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. Author manuscript; available in PMC: 2017 May 8.
Published in final edited form as: Fam Soc. 2016;97(2):111–119. doi: 10.1606/1044-3894.2016.97.12

Where Are the Beds? Housing Locations for Transition Age Youth Exiting Public Systems

Christina C Tam 1,1, Bridget Freisthler 2, Susanna R Curry 3, Laura S Abrams 4
PMCID: PMC5421640  NIHMSID: NIHMS819611  PMID: 28490852

Abstract

Transition age youth (TAY) from the child welfare and juvenile justice systems experience high rates of homelessness, but little is known about the neighborhoods to which they return after they exit these systems. This exploratory study investigates whether housing options are located in areas where TAY exit public systems and if the characteristics of areas surrounding these facilities support their transition to adulthood. Results show that housing is not related to areas where TAY exit public systems. Further, supportive housing and shelter density is related to low-income areas. Implications for practice and policy on housing locations for TAY are discussed.

Keywords: transition to adulthood, foster youth, juvenile justice youth, housing stability, risks and resources, neighborhoods

The transition to adulthood is often conceptualized as a time from the ages of 18 to 25 (Arnett, 2000) during which young adults shift from school to work, form adult relationships, and learn to live independently (Shanahan, 2000). However, the experience of this developmental period varies widely according to social class, culture, and formative child and adolescent circumstances (Arnett, 2000; Osgood, Foster, Flanagan, & Ruth, 2005). In particular, young people who are aging out of foster care or exiting the juvenile justice system face housing vulnerabilities that may negatively influence the course of their transition to adulthood. Securing stable housing is a vital component for transition age youth (TAY) in order to establish a foundation for independent living. As policymakers and practitioners are increasingly concerned about safety nets for TAY, it is important to understand not only the challenges that they face but also the neighborhood and social contexts where they are placed following their exit from public systems. Using administrative data for TAY and surveys of housing providers in Los Angeles County, California, this study explores the following questions: 1) Are housing options related to areas where TAY exit public systems? 2) What are the characteristics of the geographic areas where these housing facilities are located?

Housing Needs Related to Aging Out and Reentry

Among the array of challenges faced during the transition to adulthood, many youth from the foster care and juvenile justice systems experience difficulties accessing or paying for stable housing, thereby placing them at high risk for homelessness. Approximately 20 to 30% of aged-out foster youth experience homelessness (Courtney, Dworsky, Lee, & Raap, 2011; Pecora et al., 2006). While there is no precise documentation of homelessness among youth leaving the juvenile justice system, organizations have estimated that approximately 26% of youth or young adults with a history of juvenile justice system involvement experience homelessness (Arista et al., 2011). In addition to being at risk for homelessness, youth from these two systems are at risk for negative outcomes associated with homelessness such as substance abuse (Johnson, Freels, Parsons, & Vangeest, 1997), health disease risk (Wolitski, Kidder, & Fenton, 2007), and victimization (Gaetz, 2004). For these young people, additional risks may greatly hinder the transition to independent living.

Supportive housing and shelters can be especially beneficial for both groups of young people. These programs keep TAY from falling into homelessness and housing instability and provide a supported living environment with a gradual move toward independent living. In particular, transitional housing programs allow former foster youth to develop independent living skills while receiving financial support (Barth, 1990). Youth who are reentering the community from the juvenile justice system often have limited access to housing supports because of their legal status (Mears & Travis, 2004) and, at the time, returning to live with family members may not always be desirable for successful reintegration (Snyder, 2004). While emancipated foster youth may want to stay in contact with former foster families and previously established networks (Blakeslee, 2012), reentry youth may benefit from continuity of care between the correctional facility and community agency resources proximate to where they exit (Barton, 2006). Therefore, it is important to better understand the availability of TAY housing options and where these structures are located geographically.

