Abstract:
This paper makes a theoretical discussion and empirical test on the impact of the development of digital inclusive finance on household consumption. The conclusions show that: (1) The development of digital inclusive finance has significantly promoted household consumption in the sample period, and this promotion effect is more obvious in rural areas, in the central and western regions, and in the middle and lower income groups. At the same time, in addition to the coverage of digital inclusive financial development, the depth of use and its three sub-indicators: payment, insurance and money funds significantly promote household consumption. (2) Digital inclusive finance promotes the household consumption in the sample period mainly by easing the liquidity constraints and facilitating the payment of households. (3) Using the instrumental variable and replacing the data set with the estimation results of the Chinese labor force dynamic survey data prove that the results are robust and reliable. (4) The results of sub-sample regression of human capital differences show that the higher the education level of the head of household and the stronger the cognitive ability, the more obvious effect of digital inclusive finance on the household consumption in the sample period. (5) The development of digital inclusive finance significantly promotes consumption for clothing, housing, equipment supplies and services, transportation and other goods and services.(6) The sub-sample regression results of household debt-to-income ratio show that the development of digital inclusive finance only promotes the household consumption with low or medium debt-to-income ratio, while there is no the significant inhibitory effect on the consumption of high debt-to-income ratio households. The development of digital inclusive finance has indeed increased the debt-to-income ratio of households. Therefore, while actively promoting the development of digital inclusive finance, it is also necessary to beware of the excessive and over-growth of household debt.
易行健, 周利. 数字普惠金融发展是否显著影响了居民消费——来自中国家庭的微观证据[J]. 金融研究, 2018, 461(11): 47-67.
YI Xingjian, ZHOU Li. Does Digital Financial Inclusion Significantly Influence Household Consumption? Evidence from Household Survey Data in China. Journal of Financial Research, 2018, 461(11): 47-67.
[1] 陈斌开和杨汝岱,2013,《土地供给、住房价格与中国城镇居民储蓄》,《经济研究》第1期,第110~122页. [2] 樊纲和王小鲁,2004,《消费条件模型和各地区消费条件指数》,《经济研究》第5期,第13~21页. [3] 郭峰、孔涛、王靖一、张勋、程志云、阮方圆、孙涛和王芳,2016,《中国数字普惠金融指标体系与指数编制》,北京大学数字金融研究中心工作论文. [4] 焦瑾璞,2014,《移动支付推动普惠金融发展的应用分析与政策建议》,《中国流通经济》第7期,第7~10页. [5] 焦瑾璞、孙天琦、黄亭亭和汪天都,2015,《数字货币与普惠金融发展—理论框架、国际实践与监管体系》,《金融监管研究》第7期,第19~35页. [6] 李涛、徐翔和孙硕,2016,《普惠金融与经济增长》,《金融研究》第4期,第1~16页. [7] 沈坤荣和谢勇,2012,《不确定性与中国城镇居民储蓄率的实证研究》,《金融研究》第3期,第1~13页. [8] 万广华、张茵和牛建高,2001,《流动性约束、不确定性与中国居民消费》,《经济研究》第11期,第35~44+94页. [9] 温忠麟和叶宝娟,2014,《中介效应分析:方法和模型发展》,《心理科学进展》第5期,第731~745页. [10] 吴晓瑜、王敏和李力行,2014,《中国的高房价是否阻碍了创业?》,《经济研究》第9期,第121~134页. [11] 肖作平和张欣哲,2012,《制度和人力资本对家庭金融市场参与的影响研究—来自中国民营企业家的调查数据》,《经济研究》第S1期,第91~104页. [12] 易行健,2006,《经济开放条件下的货币需求函数:中国的经验》,《世界经济》第4期,第49~59页. [13] 臧旭恒和张欣,2018,《中国家庭资产配置与异质性消费者行为分析》,《经济研究》第3期,第21~34页. [14] 张号栋和尹志超,2016,《金融知识和中国家庭的金融排斥—基于CHFS数据的实证研究》,《金融研究》第7期,第80~95页. [15] 周洋、王维昊和刘雪瑾,2018,《认知能力和中国家庭的金融排斥—基于CFPS数据的实证研究》,《经济科学》第1期,第96~112页. [16] 朱一鸣和王伟,2017,《普惠金融如何实现精准扶贫?》,《财经研究》第10期,第43~54页. [17] Anand S, K.and K.S.Chhikara, 2013.“A Theoretical and Quantitative Analysis of Financial Inclusion and Economic Growth,”Management and Labour Studies, 38(1-2), pp. 103~133. [18] Bartik T.,2009. “How Do the Effects of Local Growth on Employment Rates Vary with Initial Labor Market Conditions?” Policy Paper No. 2009-005, W. E. Upjohn Institute for Economic Research. [19] Campbell, J. Y. and N. G. Mankiw, 1991. “The Response of Consumption to Income: A Cross-Country Investigation,”European Economic Review, 35(4), pp. 723~767. [20] Carroll C. D.,R. E. Hall and P. Zeldes, 1992. “The Buffer-Stock Theory of Savings: Some Macroeconomic Evidence,”Brookings Papers on Economic Activity, 1992(2), pp. 61~135. [21] Chen L.,2016. “From Fintech to Finlife: the case of Fintech Development in China,”China Economic Journal, 9(3), pp. 225~239. [22] Dave, D. and H. Saffer, 2008. “Alcohol Demand and Risk Preference,”Journal of Economic Psychology, 29(6), pp. 810~831. [23] Deaton A.,1991. “Saving and Liquidity Constraints,”Econometrica, 59(5), pp. 1221~1248. [24] Dupas, P. and J. Robinson, 2013. “Why Don't the Poor Save More? Evidence from Health Savings Experiments,”American Economic Review, 103(4), pp. 1138~1171. [25] Hall R.,1988. “Intertemporal Substitution in Consumption,”Journal of Political Economy, 96(2), pp. 339~357. [26] Karlan, D. and J. Zinman, 2010. “Expanding Credit Access: Using Randomized Supply Decisions to Estimate the Impacts,”Review of Financial Studies, 23(1), pp. 433~464. [27] Kang, T. S. and G. Ma, 2007. “Recent Episodes of Credit Card Distress in Asia,”Bis Quarterly Review, June, pp.55~68. [28] Kimball M. S.,1990. “Precautionary Savings in the Small and in the Large,”Econometrica, 58(1), pp. 53~73. [29] Leland H. E.,1968. “Saving and Uncertainty: The Precautionary Demand for Saving,”Quarterly Journal of Economics, 82(3), pp. 465~473. [30] Levchenko A. A.,2005. “Financial Liberalization and Consumption Volatility in Developing Countries,”IMF Staff Papers, 52(2), pp. 237~259. [31] McCallum, B. T. and M.S. Goodfriend, 1988. “Theoretical Analysis of the Demand for Money,”FRB Richmond Economic Review, 74(1), pp.16~24. [32] Mian A.,A. Sufi and E. Verner, 2017. “Household Debt and Business Cycles Worldwide,”Quarterly Journal of Economics, 132(4), pp.1755~1817. [33] Sandmo A.,1970. “The Effect of Uncertainty on Saving Decisions,”Review of Economics Studies, 37(3), pp. 353~360. [34] Sidrauski M.,1967. “Inflation and Economic Growth,”Journal of Political Economy, 75(6), pp. 796~810. [35] Welch F.,1970. “Education in Production”,Journal of Political Economy, 78(1), pp. 35~59.
