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Characteristics and Dynamics of Homeless Families with Children

Publication Date
Sep 30, 2007

Final Report

Office of the Assistant Secretary for Planning and Evaluation
Office of Human Services Policy
U.S. Department of Health and Human Services

Fall 2007

Authors:
Debra J. Rog, Ph.D., C. Scott Holupka, Ph.D., and Lisa C. Patton, Ph.D.

Prepared by:
WESTAT

Contract No: 233-02-0087 TO14

"

Acknowledgements

The authors wish to thank the numerous people who supported or provided assistance with this report. In particular, the authors wish to thank the members of the Expert Panel who provided guidance on the conceptualization of the typology.

  • Thomas Babor, Ph.D., M.P.H., Professor and Chairman, Community Medicine and Health Care, University of Conneticut School of Medicine.
  • John Buckner, Ph.D., Research Psychologist, Department of Psychiatry, Children's Hospital Boston
  • Martha Burt, Ph.D., Program Director and Principal Research Associate, Urban Institute
  • Dennis Culhane, Ph.D., Professor of Social Welfare Policy, School of Social Work, University of Pennsylvania
  • Angela Fertig, Ph.D., Public Service Faculty, Carl Vinson Institute of Government, University of Georgia
  • Jill Khadduri, Ph.D., Principal Associate, Abt Associates, Inc.
  • Paul Koegel, Ph.D., Associate Director, RAND Health, RAND Corporation
  • David Reingold, Ph.D., Associate Professor, School of Public and Environmental Affairs, Indianan University Bloomington

Additional information regarding this project can be obtained from the Federal Project Officers: Anne Fletcher (202-690-5729, anne.fletcher @ hhs.gov) and Laura Radel (690-5938, laura.radel @ hhs.gov).

1. Introduction

Homelessness among families has become a growing phenomenon. Beginning in the early 1980s, families with young children became one of the fastest growing segments of the homeless population and now comprise 34 percent of the homeless population (i.e., 23% children and 11% adults) (Burt et al., 1999). In a given year, this means that 420,000 families, including 924,000 children, experience homelessness in the United States. These numbers reveal that, over the course of a year, approximately 1.8 percent of all families are homeless at least one day, including eight percent of all poor families.

Further evidence that homelessness is a common experience for poor families comes from a national telephone survey that found 7.4 percent of U.S. adults in households with telephones had been literally homeless (i.e., sleeping in shelters, abandoned buildings, bus and train stations) at least once in their lifetime. Of those who had ever received public assistance (typically as part of families), 19.8 percent had been literally homeless at least once in their lifetime. Adding the category of doubling-up (i.e., families living with relatives or friends) to the definition of homelessness results in nearly one-third of the people (31.2%) who had ever received public assistance reporting being homeless at least once in their lifetime (Link et al., 1994).

Any available numbers are likely to underestimate the extent to which families either experience homelessness (Shinn and Bassuk, 2004) or are at imminent risk of homelessness. In particular, the size of the literally homeless family population is largely influenced by shelter policies and practices. Homeless families, unlike single individuals, rarely live on the streets. Because the majority stay in shelters, the size of the family homeless population in a given location depends in part on the number of shelter beds available. Recent reports of shelter directors turning away 32 percent of family requests for shelter (U.S. Conference of Mayors, 2005) suggest that the potential size of the homeless population is considerably greater than current estimates. The families included in these estimates are limited to those families who stay together as a unit. Much less is known about those families that are no longer intact in which the mother or father is now considered a single adult. In the National Survey of Homeless Assistance Providers and Clients (NSHAPC), for example, almost two-thirds of homeless clients have one or more children, with only 31 percent having minor children currently living with them (Burt et al., 1999).

A small body of research has provided insight into the risk factors associated with family homelessness, the housing and service needs that homeless families have, and the role that affordable housing can play in ending homelessness for many families. The studies, though varied in method, participant selection, and geographic context, provide a preliminary basis for understanding the range of experiences and needs of families. These studies have revealed considerable variability among families with respect to their residential histories and the factors that place them at risk of homelessness, as well as those factors that keep them homeless. Although there have been a variety of studies undertaken, three main studies have focused on homeless families: a 5-year followup of representative samples of first-time homeless families and families receiving public assistance in New York City (Shinn et al., 1998); a longitudinal evaluation of a nine-city program for homeless families who received subsidies for both housing and case management (Rog, Holupka, and McCombs-Thornton, 1995a); and the Worcester Family Research Project (WFRP), a case-control study of homeless families and families on welfare (Bassuk et al., 1996). In addition, the NSHAPC, directed by Burt and colleagues (1999), has contributed to the understanding of basic characteristics of homeless families across the nation, and analyses of administrative data sets in New York City and Philadelphia by Dennis Culhane and S. Metraux (1999) have improved the understanding of families' use of shelter and the interconnection of homelessness with involvement in other services and systems.

Because of the range of experiences and needs among homeless families, it is difficult to know the extent to which certain types of interventions are warranted and the ways in which they can be best delivered to meet the needs of these families and their children. The construction of a typology that identifies distinct subgroups of families with specific constellations of risk factors and needs would be helpful in guiding both practice and policy. Such a typology could enhance and improve the ability to more effectively target existing services, maximize the potential of existing programs to meet the needs of specific subgroups, and identify new opportunities to prevent homelessness for specific groups and more effectively intervene with others.

Westat, in collaboration with Vanderbilt University's Center for Evaluation and Program Improvement, was contracted by the Assistant Secretary for Planning and Evaluation (ASPE) with the U.S. Department of Health and Human Services (DHHS) to conceptualize a typology of homeless families. Through this project, and in consultation with other Federal agencies, DHHS seeks to identify opportunities and strategies to improve data about homeless families upon which future policy and program decisions may be based. The extensiveness and the quality of data on homeless families with children are substantially less robust than the available information about single homeless individuals. This project will investigate the availability of data with which to construct a typology of homeless families. Such a typology would foster a better understanding of these families' characteristics, service needs, interactions with human services systems, and the dynamics of their use of emergency shelter and other services and assistance. The purpose of this report is to identify key knowledge gaps regarding homeless families and to consider whether these gaps may most efficiently be filled through secondary analysis of existing data, adding questions or a module to planned surveys that include low-income populations, or whether additional primary data would be needed. Ultimately, it is intended that an improved understanding of the characteristics of homeless families with children will guide the development of appropriate service responses to such families and provide an empirical foundation for the design of homelessness prevention and intervention approaches.

This 24-month project, begun in September 2004, provided a vehicle for the identification of opportunities and strategies to improve data and data collection efforts regarding homeless families. The project consisted of three phases: assessing the availability of already existing data that could be mined through secondary data analysis; proposing a set of questions to modify existing and ongoing surveys that would allow for the key research questions related to homeless families to be answered, and conceptualizing various primary data collections that would specifically collect the kind of data required to develop a typology of homeless families. The research recommendations described in this report lay the foundation for future data collection efforts affecting policy and programmatic decisions for this particular population.

First, project staff explored existing data and data collection vehicles for their suitability for reanalysis or secondary data analysis related to homeless families. As an initial step toward developing data recommendations, project staff conducted a literature review examining the available data on family homelessness, including the characteristics of homeless families, key knowledge gaps, and background information for a typology conceptualization. Contractor staff then examined existing data and data collection vehicles to identify major national or multijurisdictional surveys that might include large numbers of low-income respondents (e.g., potentially homeless or homeless families).

An Expert Panel meeting was convened in July 2005 to consider topics that would need to be included as possible elements of a typology. For the purposes of discussion, four papers were developed for the Expert Panel meeting:

  • Impact of Homelessness on Children: An Analytic Review of the Literature
  • Toward a Typology of Homeless Families: Conceptual and Methodological Issues
  • Permanent Housing for Homeless Families: A Review of Opportunities and Impediments
  • The Characteristics and Causes of Homelessness among At Risk Families with Children in Twenty American Cities.

Second, expecting that even if some secondary data analysis was possible utilizing existing datasets that the absence of key questions would limit the analysis that could be done, the project staff developed a short battery of housing questions for possible use in future surveys of low-income populations, along with identifying options for primary data collection and analysis regarding the target population. The third and final phase of the project was the development of a set of approaches for a primary data collection that could fill key data gaps with respect to homeless families.

The report is structured as follows: Chapter 2 presents the literature review. Chapter 3 summarizes the Expert Panel meeting and presents feedback on four commissioned papers. Chapter 4 provides an overview of the datasets reviewed for the project, and discusses knowledge gaps about homeless families and their needs. Chapter 5 discusses the Fragile Families and Child Well-Being Study, the one dataset that held promise for secondary data analysis, in the light of a number of current research questions. Chapter 6 discusses a number of Federal surveys and explores whether these can be modified or enhanced to include questions on homelessness. Chapter 7 explores potential primary data collection opportunities by which to collect additional information that could in the development of a typology of homeless families. Chapter 8 summarizes what has been learned during this effort and suggests the next steps to take in developing a typology of homeless families.

The report concludes with a number of appendixes and a bibliography. Appendix A is a paper by John Buckner, "Impact of Homelessness on Children: An Analytic Review of the Literature." Appendix B is a paper by Rene Jahiel and Thomas Babor, "Toward a Typology of Homeless Families: Conceptual and Methodological Issues." Appendix C is a paper by Jill Khadduri and Bulbul Kaul, "Permanent Housing for Homeless Families: A Review of Opportunities and Impediments." Appendix D is a paper by David Reingold and Angela Fertig, "The Characteristics and Causes of Homelessness Among At-Risk Families with Children in Twenty American Cities." Appendix E presents the Fragile Families data set. Appendix F is the bibliography.

2. Literature Review

Building on the Existing Knowledge Base

This chapter, describing the contributions of existing research, provides the first in a series of building blocks toward developing a typology of homeless families. Although the number of studies conducted on homeless families is small, the considerable overlap in the findings suggests that there may be a sufficient knowledge base upon which to begin to develop a typology. The literature review in this chapter focuses specifically on what has been learned about the characteristics, needs, and service-use patterns of homeless parents and children to guide the development of a typology. Also highlighted are the gaps in the current base of knowledge that should be filled to construct a useful typology based on existing research in typology development.

Studies in this review include published literature, government reports, and documents identified through contacts with programs and organizations involved in these issues. Both research and evaluation studies are included if they focus on the characteristics, needs, and/or service-use patterns of families or the individuals who comprise these families. Single site and multisite studies are included, as are studies that focus on specific subpopulations and those that attempt to be more epidemiologic in scope.

The review begins with a synthesis of the research on the demographic and background characteristics of homeless families, including basic demographics of the families, human and social capital, and residential patterns (Section 2.2). Section 2.3 describes what is known about the service needs, access, and utilization patterns of homeless mothers, followed by a similar section on the service needs, access, and utilization patterns of homeless children (Section 2.4). Key findings from prior efforts in developing typologies, how current knowledge on homeless families can begin to guide the conceptualization of a typology, and what still needs to be known to fully inform these efforts are described in Section 2.5. Finally, Section 2.6 summarizes the main points of the review and outlines implications for next steps.

Demographic and Background Characteristics of Homeless Families

Age, Marital Status, Family Composition, and Ethnicity

The typical profile of a homeless family is one headed by a single woman in her late 20s with approximately two children, one or both under 6 years of age (Bassuk et al., 1996; Burt et al., 1999; LaVesser, Smith, and Bradford, 1997; Lowin, Demirel, Estee, and Schreinder 2001; Rog, McCombs-Thornton, Gilbert-Mongelli, Brito, and Holupka, 1995b; SAMHSA Homeless Families Project, 2004; Shinn, Knickman, and Weitzman, 1991). Despite the fact that homeless families are predominately headed by women, adults in homeless families are more likely to be married than individual homeless adults (23% vs. 7% in the NSHAPC survey [Burt et al., 1999]) and also more likely than adults in other poor families to be married at the point of shelter entry (Shinn et al., 1998). In fact, Shinn and her colleagues found that being married or living with a partner increased the risk of requesting shelter. The relative proportion of homeless families who are married in a particular study depends greatly on whether the homeless families are recruited from shelters that exclude men. In 2003, shelters in 57 percent of the cities involved in the U.S. Conference of Mayors (2005) report indicated that families could not always be sheltered together primarily because many family shelters excluded men and adolescent boys.

Not only are homeless families overwhelmingly households headed by women, but they are disproportionately families with young (preschool) children. The risk for homelessness is highest—and higher than the general population rate—among children under the age of 6. Furthermore, the risk increases for younger children, with the highest rate of risk among children under the age of 1 (infants), of whom approximately 4.2 percent were homeless in 1995 (Culhane and Metraux, 1999).

Pregnancy is also a risk factor for homelessness (Shinn et al., 1998). In a comparison of homeless public assistance families in New York with a sample of housed families on public assistance, 35 percent of the homeless women were pregnant at the time of the study and 26 percent had given birth in the past year, while 6 percent of the housed group were pregnant and 11 percent had given birth recently (Weitzman, 1989).

Homeless families are more likely than poor families, and both are substantially more likely than the general population, to be members of minority groups, especially African Americans (Lowin et al., 2001; Rossi, Wright, Fischer, and Willis, 1987; Susser, Lin, and Conover, 1991; Whaley, 2002). This is also true of homeless single adults. For example, in the NSHAPC, 62 percent of families and 59 percent of single adults, compared with 24 percent of the general population, were members of minority groups (Burt et al., 1999). However, the particular minority groups represented vary from city to city. Their race and ethnicity reflect the composition of the city in which they reside, with minority groups invariably disproportionately represented (Breakey, et al. 1989; d’Ercole and Struening, 1990; Rog, McCombs-Thornton, Gilbert-Mongelli, Brito, and Holupka, 1995b; Shinn et al., 1991; Lowin et al., 2001). The rates of risk are again highest among young children. For example, an annual rate of homelessness in New York City among poor African American children under the age of 5 was 15 percent in 1990 and 16 percent in 1995 (Culhane and Metraux, 1999).

Family Separations and Influence on Family Composition

One of the unfortunate experiences for a significant portion of homeless families is the separation of a child from the family, either temporarily or permanently (Cowal, Shinn, Weitzman, Stojanovic, and Labay, 2002; Hoffman and Rosenheck, 2001). The NSHAPC reported that 60 percent of all homeless women in 1996 had children below 18 years, but only 65 percent of those women lived with any of their children (and often not all of their children); similarly, 41 percent of all homeless men had minor children, yet only 7 percent lived with any of them (Burt et al., 1999). Other studies yield similar findings (Cowal et al., 2002; Maza and Hall, 1988; North and Smith, 1993; Rossi, 1989; Zima, Wells, Benjamin, and Duan, 1996). The likelihood of having one’s children separated from the family is higher for homeless mothers with a mental illness (Buckner, Bassuk, and Zima, 1993; Hoffman and Rosenheck, 2001; Smith and North, 1994; Zima et al., 1996; Zlotnick, Robertson, and Wright, 1999) and for mothers suffering from alcoholism (33%). Approximately one- to two-thirds of the mothers who reported domestic violence also experienced family separations (Browne and Bassuk, 1997; Cowal et al., 2002).

Homelessness is a major factor influencing these separations, with or without other service needs. Five years after entering shelters in New York City, 44 percent of a representative sample of mothers had become separated from one or more of their children (compared to 8 percent of poor mothers in housed families) (Cowal et al., 2002). Three factors predicted separations: maternal drug dependence, domestic violence, and (controlling for drug dependence) any institutionalization, most often for substance abuse treatment. But at any level of risk, homeless families were far more likely to become separated from their children than housed families. That is, even if a housed mother was both drug-dependent and experiencing domestic violence, she was less likely to have her children separated from her than a homeless mother who had neither of these factors (Cowal et al., 2002). Surprisingly, many of the separations occurred after families were rehoused.

There is also a link between homelessness and foster care. Although the majority of separated children in the studies reviewed were living with relatives, a substantial minority were in foster care or had Child Protective Service (CPS) involvement (26%, Cowal et al., 2002; 6%, DiBlasio and Belcher, 1992; 15%, Zlotnick, Robertson, and Wright, 1999). In a 5 year followup of a birth cohort of children in Philadelphia, being in a family that requested shelter was strongly related to CPS involvement and to foster care placement (Culhane et al., 2003). The risk for CPS involvement increased as the number of children in a family increased. Similarly, in another Philadelphia study, there was a greater risk for child welfare involvement for families with longer shelter stays, repeated homelessness, and fewer adults in the family (Park, Metraux, Brodbar, and Culhane, 2004a).

Family separations are not only disruptive to the family and the child during the separation, they can foster a multigenerational cycle of homelessness. Numerous studies have found that separation in childhood from one’s family of origin is a predictor of homelessness in adults (Bassuk et al., 1996; Bassuk, Rubin, and Lauriat, 1986; Knickman and Weitzman, 1989; Susser et al., 1991; Susser, Conover, and Streuning, 1987). In turn, homeless adults who experienced family separation as a child were more likely to be separated from their own children (Homelessness: The Foster Care Connection Institute for Children and Poverty, 1992). In fact, one study found that a large proportion of the children in foster care in the county being studied were born to parents who had histories of homelessness (Zlotnick, Kronstadt, and Klee, 1998).

Among the factors that influence separations are shelter admission rules (as noted earlier), social service policies, shelter life stresses, and parental efforts to limit the child’s exposure to shelter life (Barrow, 2004). Shelters often cannot accept larger families or children past a certain age (especially male children). The sheer stress and stigma of living in shelters can cause mothers to send their children to live with family or friends, especially among African American and Latino families (Shinn and Weitzman, 1996). Finally, homeless families and families involved in special service programs following shelter [after leaving a shelter] are subjected to high levels of professional scrutiny. Although several states have ruled out placement of children [in special programs] because of homelessness alone (Williams, 1991), at least one state training manual notes that the presence of risk factors such as homelessness, though not considered proof of abuse or neglect, “may point to a need for further investigation and future intervention” (New York State Society for the Prevention of Cruelty to Children, 1990).

Homelessness is not only a major factor in family separations; it also makes the reunification of separated families more difficult. This is particularly true if, after separation, parents lose access to income and housing supports that allow them to create a suitable environment for their children (Hoffman, Rosenheck, 2001). In particular, court-ordered separations may require that certain conditions be met before a family can be reunited, such as finding housing and employment and participating in specific treatment and parenting programs. Consequently, reunification occurs only for a subset of families (e.g., only 23% of the separated children in the New York City study were living with their mothers at the 5-year followup [Cowal et al., 2002]).

Human Capital: Education, Employment, and Income

Adults in both homeless and other poor families generally have low levels of educational attainment and minimal work histories. Compared to the national average of 75 percent of all mothers having a high school diploma or graduate equivalency diploma (GED), for example, high school graduation or GED rates for mothers in homeless families range from 35 percent to 61 percent across a number of studies (Bassuk et al., 1996; Burt et al., 1999; Lowin et al., 2001; Rog et al., 1995b; Shinn and Weitzman, 1996). In studies that compared homeless families to poor families, 46 percent of the poor mothers had at least attained high school graduation or a GED (Bassuk et al., 1996); Shinn et al. (1998) found a similar percentage of 42 percent. Overall, the educational rates for homeless families are lower than for homeless single adults (47% vs. 63% in the NSHAPC) (Burt et al., 1999) but similar to other poor families. Again, there are often regional differences reflected in education ranges, with West Coast rates of education typically higher than East Coast rates (Lowin et al., 2001; Rog et al., 1995b).

Not surprisingly, most homeless mothers are not currently working while in a shelter. In a sample of 411 homeless families being helped by shelters in Washington State (Lowin et al., 2001), only 15 percent of the respondents had worked 20 hours or more in the week prior to the interview, with 44 percent of their spouses or partners working during that period. Rog and colleagues (1995b) found that 14 percent of homeless women in the study were working upon entry into a shelter, whereas less than 1 percent were working in the Worcester Family Research Project (Bassuk et al., 1996).

The majority of homeless women in the study, however, have had work experience. Bassuk and colleagues found that 67 percent of the homeless mothers had held a job for more than 3 months. Rog and colleagues found that nearly all (92%) of the women reported working at some point in the past; 62 percent had held a job for at least 1 year (Rog et al., 1995b). Similarly, in the more recent SAMHSA Homeless Families Project, involving homeless women screened for mental health and/or substance abuse problems in eight sites across the country, 96 percent of the women reported working sometime in the past, although only 14 percent were working at baseline (SAMHSA Homeless Families Program, 2004).

The incomes of homeless mothers are significantly below the Federal poverty level (Bassuk et al., 1996, Rog et al., 1995b, Shinn and Weitzman, 1996). Homeless families’ incomes are slightly higher than the incomes of homeless single adults, because of the families’ greater access to means-tested benefit programs such as welfare, and because of more help from relatives and friends. Nonetheless, homeless families’ incomes are far too low to obtain adequate housing without subsidies (Burt et al., 1999). In the Worcester Family Research Project, more than half earned less than $8,000 per year, placing them at 63 percent of the poverty level for a family of three (Bassuk, 1996). Similarly, in the NSHAPC in 1996 the median income for a homeless family was only $418 per month, or 41 percent of the poverty line for a family of three (Burt et al., 1999).

Social Capital: Social Support, Conflict, and Violence

Social support is an important buffer for stress and a major predictor of emotional and physical well-being (Cohen and Wills, 1985). Social networks can be an important housing resource for poor families, who frequently double-up with others when they cannot afford independent housing. Findings about social networks of homeless families, however, are mixed. Several studies have found that mothers in the midst of an episode of homelessness, compared to housed poor women, have less available instrumental and emotional support, less frequent contact with network members, and more conflicted relationships (Bassuk et al., 1986; Bassuk and Rosenberg, 1988; Bassuk et al., 1996; Culhane, Metraux, and Hadley, 2001; Passero et al., 1991). Two studies found that homeless mothers were more likely to name children as sources of support (Bassuk and Rosenberg, 1988; Wood et al., 1990), although this could reflect the circumstance of living in shelter with children.

An ethnographic study of 80 homeless families found that the lack of friends or relatives, or the withdrawal of support from these people, was an important factor in the families becoming homeless (McChesney, 1995). However, Goodman (1991b) found no differences in support between homeless and housed mothers. In the New York City study of homeless families and poor housed families, Shinn and colleagues (1991) reported that newly homeless mothers had more recent contact with network members than did poor housed mothers, and over three-quarters had stayed with network members before turning to shelter. This suggests that families may exhaust social capital, rather than having less capital to begin with, than other poor families. Moreover, additional analysis (Toohey et al., 2004) 5 years later found that social networks of the (now) formerly homeless mothers in this sample were quite similar to those of their housed counterparts.

Social networks, unfortunately, can be the sources of conflict, trauma, and violence, as well as support. In the Worcester Family Research Project, homeless mothers had smaller social networks than housed women and reported more conflicted relationships in their networks. Therefore, large social networks emerged as a protective factor for homelessness, but having a network marked by interpersonal conflict was a risk factor for homelessness (Bassuk et al., 1997). For both homeless and housed mothers, conflict with family and friends was related to impaired mental health (Bassuk et al., 2002). Sibling conflict, in particular, was a stronger predictor of mental health symptoms than was parental conflict.

Homeless mothers, like poor women in general, have experienced high rates of both domestic and community violence (Bassuk et al., 2001). Many women report having been both victims and witnesses of violence over their lifetimes. In the Worcester Family Research Project, almost two-thirds of the homeless mothers had been severely physically assaulted by an intimate partner, and one-third had a current or recent abusive partner. More than one-fourth of the mothers reported having needed or received medical treatment because of these attacks (Bassuk et al., 1996). Supporting these findings, Rog and her colleagues (1995b) reported that almost two-thirds of their nine-city sample of homeless women described one or more severe acts of violence by a current or former intimate partner. Not surprisingly, many of these women reportedly lost or left their last homes because of domestic violence.

In addition to adult violent victimization, many homeless mothers experienced severe abuse and assault in childhood. The Worcester Family Research Project (Bassuk et al., 1996) documented that more than 40 percent of homeless mothers had been sexually molested by the age of 12. Women participating in the SAMHSA Homeless Families Project reported similar findings, with 44 percent reporting sexual molestation by a family member or someone they knew before the age of 18. Sixty-six percent of the women in the Worcester Family Research Project experienced severe physical abuse, mainly at the hand of an adult caretaker. Other studies have found similar results (Rog, et al., 1995b; SAMHSA, 2004).

Residential Stability

Family homelessness is perhaps most aptly described as a pattern of residential instability. Homeless episodes are typically part of a longer period of residential instability marked by frequent moves, short stays in one’s own housing, and doubling-up with relatives and friends. For example, in the 18 months prior to entering a housing program for homeless families in nine cities (Atlanta, Baltimore, Denver, Houston, Nashville, Oakland, Portland, San Francisco, Seattle) families moved an average of five times, spending 7 months in their own place, 5 months literally homeless or in transitional housing, 5 months doubled up, and 1 month in other arrangements. Overall, one-half (53%) had been homeless in the past. It is important to note, however, that this was not a random sample of families, but one selected for a variety of service needs, with “chronic homelessness” (defined as repeated episodes of homelessness) being a marker for some of the families.

Other studies document the lack of stability that homeless families experience. In a more recent study of newly homeless families who were screened for having mental health and/or substance abuse problems in eight sites across the country, less than one-half of the prior 6 months was spent in one’s own home (SAMHSA, 2004). Staying with relatives or friends was the most common living situation during that period for this sample (SAMHSA, 2004) and was also the most common living arrangement for families before entering shelter in Washington State (Lowin et al., 2001). Similarly, Shinn and colleagues found that a key predictor of first-time homelessness for families in New York was frequent mobility, as well as overcrowding (Shinn et al., 1998).

The length of time families stay homeless is a function, in part, of shelter limits on stay and the availability of subsidized housing. The availability and quality of subsidized housing also affects the number of families who return to homelessness. Research has indicated that the strongest predictor of exiting out of homelessness for families is the availability of subsidized housing (Shinn et al., 1998; Zlotnick, Robertson, and Lahiff, 1999). In a longitudinal study of first-time homeless families and a comparison random sample of families on public assistance, residential stability was predicted only by receipt of subsidized housing (Shinn et al., 1998). In followup interviews that occurred 5 years from initial shelter entry, 80 percent of the homeless families who received subsidized housing were stable (i.e., in their own apartment without a move for at least 12 months), compared to only 18 percent who did not receive subsidized housing. The 80 percent figure equaled the percentage for the comparison sample of families from the public assistance caseload. After leaving shelter, formerly homeless families were not part of special case management programs but had access to services generally available to families on public assistance. The study provided strong evidence that subsidized housing was both necessary and sufficient for families to be residentially stable (Shinn et al., 1998).

An earlier followup study of formerly homeless families in St. Louis found similar evidence of the role of subsidies in fostering stability. Of the families who had received housing placements at termination from the shelter and who could be located during the followup period (201 families out of a possible 450 families), those who had received a Section 8 certificate at termination were much less likely to have had a subsequent homeless episode than families who had received some other type of placement (6% vs. 33%) (Stretch and Krueger, 1992).

Finally, studies using administrative records in both New York City and Philadelphia provide additional support for the role of subsidized housing in ending homelessness. In New York City, families discharged from shelters to subsidized housing were the least likely to return to shelter (7.6% over 2 years). Families who were discharged to “unknown arrangements” had the highest rate of shelter return (37%) (Wong, Culhane, and Kuhn, 1997). Similarly, after a policy of placing homeless families in subsidized housing was adopted in Philadelphia, the number of families with repeated shelter visits dropped from 50 percent in 1987 to less than 10 percent in 1990 (Culhane, 1992).

Part of the success of subsidies is that they not only allow homeless people to live affordably, but they generally also allow them to live in safer, more decent housing. In a study of single adults with severe mental illness, Newman and her associates found that Section 8 certificates are associated with improved housing affordability and improved physical dwelling conditions. The quality of the physical housing, in turn, is related to other outcomes, especially residential stability (Newman, Reschovsky, Kaneda, and Hendrick, 1994).

Similarly, in a nine-city study in which homeless families received both Section 8 certificates and case management services, 88 percent of the families accessed and remained in permanent housing for up to 18 months (based on 601 families in six sites where followup data were available) (Rog and Gutman, 1997). Although all families also received some amount of case management and access to other services, the level of service provision varied greatly across and within each of the nine sites and did not appear to differentially affect housing stability. This finding was replicated in an evaluation of families participating in the 31 sites across the country receiving FY 1993 funding under the Family Unification Program (FUP). The FUP, administered by collaborating housing agencies and child welfare agencies, provides families with Section 8 rental assistance and child welfare services. The study found that 85 percent of the families were still housed after 12 months and the finding was almost universal across the 31 sites, despite different eligibility criteria and services, among other differences (Rog, Gilbert-Mongelli, and Lundy, 1998).

A smaller study in New York City in the early 1990s examined a similar intervention involving subsidized housing, coupled with short-term intensive case management, and yielded similar findings. A comparison group received subsidized housing but no special services. At the end of a 1-year followup period, the majority of families in both groups were housed, and less than 5 percent had returned to shelter. Whether or not families had received the intensive services did not affect the outcomes. Rather, the type of subsidized housing received was the strongest single predictor of who would return to shelter, with families in buildings operated by the public housing authority more stable than those in an alternative city program (Weitzman and Berry, 1994).

Although housing subsidies appear to reduce returns to shelter, some families do return after living in subsidized housing. In the New York City followup study, 15 percent of 114 families who obtained housing subsidies returned to shelters at some point during the 5 year followup period (Stojanovic, Weitzman, Shinn, Labay, and Williams, 1999). Reasons for leaving subsidized housing included serious building problems, safety issues, rats, fire or other disaster, condemnation, or the building’s failure to pass the Section 8 inspection. Informal discussions with city officials suggested that families may return to shelter because of failure to renew Section 8 certificates. Similarly, Rog and colleagues (1995b) speculated that failure to complete paperwork might explain some of the dropout of families from the Section 8 voucher program at 30 months in three sites in the nine-city study.

Service Needs, Access, and Utilization Patterns of Homeless Mothers: Health, Mental Health, Trauma, and Substance Use

Homeless mothers and their families face a number of challenges and problems, some that may stem from being homeless and others that may have contributed to becoming homeless. Homeless mothers, for instance, have more acute and chronic health problems than the general population of females under 45 years of age. Bassuk and her colleagues (1996), for example, found that 22 percent of the homeless mothers in their study reported having chronic asthma (more than four times the general population rate), 20 percent reported chronic anemia (10 times the general population rate), and 4 percent reported chronic ulcers (four times the general rate).

In the Robert Wood Johnson/Housing and Urban Development (RWJ/HUD) Homeless Families Program (Rog et al., 1995b), 26 percent of the mothers reported having two or more health problems in the past year and 31 percent characterized their health as poor or fair. Likewise, in the more recent SAMHSA Homeless Families study, 44 percent of the women in the study reported their health as being only fair, poor, or very poor when they entered the study, and 43 percent indicated that they had needed some sort of medical services in the prior 3-month period (SAMHSA Homeless Families Project, 2004). Despite the reported poor health, however, in both of these studies most women reported having had some access to health services while homeless: 75 percent in the RWJ Homeless Families Program, typically through Medicaid (Rog et al., 1995b), and 81 percent in the SAMHSA Homeless Families Project (SAMHSA Homeless Families Project, 2004).

A greater unmet health need among homeless families is dental services. The RWJ/HUD Homeless Families program found that 62 percent of the families needed dental services at baseline, while only 30 percent reported receiving services prior to entering the program (Rog and Gutman, 1997). Similarly, in the more recent SAMHSA Homeless Families project, 44 percent of the families reported needing dental services at baseline, and only 28 percent of these families reported receiving dental services in the 3 months before entering the program (SAMHSA Homeless Families Project, 2004).

Studies differ on overall prevalence of mental health and substance abuse problems among homeless mothers, largely because of how they are defined and measured (including both the actual measure and the time period being assessed) (Shinn and Bassuk, 2004). Regardless of the measurement employed, however, it is clear that the nature of the problems is far different than for single homeless adults. Depression is relatively common, as it is for poor women generally, while psychotic disorders are rare (Bassuk et al., 1998; Shinn and Bassuk, 2004). Given the high levels of stress and the pervasiveness of violence, it is not surprising that homeless mothers have high lifetime rates of posttraumatic stress disorder (PTSD) (three times more than the general female population), major depressive disorder (2.5 times more than the general female population), and substance use disorders (2.5 times more than the general female population) (Bassuk et al., 1998).

Bassuk and colleagues (1996) found, however, few differences between homeless and poor mothers. Thirty-six percent of homeless mothers had a lifetime prevalence of PTSD, with 18 percent currently reporting PTSD, while 34 percent of poor housed women experienced lifetime prevalence of PTSD, with 16 percent of poor housed women reporting current PTSD.

Similar findings have been reported by a variety of other researchers (Fischer and Breakey, 1991; Smith, North and Spitznagel, 1993; Zima et al., 1996). The most common current co-occurring disorders were major depression, substance use disorders, anxiety disorder, and PTSD (Bassuk, et al., 1998; Shinn and Bassuk, 2004). In addition, between one-quarter and one-third of homeless mothers report attempting suicide at least once in their lifetime (Bassuk et al., 1996; Rog et al., 1995). In fact, Rog reported that a majority of the mental health hospitalizations reported by women were related to suicide attempts (Rog and Gutman, 1997).

Homeless families are more likely than other poor families, but less likely than homeless individuals, to report abusing substances (Bassuk et al., 1997; Burt et al., 1999). Rates of reported lifetime use of substances range from 41 percent (Bassuk et al., 1996) to 50 percent (Rog et al., 1995b), with much lower rates reported for current use (12 percent in Rog et al., [1995b] report illicit drug use in the past year; 5 percent in Bassuk et al., [1996] report use of drugs in the past month).

Smith and North (1994) found that single homeless women have more personal vulnerabilities than homeless mothers, such as higher rates of psychiatric (e.g., schizophrenia, bipolar disorder) and substance use disorders (i.e., alcoholism); in fact, some may have lost their children as a result. In contrast, they describe homeless mothers as more socially vulnerable because of their lack of employment and the stress of caring for dependent children. The findings among homeless mothers support Belle’s (1982) argument that psychiatric disorders are more common among poorer women, largely because of the multiple stressors associated with poverty. Pervasive violence, in the context of poverty, may account for many of the emotional disorders in homeless mothers, particularly the high rates of PTSD.

Although poverty is associated with elevated risk of psychiatric and substance use disorders (Robertson and Winkleby, 1996), little empirical data exist on the prevalence, patterns, and correlates of mental health and substance abuse service use among homeless women with children. Studies that gathered data on both psychiatric status and mental health service use suggest a high proportion of homeless women have unmet treatment needs (e.g., Rog et al., 1995b; SAMHSA, 2004).

Finally, it is important to recognize that many homeless women face multiple problems and issues. Rog and her colleagues (Rog et al., 1995b), for example, noted that 80 percent of the homeless women enrolled in their study had current needs in at least two of three areas examined: human capital (poor education or lack of a job), health, and mental health (including substance abuse and trauma-related issues). One-quarter of the women had issues in all three areas.

Service Needs, Access, and Utilization Patterns of Homeless Children

Research indicates that homeless children have high rates of both acute and chronic health problems. They are more likely than their poor housed counterparts to be hospitalized, to have delayed immunizations, and to have elevated blood lead levels (Alperstein, Rappaport, and Flanigan, 1988; Parker et al., 1991; Rafferty and Shinn, 1991; Weinreb et al., 1998; the Better Homes Fund, 1999). They also have high rates of developmental delays (Bassuk and Rosenberg, 1990; Molnar and Rath, 1990) and emotional and behavioral difficulties (Bassuk and Rosenberg, 1990; Buckner and Bassuk, 1997; Molnar and Rath, 1990; Zima, Wells, and Freeman, 1994).

Masten and her colleagues found that homeless children experienced nearly twice as many stressors as a comparison group of children in poor families (Masten et al., 1993). Higher levels of stress, in turn, are associated with mental health and behavior problems. Twenty-one percent of homeless preschoolers and almost 32 percent of older homeless children (ages 9-17) in the Worcester Family Research Project, for example, had serious emotional problems with functional impairment. More specifically, the results from this study indicate that children who are homeless have more problems with internalizing behaviors (e.g., anxiety, depression, withdrawn behavior, or somatic complaints) than children in poor families (Buckner et al., 1999).

Interestingly, the relationship between length of time homeless and internalizing behaviors (as measured with the Child Behavior Checklist [CBCL]) in this study was curvilinear, which suggests that children might be adjusting to their surroundings (or scores could perhaps be a function of services being provided by shelters). It also should be noted that, while the overall CBCL scores of homeless children were higher than those in the housed group, these differences were generally minimal, and nearly equal numbers of children in both the homeless and poor but housed groups scored in the clinical range on this measure (Bassuk et al., 1997; Buckner et al., 1999).

In addition to being homeless, trauma and violence are endemic in the lives of both homeless and housed poor families, with the majority of children either witnessing violence or being directly victimized. The most powerful independent predictor of emotional and/or behavioral problems in both homeless and housed poor children in the Worcester Family Research Project was their mother's level of emotional distress, often due to trauma experienced (Buckner and Bassuk, 1997).

Homelessness appears to have negative effects on school performance. Rafferty and associates (Rafferty, Shinn, and Weitzman, 2004) used Board of Education records to trace children's performance on achievement tests before, during, and after homelessness. Prior to becoming homeless, children in families who would later become homeless had similar scores to other poor children who would remain housed. Homeless children's scores dropped significantly during homelessness and partially rebounded afterward. However, by this time, 50 percent of formerly homeless children had repeated a grade (compared to 40% of housed poor children and 25% of school children in New York City). Further, 22 percent of the homeless children had repeated two grades (compared to 8% of the housed poor children). Within the general population, grade retention is a strong predictor of failure to complete high school (e.g., Hess, 1987; Rumberger and Larson, 1998).

As noted earlier, large numbers of homeless children become separated from their families during homelessness. Research suggests that children who are separated from their families face a number of problems later in life. A study of individuals who were in the New York child welfare system as children, for example, found that children who experienced out-of-home placement were twice as likely to eventually enter the New York City homeless system as adults than those who received nonplacement preventive services (Park, Metraux, Brodbar, and Culhane, 2004b). Similarly, a study of dually-diagnosed homeless adults in three Philadelphia programs found that those who experienced out-of-home placement as children progressed worse than others in the program (Blankertz, Cnaan, and Freedman, 1993). Because most research on homeless children concerns only those who remain with their parents in shelter (e.g., SAMHSA, 2004), and because those who are separated are likely to be worse off than those who remain with families, the needs of homeless children are likely to be underestimated in the literature.

Developing a Typology of Homeless Families

The literature review provides a broad understanding of what is known about homeless families from the research conducted to date and offers a foundation for developing a typology of homeless families. There are also, as noted next, a number of unanswered questions about the population that may be important to address in moving forward. However, the purposes of the typology must first be determined to know what is pertinent from the existing literature and which knowledge gaps are the most critical to close.

Typologies are generally intended to create subgroups of cases. They may be developed for more than one purpose, including classifying individuals into groups, describing and improving the understanding of a population, matching groups to different levels or modes of service or treatment, and improving the ability to predict behavior (Harris and Jones, 1999). For this particular typology, the initial purposes are to foster a better understanding of homeless families' characteristics, service needs, interaction with human services systems, and the dynamics of their use of shelter and other services assistance. This understanding, in turn, is intended to assist in more effectively targeting existing services, maximizing the potential of existing programs to meet the needs of specific subgroups, and identifying new efforts to prevent homelessness for specific groups and to more effectively intervene with others.

Given these initial goals for the typology, it is important to understand families as they differ on levels of risk of homelessness, patterns of homelessness, service needs, and responsiveness to different interventions. It is also important to go beyond describing and predicting patterns of homelessness to determine which families can manage on their own, which need housing subsidies, and which need more help (supportive housing or something else) to exit homelessness and remain stable. For example, large families may be harder to place and, hence, family size might predict length of shelter stay, but large families without other risks might do well with only a housing subsidy.

The goals of a typology guide the selection of the overall approach, the variables to include, and the ways in which the typology can be validated. In this next section, based on a review of efforts to develop typologies in other areas, the following steps are outlined: strategies identified for developing a typology (including the selection of variables), criteria for evaluating the usefulness of a typology, and strategies for determining that these criteria are met. In reviewing these strategies, the implications of the experiences in other areas for developing a typology for homeless families are delineated in each section.

Review of the Literature on Use of Typologies

Although there has been some limited attention to typologies for homeless families (e.g., Danesco and Holden, 1998) the literature that is most helpful involves efforts to develop typologies and classification systems for a range of populations, including individuals who abuse substances (e.g., Epstein et al., 2002; German and Sterk, 2002), individuals with chronic mental illness (Braucht and Kirby, 1986), individuals in the criminal justice system (e.g., Harris and Jones, 1999), homeless single adults (Kuhn and Culhane, 1998), homeless and runaway youth (Mallett, Rosenthal, Myers, Milburn, and Rotheram-Borus, 2004; Zide and Cherry, 1992), families involved in Head Start (Ramey, Ramey, and Lanzi, 1998), and children referred to mental health treatment (Hodges and Wotring, 2000).

There are numerous dimensions along which typologies vary, including whether the typology is based on a theoretical scheme or developed empirically; whether it is developed on one variable or dimension, or multiple dimensions and variables; the nature and measurement of the variables used; and whether the variables include only risk factors or strengths as well. In addition, some typologies are developed using qualitative data (e.g., German and Sterk, 2002), while others involve quantitative data, often using cluster analysis (e.g., Babor et al., 1992). The variations often relate to the purposes of the typology, as well as to the state of the knowledge in an area.

Although typologies based on theory are found in the literature, the majority of typologies are developed through various statistical approaches (e.g., Epstein et al., 2002). Braucht and Kirby (1986) demonstrated the value of a step-wise statistical approach to developing a typology of individuals with chronic mental illness. As a first step, the authors examined 49 variables and used cluster analysis to identify subsets of the variables that correlated along a similar dimension (Tryon and Bailey, 1966). Four dimensions resulted, involving 17 of the measures. The four dimensions, or homogeneous subsets of variables, resulted in little loss of information over the use of the 49 original individual variables and became the core building blocks for the typology. For step two, a score on each dimension was computed for each client in the sample. With these four scores on each client, another cluster analysis was conducted to identify subsets or clusters of clients. Twenty-one distinct subsets or types of clients resulted. As a third step, the authors followed an iterative process to systematically condense the 21 types into a smaller number of groups. Five groups resulted and the four average dimension scores were computed for each. For the fourth and final step, to determine whether the division into the five groups was meaningful, the authors examined additional clinical and psychosocial variables to see if they distinguished the groups from each other. The pattern of statistical differences across these variables for the five groups verified that they were distinct and provided a much more complete characterization of the types.

This example illustrates the value of a multivariate approach to typology development. Although some typologies are developed using a single measure, especially those involved in classification systems that need to be used by practitioners, multidimensional typologies appear to hold the most promise for delineation of meaningful groups. Epstein and colleagues (2002) demonstrated the value of a multivariate approach to typology development of individuals with alcohol use disorders. The authors compared four prevailing typologies and examined the extent to which they overlapped using baseline data from five treatment outcome studies. Two of the typologies were multidimensional and two were single-variable, dichotomous typologies. The comparative approach the authors used was instructive in revealing the strengths and problems with each typology. The authors found that the dichotomous typologies (single variable, two groups) were not complex enough to be clinically useful and often described only a portion of the population.

As in these other areas, a multidimensional strategy appears most promising for the typology of homeless families. Past research, as reviewed earlier, has revealed that understanding the complexity of demographic, background, family composition, service need, human and social capital, and other variables is critical to fully understanding families and how their needs may best be met. In particular, understanding families at various stages of vulnerability for homelessness will be important to a more complete understanding of when and how to intervene.

Typology development, however, is sensitive to variable measurement (e.g., type of measure, cutoff points such as age cutoffs, and extent of missing data). In particular, multidimensional typologies can be sensitive to the existence of outliers and can be temporally less stable if current status variables are used in their development (Epstein et al., 2002). In the homeless families' area, it is important to understand the operationalization of the variables and how they vary across different data sets. Different measures of stability have been used across research studies, as have varying measures of mental health and other areas of service need. In addition, for any database or panel surveys that are candidates for use in this project, it will be important to understand the extent to which there are any artifacts to the data that will challenge its usefulness, such as missing data on particular variables or on subsets of the population.

Because of the many subjective decisions made in developing a typology, the strategy of developing more than one possible typology, as well as investigating multiple data sets and conducting concordance analyses, is also a useful idea for reconciling differences and developing the best, most parsimonious, and most feasible approach (Epstein et al., 2002). This strategy allows cross-validation and testing the universality of the typology.

Criteria for Evaluating a Typology. In evaluating the usefulness of a typology, several criteria can be used (Babor et al., 1992; Epstein et al., 2002; Harris and Jones, 1999). The typology can be examined to determine whether it satisfies the following conditions:

  • Results in subgroups that have homogeneity within them;
  • Results in subgroups that are nonoverlapping and have distinct nontypology characteristics (i.e., has discriminant validity);
  • Is comprehensive in its coverage of the overall population;
  • Demonstrates construct validity by having the theoretical constructs empirically supported; and
  • Has predictive validity in that members of different subgroups show different patterns of homelessness and different responses to treatments (i.e., has clinical utility).

Developing distinct homogeneous subgroups is aided by techniques such as cluster analysis and the use of rich data systems that cover the complexities of the population. One of the challenges in the study of homeless families, however, is to identify data systems that provide for comprehensive coverage of the population. Each of the typology efforts reviewed concentrated on developing the typology in one data system.

Many of the existing homeless families' data systems involve a subset of the population, such as first-time homeless families or families with multiple problems. Others are limited geographically and would have questionable external validity given the context-dependent nature of homelessness. Still others, such as NSHAPC, provide greater external validity and a less selective population but lack the richness of inquiry needed to fully understand the complexity of the individual groups. Similarly, few data sets currently available provide the longitudinal perspective needed to examine the predictive validity of the typology. Given the status of the research, it may be useful to develop a limited number of typologies in the most comprehensive data set and test them in several other candidate data sets. This would provide a greater test of the generalizability of the typology.

Knowledge Gaps

Whatever the purpose of a typology, its development entails a series of decisions and choices that require comprehensive knowledge of the population, the research that produced the knowledge, and the tradeoffs with the available approaches to typology development. There are several gaps in the knowledge of the overall homeless family population that hinder the development of a typology that can provide the most coverage and be of maximum utility for practice and policy. One gap is the lack of research on homeless families across the country, especially studies involving midwestern or southern populations, as well as those in rural areas. Much of the current knowledge is based on research in specific cities, such as New York, Boston, Worcester, Massachusetts, and the cities involved in the multisite initiatives. This is a particularly important gap to fill given the role that context plays in affecting who becomes homeless, the course of homelessness, and the service response.

In addition to lacking geographic diversity, population coverage of most of the studies that have been conducted is limited. For example, few studies focus specifically on families at risk for homelessness or families before they become homeless. Most of the research attempts to collect data retrospectively on families before they became homeless and provides only a limited understanding of the possible factors that buffer other similar low-income families from experiencing homelessness.

Little is known about the families who fall back into homelessness after receiving interventions. Although subsidized housing is shown to assist 80 to 90 percent of families out of homelessness, 10 to 20 percent of the families continue to be residentially unstable in spite of the assistance. Understanding the extent to which the difficulties are contextually-based or involve other factors is critical to understanding this key subgroup, which may end up being one of the major purposes for a homeless family typology. Also, very little is known about the subset of families who are working but remain homeless. Understanding their needs and experiences while homeless and the factors that impede their residential stability would be useful in aptly characterizing these families.

When one talks about homeless families, one almost always refers to homeless mothers and their children. Most studies have omitted two-parent families and few have collected data on the men who once were, or who remain, part of these families. Similarly, some of these families are part of extended family networks that may be critical to both prevention and intervention efforts. In addition, although studies have noted that many single homeless adults are parents, few studies have examined their familial roles.

Because many studies of homelessness have been funded by agencies charged with understanding mental health and substance abuse, much of the literature focuses on people with these conditions. As Arrigo (1998) writes, there is no mention of "modest or moderate needs" homeless families. Although efforts to identify and understand families with the greatest needs make sense for agencies that want to intervene with those most in need, these studies may distort the understanding of the levels of need in the overall population.

Finally, few studies have had a longitudinal perspective that could provide insight into the trajectories families take into and out of homelessness. Little is known about families that become homeless only once or that are residentially unstable for long periods of time.

Summary of Implications From the Literature Review

The research studies conducted on homeless families have largely focused on the characteristics and needs of homeless mothers and their children (Bassuk and Rosenberg, 1988; Wood et al., 1990; Goodman, 1991a, 1991b; Shinn et al., 1998; Weitzman et al., 1992; Rog et al., 1995a, 1995b; Bassuk, 1996; Rossi, 1989). As already noted, these studies and others that have contributed to the literature vary considerably in the definitions used, the samples selected, the designs used, and the study domains examined. Few are longitudinal in nature, only a handful use comparison groups of other poor women to contextualize the results, and the geographic areas involved are limited.

Despite the differences among these studies, this small body of research has produced the following consistent findings:

  • Homeless families are almost always headed by a single woman who on average is in her late 20s with approximately two children, one or both under 6 years of age;
  • Families at greatest risk of homelessness, as well as poverty in general, belong to ethnic minority groups;
  • Homelessness is highly linked to family separations, including foster care and involvement with child welfare services;
  • Homeless mothers have significant human capital needs, with insufficiencies in education, employment history, and income;
  • Homelessness may exhaust the social networks that some families have and may also be the source of conflict, trauma, and violence;
  • Families who become homeless often have residential histories marked by considerable mobility and instability;
  • Homeless mothers report high rates of health problems but also report high rates of access to health care;
  • Mental health problems for homeless mothers mirror those of poor women in general and are largely unmet;
  • Substance abuse reports for homeless mothers, though likely underestimates, are higher than for other women in poor families but lower than for single homeless adults;
  • Children in homeless families also have high rates of acute and chronic health problems, and the majority have been exposed to violence; and
  • The long-term effects of homelessness on children's behavior may be less than expected, but the effects on school performance appear significant and long-lasting.

Significant gaps in knowledge continue to make it difficult to know the external validity of the current base of knowledge. These gaps include the following:

  • Knowledge of homeless families across the country, especially in Midwestern, Southern, and rural areas;
  • Key population groups, including families at risk for homelessness; moderate-need families; families who fall back into homelessness after receiving interventions; families who are working but continue to be homeless; two-parent families; families living in extended family networks; and single homeless adults who are noncustodial parents; and
  • Understanding the course of residential instability and homelessness over several years and the factors that influence this course (including individual factors, contextual factors, and intervention factors).

The review of the literature suggests that a great deal is known about homeless families and their needs. There are ranges of health, mental health, child welfare, substance abuse, and other service needs and involvement, though little is known about the various responses to interventions in these areas. The literature provides guidance in the variables that may be important to include in developing a typology and the specific measures that may be most valid.

The lack of comprehensive population coverage indicates that other efforts need to be made to develop a meaningful typology. Because little is known about families prior to entering homelessness or after they leave shelter, and less is known about specific subgroups of the broader population, initial steps in conceptualizing a typology need to consider how to fill these gaps in knowledge.

3. Outcomes of Expert Panel

Expert Panel Overview

To guide the conceptualization of the typology, a one-day Expert Panel meeting was held in Washington, DC on July 25, 2005. Experts in homeless families research, homelessness research in general, welfare, and typology development were invited to participate along with several Federal representatives. The Expert Panelists discussed what constitutes a typology, potential goals for the typology, and the types of studies that would best inform these efforts. Panelists were also asked to identify critical knowledge gaps. To aid the discussion, five of the eight Expert Panelists contributed four papers focusing on a review of conceptual issues and methodological strategies for developing typology for homeless families; what is known about homeless children; families at risk of homelessness; and a review of opportunities and impediments related to permanent housing.

The meeting was intended to generate discussion that would help inform the conceptualization of a typology, including key elements to consider in its development, study options that could provide useful data, and next steps to take. The Expert Panelists focused on four goals for the development of a typology of homeless families and indicated that more than one typology is needed to guide policy and practice. The goals are as follows:

  • Prevention policy-oriented typology that would focus on identifying the risk factors for homelessness;
  • Resource allocation typology to help understand homelessness epidemiologically and guide the allocation of available resources locally;
  • Services policy typology geared toward policymakers that would identify the menu of services needed to assist homeless families; and
  • Treatment matching typology that would facilitate the matching of treatment and service intensity to particular families.

Although all goals were considered important, typologies that would guide prevention policy and resource allocation were considered the highest priorities for homeless families.

The panelists agreed that typologies should be simple in structure, easy to use, derive from available data, and have practical utility. Each typology should demonstrate predictive and construct validity and reliability and should include characteristics of homeless families, characteristics of the environment of such families, and characteristics of the interaction with the environment. Although the panel thought a range of study designs (e.g., longitudinal, cross-sectional) could inform a typology, the majority also believed a nationally geographic, representative longitudinal study that followed first-time homeless families from the shelter would provide the most guidance for constructing a comprehensive typology.

Factors Considered for Inclusion in a Homeless Families Typology

First, the panelists thought it was important to know how large a typology is needed-that is, how many variables should be considered? The caution was to keep it simple and focus on variables that provide the most differentiation. A good typology should have practical utility, be easy to derive from the data, and have the ability to predict future behaviors. A typology also should be able to facilitate conversation and command a common language among service providers, researchers, and policymakers.

There was a major emphasis on discussing the importance of considering the goals of the typology in determining the factors to be included. A key point made was that the factors that block a family from exiting homelessness or getting back into housing (e.g., bad credit, criminal record) are different from factors that predict becoming homeless or losing housing (e.g., problems with landlords; drugs). Thus, different ways of framing the problem can lead to different goal formation.

Other key factors discussed for inclusion revolved around ordering families according to levels of risk: different gradients of risk of homelessness; risk to parent/child well-being (physical risk, domestic violence, housing conditions); and probability of a quick exit (some might need a single day of shelter). This system would allow for teasing apart families in desperate need from those with moderate needs.

A major area of discussion was the interaction between family and environment and the need to overlay any family typology with an understanding of the local context (domestic violence, neighborhood, social stratification, and market). It is important to focus on the interaction and not solely the environment, as individual characteristics contribute to different personal vulnerabilities and help explain why some families experience homelessness and others in similar environments do not.

At the individual level, it is important to understand whether a family is experiencing homelessness for the first time or experiencing repeated homelessness. Routes into homelessness were also identified as a key area of differentiation. Families report different reasons for leaving housing, including economic reasons, abuse, poor health, or mental health problems. Violence is also an important factor for inclusion at the individual level. Furthermore, it is important to be sensitive to how the population views their own problems. Women in a domestic violence shelter might think violence prevention is their primary concern, for example, and not necessarily consider themselves homeless.

Even though most of the Expert Panel discussion revolved around factors needed for inclusion, some factors were also identified as unnecessary. Dr. Babor reminded the group not to include sociodemographic variables just because they are available unless they help with meaningful differentiation. Typologies should have practical utility, and extraneous variables will only hinder their effectiveness.

Types of Studies That Could Best Inform a Typology

The discussion of research studies focused on the advantages and disadvantages of longitudinal and cross-sectional designs. Some participants argued that cross-sectional designs are not helpful because they confound those who remain homeless with those who are newly homeless. It was suggested that a longitudinal study that followed first-time homeless families (not limited to urban centers) would be ideal. Others agreed that this design would be helpful but acknowledged the difficulty in tracking the population.

Others panelists believed that cross-sectional designs can be appropriate to obtaining an understanding of the current population. It was argued that cross-sectional designs are especially helpful for providing data for service providers who need to best understand the population in front of them. The majority of panelists agreed that different questions require different designs and that no single design is superior to all others.

Another main issue revolved around the importance of using administrative data. Proponents of administrative data believe that large, preexisting data sets could easily inform typology efforts. It was noted that, if administrative data were used, characteristics of those experiencing homelessness could be collected retrospectively. Others agreed, however, that most data sets are missing a housing stability field and recommended adding one to track those who are highly vulnerable and experiencing residential instability. Another recommendation was to add this field to preexisting child welfare data sets to better understand this vulnerable population.

