower-than-predicted participation are disproportionately located in the Midwest, where about 32 percent of census tracts fall into this category, versus 23 percent in the rest of the country. Outreach might benefit areas with relatively limited <abbr class="spell">SSI</abbr> participation such as these. Areas with higher-than-predicted participation are disproportionately located in the Northeast and the South; about 35 percent of census tracts in the Northeast and 32 percent in the South fall into this category, versus 16 percent in the rest of the country. The areas with higher-than-predicted participation drove much of the growth in child <abbr class="spell">SSI</abbr> caseloads from 1996 to 2015 (Wittenburg and others 2015).</p>
|
|
<p>Within counties, individual census tracts often vary in whether actual participation is higher or lower than predicted. For example, Chart 8 shows the census tracts that make up the metropolitan statistical area (<abbr class="spell">MSA</abbr>) for Detroit, Michigan. Metro Detroit has about 4.4 million people, making it the 14<sup>th</sup> largest <abbr class="spell">MSA</abbr> in the country. In 2019, it ranked 91<sup>st</sup> of 384 <abbr class="spell">MSA</abbr>s in per capita personal income (Bureau of Economic Analysis 2020). The Detroit <abbr class="spell">MSA</abbr> contains a mix of areas in which actual participation is greater than predicted (positive deviation, shown in green) and in which actual participation is less than predicted (negative deviation, in brown). This result prompts a question, which we address below: What factors are associated with local areas having higher or lower deviations? Narrowing in on these highly localized areas can help <abbr class="spell">SSA</abbr> precisely pinpoint where to target resources; for example, by helping identify specific neighborhoods in which to recruit local partners. More broadly, it can help researchers and policymakers better understand the heterogeneity of <abbr class="spell">SSI</abbr> participation at local levels, including factors such as the relative prevalence of networking (that is, learning about the program through local relationships) that might influence <abbr class="spell">SSI</abbr> dynamics and interactions with other programs.</p>
|
|
<div class="svgChart chart960" id="chart8"> <img src="v82n2p1-chart08.svg" role="img" alt="Chart 8. Deviation between actual child S S I participation and the level of participation predicted by deprivation index for the Detroit M S A, by census tract, 2019. Population density map summarized in the narrative. SOURCE: Authors' calculations using S S A program records and A C S data. NOTE: Census tracts are characterized as having actual participation greater (or less) than predicted participation if deviation is greater than the 75th percentile (or less than the 25th percentile)."> </div>
|
|
<h3>Correlations with Deviation</h3>
|
|
<p>To understand the factors associated with higher and lower levels of deviation, we next estimate regressions using equation 2. We use measures of deviation as an outcome variable with the control variables that are listed in <a href="#box1">Box 1</a>. We weight the regression by child population in the local area. These regressions explore the extent to which certain community characteristics predict positive or negative deviation. By identifying patterns common to local areas with a mismatch between deprivation and <abbr class="spell">SSI</abbr> participation, policymakers could target resources to communities with the characteristics frequently associated with high measures of deviation.</p>
|
|
<p>Areas that have a larger share of non-White residents have greater positive deviation (Table 4). Conversely, the larger the share of White residents in a local area, the lower the actual child <abbr class="spell">SSI</abbr> participation relative to predicted participation based on deprivation. In other words, a smaller positive or larger negative magnitude in the measure of deviation is associated with a larger White share of the population. This finding is consistent with evidence showing that Black individuals are about twice as likely to receive <abbr class="spell">SSI</abbr> payments as White individuals (Musumeci and Orgera 2021). The standardized coefficient for the non-White population variable has a large magnitude for counties and census tracts alike, indicating that among the chosen predictors, this one has a strong relationship with deviation.</p>
|
|
<div class="table" id="table4">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 4. </span>Correlations of selected local area characteristics with deviation in 2019</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">County level</th>
|
|
<th colspan="2" class="spanner" scope="colgroup">Census tract level</th>
|
|
</tr>
|
|
<tr>
|
|
<th scope="col">Standardized coefficient</th>
|
|
<th scope="col"><span class="nobr"><i>p</i>-value</span></th>
|
|
<th scope="col">Standardized coefficient</th>
|
|
<th scope="col"><span class="nobr"><i>p</i>-value</span></th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub0" scope="rowgroup">Percentage of population that—</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Is non-White</th>
|
|
<td>0.337</td>
|
|
<td>0.000</td>
|
|
<td>0.105</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Has a disability</th>
|
|
<td>0.276</td>
|
|
<td>0.000</td>
|
|
<td>0.146</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Region</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Northeast</th>
|
|
<td>0.255</td>
|
|
<td>0.000</td>
|
|
<td>0.159</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">South</th>
|
|
<td>0.112</td>
|
|
<td>0.004</td>
|
|
<td>0.069</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Midwest (reference variable omitted)</th>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">West</th>
|
|
<td>-0.195</td>
|
|
<td>0.000</td>
|
|
<td>-0.140</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Urbanicity</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Metropolitan</th>
|
|
<td>0.326</td>
|
|
<td>0.000</td>
|
|
<td>0.135</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Suburban (reference variable omitted)</th>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Rural</th>
|
|
<td>-0.071</td>
|
|
<td>0.000</td>
|
|
<td>-0.028</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Population density</th>
|
|
<td>0.176</td>
|
|
<td>0.049</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="rowgroup">Social capital</th>
|
|
<td>0.246</td>
|
|
<td>0.000</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="rowgroup">Opportunity Zone</th>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
<td>0.037</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="5">SOURCE: Authors' calculations using <abbr class="spell">SSA</abbr> program records, <abbr class="spell">ACS</abbr> data, and the sources cited in <a href="#box1">Box 1</a>.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="5">NOTES: A positive coefficient indicates that the characteristic is positively associated with deviation.
