The Impact of Economic Financialization on the Income Gap Between Urban and Rural Residents: Evidence from China

Abstract

Economic financialization refers to misappropriating workers’ earnings and enriching wealthy individuals through financial cycles. This process leads to an unequal distribution of wealth and income, particularly pronounced between urban and rural areas. This article examines the impact of economic financialization on the income gap between urban and rural residents by analyzing provincial-level data from China collected between 2003 and 2022. Utilizing the FE-SCC model and SDM, this study reveals that economic financialization increases the income gap between urban and rural residents, especially in eastern China and regions characterized by advanced economic development. The findings indicate that economic financialization significantly exacerbates the wage income gap between urban and rural residents but reduces the property income gap, which relates directly to the nature of work performed by urban and rural residents. The income disparity between these two groups correlates with each region’s economic financialization level. It is influenced by spillover effects from neighboring areas, evidenced by a phenomenon known as “club convergence”. Strengthening regulations on economic financialization, leveraging policy-driven financial systems, promoting regional development, and enhancing inclusive financial services could alleviate income disparity in urban–rural areas and improve the population’s overall well-being.

1. Introduction

Finance, as an essential monetary element in contemporary financial development, has turned out to be one of the major elements affecting profit distribution, and the global financial crisis of 2008 has, once again, made economic financialization an object of discussion and research. The economic financialization is derived from the development of finance. In 1993, Kevin Phillips formally defined “financialization” as the systematic separation of the real economy from finance, i.e., the rapid expansion and expansion of the financial sector, which ultimately overrides the real economy and magnifies the effect of financial wealth. This makes the financial cycle gradually replace the production cycle as the main path of capital accumulation [1] and makes financial profits the object of public pursuit. Early financialization provided business entities with a source of capital to expand reproduction to divide surplus value, collect rents, and then leave the cycle [2].

In contrast, excessive financialization increased the market dominance of the financial cycle, crowding out investment in production and causing a slowdown in the growth of employment, real wages, and consumption in the market [3]. Economic financialization has driven high-profit incomes in the financial sector, attracting large-scale social capital transfers to the financial sector [4] and prompting industrial capital to complete the evolutionary change from monetary to virtual capitalization. A large amount of social capital has formed a strong symbiotic relationship with the real estate and financial sectors, leading to a direct decline in productive investment. It has also consequently cut down the labor consumption of hired workers and limited the increase in workers’ wages [5,6]. Whether industrial capital relies on the productive transformation of financial capital or industrial capital participates in the financial game directly out of the value cycle, industrial capitalists and the profit-eating class dominate the contraction of the laboring class’s share of income in order to safeguard the economy’s profit surpluses and their capital accumulation, with the result being a change in society’s functional income structure [7]. The expansion of the wealth effect, in turn, involves the working class in the strife over financial profits, leading to the direct plunder of the working class’s income gains and capital accumulation [8], which will ultimately marginalize the low stock of capital represented by the working class, and ultimately exacerbate the conflict of economic contradictions between classes [9]. As a result, income distribution has sharply shifted in favoring capital, making it difficult for working-class families to maintain the existing consumption patterns while suppressing domestic demand.

Income distribution inequality has long been a shackle on China’s high-quality economic development and social justice, with the Gini coefficient for residents at around 0.46 for a long time and the Gini coefficient for wealth breaking through to 0.5 after 2000 and remaining above 0.7 thereafter, both higher than the international warning line of 0.4. Under the dual economic structure, the difference between the incomes of Chinese urban and rural residents has narrowed from 2.99:1 in 2010 to 2.50:1 in 2021. However, the absolute gap is still widening, and the structural imbalance of income continues. There are natural differences in resource endowment, capital scale, industrial structure, and comparative advantage between urban and rural areas, so economic financialization gives an urban and rural industrial cycle of labor value alienation and monetization or capitalization of factors of production, value plundering and appropriation, and capital appreciation and accumulation, which provides a driving force for in-depth study of the relationship between economic financialization and urban–rural residents’ income gap. Therefore, it is worth studying how to effectively explain the connotative laws of economic financialization and income distribution in the production chain and relations of production to think about the role of economic financialization in the evolution of China’s urban–rural residents’ income gap and to examine how financialization plays a role in the sizeable and functional incomes of urban and rural residents.

