Please wait a minute...
金融研究  2023, Vol. 511 Issue (1): 1-20    
  本期目录 | 过刊浏览 | 高级检索 |
信贷增长如何影响中国的收入和财富不平等
邹静娴, 张斌, 魏薇, 董丰
中国人民大学国家发展与战略研究院,北京 100872;
中国社会科学院世界经济与政治研究所,北京 100732;
北京大学国家发展研究院,北京 100871;
清华大学经济管理学院,北京 100084
How Credit Growth Affects Income Inequality in China
ZOU Jingxian, ZHANG Bin, WEI Wei, DONG Feng
National Academy of Development and Strategy, Renmin University of China;
Institute of World Economics and Politics, Chinese Academy of Social Sciences;
National School of Development, Peking University;
School of Economics and Management, Tsinghua University
下载:  PDF (1381KB) 
输出:  BibTeX | EndNote (RIS)      
摘要 本文基于中国家庭追踪调查(CFPS)数据考察了信贷增长对中国家庭收入和财富不平等的影响。整体而言,信贷增长可以缩小家庭收入不平等,主要作用机制是信贷增长通过提高中低收入群体的劳动收入和单位时薪以缩小劳动收入不平等。同时,文献中所发现的信贷增长可能恶化收入不平等的机制——扩大家庭间非货币金融资产差距,在我国表现并不明显,原因在于中国家庭的非货币金融资产比例较低,这一点对于高收入家庭也不例外,且大部分家庭难以从金融资产交易中获利。信贷增长带来了各个收入组的房屋价值上涨,但高收入家庭房产价值上涨的幅度高于中低收入家庭,因此房价上涨扩大了不同收入家庭所持有的房产价值差异。考虑到家庭调查数据往往对高收入家庭的收入和财产覆盖不完整,上述结论可能低估了信贷增长对极少数高收入家庭收入和资产的影响。本文有助于更好地理解我国信贷增长对收入分配问题的影响,为相关政策制定提供了一定启示。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
邹静娴
张斌
魏薇
董丰
关键词:  信贷  收入不平等  财富不平等    
Summary:  A common criticism of the easy monetary policy widely used in developed countries is that it increases asset prices, which asymmetrically benefits wealthy households and those working in the financial sector who own a large number of assets. Thus, it widens inequalities in income and wealth distribution. In China, there is also a widespread concern that monetary easing would worsen income and wealth distribution. Therefore, investigating these issues is important.
Income sources and asset composition vary greatly among income groups, and the effects of easy monetary policy differ among income sources and assets. These differences can significantly affect income and wealth distribution. According to the literature, easy monetary policy affects income and wealth distribution through various channels. On the one hand, easy monetary policy may worsen income and wealth distribution through two main channels. (1) The income composition channel. Middle-and low-income households mainly rely on labor income, while high-income households rely on capital income. Therefore, inequality in income and wealth distribution widens if the increase in capital income resulting from easy monetary policy is greater than the increase in labor income. (2) The financial segmentation channel. By stimulating financial activities, easy monetary policy would disproportionately benefit wealthy households, as they are generally more engaged in the financial market, and thus widen inequalities in income and wealth distribution. On the other hand, easy monetary policy may narrow the income and wealth inequality through three channels. (1) The saving redistribution channel. Decreasing interest rates or increasing inflation benefits debtors while hurting creditors, and creditors are generally wealthier than debtors, so easy monetary policy can decrease income inequality and narrow the wealth gap. (2) The earning heterogeneity channel. Studies show that the groups at the bottom of the income distribution are the most vulnerable to the effects of economic fluctuations. Specifically, during an economic downturn, those low-income groups are generally the first to lose their jobs or experience a decrease in wages, whereas those in high-income groups are less affected by economic fluctuations. Therefore, by stimulating the economy, easy monetary policy helps those low-income groups retain their jobs and increase their wages, thereby narrowing the income and wealth gaps. (3) The real estate channel. Studies find that in the United States, the inequality in wealth distribution is much milder than that in income distribution, mainly because of the appreciation of real estate held by the middle class, which prevents the continuous concentration of wealth among those in the wealthiest group.