|
|
<div class="newNote">Results are weighted by local area child population.</div>
|
|
<div class="newNote">. . . = not applicable.</div>
|
|
</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<p>Other factors that prior research has associated with <abbr class="spell">SSI</abbr> participation are also associated with deviation. For example, deviation increases with the share of the population that has a disability, consistent with the disability criteria for children to receive <abbr class="spell">SSI</abbr>. There are notable differences in deviation by region, with areas in the Northeast and the South having higher deviation than those in the Midwest and the West. Counties with higher social capital have greater deviation, indicating that places with lower participation in civic, religious, and sports organizations do not participate in <abbr class="spell">SSI</abbr> to the extent that would otherwise be expected based on the level of deprivation. Metropolitan areas have substantially higher deviation, while rural areas tend to have lower deviation.</p>
|
|
<p>We also consider an alternative specification in which the outcome is an indicator of negative deviation (that is, actual participation is less than predicted participation) rather than the continuous value of deviation (Table 5). The geographic pattern of results is similar, with counties or census tracts in the Northeast and South less likely than those in the Midwest and West to have lower-than-predicted participation. However, some of the other characteristics exhibit different patterns. For example, census tracts with a higher percentage of the population that is non-White are more likely to have lower-than-predicted participation, while the non-White share of the population is not a significant predictor at the county level. Other characteristics, such as population density, also are no longer significant predictors.</p>
|
|
<div class="table" id="table5">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 5. </span>Correlations of selected local area characteristics with lower-than-predicted child <abbr class="spell">SSI</abbr> participation in 2019</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">County level</th>
|
|
<th colspan="2" class="spanner" scope="colgroup">Census tract level</th>
|
|
</tr>
|
|
<tr>
|
|
<th scope="col">Standardized coefficient</th>
|
|
<th scope="col"><span class="nobr"><i>p</i>-value</span></th>
|
|
<th scope="col">Standardized coefficient</th>
|
|
<th scope="col"><span class="nobr"><i>p</i>-value</span></th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub0" scope="rowgroup">Percentage of population that—</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Is non-White</th>
|
|
<td>0.044</td>
|
|
<td>0.335</td>
|
|
<td>0.135</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Has a disability</th>
|
|
<td>0.100</td>
|
|
<td>0.012</td>
|
|
<td>0.047</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Region</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Northeast</th>
|
|
<td>-0.095</td>
|
|
<td>0.000</td>
|
|
<td>-0.152</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">South</th>
|
|
<td>-0.092</td>
|
|
<td>0.002</td>
|
|
<td>-0.132</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Midwest (reference variable omitted)</th>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">West</th>
|
|
<td>0.003</td>
|
|
<td>0.946</td>
|
|
<td>0.028</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Urbanicity</th>
|
|
<td colspan="4"></td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Metropolitan</th>
|
|
<td>-0.323</td>
|
|
<td>0.000</td>
|
|
<td>-0.205</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Suburban (reference variable omitted)</th>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub1" scope="row">Rural</th>
|
|
<td>0.111</td>
|
|
<td>0.000</td>
|
|
<td>0.037</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="rowgroup">Population density</th>
|
|
<td>-0.017</td>
|
|
<td>0.712</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="rowgroup">Social capital</th>
|
|
<td>-0.078</td>
|
|
<td>0.009</td>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
</tr>
|
|
<tr>
|
|
<th class="stub0" scope="rowgroup">Opportunity Zone</th>
|
|
<td>. . .</td>
|
|
<td>. . .</td>
|
|
<td>0.030</td>
|
|
<td>0.000</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="5">SOURCE: Authors' calculations using <abbr class="spell">SSA</abbr> program records, <abbr class="spell">ACS</abbr> data, and the sources cited in <a href="#box1">Box 1</a>.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="5">NOTES: Results are weighted by local area child population.
