Improving the Effectiveness of Labor Courts Through Information and Conciliation

Courts in many lower-middle and low-income countries are rendered dysfunctional by long delays in resolving cases and related low settlement rates. Mexico City’s Labor Court, one of the largest in Latin America, experiences serious backlogs and inefficiencies. Trials can drag on for years: 30% of trials started in 2011 had not finished by December 2015 and 76% of judgments result in zero payments to workers, even if they win. In this project, the research team sought to address the backlog problem by providing petitioners with better information about their rights, probability of winning, and other conciliation options.

In partnership with the Mexico City Labor Court, the researchers provided workers with a personalized prediction about the likely outcome of their case — that is, whether they’re likely to win, and if so, how much they would receive. This prediction was the result of a machine learning algorithm applied to data from past cases. Before receiving the information, plaintiffs are overconfident about the chances of winning their lawsuit and the expected size of the award. Even those with a private lawyer are uninformed about the contents of their own case. Private lawyers potentially exploit these information deficiencies by pursuing cases when workers would be better off with a settlement. Researchers found that telling workers the probability of winning their case doubles the rates of settlement out of court, largely driven by cases that the plaintiff would have lost. 

Importantly, these effects are only realized when the worker was present to receive the information, and only for workers represented by private lawyers. This suggests that lawyers did not convey the information about the opportunity to settle to their clients (presumably because the lawyer would forgo the fee they charge to file the case). The results show that “the incentives of the lawyers, who have information advantages, do not always align with those of their clients, even though private lawyers almost always receive a share of the award collected by their plaintiffs” (Sadka, Seira, and Woodruff, working paper 2020).

This intervention improves welfare for the average worker, as measured by the net present value of payouts and an improved ability to pay bills. There are also administrative benefits to the court, as the increased settlement rate reduces the case backlog and the administrative costs associated with litigating a case.

Lessons from this research influenced Mexican law, with Sadka writing parts of the labor reform bill that passed in May 2019  – one of the biggest labor law reforms in Mexican history. Read more about the impact of this project, and the working paper.