Working Papers
Working Papers
Awarded George Borts’ Prize in recognition of an outstanding Ph.D. dissertation. Department of Economics. Brown University.
Students may put insufficient effort into graduating due to inaccurate beliefs about graduation probabilities or the economic benefits of obtaining a high school diploma, missing out on large economic returns. In an experiment with senior high-school students in Argentina, I test the effectiveness of providing information about efficiently allocating effort during senior year, to correct beliefs about their graduation chances. A separate group receives information about the returns to education. The treatments increase graduation by 10 and 20 percent, respectively, and college enrollment by 38 percent. Graduation improvements are larger for students with a lower probability of graduating at baseline.
AEA Registry AEARCTR-0004511
Coverage Development Impact World Bank Blog
AI in the Classroom: Evaluating the Impact of Teacher Training on Practices and Student Outcomes Republic with Carla Z. Glave, Ezequiel Molina, and Román Andrés Zárate
Can artificial intelligence (AI) improve teaching practices and student learning in developing countries? This study evaluates whether training primary school teachers in Peru to integrate Large Language Models (LLMs) into their daily work can enhance instructional quality and student outcomes. We conduct a large-scale, multi-arm, field experiment across public schools in Lima, Peru. The main intervention is a structured teacher training program focused on the use of AI for educational practices. In addition to the primary treatment, the study includes two cross-randomized variations. The first compares two modes of ongoing support—peer-based networks versus authority-led guidance—to assess which approach better sustains AI adoption. The second introduces a complementary training that emphasizes tailoring instruction to students’ learning levels, following “Teaching at the Right Level” (TaRL) principles. By examining both implementation and impacts on teaching practices and student learning, this study provides timely evidence on the potential for AI to equitably and effectively support educators and learners in low- and middle-income contexts.
AEA Registry AEARCTR-0016336
Video Microsoft
Do Patients Value High-Quality Medical Care? Experimental Evidence from Malaria Diagnosis and Treatment with Anja Sautmann and Simone Schaner (Submitted)
Can information about the value of diagnostic tests improve provider practice and help patients recognize higher quality of care? In a randomized experiment at public clinics in Mali, health providers and patients received tailored information about the importance of rapid diagnostic tests (RDTs) for malaria. The provider training increased provider reliance on RDTs, improving the match between a patient's malaria status and treatment with antimalarials by 15-30 percent. Nonetheless, patients were significantly less satisfied with the care they received, driven by those whose prior beliefs did not match their true malaria status. The patient information intervention did not affect treatment outcomes or patient satisfaction and reduced malaria testing. These findings are consistent with highly persistent patient beliefs that translate into low demand for diagnostic testing and limit patients' ability to recognize improved quality of care.
The Socioeconomic Outcomes of Native Groups in Argentina with Pedro Dal Bó
This study uses individual-level census data from Argentina to examine the socioeconomic disparities between Native and non-Native people. Native people fare worse across a variety of indicators, including housing, education, employment, and health. On average, the observed disparities amount to 12 percent of the standard deviation and persist even after controlling for factors such as geographic location. Furthermore, there are differences in the intergenerational transmission of education between Natives and non-Natives: for each level of education of the parents, the children of Natives have, on average, fewer years of education than the children of non-Natives. Finally, the study also reveals large differences between Native groups: while some achieve average outcomes that surpass those of the non-Native population, others significantly lag behind. Notably, these differences are correlated with a characteristic of their pre-Columbian economy: the practice of agriculture.
Experimental evidence on data-driven remedial instruction in the Dominican Republic with Astrid Pineda (Draft coming soon)
Learning losses due to COVID-19 have been substantial, especially for students coming from low socioeconomic backgrounds, further worsening existing learning deficits in many developing countries. To address these losses, the use of tutoring and computer-assisted instruction holds promise for accelerating learning recovery. However, there is limited knowledge on how education systems can effectively implement these approaches at scale. Computer adaptive learning (CAL) softwares are particularly noteworthy for their ability to cater to students' individual learning levels. However, most evidence on CAL is based on after-school settings and primary-school-aged children, making it challenging to extrapolate to older students or in-school settings. Tutoring, while effective, faces scalability challenges due to cost and availability of qualified tutors. In this proof-of-concept phase, we aim to evaluate an in-school intervention combining CAL with group tutoring as a potentially more scalable alternative to accelerate learning recovery among teenagers. Moreover, we aim to generate evidence on whether using CAL-generated data for targeted tutoring and teacher support can lead to better student outcomes. We plan to use in-depth data, including on teaching practices and teachers’ time use, to investigate the underlying mechanisms behind the observed changes.
AEA Registry AEARCTR-0012383
The Personalization Trap: Does AI Tutoring Build Learners or Dependence? with Ezequiel Molina y Germán Reyes
AEA registry AEARCTR-0017917
This study evaluates the impact of an AI-powered math tutoring platform and an AI career coaching chatbot on learning outcomes and educational aspirations among 5th-year secondary students in public schools in Lima, Peru. The AI math tutor covers Peru's national math curriculum and is used during regular math class time in school computer labs, supervised by students' regular math teacher. The AI career coach helps students explore post-secondary options, understand economic returns to education, and develop concrete career plans during existing tutoría (guidance) periods. The study uses a cluster-randomized design with approximately 100 schools as the unit of randomization and three study arms: (1) AI tutoring with learning-science modifications plus career coaching, (2) AI tutoring in standard configuration plus career coaching, and (3) business-as-usual control. The learning-science modifications embed three moments of student cognitive effort (a retrieval practice opener at the start of each session, a predict-reveal-diagnose sequence on errors, and an error detection task on mastered topics) that return to the student metacognitive operations the algorithm otherwise performs on their behalf. AI tutoring platforms could produce learning gains that are substantially non-durable because the algorithm silently performs metacognitive operations (retrieval, error diagnosis, mastery evaluation) that students need to learn to perform independently—a phenomenon we term the personalization trap. The three modifications test whether returning these cognitive operations to the student makes learning gains persist after platform removal. Primary outcomes are multiple math assessments during the academic year. The study tests whether AI tutoring produces learning gains within existing public-school constraints and whether learning-science modifications to the platform's default interaction design improve the durability of those gains.