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Why Don't Housing Choice Voucher Recipients Live Near Better Schools? Insights from Big Data

Bibliographic Data

ID11684151
AuthorsIngrid Gould Ellen (0000-0002-6127-633X, corresponding author), Keren M Horn (0000-0002-1301-647X), Amy Ellen Schwartz
Year2016
Volume35
Issue4
Pages884-905
Publication date2016-06-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Policy Analysis and Management (JOURNAL)
Journal identifiersISSN: 0276-8739 • E-ISSN: 1520-6688
PublisherWiley (PUBLISHER • GB)
DOI10.1002/pam.21929
OpenAlexW2460645359
LanguageEN
Citations received12
References cited23

Housing choice vouchers provide low‐income households with additional income to spend on rental housing in the private market. The assistance vouchers provide is substantial, offering the potential to dramatically expand the neighborhoods—and associated public schools—that low‐income households can reach. However, existing research on the program suggests that housing choice voucher holders live in neighborhoods with schools that are no better than those accessible to other households with similar incomes. Households, in other words, do not seem to spend the additional income provided by the voucher to access better schools. In this analysis we rely on a large‐scale administrative data set to explore why voucher households typically do not live near to better schools, as measured by school‐level proficiency rates. We combine confidential administrative data from the Department of Housing and Urban Development on 1.4 million housing choice voucher holders in 15 states, with school‐level data from 5,841 different school districts, to examine why the average housing voucher holder does not live near to higher‐performing schools than otherwise similar households without vouchers. Specifically, we use the large‐scale administrative data set to test whether voucher holders living in areas with good schools nearby and slack housing markets move toward better schools when schools become salient for them—that is, when their oldest child becomes school eligible. We take advantage of the thick sample of households with young children provided through our administrative data to implement both a household fixed effects and a regression discontinuity design. Together these analyses shed light on whether voucher households are more likely to move toward better schools when schools are most relevant, and how market conditions shape that response. We find that families with vouchers are more likely to move toward a better school in the year before their oldest child meets the eligibility cutoff for kindergarten, suggesting salience matters. Further, the magnitude of the effect is larger in metropolitan areas with a relatively high share of affordable rental units located near high‐performing schools and in neighborhoods in close proximity to higher‐performing schools. Results suggest that, if given the appropriate information and opportunities, more voucher families would move to better schools when their children reach school age

Business · Demographic economics · Economics · Labour economics · Political science · Public economics · Regression discontinuity design · Renting · Sample (material · School choice · Survey data collection · Voucher · Housing Market and Economics · Medicine · School Choice and Performance · Urban, Neighborhood, and Segregation Studies

  • What do we know about housing choice vouchers

    Open Access•Ingrid Gould Ellen•Regional Science and Urban…•2020

  • Neighborhood Opportunity and Satisfaction Among Housing Choice Voucher Recipients

    Atticus Jaramillo, William M Rohe et al.•Housing Policy Debate•2020

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    Open Access•H Harvey, K Fong et al.•Social Forces•2020

  • Housing Voucher Take‐Up and Labor Market Impacts

    Open Access•Eric Chyn, Joshua Hyman et al.•Journal of Policy Analysis and…•2018

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    Ruoniu Wang, Rebecca J Walter•Housing Studies•2018

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    Gregg Colburn•Housing Studies•2019

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    Open Access•Amy Ellen Schwartz, Keren M Horn et al.•Journal of Policy Analysis and…•2019

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Unique citing works12
Citations per year1,5
Citation span2018 - 2022 (5)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 12
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