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Extrapolating the Value Per Statistical Life Between Populations: Theoretical Implications

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  • Hammitt, James K.

Abstract

Extrapolation of estimates of the value per statistical life (VSL) from high- to low- or middle-income populations requires attention to the possible effects of differences in income, current mortality risk, health, life expectancy, and many other factors. The standard theoretical model of VSL implies that VSL increases with income and decreases with current mortality risk. The effect of mortality risk is likely to be negligible while the effect of income is large and poorly quantified. Effects of differences in life expectancy and health are theoretically ambiguous. Effects of other factors, including differences in health care, formal and informal support networks, and cultural or religious factors that affect preferences for spending on oneself or others may be important but are unknown. Practical issues include choice of the most appropriate measure of income and possible differences in the patterns of age dependence between populations.

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  • Hammitt, James K., 2017. "Extrapolating the Value Per Statistical Life Between Populations: Theoretical Implications," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 8(2), pages 215-225, July.
    • Handle: RePEc:cup:jbcoan:v:8:y:2017:i:02:p:215-225_00
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    Citations

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    Cited by:

    1. Marcela V. Parada‐Contzen, 2019. "The Value of a Statistical Life for Risk‐Averse and Risk‐Seeking Individuals," Risk Analysis, John Wiley & Sons, vol. 39(11), pages 2369-2390, November.
    2. Claxton, Karl & Asaria, Miqdad & Chansa, Collins & Jamison, Julian & Lomas, James & Ochalek, Jessica & Paulden, Mike, 2019. "Accounting for timing when assessing health-related policies," LSE Research Online Documents on Economics 100038, London School of Economics and Political Science, LSE Library.
    3. Eiji Yamada, 2020. "Three chapters on spatial and urban economics [Trois chapitres sur l’économie spatiale et urbaine]," SciencePo Working papers Main tel-03767752, HAL.
    4. James K. Hammitt & Fangli Geng & Xiaoqi Guo & Chris P. Nielsen, 2019. "Valuing mortality risk in China: Comparing stated-preference estimates from 2005 and 2016," Journal of Risk and Uncertainty, Springer, vol. 58(2), pages 167-186, June.
    5. Claxton, Karl & Asaria, Miqdad & Chansa, Collins & Jamison, Julian & Lomas, James & Ochalek, Jessica & Paulden, Mike, 2019. "Accounting for Timing when Assessing Health-Related Policies," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 10(S1), pages 73-105, April.
    6. N. Witvorapong & T. Komonpaisarn, 2020. "The Value of a Statistical Life in Thailand: Evidence from the Labour Market," Journal of Consumer Policy, Springer, vol. 43(3), pages 491-518, September.
    7. Robinson, Lisa A. & Raich, William J. & Hammitt, James K. & O’Keeffe, Lucy, 2019. "Valuing Children’s Fatality Risk Reductions," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 10(2), pages 156-177, July.
    8. repec:hal:wpspec:tel-03767752 is not listed on IDEAS
    9. Castañeda Dower, Paul & Markevich, Andrei & Weber, Shlomo, 2021. "The value of a statistical life in a dictatorship: Evidence from Stalin," European Economic Review, Elsevier, vol. 133(C).
    10. Acland, Daniel & Greenberg, David H., 2023. "The Elasticity of Marginal Utility of Income for Distributional Weighting and Social Discounting: A Meta-Analysis," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 14(2), pages 386-405, July.
    11. José Luis Texcalac-Sangrador & Magali Hurtado-Díaz & Eunice Elizabeth Félix-Arellano & Carlos Manuel Guerrero-López & Horacio Riojas-Rodríguez, 2021. "Health and Economic Impacts Assessment of O 3 Exposure in Mexico," IJERPH, MDPI, vol. 18(21), pages 1-10, November.

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