Mapping Out Institutional Discrimination: The Economic Effects of Federal “Redlining”

Disa Hynsjo and Luca Perdoni ()

No 11098, CESifo Working Paper Series from CESifo

Abstract: This paper proposes a novel empirical strategy to estimate the causal effects of federal “redlining” – the mapping and grading of US neighborhoods by the Home Owners’ Loan Corporation (HOLC). In the late 1930s, a federal agency created color-coded maps to summarize the financial risk of granting mortgages in different neighborhoods, together with forms describing the presence of racial and ethnic minorities as “detrimental”. Our analysis exploits an exogenous population cutoff: only cities above 40,000 residents were mapped. We employ a difference-in-differences design, comparing areas that received a particular grade with neighborhoods that would have received the same grade if their city had been mapped. The control neighborhoods are defined using a machine learning algorithm trained to draw HOLC-like maps using newly geocoded full-count census records. Our findings support the view that HOLC maps further concentrated economic disadvantage. For the year 1940, we find a substantial reduction in property values and a moderate increase in the share of African American residents in areas with the lowest grade. Such negative effects on property values persisted until the early 1980s. The magnitude of the results is higher in historically African American neighborhoods. The empirical results show that a government-supplied, data-driven information tool can coordinate exclusionary practices and amplify their consequences.

Keywords: Redlining; neighborhoods; discrimination; machine-learning (search for similar items in EconPapers)
JEL-codes: J15 N32 N92 R23 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-big, nep-his and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_11098

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