Hard garbling methods for monitoring harassment at scale

Last registered on January 04, 2026
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Pre-Trial

Trial Information

General Information

Title
Hard garbling methods for monitoring harassment at scale
RCT ID
AEARCTR-0014286
Initial registration date
December 31, 2025

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
January 02, 2026, 11:47 AM EST

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

Last updated
January 04, 2026, 3:53 AM EST

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

Locations

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Primary Investigator

Name
Ada Gonzalez-Torres
Affiliation
Ben Gurion University
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Other Primary Investigator(s)

PI Name
Laura Boudreau
PI Affiliation
Columbia Business School
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PI Name
Sylvain Chassang
PI Affiliation
Princeton University
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PI Name
Rachel Heath
PI Affiliation
Washington University
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Additional Trial Information

Status
In development
Start date
2015-12-18
End date
2026-03-31
Keywords
Behavior, Crime, Violence, & Conflict, Firms & Productivity, Gender, Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This research studies how survey design affects transmission of sensitive information within organizations. We partner with a large, international apparel buyer and conduct a phone-based survey experiment with workers across several of its Bangladeshi supplier factories to study mechanisms behind garbling methods shown to increase workers' willingness to report misbehavior by managers. Boudreau et al. (2023) was the first test of hard garbling (HG) outside of a lab, building on Chassang and Padró i Miquel (2018); Chassang and Zehnder (2024). We extend our prior work to test for differences in reporting between higher and low HG rates, to assess workers' preferences over different HG rates, and to assess worker welfare consequences of the ability to report harassment in a way that allows possible deniability.
External Link(s)

Registration Citation

Citation
Boudreau, Laura et al. 2026. "Hard garbling methods for monitoring harassment at scale." AEA RCT Registry. January 04. https://doi.org/10.1257/rct.14286-1.2
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Experimental Details

Interventions

Intervention(s)
We evaluate secure survey methods to elicit information about misbehavior in organizations. We randomly assign workers to direct elicitation (DE) or hard garbling (HG) with different garbling rates, and also evaluate preferences for different elicitation methods.
Intervention Start Date
2015-12-18
Intervention End Date
2026-03-31

Primary Outcomes

Primary Outcomes (end points)
The key outcome variables are reports of physical harassment and sexual harassment from a supervisor or manager in charge of their team.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Additionally, as secondary outcomes, we study how increased reporting affects worker welfare in the short run. We also include a set of questions to study mechanisms through which HG affects workers’ responses relative to DE. In addition, we study whether garbling affects the reported incidence of fatal accidents (expecting no effect).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We experimentally vary direct and indirect survey methods (i.e. hard garbling at different garbling rates) to ask sensitive questions on management misbehavior. We also randomly assign a subset of people in the DE arm to an explanation of HG, after the module on reporting experienced misbehavior, to learn about preferences for different elicitation methods.

In the survey, we also elicit information about workers’ primary deterrents to reporting, their comprehension and preferences for different reporting systems.
Experimental Design Details
Not available
Randomization Method
The randomization is done by a computer.
Randomization Unit
The unit of randomization is a worker, stratified by factory, production team or section, and sex.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not applicable
Sample size: planned number of observations
We aim for a sample size of 5,377 workers from across 3-5 factories.
Sample size (or number of clusters) by treatment arms
Our target sample size is 1793 per treatment arm (with the DE arm having about 592 randomly assigned to the explanation of HG).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We aim for a minimum detectable effect (MDE) size of 0.15 standard deviations (SDs) for our primary outcomes.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Columbia Business School
IRB Approval Date
2024-09-03
IRB Approval Number
IRB-AAAS7822
IRB Name
Ben-Gurion University
IRB Approval Date
2025-05-06
IRB Approval Number
1099-3
IRB Name
Princeton University
IRB Approval Date
2024-09-03
IRB Approval Number
17050
IRB Name
University of Washington
IRB Approval Date
2024-07-11
IRB Approval Number
Exempted
Analysis Plan

Analysis Plan Documents

Pre-Analysis Plan

MD5: d99041e14ce3c3de882e065440efac78

SHA1: ea519591ac07ef08c8d0412ff1e1bf97b2f007fd

Uploaded At: December 30, 2025