This paper examines the differential impacts of early childhood exposure to air pollution on children’s health care use across parental income groups and vulnerability factors using French administrative data. Our quasi-experimental study reveals significant impacts on emergency admissions and respiratory medication in young children, attributed to air pollution shocks from thermal inversions. Using causal machine learning, we identify these health impacts as predominantly affecting 10% of infants, characterized by poor health indicators at birth and lower parental income. Our results suggest that intervention strategies focusing on vulnerability metrics may be more effective than those based solely on exposure levels.
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Castillo et al. (2024) studied this question.
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