We study how air pollution impacts the U.S. labor market by analyzing the effects of drifting wildfire smoke. We link satellite-based smoke plume data with labor market outcomes to estimate that an additional day of smoke exposure reduces quarterly earnings by about 0.1%. Extensive margin responses, including employment reductions and labor force exits, explain 13% of the overall earnings losses. The implied welfare costs from lost earnings due to air pollution exposure is on par with standard valuations of the mortality burden. The findings highlight the importance of labor market channels in air pollution policy responses.

Regulators often rely on regulated entities to self-monitor compliance, creating strategic incentives for endogenous monitoring. This paper builds a framework to detect whether local governments skip air pollution monitoring when they expect air quality to deteriorate. The core of our method tests whether the timing of monitor shutdowns coincides with the counties' air quality alerts – public advisories based on local governments' own pollution forecasts. Applying the method to a monitor in Jersey City, NJ, suspected of a deliberate shutdown during the 2013 “Bridgegate” traffic jam, we find a 33% reduction of this monitor's sampling rate on pollution-alert days. Building on large-scale inference tools, we then apply the method to test more than 1,300 monitors across the U.S., finding 14 metro areas with clusters of monitors showing similar strategic behavior. We assess geometric imputation and remote-sensing technologies as potential solutions to deter future strategic monitoring.