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  5. Intended and Unintended Effects of New Technologies for Monitoring and Enforcing Environmental Regulations
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Intended and Unintended Effects of New Technologies for Monitoring and Enforcing Environmental Regulations

Paper Session

Saturday, Jan. 8, 2022 12:15 PM - 2:15 PM (EST)

Hosted By: Association of Environmental and Resource Economists
  • Chair: Mary Evans, University of Texas-Austin

Enforcement and Deterrence with Certain Detection: An Experiment in Water Conservation Policy

Oliver Browne
,
Brattle Group
Ludovica Gazze
,
University of Warwick
Michael Greenstone
,
University of Chicago
Olga Rostapshova
,
University of Chicago

Abstract

New technologies are poised to transform regulatory enforcement by automating costly
inspections and driving violation detection rates to 100%. We conduct a randomized field
experiment to evaluate the adoption of smart meters for enforcing outdoor water-use regulations in
a major US city facing water scarcity. We randomize 88,905 households into 12 groups varying
enforcement method (automated or visual inspection) and fine levels. Automated enforcement
decreases water use by 3% and violations by 17%. However, due to imperfect deterrence, fines
increase by 13,800% and customer service calls increase by 545%, leading to backlash that might
make maximum enforcement politically untenable.

Strategic Shutdowns of Air Quality Monitors: Evidence from Jersey City and across the U.S.

Yingfei Mu
,
University of Oregon
Edward Rubin
,
University of Oregon
Eric Zou
,
University of Oregon and NBER

Abstract

Tolerance for gaps in environmental compliance monitoring data may induce strategic
timing in local agencies’ monitoring activity. This paper presents a framework to test whether local
governments skip pollution monitoring in expectation of a looming air quality deterioration. We
infer expectation from air quality alerts – public advisories based on local governments’ own
pollution forecasts – and test whether air quality monitor’s sampling rate falls when these alerts
occur. We first use the method to test a particulate matter monitor in Jersey City that was suspected
to have been disabled during the 2013 “Bridgegate” traffic jam. Consistent with strategic shutdowns,
the monitor’s sampling rate drops by 33% on days when Jersey City’s pollution alerts are in place.
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 behavior. We
discuss imputation methods that may help deter strategic monitoring.

Do Reminders Increase On-Time Reporting of Environmental Data? An Examination of EPA’s NetDMR System

William Wheeler
,
U.S. Environmental Protection Agency
Jay Shimshack
,
University of Virginia

Abstract

Environmental regulators rely on timely, complete, and accurate information to ensure
compliance and identify threats to public health. However, a very large percentage of U.S. water
dischargers submit required reports so late that they are in “significant noncompliance.” The U.S.
Environmental Protection Agency has set a goal to cut the rate of significant noncompliance in half;
effective tools to increase on-time submission of these reports will be crucial to meet this goal.
We examine the effect of reminders as a method to increase punctual reporting in this context. We
exploit a natural experiment: a novel system that automatically sends reminder emails to facilities
failing to submit complete and on-time reports. Copies of these emails are also sent to state
regulators. This system, NetDMR, is used by some states but not others. Across states, we analyze
different subgroups (for example, major and minor facilities) separately, because these groups receive
different levels of regulatory attention and have different on-time reporting rates.
Our results are based on a difference-in-difference estimator assigning treatment to facilities on a
stateby-state basis. (Following recent developments in the literature, we utilize “clean” controls and
examine decomposition of our estimates.) Our research design is motivated by the substantial number
of copies of the reminder emails sent to state regulators, which induced increased attention to ontime
reporting statewide.
Our results indicate that from a baseline of 1.5 percent, the automatic reminders reduced late
submission of reports by 0.33 percentage points for major facilities. And from a baseline of 9 percent,
the automatic reminders reduced late submission of reports by 3.2 percentage points for minor
facilities. These results indicate the usefulness of the reminders. State-by-state analyses indicate that
states with the highest late reporting rates also show the largest treatment effects from the
reminders.

Threshold-based Regulations and Reporting Behavior: Evidence from the Lead and Copper Rule

Tihitina Andarge
,
University of Massachusetts-Amherst
Dalia Ghanem
,
University of California-Davis
David Keiser
,
University of Massachusetts-Amherst
Gabriel Lade
,
Macalester College

Abstract

For many environmental regulations, regulatory stringency changes sharply at an arbitrary
threshold. These sharp cut-offs may incentivize agents to take undesirable actions to reduce
compliance costs. There is anecdotal evidence that public water systems regulated under the Lead
and Copper Rule engage in improper sampling and monitoring practices to ensure they are below the
thresholds contained in the rule. Improper reporting practices in this context are of concern because
lead is a potent neurotoxin, and this behavior masks true exposure to this contaminant. In this paper,
we document data irregularities and examine the reporting behavior of public water systems during
2001-2020. We employ a censored maximum likelihood approach to quantify the magnitude of data
manipulation around the thresholds. Preliminary results provide evidence consistent with threshold
manipulation, particularly for water systems serving larger populations.

Discussant(s)
Jeremy West
,
University of California-Santa Cruz
Jay Shimshack
,
University of Virginia
Joseph S. Shapiro
,
University of California-Berkeley
Mary Evans
,
University of Texas-Austin
JEL Classifications
  • Q5 - Environmental Economics
  • K4 - Legal Procedure, the Legal System, and Illegal Behavior