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Registration

Field Before After
Last Published September 28, 2021 01:44 PM November 01, 2023 09:11 AM
Study Withdrawn No
Intervention Completion Date September 30, 2018
Data Collection Complete Yes
Final Sample Size: Number of Clusters (Unit of Randomization) 88,904 households
Was attrition correlated with treatment status? Yes
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.
Public Data URL https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PSO21N
Is there a restricted access data set available on request? No
Program Files Yes
Program Files URL https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PSO21N
Data Collection Completion Date February 28, 2019
Is data available for public use? Yes
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Papers

Field Before After
Paper Abstract 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.
Paper Citation 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.
Paper URL https://doi.org/10.1162/rest_a_01316
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