Geography of Supportive Housing and Shelter Availability

Supportive housing and shelter providers choose to locate programs in certain areas based on a number of criteria. For instance, research on housing for people with serious mental illnesses suggests that provider decisions regarding where to locate these structures are influenced by local changes in the economy, shifting demographics, neighborhood opposition, and pressures on administrators to find low-cost housing options (Yanos, 2007; Wolch & Dear, 1993; Takahashi, 1997). These factors often explain why many supportive housing programs and shelters are located in disadvantaged neighborhoods (Griffiths & Tita, 2009). Members of neighborhood areas may protest when housing programs or shelters attempt to locate in their area, often claiming ‘not in my backyard’ (NIMBY) due to fear of an increase in crime or potential loss in property values (Roman & Travis, 2006). Residents may establish a base of support and constituencies for local government officials, who in turn may place pressure on higher-level officials (Marwell, 2004) to oppose locating housing facilities in their communities.

As a result, shelter or supportive housing locations often become dependent upon service provider locations and lack of community opposition in those areas (Law, 2001). TAY who use these facilities may be exposed to problems or benefit from resources that are located in these neighborhoods. Hence, this study’s second research question investigates the characteristics of neighborhoods where housing options for TAY are located, which may contain adverse or positive characteristics for TAY development and self-sufficiency.

Environmental Risks and Resources

More affluent neighborhoods have the capacity to effectively generate opposition to supportive housing and shelters, and thus areas with greater disadvantage and problems may have more housing options for those at risk of homelessness (Wolch & Dear, 1993). This is also evidenced by the density of abandoned housing in impoverished areas (Cohen, 2001). Since poor areas are more likely to have deteriorating and/or abandoned properties, nonprofit organizations are able to purchase these properties for redevelopment (Dewar, 2009). Therefore, supportive housing and shelters may be more likely to be located in low-income neighborhoods since these areas offer inexpensive properties and oftentimes do not have the resources to organize effective opposition (Wolch & Dear, 1993).

In low-income neighborhoods where supportive housing and shelters are more likely to be situated, high poverty is associated with vacant housing (Cohen, 2001). High poverty in neighborhoods is also related to high rates of violent crime (Kling, Ludwig, & Katz, 2005), greater availability of alcohol outlets (Hay, Whigham, Kypri, & Langley, 2009), and higher rates of substance use and dependency (Jones-Webb and Karriker-Jaffe 2013; Cooper et al. 2013). Housing facilities that are located in more advantaged neighborhoods may have greater densities of resources such as employment and educational opportunities (Wilson, 1997; Jargowsky, 2014) for young people transitioning from public systems. However, homeless services and beds tend to be concentrated in poor areas with other social services, missions, and support facilities (Dear & Wolch, 1987).

Present Study

Given the number of challenges that youth transitioning from foster care and juvenile incarceration face, locations of supportive housing and shelters may play a role in perpetuating or mitigating risks associated with homelessness. In this study, we explore whether housing options for TAY are related to areas where they exit public systems by zip code in Los Angeles County. Next, we investigate the characteristics surrounding the locations of these housing facilities, including known risks and available resources that may support or hinder TAY’s successful transition to independent living.

Method

The availability of housing options (i.e., number of beds) for TAY was examined using a cross-sectional, ecological design. An ecological design considers the population, rather than an individual, as a unit of analysis. The sample consisted of 272 zip codes that had their centroids within Los Angeles County. Fourteen zip codes representing administrative buildings or universities were excluded from the study, including the zip code for Catalina Island. The current study is exempt from human subjects research and IRB approval was not necessary from the authors’ institution.

Dependent Variables

The dependent variables for this study were: 1) the number of TAY-specific beds, meaning that only TAY can utilize them, and 2) the number of total beds, which were inclusive of beds for all populations and TAY-specific beds. The Los Angeles County Department of Children and Family Services (DCFS) provided a list of all agencies that stated that they provided housing or housing services to TAY. These agencies provided the names of facilities that were not on the list. We contacted all agencies with questions about the number of beds their facility provided to TAY and the number of total beds the facility offered for housing options.

