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The purpose of this project has been to conduct a number of activities designed to inform the development of a typology of homeless families. These activities included the following:
This chapter summarizes what we have learned from this constellation of activities and the directions that seem most worthwhile to take in developing a typology of homeless families.
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Consensus from the Expert Panel is that the two top goals for a typology should be a focus on prevention and resource allocation - how to match the resources that exist with the needs of the families who are homeless. Given that the factors that predict becoming homeless are likely to differ from those that predict exiting homelessness, it may be most useful to frame typologies in two different ways: a prevention typology and a resource allocation typology. In the remainder of this chapter, we identify the implications of our literature review, data exploration, analysis efforts for each of these typologies regarding what can be accomplished now, and what additional steps might be needed in developing each typology.
Definition and Guidance from Past Research. A prevention-oriented typology would provide the ability to rank families according to levels of risk for homelessness and probability of a quick exit. Such a typology would allow for distinguishing families in desperate need from those with more moderate needs.
Existing data on the risk factors for homelessness may inform the beginnings of a prevention typology. Based on our review of the literature, key factors that raise the risk of homelessness have to do with resources and life stage, including the age of the head of household, having young children, being pregnant or the mother of a newborn, being a member of a minority group (especially African-American), and having fewer housing, economic or social resources. At least one study comparing domiciled mothers with homeless mothers has identified substance use as raising the risk for homelessness. Our reanalysis of the Fragile Families and Child Well-being study data set (Chapter 4) also suggests that having mental health and substance abuse indicators may raise the risk of becoming homeless for families; in turn, their absence may help with stability. The fit of the statistical models is weak, however, suggesting that replication of the findings in other studies would be important before confirming these variables.
Past research has suggested that identifying families at risk of homelessness on a broad scale requires a complex risk profile and is likely to produce a number of "false positives" (i.e., families who would likely not enter homelessness), and yet also miss a significant percentage of the population in need. Such efforts are also likely to be extremely inefficient. Shinn and colleagues found in their New York City study that a statistical model with 20 predictor variables correctly identified 66 percent of the shelter entrants but also targeted 10 percent of the public assistance caseload that was not homeless. Similarly, our reanalysis of the Fragile Families data set suggests that, although income is related to homelessness, a percentage of the homeless families in the study lived above the poverty level. Finally, although homelessness has a larger incidence than is tolerable, it still has a relatively low occurrence, even among extremely poor populations and those at high risk.
The reanalysis of the Fragile Families data set found that, of the cohort of families who recently gave birth, a small percentage (5%) experienced homelessness during the 3-year followup. Even with the families living at 50 percent or below the poverty level, the incidence of homelessness was 8.7 percent. Therefore, targeting a broad sample of families would require a large sample size and a complex set of variables to identify the small percent of families who would ultimately experience homelessness, and yet such a strategy would still likely miss families who would experience homelessness, as well as identify families for assistance who otherwise would likely not need it.
Short-Term Study Options. There are several study approaches designed to target families as they request shelter that may be more efficient than broad sample approaches and may provide information in the shorter term to guide initial steps in developing a prevention typology. One approach would be to study current pilot service efforts to triage families as they request help (such as in Hennepin County, Minnesota). In effect, these service systems are testing their concept of a risk assessment strategy by assessing the needs of families as they request shelter and determining the level of housing assistance and services the families should receive, based on these needs. Examining the outcomes of these triaged approaches and their relative success in preventing homelessness would provide empirical evidence on what factors to consider in classifying families. It would be important to determine whether the families who were diverted from the system remain stably housed and do not return to homelessness, as well as whether those who do receive shelter and services receive the housing and services they need to remove their housing barriers and return to permanent housing.
Examining these "home-grown" typologies would likely entail descriptive study efforts that would incorporate both primary data collection and analysis of administrative data, such as the HMIS (see below). Sample sizes would depend on the communities being studied. The timeframe would likely include at least 2 years of followup data, but data even during the first 12 months will likely provide useful information on the extent to which triaging has prevented at least the initial onset of homelessness. The main limitation of this approach is that the study designs are likely to lack the rigor needed to provide definitive results.