As easy monetary policy may affect income and wealth distribution through various channels and its net effect is ambiguous, the issue calls for empirical exploration. Empirical studies of the relationship between monetary policy and income/wealth distribution in China are under supply because of data unavailability, especially a lack of micro-level, long-term household data. This paper contributes to the literature in the following ways: (1) It is the first study in China to use micro-level household data (China Household Tracking Survey, CFPS) to explore the impact of monetary policy on income and wealth distribution. (2) It also separately investigates the effects of monetary policy on various components of income and wealth, including labor income, financial asset income, net debt, and real estate values. (3) Different from the popular practice of focusing on overall indicators such as the Gini or Theil coefficients, we reveal the complete picture of inequality by specifically describing its effects on different income groups.
This paper's main findings are as follows: (1) Generally, credit growth reduces household income inequality. (2) Credit growth can significantly increase labor income and the wages of low-and middle-income groups, effectively reducing labor income inequality. (3) The impact of credit growth on financial asset inequality is insignificant because Chinese households have quite low proportions of non-monetary financial assets, even for high-income households. Moreover, most households find it difficult to profit from financial transactions. (4) Credit growth increases house prices for all income groups, but the increase is greater for high-income groups relative to low-income groups. Therefore, increases in house prices widen the wealth gaps between income groups. (5) Regarding the mechanisms through which monetary policy may affect income distribution, the two mechanisms through which easy monetary policy may worsen income distribution (the income composition and financial segmentation channels) are not significant because most families do not profit from financial transactions, even those in high-income groups. Meanwhile, among the three mechanisms through which easy monetary policy may improve income distribution, the saving redistribution and earning heterogeneity effects are supported by the data, whereas real estate has the opposite effect in China. Finally, as the most top-income households are not included in our sample, our conclusions may underestimate the effects of credit growth on income and wealth for those high-income groups.
Keywords:  Credit    Income Inequality    Wealth Inequality
JEL分类号:  D31   E51   E52  
基金资助: * 本文感谢国家社科基金重大项目(21ZDA008)、国家自然科学基金项目(72122011)、教育部人文社会科学研究规划基金项目(22YJA790096)、教育部哲学社会科学研究专项项目的资助。感谢匿名审稿人的宝贵意见,文责自负。
通讯作者:  魏 薇,博士研究生,北京大学国家发展研究院,E-mail:wwei2019@nsd.pku.edu.cn.   
作者简介:  邹静娴,经济学博士,副教授,中国人民大学国家发展与战略研究院,E-mail:zou_jingxian@163.com.
张 斌,经济学博士,研究员,中国社会科学院世界经济与政治研究所,E-mail:zbiwep@126.com.
董 丰,经济学博士,副教授,清华大学经济管理学院,E-mail:dongfeng@sem.tsinghua.edu.cn.
引用本文:    
邹静娴, 张斌, 魏薇, 董丰. 信贷增长如何影响中国的收入和财富不平等[J]. 金融研究, 2023, 511(1): 1-20.
ZOU Jingxian, ZHANG Bin, WEI Wei, DONG Feng. How Credit Growth Affects Income Inequality in China. Journal of Financial Research, 2023, 511(1): 1-20.
链接本文:  
http://www.jryj.org.cn/CN/  或          http://www.jryj.org.cn/CN/Y2023/V511/I1/1
[1] 陈军、陈彦斌、陈伟泽和邱哲圣,2013,《中国通货膨胀对财产不平等的影响》,《经济研究》第8期,第4~15页。
