Finding Out Who You Are: A Self-Exploration View of Education

Abstract

I study the optimal design of information in education. Students in my model have different priors about their talents and update their beliefs after receiving noisy signals about themselves. I show that the socially optimal signal structure depends only on the average priors of the participating students. In particular, an optimal structure encourages a career in which the average participant has a comparative advantage. In an extended model with human capital accumulation, optimal education targets the participants who respond most sensitively to information.