What Is the Relationship Between Socioeconomic Deprivation and Child Supplemental Security Income Participation?

Introduction

Recent reductions in the number of children receiving Supplemental Security Income (SSI) raise questions of how well the program currently reaches those who need it. Administered by the Social Security Administration (SSA), SSI provides cash payments to families that have children with significant disabilities and meet certain income and asset criteria. The number of children participating in SSI peaked in 2013 but has gradually declined since then, for reasons that are not yet fully understood. In addition, child applications for SSI have dropped sharply during the COVID-19 pandemic, resulting in far fewer awards than SSA had projected (SSA 2021b).

SSI 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 SSI are likely to benefit from SSI receipt. Factors that may have driven the growth in child SSI 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 SSA. For example, changes in the frequency of continuing disability reviews (CDRs) likely play an important role in driving patterns of payment receipt among SSI children (Hemmeter and others 2021).

The decline in child SSI participation since 2013 is directly relevant to SSA's responsibility under the Social Security Act to provide outreach to potentially eligible populations.¹ The act authorizes SSA 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 SSI applicants in response to the sharp decline in applications during the pandemic (SSA 2021b). In June 2021, SSA designated certain claims officers as Vulnerable Population Liaisons to support and advise over 1,100 external organizations that take in and submit SSI applications on behalf of targeted groups.

Geographic variation, especially at local levels, represents an important consideration for outreach efforts and for understanding SSI program dynamics more broadly. In 2013, per capita child SSI 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 SSI 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, SSA and its partners can try to address the underlying geographic variations in SSI participation.

One likely driver of child SSI 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 (ADI), a data set used by researchers and policymakers to study health care delivery and inform policy.² Our measure captures deprivation at the census tract level, allowing us to examine variation in SSI participation within highly localized areas. To qualify for payments under SSI's stringent asset and income limits, families must have sufficiently low resources. Almost half of child SSI 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 SSI participation.

This article examines the extent to which socioeconomic deprivation explains geographic variations in SSI participation among children. We calculate local SSI 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 ADI 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 SSI participation based on local area deprivation, which we then compare against the area's actual participation. We define this measure as deviation to highlight the difference between predicted and actual SSI participation. We also analyze the characteristics of communities that have lower-than-predicted SSI participation, which might help us understand how various factors contribute to the geographic variation in child SSI participation. Finally, we explore the extent to which areas with higher (or lower) deprivation experienced greater declines in applications after the onset of the COVID-19 pandemic.

These findings contribute to an understanding of broader trends in SSI participation, particularly in identifying the areas with the greatest unmet need for SSI, which might be best served by targeted outreach. We find that SSI participation often varies substantially within census tracts, even after controlling for measures of deprivation. As a caveat, deviations represent only one measure of SSI 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 SSI. 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.

Background

SSI 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 marked and severe functional limitations, 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 U.S.C. § 1382c[C][i]; 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.³ SSA excludes certain resources, such as the primary residential home or one vehicle (as long as it is used for transportation), in the calculation.⁴ Local field offices handle the application process.⁵ Recent research suggests that field office closures can affect local SSI 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).

In 2021, the federal maximum SSI payment was $794 per month, and 23 states exercised their option to provide a supplementation payment to children with disabilities.⁶ On average, among families that include a child SSI recipient, almost half of family income comes from SSI (Davies, Rupp, and Wittenburg 2009). Children who qualify for SSI may qualify for services from other programs as well. For example, most children who receive SSI 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).

SSA periodically reassesses the medical eligibility of SSI recipients during medical CDRs, which often result in benefit cessations. For a child whose impairment is expected to improve, SSA generally conducts a CDR within 6 to 18 months of SSI award; for a child whose impairment is judged “probable” to improve, SSA is supposed to conduct CDRs every 3 years; for a child whose impairment is not expected to improve, SSA is supposed to conduct CDRs at least every 7 years.⁷ However, the numbers of CDRs SSA conducts varies over time depending on caseload size, administrative priorities, and budgets. SSA 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.⁸ 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 SSI recipients younger than 18). The number of CDRs SSA conducts has increased substantially since 2015, which might be an important driver of the decrease in SSI participation during this time, as frequent CDRs contribute to shorter durations of payment receipt (Hemmeter and others 2021).