[2] 丁攀和李素芳,2014,《中国货币政策对城乡居民收入的有效性研究——FAVAR 模型的全视角分析》,《经济科学》第4期,第39~49页。
[3] 董兵兵,徐慧伦和谭小芬,2021,《货币政策能够兼顾稳增长与防风险吗?——基于动态随机一般均衡模型的分析》,《金融研究》第4期,第19~37页。
[4] 江春、肖祖沔和向丽锦,2018,《货币政策、收入分配及经济福利——基于 DSGE 模型的贝叶斯估计》,《财贸经济》第3期,第17~34页。
[5] 李宏瑾和苏乃芳,2020,《数量规则还是利率规则?——我国转型时期量价混合型货币规则的理论基础》,《金融研究》第10期,第38~54页。
[6] 李实,宋锦和刘小川,2014,《中国城镇职工性别工资差距的演变》,《管理世界》第1期,第53~65页。
[7] 刘帅光,2016,《货币扩张、部门融资依赖差异与劳动收入份额下降 ——基于 DSGE 模型的分析》,《中央财经大学学报》第6期,第84~92页。
[8] 卢晶亮,2017,《资本积累与技能工资差距——来自中国的经验证据》,《经济学(季刊)》第1期,第577~598页。
[9] 罗楚亮,滕阳川和李利英,2019,《行业结构、性别歧视与性别工资差距》,《管理世界》第8期,第58~68页。
[10] 聂海峰和岳希明,2016,《行业垄断对收入不平等影响程度的估计》,《中国工业经济》第1期,第5~20页。
[11] 魏下海,曹晖和吴春秀,2018,《生产线升级与企业内性别工资差距的收敛》,《经济研究》第1期,第156~169页。
[12] 杨飞,2017,《市场化、技能偏向性技术进步与技能溢价》,《世界经济》第1期,第78~100页。
[13] 姚健和臧旭恒,2022,《中国家庭收入不平等与消费不平等——基于收入冲击和消费保险视角的研究》,《经济学(季刊)》第4期,第1279~1298页。
[14] 张传勇,2018,《住房差异是否影响了家庭收入不平等?机制、假说与检验》,《南开经济研究》第1期,第67~85页。
[15] 张大永和曹红,2012,《家庭财富与消费:基于微观调查数据的分析》,《经济研究》增1期,第53~65页。
[16] 中国人民银行海口中心支行课题组,2014,《货币政策冲击的收入分配效应研究》,《南方金融》第12期,第17~24页。
[17] Bernanke, B. S., 1993, “Credit in the Macroeconomy”, Quarterly Review-Federal Reserve Bank of New York, 18, pp.50~50.
[18] Carpenter, S. B., and Rodgers, W. M., 2005, “The Disparate Labor Market Impacts of Monetary Policy”, Labor History, 46(1), pp.57~77.
[19] Coibion, O, Gorodnichenko Y, Kueng L et al., 2017, “Innocent Bystanders? Monetary Policy and Inequality”, Journal of Monetary Economics, 88, pp.70~89.
[20] Doepke, M and Schneider M., 2006, “Inflation and the Redistribution of Nominal Wealth”, Journal of Political Economy, 114(6), pp.1069~1097.
[21] Firpo, S., Fortin, N. M., and Lemieux, T., 2009, “Unconditional Quantile Regressions”, Econometrica, 77(3), pp.953~973.
[22] Firpo, S. P., Fortin, N. M., and Lemieux, T., 2018, “Decomposing Wage Distributions Using Recentered Influence Function Regressions”, Econometrics, 6(2), 28.
[23] Fisher, I., 1933, “The Debt-Deflation Theory of Great Depressions”, Econometrica: Journal of the Econometric Society, pp.337~357.
[24] Heathcote, J, Perri F and Violante G L.,2010, “Unequal We Stand: An Empirical Analysis of Economic Inequality in the United States, 1967-2006”, Review of Economic Dynamics, 13(1), pp. 15~51.
[25] Heathcote, J. and Perri, F., 2018, “Wealth and Volatility”, The Review of Economic Studies, 85(4), pp.2173~2213.
[26] Jermann, U. and Quadrini V., 2012. “Macroeconomic Effects of Financial Shocks”, American Economic Review, 102(1), pp. 238~71.
[27] Kuhn, M, Schularick M and Steins U I. 2017, “Income and Wealth Inequality in America, 1949-2013”, Cesifo Working Paper.
[28] Ledoit, O.,2011, “The Redistributive Effects of Monetary Policy”, University of Zurich Department of Economics Working Paper.
[29] Mathä, T. Y., Porpiglia, A., and Ziegelmeyer, M., 2017, “Household Wealth in the Euro Area: The Importance of Intergenerational Transfers, Homeownership and House Price Dynamics”, Journal of Housing Economics, 35, pp.1~12.
[30] Mincer, J., 1974, “Schooling, Experience, and Earnings”, Human Behavior & Social Institutions, No. 2.
[31] Romer, C D. and Romer D., 1999, “Monetary Policy and the Well-Being of the Poor”, Economic Review, 84(QI), pp.21~49.
[32] Williamson, S. D., 2009, “Monetary Policy and Distribution”, Journal of Monetary Economics,55(6), pp.1038~1053.
[33] Kling, G., Pesqué-Cela, V., Tian, L., and Luo, D., 2022, “A theory of financial inclusion and income inequality”, The European Journal of Finance, 28(1), pp.137~157.
[1] 刘承昊, 刘冲, 刘莉亚. 影子银行监管的风险防范和信贷紧缩效应——来自资管新规的证据[J]. 金融研究, 2023, 517(7): 40-56.
