Academic Performance and College Dropout: Using Longitudinal Expectations Data to Estimate a Learning Model
利用独特的预期数据估计大学辍学的动态学习模型，发现前两年45%的辍学源于学生了解自身学业表现，且表现差的学生因上学不值得而非面临退学风险而离开。
We estimate a dynamic learning model of college dropout, taking advantage of unique expectations data to greatly reduce our reliance on standard assumptions. Our simulations show that 45% of dropout in the first 2 years of college can be attributed to what students learn about their academic performance, with this type of learning playing a smaller role later in college. Poorly performing students tend to leave because staying is not worthwhile rather than because they are at risk of failing out of school. Poor performance substantially decreases the enjoyability of school and substantially influences beliefs about postcollege earnings.