Advantages and disadvantages of nationally representative samples versus local studies were also discussed. National samples offer the widest coverage of geographic locations and the larger populations provide greater validity and reliability. Panelists noted that the focus of a draft final report chapter (presented during the meeting) was solely on the potential of using national data sets for enhancement and secondary analysis. This emphasis was questioned by local-level advocates who believe that decisions on resource allocation are being made at the local level and by state/local dollars. Disadvantages of local sampling were noted, including the need to have consistency in answers across localities for any generalizable outcomes and the tendency for rural populations to be undersampled in these studies because of the placement of researchers in the field. Local-level studies are also problematic because of different community norms and regulations associated with homelessness services.

Potential Problems to Anticipate in Developing a Typology

The panelists emphasized the importance of identifying the goal of a typology before beginning to develop one. Different goals would demand different designs and more than one goal could translate into multiple typologies that need to be developed. It was agreed that more than one typology was needed to inform the policy world.

Another potential problem addressed was determining whether the goal of developing and using a typology is to house families and reduce homelessness or to also provide the services needed to achieve other outcomes (e.g., increase employment).

Another anticipated problem was the need to be aware of differences at the local level that could confound findings, such as different policies in different localities (e.g., restrictions in shelters) that interact with family homelessness. For example, local-level data in Worcester, Massachusetts and Washington, DC, would not be comparable because of differences in shelter policies (e.g., age restrictions, family status requirement), availability, and quality.

Typologies and classification systems can have potentially damaging effects if improperly designed. Problems inherent in other typologies can be used as lessons for this typology by designing one that is flexible and not static.

Some of the current typologies have little intuitive appeal and, therefore, are not used by service providers and policymakers. Finally, any typology that is practical and simple is likely to omit subgroups based on the impossibility of including all existing subtypes in a single functioning typology.

Summary and Discussion of Literature Review

Toward a Typology of Homeless Families: Building on the Existing Knowledge Base
Authors: Debra Rog, C. Scott Holupka, Kelly Hastings, Lisa Patton, Marybeth Shinn

Summary of Presentation. Chapter two of this report summarizes the available literature on homeless families, focusing on what is known about their characteristics, service needs, and service use. According to the literature, homeless families are typically female-headed with an average of two children under five years of age. These families are disproportionately young and members of ethnic minorities. Homeless families have a greater probability of experiencing child separations than nonhomeless families, even when a variety of other factors (e.g., substance abuse) are considered.

Homeless mothers have residential histories marked by mobility and general instability. Compared to other poor mothers, homeless mothers generally have limited education and work histories, are more likely to suffer from health problems despite access to health care, and have similar rates of mental health problems and substance use. Social networks can be an important resource for families but can also be a source of conflict, trauma, and violence. Homeless children also have high rates of health problems. Homeless families, like poor families overall, have high exposure to violence.

Knowledge gaps noted include the need for more research on families from different regions of the country, research on key subgroups, families at risk, moderate needs families, those who fall back into homelessness despite intervention, working homeless families, two-parent families, and families in extended family networks. Longitudinal data are needed on homeless families, as is greater information on the dynamics of their service use and residential history.

Summary of reactions and comments. Panelists concurred with the paper and mentioned additional knowledge gaps, including the need for data on family separations, especially on children who are no longer residing with their mothers. A majority of the panelists agreed that it is important to understand the various reasons, in addition to homelessness, why children can be separated from their mothers. A longitudinal design was recommended for data gathering on potential family separations. It was acknowledged, however, that family separation data can be difficult to accurately obtain as mothers may be hesitant to report the information in fear of child protection services.

Other knowledge gaps noted were data on fathers and fathers' family networks. It is important to note that fathers can enter the criminal justice system and then return to support the family, or the father's family could be an additional asset to the children and mother. More research is also needed on two-parent families and single adults versus married couples. Married couples are more likely to be in shelter, but could potentially be poorer because assets are divided across more individuals.

The importance of clarifying how past research studies have defined homelessness (e.g., whether homelessness is restricted to literally homeless or includes doubled-up situations) was noted as central to having a clear understanding of the literature and its implications for the typology.

Summary and Discussion of Prospects for Secondary Analysis

Toward a Typology of Homeless Families: Prospects for Secondary Analysis
Authors: Debra Rog, C. Scott Holupka, Kelly Hastings, Lisa Patton, Marybeth Shinn

Summary of Presentation. Chapter four of this report presents a review of 15 secondary data sets for potential enhancement and/or secondary analysis. National and state/local data sets were reviewed at both the general population and special population levels. For each data set, information was obtained on its purpose, use, size, scope, domains, and items. Each data set was then screened based on three main criteria: Were the data accessible for secondary analysis within the proposed timeframe? Did the data set include domains related to housing insufficiency, residence, and/or homelessness? Was the unit of analysis at the family level? National data sets such as the Survey of Income and Program Participation (SIPP), surprisingly, have data on characteristics and service use but do not contain data on homelessness or housing instability. The Fragile Families and Child Well-Being data set and the National Survey of America's Families (NSAF) seemed to be the best prospects for informing the typology efforts.

The Fragile Families and Child Well-Being Study follows a birth cohort of new parents and their children over a period of 5 years. A stratified random sample of 20 cities was selected from U.S. cities with 200,000 or more people and then hospitals within cities were sampled. Data were collected at baseline from both the mother and father, with followup interviews occurring at 12, 30, and 48 months. The data set included extensive information on demographics, familial relationships, child well-being, heath and development of the child, residential mobility of both parents, and a variety of homelessness identifiers. This study offers the most promise for informing the typology because it samples a high-risk population through a longitudinal design of young pregnant mothers. Finally, the study is currently available and national in scope and would offer some city-level information.

The National Survey of America's Families was designed to gather data on economic, social, and health characteristics of families and children from representative cross-sectional samples of the civilian, noninstitutionalized population under the age of 65. The NSAF provides a rich data set on both parents and children. The NSAF contains information on a range of domains, including employment, welfare receipt, social relationships, and emotional and physical well-being, and provides child-level data on social, emotional, behavioral outcomes, mental and physical health outcomes, and children's academic outcomes. A potential strength of the NSAF is that, although the homeless population is not specifically surveyed, the three administered surveys do focus on housing and economic hardship variables. The NSAF would therefore provide a rich data set to study families who are doubled-up and valuable information on those at-risk for homelessness.

Summary of reactions and comments. Even though the Fragile Families study was not as widely known by panelists, the majority agreed that the data set appeared potentially informative to typology efforts. Also noted was that, although NSAF does not directly identify homelessness, it does contain helpful doubled-up population identifiers (though it is a surprisingly small percentage of the sample). Other panelists were surprised that SIPP did not include relevant variables.

Other existing data collection efforts suggested for secondary analysis included the following:

  • Hennepin County, Minnesota homelessness program
  • Chapin-Hall, University of Chicago database on foster children
  • Multi-city Study of Urban Inequality (including life history interviews)
  • Detroit Area Studies (has ended)
  • Sampson/Raudenbush research in Chicago, IL (cluster study design of neighborhoods across city)
  • National Survey of America's Families (NSAF)
  • Survey of Income Program Participation (SIPP)

In general, the panel discussed the importance of analyzing administrative data at the local level. In particular, the Hennepin County homelessness program was identified as a good source for re-examination.

Summary of and Feedback on Commissioned Papers

Paper Title: Toward a Typology of Homeless Families: Conceptual and Methodological Issues

Paper Title: Toward a Typology of Homeless Families: Conceptual and Methodological Issues
(full text of paper can be found in Appendix B of this report)
Authors: Thomas Babor and Rene Jahiel

Summary of Presentation. This paper reviews conceptual issues and methodological strategies for developing a typology of homeless families. A typology is defined as a classification system and a set of decision rules used to differentiate relatively homogenous groups called subtypes. Taxonomic standards for an effective typology were reviewed, including the need for simplicity and practical utility, among others.

Potential functions of a homeless families typology were also discussed, including summarizing diagnostic information, providing an empirical basis for client-service matching, minimizing effects on children, and helping to prevent homelessness. Some of the decisions that need to be made in developing a typology include whether the approach should be driven by theory or blind empiricism; whether the typology is based on a single domain or is multidimensional; whether the data informing the typology come from longitudinal or cross-sectional variables; and whether one typology is sufficient or multiple typologies are needed.

Dr. Babor and Dr. Jahiel proposed that a typology should be based on three types of variables: exogenous (housing environment, housing and health/human service access); endogenous (family and individual characteristics); and situational (fit between homeless families' needs and resources accessible). As a heuristic device, the authors suggested a four-cell model identifying interactions between endogenous and exogenous factors. Using existing data sets, this model could be used to identify interactions between types of individuals and environments, resulting in subtypes that could provide a basis for matching families to appropriate levels and types of interventions and prevention efforts.

Methodological issues to develop a typology were examined, with a main focus on disadvantages and advantages of various approaches, criteria for selecting variables, measurement procedures, and statistical methods.

Summary of reactions and comments. Panel members expressed appreciation for the authors' review of conceptual and methodological issues of typology development. There was general discussion on the importance of having a typology that policymakers will use, that has practical importance, and that will actually work. Selecting criterion variables based on ease of use by policymakers (i.e., easy language like days versus stays) was discussed. There was particular interest in the interaction between individual and environmental factors and how it can be handled in a typology. In particular, community factors (e.g., earning power, rental prices, and local amount of subsidies) may be especially helpful to understand as an overlay to individual factors.

Some panelists were concerned with the wide variety of environmental factors that could be included, such as a family's culture, state of residence, and shelter requirements. Cultural differences, for example, can be important as they affect shelter usage. Asians and Latinos are less likely to come to shelters, whereas African Americans and Native Americans are more likely to come.

Also discussed by the panel were the disadvantages of the typology literature as being tautological and outlining classification techniques, but failing to describe classification with a purpose.

Paper Title: Permanent Housing for Homeless Families: A Review of Opportunities and Impediments

Paper Title: Permanent Housing for Homeless Families: A Review of Opportunities and Impediments
(full text of paper can be found in Appendix C of this report)
Authors: Jill Khadduri and Bulbul Kaul

Summary of Presentation. The presentation highlighted permanent housing options, subsidies, and other resources offered by programs for low-income renters, and outlined the barriers that homeless families experience when attempting to access these resources.

The authors argue that a typology of homeless families should differentiate between families who need permanent mainstream housing and those who need permanent supportive housing. In addition, how do we identify families for whom the inability to afford housing is not a barrier to get out of homelessness? For example, domestic violence victims might be able to afford housing but other barriers preclude their ability to access safe housing.

Another consideration for the typology should be the barriers (e.g., criminal records) that families face when attempting to use mainstream programs. Appropriate location of mainstream housing may also depend on individual circumstances of both the parent and child (e.g., domestic violence victims) and should be accounted for when developing a typology.

Knowledge gaps identified include who needs services packaged with the housing, ways in which there is a locational mismatch (e.g., in suburban and rural areas there may be a mismatch between unit sizes and the numbers of bedrooms needed by families trying to leave homelessness for housing) and how much targeting of the current programs is actually taking place (i.e., what are public housing authorities doing right now, how much preference are they giving to families trying to leave homelessness).

Summary of reactions and comments. The discussion focused on the extent to which there are families who might need services packaged with their housing and on how best to describe the permanent housing that is needed. Also reiterated was the need to differentiate between families who need permanent supportive housing and those who just need housing.

Some participants questioned whether public housing authorities would be interested in going back to establishing a priority of housing for homeless families in the absence of a Federal priority. Some suggested that it may be easier to guarantee specific providers a certain number of housing slots or to link the housing to Temporary Assistance for Needy Families (TANF). Also suggested was the need to be sensitive to how a typology is framed for favorable public opinion. It is more politically popular to inform the field on how to limit family separation versus assisting a less politically favorable group like substance abusers.

In response to the paper, it was also suggested that the number of housing slots available be considered and represented in light of the number of families on the wait lists. Another element of potential interest would be examining the methods for determining how many families would become homeless based on how long they were on the wait list.

Paper Title: The Impact of Homelessness on Children: An Analytic Review of the Literature

Paper Title: The Impact of Homelessness on Children: An Analytic Review of the Literature
(full text of paper can be found in Appendix A of this report)
Author: John Buckner

Summary of Presentation. This paper provides a review of the literature on the effects of homelessness on children's mental and physical health, behavior, and academic performance. Reasons for inconsistent findings were offered, such as contextual and policy-related differences in the communities where examinations of homeless children have taken place. Knowledge gaps identified include better understanding of contextual factors, family separations, and how homeless children overlap with housed poor children. In general, no significant subgroups of homeless children have been identified in the literature.

The author concluded that a typology should take advantage of existing data sets and take a person-centered approach. This type of approach would look across different realms of child functioning using techniques like cluster analysis versus the more typical "variable-centered" approach of previous studies. The author recommended adding other realms of child functioning (such as school attendance) to make the typology more comprehensive. The author also warned that it might be difficult to juxtapose typologies of homeless children with typologies of families if based on parental characteristics.

Summary of reactions and comments. The panelists identified a number of areas where more information on homeless children would be useful. There was interest in understanding the effects of residential instability on children's outcomes. Dr. Buckner noted that instability affects a child's school performance as it generally results in more school absences and difficulty adjusting to new environments. Mental health outcomes, however, are affected by a wider array of violence exposure situations experienced by children, and physical health is negatively affected by overcrowding and generally poor nutrition. The panelists also noted that the typology would benefit from more data on family separations.

Panel members felt that a separate typology for children would not be necessary. It was suggested that if a child was ever living in the homeless system with the parent, then it would be possible to link child welfare data and track where the child was living over time. One panelist noted that the Chapin Hall Center at the University of Chicago study is examining foster care child-level data in this manner. Sue Barrow and Judy Samuels are also conducting ethnographic work on family separation that might be examined. Additional issues discussed included how foster care children would be included in the typology.

Paper Title: Homelessness and At-Risk Families: The Characteristics and Causes of Homelessness Among At Risk Families With Children in Twenty American Cities

Paper Title: Homelessness and At-Risk Families:
The Characteristics and Causes of Homelessness Among At Risk Families With Children in Twenty American Cities

(full text of paper can be found in Appendix D of this report)
Authors: David Reingold and Angela Fertig

Summary of Presentation. This paper attempts to understand whether (and how) the family homelessness problem has changed over the last ten years and what factors seem to predict family homelessness today. The first section of the paper reviewed the existing poverty literature to determine whether various macroeconomic and social conditions (welfare reform, decreasing wage returns, low income housing, housing affordability, and incarceration of homeless parents due to drug abuse and violence) have altered the extent and degree of homelessness among families.

The authors conclude that evidence on welfare reform suggests that it has not pushed more at-risk families into homelessness, though there may be a small increase in homelessness among welfare leavers in some states. Wages upon returning to work for those at the bottom of the wage distribution has worsened over the past few years but, because the working poor rate is below the 1993 high, it is unlikely that changes in the labor market have exacerbated family homelessness in recent years. Data on the effects of low-income housing reform on homelessness suggests that the changes in HUD HOPE VI Program may be increasing homelessness for illegal residents. The authors recognized the link between the lack of affordable housing and family homelessness; however, they believe that the shortage has not worsened over the past ten years and thus is unlikely to have forced more families into homelessness. Finally, there may be a link between increases in the number of families who become homeless and patterns of reentry of incarcerated parents, but not enough is known to understand the link.

The second part of the paper focused on a brief reanalysis of data from the Fragile Families and Child Well-Being Study, examining the factors that predict homelessness. The analysis focused on two different subsamples: those who report being homeless at the 12-month interview (n=140) and those who report being homeless at the 36-month interview (n=110). Homelessness in this sample is associated with race, educational attainment, welfare receipt, less employment, lower earnings, experiences of hardships (e.g., inability to pay utilities or rent), living in public housing, paying a greater proportion of income toward rent, higher rates of prior incarceration of the father, drug use, emotional distress, domestic violence reports, and less access to social support. Regression results seem to indicate that health, drug use, and domestic violence are the best predictors of family homelessness though these account for only a small amount of the variance explaining whether or not a family becomes homeless.

Summary of reactions and comments. For the literature review analysis portion of the paper, there was discussion about the problem of limiting the timeframe to the past ten years, especially if the conclusion is true that the affordable housing and homelessness situations have had less change during this period. It is difficult to examine the effects of a condition on the problem if the condition has been relatively constant. Taking a longer time perspective may provide a more valid assessment of the relationship among these conditions.

Much of the discussion focused on concerns about the analyses conducted on the Fragile Families database and strategies for strengthening them. First, the analysis compared the families who had reported being homeless at one point or another with all other families. As this data set was not restricted to poor families, some of the findings (such as receipt of a housing subsidy increasing the risk of being homeless) may actually be indicators of poverty. If the control group was restricted to just poor families (e.g., those who are at 50% of the poverty level) the analyses could find that a housing subsidy actually acts as a protective factor. It was also noted that, because the community was known, it may be possible to adjust the poverty definition by the area median income.

There was some concern voiced that the sample size was small and thus might be sensitive only to moderate and large effects.

Some of the discussion focused on whether it would be possible to link people to the housing market conditions in which they live to be able to understand the housing market factors related to homelessness.

In addition to examining individuals who had experienced homelessness, it might be useful to know if families are doubled up. If the individual is on a lease, it may be possible to determine if he or she is living with others or has others living in the residence. It would also be useful to see if the person is paying anything toward rent. The fact that a family is not contributing toward rent may help them stay housed.

Finally, there was a great deal of discussion sparked about the ability to predict homelessness among families and the implications for the typology. The authors noted that the R-squares on their regressions were quite small and questioned whether homelessness was an event that was due to the idiosyncrasies of the population and environments. It was noted that R-square or variance explained may not be as useful as success of prediction. Even excellent predictors can't predict much variance when their distributions are quite different from the distribution of the criterion variable.

It was also noted that homelessness is relatively rare in a restricted time frame but can become a much more common event for poor families over a longer period of time. It was noted again that, from a prevention standpoint, it may be hard to predict who will become homeless and thus it may be necessary to wait until families present as homeless to intervene and triage.

Directions for Typology Development

Following the paper presentations and discussions, the group discussed how best to proceed with the task of typology development. The following general guidelines emerged from the discussion.

The two top goals for a typology should be a focus on prevention (in hopes of minimizing the population) and resource allocation. From the Federal perspective, having data on how best to match the resources that exist with the needs of the population is important. With multiple, equally important goals, it was concluded that more than one typology is necessary to best inform the field.

Dr. Babor's recommendation of a four-cell model between environment (facilitators/barriers) and service needs of families (minor and major needs) should be explored. Dr. Babor thought the empirical question is whether these levels are adequate and appropriate for differentiation. The suggestion was to identify the services that are available for allocation, including mental health, substance abuse, medication, STD clinics, prenatal services, domestic violence, trauma, employment, education, and legal services. Then put all variables together and see if different clusters form. The results might show two large groups emerging, one of high needs and the other of low needs. The high needs group might cluster around history of domestic violence, mental health, substance abuse, and poor employment, whereas the other group might have relatively few problems. For children, include child person-variables such as education needs, domestic situation, CBCL scores, and ages and see how those variables cluster. Dr. Babor reiterated that a goal of the typology is to define subgroups.

A prevention focus might be best addressed by waiting until families are present at the shelter door for the first time and then triage from there. In this vein, it was recommended that we pursue the use of existing administrative data of Hennepin County, Minnesota and other communities (e.g., Arizona) where they are attempting to assess needs and triage in real time. Hennepin has developed a classification system for treatment matching of shelter usage by assessing needs and triaging in real time. Classification is used at a very practical level and provides a method for service providers to use when deciding who receives shelter (i.e., level 1 and level 2 are referred elsewhere, level 3- referred to the shelter).

The group determined that a priority is to continue to explore methods for informing the knowledge gaps discussed and described earlier in Chapter 2:

  • Family separation;
  • Different family structures (couple vs. married);
  • Father's support network;
  • Data on families across different regions of the country;
  • Families at risk;
  • Moderate needs families;
  • Those who fall back into homelessness despite intervention;
  • Working homeless families;
  • Two-parent families;
  • Families in extended family networks;
  • Longitudinal studies of homeless families; and
  • Studies that focus on homeless children.

4. Prospects for Secondary Analysis

Introduction

The literature review in Chapter 2 identifies key knowledge, as well as gaps in that knowledge, related to homeless families and families at risk of homelessness that will be critical to the development of a relevant typology for the purposes of this study. While there is a considerable amount known about currently homeless families and their needs, there are also significant gaps in the knowledge because of limitations in population coverage (focus on the currently homeless and small samples that do not permit subgroup analysis), the cross-sectional nature of many of the studies, the lack of focus on intervention, and the lack of data on children (Table 4-1).

Knowledge gaps Type of research needed to address gap
Table 4-1. Knowledge gaps
Geographic coverage gaps A. National sample
B. Multisite sample
C. Aggregation of numerous site-specific samples
Population coverage D. Data on a population broader than homeless population only
Longitudinal studies E. Track study participants over time
Subgroup gaps F. Families at risk of becoming homeless, working but still homeless, episodically homeless, two-parent homeless families, families that fall back into homelessness, moderate needs homeless families, families living in extended family networks, noncustodial homeless parents
Focus on prevention/intervention G. Track services used, government support (welfare, housing subsidies, etc.)
Focus on children H. Track children and collect data

The lack of comprehensive population coverage in previous studies is due to several factors, including a dominant focus on currently homeless families, relatively small study sample sizes, and a concentration of research in East Coast cities. The focus on currently homeless families provides an understanding of the characteristics of those who become homeless, but generally explains little about families prior to entering homelessness (and, even then, only retrospectively) and does not provide any knowledge of the specific subgroups of the broader population who may be at risk of homelessness. In addition, because only a few studies have tracked homeless families for 12 months or longer, little information is available on families after they leave shelter or about their long-term stability.

The small study samples generally inhibit the ability to examine specific subgroups. For example, survey questions may be asked about families who are currently working but, because the percentage of working families in currently homeless samples is typically 20 percent or less, the overall study samples are generally not large enough (e.g., 500 or more) to provide subsamples of sufficient size. Other key subgroups with inadequate sample sizes in current studies include those who are episodically homeless (because they are homeless for such short periods of time and generally are not represented in studies with restricted recruitment patterns or with criteria that require a minimum period of homelessness before being included in the study); families with two parents; moderate-need families; families living in extended family networks; and chronically homeless families. Although one study (Burt, M., Aron, L.Y., Douglas, T., Valente, J., Lee, E., and Iwen, B., 1999) had a large sample of families, only limited information on the families was collected because it was part of a larger effort.

A final limitation with respect to population coverage is the fact that many studies concentrated their data collection in East Coast cities. Because of the contextual nature of homelessness and the diversity in labor markets, housing markets, and service systems, the lack of attention to other geographic areas of the country-especially the Midwest, South, and rural areas-limits the generalizability of the findings and would likely distort any typology efforts that were based solely on existing data.

Although a few past studies had longitudinal study designs, only one study tracks families over a 5-year period and even then only two waves of data were collected. Longitudinal, ongoing data on families who have experienced homelessness would increase the understanding of the course of residential instability and homelessness and the factors that influence this course (including individual, contextual, and intervention factors).

There is also a paucity of data on the role of prevention efforts in keeping families from becoming homeless and intervention efforts to help them exit homelessness. Finally, most of what is understood about homeless families is either about the mother or from the mother's perspective; few studies have focused on the children in the families.

Most of the data that is available on homeless families has been drawn from research studies that focus exclusively on homeless families, as opposed to the population at large or even studies that have explored the needs of low-income families or families living in poverty. A number of existing data sets that include low-income families potentially contain information to support the development of a typology of homeless families. In order to be useful, a data set must include information on each family's housing status or housing history to determine if the family is or has been at risk of homelessness or has experienced homelessness.

This chapter summarizes our review of data sets that focus on or include low-income families (i.e., families who have the greatest probability of experiencing housing instability), including the stepwise approach taken to identify and screen the data sets to determine if they have the necessary housing information. The purpose of this undertaking was to identify existing prospects for secondary analysis-that is, data already being collected that could serve to inform the development of a homeless family typology. Project staff examined major national or multijurisdictional surveys that might include large numbers of low-income respondents (e.g., potentially homeless or homeless families) and the types of data currently being collected. This chapter highlights what can and cannot be answered with existing data.

Identification of Potential Data Sets

Data sets were sought that could extend the understanding of homelessness beyond currently homeless families to a broader sample of families who may have been homeless in the past or may be at particular risk of homelessness in the future. Some of the candidate data sets are ones that Westat has previously analyzed, such as the Survey of Income and Program Participation (SIPP), the National Health Interview Survey (NHIS), and the National Household Survey on Drug Abuse (currently called the National Survey on Drug Use and Health [NSDUH]). Other data sets reviewed include the National Survey of America's Families (NSAF), the California Health Interview Survey, the Current Population Survey (CPS), the Panel Study of Income Dynamics (PSID), the Survey of Program Dynamics (SPD), the National Longitudinal Surveys of Labor Market Experience (NLS), the Fragile Families and Child Well-being Study, and the National Survey of Child and Adolescent Well-Being (NSCAW). Three other studies-the Women's Employment Study, Three-City Study, and the Chicago Women's Health Risk Study-were identified through the review of the literature and the Internet, and through contacts with colleagues in the field.

For each data set, information was obtained on its purpose, use, size, scope, domains, and individual variables and each was initially screened based on three criteria:

  1. The data set was public and could be readily obtained (e.g., through electronic download);
  2. The data set contained information on a family's housing status or history so that it was possible to determine if a family was, or had been, at risk of homelessness or had experienced homelessness; and
  3. The data set was organized by family so that analyses could examine the family-level information that related to housing status.

The first criterion was essential so that any secondary analyses could be conducted within the time frame of this project. The second criterion relates to the study's relevance to our typology efforts; data sets may contain housing information but, if there is no information on homelessness or other unstable housing situations, there is little to inform how we would define a typology of homeless families. Finally, data need to be available at the family level to permit analyses that can examine the factors that put families at greater risk for homelessness or buffer them from the experience. Some data sets provide data only at the aggregate level (i.e., by city or community) and do not allow for individual family analyses.

Table 4-2 displays the data sets that were screened and the results of the screening. The review is divided into two sections, focusing on the general population surveys first, followed by the special population studies. Studies were classified as "General Population" if the sample was designed so that estimates could be made for a national (or state) population, even if, as in some cases, the study also oversampled low-income or other groups. "Special Population" studies focused on specific subsets, such as families involved in the welfare system (NSCAW), low-income families (Chicago Women's Health Study, Three-City Study, and Women's Employment Study), and children born to unwed mothers (Fragile Families). Results from these studies cannot be generalized to a national or state level. The table displays information on the population scope and design for each data set, as well as the content relevant to the typology development, and the data sets are listed by their scope and population focus.

Of the national population studies identified, only the NLS and PSID met all three screening criteria. All others lacked information on housing stability or homelessness, with the exception of the American Housing Survey (AHS), which collected data at the housing unit level, rather than the individual or family level. The four "special population" studies that focus on discrete populations of women and their families met the criteria. All seven data sets that met the criteria were then reviewed more closely to determine their benefit for secondary analysis.

Table 4-2. Data sets screened for secondary analyses
  Structure Domains Addressed
National sample Longitudinal design Housing/homelessness domain Housing subsidies Employment/income data Agency service involvement
General population studies
National
American Housing Survey* (AHS) Y Y Y Y Y Y
Current Population Survey (CPS) Y No No No Y Y
National Health Interview Survey (NHIS) Y No No No Y Y
National Longitudinal Surveys of Labor Market Experience (NLS) Y Y Y Y Y Y
National Survey on Drug Use and Health (NSDUH) Y No No No Y No
Panel Study of Income Dynamics (PSID) Y Y Y Y Y Y
Survey of Income and Program Participation (SIPP) Y Y No Y Y Y
Survey of Program Dynamics (SPD) Y Y No Y Y Y

State/local

California Health Interview Survey No No No No Y Y
Special population studies
National
National Survey of America's Families (NSAF) Y No Y Y Y Y
National Survey of Child and Adolescent Well-being (NSCAW) Y Y No No Y Y

State/local

Chicago Women's Health Risk Study No Y Y No Y Y
Fragile Families Study Y Y Y Y Y Y
Three-City Study No Y Y Y Y Y
Women's Employment Study+ No Y Y Y Y Y
* Family-level data unavailable.
+ Data currently unavailable.

Review of Data Sets

Seven data sets were identified as warranting further consideration for possible reanalysis. In this section, each of these data sets is reviewed in detail, including their structure and content. Then, the nature of the reanalysis that is indicated, including the type of questions that could be addressed, and how the results could inform the typology efforts, is presented.

General Population Studies

Three of the data sets are ongoing, general population studies that are widely known and have been analyzed for a variety of research purposes. Two, the NLS and the PSID, are national, longitudinal studies, and the other is a large, national cross-sectional survey of families, NSAF. The three national data sets identified have potential for informing the efforts to conceptualize a typology of homeless families. In the following section, each study is described in detail and information on the structure, content, and strengths of the data set is further outlined in an accompanying table.

National Longitudinal Survey of Labor Market Experiences (NLS). The NLS (see Table 4-3) is a series of longitudinal cohort studies. Four initial cohorts were selected in the mid-1960s, including samples of both young and mature men and women. Tracking of the two male cohorts was stopped in the early 1990s, while the two groups of women continue to be monitored. Tracking began of another cohort of 12,686 youth between the ages of 14 and 22 in 1979 (NLS79). Annual surveys of this cohort were conducted for the next 25 years and, since that time (1994), biennial surveys have been conducted. In 1986, surveys were begun with children from the NLS79 cohort. Information was initially collected on these children in 1986 and has been biennially updated since 1988. A sixth cohort NLSY97 sample of 9,000 youths who were 12 to 16 years of age as of December 31, 1996, has been tracked annually since 1997.

Table 4-3. National Longitudinal Survey of Labor Market Experience (NLS79)
Structure
Sample Nationally representative sample of youths who were 14 to 22 years old in 1979
Size 12,686 youths
Timeframe First interviewed in 1979. Interviewed annually through 1994 and biennially since then.
Content
Housing/homelessness Information collected on current residence and on moves since the previous interview. Homelessness (e.g., living on the streets or in a shelter) is not recorded
Specific housing questions
  • What is the address of your current residence?
  • What type of living quarters? (Answer choice- Other- Temporary individual quarters)
Demographics Work history, education, high school transcript, income and assets
Family Marital status event history, child births, and family composition
Service needs Health conditions, alcohol and substance abuse, insurance coverage
Agency/service involvement Event histories of participation in government programs such as unemployment insurance and AFDC
Strengths for typology — Knowledge gaps answered
Geographic coverage Yes, large, national representative sample
Population coverage
(Broader than homeless)
Yes
Subgroups available Yes, to identify those at risk, provides ability to examine role of risk factors and protective factors as they relate to housing stability, work, and family
Prevention/intervention services
(agency involvement)
Yes, data on government programs, including housing subsidies
Data on children Yes, limited data on children of NLS79 cohort's mothers
Weaknesses Possibly biased sample if did not successfully track those who became homeless; does not collect any information on homelessness
Conclusion Cannot be used for typology — no information on homelessness

The four initial cohorts are unlikely to yield information relevant to family homelessness. By the time this topic began to emerge as a national issue in the mid-1980s, most of the original 1966 and 1967 samples were too old to have young children and less likely to have been at risk of homelessness. Conversely, the latest cohort, the NLSY97 sample, is just beginning to reach the prime age for entering homelessness as families. Data available on this cohort, however, exist only through 2000, when most of the youth in the sample had not yet reached their 20s. This data set, because it specifically collects information on whether a respondent was living in a shelter or on the street, may be important to examine in the future.

Only the NLSY79 sample is likely to have experienced homelessness, with the group entering their 20s during the mid-1980s. A review of the data set revealed that, in addition to labor force behavior, information has been collected on a wide range of key domains, such as welfare receipt, educational attainment, income, health conditions, alcohol and substance abuse, family histories, and residential history. Contacts with individuals at the Bureau of Labor Statistics (BLS) indicated that the NLS does not provide any measure of homelessness, though the database is built on panel surveys that track living arrangements over time. At this time, only the addresses, not types of location, are coded. Thus, a shelter cannot be distinguished from a stable living arrangement. In addition, even if the type of location could be discerned, it is likely that because of the difficulty in locating homeless people for followup interviews, individuals who are not stably housed would be underrepresented.

If coding of homelessness and precariously housed arrangements did exist in a reliable and valid fashion, a reanalysis of this data set could make an important contribution to understanding the dynamics of residential instability from early adulthood on and the role that labor force involvement, welfare, and some basic health issues play in these dynamics. The size, scope, and longitudinal nature of the data set would amplify its potential importance for the efforts as long as there could be some determination of the representativeness of the study sample with respect to unstable families. As it currently stands, however, the NLS does not provide this information.

Panel Survey of Income Dynamics (PSID). The PSID (see Table 4-4) is a nationally representative, longitudinal study that began in 1968. The initial PSID study consisted of two independent samples: a cross-sectional national sample of approximately 3,000 families and a national sample of 2,000 low-income families. From 1968 to 1996, individuals from these families were interviewed annually, whether or not they were living in the same dwelling unit or with the same people. As a result of both low attrition of the original sample and additional followups of the children as they formed their own families, the PSID grew to a size of more than 65,000 individuals, clustered into families branching off from the original family sample. To keep the PSID sample representative of the U.S. population, adjustments were made in 1997 that reduced the number of core families and added a refresher sample of post-1968 immigrant families, particularly Latino and Asian households.

Table 4-4. Program Survey of Income Dynamics (PSID)
Structure
Sample Representative, national sample of families, including a national sample of low-income families in 1968, refreshed in 1997
Size Initial sample of 4,800 families, grown to 7,100 by 2001, with data on over 65,000 individuals
Timeframe First survey conducted in 1968, annual surveys administered until 1997, starting in 1999 surveys administered biennially
Housing/homelessness Residential followback calendar for all places lived in during the previous 2 years; however, homeless not directly coded
Content
Specific housing questions
  • Asks for a residential follow-back calendar of all places lived during the previous 2 years (lists addresses).
  • Is this house in a public housing project; that is, is it owned by a local housing authority or other public agency?
  • Are you paying no rent because the government is paying all of it?
Demographics Education, ethnicity, religion, military service, parents' education, occupation, poverty status, income
Family Family composition and changes
Service needs Physical health, emotional distress
Agency/service involvement Public assistance in the form of food or housing
Strengths for typology — Knowledge gaps answered
Geographic coverage Yes, large nationally representative sample
Population coverage
(Broader than homeless)
Yes, with a subsample of low-income individuals from 1997
Subgroups available Yes, provides ability to examine role of risk and protective factors as they relate to housing, family, and employment for those at-risk for homelessness.
Prevention/intervention services
(agency involvement)
Yes, housing and food public assistance
Data on children Limited
Weaknesses Does not collect any information on homelessness
Conclusion Cannot be used for typology — no information on homelessness

The PSID collects information on a broad range of core topics, including income sources and amounts, poverty status, public assistance, marital status, childbirth, employment status, military service, and health. Supplemental questions also have been added to various waves of the PSID. For example, various types of health questions have been included in several different years. Retrospective questions also have been asked to clarify relationships between people identified in the early years of the PSID and to obtain more detailed work histories from participants.

The PSID collects housing and mobility information but does not include homelessness as a specific location. For example, it obtains information such as when and why people have moved, whether they own or rent, and how much they pay for housing. It is possible that homelessness or other information related to homelessness is collected but coded as other.

A potential strength of the PSID for this effort is oversampling of low-income families. However, because the percentage of families that experience long-term poverty is fortunately relatively small, the number of families experiencing long-term poverty in the PSID is not large (Gottschalk and Ruggles, 1994).

National Survey of America's Families (NSAF). The NSAF (see Table 4-5), consists of representative cross-sectional samples of the civilian, noninstitutionalized population under the age of 65, and was designed to gather data on economic, social, and health characteristics of families and children. Individuals were contacted through either random-digit dialing (RDD) or, for households without a telephone, face to face. The NSAF is a national sample, but it oversamples 13 states to provide more accurate state-level numbers. The survey was administered to 44,461 households in 1997, 46,000 households in 1999, and 43,157 households in 2002.

The NSAF provides a rich data set on both parents and children. In households with children, up to two children were randomly sampled, one child under the age of 6, and another child between the ages of 6 and 17. Information on children in the household was gathered by asking questions of the adult with the most knowledge regarding the children's education and health care. The NSAF contains information on a range of domains, including employment, welfare receipt, social relationships, and emotional and physical well-being and provides child-level data on social, emotional, behavioral outcomes, mental and physical health outcomes, and academic outcomes.

Another potential strength of the NSAF is that, although the homeless population is not specifically surveyed, the three administered surveys focus on housing and economic hardship variables. The survey includes questions that identify families who were forced to live with other families because of the inability to pay the monthly mortgage, rent, or utilities. Additional questions that capture families at risk for homelessness identify the use of emergency food banks and the inability to pay monthly rent. The NSAF would, therefore, provide a rich data set to measure families who are doubled-up and provide valuable information to identify those at risk for homelessness. A potential limitation of the NSAF is that the cross-sectional design would not provide information on the same families across points in time.

Table 4-5. National Survey of America's Families (NSAF)
Structure
Sample Representative sample of the civilian, noninstitutionalized population under the age of 65, oversampling people with low incomes
Size 44,460 households surveyed in 1997; 46,000 households surveyed in 1999; approximately 40,000 households surveyed in 2002
Timeframe Cross-sectional design, surveys conducted in 1997, 1999, and 2002
Content
Housing/homelessness Asks if family had to move in with another family because of inability to pay mortgage, rent, or utility bills (doubled-up population identifier)
Specific housing questions
  • How much paid for rent?
  • Are you and your family paying lower rent because the Federal, state, or local government is paying part of the rent?
  • During the last 12 months, did anyone move into your home even for a little while because they could not afford their own place to live or because their parents could not support them?
  • During the past 12 months, was there a time when you and your family were not able to pay your mortgage, rent, or utility bills?
Demographics Gender, education, employment, ethnicity
Family Births/pregnancies, parent-child interactions, family formation, and stability/living arrangements
Service needs Adult health, physical, and emotional well-being, children’s mental/physical heath
Agency/service involvement Welfare, mental health services, medical services
Strengths for typology — Knowledge gaps answered
Geographic coverage Yes, three very large, national representative samples
Population coverage
(Broader than homeless)
Yes, oversamples low-income individuals
Subgroups available Yes, provides ability to examine role of risk and protective factors as they relate to housing, family, and employment for those at risk for homelessness. Provides ability to track the hardships families face, the role of welfare and other services in affecting the course of the hardships, and the role of family interactions and stability as both factors in shaping hardships and buffering hardships
Prevention/intervention services (agency involvement) Yes, housing and food public assistance
Data on children Yes, child-level data collected
Weaknesses Does not collect any information on homelessness
Conclusion The data set may provide valuable information on those doubled-up and at risk for homelessness.

At this point, the specific size of the doubled-up population has not been identified; however, interim analytical findings suggest that 3 in 10 low-income families answered that they were unable to pay for a month's rent, utility bills, or mortgage payment and nearly half of the low-income families reported food affordability problems (Nelson, 2004). These findings suggest that an ample-sized, at-risk population exists and should be further examined on all variables.

Special Population Studies

The remaining four data sets examined are from studies that contain data on specific populations in selected areas of the country. Three of the studies are focused on low-income families in one or more selected cities across the country. One study, the Chicago Women's Health Risk Study (CWHRS), includes a one-time sample of women in Chicago seeking treatment. Each of these studies is described below.

Chicago Women's Health Risk Study (CWHRS). Funded by the National Institute of Justice, the CWHRS (see Table 4-6) was designed to identify risk factors that place a physically abused woman or her partner in significant danger of life-threatening injury (Block, 2000). The study collected extensive baseline information on several different samples: women who had been abused in the 12 months prior to seeking general health care (n=497), women who did not report being abused during that same period (n=208), and victims of intimate homicide (based on proxy interviews) (n=87) (Block, Stevenson, Leskin, and Thomas, 2002; Block, 2000; Block, Engel, Naureckas, and Riordan, 1999). Because the CWHRS sought to include the hidden population of women who are experiencing intimate partner violence but who are unknown to service agencies, women were screened for abuse at a county hospital or at community health clinics located in neighborhoods with high rates of intimate partner homicide.

The study focused on the 49a7 women who had been physically abused at least once in the year prior to seeking general health care, collecting descriptive data on each abuse incident during the 12 months prior to seeking treatment, and reinterviewing the women one time for varying periods up to 12 months following the initial interview. Sixty-six percent (323) of the original abuse sample was reinterviewed. Data were collected on an array of risk and protective factors for abuse across the retrospective and prospective periods. These included one's living situation (with specific attention to homelessness), family composition and child separations, marital status, physical health, pregnancy, drug and alcohol use, mental health (posttraumatic stress disorder [PTSD] and depression/suicide feelings), race/ethnicity, occupation and income, immigrant status, resource and social support network, intervention, and help seeking. Specifically, help seeking included whether assistance was sought from alcohol and drug treatment providers, a domestic violence agency, a medical provider, and/or the police.

Table 4-6. Chicago Women's Health Risk Study (CWHRS)
Structure
Sample Women seeking treatment at medical centers in areas with high rates of intimate partner homicide in Chicago
Size 705 total women interviewed, 497 experienced intimate violence in past year, 208 were in the comparison sample
Timeframe Baseline interviews conducted 1997-1998, one followup conducted from 1998-1999
Content
Housing/homelessness Homelessness, living in a treatment center, shelter, number of people living in household (including her children), changes to household structure
Specific housing questions Was the mother homeless or living in a treatment center or shelter?
Demographics Age, race, education level, employment status, birthplace, marital status
Family Age and gender of children living in and outside of the household with mother
Service needs Physical and mental health, including general well-being, type and duration of any physical or emotional limiting condition, amount of bodily pain experienced, pregnancy outcomes, medical outcomes study, scale of depression
Agency/service involvement Alcohol/drug treatment, contacting a domestic violence-related agency or counselor, seeking medical help, and contacting the police
Strengths for typology — Knowledge gaps answered
Geographic coverage No, only in Chicago
Population coverage
(Broader than homeless)
Yes, samples from medical centers with high numbers of intimate violence
Subgroups available Yes, subgroups include working but still homeless; noncustodial homeless parent; those at-risk providing the ability to examine role of risk and protective factors as they relate to family, work, and physical/emotional health.
Prevention/intervention services (agency involvement) Somewhat, physical and mental health services
Data on children Very limited
Weaknesses Not a representative, national sample and only has one followup with a portion of the original sample
Conclusion Cannot be used for typology — data are not generalizable to national population and the sample size is small

The CWHRS provides additional samples of women at risk for homelessness, as well as those who are homeless, and any transitions they make over the course of 12 months. The study also provides information on women currently being abused that would augment knowledge contributed by the Worcester Family Research Project and the SAMHSA Homeless Families Project. A specific question of interest for reanalysis would be if the help-seeking patterns of those who are homeless differ from individuals who are currently housed. The major drawback is that this is a single-site study with a relatively small sample that therefore is likely not representative of all women being abused.

Fragile Families and Child Well-being Study. The Fragile Families and Child Well-being Study (see Table 4-7), also referred to as the Survey of New Parents, follows a birth cohort of new parents and their children over a 5-year period. The purpose of the study is to provide new information on the strengths, conditions, and relationships of unwed parents and how Federal and state policies affect family composition and child well-being.

The study used a three-stage sampling process. First, a stratified random sample of 20 cities was selected from all 77 U.S. cities with 200,000 or more people. The stratification was based on three variables: welfare generosity, the strength of the child support system, and the strength of the labor market (Reichman, Teitler, Garfinkel, McLanahan, 2001). Second, hospitals within cities were sampled, based on the proportion of nonmarital births in the hospitals or, in New York and Chicago, randomly from the pool of hospitals with over 1,000 nonmarital births per year. Third, random samples of both married and unmarried births were selected in each hospital per preset quotas. Samples were designed to be representative of the nonmarital births taking place in each of the 20 cities, but not necessarily to be representative of the marital births, since hospitals were sampled that had the most nonmarital births. Interviews were conducted with both the birth mother and the birth father. The final sample was composed of 3,712 nonmarital births and 1,186 marital births.

Data were collected at baseline, with initial interviews with mothers occurring within 24 hours of the child's birth and with fathers as soon after the birth as possible. Followup interviews were conducted with both parents when the child reached 12, 30, and 48 months. An in-home child assessment was also conducted with the child at 30 and 48 months. Data were collected on current housing situation and residential mobility from both parents at all data collection points and included homelessness as a response option. The data set also included extensive information from both parents on demographics; partner, child, and familial relationships; marriage attitudes; child well-being; the health and development of the child and the respondent; social support; environmental factors; government programs; incarceration; and employment, income, and economic well-being.

Table 4-7. Fragile Families and Child Well-being Study
Structure
Sample Stratified random sample of U.S. cities with a population of 200,000 or more, containing samples of families with nonmarital and marital births
Size Approximately 3,800 unwed couples and 1,200 married couples
Timeframe Baseline collected between 1998-2000, followups conducted 1 year, 3 years (not yet available), and 5 years (not yet available)
Content
Housing/homelessness Current housing situation (street, homeless is a choice), various doubled-up population identifiers
Specific housing questions
  • In 1-year followup instrument: Asks the mother what the current housing situation is (answer choices include on the street, homeless); question is also present in the 3-year and 5-year followup
  • What are the reasons that you and the baby’s father are not planning to live together? Answer choice: housing reasons (no place to live)
  • In the past 12 months, did you not pay the full amount of rent or mortgage payments?
  • In the past 12 months, were you evicted from your home or apartment for not paying the rent or mortgage?
  • In the past 12 months, did you move in with other people even for a little while because of financial problems?
  • In the past 12 months, did you stay at a shelter, in an abandoned building, an automobile or any other place not meant for regular housing for even one night?
Demographics Race, education, employment status, of mother and father
Family Followups: Family characteristics, relationships with family members, mother’s family background and support
Service needs Mother’s physical and emotional health; child’s social/emotional/behavioral outcomes, cognitive skills, overall development, academic outcomes, child mental/physical health
Agency/service involvement Baseline: drug treatment; Followup: welfare, employment office, Healthy Start, Head Start
Strengths for typology — Knowledge gaps answered
Geographic coverage Yes, nationally representative sample
Population coverage
(Broader than homeless)
Yes, provides ability to examine subgroups of families from initial development through various changes
Subgroups available Yes, relevant subgroups include working but still homeless, episodically homeless, two-parent homeless families, families that fall back into homelessness; “moderate needs” homeless

Also provides data on those at risk, ability to examine the role of risk and protective factors as they relate to homelessness, family, and work.

Prevention/intervention services (agency involvement) Yes, housing subsidies, welfare, drug treatment
Data on children Yes
Weaknesses Sample size of the literally homeless might be small
Conclusion This sample would definitely inform a typology of homeless families

Of all the data sets identified, this study offers the most promise for informing the typology efforts. For the purposes of this current effort, the project team conducted a reanalysis of the Fragile Families data set, focusing on specific research questions described in Chapter 5, along with the findings from the reanalysis. The data set contains a high-risk sample for homelessness, in that pregnancy is one of the major risk factors found to precede homelessness (Weitzman, 1989) or its reoccurrence (Rog and Gutman, 1997). Because it is a longitudinal panel study, it affords the ability to track families over time into various residences and presumably homelessness, and to examine the role of various other factors in their lives operating as either risk or protective factors. The database has the added benefits of being readily available and national in scope on the nonmarital births, offering some specific city information. Finally, the study contains a wealth of information on children from birth to 5 years and would provide an invaluable comparative perspective on the development of children living in various environments and experiencing different patterns of residential and familial instability.

Welfare, Children, and Families: Three-City Study. This research project is an intensive study of households with children in low-income neighborhoods in Boston, Chicago, and San Antonio. The study (see Table 4-8) is designed to better understand the effects of welfare reform on the well-being of children and families, especially as welfare reform evolves. The study has three interrelated components-longitudinal surveys, an embedded development study, and ethnographic studies.

Table 4-8. Welfare, children, and families: Three-city study
Structure
Sample Random sample of households with children in low-income neighborhoods in Boston, Chicago, and San Antonio
Size Approximately 2,400 households; approximately 256 women
Timeframe Baseline conducted in 1999, first followup in 2000, second followup in 2002
Content
Housing/homelessness
  • What did you do to get by without welfare (answer choice is “went to a shelter”)
  • Doubled-up population identifying question
Specific housing questions
  • What did you do to get by instead of going on welfare? (Answer choice- “went to a shelter”)
  • What did you do to get by when the welfare benefits stopped? (Answer choice “went to a shelter”)
  • During the past two years, did anyone move into your house/apartment because they could not afford their own place to live? (doubled-up population)
  • In the past two years, were you forced to move from a residence or home because you could not afford the rent or mortgage?
  • Does your household pay less rent because the government pays for part, such as Section 8?
Demographics Education, basic demographics
Family Family routines, family background, father involvement, mother-child activities
Service needs Domestic violence, schooling, pregnancies, mother’s emotional and physical well-being
Agency/service involvement Welfare participation
Strengths for typology — Knowledge gaps answered
Geographic coverage No, sampled in only three cities
Population coverage
(Broader than homeless)
Yes
Subgroups available Yes, subgroups include episodically homeless, families that fall back into homelessness, those at risk for homelessness
Prevention/intervention services
(agency involvement)
Yes, housing subsidies, welfare
Data on children Yes
Weaknesses Unrepresentative sample
Conclusion Cannot be used for the typology even though the sample identifies homeless families; the sample is nationally unrepresentative

The longitudinal component includes three rounds of interviews with a random sample of 2,400 households selected in 1999 (with an oversampling of welfare families). Each household had a child either between the ages of 0 to 4 or between the ages of 10 to 14 at the time of the baseline interview. Two followup interviews were conducted, one in 2000/01 and the second beginning in 2002. Personal interviews were conducted with the adults and the older children. Assessments were conducted with the younger children. With respect to homelessness, the survey identifies families who indicate that they went to a shelter instead of receiving welfare and those who indicate that they went to a shelter when benefits stopped or were cut. Unfortunately, the code "moving in with others" as a response to either not receiving welfare or what they did after benefits stopped is combined with "moving to cheaper housing;" therefore, a transition can be noted but is not well defined. In addition, data are collected on whether another individual or individuals have moved in with the household because they could not live on their own.

The developmental study includes more intensive testing and evaluation of approximately 700 children aged 2 to 4. This includes videotaping and coding interactions, time-diary studies, and observations of child care settings. Ethnographies are also being conducted in each city, focused on how changes in welfare policy affect the daily lives of welfare-dependent and working poor families; 215 families are to be followed for 4 years.

This study may hold some promise for informing the typology. It will depend on the extent to which people indicate that homelessness, or moving to another residence/being doubled up, are options they chose in order to not receive welfare. It will also depend on how they survived once welfare was terminated. Because these are not direct questions but rather open-ended response options, it is up to the respondent to offer this information. Moreover, it is unlikely in most cases that people moved into shelter to avoid going on welfare or as a direct result of benefits being cut. Doubling-up with others is a more likely result, but it may not happen immediately after welfare is cut; it is more likely that families will weather an eviction or two before moving to other housing or in with family or friends. Thus, the usefulness of these data depends on how valid the responses are and the extent to which the relevant options are used.

Women's Employment Study. The Women's Employment Study (see Table 4-9) consists of a random sample of 874 single mothers who were on the welfare rolls in a Michigan metropolitan area in 1997. Cases were proportionately selected by ZIP Code, race, and age. Eligibility was also restricted to White or Black women who were U.S. citizens and not classified as exempt from work requirements. Four waves of data were collected, generally at 1-year intervals with the baseline conducted in 1997. The purpose of the study is to examine barriers to employment among welfare mothers. In-person interviews cover a comprehensive set of possible barriers, including education; work experience, skills, and readiness; physical health, mental health, and substance abuse problems; family stress; and domestic violence.

Table 4-9. Women’s employment study
Structure
Sample Random sample of single welfare mothers who live in a Michigan metropolitan area
Size 753 current and former welfare recipient families
Timeframe 1997-2003; baseline collected 1997, 1-year followup in 1998, 2-year followup in 1999
Content
Housing/homelessness
  • Homelessness
  • Length of homelessness
Specific housing questions
  • Have you ever been homeless? 
  • For how many days or weeks were you homeless?
  • Have you ever been evicted?
  • In the next two months, how much do you anticipate that you and your family will experience actual hardships such as inadequate food, housing, or medical care?
  • Do government programs like Section 8 pay part of housing costs?
Demographics Employment, education
Family Violence in family, births/pregnancies, parent-child interactions, family and relationship outcomes, parenting attitudes, parenting skills
Service needs Child development, substance abuse, emotional and physical well-being
Agency/service involvement Case management, counseling, substance abuse, child protection agencies, domestic violence, or mental health treatment
Strengths for typology — Knowledge gaps answered
Geographic coverage No, only from Michigan
Population coverage
(Broader than homeless)
Yes, sample of single welfare mothers
Subgroups available Yes, subgroups include working but still homeless, episodically homeless, families that fall back into homelessness, moderate needs homelessness, those at-risk
Prevention/intervention services
(agency involvement)
Yes, housing subsidies, CPS, mental health treatment
Data on children Yes
Weaknesses Small, unrepresentative sample
Conclusion Cannot be used for typology; even though homelessness data are collected, the sample is unrepresentative and small.

Key to typology interest is the measurement of housing affordability, residential mobility, and homelessness in the first followup wave. Respondents rated the difficulty of living on their total household income and the likelihood of experiencing hardships such as inadequate housing, food, or medical care in the next 2 months. They also were asked if they had their gas or electricity turned off, had been evicted, or had been homeless since the previous interview. If a respondent indicated that they had been homeless, the amount of time spent homeless was recorded.

Unfortunately, the Women's Employment Study database is not in the public domain at this time. However, since the study has an active research team, additional analyses relevant to the typology may be ongoing or may be solicited. In particular, the study represents another examination of families at risk of becoming homeless and the various factors that place them at risk or that may cause them to fall into homelessness.

Summary

The current homelessness research provides an extensive understanding of currently homeless families' characteristics and service needs and, to some degree, the patterns of residential instability they faced prior to becoming homeless. However, as a whole, the existing studies lack geographic diversity and do not provide the ability to understand subsets of families. Moreover, there is not sufficient data tracking of families at risk of homelessness or those that fall back into homelessness over time. In addition, the small sample sizes of the more general homeless family population studies restrict the ability to focus on subgroups of families. There is little study of the role that prevention and intervention efforts play in the lives of the families or the role that specific government programs have in preventing or intervening with homelessness.

The majority of studies reviewed for this effort, especially the general population studies, do not hold the prospect of filling the knowledge voids. Those studies that focus on, or include, key at-risk populations and that are national in scope lack questions on homelessness. Those that do include a housing or living arrangement question or domain may not have an explicit code for homelessness. For example, after extensive review of the NLS, it was discovered that addresses, and not types of locations, are coded, thus making it impossible to distinguish a shelter address from a housing address. If nothing else, the database investigation has revealed shortcomings in some of the nation's major data sets that are clearly missing a significant segment of the population. Remedies for improving some of the data sets' ability to inform the efforts would range from adding codes to the "other responses" to adding probes or questions.

Proposed Secondary Analyses

Among the studies reviewed, there are three data sets that hold the greatest promise for informing these typology efforts. These data sets include the NSAF, Women's Employment Study, and the Fragile Families and Child Well-being Study. The best prospect is the Fragile Families and Child Well-being Study, which has the following strengths:

  • Contains a high-risk sample for homelessness (i.e., new parents);
  • Is a longitudinal panel study that is national in scope;
  • Measures residential moves, including homelessness, so it can provide a sensitive understanding of the dynamics of homelessness and housing instability;
  • Has a number of questions for the prior year that measure incidence of risk factors for homelessness (e.g., being evicted; having utilities turned off), and the incidence of homelessness itself (e.g., staying for at least one night with others; staying at least one night in a literally homeless situation);
  • Examines various other factors in their lives that can operate as either risk or protective factors, and can help differentiate those who become homeless from those who do not; and
  • Includes developmental information on a cohort of children from birth to 4 years old.

The Fragile Families data set is readily available, free of charge, and has considerable documentation on the web. Given its potential and easy availability, reanalysis of the original data, presented in Chapter 5, has been conducted.

The NSAF is a second data set that has potential for providing data about families at risk of homelessness, and families who are homeless by virtue of being doubled up. As a large national database, it offers the potential to provide a strong understanding of the at-risk population, however, since it is only a cross-sectional study, the data will be a snap-shot of the population. This data set is also readily available and is well documented on the web site.