To achieve this objective, this study uses a provincial sample of China from 2003 to 2022 to explore the gap between economic financialization and the income of urban and rural residents. First, the potential path mechanism between economic financialization and the income gap between urban and rural residents is theoretically analyzed. Second, this study establishes a comprehensive indicator system to scientifically and objectively measure the development level of economic financialization in China. Finally, through the FE-SCC model and the construction of a spatial effect model, the effect of China’s economic financialization development on the income gap between urban and rural residents is empirically analyzed. The study’s results indicate that the financialization of China’s economy significantly increases the income disparity between urban and rural residents. Specifically, the effects of economic financialization on income differences vary; it greatly expands the wage income gap between urban and rural residents while notably reducing the property income gap between these groups. Economic financialization has a spatial spillover effect that influences the income gap between urban and rural residents. This disparity is not just linked to the level of economic financialization within a specific region, but is also shaped by the spillover effects from financialization occurring in neighboring regions. Compared with previous studies, this paper provides possible marginal contributions in two aspects: (1) Unlike the single-indicator measurements used in previous studies, this paper enhances the understanding of economic financialization by developing a more comprehensive evaluation system. It examines various dimensions, including monetization, virtualization, the influence of profit-seeking entities, and the overall trend of market pan-financialization. This approach aims to create a more detailed and specific measurement of economic financialization. (2) It examines the effects of economic financialization on income distribution by breaking it down from a national perspective to focus on urban and rural residents. Specifically, it investigates how economic financialization impacts the income distribution of urban and rural residents through two aspects: the scale income gap and the functional income gap. (3) The text confirms that economic financialization has spatial spillover effects. The income gap between urban and rural residents is influenced not only by local factors but also by the effects of economic financialization occurring in other regions. This highlights the dynamic nature of economic financialization.

The remainder is arranged as follows: Section 2 provides a review of the literature, which mainly compiles, analyzes, and summarizes the related research on the income distribution effect of economic financialization and explores the income distribution effect of economic financialization and the impact on the income gap between urban and rural residents. Section 3 discusses the mechanisms and hypotheses derived from analyzing the impact of economic financialization on income distribution. It explains how economic financialization influences the income gap between urban and rural residents, examines the spatial relationship between the two, and presents the research hypotheses of this paper. Section 4 provides the variable data and the model, which mainly explains the variables required in the empirical analysis of this paper and their selection criteria. It also includes the sources and statistics of data and the model setting. Section 5 presents the results of the empirical analysis, focusing on the impact of economic financialization on the income gap between urban and rural residents. The analysis divides samples based on geographic regions and the quality of regional economic development to test for heterogeneity. Additionally, it examines how economic financialization affects the functional income gap between urban and rural residents and discusses the spatial distribution effects of economic financialization. Section 6 discusses the results of the empirical analysis. Section 7 summarizes the study’s conclusions and proposes corresponding countermeasure suggestions.

2. Literature Review

2.1. Research Status of Financial Development and Income Distribution

Relevant studies on the distributional effects of financial activities take neoclassical financial development theory as the research kernel, and these studies focus on digging into the market efficiency of the economic financialization and the operation of the system, as well as its income distributional effects, and the conclusions are mainly classified into three categories. In the first category, there is a significant Kuznitz curve effect and threshold effect between financial development and income disparity. Scholars represented by Greenwood find that financial services are more biased toward the wealthy class in the early stage of financial development. When capital accumulation exceeds the threshold critical value, the poor class begins to enjoy financial benefits, the wealth gap between classes converges, and the overall distribution pattern tends to stabilize [10,11]. Wang and Wen found that urban and rural financial capital undergoes unbalanced agglomeration, resulting in a unique stage-dependent nonlinear relationship with the urban–rural income gap [12]. In the second category, where financial activities exacerbate income distribution inequality, Jing et al. found that financial development inhibits the contraction of the income gap between urban and rural residents and that this inhibitory effect is most prominent in western China [13], which is fundamentally due to the greater concentration of indirect financial institutions in the urban sector [14], and that the low penetration of financial services in rural areas hampers the rural residents from income growth [15]. In the third category, financial activities help to alleviate the residents’ income gap. Wen et al. also confirmed that increasing the investment in human capital will promote the financial activities to converge the urban–rural income gap, and the regional heterogeneity is significant [16]. The financial system improves the income structure by regulating the financial structure, financial efficiency, and financial scale [17,18].