|
|
<div class="newNote">. . . = not applicable.</div>
|
|
</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<h3><abbr class="spell">SSI</abbr> Applications During the <span class="nobr"><abbr>COVID</abbr>-19</span> Pandemic</h3>
|
|
<p>We estimate that the number of child <abbr class="spell">SSI</abbr> applications filed during 2020 fell to 310,688, a decline of 17.5 percent from the 376,557 child <abbr class="spell">SSI</abbr> applications filed during 2019.<sup><a href="#mn22" id="mt22">22</a></sup> Counties with higher deprivation had slightly larger declines in child <abbr class="spell">SSI</abbr> applications in 2020 (Table 6). For each successively higher decile of deprivation, <abbr class="spell">SSI</abbr> applications declined by an additional 0.5 percentage points, indicating that these changes contributed a very small fraction to the total decline in child <abbr class="spell">SSI</abbr> applications during 2020. In addition, counties with greater deviation saw larger declines in child <abbr class="spell">SSI</abbr> applications. Counties that had smaller positive deviation (or larger negative deviation) likely began 2019 with low application levels because actual participation was already less than predicted participation, making application numbers in those areas unlikely to decline.</p>
|
|
<div class="table" id="table6">
|
|
<table>
|
|
<caption><span class="tableNumber">Table 6. </span>Correlations of the county-level decline in child <abbr class="spell">SSI</abbr> applications from 2019 to 2020 with deprivation and with the deviation between actual and predicted child <abbr class="spell">SSI</abbr> participation</caption>
|
|
<colgroup span="1" style="width:12em"></colgroup>
|
|
<colgroup span="2" style="width:7em"></colgroup>
|
|
<thead>
|
|
<tr>
|
|
<th class="stubHeading" scope="col">Measure</th>
|
|
<th scope="col">Deprivation</th>
|
|
<th scope="col">Deviation</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<th class="stub0" scope="row">Coefficient</th>
|
|
<td>-0.053</td>
|
|
<td>-0.799</td>
|
|
</tr>
|
|
<tr class="topPad1">
|
|
<th class="stub0" scope="row">Standard error</th>
|
|
<td>0.024</td>
|
|
<td>0.073</td>
|
|
</tr>
|
|
<tr class="topPad1 shaded">
|
|
<th class="stub0" scope="row">Number of observations</th>
|
|
<td class="center" colspan="2">3,130</td>
|
|
</tr>
|
|
</tbody>
|
|
<tfoot>
|
|
<tr>
|
|
<td class="firstNote" colspan="3">SOURCE: Authors' calculations using <abbr class="spell">SSA</abbr> program records and <abbr class="spell">ACS</abbr> data.</td>
|
|
</tr>
|
|
<tr>
|
|
<td class="lastNote" colspan="3">NOTES: Correlation coefficients reflect the regression of the percentage change in <abbr class="spell">SSI</abbr> applications.