In this study, 146 facilities in Los Angeles County provided beds, of which 111 facilities were available for TAY use. We used ArcGIS version 9.3 (Ormsby, Napoleon, Burke, Groessl, & Bowden, 2009) to geocode 140 of 146 separate locations (96%). The remaining six facilities were domestic violence shelters and these addresses remained confidential to ensure client safety. There were a total of 3,454 beds in the county, of which 822 were specifically set aside for TAY. The numbers of total beds and TAY beds were aggregated, i.e., spatially joined the geocoded locations to zip codes, to yield a number of beds available by zip code.

Independent Variables

Foster Care Entry

Foster care data were obtained from the Center for Social Services Research at the University of California, Berkeley (Needell et al., 2013). Because the numbers corresponding to foster care emancipation were not available, we used first time foster care entries for children as a proxy for emancipation2. Foster care entries were aggregated by Census tract. Using ArcGIS, we spatially joined Census tracts (based on the centroid of the Census tract) with 272 zip codes to yield a total number of foster care entries per zip code. In 2008, 2,956 children aged 11 to 17 entered foster care. The average number of foster care entries per zip code was 10.87.

Juvenile Reentry

Juvenile reentry data were obtained from the Data Management Unit of the Los Angeles County Department of Probation. These data included a listing of all offenders who reentered the community from a correctional setting and were on probation at the time of data extraction. The total number of reentries in Los Angeles County was available by zip code. In 2008, over 4,000 youth aged 10 to 19 reentered the community. The average number of juvenile reentries per zip code was 16.63.

Neighborhood Resources

The Rainbow Directory of Social Service Agencies (2006) provided the data for neighborhood resources. This directory contains over 25,000 agencies in Los Angeles County for 58 different social service categories (e.g., employment, mental health, legal assistance, education). These agencies were aggregated by zip code and divided by the area to create the density of resources. The geocoding rate for social service agencies was 97%.

Neighborhood Risks

Environmental risks related to juvenile justice reentry and foster care emancipation that may affect young people’s transition to independence included alcohol outlets, assaults, and rates of substance abuse and dependence.

Alcohol Outlets

Alcohol outlets included densities of three types of alcohol outlets per area: 1) off-premise outlets (e.g., liquor stores, convenience stores, grocery stores), 2) restaurants that serve alcohol, and 3) bars or pubs. The type of alcohol outlet is related to violence rate and type, such that bars were associated with greater rates of assaults and restaurants were related to less violence (Lipton & Gruenewald, 2002). This study used establishments with active licenses with the California Department of Alcoholic Beverage in January 2007. The geocoding rate for alcohol outlets was 99%.

Assaults

Assaults consist of physical attacks that are more likely to have community witnesses compared to other violent crimes such as murder, rape, and robbery. Rates of assaults per 1,000 individuals by zip code were obtained from assault injuries for the residence of patients from California’s Office of Statewide Health Planning and Development for the year 2007. However, assaults that were not captured by authorities were not included in these estimates. The geocoding rate for assaults was 99%.

Substance Abuse and Dependence

Substance use is generally difficult to capture at one point in time, and thus substance abuse and dependence rates were used in the analyses. Rates of substance abuse and dependence per 1,000 individuals were obtained from California’s Office of Statewide Health Planning and Development for the year 2007. The geocoding rate for substance abuse and dependence was 99%.

Sociodemographic Covariates

Sociodemographic control variables per zip code included the number of youth aged 10 to 19, the density of these youth per area, and the percentages of the following: African American population, Asian American population, Hispanic population, households with incomes less than $25,000, female-headed households, and vacant housing. These data were obtained from GeoLytics (2007). Youth population may explain the densities of TAY exiting public systems in zip code areas. The percentages of race and socioeconomic covariates are indicators for ethnic homogeneity (i.e., inversely related to violent crime) (Altheimer, 2007) and disadvantage. Descriptive statistics for all modeled variables are displayed in Table 1.

