What’s Missing in Environmental (Self-)Monitoring: Evidence from Strategic Shutdowns of Pollution Monitors / Yingfei Mu, Edward A. Rubin, Eric Zou.

By:

Mu, Yingfei

Contributor(s):

Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w28735.Publication details: Cambridge, Mass. National Bureau of Economic Research 2021.Description: 1 online resource: illustrations (black and white)Subject(s):

Online resources:

Available additional physical forms:

Hardcopy version available to institutional subscribers

Abstract: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w28735 (Browse shelf(Opens below))	Not For Loan

Total holds: 0

April 2021.

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.