|
|
<div class="newNote">Regressions are weighted by county child population.</div>
|
|
</td>
|
|
</tr>
|
|
</tfoot>
|
|
</table>
|
|
</div>
|
|
<h2>Conclusion</h2>
|
|
<p>We find substantial differences in child <abbr class="spell">SSI</abbr> participation across geographic areas even after controlling for deprivation. These differences existed before the drop in applications associated with the <span class="nobr"><abbr>COVID</abbr>-19</span> pandemic, yet high-deprivation areas saw somewhat larger declines in application volume during the first year of the pandemic. In response to that drop, <abbr class="spell">SSA</abbr> increased outreach efforts in <span class="nobr">at-risk</span> communities and for populations facing barriers to participation (<abbr class="spell">SSA</abbr> 2021b). The agency established new liaisons and partnerships to facilitate application and released public service announcements focusing on children with disabilities.</p>
|
|
<p>Our research can support <abbr class="spell">SSA</abbr> by suggesting a metric with which to target areas for more effective outreach. A deprivation metric succinctly identifies areas with multiple characteristics that are likely to be associated with barriers to participating in <abbr class="spell">SSI</abbr> (and other programs). As such, deprivation could be more useful than single-measure identifiers such as poverty rate. By identifying specific geographic areas with notably lower-than-expected <abbr class="spell">SSI</abbr> participation, <abbr class="spell">SSA</abbr> can effectively pinpoint its outreach efforts.</p>
|
|
<p>Although the deprivation index is one potential metric, our work highlights several additional local-area factors, such as race, disability prevalence, and social capital, that are correlated with gaps between predicted and actual <abbr class="spell">SSI</abbr> participation. Other factors beyond the scope of this article that could also inform targeted outreach include aspects of the local program environment such as the availability of services and supports, which vary substantially by region and within counties (National Academies of Sciences, Engineering, and Medicine 2018); <abbr class="spell">SSA</abbr> field office proximity (Deshpande and Li 2019); and <abbr class="spell">CDR</abbr> frequencies.</p>
|
|
<p>Although our deviation metric is a useful starting point for understanding geographic variation in program dynamics, it has limitations. Deviation is measured relative to the average national caseload, so it can only capture whether <abbr class="spell">SSI</abbr> participation is low relative to all other areas, not whether all who are eligible are participating. Further, deviation may not reveal some of the systemic barriers that can influence outcomes. For example, residential segregation resulting from redlining and other discriminatory practices (Aaronson, Hartley, and Mazumder 2021) might unevenly affect the underlying input measures, which include housing variables, as the extent of such practices varies from location to location. If the measure of deprivation underestimates or overestimates the need for <abbr class="spell">SSI</abbr> in communities with a larger non-White population because it cannot distinguish the relative effects of such systemic factors, our ability to draw conclusions from the model may be limited.<sup><a href="#mn23" id="mt23">23</a></sup> Despite these limitations, using the deviation measure provides <abbr class="spell">SSA</abbr> a useful starting point for identifying potentially underserved populations.</p>
|
|
<p>Although we focus on areas with high deprivation and low child <abbr class="spell">SSI</abbr> participation, understanding more about the areas where actual participation exceeds predicted participation is also important. Perhaps through stronger community ties (such as social capital) and greater understanding of available programs, people in such areas take better advantage of services and supports available to them. Yet many people do not take up benefits for which they are eligible (Currie 2006). Although these areas have greater-than-predicted participation relative to the national average, such areas might nevertheless have many children who are eligible for <abbr class="spell">SSI</abbr> but do not participate and thus might also benefit from outreach efforts.</p>
|
|
<div id="notes">
|
|
<h2>Notes</h2>
|
|
<p> <a href="#mt1" id="mn1">1</a> For the Social Security Act section requiring <abbr class="spell">SSA</abbr> outreach to children who are potentially eligible for <abbr class="spell">SSI</abbr> payments, see <a href="/OP_Home/ssact/title16b/1635.htm">https://www.ssa.gov/OP_Home/ssact/title16b/1635.htm</a>.</p>
|
|
<p> <a href="#mt2" id="mn2">2</a> Singh (2003) developed the <abbr class="spell">ADI</abbr> methodology. The University of Wisconsin maintains and updates the <abbr class="spell">ADI</abbr> data set.</p>
|
|
<p> <a href="#mt3" id="mn3">3</a> A child can qualify for <abbr class="spell">SSI</abbr> if her or his own countable resources do not exceed $2,000. Parental resources deemed to the child affect the eligibility threshold; in a <span class="nobr">2-parent</span> household, for example, resources can be as high as $5,000 before the child is no longer eligible.</p>
