Working Paper: NBER ID: w22796
Authors: Tatyana Deryugina; Garth Heutel; Nolan H. Miller; David Molitor; Julian Reif
Abstract: 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 and a novel instrument for air pollution: changes in local wind direction. We develop a new approach that uses machine learning to estimate the life-years lost due to pollution exposure and show that our procedure reduces bias relative to previous methods. 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.
Keywords: air pollution; PM2.5; mortality; health care costs; Medicare; instrumental variables
JEL Codes: I1; Q53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| PM2.5 exposure (I14) | mortality (I12) |
| PM2.5 exposure (I14) | emergency room visits (I19) |
| PM2.5 exposure (I14) | higher inpatient spending (H51) |
| PM2.5 exposure (I14) | life-years lost (J17) |
| lower life expectancy (I14) | higher vulnerability to pollution (F64) |
| chronic conditions and medical histories (I12) | life-years lost (J17) |