Longer-term Study Options. Another strategy, though more costly, would be to conduct a longitudinal study of families requesting shelter for the first time. Although this study may better inform a resource allocation typology (see below), to the extent that there are data on families who are at risk and diverted from entering shelter, (or a comparable sample of poor families at risk of homelessness) the study could track the factors that assist the family in preventing homelessness and the services that contribute to their ability to avoid homelessness.
An efficient, though long-term, strategy for informing a prevention typology would be to enhance ongoing national studies. From an extensive review of ongoing or planned data sets, two emerged as strong candidates for enhancements that could improve our understanding of families who have experienced homelessness, as well as those who are at risk of homelessness. Both have large sample sizes that should yield sufficiently large numbers of families that are either currently homeless or at risk of becoming homeless.
The American Community Survey (ACS), conducted by the U.S. Census Bureau, is a national area probability study that currently surveys three million households annually. This study replaces the decennial census long form. The ACS is designed to collect the same information as the long form, including demographic, housing, social, and economic data. Data are collected on every person in the household, through a self-administered survey, by telephone, or by-person interviews. Because of its large sample size, the study can provide valid estimates for each state, as well as cities, counties, and metropolitan areas with 65,000 people or more. Data for smaller areas will be aggregated over a 3- to 5 year period to produce a sufficiently large sample for analysis.
Adding questions on homelessness and the risk of homelessness to the ACS would provide the opportunity to look at homelessness in specific geographic areas (which would help the resource allocation purposes, as discussed below), but would also help to examine the extent to which families have the risk factors that make them vulnerable to homelessness. The incidence of at-risk and homeless experiences also could be examined in relationship to market forces, social capital, and other community and contextual variables that could provide structural guidance for preventing homelessness.
Among the set of ongoing panel studies that could be enhanced with homelessness questions to inform a prevention typology, the National Longitudinal Survey of Youth 1997 (NLSY97) has the best potential. The sample consists of two independent national probability samples: a cross-sectional sample of 6,748 people between the ages of 12 and 17 in 1997, and a supplemental sample of 2,236 individuals designed to oversample Latino and Black youth. The purpose of the survey is to collect information on labor force experience, education, and the transition into the labor market. There is precedent for adding questions to the survey by other agencies, including NICHD and NIJ. Adding questions to this survey would provide an opportunity to help identify the factors that lead to people becoming homeless, as well as the factors that help predict exits from homelessness.
Typology Framework. As Dr. Thomas Babor recommended in his paper (see Appendix B) and reinforced during the Expert Panel meeting, a four-cell model that crosses the facilitators and barriers in an environment with the needs of a family (minor and major) should be explored in developing a typology (see Figure 8-1). An environment with a large number of barriers (e.g., high unemployment, lack of affordable housing) is likely to include homeless families with only minor or moderate service needs while in a more facilitating environment (e.g., low unemployment, adequate affordable housing) only families with major service needs are likely to be found homeless. Data may first come from the existing body of literature, enhanced by one or more of the approaches described above. This initial model may help us understand the relationship between the resources in a community and the presenting needs of families. As a second step, the high needs group may be further differentiated by the type of needs presented, including housing, health, and social service needs, among others.
| Environment Characteristics: | |||
|---|---|---|---|
| Facilitators | Barriers | ||
| Service Needs of Families: | Minor | ||
| Major | |||
Definition and Guidance from Past Research. A second typology, focused on families who have already become homeless, would classify families by the factors that block their ability to exit homelessness (e.g., poor credit; past justice involvement), as well as challenges they may have to maintain stability and self-sufficiency. Some families exit shelters and emergency housing quickly (within a month or less), while others stay for relatively longer periods of time, depending on the system. Some families experience repeated episodes of homelessness.