The Women's Employment Study database is the final data set that appears useful to reanalyze with a focus on homelessness. This data set provides data on families on welfare, their struggles with income insufficiency, and the impact that welfare reform is having, especially on housing stability and affordability. In particular, the study has potential for explaining the dynamics of shelter use and residential instability among welfare families. The drawbacks of the study are that the data are currently not in the public domain, the study is concentrated in a single site, the study includes a relatively small sample, and the number of homeless families in the data set could be too small for analysis. However, as it is an active research team, additional analyses may be ongoing or may be solicited.

Two other studies offer less information for the time and effort it would take to access, understand, review, and reanalyze the data. The Three-City Study, for example, could be useful to the typology development if there is a sufficient sample of families who reveal that they have used shelter or have been doubled up with others. However, the indirect nature of these questions suggests that this is unlikely to be the case.

The CWHRS contains key information on housing affordability, residential mobility, and homelessness of women currently being abused. From a prior examination of this data set, significant subsets of families in the data set are currently homeless. A key analytic question would be if the help-seeking patterns of those who are homeless differ from individuals who are currently housed, and what other factors are related to their help-seeking behaviors. However, the fact that it is a single study, has only two waves of data (with the second wave only a year or less after the baseline), and focuses on only one subset of the overall homeless families population lowers its priority for reanalysis.

5. A Reanalysis of the Fragile Families and Child Well-Being Study

Introduction

As noted in Chapter 4, through an extensive review of existing data sets, a data set was identified with potential for informing the development of a typology of homeless families. The Fragile Families and Child Well-Being Study (Reichman, N.E., Teitler, J.O., Garfinkel, I., McLanahan, S., 2001) follows a birth cohort of new parents and their children over a 5-year period beginning in 1998. This sample is at high risk for homelessness in that pregnancy is one of the major risk factors found to precede homelessness (Weitzman, 1989) or loss of housing (Rog and Gutman, 1997). In addition, because the study oversampled unmarried women, the sample contains a higher proportion of women potentially more vulnerable to residential instability. Furthermore, Fragile Families is a national longitudinal panel study that includes measures of residential instability and risk (e.g., being evicted, having utilities turned off), as well as the incidence of being doubled-up (i.e., staying for at least one night with others) and literal homelessness (i.e., staying at least one night in a literally homeless situation).1 These data thus afford the ability to track families over time and to examine the role of various risk or protective factors on residential stability.

This chapter describes a reanalysis of the Fragile Families data set focused on the following research questions:

  • What are the risk and protective factors that differentiate, among a cohort of poor families, those families who:
    • Experienced homelessness and those who remained stably housed?
    • Experienced homelessness and those who become doubled up or residentially unstable (i.e., at risk of homelessness)?
    • Became doubled up or residentially unstable and those who remained stably housed?

The reanalysis is intended to inform our conceptualization of a typology of homeless families. As a multisite database of high-risk families, it provides an opportunity to examine the incidence of homelessness over multiple geographic areas, over time, and in contrast to a comparison population of poor families experiencing a range of residential arrangements.2

The chapter begins with a brief description of the data set, the sample selected for the re-analyses, the creation of residential groups and other relevant measures, and the analyses performed. Then, the results of the reanalysis are provided, followed by a summary of the findings and a discussion of the study's implications for filling knowledge gaps, guiding typology development, and directing future research.

Methodology

Database Description

The Fragile Families and Child Well-being Study, also referred to as the "The Survey of New Parents," is designed to track a cohort of new parents and their children over a 5-year period. The purpose of the study is to provide new information on the strengths, conditions, and relationships of both wed and unwed parents and how Federal and state policies affect family composition and child well-being. The study is a stratified random sample of U.S. cities with a population of 200,000 or more, designed to provide a representative sample of nonmarital births in U.S. cities with populations over 200,000. Mothers were approached and interviewed at the hospital within 48 hours of giving birth, and fathers were interviewed at the hospital or elsewhere as soon as possible after the birth.

Eventually, four waves of data will be available. Baseline data were collected between 1998 and 2000, and followup interviews were conducted at 1 year, 3 years, and 5 years after the baseline. Currently, three waves are available for reanalysis: baseline, Year 1 followup, and Year 3 followup. Baseline data are available on a sample of 4,898 families (3,712 nonmarital births and 1,186 marital births). One-year followup data are available on a total of 4,365 mothers and 3,367 fathers, and Year 3 followup interviews are available on 4,231 mothers and 3,299 fathers. At least one wave of followup is available on 94 percent of the mothers, while 82 percent of the mothers were interviewed at both followups.3 The database constructed for this reanalysis focuses on the mother, with data about the father given where appropriate.

Defining the Sample for Reanalysis

The Fragile Families data set includes families from diverse income backgrounds, ranging from those far beneath the income poverty level to those who have relatively high levels of income. For an analysis examining the risk factors for homelessness, it is important that the groups being compared have an equivalent probability of experiencing the condition. Therefore, an income limit (i.e., household income at or below 50% of national poverty) was used to define the sample. In addition, the sample was limited to families in which the mother was 18 years of age or older. Finally, the sample was selected based on the families completing the Year 1 interview (n = 4365) because residential information was only collected during the followup interviews. A total of 838 families (19.2% of the Year 1 Fragile Families data set) met the income and age criteria and constitute the primary sample used for this study.

Creating and Describing Residential Outcome Groups

Detailed residential information was collected on participants in the Fragile Families study at the Year 1 and Year 3 followup surveys. This residential information found in each survey included:

  • # — Moves: Number of moves since birth of child/last interview;
  • Residential Risk Indicators: Indicators of residential risk in past 12 months (i.e., had not paid full amount of rent or mortgage; had been evicted from home or apartment; had not paid full amount of a gas, oil, or electric bill; had phone service disconnected because payments were not made; had to borrow money from friends or family to help pay bills);
  • Doubled-Up: Whether the family was currently living with family or friends and paying no rent, or had moved in with other people even for a little while due to financial problems in last 12 months; and
  • Homeless: Whether the family was currently living on the street, in temporary housing or a group home, or spent at least one night in a shelter, abandoned building, automobile, or other place not meant for regular housing in the past 12 months.

For descriptive analyses, each mother was categorized into one of four residential groups at Year 1 and Year 3 based on the pattern of responses to these residential indicators (# moves, residential risk indicators, doubled-up, homeless).

As Table 5-1 shows, residentially stable households in Year 1 and Year 3 were defined as having less than two moves, no residential risks, and had not been doubled-up or homeless during the prior 12 months. At-Risk households reported two or more moves and/or one or more residential risks, and also had not been doubled-up or homeless in the last 12 months. Doubled-up households were ones that were currently or recently doubled-up and had not been homeless, regardless of the number of moves or residential risks they reported in the past 12 months. Homeless households were ones that reported currently living on the street, in temporary housing or group home, or had spent at least one night in the past 12 months in a shelter, abandoned building, automobile or other place not meant for regular housing.

Data Collection Timeframe Residentially Stable Residentially At-Risk Doubled-Up Homeless
Table 5-1. Defining Residential Groups
Year 1 (n=838) 35% 39% 21% 6%
Year 3 (n=754) 42% 37% 16% 5%
Year 1 or Year 3 Criteria < 2 moves yearly 2+ moves yearly OR N/A N/A
No risk Indicators 1+ risk indicators N/A N/A
Not doubled-up Not doubled-up Current/recently doubled-up N/A
Not homeless Not homeless Not homeless Current/recently homeless
Combined Criteria* 22%
  • Residentially stable Year 1 AND Year 3
41%
  • At-risk Year 1 OR Year 3
  • Never doubled-up or homeless
28%
  • Doubled-up Year 1 OR Year 3
  • Never homeless
8%
  • Homeless Year 1 OR Year 3
* Combined Year 1 and Year 3 (n= 838)

In addition to categorizing households into one of these four residential groups at Year 1 and Year 3, a combined residential group was also created based on the most severe residential category experienced in the two waves. A family who was residentially stable during Year 1 but doubled-up at Year 3, for example, would be classified as doubled-up. In order to be considered residentially stable, a family would need to meet the stable criteria for both Year 1 and Year 3. Conversely, to be put into the homeless group a family only had to report being homeless in Year 1 or Year 3.

Potential Risk and Protective Factors

Variables to be examined were selected in part based on characteristics that were found to be important in past research along with those proposed by members of an Expert Panel, convened to guide the conceptualization of the typology (a detailed meeting summary is included in Chapter 3). Demographic and background variables were examined, including the mother's age, race, and whether her first birth was as a teenager, as were several household characteristics, such as the number of children in the household and whether the mother was living with her spouse/partner, living with her own mother, or living with other adults (not including her spouse/partner). Variables were also examined that allow us to describe services used by these households, including receipt of health services, employment training, child care, and housing-related services, such as living in public housing or receiving housing assistance. Changes in health status, alcohol and substance use, and mental health have also been examined. Reports of whether the mother had recently been hit or slapped by her partner/spouse/child's father were also combined to create a measure of domestic violence. Table 3-1 provides a complete list of the variables that were examined.

Descriptive analyses, described below, were conducted with all of the variables shown in Table 5-1. Only those variables that showed substantial variation between housing groups (e.g., there were statistically significant differences between stably housed or homeless and at least two of the three other groups) or were considered important background and demographic characteristics, however, were included in the multivariate analyses.

Table 5-2. Variables from Fragile Families data set to be examined in descriptive reanalyses
Demographics (Mother and Father)
  • Age
  • Race (% African American)
  • Income

Household Composition

  • Live with partner/spouse
  • Live with mother
  • Number of other adults (not spouse/partner) in household
  • Number of children (<18) in household

Background (Mother):

  • Whether the mother first gave birth as a teenager (<18); age first gave birth
  • Currently attend any school/training
  • Whether mother has worked since target child was born; currently working
  • Receive health care during pregnancy
  • Any new pregnancies or children
  • Mother living with parents at age 15
  • Spouse/partner working
  • Other adult in household working

Problems Making Ends Meet

  • Receive free food/meal in last 12 months
  • Children ever go hungry last 12 months
  • Mother ever go hungry last 12 months

Government Assistance

  • Any income assistance (e.g., unemployment insurance, workers’ compensation, SSI, etc.)
  • Receive TANF
  • Receive food stamps
  • Applied for EITC
Housing
  • Does mother live in a housing project?
  • Mother receiving subsidized housing
  • Safety of streets around home

Mother’s Services

  • Income from public assistance, welfare, food stamps, unemployment insurance, workmen’s compensation, disability, or Social Security benefits
  • Have any health insurance

Supports

  • Did mother receive financial support from anyone (other than child’s father)
  • Could mother count on someone for a $200 loan? $1,000 loan?
  • Could mother count on someone giving her a place to live?
  • Could mother count on someone to provide child care/babysitting?

Health, Mental Health, and Substance Abuse

  • Mother’s health
  • Use alcohol
  • Use drugs
  • Whether drinking or drugs has interfered with work
  • Whether mother sought help or was treated for drug or alcohol problems
  • Mother’s depression and anxiety levels

Conflict/Domestic Violence

  • Hit or slapped by a partner/spouse

Descriptive Analyses and Results

Descriptive analyses were conducted with all of the variables shown in Table 5-2. These analyses were conducted to examine differences among the combined residential groups on the range of variables listed in Table 5-2. Alcohol and drug use were combined to create a single substance use variable. Several measures of mental health status — currently feeling sad or depressed, recently lost interest in hobbies/work, or recently feeling tense/anxious — were also combined into a single mental health indicator. Means, standard deviations, percentages and other descriptive statistics were computed for these variables for each residential group. The appropriate comparative analysis — chi-square, t-test — was then used to determine if the residential groups statistically differed on each of these variables. These analyses allow us to determine how these individual groups compared and contrasted.

The number of families in each residential group varies, both over time and when combined. At Year 1 and Year 3, for example, over one-third of families can be classified as residentially stable (35% and 42%) or at-risk (39% and 37%), while approximately one-fifth were doubled-up (21% and 16%) and only one-twentieth were currently or recently homeless (6% and 5%). When the two years are combined, however, the number of residentially stable families declines to only 22%. The percentage of families at-risk across both time periods increases to 41%, and Doubled-Up to 28%. The percentage of families ever homeless also increases to 8%.

The tables found in Appendix E provide the descriptive comparisons of the four combined residential groups for households at or below 50 percent of the poverty level on all key variables. The table also shows statistical differences between and among the groups.

This section provides a brief summary of these findings, highlighting the patterns of differences among the groups. First, as has been found in past studies (see Chapter 2), there were no demographic and background differences between the various residential groups. The mother's age at baseline, for example, was almost identical between the four groups (24 to 25 years old, on average), and comparable percentages of women were African-American (61% - 70%). There were also no major differences in the percentage of women with a high school degree (45% - 49%), currently attending any school or training (14% - 20%), or working (34% - 38%).

There were, however, distinguishing characteristics for each of the groups. Stable families were most distinct from all other families on a full host of health, mental health and substance use variables. Compared to each of the other groups, families who were residentially stable both years reported statistically:

  • Better health
  • Less alcohol use
  • Less drug use
  • Less smoking
  • Less daily interference from drug and alcohol use
  • Less depression or other mental health issues
  • Less likelihood of being hit or slapped by a spouse/partner

The other area of pronounced difference between stable families and all other groups of families involved resources. Of all four groups, those stably housed were most likely to have a spouse working and have someone who could co-sign for a loan. They were least likely of all groups to receive food stamps or free food in the past year, to report going hungry, and to apply for the Earned Income Tax credit. Although there are other differences between the residentially stable groups and others, the patterns of resources and problems are the strongest and most consistent.

Among the groups, at-risk families were the least likely to have lived with their mother at any interview time point, had the fewest number of adults in the family, and were most likely to have received a housing subsidy since the baseline. Doubled-up families, not surprisingly, are the most likely of all groups to have more adults in their household. Compared to at-risk families, doubled-up families are more likely to live with their mother, less likely to have a spouse or partner working, but more likely to have another adult working in the household, and less likely to have a housing subsidy.

Homeless families, compared to all groups, are most likely to have received free food in the past year, yet also most likely to have gone hungry, least likely to have someone in their family offer a place to live or to have someone who could co-sign a loan, and most likely to report using drugs and report mental health symptoms.

These descriptive comparisons show a variety of differences between the groups, but most clearly show differences between the groups on household composition, resources and receipt of benefits, and on health, mental health, and substance use.

Predicting Residential Stability and Homelessness

A second set of analyses were performed to answer the questions:

  • What are the risk and protective factors that differentiate homeless families from all others?
  • What are the risk and protective factors that differentiate residentially stable families from all others?

To answer these questions, statistical procedures (logistic regressions) were used that could test for the effects of all relevant variables at one time (rather than one at a time, as in the descriptive analyses). By looking at all variables simultaneously, it is possible to identify variables that are relatively more important in distinguishing residentially stable families from all others or those that are relatively more important in distinguishing homeless families from all others. The variables that set residentially stable families apart from others may be considered "protective" factors for homelessness and residential risk, while the factors that distinguish homeless families from all others can be considered potential "risk" factors for homelessness.

Logistic regressions were computed for Year 1 groups, Year 3 groups, and the combined residential groups. Only those variables that showed substantial variation between housing groups (e.g., there were statistically significant differences between stably housed or homeless and at least two of the three other groups) or were considered important background and demographic characteristics were included in these analyses. Each logistic model began by entering all of the variables in the model, and then removing non-significant variables4. Tables 5-4 and 5-5, which show the results from these logistic analyses, list all of the variables that were initially included in the model (e.g., non-shaded variables), but parameter estimates are only shown for those variables that were statistically significant at the .05 level in the final models.

Three models examined the factors that related to a family experiencing recent homelessness at Year 1, Year 3, and at either time-point. Three additional models examined the factors that related to a family remaining residentially stable at Year 1, Year 3, and at both time-points.

Homelessness. Table 5-3 presents the results of the three homeless models (Year 1, Year 1 and 3, and Year 3). The Nagelkerke R2 (a pseudo- R2 statistic that measures the amount of variance explained by the model) for the Year 1 and Year 1-3 models are both less than .2, indicating that neither model is doing a very good job of fitting the data. The Year 3 model has a Nagelkerke R2 of .333, however, indicating that this is a better fitting, more powerful model (closer to Cohen's definition of a medium effect).

Only one variable, income, is significant in all three models. Families with relatively higher household incomes were consistently less likely to experience homelessness, an effect that was strongest for the Year 3 model (parameter estimate of -.303).

A few variables were significant in two of the three models. Receiving housing assistance (local, state, or Federal) appears to be a protective factor. People who reported receiving housing assistance at baseline or Year 1, as well as those who obtained housing assistance during the followup period, (having a negative coefficient for the change score) were also less likely to experience homelessness.

Mental health issues, substance abuse issues, and reports of domestic violence were also somewhat related to a greater likelihood of experiencing homelessness. Finally, receipt of TANF was positively related to the likelihood of becoming homeless, but was likely a proxy for need and lack of income rather than a predictor of homelessness.

  Year 1 Model Year 1 or 3 Model Year 3 Model
Table 5-3.5
Logistic regression models year 1 and year 3 homeless households
at least 50 percent below poverty line
Nagelkerke R2 n=778 n=775 n=688
.157 .166 .333
Age      
Race (% Black)      
Live with both parents @ 15      
Teen Birth     .872*
Pregnant @ Year 1      
Pregnant @ Year 3      
Partner – Baseline      
Partner – Yr 1      
Change partner B-1      
Change partner 1-3     -1.536***
Live with mother – Base      
Live with mother – Yr 1     1.007*
Change live Mom B-1      
Change live Mom 1-3      
Number adults in household – Base      
Number adults in household – Yr 1      
Number adults in household – Yr 3     .509**
Number of children – Baseline      
Number of children – Yr 1      
Number of children – Yr 3      
Social Support – Base
(# Sources 0-3)
     
Social Support – Yr 1      
Social Support – Yr 3      
$1,000 Loan – Yr 1      
$1,000 Loan – Yr 3     -1.303*
Education – Baseline (<HS/HS+)      
Mother working – Base      
Mother working – Yr 1     -1.537*
Change Mom work B-1      
Change Mom work 1-3     -1.803**
  Year 1 Model Year 1 or 3 Model Year 3 Model
Table 5-3.
Logistic regression models year 1 and year 3 homeless households at least 50 percent below poverty line (continued)
Nagelkerke R2 n=778 n=775 n=688
.157 .166 .333
Income – Year 1 (ln) -.155* -.182** -.303***
Partner working – Base      
Partner working – Yr 1      
Change partner work B-1      
Change partner work 1-3      
Other adult work – Base      
Other adult work – Yr 1      
Other adult work – Yr 3      
Health status – Base
(1:Excellent to 5:Poor)
     
Health status – Yr 1      
Health status – Yr 3      
Ever use SA – Base and Yr 1 1.076*    
SA ever interfere – B and Yr 1   .781*  
Ever DV – B and Yr 1 1.092** .764*  
MH Prob – Yr 1 .306 .473***  
Ever use SA – Base, 1, 3      
SA ever interfere – B, 1, 3      
Ever DV – B, 1, 3      
MH Prob – Yr 3     .637**
Neigh Safety – Baseline
(1 Very Safe to 4 Very Unsafe)
    .535*
Public housing – Base      
Public housing – Yr 1      
Change public housing B-1      
Change public housing 1-3      
Housing assistance – Baseline   -.815*  
Housing assistance – Yr 1     -1.473*
Change housing assistance
B-1
-1.029*** -1.359***  
Change housing assistance
1-3
     
TANF/Food Stamps – Base      
Receive TANF – Yr 1 .995** 1.029*** .759
Change TANF 1-3      
Receive food stamps – Yr 1      
Change food stamps 1-3      
* Significant at P<.05
** Significant at P<.01
*** Significant at P<.001

Stably Housed. The descriptive analyses showed that it was often the Stably Housed group that differed the most from the other residential groups. Table 5-4 presents models that examine factors to predict who was residentially stable at Year 1, at Year 3, as well as Year 1 AND Year 3. The overall fit of all three models is fairly consistent and low; Nagelkerke R2 of .221 for the Year 1 model, .183 for the Year 3 model, and .197 for the Year 1-3 model (all would be considered small effects). Table 5-4 presents the results for the stably housed group.

  Year 1 Model Year 1 or 3 Model Year 3 Model
Table 5-4.
Logistic regression models for year 1 and year 3 stably housed households
at least 50 percent below poverty line
Nagelkerke R2 n=778 n=775 n=688
.221 .197 .183
Age   .033  
Race (% Black)      
Live with both parents @ 15      
Teen birth      
Pregnant @ Year 1      
Pregnant @ Year 3      
Partner – Baseline .530** .548*  
Partner – Yr 1      
Change partner B-1 .456*    
Change partner 1-3     -.303
Live with mother – Baseline      
Live with mother – Yr 1      
Change live Mom B-1 .336    
Change live Mom 1-3     -.479**
Number of adults in household – Baseline .186* .210*  
Number of adults in household – Yr 1      
Number of adults in household – Yr 3      
Number of children – Baseline .194***    
Number of children – Yr 1      
Number of children – Yr 3      
Social Support – Base
(# Sources 0-3)
     
Social Support – Yr 1      
Social Support – Yr 3      
$1,000 Loan – Yr 1 .291    
$1,000 Loan – Yr 3      
Education – Baseline (<HS/HS+)      
Mother working – Baseline -.283    
Mother working – Yr 1      
Change Mom work B-1      
Change Mom work 1-3     .383**
Income – Yr 1 (ln) .091 .112  
Partner working – Base      
Partner working – Yr 1      
Change partner work B-1 .705** .881***  
Change partner work 1-3      
  Year 1 Model Year 1 or 3 Model Year 3 Model
Table 5-4.
Logistic regression models for year 1 and year 3 stably housed households
at least 50 percent below poverty line (continued)
  n=778 n=775 n=688
Nagelkerke R2 .221 .197 .183
Other adult working –Base      
Other adult working – Yr 1      
Other adult working – Yr 3      
Health status – Base
(1:Excellent to 5:Poor)
-.149 -.323***  
Health status – Yr 1     -.130
Health status – Yr 3      
Ever use SA – Base and Yr1 -.473** -.644**  
SA ever interfere – B and Yr 1      
Ever DV – B and Yr 1 -1.037*** -.928*  
MH Prob – Yr 1 -.546*** -.625***  
Ever use SA – Base, 1, 3     -.692***
SA ever interfere – B, 1, 3      
Ever DV – B, 1, 3      
MH Prob – Yr 3     -.583***
Neigh Safety – Baseline
(1 Very Safe to 4 Very Unsafe)
     
Public housing – Base   .823**  
Public housing – Yr 1     .528**
Change public housing B-1   .548*  
Change public housing 1-3      
Housing assistance – Baseline      
Housing assistance – Yr 1      
Change housing assistance B-1   .352  
Change housing assistance 1-3      
TANF/food stamps – Base      
Receive TANF – Yr 1     -.304
Change TANF 1-3      
Receive food stamps – Yr 1      
Change food stamps 1-3     -.508**
* Significant at P<.05
** Significant at P<.01
*** Significant at P<.001

Looking for results that were significant in more than one model showed that living with a partner/spouse, at least at baseline, made it more likely that a mother would be residentially stable. Changes in this relationship, however, had contradictory effects in different models; in the Year 1 model, having a partner join the household was associated with greater likelihood of being stable, whereas in Year 3, the household was less stable if a partner joined (or more stable if the partner left). Perhaps this was due, in part, to the decrease in partner employment noted earlier in Year 3.

The more adults there are living in the household, and having a spouse/partner who is working or who has found employment, all make it more likely that a mother will be residentially stable. Living in public housing was also frequently associated with being stably housed, while obtaining public housing was significant only for the combined Year 1/Year 3 outcome.

Factors that made it less likely that someone would be residentially stable somewhat mirror the results of the homeless analyses. Reported substance use and mental health issues made it less likely that a woman would be residentially stable in all three models. Poorer reported physical health was also associated with a decreased risk of residential stability in the combined model, and reported domestic violence was significant in two of the models.

Discussion

Summary of Results

The reanalysis of the Fragile Families database shows that even among women who are extremely poor (at or below 50% of the poverty level), the risk of being homeless is not very large. Using a very broad definition of homelessness, less than one in ten (8%) of the women in this poverty sample indicated that they had been homeless for even 1 night over a 1-to-3 year period. However, only 22 percent reported being residentially stable (moving no more than once, not reporting any problems making ends meet) for the entire period, while the largest group (40%) of women were generally residentially stable but experienced some sort of financial issues (e.g., had problems paying for food, housing and/or utilities), but had not been homeless, doubled-up, or had to move frequently.6

The Fragile Families reanalysis also shows that there are characteristics and experiences that distinguish between these residential outcomes. Bivariate analyses indicate that the residential groups are distinct on a number of variables, often in a linear fashion, from those who experience the least stability to those experiencing the most stability. The most consistent findings relate to mothers' health, mental health, and substance use, suggesting that these conditions heighten their vulnerability to become homeless and their absence helps a mother remain stable. Overall, however, the results of the logistic models find few variables that have strong predictive value in differentiating those who experience homelessness from all others living in vast poverty, or those who remain residentially stable from all others. Having higher incomes and receiving housing assistance appear to serve as protective factors in the homeless models, whereas the health, mental health, and substance use issues appear to place a mother at risk (though the findings are not entirely consistent). In predicting stability living with a partner relates to greater stability, especially if the partner is working. Having other adults in the household also appears to increase a mother's likelihood of remaining stable and, not surprisingly, having substance use and mental health issues lessens a mother's likelihood of remaining stable.

Caveats and Qualifications

Several important qualifications need to be kept in mind when reviewing all of these findings. One issue is the relatively small number of households in this poverty sample that were ever homeless during the period examined (less than 100). The small number of cases limits how much can be said even descriptively about these families. In addition, little information was obtained on the homeless experience. Thus, the group could include families who spent one night in shelter to those who spent many nights and had multiple episodes of homelessness.

For the logistic regression models, the relatively poor fit of most of the models (with Nagelkerke R2 scores typically only around .2) should serve as a reminder to treat these findings with some caution. Although it is plausible that the low fit for the various homeless models could be attributed to the small number of cases in the condition or to the heterogeneous nature of the outcome variable, the fact that low model fits were found with the stably housed models where the numbers were greater and the definition of stable more solid makes this explanation less likely. It is more likely that the poor fit of these models is due to the reliance on individual-level variables and the absence of any contextual variables.

These issues notwithstanding, though, the reanalysis of the Fragile Families database has still provided an opportunity to address some of our knowledge gaps with respect to homeless families, guide our conceptualization of a typology, and inform designs for future research.

Filling Knowledge Gaps

Although not designed to provide information on homeless families, the Fragile Families database has provided information that is useful in filling some of our knowledge gaps with respect to homeless families. One important gap that this data set helps fill is providing information on a national sample of homeless families, rather than being restricted to a single city. In fact, looking more closely at the geographic location of families (e.g., were homeless families more likely than others to come from some metropolitan areas?), might be another useful analysis. Unfortunately, geographic data were not readily available to those who had access to the public data sets. Reingold and Fertig, in "The Characteristics and Causes of Homelessness Among At Risk families with Children in Twenty American Cities" included as Appendix D, had unrestricted access to the Fragile Families data and did examine a few contextual variables. However, only the number of shelter beds in a city related to the probability of experiencing homelessness. It is possible, however, that unexplored contextual variables may be important to examine in predicting not only homelessness but residential stability as well.

The Fragile Families data set is also useful in that it provides information on a broader sample of at-risk families. As already noted, a key finding from this reanalysis is that there is a range of residential patterns experienced by even very poor families and that it is as useful to determine what keeps families stable as it is to know what predicts homelessness. This type of analysis is difficult to do with the typical homeless database but was possible in this reanalysis.

The fact that the Fragile Families project has collected information over time is also important, providing a longitudinal perspective that is often missing from studies. The longitudinal analyses not only showed that the incidence of homelessness was relatively rare (less than 10% ever homeless), even in this extreme poverty sample, it also showed that only a handful of households (9 households, 1% of the eligible families) reported being homeless in more than one time period. It is also true, however, that less than a quarter of the families remained stable throughout both time periods.

The eventual release of the 5-year followup survey should provide even more opportunities to examine the residential patterns of these various households, including a chance to examine households that fall back into homelessness, as well as what predicts long-term stability. The small number of families that experience homelessness, however, will likely make it difficult to do many analyses with such a group even if they could be identified. The small number also makes it difficult to use the Fragile Families data set to examine any subsets of homeless families, such as those who are working or two-parent families.

Guiding the Typology Development

The relatively poor fit of the logistic regression models, examining both homelessness and residential stability, limits how much guidance this reanalysis of the Fragile Families database can provide for developing a typology of homeless families. The results do suggest that mental health and substance use issues (and to a lesser degree, domestic violence) increase a family's vulnerability to homelessness and that the absence of these issues heightens a family's probability of remaining stable. Housing assistance (such as receiving a subsidy) and having more money, not surprisingly, help families avoid homelessness, as has been found in prior studies.

As noted, the relatively poor fit of these models suggests that individual-level characteristics such as these are not the only factors involved in predicting who will become homeless. For those who are struggling well below the poverty level, it is likely that contextual factors, such as the availability of affordable housing in an area, play an even more important role in determining the likelihood of becoming homeless or staying in stable housing.

Directing Future Research

As noted, several suggestions on how the Fragile Families data set could be used for future research include looking more closely at geographic differences, as well as taking advantage of the next wave of surveys. More broadly, this reanalysis has shown the utility of looking at a broader range of families that may be at risk of becoming homeless. While the factors associated with being residentially stable somewhat mirror the factors related to who becomes homeless, there are also important differences that can be seen only when it is possible to examine each group separately.

Additional new research may benefit from exploring more clearly the role that other family members (e.g., partner, other adults) play in fostering stability, as well as how the various health/mental health/substance use/domestic violence issues increase one's vulnerability. Do these issues make it difficult for a mother to work and thus rise out of poverty? Do they make her more vulnerable to being evicted or being thrown out of other relatives' homes? Do they make it difficult for other adults to remain living with them? Understanding the role these factors play may help in developing interventions that can prevent homelessness, especially among those who may have had and lost subsidies.

Although our analyses did not focus squarely on those living at risk or doubled-up, it is clear that these groups experience a number of stresses and their share of health, mental health, and related issues. Understanding their vulnerability and interventions that can help them rise to greater stability would be important to decreasing the daily challenges these families experience.

Lastly, studies should investigate how context interplays with individual-level factors and determine what community factors can play a role in fostering greater stability and decreasing the risk of homelessness.

Endnotes

1 U.S. Department of Health and Human Services. For the purposes of some Federal definitions, being doubled up is considered homelessness whereas in other programs it is not.

2 Reingold and Fertig also conducted analyses on the Fragile Families data set for Appendix D in this volume (Reingold and Fertig, 2006) in response to a request to write a paper on at-risk families. Their paper was designed to examine homelessness among poor families with children while this chapter, derived from our exploration of relevant secondary data sets, looks more broadly at the different residential histories of poor women (50% or below the poverty level) and the factors that predict both homelessness and stability. Where the analyses overlap, similar results are found in both studies.

3 More detailed information on the Fragile Families data set can be found in Reichman et al. (Reichman, Teitler, Garfinkel, and McLanahan, 2001), as well as on the study's web site.

4 More specifically, the backward stepwise procedure removed non-significant variables one-by-one. Once all appropriate variables had been removed, however, the program re-examines all of the removed variables to see if any should be re-entered.

5 The outcome tables show all of the variables that were initially included in the model (nonshaded parameters), but parameter estimates are shown only for those variables used in the final model.

6 An interesting observation is that homelessness in this sample does not appear to be completely correlated with poverty. A total of 230 families (5% of the total sample) experienced homelessness at some point during the followup period; only one-third of these families were living at 50 percent of or below the poverty level and 29 percent were living above the poverty level. Additional analyses of these groups may provide further insights into the factors related to families becoming homeless.

6. Prospects for Enhancing Federal Surveys

Introduction

As noted in previous chapters, the current literature provides an extensive understanding of the characteristics and service needs of currently homeless families, yet there remain substantial knowledge gaps that make it difficult to develop an accurate and useful typology of homeless families. These gaps include the following:

  • Data on homeless families across various regions of the country;
  • Data on key subgroups, such as:
    • Families at risk of becoming homeless;
    • Moderate need homeless families;
    • Families that fall back into homelessness despite intervention;
    • Working homeless families; and
    • Two-parent homeless families.
  • Longitudinal studies of homeless families; and
  • More intensive studies of homeless children.

It was noted in Chapter 4 that none of the general population studies currently or recently conducted by the Federal Government, such as the Current Population Survey (CPS), the Panel Study of Income Dynamics (PSID), or the National Longitudinal Surveys of Labor Market Experience (NLS) can address these knowledge gaps in their present form.

Given the size, scope, and resources already invested in conducting various national surveys, it would be useful to determine if there are surveys that are ongoing, or planned for the future, that might potentially be enhanced to fill these gaps. In this chapter, current and planned survey efforts are examined and three surveys are identified that could be enhanced to provide useful information on families who have experienced homelessness one or more times, and families who are at risk of homelessness. A short battery of questions is proposed that could be added to each identified survey to strengthen the ability of each to address one or more of the gaps in the knowledge and understanding of homeless families.

Overview of National Survey Efforts

A number of national surveys are regularly conducted to address a myriad of information needs. These surveys are generally sponsored, if not actually conducted, by the Federal Government, from the basic census task of describing how many people live in the country in order to apportion congressional seats and Federal spending, to more focused efforts designed to provide both private and public officials with timely, reliable, and accessible information on such topics as labor force participation and income, housing, and health and nutrition.7 In general, these survey efforts can be divided into three broad types:

  1. Ongoing cross-sectional studies;
  2. Short-term longitudinal studies; and
  3. Long-term longitudinal studies.

Each of these survey types provides a different set of opportunities and challenges with respect to the information it can already provide on families that are at risk and/or have experienced homelessness, as well as for its potential to be enhanced to provide such information.

Review of Cross-Sectional Surveys

The national cross-sectional surveys currently in operation are designed to provide current information on various topics (e.g., the percentage of the population currently working, health status of people, or the extent of illegal substance abuse). These surveys typically collect information on a large number of people in order to be able to provide accurate and reliable estimates not only at the national level, but also for smaller geographic subunits, such as the state, metropolitan region, city, or even census tract level.

In terms of providing useful information on families that are, have been, or may be homeless in the future, these cross-sectional, general population surveys have several major advantages:

  • The ability to understand factors that helped families exit homelessness;
  • Depending on the size and structure of the data set, the ability to examine at-risk and literal homelessness for subgroups of families, including:
    • Working poor families;
    • Moderate-need poor families; and
    • Two-parent poor families.
  • The ability to develop estimates (albeit, likely underestimates) of the incidence and prevalence of homelessness among families over a specific period of time at the national level and, depending on the size and structure of the data set, at the regional and/or state level, and the ability to examine change in the incidence, prevalence, and characteristics of homeless families over time.

Cross-sectional studies also have two major limitations for use in the current effort. First, depending on the sampling frame and data collection methods used, a study may exclude currently homeless families. A study that recruits participants from a list of addresses that includes only homes, apartments, and condominiums, for example, would exclude not only those who are living on the streets, but those living in emergency shelters and other types of temporary housing. Likewise, a survey that collects information only by phone could not include people who do not have their own phone, which is likely to be true for most homeless families (as well as families at risk of becoming homeless). As a result, these studies would provide an underestimate of the overall incidence and prevalence of homelessness. Second, these studies can only examine past homelessness, with no opportunity to examine families prospectively. These surveys generally offer large samples, but either select different samples each time data are collected or do not provide the ability to link responses across different collection points.

Table 6-1 presents a summary of the nine major, cross sectional surveys that were identified and reviewed for this effort. Each survey is described according to the type of sampling frame used (i.e., how the sample was initially drawn or identified), the size and composition of the sample (i.e., if the data are collected on individuals, households, or both), the frequency of data collection, whether the sampling frame is supplemented by a specific oversample (e.g., oversample of low-income households), how the data were collected (e.g., in person, by telephone, or some combination), the primary content focus of the survey, and any other notes that help us understand the suitability of the survey for informing the typology.

Survey Sampling frame Sample size and type Frequency Oversamples How data collected Primary focus Other notes
Table 6-1. Overview of Federal cross-sectional survey efforts
American Community Survey (ACS)
(Conducted by Census Bureau)
National area probability

Currently excludes group quarters, expected to include in 2006

800,000 households

3 million households starting in 2005

Data collected on all household members

Annually None Mail (50%)

Computer-assisted telephone surveys (CATI)

In-person (sample of nonresponders)

Demographic
Housing
Social
Economic
ACS replaces the decennial census long form
American Housing Survey (AHS)
(Conducted by Census Bureau)
National area probability (excludes group quarters)

Metropolitan area probability surveys collected as well

55,000 households - national survey

3,200 households - for each metropolitan survey

Biannually - national survey

Every 6 years for each metropolitan survey, conducting 14 per year

None CATI
In-person
Size, composition, and state of housing stock Survey returns to the same address for each wave, even if the household has changed
Current Population Survey (CPS)
(Conducted by Census Bureau)
National area probability 60,000 households, 130,000+ people

Data collected on all household members

Monthly

Households in survey for 4 months, out 8, in 4, and then dropped

Latinos (March sample of each year) Initial Interview In-person

In-person or CATI for followups

Labor force participation Supplemental questions regularly added:
  • March: Annual demographic survey
  • Housing vacancy survey
National Health and Nutrition Examination Survey (NHANES)
(Conducted by Nat. Center for Health Statistics)
National area probability 5,000 people Annually Low-income Whites, Adolescents, Persons 60+, Blacks, and Latinos In-person

Additional medical exams at a mobile exam center

Health, Nutrition Data combined and released in 2-year waves
National Health Interview Survey (NHIS)
(Sponsored by Nat. Center for Health Statistics)
National area probability (includes group quarters) 43,000 households, 106,000 people

Data collected on all household members

Annually Blacks and Latinos In-person Health and illness, Disability Topical supplemental modules regularly included
National Household Education Survey (NHES) (Conducted by National Center for Education Statistics) National RDD '2003 - 32,000 Households

Limited household data, more on selected adults and children

Biannually Blacks and Latinos CATI Various educational activities of adults and/or children  
National Immunization Survey (NIS)
(Sponsored by National Center for Health Statistics)
National RDD

Screen 1 million households to find families with children 19 to 35 months

35,000 households, 94,000+ people

Data collected on family, sample adult, and sample child, if available

Annually None CATI Immunization  
National Survey of America's Families (NSAF)
(Conducted by Urban Institute)
National RDD supplemented with area probability in poorer neighborhoods Three cohorts:
1997 - 45,000 households
1999 - 46,000 households
2002 - 40,000 households

Data collected on adults and one child if available

Three separate cohorts

No future surveys scheduled at this time

Oversampled in 13 large states

Low-income

CATI (majority)

In-person for households w/o phones

Employment
Education
Social services
Financial services
 
National Survey on Drug Use and Health (NSDUH)
(formerly National Household Survey on Drug Abuse)
(Sponsored by SAMHSA)
Area probability sample by state (to provide valid state estimates)

Includes group quarters (e.g., shelters, rooming houses)

70,000 people

Randomly selected persons per household

Annually Not currently (earlier oversampling of Blacks and Latinos stopped when the sample size was increased) In-person

(including audio computer assisted self-interviewing ACASI)

Cigarette use
Illicit drug use
Alcohol use
Mental illness
Mental health treatment
NSDUH notes that the sample sizes for group quarters are too small to provide valid estimates

These features were examined to identify surveys that offer the best opportunity to be enhanced to inform efforts to develop a typology of homeless families. Four criteria were used to select candidates for enhancement:

  • Whether the survey is still being conducted;
  • Whether the sample design (frame, size, type, and frequency) and data collection methods are more likely to include recently homeless families, as well as currently unstable families;
  • Whether the data are collected on family characteristics; and
  • Whether the sample size is large enough to examine subpopulations, regional, and state differences in homeless families, and families who are doubled-up.

Only two studies, the Current Population Survey (CPS) and the American Community Survey (ACS), met all four of these criteria. In this section, the rationale for eliminating the seven other studies from further review is explained and then the opportunities offered by the CPS and ACS surveys are described in more detail.

Studies No Longer Being Conducted

The National Survey of America's Families was eliminated from further consideration as there are no current plans for extending its data collection to a fourth cohort of respondents. It has a number of features that would have made it a good candidate for enhancement, including an oversampling of poorer neighborhoods, relatively large samples, and a focus on a number of data elements that could be fruitfully used to address questions about homeless families, including employment, education, and social service use. If a new NSAF study is mounted, however, it might be useful to consider including questions about homelessness.

Study Design and Structure Likely to Exclude Recent Homeless or Residentially Unstable Families

Three studies-the National Immunization Survey (NIS), National Household Education Survey (NHES), and American Housing Survey (AHS)-are not good candidates for enhancement because they use sample designs and/or data collection methods that are likely to exclude current and recently homeless families, as well as families that are currently residentially unstable. The NIS and NHES surveys use random-digit dialing (RDD) to identify study participants. Random digit dialing involves selecting telephone numbers at random from a frame of all possible telephone numbers. While RDD is a reliable and efficient method for randomly selecting a national sample, unless a currently homeless person or family happens to have a cell phone, RDD will exclude people and families who are currently living on the streets and/or in shelters. It is also likely to undersample those who are precariously housed, since they are likely to be part of the small percentage of households that do not have a phone or have phone numbers that are routinely disconnected.

In addition to these problems with their sampling frames, the NIS and NHES use computer-assisted telephone surveys (CATI) to collect data. Reliance on the telephone to collect data is further likely to lead to an underreporting of both current and recently homeless people and families. The AHS uses an area probability sample to identify study participants. In this approach, the country is broken down into various geographic units, with the smallest often having only 100 to 200 housing units (e.g., street addresses), and various methods are then used to randomly select these small geographic units or segments. Area probability samples have a better chance of including homeless families in their data sample, however, when they include group quarters, such as homeless shelters and transitional housing, in their sample frame. Unfortunately, the AHS excludes group quarters from its sample design. Furthermore, the AHS design of interviewing households living at the same address initially selected (e.g., returning to 100 Main Street each time), even if the household living there has changed since the previous survey, minimizes the likelihood of identifying homeless and at-risk families.

Family Data Not Collected

Two studies, the National Health and Nutrition Exam (NHANES) and the National Survey on Drug Use and Health (NSDUH), were dropped from consideration because both collect data mainly on a specific individual rather than a family or household. This is a particularly unfortunate feature, since the NSDUH annually collects data on a large number of people (more than 70,000), with samples designed to provide valid estimates at the state level and using a sample frame that includes group quarters. Furthermore, the NSDUH collects information on a number of domains that might be useful to examine in relationship to both prior homelessness and the risk of homelessness, including illicit drug use, alcohol use, mental health status, and mental health treatment.

Studies Unable to Examine Subpopulations or Regional/State Differences

The National Health Interview Survey (NHIS) uses a national area probability sample; collects information on health, illness, and disability that could be usefully examined in relationship to literal and at-risk homelessness; and regularly includes supplemental questions. The major challenge with the NHIS is its sample size. With a total sample of 43,000 and using a 1.5 percent yearly incidence rate of family homelessness (Burt et al., 1999), the NHIS would likely produce 600 to 700 cases per cohort and would not provide the ability to examine specific subgroups or data on homelessness at any level other than national.

Studies that Met Primary Selection Criteria

As noted earlier, only two studies meet all four of the primary selection criteria: the Current Population Survey and the American Community Survey.

Current Population Survey. The CPS is the main source of labor statistics in the United States. Conducted monthly by the Census Bureau for the U.S. Bureau of Labor Statistics, the CPS typically interviews a nationally representative sample of approximately 50,000 households. Respondents are selected using a national area probability sample. Part of the sample is changed each month; that is, a selected household or address is in the sample for 4 months, taken out for 8 months, put back in for 4 months, and then entirely removed. Given this rotation process, three-fourths of the sample stays the same from one month to the next, and half of the sample is surveyed in the same month from one year to the next. The monthly responses are not linked, however.

The CPS collects information on each member of the selected household aged 15 or older (although published reports focus on people ages 16 or over). Information collected includes data on employment, hours of work, and income, in addition to such demographic characteristics as age, sex, race, marital status, and educational attainment. Supplemental questions are also frequently included with the CPS. The results from each March survey, for example, are used to develop the Annual Demographic Supplement for the U.S. Census. In order to provide an adequate sample to do in-depth analyses of the Latino population, additional Latino sample units are added to the survey in this month.

With approximately 50,000 households selected each month, the CPS provides an opportunity to identify families that have been recently homeless or are at risk of becoming homeless. The broad geographic spread of the survey could help determine rates of homelessness across various regions of the country, as well as differences among urban, suburban, and rural areas. Information obtained over time could also be used to monitor changes in the percentage of families/individuals that have been homeless. In order to provide this sort of information, though, questions would need to be added about recent homeless and housing experiences.

American Community Survey. The ACS is a new survey effort being conducted by the Census Bureau and is designed to replace the long form of the decennial census. The main reason for this change is that the information provided by the long form tends to be increasingly out of date later in the decade. The ACS will enable the Census Bureau to provide more frequently updated information on the same range of topics that are covered in the decennial census.

Respondents for the ACS will be selected using a national area probability sample. Since the ACS is still being field tested, the survey initially included only 800,000 households, and group quarters were excluded from the sample. By 2006, however, group quarters, including emergency homeless shelters, transitional shelters, temporary housing, and hotels or motels used to provide housing for people without conventional shelter, were to be included.8

The ACS is designed to collect the same information as the long form, such as demographic, housing, social, and economic data. Information is obtained on every person in the household. Data for the ACS will be collected using three data collection methods. The first step will be self-administered mail surveys; it is expected that at least half of the responses will be obtained this way. Households that have not responded by mail will then be contacted by telephone. Finally, attempts will be made to conduct in-person interviews with at least a sample of those still remaining.

When it is fully operational, the ACS is expected to collect information on over three million households annually, making it by far the largest survey effort in the country. The sample size of the ACS should be large enough to provide valid annual estimates for every state, as well as all cities, counties, and metropolitan areas with 65,000 people or more. For smaller areas, such as rural areas or individual census tracts, results will have to be aggregated over a 3- to 5-year period to produce a sufficiently large sample.

Prospects for Survey Enhancement. Of the eight national cross-sectional surveys examined and summarized in Table 6-2, only the CPS and the ACS offer benefits for obtaining information on at-risk and literally homeless families. Of these two surveys, the ACS is the more useful for several reasons. First, the ACS has a much larger sample than the CPS. Questions about homelessness and the risk of homelessness added to the ACS would be asked to over three million households annually, while supplemental questions to the CPS would likely be asked only one month a year, to a sample of 50,000 households. Second, the data collection methods used for the ACS are more likely to locate and include precariously housed families, as the survey will eventually include families living in emergency homeless shelters and temporary housing. The data collection procedures used by the CPS provide much less opportunity to locate people who cannot be contacted initially. Finally, the CPS collects a relatively small amount of information compared to the ACS, with a major emphasis on labor force participation that is likely to be less useful in developing a typology of homeless families.

Given these additional considerations, the ACS offers the best prospects for addressing knowledge gaps about homeless families, if it is enhanced. Given its large sample size of over 3 million households a year, for example, the ACS could provide an opportunity to look at homelessness in specific geographic areas, providing an ability to examine how market forces, social capital, and other contextual variables relate to the incidence of family homelessness.

Selection Criteria American Community Survey American Housing Survey Current Population Survey National Health and Nutrition Survey National Health Interview Survey National Household Education Survey National Survey of America's Families National Survey on Drug Use and Health
Table 6-2. Cross-sectional surveys that meet selection criteria for possible enhancement
Surveys still being conducted Y Y Y Y Y Y Y Y
Sample design and data collection methods less likely to exclude recently homeless and currently unstable families Y No Y Y Y No Y Y
Data collected on family characteristics Y Y Y No Y Y Y No
Sufficient sample size to examine:
  • Subpopulations
  • Regional/state differences
  • Doubled-up
Y Y Y No No Y Y Y
Candidate for enhancement? Y No Y No No No No No

The sampling frame for the ACS already plans to begin to include overnight shelters and other facilities where homeless families could be found. Even if the ACS sample includes only a percentage of families found in the nontraditional housing settings, its large sample should still yield a large absolute number of homeless families that could be examined. Again, using a yearly incidence rate of family homelessness of 1.5 percent (Burt et al., 1999), the ACS could produce a sample of 45,000 homeless households a year. Even at half that rate, there would still be 20,000 to 25,000 homeless households in the sample. Furthermore, because the ACS is still being developed and refined, it may be possible to refine the sampling procedures to better ensure that emergency and transitional shelter facilities that serve homeless families and individuals are part of the sample frame.

Review of Longitudinal Studies

In addition to the cross-sectional surveys, there are several longitudinal studies that track the same person, family, or household over time. Because of the challenges and costs involved in tracking respondents, these surveys typically involve much smaller samples than cross-sectional studies and are often much more focused on specific populations and/or topics. Some of these surveys, such as the Survey of Income and Program Participation (SIPP) or the Medical Expenditure Panel Survey (MEPS), track people or households for only a few years. There are also two well-known, long-term longitudinal studies to consider: the Panel Study of Income Dynamics (PSID), begun in 1960, and the National Longitudinal Survey of Labor Market Experiences (NLS).

Longitudinal studies offer many of the same potential advantages as cross-sectional studies, and they have the added potential benefit of tracking people over time and thus may provide an opportunity to examine entries into, and exits out of, homelessness (depending on their tracking methods). However, as discussed in a later section, the longitudinal studies are smaller in overall sample size and lack the ability offered by cross-sectional studies to examine regional differences as well as various subgroups.

Table 6-3 presents a summary of the eight longitudinal surveys that were identified and reviewed for this effort according to the same features used to review the cross-sectional surveys.9 As with the cross-sectional surveys, the longitudinal surveys were initially examined according to four key selection criteria to identify surveys that offer the best opportunity to be enhanced to inform efforts to develop a typology of homeless families. Six of the eight surveys were deemed inappropriate candidates for enhancement, as discussed later. Only two surveys-the NLS 1979 cohort study and the NLS 1997 cohort study-met all four of the initial criteria.

Survey Sampling frame Sample size
and type
Frequency Oversamples How data collected Primary focus Other notes
Table 6-3. Overview of Federal longitudinal surveys
Early Childhood Longitudinal Study – Birth Cohort (ECLS – Birth)
(Sponsored by National Center for Education Statistics)
National random sample of birth certificates (or hospital records) 13,500 children born in 2001 Five waves of data collection:
  • 9 month
  • 18 month
  • 4 years
  • Kindergarten
  • 1st grade

Data collection ending in 2008

Asian, Pacific Islander, Chinese

Low and moderately low birthweight

Twins

In-home interviews with parent/ guardian

1-1 child assessments

Child and family characteristics that influence school preparedness  
Early Childhood Longitudinal Study – Kindergarten Sample (ECLS – K)
(Sponsored by National Center for Education Statistics)
National area probability of elementary schools 22,000 children in kindergarten 1998-99

Information collected on/from:

  • children
  • parents
  • teachers/school administrators
Most data collected annually for 6 years (K-5th grade) 

Some data collected semi-annually for first 2 years

Data collection ended in 2004

None Various methods:
  • 1-1 assessment
  • child interviews
  • CATI (parents)
  • self-administered (teachers, administrators)
Impact of early and middle-childhood education  
Early Childhood Longitudinal Study – Birth Cohort (ECLS – Birth)
(Sponsored by National Center for Education Statistics)
National random sample of birth certificates (or hospital records) 13,500 children born in 2001 Five waves of data collection:
  • 9 month
  • 18 month
  • 4 years
  • Kindergarten
  • 1st grade

Data collection ending in 2008

Asian, Pacific Islander, Chinese

Low and moderately low birthweight

Twins

In-home interviews with parent/ guardian

1-1 child assessments

Child and family characteristics that influence school preparedness  
Early Childhood Longitudinal Study – Kindergarten Sample (ECLS – K)
(Sponsored by National Center for Education Statistics)
National area probability of elementary schools 22,000 children in kindergarten 1998-99

Information collected on/from:

  • children
  • parents
  • teachers/school administrators
Most data collected annually for 6 years (K-5th grade)

Some data collected semi-annually for first 2 years

Data collection ended in 2004

None Various methods:
  • 1-1 assessment
  • child interviews
  • CATI (parents)
  • self-administered (teachers, administrators)
Impact of early and middle-childhood education  
Medical Expenditure Panel Survey (MEPS)
(Sponsored by Agency for Healthcare Research and Quality)
National area probability

(Based on NIS RDD sample)

 7,000 – 13,000 households

New waves added annually

Five interviews conducted over 2 years Blacks and Latinos

Low income

Elderly

In-person

CATI

Health care use and expenditures  
National Survey of Child and Adolescent Well-Being (NSCAW)
(Sponsored by Administration for Children and Families)
Children in welfare agencies nationwide

(97 different agencies)

5,400 children

700 supplemental sample

Three to four waves of data collection:
  • baseline
  • 12 month
  • 18 month
  • 36 month possible

Project ending in 2005

Supplemental sample (700 children) in foster care In-person Demographic characteristics of children and families

Pathways and services utilized

 
Survey of Income and Program Participation (SIPP)
(Conducted by Census Bureau)
National area probability 2001 cohort – 36,700 households

(Only original sample members reinterviewed)

Every 4 months over 3 to 4 years

2001 cohort just ended

Low-income In-person

CATI

Labor force

Income

Program

Participation and eligibility

 
National Longitudinal Surveys of Youth 1979 (NLSY79)
(Sponsored by Bureau of Labor Statistics)
National area probability sample youth/young adults

Initial NLS samples started in 1968, ended 1981

New cohorts added in 1979 and 1997

12,686 youth ages 14 to 22 in 1979

7,724 respondents in 2002 sample

Annually 1979-94

Biennially starting in 1994

Latino, Black, and economically disadvantaged nonminority

Young adults in the military (discontinued in 1985)

Initially in-person

Mostly CATI in recent years

Labor market activities Supplemental questions have been added at various waves
National Longitudinal Surveys of Youth 1997 (NLSY97)
(Sponsored by Bureau of Labor Statistics)
National area probability youth/young adults 8,984 youth ages 12 to 17 in 1997 Annually, 1997-2003 Black or Latino youth Initially in-person

CAPI

ACASI

Education

Labor market behavior

Family and community

Background

Supplemental questions have been added at various waves
Panel Study of Income Dynamics (PSID)
(Conducted by University of Michigan)
National area probability sample

Supplemental sample of low-income families

4,800 households

65,000+ people

Annually 1968-97

Biennially starting in 1999

Initial supplemental sample of low-income families

Refresher sample added in 1997

Initially in-person

Mostly CATI more recent years (97%)

Income

Labor force

Marital status

Supplemental questions have been added at various waves

Studies No Longer Being Conducted

Two of the longitudinal studies described in Table 4-3 were not considered appropriate candidates for enhancement, either because they have just finished or will soon end data collection. These include both the kindergarten and birth cohort samples of the Early Childhood Longitudinal Study (ECLS) and the National Survey of Child and Adolescent Well-Being (NSCAW). The ECLS kindergarten cohort ended data collection in 2004, while the birth cohort is expected to end data collection in 2008. The NSCAW study ended late in 2005.

Study Design and Structure Likely to Exclude Recent Homeless Families or Residentially Unstable Families

The MEPS was not considered a good candidate for enhancement because it uses a sample design that appears to make it more difficult to include recently homeless families as well as families that are currently at risk of being homeless. While most of the longitudinal studies use some sort of national area probability sample to select their respondents, the MEPS sample is selected from households identified through the NIS, which in turn identifies families using RDD. As previously discussed, it is expected that the use of RDD to identify study participants will further reduce the likelihood of a study including currently homeless people or families, and those who have been recently homeless or who are residentially unstable.

Studies Unable to Examine Subpopulations or Regional/State Differences

Two longitudinal studies, the SIPP and the PSID, that in many respects appeared to be good candidates for enhancement, were eventually considered to have samples that were too small to provide reliable estimates of recently homeless or residentially unstable families.

