Research examining the educational attainment of low-income students has often focused on financial factors such as credit constraints. We use unique longitudinal data to provide direct evidence about a prominent alternative explanation—that departures from school arise as students learn about their academic ability or grade performance. Examining college dropout, we find that this explanation plays a very prominent role; our simulations indicate that dropout between the first and second years would be reduced by 40% if no learning occurred about grade performance/academic ability. The article also contributes directly to the understanding of gender differences in educational attainment.

This paper studies wage determination using the first longitudinal data set containing job-level task information for individual workers. Novel quantitative task measures detail the amount of time spent performing people, information, and objects tasks at different skill levels. These measures suggest natural proxies for on-the-job human capital accumulation and provide new insights about wage determination. Current job tasks are quantitatively important, with high-skilled tasks being paid substantially more than low-skilled tasks. There is no evidence of learning by doing for low-skilled tasks but strong evidence for high-skilled tasks. Current and past high-skilled information tasks are particularly valuable.