摘要
实现全体人民共同富裕是中国式现代化的本质要求。文章基于共同富裕的内涵与特征,构建包含富裕程度和共享程度两个维度共24项指标的共同富裕评价指标体系,对中国内地除西藏外30个省域2013—2020年共同富裕水平进行测度,并依次对全国整体、东中西三大区域以及各省域共同富裕水平展开分析,利用核密度估计和空间自相关分析探究共同富裕水平的分布动态及空间关联特征。研究发现:中国共同富裕整体水平尚低但提升明显;三大区域共同富裕水平均呈稳步增长态势,东部地区明显高于中西部地区;各省域之间共同富裕水平差异较大,优势和短板各异,且存在显著的空间集聚特征。本研究有助于识别各省域共同富裕实现过程中的优劣势,为各级政府推进共同富裕精准施策提供参考。
Achieving common prosperity for all is an essential requirement of the Chinese modernization.On the basis of the connotation and features of common prosperity,this paper constructs a common prosperity index system including two dimensions,namely prosperity and sharing,and then measures the common prosperity level of 30 provinces in China's Mainland except Tibet from 2013 to 2020.Furthermore,we analyze the common prosperity level of the nation as a whole,the three major regions of the east,the middle and the west,and provinces.Finally,we use the kernel density estimation method and spatial autocorrelation analysis to explore the distribution dynamic and spatial correlation characteristics of common prosperity level.The results show that:the overall level of common prosperity in China is still low,but the upward trend is obvious.The level of common prosperity in the three regions has shown a steady increase,but the eastern region is significantly higher than the middle and western regions.The level of common prosperity among provinces varies greatly,with different advantages and disadvantages,and there are significant spatial agglomeration characteristics.This research can help identify the advantages and disadvantages in the development process of provincial common prosperity,and provide a reference for governments to promote common prosperity precisely.
作者
陈钰芬
胡思慧
CHEN Yufen;HU Sihui(School of Statistics and Mathematics,Zhejiang Gongshang University,Hangzhou 310018,China)
出处
《商业经济与管理》
北大核心
2023年第6期89-104,共16页
Journal of Business Economics
基金
国家社会科学基金重大项目“高质量发展视域下创新要素配置的统计测度与评价研究”(19ZDA122)
浙江省重点建设高校优势特色学科(浙江工商大学统计学)和“统计数据工程技术与应用协同创新中心”资助。
关键词
共同富裕
指标体系
统计测度
核密度估计
空间自相关性
common prosperity
index system
statistical measurement
kernel density estimation
spatial autocorrelation