Table 1.

Descriptive Statistics for Study Variables for Zip Codes (n = 272)

Min Max Mean Std. Dev.
Outcome Measures
  Total Number of Beds 0.00 1025.00 17.39 85.32
  TAY-specific Beds 0.00 136.00 4.66 15.88
TAY Housing Need
  Number of Reentry Youth 0.00 139.00 15.63 22.37
  Number of Foster Care Entries 0.00 101.00 10.87 13.69
Population
  Youth population 5.00 24297.00 5537.28 4267.64
  Youth population (per area) 0.84 7473.33 1306.95 1207.51
Neighborhood Demographics
  Percent African American 0.20 88.30 9.17 14.66
  Percent Asian American 0.20 69.50 13.40 13.13
  Percent Hispanic 3.00 98.80 37.38 27.67
  Percent income < $25,000 0.00 88.00 28.43 14.19
  Percent female-headed households 0.00 21.60 7.51 4.06
  Percent vacant housing 1.30 30.50 4.47 3.14
Resources
  Social Services (per area) 0.00 262.50 9.53 22.36
Risk Factors
  Off Premise outlets (per area) 0.00 60.00 6.34 6.65
  Restaurants (per area) 0.00 200.00 9.06 18.39
  Bars or Pubs (per area) 0.00 20.00 1.40 2.34
  Assault rate 0.00 8.55 0.50 0.70
  Substance Abuse/Dependence rate 0.00 122.54 6.65 10.81
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Analysis

Four models for each outcome variable (TAY beds and total beds) were estimated using zero inflated negative binomial models. We first modeled outcome variables on the count of juvenile justice reentry youth; second, we modeled outcome variables on the count of foster care youth. Models 1 and 3 for TAY beds and total beds incorporated youth population and number of juvenile reentries or foster care entries, while the full models (Models 2 and 4) included all covariates.

Compared to a Poisson approach, negative binomial models provide a flexible approach to modeling count data that allows for overdispersion. Relative to a negative binomial distribution, the zero inflated negative binomial model accounts for an excess of zeros (inflation may be due to unobserved zeros in addition to the actual count) (Greene, 2002). In this study, we incorporated counts of zero beds as well as zeros for locations where we were unable to obtain these data. Model diagnostics for overdispersion that indicate the need for a negative binomial model versus a Poisson model and a zero-inflation versus a non-inflated negative binomial model were estimated. Assessments of overdispersion and zero-inflation for all analyses were significant.

Ecological analyses often suffer from issues related to spatial autocorrelation or the lack of independence that occurs when neighboring zip codes have similar values on study variables. To address this concern, we examined the residuals from our final models to assess whether spatial autocorrelation was present. The presence of spatial autocorrelation in residuals would suggest the need for models that can assess and control for this source of error. The Moran’s I value for the residuals for all four models presented here were not statistically significant, suggesting that the residuals were not spatially autocorrelated errors for any of the four models. Thus, the use of zero-inflated negative binomial models was warranted.

Results

The results for juvenile reentry and foster care entries related to the number of TAY beds and total beds are presented in Tables 2 and 3, respectively.

Table 2.

Zero-Inflated Negative Binomial Regression Model Assessing the Relationship between Number of Transition Age Youth and Total Beds, Number of Juvenile Reentry Youth, Neighborhood Demographics, Resources, and Risks (n = 272)

