按揭贷款中的历史偏差、再限制以及对不确定地理背景问题的影响:达拉斯和波士顿结构性住房歧视研究》(Historical Bias in Mortgage Lending, Redlining, and Implications for the Uncertain Geographic Context Problem: A Study of Structural Housing Discrimination in Dallas and Boston.
Alaina M Beauchamp, Jasmin A Tiro, Jennifer S Haas, Sarah C Kobrin, Margarita Alegria, Amy E Hughes
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We studied the Boston-Cambridge-Newton and Dallas-Fort Worth metropolitan statistical areas to examine distinct historical trajectories and urban development. We estimated the odds of mortgage denial for census tracts. Overall, all tracts in Boston-Cambridge-Newton (N = 1003) and Dallas-Fort Worth (N = 1312) displayed significant change, with greater odds of bias over time in Dallas-Fort Worth and lower odds in Boston-Cambridge-Newton. Historically redlined areas displayed the strongest persistence of bias. Results suggest that temporal data can identify persistence and improve sensitivity in measuring neighborhood bias. 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Historical Bias in Mortgage Lending, Redlining, and Implications for the Uncertain Geographic Context Problem: A Study of Structural Housing Discrimination in Dallas and Boston.
According to the uncertain geographic context problem, a lack of temporal information can hinder measures of bias in mortgage lending. This study extends previous methods to: (1) measure the persistence of racial bias in mortgage lending for Black Americans by adding temporal trends and credit scores, and (2) evaluate the continuity of bias in discriminatory areas from 1990 to 2020. These additions create an indicator of persistent structural housing discrimination. We studied the Boston-Cambridge-Newton and Dallas-Fort Worth metropolitan statistical areas to examine distinct historical trajectories and urban development. We estimated the odds of mortgage denial for census tracts. Overall, all tracts in Boston-Cambridge-Newton (N = 1003) and Dallas-Fort Worth (N = 1312) displayed significant change, with greater odds of bias over time in Dallas-Fort Worth and lower odds in Boston-Cambridge-Newton. Historically redlined areas displayed the strongest persistence of bias. Results suggest that temporal data can identify persistence and improve sensitivity in measuring neighborhood bias. Understanding the temporality of residential exposure can increase research rigor and inform policy to reduce the health effects of racial bias.
期刊介绍:
The Journal of Urban Health is the premier and authoritative source of rigorous analyses to advance the health and well-being of people in cities. The Journal provides a platform for interdisciplinary exploration of the evidence base for the broader determinants of health and health inequities needed to strengthen policies, programs, and governance for urban health.
The Journal publishes original data, case studies, commentaries, book reviews, executive summaries of selected reports, and proceedings from important global meetings. It welcomes submissions presenting new analytic methods, including systems science approaches to urban problem solving. Finally, the Journal provides a forum linking scholars, practitioners, civil society, and policy makers from the multiple sectors that can influence the health of urban populations.