The spillover effects of source of income anti-discrimination laws on public housing

Abstract

This paper examines how source of income (SOI) anti-discrimination laws in the United States affect the sociodemographic composition of households living in public housing. SOI laws make it illegal for landlords to discriminate against the source of rent payment, including housing vouchers. Landlord discrimination is a major barrier to voucher utilization, disproportionately affecting extremely low-income families and racial minorities. Thus, SOI laws may affect the pool of applicants and recipients of public housing that operate within the same local housing authority service areas. I use housing authority-level data and a difference-in-differences approach to examine the changes in the composition of households living in public housing. SOI laws significantly reduce the shares of poor and extremely poor residents in public housing, along with a decline in new entries to public housing. Results suggest potentially positive spillover effects of SOI laws, alleviating ‘concentration of poverty’ in public housing as a consequence of a policy attempt to improve accessibilities to an alternative program.

Keywords:

• Public housing
• anti-discrimination laws
• program interaction

Acknowledgements

I thank Amy Schwartz, Michah Rothbart, Bob Bifulco, Johnny Yinger, Ying Shi, Robert Collinson, Jeff Zabel, Vincent Reina, and Hannah Patnaik for comments and feedback. I also thank the three anonymous reviewers and seminar participants at the Maxwell School at Syracuse University, the Bush School at Texas A&M University, the Association for Public Policy Analysis and Management, and the Ohio State University PhD Conference on Real Estate and Housing. This study was supported by Horowitz Foundation for Social Policy through its dissertation award. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1 However, a more recent set of studies on New York City public housing has shown that public housing projects are not uniformly located in high-poverty neighbourhoods and that some may offer high-quality neighbourhood amenities, leading to positive health and academic outcomes among children (e.g. Dastrup & Ellen, 2016; Han et al., 2020; Han & Schwartz, 2021; Rick et al., 2023).

2 HUD’s Moving to Opportunity experiment, for example, provided housing vouchers to participating households for the purpose of examining whether moving into low-poverty neighbourhoods improved household outcomes.

3 If rent exceeds the maximum payment standard, tenants have to pay the difference in addition to 30 percent of their adjusted income. Some PHAs only allow the rent to exceed the maximum standard for a limited amount of time.

4 Unfortunately, there is no centralized dataset, public or restricted, that documents the waiting list policies or keeps track of when waiting lists were open or closed for public housing programs (Waldinger, 2021). HUD’s Office of Policy Development and Research has conducted a cross-sectional web-based survey of 3,210 PHAs in 2012-2013 and finds that 50 percent of the PHAs administering HCV and 94 percent of those administering public housing had their waiting lists open (Dunton et al., 2014). More recently, Hembre and Urban (2022) hand-collected data by emailing and post-mailing requests to PHAs for information on whether waiting lists were open or closed for HCV (not public housing). They collected data on 150 PHAs in 2010-2017 and find that, on average, the waiting lists for HCV were open for seven months per year. However, a study on shrinking cities across the nation find that 80 percent of 71 cities were not accepting applicants for HCV in a given year (Tighe & Ganning, 2016).

5 Due to data limitations, it is difficult to determine the number of households that are present on multiple waiting lists (Acosta & Gartland, 2021). Carder et al. (2016) find in a survey that some individuals on the waiting lists for public housing and HCV have applied to both, but they do not provide the number of such individuals.

6 But, again, staying on the waiting list may not incur opportunity costs for either better-off or worse-off households and, thus, may not affect the willingness to wait across different households.

7 For example, a PHA with an average waiting time of 12 months for every year between 2009 and 2018 would have an average waiting time of 12 months, and a PHA with an average waiting time of 24 months but for every other year between 2009 and 2018 (thus missing five years of average waiting time data) would have an average waiting time of 24 months instead of 12 months.

8 ‘Future SOI’ PHAs include those without SOI laws in the sample period but had SOI laws passed in 2019 or 2020. They are different from the Never SOI PHAs that do not have SOI laws until 2020.

9 Event study results by average waiting time terciles depicted in Figure A3 also show that the direction of the point estimates for the shares of poor and extremely poor households are not different by average waiting time but suggest that the main effects may be driven mainly through PHAs with shorter average waiting time (in the first tercile).

10 Event study results in Figure A4 also suggest that the increase in the share of Black households in the main effects may be driven by PHAs with shorter waiting time (in the first or second terciles), while PHAs with longer waiting time experience a slight decline in the share of Black households.

Additional information

Notes on contributors

Jeehee Han

Jeehee Han is an Assistant Professor in the Department of Public Service and Administration at Texas A&M University’s Bush School of Government and Public Service.