摘要
消除多维机会不平等是推进共同富裕的重要抓手。本文基于不平等厌恶社会福利函数和定序数据改进多维机会不平等的事前非参数测度与分解方法,然后利用2010—2020年中国家庭追踪调查数据库(CFPS),从收入、教育和健康三个维度出发实证测算我国多维机会不平等,并解析其中的演进动态与个体和区域异质性特征。结果表明:多维机会不平等在不平等中的占比持续稳定在60%左右,收入与教育维度的机会不平等在其中发挥主导作用,维度关联性会放大多维机会不平等。女性、“50后”和最高财产组群体面临的机会不平等更高,女性、“50后”和最低财产组群体的维度关联性的放大作用较强。经济发展与教育、医疗资源配置水平更高区域往往面临更高水平的机会不平等,经济发展与教育、医疗资源配置水平更低区域的维度关联性的放大作用较强。
Addressing opportunity inequality is a crucial lever for advancing shared prosperity.Combined with its data types,this paper proposes an ex ante non-parametric multidimensional inequality of opportunity improving method based on the social welfare function of inequality aversion and ordinal data according to China Family Tracking Survey Database(CFPS).Considering the dimensions of income,education,and health,it calculates the multidimensional opportunity inequality(MIOP)in China from 2010 to 2020,and explores the dynamic evolution mechanism and individual and regional heterogeneity.The findings indicate that:Multidimensional inequality of opportunity remains relatively stable at around 60%,with income and education dimensions playing a dominant role in the overall inequality.The interrelationships amplify multidimensional inequality of opportunity.Women,post-50s,and the highest wealth group face higher levels of opportunity inequality,while there is a stronger amplifying effect of the dimension correlations for women,post-50s,and the lowest wealth group.Regional disparities manifest as higher levels of inequality of opportunity in regions with greater economic development,education,and healthcare resource allocation.In regions with lower levels of economic development,education,and healthcare resource allocation,the amplifying effect of the dimension association is stronger,resulting in higher concentrations of inequality of opportunities.
作者
吕光明
杜子青
Lv Guangming;Du Ziqing
出处
《财政研究》
CSSCI
北大核心
2023年第12期16-33,共18页
Public Finance Research
基金
国家社会科学基金重大项目“新时代共同富裕实现程度的统计测度及实现路径研究”(22&ZD155)。
关键词
多维机会不平等
事前非参数法
定序数据
异质性特征
Multidimensional Inequality of Opportunity
Ex ante Non-Parametric Method
Ordinal Data
Heterogeneity
