Does the value of a statistical life vary with age and health status? Evidence from the US and Canada *
Bibliographic record
Abstract
This paper provides an empirical assessment of the effects of age and baseline health on willingness to pay (WTP) for mortality risk reductions by reporting the results of two contingent valuation surveys: one administered in Hamilton, Ontario and the other to a national sample of US residents. Respondents for both surveys were limited to persons aged 40 years and older to examine the impact of age on WTP. Using the WTP responses and those regarding respondent’s own and family health histories, we find weak support for the notion that WTP declines with age, and then, only for the oldest respondents (aged 70 or above). Furthermore, we find no support for the idea that people with chronic heart or lung conditions, or cancer, 366 are willing to pay less to reduce their risk of dying than people without these illnesses. If anything, people with these illnesses are willing to pay more.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".