重庆工商大学学报（社会科学版）2024，Vol.41Issue(4)：69-88,20.DOI:10.3969/j.issn.1672-0598.2024.04.006
多维相对贫困的精准测度与分解
Precision Measurement and Decomposition of Multidimensional Relative Poverty
摘要
Abstract
Alleviating relative poverty is a fundamental prerequisite for achieving common prosperity.This study constructs a multidimensional relative poverty index system,incorporating factors such as mental health and environmental quality,and adopts the A-F poverty framework system.Utilizing data from the China Family Panel Studies(CFPS)and employing the neural network random weight(NNRW)method from machine learn-ing,it precisely measures and decomposes the breadth,depth,and intensity levels of multidimensional relative poverty among urban-rural and regional residents in China.The research finds that regardless of urban-rural or regional disparities,as the dimensions of relative poverty increase,the breadth,depth,and intensity indices of multidimensional relative poverty all decrease,indicating a gradual reduction in the number of residents experi-encing extreme multidimensional relative poverty.Meanwhile,residents'multidimensional relative poverty indi-ces exhibit a"high in the west and low in the east"trend,with the overall level of multidimensional relative poverty among residents roughly equivalent to that of the central regions.Rural residents'multidimensional rel-ative poverty levels are significantly higher than urban residents',and rural residents'multidimensional relative poverty levels are similar to those of the western regions;while urban residents'multidimensional relative pover-ty levels are roughly equivalent to those of the eastern regions.Additionally,the decomposition results of the multidimensional relative poverty index show that factors such as financial products,living environment,durable goods,and per capita net income are the main reasons for relative poverty among urban-rural and regional resi-dents,but the contribution rates of poverty determinants to the breadth,depth,and intensity differ.The re-search conclusions provide theoretical references and policy bases for formulating long-term mechanisms to ad-dress multidimensional relative poverty.关键词
多维相对贫困/相对剥夺/随机权神经网络/环境质量Key words
multidimensional relative poverty/relative deprivation/neural network random weight/envi-ronmental quality分类
管理科学引用本文复制引用
车四方..多维相对贫困的精准测度与分解[J].重庆工商大学学报（社会科学版）,2024,41(4):69-88,20.基金项目
国家社会科学基金青年项目(21CTJ007)"基于机器学习的多维相对贫困精准识别、测度与预警机制研究" （21CTJ007）
重庆市教委人文社会科学项目(21SKGH114)"重庆市多维相对贫困的评价、测度与治理研究" （21SKGH114）
重庆工商大学高层次人才科研启动项目(19550332)"社会保险对农村多维贫困的影响机理研究" （19550332）
重庆市自然科学基金项目(cstc2021jcyj-bshX0123)"重庆市经济高质量发展水平测度与动态监测机制研究" （cstc2021jcyj-bshX0123）