|
|
<p> <a href="#mt4" id="mn4">4</a> For more details on resource limits, see <a href="/ssi/spotlights/spot-resources.htm">https://www.ssa.gov/ssi/spotlights/spot-resources.htm</a>.</p>
|
|
<p> <a href="#mt5" id="mn5">5</a> Applicants can be assisted in person or by phone. For more details on the child <abbr class="spell">SSI</abbr> application process, see <a href="/ssi">https://www.ssa.gov/benefits/disability/apply-child.html</a>.</p>
|
|
<p> <a href="#mt6" id="mn6">6</a> The Policy Surveillance Program provides details on state supplementation payments for child <abbr class="spell">SSI</abbr> recipients at <a href="https://lawatlas.org/datasets/supplemental-security-income-for-children-with-disabilities">http://lawatlas.org/datasets/supplemental-security-income-for-children-with-disabilities</a>.</p>
|
|
<p> <a href="#mt7" id="mn7">7</a> For <abbr class="spell">SSA</abbr>'s policies on the frequency of <abbr class="spell">CDR</abbr>s, see <a href="/OP_Home/cfr20/404/404-1590.htm">https://www.ssa.gov/OP_Home/cfr20/404/404-1590.htm</a>.</p>
|
|
<p> <a href="#mt8" id="mn8">8</a> Unlike those for children, the adult criteria rely on a disability definition that focuses on work (the inability to engage in substantial gainful activity, which in 2021 was defined as monthly earnings above $1,310 for a nonblind individual). In making <span class="nobr">age-18</span> redeterminations, <abbr class="spell">SSA</abbr> uses the same medical, income, and asset criteria it uses in adult application decisions. Among children receiving <abbr class="spell">SSI</abbr> payments on reaching age 18, 82 percent have a redetermination at that time; the others do not have redeterminations until after age 18, for various reasons (Hemmeter and Bailey 2015).</p>
|
|
<p> <a href="#mt9" id="mn9">9</a> For a history of <abbr class="spell">SSI</abbr> program changes in (and before) 1996, see Wittenburg and Livermore (2021) and Berkowitz and DeWitt (2013).</p>
|
|
<p><a href="#mt10" id="mn10">10</a> One example of a study using census tract–level analysis is Chetty, Hendren, and Katz (2016).</p>
|
|
<p><a href="#mt11" id="mn11">11</a> The other seven measures are (1) the federal statutory definition of Low-Income Community; (2) the Internal Revenue Service designation as a Qualified Opportunity Zone; (3) the Centers for Disease Control and Prevention's Social Vulnerability Index; (4) diversitydatakids.org's Child Opportunity Index; (5) the Robert Graham Center's Social Deprivation Index; (6) the Economic Innovation Group's Distressed Communities Index; and (7) Kim and Loh's adaptation of the “persistent poverty counties” classification used by the Department of Agriculture's Economic Research Service.</p>
|
|
<p><a href="#mt12" id="mn12">12</a> The United States is composed of about 74,000 census tracts, which are designed to have about 4,000 people each, although their populations range roughly from 2,500 to 8,000.</p>
|
|
<p><a href="#mt13" id="mn13">13</a> The <abbr class="spell">ADI</abbr> provided by the University of Wisconsin is only available at the census block group level and captures a relative ranking. Because the <abbr class="spell">ADI</abbr> is a relative ranking, we could not convert the census block group values to census tract or county values simply by aggregating the narrower units into the broader ones and then computing an average. Rather, we needed the underlying raw score, which we could use to construct a relative percentile at the geographic variable of interest. Even so, there is a strong positive correlation (greater than 0.80) between the percentile computed by averaging the values for an area's component subunits and the percentile calculated from the raw data.</p>
|
|
<p><a href="#mt14" id="mn14">14</a> Input variables are missing for as many as 5.9 percent of census tracts. In these instances, we imputed the tract-level value using the county-level value (when available), following the same procedure used to create the <abbr class="spell">ADI</abbr>.</p>
|
|
<p><a href="#mt15" id="mn15">15</a> The choice of 25<sup>th</sup> and 75<sup>th</sup> percentiles is somewhat arbitrary, but the interquartile range provides a reasonable definition of low and high deviation. Alternative thresholds, such as those based on the standard deviation, could also be used.</p>
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<p><a href="#mt16" id="mn16">16</a> Many of these measures are correlated with both deprivation and <abbr class="spell">SSI</abbr> participation. However, this regression seeks to correlate each measure with <i>deviation,</i> not directly with either deprivation or actual <abbr class="spell">SSI</abbr> participation. In other words, a measure that is correlated with both deprivation and <abbr class="spell">SSI</abbr> participation is not necessarily correlated with the <i>gap</i> between actual <abbr class="spell">SSI</abbr> participation and a level of <abbr class="spell">SSI</abbr> participation that is predicted based on deprivation.</p>
