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Effect of FinCredit on income inequality: the moderating role of financial inclusion

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Abstract

This paper examines the direct effect of FinCredit on income inequality and the moderating effect of Financial Inclusion. Using a dual-process variable selection approach, we develop an optimal model to test two hypotheses and explore how FinCredit influences direct and indirect income inequality. Our study has made a pioneering contribution to the literature by identifying the direct positive impact of FinCredit on income inequality, which previous research had not yet revealed. Additionally, we have discovered new evidence on the crucial role of Financial Inclusion in enhancing the positive effects of FinCredit on reducing income inequality. We provide reliable scientific empirical evidence that clarifies the confusion surrounding fintech credit and encourages its development. Practical managerial implications can be inferred from the moderating role of financial inclusion on the positive effect of fintech credit on income inequality. These results have significant policy implications for promoting FinCredit and Financial Inclusion to reduce income inequality in the era of digital transformation.

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References

  • Appiah-Otoo I, Song N (2021) The impact of fintech on poverty reduction: evidence from China. Sustainability 13(9):5225

    Article  Google Scholar 

  • Aslan G et al (2017) Inequality in financial inclusion and income inequality. International Monetary Fund

  • Asongu A, Nwachukwu JC (2018) Comparative human development thresholds for absolute and relative pro-poor mobile banking in developing countries. Inform Technol People 31(1):63–83

    Article  Google Scholar 

  • Asongu SA, Odhiambo NM (2019) Mobile banking usage, quality of growth, inequality and poverty in developing countries. Inform Dev 35(2):303–318

    Article  Google Scholar 

  • Banerjee AV, Newman AF (1993) Occupational choice and the process of development. J Polit Econ 101(2):274–298

    Article  Google Scholar 

  • Beck T (2020) Fintech and financial inclusion: opportunities and pitfalls. ADBI working paper series

  • Chinoda T, Mashamba T (2021) Fintech, financial inclusion and income inequality nexus in Africa. Cogent Econ Financ 9(1):1986926

    Article  Google Scholar 

  • Claessens S et al (2018) Fintech credit markets around the world: size, drivers and policy issues. BIS Q Rev Sept

  • Cornelli G et al (2020) Fintech and big tech credit: a new database

  • Cox I, Gaudard M (2013) Discovering partial least squares with JMP. SAS Institute, Cary

    Google Scholar 

  • de Haan J, Sturm J-E (2016) How development and liberalisation of the financial sector is related to income inequality: some new evidence. BAFFI CAREFIN Cent Res Pap (2016-33)

  • Demirgüç-Kunt A et al (2017) Asli Demirgüç-Kunt the global findex database

  • Demirguc-Kunt A et al (2018) The global Findex Database 2017: measuring financial inclusion and the fintech revolution. World Bank Publications

  • Demirgüç-Kunt A et al (2022) The global Findex Database 2021: financial inclusion, digital payments, and resilience in the age of COVID-19. World Bank Publications

  • Demirgüç-Kunt A et al (2020) The global findex database 2017: measuring financial inclusion and opportunities to expand access to and use of financial services. World Bank Econ Rev 34(Supplement1):S2–S8

    Article  Google Scholar 

  • Dimova R, Adebowale O (2018) Does access to formal finance matter for welfare and inequality? Micro level evidence from Nigeria. J Dev Stud 54(9):1534–1550

    Article  Google Scholar 

  • Dotti V (2020) Income inequality, size of government, and tax progressivity: a positive theory. Eur Econ Rev 121:103327

    Article  Google Scholar 

  • Erlando A et al (2020) Financial inclusion, economic growth, and poverty alleviation: evidence from eastern Indonesia. Heliyon 6(10):e05235

    Article  PubMed  PubMed Central  Google Scholar 

  • Fan J, Lv J (2008) Sure independence screening for ultrahigh dimensional feature space. J R Stat Soc Ser B (Stat Methodol) 70(5):849–911

    Article  MathSciNet  Google Scholar 

  • Fan J, Lv J (2010) A selective overview of variable selection in high dimensional feature space. Stat Sin 20:101–148

    MathSciNet  PubMed  PubMed Central  Google Scholar 

  • Galor O, Zeira J (1993) Income distribution and macroeconomics. Rev Econ Stud 60(1):35–52

    Article  Google Scholar 

  • Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157–1182

    Google Scholar 

  • Guzi M, Kahanec M (2019) .Income inequality and the size of government: a causal analysis. Available at SSRN 3318771

  • De Haan J, Sturm J-E (2017) Finance and income inequality: a review and new evidence. Eur J Polit Econ 50:171–195

    Article  Google Scholar 

  • Hermes N (2014) Does microfinance affect income inequality? Appl Econ 46(9):1021–1034

    Article  Google Scholar 

  • Hodula M (2023) Fintech credit, big tech credit and income inequality. Financ Res Lett 51:103387

    Article  Google Scholar 

  • Honohan P (2008) Cross-country variation in household access to financial services. J Bank Financ 32(11):2493–2500

