Economic Benefits of Studying Economics in Canada: A Comparison of Wages of Economics Majors with Wages in Other Disciplines, Circa 2005
Bibliographic record
Abstract
We compared the wages of economics degree holders with of those in 49 other fields of study using data from the 2006 Canadian population census. At the undergraduate level, economics majors earned the sixth highest average wage in 2005. When demographic controls were applied, they ranked ninth on the salary scale. When we compared the wages in 15 fields that require students to take math courses, economists ranked in the middle, as they also did when working as managers and professionals. When working as business and finance professionals, economists had wages surpassed only by finance majors. At the graduate level, economics majors had a greater wage advantage over all of the other fields except for business majors. These results are useful for Canadian university economics departments that have been experiencing declining enrolments over the past few years. In addition, we hope they will enable students to make more informed choices regarding their academic discipline. The results also highlight the need to direct greater policy attention towards developing mathematical skills among incoming university students as a prerequisite for them to build analytical skills, the demand for which in the labour market has been demonstrated in some Canadian and US studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".