Doctoral Dissertation Research in DRMS: A Comparison of Value of Statistical Life Estimates Derived from Revealed and Stated Preferences

DRMS 的博士论文研究：根据显示和陈述的偏好得出的统计寿命估计值的比较

• 批准号: 2116716
• 负责人: Mary Hansen
• 金额: $ 2.99万
• 依托单位: American University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2116716&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; DRMS; Comparison

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Every day, people consider tradeoffs they are willing to make to reduce risks. The Value of a Statistical Life (VSL) describes the tradeoffs in aggregate. The VSL is used to evaluate safety regulations across many areas of life. Accurate measurement is critical, but VSL estimates vary widely. This dissertation advances the national health, prosperity, and welfare by investigating three sources of variability in the VSL. First, it compares the methods of discovering people’s risk preferences. This is important because preferences are a key input into the VSL metric. Second, it examines whether people’s risk preferences differ across contexts when the probability of death does not. This is important because VSL estimates derived from labor market data are used to guide regulation in unrelated areas. Finally, it explores people’s subjective experiences of objective risks. This is important because use of the VSL requires that people accurately comprehend risk. The project contributes to the advancement of the decision sciences and economics by testing prevailing assumptions about the nature and measurement of risk preferences. Findings promote a more complete theory of how perceptions of risk guide decision making. Findings may also reshape how regulatory agencies approach cost-benefit analyses of safety regulations. This is of particular importance in promoting the health, prosperity, and welfare of vulnerable populations, who are more susceptible to fatality risks and health hazards.The Value of a Statistical Life (VSL) captures the trade-offs people are willing to make to reduce the probability of death. The VSL is used widely in safety regulation. Questions remain, however, regarding how well the VSL captures risk preferences. First, VSL estimates vary considerably by measurement approach (revealed vs. stated preferences), reflecting different assumptions about how people evaluate risk. Second, VSL estimates from labor contexts are leveraged for non-labor regulations, which may not be justifiable if people value risk mitigation differently across contexts. Finally, use of the VSL relies on the assumption that people accurately comprehend risk; this ignores differences in subjective experiences of risk. To address the first two issues, subjects complete a revealed preferences (RP) survey about the labor market and three discrete choice experiments (DCE) eliciting stated preferences (SP) to reduce risks in other contexts. To assess the VSL’s criterion validity, RP responses are predicted from the labor SP DCE. To test the stability of preferences across contexts, comparisons of people’s wealth/risk trade-offs across the DCEs are examined with machine learning techniques. To address the third question, baseline risk preferences for each subject and their perceptions of risk in their jobs, and in each DCE are measured and compared. The data are used to develop and test a model of risk preferences in contexts significant to VSL estimation. The work advances understanding of how decision making is shaped by context and how subjectivity in experiencing risk affects the VSL over and above the objective risky features of one’s environment. Ultimately these findings have the potential to inform how governments allocate public resources to increase safety.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。每天，人们都在考虑他们愿意做出的权衡，以降低风险。统计寿命的价值(VSL)描述了总体上的权衡。VSL用于评估生活中许多领域的安全法规。准确的测量是至关重要的，但VSL的估计差别很大。本文通过研究VSL的三个变异性来源，提出了国家的健康、繁荣和福利。首先，比较了发现人们风险偏好的方法。这一点很重要，因为首选项是VSL指标的关键输入。其次，它考察了当死亡概率没有变化时，人们的风险偏好是否在不同的背景下有所不同。这一点很重要，因为根据劳动力市场数据得出的VSL估计值被用来指导无关领域的监管。最后，探讨了人们对客观风险的主观体验。这一点很重要，因为VSL的使用要求人们准确地理解风险。该项目通过测试关于风险偏好的性质和测量的普遍假设，促进了决策科学和经济学的进步。这些发现促进了关于风险感知如何指导决策的更完整的理论。这些发现还可能重塑监管机构对安全法规进行成本效益分析的方式。这对于促进弱势群体的健康、繁荣和福利尤其重要，因为弱势群体更容易受到死亡风险和健康危险的影响。统计寿命(VSL)的价值体现了人们愿意进行的权衡，以降低死亡概率。VSL在安全监管中有着广泛的应用。然而，关于VSL在多大程度上捕捉风险偏好的问题仍然存在。首先，VSL估计因测量方法的不同而有很大差异(透露的偏好与声明的偏好)，反映了人们如何评估风险的不同假设。其次，来自劳工环境的VSL估计被用于非劳工法规，如果人们对风险缓解的重视在不同环境中有所不同，这可能是不合理的。最后，VSL的使用依赖于人们准确理解风险的假设；这忽略了风险主观体验的差异。为了解决前两个问题，受试者完成了关于劳动力市场的揭示偏好(RP)调查和三个离散选择实验(DCE)，以引出声明的偏好(SP)以降低在其他情况下的风险。为了评估VSL的标准效度，我们预测了劳工SP DCE的RP反应。为了测试偏好在不同背景下的稳定性，我们使用机器学习技术对不同地区的人们的财富/风险权衡进行了比较。为了解决第三个问题，对每个受试者的基线风险偏好以及他们对其工作和每个DCE中的风险的看法进行了衡量和比较。这些数据被用来开发和测试对VSL估计有重要意义的背景下的风险偏好模型。这项工作促进了对决策是如何由环境塑造的，以及经历风险的主观性如何影响环境的客观风险特征对VSL的影响的理解。最终，这些发现有可能为政府如何分配公共资源以提高安全性提供信息。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。