Can FinTech curb income inequality in China?
Abstract
The effect of FinTech on income inequality in China and the characteristics of the existing thresholds are examined in this study based on China provincial panel data from 2011 to 2020 by combining dynamic panel differential GMM with panel threshold models. As revealed by this study, (1) FinTech can significantly curb income inequality. (2) FinTech can mitigate income inequality in all regions, and the degree of mitigation is more significant in the central and western regions of China. (3) The improvement of FinTech development can reduce income inequality in all quantiles. The regions with high-income inequality and low-income inequality are compared. The comparison results reveal that FinTech can reduce income inequality to a greater extent in regions with lowincome inequality. (4) FinTech can restrain income inequality under different threshold variables, and the restraining effect of economic growth is the most significant. The policy significance of this study is to fully exploit the empowerment and income-generating role played by FinTech, build a more inclusive financial system, create a good financial environment, cultivate residents’ financial knowledge level, enhance the ability of low-income groups to obtain income from financial services and reduce income inequality, to fulfill the development goal of common prosperity.
First published online 09 January 2024
Keyword : FinTech, income inequality, differential GMM, threshold model, income distribution, financial inclusion, common prosperity
How to Cite
Liu, K., Hao, Y., Ge, Y., & Mu, W. (2023). Can FinTech curb income inequality in China?. Journal of Business Economics and Management, 24(6), 960–975. https://doi.org/10.3846/jbem.2023.20653
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Ureta, S. (2008). Mobilizing poverty?: Mobile phone use and everyday spatial mobility among low-income families in Santiago, Chile. Information Society, 24(2), 83–92. https://doi.org/10.1080/01972240701883930
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Zhang, X., Zhang, J., & He, Z. (2018, August). Is FinTech inclusive? Evidence from China’s household survey data [Conference presentation]. 35th IARIW General Conference. Copenhagen.
Aker, J. C., & Mbiti, I. M. (2010). Mobile phones and economic development in Africa. Journal of Economic Perspectives, 24(3), 207–232. https://doi.org/10.1257/JEP.24.3.207
Asongu, S. A. (2015). The impact of mobile phone penetration on African inequality. International Journal of Social Economics, 42(8), 706–716. https://doi.org/10.1108/IJSE-11-2012-0228
Asongu, S. A., & Nwachukwu, J. C. (2018). Comparative human development thresholds for absolute and relative pro-poor mobile banking in developing countries. Information Technology & People, 31(1), 63–83. https://doi.org/10.1108/ITP-12-2015-0295
Asongu, S. A., & Odhiambo, N. M. (2018). Human development thresholds for inclusive mobile banking in developing countries. African Journal of Science, Technology, Innovation and Development, 10(6), 735–744. https://doi.org/10.2139/ssrn.3200547
Asongu, S. A., Odhiambo, N. M., & Rahman, M. (2023). Information technology, inequality, and adult literacy in developing countries. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-023-01307-8
Bahia, K., Castells, P., Cruz, G., Masaki, T., Rodriguez-Castelan, C., & Sanfelice, V. (2023). Mobile broadband, poverty, and labor outcomes in Tanzania. World Bank Economic Review, 37(2), 235–256. https://doi.org/10.1093/wber/lhad003
Bhallamudi, I. (2022). Daughters, devices and doorkeeping: How gender and class shape adolescent mobile phone access in Mumbai, India. Information, Communication & Society, 25(6), 851–867. https://doi.org/10.1080/1369118X.2022.2056499
Billari, F. C., Rotondi, V., & Trinitapoli, J. (2020). Mobile phones, digital inequality, and fertility: Longitudinal evidence from Malawi. Demographic Research, 42, 1057–1096. https://doi.org/10.4054/DemRes.2020.42.37
Brei, M., Ferri, G., & Gambacorta, L. (2023). Financial structure and income inequality. Journal of International Money and Finance, 131, Article 102807. https://doi.org/10.1016/j.jimonfin.2023.102807
Demirguç-Kunt, A., & Klapper, L. (2013). Measuring financial inclusion: Explaining variation in use of financial services across and within countries. Brookings Papers on Economic Activity, 2013(1), 279–340. https://doi.org/10.1353/eca.2013.0002
Demirgüç-Kunt, A., Klapper, L., Singer, D., Ansar, S., & Hess, J. (2018). The Global Findex Database 2017: Measuring financial inclusion and the FinTech revolution. The World Bank. https://doi.org/10.1596/978-1-4648-1259-0