Although past research has indicated that housing subsidies are a major predictor of successful, stable exits, it is clear that there are not enough subsidies to meet the needs of all families that are homeless. In addition, some families may need less than a subsidy to exit homelessness and others may need additional supports. For example, domestic violence victims may be able to afford housing but other barriers preclude their ability to access safe housing. In addition, research has indicated that some families do still return to homelessness, despite having had a subsidy in the past. Therefore, it is important to understand the factors that help families exit homelessness quickly, as well as the contextual and personal barriers that block families from exiting homelessness. This understanding could help classify families who need minimal resources to exit and those that need additional assistance. In particular, as Dr. Jill Khadduri emphasized in her paper (see Appendix C), it is important that a typology differentiate between families who need permanent mainstream housing and those who need permanent supportive housing.
A resource allocation typology could also further classify families by the other needs they have that may block their ability to achieve other favorable outcomes. For example, homeless families, even after obtaining housing, have a greater probability of experiencing child separations than nonhomeless families. A resource allocation typology may identify families having needs for family preservation and/or reunification, as well as families that have other needs for their children. In addition, research currently in press indicates that a group of homeless families with psychiatric and/or substance use conditions show less improvement over time in other outcome areas because of ongoing conflict and trauma. Identifying those needs and strategies for dealing with them may be important in typology development. Finally, social capital outcomes, such as education and employment, may be critical targets for a typology. Research in progress with the SAMHSA Homeless Families Program suggests that employment correlates with improvements in other outcome areas, so strategies for helping homeless women secure and maintain employment could be a priority area for resources. Developing a typology, therefore, that identifies the family support needs, broad health needs (including mental health and substance use), and social capital needs of a family, as well as specific housing needs, may be important to helping families obtain and maintain stable housing. Adding the needs of children into this mix, rather than creating a separate typology for children, also was the consensus of the Expert Panel. This approach is further supported by the synthesis of findings on homeless children provided by Dr. John Buckner (see Chapter 3 and Appendix A for a complete copy of his paper).
As noted earlier, having a typology that incorporates environmental variables is important, especially given the role that context plays in homelessness. Drs. Reingold and Fertig's contribution in this volume (Appendix D) suggests that, of the contextual variables they were able to examine in the Fragile Families data base, high unemployment rates and high fair market rents were associated with higher risks of becoming homeless. Shelter availability and the existence of anti-loitering laws also were associated with homelessness, but admittedly were likely to be acting as community indicators of high levels of homelessness and not necessarily elements that contribute toward an increase or decrease in the probability of homelessness.
Short-Term Study Options. As with the development of the prevention typology, a staged approach to informing the resource allocation typology can be envisioned. One of the most expedient strategies for providing data on families living in shelters and their exit patterns would involve an analysis of the HMIS data sets. As noted earlier, in 2001, Congress directed HUD to provide more detailed information on the extent and nature of homelessness and on the effectiveness of programs funded by the McKinney-Vento Act. As a result of this mandate, HUD is requiring each local CoC to develop its own HMIS, a computerized data collection system on homeless individuals and families. By requiring programs and communities to collect demographic, service, and outcome data using standardized data elements, the HMIS system provides a unique opportunity to examine homeless families across programs, providers, and communities.
With data on the types of services homeless families use and how these services relate to outcomes, such as the length of time families are homeless, whether they stay out of the homeless system once they leave, and how many exit to more stable housing arrangements, the HMIS data can help allocate appropriate resources to appropriate services. Knowing which families benefit from the various types of services also can inform the development of better treatment matching efforts (e.g., matching families to the appropriate level and intensity of services required).
Longer-term Study Options. As with the prevention typology, adding questions on homelessness to the American Community Survey would provide the opportunity to look at homelessness in specific geographic areas and examine how the community and contextual variables relate to changes in the incidence and prevalence of homelessness over time. This procedure would use the community itself as the unit of analysis, rather than the individual family and, given the vastness of the data set, should provide key guidance on whether communities that implement different types of interventions and service efforts affect homelessness for families with different constellations of needs. These efforts could also be examined in tandem with variables such as changes in the housing market and other contextual factors.