2.2. Research Status of Economic Financialization and Income Inequality

Most of the established studies on the measurement of economic financialization are from the perspective of financial development. Specific metrics indicators include the share of returns on financial assets in national wealth [19], the index of financialization [20,21], the share of value added in the FIRE sector [22], and McClellan’s indicator, among others. Given that China’s financial system is a typical indirect financial system dominated by commercial banks, Wu referred to McClellan’s indicator to construct the FIR indicator, the ratio of the level of total market indebtedness to GDP to measure the degree of China’s financialization development [23]. However, the above measurements make it difficult to highlight the capital agglomeration qualities of the economy’s financialization and the complexity of its connotations. Therefore, Jiao and Chen developed a multi-indicator evaluation system for economic financialization. This system took into account the monetization and virtualization aspects of economic financialization, as well as the characteristics of market pan-financialization and groups that profit from financial activities to address the measurement shortcomings that arise from relying on a single indicator to assess economic financialization [24].

In contrast to the findings of various studies on the impact of financial activities on income distribution, economic financialization is regarded as one of the powerful threats to the equal distribution of income, which spontaneously drives the unequal distribution of income. Tests based on empirical studies have found that the expansionary effect of economic financialization on income disparity is noticeable. Wen and Wang, using financial sector expansion and financial labor force expansion as proxies for financialization, found that there is a robust positive correlation between financialization and income gap between urban and rural areas, and that the relationship is heterogeneous across provinces [25]. DeVita and Luo found that income inequality is sensitive to households’ financialization and that the level of households’ indebtedness is the main factor that widens the income gap [26]. Alexiou et al., using data from OECD countries, revealed that only a certain degree of financialization widens income inequality [27]. Similarly, corporate financialization has a positive impact on the share of labor income received by executives, simultaneously increasing the internal pay gap within organizations. This pay gap tends to widen further in situations where financing constraints are more significant. Additionally, the positive effects of corporate financialization are more pronounced and vary by region [28]. Similarly, the uneven distribution of financial capital between urban and rural areas has caused the financialization of both economies at varying stages of development. This has resulted in a unique, nonlinear relationship between the progression of financialization and the income gap between urban and rural regions [29,30].

In fact, economic financialization worsens income inequality within the national economy. It is deeply rooted in the institutional context, particularly the labor system that shapes the relationship between different income groups [31]. Economic financialization creates and dominates varied market incomes. It tends to concentrate wealth in the hands of the affluent and the rich, while income growth for the middle class and the poor is stifled [32]. This dynamic exacerbates inequality between the income distribution’s upper and lower ends and intensifies the conflicts between labor and capital [32,33,34].

2.3. Research Status of Spatial Spillovers from Economic Financialization

Existing studies on the distribution of the impact of economic financialization ignore that financialization as an economic variable has spatial characteristics, and there is a lack of exploration of the spatial effects of economic financialization. Some scholars have found a significant spatial spillover effect of economic financialization on real economic development [35,36]. Chen et al. take the difference between urban and rural financial development gradient as the research object and find that urban financial development is more conducive to improving rural residents’ income than rural financial development, mainly related to the number of urban absorption of the labor force, and the spatial effect of this impact is pronounced [37]. Xu points out that the spatial correlation effect of China’s real estate financial risk is incremental and shows multiple superposition characteristics and spatial spillover effect, which proves, to a certain extent, that the financial threats implied by overheated financialization development have spatial transmission effects [38]. In summary, the spatial spillover effect of financialization has a significant impact on the operation of financial markets, which is reflected in the spatial convergence of the operation of financial markets, which broadens the transmission channels of financial mechanisms [39], and then causes spatial changes in the financial structure, which ultimately influences the changes in the financial vulnerability and the inequality of income distribution [40].

