Human capital investment and debt constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it