Analysis of Business Environment and Medical Insurance Coverage Rates in the Destination of China’s Migrant Population: Based on Geographically and Temporally Weighted Regression Model for Panel Data
Analysis of Business Environment and Medical Insurance Coverage Rates in the Destination of China’s Migrant Population: Based on Geographically and Temporally Weighted Regression Model for Panel Data
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中国流动人口流入地营商环境与医疗保险覆盖率分析——基于面板数据的地域和时间加权回归模型
DOI：
10.1155/2022/6540663
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发表时间：
2022-11
影响因子：
--
通讯作者：
章雨杰
中科院分区：
文献类型：
--
作者：
刘杨;陈晓宇;章雨杰
健康风险是人口空间流动过程中的一个重要问题，也是我国城市化进程中的一个重要问题。本研究利用2011 - 2018年中国流动人口动态监测数据和31个省级商业environment.data），系统考察了流动人口在目的地地区医疗保险参保的空间分布和演变特征，并结合面板数据的地理和时间加权回归模型（PGTWR）分析了区域商业环境对流入地地区医疗保险覆盖率的影响。研究结果表明：（1）我国农村保险覆盖率的空间分布格局是东部高西部低;参保率高的地区主要分布在全国三大经济圈和山东、新疆等省份。第二，覆盖率在空间上存在显著autocorrelation.in，表明参与率高的城市在地理空间上倾向于形成集聚区，参与率低的城市在地理空间上也是如此.热门地区覆盖率分散，冷门地区集中。第三，对Panel Data的地理和时间加权回归模型的估计结果表明，经营环境中的宏观经济指标和基础设施指标对保险费率的影响较大，而政策环境指标的影响相对较弱。但是，the.business环境的整体改善能够显著提高流动人口在流入地参加医疗保险的概率。
Health risk is an important issue in the process of population spatial mobility, and it is also an important issue in the process of.urbanization in China. Using the dynamic monitoring data of China’s migrant population and 31 provincial business environment.data from 2011 to 2018, this study systematically investigated the spatial distribution and evolution characteristics of the.migrant population’s participation in medical insurance in the destination areas and combined it with the Geographically and.Temporally Weighted Regression Model for Panel Data (PGTWR) to analyze the impact of the regional business environment on.the medical insurance coverage rate of the in:ow area. 'e results are as follows: ;rst, the spatial pattern of the insurance coverage.rate is high in Eastern China and low in Western China. 'e areas with high insurance coverage rates are mainly distributed in the.three major economic circles of China and provinces such as Shandong and Xinjiang. Second, there is significant spatial autocorrelation.in the coverage rate, which shows that cities with high participation rates tend to form agglomeration areas in.geographical space, and so do cities with low participation rates. 'e coverage rate of the popular areas is scattered, while the.unpopular areas are concentrated. 'ird, the estimation results of the Geographically and Temporally Weighted Regression Model.for Panel Data show that the macroeconomic and infrastructure indicators in the business environment have a greater impact on.the insurance rate, while the impact of policy environment indicators is relatively weak. However, the overall improvement of the.business environment can signi;cantly improve the probability of the migrant population participating in medical insurance in.the inflow area.
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DOI：
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发表时间：
2008-02
期刊：
Zhonghua xin xue guan bing za zhi
影响因子：
--
作者：
Bing Li;Xin Lu
通讯作者：
Bing Li;Xin Lu
影响因子：
--
作者：
B. Kable
通讯作者：
B. Kable
DOI：
10.1017/cbo9781139207249.009
发表时间：
2012
期刊：
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影响因子：
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作者：
W. Marsden
通讯作者：
W. Marsden
DOI：
10.1016/j.jjie.2012.05.004
发表时间：
2012-05
期刊：
IO: Regulation
影响因子：
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作者：
J. Haidar
通讯作者：
J. Haidar
DOI：
10.3138/9781442678286
发表时间：
2005-11
期刊：
--
影响因子：
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作者：
G. Nelson;K. Hayward;S. Pancer
通讯作者：
G. Nelson;K. Hayward;S. Pancer