2.4. Research Gaps

The existing literature consistently concludes that “economic financialization exacerbates the inequality of income distribution”, which gives some theoretical support and reference basis for subsequent studies on the impact of economic financialization on the income gap between urban and rural residents. However, there is also room for improvement: first, the measurement of the degree of economic financialization from the neoclassical theory of financial development is slightly unitary, and the limited indicators can hardly reflect the complexity and multi-dimensional qualities of economic financialization, ignoring the role of financial capital accumulation, the rise of the financial bourgeoisie, and the pan-financialization of the market in promoting economic financialization. Second, there is a lack of logical discussion on whether the spontaneous distributional effects of economic financialization originate from the profit-grabbing class’s division of laborers’ wage income and crowding out of labor value and whether total value redistribution occurs. Third, the income gap between urban and rural residents, as one of the realities of China’s current inequality in income distribution, is in urgent need of improvement, as few scholars have explored the link between economic financialization and the gap between urban and rural residents at a deeper level, and the existing research conclusions have only thought in terms of urban and rural residents’ household-size incomes, while ignoring their impact on the functional income structure of urban and rural residents, and lacking deep exploration from a spatial perspective.

3. Mechanisms and Hypotheses

3.1. The Dialectic Between Economic Financialization and Income Distribution

Marx’s distribution logic depends on the value distribution in the production chain, where workers are paid for the necessary labor during the necessary labor time. Surplus value is produced along with the production of material goods. After separating the costs of various types of labor, the surplus value is converged into the wealth of the industrial capitalists and their consumption funds. In order to maximize the profit rate, industrial capitalists continue to capture surplus value (industrial profit) by extending the surplus labor time or shortening the necessary labor time. However, laborers are only rewarded during the necessary labor time. Hence, the income disparity between labor and capital directly reflects the conflict between the two. The industrial capital cycle eventually completes the accumulation of surplus value and capital surplus of industrial capitalists. However, industrial profits tend to be balanced with the formation of large-scale product markets. The existing scale of production makes it difficult to sustain the pursuit of high-profit margins of industrial capitalists. Some industrial capitalists begin to change their identities and monetize their capital (or borrowing). At this time, the industrial capital is endowed with financial attributes that define the identity of the money capital: re-enter the industrial cycle, use financial rent (or lending interest) as the rental compensation, and finally exit the industrial cycle. This process provides a new channel for the expansion of excess capital, financial services, and financial capital formed spontaneously to alleviate the financing constraints of some industrial capitalists, reduce costs, increase efficiency, and share risks. Financial services focus on the use of financial capital to solve the problem of intertemporal value storage and claim for future earnings; the borrower of money capital additional capital investment to complete the expansion of reproduction; while money capitalists participate in the distribution of surplus value by the ownership of money capital. The newborn surplus value is divided into borrowing and lending capital, interest, and profits from the production in turn, and the new distribution makes the money capital experience the transformation of industrial capital, commodity capital, and then to new money capital. The industrial cycle has long been dependent on borrowed capital, and financial indebtedness has severely reduced the production’s net profitability, so the surplus value rate in the industrial cycle can only be maintained by lowering variable costs (labor compensation). Although both borrowers and lenders of money capital complete the accumulation of new value, workers only receive wage income, which, coupled with the fact that the supply of labor in society has long exceeded the demand for labor, has led to a steady decline in the share of labor compensation in the initial distribution [41]. Ultimately, the sizeable income gap between industrial capitalists, money capitalists, and laborers has gradually widened, forming a pattern of triple division among nonfinancial enterprises, the financial sector, and the working class.

