Worker replacement and cost-benefit analysis of life-saving health care programs, a precautionary note
Bibliographic record
Abstract
The assumption according to which ill individuals can be replaced at work that underpins the 'friction cost method' (FCM) to value productivity costs has been primarily discussed within the framework of cost-utility analysis. This paper investigates the consequences of this assumption for cost-benefit analysis (CBA). It makes three contributions. First, it provides the first analytical account of the overall consequences of ill worker replacement on social welfare and it analyzes the associated compensation effects within a CBA framework. Second, it highlights a double counting problem that arises when ill worker replacement is assumed in the CBA of life-saving health care programs. To the best of our knowledge, no satisfactory solution to this problem has yet been provided in the literature. Third, this paper suggests and discusses two original ways to address this double counting issue. One consists in adjusting value of a statistical life estimations for the well-being provided by future incomes. Another possibility lies in the estimation of marginal rates of substitution between health and wealth so as to directly monetize the value of life over and above consumption. We show that both solutions raise unresolved questions that should be addressed in future research to enable appropriate use of the FCM in CBA.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".