Empirical analysis of the impact of financial development on the income gap between urban and rural residents in the context of large data using fuzzy Kmeans clustering algorithm

Abstract

With the rise of the tertiary industry, the financial industry has achieved unprecedented development, which is mainly reflected in the rapid growth of economic aggregate, the increasingly balanced financial structure system and the increasingly diversified financial products. However, with the rapid development of financial industry, the income of urban and rural residents is increasingly unbalanced. The increasing income gap between urban and rural areas has caused a large number of adverse phenomena in the process of economic development, seriously affecting the income distribution of the people and even causing social instability. Therefore, in today’s big data era, it is necessary to systematically study and analyze the impact of financial industry development on the national income gap between urban and rural areas. At the same time, it is of great significance to improve the problem of excessive income gap between urban and rural areas. This paper mainly analyses the relationship between the three effects of the development of financial industry and the income gap between urban and rural residents. In the empirical aspect, the paper creatively uses the fuzzy Kmeans clustering algorithm to regression analysis the panel data of a certain area from 2010 to 2018. At the same time, in the empirical data analysis, this paper creatively replaces the European norm measure of the Kmeans clustering algorithm with the AE measure, and puts forward a proposal. The index of financial development level is based on the proportion of loans from financial institutions. Through theoretical and empirical analysis, this paper draws the following conclusions: the financial scale in the financial industry will have a huge impact on the income gap between urban and rural areas. Finally, based on the above problems and current situation, this paper puts forward relevant improvement suggestions.

Keywords

Big data financial industry development urban-rural income gap fuzzy Kmeans clustering algorithm AE measurement

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References

Vincens

Emmelin

Stafström

Social capital, income inequality and the social gradient in self-rated health in Latin America: a fixed effects analysis.

Soc Sci Med 2018; 196: 115–122.

Ward

Viner

RM.

The impact of income inequality and national wealth on child and adolescent mortality in low and middle-income countries.

BMC Public Health 2017; 17: 429.

Kiran

Glazier

Moineddin

, et al. The impact of a population-based screening program on income and immigration-related disparities in colorectal cancer screening. Cancer Epidemiol Biomark Prev 2017; 26: 1401–1410.

Zheng

Dong

, et al. Comparing the needs and utilization of health services between urban residents and rural-to-urban migrants in China from 2012 to 2016. BMC Health Serv Res 2018; 18: 717.

Demuzere

Harshan

Järvi

, et al. Impact of urban canopy models and external parameters on the modelled urban energy balance in a tropical city. Q J R Meteorolog Soc 2017; 143: 1581–1596.

Wang

Chen

, et al. The income gap between urban and rural residents in China: since 1978. Comput Econ 2018; 52: 1153–1174.

Lei

Huang

The impact of transfer payments on urban-rural income gap: based on fuzzy RD analysis of China’s midwestern county data.

China Financ Econ Rev 2016; 4: 20.

Yeldan

AE.

The impact of financial liberalization and the rise of financial rents on income inequality: the case of Turkey.

Res Pap 2004; 51: 615–621.

Sehrawat

Giri

AK.

The impact of financial development, economic growth, income inequality on poverty: evidence from India.

Empir Econ 2017; 55: 1–18.

10.

Haan

Pleninger

Sturm

JE.

Does the impact of financial liberalization on income inequality depend on financial development? Some new evidence.

Appl Econ Lett 2018; 25: 4–6.

11.

Wang

Cai

Fiscal expenditure structure and the income gap between urban and rural residents: empirically analyses based on provincial panel data of eastern, central and western regions in China.

J Shanghai Univ Financ Econ 2010; 30: 240–241.

12.

Islam

Tiwari

Shahbaz

Does financial development increase rural‐urban income inequality?: cointegration analysis in the case of Indian economy.

MPRA Pap 2010; 40: 151–168.

13.

Jingjing

Huang

Dual economic structure, government policies and the urban-rural income gap in China – empirical analysis based on provincial panel data. Financ Trade Econ 2013; 34: 121–129.

14.

Gupta

Muttoo

Pal

SK.

Fuzzy C-means clustering and particle swarm optimization based scheme for common service center location allocation.

Appl Intell 2017; 47: 624–643.

15.

Nongmeikapam

Kumar

Singh

AD.

Fast and automatically adjustable GRBF kernel based fuzzy C-means for cluster-wise coloured feature extraction and segmentation of MR images.

IET Image Process 2018; 12: 513–524.

16.

Gavankar

Ghosh

SK.

Object based building footprint detection from high resolution multispectral satellite image using K-means clustering algorithm and shape parameters.

Geocarto Int 2018: 2019; 34: 626–643.

17.

Research on the risk management of the accounting information of public–private partnership financing mode in the pricing of listed companies’ bonds. Int J Electr Eng Educ 2018; 57: 175–185.

18.

Wang

Identification and decision making of social entrepreneurship opportunities based on family social capital and prior knowledge.

Int J Electr Eng Educ 2018; 57: 73–84.