Risk and Protective Factors Associated with Formal and Informal Income Generation Among Homeless Young Adults in Three U.S. Cities

Background:

Formal employment is key in aiding youth to exit homelessness, yet their rates of participation in formal employment are low. To supplement formal wages, many homeless youth earn income from informal sources, or “survival behaviors”, which can be legal or illegal. Although research indicates that this population participates in various forms of economic activity, it remains unclear which specific risk and protective factors contribute to their decisions to engage in formal or informal employment. Thus, the purpose of this study was to examine predictors of formal (e.g., full-time, part-time, or paid, temporary work) and informal (e.g., selling drugs, theft, prostitution) economic activity by homeless young adults in three U.S. cities. A theoretical model was constructed using the risk and resilience framework. Two hypotheses were tested. First, a greater number of formal income sources would be reported by homeless young adults who: 1) have shorter homelessness histories; 2) are less transient; 3) have fewer mental health challenges; 4) report fewer problem behaviors; and 5) are more resilient. Second, a greater number of informal income sources would be reported by those who: 1) have longer homelessness histories; 2) are more transient; 3) have greater mental health challenges; and 4) report more problem behaviors.

 Methods:

This study used a cross-sectional design and purposive sampling to recruit 601 homeless young adults (ages 18-24) from agencies in Los Angeles (n=200), Austin (n=200), and Denver (n=201). Face-to-face interviews used a quantitative retrospective questionnaire that included standardized and researcher-developed measures to assess demographics, homelessness history, mental health (Mini International Neuropsychiatric Interview), problem behaviors, resilience (Resilience Scale), and income generation (two count variables of all sources of formal and informal income). Using maximum likelihood parameter estimation, the theoretical model was tested via structural equation modeling, regressing the two dependent variables (formal and informal income sources) on independent variables (age, gender, education, length of homelessness, transience, mental health, problem behaviors, and resilience) according to the hypothesized pathways described above.

Results:

The model represented an acceptable fit (X²=385.59, df=155, p<0.001, CFI=0.904, RMSEA=0.050).

Standardized path coefficients from the predictors to formal and informal income sources ranged from 0.087 to 0.565. Collectively, predictors explained 9% of the variance in formal income generation and 33% of the variance in informal income generation. Income generation from formal sources was associated with being male (β=.-140, p=.001), having a greater number of problem behaviors (β=.187, p=.001), and reporting higher levels of resilience (β=.158, p=.001). Informal income generation was associated with younger age (β=.-097, p=.01), being transient (β=.087, p=.03), and reporting a greater number of problem behaviors (β=.565, p=.001).

 Conclusions:

Two noteworthy patterns emerged from this study. First, a host of assumed risk factors (e.g., substance use, arrest history, substance-using peers) were associated with greater sources of both formal and informal income, suggesting even youth who engage in risky behaviors have the potential to obtain formal income. Second, young adults who reported greater resilience earned income from a greater number of formal sources, indicating that efforts to build resilience may diversify youths’ options for pro-social income.