The impact of air pollution on birthweight: evidence from grouped quantile regression

Martina Pons ()
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Martina Pons: University of Bern

Empirical Economics, 2022, vol. 62, issue 1, No 12, 279-296

Abstract: Abstract Estimates of the average effect of pollution on birthweight might not provide a complete picture if more vulnerable infants are disproportionately more affected. To address this, I focus on the distributional effect of particulate matter pollution (PM $$_{2.5}$$ 2.5 ) on birthweight. To estimate the impact, this paper uses grouped quantile regression, a methodology developed by Chetverikov et al. (Econometrica 84(2): 809–833, 2016), which allows estimating the impact of a group-level treatment on an individual-level outcome when there are group-level unobservables. The analysis reveals nonhomogeneous effects indicating that pollution disproportionately affects infants in the lower tail of the conditional distribution, whereas average effects suggest only minimal and not economically significant impact of pollution on birthweight. The findings are also consistent across different specifications.

Keywords: Air pollution; Birthweight; Infant health; Quantile regression (search for similar items in EconPapers)
JEL-codes: I18 Q52 Q53 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00181-021-02048-w

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