Bibliographic record
Abstract
Health has long been considered as a fundamental commodity in economic analyses; Michael Grossman (2000) cites Bentham as recognizing that the ‘relief of pain’ is one of the basic arguments in the utility function. Health was viewed both as an investment in human capital and as an output of a household production process by Grossman (1972a & b), the founding father of demand for health models. In the Grossman model, health is both demanded for utility reasons - it is good to feel well - and for investment reasons – to make more healthy time available for market and non-market activities. Grossman developed a dynamic model for health and solution of the dynamic optimisation problem leads to optimal life-cycle health paths, gross investment in each period, consumption of medical care (which is seen as a derived demand) and time inputs in the gross investment function in each period. By comparing maximum lifetime utility for different lengths of life, it also allows endogenous determination of the length of life. Usually the comparative static and dynamic analyses are performed on sub-models where either the consumption benefits are assumed to equal zero (the investment model), or the investment benefits are assumed to equal zero (the consumption model). We focus on the investment model as sharper predictions are available; this model results in a condition which determines the optimal stock of health in any period and shows that the rate of return on capital (or, marginal efficiency of capital, MEC) must equal the opportunity cost of capital. Increases in the depreciation rate over time cause the optimal stock of health to decrease, as the opportunity cost of capital increases. However, if the MEC curve is inelastic, gross investment grows over time. Thus the model predicts older people to have more sick time, to consume more medical care and devote more time to investment
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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 | 0.004 |
| 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 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".