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<p><a href="#mt17" id="mn17">17</a> <a href="#table2">Table 2</a> also includes an alternative specification that replaces deprivation with a measure of poverty rate—specifically, the percentage of the local population earning less than 150 percent of the federal poverty limit, converted to a percentile score—which yields remarkably similar results.</p>
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<p><a href="#mt18" id="mn18">18</a> Children may have more than one diagnosis; however, not all are recorded in <abbr class="spell">SSA</abbr>'s administrative records. Additionally, the primary diagnosis may or may not reflect the condition causing the most significant functional barriers to the child. Whether a given condition is identified as the primary diagnosis may reflect underlying differences in access to medical care or <abbr class="spell">SSA</abbr>'s disability determination process itself.</p>
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<p><a href="#mt19" id="mn19">19</a> On average, nearly one child <abbr class="spell">SSI</abbr> recipient in five had a primary diagnosis of autistic disorders. In the highest deprivation areas, fewer than 15 percent had that primary diagnosis, while in the lowest deprivation areas, nearly 30 percent did.</p>
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<p><a href="#mt20" id="mn20">20</a> On average, about 30 percent of child <abbr class="spell">SSI</abbr> recipients had a primary diagnosis of either developmental disorders or other childhood and adolescent disorders. In the highest deprivation areas, more than 40 percent had one of these two conditions as a primary diagnosis, while in the lowest deprivation areas, only about 23 percent did.</p>
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<p><a href="#mt21" id="mn21">21</a> Because <a href="#table2">Table 2</a> indicates a similar relationship between our poverty measure and child <abbr class="spell">SSI</abbr> participation, we constructed an alternative measure of deviation based on the regression on poverty. This alternative deviation is highly correlated with deviation based on the regression on deprivation. The correlation is about 0.95 at the county level and 0.99 at the census tract level. Using a simpler measure would yield nearly identical findings but would not explicitly account for other socioeconomic factors.</p>
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<p><a href="#mt22" id="mn22">22</a> Our estimates may not match official <abbr class="spell">SSA</abbr> statistics because of differing estimation methodologies for cases involving individuals with more than one application or applications that are not recorded timely.</p>
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<p><a href="#mt23" id="mn23">23</a> As another example, health care outcomes are worse for Black patients than for White patients with the same levels of spending, suggesting differential access to care (Obermeyer and others 2019). This leads to bias in comparing measures of spending across racial groups. If similar issues affect the deprivation inputs—and thus <abbr class="spell">SSI</abbr> participation—our findings may be compromised.</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>Aaronson, Daniel, Daniel Hartley, and Bhashkar Mazumder. 2021. “The Effects of the 1930s <abbr class="spell">HOLC</abbr> 'Redlining' Maps.” <i>American Economic Journal: Economic Policy</i> 13(4): <span class="nobr">355–392.</span></p>
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<p>Berkowitz, Edward D., and Larry DeWitt. 2013. <i>The Other Welfare: Supplemental Security Income and <abbr>U.S.</abbr> Social Policy</i>. Ithaca, <abbr title="New York">NY</abbr>: Cornell University Press.</p>
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<p>Bureau of Economic Analysis. 2020. “Real Personal Income for States and Metropolitan Areas: 2019.” <a href="https://apps.bea.gov/regional/histdata/releases/1220rpi/index.cfm">https://apps.bea.gov<wbr>/regional<wbr>/histdata<wbr>/releases<wbr>/1220rpi/index.cfm</a>.</p>
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<p>Census Bureau. 2021a. “2010 Census Regions and Divisions of the United States.” <a href="https://www.census.gov/geographies/reference-maps/2010/geo/2010-census-regions-and-divisions-of-the-united-states.html">https://www.census.gov/geographies/reference-maps/2010/geo/2010-census-regions-and-divisions-of-the-united-states.html</a>.</p>
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<p>———. 2021b. “Gazetteer Files.” <a href="https://www.census.gov/geographies/reference-files/time-series/geo/gazetteer-files.html">https://www.census.gov/geographies/reference-files/time-series/geo/gazetteer-files.html</a>.</p>
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<p>Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment.” <i>American Economic Review</i>106(4): <span class="nobr">855–902.</span></p>
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<p>Currie, Janet. 2006. “The <span class="nobr">Take-Up</span> of Social Benefits.” In <i>Public Policy and the Income Distribution</i>, edited by Alan J. Auerbach, David Card, and John M. Quigley <span class="nobr">(80–148).</span> New York, <abbr title="New York">NY</abbr>: Russell Sage Foundation.</p>