Demir, A., Pesque-Cela, V., Altunbas, Y., & Murinde, V. (2022). Fintech, financial inclusion and income inequality: A quantile regression approach. The European Journal of Finance, 28(1), 86–107. https://doi.org/10.1080/1351847X.2020.1772335
Fu, Y., & Liu, L. (2023). On the accessibility of financial services and income inequality: An international perspective. Technological and Economic Development of Economy, 29(3), 814–845. https://doi.org/10.3846/tede.2023.18722
Gong, S. E., & Fan, C. L. (2012). Income inequality, credit supply and consumption volatility. Economic Research Journal, 47(12), 4–14
Hansen, E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93(2), 345–368. https://doi.org/10.1016/S0304-4076(99)00025-1
Hodula, M. (2023). Fintech credit, big tech credit, and income inequality. Finance Research Letters, 51, Article 103387. https://doi.org/10.1016/j.frl.2022.103387
Khan, H., Weili, L., & Khan, I. (2022). The effect of political stability, carbon dioxide emission and economic growth on income inequality: Evidence from developing, high income and Belt Road initiative countries. Environmental Science and Pollution Research, 30, 6758–6785. https://doi.org/10.1007/s11356-022-22675-9
Lee, J. N., Morduch, J., Ravindran, S., Abu, S., & Zaman, H. (2021). Poverty and migration in the digital age: Experimental evidence on mobile banking in Bangladesh. American Economic Journal: Applied Economics, 13(1), 38–71. https://doi.org/10.1257/app.20190067
Li, C. T., Yan, X. W., Song, M., & Yang, W. (2020). Fintech and corporate innovation – Evidence from Chinese NEEQ-listed companies. China Industrial Economics, 2020(01), 81–98. https://doi.org/10.19581/j.cnki.ciejournal.2020.01.006
Liu, Y. W., Ding, L. P., Li, Y., & Hu, Z. Y. (2018). The measure of financial inclusion in China and its economic growth effect. China Soft Science, 2018(03), 36–46. https://doi.org/10.3969/j.issn.1002-9753.2018.03.004
Lu, M., Chen, Z., & Wan, G. H. (2005). Equality for the sake of growth: The nexus of inequality investment education and growth in China. Economic Research Journal, (12), 4–14.
Luo, S. M., Sun, Y. K., & Zhou, R. (2022). Can fintech innovation promote household consumption? Evidence from China family panel studies. International Review of Financial Analysis, 82, Article 102137. https://doi.org/10.1016/j.irfa.2022.102137
Muralidharan, K., Niehaus, P., & Sukhtankar, S. (2014). Payments infrastructure and the performance of public programs: Evidence from biometric smartcards in India (National Bureau of Economic Research Working Paper Series No. 19999). https://doi.org/10.3386/w19999
Odhiambo, N. M. (2022). Information technology, income inequality and economic growth in sub-Saharan African countries. Telecommunications Policy, 46(6), Article 102309. https://doi.org/10.1016/j.telpol.2022.102309
Philippon, T. (2020). On fintech and financial inclusion. Bank for International Settlements (BIS Working Papers No. 841).
Rajkhowa, P., & Qaim, M. (2022). Mobile phones, off-farm employment, and household income in rural India. Journal of Agricultural Economics, 73(3), 789–805. https://doi.org/10.1111/1477-9552.12480
Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288–1292. https://doi.org/10.1126/science.aah5309
Sheng, T. X., & Fan, C. L. (2020). Fintech, optimal banking market structure, and credit supply for SMEs. Journal of Financial Research, 480(6), 114–132. http://www.jryj.org.cn/CN/abstract/abstract755.shtml
Tian, X. J., Li, R., & Yang, G. (2021). A study of the effect of financial technology on the development of real economy: An empirical analysis based on the dual path of financial innovation and scientific and technological innovation. Social Sciences in Guangdong, (5), 5–15. https://doi.org/10.3969/j.issn.1000-114X.2021.05.001
Ureta, S. (2008). Mobilizing poverty?: Mobile phone use and everyday spatial mobility among low-income families in Santiago, Chile. Information Society, 24(2), 83–92. https://doi.org/10.1080/01972240701883930
Wei, Z. Y., & Mukherjee, S. (2023). Examining income segregation within activity spaces under natural disasters using dynamic mobility network. Sustainable Cities and Society, 91, Article 104408. https://doi.org/10.1016/j.scs.2023.104408
Zhao, J. C., & Fan, C. L. (2020). Income inequality, financial inclusive, and pro-poor growth. World Economy Studies, 2020(8), 101–116. https://doi.org/10.13516/j.cnki.wes.2020.08.008
Zhang, Y., & Wang, W. Q. (2021). Can financial technology alleviate income inequality? – Research based on multinational panel data. Shanghai Finance, 2021(6), 59–71. https://doi.org/10.13910/j.cnki.shjr.2021.06.006
Zhang, X., Zhang, J., & He, Z. (2018, August). Is FinTech inclusive? Evidence from China’s household survey data [Conference presentation]. 35th IARIW General Conference. Copenhagen.