Location of Repository

Math or Science? Using Longitudinal Expectations Data to Examine the Process of Choosing a College Major

By Todd R. Stinebrickner and Ralph Stinebrickner


Due primarily to the difficulty of obtaining ideal data, much remains unknown about how college majors are determined. We take advantage of longitudinal expectations data from the Berea Panel Study to provide new evidence about this issue, paying particular attention to the choice of whether to major in math and science. The data collection and analysis are based directly on a simple conceptual model which takes into account that, from a theoretical perspective, a student’s final major is best viewed as the end result of a learning process. We find that students enter college as open to a major in math or science as to any other major group, but that a large number of students move away from math and science after realizing that their grade performance will be substantially lower than expected. Further, changes in beliefs about grade performance arise because students realize that their ability in math/science is lower than expected rather than because students realize that they are not willing to put substantial effort into math or science majors. The findings suggest the potential importance of policies at younger ages which lead students to enter college better prepared to study math or science.

OAI identifier:

Suggested articles



  1. (2004). Ability Sorting and the Returns to College Major,”
  2. (2008). College Major Choice and the Gender Gap,” working paper
  3. (1966). Consumer Buying Intentions and Purchase Probability: An Experiment in Survey
  4. (1998). Earnings Expectations, Revisions, and Realizations,” The Review of Economics and Statistics,
  5. (1996). Eliciting Student Expectations of the Returns to Schooling,” Winter
  6. (2005). Estimating Distributions of Counterfactuals with an Application to the Returns to
  7. (2002). How Do Young People Choose College Majors?,”
  8. (2003). How Should We Measure Consumer Confidence (sentiment)? Evidence form the Michigan Survey of Consumers,” Working paper 9926, National Bureau of Research,
  9. (2009). Learning about Academic Ability and the College Drop-Out Decision”
  10. (2004). Measuring Expectations,”
  11. (2010). Modeling College Major Choices using Elicited Measures of Expectations and Counterfactuals,” Duke University working paper,
  12. (1997). Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement Survey,” The Quarterly Journal of Economics,
  13. (2007). Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future.” The National Academies Press
  14. (1989). Schooling as Experimentation: a reappraisal of the post-secondary drop-out phenomenon,”
  15. (2005). Separating Uncertainty from Heterogeneity
  16. (1993). The Demand for and Return to Education When Education Outcomes are
  17. (2008). The Effect of Credit Constraints on the College DropOut Decision: A Direct Approach Using a New Panel Study,” American Economic Review.
  18. (1990). The Use of Intentions Data to Predict Behavior: A Best Case Analysis,”
  19. (2004). Time-Use and College Outcomes,”
  20. (2010). Using Elicited Choice Probabilities to Estimate Random Utility Models: Preferences for Electricity Reliability,” International Economic Review,
  21. (1997). Using Expectations Data to Study Subjective Income Expectations,”

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.