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<p>Davies, Paul S., Kalman Rupp, and David Wittenburg. 2009. “A Life-Cycle Perspective on the Transition to Adulthood Among Children Receiving Supplemental Security Income Payments.” <i>Journal of Vocational Rehabilitation</i> 30(3): <span class="nobr">133–151.</span></p>
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<p>Deshpande, Manasi, and Yue Li. 2019. “Who Is Screened Out? Application Costs and the Targeting of Disability Programs.” <i>American Economic Journal: Economic Policy</i> 11(4): <span class="nobr">213–248.</span></p>
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<p>Duggan, Mark, Melissa S. Kearney, and Stephanie Rennane. 2016. “The Supplemental Security Income Program.” In <i>Economics of Means-Tested Transfer Programs in the United States, Volume 2</i>, edited by Robert A. Moffitt <span class="nobr">(1–58).</span> Chicago, <abbr title="Illinois">IL</abbr>: University of Chicago Press.</p>
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<p>Economic Research Service. 2020. “Rural-Urban Continuum Codes: Documentation.” <a href="https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation">https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/</a>.</p>
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<p>Emanuel, Gabrielle. 2021. “Applications for Aid Program Helping Seniors and People with Disabilities Plunges,” National Public Radio, <i>Morning Edition,</i> February 19. <a href="https://www.npr.org/2021/02/19/969351627/applications-for-aid-program-helping-seniors-and-people-with-disabilities-plunge">https://www.npr.org/2021/02/19/969351627/applications-for-aid-program-helping-seniors-and-people-with-disabilities-plunge</a>.</p>
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<p>Government Accountability Office. 2012. <i>Supplemental Security Income: Better Management Oversight Needed for Children's Benefits.</i> Report <abbr title="Number">No.</abbr> <span class="nobr"><abbr class="spell">GAO</abbr>-12-498<abbr class="spell">SP</abbr>.</span> Washington, <abbr class="spell">DC</abbr>: <abbr class="spell">GAO</abbr>.</p>
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<p>Hemmeter, Jeffrey, and Michelle Stegman Bailey. 2015. “<a href="/policy/docs/rsnotes/rsn2015-03.html">Childhood Continuing Disability Reviews and <span class="nobr">Age-18</span> Redeterminations for Supplemental Security Income Recipients: Outcomes and Subsequent Program Participation</a>.” Research and Statistics Note <abbr title="Number">No.</abbr> <span class="nobr">2015-03.</span> Washington, <abbr class="spell">DC</abbr>: <abbr class="spell">SSA</abbr>.</p>
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<p>Hemmeter, Jeffrey, Michael Levere, Pragya Singh, and David C. Wittenburg. 2021. “<a href="/policy/docs/ssb/v81n2/v81n2p17.html">Changing Stays? Duration of Supplemental Security Income Participation by First-Time Child Awardees and the Role of Continuing Disability Reviews</a>.” <i>Social Security Bulletin</i> 81(2): <span class="nobr">17–41.</span></p>
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<p>Hu, Jianhui, Amy J. H. Kind, and David Nerenz. 2018. “Area Deprivation Index Predicts Readmission Risk at an Urban Teaching Hospital.” <i>American Journal of Medical Quality</i> 33(5): <span class="nobr">493–501.</span></p>
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<p>Kim, Joanne, and Tracy Hadden Loh. 2020. “How We Define 'Need' for Place-Based Policy Reveals Where Poverty and Race Intersect.” Washington, <abbr class="spell">DC</abbr>: Brookings Institution.</p>
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<p>Kind, Amy J. H., Steve Jencks, Jane Brock, Menggang Yu, Christie Bartels, William Ehlenbach, Caprice Greenberg, and Maureen Smith. 2014. “Neighborhood Socioeconomic Disadvantage and <span class="nobr">30-Day</span> Rehospitalization: A Retrospective Cohort Study.” <i>Annals of Internal Medicine</i> 161(11): <span class="nobr">765–774.</span></p>
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<p>Musumeci, MaryBeth, and Kendal Orgera. 2021. “Supplemental Security Income for People with Disabilities: Implications for Medicaid.” <abbr class="spell">KFF</abbr> Issue Brief. San Francisco, <abbr title="California">CA</abbr>: Kaiser Family Foundation.</p>
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<p>National Academies of Sciences, Engineering, and Medicine. 2018. <i>Opportunities for Improving Programs and Services for Children with Disabilities</i>. Washington, <abbr class="spell">DC</abbr>: National Academies Press.</p>
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<p>Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations.” <i>Science</i> 366(6464): <span class="nobr">447–453.</span></p>
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<p>Romig, Kathleen. 2017. “<abbr class="spell">SSI</abbr>: A Lifeline for Children with Disabilities.” Washington, <abbr class="spell">DC</abbr>: Center on Budget and Policy Priorities.</p>
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<p>Rupasingha, Anil, Stephan J. Goetz, and David Freshwater. 2006. “The Production of Social Capital in <abbr class="spell">US</abbr> Counties.” <i>Journal of Socio-Economics</i> 35(1): <span class="nobr">83–101.</span></p>
