Estimating the Causal Effect of Redlining on Present-day Air Pollution

Abstract

Recent studies have shown associations between redlining policies (1935-1974) and present-day fine particulate matter (PM${}_{2.5}$) and nitrogen dioxide (NO${}_2$) air pollution concentrations. In this paper, we reevaluate these associations using spatial causal inference. Redlining policies enacted in the 1930s, so there is very limited documentation of pre-treatment covariates. Consequently, traditional methods fails to sufficiently account for unmeasured confounders, potentially biasing causal interpretations. By integrating historical redlining data with 2010 PM${}_{2.5}$ and NO${}_2$ concentrations, our study aims to discern whether a causal link exists. Our study addresses challenges with a novel spatial and non-spatial latent factor framework, using the unemployment rate, house rent and percentage of Black population in 1940 U.S. Census as proxies to reconstruct pre-treatment latent socio-economic status. We establish identification of a causal effect under broad assumptions, and use Bayesian Markov Chain Monte Carlo to quantify uncertainty. Our analysis indicates that historically redlined neighborhoods are exposed to notably higher NO${}_2$ concentration. In contrast, the disparities in PM${}_{2.5}$ between these neighborhoods are less pronounced. Among the cities analyzed, Los Angeles, CA, and Atlanta, GA, demonstrate the most significant effects for both NO${}_2$ and PM${}_{2.5}$.