(Housing, Community Development, and Urban Policy)
*Names in bold indicate Presenter
Reducing homelessness remains an important but difficult to achieve goal. While national point-in-time count estimates of the extent of homelessness have trended downward since 2007, more than 500,000 people were homeless on a given night in January as of the last count. Progress has varied widely with homelessness counts increasing by 57 and 31 percent since 2012 in Los Angeles County and New York City, respectively.
However, quantitative evaluations that credibly measure the consequences of policy responses to homelessness remain relatively rare. The examples that do exist tend to be tied to pre-planned randomized control trials and/or large-scale federal demonstration projects, and the cost of these approaches limits many potentially informative evaluations. Better data access or creative use of naturally occurring quasi-experiments could fill many evidence gaps. This session brings together a set of papers making progress on these two dimensions.
The first two papers bring new, large-scale datasets to bear on the study of homelessness. The first paper (von Wachter) examines the relationship between employment and housing stability. While over a half million people across the United States experienced homelessness in 2017, we still know little about these individuals’ employment and earnings before, during, and after homelessness spells. This paper uses homeless service data provided by LA County and quarterly employment and earnings data provided by the state of California to explore labor market participation and earnings among people experiencing homelessness in Los Angeles between 2010 and 2018. The second paper (Phillips) shows how address histories from consumer reference data can be used to measure housing stability. Consumer data tracks housing moves throughout the entire United States for most of the adult population. This paper shows that such data can measure housing stability for groups with very low income and extreme instability. For example, the data can track housing moves during natural disasters, at demolition of public housing, and for households at high risk of homelessness.
The latter two papers identify naturally occurring policy experiments whose effects can be measured in existing administrative data. The third paper (Cassidy) investigates how variation in government benefits affects decision making by homeless families. It evaluates New York City's longstanding strategy of placing families in shelters in the neighborhoods where their youngest children attend school. Capacity constraints create a natural experiment plausibly suitable for assessing the policy’s effects on length of stay in shelter, public benefit use, parental employment, and children's educational outcomes. The fourth paper (Collinson) examines whether an offer of a unit in public housing versus a housing voucher affects the characteristics of those who lease-up with assistance, as well as the effectiveness of this assistance in reducing subsequent housing instability. Using a unique housing assistance lottery linked to administrative data on housing instability and homelessness from three sources (HMIS data, school records, and consumer data), the paper examines how the design of housing subsidies impact their targeting efficiency and their effectiveness in reducing housing instability. The authors estimate the relative effectiveness of each type of assistance using the lotteries.