Characteristics and Dynamics of Homeless Families with Children. 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.

View full report


"report.pdf" (pdf, 4.18Mb)

Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®