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通过事前政策设计降低市场化私人养老服务供应商的质量风险:来自委托代理理论分析的经验教训。

Reducing the quality risk of elderly care services in government procurement from market-oriented private providers through ex ante policy design: lessons from the principal-agent theory analysis.

机构信息

Zhou Enlai School of Government, Nankai University, No.38 Tongyan Road, Jinnan Campus, Tianjin, 300350, China.

出版信息

BMC Health Serv Res. 2020 Dec 10;20(1):1135. doi: 10.1186/s12913-020-05994-w.

DOI:10.1186/s12913-020-05994-w
PMID:33302941
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7727223/
Abstract

BACKGROUND

Government procurement of elderly care services from market-oriented private providers has become an important way to respond to the growing demands of elderly care. However, the government cannot accurately identify the actual quality efforts of these providers, and the government pursues social benefits while the providers pursue economic interests. The existence of asymmetric information and goal divergence increases the quality risk of services. From the perspective of maximizing the government's net benefits, this study aimed to analyze how to reduce the quality risk through ex ante policy design.

METHODS

On the basis of the principal-agent theory, this study defined the asymmetric information of market-oriented private providers' efforts on quality as a random variable, and constructed the theoretical model in the case of asymmetric information to compare with the one in the reference case of complete information, in both of which the government is the principal and market-oriented private providers are the agents. And the models also introduced several parameters to describe key factors that affect the contract results, including the physical health of the elderly, the spillover benefits to the government and market-oriented private providers, and the market risks.

RESULTS

The optimal results of the models in the two cases were obtained respectively, and the validity of the theoretical models was verified in a numerical example. Taking the case of complete information as the basic frame of reference, the difference of the optimal results in both cases showed the extent of negative impacts of asymmetric information, and highlighted the role of ex ante policy design in minimizing asymmetric information and reducing its negative impacts. Some ex ante policies that can improve the supervision of market-oriented private providers and their quality efforts, as well as have positive effects on key factors, were also recommended.

CONCLUSIONS

The government should attach importance to ex ante policy design to reduce the quality risk of elderly care services supplied by market-oriented private providers in government procurement. Our study provides main framework and critical directions for ex ante policy design, which is conducive to the realization of real and sustained quality improvement.

摘要

背景

政府从市场化的私人供应商处采购养老服务,已成为满足不断增长的养老需求的重要途径。然而,政府无法准确识别这些供应商的实际质量努力,政府追求社会效益,而供应商则追求经济利益。信息不对称和目标分歧的存在增加了服务的质量风险。从政府净收益最大化的角度出发,本研究旨在分析如何通过事前政策设计来降低质量风险。

方法

本研究基于委托代理理论,将市场化私人供应商在质量方面的努力的信息不对称定义为一个随机变量,并构建了在信息不对称情况下的理论模型,与完全信息情况下的参考模型进行比较,其中政府是委托人,市场化私人供应商是代理人。模型还引入了几个参数来描述影响合同结果的关键因素,包括老年人的身体健康、政府和市场化私人供应商的溢出效益以及市场风险。

结果

分别得出了两种情况下模型的最优结果,并通过数值示例验证了理论模型的有效性。以完全信息情况为基本参照框架,比较两种情况下的最优结果差异,揭示了信息不对称的负面影响程度,并强调了事前政策设计在最小化信息不对称及其负面影响方面的作用。还推荐了一些事前政策,这些政策可以加强对市场化私人供应商的监督及其质量努力,并对关键因素产生积极影响。

结论

政府应重视事前政策设计,以降低政府采购中市场化私人供应商提供的养老服务的质量风险。本研究为事前政策设计提供了主要框架和关键方向,有利于实现真正和持续的质量改进。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/7727223/2f578f062a3a/12913_2020_5994_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/7727223/2112f58f65bc/12913_2020_5994_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/7727223/ffa3dd310c85/12913_2020_5994_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/7727223/473bec0cfab1/12913_2020_5994_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/7727223/2f578f062a3a/12913_2020_5994_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/7727223/2112f58f65bc/12913_2020_5994_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/7727223/ffa3dd310c85/12913_2020_5994_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/7727223/473bec0cfab1/12913_2020_5994_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/7727223/2f578f062a3a/12913_2020_5994_Fig4_HTML.jpg

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