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<p>Rupp, Kalman, Paul S. Davies, Chad Newcomb, Howard Iams, Carrie Becker, Shanti Mulpuru, Stephen Ressler, Kathleen Romig, and Baylor Miller. 2005/2006. “<a href="/policy/docs/ssb/v66n2/v66n2p21.html">A Profile of Children with Disabilities Receiving <abbr class="spell">SSI</abbr>: Highlights from the National Survey of <abbr class="spell">SSI</abbr> Children and Families</a>.” <i>Social Security Bulletin</i> 66(2): <span class="nobr">21–48.</span></p>
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<p>Schmidt, Lucie, and Purvi Sevak. 2004. “<abbr class="spell">AFDC</abbr>, <abbr class="spell">SSI</abbr>, and Welfare Reform Aggressiveness: Caseload Reductions Versus Caseload Shifting.” <i>Journal of Human Resources</i> 39(3): <span class="nobr">792–812.</span></p>
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<p>———. 2017. “Child Participation in Supplemental Security Income: Cross- and Within-State Determinants of Caseload Growth.” <i>Journal of Disability Policy Studies</i> 28(3): <span class="nobr">131–140.</span></p>
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<p>Shogren, Karrie A., and David Wittenburg. 2020. “Improving Outcomes of Transition-Age Youth with Disabilities: A Life Course Perspective.” <i>Career Development and Transition for Exceptional Individuals</i> 43(1): <span class="nobr">18–28.</span></p>
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<p>Singh, Gopal K. 2003. “Area Deprivation and Widening Inequalities in <abbr class="spell">US</abbr> Mortality, <span class="nobr">1969–1998.</span>” <i>American Journal of Public Health</i> 93(7): <span class="nobr">1137–1143.</span></p>
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<p>Singh, Gopal K., and Michael D. Kogan. 2007. “Persistent Socioeconomic Disparities in Infant, Neonatal, and Postneonatal Mortality Rates in the United States, <span class="nobr">1969–2001.</span>” <i>Pediatrics</i> 119(4): <span class="nobr">e928–e939.</span></p>
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<p>Singh, Gopal K., and Mohammad Siahpush. 2006. “Widening Socioeconomic Inequalities in <abbr class="spell">US</abbr> Life Expectancy, <span class="nobr">1980–2000.</span>” <i>International Journal of Epidemiology</i> 35(4): <span class="nobr">969–979.</span></p>
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<p>[<abbr class="spell">SSA</abbr>] Social Security Administration. 2021a. <i>Annual Statistical Supplemental to the Social Security Bulletin, 2021.</i> <abbr class="spell">SSA</abbr> 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/2021/index.html">https://www.ssa.gov/policy/docs/statcomps/supplement/2021/index.html</a>.</p>
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<p>———. 2021b. “<abbr class="spell">SSA</abbr> Budget Information: <abbr class="spell">FY</abbr> 2022 Budget Request.” https://www.ssa.gov/budget/FY22Files/2022BO.pdf.</p>
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<p>———. 2021c. <i><abbr class="spell">SSI</abbr> Annual Statistical Report, 2020.</i> <abbr class="spell">SSA</abbr> 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/2020/index.html">https://www.ssa.gov/policy/docs/statcomps/ssi_asr/2020/index.html</a>.</p>
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<p>Thomas, Pauline, Walter Zahorodny, Bo Peng, Soyeon Kim, Nisha Jani, William Halperin, and Michael Brimacombe. 2012. “The Association of Autism Diagnosis with Socioeconomic Status.” <i>Autism</i> 16(2): <span class="nobr">201–213.</span></p>
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<p>Wheaton, Laura, Sarah Minton, Linda Giannarelli, and Kelly Dwyer. 2021. “2021 Poverty Projections: Assessing Four American Rescue Plan Policies.” Washington, <abbr class="spell">DC</abbr>: Urban Institute.</p>
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<p>Wittenburg, David. 2011. “Testimony for Hearing on Supplemental Security Income Benefits for Children.” Washington, <abbr class="spell">DC</abbr>: Mathematica Policy Research, Center for Studying Disability Policy. <a href="https://www.mathematica.org/publications/testimony-for-hearing-on-supplemental-security-income-benefits-for-children">https://www.mathematica.org/publications/testimony-for-hearing-on-supplemental-security-income-benefits-for-children</a>.</p>
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<p>Wittenburg, David, and Gina Livermore. 2021. “Youth Transition.” In <i>Lessons from <abbr class="spell">SSA</abbr> Demonstrations for Disability Policy and Future Research</i>, edited by Austin Nichols, Jeffrey Hemmeter, and Debra Goetz Engler <span class="nobr">(223–260).</span> Rockville, <abbr title="Maryland">MD</abbr>: Abt Associates.</p>
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<p>Wittenburg, David, John Tambornino, Elizabeth Brown, Gretchen Rowe, Mason DeCamillis, and Gilbert Crouse. 2015. “The Child <abbr class="spell">SSI</abbr> Program and the Changing Safety Net.” <abbr class="spell">ASPE</abbr> Research Brief. Washington, <abbr class="spell">DC</abbr>: Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation.</p>
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