Learning from What: The informational Value of Grades and Wages. 2024 Miltos Makris Award.
Abstract: When individuals face uncertainty about their academic ability, their education and the labour market histories carry valuable information. I develop a model in which individuals choose to participate in education and the labour market sequentially and are consequently able to adjust their choice based on new information provided by their realization of grades and wages. Particularly for low-income, high-ability individuals, joining higher education after some labour market experience is welfare-improving because participating in the labour market provides information about ability at relatively low costs, removing uncertainty from the higher education choice with no significant crowd-out effects.
The Role of Financial Aid for Low-Income Low-Achievers.
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 Flow Accounting: A Matching Function Approach (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.