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具有内生生存的生命周期模型中的外部性。

Externalities in a life cycle model with endogenous survival.

作者信息

Kuhn Michael, Wrzaczek Stefan, Prskawetz Alexia, Feichtinger Gustav

机构信息

Austrian Academy of Science, Vienna Institute of Demography/Wittgenstein Centre, Wohllebengasse 12-14, A-1040 Vienna, Austria.

Institute of Mathematical Methods in Economics (Research Group on Operations Research and Control Systems), Vienna University of Technology, Argentinierstrasse 8, 1040 Vienna, Austria; Austrian Academy of Science, Vienna Institute of Demography/Wittgenstein Centre, Wohllebengasse 12-14, A-1040 Vienna, Austria.

出版信息

J Math Econ. 2011 Aug;47(4-5):627-641. doi: 10.1016/j.jmateco.2011.08.002.

DOI:10.1016/j.jmateco.2011.08.002
PMID:28298810
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5341756/
Abstract

We study socially vs individually optimal life cycle allocations of consumption and health, when individual health care curbs own mortality but also has a spillover effect on other persons' survival. Such spillovers arise, for instance, when health care activity at aggregate level triggers improvements in treatment through learning-by-doing (positive externality) or a deterioration in the quality of care through congestion (negative externality). We combine an age-structured optimal control model at population level with a conventional life cycle model to derive the social and private value of life. We then examine how individual incentives deviate from social incentives and how they can be aligned by way of a transfer scheme. The age-patterns of socially and individually optimal health expenditures and the transfer rate are derived. Numerical analysis illustrates the working of our model.

摘要

我们研究了消费和健康在社会最优与个人最优的生命周期配置,此时个人医疗保健虽能降低自身死亡率,但也会对他人存活产生溢出效应。例如,当总体层面的医疗保健活动通过干中学(正外部性)引发治疗改善,或因拥堵导致护理质量下降(负外部性)时,就会出现这种溢出效应。我们将人口层面的年龄结构最优控制模型与传统生命周期模型相结合,以推导生命的社会价值和私人价值。然后,我们研究个人激励如何偏离社会激励,以及如何通过转移方案使其保持一致。我们得出了社会最优和个人最优健康支出的年龄模式以及转移率。数值分析说明了我们模型的运行情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/d74f5a86f7ab/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/3a0c2d5a1cbc/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/c05761900fdd/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/acf2b3820109/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/ede9e338a8db/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/f83ebe35a317/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/5a4b7cafdac4/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/4cd14e795837/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/d74f5a86f7ab/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/3a0c2d5a1cbc/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/c05761900fdd/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/acf2b3820109/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/ede9e338a8db/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/f83ebe35a317/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/5a4b7cafdac4/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/4cd14e795837/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0671/5341756/d74f5a86f7ab/gr8.jpg

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