JMP: The informational Value of Grades and Wages. 2024 Miltos Makris Award. [PDF here]
Abstract: Individuals make education and career decisions under uncertainty about their own ability. This paper quantifies the informational value of two ability signals, grades and wages, and examines how they shape schooling and work choices. Using administrative Brazilian data linking students’ academic records to complete employment histories, I estimate a Bayesian learning model in which individuals update beliefs as they observe grades and wages. I find that wages convey more precise information than grades, and that this aggregate dominance is driven mainly by slow learners in fast-paced learning environments; for other learning types, the two signals are roughly equally informative. Simulations show that learning from wages delays sorting on ability and reduces income inequality, but less so than learning from grades, because wage signals are less noisy and arrive while individuals accumulate work experience. By contrast, learning primarily through grades produces noisier updating, more course switching and dropout, and lower early-career earnings due to forgone work experience. As a result, learning through wages increases graduation rates and reduces income inequality relative to both full-information and learning-from-grades benchmarks. These findings suggest that work experience can be a cost-effective channel for ability discovery and for reducing attrition in human-capital investment.
The Role of Financial Aid for Low-Income Low-Achievers. [PDF here]
Abstract: I use a series of discontinuities in policy eligibility to uncover the impact of different types of financial aid directed to low-income students, along their exam score distribution. Results show that low-interest loans are more effective than full and 50% grants in securing College completion but no significant effects on enrolment for students of similar low socioeconomic backgrounds. This is because loans act as a commitment device by introducing a penalty in case of dropout.
Sectoral Labour Flows (with Carlos Carrillo-Tudela, Alex Clymo, Ludo Visschers and David Zentler-Munro). [Slides here.]
Abstract: We develop a data-led structural framework for 1) measuring how workers direct their search effort towards different industries or occupations, and 2) providing measures of market tightness by industry and occupation. The novelty is to use realised worker flows to infer worker search effort and direction, when combined with vacancy data through the lens of a model of sector-specific matching functions.