SSI Caseload Trends

The number of child SSI 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 SSI payments, with the most notable example being the large increase in CDRs in recent years. In the first years after the current SSI eligibility rules were implemented as part of larger welfare reforms in 1996, SSI caseloads dipped.⁹ 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, SSI child-recipient caseloads have declined; 1.1 million children received SSI as of December 2020. Caseloads have declined further during the COVID-19 pandemic, with the closure of SSA 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 SSI participation.

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, SSI 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 SSI caseloads. Several studies identified other factors that could affect local caseload trends, such as availability of advocacy networks, proximity and access to SSA field offices, information about SSI 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).

Understanding the drivers of recent geographic variation in child SSI participation is important to ensure equitable access to the program. SSA 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 COVID-19 pandemic (SSA 2021b).

To better inform outreach efforts, and to understand SSI program dynamics more generally, this article addresses three notable gaps in the existing literature. First, recent geographic variation in child SSI participation is not well understood. Most studies analyze the period of large program growth through 2013, but recent declines in child SSI 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.¹⁰ Finally, quantitative information that could be used to identify promising targets for potential outreach (or to learn how child SSI participation in those areas is correlated with demographic and other characteristics) is not widely available.

Deprivation

We incorporate a measure of local area socioeconomic deprivation into our geographic analysis of child SSI participation rates. Our measure is based on the ADI, which was initially developed by the Health Resources and Services Administration. The ADI, 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 ADI, 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 SSI participation with the full list of our deprivation input variables, which are based on data from the Census Bureau's American Community Survey (ACS).

Researchers and policymakers use measures of deprivation such as the ADI 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).

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 ADI.¹¹ 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 high-need communities fare worse than other communities on a range of alternative measures, such as greater prevalence of employment in low-wage occupations. Although our ADI-based measure of deprivation captures an essential component of need, Kim and Loh showed that of the eight measures, ADI identified relatively few high-need areas in the West and Midwest. Hence, using this measure of deprivation might lead to characterizations of high-need local areas that differ from those of other measures, a notable caveat when targeting localized outreach efforts.

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 SSI. Local areas with low child SSI participation relative to the level of participation one might expect based on a deprivation measure could therefore be suitable targets for outreach.

Data and Methods

We used administrative data from the Supplemental Security Record, SSA's main system of records for the SSI program, to measure the number of children receiving SSI payments at the census tract and county levels in 2019. We also measured the number of child SSI 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 SSI recipients.¹² 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.

Our primary outcome measure is the number of child SSI recipients per 1,000 children in the geographic unit. We gathered data on the population of individuals aged 0–17 from ACS 5-year estimates for the period 2015–2019. These data were available at the census tract and county levels.

We also explored the characteristics of child SSI recipients in each local area. Specifically, we measured the percentage distributions of child SSI recipients in each local area by sex, age (0–4, 5–13, 14–17), and primary diagnosis. For the latter, we used the standard list of primary diagnoses presented in the SSI Annual Statistical Report (SSA 2021c).

Chart 2 shows the prevalence of child SSI participation in 2019 by county. As previous studies have shown, we find heavier concentrations of children receiving SSI in the southern and northeastern states.

We measured deprivation using ACS 5-year estimates for the period 2015–2019. We calculated deprivation at the county and census tract levels by following the process described in Singh (2003).¹³ Specifically, we gathered data on the components of the ADI.¹⁴ 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.

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 SSI participation (shown in Chart 2). However, many counties with relatively high deprivation also have lower levels of SSI participation, for example in North Dakota and South Dakota, and some counties combine lower deprivation with higher SSI participation.

To better understand the relationship between deprivation and child SSI participation, we developed a regression framework to examine correlations between the two measures. We first estimated a simple linear regression of child SSI participation on deprivation as shown in equation 1:

We weighted this regression by the child population in the geographic unit. Using the coefficient $\beta$ from the regression, we created a predicted value of child SSI 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 $g$).

