摘要
为了探讨民生领域投入绩效水平及其影响因素,首先利用熵权优劣解距离法(technique for order preference by similarity to an ideal solution,TOPSIS)模型,对中国2009-2018年民生领域投入绩效进行分析,其次根据TOPSIS得分进行聚类,最后运用主成分回归模型分析民生领域投入绩效的影响因素。实证研究结果表明,中国的民生领域投入绩效水平可分为3类:山东、广东等6个省(自治区、直辖市)为高水平,吉林、陕西等23个省(自治区、直辖市)为中等水平,西藏和青海为低水平;对外开放度、财政分权、人均国内生产总值(gross domestic product,GDP)、城镇化水平及受教育程度对民生领域投入绩效水平的提高具有正向作用,而政府规模的扩大将会降低绩效水平。本研究结果可为各地政府如何有针对性地提高民生领域投入绩效水平提供参考。
In order to explore the level of investment performance and its influencing factors in people's livelihood,the technique for order preference by similarity to an ideal solution(TOPSIS)model was used to analyze the investment performance in the field of people's livelihood in China from 2009 to 2018.Then,the TOPSIS score was clustered.Finally,the principal component regression model was used to analyze the influencing factors of investment performance in the field of people's livelihood.The empirical results show that China's investment performance in people's livelihood can be divided into three levels:six provinces(autonomous regions,municipalities)including Shandong,Guangdong are at a high level,and 23 provinces(autonomous regions,municipalities)such as Jilin,Shaanxi,are at a medium level,and Tibet and Qinghai are at a low level.The degree of openness,fiscal decentralization,gross domestic product(GDP)per capita,urbanization level and education level have positive effects on the improvement of investment performance in people's livelihood,while the expansion of government scale will reduce the performance level.The research results can provide reference for local governments to improve the investment performance level in people's livelihood.
作者
敖爽
章迪平
AO Shuang;ZHANG Diping(School of Sciences,Zhejiang University of Science and Technology,Hangzhou 310023,Zhejiang,China)
出处
《浙江科技学院学报》
CAS
2021年第4期267-274,共8页
Journal of Zhejiang University of Science and Technology
基金
浙江省教育厅一般科研项目(研究生专项)(Y202043852)。
关键词
民生领域投入绩效
影响因素
熵权TOPSIS法
主成分回归
investment performance in people's livelihood
influencing factors
entropy weight TOPSIS method
principal component regression