What Should We Spend to Save Lives in a Pandemic? A Critique of the Value of Statistical Life

M. Adler
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引用次数: 17

Abstract

The value of statistical life (VSL) is a risk-to-money conversion factor that can be used to accurately approximate an individual’s willingness-to-pay for a small change in fatality risk. If an individual’s VSL is (say) $7 million, then she will be willing to pay approximately $7 for a 1-in-1-million risk reduction, $70 for a 1-in-100,000 risk reduction, and so forth. VSL has played a central role in the rapidly emerging economics literature about COVID-19. Many papers use VSL to assign a monetary value to the lifesaving benefits of social-distancing policies, so as to balance those benefits against lost income and other policy costs. This is not surprising, since VSL (known in the U.K. as “VPF”: value of a prevented fatality) has been a key tool in governmental cost-benefit analysis for decades and is well established among economists. Despite its familiarity, VSL is a flawed tool for analyzing social-distancing policy—and risk regulation more generally. The standard justification for cost-benefit analysis appeals to Kaldor- Hicks efficiency (potential Pareto superiority). But VSL is only an approximation to individual willingness to pay, which may become quite inaccurate for policies that mitigate large risks (such as the risks posed by COVID-19)—and thus can recommend policies that fail the Kaldor- Hicks test. This paper uses a simulation model of social-distancing policy to illustrate the deficiencies of VSL. I criticize VSL-based cost-benefit analysis from a number of angles. Its recommendations with respect to social distancing deviate dramatically from the recommendations of a utilitarian or prioritarian social welfare function. In the model here, it does indeed diverge from Kaldor- Hicks efficiency. And its relative valuation of risks and financial costs among groups differentiated by age and income lacks intuitive support. Economists writing about COVID-19 need to reconsider using VSL
在大流行中,我们应该花什么钱来拯救生命?对统计生命价值的批判
统计寿命值(VSL)是一种风险-金钱转换因子,可以用来准确地估计个人为死亡风险的微小变化支付的意愿。如果一个人的VSL(比如说)是700万美元,那么她将愿意为百万分之一的风险降低支付大约7美元,为10万分之一的风险降低支付大约70美元,以此类推。VSL在快速涌现的关于COVID-19的经济学文献中发挥了核心作用。许多论文使用VSL来为社交距离政策的救命效益分配货币价值,以便在这些效益与收入损失和其他政策成本之间取得平衡。这并不奇怪,因为VSL(在英国被称为“VPF”:预防死亡的价值)几十年来一直是政府成本效益分析的关键工具,在经济学家中得到了广泛认可。尽管它很熟悉,但VSL在分析社会距离政策和更普遍的风险监管方面是一个有缺陷的工具。成本效益分析的标准理由诉诸于卡尔多-希克斯效率(潜在的帕累托优势)。但VSL只是个人支付意愿的近似值,对于减轻重大风险(如COVID-19带来的风险)的政策来说,这可能会变得非常不准确,因此可以推荐无法通过卡尔多-希克斯测试的政策。本文利用社会距离政策的仿真模型来说明VSL的不足。我从多个角度批评了基于vsl的成本效益分析。它关于社会距离的建议与功利主义或优先主义社会福利功能的建议大相径庭。在这个模型中,它确实偏离了卡尔多-希克斯效率。它对不同年龄和收入群体的风险和财务成本的相对评估缺乏直观的支持。撰写COVID-19的经济学家需要重新考虑使用虚拟语言
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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