The SIPP is a series of national panel studies designed to collect information on income, labor force participation, and participation and eligibility for various government programs. The length of time each panel is followed has varied in recent years, from 2.5 to 4 years. Sample sizes have also varied from cohort to cohort within the panel studies, from 14,000 to 36,700 households in the 2001 study. Even at its largest, however, the SIPP study is likely to identify only 500 or 600 recently homeless families at most (based on a 1.5% annual homeless rate). Although this would be a sufficiently large sample to examine national trends, it would not provide a large enough sample to reliably examine any regional or geographic differences in homelessness. Combined with the fact that the SIPP tracks families for only a few years, it does not appear to be a good candidate for enhancement.

Initially, the PSID offered the best prospects for informing national efforts toward homeless prevention and resource allocation. Begun in 1968 and conducted by the University of Michigan, the PSID originally consisted of two independent samples-a cross-sectional national sample of approximately 3,000 families and a national sample of 2,000 low-income families. From 1968 to 1996, individuals from these initial samples were interviewed annually, including people who may no longer have been living in the original sampled household (e.g., children of the originally selected households). Because it tracked everyone associated with the originally sampled household, by 1996 the PSID had grown to over 65,000 individuals. In order to keep the sample more manageable, as well as to readjust the sample to better reflect the U.S. population, adjustments were made to the sample in 1997 that reduced the number of "core" families and added a new sample of families, particularly Latino and Asian households. The distinct advantages of the PSID with respect to being able to address knowledge gaps about homeless families are the following:

  • Longitudinal, currently conducted every 2 years;
  • Long history, starting in 1968;
  • An oversample of low-income households, who have a higher probability of having been or becoming homeless than the general population;
  • A residential followback as part of its data collection, so many of the changes could be adding questions to that part of the instrument; and
  • A wealth of data that have been consistently collected over time, such as income sources and amounts, employment, family composition, and demographic changes.

The major limitation of the PSID, however, is its sample size. If 1.5 percent of the households in the current PSID sample experienced homelessness in any given year, this would produce a sample of only 75 families to examine given the current overall sample of 4,800 households. A further complication with the PSID, or with any longitudinal study, is the ability to track and maintain contact with more difficult-to-reach study participants, such as people or families who become homeless. The response rates for the PSID have generally been very high, averaging 97 percent to 98 percent a year.10 As is noted in the PSID guide, though, even small rates of attrition from wave to wave can create problems over time. In 1988, for example, the response rate for individuals who lived in 1968 households was only 56 percent. Furthermore, the PSID does not make an attempt to recontact households that drop out, so even a small level of attrition may severely impact the likelihood of identifying families that have been or become homeless. Thus, despite its many potential advantages, concerns over sample size and composition make the PSID a less than ideal candidate for enhancement.

The only two longitudinal surveys that do seem to have some potential for addressing knowledge gaps about homeless families are the two recent NLS cohorts: the National Longitudinal Surveys of Youth 1979 (NLSY79) and the National Longitudinal Surveys of Youth 1997 (NLSY97). Both the NLSY79 and the NLSY97 are part of the National Longitudinal Surveys conducted for the U.S. Department of Labor, BLS.

Studies that Met Primary Selection Criteria

National Longitudinal Surveys of Youth 1979. The NLSY79 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.).

Respondents were selected using a multistage, stratified national area probability sample of dwelling units and group quarters. Three independent probability samples were recruited:

  • Cross-sectional sample of 6,111 people designed to be representative of the young adult population living in the United States at that time;
  • Supplemental set of 5,295 people designed to oversample Latino, Black, and economically disadvantaged, non-Latino, non-Black youth; and
  • A military sample of 1,280 people designed to represent the population born between January 1, 1957 and December 31, 1961, serving in the military as of September 30, 1978. Interviewing of the full military sample stopped in 1985.

Data for the NLSY79 have usually been collected using personal interviews, but telephone interviews have also been used and, in fact, are becoming more common. The NLS studies are primarily designed to study the transition of young people into the labor market. As a result, questions are typically asked about education, work, and training. Information is also collected on everyone living in the household of the initial respondent.

New topics have been frequently added to the NLS surveys. The National Institute on Alcohol Abuse and Alcoholism, together with the National Institute on Drug Abuse, for example, has added questions on alcohol and substance abuse on various NLS waves, while the National Institute of Education added a set of time-use questions to the 1981 survey.

In 1986, the NLS79 was further enhanced with a survey of children from the NLSY79 sample, funded by the National Institute of Child Health and Human Development (NICHD), along with a number of other government agencies and private foundations. These supplemental questions have collected information on the development of children born to NLSY79 women and, starting in 1994, a separate survey was administered to children age 15 or older.

National Longitudinal Surveys of Youth 1997. With the aging of the NLSY79 sample, a new cohort of young adults was selected in 1997 to participate in the NLSY97 survey. The NLSY97 sample consists of two independent national probability samples:

  • Cross-sectional sample of 6,748 people between the ages of 12 and 17 in 1997 designed to be representative of the young adult population living in the United States at that time and
  • Supplemental set of 2,236 people designed to oversample Latino and Black respondents.

Data are usually collected using in-person interviewers although, as with the NLSY79 study, telephone interviews are also conducted and are becoming more common over time.11 While much of the interview is conducted using a computer-assisted personal interview (CAPI) system, questions about particularly sensitive issues are asked using an audio computer-assisted self-interview (ACASI) procedure. While respondents are living with their parents or other legal guardian, many of the household questions are asked directly to the parents. When the initial respondent is living elsewhere, information is collected on everyone in the respondent's household. Followup surveys are conducted annually, although the gap between the initial survey and the second round turned out to be a little longer, approximately 18 months.

As with all of the NLS surveys, the primary purpose of the NLSY97 is to collect information on labor force experience, education, and the transition into the labor market. A number of additional questions have also been added, however, including a set of questions on crime and criminal activities sponsored by the U.S. Department of Justice, as well as development questions added by NICHD.

Prospects for Survey Enhancement. Although several longitudinal studies were initially thought to be able to provide information on knowledge gaps on homeless families, at least if they were enhanced, this review suggests that only the two latest NLS surveys — NLSY79 and NLSY97 — may be particularly good candidates. Of these two, the NLSY97 may offer the better opportunity. A major challenge with the NLSY79 cohort is that the primary respondents are moving out of the age when homelessness seems to be most likely to occur. As noted in Chapter 1, the risk of becoming homeless seems to be higher when people are in their mid to late-20s. Therefore, the NLSY79 sample would have been most likely to have experienced homelessness from the mid-1980s to early 1990s. By now, with the youngest members of the NLSY79 sample already 40 years old, this cohort may be too old to provide a good opportunity to examine homelessness, at least prospectively.

The NLSY79 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.

The NLSY97 sample provides the best opportunity to examine family homelessness prospectively, which could help answer questions about the factors that lead to people becoming homeless and factors that help predict exiting out of homelessness. The attrition rate for the NLSY97 sample has so far been fairly low, making it more likely to still include respondents whose families have been homeless or who are at risk of becoming homeless. For example, as of the last reported round of the NLSY97 surveys (Round 5), 88 percent of the initial sample had been interviewed. The primary reason for not conducting an interview has generally been because the respondent refused the interview rather than an inability to locate the respondent (65% of the nonresponses in Round 5 resulted from refusals).

Proposed Housing Questions

As noted in Chapter 2, existing studies, including the NLS and ACS, do not provide enough information to identify families that are currently or have recently been homeless. The major enhancements that these surveys need include adding questions and/or adding response categories that make it possible to identify homeless families.

American Community Survey. Enhancements of the ACS would focus on the housing section of the survey. These enhancements would include questions to determine whether household members are currently living in some sort of emergency or transitional housing, and whether they have been homeless or at risk of being homeless in the past 12 months. Also proposed is a question on whether anyone in the household has a housing subsidy. Table 6-4 shows in which sections of the survey instrument those enhancements could be made.

Table 6-4. Possible enhancements to the American Community Survey
Current Living Situation: Possibly add after Question 1 in the Housing section:
  • Are you currently living in an emergency or transitional housing unit or in some other sort of temporary housing? Y/N
Recent Homelessness or Risk of Homelessness:* Possibly add at the end of the Housing section: In the past 12 months:
  • Did you ever not pay the full amount of rent or mortgage payments? Y/N
  • Were you ever evicted from your home or apartment for not paying the rent or mortgage? Y/N
  • Did you move in with other people even for a little while? Y/N
  • Did you stay at a shelter, in an abandoned building, an automobile or any other place not meant for regular housing, for even one night? Y/N
Housing Subsidy: Possibly add to Question 15 in the Housing section, which currently asks about food stamps:
  • At any time in the past 12 months did anyone in the household receive a housing subsidy? Y/N
* These are modified versions of questions asked in the Fragile Family Study.

National Longitudinal Surveys of Youth 1997. The NLSY97 already collects housing and mobility information. In fact, the NLSY97 uses a set of responses to describe the respondent's current living situation that already includes "Shelter (for homeless or abused) or on street…" It then follows up with a question concerning how long the person has been living in this place. The NLS97 also includes a number of questions about various risk and protective factors. Many of these questions, including such topics as illegal drug use, criminal behavior, and arrests, are asked as part of an ACASI section.

What is not collected in the NLS97 survey is whether the respondent was homeless at some point between the current and previous interviews for those who moved, and whether the respondent was ever at risk of being homeless. Finally, depending on the length of time it takes to add any of these questions into the NLS, it may also be necessary to include homeless history questions at least once. Table 6-5 shows possible enhancements in the NLSY97.

Table 6-5. Possible enhancements to the National Longitudinal Surveys of Youth 1997
Recent Homelessness or Risk of Homelessness.* Possibly add in the Household Information section: In the past 12 months:
  • Did you ever not pay the full amount of rent or mortgage payments? Y/N
  • Were you ever evicted from your home or apartment for not paying the rent or mortgage? Y/N
  • Did you move in with other people even for a little while? Y/N
  • Did you stay at a shelter, in an abandoned building, an automobile or any other place not meant for regular housing, for even one night? Y/N
History of Homelessness: It may be possible to determine whether current respondents were ever homeless and to link that information to NLS data that have already been collected.

Was there any time during your lifetime in which you:

  • Lived with others due to cost?
  • Lived in places not intended for habitation?
  • Lived in an emergency shelter?
  • Lived on the streets (including car, campsite)?

If yes to any of the above:

  • When did it occur? (month/year)
  • Who were you living with at the time:
    • Living alone
    • Partner/spouse
    • Children
    • Other family member(s) (e.g., mother, cousin)
  • How long did you live there?
* These are modified versions of questions asked in the Fragile Families Study.

Endnotes

7 The surveys examined in this chapter were identified using a variety of sources. In addition to the surveys identified and examined in Chapter 2 and recommendations made by members of an Expert Panel brought together in July 2005 [see Chapter 3], surveys were identified through various web searches. Summaries and lists of databases, such as the list of public databases maintained by the American Sociological Association were also reviewed. Two recent government reports were also reviewed that discussed similar recent efforts at examining various Federal surveys to make more efficient use of these data collection sources. One was an inventory of Federal databases conducted for the HHS Office of the Assistant Secretary for Planning and Evaluation, as part of an assessment of major Federal databases for analyses of Latinos and Asian or Pacific Islander subgroups and Native Americans (Waksberg, Levine, and Marker, 2000). The second was a more recent review of Federal health surveys sponsored by The Robert Wood Johnson Foundation and the CDC's National Center for Health Statistics (AcademyHealth 2004).

8 In order to protect the confidentiality of their locations, group quarters will not include domestic violence shelters.

9 Since the NLS79 and NLS97 studies collect information on two distinct cohorts of households, and even use different data collection.

10 These numbers are from the PSID Guide available online at: http://psidonline.isr.umich.edu/Guide/ug/chap5.html.

11 Only 3 percent of the initial NLSY97 interviews were done over the telephone, for example, compared to 8.7 percent of the interviews in 2000.

7. Options for Additional Primary Data Collection and Analysis

Background

The existing body of literature related to homeless families provides substantial information on the characteristics and service needs of currently homeless mothers and their dependent children but is not robust enough to provide sufficient data with which to develop a typology of homeless families. In order to fill this knowledge gap, this project has employed a step-wise approach to seeking opportunities to collect additional information about homeless families and families at risk of homelessness that could be used in the development of a typology. The first step in the process identified existing major national and multijurisdictional surveys that might yield information through secondary data analysis. A closer analysis of data collected through the Fragile Families study further illuminated additional findings about homeless families and families at risk of homelessness. However, the data were still insufficient to fully inform a typology.

A second step included reviewing ongoing and planned surveys and developing a short battery of housing questions that could be considered for use in future surveys of low-income populations. The third and final step in the process is to identify and develop three separate approaches that Health and Human Services could consider for a future specialized data collection to fill key data gaps with respect to homeless families.

Proposed Study Options

Based on previous chapters and the Expert Panel meeting, three options for future research to inform the typology are proposed (see Table 7-1). First, there remains a need to understand the exits and pathways out of homelessness and subsequent residential patterns. A longitudinal, nationally representative study of first-time homeless families requesting shelter would provide critical information on multiple gaps identified.

Key knowledge gaps Option 1:
National longitudinal study of exit patterns and shelter requests of homeless families using primary data
Option 2:
Longitudinal, cross-regional study of families utilizing homeless shelters (HMIS)
Option 3:
Testing of promising practices to use a "typology" to prevent homelessness and/or expedite exit from homelessness
Table 7-1. Knowledge gaps informed by three options
Geographic diversity Y Y No
Families over time, as they move from homelessness into other arrangements Y No No
Factors that prevent imminent homelessness Y No Y
Dynamics of service use Y Y (Y)
Homeless children Y No No
Father and father's social networks No No No
Key subgroups
Families that fall back into homelessness despite intervention Y Y Y
Families at risk of becoming homeless No No No
Moderate needs families No (Y) Y
Family separations Y No No
Working homeless families Y (Y) (Y)
Families in extended family networks (Y) No No
Two-parent homeless families Y Y (Y)
(Y) — Could potentially fill the gap.

The second option is an analysis of Homeless Management Information System (HMIS) data from a national sample of communities. The analysis of universal items would provide an understanding of the demographic characteristics of families in and across different regions, while the analysis of program-specific data, if available, would permit an examination of the patterns of service use over time and their relationship to outcomes for subgroups of homeless families.

The third option would be targeted to understanding how best to prevent homelessness, with an examination of existing efforts to triage families, such as in Hennepin County's (Minnesota) Homelessness Prevention program in which they use a risk assessment to make decisions on how to prevent homelessness locally. This option, in many respects, would examine "test runs" of typologies in action in different communities.

Potential Goals of a Typology

Expert Panel members all agreed that more than one typology relevant to homeless families would be needed, depending on the purposes for developing the particular typology. After much discussion, four possible goals for a typology were summarized:

  • Prevention Policy. One goal for a typology of homeless families would be to identify the risk factors for homelessness. Most participants agreed this goal should be a priority because it would strive to minimize the population.
  • Services Policy. A typology that would guide services policy would identify the menu of services needed to help homeless families. However, this could potentially blur the lines of services for the general poor population.
  • Resource Allocation. This goal would result in a typology that would help us understand homelessness epidemiologically and guide the allocation of available resources/money locally.
  • Treatment Matching. This design would have the ability to predict the services and housing that a particular family needs from a clinical provider perspective. Different approaches have been implemented at the local level, usually following a basic model of three levels: one, a family needs support services; two, a family needs just housing, and three, a family needs both housing and support services. Unlike the service policy typology, a typology to guide treatment matching would be developed primarily for service providers rather than for policymakers.

Option 1: Longitudinal Study of Homeless Families

Study Overview

The Longitudinal Study of Homeless Families is a proposed national longitudinal study of exit patterns and shelter requests of homeless families using primary data. The major research questions could include the following:

  • What are the exit patterns from homelessness for families requesting shelter for the first time (e.g., time to exit; residential arrangement upon exit; stability following exit)? How do they compare with families with multiple homeless episodes?12
  • What are the individual and contextual factors13 that facilitate and inhibit exiting homelessness? What are the characteristics of families who are least likely to exit quickly? Most likely to return? What families are most likely to exit quickly on their own? What type and level of service use relate to length of stay in shelter/homelessness?
  • What factors assist a family in preventing the imminent risk of homelessness? What type and level of service use relate to their ability to successfully avoid homelessness?14

Rationale

Much of the past research involving homeless families has focused on the pathways into homelessness and the characteristics of families who become homeless in comparison to poor families in general. There has not been comparable attention paid to understanding how families exit homelessness and their subsequent residential patterns. During an overall period of lean fiscal times and reduced Section 8 certificates and other forms of public housing, other factors need to be identified that both facilitate families leaving homelessness and block successful exits. Information on both factors should inform intervention efforts, as well as efforts in targeting the limited housing resources to families least able to exit homelessness on their own. Likewise, there is a need to more clearly understand factors that both protect families from, and increase risk for, future homelessness episodes.

Few studies have had a longitudinal perspective that could provide insight into the trajectories families take out of homelessness. Little is known about the types of assistance that families receive and whether they take full advantage of services or benefits for which they may be eligible in order to exit. Research has not been conducted 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 has sufficient research been conducted on the factors that influence repeat homelessness among families.

Typologies and Knowledge Gaps it Could Inform

Data collected through a national longitudinal study of homeless families would help with resource allocation; understanding the needs of the population enables resource matching. Basic study design could provide data on the following:

  • Families while homeless and subsequent to homelessness;
  • Dynamics of service use and residential history/arrangements;
  • Family separations during and following homelessness;
  • Those who fall back into homelessness despite intervention;
  • Families who are working (depending on sample size and selection); and
  • Two-parent and father-only families (depending on sample size and selection).

If the study includes multiply homeless families at baseline, there will be greater understanding of repeat homelessness among families. If the study includes a sample of at-risk families, factors that prevent families from becoming homeless will be learned. If the sample is large enough to look at subgroups in regions, contextual factors will be identified that interact with individual factors and family homelessness.

Methodology

Sample. The basic sample would be a random sample of families requesting shelter for the first time. Depending on resources, the sample could include oversamples of families who come from two-parent families, father-only families, and families who are working to allow greater attention to these understudied groups.

The study could be enhanced by the addition of two other cohorts: families who have previously been homeless at least once, and families who are comparably poor, but domiciled and never homeless. This latter group would need to be selected from a separate sampling frame, such as Temporary Assistance for Needy Families (TANF) rolls.

To achieve a nationally representative sample of shelter requests, a stratified, multistage cluster sample would be used. Similar to the design used in the National Survey of Homeless Assistance Providers and Clients (NSHAPC) (Burt, Aron, Douglas, Valente, Lee, and Iwen, 1999), the first stage of the proposed design would include sampling of metropolitan statistical areas (MSAs) and, for non-MSAs, Community Action Agency (CAA) catchment areas. These sampling units would be clustered according to geography, population size, and economic indicators (e.g., per capita income, percent unemployed). Random samples of MSAs and CAAs within each cluster would then be chosen. All homeless shelters within each MSA or CAA would be identified and, if the number is too large, a random sample of these programs would be chosen. If there are specific subgroups that need to be oversampled, such as two-parent families, shelters could be clustered by type of populations served. Depending on resources, either a complete census of families requesting shelters for the first time or a random sample of these families could be sampled.15, 16

Time Frame. Families would be contacted to participate in the study at the time of the shelter request and would be followed for at least two years, and up to five years, following the shelter request.

Data Collection

Primary Data Collection. Interviews with the heads of household would be conducted within two weeks of the shelter request; at the time of exit or six months into shelter; and at six- or 12-month intervals subsequent to exit for a period of two to five years. Each interview would include questions on family demographics; family background, including credit history; criminal and legal involvement; residential background (residential follow-back calendar); homeless and shelter background; family separations; service need and use information; current and past trauma, conflict, and violence; and supports available. Data collection would be conducted by local interviewers in each selected community.

Administrative Data. In addition to collecting information through interviews, information could be obtained through the use of administrative databases, particularly the Homeless Management Information System.17

Although more in-depth information can be obtained through individual surveys, local HMIS systems can be used to determine the following:

  • Family exits from the homeless system;
  • Family reentry into the homeless system;
  • Possible validation of services received (depending upon the extensiveness of the HMIS system); and
  • Possible linkage to other administrative databases, such as public housing or welfare, to examine whether and how these other resources are used and what impact that has on staying out of homelessness.

A major advantage of using local HMIS systems is that information can be obtained even for families that cannot be located for a given followup, reducing the amount of missing data. This can be particularly useful in tracking families that return to shelters.

Because the U.S. Department of Housing and Urban Development (HUD) requires only the submission of aggregate HMIS data, however, and has explicitly stated that there will be no Federal effort to track homeless people and their identifying information beyond the local level, access to the local HMIS data will need to be negotiated with each Continuum of Care (CoC) in the targeted sampling areas.

Advantages and Limitations

The advantages of a national longitudinal study of homeless families include the ability to:

  • Focus on data collection at the exit time point;
  • Obtain data on patterns and pathways out of homelessness over time;
  • Determine families who are diverted from shelter;
  • Identify the characteristics and services used by families who leave shelter early; and
  • Collect more extensive and potentially more valid data than existing administrative data sets.

The likely cost of such a study is greater than other study alternatives. There may be various strategies that could be used to limit costs, such as relying on HMIS data in all communities and including primary data collection in a subset of communities.

Option 2: Homeless Management Information System

Study Overview

The Homeless Management Information System (HMIS) is a longitudinal, cross-regional study of families using homeless shelters. Using the HMIS universal data elements, the following questions can be investigated:

  • Are there regional differences in the number and demographic characteristics of homeless families?
  • How large are various subgroups of homeless families, such as families that return to shelters and two-parent families?
  • What is the length of stay for various demographic and regional subgroups of families?
  • What are the demographic characteristics of families that return to shelter?

Using the program-specific HMIS data elements:

  • What are the needs of different subgroups of families?
  • What services do homeless families use? Are there differences among various subgroups with respect to their service needs and homeless patterns?
  • Is there a relationship between family characteristics, services received, and time until exit and type of destination?

Rationale

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 Homeless Assistance 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. As of 2004, there were 444 CoCs operating across the country, with more being established every year. Of these 444 CoCs, 60 percent were already implementing or expanding their HMIS systems, while only one percent were not yet considering any such data collection effort.

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. Analyzing HMIS data, particularly from a national sample of CoCs, can help address a number of gaps in what is known about homeless families.

In particular, by showing what services homeless families use and how these services relate to outcomes (such as the length of time a family is 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).

Typologies and Knowledge Gaps the HMIS Could Inform

Using the HMIS universal data elements would help with resource allocation, as these would identify the size and composition of the population to enable resource matching.

Using the program-specific HMIS data elements would help provide data on the following:

  • Treatment matching-understand the services and housing needed by particular families to exit homelessness and
  • Resource allocation-understand the needs of the population to enable better resource matching.

An advantage of using HMIS data is that the information is already being collected in a number of communities around the country. One problem with the use of such administrative data, however, is that the only information available is that which is already being collected. Although HUD is encouraging CoCs to collect a wide range of information on everyone receiving homeless services, only a smaller set of items is required to be collected on every person. As a result, the knowledge gaps that an analysis of HMIS systems might address will depend upon the comprehensiveness of data collection in the specific HMIS systems examined.

The universal HMIS data elements required to be collected on everyone are as follows:

  • Identifying variables (e.g., name, Social Security number);
  • Personal identification number;
  • Household identification number;
  • Date of birth;
  • Ethnicity/race;
  • Gender;
  • Veteran's status;
  • Disability status (dichotomy);
  • Residence prior to program entry;
  • ZIP Code of last permanent address;
  • Program entry date; and
  • Program exit date.

If only these basic, universal data elements are available, an analysis of HMIS databases from CoCs around the country could provide the following:

  • Information on regional differences in the number and demographic composition of homeless families;
  • Information on the number and size of some subgroups of homeless families (e.g., two-parent families); and
  • Information on the number, size, and characteristics of families that return to shelters after receiving services.

More detailed, program-specific data elements are also collected as part of the HMIS. This information must be collected on all individuals and families participating in various HUD-funded programs, including the Supportive Housing Program, Shelter Plus Care, and Housing Opportunities for Persons with AIDS (HOPWA). CoCs are encouraged to collect this information on everyone tracked in the HMIS, but since this is not mandated, the extent to which this information is available would need to be determined on a case-by-case basis. These program-specific and outcome data elements include the following:

  • Income (total monthly and sources);
  • Noncash benefits (e.g., food stamps, Medicaid, TANF);
  • Physical disability (dichotomy);
  • Development disability (dichotomy);
  • HIV/AIDS (dichotomy);
  • Mental health (if experiencing [dichotomy] and if problem is expected to be long-standing);
  • Substance abuse (if experiencing [dichotomy] and if problem is expected to be long-standing);
  • Domestic violence (if experiencing and for how long);
  • Services received; and
  • Destination (for those who leave the homeless system).

If this more detailed information on family characteristics, service use, and outcomes can be obtained, then a study of HMIS databases could also provide the following:

  • Information on the needs and services used by homeless families; and
  • Information on differences in the types of services used by homeless families and whether these are related to family differences and/or to outcome differences.

Finally, it might be possible in a number of communities to link HMIS data with information from other government databases, such as public assistance or public housing data. This would provide even more information about each family that could be used both descriptively and to better understand what characteristics and services are related to exiting and staying out of homelessness.

Methodology

Sample. As already noted, by a congressional mandate, HUD is requiring local communities to develop a computerized data collection system. Since 2001, HUD has been working with local jurisdictions to develop and implement the HMIS. Individual CoCs will soon be required to submit information to HUD electronically based on Federal HMIS guidelines published in July 2004. These guidelines outline a set of universal elements that every CoC will be required to collect on all persons receiving homeless services, more detailed information that needs to be collected on everyone receiving services through McKinney-Vento-funded programs, along with a set of additional, recommended data elements.

Individual CoCs will be required to annually submit only aggregate information to HUD, however. As noted earlier, HUD has made it clear that "the HMIS initiative will include no Federal effort to track homeless people and their identifying information beyond the local level." As a result, the Federal guidelines state that "any research on the nature and patterns of homelessness that uses client-level HMIS data will take place only on the basis of specific agreements between researchers and the entity that administers the HMIS."18 Since it would not be feasible, nor necessary, for a study to coordinate with more than 400 CoCs operating across the nation, a sample of CoCs would need to be created.

To identify CoCs to approach for being in an HMIS study, a stratified, multistage cluster sample would need to be used. The CoCs would first be clustered on the basis of geography (e.g., programs in the South or Northeast), as well as possibly by community size (total population), and estimated size of the homeless population (based on prior research). One important set of criteria would also likely be the extent to which the HMIS is operational in a community, including the number of homeless service providers participating in the HMIS effort and the extent to which detailed information is being collected on everyone in the homeless assistance system. Once various clusters of CoCs have been established based on this sort of criteria, communities could be randomly selected to provide a comprehensive national sample of CoCs and, by extension, homeless families.

This sort of multistage cluster sampling procedure has already been used to select communities involved in the first Annual Homeless Assessment Report (AHAR). Although the AHAR will eventually include information from all CoCs, a sample of 80 communities was selected to provide information for the first annual report. Of these 80 communities, 18 were chosen because they have the largest homeless populations (e.g., New York City, Chicago, Los Angeles). The remaining communities were randomly selected after clustering them by their population size and region. The result is a nationally representative sample of communities.

After a sample of CoCs has been selected, each agency administering the HMIS that agreed to participate in the study would provide client-level data to be analyzed. The data submitted could include retrospective data on people and families already served, as well as periodic updates to enable researchers to track families over time.

Time Frame. The HMIS is designed to track people and families over time and record their history within the homeless service system. As a result, it would be possible to examine families from the beginning of each community's HMIS system. In order to compare results across HMIS systems, however, a common starting point would need to be established. When to set that starting point would be a function of the implementation histories of the HMIS systems in the selected communities.

Another data collection factor that would need to be taken into account, either in selecting communities or determining the starting point for data collection, is the extent of HMIS coverage. In order to be confident in the results obtained from any analyses, the Federal Government recommends that the HMIS cover at least 75 percent of the emergency and transitional housing beds in the community. Since it may have taken each CoC some time to begin collecting information on 75 percent or more of the homeless beds, the date when information can be reliably obtained from an HMIS is, therefore, likely to be later than the date when data collection initially started.

Data Collection

Homeless Management Information System. One advantage of using an administrative database such as the HMIS is that information is being collected on an ongoing basis. Therefore, instead of collecting data through repeated waves of interviews, as is typically done in a survey effort, HMIS data can be collapsed into any time frame desired, such as annually, quarterly, or monthly. There is less flexibility in the extent of information available on each family, or family member, from the HMIS system, however. The universal data elements, listed earlier, are the only variables that will be available on everyone in every community implementing an HMIS. Although this is not a very extensive amount of information, even these data can be used to help address some of the major research questions:

  • The percentage of homeless families among the total homeless population in a community;
  • Basic descriptive information on homeless families, including the number of people in the household, age of the parent(s) and children, and whether more than one adult is part of the family; and
  • Information on the number/percentage of families that return to shelters over whatever time frame can be examined.

More detailed, program-specific data elements can also be collected as part of the HMIS. This information must be collected on everyone involved in various HUD-funded programs, including the Supportive Housing Program, Shelter Plus Care, and HOPWA. The CoCs are encouraged to collect this information on everyone tracked in the HMIS system but, since this is not mandated, the extent to which the information is available would need to be determined on a case-by-case basis. The availability of this more detailed information, also listed earlier, would make it possible to expand the descriptive information available on each family and to create more refined subgroups of families (e.g., families experiencing domestic violence or substance abuse). It would also be possible to examine the services that families received and explore the relationship between services and basic outcomes, such as length of time in the homeless system and whether the family unit, or individual family members, fall back into homelessness over time.

Finally, there are a handful of data elements that are not required for anyone in the HMIS system but that CoCs are encouraged to collect: employment, education, health, pregnancy, more detailed veteran's data, and information on children's education participation. If this level of information is available on most people in the HMIS systems examined, then it would be possible to examine even more closely the relationships among family characteristics, services received, and various types of outcomes, such as finding a job or keeping children enrolled in school.

Other Administrative Data. Another important feature of the HMIS system is that information is collected that can be linked with other databases. Individual CoCs, for example, have been able to link their HMIS records with databases from the following:

  • Parole/justice/jails;
  • Public assistance (TANF, general assistance, food stamps);
  • Public health;
  • Health services; and
  • Housing (public housing, Section 8 programs).

If these linkages could be established for CoCs involved in a national study, they would provide an opportunity to examine even more about each family. Public assistance records, for example, can help show how many families were receiving services before they became homeless, how many obtained services after becoming homeless, whether public assistance came before or after exiting the homeless system, and whether receipt of public assistance is related to whether a family falls back into the homeless system.

Advantages and Limitations

There are a number of advantages to this option:

  • Data collection systems are in place in most CoCs in the country;
  • There is the ability to maximize the existing HMIS data for study purposes; and
  • The cost and burden are relatively low since CoCs are already required to collect this information.

There are also limitations to this option.

Extent of Coverage of Providers Within a Community. Not all homeless service providers necessarily need to participate in the HMIS, and it may take a while for some CoCs to get the participation of most, if not all, providers. To the extent that the HMIS system does not cover all homeless providers, it may miss some homeless families. In particular, there may be biases in the information available because of the lack of participation by certain types of providers. Many domestic violence shelters, for example, have expressed concerns regarding security and client privacy within the HMIS.

Extent of Coverage of Families. The HMIS is limited to providing information on families that receive services from homeless service providers. While it is likely to include most, if not all, families who live in shelters, the HMIS could miss families living in motels, living on the streets, or those who are doubled-up.

Variation in Data Quality. The Federal guidelines provide sites with a great deal of flexibility in how data are collected, including interviews with clients, interviews with staff, review of staff notes, and the like. In addition, many complex variables, such as disability or mental health status, are only grossly measured (Yes/No) and may or may not be based on solid, clinical information. The data also provide little indication of the level of services needed. Finally, the degree to which complete information is available on every person would need to be assessed on a case-by-case basis.

Data May not be Readily Available. As noted earlier, any study that relies on HMIS data would need to negotiate with each individual CoC for access to client-level data. Obtaining approval from multiple CoCs could well be a very cumbersome process and there is no guarantee that any selected CoC will agree to participate in a study. Providing adequate time and resources to establish a good working relationship with any selected CoC is thus likely to be an important aspect of any study involving HMIS records. Furthermore, there is likely going to be a tradeoff in the number of CoCs from which data can be obtained and the depth of information that can be collected. The most detailed studies, those that take advantage of both rich HMIS databases and the ability to link to other databases, can probably be conducted in only a handful of sites at one time, limiting the national representativeness of the study. Conversely, studies that try to use the large number of CoCs operating or developing will likely need to be satisfied with using only the more basic, universal data elements.

Option 3: Examining Efforts to Prevent Homelessness

Study Overview

This option tests promising practices to use a typology to prevent homelessness and/or expedite exit from homelessness. The following questions can be investigated:

  • Does a triaged approach to shelter result in long-term prevention of imminent homelessness for families?
  • What are the characteristics of families for whom the prevention approach works best?

Rationale

One goal for a typology of homeless families would be to identify families' risks for homelessness and barriers to housing in order to address the issues prior to entering shelter so that the incidence of homelessness among families could be reduced. In particular, a prevention-oriented typology would provide the ability to rank families according to levels of risk of homelessness and the probability of a quick exit. Such a typology would allow for distinguishing families in desperate need from those with moderate needs.

There are two concerns with trying to identify families at risk of homelessness on a broad scale, however. First, it is likely that an identification strategy that has fewer "false positives" will be based on a complex risk profile, rather than on one or two factors. As an example, Shinn and colleagues in New York City developed a model including 20 predictors to distinguish new applicants for shelter from the public assistance caseload in 1988 and correctly identified 66 percent of shelter entrants, while targeting 10 percent of the public assistance caseload (Shinn, Weitzman, Stojanovic, Knickman, Jiminez, Duchon, James, and Krantz, 1998). Second, the incidence of homelessness, even among poor families, is still too small to make widespread screening and prevention efficient. Resources targeted to an at-risk population are likely to be spent on more families that would never become homeless, than to reach those families whose homelessness could have been prevented.

A more efficient method for identifying families at risk of homelessness and in need of prevention services might be to use a risk assessment strategy to triage families who present at the shelter door for the first time. Several communities around the country are implementing systems that are using various levels of information to try to determine who can be diverted from the shelter; perhaps with some level of resources, who can be referred elsewhere; and who may require shelter services.

In Hennepin County, Minnesota, homeless service providers have developed a classification system for treatment matching of shelter usage by assessing needs and triaging families in real time. Classification is used at a very practical level and provides a method for service providers to use when making decisions about who receives shelter. In particular, Hennepin County operates the Rapid Exit Program, an innovative program that facilitates rapid rehousing by relying on early identification and resolution of a family's or individual's "housing barriers" and provides the assistance necessary to facilitate their return to permanent housing.

A study of Hennepin County or similar systems would, in effect, provide an opportunity to validate the utility of home-grown typologies.

Typologies and Knowledge Gaps It Could Inform

A basic study of a prevention practice would provide information on the following:

  • Prevention — identify risk factors for homelessness;
  • Treatment matching — understand the services and housing needed by particular families to exit homelessness;
  • Families at risk for homelessness or the identification of families before they become homeless;
  • Factors that prevent imminent homelessness, including individual and programmatic factors;
  • Moderate need families; and
  • Families who become homeless despite intervention.

Methodology

Basic Study Design. The basic study design would be an evaluation of one or more existing best practices at the county or state level where homeless service providers are using an empirical approach to determine need for preventive services. The goal would be to determine how effectively and appropriately the system matches services to needs. Rather than impose a classification system upon communities, this project would seek to find existing or developing systems that could be assessed and tracked over time, using HMIS or other administrative data in addition to primary data.

The first step would be to either issue a call for proposals to systems implementing such programs or to fund a scan of states and communities to identify these initiatives in place. Based on this first step, one or more best practices could be selected for examination.

The major evaluation question would be to determine how effectively the system prevents future homelessness for those diverted at the front door of the system. The study would involve examining the characteristics of each family, the resources and services available and accessed, and the residential arrangements following triaging. The outcome studied would be incidence of homelessness and the length of the homeless episode for each subgroup of families having various constellations of needs and receiving specific levels of service.

The basic study design would be descriptive, tracking families over time with respect to the interventions received and changes in family stability (including both residential stability and family composition). The HMIS data could be used if program-specific elements are included.

Alternate Study Designs. In systems where more than one preventive approach is being used, a randomized study might be possible in which families receiving the same assessment ratings would randomly receive different levels of preventive service. An alternative comparative approach would involve assessing and tracking families in a comparable community where the best practice triaging approach did not exist. Data would be compared over time on homelessness rates and service use.

Sample. The sample would be families who request shelter, are at imminent risk of homelessness, and have not been homeless in the past.

Timeframe. Families would be followed for at least two years, and up to five years, following the shelter request. Data even in the first 12 months may provide an indication of the effectiveness of the triaging in preventing at least the initial onset of homelessness.

Data Collection

Administrative Data. Ideally, administrative data could be accessed through the HMIS system that would provide information on the family background and demographics, service needs, past and ongoing service use, family composition and stability, and family residential arrangements.

Primary Data. Primary data collected through baseline interviews with the families could be used to supplement administrative data if needed. Followup interviews also could be included if administrative data are lacking on key domains such as family stability, residential arrangements, and service use.

Advantages and Limitations

The advantage to this option is the ability to examine the effectiveness of typologies in place. Limitations to this proposed approach include:

  • Not likely to allow for a controlled study and
  • What is in place may not concur with guidance from other research.

Endnotes

12 This is relevant if the study involves a cohort of multiply homeless families in addition to first-time homeless.

13 This can be investigated only if the study is national with sufficient local samples or a set of local studies.

14 This is relevant only if the study includes a comparable sample of poor families who are at risk of homelessness.

15 In some communities, the sample would be selected from a central screening center rather than from individual shelters.

16 The sample, depending on interest, could be expanded to include all families requesting shelter, not just first-timers.

17 If the study is designed to use HMIS data, then it may make sense to use local Continuum of Care (CoC) as the primary sampling unit. CoC's could further be clustered by geography, location (e.g., rural/urban), and whether they have an operating HMIS system in order to select a final sample.

18 From Federal Register, July 30, 2004, Homeless Management Information Systems (HMIS): Data and Technical Standards of Final Notice, Docket No. FR 4848-N-02.

8. Beginning to Conceptualize a Typology: Implications

Conclusions

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:

  • Reviewing the relevant literature on homeless families, as well as on typology development;
  • Reviewing existing data sets for reanalysis and conducting a reanalysis of a data set on a sample at high risk of homelessness;
  • Reviewing relevant ongoing studies and identifying how they may be modified to collect data on homelessness;
  • Convening a group of research experts on topics ranging from homelessness research in general, to homeless families, welfare, and typology development. Several Federal participants attended a one-day panel meeting to generate discussion related to conceptualizing a typology; and
  • Identifying study options that could provide additional information to guide the typology development.

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.

The Need for Multiple Typologies

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.

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    
Major    

Resource Allocation Typology

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.

Summary

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:

  • Results in subgroups that have homogeneity within them;
  • Results in subgroups that are nonoverlapping and have distinct nontypology characteristics (i.e., has discriminant validity);
  • Is comprehensive in its coverage of the overall population;
  • Demonstrates construct validity by having the theoretical constructs empirically supported; and
  • Has predictive validity in that members of different subgroups show different patterns of homelessness and different responses to treatments (i.e., has clinical utility).

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.

Appendix A: Impact of Homelessness on Children: An Analytic Review of the Literature

This paper reviews published research conducted in the United States pertaining to the effects of homelessness on the mental health, behavior, health, and academic performance of children who are homeless with their families. This has been the central aim of most of the studies involving homeless children that have been conducted to date. A primary intent of the chapter is to describe what has been learned as well as to discuss some of the issues that may have led to inconsistent study findings over the years. In addition, the paper identifies gaps in the understanding of homeless children, one of which is the lack of information on different subgroups of homeless children based on varying constellations of problems or needs.

Part I: Literature Review

Using data from the National Survey of Homelessness Assistance Providers conducted in 1996, The Urban Institute (2000) estimated that families with children account for about 39 percent of the homeless population in this country on any given night.1 Based on this survey, researchers at The Urban Institute estimated that somewhere between 874,000 and 1,360,000 children experienced a homeless episode2 at some point in 1996. This implies that about 9 percent of poor children in the United States had a spell of homelessness that year. In most cases, a homeless family is comprised of a single mother with one or two young children in tow. This is particularly true in the Northeast, where, for instance, in Massachusetts about 95 percent of homeless families are single parent female headed (Bassuk et al., 1996). In some parts of the country it is more common to also encounter two-parent (or couple) families or families headed by a single father (U.S. Conference of Mayors, 2001).

The research literature on homeless children now spans about 18 years, with the earliest studies having been published around 1987. One approach to reviewing empirical studies of homeless children is to summarize findings according to topical domain (e.g., mental health, health, education). To some extent, this chapter adopts this approach as well as it facilitates meaningful comparisons and inferences across studies. However, in an effort to make better sense of incongruities in various investigations of homeless children that have made their way to the published literature, it is also helpful to organize them in chronological order. Toward this end, it is useful to distinguish between a set of “first generation” studies and a second stage of research investigations on homeless children. Not all studies in the literature can be grouped so neatly, but such a distinction is reasonable in most cases. This review is not an exhaustive attempt to describe every study that has been published but covers many of the empirical investigations, particularly those that have included a housed comparison group children as it is very difficult to gauge the impact of homelessness, per se, on children by only involving homeless children in a study.

The first studies that were conducted on homeless children sounded an alarm (cf. Alperstein, Rappaport, and Flanigan, 1987; Bassuk and Rubin, 1987; Miller and Lin, 1988; Rescorla, Parker, and Stolley, 1991; Wood, Valdez, Hayashi, and Shen, 1990). Their findings indicated that homeless children had a range of health and mental health problems that called for immediate attention. Data for these investigations were collected in the mid-1980s, not long after the issue of homelessness for families became apparent. Families who required emergency shelter during this period in time encountered a shelter system in the United States that was only beginning to determine how to handle the needs of parents with young children and it is conceivable that shelter conditions were at their worst during the period in which these studies were conducted.

A second generation of studies on homeless children followed in the early 1990s spearheaded by these earlier findings. Some of these studies were funded by the National Institute of Mental Health (NIMH), while others were supported by foundations and local grants. Investigators who included homeless children in their studies attempted to advance an understanding of the impact of homelessness on children by involving larger study populations, a greater breadth and quality of assessment instruments, and more advanced statistical techniques with which to analyze the data (cf. Bassuk, Weinreb, Dawson, Perloff, and Buckner, 1997; Buckner and Bassuk, 1997; Buckner, Bassuk, Weinreb, and Brooks, 1999; Buckner, Bassuk, and Weinreb, 2001; Garcia Coll, Buckner, Brooks, Weinreb, and Bassuk, 1998;; Masten, Miliotis, Graham-Bermann, Ramirez, and Neemann, 1993; Masten, Sesma, Si-Asar, Lawrence, Miliotis, and Dionne, 1997; Rafferty, Shinn, and Weitzman, 2004; Rubin, Erickson, San Agustin, Cleary, Allen, and Cohen, 1996; Schteingart, Molnar, Klein, Lowe, and Hartmann, 1995; Weinreb, Goldberg, Bassuk, and Perloff, 1998).

Mental Health and Problem Behaviors

The mental health of homeless children has been a central concern for service providers as well as researchers. The most widely used instrument in homelessness research with children has been the Child Behavior Checklist (CBCL) (Achenbach, 1991; Achenbach and Rescorla, 2001). The CBCL is an instrument that is administered to the parent of a child and assesses the signs (i.e., observable manifestations) as opposed to the symptoms of mental health problems. The CBCL has two versions, one intended for preschoolers and the other for school-age children. Both versions of the CBCL are comprised of specific syndrome scales as well as composite "internalizing" and "externalizing" global scores.3 The internalizing dimension of the CBCL assesses observable behaviors that are indicative of anxiety and depression as well as withdrawn behavior and somatic complaints. The externalizing dimension is derived from items that assess delinquent and/or aggressive behavior in older kids and attention problems and aggressive behavior in younger children. Raw scores on the syndrome and global scales can be converted into T-scores with the mean set to 50. Higher scores are indicative of more problematic behaviors.4

Bassuk and Rosenberg (1990) published the first study comparing homeless and housed children in which the CBCL was employed. Homeless children were enrolled from emergency shelters in Boston during 1985 and a comparison group of families living in low-income housing were interviewed a year later. Bassuk and Rosenberg (1990) used the CBCL to assess children ages 6 to 16 in their study and found that 39 percent of the 31 homeless children and 26 percent of the 54 housed children scored in the clinical range. This difference did not reach statistical significance, most likely a function of the relatively small sample size. Homeless girls had higher scores than homeless boys and older homeless youths (ages 12–16 years) were more likely to score in the clinical range than younger children (ages 6–11 years). A widely used self-report measure of depression, the Children’s Depression Inventory (CDI), was also part of the assessment protocol in this study and homeless children averaged 10.3 on this measure compared to 8.3 for the housed children. While this difference was also not statistically significant, such levels on the CDI are of some clinical significance and represent depressive symptoms of moderate severity.

In Philadelphia during the late 1980s, Rescorla, Parker, and Stolley (1991) conducted a study involving 83 homeless children between the ages of 3 and 12 years who were living in 1 of 13 shelters throughout the city and compared them to 45 children whose families were randomly selected from the waiting room of a pediatric clinic. The children were given an assessment battery that included the CBCL and various measures of cognitive abilities (IQ) and reading achievement. The authors compared preschool and school-age children separately. Across the various indices of intelligence and achievement, homeless children in both age groups scored lower than the clinic group although only some of the differences reached statistical significance. If the study had had a greater sample size, it would have found more differences between the two groups reaching statistical significance. Similarly, on the CBCL, homeless children in both age groups had more elevated indices of internalizing and externalizing problems compared to the clinic enrolled children, with differences particularly acute among the preschool-age children.

The authors did not use multivariate statistics to control for potential imbalances on other explanatory variables and collected very little data on the mothers of children in these two groups, making it hard to discern how well the two groups were matched. Thus, it is not possible to determine to what extent the differences found between homeless and housed children is a function of housing status or other family/mother factors that are associated with both vulnerability to becoming homeless and child outcomes. Despite the difficulty of making causal inferences about whether housing status or other unmeasured variables accounted for the differences seen between the homeless and clinic children in this study, the absolute scores that Rescorla et al. (1991) reported for the homeless children on measures of intelligence, achievement, and problem behaviors are the most problematic that can be found in the published literature. Indices of IQ and achievement were a good one standard deviation below the national average (e.g., 85 instead of the norm of 100) and CBCL scores, on average were in the high 50s, with internalizing and externalizing CBCL scores at 59 for the homeless preschool group (the borderline clinical range begins at 60).

In a study conducted in the early 1990s in New York city involving 82 homeless and 62 housed children ages 3 to 5 and their mothers, Schteingart, Molnar, Klein, Lowe, and Hartmann (1995) found that the two groups had equivalent scores on both the internalizing and externalizing dimensions of the CBCL as well as on a measure of developmental status. In multivariate analyses, maternal depressive symptoms predicted internalizing CBCL scores, but housing status did not. Overall, CBCL scores for this group of low-income preschool-age children were in the low 50s, indicating slightly more problem behaviors than would be expected based on the instrument’s standardization group.

A study with similar no difference findings involved 145 homeless and 142 housed school-age children in Madison, Wisconsin. Using the teacher-report version of the CBCL, Ziesemer, Marcoux, and Marwell (1994) found that both groups scored appreciably higher than test norms on the total problem behaviors index (T-scores of about 58 on average for the homeless and 60 for the housed children). Also, the two groups were comparable on a measure of self-esteem and academic functioning. The authors stressed that broader issues of poverty, rather than homelessness per se, accounted for these results (Ziesemer, et al., 1994).

Several years after her Boston study, Ellen Bassuk and colleagues mounted a “second generation” study of 220 homeless and 216 housed single parent, female-headed families, which took place in Worcester, Massachusetts. These families were enrolled into this longitudinal study and received their initial (baseline) interview between1992-95. The findings to follow predominantly come from the data collected during this cross-sectional phase of the study. Homeless mothers were enrolled from nine of Worcester’s emergency shelters while the comparison group consisted of low-income, never homeless, mothers who were receiving public assistance in the form of Aid to Families with Dependent Children (AFDC). The CBCL was administered to the mothers of both preschool-age (2-½ – 5 years old) and school-age (6–17 years old) children and data for the two age groups were analyzed separately due to different assessment protocols for these two cohorts.

As reported in Bassuk, Weinreb, Dawson, Perloff, and Buckner (1997), for the preschool children, scores on both the internalizing and externalizing dimensions of the CBCL were slightly higher for homeless children compared to their housed peers (52.5 vs. 49.9 on the internalizing dimension and 54.8 vs. 51.2 for the externalizing score). Only the difference in externalizing scores was statistically significant between the two groups. Approximately 12 percent of children in both groups were in the clinical range on the internalizing score and 15 percent in both groups on the externalizing dimension. This compares to about 10 percent in the general population based on CBCL test norms. Importantly, the two best predictors of children’s CBCL scores were a measure of mother’s psychological distress and a measure of her parenting practices (negative parenting practices were associated with more elevated CBCL externalizing scores).5 Housing status (whether the child was homeless or housed) was also predictive of externalizing scores, but to a lesser degree.

Among school-age children ages 6 to 17 years in the Worcester study, Buckner, Bassuk, Weinreb, and Brooks (1999) found a similar pattern of findings; although homeless children in this older age group were evidencing more problem behaviors than their low-income housed counterparts.6 On the internalizing dimension of the CBCL, the 80 homeless school-age children scores averaged 56.1 compared to 50.2 for their 148 housed peers. About 47 percent of the homeless school age children were in the borderline-clinical or clinical range on the internalizing subscale of the CBCL as compared to 21 percent of the youths in the housed group and 16 percent in the general population. Controlling for other explanatory variables such as negative life events, abuse history, mother’s distress, and social support, housing status remained a significant predictor (Buckner, et al., 1999).

On the externalizing dimension of the CBCL, homeless children also were reported to have elevated behavior problems compared to the general population but their scores were only slightly higher than the housed poor comparison group (53.7 vs. 51.4). Supporting the CBCL internalizing dimension finding, homeless youths were also more symptomatic on self-reported measures of depression and anxiety. For instance, CDI scores for homeless youths averaged 10.9 versus 9.2 for housed children.7 This difference in CDI scores was not statistically significant, and both levels indicate depressive symptoms of moderate severity. Among school-age children in the Worcester study there was some evidence of a link between homelessness and mental health/behavioral problems. This link was not evidenced among preschool children, however.

Among homeless school-age children, there was some indication that a “dose-response” relationship existed between length of time in shelter and children’s internalizing CBCL scores (Buckner et al., 1999). Such problem behaviors appeared to gradually increase the longer a child had been homeless and peak at about 15 weeks and then were less for those children who had been homeless a longer duration (e.g., 18-45 weeks). While this curvilinear (rainbow-shaped) trend was rather apparent in the data, the finding was a tentative one as it involved a cross-sectional comparison of separate children who had been homeless for different lengths of time. Stronger evidence for such a dose-response curve could be had if a group of children were repeatedly measured during their shelter stays and the same trend was noted in their individual “change trajectories.” The meaning of this curvilinear trend, if valid, is not clear. It could suggest that children habituate some to shelter conditions over time and have fewer internalizing problems once they get used to living there. It might also be the case that after several months of observation, shelter staff pick up on the problems of some children and take measures to ameliorate their distress. It might also be the case that mothers’ perceptions of their children’s behavior changes over time as they become more accustomed to living in a shelter.

Buckner and Bassuk (1997), assessed the mental health of homeless and housed youths in the Worcester study using a diagnostic instrument. Both parent and self-report versions of the Diagnostic Interview Schedule for Children (DISC Version 2.3) were administered to 94 children 9 to 17 years of age (and their mothers) in the Worcester study.8 To meet criteria for a disorder, a child needed to fulfill the specific DSM-III-R criteria and have impairment in functioning as a result of that disorder. About 32 percent of youths in each of the homeless and housed groups (i.e., the proportions were nearly identical in the two groups) met criteria for one or more disorders in the past 6 months (Buckner and Bassuk, 1997). This compares to a rate of 19 percent that has been reported for children of similar age in the general population (Shaffer, Fisher, Dulcan et al., 1996). The most prevalent disorders for these low-income children were anxiety, mood, and conduct problems. Differences found between homeless and housed youths on the CBCL (Buckner et al., 1999), were not apparent when examining these youths in terms of diagnostic criteria, whether looking across all assessed disorders or only those pertaining to disorders of an internalizing (e.g., depressive and anxiety disorders) nature.9 The more important finding was that these low-income children had much higher prevalence rates of mental health problems than has been found among youths of similar age in the general population (32% versus 19% prevalence rate for meeting criteria in the past 6 months for at least one disorder that was causing impairment).

The Worcester study also involved a longitudinal component in which followup data were collected on study participants at 12 and 24 months following their baseline interviews. Among children in the school-age cohort, the longitudinal interviews found all formerly homeless children now living in permanent housing. At followup, the impact of this homeless experience seemed to have dissipated, whereas other negative life events, particularly exposure to violence in the home or community, was much more associated with mental health symptoms (Buckner, Beardslee, and Bassuk, 2004). Unpublished results from the Worcester study’s preschool cohort showed a similar pattern with initial differences between homeless and housed children at baseline assessment converging at followup when most children were living in permanent housing.

An entirely separate study to the Worcester investigation, but somewhat similar in its methodology, is that of Masten, Miliotis, Graham-Bermann, Ramirez, and Neemann (1993). They interviewed 159 homeless children ages 8 to 17 years who were living in a large emergency shelter in Minneapolis during the summer of 1989 and compared them to 62 low-income children of similar age living in permanent housing. The CBCL and CDI were their principal outcome measures. On the internalizing CBCL score, homeless children scored 52.2 on average compared to 49.4 percent for the housed group. Twenty-seven percent of homeless youths had T-scores of 60 and higher (borderline clinical range and above) compared to 17 percent of housed youths and 16 percent in the general population based on the tests normative data. On the externalizing dimension, homeless youths had scores that averaged 56.0 (40% had a T-score of 60 or higher) versus 53.4 for housed youths (with 30% having a T-score of 60 or higher). For homeless youths, these internalizing scores are lower than those reported by Buckner et al. (1999) in the Worcester study but about the same for externalizing scores. Controlling for other explanatory variables, Masten et al. (1993) did not find that housing status was a significant predictor of either internalizing or externalizing CBCL scores. Scores on the CDI were equivalent between the two groups and of similar magnitude in severity (mild to moderate) to what was found by Buckner et al. (1999) in the Worcester study.

In summarizing their findings with an eye toward the bigger picture, Masten et al. (1993) described a “continuum of risk.” By this they meant that behavior problems seemed to be more severe according to how much “risk” children had experienced. Based on indices of adversity such as stressful life events, homeless children in the Minneapolis study had the most risk, followed by low-income housed children who, in turn, looked worse off than children from more advantaged backgrounds. This continuum-of-risk concept is an appropriate summary of the Worcester study’s findings, with both homeless preschool and school-age children experiencing the most adversity and having more problem behaviors.

Developmental Status

Among infants and preschool age children, assessing cognitive and motor development in relation to specific developmental milestones is useful in understanding a child’s “developmental status” and whether the child appears to have developmental delay(s) in one or more realms. For instance, a child who is not walking by the age of 2 or not speaking simple sentences by the age of 3 may be delayed in this sphere of development compared to the majority of children of similar age. Three studies examined young homeless children on this dimension. Two of the studies, Wood et al. (1990) in Los Angeles, and Bassuk and Rosenberg (1990) in Boston used the Denver Developmental Screening Test (DDST), whereas the third study, Garcia Coll, Buckner, Brooks, Weinreb, and Bassuk (1998), which involved the infant and toddler cohort from the Worcester study, used the Bayley Scales of Infant Development (“Bayley”). As the name implies, the DDST is an easy-to-use screening instrument for identifying developmental delays in children. The Bayley is the gold standard measure of developmental status in infants and young children and requires specialized training to administer. The DDST is a set of questions asked of a parent or guardian about the child (usually with the child present), whereas the Bayley is administered by a trained tester via direct observation and interaction with the child.

