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This study examines the relationship between fine particulate matter air pollution (PM2.5), socioeconomic conditions, and crime rates using a dataset of 230 observations from 10 highly polluted U.S. states (2000–2022). Moving beyond aggregated analyses, this study applies a panel data approach to capture within-state dynamics often overlooked in prior research. Annual state-level data on PM2.5 concentrations, crime rates, poverty, and unemployment were analyzed using pooled ordinary least squares (OLS) and fixed effects models, with state-clustered standard errors. Although pooled results showed no significant association between PM2.5 and crime, the inclusion of state-and-year fixed effects reveals a statistically significant positive relationship. Specifically, a 1 µg/m³ increase in PM2.5 is associated with approximately 5 additional crimes per 100,000 population within states. Poverty remains insignificant, whereas higher unemployment rates show a statistically significant association with lower crime rates. The preferred model explains substantial within-state variation ( R 2 = .71), underscoring the importance of accounting for unobserved heterogeneity. These findings highlight how model specification shapes inference and suggest that short-term increases in air pollution can influence social behavior. Although the results of this study cannot establish causality, the findings do contribute novel evidence linking environmental exposure to crime dynamics, with implications for integrated environmental and public policy.
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Sadiq Melhim (2026) studied this question.
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