![]() We are the first to formalize these problems of optimal insurance demand in one efficient model. Specifically, we consider the effects of probability weighting on coinsurance, deductible choice, insurance demand for low-probability, high-impact (LPHI) risks versus high-probability, low-impact (HPLI) risks, and insurance demand in the presence of nonperformance risk. We explore a number of common insurance demand problems. Our model allows for clear comparisons to the predictions obtained under the standard EU model. In this paper, we provide a comprehensive analysis of the impact of probability weighting on optimal insurance demand in a unified framework. Incorporating probability weighting into the decision model is a common approach to address these shortcomings (e.g., Barseghyan et al. From a descriptive standpoint, utility curvature alone is often too rigid to explain insurance choices in the field and may require implausibly high levels of risk aversion (e.g., Sydnor 2010). In the expected utility (EU) model, the curvature of the utility function measures the individual’s degree of risk aversion (Pratt 1964), which then drives optimal insurance demand (Mossin 1968). Researchers have aspired to find a valid descriptive model of insurance choice under risk for decades to be able to predict individual behavior and conduct policy analysis. Insurance choices are important financial decisions for households and can have a significant impact on their welfare (Bhargava et al.
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