Estimating the Financial Return to Education Between Fields of Study
Bibliographic record
Abstract
The Mincer regression equation was utilized to compute the expected financial return to education from additional years of education across the 2016 Canadian population. Data was taken from the 2016 Canadian Census of Population to create the populations of interest. Three sub populations were then derived from the collected data to represent Canadians with different major areas of study namely, business, humanities, and engineering. Mincerian regressions were run using these subsections to determine how the financial return to education differs between distinct majors. Additional multiple regressions included an interaction term between sex and years of schooling in an attempt to determine whether an individual’s sex affects their expected return to education given a specific area of study. The regression results indicated that business majors boast the largest average expected return to education while engineering majors boast the lowest. Subsequently, in relation to business majors, sex was not found to have an impact on expected financial returns. Future research may build off the findings of this paper by expanding the scope to include all areas of study in addition to deciphering whether the expected return to education for a given major is consistent throughout all major Canadian universities.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".