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 They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

It is widely hypothesized that incomes in wealthy countries are insulated from environmental conditions because individuals have the resources needed to adapt to their environment. We test this idea in the wealthiest economy in human history. Using within-county variation in weather, we estimate the effect of daily temperature on annual income in United States counties over a 40-year period. We find that this single environmental parameter continues to play a large role in overall economic performance: productivity of individual days declines roughly 1.7% for each 1°C (1.8°F) increase in daily average temperature above 15°C (59°F). A weekday above 30°C (86°F) costs an average county $20 per person. Hot weekends have little effect. These estimates are net of many forms of adaptation, such as factor reallocation, defensive investments, transfers, and price changes. Because the effect of temperature has not changed since 1969, we infer that recent uptake or innovation in adaptation measures have been limited. The non-linearity of the effect on different components of income suggest that temperature matters because it reduces the productivity of the economy's basic elements, such as workers and crops. If counties could choose daily temperatures to maximize output, rather than accepting their geographicallydetermined endowment, we estimate that annual income growth would rise by 1.7 percentage points. Applying our estimates to a distribution of "business as usual" climate change projections indicates that warmer daily temperatures will lower annual growth by 0.06-0.16 percentage points in the United States unless populations engage in new forms of adaptation.

We thank Jesse Gregory, Bruce Sacerdote, and participants at the 2014 ASSA meetings and 2014 NBER Summer Institute for helpful comments. Erin Robertson and Eric Andersen provided excellent research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research or the policy of the U.S. Department of Treasury. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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