A theory of financial inclusion and income inequality

Abstract

We develop a theory linking financial inclusion, defined as access to formal loans and financial assets, to income inequality. Initial inequality of households is modeled by a random variable determining initial endowments. These initial endowments can be used to invest instantaneously in human capital and financial assets. Human capital translates into income based on a strictly concave production function, suggesting optimal levels of investment. Financial assets earn yields which do not depend on the amount invested by individuals. Theoretical predictions are tested using the China Household Finance Survey (CHFS) for 2011 and 2013. Initial conditions modeled by a random variable are replaced by an actual distribution of income or assets to derive theoretical predictions regarding the proportion of the population that might benefit from financial inclusion. Financial inclusion does mitigate under-investment in education – but formal loans do not contribute. Income inequality worsens if households rely on formal or informal loans, whereas access to bank accounts improves households' prospects in the future income distribution. However, households below the 40th percentile of household income do benefit from informal loans.

Keywords:

• Financial inclusion
• income inequality
• education
• theory of financial inclusion

Acknowledgments

We are very grateful for the financial support provided by the ESRC and NSFC (Newton Fund). This paper is part of the research project entitled Research on China's Financial System towards Sustainable Growth: The Role of Innovation, Diversity and Financial Reg- ulation (ESRC: ES/P005241/1 and the National Natural Science Foundation of China: 71661137002. In addition, this research was supported by the National Social Science Foundation of China (Project Number: 17ZDA071).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Between 1983 and 2016, China's Gini coefficient increased from 0.28 to about 0.46 (Naughton 2018)

2 See recent reviews of this literature by Claessens and Perotti (2007), Demirgüç-Kunt and Levine (2009), De Haan and Sturm (2017), and Cihak and Sahay (2020).

3 The intensity of use of financial services is proxied by the share of individuals having an account at a financial institution, saving at or borrowing from a financial institution, and making or receiving digital payments (Aslan et al. 2017).

4 These various dimensions of financial inclusion are measured by: the number of automated teller machines and commercial bank branches per 100,000 adults; the number of borrowers from, and depositors with, commercial banks per 1,000 adults; and the ratio of domestic credit to GDP (Park and Mercado 2018).

5 Specifically, they find that financial inclusion reduces rural-urban income inequality in the long run, but can increase it in the short term, due to initial disparities between rural and urban areas in terms of access to financial infrastructure and education (Huang and Zhang 2019).

6 Splitting the sample into the bottom 40% of households and the top 60% in terms of household income is in line with Demirguc-Kunt, Klapper, and Singer (2017, 18).

Additional information

Funding

Deming Luo is grateful for the financial support from the National Natural Science Foundation of China, the NSFC-RCUK-ESRC Joint Research Project (Grant No. 71661137002), the MOE project of Key Research Institute of Humanities and Social Sciences at Universities (Grant No. 16 JJD790052), the Fundamental Research Funds for the Central Universities, and the Leading Talents of Zhejiang Province in Humanities Social Sciences (ZJWR 0204018).