 Both the Los Angeles and Boston studies found that homeless preschool children were experiencing a greater proportion of developmental delays than the comparison groups of poor housed children. In the Wood et al. (1990) study, 15 percent of homeless children were found to have one developmental delay and 9 percent had two or more. These rates are significantly higher than that found in the general child population.10 The most common type of delay was in language. Bassuk and Rosenberg (1990) found much higher rates of developmental delay in their Boston study, with 54 percent of homeless children evidencing at least one delay versus 16 percent for children in the housed comparison group. Developmental tasks in the areas of language and social behavior were the two areas in which homeless children were having the most difficulty. In contrast to these two studies, Garcia Coll et al. (1998) found no differences between homeless and low-income housed infants/toddler’s developmental status on the Bayley. In fact, homeless children looked slightly better on both the mental and motor development subscales of this instrument (scores of 105 in both realms vs. about 101 for the housed comparison group). Moreover, scores on the Vineland Screener (a measure of adaptive behavior that asks a parent about a child’s communication, daily living, socialization, and motor skills) were almost identical. These low-income infant and toddlers’ scores were in the low-normal to normal range based on normative data for this instrument.

Health Outcomes

The early studies of homeless children that assessed health outcomes found a higher prevalence of health-related problems compared to low-income housed children or children in the general population. For instance, Alperstein et al. (1987) in a study of outpatient medical records in a New York City pediatric clinic, compared 265 homeless children under the age of five in New York City with poor housed children attending the same clinic. Homeless children were behind in their immunizations and had elevated blood lead levels compared to housed children. Homeless children also had higher rates of hospital admissions and reports of child abuse/neglect. The two groups were comparable in terms of height, weight, and free erythroprotoporphyrin (FEP) levels (a measure of iron deficiency).

Miller and Lin (1988) conducted a survey in King County, Washington, involving a representative sample of 82 homeless families living in emergency shelters. A total of 158 children ranging from 1 month to 17 years of age were assessed, and the investigators compared their findings on these homeless children to normative data in the general population. Although Miller and Lin (1988) found that the majority of children were described as in “good” or ”excellent“ health, the proportion whose health was described as ”fair“ or “poor” was 4 times that of the general U.S. pediatric population (13% vs. 3.2%) and 2 times higher than low-income children (13% vs. 6.5%). Homeless children in this study were also found to lack a regular health care provider (true for 59%), use emergency rooms a rate 2 to 3 times higher than in the general population, and were more likely to lack standard immunizations and preventative health care.

Another health outcome study took place in Los Angeles and involved a comparison of 196 homeless families to 194 stably housed poor families (Wood et al., 1990). Children in both groups had compared global ratings of their health status (i.e., excellent, good, fair, poor) and similar rates of symptoms (e.g., fever, cough, vomiting, diarrhea) indicative of an illness during the past month. However, these rates were 2 to 5 times higher than those reported in the general child population. Children in both groups had poor dietary intakes and problems with obesity. Homeless children were more likely than housed children to have experienced an episode of hunger in the past month (21% vs. 7%).

The only second generation study involving health outcomes is that of Weinreb, Goldberg, Bassuk, and Perloff (1998), which was part of the Worcester study that took place during the mid 1990s. They compared 293 homeless children ranging from 2 months to 17 years of age to 334 low-income housed (never homeless children). Their results are fairly consistent with prior studies, although the study is more rigorous because they used multivariate analyses to statistically control for imbalances between the two groups in order to better isolate genuine differences between the two groups. Eighty-eight percent of the homeless children and 94 percent of low-income housed children were reported to be in “good” to “excellent” health, while about 12 percent of the homeless children and 6 percent of the housed children’s health were rated as “fair or poor.” Overall, the difference in health ratings between the two groups was statistically significant at the p <.05 level. Rates of acute illnesses in the past month were generally comparable between the two groups although homeless children had higher rates of ear infections and asthma. Homeless children had higher service use rates, including visits to an emergency room and outpatient clinic visits.

Education-related Outcomes

When the crisis of family homelessness emerged in the 1980s, most school systems were unprepared to deal with the complex needs of homeless children. Many homeless children were denied access to education with school districts claiming that families living in shelter did not meet permanent residency requirements and, therefore, were not eligible for enrollment (Rafferty, 1995). The most frequent impediments to adequate education for homeless children were residency, guardianship, immunization requirements, availability of records, and transportation to and from school (Stronge, 1992). It is not difficult to imagine that if homelessness causes children to miss school, such absence will likely be detrimental to their academic performance.

Part of The Stewart B. McKinney Homelessness Assistance Act, which Congress passed in 1987, was the establishment of the Education of Homeless Children and Youth (EHCY) program to ensure that homeless children had the same access to public education as all other children. Since then, the EHCY program has provided formula grants to state educational agencies to review and revise policies that may act as barriers to school enrollment and attendance as well as to fund direct services such as transportation and tutoring. Anderson, Janger, and Panton (1995) conducted a national evaluation of the EHCY program and found that over 85 percent of homeless children and youth were regularly attending school, indicating a marked improvement in school access compared to pre-EHCY program attendance rates.

Studies of homeless children that were conducted prior to and shortly after the creation of the EHCY program have consistently documented disrupted school attendance and academic underperformance. For instance, Bassuk and Rubin (1987) reported that 43 percent of students living in Massachusetts shelters had repeated a grade, 25 percent were in special classes, and 42 percent were failing or doing below-average work. Masten et al. (1993) found that 64 percent of the homeless children they surveyed in Minneapolis in 1999 had changed schools in the past year, significantly higher than the 40 percent rate experienced by housed poor children. In a separate study of 73 homeless children ages 6 to 11, Masten and colleagues determined that academic achievement scores were lower on average than would be expected among children in the general population (Masten, Sesma, Si-Asar, Lawrence, Miliotis, and Dionne, 1997).

Zima, Wells, and Freeman (1994) reported that 16 percent of their sample of school-age homeless children in Los Angeles had missed more than 3 weeks of school over the past 12 weeks. Thirty-nine percent exhibited reading delays and almost half were at or below the 10th percentile on a measure of receptive vocabulary. Zima and colleagues also found a high level of unmet need for special education evaluations (and perhaps special education programs) based on the high proportion of children with a probable behavioral disorder, learning disability, or mental retardation (Zima, Bussing, Forness, and Benjamin, 1997).

In a longitudinal study in New York City, Rafferty, Shinn, and Weitzman (2004) compared the academic achievement scores of 46 youths who had a history of homelessness with 87 housed (never homeless) adolescents at three time points during the early to mid-1990s. They found an apparent detrimental effect of homelessness on achievement scores over the short term but not 5 years later. A subtest of the Wechsler Intelligence Scale for Children-Revised was equivalent between the two groups. Youths who had previously been homeless had more school mobility and grade retention than their housed peers (Rafferty et al., 2004).

Between 1990-92, Rubin, Erickson, San Agustin, Cleary, Allen, and Cohen (1996) conducted a comparative study of homelessness and poor housed children ages 6 to 11 in New York City to examine the relation among housing status, cognitive functioning, and academic achievement. Similar to other studies, they reported that homeless children had missed more days of school in the past year and were more likely to have repeated a grade compared to low-income housed children. Controlling for sociodemographic variables, Rubin et al. (1996) did not find differences between the two groups on measures of verbal and nonverbal intelligence. However, academic achievement scores (reading, spelling, math) were worse for homeless children compared to their housed counterparts, adjusting for demographic factors. Rubin et al. (1996) reported that the effect of housing status on reading achievement was mediated by the number of school changes a child had experienced in the previous 2 years, whereas housing status was linked to spelling achievement through having repeated a grade.

In contrast to some of these studies, Buckner, Bassuk, and Weinreb (2001) found no evidence of higher school absenteeism or lower academic achievement scores among homeless school age children in the Worcester study as compared to low-income housed children. Children in each group had missed an average of 6 days of school in the past year and scores on a composite measure of academic achievement were identical for both groups (92.8 with 100 the average in the general population). IQ scores were also equivalent in the two groups (92.5 for homeless children vs. 93.5 for housed youths with a score of 100 the norm). Rates of school suspension, grade retention, and special classroom placement were actually higher in the housed comparison group. The only notable difference in the “expected” direction was that homeless children had been enrolled in more schools in the past year (a median of 2 vs. 1 for housed school-age children).

It is likely that the lack of differences in the Worcester study between homeless and housed school-age children on school and education-related variables had to do with successful implementation of the EHCY program in that city. For the most part, data collection for the other investigations cited above occurred prior to the full implementation of EHCY programs in cities in which these studies were conducted. Since EHCY programs target likely mechanisms by which homelessness could adversely impact academic achievement—namely school access and school attendance—it is not surprising that subsequent studies of homeless children that took place after EHCY programs had been actively implemented (such as in Worcester) would find fewer differences between homeless and housed children on measures of school-related problems and achievement. The findings offer encouraging evidence that it may be possible to eliminate education-related problems for homeless children if barriers to accessing education can be removed.11

A summary of all the studies described above is presented in Table 1-1. The “Findings” column of this table gives a simplified synopsis of the results of the study in terms of how homeless children looked on the main outcome measure(s) compared to housed children and children in the general population. As can be seen by reading down this column, past studies that can speak to the matter of if and how homelessness has an impact on children are decidedly mixed in their findings, particularly when comparing homeless to low-income housed children.12 In virtually all instances, these two groups of low-income children look worse on various outcome measures compared to children in the “general population” (i.e., for whom the tests were normed). However, overall it appears that homelessness is associated with worse outcomes, particularly those pertaining to health and education-related measures. Study results in the areas of mental health, problem behaviors, and developmental status are somewhat less consistent, both within and across investigations. The magnitude of severity of problems found among homeless (and low-income housed) children tend to be in the mild to moderate range.

Table 1-1. Summary of published homelessness studies 1987-2004 by domain

Mental health/behavior problems

Publication Location Sample Age Outcomes Findings Comments
 

Bassuk and Rubin (1987)

 

Massachusetts

 

156 homeless children

 

0-18 years

 

CBCL, CDI

 

Hom > GP

 

First study to involve homeless children

 

Bassuk and Rosenberg (1990)

 

Boston

 

134 homeless children

81 housed children

 

0-18 years

 

CBCL, CDI, etc.

 

Hom > Hou > GP

 

Mostly the same homeless sample as
Bassuk and Rubin (1987)

 

Rescorla et al. (1991)

 

Philadelphia

 

83 homeless children

45 housed/clinic children

 

3-12 years

 

CBCL, etc.

 

Hom > Hou > GP

 

Homeless children much worse on CBCL than housed peers

 

Masten et al. (1993)

 

Minneapolis

 

159 homeless children

62 housed children

 

8-17 years

 

CBCL, CDI

 

Hom = Hou > GP

 

Multivariate analyses controlled for other explanatory variables

 

Zima et al. (1994)

 

Los Angeles

 

169 homeless children

 

6-12 years

 

CBCL, CDI

 

Hom > GP

 
 

Ziesemer et al. (1994)

 

Madison, WI

 

145 homeless children

142 housed children

 

School-age

 

CBCL-Teacher

 

Hom = Hou > GP

 

Teacher version of CBCL used, not parent version as in the other studies

 

Schteingart et al. (1994)

 

New York City

 

82 homeless children

62 housed children

 

3-5 years

 

CBCL

 

Hom = Hou > GP

 

Multivariate analyses controlled for other explanatory variables

 

Bassuk et al. (1997)

 

Worcester, MA

 

77 homeless children

90 housed children

 

2-5 years

 

CBCL

 

Hom > Hou > GP

 

Multivariate analyses. Difference between Homeless/housed on CBCL-Externalizing only

 

Buckner et al. (1999)

 

Worcester, MA

 

80 homeless children

148 housed children

 

6-17 years

 

CBCL, CDI, etc.

 

Hom > Hou > GP

 

Multivariate analyses. Difference between Homeless/housed on CBCL-Internalizing only

 

Buckner and Bassuk (1997)

 

Worcester, MA

 

41 homeless children

53 housed children

 

9-17 years

 

DISC

(DSM-III-R diagnoses)

 

Hom = Hou > GP

 

Children age 9 and older in Worcester study.

Only study to report DSM diagnoses

Developmental-related problems

Publication Location Sample Age Outcomes Findings Comments
 

Bassuk and Rosenberg (1990

 

Boston

 

134 homeless children

81 housed children

 

0-5 years

 

DDST

 

Hom > Hou > GP

 

DDST is a brief screening instrument

 

Wood et al. (1990)

 

Los Angeles

 

194 homeless children

 

0-5 years

 

DDST

 

Hom > GP

 

Housed children were not assessed

 

Garcia Coll et al. (1999)

 

Worcester, MA

 

127 homeless children 91 housed children

 

0-3 years

 

Bayley

 

Hom = Hou = GP

 

Bayley is the “gold-standard” measure of Developmental status

Health-related problems

Publication Location Sample Age Outcomes Findings Comments
 

Alperstein et al. (1987)

 

New York City

 

265 homeless children

1600 housed children

 

0-5 years

 

Miscellaneous

 

Hom > Hou> GP

 
 

Miller and Lin (1988)

 

King County, WA

 

158 homeless children

 

0-17 years

 

Miscellaneous

 

Hom > GP

 
 

Wood et al. (1990)

 

Los Angeles

 

194 homeless children

193 housed children

 

0-5 years

 

Miscellaneous

 

Hom > Hou > GP

 
 

Weinreb et al. (1998)

 

Worcester, MA

 

293 homeless children

334 housed children

 

0-17 years

 

Miscellaneous

 

Hom > Hou > GP

 

Multivariate analyses.

Education-related problems

Publication Location Sample Age Outcomes Findings Comments
 

Bassuk and Rubin (1987)

 

Massachusetts

 

156 homeless children

 

0-18 years

 

Attendance, etc.

 

Hom > GP

 
 

Rescorla et al. (1991)

 

Philadelphia

 

83 homeless children

45 housed/clinic children

 

3-12 years

 

WRAT-Reading

 

Hom > Hou > GP

 

Homeless children worse in reading achievement than housed peers

 

Masten et al. (1993)

 

Minneapolis

 

159 homeless children

62 housed children

 

8-17 years

 

Changes in school

 

Hom > Hou

 
 

Masten et al. (1997)

 

Minneapolis

 

73 homeless children

 

6-11 years

 

WIAT-S, etc.

 

Hom > GP

 

Compared to children for whom the test was normed, homeless children scored lower in achievement

 

Ziesemer et al. (1994)

 

Madison, WI

 

145 homeless children

142 housed children

 

School-age

 

CBCL-Teacher

 

Hom = Hou > GP

 

Ratings of academic performance using teacher version of CBCL

 

Zima et al. (1994; 1997)

 

Los Angeles

 

169 homeless children

 

6-12 years

 

Attendance, reading

delays, unmet need for

special ed., etc.

 

Hom > GP

 

Homeless children have elevated rates of academic problems, unmet need for special education, etc.

 

Rubin et al. (1996)

 

New York City

 

102 homeless children

178 housed children

 

6-11 years

 

WRAT-R

 

Hom > Hou > GP

 

Multivariate analyses. No differences between homeless and housed on IQ measure

 

Buckner et al. (2001)

 

Worcester, MA

 

80 homeless children

148 housed children

 

6-17 years

 

Attendance, WIAT-S,

KBIT-Non-verbal

 

Hom = Hou = GP

 

Multivariate analyses. No differences between homeless and housed on any measure, including IQ

 

Rafferty et al. (2004)

 

New York City

 

46 formerly homeless children

87 permanently housed children

 

11-17 years

 

Changes in school,

WISC-R Similarities,

Reading achievement

 

Hom > Hou

 

No differences on IQ measure

Key:
Hom = Homeless group; Hou = Low-income housed comparison group; GP = Children in the general population; “>” means “greater problems than”
CBCL = Child Behavior Checklist; CDI = Children’s Depression Inventory; DISC = Diagnostic Interview Schedule for Children;
DDST = Denver Developmental Screening Test; Bayley = Bayley Scales of Infant Development;
WRAT-R = Wide Range Achievement Test – Revised; WIAT-S = Wechsler Individual Achievement Test- Screener; KBIT – Kaufman Brief Intelligence Test;
WISC-R = Wechsler Intelligence Scale for Children-Revised.

The notion of a continuum of risk is a useful in describing how results tend to fall out when comparing homeless to low-income housed children as well as children in the general population. That is, compared to children in the general population, low-income housed children appear to be doing worse on most outcome measures with homeless children looking the most problematic. (In the next section a range of different factors are discussed that might account for the lack of dependable findings in studies that have compared homeless to housed children.) In addition to the table, Figure 1-1 provides a means by which to summarize both the intentions and the findings of the studies discussed in this section. It is intended as an explanatory device: The figure does not portray actual findings from any particular study and the quantitative values suggested by the lines on the y axis should not be taken literally. The figure portrays the continuum-of-risk concept mentioned by Masten et al. (1993), which is a consistent pattern of results across studies involving homeless and low-income housed children. In the figure, an “average degree of problem severity” is assigned to each of three different grouping of children: children in the general population, housed children living in poverty, and homeless children. Each group’s level of “problem severity” is apportioned to up to three different sources or risk. Children in the general population have just one source of risk (“normative stressors”), those who are from low-income families living in housing have two sources of risk (normative stressors plus “non-homeless, poverty-related” stressors) and homeless children have three sources (normative, poverty-related, and “homelessness-specific” stressors).

Figure 1-1: Continuum-of-Risk Concept

Figure 1. Continuum-of-Risk Concept. See text for explanation.

To interpret this graph, assume that the y axis refers to values indicative of a problem of some sort, with higher values indicating greater severity. The graph illustrated a finding that is typical across the studies reviewed earlier, namely that the degree of problem severity is highest for homeless children, followed by low-income housed children, with children in the general population (based on test norms) scoring lowest. The continuum-of-risk notion posits that those with exposure to greater risk have heightened problems, with homeless children experiencing the most risk, hence more severe problems followed by poor housed children, followed by children in the general population. An implicit assumption is that all three groups of children share some common risk factors that are not related to poverty. These are labeled problems attributable to “normative risk factors” and assigned equal values in all three groups. Children in the low-income housed and homeless groups share in common a set of “poverty-related” risk factors. These would be mostly environmental and family variables that children from more advantaged backgrounds are rarely or never exposed to. Furthermore, these poverty-related risk factors are not related to homelessness. Equal values are assigned to both the low-income housed children and homeless children, but no value to children in the general population. Lastly, a value of risk exposure is assigned to the group of homeless children that represents their exposure to risks that are “homelessness-related.” Of course, only children in the homeless group receive such exposure.

Some of the studies reviewed earlier reveal a pattern of results that match up nicely to this figure. For instance, those studies listed in Table 1-1, in which the finding “Homeless Group > Housed > General Population” seems to fit a pattern of findings consistent with the continuum-of-risk notion.13 As described earlier, a goal of many of the studies, especially those involving both homeless and housed children and multivariate statistics, was to determine whether homeless children had heightened problems; and, if so, whether these could be attributed to homelessness or if it were simply the case that homeless children got a higher dose of poverty-related risk exposure than the low-income housed group. So, for example, Buckner et al. (1999) found that homeless school-age children had more internalizing mental health problems than their low-income housed counterparts. Furthermore, through the measurement and statistical control of other risk factors (such as negative events, chronic strains, abuse history, mother’s mental health), the study determined that homelessness, per se, seemed to be playing a role in these elevated internalizing problems. Put another way, it was unlikely that this was a spurious association between housing status and internalizing problems brought about by homeless children having been exposed to more poverty-related (non-homeless) risks than the low-income housed group. This is one of the few studies that has found both an elevated problem severity in homeless children and has been able to convincingly demonstrate that this heightened degree of problem severity is likely the result of homelessness-related stressors and not non-homeless poverty-related factors.

Part II: Why Studies of Homeless Children Have Produced Inconsistent Findings

The previous section reviewed many of the published empirical articles that address the potential impact of homelessness on children. The continuum-of-risk figure (Figure 1-1) is helpful in summarizing various study findings. A rather consistent result across studies is noting elevated problems among homeless and low-income housed children compared to children in the general population. In essence, most studies have documented an apparent negative effect caused by exposure to a common set of “poverty-related” risks. What is less consistent across studies is whether an additional elevation in problems among homeless children as compared to low-income housed children is also found. Moreover, when differences are detected, limitations in methodology (such as not adequately measuring additional risk factors and/or not using multivariate analyses to control for them) call into question whether homelessness, per se, is behind the heightened severity of problems. In other words, it is hard to demarcate where poverty-related sources of risk end and homelessness-specific risks begin.

While the overall pattern of findings across studies does suggest that, more often than not, children’s exposure to homelessness increases their risk of adverse outcomes, it is difficult to make strong and definitive assertions about the impact of homelessness on children due to inconsistent study results. Rather, the effect that homelessness appears to have on children would seem to be dependent on a range of contextual factors and “effect modifiers.” Put simply, whether homelessness has an impact on children may depend. On the other hand, studies are much more consistent in discerning a negative impact of poverty on children (i.e., both low-income housed and homeless) across outcome domains and among different age groups within domains.

The remainder of this section offers some explanations as to why various studies involving homeless children have not been able to reliably produce findings suggestive of a negative impact of homelessness above and beyond the effects of broader poverty-related risks.

Methodological Differences

Studies of homeless children have differed in terms of the assessment instruments employed, the degree of statistical power afforded by sample size, selection of comparison groups, enrollment procedures, and other factors. While there are methodological shortcomings in some studies, this probably is not a major reason for the inconsistencies in study findings. For one, some of the more methodologically rigorous studies are internally inconsistent. For example, in the Worcester study, differences were found between homeless and housed children on behavior problems (but only for internalizing problems in school-age children and externalizing problems for preschool children). Infants and toddlers in both groups appeared equivalent and no differences were found on measures of academic achievement among the older children. The Rescorla et al. (1991) study in Philadelphia made the questionable choice of having children in a health clinic serve as the housed comparison group. Yet, the magnitude of problems they assessed in their sample of homeless children was very high and they were likely to have found statistically significant differences between this group and whichever comparison group they might have selected.

Pointing out methodological differences between studies (or problems within studies) yields an uncompelling argument for why studies of homeless children paint such a confusing picture as to the impact of homelessness on children. Rather, inconsistencies across these studies may have more to do with the fact that these investigations have involved different study groups in different communities at different points in time during the fast changing history of family homelessness in America. These other factors, which are largely outside the realm of what is described in an article’s methodology section, are discussed in the pages to follow.

Historical Factors

The early studies of homeless children took place in contexts in which the problem of family homelessness had recently emerged and where communities had not had sufficient time nor had marshaled adequate resources to address the needs of this new homeless subgroup. While difficult to document, it is likely that shelter conditions for families have improved in most cities between the mid-1980s and mid-1990s. What a typical child who was homeless in Washington, DC, in 1985 experienced versus what a child who was homeless in Worcester, Massachusetts, encountered in 1995 are very likely quite different. The contrast to 1985 is probably even greater now. The Stewart B. McKinney Act, which was passed in the late 1980s, has funneled hundreds of millions of dollars each year to communities to use in improving housing options and services available to homeless single adults, families, and unaccompanied youths. Legal changes and funding to reduce educational obstacles for homeless children could have made a difference in some communities as evidenced by findings in Worcester (Buckner et al., 2001) and more broadly (Anderson et al., 1995). It is safe to say that, were it not for Federal, state, and local funding to address the needs of homeless individuals and families, their plight would clearly be much worse.

One can make a bit more sense out of the inconsistencies across studies of homeless children by recognizing that the time span between some of these investigations was long enough that what investigators were observing in the later studies entailed a much greater societal response to the issue of homelessness that what the earliest studies had witnessed. For instance, it is probable that the “null” findings regarding school and education-related outcomes for homeless children in Worcester in the mid-1990s (Buckner et al., 2001) would not have emerged had the same investigation in the same city been conducted a decade earlier, before implementation of the McKinney Act and other responses, which began to rectify difficulties that homeless children were having in attending school.

Contextual and Policy-related Factors

Across communities at any given moment, the extent of structural imbalance between the supply of affordable housing and its demand will vary with some areas having greater disequilibrium between the supply of housing and demand than others. Likewise, within any given community over time, the degree of structural imbalance is not static but in a state of flux. For instance, Massachusetts, like many other regions of the United States, has had a shortage of affordable housing for many years and this structural imbalance between supply and demand has worsened over the past 10 years. Evidence of this has been increased length of time on waiting lists for eligible households to receive Section 8 housing assistance and longer average duration of shelter stays before families can secure permanent housing (U.S. Conference of Mayors, 2001). Interestingly, it is quite possible that changes in this structural imbalance, for better or for worse, may have ramifications for what researchers uncover at the individual-level among homeless individuals and families. How could this be so?

In understanding the root causes of homelessness, it is important to differentiate between a structural imbalance in the supply and demand for housing, which is the fundamental cause of homelessness, from individual-level vulnerability factors. As a structural imbalance emerges within a locale, such that there is a shortage of affordable housing, it is those who are least able to “compete” who are first to become homeless. Such persons may have multiple vulnerability factors so that, compared to a broader group of persons at risk, they are the least competitive (Buckner, 1991; Buckner, Bassuk, and Zima, 1993; Shinn, 1992). For instance, among families, where caring for children in and of itself leaves adults more vulnerable to homelessness, this could include having health, mental health, or substance use problems as additional risk factors. As the structural imbalance progresses, those who become homeless next will have fewer vulnerabilities than the earliest victims. In other words, when a community begins to encounter a lack of affordable housing appropriate for families, it will be the most vulnerable families who become homeless first. If the problem worsens over time, those families who become homeless thereafter will increasingly look less susceptible compared to the first entrants into homelessness.

The implication this has for homelessness research is that, all other things being equal, in a gradually worsening housing market, early studies may reveal greater problems among shelter residents (adults and children) than do later studies. The rationale being that a gradually tightening housing market “selects” out those families first with the most vulnerabilities (i.e., least ability to compete successfully for housing) followed by families with fewer vulnerabilities. Over time, early disparities between homeless and low-income housed families would tend to lessen. Hence, a comparative study conducted shortly after a structural imbalance in the supply and demand for housing emerges may end up seeing starker differences between the homeless and housed group (e.g., more ADM disorders with the mother). However, these may be factors that entered into the selection process for which families became homeless. If these factors also have a role in influencing a children’s mental health (or other aspect of child functioning) then it may appear as though housing status is the reason for heightened problems among children, when in fact the association is not a causal one. For this reason, it is important to measure other factors that can influence a child’s mental health (or other relevant outcome) so as to make a clearer determination about the specific contribution of housing status (i.e., homelessness) to such outcomes.

Housing assistance policy is another area that could change the complexion of sheltered homeless families over time. If housing policy is such that being homeless reduces a family’s wait for a Section 8 housing certificate/voucher or some other form of housing assistance, then some families may decide it is worth it in the long run to seek admittance to a family shelter. A situation then arises where homelessness is not something that is avoided by all. Should a modest proportion of families in shelter be there as a matter of “choice” rather than necessity, a comparison of homeless to low-income housed families would most likely reveal fewer differences than if all families in shelter were in shelter unwillingly.14

Conceivably, some of these contextual and/or housing policy-related factors could have played a role in accounting for different results between Ellen Bassuk’s and colleagues study of homeless and housed families in Boston during the 1980s (Bassuk and Rosenberg, 1988; Bassuk and Rosenberg, 1990) and a similar but more comprehensive investigation of homeless and housed mothers in Worcester that she led 8 or so years later (Bassuk, Weinreb, Buckner, Browne, Salomon, and Bassuk, 1996; Bassuk, Buckner, Weinreb, Dawson, Browne, and Perloff, 1997; Bassuk, Buckner, Bassuk, and Perloff, 1998). In the earlier study, homeless mothers had greater difficulties than a comparison group of low-income mothers on a range of factors, including history of abuse in childhood and adulthood, greater psychiatric problems, and less supportive social networks (Bassuk and Rosenberg, 1998). In contrast, in the Worcester study, the two groups were quite similar across many different measures, including abuse histories, alcohol, drug, and mental health problems, health conditions, and social networks. In fact, the two groups were similar enough on so many different dimensions, especially histories of violent victimization and mental health problems, that it was almost as if they had been sampled from the same population. Conceivably, this contrast in study findings between Boston in the 1980s and Worcester in the 1990s is partly explained by a gradual worsening of the housing market in Massachusetts. Or perhaps housing policy shifted appreciably such that more low-income families were entering shelter to accelerate receiving housing assistance. Either way, what was observed in mothers in each of the two studies likely related to what was assessed in their children. In other words, the greater differences between homeless and housed children in the Boston study as reported by Bassuk and Rosenberg (1990) as compared to the Worcester study (Bassuk et al., 1997; Buckner and Bassuk, 1997; Garcia Coll et al., 1998; Buckner et al., 1997) could have partly been a function of there being more troubled families in the Boston homeless sample than the Worcester homeless sample.

While the above discussion is somewhat speculative, there are compelling reasons to warrant researchers taking a step back and evaluating possible contextual and/or policy-related factors that may play a role in study findings of homeless individuals and families. This is not to argue that differences in contextual or policy factors explain all the inconsistencies seen across the different investigations of homeless children (and families), but that they could account for some portion of the variability in results.

Homelessness is Not a Homogenous Experience

It is important to recognize that people experience homelessness in many different ways. For example, if one were to examine the residential histories of those children (or adults) who are homeless across the United States on any given night, one would find that a number of different circumstances have led to their present situation. Likewise, the ultimate pathways they shall take out of homelessness will vary as well. While homeless, these children will experience different durations of shelter stay, the conditions of shelters will vary both within and across cities, and shelter rules will be quite different. For instance, a few shelters require a family to leave during the daytime while others do not force such a requirement (U.S. Conference of Mayors, 2001). As such, homelessness is not a homogenous experience for children and it can be challenging to make generalized statements about the impact of “homelessness” on children because “homelessness” is not the same thing for all those who experience it. This can be the case as well for other stressful events, but there may be an especially high degree of variation in what homeless children encounter, both within and across locales and time periods.

Shelter conditions are probably an especially important factor in moderating the impact of homelessness for a child. Yet, previous investigations involving homeless children have not sought to measure attributes of a shelter or ecological indices to see if they relate to child outcome. No doubt this would be a challenging task and most studies have not had enough contrast in shelters from which families were enrolled to examine such issues. Nonetheless, it stands to reason that there are important qualities to shelters that may worsen or buffer a child’s experience while living there. These could include the amount of privacy accorded to families, the crowdedness of the facility, the extent to which rules are strictly enforced, the warmth of shelter staff, the size of the facility, its location, and whether families are asked to leave during the day or can remain on the premises.

Shelter as an Intervention

On the surface, a shelter stay may seem like a negative experience for a child in a low-income, but as Bassuk and Rosenberg (1990) remarked, “for some children, their stays in a neighborhood-based family shelter have been the most stable and predictable experiences of their young lives (p.261).” In fact, a stay in a family shelter (especially if it is neighborhood based and not a barrack-like shelter or a motel) accords some families the opportunity to receive assistance from case management staff in applying for assistance programs for which they may be eligible as well as referrals to professionals for treatment of one sort or another. As a general rule, the staffs of family shelters have good intentions and, over time, shelter staffs aim to improve their programs and be more responsive to their guests. Hence, some shelters may be providing useful assistance to families, thereby ameliorating other factors that can have a negative impact. In contrast, low-income families who have never been homeless can sometimes be quite isolated and far removed from a range of services and treatment programs that may be beneficial. The implication for research on the impact of homelessness on children is that a shelter stay is not always a negative event for a child. Were it the case in studies that homeless children had been literally without shelter (e.g., living in a car or in campgrounds), than the contrast in living conditions would be much more striking than is sometimes the case. In reality, studies that compare sheltered homeless versus low-income housed children are dealing with a much more complex underlying set of residential circumstances in the lives of each group of children than is generally appreciated. Said differently, the living conditions of children living in shelter are not always as bad as they might seem while the conditions of children in low-income housed settings can be much worse than imagined.

Similarities Between Homeless and Low-income Housed Children

As stated at the beginning of this section, it seems easier to discern a poverty-related effect in studies of homeless and low-income children than a homelessness-specific effect. A simple explanation is that both groups tend to differ far more from children in the general population, in terms of exposure to risk factors detrimental to various measures of outcomes than they do to one another. Despite differences in current housing status, homeless children and low-income housed children have more similarities than differences in what they have been exposed to. Even on housing status, it is important to note that homelessness is a temporary state through which people pass, not a permanent trait emanating from individual deficits (Shinn, 1997).15 Also, the living conditions of housed low-income children can be quite decrepit thereby attenuating the contrast between them and children who are living in shelter.

Children from low-income families, whether homeless or housed, face an array of chronic strains and acute negative life events that stem from the broader conditions of poverty.16 These adversities may loom large over the specific detrimental effects that homelessness can have on a child (especially when looked at over the long term). In other words, problems attributable to poverty-related stressors may be much greater than those that are homelessness specific. When viewed in the context of a much broader range of adversities, it is apparent that homelessness is but one of many stressors that children living in poverty all too frequently encounter. For most children, homelessness as a stressful event, may rank somewhere in the moderate range in terms of severity. It has the potential to be more stressful than many experiences, but not to the degree that some events hold, such as witnessing or being the victim of abuse or violence; events that are not uniquely experienced by children when homeless.

Part III: Future Directions for Research

Research conducted to date on homeless children has illuminated the knowledge on current needs and the impact of homelessness. Additional studies of homeless and housed children along the lines of previous investigations may do little to clarify the inconsistencies in findings. If future research is conducted that specifically addresses the question of how and to what degree homelessness impacts children, it should address some of the issues brought up earlier. However, this is no small task because it would be impossible to control on historical factors that may have affected past results and it would be very difficult to account for contextual factors, such as the extent of a housing shortage in a community or shelter conditions, without conducting a large multisite study. Clearly there are variables that moderate the relationships between housing status and important indices of children’s well-being, but many of these variables may be at levels of analysis higher than the individual (e.g., shelter, community, etc.) and are difficult to investigate. Nonetheless, to advance this area of research to be more practical for policymakers and service providers, it would be helpful to understand some of the contextual, moderating influences raised here.

Topics for Further Inquiry

There is sparse data concerning some issues on homeless and low-income children. One issue is to better understand homelessness in the context of other adversities that children living in poverty frequently encounter. As mentioned previously, in comparing homelessness to other stressors that children living in poverty may encounter, homelessness is a moderate stressor, not as problematic as exposure to violence but capable of causing mental health and educational problems in children under certain circumstances (Buckner et al., 2004). Future studies to clarify the negative life events and chronic strains that are the most problematic for children would be helpful in targeting treatment resources and preventive efforts to those children living in poverty who are the most in need.

It is also useful to understand factors both internal and external to a child that lead to positive outcomes despite the adversities of poverty. Such findings lend themselves to more strengths-based interventions, which attempt to promote positive factors as opposed to only trying to eliminate risk factors. Two characteristics of children were identified in the Worcester research that were quite useful in distinguishing those who were resilient from those who were not doing as well on multiple indicators of mental health and adaptive functioning (Buckner, Mezzacappa, and Beardslee, 2003). One of these factors, which is external to a child, was parental monitoring. A child whose parent(s) engaged in active awareness of where and with whom there child was on a daily basis tended to exhibit more resilience. Another, even more important, factor distinguishing resilient from nonresilient children was an internal set of cognitive and emotion regulation skills that researchers refer to as “self-regulation.” Self-regulation comprises a set of skills that are invoked in order to accomplish goals, whether they are fairly proximal or distal in nature. In the Worcester study, children high in self-regulation looked much better on measures of mental health, behavior, adaptive functioning, and academic achievement than children low in self-regulation (Buckner, Mezzacappa, and Beardslee, paper in review). Furthermore, those high in self-regulation appeared to be better able to cope with stressors in their lives. Variables such as parental monitoring and self-regulation may offer promising leads for positive or strengths-based interventions to promote resilience in homeless children and other children living in poverty.

An additional area of importance to homeless children for which relatively little is known concerns the issue of children who are separated from their parent(s) due in part to a homeless episode. Such separation is sometimes the choice made by a parent, usually the mother, in deciding the best interests of a child or can be a decision forced upon her by the child welfare system, shelter staff, or relatives (Cowal, Shinn, Weitzman, Stojanovic, and Labay, 2002; Park, Metraux, Brodbar, and Culhane, 2004). Cowal et al. (2002) conducted the most involved investigation to date on this issue. Their study, which took place in New York City during the early 1990s involved 543 poor families, 251 of whom had experienced homelessness at some point in the 5 prior years. They found that 44 percent of the homeless families had had a child separation compared to only 8 percent of low-income never homeless families. Even when accounting for histories of mental health and substance abuse problems as well as domestic violence (directed at the mother), homelessness was strongly associated with a family experiencing such a separation (Cowal et al., 2002). The reasons why the risk of parent-child separation increases when a family becomes homeless is not entirely clear but it is likely there are multiple factors at work. The “fishbowl hypothesis” (Park et al., 2004) posits that shelters scrutinize the parenting practices of adult family members much more so than what they would experience if living in housing, and this poses a risk for child welfare placement. Alternatively, in some cases, a soon-to-be homeless mother will ask that relatives care for a child of hers so that the child can continue attending the same school. In other instances, shelters may not allow adolescents, especially males, to stay in their shelter, thereby forcing a family-child separation.

Looking at this matter of parent-child separation within the parameters of families living in homeless shelters masks an even larger issue because residents of family shelters must include at least one parent and at least one child. What about parents who are separated from their only child or all of their children? They would not be welcomed at a family shelter and instead would be placed in a shelter for “single” adults. Hence, the residents of shelters intended for single adults can and do include some individuals who would otherwise be in a family shelter if they were presently caring for their child(ren). This is borne out in a study conducted in Alameda County, California by Zlotnick, Robertson, and Wright (1999), who interviewed 171 homeless women drawn from a countywide probability sample. Of these women, 84 percent were mothers and 61.5 percent of these homeless mothers had a child under the age of 18 living either in foster care or some other out-of-home placement.

Another topic for future inquiry involves the issue of residential instability as a predictor of adverse outcomes in low-income children. Moving from place to place is certainly a common event for homeless children in the months before and sometimes after a shelter stay, but such residential instability can also be experienced among children who remain in permanent housing and do not ever spend time living in a homeless shelter. The impact that residential instability has on child outcomes is not presently well understood.

Overlapping Issues of At-risk Groups

Homeless children, because of their impoverished circumstances and residential instability share commonalities with another at-risk group of children, namely dependents of migrant farm workers. Mostly Latino of Mexican and Central American heritage, migrant farm workers provide a low-cost source of labor for American farmers who seasonally require large numbers of temporary workers to harvest their crops. About one-third of such workers lead a transient lifestyle as they travel from one state to another in the course of a year, laboring to harvest the different types of produce grown in each region. They are paid low-wages, usually with no or minimal benefits and must live in crowded makeshift abodes. It is estimated that about 42 percent of the 2 million farm workers in the United States are migrant workers. The National Commission on Migrant Education (1992) estimated that about 600,000 children belong to migrant farm worker families. Older children sometimes work alongside adults in the fields while younger children are loosely supervised during working hours. Studies of children of migrant farm workers have observed problems of a similar nature to that of homeless children, including higher rates of health and mental health problems compared to children in the general population, elevated rates of physical abuse, and academic problems (Kupersmidt and Martin, 1997; Larson, Doris, and Alvarez, 1987; Research Triangle Institute, 1992; Slesinger, Christenson, and Cautley, 1986). While the residential instability of migrant workers is somewhat more elective and predictable than for homeless families, it nonetheless can lead to similar problems, particularly difficulties in attending school and graduating (National Commission on Migrant Education, 1992).

Typology Efforts

As previously characterized, the emphasis on research to date involving homeless children has been to discern the nature and extent of impact that homelessness can have on children. Referring back to Figure 1-1, studies have tried to identify and quantify, to some extent, a homelessness-specific effect on children above and beyond a poverty-related impact. Because of this focus, much less is understood about homeless children themselves in terms of having different constellations of needs. For instance, studies of homeless children typically use measures of central tendency when summarizing results rather than focusing on a range (or extremes) in outcomes. There is work to be done on better understanding the needs of subgroups of homeless children who have significant problems in one realm and/or across different dimensions of functioning. For instance, it could be the case that a subgroup of homeless children with demonstrable needs require much more in the way of services than they are presently receiving while in shelter; whereas other homeless children, those with fewer problems, do not stand to benefit from the services than they presently getting. A better understanding of this issue would help in allocating preventive and treatment services for homeless children in the most sensible manner possible.

The studies that have been conducted to date on homeless children can be characterized as having predominantly taken a variable-centered approach to analyses. In other words, variables in specific domains (e.g., CBCL scores as indices of mental health and problem behaviors; academic achievement scores; indices of developmental status) are highlighted. In such analyses, little if any attention is paid to how, for instance, there may be subgroups of children with quite different patterns in the type and severity of their problems or needs. In contrast, a person-centered approach to data analysis (e.g., cluster analysis) would be needed to empirically identify different subgroups of children based on a range of outcome measures.17 Fortunately, the data sets of many existing studies of homeless children could be reexamined to better understand these different clusterings, but it would require a person-centered approach to data analyses. Little, if any, work in this area has been done to date, for the simple reason that it has not been a question that researchers have been trying to address (at least in the published literature), although it could have been examined. Nonetheless, those data sets from studies of homeless children that have a range of relevant outcome measures could be analyzed using cluster analytic and other person-centered procedures to rather readily identify subgroups based on problems or needs.

What are some of the things that might be found by looking at how problems in homeless children cluster together? Internalizing and externalizing mental health problems co-occur as can be seen in the high correlations (r = .40 - .50) between CBCL indices as well as in children who come to an outpatient clinic presenting with disruptive behavior problems and with internalizing issues (e.g., a child who is acting out but also manifests symptoms of depression and anxiety). In terms of how school-age children present with problems, it is common to see co-occurring difficulties in the realms of mental health and academic functioning, although it is difficult to discern if one is the cause of the other (e.g., is a child doing poorly in school because she is depressed or is her low self-esteem and dysphoric mood the result of poor academic performance?). Most of the time, mental health issues and academic performance influence each other in a reciprocal manner.

Conclusion

In summary, the literature on homeless children conducted over the past 18 years has focused on trying to understand if, how, and to what extent homelessness has an impact on children. Studies involving both homeless and low-income housed children have consistently found evidence for a poverty-related impact on children; that is finding that both groups have more problems on measures compared to children from nonpoverty backgrounds. Discerning an additional, homelessness-specific, impact in different realms of child functioning has been more difficult; although, not surprisingly, the preponderance of the evidence does suggest that homelessness is detrimental to the well-being of children across various realms of functioning. Yet, enough studies having the methodological capability of finding effects of homelessness (above and beyond poverty) on children have not done so, making it seem that a range of potential effect modifiers and contextual variables are operating, such that homelessness-specific effects are sometimes, but not always, detected by researchers. Additional areas in which further research is needed include trying to better understand parent-child separations that can occur because of a homeless episode and the effects this has on family members. Also, very little attention has been given to understanding whether there are distinct subgroups of homeless children based on different constellations of problems or needs.

As studies have indicated, homeless families are not a static and isolated group. Homeless families emerge from a broader population of low-income families living in housing and eventually return to this larger group. Because homelessness is but one of many stressors that children living in poverty must encounter, it is wise to always be mindful of the broader context of poverty in terms of understanding the needs and issues of homeless children. Many of their problems and needs will be quite similar to housed children who are living in poverty. That said, it is also vitally important to appreciate the specific problems that children encounter when homeless and attempt to rectify them.

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Endnotes

1 This estimate is children who are part of families and does not include unaccompanied adolescents.

2 This is a period (e.g., 12-month) prevalence estimate for a homelessness episode of any duration. A point prevalence estimate (e.g., the number of children homeless on any given night) would be a substantially smaller number.

3 There is some minor variation in syndrome scales for the two age groups, but the composite internalizing and externalizing global scores can be calculated for each version thereby providing a useful means for aggregating data across the two age ranges.

4 Children are considered in the clinical range of the instrument, suggesting the need for further assessment and possibly treatment by a mental health care provider, with T-scores of 64 or greater. T-scores from 60 to 63 are considered to be in the “borderline-clinical” range.

5 The association between mothers’ psychological distress and CBCL ratings of their children’s problem behavior is a consistent finding in the literature. However, the nature of the link is unclear. One possibility is that a mother’s mental health influences her child’s behavior but the reverse could also be true. Furthermore, a mother who views the world in negativistic terms may report herself as having more distress as well rate her child’s behavior as more problematic.

6 This is consistent with anecdotal reports and conjecture that older children experience more distress as a result of being homeless as compared to younger children. Possible reasons include older children’s increased awareness of their external surroundings and the greater likelihood of encountering stigmatization from peers.

7 These CDI scores are nearly identical to those found by Bassuk and Rosenberg (1990) about 8 years earlier in Boston.

8 Children ages 6 to 8 years who were included in the Buckner et al. (1999) report were not part of this paper because they were too young to be directly administered the DISC.

9 A possible explanation is that the CBCL is better at picking up the effects of recent events than is the DISC, although both assessments use the same 6-month retrospective time frame. Also, diagnostic criteria for mental disorders versus behavior problem checklists do not correspond exactly, so the instruments may be assessing somewhat different things. The discrepancy could also be due to the source of the information (the CBCL is based on parent report, whereas the information taken to arrive at diagnoses for children regarding internalizing disorders came from the youth him or herself).

10 The DDST was not administered to housed children in this study.

11 In contrast, one could speculate that the results of Rubin et al. (1996) and that of Rafferty et al. (2004) (both which were conducted in New York city at about the same time), which each found higher school absences and lower academic achievement among homeless children, suggest that the EHCY program in this city was not as successfully implemented as compared to in Worcester 5 years later.

12 As shown in Table 1-1, the “Ho > Hou > GP” abbreviation can be interpreted to mean that the “homeless group had more problems on the outcome measure(s) than the low-income housed comparison group, which in turn had more problems than children in the general population/normative data.”

13 However, only some studies collected assessments of a range of adversities that children living in poverty experience, so it is not always possible to document how much risk children in the homeless and housed groups were exposed to.

14 In Massachusetts, the Department of Transitional Assistance (DTA), which is in charge of the emergency shelter system (as well as other assistance programs for persons with low-income), refers to this as “rendering oneself homeless.” This term is an acknowledgement of the reality that some families decide a temporary stay in a family shelter may be worth it if it speeds up the process of securing permanent housing; especially if the alternative is to continue living in crowded, “doubled-up” quarters with relatives or friends. In general, DTA disapproves of rendering oneself or family homeless.

15 As illustration of this, in the Worcester study when families in both groups were re-interviewed a year after the initial baseline interview, 92 percent of the initially homeless families were now in permanent housing and 8 percent were still homeless. By the same token, 92 percent of the housed families were still in permanent housing but 8 percent were now homeless. In other words, one year after enrollment, exactly the same proportion of “homeless” and “housed” families in our longitudinal study were living in permanent housing.

16 Chronic strains include such things as feeling hungry, being cold in the winter, worrying about the safety of one’s relatives, feeling a lack of privacy. These are circumstances that can be experienced on a regular basis, and children were asked if they had experienced a strain, how frequently, and how much they were worried or bothered by it. Life events are more acute in nature and tend to have an onset and endpoint. They can include extreme events, such as witnessing violence, having a relative die, having a parent be arrested, and more normative events, such as changing schools or having a new sibling born into one’s family.

17 A primary goal of cluster analysis is to take a group of variables (e.g., indices of mental health and other outcome measures) and try to identify subgroups where members are similar to one another but different from other subgroups. A goal is to minimize within-group variation on the values of variables used in the clustering, but maximize differences between groups. This yields an empirical typology.

Appendix B: Toward a Typology of Homeless Families: Conceptual and Methodological Issues

Introduction

Despite the general conviction that homelessness is a unitary phenomenon, there is ample evidence that persons without permanent living arrangements differ significantly among themselves (Culhane and Metraux, 1999).  Recognition of this heterogeneity has led to attempts to classify subgroups of homeless persons (herein referred to as subtypes) according to a variety of characteristics and dimensions, such as (chronicity, substance abuse, psychopathology, and childhood vulnerability factors).  An important consideration in the search for subtypes of homeless persons is the specification of essential environmental, situational, and personal characteristics that have a direct role in the development, patterning, and course of homelessness. 

The goal of this chapter is to review conceptual issues and methodological strategies for developing a typology of homeless families with children.  In particular, the chapter examines the feasibility of using a multidimensional conceptual and analytic strategy to determine how best to identify distinct subgroups of families with specific constellations of risk factors and service needs. The ultimate goal of this chapter is to inform both clinical practice and public policy, including the need for effective interventions and prevention programs.

Background Issues

The chapter begins with a review of the relevant scientific and clinical issues guided by the following questions: What is the purpose of typological classification? How can current knowledge about the epidemiology of homeless families contribute to the development of a typology?  What are the existing typologies and risk factors relevant to typological classification, as well as methodological approaches used to derive typologies?  What is the experience from other fields such as psychiatry, criminology, alcoholism?

What is the purpose of the typological classification?

Several possible functions suggest themselves: theoretical, clinical, and practical. Theoretical functions are those that deal with fundamental questions about the mechanisms through which individuals and families become homeless and continue in this condition.  The condition of living in stable housing within a stable community that is supported by local and national government bodies is considered a fundamental right of a civil society.  Why some members of society are excluded from this fundamental right is critical to the development of effective methods of remediation and prevention. 

A second function of typological formulations is to facilitate client-service matching. Here the concern is with efficient use of scarce resources, including cost-effectiveness. The idea of treatment matching has been popular in psychiatric research, guided by the assumption that treatment outcomes can be improved by matching patients with the most appropriate level, modality, and intensity of care.  Service matching is a broader perspective that includes not only clinical interventions but other kinds of services, such as housing, income supplements, and case management, among others.

How can current knowledge about the epidemiology of homeless families contribute to the development of a typology?

Research over the past 25 years has yielded an extensive body of knowledge on the prevalence and determinants of persons who are homeless and, of particular relevance to the present project, families that are homeless.  Some key epidemiological findings are summarized as follows:

  • The population is heterogeneous with regard to homelessness history. Population-based longitudinal studies in New York and Philadelphia show that 80 percent of persons using shelters are newly homeless with a short duration of homelessness; 10 percent are recurrently homeless; and 10 percent are long-term homeless (more than a year)  (Culhane and Metraux, 1999). Homeless families show a similar distribution.  In New York City,  homeless families were grouped in three categories; 52 percent were transitional (average of 1.2 episodes of homelessness, of average duration of 59 days); 43 percent were intermediate (average of 1.2 episodes of homelessness of average duration of 211 days); and 5 percent were episodic (average of 3.3. episodes of homelessness, of average duration of 345 days (Culhane, 2004)
  • The homeless population is very large. Earlier studies underestimated the extent of homelessness in part because of designs that selected the long-term homeless, and in part because of the hidden nature of a good part of the population, especially those that are doubled up with families or friends. Later studies correcting for some of these factors, especially retrospective telephone surveys of the general population (Link et al., 1994), showed a much larger prevalence of homelessness at some point in life. Homeless families with children have been the fastest growing segment of the homeless population during much of the past 2 decades.
  • The one feature that homeless people, including homeless families, have in common is poverty (IOM, 1988; Jahiel, 1992a).  Many poor people are not homeless, but nearly all homeless people are very poor. Because of this they contribute to an excess demand for low-cost housing, and those with features that might provide an additional barrier to housing are at a competitive disadvantage.
  • Certain types of homeless families are much more prevalent than others (Bassuk et al., 1996; Weitzman, Knuckman, and Shinn, 1990; McChesney, 1995; Culhane, 2004): single mother families; families where the parent was a foster child or never had a real home; families where the parent has had a long history of abuse; families fleeing imminent or continuing abuse; and African American and Hispanic ethnic minorities.
  • A small proportion of homeless individuals and homeless families are more salient and consume shelter and other services disproportionate to their numbers (Kuhn and Culhane, 1998).  They include people or families that are chronically homeless, and families in which one or more members have mental disorders, substance abuse, illiteracy, and not infrequently physical or mild mental disabilities; often, there is significant overlap of these problems in the same individual.  Given the hardships of homeless life, the word “multiproblem” is an understatement for these families.
  • The number of homeless children has been estimated at 1.3 million in 2000 by the Urban Institute and 1.2 million in 2001 by the National Coalition for the Homeless.  Despite better controlled studies of homeless children (Buckner, 2005), there still is relatively little in the way of systematic research on children whose families are homeless. Severe hunger is more frequent among homeless children than housed low-income controls (Weinreb et al., 2002).  In addition, multiple barriers to education have been reported, including lack of schooling, multiple transfers, transportation problems, and lack of needed educational services such as special education (Rafferty and Rollins, 1989; Rafferty and Shinn, 1991; Whitman et al., 1992; Vostanis and Cumella; 1999; Masten et al., 1997).  These children also have an increased rate of being in foster care or welfare service if parents are or have been homeless (Zlotnick et al., 1998; Culhane et al., 2003).  Education reform through the McKinney Act has improved the situation somewhat but much remains to be done.
  • Pregnancy has an elevated prevalence in homeless women. Pregnancy is relevant to a potential typology in several ways: it is a risk factor for homelessness (Shinn et al., 1998), it is associated with increased perinatal morbidity, and is sometimes followed by disorders in bonding (Whitman et al., 1992).

In summary, epidemiology provides valuable information about prevalence, incidence and determinants of homelessness.  The epidemiology of homelessness and of homeless families provides important insights into the potential usefulness of an empirical typology.  First, homeless people and homeless families are homogeneous with regard to poverty, but heterogeneous in terms of their personal characteristics and service needs.  Second, there seems to be a simple dichotomy separating complicated, multiproblem homeless families from relatively uncomplicated homeless families, who are more likely to be temporarily homeless and require fewer services.  Third, epidemiology suggests that the prevalence of homelessness changes with a variety of economic and social conditions, as does incidence.  Political considerations and public policy, particularly policies affecting the public “safety net” and resource allocations for social welfare programs, can have dramatic effects on the number of homeless persons and their personal and demographic characteristics. Without putting homelessness into a proper historical and socioeconomic perspective, any typology of homeless families may turn out to be a historical artifact.

What are the existing typologies and risk factors relevant to typological classification, as well as methodological approaches used to derive typologies? 

From common knowledge in the field, one would expect three main groups to emerge in general discussions of a useful typology: (1) families that are homeless for economic reasons (e.g., cannot pay rent, loss of employment, low paying jobs that cannot cover the rent, loss of welfare support); (2) families that have left one family member’s home because of abuse or fear thereof, usually a single female headed family; and (3) families that can be indexed as having a serious health or social problem (substance abuse, mental health, chronic illness or disability, criminal record, etc).  There are also two smaller groups: (4) families that have lost their home in a disaster (earthquake, war, etc); and (5) migrant families (families that have a home elsewhere but have moved to another area (in the same or different country) where they do not have a home). 

Typologies Based on Features of Homeless Persons

The first approaches to typologies of homeless persons were based on differing features of certain groups of homeless people, developed in part to describe the population and in part to ascribe a causal relation of these features to homelessness. Such studies, published from 1912 to the 1980s, have been reviewed by Louisa Stark (1992). Nearly all of these studies were derived from surveys of single homeless persons and were based on homeless shelter-based populations. Despite the fact that homeless people were typecast in different ways at different times, several major types were described:  First, people were classified as unemployed workers, alcoholics, mentally ill, and chronically physically ill or disabled.  Elderly people and “bums” constituted two additional, albeit much smaller, groups. Recognizing the heterogeneity of homeless people, Bahr and Caplow (1973) attempted to reduce this diversity to a single operational feature.  They postulated a Durkheim-like concept of disaffiliation, a detachment from social roles and institutions, as a common pathway to homelessness. They distinguished three major categories of disaffiliation resulting from external changes that leave the individual with few affiliations: (1) society withdrawing from the individual in periods of economic depression, war, persecution, etc; (2) from individual choice (opting out of societal roles); and (3) handicap or lifetime “unsocialization” resulting from mental illness or other chronic disorders (Bahr and Caplow, 1973). This theory lost ground in the next 2 decades as studies showed that homeless people had a network of social roles and institutional or personal affiliations, albeit usually not with rich people.

