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金融研究  2023, Vol. 521 Issue (11): 97-114    
  本期目录 | 过刊浏览 | 高级检索 |
中国财富机会不平等的测度与源泉识别——兼论共同富裕的路径选择
孙三百, 张青萍, 李冉
中国人民大学应用经济学院,北京 100872;
北京大学经济学院,北京 100871;
北京大学全球健康发展研究院/北京大学国家发展研究院,北京 100871
Measurement and Source Identification of Opportunity Inequality of Wealth in China: Path Common Prosperity
SUN Sanbai, ZHANG Qingping, LI Ran
School of Applied Economics, Renmin University of China;
School of Economics, Peking University;
Institute for Global Health and Development/National School of DeveLopment, Peking University
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摘要 本文基于中国家庭金融调查(CHFS)数据,采用平均对数偏差刻画财富不平等,使用事前参数法和机器学习方法构建“反事实”财富,测算机会不平等在财富不平等中的占比,并通过Shapley值法对财富机会不平等的成因进行分解。研究发现:(1)2017年,机会不平等对财富不平等的影响远大于其对收入不平等的影响;(2)“50后”和“70后”的组内财富机会不平等程度明显高于其他出生组;(3)房价增长率、户籍类型、金融可得性、青少年时期所在地区类型和省份经济发展水平,以及父辈受教育水平,对财富机会不平等的贡献率位居前列,合计超过85%;(4)个人特征、家庭背景、地域因素和宏观因素这4类环境因素,对各出生组的影响程度存在差异;(5)教育间接作用渠道对各出生组财富机会不平等的贡献率均超过10%,就业间接作用渠道对“50后”财富机会不平等的贡献率在各出生组中最高,而投资间接作用渠道对各出生组财富机会不平等的贡献率相对较小。根据本文研究结论,优化住房调控政策、缩小城乡与区域差距、完善地区金融体系和提高代际流动性,可降低财富机会不平等,是实现共同富裕的重要路径。
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孙三百
张青萍
李冉
关键词:  财富不平等  财富机会不平等  共同富裕  事前参数法  机器学习    
Summary:  Common prosperity refers to a state where all people achieve a decent standard of living through hard work and mutual assistance, signifying the elimination of extreme wealth inequality and poverty. Achieving common prosperity for all citizens is the essential requirement for promoting Chinese path to modernization. To achieve common prosperity, it is necessary to identify the sources of wealth inequality, so as to find an effective way to promote common prosperity. According to Roemer's “circumstance-effort” framework, personal wealth is determined by both environmental and effort-related factors. Inequality arising from environmental factors, or inequality of opportunity, should be eradicated through public policy, whereas inequality solely due to effort is considered reasonable and can be allowed within certain limits. Therefore, in promoting common prosperity, it is essential to permit a reasonable degree of wealth inequality while focusing on reducing inequality of opportunity in wealth, which is caused by environmental factors.
Based on the 2017 China Household Financial Survey (CHFS) data, this study employs the Mean Logarithmic Deviation to measure inequality levels and uses ex-ante parameter method and the conditional inference tree method (machine learning) to construct “counterfactual wealth.” We identify and calculate the proportion of inequality of opportunity within wealth inequality and decompose the sources and indirect channels of inequality of opportunity in wealth using the Shapley value method. The findings are as follows: ① In 2017, the inequality of opportunity in wealth far exceeds that in income significantly, with inequality of opportunity accounting for approximately 57% to 72% of the factors contributing to wealth inequality and about 32% for income inequality. ② The inequality of opportunity in wealth exhibits marked variations across birth cohorts, with those born in the 1950s and 1970s experiencing notably higher levels of inequality of opportunity in wealth than other cohorts. ③ Among the sources of inequality of opportunity, factors related to housing and finance contribute the most. Respondents' initial household registration type, parental education level, provincial economic development during their adolescence, the region in adolescence, housing price, and financial access rank among the foremost contributors, collectively accounting for over 85% of inequality of opportunity in wealth. ④ The impact of environmental factors—comprising individual characteristics, family backgrounds, regional elements, and macroeconomic elements—varies across birth cohorts. The contribution rate of family background to inequality of opportunity in wealth among the “1950s” to “1980s-1990s” cohorts progressively increases with each birth generation, while the contribution rate of regional factors generally shows a declining trend. ⑤ In terms of indirect channels, the education channel contributes over 10% to inequality of opportunity in wealth across all birth cohorts, the employment channel has the highest contribution to inequality of opportunity in wealth for those born in the 1950s, and the investment channel has a limited impact across all cohorts.
In light of our conclusions, it is evident that environmental factors significantly influence wealth accumulation. Therefore, addressing inequality of opportunity is a critical step in promoting common prosperity. The implementation of public policies aimed at optimizing housing regulation, narrowing regional gap, improving the regional financial system, and enhancing intergenerational mobility to progressively diminish the inequality of opportunity in wealth is a vital pathway to achieve common prosperity.
Our paper makes the following three contributions. First, it extends the study of inequality of opportunity from income disparity to wealth disparity. Most of the literature focuses on inequality of opportunity in income, analyzing the causes of income inequality. Considering the significant differences between wealth and income, this paper identifies and calculates the proportion of inequality of opportunity within wealth inequality, thus enriching and expanding the literature on the inequality of opportunity in China. Second, this paper identifies the sources and indirect channels of inequality of opportunity in wealth using the Shapley value method. Given the significant differences between income and wealth, this paper introduces factors such as economic support from parents for housing purchases, property inheritance, and housing-related environmental factors (including housing demolition, housing price growth rates, and housing system reforms) to analyze the important roles of housing wealth and inherited wealth in inequality of opportunity in wealth. Furthermore, it incorporates factors such as financial investment and property acquisition to examine the specific indirect channels through which inequality of opportunity in wealth is manifests. Third, considering the evident differences in the impact of environmental factors on each birth cohort, and the different life stages of these cohorts, this paper categorizes individuals according to their birthyear and explores the intergenerational trends of inequality of opportunity in wealth.
Keywords:  Wealth Inequality    Inequality of Opportunity in wealth    Common Prosperity    Ex-ante Parameter Method    Machine Learning
JEL分类号:  D31   D33   D63  
基金资助: * 本文感谢国家自然科学基金项目(72073137、72373155)的资助。感谢匿名审稿人的宝贵意见,文责自负。
通讯作者:  张青萍,博士研究生,北京大学经济学院,E-mail:m18059503898@163.com.   
作者简介:  孙三百,经济学博士,副教授,中国人民大学应用经济学院,E-mail:sunsanbai@ruc.edu.cn.
李冉,博士研究生,北京大学全球健康发展研究院,北京大学国家发展研究院,E-mail:13946385735@163.com.
引用本文:    
孙三百, 张青萍, 李冉. 中国财富机会不平等的测度与源泉识别——兼论共同富裕的路径选择[J]. 金融研究, 2023, 521(11): 97-114.
SUN Sanbai, ZHANG Qingping, LI Ran. Measurement and Source Identification of Opportunity Inequality of Wealth in China: Path Common Prosperity. Journal of Financial Research, 2023, 521(11): 97-114.
链接本文:  
http://www.jryj.org.cn/CN/  或          http://www.jryj.org.cn/CN/Y2023/V521/I11/97
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