We estimate the causal effects of acute fine particulate matter exposure on mortality, health care use, and medical costs among the US elderly using Medicare data. We instrument for air pollution using changes in local wind direction and develop a new approach that uses machine learning to estimate the life-years lost due to pollution exposure. Finally, we characterize treatment effect heterogeneity using both life expectancy and generic machine learning inference. Both approaches find that mortality effects are concentrated in about 25 percent of the elderly population. (JEL I12, J14, Q51, Q53)
, and the WCERE for helpful comments. Dominik Mockus and Eric Zou provided excellent research assistance. We thank Jean Roth for assistance with the Medicare data and Daniel Feenberg and Mohan Ramanujan for system administration. Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R01AG053350. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health nor of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.