This is a study of decision problems under two-dimensional risk. We use an existing index of absolute correlation aversion to conveniently classify bivariate preferences, with respect to attitudes toward this risk. This classification seems to be more important than whether decision makers are correlation-averse or correlation-seeking for the study of insurance demand when a loss has a multidimensional impact. On this note, we also re-examine Mossin’s theorem under bivariate preferences, where full insurance is preferred with a fair premium, while less than full coverage is preferred with a proportional premium loading. Furthermore, based on the comparative statics of this two-dimensional insurance model for changes in correlation aversion, we derive testable implications about the classification of bivariate utility functions. For the particular case when the two-dimensional risk can be interpreted as risk on income and health, we identify the form of separable utility functions depending on health status and income that is consistent with household disability insurance decisions.

中文翻译：

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论相关厌恶与保险需求

这是二维风险下决策问题的研究。我们使用现有的绝对相关厌恶指数来方便地根据对这种风险的态度对二元偏好进行分类。当损失具有多维影响时，对于保险需求的研究，这种分类似乎比决策者是厌恶相关性还是寻求相关性更重要。在这一点上，我们还重新审视了双变量偏好下的莫辛定理，其中全额保险优先于公平保费，而低于全额保险则优先于比例保费负担。此外，基于这种二维保险模型对相关厌恶变化的比较静态，我们得出了关于二元效用函数分类的可检验的含义。对于二维风险可以解释为收入和健康风险的特殊情况，我们根据健康状况和收入确定可分离效用函数的形式，这与家庭残疾保险决策一致。