Environmental Engel curves: A neural network approach

Tullio Mancini, Hector Calvo‐Pardo and Jose Olmo
Authors registered in the RePEc Author Service: Hector Fernando Calvo Pardo

Journal of the Royal Statistical Society Series C, 2022, vol. 71, issue 5, 1543-1568

Abstract: Environmental Engel curves describe how households' income relates to the pollution associated with the services and goods consumed. This paper estimates these curves with neural networks using the novel dataset constructed in Levinson and O'Brien. We provide further statistical rigor to the empirical analysis by constructing prediction intervals obtained from novel neural network methods such as extra‐neural nets and MC dropout. The application of these techniques for five different pollutants allow us to confirm statistically that Environmental Engel curves are upward sloping, have income elasticities smaller than one and shift down, becoming more concave, over time. Importantly, for the last year of the sample, we find an inverted U shape that suggests the existence of a maximum in pollution for medium‐to‐high levels of household income beyond which pollution flattens or decreases for top income earners.

Date: 2022
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https://doi.org/10.1111/rssc.12588

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Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:71:y:2022:i:5:p:1543-1568

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