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

Last registered on November 01, 2023


Trial Information

General Information

Enforcement and Deterrence with Certain Detection: An Experiment in Water Conservation Policy
Initial registration date
September 24, 2021

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
September 28, 2021, 1:44 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
November 01, 2023, 9:11 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.


Primary Investigator

University of Warwick

Other Primary Investigator(s)

PI Affiliation
University of Chicago
PI Affiliation
The Brattle Group
PI Affiliation
University of Chicago

Additional Trial Information

Start date
End date
Secondary IDs
Pre-analysis plan registered on June 14, 2018 via OSF Registries doi:10.17605/OSF.IO/XYFU3
Prior work
This trial does not extend or rely on any prior RCTs.
The emergence of new technologies that allow perfect detection of violations at near zero marginal cost can revolutionize the enforcement of environmental regulations. We conducted a field experiment to evaluate enforcement though smart water meters with Fresno, CA. Nearly 100,000 single-family households in Fresno were experimentally assigned combinations of enforcement method (automated or visual inspection) and fine levels. We measure the impact of treatment assignment on outcomes related to enforcement and compliance, water use and customer contact with the water utility.
External Link(s)

Registration Citation

Browne, Oliver et al. 2023. "Enforcement and Deterrence with Certain Detection: An Experiment in Water Conservation Policy." AEA RCT Registry. November 01. https://doi.org/10.1257/rct.8290-2.0
Sponsors & Partners

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Experimental Details


The purpose of this study is to test the effectiveness of a new, automated outdoor watering enforcement system in reducing overall water use while minimizing the number of fines issued. The intervention is targeted to the universe of single-family households in Fresno, California with smart water meters.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes of the experiment can be grouped into three categories:
i. Hourly Water Use: This contains hourly water use mapped to a household ID for each household in the study.
ii. Enforcement Actions: This contains enforcement actions taken by the city, i.e. warnings and fines that have been issued by the city for non-compliance with the written ordinance. This information contains date of enforcement action and enforcement action type, and is linked to the household ID.
iii. Other City Actions: This contains information on other actions the City of Fresno has taken to foster water conservation, such as rebates that have been given to households for purchasing water-efficient appliances, free water timer tutorials that have been provided, and other initiatives including a customer support hotline.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Between July 1, 2018 and September 30,2018, all single-family residential households with smart water meters in the city of Fresno, California were randomly assigned to groups that vary along two dimensions: Enforcement Method and Fine Level.

Along the Enforcement Method axis, households were assigned to one of two groups: Visual Inspection (household receives a fine if a violation is detected by visual inspection–this was the system in place until 2017, or the control), or Automated Enforcement (household receives a fine if a pre-installed water meter measures hourly water usage above a given threshold–this is the treatment or “intervention”). The Automated Enforcement group is further randomized into three groups defining the amount of water utilization that triggers an enforcement action:
i. Baseline (300 gallons/hour threshold)
ii. Reduced Enforcement 1 (500 gallons/hour threshold)
iii. Reduced Enforcement 2 (700 gallons/hour threshold)
The Reduced Enforcement thresholds were chosen, respectively, to reduce the expected number of automated fines issued by 50% and 75% relative to the baseline threshold of 300 gal/hr.

Along the Fine Level axis, households were randomized to receive different fine amounts. Violators received a maximum of one fine per month. The municipal code outlines the following fine schedule:

i. First month in violation: $0
ii. Second Month in violation: $50
iii. Third Month in violation: $100
iv. Fourth month in violation: $200

All households were randomly assigned to be subject to one of three fine levels:
i. Baseline ($0, $50, $100, $200)
ii. 50% Fine Discount ($0, $25, $50, $100)
iii. 75% Fine Discount ($0, $12.50, $25, $50)
In total, all households were randomly assigned to one of 11 treatment groups or a control group.
Experimental Design Details
Randomization Method
Randomization was completed in an office using a computer.
Randomization Unit
Randomization was completed at the household-level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
89,581 households
Sample size: planned number of observations
89,581 households
Sample size (or number of clusters) by treatment arms
- Control Group
1. Non-automated enforcement, Baseline fine group: 40,311
- Treatment Groups
Non-automated enforcement:
2. 50% fine level: 4,479
3. 25% fine level: 4,479
Automated: 300 gal/hr water use threshold:
4. Baseline fine: 4,479
5. 50% fine level: 4,479
6. 25% fine level: 4,479
Automated: 500gal/hr water use threshold:
7. Baseline fine: 4,479
8. 50% fine level: 4,479
9. 25% fine level: 4,479
Automated: 500gal/hr water use threshold:
10. Baseline fine: 4,479
11. 50% fine level: 4,480
12. 25% fine level: 4,479
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
University of Chicago Social and Behavioral Sciences IRB
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Intervention Completion Date
September 30, 2018, 12:00 +00:00
Data Collection Complete
Data Collection Completion Date
February 28, 2019, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
88,904 households
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
7,734,648 household-day observations
Final Sample Size (or Number of Clusters) by Treatment Arms
40,311 households in the control group and 4,479 households in each of the following treatment groups: non-automated enforcement at the 50% fine level, non-automated enforcement at the 25% fine level, and automated enforcement of every possible combination of 300, 500, and 700 gallons per hour limits at the baseline, 50%, and 25% fine levels.
Reports, Papers & Other Materials

Relevant Paper(s)

New technologies allow perfect detection of environmental violations at near-zero marginal cost, but take-up is low. We conducted a field experiment to evaluate enforcement of water conservation rules with smart meters in Fresno, CA. Households were randomly assigned combinations of enforcement method (automated or in-person inspections) and fines. Automated enforcement increased households' punishment rates from 0.1 to 14%, decreased summer water use by 3%, and reduced violations by 17%, while higher fine levels had little effect. However, automated enforcement also increased customer complaints by 1,102%, ultimately causing its cancellation and highlighting that political considerations limit technological solutions to enforcement challenges.
Browne, Oliver R., Ludovica Gazze, Michael Greenstone, and Olga Rostapshova. "Man vs. machine: Technological promise and political limits of automated regulation enforcement." The Review of Economics and Statistics (2023): 1-36.

Reports & Other Materials