    Article  Google Scholar 

  • Huang C et al (2019) Feature selection method based on partial least squares and analysis of traditional Chinese medicine data. Comput Math Methods Med 2019

  • Jauch S, Watzka S (2016) Financial development and income inequality: a panel data approach. Empir Econ 51:291–314

    Article  Google Scholar 

  • Jović A et al (2015) A review of feature selection methods with applications. In: 2015 38th international convention on information and communication technology, electronics and microelectronics, MIPRO, IEEE

  • Kim J-H (2016) A study on the effect of financial inclusion on the relationship between income inequality and economic growth. Emerg Mark Financ Trade 52(2):498–512

    Article  MathSciNet  Google Scholar 

  • Kochar A (2011) The distributive consequences of social banking: a microempirical analysis of the Indian experience. Econ Dev Cult Change 59(2):251–280

    Article  Google Scholar 

  • Lacalle-Calderon M et al (2019) Microfinance and income inequality: new macrolevel evidence. Rev Dev Econ 23(2):860–876

    Article  Google Scholar 

  • Mangal A, Holm EA (2018) A comparative study of feature selection methods for stress hotspot classification in materials. Integr Mater Manuf Innov 7:87–95

    Article  Google Scholar 

  • Mitchell TM (1997) Does machine learning really work? AI Mag 18(3):11–11

    Google Scholar 

  • Mookerjee R, Kalipioni P (2010) Availability of financial services and income inequality: the evidence from many countries. Emerg Markts Rev 11(4):404–408

    Article  Google Scholar 

  • Mulaudzi R, Ajoodha R (2021) Improving the performance of multivariate forecasting models through feature engineering: a South African unemployment rate forecasting case study. In: Interdisciplinary research in technology and management. CRC Press, pp 420–425

  • Neaime S, Gaysset I (2018) Financial inclusion and stability in MENA: evidence from poverty and inequality. Financ Res Lett 24:230–237

    Article  Google Scholar 

  • Ngo FT et al (2018) Traditional regression methods versus the utility of machine learning techniques in forecasting inmate Misconduct in the United States: an exploration of the prospects of the techniques. Int J Crim Justice Sci 13:2

    Google Scholar 

  • Ozili PK (2018) Impact of digital finance on financial inclusion and stability. Borsa Istanbul Rev 18(4):329–340

    Article  Google Scholar 

  • Park C-Y, Mercado R Jr (2018) Financial inclusion, poverty, and income inequality. Singap Econc Rev 63(01):185–206

    Article  Google Scholar 

  • Pes B (2017) Feature selection for high-dimensional data: the issue of stability. In: 2017 IEEE 26th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), IEEE

  • Polson N et al. (2021) Deep learning partial least squares. https://arxiv.org/abs/2106.14085

  • Reinartz W et al (2009) An empirical comparison of the efficacy of covariance-based and variance-based SEM. Int J Res Mark 26(4):332–344

    Article  Google Scholar 

  • Sahay MR et al (2015) Financial inclusion: Can it meet multiple macroeconomic goals? International Monetary Fund

  • Sawadogo R, Semedo G (2021) Financial inclusion, income inequality, and institutions in sub-saharan Africa: identifying cross-country inequality regimes. Int Econ 167:15–28

    Article  Google Scholar 

  • Turegano DM, Herrero AG (2018) Financial inclusion, rather than size, is the key to tackling income inequality. Singap Econ Rev 63(01):167–184

    Article  Google Scholar 

  • Von Fintel D, Orthofer A (2020) Wealth inequality and financial inclusion: evidence from South African tax and survey records. Econ Model 91:568–578

    Article  Google Scholar 

  • Valdebenito A, Pino G (2022) Local financial access and income inequality in Chile. Econ Lett 210:110170

    Article  Google Scholar 

  • WB (2013) Global financial development report 2014: financial inclusion. World Bank Publications

  • Wold H (1975) Soft modelling by latent variables: the non-linear iterative partial least squares (NIPALS) approach. J Appl Probab 12(S1):117–142

    Article  MathSciNet  Google Scholar 

  • Wold S et al (1984) The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J Sci Stat Comput 5(3):735–743

    Article  Google Scholar 

  • Zhang Q, Posso A (2019) Thinking inside the box: a closer look at financial inclusion and household income. J Dev Stud 55(7):1616–1631

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by the University of Economics and Law, Vietnam National University, Ho Chi Minh City, Vietnam.

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Authors and Affiliations

  1. University of Economics and Law, Ho Chi Minh City, Vietnam

    Xuan T. T. Pham & Thu B. Luu

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Xuan T. T. Pham & Thu B. Luu

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  1. Xuan T. T. Pham
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  2. Thu B. Luu
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Correspondence to Thu B. Luu.

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Pham, X.T.T., Luu, T.B. Effect of FinCredit on income inequality: the moderating role of financial inclusion. Rev Quant Finan Acc 62, 953–969 (2024). https://doi.org/10.1007/s11156-023-01226-4

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