Working Paper: NBER ID: w28735
Authors: Yingfei Mu; Edward A. Rubin; Eric Zou
Abstract: Regulators often rely on regulated entities to self-monitor compliance, potentially 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.
Keywords: Environmental Self-Monitoring; Air Quality; Pollution Monitors; Strategic Shutdowns
JEL Codes: C12; H77; Q53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Local governments' incentive to strategically time shutdowns (H73) | Strategic shutdown of pollution monitors (Q52) |
| Timing of monitor shutdowns (C41) | Issuance of air quality alerts (Q53) |
| Being located in a noncompliant county (R33) | Strategic behavior of monitors (L12) |
| Strategic shutdowns (D25) | Misrepresentation of air quality data (Y10) |
| Had monitors been operational on unmonitored days (E01) | PM2.5 levels would have exceeded regulatory standards (Q53) |