Estimating the Causal Effect of Redlining on Present-day Air Pollution
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| Format: | Preprint |
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2025
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| _version_ | 1866909537740521472 |
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| author | Zhou, Xiaodan Yang, Shu Reich, Brian J |
| author_facet | Zhou, Xiaodan Yang, Shu Reich, Brian J |
| contents | 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$_{2.5}$. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_16958 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating the Causal Effect of Redlining on Present-day Air Pollution Zhou, Xiaodan Yang, Shu Reich, Brian J Applications 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$_{2.5}$. |
| title | Estimating the Causal Effect of Redlining on Present-day Air Pollution |
| topic | Applications |
| url | https://arxiv.org/abs/2501.16958 |