As noted above, adding questions to the NLSY97 sample would not only help identify factors that lead to people becoming homeless but, over time, could also help to identify the factors that help predict exits out of homelessness. These data collected over time should provide the ability to look at different exit trajectories for families and determine the service variables and other factors that help to predict an exit for different classifications of families.
A related data set, The National Longitudinal Survey of Youth 1979 (NLSY79), described in Chapter 4, is a series of surveys with a nationally representative sample of 12,686 young men and women who were between the ages of 14 and 22 in 1979. Annual interviews were conducted from 1979 until 1994; since then, respondents have been interviewed every other year (1996, 1998, etc.). A major challenge with the NLSY79 cohort is that the primary respondents are now 40 years of age or older and may be too old to provide a good opportunity to examine homelessness among families. The sample does include a subsample of children born to initial study participants whose ages would make them more likely to be currently experiencing homelessness. Adding questions to the NLSY79 sample about their history of homelessness, as well as to the NLSY79 Children and Young Adult surveys about both their history and current incidence of homelessness, would, therefore, provide a rare opportunity to examine the intergenerational effects and impact of homelessness. However, the smaller sample size of the children's sample (only children born to women in the NLSY79 sample are surveyed) makes this a less promising approach than examining the NLYS79.
Finally, a national longitudinal study of exit patterns and shelter requests of homeless families could answer questions about the exit patterns that families have, the individual and contextual factors that facilitate and inhibit exiting homelessness, the characteristics of families least likely to exit quickly and those most likely to return, as well as the relationship between type and level of service use to length of stay in shelter or homelessness. This type of study would require the collection of primary data, as a longitudinal prospective study focused on how families exit homelessness and their subsequent residential patterns has not been conducted.
Few studies have had a longitudinal perspective that could provide insight into the trajectories families take out of homelessness, and little is known about the types of assistance that families receive or whether they take full advantage of services or benefits that they may be eligible for in order to exit. There is also a lack of rigorous knowledge on the extent to which having bad credit, a criminal record, multiple children, and other factors hinder a family's ability to exit a homeless situation, nor are there data on the factors that influence repeat homelessness among families. Thus, this information would help classify families into level of need at entry into homelessness and during their homeless experience and help inform how these needs relate to length of stay in homelessness, as well as reentries into homelessness. If the sample is large enough to look at subgroups in regions, it would be possible to examine the relationship among contextual factors, individual factors, and family homelessness.
Of all the study options, a national longitudinal study of exit patterns and shelter requests of homeless families would likely provide some of the more intensive information on patterns and pathways out of homelessness and the role that services and resources have in that process. However, it would also be the costliest of the different study strategies proposed to inform the development of the typologies.
Typology Framework. With respect to the initial framework of a resource allocation typology, Dr. Babor proposed that it be based on three types of variables: exogenous (housing environment, housing, and health and human service access); endogenous (family and individual characteristics); and situational (the fit between the families' needs and accessible resources). The key will be to develop a typology that is useful and has practical importance. Selecting criterion variables based on ease of use was stressed by Expert Panel members as important to ensure its usefulness and replication. Environmental factors such as culture and geographic residence are considered important, but careful consideration of which variables to include is recommended since the sheer number of such variables could overwhelm a typology and dilute its usefulness.
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In summary, this project has identified a staged approach to developing typologies of homeless families and families who are at risk of homelessness. Data from existing sources provide some indication of the types of variables to be examined in order to develop classifications, but the variability among the studies in sample selection, measurement, and geographic focus limits their usefulness for typologies that could have wide-ranging relevance. Embarking upon some initial short-term efforts (e.g., studying local triaging attempts; analyzing HMIS data) can begin to further inform typology development, but it appears that the strongest data would come from enhancements of existing surveys, as well as the development of a national longitudinal study of exit patterns and shelter requests of homeless families.
In evaluating the usefulness of any developed typology, several criteria include the extent to which it:
Most importantly, regardless of what type or how many are developed, any proposed typology must be simple to use, be developed with sufficient attention to the broad population of homeless families, and incorporate the relevant individual and environmental level factors to provide for identifiable, discrete groupings of families that have practical significance to both service providers and policymakers.
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