Do Universities Benefit Local Youth? Evidence from University and College Participation, and Graduate Earnings Following the Creation of a New University
Bibliographic record
Abstract
In this study, I explore the relationship between the presence of a local university in a city and university and college participation among local youth. The evidence is drawn from Census data, along with information on the creation of new university degree-granting institutions in Canada. Students who do not have access to a local university are far less likely to go on to university than students who grew up near a university, likely due to the added cost of moving away to attend, as opposed to differences in other factors (e.g., family income, parental education, academic achievement). When distant students are faced with a local option, however, their probability of attendance substantially increases. Specifically, the creation of a local degree-granting institution is associated with a 28.1% increase in university attendance among local youth, and large increases were registered in each city affected. However, the increase in university participation came at the expense of college participation in most cities. Furthermore, not everyone benefited equally from new universities. In particular, students from lower income families saw the largest increase in university participation, which is consistent with the notion that distance poses a financial barrier. Also, local aboriginal youth only saw a slight increase in university participation when faced with a local university option.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".