Last registered on August 07, 2017


Trial Information
General Information
Initial registration date
August 06, 2017
Last updated
August 07, 2017 10:16 AM EDT

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Primary Investigator
Instituto Tecnologico Autonomo de Mexico
Other Primary Investigator(s)
PI Affiliation
PI Affiliation
Additional Trial Information
On going
Start date
End date
Secondary IDs
Labor courts are essential for the workings of well-functioning labor markets. However, the courts in many lower-middle and low-income countries are rendered dysfunctional by large backlogs in resolving cases, corruption, low settlement rates, poor enforcement, and limited access to justice. The labor courts in Mexico exemplify these issues. Data from surveys conducted as part of a pilot project show that parties are overly optimistic about their chances of winning the case. There is suggestive evidence that those represented by private lawyers are particularly prone to excessive optimism. We posit that this owes partly to the incentives of private lawyers and agency problems. We aim to understand whether providing plaintiffs and defendants with unbiased statistical information and conciliation services increases settlement rates. We conduct an experiment with three scalable treatments (plus a control group). The first treatment provides parties to cases with objective statistical information about expected outcomes. The information is customized to the filing and comes from analysis of more than 5000 recent cases. The second treatment provides statistical information plus reimbursement for the cost to travel to the public attorney’s office. The third treatment provides information and encouragement to use pre-judicial conciliation. These treatments are organized in one pure control group and 4 intervention groups, varying information provided and transport assistance to the public attorney's office across them. We conjecture that each of these treatments will cause parties to be better informed about their case and the likely outcomes, and that conciliation will increase, and that welfare will increase.

We seek to answer the following questions:

1. Are worker’s expectations about suing and winning the trial unbiased on average relative to outcomes of recent lawsuits with similar characteristics?
2. Does providing statistical information about outcomes of cases like theirs lead to expectation updating? Does it lead to more out-of-court settlement? Does it lead to larger welfare in terms of having a job, self-reported welfare, and amounts earned?
3. Does giving information about the existence of public (and free of cost) lawyers early on lead to more conciliation and improved self-reported welfare?
4. Do private lawyers inflate expectations and reduce conciliation?
External Link(s)
Registration Citation
Bejarano, Enrique, Joyce Sadka and Christopher Woodruff. 2017. "IMPROVING THE EFFECTIVENESS OF LABOR COURTS THROUGH INFORMATION AND CONCILIATION." AEA RCT Registry. August 07.
Experimental Details
Logistics: The court receives about 10 daily visits from workers claiming to be unfairly dismissed and either planning to file a suit or just looking for legal advice. But the court does not provide advice in its building. Free legal advice is provided at a building more than 3 km away, a half hour or more away by public transport. Every week day we will put a module (stand) at the court’s entrance. We will randomize at the day level whether the module gives one of four treatments described below. The target population of this pilot study is workers searching for legal advice, conciliation or preparing to sue. We hypothesize that the effect of information to induce settlement out of court will be far larger before workers have private lawyers.

1a) Control group: We will tell incoming workers that the court is collecting information to provide better services and understand the parties’ problems better. We will then do and entry survey to collect basic information on their case (data necessary for the calculator –even though we will not give calculator), including expectations on case outcomes if a suit is filed, date of firing, name of company, and their name, phone and address. We will then tell them that we will re-contact them in 6 months after intervention by phone or visit to follow up on their case.

1b) Public lawyer information: Same as 1a, except that we will provide detailed information on the public lawyers that are available , a map of where they are located, and will be offered an immediate mode of transport to the public lawyers’ office.

2) Calculator information: Same as 1b except that we will also give them the calculator information. The calculator will be implemented in situ using the information provided on the baseline survey. We have found that 5 variables are enough to give an accurate prediction of outcomes on the probability of winning, the amount of winnings conditional on a positive result at trial, and the average amount that can be obtained in a settlement. This calculator will consist of a point estimate of the probability of recovering a positive amount if the worker sues and continues the litigation to a court judgment, and an interval estimate of the amount of compensation obtained in a settlement, measured in days of salary. We will re-elicit (follow-up) expectations about the probability of winning conditional on suing, and amount earned conditional on winning, and delay. This will enable us to analyze updating because of the information we provided. After receiving the calculator information, subjects will receive information about the public lawyers’ office, the map, and the offer of immediate transport to that location.

3) Early free conciliation help: Same as 2 but in addition –after providing the calculator information and re-eliciting (follow-up) expectations-- the court will offer help setting up and conducting a conciliation meeting with the employer. If the worker accepts this help, the court will give her an official letter of appointment addressed to the firm. The worker will take this letter of appointment to the firm and we will confirm by phone that it was delivered. The letter of appointment will be printed on the court’s letterhead, and will signed by the General Secretary for Individual Labor Disputes (the boss of all the judges of the sub-courts), and will contain a specific date/time for the conciliation meeting. At the meeting time --if both parties show up-- we will provide to the firm the same calculator information already provided to the worker, and sit them down with a court conciliator to attempt a settlement agreement. If they make an agreement, the module personnel will help draft the formal agreement and the court decree formalizing the settlement, and send these documents to the office for “settlements without a lawsuit” to register the agreement, giving it force of law. If the firm does not show up to the conciliation meeting, or the parties are unable to reach a settlement, then we will recommend the worker to the public prosecutor’s office in case she wants to pursue a lawsuit, provide her with the map, and offer immediate transport.

It is important to highlight that the research team will not be giving legal advice or conducting the conciliation hearings. These will be done by the court under their current procedures. The research team will only provide information. A

This study is intended to run for about 250 week days, and we plan to have 10 subjects per day, giving us a total of 2500 subjects, that is 625 per arm. We think this is a very reasonable number to test the promise of the pilot, logistics and materials, and believe the sample may be of sufficient size to generate statistically significant results from the pilot. Sample sizes may vary depending on financing for the project
Intervention Start Date
Intervention End Date
Outcomes (end points)
We will test if the following outcome variables are different across the 5 groups: o Expectation updating (comparing same-day baseline vs follow up survey) o If they file a suit or not within 90 days (dummy) o If they conciliated out-of-court or not within 90 days (dummy) o Conditional on suing: if they got a public vs a private lawyer. o The “quality” of the law suit, as assessed by a team of lawyers from ITAM, blinded as to treatment and whether the lawyer is public or private, including the amount demanded in the suit relative to the calculator prediction based on the worker/job characteristics. Conditional on financing and cost, we will also conduct surveys to measure welfare proxies like having a job, buying durables, wage, expenses.
Outcomes (explanation)
Experimental Design
Experimental Design
Some of this is described above in the "Intervention" section.

We will have 5 treatment groups described above. Randomization will be conducted at the day level (ICC of conciliation at the day level is about 0.008).

We will implement a short baseline survey at first contact. Most outcomes will be measured with administrative information. Including whether they sued or not, if they got a public or a private lawyer, and the quality of their law suit. We plan to do a follow up survey to measure if they conciliated out-of-court and in which terms, as well as proxies of welfare mentioned in the "Outcomes" section.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Day level.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Close to 250 days
Sample size: planned number of observations
about 2500
Sample size (or number of clusters) by treatment arms
About 10 per day.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
This is a pilot trial. We need to have data for this population to answer this exactly. In a previous pilot for lawsuits already filed, 300 observations per arm were sufficient to detect effects of 4pp on conciliation, statistically significant at the 5% level. Power calculations suggest that we can detect effect of 3pp-4pp with 90% power and 95% confidence
IRB Name
Instituto Tecnologico Autonomo Mexico
IRB Approval Date
IRB Approval Number