Moral Hazard and Adverse Selection in Private Health Insurance

David Powell, D. Goldman
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引用次数: 4

Abstract

Moral hazard and adverse selection create inefficiencies in private health insurance markets. The authors use claims data from a large firm to study the independent roles of both moral hazard and adverse selection. Previous studies have attempted to estimate moral hazard in private health insurance by assuming that individuals respond only to the spot price, end-of-year price, average price, or a related metric. There is little economic justification for such assumptions and, in fact, economic intuition suggests that the nonlinear budget constraints generated by health insurance plans make these assumptions especially poor. They study the differential impact of the health insurance plans offered by the firm on the entire distribution of medical expenditures without parameterizing the plans by a specific metric. They use a new instrumental variable quantile estimation technique introduced in Powell [2013b] that provides the quantile treatment effects for each plan, while conditioning on a set of covariates for identification purposes. This technique allows us to map the resulting estimated medical expenditure distributions to the nonlinear budget sets generated by each plan. Their method also allows them to separate moral hazard from adverse selection and estimate their relative importance. They estimate that 77% of the additional medical spending observed in the most generous plan in their data relative to the least generous is due to adverse selection. The remainder can be attributed to moral hazard. A policy which resulted in each person enrolling in the least generous plan would cause the annual premium of that plan to rise by over $1,500.
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私人医疗保险中的道德风险与逆向选择
道德风险和逆向选择导致私人健康保险市场效率低下。作者使用一家大公司的索赔数据来研究道德风险和逆向选择的独立作用。先前的研究试图通过假设个人只对现货价格、年终价格、平均价格或相关指标做出反应来估计私人健康保险的道德风险。这种假设几乎没有经济上的理由,事实上,经济直觉表明,健康保险计划产生的非线性预算限制使这些假设特别不成立。他们研究了公司提供的健康保险计划对整个医疗支出分布的不同影响,而没有用特定的指标来参数化这些计划。他们使用了Powell [2013b]中引入的一种新的工具变量分位数估计技术,该技术为每个计划提供分位数处理效果,同时对一组协变量进行调节以进行识别。这种技术允许我们将结果估计的医疗支出分布映射到每个计划生成的非线性预算集。他们的方法还允许他们将道德风险与逆向选择分开,并估计其相对重要性。他们估计,在他们的数据中,相对于最不慷慨的计划,最慷慨计划中77%的额外医疗支出是由于逆向选择。其余的可以归结为道德风险。一份导致每个人参加最不慷慨计划的保单将导致该计划的年保费增加超过1,500美元。
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