Mexico City’s Labor Court, one of the largest in Latin America, experiences extreme 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.
Researchers Joyce Sadka (ITAM), Enrique Seira (ITAM), and Christopher Woodruff (Oxford)—with funding from the Economic Development and Institutions (EDI) program, managed in part by CEGA—sought to address the backlog problem, ultimately contributing to one of the biggest labor law reforms in Mexican history.
Researchers established an information booth on the steps of the court to provide free information to workers about their rights and the judicial process, and used data from past cases to predict whether they were likely to win (using a machine learning “calculator”). They found that telling workers the probability of winning their case led to higher rates of resolution (a settlement out of court, or being reinstated at one’s job) and that setting up a meeting with the employer increased the rate of conflict resolution. In short, providing basic information to individual claimants significantly changed their decision-making and eased strain on the court system overall.
Lessons from this research influenced Mexican law, with Sadka writing parts of the labor reform bill that passed in May 2019 using lessons from the research. In addition, the Labor Court decided to keep a permanent booth in front of the court, and the research team is helping the court hire and train staff to take charge of its operation. The government has also expressed interest in scaling the calculator’s predictions across routine case management.
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