TAY Beds Total Beds
Model 1 Model 2 Model 3 Model 4
Variable b se p b se p b se p b se p
Constant 2.543 0.277 *** 1.884 1.134 1.629 0.109 *** 1.249 0.785
Population
  Youth population (×100) −0.004 0.005 −0.008 0.008 0.001 0.004 0.010 0.005
  Youth population (per area) (×100) 0.030 0.010 ** −0.020 0.040 0.050 0.020 ** 0.020 0.030
TAY Housing Need
  Number of Reentry Youth (x100) −0.690 1.030 2.150 1.880 1.070 1.220 2.190 1.340
  Number of Foster Care Entries -- -- -- -- -- -- -- --
Neighborhood Demographics
  Percent African American 0.027 0.029 0.047 0.024
  Percent Asian American 0.002 0.019 −0.025 0.012 **
  Percent Hispanic 0.004 0.014 0.006 0.011
  Percent income < $25,000 0.093 0.032 ** 0.101 0.029 ***
  Percent female-headed households −0.086 0.120 −0.308 0.099 **
  Percent vacant housing −0.323 0.147 * −0.239 0.138
Resources
  Social Services (per area) 0.003 0.029 0.001 0.020
Risk Factors
  Off Premise outlets (per area) 0.042 0.087 −0.028 0.071
  Restaurants (per area) −0.020 0.036 −0.014 0.028
  Bars or Pubs (per area) 0.030 0.152 0.003 0.013
  Assault rate −1.717 1.599 −1.020 1.123
  Substance Abuse/Dependence rate 0.043 0.135 0.072 0.721
Overdispersion 1.48 .0.36 *** 1.15 0.27 *** 5.92 0.53 *** 3.40 0.37 ***
Zero Inflation 0.21 0.08 ** 0.18 0.06 ** −0.48 0.12 *** −0.37 0.10 ***
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Note:

***

p<.001

**

p<.01

*

p<.05

Table 3.

Zero-Inflated Negative Binomial Regression Model Assessing the Relationship between Number of Transition Age Youth and Total Beds, Number of Foster Care Youth, Neighborhood Demographics, Resources, and Risks (n = 272)

TAY Beds Total Beds
Model 1 Model 2 Model 3 Model 4
Variable b se p b se p b se p b se p
Constant 2.253 0.032 *** 1.62 0.860 1.613 0.095 *** 0.733 0.759
Population
  Youth population (×100) −0.009 0.005 0.002 0.008 0.005 0.004 0.020 0.006 **
  Youth population (per area) (×100) 0.030 0.010 ** −0.030 0.040 0.060 0.010 *** 0.010 0.030
TAY Housing Need
  Number of Reentry Youth -- -- -- -- -- -- -- --
  Number of Foster Care Entries (×100) −0.120 1.470 −0.030 0.040 −0.460 1.180 −1.580 2.200
Neighborhood Demographics
  Percent African American 0.005 0.025 0.032 0.025
  Percent Asian American −0.004 0.016 −0.027 0.012 *
  Percent Hispanic −0.008 0.014 0.003 0.011
  Percent income < $25,000 0.095 0.029 ** 0.098 0.033 **
  Percent female-headed households 0.006 0.109 −0.215 0.110 *
  Percent vacant housing −0.267 0.149 −0.146 0.128
Resources
  Social Services (per area) 0.014 0.281 −0.001 0.024
Risk Factors
  Off Premise outlets (per area) 0.028 0.082 −0.025 0.076
  Restaurants (per area) −0.022 0.035 −0.008 0.033
  Bars or Pubs (per area) 0.031 0.135 −0.002 0.133
  Assault rate −0.371 1.407 0.012 1.111
  Substance Abuse/Dependence rate −0.063 0.117 0.006 0.072
Overdispersion 1.43 0.36 *** 1.14 0.29 *** 5.69 0.49 *** 2.94 0.33 ***
Zero Inflation 0.22 0.08 ** 0.19 0.08 * −0.43 0.11 *** −0.27 0.08 ***
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Note:

***

p<.001

**

p<.01

*

p<.05

Juvenile Reentry

As Table 2 shows, the number of reentry youth was not related to the number of TAY beds or total beds per zip code.

TAY Beds

In Model 1, the number of juvenile reentries was not related to the number of TAY-specific beds. In Model 2 (full model for TAY beds), a higher percentage of residents with incomes with less than $25,000 and a lower percentage of vacant housing were related to areas with a higher number of TAY beds per zip code.

