Does the Value of a Statistical Life Vary with Age and Health Status? Evidence from the United States and Canada
Bibliographic record
Abstract
Much of the justification for environmental rulemaking rests on estimates of the benefits to society of reduced mortality rates. Yet the literature providing estimates of the willingness to pay (WTP) for mortality risk reductions measures the value that healthy, prime-aged adults place on reducing their risk of dying, whereas the majority of statistical lives saved by environmental programs, according to epidemiological studies, appear to be the lives of older people and people with chronically impaired health. This paper provides an empirical assessment of the effects of age and baseline health on WTP for mortality risk reductions by reporting the results of two contingent valuation surveys designed to test the above hypotheses. One survey was administered in-person to residents of Hamilton, Ontario, and the other to a nationally representative sample of U.S. residents using the Internet. Both surveys elicited respondents’ WTP for reductions in mortality risk of different magnitudes. Respondents were limited to persons aged 40 years and older, including those older than 60, to examine the impact of age on WTP. Extensive information was collected about each respondent’s health status to see whether it systematically influenced WTP. Our results provide weak support for the notion that WTP declines with age, but only after age 70. Specifically, in our Canadian sample, WTP declines by about 30% after age 70 compared with WTP at younger ages. There is no such statistically significant decline, however, in the U.S. sample. We similarly find no support for the idea that people who have cancer or chronic heart or lung disease 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 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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".