Can Transparency in Effluent Environmental Monitoring Reduce Pollution?

Last registered on August 19, 2020
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Pre-Trial

Trial Information

General Information

Title
Can Transparency in Effluent Environmental Monitoring Reduce Pollution?
RCT ID
AEARCTR-0006226
Initial registration date
August 18, 2020

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
August 19, 2020, 11:58 AM EDT

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

Locations

Country
Uruguay
Region
Montevideo

Primary Investigator

Name
Felipe Carozzi
Affiliation
London School of Economics
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Marcelo Caffera
PI Affiliation
Universidad de Montevideo

Additional Trial Information

Status
In development
Start date
2020-09-01
End date
2022-06-15
Keywords
Environment & Energy
Additional Keywords
JEL code(s)
Secondary IDs
Abstract
We want to study how transparency and access to information regarding firms' polluting activities shapes subsequent polluting behaviour by these firms in Montevideo, Uruguay. Specifically, we want to test whether transparency regarding compliance with environmental regulations in waste product processing can improve the ecological footprint of these firms. In association with the effluent inspectors unit of the Montevideo municipal government, we create Ykarai, an observatory of firm compliance which uses an easily interpretable coulour-coded system to report the track record of manufacturing firms in terms of liquid pollutants. Using this observatory, we progressively disseminate data on firms' behaviour for up to 129 firms. We introduce firms in three randomly selected batches in four month periods until all firms are included. We use this staggered inclusion process to study how firms react to publication of information on compliance. Our outcomes include reported pollution, measured pollution and communications with the monitoring body.
External Link(s)

Registration Citation

Citation
Caffera, Marcelo and Felipe Carozzi. 2020. "Can Transparency in Effluent Environmental Monitoring Reduce Pollution? ." AEA RCT Registry. August 19. https://doi.org/10.1257/rct.6226-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We create an online observatory to release information on compliance with industrial effluent regulation for manufacturers operating in Montevideo, Uruguay.
Intervention Start Date
2020-09-01
Intervention End Date
2022-06-15

Primary Outcomes

Primary Outcomes (end points)
We have three main outcomes. Reported pollution levels, measured pollution levels, and the frequency of interactions with the regulator (communications, complaints, etc.). The first variables correspond to pollutant concentration levels as reported by the firm in each period. There is one time series of reported levels for each pollutant. We will also use measured pollution levels as recorded by the municipal inspectors each month (i.e. the variables underlying the colour coded classification of compliance discussed above). We will also know the frequency of formal contacts between the regulator (Unidad de efluentes) and the firms.
Primary Outcomes (explanation)
For pollution measures (reported or measured by inspectors), the outcome will be raw pollutant concentrations, a constant weight index computed using all pollutants, and a categorical variable exactly mimicking the colour-coded categories that are reported in the observatory.

For measures of intensity of communication we will measure the frequency of interactions in a month (both a count of the total number of contacts and a dummy taking value 1 if a contact took place).

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We randomly assign monitoring firms to different groups and then proceed to launch the Observatory with staggered information releases based on those groups. This induces experimental variation in the availabilty of contemporaneous information on compliance at the firm level. Randomization is based on stratified random sampling, with strata related to firm characteristics (historical record of compliance, frequency of inspections and broadly-defined sector).
Experimental Design Details
Monitored firms (129 in total) are randomly assigned to three groups. Randomization is stratified by inspection frequency, as different firms are inspected at different rates by the Unidad de Efluentes run by the local city government. We label our groups of firms as T1, T2 and C. We conduct a staggered release of information on polluting activities to the public, first releasing inspection results for group T1 for two quarters, adding the results for group T2 for another two quarters, and finally adding group C in the final two quarters. The website may remain in operation permanently with all firms included if our partners in the local government decide to make it so.

Randomization is based on stratified random sampling, with strata chosen based on firm characteristics: historical record of compliance, frequency of inspections (three groups) and broadly-defined sector (2 groups).


The staggered information release will be used for identification of contemporaneous effects of information disclosure across the set of outcomes described above. A new set of firms is added to the site every eight months, inducing variation in available information on the firms' polluting behaviour. Treated firms are aware of the website before information release begins, so the intervention is not a surprise to regulated units. Moroever, we inform treated units that their specific information will be released some weeks before that release takes place. This condition was imposed by our partners.


Randomization Method
Randomization done in office by a computer (Stata). Stratified by urn, the level of infringements before treatment and sector (food producer or not).
Randomization Unit
Firms.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
128
Sample size: planned number of observations
128
Sample size (or number of clusters) by treatment arms
45, 49, 34
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials