Scaling Up Information to Expedite Conciliation

Last registered on July 23, 2026
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Pre-Trial

Trial Information

General Information

Title
Scaling Up Information to Expedite Conciliation
RCT ID
AEARCTR-0019075
Initial registration date
July 19, 2026

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
July 23, 2026, 8:07 AM EDT

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

Locations

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Primary Investigator

Name
Joyce Sadka
Affiliation
ITAM
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Other Primary Investigator(s)

PI Name
Christopher Woodruff
PI Affiliation
Oxford
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Additional Trial Information

Status
In development
Start date
2027-08-01
End date
2028-03-31
Keywords
Crime, Violence, & Conflict, Firms & Productivity, Governance, Labor
Additional Keywords
labor disputes, conciliation, legal information, statistical information, dispute resolution, settlement, access to justice, administrative data
JEL code(s)
K31, K41, K42, J52, J83
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study evaluates whether providing personalized statistical information can improve the resolution of labor disputes in Mexico’s Labor Conciliation Centers. In Mexico, workers who are dismissed must seek severance payments through a mandatory conciliation process before filing a lawsuit. However, many workers and firms have limited information about likely case outcomes, expected settlement amounts, and the benefits of reaching an agreement quickly. This lack of information may contribute to unrealistic expectations, reliance on costly legal intermediaries, and lower settlement rates.
The intervention provides workers, firms, and conciliators with information from a statistical calculator embedded in SINACOL, the national labor conciliation case management system used in most states of Mexico. The calculator uses historical administrative data from similar cases to generate personalized predictions about the probability of settlement and expected settlement amounts. The goal is to help parties form more realistic expectations and support fairer and faster dispute resolution.
The study will be implemented in Mexico City and several additional Mexican states. Randomization will occur at the day level. Depending on the assigned day, workers may receive the calculator information when they file their conciliation request, workers and firms may receive it at the beginning of the hearing with an explanation from the conciliator, both forms of communication may be used, or the case may proceed under the standard process. The study will measure effects on settlement rates, settlement amounts, case outcomes, understanding of the information, satisfaction with the process, and subsequent intentions to file a lawsuit.
The project builds on prior experimental evidence showing that statistical information can increase settlement in labor disputes without reducing worker compensation. By testing different ways of delivering this information through an existing public case management system, the study aims to inform how labor authorities can scale the intervention effectively and sustainably
External Link(s)

Registration Citation

Citation
Sadka, Joyce and Christopher Woodruff. 2026. "Scaling Up Information to Expedite Conciliation." AEA RCT Registry. July 23. https://doi.org/10.1257/rct.19075-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
This intervention provides personalized statistical information to parties in labor conciliation cases in Mexico. The information is generated by a statistical calculator embedded in SINACOL, the case management system used by most Labor Conciliation Centers. Using historical administrative data from similar cases, the calculator provides information on likely case outcomes, including the probability of reaching a settlement and expected settlement amounts.
The intervention is designed to help workers, firms, and conciliators form more realistic expectations during the conciliation process. By reducing information gaps about typical outcomes in similar labor disputes, the intervention aims to support faster, fairer, and more informed dispute resolution before cases proceed to labor court.
Intervention Start Date
2027-08-01
Intervention End Date
2028-03-31

Primary Outcomes

Primary Outcomes (end points)
1. Settlement reached during the conciliation process: an indicator equal to one if the worker and firm reach a settlement agreement before the case proceeds to labor court, and zero otherwise.
2. Settlement amount: the monetary compensation agreed upon in the settlement, measured both in Mexican pesos and in daily wages. We will analyze this outcome conditional on settlement and, in secondary specifications, unconditionally assigning zero to cases without settlement.
3. Case outcome at the end of the conciliation stage: whether the case ends in settlement, is dropped, or otherwise proceeds toward litigation (no conciliation).

These outcomes will be measured using administrative data from SINACOL, Mexico’s labor conciliation case management system. The main endpoint is the final recorded outcome of the conciliation process for each case. The central hypothesis is that providing statistical information will increase settlement rates without reducing the compensation workers receive in settled cases.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
1. Understanding of the calculator information: whether workers report understanding the information provided by the statistical calculator, including the predicted likelihood of settlement and expected settlement amount.
2. Knowledge of labor rights and conciliation procedures: workers' knowledge about severance pay, the conciliation process, and whether legal representation is required during conciliation.
3. Satisfaction with the conciliation process and outcome: workers' reported satisfaction with the information received, the hearing process, and the final case outcome.
4. Intention to file a lawsuit: among cases that do not settle during conciliation, whether the worker reports intending to proceed to labor court.
5. Use of legal or other representatives: whether the worker is accompanied by a "person of confidence" during the conciliation process, and whether this person is a licensed lawyer. Legally, workers are not allowed to bring legal representatives that are active during the hearings, but they can be accompanied by them.
6. Hearing attendance and procedural compliance: whether workers and firms attend the scheduled hearing and whether the case proceeds according to the expected administrative timeline.
7. Post-conciliation labor market outcomes: workers' employment status, wages, and job search activity after the conciliation process, when available through survey data.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study is a randomized evaluation of an informational intervention in Mexico’s Labor Conciliation Centers. The intervention provides parties in labor disputes with personalized statistical information generated from historical administrative data on similar cases. The information is intended to help workers and firms form more realistic expectations about likely case outcomes and settlement amounts.
The evaluation will be implemented through SINACOL, the case management system used by Labor Conciliation Centers in participating Mexican states. Randomization will occur at the day level. Depending on the assignment, cases initiated or heard on a given day may receive the statistical information at different moments of the conciliation process, or may proceed under the standard conciliation process.
The study will use administrative data from SINACOL to measure settlement rates, settlement amounts, and final case outcomes. Survey data collected through SINACOL will be used to measure understanding of the information, satisfaction with the process, and related secondary outcomes. The study is designed to inform how labor authorities can most effectively incorporate statistical information into routine conciliation procedures.
Experimental Design Details
Not available
Randomization Method
Randomization will be done in office by a computer. The research team will randomly assign eligible implementation days to treatment arms using a reproducible computer-generated randomization procedure. Randomization will be at the day level, with days assigned to the control condition or to one of the calculator delivery conditions before the start of the intervention period. Treatment status will then be implemented through SINACOL according to the pre-specified assignment schedule.
Randomization Unit
The unit of randomization will be the day. Eligible implementation days in each participating Labor Conciliation Center will be randomly assigned to one of the experimental arms. All eligible cases filed or heard on a day assigned to a given arm will receive the corresponding condition according to the pre-specified treatment schedule. There is no individual-level randomization.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
The planned number of clusters is 180 implementation days, with approximately 45 days assigned to each of the four experimental arms.
Sample size: planned number of observations
Approximately 135,000 conciliation requests/cases.
Sample size (or number of clusters) by treatment arms
Control group: 45 implementation days, approximately 33,750 conciliation requests. Treatment 1: 45 implementation days, approximately 33,750 conciliation requests. Treatment 2: 45 implementation days, approximately 33,750 conciliation requests. Treatment 3: 45 implementation days, approximately 33,750 conciliation requests. Total: 180 implementation days and approximately 135,000 conciliation requests.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The minimum detectable effect for the main settlement outcome is approximately 2 percentage points, from a baseline settlement rate of 47%, accounting for day-level clustering. For settlement amounts conditional on settlement, the mean is approximately 82.36 daily wages with a standard deviation of 68.77 daily wages, and the minimum detectable effect is approximately 4% of the mean, or about 3.3 daily wages. These calculations assume 45 randomized implementation days per treatment arm and approximately 750 conciliation requests per day across participating states.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
ITAM
IRB Approval Date
2025-10-29
IRB Approval Number
N/A
Analysis Plan