The power of human capital in lifecycles. Insights from a flexible framework.
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
This paper develops a novel and flexible life-cycle framework, where borrowing human capital plays an explicit role in modeling and decision-making, explicitly impacting risk-aversion levels, borrowing rates, and inter-temporal discount rates. We find the pre-commitment solution to this new ‘double’ optimization problem in semi-closed form in a region of the control/policy space while developing a numerical procedure to approximate the remaining region using the solvable cases. We carry out numerical case studies revealing two unprecedented conclusions. First, the optimal level of human capital borrowings depends non-trivially on many characteristics of the investor and the market, e.g. range of borrowing cost and risk aversion, subjective discount rate, future income level, and size of their initial endowment. Second, we observe a high level of welfare losses when investors fail to take advantage of their human capital; for instance, investors with high endowment could experience a welfare loss exceeding 70%, while investors with high income could see a 20% welfare loss.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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