Based on this regression, we then calculated deviation, which captures the gap between actual SSI participation and a prediction based on deprivation. In short, the deviation is the residual from the regression (${\epsilon }_g$).

Deviation can be negative or positive. A negative deviation indicates that actual child SSI participation was lower than predicted participation. Conversely, a positive deviation indicates that actual SSI 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^th 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^th percentile of the deviation distribution.¹⁵ All metrics, even those presented for specific local areas, are based on the national distribution of deviation.

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 (${\epsilon }_g$ in equation 1) on the list of measures from Box 1, signified as $X_g$ in equation 2:¹⁶

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 p-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.

Finally, we explore how the COVID-19 pandemic has affected the underlying relationship between deprivation and child SSI participation. Specifically, we assess whether the change in SSI applications from 2019 to 2020 was associated with deprivation and deviation. SSI applications for children declined by 17 percent in 2020 (SSA 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.

Results

There is a strong positive relationship between deprivation and child SSI participation (Chart 4), which is expected because SSI serves low-income populations. For each successive decile of deprivation (for example, the 20^th percentile relative to the 10^th), child SSI 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.¹⁷ The R² 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 SSI receipt, much variation remains in predicting local area SSI participation.

The distribution of child SSI recipients by primary diagnosis¹⁸ 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 SSI recipients in each census tract, we find that communities with higher levels of deprivation have a lower percentage of child SSI recipients with autistic disorders as their primary diagnosis.¹⁹ 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.²⁰ The age and sex distributions of child SSI recipients are mostly constant across communities regardless of the level of deprivation.

Geographic Heterogeneity and Deviation Between Predicted and Actual Child SSI Participation

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 top-codes values at 65, representing the 99^th percentile of deviation, to simplify the presentation.

Chart 7 shows that child SSI 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^th percentile, and lower-than-predicted participation if the measure is below the 25^th percentile, of the deviation distribution.²¹

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 SSI 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 SSI caseloads from 1996 to 2015 (Wittenburg and others 2015).

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 (MSA) for Detroit, Michigan. Metro Detroit has about 4.4 million people, making it the 14^th largest MSA in the country. In 2019, it ranked 91^st of 384 MSAs in per capita personal income (Bureau of Economic Analysis 2020). The Detroit MSA 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 SSA 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 SSI 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 SSI dynamics and interactions with other programs.

Correlations with Deviation

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 Box 1. 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 SSI participation, policymakers could target resources to communities with the characteristics frequently associated with high measures of deviation.

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 SSI 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 SSI 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.

Other factors that prior research has associated with SSI 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 SSI. 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 SSI 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.

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.

SSI Applications During the COVID-19 Pandemic

We estimate that the number of child SSI applications filed during 2020 fell to 310,688, a decline of 17.5 percent from the 376,557 child SSI applications filed during 2019.²² Counties with higher deprivation had slightly larger declines in child SSI applications in 2020 (Table 6). For each successively higher decile of deprivation, SSI applications declined by an additional 0.5 percentage points, indicating that these changes contributed a very small fraction to the total decline in child SSI applications during 2020. In addition, counties with greater deviation saw larger declines in child SSI 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.

Conclusion

We find substantial differences in child SSI participation across geographic areas even after controlling for deprivation. These differences existed before the drop in applications associated with the COVID-19 pandemic, yet high-deprivation areas saw somewhat larger declines in application volume during the first year of the pandemic. In response to that drop, SSA increased outreach efforts in at-risk communities and for populations facing barriers to participation (SSA 2021b). The agency established new liaisons and partnerships to facilitate application and released public service announcements focusing on children with disabilities.

Our research can support SSA 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 SSI (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 SSI participation, SSA can effectively pinpoint its outreach efforts.

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 SSI 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); SSA field office proximity (Deshpande and Li 2019); and CDR frequencies.

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 SSI 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 SSI 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.²³ Despite these limitations, using the deviation measure provides SSA a useful starting point for identifying potentially underserved populations.

Although we focus on areas with high deprivation and low child SSI 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 SSI but do not participate and thus might also benefit from outreach efforts.