健康不平等对终生收入不平等有多重要?

R. Hosseini, K. Zhao, K. Kopecky
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引用次数: 20

摘要

健康和收入呈正相关,这有几个原因。首先,健康状况不佳的人比健康的人更不可能工作。第二,以工作为条件,健康状况不佳的人平均工作时间较短。第三,健康状况不佳的人平均工资较低。我们使用一个客观的健康衡量标准来记录这些事实,我们为PSID受访者构建了一个脆弱指数。虚弱指数衡量的是个人可观察到的健康缺陷的比例。在以前的工作中,我们记录了以虚弱指数衡量的健康状况,与自我报告的健康状况相比,随着年龄的增长,健康状况恶化得更快,而且分散性增加得更大。它在整个生命周期中也更加持久。这些事实综合起来表明,生命周期中的健康不平等可能是终生收入不平等的一个重要驱动因素。为了评估这一说法,我们开发了一个健康和收入在生命周期中的联合动态模型。模型中的个体面临健康、收入和失业风险,并在集约边际和粗放边际上选择最优劳动力供给。通过政府经营的失业和残疾保险项目,代理人可以部分投保这些风险。我们为模型中的代理提供了一个使用PSID数据估计的脆弱性(健康)动态过程。基于对选择的考虑,利用该模型和矩估计方法对agent的生产率过程,包括脆弱性对生产率的贡献进行了估计。目标时刻是根据工资、工作时间以及虚弱和年龄的参与情况来构建的。这些分布是从使用PSID数据估计的辅助模拟模型中获得的。我们发现,健康不平等可以在很大程度上解释70岁老人一生收入的差异。造成这种影响的主要原因是,不健康的人退出劳动力市场的年龄比健康的人要小得多。我们发现健康不平等对收入不平等的影响比以前的文献更大,原因有两个。首先,我们的模型是本文献中第一个允许健康通过参与、工作时间和工资(生产率)这三个方面影响收入的模型。第二,以前的文献使用自我报告的健康状况来衡量健康,因此低估了一些人的健康随年龄增长而恶化的程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How Important Is Health Inequality for Lifetime Earnings Inequality?
Health and earnings are positively correlated and this is for several reasons. First, individuals who are in poor health are significantly less likely to work than healthy individuals. Second, conditional on working, individuals in poor health work fewer hours on average. Third, individuals in poor health earn lower wages on average. We document these facts using an objective measure of health called a frailty index which we construct for PSID respondents. The frailty index measures the fraction of observable health deficits an individual has. In previous work, we documented that health, as measured by the frailty index, deteriorates more rapidly and has a larger increase in dispersion with age than self-reported health. It is also more persistent over the life-cycle. These facts put together suggest that health inequality over the life cycle may be an important driver of lifetime earnings inequality. To assess this claim we develop a model of the joint dynamics of health and earnings over the life cycle. Individuals in the model face health, earnings and unemployment risk, and optimally choose labor supply on both the intensive and extensive margin. Agents are partially insured against these risks through government-run unemployment and disability insurance programs. We give agents in the model a dynamic process for frailty (health) that is estimated using the PSID data. Because of selection concerns, agents' productivity processes, including the contribution of frailty to productivity, are estimated using the model and a method of moments estimation. Targeted moments are constructed off distributions of wages, hours, and participation by frailty and age. These distributions are obtained from an auxiliary simulation model that is estimated using PSID data. We find that health inequality can account for a significant share of the variation in lifetime earnings among 70 year-olds. Most of this effect is due to the fact that unhealthy individuals exit the labor force at much younger ages than healthy ones. We find that health inequality has a larger impact on earnings inequality than previous literature for two reason. One, our model is the first in this literature that allows health to impact earnings through all three margins: participation, hours, and wages (productivity). Two, previous literature measured health using self-reported health status and thus understated the extent to with health deteriorates with age for some individuals.
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