Taking advantage of unique longitudinal data, we provide the first characterization of what college students believe at the time of entrance about their final major, relate these beliefs to actual major outcomes, and provide an understanding of why students hold the initial beliefs about majors that they do. The data collection and analysis are based directly on a conceptual model in which a student's final major is best viewed as the end result of a learning process. We find that students enter school quite optimistic about obtaining a science degree, but that relatively few students end up graduating with a science degree. The substantial overoptimism about completing a degree in science can be attributed largely to students beginning school with misperceptions about their ability to perform well academically in science. Copyright 2014, Oxford University Press.

The labor supply decisions of certified elementary and high school teachers are examined using data from a general longitudinal survey. A significant decrease in the teaching participation rate takes place over time after teacher certification. Previously unavailable marital and fertility variables provide new insight into reasons for this decrease. Descriptive statistics indicate that high‐ability teachers choose to teach a smaller proportion of time than other teachers. A dynamic, discrete‐choice model, which accommodates Serial Correlation in the wage process for teachers, is used to analyze responsiveness of the overall sample and different types of teachers to two potential types of wage increases.

Unique new data from a college with a mandatory work‐study program are used to examine the relationship between working during school and academic performance. Particular attention is paid to the importance of biases that are potentially present because the number of hours that are worked is endogenously chosen by the individual. The results suggest that, even if results appear reasonable, a researcher should be cautious when drawing policy conclusions about the relationship between hours worked and a particular outcome of interest unless he or she is confident that potential problems associated with the endogeneity of hours have been adequately addressed.

This paper utilizes recent simulation techniques in a two-stage estimation method which is applicable for a wide range of statistical models in the presence of missing data. The first stage of the method provides a way to estimate (and simulate from) the joint distribution of missing variables when the missing variables are continuous, binary, or ordered discrete. The second stage uses the first-stage estimates to “integrate” out the effects of the missing variables and obtain model estimates. The implementation of the method in this paper allows theoretically important, partially missing wage and school characteristic variables-which are not necessarily independently determined-to be included in a proportional hazard model of teacher attrition.