Choice of Fields of Study of Canadian University Graduates: The Role of Gender and their Parents’ Education
Bibliographic record
Abstract
This paper examines the determinants of the choice of field of study by university students using data from the Canadian National Graduate Survey. The sample of 18,708 graduates holding a Bachelor degree is interesting in itself knowing that these students completed their study and thus represent a pool of high quality individuals. What impact expected post-graduation lifetime earnings have in choosing their field of study respectively to their non pecuniary preferences? Are these individuals less or more influenced by monetary incentives on their decision than was found in previous literature with samples of university students not all completing their studies successfully? Unlike existing studies, we account for the probability that students will be able to find employment related to their field of study when evaluating lifetime earnings after graduation. The parameters that drive students' choices of fields of study are estimated using a mixed multinomial logit model applied to seven broadly defined fields. Results indicate that the weight put by a student on initial earnings and earnings' rate of growth earnings depends upon the education level of the parent of the same gender. Surprisingly, lifetime earnings have no statistically significant impact when the parent of the same gender as the student has a university education. Results show that men are, in general, more sensitive than women to initial income variations, whilst women are more sensitive than men to the earnings' rate of growth variations. Marital status, enrolment status and the vocation identified with each field of study are influential factors in students' choices. From a policy perspective, a substantial increase in lifetime earnings, while all other factors remain constant, would be necessary to draw students into fields of study they are not inclined to choose initially
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".