[2] 许家云, 方森辉, 毛其淋. 僵尸企业、信贷约束与中国出口升级[J]. 金融研究, 2023, 516(6): 113-131.
[3] 成程, 田轩, 徐照宜. 供应链金融与企业效率升级 ——来自上市公司公告与地方政策文件的双重证据[J]. 金融研究, 2023, 516(6): 132-149.
[4] 李俊成, 彭俞超, 王文蔚. 绿色信贷政策能否促进绿色企业发展?——基于风险承担的视角[J]. 金融研究, 2023, 513(3): 112-130.
[5] 田鸽, 黄海, 张勋. 数字金融与创业高质量发展:来自中国的证据[J]. 金融研究, 2023, 513(3): 74-92.
[6] 王亚柯, 刘东亚. 信贷约束与家庭金融市场参与[J]. 金融研究, 2023, 512(2): 171-188.
[7] 马理, 张人中, 马威, 牛慕鸿. 能源结构有序调整与绿色信贷政策调控[J]. 金融研究, 2023, 511(1): 94-112.
[8] 宋弘, 张庆, 陆毅. 消费信贷与家庭人力资本投资[J]. 金融研究, 2023, 511(1): 131-149.
[9] 司登奎, 李颖佳, 李小林. 中国银行业竞争与非金融企业影子银行化[J]. 金融研究, 2022, 506(8): 171-188.
[10] 郭杰, 饶含. 土地资产价格波动与经济中的流动性供给——基于以地融资视角的研究[J]. 金融研究, 2022, 505(7): 76-93.
[11] 宣扬, 靳庆鲁, 李晓雪. 利率市场化、信贷资源配置与民营企业增长期权价值——基于贷款利率上、下限放开的准自然实验证据[J]. 金融研究, 2022, 503(5): 76-94.
[12] 金祥义, 张文菲, 施炳展. 绿色金融促进了中国出口贸易发展吗?[J]. 金融研究, 2022, 503(5): 38-56.
[13] 丁杰, 李仲飞, 黄金波. 绿色信贷政策能够促进企业绿色创新吗?——基于政策效应分化的视角[J]. 金融研究, 2022, 510(12): 55-73.
[14] 郭晔, 未钟琴, 方颖. 金融科技布局、银行信贷风险与经营绩效——来自商业银行与科技企业战略合作的证据[J]. 金融研究, 2022, 508(10): 20-38.
[15] 马慧, 陈胜蓝, 刘晓玲. 担保物权制度改革与企业劳动力结构[J]. 金融研究, 2022, 508(10): 153-169.
[1] 王曦, 朱立挺, 王凯立. 我国货币政策是否关注资产价格?——基于马尔科夫区制转换BEKK多元GARCH模型[J]. 金融研究, 2017, 449(11): 1 -17 .
[2] 刘勇政, 李岩. 中国的高速铁路建设与城市经济增长[J]. 金融研究, 2017, 449(11): 18 -33 .
[3] 况伟大, 王琪琳. 房价波动、房贷规模与银行资本充足率[J]. 金融研究, 2017, 449(11): 34 -48 .
[4] 祝树金, 赵玉龙. 资源错配与企业的出口行为——基于中国工业企业数据的经验研究[J]. 金融研究, 2017, 449(11): 49 -64 .
[5] 陈德球, 陈运森, 董志勇. 政策不确定性、市场竞争与资本配置[J]. 金融研究, 2017, 449(11): 65 -80 .
[6] 牟敦果, 王沛英. 中国能源价格内生性研究及货币政策选择分析[J]. 金融研究, 2017, 449(11): 81 -95 .
[7] 高铭, 江嘉骏, 陈佳, 刘玉珍. 谁说女子不如儿郎?——P2P投资行为与过度自信[J]. 金融研究, 2017, 449(11): 96 -111 .
[8] 吕若思, 刘青, 黄灿, 胡海燕, 卢进勇. 外资在华并购是否改善目标企业经营绩效?——基于企业层面的实证研究[J]. 金融研究, 2017, 449(11): 112 -127 .
[9] 姜军, 申丹琳, 江轩宇, 伊志宏. 债权人保护与企业创新[J]. 金融研究, 2017, 449(11): 128 -142 .
[10] 刘莎莎, 孔高文. 信息搜寻、个人投资者交易与股价联动异象——基于股票送转的研究[J]. 金融研究, 2017, 449(11): 143 -157 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《金融研究》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn
京ICP备11029882号-1