Research Seminar in Economics, April 30

.

This paper estimates the causal effects of federal “redlining” – the mapping and grading of US neighborhoods by the Home Owners’ Loan Corporation (HOLC) – with a novel empirical strategy. In the late 1930s, a federal agency developed 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. HOLC maps had a negative impact on neighborhoods colored in red, reinforcing patterns of urban disadvantage, with a particular burden on African-American communities. In the short term, we estimate a reduction in property prices and an increase in the percentage of African American residents. Significantly higher percentages of Black Americans can be detected in D communities in the long term, up until the early 2000s. For property prices, we find negative effects in C and D neighborhoods until the early 1980s. Our empirical results show that a government-supplied, data-driven information tool can coordinate exclusionary practices and amplify their consequences.