This typological approach continued even after the growth of homelessness and changes in the homeless population that included younger homeless single people and families in the 1980s.  For instance, Fischer and Breakey (1985) grouped mission users into the chronically mentally ill, the chronic alcoholic, street people, and the “situationally distressed.”  Other typologies of some of these groups were subsequently published, some of which were highly disaggregated.  For instance, Shepherd (2000), who used cluster analysis with a population of homeless adults, distinguished 11 profiles (malingerers, depression with alcoholism, symptom minimizers, psychotic avoiders, service avoiders, newly homeless, local ethnic minority, women with children, healthy family, other-Caucasian, and nondrug users).

The 1980s saw homelessness emerge as a major social problem, and several streams of research on the homeless population were initiated (see Institute of Medicine [1988] and Jahiel [1992a] for reviews).  The only common factor in this very heterogeneous homeless population was extreme poverty, associated with a decrease in low income housing in the late 1970s and 1980s (e.g., Calsyn and Roades, 1994). The concept of homelessness as a manifestation of extreme poverty began to replace that of homelessness as social disaffiliation.  Homelessness was seen as an aggregate rather than an individual problem due to the disequilibrium between the number of poor people and the number of low-income housing units:  a certain number of people had to become homeless at a given time unless the housing supply was increased, and environmental, situational, and personal characteristics determined who was most vulnerable to become part of that population (McChesney, 1992a).

Some years ago, Jahiel (1987) described a dichotomy between two types of homelessness: benign homelessness and malignant homelessness.  Benign homelessness means that the state of homelessness causes relatively little hardship, lasts for a short time and does not recur soon. For these people, it is relatively easy to gain back a home and a stable tenure on that home. Malignant homelessness means that the state of homelessness is associated with considerable hardship or even permanent damage to the person who is homeless. It lasts for a relatively long time or recurs at short intervals; extraordinary efforts must be expended to gain back a home with a stable tenure, and these efforts are often unsuccessful.

Typologies Based on Trajectories of Homelessness

In the 1980s a series of national and local studies were undertaken to enumerate homeless people. Although these studies had considerable methodological difficulties, they revealed the great variety of sites used by homeless people.  Some classifications of homeless persons were proposed according to where homeless people spend their nights. For instance, based on field studies of samples throughout Ohio, Roth et al., (1985) classified homeless people as street people, shelter people, and resource people (the latter including people who doubled up with family or friends).  Doubled-up people, the largest category by far, had not been studied before the 1980s.  Further studies showed that they were a large source of “literal homelessness” (Weitzman, Knickman, and Shinn, 1990) and that there was considerable back and forth movement among these three groups. 

The same cohort of 1980s studies also provided valuable information about the way people became homeless, yielding two main groups: the majority became homeless because they could not pay for their housing; a lesser number became homeless because they fled abusive environments (battered spouses, runaway youth) or were thrown away from their home by parents or partners.  Finally, the same studies showed that many people were recurrently homeless and pointed to three groups of homeless persons: new (homeless for the first time), episodic (recurrent homelessness) and chronically homeless (continuously for more than a year [see, for instance, Ropers, 1988]).

A more recent contribution (Mackenzie and Chamberlain, 2003) introduces the concept of homelessness careers. It identifies homelessness as a career process for a series of transitional stages in the development of any form of biographical identity, (i.e., people passing through various phases before they acquire the identity of homeless persons). They distinguish three pathways: (1) the housing crisis career, with poverty, accumulating debt, unstable housing, and eviction preceding homelessness; (2) the family breakdown career, with abuse or violence associated frequently with return to an abusive home and recurrence of that process until a final break occurs; and (3) the youth homelessness career continuing into adulthood for people who have been homeless since their teens.

By focusing on people in homeless shelters in two cities and developing a city-wide information retrieval of administrative data from shelters, Dennis Culhane opened the way for very large and relatively accurate data collection projects. Kuhn and Culhane (1998) applied cluster analysis together with an information retrieval system to trace homeless persons through the shelters in Philadelphia and New York to produce three groups of homeless persons—transitionally, episodically, and chronically homeless—by number of shelter days and number of shelter episodes. Transitional, episodically, and chronically homeless constituted, respectively 80 percent, 10 percent and 10 percent of shelter users. However, the latter group consumed over 50 percent of shelter beds. These data were cited in congressional hearings that led to Federal appropriation of funds for initiatives to end chronic homelessness (U.S. Department of HUD, 2002 and 2004).

Kuhn and Culhane reported differences in racial origin, age, and physical and mental conditions among the three groups.  However, they dealt with a selected population (shelter only and two cities). In studies of the users of a Toronto shelter, Goering and colleagues (2002) found little difference between transitional and episodic groups.  In studies of chronically, episodic, and housed adults attending a detoxification program who were followed for 2 years, chronic homelessness was associated with poorer scores over time on a mental health instrument but not on a health-related quality of life instrument (Kertesz et al., 2005).

Typologies of the Homeless Environment

The European Homelessness organization FEANTSA (The European Federation of National Organizations Working with the Homeless) recently presented a European Typology of Homelessness and Housing Exclusion (ETHOS) with four main conceptual categories (Roofless, Houseless, Insecure Housing, and Inadequate Housing) and a large number of operational subcategories (FEANTSA, March 2005). This is a new perspective on typology: a typology of the environments associated with becoming and being homeless.

Typologies of Homeless Families

While homeless families have been a topic of concern prior to 1980, studies of homeless families started only in the 1980s.  Early studies of homeless families are reviewed by McChesney (1995). More recent studies of homeless families have revealed several risk factors and protective factors (Bassuk et al., 1997; Rog et al., 1995).  Wong et al., (1997), using Culhane’s methodology, have investigated predictors of exit and re-entry among family shelter users in New York City.  Families with housing vouchers had fewer re-admissions to shelters, and those with more children, minority status, pregnancy, and public assistance had more re-admissions.  Bassuk et al., (2001) compared multiply homeless women with first-time homeless.  A history of childhood abuse and adult partner violence were predictors of recurrence of homelessness.  Qualitative studies have yielded more evidence on which to build typologies of homeless families. Based on ethnographic studies in Los Angeles, McChesney (1992b) described four types of homeless families: unemployed couples; mothers leaving relationships; mothers receiving Aid to Families with Dependent Children (AFDC), and mothers who had been homeless teens (the latter includes a subtype of mothers who have never had a home in their entire life).

Summary

Based on the literature on subtyping of homeless individuals and families, there is some evidence to suggest that most of the attempts to classify this population, either according to a priori domains or according to multivariate statistical techniques, have identified two broad types of homelessness that can be arranged on a single continuum ranging from relatively simple, benign, time-limited, uncomplicated cases (e.g., situationally distressed, resource people, new homeless, transitional) to more complicated, “malignant” chronic, multiproblem cases (e.g., chronically mental ill, chronic alcoholic, street people (Fischer and Breakey, 1985), shelter people (Roth et al., 1985), episodic, chronic (Ropers, 1988; Kuhn and Culhane, 1998), multiply homeless (Bassuk et al., 2001).  As discussed later, this simple dichotomy may be a good place to begin in the development of a useful typology of homeless families.

What is the experience from other fields such as psychiatry, criminology, and alcoholism?

There is along tradition of typological research in psychiatry, alcoholism, and criminology that may be useful in the development of typological approaches to the description and management of homeless families.  For example, the fourth edition of the Diagnostic and Statistical Manual of the American Psychiatric Association (APA, 1994), which is used primarily for clinical and reporting purposes, describes subtypes for schizophrenia, schizoaffective disorder, anxiety disorders, affective disorder, delusional disorder, and substance induced psychotic disorder.  These subtyping schemes are derived primarily from clinical experience rather than from empirical research, and each one relies on a different organizing principle.  The subtypes of schizophrenia (paranoid, catatonic, disorganized, undifferentiated, and residual), for example, are organized on the basis of “the clinical picture,” which presumably refers to presenting symptoms.  The subtypes of schizoaffective disorder (bipolar type, depressive type) are organized according to affect disturbance.  The subtypes of delusional disorder (erotomanic, grandiose, jealous, persecutory, somatic, mixed) are organized according to the predominant delusion.  What these psychiatric subtyping schemes have in common is their attempt to classify psychiatric patients who share the same general condition into more meaningful or clinically useful subgroups.

In the field of alcoholism, the tradition of clinical subtyping according to single domains extends back to the 19th century (Babor, 1998; Babor and Dolinsky, 1988) and includes the domain of childhood vulnerability factors, family history of alcoholism, onset age, dependence, severity, and co-morbid psychopathology. Over the past century there has been an evolution of typological theory from these single domain subtypes, such as familial and nonfamilial alcoholism, to multidimensional typologies, based on a variety of defining characteristics, such as etiological elements, personality characteristics, drinking patterns, and course of illness (Babor, 1998).  This evolution in typological thinking has been in part influenced by the development of multivariate statistical techniques as well as reliable and valid measurement procedures that make it possible to search for homogeneous subgroups within a population of alcoholics.  Similar to the simple dichotomy suggested above in the review of the homeless typology literature, the alcoholism typology literature has identified a low severity, low vulnerability subgroup (Type A) and a high vulnerability, high severity subgroup (Type B) (Babor et al., 1992).

Conceptual Issues

Definition of Terms and Important Concepts

A number of important terms and concepts have been introduced in the introductory sections of this chapter that should now be more formally defined. 

What is a typology? A typology is a classification system and a set of decision rules used to differentiate relatively homogeneous groups called subtypes.  A subtype is an abstract category organized according to some conceptual, theoretical, and clinical principle.  According to one student of clinical subtyping (Millon, 1991), subtypes of complex clinical phenomena are “splendid fictions” because nature was not made to suit the conceptual need for a well-ordered universe. As noted above with typologies of alcoholism, different concepts and categories can be formulated and labeled in a variety of ways, but bear in mind that these labels are not necessarily “realities.”  This realization should not discourage one from attempting to make sense of complex clinical phenomena and heterogeneous groups if the primary purpose is kept in mind to benefit people in need and make the most efficient use of resources.

What is a “homeless family”?  Although this term appears to be self-evident, it is important to note that “homeless” should include both literal homelessness and families who are doubling up with others by necessity, and “family” should include couples without children, couples with children, and the large category of single parent with children.

Treatment and Service Matching

The concept of treatment or service matching refers to decision rules designed to facilitate matching to optimal treatment modality, service intensity, and ancillary services.  An important consideration in the development of a typology of homeless families is the kinds of services that the typology might relate to in terms of treatment, prevention, and other needs.  Obviously, the typology should be relevant to the types of services that are appropriate, feasible, and available to homeless families.  These services include the following:

  • Shelter facilities to deal with immediate and short-term housing needs
  • Child care, preschool, and school placement to deal with children’s needs
  • Housing subsidies to deal with economic barriers to housing
  • Supported housing and other housing programs to deal with long-term housing needs
  • Services to keep families intact and to improve family dynamics
  • Employment counseling
  • Welfare programs to provide for basic needs
  • Medical care, including sexually transmitted diseases and pregnancy care
  • Clinical preventive services, including family planning, HIV prevention, and childhood immunizations
  • Mental health counseling, especially for PTSD, depression, and domestic violence
  • Treatment for substance abuse
  • Case management to integrate and coordinate individual services

Services to help with stable housing or to rectify personal problems have to face unusually high obstacles. No matter how much help is given to finding housing, low income housing is often so limited (McChesney, 1992a) that only a small number can be rehoused unless the supply is increased.

Services to help with stable housing or to rectify problems have to be appropriately gauged to avoid a mix of insufficient and wasteful services. There is now ample evidence that the majority of homeless families can achieve stable housing based only on housing subsidies (Shinn et al., 1998; Stretch and Krueger, 1992; Wong et al., 1997).  In other studies when subsidies and a variable set of support services or case management were given, the strongest predictor of housing stability was subsidized housing regardless of the intensity of services (Weitzman and Berry, 1994; Rog, Gilbert-Mongelli and Lundy, 1998). Thus in the majority of instances, housing subsidies should be sufficient to achieve stable housing, and there is no need to provide additional case management for those families.  However, a small proportion of families return to homelessness during a 5-year follow-up period (Stojanovic, Weitzman Shinn et al., 1999). Thus an important role of a typology of homeless families would be to help in the identification of those families that need supportive services in addition to housing subsidies, and what kind of services are needed (e.g., case management, intensive case management, specialized services).

Possible Functions of a Typology

Given the nature of typological formulations and their history in clinical decision-making, an important conceptual issue is the possible functions of a typology for the management of homeless families.  The major uses of clinical typologies that have been proposed in these various literatures are the following:

  • Summarize important diagnostic, prognostic and descriptive information in a simple, understandable classification scheme.
  • Provide an empirical basis for client-service matching, such as programs to help with stable housing, psychological problems, medical care, social services, child care, or substance abuse.
  • Minimize or remediate effects on children.
  • Improve specificity of prediction of short-term as well as long-term outcomes in relation to services received.
  • Help to prevent family homelessness.

Optimal Taxonomic Standards of a Good Typology

Based on the experience of typological research in psychiatry and substance abuse (Babor and Dolinsky, 1988), a set of taxonomic standards can be suggested as the characteristics of a good typology.  Optimally, a typology of homeless families should:

  • Be simple in its structure;
  • Have practical utility (e.g., mediate judgments about clinical evidence);
  • Allow matching to clinical and preventive services;
  • Be easy to derive from available data;
  • Permit inferences to underlying causes;
  • Predict future behavior;
  • Facilitate communication;
  • Demonstrate empirical validity and reliability; and
  • Identify subtypes that are homogeneous within categories, remain stable over time, and are comprehensive in their coverage of the homeless population.

A typology of homeless families with children is relevant to at least three public health issues: (1) how to  help such families gain stable housing; (2) how to help them with personal problems, including but not limited to those affecting housing; and (3) how to protect homeless children in situations  that may interfere with  their healthy development. The same typology may not be optimal for these three challenges. Therefore, it is possible that more than one typology of homeless families may be indicated.

Classification Issues

If there is general agreement that typological formulations are appropriate to consider for the description and management of homeless families, the following questions need to be addressed before beginning the search for subtypes:

  • Should the approach be theory driven or directed by blind empiricism? [Does it have to be an either-or?]
  • Should the typology work within a single domain of variables or should it be multi-dimensional?
  • Should the working material for the typology include individual and group strengths as well as risk factors?
  • Should the working material include cross sectional or longitudinal variables, family variables or individual characteristics?  What are the relative merits of disaggregating families from individuals? 
  • Is one typology going to be sufficient, or should there be several?
  • Is it better to focus on the causes or the consequences of homelessness?

Question One: Theory Driven or Blind Empiricism?

Regarding the first question (whether the typology should be theory driven or directed by blind empiricism), it is first necessary to evaluate the quality of theory. There are essentially five theories: (1) Homeless people belong to an underclass with a culture of its own that lacks the necessary personal structuring needed to develop a home life, employment etc, (Schiff, 1990). There is little, if any, evidence for this view.  (2) Homeless people have “lost out in the battle for acceptance” and have gone through aversive learning experiences and as a result, they value their retirement from any institutional constraint (Levinson, 1963).  This theory is compatible only with a very small fraction of homeless people, chiefly single men.  (3) Homeless people have a faulty relationship with society, a “social disaffiliation” that may be brought about in various ways, for example, by mental illness, drug use, or other causes (Bahr and Caplow, 1970).  This theory, which was popular for a while, fails to take into account the extensive social networks that recent empirical research has demonstrated for homeless people.  (4) Homelessness as an extreme form of poverty resulting from the gap between income and available low-income housing. There is no single theoretical paper about this theory, but a lot of empirical evidence suggests an association between homelessness and severe poverty and unavailability of low income housing. (5) Societal disinvestment theory (Jahiel, 1992) accepts the premises of hypothesis but looks beyond it to decisions made by society to disinvest in certain geographic areas, types of work, or types of welfare support.  It also fits the empirical evidence. 

Based on this brief review, there seems to be little consensus around an explanatory theory, and virtually no theories specific to homeless families.  Nevertheless, it would seem like theory may offer some guidance on the selection of candidate variables for further empirical exploration.  For example, there is good support for theory 4 at the aggregate level (to account for the size of the homeless population).  At the individual level, some vulnerability factors (ethnicity, pregnancy, substance abuse, past homeless history, various disabilities, physical abuse by spouse, and others as well as being in the wrong place at the wrong time) account for who is selected by society (societal disinvestment) or by self (societal disaffiliation) to become homeless.

Question Two: Single Domain or Multidimensional Typology?

Regarding the second question (single domain or multidimensional typology), if a single domain is chosen, the only one that is general enough is the low-income housing/poverty relationship. In this instance, the typology should include exogenous variables (availability, accessibility of housing), personal variables (need for mere housing subsidies or subsidies plus support services) and situational variables (acceptability, appropriateness of the housing that is provided).  That domain would be best adapted to a majority of homeless families, judging from the literature reviewed above.  In addition, that domain could be used with a preventive approach to homelessness (including such additional variables as eviction preventive programs, and low-income housing guidance).  A multidimensional approach would be better adapted to a (multidisciplinary) client-service matching strategy with two possible purposes: (1) ending homelessness (here the housing /income disciplines would be predominant); (2) alleviating or perhaps diminishing the adverse effects of homelessness (here the effects on homeless children would have top priority, with mental, physical and re-adaptive services as a second priority for specific families).

Approaches to Understanding Homelessness

Another issue is whether homelessness should be approached in a cross-sectional way or situated in the larger context of developmental experience.  Some types of homelessness may be developmentally cumulative, becoming progressively worse over time, whereas others may be developmentally limited (e.g., only during periods of economic depression and only when children are in care of parents).  The successful negotiation of major life events such as completing an education, assuming adult roles, choosing a profession, marriage, and having children, may have important implications for the determination of which homeless families become economically self-sufficient and which ones deteriorate and remain chronically homeless.  A cross-sectional approach may be the simplest one for a client-service matching, but it may not be without limitations, and it tends to select chronically homeless people unless statistical corrections are made for the effect of homelessness duration on the chance of being selected in the study.  Homelessness, even of short duration, is often preceded by a period of considerable financial or emotional stress and poor quality of life. Thus, the variables included in the typology should not be limited to the period of homelessness, but also to preceding stressful periods and following periods of re-adaptation to having a home. Several studies have shown that the risk of homelessness is markedly increased by several distant developmental antecedents, such as physical and sexual abuse during childhood  (Bassuk, Perloff, and Dawson, 2001); foster care or institutional placement during childhood, housing instability during childhood (McChesney, 1992b) or homelessness as a youth (Mackenzie and Chamberlain, 2003).

Family Variables vs. Individual Characteristics

Regarding the issue of family variables vs. individual characteristics, it would seem logical and necessary to consider both in any typology of homeless families. There are typologies of homeless youth, (i.e., youth who are homeless by themselves), with categories of runaway; throw away and “system” (e.g. foster care) youth (Farrow, Deisher, Brown, Kilg, Kipner, 1992). Very little has been done, to our knowledge, regarding a typology of children whose family is homeless. Daniesco and Holden (1998) proposed a typology of homeless families in which one type was associated with higher rates of parenting stressors, major life concerns, and children with cognitive, academic, and adaptive behavior problems.

Methodological Issues

Having described the conceptual issues that justify the development of typological formulations, particularly in relation to homelessness and homeless families, this section considers the benefits and disadvantages of various methodological approaches for typology development, as well as criteria for selecting variables, measurement procedures and statistical methods for the identification of homogeneous subgroups.  Among the methodological approaches that have been employed in typological research on psychiatric populations are clinical description, statistical discrimination, and response to treatment. 

Clinical description is based on observation of clinical cases that come to the attention of service providers.  A major limitation of early attempts at clinical description is the failure or inability to use objective measurement techniques to provide a basis for testing assumptions about differences between subtypes.

With the advent of structured interview schedules, psychiatric diagnostic criteria, personality inventories, and administrative data bases used to collect descriptive information, quantitative procedures have been used to identify homogeneous groups.  For example, subtype discrimination and identification can be brought to a higher level by using statistical clustering techniques that identify homogeneous subgroups based on correlations among individuals sharing similar characteristics.

From the experience gained in other areas of clinical research, it is clear that classification theory and clinical practice should both be grounded in objective clinical assessment and sound research methodology.  It is, therefore, important to focus on the selection of classification variables and their measurement as the most fruitful empirical approach to the development of a typology.

Selection of Classification Variables and Data Sets

Several criteria may be helpful to guide the selection of variables.  These are simplicity, ease of measurement, theoretical relevance, minimal measurement overlap, coverage of major domains of interest, and practical usefulness in service matching. 

From the perspectives of efficiency and economy, the availability of current instruments is certainly an important practical consideration. Progress would be much faster if one could use existing instruments and data sets than if one had to devise new instruments.  However, one should not be guided in the choice of a variable by availability of instruments for that variable. The choice of variables should be determined by its theoretical and practical value.  However, one should also consider developing a new instrument for key constructs to the extent they are considered important.

A variety of measurement techniques and standardized assessment procedures have been developed to measure many of the variables relevant to typological formulations.  Techniques include self-report questionnaires, personal interview schedules, and administrative data, including demographic characteristics.  Assessment procedures include measures of psychopathology, substance use disorders, personal resources, and multiple problem inventories, such as the Addiction Severity Index (McLellan et al., 1992), which covers employment, psychiatric severity, substance abuse, family functioning, and criminal activity.  Additional considerations important in the selection of measurement instruments are response burden, administrative load, and the availability of data sets to develop typologies.

A decision point in considering variables is whether to focus on endogenous variables (i.e., characteristics of the homeless families), exogenous variables (i.e., characteristics of the environment of such families), situational variables (i.e., characteristics of the interaction with the environment or of situation in the family’s homelessness history), or all the above.

There has been very little use of available data on the environment of homeless families. Yet such data are of critical importance since homelessness is the result of interactions between persons and their environment (Jahiel, 1992b). There are several readily available sources of data with environment as a unit of analysis that could be used: (1) as a typology of homeless environments, or (2) in a typology of homeless situations (matching homeless persons’ needs and environmental capacity to meet these needs). Environmental data fall into several categories: (1) housing-related; (2) welfare-related (3) employment-related, (4) health-related, (5) mental health and substance abuse related. A partial listing of available secondary data resources that are relevant to critical environmental factors is given in Appendix B.3.

Another decision point is whether to start with “epidemiological type variables” (i.e., variables shared with other environmental problems), or empirically derived variables (variables elicited in qualitative or quantitative empirical studies in the field of homelessness, that are often more complex than the first type, and that have sometimes been used in developing typologies of homeless persons).

To the extent that the goal is to develop a typology that can be justified quantitatively and be useful in quantitative studies, the selection of variables is very important, particularly with regard to the state of disaggregation (to avoid noise), the locus of the variable (the one that best explains), and the specificity or the relevance of the variables for homelessness as it may be revealed by previous qualitative studies or published typologies. Appendix B.4 provides some examples to guide a starting point for an empirically derived typology.

A related consideration in the selection of variables is the availability of data sets that include various measures of homeless families.  There are four existing longitudinal data sets on homeless families: the New York City Homeless Family Study (NYC HF, Shinn et al., 1998), the Worcester Family Research Project (WFPR, Bassuk, Buckner, Perloff and Bassuk, 1998)), the Robert Wood Johnson /U.S. Department of Housing and Urban Development data set (RWJ/HUD HF, Rog and Gutman, 1997), and the Substance Abuse and Mental Health Services Administration Homeless Families Program (SAMHSA.HF, SAMHSA, 2004)) and one cross-sectional study (the NSHAPC, Burt et al., 1999). Together the four longitudinal studies have 3,878 subjects. Each of these studies has a set of demographic data (age, race, marital status, work, education, currently pregnant) and certain service needs (health, mental health, substance abuse, trauma, legal history). Three of them have measures of income and foster care history.  There are differences in the instruments used to measure these variables but there is enough similarity among them to make it possible to do replication studies, with appropriate correcting factors. There are marked differences in the selection of the study populations. Two of them (NYC-HF and WFPR) have populations of families on welfare and families in shelters.  One (the RWJ/HUD HF) has families with multiple needs entering enriched housing. Another (the SAMHSA.HF) has families with mental illness, substance abuse, or both.  Thus there are marked selection differences among families in the four studies, including differences in service needs and differences in the stage during the trajectory of homelessness when these families are studied. Furthermore, all studies underselect families that are doubled up (as opposed to literally homeless) and families that are in shelter for battered women, as well as families that have little or no contact with services. Thus, the four studies cannot be considered representative of the homeless family population at large.  Further, families with multiple or severe service needs are selected in at least the two largest studiesNevertheless, the advantage of the large sample sizes of the four combined longitudinal studies cannot be overlooked. They might yield typologies that are robust in the presence of differences in types of populations selected, for instance, typologies reflecting the intensity of service needs.

The only study able to provide good data on families identified before they are homeless and followed longitudinally, including those that remain stably housed and those that do not and have episodes of homelessness, is the National Survey of America’s Families (NSAF) (Abi Habib et al, 2005). NSAF has data from a representative sample of the civilian population with an oversampling of people with low income, with a large sample (n=> 40,000) surveyed in a cross-sectional design every 2 to 3 years.  It is the best available source of information on doubling up, since it has a specific question asking whether the family had to move in with another family because of inability to pay mortgage, rent, or utilities. This data set would be very useful in investigating possible typologies of pre-literal homeless trajectories, as well as typologies related to history of doubling up.  Further, it has demographic and service need data that might be used in conjunction with the five studies of homeless families.

Eventually studies designed to collect primary data will be necessary to achieve a nationally representative sample of homeless families or families at risk of homelessness and to have sufficient numbers to allow adequate statistical analysis.  Ideally, such primary data studies should include longitudinal followup (e.g., 5 years).

Statistical Methods

A number of statistical procedures are available to identify homogeneous subtypes for the development of empirical typologies.  Important considerations in the selection of a statistical procedure are the size of the data set, the value of classifying all cases, the relative importance of working with smaller rather than larger numbers of subtypes, and the need to confirm or reject subtypes reported in the literature. 

Cluster analysis typically focuses on patterns of individual symptom clustering (e.g., syndrome manifestation).  Most investigators apply cluster analysis to cases, rather than attributes.  One advantage of empirical clustering techniques like the k-means clustering procedure is that all cases can be classified, and the method tends to favor the identification of a small (e.g., 2-5) rather than a larger number of groups.

In addition to cluster analysis, a variety of alternative procedures are available for representing structure.  DelBoca (1994) has argued that nonmetric multidimensional scaling (MDS) can be useful to identify major dimensions along which members of a particular heterogeneous group can be ranked.  This approach is particularly suited for finding a relatively small number of important dimensions that underlie the similarities or differences among cases or attributes (“objects”).  Based on the degree of similarity or dissimilarity between each pair of objects, the procedure produces an array of objects in n-dimensional space.  The reference axes in the resulting MDS spatial configuration are arbitrary but multiple regression can be used to fit substantive dimensions in the space.

Latent class analysis (LCA) is a multivariate statistical technique used to explore the structure and number of unobserved subgroups.  LCA assumes that there are qualitatively meaningful groups (or classes) that exist in a population and that symptom frequency can be explained by the existence of a small number of mutually exclusive classes, with each class having a distinct profile of item endorsement probabilities.  Another important assumption is that the variables are statistically independent and conditional on class membership.

Each approach has its strengths and limitations.  With many different variables, possibly in different categories (exogenous, endogenous, etc), multidimensional scaling might be the method of choice for more complicated modeling.

Validation Procedures

The validity of a classification or typology can be established in a variety of ways.  The approach most frequently emphasized in clinical research is predictive validity, which refers to the ability of a classification scheme to suggest the most likely course and treatment response for a given member of a class. Another approach is construct validity, which refers to the “goodness of fit” between a theoretical construct (e.g., an ideal type of homeless family) and a set of statistical relationships observed empirically.  Discriminative validity means that the subgroups classified by a typological theory can be clearly discriminated from one another in terms of major defining characteristics and correlates of homelessness, such as demographic factors, situational variables, service utilization or exogenous factors.

The following is an example of a validation procedure that can be applied to subtypes derived from empirical clustering procedures.  Once a satisfactory solution has been achieved: (1) compare the clusters using variables excluded from the original analysis as evidence of discriminant validity; (2) compare clusters on measures of clinical course following a service intervention (predictive validity); (3) examine subtypes in terms of their fit with theoretical constructs of homelessness (construct validity); (4) determine whether there are differential outcomes for subtypes matched to optimal services.  Other criteria for evaluating a typology are homogeneity within subgroups, comprehensiveness, simplicity, and practical utility.

Conclusions

A typology of homeless families should build on the existing knowledge. Most homeless families are experiencing severe poverty and that subsidized housing is enough in the majority of instances to help them gain a stable home. There are smaller groups for whom this does not seem to work, presumably because other environmental, personal, or situational factors. There may be environmental barriers to housing subsidies and other services.  

Aside from their extreme poverty, homeless families belong to a heterogeneous population. They fall into three groups: newly and recently homeless, recurrently homeless, and chronically homeless. The first group is the largest and the third is much smaller. Personal factors  associated with family homelessness are loss of employment, welfare support, spouse or partner; eviction from current living quarters; recent violence; physical or sexual abuse in childhood and/or foster care or lack of stable housing during developmental phases; belonging to African American or Hispanic minorities; pregnancy; hospitalization; and substance abuse, medical problems as well as mental disabilities.  At the individual or family level, these findings are consistent with a theory that homelessness is associated with severe poverty, lack of access to housing, and exposure to traumatic events, some of which go back to childhood.  At the population level, the theory that homelessness is associated with a gap between the number of low-income families and the availability of low-income housing units (the homelessness equation) is well suited to the facts.

There is evidence that, while children can be quite resilient, homelessness provides them with serious hazards shared, at least in part, with children experiencing severe poverty in their home (Buckner, 2005). Such hazards include hunger, poor physical health, poor access to health care, disrupted education, barriers to home work; exposure to bias associated with stigma; insults to self-image and to parental image; exposure to violence, psychological abuse; drug abuse; separation from family members; separation from parents and foster care placement; and lack of a stable, secure home during development. Research on children who are homeless with their families is far less advanced than research on homeless youth.

Research on homelessness, in general, and on homeless family typologies in particular, should be guided by the context in which research policies are developed. In the historical context of the 1980s, the problem was focused on single homeless persons and on mental disorder (“the homeless mentally ill”) and abuse of various substances. Thus, at the Federal level, the problem was “owned” by mental health and substance abuse agencies, and those agencies funded the waves of research in the 1980s and 1990s, and the characteristics of homeless persons were targeted. The rapid increase in the number of adults and children that are homeless as a family group led to additional research funded by private foundations and local government, as well as demonstration projects to address such homelessness. The results refocused the problem on housing and, therefore, housing subsidies, and on the developmental damage done by unstable housing situations, poverty, and sexual or physical abuse. The simplistic view that treating mental disorder or substance abuse would solve the homelessness problem is no longer tenable. Rather, the solution has to be systemic, a point that is reflected in the organization of the Federal U.S. Interagency Council on Homelessness. In the Department of Health and Human Services (HHS), the problem now requires planning and evaluation of the role of HHS’s various social and health divisions, as well as a concerted collaborative effort involving the Department of Housing and Urban Development (HUD).

In this new context, typologies of homeless families must include exogenous (housing environment, housing and health/human services access), endogenous (characteristics and history of homeless families and their members), and situational (fit between homeless families’ needs and accessible environmental resources) components. A systematic approach to developing a typology should take into account their practical value for: (1) preventing homelessness; (2) securing a home for homeless families; (3) preventing recurrence of homelessness; (4) providing human and health services to meet the needs of homeless families and their members; and (5) offsetting the harmful developmental effects of homelessness on children.

The typologies could be used to assign homeless families or children to groups that would be relatively homogeneous with regard to policy development at Federal and local governmental levels, and service provision at the provider level.  At the governmental levels, the prevalence estimates and distribution of the population of homeless families among the various groups would guide the development of programs among and between agencies. At the provider level, the classification of the client families among the classification categories would guide various providers in the selection of interventions at various stages in the process of experiencing and responding to homelessness: imminent eviction by landlord or flight from abusive home, presentation to initial service agency, assignment to initial shelter, interaction with welfare, employment and other agencies, provision of needed personal services to adults and children, temporary housing, with or without support, and finally, permanent housing.

A simple heuristic device that could be used to guide further work in typology development is shown below in terms of a four-celled model:

  Environment with
Facilitators Barriers
Service Needs of Families: Minor    
Major    

Differing typologies within this general format might be applied to housing, health and human services, and education of children.  Detailed typologies might be developed within each dimension (i.e., service needs based on endogenous variables, environmental context based on exogenous).  Ultimately, the interaction between endogenous and exogenous factors needs to be investigated, to the extent that the distribution and prevalence of service need subtypes is likely to vary with the environmental context, with environments having a high density of barriers (e.g., high unemployment, lack of services, poor housing stock) more likely to include families with minor or moderate service needs, whereas facilitating environments (e.g., ample services, low unemployment, adequate low income housing) more likely to include families with major service needs.

Homeless individuals or families are often classified as being newly homeless, on the one hand, or recurrently or chronically homeless, on the other hand.  Sometimes a third group of recurrent (but not chronic) cases is included.  The smaller, chronic group utilizes a disproportionate amount of shelter and other services. It has been proposed (and introduced as policy to end homelessness) that efforts be targeted to this small chronic group. This has been countered by advocates of homeless families who point to the significant needs of the much larger group of new and recurrent homeless families.

Recommendations

Here are recommendations for two types of typological research: (1) new data collection efforts targeted at developing and validating one or more typologies; (2) studies using existing data sets.

Recommendations for New Studies to Develop Typologies Relevant to Homeless Families

New cohort studies should be conducted with children in homeless families, with follow up until adulthood, including data on the variables listed in Appendix B.4. This is given the highest priority because damage to children, whether associated with severe poverty or homelessness, may have long-term repercussions on their emotional, social, intellectual, and physical development. Most studies of homeless children have had relatively short follow up. The findings in homeless and housed children should be disaggregated to identify subtypes associated with more severe social or developmental outcomes. Duration/frequency of homelessness, context of homelessness (shelter, street, doubling up), prehomeless history of the family, social isolation of the family, continuity of family life during homelessness, personal family conflicts and conflicts of the family or children with the law, nature of public and private services received, and community support, indifference, or stigmatization are examples of categories that might be significant in building a typology.  The objects of the typology would be to identify groups of children at risk of developing long-standing ill effects of childhood homelessness and protective factors thereof, as well as grouping children by service needs.

In addition, new cross-sectional and longitudinal (cohort) studies should be conducted to group homeless families based upon the environmental housing variables in the locality, and the housing, employment and financial needs of the families. Different typologies might be needed for the differing objectives of preventing eviction; securing housing for homeless families; and preventing recurrence of homelessness. This is needed to develop effective and efficient housing policies and services and perhaps could be accomplished by research on social indicators and other population statistics within states, metropolitan statistical areas, and other geographic or governmental subdivisions.

Finally, new cross-sectional and longitudinal (cohort) studies should be conducted to group homeless families according to needs for services and environmental access to needed services. This is needed to provide the services needed by this high risk population for adverse health and social effects and to help to offset some of the human damage caused by the homeless situation.

Recommendations for Studies with Existing Data

Readily available data sources on homeless families include four longitudinal studies (Shinn et al., 1998; Bassuk et al., 1998; Rog and Guttman, 1997; SAMHSA, 2004), two in a single city (New York and Washington, DC, respectively) and two in several sites. There is also one cross-sectional study (Burt et al., 1999). The two studies in a single city select single female headed families on welfare. The two longitudinal studies in several sites select families with serious problems, requiring health, substance abuse, and mental health services. The cross sectional study is based on a shelter population.  There is considerable heterogeneity in the design and instruments used in these studies. A retrospective meta-analysis would require considerable statistical sophistication. One of the longitudinal studies of low-income housed families is likely to include families that experienced homelessness during the followup period. As a prospective study with good national sampling and little evidence of selection of families, it is a good candidate to study the development of family homelessness, provided there are enough instances of homelessness in the study population. 

Clearly the currently available data do not include all types of homeless families with children and, at least in some studies, there has been a tendency to oversample those with mental disorder, substance abuse, and frequent service use. There is an exception in the instance of NSAF. Because its cohort begins as housed families, it is unlikely to select particular pathways or subgroups of homeless families.  Thus, a secondary data-based approach to a typology might first find whether there are enough families in NSAF and enough variables relevant to homelessness in that study to warrant using it in clustering studies.  Another approach would be to use the five homelessness studies very cautiously, with analysis of resulting clusters for dependence upon the excess categories described above.  Along these lines, the following secondary analysis projects are suggested:

  1. The longitudinal study of low-income housed families should be examined to determine whether it will yield a sufficient number of homeless families to warrant attempts to develop a typology.  If so, the careful selection of available severity indicators could be recommended, within the context of the four-celled approach described above.
  2. Preliminary typological analysis of the five homeless family studies should be performed to assess the extent to which the design and instruments are compatible with pooling their data to develop a typology; and find how much effort would be needed to index the subjects in these studies with environmental data as described in Appendix B.3 and Appendix B.4.  If the studies pass both tests, they might be worth further analyses both as pilot projects for the new studies and as a provisional source of data to guide policy and service delivery.

Available data in the five homeless family studies include the approximate dates when the findings were obtained and the localities where the homeless families were situated. Therefore, it should be possible to link demographic and endogenous measures from these studies with data on housing and other environmental variables listed in Appendix B.3 using date and locality information from Federal and local agencies and advocacy sources. Thus, it might be feasible derive a rough four-celled typology model from those linked data.

From a very practical point of view, perhaps it would be best to start with an attempt to create a relatively simple typology using readily available endogenous (e.g., psychopathology/psychiatric severity; substance abuse) and personal history variables (e.g., chronicity of homelessness, minority status) that are particularly relevant to women with children, and to test their interactions with environmental factors as suggested above.  This approach could be applied to existing data sets (both longitudinal and cross-sectional) and might lead to a relatively easy way to provide a simple classification into the uncomplicated and complicated subtypes suggested in the literature.  If replicated subtypes could be identified, they could provide a basis for some relatively straightforward decisions matching families to the most appropriate levels and types of intervention, including housing, social services, medical services and psychiatric care, with the more severe, chronic subtype perhaps being the subject of additional subtyping analyses to develop a more refined classification into service need categories.  New research on primary data sources should also proceed in concert.

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Appendix

B.2: European Typology of Homelessness and Housing Exclusion (ETHOS)

Conceptual category Operational category Sub-Category Description
Roofless
  1. Living in a public space (no abode)

  2. Stay in a night shelter and/or forced to spend several hours a day in a public space

1.1

1.2

2.1

2.2

2.3

Sleeping Rough

Contacted by outreach services

Low-threshold / direct access shelter

Arranged (e.g. low budget hotel)

Short-stay hostel

Houseless

  1. Homeless hostel / temporary accommodation

3.1

3.2

3.3

3.4

Short-stay homeless hostel

Temporary housing (no defined time)

Temporary housing (transitional defined)

Temporary housing (longer stay)

 
  1. Women’s shelter / refuge

4.1

4.2

Shelter accommodation

Supported accommodation

 
  1. Accommodation for asylum seekers and immigrants

5.1

5.2

5.3

Reception centers (asylum)

Repatriate accommodation

Migrant workers hostels

 
  1. Institutional Release
6.1

6.2

Penal institutions (period defined nationally)

Institutions (care and hospital)

 
  1. Specialist Supported Accommodation (for homeless people)

7.1

7.2

7.3

7.4

Supported accommodation (group)

Supported accommodation (individual)

Foyers

Teenage parent accommodation

Insecure housing
  1. No tenancy
  2. Eviction Order
  3. Violence

8.1

8.2

9.1

9.2

10.1

Living temporarily with family or friends (not through choice)

(Housing /Social Service records)

Living in dwelling without a standard legal (sub) tenancy (excludes squatting)

Legal orders enforced (rented housing)

Re-possession orders (owned housing)

Living under threat of violence from partner or family (police recorded incidents)

Inadequate housing
  1. Temporary structure
  2. Unfit Housing
  3. Extreme Overcrowding
11.1

11.2

11.3

12.1

13.1

Mobile home / caravan (which is not holiday accommodation)

Illegal occupation of a site (e.g. Roma / Traveller / Gypsy)

Illegal occupation of a building (squatting)

Dwellings unfit for habitation under national legislation (occupied)

Highest national norm of overcrowding

B.3: Sources of Data on Environmental Factors

  1. Data on housing and shelters
    1. Low income housing data. The National Low Income Housing Coalition provides a report initiated by Dolbeare Cushing and updated every year or few years on the cost of rental housing at a very disaggregated level (town or county), and relates it to wages and other income. These data are essential to the understanding differences in rates of homelessness or of recovery from homelessness.
    2. Data from HUD. HUD has a wealth of data on low income housing resources disaggregated to town and county levels. There are several relevant programs, and only the most salient ones are included below.
      1. Section 8 certificates (local numbers and utilization rate);
      2. Section 202 buildings;
      3. HOME investment partnership program (this would be significant for people already having a job who are coming out of homelessness, to help them with mortgage);
      4. Section 232 providing mortgage insurance for assisted living facilities and board and care homes; and
      5. Public and Indian Housing Resident Opportunities and Self-sufficiency (ROSS) program.
    3. Data from the Department of Agriculture on Rural Housing Services Rent Assisted programs.
    4. Data from the periodic surveys (e.g., ‘‘the continuing growth of homelessness and poverty in American cities’’ conducted by the United States Conference of Mayors).
    5. Local area data (town or state level).
      1. Anti-eviction programs;
      2. Housing subsidies for homeless families;
      3. Mortgage assistance programs
      4. Local data on items discussed in a) and b).
  2. Data on income related programs
    1. HHS data on federal supplemental security income (SSI) and state supplements to the federal SSI payments.
    2. Local town or state welfare programs (eligibility, amount of support).
    3. Data from local transportation departments on cost of transportation and availability of transportation vouchers.
    4. Data from local government social service and from Department of Labor on availability of jobs and rates of pay and unemployment rates in different localities.
  3. Data on health related services
    1. Location of community health centers.
    2. Location of mental health services.
    3. Location of substance abuse preventive and detoxification service.
    4. Location of McKinney homeless programs.
    5. Local availability/accessibility of other kinds of health related services.

B.4: List of Exogenous, Endogenous, and Situational Variables

Exogenous Variables
  1. Area housing resource indicators:
    1. State or local eviction prevention policies
    2. Title 8 vouchers
    3. Waiting time for 202 or other public housing
    4. Local area occupancy ratio of rental housing
    5. Local ratio of low income housing rent to minimum wage
    6. Local availability of SRO housing
    7. Local availability of housing subsidies: hotel rooms
    8. Local availability of housing subsidies: apartments, rental house
    9. Local availability of down-payment assistance programs
    10. Local mortgage assistance
  2. Area shelter resources
    1. Homeless shelter-occupancy ratio
    2. Homeless shelters with extended stay-occupancy ratios
    3. Family shelters with both parents-occupancy ratio
    4. Family shelters with only one parent-occupancy ratio
  3. Income sources in area
    1. Welfare policy indicators
    2. Area welfare income rate per family size
    3. Area hourly, weekly or monthly minimum wage
    4. Availability of jobs paying less than minimum wage
    5. Area unemployment rate
    6. Average number of applicants per low paying job
  4. Environmental safety indicators in the area
    1. Nutritional: availability and quality of soup kitchen and other free foods
    2. Temperature extremes for season
    3. Drug dealing activity indicators
    4. Crimes (assault, robbery, rape) rates
    5. Infectious diseases (rates of respiratory, STD, skin, GI, etc)
    6. Building safety re: collapse, arson, etc.
  5. Social environment in the area regarding people who are homeless
    1. Hostile: NIMBY
    2. Live and let live
    3. Supportive
  6. Local service resources
    1. Educational
    2. Physical health
    3. Mental health
    4. HIV/AIDS
    5. Drug abuse
    6. Job training
    7. Legal/administrative assistance
    8. Sheltered workshops
    9. Homeless clients work programs
Endogenous Variables
  1. Demographics
    1. Single or two-parent family
    2. Number of children
    3. Age of children (infants, pre-school, school, adolescents)
    4. Pregnancy
    5. Age of parents
  2. Social capital
    1. Education of parents
    2. Parenting ability of parents
    3. Work skills and habits
    4. Helpful informal network
    5. Prison/jail record
    6. Illegal alien
    7. History of institutionalization (hospital, training school, etc)
  3. Financial capital
    1. Income
    2. Savings
    3. Credit status
    4. Valuable possessions
  4. Health status of family members
    1. Chronic illness
    2. Physical disability
    3. Mental disorder
    4. Intellectual disability
    5. Substance use and abuse
    6. HIV/AIDS
  5. Past traumatic history and PTSD (parents)
    1. Physical abuse in childhood (parents)
    2. Sexual abuse in childhood (parents)
    3. Physical abuse as adults (parents)
    4. Sexual abuse as adults (parents)
  6. Linguistic and cultural resources
    1. Mainstream culture: English
    2. Marginal culture: good English
    3. Marginal culture: poor English
    4. Ethnicity: nationality
Situational Variables
  1. Precipitating factor
    1. Natural disaster or condemned housing
    2. Eviction by landlord or by foreclosure for lack of payment
    3. Immediate post-hospitalization loss of housing
    4. Immediate post-release from jail inability to find housing
    5. Loss of job
    6. Gradually increasing financial distress
    7. In and out of homelessness with short turnaround (less than a month)
    8. Moving to another town (state, country) and cannot find housing
    9. Evicted by parent or mate
    10. Physical or emotional battering by parent or mate
    11. “I cannot stand that home environment”
    12. Evicted for using drugs or being drunk
    13. Other
  2. Current homeless situation
    1. Doubled-up with friend or family
    2. Supported or transitional housing
    3. Referred domicile (e.g. paid hotel room)
    4. Family shelter
    5. Individual shelter
    6. Own car
    7. Squatting
    8. The street or equivalent (e.g. airport, etc)
  3. Time limits of domicile
    1. Number of days, weeks or months allowed
    2. Number of hours per day allowed
  4. Relations with domiciliary setting
    1. Supportive
    2. Neutral
    3. Tense, conflicted
    4. About to be evicted
  5. Reaction to homelessness
    1. Early crisis reaction
    2. Early adaptation reaction
    3. Short term efforts to regain a home
    4. Long term efforts to regain a home
    5. Resigned to homelessness, not trying, but not adapted
    6. Adapted to long term homelessness
  6. Parents’ general stress and coping with obligations of daily living
    1. Low stress, high coping
    2. Low stress, low coping.
    3. High stress, high coping
    4. High stress, low coping
  7. Parenting stress and coping
    1. Same categorization as in 5)
  8. Work situation of parent or older youth during current homelessness
    1. Work full time (satisfied with work or not)
    2. Work part-time
    3. Occasional work
    4. Looking for work
    5. Has given up looking for work
  9. Work situation of parent or older youth before current homelessness
    1. Employed or otherwise worked
    2. Failed welfare to work transition
    3. Had never worked
  10. Non-work income situation of parent or older youth during current homelessness
    1. General welfare
    2. SSI
    3. SS (retired or disabled worker)
    4. Other (e.g., private pension, VA, etc)
    5. Underground economy
    6. No source of income
  11. Informal social support (family, friends, other homeless)
    1. Quality of support: supportive, neutral or negative
    2. Reliability of support: steady, intermittent, unreliable
    3. Perception of support: perceived as meeting or not meeting needs
Additional Questions for Children
  1. Housing history of the child
    1. Duration of current episode of homelessness for the child
    2. New or recurrent homelessness for the child
    3. Housing instability of the child prior to homelessness
    4. With parent(s) in shelters or separated from parent(s)
    5. History of foster care
    6. Child’s appraisal of homeless situation
  2. Traumatic history of the child
    1. Physical abuse or neglect
    2. Sexual abuse
    3. Psychological abuse or neglect
    4. Welfare involvement
    5. Accidents
  3. Situation of school-aged child in family
    1. Expected to earn money (work, panhandle, etc.)
    2. Expected to care for siblings or parent
    3. Significant sibling rivalries
    4. Significant home behavioral problem
    5. Source of strength for family
  4. Educational situation of school-age children without special needs
    1. In school with continuity of schooling
    2. In school: more than 2 schools during time homeless
    3. Not in school, were in school before
    4. Never in school
  5. Educational situation of school-age children with special needs
    1. Evaluated, in special needs program with continuity
    2. Evaluated, in special needs program, without continuity
    3. Evaluated, in general schooling
    4. Evaluated, not in school
    5. Not evaluated, in school
    6. Not evaluated, not in school
  6. Home or community factors related to education
    1. Ability to study at home (light, space, noise, other duties, etc.)
    2. Sleeping conditions, number hours sleep at night
    3. Transportation to school
    4. Negative/positive attitude of parents toward schooling
    5. Negative/positive attitude of school mates toward homeless child
    6. Negative/positive attitude of teachers toward homeless child
    7. Language, cultural, or disability barrier to communication and understanding
    8. Integrated in regular school or in school for homeless children
  7. Pre-school children: daily life activities
    1. In Headstart or other program
    2. Adequate maternal bonding and conversation
    3. Regular schedule of feeding, sleep, activities
    4. Interactions with other than parents
  8. Health: General
    1. Hunger
    2. Nutritional status
    3. Growth rate, stunting
    4. Blood count (anemia, etc)
    5. Immunization status
    6. Exposure to lead or other environmental toxins
    7. Chronic illnesses, allergies
    8. Frequent acute illnesses
  9. Health: Disability
    1. Vision
    2. Hearing
    3. Motor
    4. Intellectual
    5. Attention deficit/hyperactive
    6. Emotional (mental disorder), substance abuse
    7. Social (passivity, shyness, aggressiveness, etc.)
  10. Health: Services
    1. Has regular primary care
    2. Specialist care
    3. Emergency services visits
    4. History of hospitalization
    5. Medications
    6. Regular dental care
    7. Self or alternative health care
  11. Health care coverage
    1. Medicaid
    2. McKinney program
    3. Other
    4. No coverage
  12. Attitudes of the child
    1. Sense of self (identity) and self-worth
    2. Sense of locus of control
    3. Attitude toward a home and toward parents
    4. Antonovsky’s sense of coherence
    5. Detachment
    6. Feelings of isolation or of belonging
    7. Feelings of humiliation, feelings of dignity
    8. Rage to serenity score
    9. Bonding to other youths, gangs
    10. Role models

Appendix C: Permanent Housing for Homeless Families: A Review of Opportunities and Impediments

Achieving stability in permanent housing is considered by many to be the overriding goal of the system of services for people who are homeless. Providers of service may have other important objectives tailored to the particular needs of their client population and associated with helping people to become as self-reliant as possible through employment, connection to mainstream services, and addressing medical and other needs associated with disabilities. However, the aspect of self-reliance most central to ending homelessness is moving to permanent housing and not returning to a shelter, transitional housing, or the street.

In addition to being an end in itself, stable, permanent housing is often closely associated with achieving other types of self-reliance. For families with children, in particular, a place to live may make it possible for a child who has become separated from a parent to be reunited with that parent. Such family reunification can enhance the long-term well-being of children, even when the parent or parents are in recovery from behavioral health problems. Family reunification also can serve as an important motivation for the adult experiencing homelessness to try to overcome circumstances that contributed to her becoming homeless, such as unemployment, substance abuse, or failure to comply with a treatment program for mental illness.

This chapter focuses on the role of federally funded rental housing subsidies in helping parents who have become homeless achieve permanent housing. There is strong evidence that families who receive housing assistance are more likely to remain stably housed than those without such assistance (Rog et al., 2005). This is not surprising, given that most adults who become homeless have limited education and earnings potential and, therefore, limited ability to pay market rents (or buy a housing unit) even when they are employed full time.

The purpose of this chapter is to explore the type of permanent housing that homeless families need, the resources potentially available for homeless families from programs that provide housing subsidies to low-income renters, and the barriers that may prevent the use of those housing resources by people attempting to leave homelessness.

This chapter is part of an effort sponsored by the Office of the Assistant Secretary for Planning and Evaluation at the Department of Health and Human Services (HHS) to develop a typology of homeless families. Part of that typology might be the type of permanent housing placement suitable for a particular family. Alternatively, the potential availability of suitable permanent housing for families with particular characteristics might be one of the indicators used in a typology of families at risk of becoming homeless or of families attempting to leave homelessness.

The chapter is organized into five sections. Section 1 provides estimates of the number of families with children who are homeless and the number who need three types of permanent housing: unsubsidized mainstream housing, subsidized mainstream rental housing, and permanent supportive housing. Section 2 describes the subsidy programs for mainstream rental housing, estimates the resources available from those programs that might be used by families attempting to leave homelessness, and discusses the barriers to the use of these programs by parents who have become homeless.

Section 3 discusses current and potential resources for permanent supportive housing for formerly homeless families with children. Section 4 evaluates whether current proposals for additional housing subsidies could create additional resources for helping families leave homelessness. Section 5 discusses implications for housing policies at the Federal, state, and local levels, implications for a typology of homeless families, and gaps in knowledge that need to be filled in order to develop a typology.

Section 1: How Many Families with Children are Homeless and What Kind of Permanent Housing Do They Need?

An estimate based on the National Survey of Homeless Assistance Providers and Clients (NSHAPC) is that in 1996 there were 60,860 families with children currently experiencing homelessness.1, 2 Another 101,840 homeless adults were parents of children under the age of 18 whose children do not live with them. While it is unrealistic to assume that all of the parents whose children do not live with them will be reunited with their children, this is often a basic objective both for the parents themselves and for the service providers that help them to set and achieve goals. Thus, the number of permanent housing units needed for families who are attempting to leave homelessness and become permanently housed is somewhere between 60,900 and 162,700.

This NSHAPC estimate is a point-in-time count of families who were homeless at the time the survey was conducted. The NSHAPC also can provide estimates of the number of parents who have experienced homelessness at some time in their life. Administrative data on homelessness from Homeless Management Information System (HMIS) ultimately will make possible more sophisticated estimates of the number of families who are homeless at some time during a calendar year and the average length of time they are homeless. These analyses of the flow of families in and out of homelessness will be superior for analyzing both the type of permanent housing needed by families who become homeless and the number of permanent housing units needed. For the time being, however, the information from the NSHAPC on parents and children homeless at a point in time in 1996 is the best available for determining the numbers and characteristics of permanent housing units needed by homeless families and for comparing those numbers and characteristics to resources available from housing subsidy programs.

Families who are doubled up but not at imminent risk of homelessness (i.e., did not tell the interviewers that they were about to have to leave) are not included because it is very difficult to determine how many of those families will actually become homeless. Without tools for predicting homelessness superior to those available now, any estimate that included high risk families would merge into estimates of families severe housing needs for housing assistance, such as the Department of Housing and Urban Development’s (HUD’s) estimates of “worst case needs” among unassisted renters with very low incomes. Similarly, in the following sections, resources or policies for preventing homelessness are not focused on. An updated estimate of the total number of people who are homeless is not used because no one knows whether that number has grown or decreased since 1996.

What types of housing units do families exiting homelessness need? Exhibit 1 provides estimates of the units of different sizes needed by parents who are homeless, derived from NSHAPC. The standard policy assumption is that all families with children need two sleeping rooms—one for the parent(s) and one for the child or children. A parent or parents with an infant could get along with only one bedroom for a time, but because the interest is in having families become stable in permanent housing, one assumes that children need their own sleeping room. Families with two or more children may need three bedrooms, depending on the numbers and ages of the children and the gender of older children in families with two or more.

Since the NSHAPC has limited information on the gender of children, the estimate in this exhibit is based just on numbers of children under 18, assuming that a parent with one or two children needs two bedrooms, with three or four children needs three bedrooms, and with five or more children needs at least four bedrooms. The exhibit provides two estimates. It shows, first, the number of units with two, three, and more than three bedrooms needed by the family members who are in the homeless services system as a family unit. Thus, if permanent housing were provided for all homeless families counting only the children who are with the homeless parent, the total units of permanent housing needed would be 60,860 and 14,700 of those units would need to have three bedrooms.

Category Number of units needed for family members homeless together Number of units needed for all family members of homeless parents Number of units needed if 25 percent of parents homeless alone are reunited with their children Percentage of units needed that have number of bedrooms
Exhibit 1:
Permanent housing units needed by parents who have become homeless and their children, by unit size
Two bedrooms 44,350 108,110 63,040 73
Three bedrooms 14,700 40,670 20,230 23
More than three bedrooms 1,810 13,920 3,050 4
Total 60,860 162,700 86,320 100

The exhibit then shows the number of units of different sizes that would be needed if the exit from homelessness to permanent housing always resulted in all children under the age of 18 becoming part of the household living in the permanent housing unit. The total units needed increases to 162,700, and the total of three bedroom units increases to 40,670.

