Bibliographic record
Abstract
When young individuals face binding debt constraints, their human capital investments will be insufficiently financed by private creditors. If generations overlap, then a well-designed fiscal policy may be able to improve human capital investments by replacing missing capital markets with an intergenerational transfer scheme. The optimal (balanced budget) fiscal policy in this context entails the joint provision of an education subsidy for the young and a pension program for the old, financed with a tax on those in their peak earning years. We demonstrate, however, that the desirability of such a cradle-to-grave policy depends crucially on the assumption of an exogenous debt constraint. If debt constraints arise endogenously for reasons of limited commitment, then the optimal (balanced budget) fiscal policy looks radically different. Furthermore, we find that cradle-to-grave type policy interventions may actually lead to lower levels of human capital investment as altered default incentives induce private creditors to contract the supply of student loans by an amount greater than the subsidy. In some cases, the constrained-optimal policy entails zero intervention. These results highlight the importance of taking seriously the reasons for why debt constraints exist.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".