Student loans and the allocation of graduate jobs
Bibliographic record
Abstract
Abstract Higher education is not just a costly signal of native talent but also a means of raising a person's ability to hold a graduate job (and at least a certain educational achievement is required to get one). Graduate jobs differentiated by quality are allocated to graduates differentiated by native talent and parental wealth through a tournament. Non‐graduates jobs pay a fixed wage to those who do not participate in the tournament. Assuming that credit is rationed, some poor school leavers will go straight into the non‐graduate labour market even if they are talented enough to get a higher education and participate in the tournament. Some others will buy the same amount of higher education and end up doing graduate jobs of the same quality as less talented but richer school leavers. We show that student loans improve job matching and bring educational investments closer to efficiency. If the size of the loan is not very large, some poor school leavers will still be liquidity‐constrained and thus buy the same amount of higher education as less talented but richer ones. In that case, the former will get a productivity bonus. But raising the size of the loan to such a level that nobody is liquidity‐constrained could be socially optimal only if social preferences were extremely egalitarian.
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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 | 0.001 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".