Total Beds

In Model 3, which only contained variables related to youth density and number of juveniles reentering, the number of juvenile reentries was not related to the number of total beds. In Model 4 (full model for total beds), a higher percentage of residents with incomes less than $25,000, a lower percentage of female-headed households, and a lower percentage of Asian American residents were related to a higher number of total beds per zip code.

Foster Care Entries

Similar results were found for the analyses of foster care entries in Table 3, in that the number of foster care entries was not related to the number of TAY beds or total beds per zip code.

TAY Beds

In Model 1, the number of youth entering foster care was not related to the number of TAY beds. As with the juvenile reentry models in Model 2, a higher percentage of residents with incomes less than $25,000 were related to higher numbers of TAY beds and total beds in Models 2 and 4.

Total Beds

According to Model 3, the number of youth entering foster care was not related to the number of total beds. In Model 4, a higher percentage of residents with incomes less than $25,000, a lower percentage of female-headed households, and a lower percentage of Asian American residents were related to higher numbers of total beds.

The variables related to risk factors and resources were not related to the number of TAY beds and total beds by zip code in the models.

Discussion

This study investigated the locations of housing facilities in relation to where TAY exit the foster care or juvenile justice systems in Los Angeles County. In doing so, we assessed whether beds are located in those areas where youth exit public systems and identified neighborhood characteristics where supportive housing and shelters are located. Our analyses found that the locations of beds are not related to the zip codes where youth are transitioning out of foster care or the juvenile justice system. Further, regardless of whether they are TAY-specific, all beds are located in low-income zip codes.

The results indicate that housing facilities are not related to where youth age out of foster care or where youth reenter the community following confinement. While this exploratory study yielded no relationship between TAY exiting public systems and housing options for them, it is possible that future research using more nuanced data may uncover an association that bears myriad implications, whether positive or negative. For example, it may be beneficial for housing facilities to be located where young people exit public systems since they may already be familiar with their surroundings, are closer to kin or foster families, and may establish continuity of care with local service providers. Housing facilities that are located outside of areas where TAY transition from public systems may limit accessibility to established supports and continuity of care (Barton, 2006). On the other hand, formerly incarcerated youth may wish to move away from their pasts to establish meaningful networks integral to their foundation for a successful transition to adulthood (Abrams & Terry, 2014). As such, it could be argued TAY housing that is not located where youth exit correctional facilities may be beneficial for their transition to adulthood experiences and expanded opportunities for positive peer socialization, employment, or a fresh start.

Supportive housing and shelters for TAY are predominantly located in low-income areas. While this may be a pragmatic decision on the part of service providers due to cheaper housing and the absence of NIMBYism, these locations may not provide optimal settings for youth to successfully transition to adulthood. The ability to secure and maintain stable housing is an essential component of the successful transition to independence for TAY, but supportive housing that is located in these neighborhoods may not have employment and educational opportunities compared to more advantaged areas (Jargowsky, 2014; Wilson, 1997). Despite this, findings point to the lack of association between housing facilities and higher rates or densities of assaults, substance abuse and dependence, and alcohol outlets in these areas that may place TAY at higher risk for other problems that may limit successful transition to independent living. This contrasts the popular notion that housing facilities and shelters are associated with neighborhood disorganization and crime (Wuerstle, 2010).

There are several limitations in this study. First, the foster care data only reflect youth entering the child welfare system and thus we can only postulate about whether they are the same locations where foster youth exit the system. Oftentimes children are placed close to their biological parents with the hopes of reunification and therefore are also likely to exit the system within the same areas. Investigating whether foster youth enter and exit the same neighborhoods in further research will help to extend this line of inquiry. Second, the availability of the data limits the analysis to the zip code level, which does not necessarily capture clustering within these boundaries. Finally, given that there is no available database on shelter and supportive housing beds in Los Angeles County, our survey of service providers offers the best available data on bed locations. Despite these limitations, however, this study provides an initial examination of resource availability and risks for vulnerable TAY exiting public systems in a large metropolitan area.

