Bibliographic record
Abstract
At least a quarter of college students in the United States graduate with more than one undergraduate major. This article investigates how students choose the composition of their majors conditional on pursuing more than one major, that is, whether the majors that they choose are substitutes or complements. As the students use both their preferences and expectations about the realizations of future major‐specific outcomes when choosing their college majors, I collect innovative data on subjective expectations from a sample of Northwestern University sophomores. Although there is substantial heterogeneity in beliefs across students, they seem to be aware of differences across majors and have sensible beliefs about the outcomes conditional on major. Students believe that their parents are more likely to approve majors associated with high social status and high returns in the labor market. I incorporate the subjective data in a choice model of double majors that also captures the notion of specialization. I find that enjoying studying the coursework and gaining approval of parents are the most important determinants in the choice of majors. The model estimates reject the hypothesis that students major in one field to pursue their own interests and in another for parents' approval. Instead I find that gaining parents' approval and enjoying studying and working in a field of study are outcomes that are important for both majors in a student's major pair. However, I do find that students act strategically in their choice of majors by choosing majors that differ in their chances of completion and difficulty, and in finding a job upon graduation. ( JEL D8, I2, J1)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".