What’s Missing in Environmental (Self-)Monitoring: Evidence from Strategic Shutdowns of Pollution Monitors
Regulators often rely on self-reported data to determine compliance. Tolerance for missingness in self-monitoring data may create incentives for local agents to strategically decide when (not) to monitor regulated activities. This paper builds a framework to detect whether local governments skip air pollution monitoring when they expect air quality to deteriorate. We infer this expectation from air quality alerts – public advisories based on local governments’ own pollution forecasts – and test whether monitors’ sampling rates fall when these alerts occur. We first use this method to test an individual pollution monitor in Jersey City, NJ, suspected of a deliberate shutdown during the 2013 “Bridgegate” traffic jam. Consistent with strategic shutdowns, this monitor’s sampling rate drops by 33% on days that Jersey City issues pollution alerts. Building on large-scale inference tools, we then apply the method to test over 1,300 monitors across the U.S., finding at least 14 metro areas with clusters of monitors showing similar strategic behavior. We discuss imputation methods and policy responses that may help deter future strategic monitoring.
We thank Michael Anderson, Trudy Ann Cameron, Eric Edwards, Dave Evans, Mary Evans, Cynthia Giles, Corbett Grainger, Andreas Hagemann, Alex Hollingsworth, Nicolai Kuminoff, Shanjun Li, Julian Reif, Michelle M. Rubin, Ivan Rudik, William Wheeler, Jianwei Xing, officials at the U.S. Environmental Protection Agency, and seminar participants at Arizona State University, the Online Summer Workshop in Environment, Energy, and Transportation Economics, and the Society for Benefit-Cost Analysis Annual Meeting for helpful comments. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.