We develop validity tests for application to stated-preference estimates of WTP to reduce mortality risk, i.e., value per statistical life (VSL), and apply these to data obtained by surveying a representative sample of French adults over the internet. These tests (WTP nearly proportional to risk reduction, insensitive to small differences in baseline risk, increasing in income, and consistent with budget constraints) are satisfied by a conventional single-regression analysis of our data. Using latent class analysis (LCA), we identify important differences between respondents in their consistency with the validity tests and control for much of this heterogeneity. Estimates of VSL from the latent class that is consistent with the validity tests are smaller than estimates from the standard analysis. We estimate mean VSL for adults of about 2 million e and for children (based on parents' WTP

We present an integrated valuation model for diseases that are life-threatening. The model extends the standard one-period value-per-statistical-life model to three health prospects: healthy, ill, and dead. We derive willingness-to-pay values for prevention efforts that reduce a disease's incidence rate as well as for treatments that lower the corresponding health deterioration and mortality rates. We find that the demand value of prevention always exceeds that of treatment. People often overweight small risks and underweight large ones. We use the rank dependent utility framework to explore how the demand for prevention and treatment alters when people evaluate probabilities in a non-linear manner. For incidence and mortality rates associated with common types of cancers, the inverse-S shaped probability weighting found in experimental studies leads to a significant increase in the demand values of both treatment and prevention.

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