The reality is that not all children who have become separated from their parents will be reunited with them when the parent leaves homelessness. Some children will have been adopted, and many will continue to live with a custodial parent who is not homeless. Many of the parents who told the NSHAPC interviewers about children who did not live with them were men; and 46 percent of the minor children of homeless parents were reported to be living with the other parent (Burt, Aaron, and Lee, 2001). Other children will continue to live with a grandparent or other relative. In some cases the child protection system will not be willing to return legal or physical custody of the child to a parent leaving homelessness, even if that parent is able to acquire suitable and stable housing. Therefore, in the third column of the table, an intermediate estimate is provided based on the crude assumption that one-fourth of the parents without children present will be reunited with their children.3

The total number of units needed is 86,320, and of those units 73 percent are two-bedroom units, 23 percent are three bedroom units, and 4 percent have more than three bedrooms. These are the numbers that will form the starting point for comparison with available permanent housing units.

Another important aspect of the permanent housing needed by families leaving homelessness is the nature and intensity of supportive services that formerly homeless families will need in permanent housing and whether those services require housing with features not generally provided in private market housing or in subsidized housing developments. How many homeless families need permanent housing with intensive supportive services? The literature suggests that severe mental illness is not common among the adults in families that are homeless (Rog et al., 2005). Depression and post-traumatic stress disorder (PTSD) are common, but do not imply intensive services linked to housing. Substance abuse is common, but recovery based on treatment that is limited in duration may be more than likely for parents trying to leave homelessness with children than it is for homeless individuals and thus may not require the ongoing, intensive services associated with permanent supportive housing.

One turns again to the NSHAPC to attempt to estimate the number of families that need permanent supportive housing rather than mainstream rental housing. To estimate the size of the group needing permanent supportive housing, those families were included who met either of the following criteria:

  • They reported having a alcohol, drug, or mental health problem in the past month and
    • They had been homeless more than once and the current episode lasted more than 6 months, or
    • They reported receiving Supplemental Security Income (SSI) or Social Security Disability Insurance (SSDI) benefits in the past
  • They reported receiving SSI or SSDI benefits in the past and never having owned or rented a place where their name was on the lease.

Based on these assumptions, 22,130 families with children who were homeless at a point in time in 1996 needed permanent supportive housing (Exhibit 2). This is a very crude estimate and clearly is one of the key areas that need more work before a good typology of homeless families can be developed. The ability of parents with long-term disabilities or patterns of chronic homelessness may have been underestimated nonetheless to live in mainstream permanent housing without intensive services.

Among families who are homeless at a point in time, there are some whose homelessness is a single event of short duration and who can return to permanent housing without the help of a rental subsidy. To estimate the size of this group, one assumes that those who told the NSHAPC interviewers that help with their housing was not one of their priority needs were making an accurate assessment of their situation only if, in addition to this self-assessment of their housing need, they met all of the following three criteria:

  • This was their first episode of homelessness,
  • They had been homeless for 6 months or less, and
  • They did not report receiving SSI or SSDI benefits.

Based on these assumptions, there were 8,740 families who were homeless in 1996 but could have left homelessness for unsubsidized rental housing.  After subtracting the group that can use unsubsidized permanent housing and the group that needs permanent supportive housing, consider that the residual group needs subsidized mainstream housing: 55,450 families with children.

Category

Unsubsidized mainstream housing

Subsidized mainstream housing

Permanent supportive housing

Exhibit 2:
Permanent housing units needed by parents who have become homeless and their children,
by type of permanent housing and unit size
Two bedrooms 6,890 39,430 16,820
Three bedrooms 1,320 14,290 4,190

More than three bedrooms

530 1,730 1,120
Total 8,740 55,450 22,130

These point-in-time estimates do not capture the total number of homeless families who will need subsidized mainstream housing or permanent supportive housing during the course of a year. Between 2½ and 4 times as many adults probably were homeless at some time during 1996 as were during the short period during which NSHAPC data were collected (Burt, Aaron, and Lee, 2001).

On the other hand, multiplying the NSHAPC-based estimates by as large a factor as 4 would overstate the number of parents who need subsidized mainstream housing when leaving homelessness, compared to those who can return to permanent housing without a subsidy. Those who remain homeless for longer periods and, therefore, form a larger fraction of point-in-time estimates are more needy on average than those with single or shorter episodes of homelessness.4 Therefore, for comparisons in the next section of the chapter with the number of units of assisted housing that turn over each year, the number of parents needing mainstream subsidized housing shown in Exhibit 2 has been multiplied by 3.

Section 2: Housing Resources for Permanent Housing for Parents Who Have Become Homeless and Their Children: Mainstream Subsidized Rental Housing

What housing resources are available to provide mainstream permanent housing for homeless families? This section discusses the Federal programs that provide subsidized rental housing to low-income families and individuals. To understand the potential of each of these programs for serving families leaving homelessness, it is important to distinguish between two general types of programs: assisted housing programs and affordable housing programs. The key distinction is the system for determining the rent paid by the resident family.

In assisted housing programs, the family pays for rent 30 percent of actual income, regardless of how low that income is.5 The major assisted housing programs are public housing, project-based Section 8, and Housing Choice Vouchers. The nominal income limits for assisted housing programs are quite high—80 percent of the local median income, which is about twice the Federal poverty level. However, for two reasons, the assisted housing programs heavily serve poor families—those with incomes below 30 percent of area median income, which on average is about at the poverty level. The first reason is that, because the subsidy varies with the actual income of the household, it is most valuable for the poorest households, who are the most likely to get on the waiting lists for the programs and to accept assistance when it is offered. The second reason is that Federal law requires that a certain percentage of the families and individuals chosen from the waiting list for each program have incomes below 30 percent of area median income (also known as “extremely low income.”)

In affordable housing programs, all families within an income range (e.g., up to 50 percent of area median income) who occupy a certain size unit pay the same rent for that unit. The rent has been set at an affordable level by the owner of the housing development. The rules of the Federal funding program establish the maximum rent the owner may charge at 30 percent of the income that is the upper limit of the income range for the size household expected to occupy the unit—for example, 30 percent of 50 percent of area median income (i.e., 15 percent of area median income) for a 3-person family. The major affordable housing programs are the Low Income Housing Tax Credit (LIHTC) program and the HOME Investment Partnerships (HOME) program.

2.1 Assisted Housing Programs

Assisted housing programs are examined first, which, of the two groups of programs, is the more likely to be usable by parents who have become homeless and are seeking permanent housing. Because assisted housing programs charge rent on the basis of actual income, however low, any family exiting homelessness should be able to afford to live in an assisted housing unit.

Administrative data collected by HUD make it possible to know a great deal about the units in the assisted housing programs: how many there are, how many bedrooms they have, whether families with children are living in them, and where they are. In 1998 HUD released a public-use data set called Pictures of Subsidized Housing that contains this information.6 The estimates of numbers of units in Exhibit 3 are based on the percentage distributions of different types of assisted housing units as of 1998 but updated to reflect the numbers of public housing, project-based Section 8, and voucher units as of 2004 that are shown in January 2005 HUD budget materials (U.S. Department of Housing and Urban Development [HUD], 2005a).

Of the 4.8 million total units of assisted housing, 2.6 million have two bedrooms or more and potentially could be occupied by families with children. More than half a million of these assisted rental units (511,000) turn over each year and might be used by families exiting homelessness. Exhibit 3 shows how these units are distributed across the three major programs and also shows how many have more than two bedrooms.

Category

Public housing

Section 8 projects1

Housing Choice Vouchers

Total assisted housing units

Exhibit 3:
Assisted housing units that could serve families with children

Two-bedroom units

280,000

408,000

779,000

1,467,000

Annual turnover

50,000

73,000

164,000

287,000

Three or more bedroom units

290,000

201,000

646,000

1,137,000

Annual turnover

52,000

36,000

136,000

224,000

Total units that could serve families with children

570,000

609,000

1,425,000

2,604,000

Annual turnover

103,000 110,000 299,000  512,000
1 Units produced under the Section 8 Moderate Rehabilitation program are classified as belonging in Section 8 projects. This is the “mainstream” Section 8 Moderate Rehabilitation program, which has many units with multiple bedrooms, as distinct from the “SRO Moderate Rehabilitation” program, which is one of the HUD McKinney-Vento programs for the homeless and has mainly zero or one-bedroom units.

Public Housing. The oldest of the assisted housing programs (created in 1937), public housing now has approximately 1.2 million units. Fewer than half of those units, approximately 570,000, have multiple bedrooms. While the popular image of the public housing program is that of a family program, a large portion of the program consists of developments that have been designated for occupancy by the elderly. In addition, “general occupancy” public housing developments often have some zero or one-bedroom units, as well as units with multiple bedrooms.

The public housing program is not growing. New units of public housing have been produced in the past 2 decades in very small numbers and only when a public housing authority (PHA) has received capital funds that may be used to replace public housing units that have been demolished or otherwise retired from the stock of public housing. The current (as of 2005) scenario is that a few PHAs may be able to amass sufficient “replacement factor” capital funds to undertake the development of new projects. Such developments would not add to the overall stock of public housing that might serve families leaving homelessness. On the other hand, it might be possible to persuade one or more PHAs to agree that some (or all) units in a replacement factor project should be used for parents who are exiting homelessness. The use of replacement factor funds requires approval from HUD.

The number of multiple bedroom public housing units has dropped slightly over the past decade, as distressed public housing developments have been redeveloped under the HOPE VI program or otherwise retired from the public housing stock. The estimates in Exhibit 3 reflect the loss of about 100,000 public housing units since 1998 and are based on an assumption that most of the reduction has been in family units. Many of those units are not lost to the entire system of assisted housing, because demolished public housing units are replaced by Housing Choice Vouchers.

For the public housing program as a whole, recent analysis of administrative data shows that between 10 and 14 percent of all households in the program are newly admitted each year (HUD, 2002). An analysis focusing just on families with children suggests that 18 percent of units occupied by families with children become vacant each year (Lubell, Shroder, and Steffen, 2003).7 Applying this 18 percent rate to public housing units with two or more bedrooms, it is estimated that 103,000 units that could be occupied by families turn over each year, including units with two bedrooms and units with three or more bedrooms (Exhibit 3).

Public housing developments are owned and operated by PHAs. Both the capital and the operating costs for public housing are funded by grants from the Federal Government. Waiting lists for the program are maintained by PHAs and, in recent years, Federal law has given PHAs fairly broad discretion for setting priorities for who gets selected from the waiting list to fill vacant units on the basis of income level, household type, and other preferences that could include, for example, work effort or special needs.

Project-based Section 8. Project-based Section 8 is actually a family of programs that produced subsidized rental housing during the 1960s, 1970s, and 1980s. Section 8 projects either were built in the first place with rental subsidies that follow the assisted housing rules (30 percent of actual income is charged for rent) or had such Section 8 subsidies added to them later in order to make them more affordable for current residents or to help maintain the financial viability of the housing developments, or both.

Section 8 projects are privately owned, and the private owner contracts directly with HUD to receive for each occupied unit a subsidy equal to the difference between 30 percent of the household’s income and a total rent agreed to by HUD as necessary to operate the housing development and pay its debt. Private owners of Section 8 projects have somewhat less discretion than PHAs to set their own priorities for their waiting lists, and many owners take households from their waiting lists on a first come, first served basis. They may—and most do—screen potential tenants for such things as credit ratings, rent payment histories, and criminal records.

As shown by Exhibit 3, there are 609,000 multiple bedroom units that might be occupied by families with children in privately owned Section 8 projects. About 408,000 of these units have two bedrooms, and 201,000 have three or more bedrooms. It is assumed that the annual turnover rate for family units is the same as for public housing, 18 percent. Thus, 110,000 units that could be occupied by families with children become available each year, including units with two bedrooms and units with three or more bedrooms.

Like public housing, the project-based Section 8 program is shrinking at a modest rate, rather than growing. Owners of Section 8 projects have the legal authority to end their contracts with HUD when those contracts come to the end of the term (the number of years) originally agreed to. Many Section 8 projects have reached that “opt out” point, and some owners have chosen to leave the program and convert their property to market rate rental housing or to something else. The numbers in Exhibit 3 reflect a reduction of about 5 percent of the project-based Section 8 units between 1998 and 2004. As is the case for public housing, some of the “lost” units have been replaced by vouchers, and these increases in the size of the voucher program are reflected in Exhibit 3.

Housing Choice Vouchers. Housing Choice Vouchers are tenant-based rather than project-based. Families and individuals use subsidies administered by PHAs (usually, but not always, the same entities that own and operate public housing) to rent private market housing. The housing must pass a housing quality inspection, and the landlord must be willing to participate in the program.

Vouchers do not have a predetermined distribution of unit sizes. As a household comes off a PHA’s waiting list, the PHA issues a voucher for the unit size needed by the household. In the earliest years of the voucher program and its predecessor Section 8 certificate program, PHAs kept separate waiting lists for different unit sizes, but that practice ended many years ago. Over time the voucher program has become a program serving mainly families with children. As shown by Exhibit 3, more than 1.4 million of the approximately 1.9 million vouchers are used to rent units with two or more bedrooms: 779,000 in two-bedroom units and 646,000 in units with three or more bedrooms. The voucher program is by far the largest component of assisted housing for serving families with children. The annual turnover rate for vouchers is 21 percent (Lubell, Shroder, and Steffen, 2003). Assuming that families with children come to the top of waiting lists at the same pace as before, it is estimated that 299,000 vouchers each year become available for use by new households.

The voucher program began in the mid-1970s, grew at a rapid pace during the 1980s, grew at a slower pace during the 1990s, and is now static in size, except for the growth associated with vouchers that are allocated to PHAs to replace public housing units retired from the stock and units in Section 8 projects with owners that opt out of the housing assistance system.8

PHAs administering the voucher program have been given increased flexibility to determine their own priorities among the households on the waiting lists for the program. During the 1990s, the funds appropriated by Congress for additional vouchers often included special set-asides of units for the homeless or for people with disabilities, but these set-asides have disappeared and the units have been absorbed into the mainstream voucher program. In addition, housing legislation in the late 1990s eliminated a system of Federal preferences that put at the top of voucher waiting lists households with extreme rent burdens (paying more than 50 percent of their income for housing), households living in substandard housing, and people who were homeless. Instead, an income-based rule applies: at the time vouchers are first used, 75 percent of those using them must have incomes below 30 percent of the local median (“extremely low” incomes). Subject to this constraint, PHAs can set their own preferences for admission to the voucher program.

Exhibit 4 compares the NSHAPC-based number of families with children who need permanent mainstream housing from Exhibit 2, and the unit sizes they need, with the annual turnover of assisted housing units from Exhibit 3. To compare annual turnover in assisted housing with a very rough estimate of the number of parents leaving homelessness with children during the course of the year, the point-in-time estimates shown on Exhibit 2 have been multiplied by 3.

Exhibit 4:
Comparison of annual turnover of assisted housing units with homeless families
that need mainstream permanent housing

 

Annual turnover in assisted housing

Category

Public housing

Section 8 projects

Vouchers

Total

Total mainstream units needed/ratio

Two bedrooms

50,000

73,000

164,000

287,000

118,290 2.4 to 1

Three or more bedrooms

52,000

36,000

136,000

224,000

48,060 4.7 to 1

Total 103,000 110,000 299,000 512,000 166,350 3.1 to 1

There are about three units of assisted housing turning over each year for every homeless parent who needs permanent mainstream housing. Furthermore, for the nation as a whole, there is no relative shortage of units that could serve families who need three or more bedrooms. The ratio of units turning over to needs for such units is almost 5 to 1.

Almost 60 percent of the units potentially available to parents who have become homeless and their children are in the Housing Choice Voucher program, close to 300,000 units. There are slightly more family units in the privately owned Section 8 stock than in the public housing program, although the public housing program has substantially more units turning over each year for families who need three or more bedrooms.

The next two sections (2.2 and 2.3) examine the degree to which the affordable housing programs, HOME and the LIHTC, may provide an additional potential resource for families leaving homelessness. Section 2.4 discusses the barriers that may prevent parents who have become homeless from using assisted or affordable housing and describes some strategies for overcoming those barriers.

2.2 Affordable Housing Programs

Since 1990, most of the growth in rental housing subsidy programs has been in affordable housing rather than assisted housing. The LIHTC was enacted in 1987 and, as of 2004, had produced about 1.2 million units of rental housing. In other words, the program is about the same size as the public housing program, and unlike public housing it continues to grow each year. The HOME program was enacted in 1990 and is a block grant to cities and states that can be used for a variety of purposes, including the production of rental housing and tenant-based rental assistance, as well as subsidies to homebuyers and homeowners.

Each year, authority to allocate LIHTC tax credits to developers of rental housing is issued to the states by the Internal Revenue Service, initially in an amount of $1.25 per capita, which was increased to $1.75 per capita in 2002 and indexed to inflation thereafter. The total annual tax credit authority (the equivalent of the program’s budget) is about $5 billion (Climaco et al., 2004). HOME is based on appropriated funds allocated to local governments and states through a needs-based formula. Its annual budget in recent years has been around $1.9 billion.

HOME. Sixty percent of HOME funds are allocated to local governments, and 40 percent are allocated to states. Only local governments large enough to receive a formula allocation of a certain size receive direct allocations. State funds may be used anywhere in the state, including within local government “participating jurisdictions.”

The use of HOME funds is based on a plan that each state or local participating jurisdiction must develop as part of the jurisdiction’s Consolidated Plan for using HUD funds. The Consolidated Plan process includes public hearings and other opportunities for input from advocates and provider organizations.

Most jurisdictions use a substantial part of their HOME allocation for the production of rental housing. Information is not available on the number of bedrooms in HOME units, but as of 2002, 48 percent of HOME rental production units served two to four people, and another 7 percent served five or more people (Turnham et al., 2004). Assuming that all of these units have two or more bedrooms, 55 percent of the HOME program may be usable by families with children, about 120,000 units as of early 2005. In addition, there is a “pipeline” of 66,000 HOME units with two or more bedrooms for which funds have been committed but which have not been completed. At current budget levels for the HOME program, funds could be committed for an additional 15,000 units with two or more bedrooms each year (Exhibit 5, based on HUD, 2005b).

As a “flat rent” program (with maximum rents generally 30 percent of 50 percent of area median income), HOME rental production does not necessarily produce housing that families leaving homelessness with limited earnings or benefit income could afford. However, a substantial fraction (42 percent) of units in HOME rental projects does serve households with incomes below 30 percent of area median income (HOME Program National Production Report, June 2005). In many cases, this is because of the families and individuals using Housing Choice Vouchers occupy the HOME units. About one-fifth (22 percent) of HOME rental production units are occupied by households with tenant-based assistance, and another 18 percent have some other type of rental subsidy (Herbert et al., 2001).

Thus, there are only a few HOME rental production units (probably less than 5 percent) that do not also have an assisted housing subsidy but nonetheless have flat rents low enough to be affordable for poor homeless families.

In addition to rental production, state and local participating jurisdictions may use HOME funds for tenant-based rental assistance similar to vouchers. Tenant-based rental assistance is a relatively small use of HOME. About 15,000 units are subsidized each year, typically for 2-year periods, with a total of 123,000 households ever subsidized as of 2005.9 Sixty percent of the households using HOME tenant-based rental assistance have two to four members, and another 12 percent have five or more members (Turnham et al., 2004). As of 2004, it is estimated that 22,000 families with two or more members were using HOME tenant-based rental assistance. HOME tenant-based rental assistance it is heavily used for households with extremely low incomes: 81 percent have incomes below 30 percent of area median.

There is anecdotal information that HOME tenant-based rental assistance is often used for special needs housing and that participating jurisdictions’ choice to fund tenant-based assistance results from demand for that use by advocacy and provider groups. It is not known whether this is permanent supportive housing or mainstream permanent housing targeted for use by families and individuals with special needs.

Category

HOME Rental Production

HOME Tenant-based Rental Assistance1

Low Income Housing Tax Credit2

Exhibit 5:
HOME and Low Income Housing Tax Credit units that could serve families with children

Two bedroom or two to four people

105,000

18,000

570,000

Three bedroom or five or more people

15,000

4,000

310,000

Total family 2004

120,000

22,000

880,000

Annual turnover

30,000

Not applicable

220,000

Pipeline as of 2004

66,000

Not applicable

120,000

Annual increments 15,000

Not applicable

60,000
1Assumes all tenant-based rental assistance used by more than one person serves families. The total two- or more bedroom units is an estimate of the number of units under subsidy at a point in time. HOME tenant-based rental assistance typically is only committed for 2 years, so the total commitments since the beginning of the program, 123,000 as of 2005, do not equate to a current program size. It is assumed that incremental use of HOME for tenant-based rental assistance sustains the current program level by renewing subsidies for current households.

2It is assumed that the size distribution of units placed in service 1987-94 and 2003-2004 is the same as the size distribution of units placed in service 1995-2002.

LIHTC. States use their annual allocations of LIHTC authority on the basis of a Qualified Allocation Plan (QAP) that, like the Consolidated Plan that informs the use of HOME funds, provides an opportunity for public input. There is no national administrative data on LIHTC. HUD conducts each year a survey of the state agencies that administer the LIHTC program, collecting information on some of the characteristics of the rental developments placed in service that year, including the number of bedrooms in each unit in the development and the development’s address. A survey conducted in the early 1990s by the General Accounting Office (GAO) provides the only information on the occupants of LIHTC developments, and that information is both limited and dated.

Thus, little information is available on the incomes of the households occupying LIHTC units (other than the presumption that, when they moved in, they had incomes below the program’s usual limit of 60 percent of area median income) or on the rents actually charged in LIHTC developments (as distinct from the maximum rents permitted by the program rules, usually 30 percent of 60 percent of area median income). Approximately 40 percent of all LIHTC developments placed in service between 1995 and 2002 have at least one household using a Housing Choice Voucher (Climaco et al., 2004).

The administration of the LIHTC program on the state level should provide an opportunity to coordinate the development of LIHTC housing with state programs focused on mental health, developmental disabilities, substance abuse, and other special needs. The extent to which LIHTC is used by states for developments targeted for occupancy special population groups is not known, although there is anecdotal evidence that some states create set-asides of this nature.10 LIHTC developments—or set-asides of units within developments—could be used either for mainstream permanent housing or for permanent supportive housing.

About 30 percent of LIHTC developments have nonprofit sponsors. Developments with nonprofit sponsors may be particularly likely to serve families with vouchers or to set rents lower than the LIHTC maxima on the basis of multiple sources of subsidy, often including HOME funds. Besides HOME, other supplementary subsidies that can make it possible to cover a development’s costs at rents that are more affordable for families with extremely low incomes include the Federal Home Loan Bank Board’s Affordable Housing Program (AHP) and the Community Development Block Grant (CDBG) program. Nonprofit owners may be especially willing to agree to long-term rental arrangements with providers of services for special needs populations.

The data set based on the annual survey of state agencies provides information on the number of LIHTC units placed in service between 1995 and 2002 that have multiple bedrooms. Based on that information (Climaco et al., 2004), Exhibit 5 provides estimates of the numbers of LIHTC units with two bedrooms and with three or more bedrooms placed in service between the first year of the program and 2004. As of 2004 there were a total of 880,000 units that potentially could be occupied by families with children.

2.3 Assisted and Affordable Rental Subsidy Programs as a Resource for Homeless Parents Who Need Mainstream Permanent Affordable Housing

There is less information about the rate at which HOME and LIHTC units turn over than there is about the assisted housing programs. It is likely that affordable housing units turn over at a more rapid rate than assisted housing units because they are less likely to represent a unique opportunity for the households occupying them to live in units they can afford. With flat rents at 15 or 18 percent of area median income, HOME and LIHTC units often are in competition with other moderately rental priced housing in the same area, and residents of housing with flat rents may have incomes at a level that makes it possible for them to buy moderately priced homeownership units. Typical market rate rental housing turns over at about 50 percent per year, but this includes many childless households with very high mobility rates. If it is assumed that a 25-percent turnover rate—higher than assisted housing but substantially lower than for all types of households in market rate rental housing—there are 30,000 multiple bedroom units in HOME rental developments and 220,000 units in LIHTC developments that become available each year for occupancy by new families. In addition, these programs have current pipelines that are likely to include 66,000 units of multiple bedroom rental housing (HOME) and 120,000 units (LIHTC). At current budget levels, there will be further annual increments of 15,000 HOME units and 60,000 LIHTC units (Exhibit 5).

It is not appropriate to add together assisted housing units, HOME rental development units, and LIHTC units, because many HOME and LIHTC units are also assisted housing units (residents use vouchers or the units also have project-based Section 8 subsidies). In addition, HOME and LIHTC often are used for the same developments and units.

Exhibit 6 provides the order-of magnitude estimates of the total units of assisted and affordable rental housing that have multiple bedrooms and that might be available to parents leaving homelessness. The estimates for affordable housing do not include units that have housing assistance, and they do not double count units that have both LIHTC and HOME subsidies.11 Based on turnover of rental units already placed in service and of current voucher slots, there are 721,000 units each year that might be used by families with children attempting to leave homelessness for mainstream permanent housing.

Category

Total units

Annual turnover units

Exhibit 6:
Total units of assisted and affordable housing with two or more bedrooms, 2004

Assisted housing (Exhibit 3)

2,610,000

512,000

Affordable housing

Total HOME plus LIHTC (Exhibit 5)

1,000,000

Minus HOME units with LIHTC/rental assistance

(75,000)

Minus other LIHTC units with rental assistance

(88,000)

Net affordable housing

837,000

209,000

Total assisted and affordable housing 3,447,000 721,000

2.4 Barriers to the Use of Assisted and Affordable Housing Programs by Parents Leaving Homelessness and Their Children

The assisted and affordable housing programs have almost 3.5 million units of subsidized rental housing large enough for families with children, and it is likely that about 720,000 million of these units turn over each year (Exhibit 6). By comparison, there are only 55,000 parents who are homeless at a point in time who need mainstream subsidized rental housing (Exhibit 2) and perhaps three times (166,000) that number over the course of a year. In theory, then, with a ratio of more than 4 to 1 between units available and units needed, the assisted and affordable housing programs should provide a substantial source of permanent housing for families leaving homelessness.

However, there are also serious barriers to the use of assisted and affordable housing by homeless families:

  • Competition from housed families who get on waiting lists for assisted housing programs;
  • Affordability issues already touched upon in the discussion of HOME and the LIHTC;
  • Shifting priorities for assisted and affordable housing programs that may make those programs less available as permanent housing for homeless families;
  • Requirements for occupancy of assisted and affordable housing other than rent;
  • Discrimination against people who have been homeless and against racial and ethnic minorities; and
  • Inappropriate locations of some assisted housing developments.

Competition from Housed Families with Severe Housing Needs. Families living with extreme housing cost burdens or in substandard or overcrowded conditions have a strong incentive to get on waiting lists for vouchers and public and assisted housing projects. HUD’s most recent estimates show that, in 1999, there were 1.8 million housed12 families with children, incomes below 50 percent of area median, and rent burdens greater than 50 percent of income or living in housing with severe physical problems. Most of these families (1.4 million) had incomes below 30 percent or area median, which is about the equivalent of the poverty level. In addition to the 1.8 million families considered by HUD to have “worst case needs” for housing assistance, another 600,000 were in crowded conditions (Nelson et al., 2003)

Thus, more than 2 million housed families are in direct competition for assisted housing with the 50,000 families who are homeless and seeking mainstream assisted housing. Some of these housed families may recently have experienced a drop in income or a change in household composition, or they may expect their poverty to be temporary, or they may have other reasons for not placing themselves on waiting lists for assisted housing. However, at any point in time, many families with severe housing needs will have been on waiting lists for assisted housing for months or for years. Under a first-come-first-served system or a system that provides only an income-based preference for extremely low-income households, these housed families will be selected from waiting lists ahead of homeless families entering the waiting lists more recently.

One approach used by homeless service providers for overcoming this barrier is to help families apply for assisted housing as soon as they have entered a shelter or a transitional housing facility, so that when they are ready to move to permanent housing some months in the future, they will be at the top of the list. How successful this approach can be depends on the length of waiting lists for assisted housing, which varies a great deal from location to location. It also may not help much for implementing a policy that tries to get families out of shelters or transitional housing facilities and into permanent housing as quickly as possible.

A more direct approach is to persuade PHAs to give people who are homeless a preference for receiving a voucher or a vacant public housing unit. PHA staff (and boards of directors) may be reluctant to establish a general preference for people who are homeless, especially in a jurisdiction where there is a large shelter population, for fear of crowding out other needy families who are precariously housed. In addition, PHAs or private owners of assisted housing developments may be reluctant to add families with histories of special needs to rental developments that already have a concentration of families with challenges.

An alternative is for providers in the homeless service system to negotiate with PHAs, or with owners of Section 8 projects, for a set-aside of vouchers or of units in a development for occupancy as needed by graduates of the provider’s program. It is important for advocacy organizations and providers to remember that PHAs are not the only providers of assisted housing and to take the effort required to reach out to private owners of Section 8 projects. The fact that the Section 8 projects in an area have many different owners in a local area may actually have some advantages, because special arrangements may be simpler to achieve than with PHAs. For a PHA such arrangements may imply policy decisions for all of the public housing projects—or vouchers—in a local area and require the approval of the PHA board.

Affordability of HOME and LIHTC Rents. The median income for homeless families is only 41 percent of the poverty income level, and most HOME and LIHTC rents are not affordable even by families with incomes at 100 percent of poverty, unless those families are using a housing voucher. There may be some homeless families that have incomes at a level low enough that they need affordable housing but high enough to afford HOME and LIHTC flat rents. For example, some parents may become homeless because of domestic violence or temporary behavioral health issues but have sufficient human capital to have jobs at which they earn between 40 and 60 percent of area median income at the time they leave homelessness for permanent housing.

For families who are able to obtain Housing Choice Vouchers, HOME and LIHTC developments can serve as available housing in which to use the voucher. This is not always the case for LIHTC developments, which, depending on the part of the country in which they are located, often are permitted to have rents above the voucher program’s payment standard. This happens in locations that have relatively low private market rents (on which voucher payment standards are based) compared with local median incomes (on which LIHTC maximum rents are based).

One of the ways to make HOME and LIHTC developments available for families leaving homelessness, with and without vouchers, is for providers to negotiate with owners for a set-aside of units within the development to be made available, as needed, to the provider’s clients seeking permanent housing. A good time to do this is when the rental housing developer is applying to the state or locality for an allocation of HOME dollars or LIHTC tax credit authority, because such a commitment may make the proposed development score higher in the competition for these resources. On a system-wide level, advocates can encourage the state and local agencies administering the affordable housing programs to give such arrangements priority in the competitions for HOME and LIHTC.

Shifting Priorities of Housing Programs. The most recent enacted legislation affecting the assisted housing programs, the Quality Housing and Work Responsibility Act (QHWRA) of 1998, accelerated a trend toward giving PHAs more discretion over both the management of their waiting lists and the rents charged for assisted housing. The earlier system of Federal preferences, which included homelessness and otherwise favored the poorest households, was replaced by requirements that 75 percent of the newly issued vouchers go to households with incomes below 30 percent of area median, and that only 40 percent of households newly admitted to public housing or project-based Section 8 have such extremely low incomes.

Because the income-based subsidy formula is such a powerful force in targeting assisted housing to the poorest households and because of decisions made by many PHAs to continue to serve the neediest households, the incomes of families in the assisted housing programs did not change much following the enactment of QHWRA. As of 2001, 71 percent of families and individuals living in public housing had incomes below 30 percent of area median, and 75 percent of families using vouchers had these extremely low incomes (HUD, 2002.) However, there is a parallel and growing trend to move away from 30 percent of income rents in order to create incentives to work within the assisted housing programs.13 A number of PHAs have been given the authority to do this under a demonstration called Moving to Work. In addition, under the basic QHWRA authority, PHAs may establish preferences for families with employment income. Furthermore, current proposals for “rent reform” favored by some PHA interest groups could make alternatives to 30 percent of income rents applicable to the assisted housing programs as a whole (Public Housing Directors Association, 2005).

Meanwhile, the Bush Administration has proposed legislation that would make fundamental changes to the Housing Choice Voucher program, renaming it the Flexible Voucher program and to turning it into a block grant with even broader discretion for PHAs to set priorities for the families and individuals who get vouchers and to alter the subsidy formula (HUD, 2005a). Possibly the most important aspect of this proposal has already been enacted in the most recent appropriation of HUD funds. Unless reversed by later appropriations acts, the voucher program now provides a fixed pot of money to a PHA, rather than the amount needed to maintain the current size of the voucher program regardless of the incomes of the households actually served by the program. This has created powerful incentives for PHAs to avoid serving the neediest families or to shrink the size of the program (the number of vouchers in use) below historical levels. Both possibilities are extremely threatening to the potential use of vouchers for enabling families leaving homelessness to achieve permanent housing.14

Another shift in the priorities for Federal programs that may affect the availability of subsidized housing for families with children is the increased emphasis on using HOME for homeownership rather than rental activities. However, even though there is now a set-aside of HOME funds that must be used for first-time homebuyer programs, the fundability of HOME funds means that so far there has not been an appreciable drop in the overall percentage of HOME funds used for rental housing production or for tenant-based rental assistance (HUD, 2005b).

Occupancy Requirements. Both PHAs and private owners are permitted by law and regulation to screen tenants for their ability to be lease-compliant tenants before offering them a unit in a public housing or Section 8 project. Screening often includes checks on credit history, on criminal records of household members, and on whether the family has a history of eviction from rental housing for nonpayment of rent or for other lease violations. Such screening may be difficult for some parents attempting to move from homelessness to permanent rental housing. Providers in the homeless service system report, in particular, that many of their clients have been evicted from housing in the past. Many have lived in assisted housing at some point in their lives, and some are barred from waiting lists for vouchers and public housing because they have outstanding debts to the PHA.

In the past, PHAs were less rigorous than private owners of Section 8 projects in screening families before admitting them to public housing. That is now changing, as PHAs attempt to create mixed-income communities in public housing developments. In addition, the retirement from the public housing stock of many of the most distressed public housing developments means that PHAs that formerly would take virtually anyone in order to fill vacant units no longer feel that pressure to the same extent.

For the Housing Choice Voucher program, major responsibility for screening prospective tenants rests with the owners of the private market rental housing in which families seek to use the voucher, rather than with the PHA. However, PHAs now screen families for criminal records during the process of qualifying them to receive a voucher, and they also may disqualify households that have violated voucher program rules in the past. Private owners of rental housing may legitimately refuse to rent to voucher holders for any of the same reasons, such as poor credit history, that they may refuse to rent to unsubsidized households.

Occupancy requirements can also present a challenge for retaining formerly homeless families in assisted housing. Anecdotal information from homeless service providers suggests that eviction from public housing or loss of subsidies under the voucher program because of rule violations may be a common path into homelessness.

Ways of overcoming these barriers to the use of the assisted housing programs by families attempting to leave homelessness include making payment of outstanding housing debts and repair of other credit problems immediate goals for parents who become homeless. A homeless service provider can also play an active role in the housing search process, making assurances to an owner of rental housing that a family will continue to receive case management and other support during a period of stabilization in permanent housing. It may even be possible for some providers to guarantee rent payments—for example, based on a revolving fund set up for this purpose.

Overcoming criminal records may be more difficult, and it may be that criminal histories recent enough to bar them from admission to assisted housing may be a characteristic worth including in a typology of homeless families.

Discrimination. In addition to the legitimate reasons owners of rental housing may refuse to accept families, such as a prior history of nonpayment of rent, private owners may discriminate against families simply because they have been in shelters. The owner may believe that becoming homeless is a predictor of disruptive behavior or lease violations. And homelessness may simply bring with it a stigma with which owners do not wish to be associated. There may be a few jurisdictions that include people who have been homeless among those protected by fair housing law.

Another type of discrimination, clearly protected by fair housing law but nonetheless common, is discrimination against racial and ethnic minorities. The most recent study of housing discrimination in U.S. metropolitan housing markets, based on paired testing conducted in 2000, found that, among those seeking rental housing, African Americans received unfavorable treatment compared to Whites 21.6 percent of the time.15 For renters, the adverse treatment measured included “the availability of advertised and similar units, opportunities to inspect units, housing costs, and the encouragement and assistance from rental agents.” (Turner, Ross, Galster, and Yinger, 2002).

Parents who become homeless are disproportionately members of minority groups and are particularly likely to be African American. Based on the NSHAPC, 37 percent of parents who are homeless together with at least one child are African American, as are 45 percent of homeless parents who do not have their children with them. The proportion of homeless parents who are Hispanic is not high compared to the representation of Hispanics in the overall U.S. population, but a surprising 11 percent of homeless parents who are separated from their children are Native American. Especially in localities where the homeless population is heavily minority and the rest of the population is not, it may be difficult for parents attempting to use vouchers to rent permanent housing to find willing landlords.16

Once again, the most effective way of overcoming these barriers may be active participation in the housing search process by homeless service providers. Reaching out to owners of rental housing and informing them about the ongoing support families leaving homelessness will receive in permanent housing can help allay stereotypes associated with minority status, with disabilities, or with homelessness itself.

Inappropriate Locations. A final barrier to the use of the 720,000 units of assisted and affordable housing that turn over each year by parents who have become homeless may be that assisted housing projects and programs are located in the wrong place. This could happen in a number of ways. For example, some public housing and Section 8 developments may be located in neighborhoods where it is hard for people recovering from substance abuse to remain clean and sober, and programs helping homeless people overcome such challenges may not want to encourage their clients to seek housing in those places. Another challenge may be that some public housing or Section 8 developments in metropolitan areas may be in locations from which it is not easy to find jobs or to travel to work. Or they may require a child to leave a school in which he has become stabilized or to move away from a relative who provides important emotional support. The inflexibility of assisted housing with a fixed location also may result in parents living in places that are inconvenient for continuing their treatment programs.

In rural areas, assisted housing may be in locations too far from the locations where parents have become homeless or are receiving services to help them leave homelessness. The NSHAPC suggests that as many as 20 percent of parents who become homeless are in nonmetropolitan areas. A substantially higher proportion of assisted and affordable subsidized housing is in nonmetropolitan areas, but there nonetheless may be a mismatch between the locations of housing subsidies and the locations of homeless families. For project-based assisted housing in particular suburban and rural areas, there may be a mismatch between unit sizes and the numbers of bedrooms needed by families trying to leave homelessness for permanent housing.

Finally, victims of domestic violence may need to find permanent, affordable housing in locations where they will be safe from their abusers. Housing vouchers may be a more appropriate form of permanent housing for victims of domestic violence than public housing or Section 8 projects, especially in cities that are small enough that there are only a small number of easily identifiable public and assisted housing developments.

Section 3: Housing Resources for Permanent Housing for Parents Who Have Become Homeless and Their Children: Permanent Supportive Housing

In Exhibit 2 a very rough estimate was presented that there were 22,000 families with children who need to leave homelessness for permanent supportive housing at a point in time in 1996. This may be an overestimate of the number of permanent supportive housing units needed for families, as it may underestimate the ability of parents to live in mainstream permanent housing despite having experienced multiple or lengthy episodes of homelessness or having the type of disability that qualifies for SSI.

The vehicles for subsidizing permanent supportive housing that are the most well-known are the HUD McKinney-Vento grant programs: Shelter Plus Care and the Supportive Housing Program (SHP). As of the end of 2003, program grantees reported to HUD that there were 7,355 families living in permanent supportive housing subsidized by these two programs.17 About half of these families, 3,710, were receiving tenant-based rental assistance funded by the Shelter Plus Care program. Assuming there are some vacancies associated with unit turnover and new units of subsidy just coming on line, there may be as many as 8,000 units of permanent supportive housing for families supported by the HUD McKinney-Vento programs.

Some cities and states use the HOME rental production option for permanent supportive housing. Data on HOME rental production as of 2000 showed that 5 percent of all units were in single room occupancy developments or group homes (Herbert et al., 2001). Because of the type of housing, these are likely to be for individuals with special needs rather than for families. It is not known whether there are any HOME rental developments that provide permanent supportive housing for families with children. The HOME tenant-based rental option, on the other hand, includes a substantial fraction of units with two or more bedrooms, and it is possible that some of them are used for permanent supportive housing for families.

Some states use LIHTC for permanent supportive housing, but there is no estimate of how many of such units there are in total or of how many have two or more bedrooms or are explicitly targeted to families. Another source of funds sometimes used for permanent supportive housing is housing trust funds based on dedicated sources of state revenue. Again, there is no estimate of the number of additional permanent supportive housing units that are created in this way.

HUD’s housing assistance production programs for people with disabilities, Section 811 and the older Section 202 program for people with disabilities, have only a tiny number of units with two or more bedrooms,18 and it is likely that these units serve individuals living as roommates or with caregivers rather than families.

With little information at the present on permanent supportive housing for homeless families subsidized outside the 7,300 to 8,000 units provided by the HUD McKinney-Vento programs, it is not known how large the gap is between the total amount of such housing and the 22,000 families estimated to need such housing.

Section 4: Proposed New Resources for Subsidized Mainstream Rental Housing

Housing assistance is not an entitlement, and there is a very large gap between the number of families who need subsidies for affordable rental housing and the number of available subsidies. This puts families attempting to leave homelessness for mainstream subsidized permanent housing in direct competition with families who are housed but who have severe or worst case needs for housing assistance. This section considers two current proposals for increasing the resources available for housing subsidies: an expansion of the Earned Income Tax Credit (EITC) and enactment of a National Housing Trust (NHT).

Adding a Housing Subsidy to the EITC. Proposals discussed in a recent issue of Housing Policy Debate (Stegman, Davis, and Quercia, 2004) would use the EITC as a vehicle for reducing severe housing cost burdens. These proposals would tie the amount of the EITC explicitly to housing costs either: (1) by providing a supplement to EITC equal to the difference between 50 percent of earnings and actual housing costs or (2) by providing a supplement based on national median housing costs that retains the structure of the EITC subsidy, with families with greater earnings receiving greater subsidies up to the beginning of a phase-out point, after which the subsidy diminishes with increased earnings.

Attaching a housing subsidy to the EITC focuses on families with children, as does the EITC itself. However, the subsidy levels proposed would be too shallow to be used for placing families exiting homelessness in stable, affordable housing. The first approach would be most useful for those who already are housed but need some relief from a severe rent burden. For those attempting to move into a rental unit, a subsidy paying only the difference between rent and 50 percent of earnings would not be large enough to make the rent affordable and the owner of a housing unit willing to accept such a rental agreement.

The second approach would provide higher subsidy amounts. Nonetheless, because it is tied to earnings, it could not help families entirely dependent on benefit income such as SSI. Also, because this approach provides a benefit that increases with earnings (at the lowest levels of earnings), it would also not provide a large enough supplement for families with low wage jobs and less than full time work to be able to afford the rents in unsubsidized housing or in affordable housing with flat rents.

The greatest benefit of an expanded EITC—or of any program or policy that increases substantially the incomes of poor families—is that it would reduce the number of housed families with severe rent burdens, making them less likely to get on waiting lists for housing assistance and thereby taking them out of competition with families attempting to leave homelessness. An expanded EITC might also reduce the number of families who become homeless because they are evicted for failing to pay the rent.

NHT. The proposed NHT would provide a permanent, dedicated source of revenue for the production of affordable housing. The goal of the proponents of this legislation is to provide an amount needed to produce 1.5 million units of affordable housing over a 10-year period. Seventy-five percent of NHT funds would be used for rental housing, and at least 45 percent of NHT funds would be used for housing affordable to those with incomes at 30 percent of area median income. NHT funds would be allocated (as is the HOME program), 40 percent to states and 60 percent to local governments.

The proposal contemplates the use of NHT funds in mixed-income developments that also have other locally controlled Federal dollars, including HOME and LIHTC. NHT funds would be used to write down the rents of a portion of the units to levels affordable at 30 percent of area median income. Locally controlled Federal dollars could count as a one-to-one match needed to access NHT funds. A state or local government using its own revenue (or private revenue) for the match would need to provide only $1 for every $2 of NHT funds.19

The NHT is, in effect, a supplement to the HOME and LIHTC programs, and it would likely be used for many rental developments that also use one or both of those funding sources. Its relevance for providing mainstream permanent rental housing for homeless families is that, if enacted as intended as a supplement rather than a replacement for HOME and LIHTC funds, it could bring the flat rents of those programs within the reach of families with incomes below the poverty level. In that sense, it would play a role similar to Housing Choice Vouchers when vouchers are used in HOME or LIHTC developments. The difference is that vouchers are portable subsidies that can be used by a formerly homeless family to move among rental housing developments (subsidized or unsubsidized), whereas the deeply subsidized rents enabled by NHT funds would be tied to particular rental housing developments.

When used outside HOME and LIHTC developments, the NHT, like those programs, would be unlikely to provide rents affordable for most families leaving homelessness, unless those families also had vouchers.

Section 5: Implications

This section considers, first, which housing policies controlled at the Federal, state, and local levels would help provide mainstream subsidized rental housing for parents who have become homeless. Then, the implications of the information provided in this chapter for developing a typology of homeless families are discussed.

5.1 Federal, State, and Local Housing Policies

Federal Policies. The broadest implication of the information reviewed in this chapter and the implication most relevant at the Federal level is that there is a need for more funding for housing subsidies that follow the assisted housing model so that families trying to leave homelessness are not in direct competition with housed families with severe needs for housing assistance. In particular, the Housing Choice Voucher program should be kept as a program targeted to the neediest households with a subsidy formula that can help any family, however low its income, move into permanent housing. The voucher program should be expanding rather than static.

State Policies. In the current housing policy environment, affordable housing programs provide the growth opportunity both for mainstream subsidized rental housing and for permanent supportive housing. The entire LIHTC program is controlled at the state level, and states receive 40 percent of HOME funds. State governments also control many of the funding streams for services needed by homeless parents and, therefore, are in an excellent position to plan and coordinate housing and service resources.

States should provide a competitive advantage to LIHTC and HOME developments that have a preference or set-aside for homeless families needing permanent mainstream housing or that provide permanent supportive housing. Advocates and providers serving homeless families should be active at the state level in working for such choices by state policymakers. States should also be encouraged to create or expand housing trust funds and to dedicate a portion of these funds to mainstream or permanent supportive housing for families attempting to leave homelessness.

Local Policies. Local governments control 60 percent of HOME funds and, like states, should plan for the use of those funds in a way that helps homeless families achieve permanent housing. A particular use of HOME funds that can be chosen, and advocated for, at the local level, is tenant-based rental assistance funded by HOME.

PHAs are local public entities that have increasing discretion over the targeting of two of the three assisted housing programs: public housing and the Housing Choice Voucher program. PHAs should consider ways of reestablishing preferences for families exiting homelessness and they should also work with providers of services for homeless families on other types of policies that make it easier for families trying to leave homelessness to use PHA-administered programs—for example, policies that help families repay past debts to the PHA.

5.2 Implications for a Typology of Homeless Families

A key distinction made in this chapter and one most relevant to any typology of homeless families is between families who need permanent mainstream housing and those who need permanent supportive housing. That distinction relates to the intensity of the service needs of parents leaving homelessness, whether those services are so essential to the particular family that they must be packaged along with the housing subsidy, and whether any of those services require staff to be on-site—for example, on-site case managers or mentors.

Another dimension that should be considered is the barriers certain families face when trying to use mainstream programs. The barrier that is the most difficult to overcome and that is a clear distinguishing feature of certain families is having a criminal record.

Another area to consider might be special factors relating to the appropriate location of subsidized mainstream housing. For example, it might make sense to classify homeless families who can use subsidized mainstream housing into those for whom location is not an important factor and those for whom it is. Those for whom it is may include victims of domestic violence who need to be protected from further harm and recovering substance abusers who need to avoid trigger neighborhoods.

An issue that has not been explored in this chapter is the needs of the children who will live in the permanent housing unit. Just as parents may need to avoid trigger neighborhoods so might older children who have exhibited risky behavior or who have been involved with the criminal justice system. Alternatively, there may be some children for whom school stability or the added support of an extended network of family (perhaps including those who have been caregivers during an episode of parental homelessness) means that it is a good thing to remain living near those people, even if that means living in a neighborhood with a high poverty rate or racial concentration.

Yet another issue, relevant to both parents and children, is whether the family has ties to a particular institution—for example, a mental health facility or a religious institution—and whether this is relevant to the location of permanent housing.

5.3 Gaps in the Knowledge Needed to Develop a Typology of Homeless Families

The most important areas on which information is needed are how many homeless families need intensive ongoing supportive services to maintain stable housing and when those services need to be packaged with a housing subsidy or delivered on site. It is estimated that 22,000 families need permanent supportive housing based on some rough and ready decisions on how to use the variables available in the NSHAPC. This is an area that clearly needs more work, including on a clear definition of what supportive housing is. For example, some of the permanent supportive housing funded by Shelter Plus Care and the Supportive Housing Program may be better characterized as transitional housing without a time limit, given the expectation of its sponsors that families living there eventually will graduate to mainstream permanent housing. Does this sort of continuum make sense for a particular type of homeless family, or has it simply grown out of the practice of the homeless services system?

Another area that needs more work is how many families can leave homelessness quickly without needing subsidized housing. Again, a crude estimate has been provided based on NSHAPC, but HMIS data over time will give a much better picture of movement patterns out of homelessness that will make possible a much more definitive assessment. A related question is how many homeless families can afford the flat rents provided by LIHTC and HOME (and potentially the NHT) and do not need a voucher to enable them to live in the growing stock of subsidized housing that follows the affordable housing rather than the assisted housing model.

Other questions have to do with the current use of assisted and affordable housing programs. A substantial gap in the information available for this chapter is the extent to which LIHTC and HOME already are used for housing targeted to homeless families or to families with special needs—clearly an area in which additional information gathering will be important for developing a complete picture both of resources available and of barriers to their use.

A similar question is, without the earlier special set-asides of Federal allocations of vouchers, how extensively do PHAs now make vouchers and public housing available to families leaving homelessness? Much of what has been said in this chapter about barriers to the use of mainstream assisted housing resources as permanent housing for parents who have become homeless has been based on anecdotal information. There is a clear need for more systematic information gathering on this topic as well.

References

Burt, M., Laudan, Y.A., and Lee, E. (2001). Helping America’s homeless: Emergency shelter or affordable housing? Washington, DC: Urban Institute Press.

Climaco, C., Finkel, M., Nolden, S., and Rich, K. (2004). Updating the Low Income Housing Tax Credit database: Projects placed in service through 2002. Cambridge, MA: Abt Associates, Inc.

Culhane, D.P., and Kuhn, R. (1997). Patterns and determinants of shelter utilization among single homeless adults in New York City and Philadelphia: A longitudinal analysis of homelessness. Journal of Policy Analysis and Management, 17(1), 23-43.

Finkel, M., and Buron, L. (2001). Study on Section 8 voucher success rates. Cambridge, MA: Abt Associates, Inc.

Finkel, M., and Kennedy, S.D. (1992). Racial/ethnic differences in utilization of Section 8 existing rental vouchers and certificates. Housing Policy Debate, 3, 2.

Herbert, C.E., Bonjorni, J., Finkel, M., Michlin, N., Nolden, S., Rich, K., and Sninath, K.P. (2001). Study of the ongoing affordability of HOME Program rents. Cambridge, MA: Abt Associates, Inc.

Lubell, J.M., Shroder, M., and Steffen, B. (2003). Work participation and length of stay in HUD-assisted housing. Cityscape, 6(2), 207-223.

Nelson, K.P., Vandenbroucke, D.A., Lubell, J.M., Shroder, M.D., and Reiger, A.J. (2003). Trends in worst case needs for housing, 1978-1999. Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy Development and Research.

Public Housing Directors Association (PHDA). (2005). Rent reform: Fair and simple solutions. Washington, DC.

Rog, D., Holupka, S., Hastings, K., and Shinn, B. (2005). Toward a typology of homeless families: Building on the existing knowledge base. Paper presented at the Expert Panel Meeting: Homeless Families Typology Development. Washington, DC.

Stegman, M.A., Davis, W.R., and Quercia, R. (2004). The Earned Income Tax Credit as an instrument of housing policy. Housing Policy Debate, 15(2), 203-260.

Turner, M.A., Ross, S.L., Galster, G., and Yinger, J. (2002). Discrimination in metropolitan housing markets: Results from HDS2000. Washington, DC: The Urban Institute.

Turnham, J., Herbert, C., Nolden, S., Feins, J., and Bonjorni, J. (2004). Study of homebuyer activity thought the HOME Investment Partnerships Program. Cambridge, MA: Abt Associates, Inc.

U.S. Department of Housing and Urban Development. (2002). Fourth Annual Report to Congress on the Effects of the Quality Housing and Work Responsibility Act of 1998. Washington, DC.

U.S. Department of Housing and Urban Development. (2004). FY 2005 Annual Performance Plan. Washington, DC.

U.S. Department of Housing and Urban Development. (2005a). Congressional Justifications for 2006 Estimates. Washington, DC.

U.S. Department of Housing and Urban Development. (2005b). HOME Program National Production Report as of 5/31/05. Washington, DC.

U.S. Department of Housing and Urban Development. (forthcoming). First Annual Homeless Assessment Report. Washington, DC.

Wong, I., Culhane, D.P, and Kuhn, R. (1997). Predictors of exist and re-entry among family shelter users in New York City. Social Service Review, 441-462.

Endnotes

1 The NSHAPC classifies “currently homeless” families as those who reported that, on the day of the survey or during the 7-day period prior to being interviewed, they stayed in an emergency shelter or transitional housing program; or a hotel or motel paid for by a shelter voucher; or an abandoned building, a place of business, a car or other vehicle; or anywhere outside. In addition, families are classified as currently homeless if they report that the last time they had “a place of [their] own for 30 days or more in the same place” was more than 7 days ago; or said their last period of homelessness ended within the last 7 days; or were selected for inclusion in the NSHAPC client survey at an emergency shelter, transitional housing program; or reported getting food from “the shelter where you live” within the last 7 days; or, on the day of the interview, said they stayed in their own or someone else’s place but that they “could not sleep there for the next month without being asked to leave.”

2 The NSHAPC data are available at http://www.census.gov/prod/www/nshapc/NSHAPC4.html. NSHAPC data were weighted up to national totals by applying to the “rescaled” weight variable (CLIWGT) in the NSHAPC data set a factor derived by dividing the NSHAPC-based estimate of the total number of currently homeless households by the rescaled weight for these households. Burt, Aron, and Lee (2001), derive from NSHAPC an estimate that there were 346,000 homeless households during an average week in October-November 1996. A slightly different definition is applied to the NSHAPC for parents who are homeless together with their children, by including parents who say their children are living with them even if they were not physically present in a shelter at the time the survey was conducted. These children are highly likely to be reunited with their parents.

3 The gender distribution of parents in the NSHAPC who are homeless without their children were analyzed and found that 80 percent of these parents are male and 20 percent are female. Assuming that 10 percent of male parents are reunited with their children and 75 percent of female parents are reunited with their children, about 25 percent of all such parents will be reunited with their children. The estimate that 25 percent of homeless parents will be reunited with their children is conservative—that is, more likely results in an overestimate of the need for permanent housing for families who have become homeless than an underestimate.

4 It also would overstate the number who need permanent supportive housing. For the way in which the profile of those homeless over the course of a year differs from those homeless at a point in time, see Wong et al., (1997) and Culhane and Kuhn (1997). The average number of days homeless is greater for families than for individuals, implying that the multiplier used to go from a 1-week estimate to a 1-year estimate should be lower for families (U.S. Department of Housing and Urban Development, forthcoming 2006). This further supports the use of 3 rather than 4 as the multiplier.

5 There are minimum rent provisions, but the minimum rents are so low that they do not affect families that have some source of income, even if the amount is small. In addition, families using Housing Choice Vouchers may choose housing units with rents above the program’s subsidy (payment) standard and pay the additional cost without a subsidy, resulting in a rent greater than 30 percent of income. The program rules specify that their total housing cost may not be greater than 40 percent of their income at the time they first use the voucher.

