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作 者:任镇鹏
机构地区:[1]上海工程技术大学管理学院,上海
出 处:《运筹与模糊学》2023年第5期5073-5084,共12页Operations Research and Fuzziology
摘 要:本文首先利用数据包络分析方法,对我国31个省市的工伤保险运营效率进行评估。其次运用K-means聚类方法对效率评估结果进行聚类分析。最后通过Malmquist指数对工伤保险运营效率的时空变化进行分析。研究发现,我国工伤保险运营的综合效率均值为0.824,纯技术效率均值为0.863,规模报酬效率均值为0.956。总体来看,我国工伤保险运营效率处于较高水平,但仍有提升空间。在未剔除环境因素和随机变量的情况下,有8个省市达到了效率最优。在聚类分析中,研究发现纯技术效率为多数省市未达到效率最优的短板。为进一步提高我国工伤保险的运营效率,提出以下对策建议:工伤保险管理机构应着重提高自身的技术效率,优化流程,简化申报和理赔流程、优化数据收集和处理过程,减少冗余和重复操作,提高处理效率。This study first utilizes the Data Envelopment Analysis (DEA) method to assess the operational efficiency of occupational injury insurance across 31 provinces and cities in China. Subsequently, the K-means clustering method is employed to perform cluster analysis on the efficiency evaluation results. Finally, the Malmquist index is used to analyze the spatiotemporal changes in the operational efficiency of occupational injury insurance. The research findings indicate that the average comprehensive efficiency of occupational injury insurance operation in China is 0.824, the average pure technical efficiency is 0.863, and the average scale efficiency is 0.956. Overall, the operational efficiency of occupational injury insurance in China is at a relatively high level but still has room for improvement. Without eliminating environmental factors and random variables, eight provinces and cities have achieved optimal efficiency. In the cluster analysis, the research highlights that the majority of provinces and cities fall short in terms of pure technical efficiency, which hasn’t reached the optimal level. To further enhance the operational efficiency of China’s occupational injury insurance, the following recommendations are proposed: occupational injury insurance administrative institutions should focus on improving their own technical efficiency, optimizing processes, simplifying application and claims procedures, enhancing data collection and processing processes, reducing redundancy and repetitive operations, and improving processing efficiency.
关 键 词:工伤保险 效率评价 数据包络方法 聚类分析 MALMQUIST指数
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