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This paper presents the first value of statistical life (VSL) meta-analysis that empirically estimates correction factors for 'out of context' benefits transfer (BT) purposes. In the field of mortality risk reductions elicited willingness to pay values in one risk context, say road safety, are frequently applied in other risk contexts like air pollution. However, differences in risk perception and the population at risk across contexts are likely to result in diverging VSL estimates. In a meta-analysis of 26 international stated preference studies, a Bayesian model is estimated regressing contingent values for mortality risk reductions, originating from three different risk contexts, on the characteristics of the risk reduction itself and additional variables characterizing the underlying studies. A willingness to pay (WTP) premium for mortality risk reductions in the air pollution and general mortality risk context relative to improving road safety is observed. Evaluated at the mean, road safety VSL estimates should be multiplied by a factor 1.8 before being applicable in the air pollution context. Moreover, in an illustrative BT exercise we find limited overlap in the set of context specific predictive VSL distributions. Consequently, 'out of context' BT results in a substantial overor underestimation of the VSL.

This study investigates the short‐ and long‐run impact on population dynamics of the major flood in the Netherlands in 1953. A dynamic difference‐in‐differences analysis reveals that the flood had an immediate negative impact on population growth, but limited long‐term effects. In contrast, the resulting flood protection program (Deltaworks), had a persisting positive effect on population growth. As a result, there has been an increase in population in flood‐prone areas. Our results suggest a moral hazard effect of flood mitigation leading to more people locating in flood‐prone areas, increasing potential disaster costs.

This paper studies the adoption and diffusion of energy-saving technologies in a vintage model. An important characteristic of the model is that vintages are modeled as being complementary: there are returns to diversity of using different vintages. We analyse how diffusion patterns and adoption behaviour are affected by complementarity and learning-by-using. It is shown that the stronger the complementarity between different vintages and the stronger the learning-by-using, the longer it takes before firms scrap (seemingly) inferior technologies. We argue that this is a potentially relevant part of the explanation of the energy-efficiency paradox. Furthermore we explore the effects of energy tax policies.