6 This database can be found at www.huduser.org/data sets/assthsg/statedata98/index.html.

7 The mean length of stay in public housing for families with children is 5.59 years.

8 The estimates in Exhibit 3 are based on a voucher program of 1.9 million units. This is 100,000 fewer than the number of vouchers shown in the January 2005 HUD budget estimates and is a more realistic estimate of numbers of vouchers likely to be placed under lease at current budget levels.

9 HOME National Production Report, June 2005; HUD’s Annual Performance Report for FY 2004, p. 2-39.

10 For example, Illinois’ new Comprehensive Housing Plan commits 15 percent of capital development resources for multifamily housing targeting families and individuals who are homeless.

11 Units that are both assisted and affordable housing are counted as assisted housing in the exhibit. HOME units are reduced by 62 percent: 40 percent because they also have rental assistance and by another 22 percent because, altogether, 62 percent of HOME units use the LIHTC. In effect, it is assumed that all of the HOME units with rental assistance also have LIHTC. It is difficult to tell how many LIHTC units do not have HOME and do have rental assistance. LIHTC units are reduced by 10 percent to reflect this phenomenon. The application of these assumptions probably results in a conservative estimate (the error is in the direction of a slight underestimate) of the net number of affordable housing units that do not have rental assistance.

12 These estimates include only housed families, because they are derived from the American Housing Survey, which is a survey of housing units.

13 Rents charged as a percentage of income are often referred to as “Brooke Rents,” after former Senator Edward Brooke. Brooke Rents originally were 25 percent of income. Legislation increased assisted housing rents to 30 percent of income in the early 1980s.

14 A good source of information on these proposed and actual changes to the voucher program and their potential impact is the web site of the Center on Budget and Policy Priorities, www.cbpp.org.

15 These are net figures, subtracting from the total cases of unfavorable treatment those cases in which White, non-Hispanic households received less favorable treatment than minorities seeking rental housing. As such, they are considered by the researchers to represent lower bound estimates of discrimination against minorities seeking rental housing.

16 While overall the rates of success in using vouchers are as high for members of minority groups as they are for White non-Hispanic households (Finkel and Buron, 2001). Finkel and Kennedy (1992) found that, in the late 1980s, success rates for minorities were higher on average in jurisdictions that had a relatively high minority population, and lower on average in jurisdictions that did not.

17 Based on analysis of HUD’s database of Annual Performance Reports (APR) for 2003. Because of the nature of the housing stock, the Section 8 Moderate Rehabilitation Single Room Occupancy program does not serve families.

18 Based on a HUD data set of units under subsidy in the Section 811 and Section 202 programs.

19 This description is based on “The National Housing Trust Campaign: Proposal for Legislation,” National Low Income Housing Coalition, February 2, 2005.

Appendix D: The Characteristics and Causes of Homelessness among At Risk Families with Children in Twenty American Cities

Abstract

This paper explores the characteristics and causes of homelessness among poor families with children. In so doing, it attempts to develop a conceptual framework on family homelessness that shifts the dominant focus on individual characteristics or structural factors to consider their combined role in fostering homelessness episodes.  Analyzing data from 4,900 poor families in twenty cities who took part in the Fragile Families and Child Wellbeing Study, the authors find that homelessness episodes are more closely linked to mother’s physical health, exposure to domestic violence and social connectedness, as well as housing affordability, local unemployment rates, receipt of housing subsidies, the availability of shelter beds, and anti-homeless laws.  However, basic socio-economic and demographic characteristics thought to influence family homelessness were not observed to have any effect, including educational attainment, labor force participation, welfare receipt, martial status, or race.  Moreover, poor families living in cities with severe weather, higher housing vacancy rates, and higher poverty rates were not at an increased risk of becoming homeless.

Introduction

Scholarly research over the past twenty-five years has firmly established the emergence and persistence of family homelessness in the United States.   The homeless household – usually a mother and her children – represents a departure from the stereotypical image of skid-row residents (sometimes referred to as the “old homeless”) who are predominately single, working age males. Homeless households (or families), frequently perceived as a component of the “new” homeless, are thought to have emerged in the late 1970s as a result of changes in the labor market, largely caused by deindustrialization, as well as shifting marriage patterns, the decline in value of in-cash welfare benefits, rising housing costs, and the crack epidemic (For additional historic trends and potential explanations for the emergence and growth of family homelessness in the United States see Jencks 1994; Rossi 1994).

Recent estimates from the U.S. Department of Housing and Urban Development (1999) suggest that 15 percent of homeless households are families (that is one or more people represented by each client in the 1996 National Survey of Homeless Shelters). This means that between 900,000 and 1.4 million children experience a homelessness event with their parents (Burt, 2001).  These children are evenly distributed by age – 22 percent are 0 to 2 years old, 22 percent are 3 to 5 years old, 33 percent are 6 to 11 years old, and 20 percent are 12 to 17 years old.  These estimates of the family homelessness problem are consistent with a systematic review of sixty data collection exercises of homelessness enumeration between 1980 and the early 1990s (Shlay and Rossi, 1992).

Unfortunately, we do not know how much the family homelessness problem has changed since the mid-1990s – the last time HUD administered the National Survey of Homeless Shelters.  However, in places where consistent records are kept the family homelessness problem appears to be getting substantially worse.  For example, the number of homeless families in Minnesota tripled to 1,341 in 2003 a night from 434 in 1991 when the State started collecting consistent homeless shelter counts and censuses of people living in public spaces (Kaufman, 2004).

Our understanding of the family homelessness problem comes from a growing literature designed to measure the characteristics of homeless families, as well as the circumstances that are responsible for causing homelessness spells among parents and their children.  For the most part, these studies have substantially improved our ability to identify the correlates of family homelessness (see, for example, Bassuk and Rodenberg, 1998; Wood et al, 1990; Bassuk et al, 1996; 1997; Shinn, Knickman and Weitzman, 1991; Shinn et al, 1997; Caton et al, 2000; Goodman, 1991; Quigely, Raphael, and Smolensky 2001; Main, 1996).

Unfortunately, much of this research presents family homelessness as the product of individual characteristics without adequate attention to community (or structural) circumstances.  The prominence of individual forces, such as mental illness, drug use and domestic violence, in this research stems from a reliance on studies from a single city, limiting what we can say about the relative importance of community variation in local housing market conditions, climate, the supply of shelter beds, and the presence of local anti-loitering laws. 

When the literature attempts to bring-in the structural aspects of family homelessness, it has done so with research strategies that omit the individual from the analysis.  The typical approach is to predict municipal-level homelessness rates or counts with selected characteristics of the city and its population. This aggregate approach may tell us how family homelessness rates vary by a city’s housing affordability, among other city-level factors of interest, but it cannot tell us whether the lack of affordable housing actually increases an individual household’s chance of becoming homeless or whether housing affordability has a stronger effect on family homelessness than individual mental illness, drug abuse or domestic violence.

Another shortcoming of the family homelessness literature (and the homelessness literature in general) is the tendency to collect data from individuals who have sought assistance from homeless shelters without collecting comparable information from non-shelter beneficiaries.  These studies tend to use duration of shelter use or repeat shelter use as the dependent variable, seeking to explain why some families remain homeless for extended periods of time or experience multiple homelessness spells (sometimes referred to as chronic homelessness).  As a result, these studies are unable to provide much information on why some at risk families become homeless and others do not.

These shortcomings have resulted in a research literature that compartmentalizes the problem as one that derives only from the individual or only from the community – failing to integrate across the micro- and macro-levels.  That is, we know individual characteristics matter and we know structural characteristics matter; however, we do not know if individual characteristics matter when controlling for city-level variation in structural characteristics or if structural characteristics matter when controlling for individual variation. To answer this type of question, we need detailed life-history information from a representative sample of households (some who have been homeless and other that have not) across a number of different geographic units (such as cities) that vary by characteristics thought to be responsible for creating conditions that can lead to homelessness.

Fortunately, the Fragile Families and Child Well Being Study meets all of these basic criteria. This new longitudinal birth-cohort sample includes nearly 5,000 children born between 1998 and 2000 in twenty cities in the United States with populations over 200,000.  The survey over-samples births to unmarried parents and follows the families from birth through 3 years of age thus far.  The Fragile Families data is well-suited to the goals of this analysis and allows us to overcome many of the limitations of prior research.  That is, these data allow us to measure the impact of structural variation (at the city-level) on the probability of becoming homeless, while comparing low-income households that have been homeless to those that have not, controlling for a wide-range of individual socio-economic and demographic characteristics, physical and mental health conditions, drug use, exposure to domestic violence, and access to informal and formal social support.

This paper is organized into four sections.  First, we provide a critique of the existing family homelessness literature and argue these studies have been unable to adequately capture the conceptual typology of individual versus structural causes of homelessness.  Second, we explore the limitations of the family homelessness literature and its disconnect with trends in risk factors thought to shape family homelessness and the well-being of at risk families.  Third, we describe our empirical strategy, data, and results.  Forth, we summarize our findings and discuss the limitations and implications of our analysis.

Theoretical Background

Typologies are ubiquitous in the social sciences.  They are frequently used to help provide clarity and improve understanding of a given population, situation, or pattern of social organization.  They are not designed to capture all subtle variation in a given context, but function as a heuristic device to provide theoretical clarity to help formulate basic understanding.  As one might expect, some typologies are more helpful than others.

In the homelessness literature, the event of becoming homeless is frequently differentiated along a single causal dimension: individual versus structural explanations.  Individual explanations include those micro-characteristics that reside within people, such as physical and mental health, substance abuse and addiction, domestic violence, and educational attainment, among others.  Structural explanations include those macro-characteristics beyond the individual, such as lack of affordable housing, slack labor markets, and the availability of homeless shelters, among others.

The individual-structural homelessness typology represents an overly simplified version of general typologies found in the social science poverty literature. For the past fifty years, poverty scholars have conceptualized poverty as a function of social structure, culture and individual behavior.  Social structure (sometimes referred to as structural causes) includes macro-economic and social constraints thought to block economic opportunity, including changes in the labor market, discrimination, and lack of access to educational opportunities. These constraints interact with and produce cultural adaptations, including beliefs, values and strategies for daily living, that sometimes reinforce the effects of these structural barriers or create cultural adaptations that help individuals overcome these barriers.  The interaction between structural and cultural dimensions is thought to shape individual behavior which predisposes certain people and groups to poverty (for a more detailed treatment of this conceptual framework, see Rainwater, 1987; Wilson, 1987; and Newman, 1992).

The role of culture in the homelessness literature is not well developed. However, it tends to be equated with access to and willingness to receive informal help from friends family, and strangers, as well as the changes in belief systems that may be associated with illegal activity, drug use, and mental illness where these conditions are thought to mediate individual behavior that puts an individual or group at greater risk of being homeless.

The individual-structural dichotomy can be complicated by introducing the idea that some factors at both the micro- and macro-levels are associated with increased risk of becoming homeless, while other protective factors may reduce the likelihood of becoming homeless.  Risk versus protective factors can be further distinguished as distal or proximate.  That is, those events and conditions at the micro- and macro-levels can take place early in life (distal) and are distinct from those that take place later in life (proximate) (Bassuk et al, 1997).  So, it is possible to think of distal and proximate factors that increase the risk of being homeless or help protect against homelessness at the individual- and structural-levels.

This approach is useful for untangling the complex social problem of homelessness, but it tends to reinforce the way in which the debate on homelessness (including family homelessness) has unfolded.  Most of the data used to test this typology can only speak to one dimension.  That is, it focuses on either the micro- or macro-level and it rarely accounts for risks and protective factors that are proximate and distal to homelessness spells.  As a result, we know a great deal about each discrete cell in this typology, but we have very little empirical work that explores the relative influence of individual versus structural factors, risk versus protective factors, and distal versus proximate factors.  This has shaped the scholarly debate around homelessness into finite findings that investigate a segment of the original theoretical approach.  So, we know individual factors are associated with family homelessness, but we do not know whether these relationships hold when controlling for structural conditions. Similarly, we know that structural conditions matter, but we do not know whether these relationships remain robust when controlling for individual distal and proximate protective factors of informal social support. 

Overall, the homelessness literature is fragmented and overly deterministic without adequate data to support global statements about what causes this social problem.  More important, this fragmentation has created a very confusing picture to public policy makers and others interested in fighting family homelessness, since it is hard to tell from the literature what policy levers really matter and will produce the greatest effect on pushing down the number of poor mothers and children living on the street or in shelters.   As a result, the social science literature on homelessness has been unable to offer coherent and comprehensive knowledge that can effectively shape the policy debate.

At Risk Families & Homelessness

This fragmentation in the literature has created a perception that many hypothesized correlates of family homelessness matter equally.   Whether it is domestic violence, drug use, weak labor force attachment, lack of informal social support, high housing costs, climate, the decline in the value of cash welfare benefits, etc., all have some empirical support to suggest that each is a cause of the family homelessness problem.  The vast number of causes associated with the problem creates an environment where it is easy for policy makers to downplay the problem family homelessness or declare it is too complicated to combat.

For instance, we know that family homelessness is closely associated with female-headed households, unwed childrearing, and the economic hardships of single-mothers (Bassuk et al, 1996; Early, 2004).  However, this observation rings hallow when we look at some of the national trends on these risk factors and attempt to link them to recent increases in family homelessness.  From 1996 to 2001 the teen birth rate declined by 24 percent, the high-school drop out rate declined by 10 percent, the percent of youth not attending school and not working declined by 11 percent and the percent of families where no parent has a full-time, year round job dropped 11 percent (Kids Count Databook, 2004:33).  These trends are supported by more detailed studies which indicate that the economic hardship experienced by mother-only families declined at a faster rate during the economic expansion of the 1990s than during prior periods of economic growth (Winship and Jencks, 2004).  How is it possible that family homelessness at the micro-level is being driven by out-of-wedlock births and economic hardship among single mothers when these factors at the macro-level are improving?

A similar disconnect seems to exist with respect to welfare reform, at risk families and family homelessness.  Micro-level studies indicate welfare and other cash benefit programs have a protective effect against family homelessness (Salomon, Bassuk, and Brooks, 1996).  However, at the micro-level the welfare reforms of 1996, introducing time limits and work requirements, would seem to make at risk families more vulnerable to homelessness; however, this does not appear to be the case.  A number of studies have tracked welfare recipients and studied the impact of these policy changes on socio-economic outcomes, including the degree to which they have pushed at-risk families into economic hardship, such as homelessness.  According to the 1997 National Survey of American Families, 7.1 percent of former welfare recipients reported that they had to move in with others because they couldn’t pay mortgage, rent, or utility bills (Loprest, 1999).  This figure is a bit lower than more recent cross-sectional evaluations of welfare reform in specific states.  For example, a comprehensive evaluation of Indiana’s welfare reform indicates that nearly 9 percent of current and former TANF recipients became homeless approximately 3 years after these reforms took place, over 25 percent had their utilities turned off; about 8 percent had been evicted; and about 17 percent indicated they either moved in with others or obtained roommates to defray rental costs (Institute for Family and Social Responsibility, 2000). 

However, more rigorous experimental data from Connecticut and Florida provide little or no evidence that welfare time-limits and work requirements have increased homelessness.   In Connecticut, 2.6 percent of the treatment (TANF) group reported being homeless following welfare reform compared to 1.5 percent of the control group (those not subject to time-limits and work requirements).  This difference is statistically significant, but the percentages are very small. In contrast, Florida’s experimental evaluation indicated no statistically significant difference between the treatment and control groups.  As the authors of this report indicate, “homelessness has been quite rare” among welfare leavers (compared to those recipients operating under the old rules). These authors go on to summarize the welfare reform literature and its effects on homelessness:

Relatively few respondents reported experiencing the most serious kinds of housing distress: eviction and homelessness. Almost all the studies reported the percentage of respondents who had been homeless since leaving welfare. Although the definitions vary, all the figures are 2 percent or below. Three studies reported the percentage who had been evicted since leaving welfare: Florida FTP (8 percent), Ohio (8 percent), and Utah (5 percent). Other studies found that relatively few recipients had moved to worse living arrangements since leaving welfare (in fact, respondents who had moved were more likely to have moved to better arrangements). As noted earlier, relatively large proportions of time-limit leavers are living in public or subsidized housing; it is possible that housing subsidies are protecting some families from severe housing distress (Bloom, Farrell, and Link, 2002:91).

Therefore, the evidence seems to indicate that welfare reform has not pushed more at-risk families into homelessness.  While it is true that the implementation of welfare reform in some states has resulted in higher rates of homelessness among welfare leavers, the numbers are extremely small.  So, how is it possible that one of the protective factors (welfare) that keeps families from homelessness has been dramatically changed without any observed increase in more homeless families because of these reforms?

It is worth noting two other areas where the family homelessness literature seems disconnected from the macro-trends: (1) Housing affordability; and (2) The intersection between crime, mental illness, and drug use.  Each is discussed in turn.

The housing affordability literature seems to indicate that aggregate levels of homelessness are associated with higher housing cost metropolitan areas (Quigely, Raphael, and Smolensky, 2001; Price-Spratlen and Kanon, 2003).  While the United States has long had a shortage of affordable and available housing units,” [t]he economic boom of the 1990s did little to improve the mismatch between the number of renters with household incomes of $16,000 or less and the number of affordable and available (not occupied by households with higher incomes) rentals. Indeed, between 1993 and 2003, the shortfall in affordable and available units remained essentially unchanged at 5.2 million” (2005:23).  Again, how is it possible that housing affordability is increasing homelessness when the problem of housing affordability has not changed much since 1993?

A similar pattern exists for the intersection between family homelessness, crime, mental illness, and drug use.  The family homelessness literature makes a clear connection to the role of domestic violence (crime), drug use and mental illness as risk factors in becoming homeless (Wood et al, 1990; Goodman, 1991; Bassuk et al, 1998).  That is, relatively high rates of domestic violence, drug use and mental illness create circumstances where households may be unable to manage the challenges of daily life, resorting to life on the street or in a shelter.  However, macro-level trends indicate the rate of sexual assault between 1993 and 2003 declined by 62.5 percent (Catalano, 2004), usage rates for marijuana, cocaine, and hallucinogens have remained constant (or increased modestly) in the past decade (Substance Abuse and Mental Health Service Administration, 2004), and rates of mental illness have remained unchanged (or are declining because of new pharmacological treatments) during a time of rising family homelessness. Again, how is it possible that domestic violence, mental illness and drug use are causing the family homelessness problem when rates of domestic violence, drug use and mental illness have declined or remained unchanged?

It is important to note that there are at least two areas where the family homelessness literature is supported by macro-trends on at risk families: (1) The decline in wages for unskilled workers, and (2) the destruction of public housing that provided a buffer against family homelessness.  Each is discussed in turn.

The family homelessness literature clearly indicates the importance of family income and labor force participation as determinants of unstable housing situations that lead to homelessness (Wood et al, 1990; Shinn at al, 1998).  In fact, according to a detailed study of 164 working poor mothers, 12 percent reported being homeless for some period of time in the past twelve months (Edin and Lein, 1997:113), and some of these spells are likely due to problems in the labor market (underemployment, unemployment, joblessness, or low-wages).  The link between family homelessness and limited labor market opportunities for unskilled workers may have been exacerbated by the growth in full-time workers who remain in poverty.  According to the Bureau of Labor Statistics, 5.9 percent of working families lived below the poverty level in 2001, up from 5.6 percent in the previous year (Mosisa, 2003). This number increased to 6.3 percent of working families in 2002 where it has remained unchanged (U.S. Department of Labor, 2005).  As one might expect, two earner families are less likely to be working poor than single earner families.  Mother-headed households had a working-poor rate of 23.0 percent and father-only households had a working-poor rate of 13.5 percent, while married parents had a working-poor rate of only 5.8 percent in 2003 (U.S. Department of Labor, 2005).

The overall 0.7 percent increase (from 5.6 percent in 2000 to 6.3 percent in 2002) in the working poor family rate may appear small; however, it represents hundreds of thousands of households.  If ten percent of this population were to become homeless, that would represent a substantial increase in the family homelessness problem.  Therefore, it would appear that the wage returns to work for those at the bottom of the wage distribution has worsened over the past few years, and may be causing upward pressure on the number of homeless – including families.  However, the working poor rate remains below the 1993 high, so it is unlikely that changes in the low-wage labor market have aggravated the family homelessness problem beyond where it was in the early 1990s.

Much like having a well-paying job is a protective factor against homelessness, public housing and other low income housing subsidies have been shown to protect families from experiencing multiple homelessness spells (Bassuk et al, 1997) – even though these subsidies are not well-targeted to the homeless (Early, 2004).  However, low income housing programs and policy especially public housing, has undergone substantial changes in the past ten years.  In particular, the HOPE VI effort, combined with the low-income housing reform Act of 1998, has attempted to address substantial deterioration of the public housing stock, while promoting mixed-income replacement housing. Moreover, the 1998 Act gave local housing authorities increased flexibility in how they operate their programs, including giving preference to higher-income tenants even at the expense of the very poor.  Over the past decade, hundreds of thousands of run-down public housing units have been demolished and replacement housing has been provided in a limited number of new mixed-income housing developments, as well as rental-based Section 8 vouchers.  Low-income housing advocates are correct to point out that the number of replacement units is far less than the number of units that have been torn-down.  These same advocates fear that the transformation of public housing has increased the number of low-income families who have no place to live. 

Unfortunately, evidence on how these changes in the public housing program have impacted residents is not very comprehensive or systematic.  However, we do have some indication of these effects (and whether they are causing more families to become homeless) from selected cities across the United States.  The only national-level effort to track families affected by these low-income housing policy changes is being conducted by The Urban Institute.  According to the study’s director, “…we did indeed find a few people who had become homeless, but for the most part the unassisted renters were doing well. Several of them had earned their way off housing assistance; a few of them have become homeowners.  So at this point…we did not find big indications of people becoming homeless” (Popkin, 2004).

In contrast, detailed research on Chicago’s public housing transformation efforts indicates these structural policy changes may be having a dramatic effect on the family homeless problem.  For instance, “[n]early 52 percent of squatters [i.e., non-lease tenants] report experiencing homelessness in the year after building closure. On average, a squatter moves at least twice (the mean is 2.7 times) in the year after building closure. 4% stay in Single Room Occupancy (SRO) dwellings and shelters, while 5% stay with friends or relatives…[And] one year after building closure 13 percent of the squatter population is homeless” (Venkatesh, et al., 2004).

A primary reason for these contradictory findings is that the national Urban Institute study is only tracking individuals and families with formal lease agreements with local housing authorities, whereas the Chicago study is also tracking those individuals who live in public housing illegally.  Unfortunately, we have little or no national data on the number of public housing squatters, making it impossible to know exactly how many of these individuals and/or families may be at risk of homelessness as a result of national efforts to revitalize public housing.  Nevertheless, the Chicago data seem to indicate that the changes being introduced via HOPE VI and the 1998 housing reform act, may be exacerbating the homelessness problem – particularly among illegal residents and families living in public housing.

In sum, the family homelessness literature has unfolded in a piece-meal fashion, and is disconnected from the theoretical paradigm that simultaneously recognizes both individual and structural factors that generate risk, as well as protection from homelessness where these factors can be immediate (proximate) or cumulative from the past (distal).  The proliferation of family homelessness research, focused on narrow pieces of this paradigm (or typology), has established findings that appear counter-intuitive to many macro-trends for at risk families.  As a result, the value of this literature to policy makers has been diminished since it has been unable to conduct research that can accommodate the complexity of the problem.

Families at-Risk of Being Homeless: A Comprehensive Empirical Inquiry

In order to expand our understanding of the current characteristics of homeless families, while comparing the importance of individual and structural factors that represent both proximate and distal risk and protective dimensions, we analyze data from the Fragile Families and Child Wellbeing Study.  This new longitudinal birth-cohort sample of approximately 4,900 children born between 1998 and 2000, includes data on 3,712 children born to unmarried parents and 1,186 children born to married parents, as well as independent interviews with mothers and fathers at the time of the child’s birth, one year after birth, three years after birth and five years after birth.  Since the last wave of data collection is still ongoing, the proceeding analysis is restricted to the first three waves.

Data were collected in twenty U.S. cities with populations above 200,000, and the random stratified sampling strategy is designed to produce a representative cohort of non-marital births in large U.S. cities.  The Fragile Families data is well-suited to the goals of this analysis because it captures a population of at-risk families and collects information on whether a respondent was homeless or in a shelter for at least one night in the year prior to the interview.  In addition, the survey contains a large amount of socio-demographic and life-history information on each respondent, including questions that capture some of the structural dimensions that may be responsible for recent changes in the family homelessness problem discussed above.  To supplement these individual measures, we collected detailed city-level data on the local economic environment, climate, housing affordability and availability, access to shelter beds and anti-loitering laws.

Overall, the number of homeless respondents in these data is small.  At the 12 month follow-up, 140 mothers report being homeless in the prior year (or 3.2 percent), 98 fathers report being homeless in the prior year (or 2.9 percent), and 49 mothers and fathers both report being homeless in the prior year.  At the 36 month follow-up, 110 mothers report being homeless in the prior year (or 2.6 percent), 54 fathers report being homeless in the prior year (or 1.6 percent), and 4 mothers and fathers both report being homeless in the prior year.  Given the small number of fathers who report being homeless, as well as households where both the mother and the father report being homeless in the prior year, we focus our analysis on the sub-sample of mothers in the Fragile Families data. 

Specifically, our analysis focuses on two different sub-samples of households at 50% of the federal poverty threshold:  those mothers who report being homeless at the 12 month interview (n=140), and those mothers who report being homeless at the 36 month interview (n=110).  It is important to note that these are two separate sub-samples with very little overlap; only 18 mothers report a homeless spell at both the 12 and the 36 month follow-up interviews.  We categorize a mother as having been homeless if she answers positively to the following question: In the past 12 months, did you stay at a shelter, in an abandoned building, an automobile, or any other place not meant for regular housing even for one night?

Our analysis is organized around three research questions: What are the current characteristics of homeless families compared to a similar sub-group that did not experience a homeless spell during the study period? What is the relative impact of individual versus structural factors in explaining a family’s exposure to a homeless spell?  And, what factors seem to inoculate at risk families from experiencing homelessness?

Our strategy of analysis is to utilize our measure of homelessness as a dependent variable, comparing individual-level characteristics of those respondents that were homeless and those that were not.  We attach the city-level characteristics to each individual record and estimate the impact of our individual-background characteristics and city-level measures on the likelihood of experiencing a homeless spell.

Table 1 provides a large number of characteristics of mothers and their households for each of the two homeless sub-samples.  For comparison, we also show the corresponding statistics for a group of mothers who did not report a homeless spell at the appropriate follow-up interview but who were in households at 50 percent of the poverty line at the 1-year interview.  We argue that this group of mothers is at-risk of homelessness.  We first provide some basic demographic characteristics and then show characteristics pertaining to a mother’s housing, economic status, health, drug use and violence, parental support, and community connectedness.  As these latter characteristics can change over time, we show the mother’s reports at the baseline interview and then at the 1-year interview.  For each characteristic, we report whether the difference in the mean for the homeless sample is significantly different from the mean for the non-homeless comparison group. 

Those mothers who report being homeless are slightly older on average and less likely to be immigrants compared to those that who report no homeless episodes during the study period.  However, homelessness among these families is not linked to race, marital status, or the number of children.  Mothers who report homelessness are more likely to have drug, health and violence problems, and their families are less able to support them in times of trouble.  At the bottom of Table 1, we also see that mothers who experience homelessness at the 3-year interview were more residentially mobile prior to the 1-year interview.

Table 2 displays a few characteristics of the cities which may influence the probability of family homelessness.  There is a lot of variation across the twenty Fragile Families cities in these characteristics.  The economic strength of the cities range from Newark, New Jersey, whose poverty rate is 28% and whose median family income is less than $27,000 a year, to San Jose, California whose poverty rate is 9% and whose median family income is over $70,000 a year.  The climate may have a large influence on the homeless population and this data includes three cities in Texas with very high year-round temperatures and cities like Milwaukee, Wisconsin whose average minimum temperature in January is 12 degrees Fahrenheit.

We measure housing affordability and availability of a city with three variables – fair market rent, the percent of apartments whose rent is less than 30 percent of the median family income, and the rental vacancy rate.  These variables all come from the U.S. Department of Housing and Urban Development.  As we can see in Table 2, there is substantial variation in these three measures across cities. Finally, we measure city-level homelessness policy with three variables – the number of shelter beds per 1000 people in the city, the percent of the total number of shelter beds that are reserved for families, and the number of anti-homeless laws which a city has enacted.  Examples of anti-homeless laws include whether or not a city has established laws that prohibit vagrancy (closure of particular public places; obstruction of sidewalks/public places), loitering (loitering, loafing in particular public places or city-wide), sitting-lying (sitting or lying in particular public places or city-wide), camping (camping in particular places or city-wide), sleeping (sleeping in particular public places or city-wide), begging (aggressive panhandling, begging in particular public places or city-wide), and sanitation (urinating or defecating in public places or bathing in particular public waters).  Data on anti-homelessness laws were compiled by the National Coalition for the Homeless – a non-profit advocacy organization – and represent the presence of such laws in 2004.  Shelter bed data were tabulated by the U.S. Department of Housing and Urban Development’s Continuum of Care initiative and capture the number of beds per city in 2004.

To evaluate the constellation of factors that best explain variation in family homelessness, we estimate the effect of these variables on the probability of being homeless at a future interview in Table 3.  In the first column, we regress homelessness at 1 year on the characteristics of the mother at baseline to determine if any of these dimensions of her life at the time of her child’s birth can predict her future homeless spell.  In the third column, we regress homelessness at 3 years on the characteristics of the mother at the 1-year interview.  In columns 2 and 4, we omit the homeless policies variables from the regressions because we believe that these variables may be acting as an indicator for cities with high levels of homelessness.  That is, cities with a lot of homeless may be more likely to fund homeless shelters and pass anti-homeless laws.  Thus, when we control for homeless policies, these variables may be absorbing all of the effects which may actually be attributable to other factors.

We find that immigrant status reduces a mother’s risk for homelessness.  At the 3-year interview, public housing residence and housing subsidies also appear to insulate mothers from the risk.  Parental support is protective at the 1-year interview.  Health problems, domestic violence, and high rates of residential mobility appear to be predictors of homelessness.  Finally, the most important city-level predictors of homelessness are homeless policies.  This finding has two possible interpretations.  First, it may be that an abundance of homeless shelters encourages homelessness.  Second, it may be that cities with high numbers of the homeless build more homeless shelters and institute anti-homeless regulations, as mentioned above.  When the homeless policies are omitted in columns 2 and 4, we see that families living in high-rent cities are more likely to experience a homeless spell.

Discussion & Conclusion

Our analytic approach is designed to measure the multiplicity of individual and structural factors that may be associated with increased risk of becoming homeless, while also measuring those characteristics thought to protect families from this unfortunate hardship.  The design of the Fragile Families Study allows us to estimate these effects on the actual experiences of at risk families.  As a result, we are able to overcome the limitations of prior research that frequently omits structural factors because these studies are conducted in a single city, focuses only on those families that have been homeless without an adequate comparison group, or lacks potentially important individual and household characteristics on health, drug use, domestic violence, and informal social support.

Our approach provides the kind of analysis needed to establish the relative importance of those dimensions thought to shape homeless spells among low-income families.  In so doing, it provides the kind of information that can help untangle the complex matrix of factors thought to influence family homelessness.  We believe this type of analysis can help policy makers prioritize strategies that can have the greatest effect on reducing the number of families living in shelters or on the street.

Our analysis demonstrates the importance of both individual and structural factors.  In particular, poor health, domestic violence, and residential mobility significantly increase the likelihood of homelessness even when controlling for city-level variation in housing affordability, local economic conditions, climate, shelter availability, and anti-loitering laws.  Moreover, high unemployment, lack of affordable housing, shelter availability, and anti-loitering laws all significantly increase the odds of a family experiencing a homeless spell independent of individual- and household-level socio-demographic characteristics.

It is important to note that a number of factors thought to be important in explaining family homelessness did not help explain why some families in the Fragile Families Study became homeless.  In particular, race, educational attainment, labor force participation, out of wedlock birth, welfare receipt, and drug use were not associated with homeless spells.  Similarly, housing vacancy rates and climate had little or no effect. At risk families in our analysis do experience lower rates of homelessness because of protective factors.  We observed small but statistically significant effects for informal familial social support, as well as some benefits of reduced homelessness as a result of receiving low-income housing subsidies.

Before concluding, it is important to highlight several limitations in our analysis.  First, our measure of homelessness is somewhat crude and does not capture variation in the severity of homelessness spells.  Sleeping in a homeless shelter for a few nights is substantially different from sleeping on the street for months; however, the Fragile Families Study is unable to distinguish the severity of homelessness spells or the number of homelessness spells.  Second, the number of homeless families in the sub-samples is large enough to generate reliable estimators, but limits our ability to establish statistically significant coefficients. The robustness of our findings would likely improve with a larger sample of homeless families.  Third, our study is restricted to the twenty cities in the Fragile Families Study and it is unclear whether our findings can be generalized beyond these places.  This external validity threat may be overstated given the mix of cities by region, size, and level of deprivation; however, it is important that these results not be interpreted beyond the study sample.  Forth, the assumed causal direction between our dependent and independent variables, i.e., simultaneity, may be reversed. For instance, we observed a strong positive relationship between a city’s unemployment rate and the likelihood of a respondent reporting a homeless spell.  This effect is observed at the 3 year interview but not at the one year interview and may indicate high-unemployment in a city causes family homelessness but it is also possible that homelessness causes higher unemployment rates in a city. Unfortunately, this problem can only be addressed though the longitudinal design of the Fragile Families Study and the use of future waves of data collection.

While future research will have to overcome these limitations, we believe our analytic approach is an innovative strategy for the study of family homelessness (and homelessness in general), and provides a framework for improving what we know about the problem of family homelessness.  It provides a more coherent and comprehensive approach to the study of this complex social problem, while providing policy makers with the type of knowledge and understanding they need to craft effective interventions designed to keep at risk families from becoming homeless.

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Endnotes

* Direct all correspondence to: David Reingold, Associate Professor, Indiana University, School of Public & Environmental Affairs, 1315 E. 10th Street, Rm. 410, Bloomington, Indiana 47405.  Reingold@indiana.edu.

Biographical Statements

David Reingold is Associate Professor of Public and Environmental Affairs at Indiana University-Bloomington and Director of Public Affairs and Public Policy Doctoral Programs.  His teaching and research areas include urban poverty, social policy, civil society, and government performance.  His research has appeared in numerous social science journals, including The Journal of Policy Analysis and Management, Urban Studies, the Journal of Urban Affairs, and Housing Studies, among others.  From 2002 to 2004, he was Director of Research and Policy Development at the Corporation for National and Community Service, a member of the White House Task Force for Disadvantaged Youth and Chairman of the Task Force’s Research, Accountability and Performance Committee. A former Housing Commissioner and Vice-Chairman of the Bloomington Housing Authority Board, he is currently the Chairman of the Indiana Commission on Service and Volunteerism and a board member of the South Central Community Action Program in southern Indiana.  He has served on expert panels for the National Academy of Public Administration and the Office of the Assistant Secretary for Planning and Evaluation in the U.S. Department of Health and Human Services.  He is an elected member the Association for Public Policy and Management’s Policy Council, and is the Managing Editor and Co-Editor of the Journal of Policy Analysis and Management. He is also on the editorial board of the Journal of Urban Affairs.  Professor Reingold received his Ph.D. in Sociology from the University of Chicago in 1996.

Angela Fertig is an assistant professor in the College of Public Health at the University of Georgia in Athens, with a joint appointment in the Carl Vinson Institute of Government.  Prior to her current appointment, she was an assistant professor of economics at Indiana University in Bloomington and a post-doctoral research fellow at Princeton University.  Her research focuses on issues related to families and health.  Current projects include studying prenatal smoking and alcohol exposure, childhood obesity, child support enforcement policies, domestic violence, Medicaid take-up, public housing, and homelessness.  Professor Fertig received her Ph.D. in economics from Brown University in 2001.

Appendix E: Fragile Families Data Set

E.1 Outcome Tables for Fragile Families Data Set

Table E-1.
Demographic and background characteristics households
at least 50 percent below poverty line
  Stably       Significant Group Comparisons
Stable At Risk Dbld
Housed At-Risk Doubled-Up Homeless vs. vs. vs.
Variable n = 187 (%) n = 347 (%) n = 231 (%) n = 73 (%) A D H D H H
Demographics – Mother
(Baseline)
Race – All Categories
White 16 18 22 12            
Black 63 66 62 70            
Asian 2 2 1 3            
American Indian 7 6 7 4            
Hispanic -- 0.3 -- --            
Other 13 9 9 11            

Race –
% African American

62 65 61 70            

Age
(mean, standard deviation)

24.7
(5.5)
24.2
(5.5)
24.1
(6.1)
24.0
(6.0)
           
Mother’s Income (%)
< $5,000 46 51 55 49            
$5,000 – $10,000 36 34 26 37            
> $10,000 17 15 20 14            
Demographics – Father
(Baseline)
Race – All Categories
White 14 16 21 10           X
Black 63 69 62 79           X
Asian 2 1 0.4 1            
American Indian 7 5 7 1            
Hispanic -- -- -- --            
Other 15 9 11 9            

Race –
% African-American (mother’s report)

54 59 58 60            
Age
(mean, standard deviation)
16.5
(17.5)
18.4
(16.3)
16.8
(16.2)
19.2
(17.5)
           
Father’s Income (%)
< $5,000 18 17 15 8            
$5,000 – $10,000 9 15 19 17            
> $10,000 73 69 66 75            
Table E-1.
Demographic and background characteristics households
at least 50 percent below poverty line (continued)
  Stably       Significant Group Comparisons
Stable At Risk Dbld
Housed At-Risk Doubled-Up Homeless vs. vs. vs.
Variable n = 187 (%) n = 347 (%) n = 231 (%) n = 73 (%) A D H D H H
Background – Mother

Living with both biological parents at age 15

39 30 35 29 X          

Had first birth as a teen

30 30 27 36            

Mothers Age at 1st Birth

19.8 19.4 20.0 19.1            

Any new pregnancies or children?

Year 1 27 16 11 16            

Year 3

41 35 36 45            

Mother’s Education – Baseline

< HS 54 51 55 55            
HS + 47 49 45 45            

Currently attend any school/training – 1 year

17 20 14 18            

Mother has worked since child’s birth – 1 year

65 73 75 73            

Do any regular work for pay last week?

Year 1 35 38 37 34            
Year 3 51 47 44 32     X   X  

Visited a doctor/health care professional
to check on the pregnancy – Baseline

93 94 98 92       X   X
Table E-1.
Demographic and background characteristics households
at least 50 percent below poverty line (continued)
  Stably       Significant Group Comparisons
Stable At Risk Dbld
Housed At-Risk Doubled-Up Homeless vs. vs. vs.
Variable n = 187 (%) n = 347 (%) n = 231 (%) n = 73 (%) A D H D H H
Mother’s Household Composition

Lives with Partner/Spouse

Baseline 42 44 38 40            
Year 1 57 48 35 40   X X X    
Year 3 45 45 36 30     X X X  

Lives with Mother (i.e. child’s grandmother)

Baseline 27 18 27 23 X     X    
Year 1 16 10 22 14       X    
Year 3 9 6 8 18   X   X   X
Child lives with mother
Baseline 98 99 100 94   X     X X
Year 1 97 99 94 93       X X  
Year 3 97 98 95 89     X   X  

Number of adults in Household (not partner)

Baseline .80 .52 .87 .59 X     X   X
Year 1 .67 .39 .97 .62 X X   X   X
Year 3 .69 .58 1.04 .87   X   X X  

Number of  children in household

Baseline 1.89 1.57 1.56 1.44 X X X      
Year 1 2.83 2.68 2.86 2.56            
Year 3 2.94 2.79 2.76 2.77            
Spouse/Partner Working
Baseline 34 37 31 30            
Year 1 41 31 20 18 X X X X X  
Year 3 16 13 7 5   X X X    
Table E-1.
Demographic and background characteristics households
at least 50 percent below poverty line (continued)
  Stably       Significant Group Comparisons
Stable At Risk Dbld
Housed At-Risk Doubled-Up Homeless vs. vs. vs.
Variable n = 187 (%) n = 347 (%) n = 231 (%) n = 73 (%) A D H D H H
Other adult in household working
Baseline 31 20 39 25 X     X   X
Year 1 22 13 31 19 X X    X   X
Year 3 33 28 44 38   X   X    
Housing
Mother lives in housing project
Baseline 23 24 18 23            
Year 1 33 31 18 19   X X X X  

Year 3

28 26 21 27            

Mother receives housing subsidy

Baseline 28 35 20 38       X   X
Year 1 31 34 19 14     X X X X
Year 3 32 34 22 29   X   X    

Safety of streets around home at night – Baseline

Very Safe 19 13 18 16            
Safe 61 60 56 47     X      
Unsafe 16 21 21 27     X      
Very Unsafe 4 6 5 10            
Problems Making Ends Meet

Received free food/meal in past 12 months

Year 1 3 15 20 34 X X X   X X

Year 3

2 15 16 35 X X X   X X

In past 12 months, children went hungry – Year 1

0 2 2 5     X      

In past 12 months, mother went hungry – Year 1

2 7 12 22 X X X   X X
Social Support

During pregnancy, received financial support
other than baby’s father? – Baseline

53 57 67 67   X X X    
Next year, would someone in family loan you $200?
Baseline 85 81 80 79            
Year 1 74 74 67 64            
Year 3 82 72 75 74 X          

Next year, would someone in family give you a place to live?

Baseline 90 86 88 85            
Year 1 82 72 80 64 X   X X    
Year 3 82 74 79 62     X   X X
Next year, would someone help you with babysitting/ child care?
Baseline 92 37 88 89            
Year 1 82 79 81 81            
Year 3 90 77 84 75 X   X X    
Count on someone to co-sign loan for $1000
Year 1 50 37 42 33 X   X      

Year 3

54 40 37 26 X X X   X  
Government Assistance

In last year, had income from public assistance,
welfare, or food stamps – Baseline

60 69 58 74 X   X X   X

In last year, had income from unemployment insurance,
worker’s compensation, disability, or SSI - Baseline

8 8 17 12            

Received food stamps in past 12 months

Year 1 68 75 68 82     X     X
Year 3 60 71 74 76 X X X      

Complete tax form – Year 1

44 47 44 36            

Applied for Earned Income Tax Credit? – Year 1

43 72 65 74 X X X      

Have any health insurance

Year 1 88 90 84 85       X    
Year 3 90 93 93 97            
Health, Mental Health, and Substance Abuse
Mother’s health (avg.)
Baseline 2.07 2.28 2.32 2.48 X X X      
Year 1 1.95 2.39 2.42 2.55 X X X      

Year 3

2.05 2.45 2.45 2.65 X X X      
Use Alcohol
Baseline 9 12 13 25     X   X X
Year 1 17 28 28 29 X X X      
Year 3 30 47 49 49 X X X      
Use Drugs
Baseline 7 5 13 25   X X X X X
Year 1 1 3 5 5   X X      

Year 3

1 9 9 20 X X X   X X
Use Cigarettes
Baseline 20 29 30 40 X X X      
Year 1 25 36 39 47 X X X      

In past year, has alcohol or drugs interfered with work/relationships?

Baseline 1 4 6 4 X X        
Year 1 2 1 6 5   X   X X  

Year 3

3 6 7 16 X   X     X

Felt sad/depressed 2 or more weeks in a row

Year 1 12 24 24 46 X X X   X X

Year 3

15 31 38 39 X X X      
Table E-1.
Demographic and background characteristics households
at least 50 percent below poverty line (continued)
  Stably       Significant Group Comparisons
Stable At Risk Dbld
Housed At-Risk Doubled-Up Homeless vs. vs. vs.
Variable n = 187 (%) n = 347 (%) n = 231 (%) n = 73 (%) A D H D H H

Lost interest in hobbies/work for 2 or more weeks in a row

Year 1 3 16 19 20 X X X      

Year 3

6 14 15 24 X X X      

Felt tense/anxious for month or longer

Year 1 7 20 20 36 X X X   X X
Year 3 5 22 24 39 X X X   X X

Sought help or was treated for drug or alcohol problems

Year 1 4 4 6 12     X   X  
Year 3 1 1 6 6   X X X X  

Hit or slapped by spouse/partner

Baseline 2 4 7 8   X X      
Year 1 4 10 12 19 X X X   X  

Year 3

8 13 16 25   X X   X  
  Year 1 Model Yr 1 or 3 Model Year 3 Model
Table E-2.
Logistic regression models year 1 and year 3 homeless households
at least 50 percent below poverty line 
  n=778 n=775 n=688
Nagelkerke R2 .157 .166 .333
Age      
Race (% Black)      
Live Both Parents @ 15      
Teen Birth     .872*
Preg @ Year 1      
Preg @ Year 3 # #  
Partner – Baseline     #
Partner – Yr 1 # #  
Change Partner B-1     #
Change Partner 1-3 # # -1.536***
Live with Mother – Base     #
Live with Mother – Yr 1 # # 1.007*
Change Live Mom B-1     #
Change Live Mom 1-3 # #  
# Adults in Hhld – Base     #
# Adults in Hhld – Yr 1      
# Adults in Hhld – Yr 3 # # .509**
# Kids – Baseline     #
# Kids – Yr 1      
# Kids – Yr 3 # #  
Social Support – Base
(# Sources 0-3)
    #
Social Support – Yr 1      
Social Support – Yr 3 # #  
$1000 Loan – Yr 1      
$1000 Loan – Yr 3 # # -1.303*
Educ Level – Baseline (<HS/HS+)      
Mother Working – Base     #
Mother Working – Yr 1 # # -1.537*
Change Mom Work B-1     #
Change Mom Work 1-3 # # -1.803**
Income – Year 1 (ln) -.155* -.182** -.303***
Partner Working – Base     #
Partner Working – Yr 1 # #  
Change Partner Work B-1     #
Change Partner Work 1-3 # #  
Other Adult Work – Base     #
Other Adult Work – Yr 1      
Other Adult Work – Yr 3 # #  
# Indicates cell that is gray.
  Year 1 Model Yr 1 or 3 Model Year 3 Model
Table E-2.
Logistic regression models year 1 and year 3 homeless households
at least 50 percent below poverty line (continued) 
  n=778 n=775 n=688
Nagelkerke R2 .157 .166 .333
Health Status – Base
(1:Excellent – 5:Poor)
    #
Health Status – Yr 1      
Health Status – Yr 3 # #  
Ever Use SA – Base and Yr 1 1.076*    

SA Ever Interfere – B and
Yr 1

  .781* #
Ever DV – B and Yr 1 1.092** .764* #
MH Prob – Yr 1 .306 .473*** #
Ever Use SA – Base, 1, 3 # #  
SA Ever Interfere – B, 1, 3 # #  
Ever DV – B, 1, 3 # #  
MH Prob – Yr 3 # # .637**

Neigh Safety – Baseline
(1 Very Safe – 4 Very Unsafe)

    .535*
Public Hsng – Base     #
Public Hsng – Yr 1 # #  
Change Pub Hsng B-1     #
Change Pub Hsng 1-3 # #  
Hsng Assist – Baseline   -.815* #
Hsng Assist – Yr 1 # # -1.473*
Change Hsng Assist B-1 -1.029*** -1.359*** #
Change Hsng Assit 1-3 # #  
TANF/Food Stamps – Base     #
Receive TANF – Yr 1 .995** 1.029*** .759
Change TANF 1-3 # #  
Receive Food Stamps – Yr 1      
Change Food Stamps 1-3 # #  
*Significant at P<.05
**Significant at P<.01
***Significant at P<.001
# Indicates cell that is gray.
  Year 1 Model Yr 1 or 3 Model Year 3 Model
Table E-3.
Logistic regression models year 1 and year 3 stably housed households
at least 50 percent below poverty line
  n=778 n=775 n=688
Nagelkerke R2 .221 .197 .183
Age   .033  
Race (% Black)      
Live Both Parents @ 15      
Teen Birth      
Preg @ Year 1      
Preg @ Year 3 # #  
Partner – Baseline .530** .548* #
Partner – Yr 1 # #  
Change Partner B-1 .456*   #
Change Partner 1-3 # # -.303
Live with Mother – Baseline     #
Live with Mother – Yr 1 # #  
Change Live Mom B-1 .336   #
Change Live Mom 1-3 # # -.479**
# Adults in Hhld – Baseline .186* .210* #
# Adults in Hhld – Yr 1      
# Adults in Hhld – Yr 3 # #  
# Kids – Baseline .194***   #
# Kids – Yr 1      
# Kids – Yr 3 # #  

Social Support – Base
(# Sources 0-3)

    #
Social Support – Yr 1      
Social Support – Yr 3 # #  
$1000 Loan – Yr 1 .291    
$1000 Loan – Yr 3 # #  

Educ Level – Baseline (<HS/HS+)

     
Mother Working – Baseline -.283   #
Mother Working – Yr 1 # #  
Change Mom Work B-1     #
Change Mom Work 1-3 # # .383**
Income – Yr 1 (ln) .091 .112  
Partner Working – Base     #
Partner Working – Yr 1 # #  
Change Partner Work B-1 .705** .881*** #
Change Partner Work 1-3 # #  
Other Adult Working –Base     #
Other Adult Working – Yr 1      
Other Adult Working – Yr 3 # #  
# Indicates cell that is gray.
  Year 1 Model Yr 1 or 3 Model Year 3 Model
Table E-3.
Logistic regression models year 1 and year 3 stably housed households
at least 50 percent below poverty line (continued) 
  n=778 n=775 n=688
Nagelkerke R2 .221 .197 .183
Health Status – Base
(1:Excellent – 5:Poor)
-.149 -.323*** #
Health Status – Yr 1     -.130
Health Status – Yr 3 # #  
Ever Use SA – Base and Yr1 -.473** -.644** #
SA Ever Interfere – B and Yr 1     #
Ever DV – B and Yr 1 -1.037*** -.928* #
MH Prob – Yr 1 -.546*** -.625*** #
Ever Use SA – Base, 1, 3 # # -.692***
SA Ever Interfere – B, 1, 3 # #  
Ever DV – B, 1, 3 # #  
MH Prob – Yr 3 # # -.583***
Neigh Safety – Baseline
(1 Very Safe – 4 Very Unsafe)
     
Public Hsng – Base   .823** #
Public Hsng – Yr 1 # # .528**
Change Pub Hsng B-1   .548* #
Change Pub Hsng 1-3 # #  
Hsng Assist – Baseline     #
Hsng Assist – Yr 1 # #  
Change Hsng Assist B-1   .352 #
Change Hsng Assit 1-3 # #  
TANF/Food Stamps – Base     #
Receive TANF – Yr 1     -.304
Change TANF 1-3 # #  
Receive Food Stamps – Yr 1      
Change Food Stamps 1-3 # # -.508**
*Significant at P<.05
**Significant at P<.01
***Significant at P<.001
# Indicates cell that is gray.

E.2 Overview of Fragile Families Data Set

Fragile Families and Child Well-being Study
Conducted by Princeton University’s Center for Research on Child Wellbeing and Columbia University’s Social Indicators Survey Center

Principal Investigators:
Sara McLanahan, Irwin Garfinkel, Jeanne Brooks-Gunn and Christina Paxson

Funders:
- National Institute of Child Health and Human Development
- National Science Foundation
- U.S. Dept. of Health and Human Services
- Over 20 foundations including: Commonwealth Fund, Ford Foundation,  William T. Grant Foundation, William and Flora Hewitt Foundation, Hogg Foundation, John D. and Catherine T. MacArthur Foundation, Charles Stewart Mott Foundation, David and Lucile Packard Foundation, Robert Wood Johnson Foundation

 
Sample

The study is a stratified random sample of US cities with a population of 200,000 or more. The sample is representative of non-marital births in each of the 20 cities and also representative of non-marital births in US cities with populations over 200,000.

The sample is new, mostly unwed mothers approached and interviewed at the hospital within 48 hours of giving birth, and fathers were interviewed at the hospital or elsewhere as soon as possible after the birth. Hospitals were chosen over prenatal clinics because of higher response rates from the fathers and to gain a more representative sample of all non-marital births.

Baseline interviews were conducted across the United States in: Austin, TX; Pittsburgh, PA; Boston, MA; Oakland, CA; Baltimore, MD; San Antonio, TX; Philadelphia, PA; Detroit, MI; New York City, NY; Jacksonville, FL; San Jose, CA; Indianapolis, IN; Chicago, IL; Toledo, OH; Newark, NJ; Richmond, VA; Milwaukee, WI; Corpus Christi, TX; Norfolk, VA; and Nashville, TN.

 
Size Baseline datasets include 4,898 completed mother interviews (1,186 marital births and 3,712 non-marital births) and 3,830 completed father interviews. One year followup dataset includes 4,365 completed mother interviews and 3,367 completed father interviews.
 
Timeframe Baseline collected between 1998-2000, followups conducted 1 year, 3 years, and 5 years
 
Data availability Baseline, one year and three-year followup currently available. Five-year followup available Spring/Summer 2007
 
Knowledge Gaps At-risk for homelessness factors (including, doubled-up)

Pregnant Mothers

Specific city information

Data on children from birth to 5 years

Longitudinal design that tracks risk and protective factors

 
Relevant Variables At-risk for Homelessness. Questions related to at-risk for homelessness predictors include whether the mother needed financial support from family or friends, whether or not there was someone who could provide the mother with a place to live, whether family lives in a house owned by another family. Other relevant questions regarding the previous 12 months, assess family hunger, eviction, inability to pay utility bills, borrowing money to pay bills, moving in with others while experiencing financial problems, staying in a shelter, abandoned building, or automobile or any other place not meant for regular housing even for one night.

Demographics. Background data on the mother includes, race, education level, and employment status (including income).

Domestic Abuse. Father and mother’s physical relationship was assessed through questions about sexual, physical, and verbal abuse, including if hospitalization was necessary from abuse.

Family Separation. If mother and child were separated, describes where child stayed during separations and why mother and child were originally separated.

Government Programs. Utilization of government programs for children including, Healthy Start nurses, Head Start, childcare referral agencies, and WIC. Other governmental programs questioned, include, TANF, SSI, energy and housing assistance, food stamps, worker’s compensation.

Housing Composition. The number of people currently living in the house
(i.e., children, husband, mother). Provides data on name, gender, age, relationship, and place of employment.

Marital. Marital status and whether the mother is currently pregnant or recently given birth.

Mental Health. The mother’s level of depression, anxiety, and general mental health.

Physical Health. The mother’s general level of physical health is assessed.

Substance Abuse. Drug use and treatment for alcohol and drug usage assessed.

E.3 Measuring Household Income and Poverty Sample

As noted in the report, two samples of families from the Fragile Families dataset were selected for re-analysis. An initial sample was limited to families where the mother is 18 years of age or older and has a household income at their Year 1 interview at or below the national poverty threshold based on the year of their interview (1999 through 2001). The second sample, the primary sample used for these analyses, is limited to families where the household income at Year 1 was at or below 50 percent of the national poverty rate.

The one-year followup was used as the time period to measure household income, instead of the baseline, because residential information was not collected until the Year 1 followup, so it matches the time point that homelessness could first be measured. Analyses also showed that approximately one-fifth of the households classified as being below the poverty line at baseline were above the poverty line at Year 1, indicating that the use of baseline income data might too widely broaden the pool of households in the analyses.

A question concerning household income included in the Year 1 Fragile Families survey was the first source of income data used. Of the 4,365 households in the Year 1 sample, 2,525 (58%) gave their total household income. For those women who could not give an exact dollar amount, a followup question asked them for at least an income range. An additional 1,426 woman (33%) answered this question. Using the midpoint of the range as an estimate, household income data was thus available on 91 percent of the Year 1 sample.

The household income information, together with information on household composition (number of children and other adults) was used to determine whether a household was above or below the poverty line. In 1999, for example, a household with one adult and one child needed to have a household income below $11,483 to meet the poverty threshold, while a family with two adults and two children had to be below $16,895. For those families missing any household income information, questions about the receipt of welfare/TANF or Food Stamps were used to indicate whether the family met the poverty criterion. Using these various measures, a total of 1,756 (36%) families were considered part of the poverty sample.

To determine whether families were at or below 50 percent of the poverty threshold, each income criteria (specified by household size) was divided in half. Since no other proxy measure, such as receiving TANF of Food Stamps, appeared to be a reliable indicator of being 50 percent below the poverty level, households with missing income data were excluded from this sample. A total of 838 families (17% of the entire Fragile Families dataset; 48% of the poverty sample) meet these more stringent income qualifications.

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