Post-Schooling Outcomes of University Graduates: A Tax Data Linkage Approach
Bibliographic record
Abstract
This paper is rooted in the construction of a new and unique dataset which links administrative data on students who graduated from the University of Ottawa (a large Canadian urban university) from 1998 through 2010 with Canadian tax record data. We track students’ post-schooling earnings on a year-by-year basis, and all graduates are followed through to 2011, which means we are able to track earnings over as much as 13 years for the earliest cohorts. We break earning profiles down by area of study and follow each graduating cohort separately, allowing us to compare patterns in starting earnings levels and earnings growth across graduating cohorts by area of study. This yields some interesting and important patterns. We also compare male-female earnings, and compare earnings quintiles across areas of study. This kind of analysis is not only useful for understanding higher education earnings premia and the returns to HE, but is also valuable for young people making schooling choices, for individual HE institutions and HE systems making program decisions, and for policy makers concerned with skills and skill shortages. The work is currently being extended to relate earnings outcomes to additional student characteristics and schooling experiences, and to include additional various additional sets of Canadian HE institutions in the analysis.
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".