Characteristics and Dynamics of Homeless Families with Children. Prevention 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.

Figure 8-1. Simple heuristic for Homeless Families Typology
  Environment Characteristics:
Facilitators Barriers
Service Needs of Families: Minor    

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