Findings from the current study warrant further research in several arenas. Given the exploratory nature of this study, it would be advantageous to confirm in future research whether there is indeed a lack of a relationship between TAY exiting public systems and the availability of housing using a smaller unit of analysis compared to the zip code. To expand upon this research on an ecological level, it would be key to examine the availability of transportation options surrounding supportive housing and shelter facilities with regards to accessing educational and employment opportunities outside of these areas. Further, a natural segue would be to continue to build on individual-level studies on TAY and their perceptions of housing availability and their usage of these facilities (c.f., Curry & Abrams, 2015).

Implications for Practice and Policy

Creating supportive housing and shelters in optimal places has historically been a complex, multilevel challenge. This study reveals that housing options for TAY are located in lower-income areas despite this population’s need for continued supports during their transitions from public systems. Several steps may be taken on an ecological level to help generate the capacity to combat NIMBYism in less distressed locations. First, social service agencies are encouraged to partner with community stakeholders to ensure that housing facilities are located in more resourced areas. Stakeholders include business owners and long-term residents who have an influence over other community members. To garner buy-in from community residents, one strategy is to ensure that the community agency (i.e., housing facility) will give back to its neighborhood and to maintain cleanliness of the exterior of its associated buildings. Many communities mitigate potential NIMBYism by introducing “Good Neighbor Plans” to address such issues as hours of operation and safety measures (Gilderbloom, Squires, & Wuerstle, 2013). Further, local businesses may wish to establish internship programs for youth who live in these housing facilities for work experience, and housing facilities may establish regular “community clean-up days” or other volunteer activities to encourage positive engagement with the community. Accordingly, both the community and youth are able to benefit from this arrangement.

It is important to emphasize that social workers want former systems-involved youth to lead meaningful, productive lives much like their more advantaged counterparts. Doing so means that they are afforded equal opportunity and access to safe spaces and educational and academic resources. Establishing a reciprocal relationship between social service agencies and neighborhood stakeholders will facilitate community buy-in. Additionally, engaging residents in all steps of the process in locating housing facilities for vulnerable youth by garnering their feedback will increase the chances that beds are located in optimal areas. Finally, from an administrative standpoint, nonprofits and policymakers for TAY housing should build effective partnerships to ensure that policymakers fully understand the neighborhood forces that may pose barriers to creating housing in optimal places, including zoning and other restrictions on locations. Further understanding the processes at the ground level will help to create more effective strategies in implementing housing facilities in places that support youth in their transition from public systems.

Acknowledgments

This project was supported by grant number P60-AA-006282 from the National Institute on Alcohol Abuse and Alcoholism. The content is solely the responsibility of the authors and does not necessarily represent the National Institute on Alcohol Abuse and Alcoholism or the National Institutes of Health.

Footnotes

2

In order to increase chances of reunification with their biological parents, caseworkers strive to place children in close proximity to their parents to ease visitation and ultimately increase chances of reunification (K. T. Jackson [DCFS Caseworker], personal communication, July 10, 2015). Once children exit or emancipate from foster care, they may tend to stay in these same neighborhoods because of the availability of established supports (Blakeslee, 2012).

Contributor Information

Christina C. Tam, Department of Social Welfare, University of California, Los Angeles, christinatam@ucla.edu, (424) 258-4564, f: (310) 206-7564.

Bridget Freisthler, Department of Social Welfare, University of California, Los Angeles, freisthler@luskin.ucla.edu, (310) 206-1602, f: (310) 206-7564.

Susanna R. Curry, Department of Social Welfare, University of California, Los Angeles, scurry@ucla.edu, (424) 258-0954, f: (310) 206-7564.

Laura S. Abrams, Department of Social Welfare, University of California, Los Angeles, abrams@luskin.ucla.edu, (310) 206-0693, f: (310) 206-7564.

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