Based on Marx’s theoretical account of “finance capital” in the third volume of Capital, Marxist political economy and the radical political economy school focus on economic financialization from the perspective of capital accumulation [42], which is summed up as an economic phenomenon that combines the monetization, the capitalization of money, and the virtualization of capital, and accordingly forms the relations of appropriation of capital and income through these mediums [43,44]. Economic financialization strips industrial capital from the production chain and undergoes monetization, monetary capitalization, and capital virtualization until it is transformed into financialized capital; the mode of social operation transitions from the production cycle to the financial cycle, and market players begin to pay attention to the anticipation and seizure of financial profits, neglecting the physical, economical operation, and value production, and ending the over-expansion of the financialized capital agglomeration and the integration of the economic development of the hollowing-out and virtualization of the economy [45]. Capital for profit is the fundamental driving force of the transformation of economic financialization [46]. When the return rate of financial investment is higher than the profit rate of production, financial capital in the financial internal cycle obtains a large number of proliferation and accumulation. This low-cost return process attracts a larger scale of functional capital from physical production to financial activities. The cycle repeats itself, and the economy is “deconcentrated” and “virtualized”. As the cycle continues, the “de-realization” of the economy and the virtualization of capital become increasingly severe [47], and economic financialization also becomes increasingly inflated. Workers in the financial industry design and produce financial products and services in the value creation process, unlike the entity material value created through the live labor of workers. Securities, financial derivatives, and forward income claims, and other financial products and real estate transactions do not create value. The product comes with speculative attributes, the price expectations and income determination are very flexible, financial investors will be directly invested in the financial transactions of the monetary capital, by buying and selling financial services or products to earn the price difference, and finally, the principal and income together back to their own hands, to form a whole set of financial cycle [48]. Therefore, economic financialization accelerates financial innovation and transactions, promotes the zero-sum game between buyers and sellers, and contributes to the “de-realization” of the economy. High financial profits become the object of public pursuit but are no longer the product of the stripping of the new value, which is realized through the transfer of the value and wealth of those who enter the market after the transfer of the value and wealth of the profit-eating class, i.e., the capital capture and value appropriation of the profit-eating class. Moreover, with value appropriation by the profit-eating class, the market’s total value remains unchanged, only the redistribution of existing value. Excessive financialization emphasizes the pursuit, appropriation, accumulation, and even domination of money capital. The claiming power of money capital and the various certificates of rights by the profit-eating class (or money capitalists) and the financial bourgeoisie, anticipating the pursuit of high yields and high expectations by various market players, has plunged into the “Shilohole” of the competition for profits. Relying on its mighty market power, it transfers the division of labor value and money capital by creating and trading financial products, hiding the objective fact that financial profits come from the exploitation of value and the appropriation of capital. The working class, whose gaming strength is so different, is left to be “slaughtered” or has no choice but to withdraw from the competition. Coupled with the shrinking of the industrial cycle and the squeezing of labor remuneration [49], the imbalance in the distribution of functional income is becoming worse and worse, forming a distribution pattern in which “the rich are getting richer, and the poor are getting poorer and poorer”.

3.2. Impact of Economic Financialization on the Income Gap Between Urban and Rural Residents

The income gap between urban and rural residents is a distinctive feature of the imbalance in income distribution under the dualistic economic structure. Marx argued from the historical materialist perspective of the social division of labor and the development of productive forces that the separation of urban and rural areas is the result of urban–rural antagonism and the antagonistic conflict between the urban economic civilization, which takes the industrialization of towns and cities as a spatial carrier, and the agricultural economic civilization, which takes agricultural productivity in the countryside as a spatial carrier, is pronounced. The well-developed system of the division of labor in towns and cities has made rural areas subordinate to the development of towns and cities [50]. The towns and cities have long relied on a crude economic growth model, with labor-intensive industries dominating urban enterprises to expand reproduction through financial financing channels, absorbing a large amount of rural surplus labor and accelerating the nonagricultural transfer of rural laborers. However, the rise in industrial profits has been weak, while the high-return financial and real estate industries have crowded out the profit space of the real economy. The exodus of functional capital to the financial and real estate industries has rapidly expanded the “pan-financialization” of the economy [51]. The pan-financial industry became the main driving force of economic growth in towns and cities, to the point where the income of urban financial workers was significantly higher than the average wage of manufacturing workers. Although agricultural laborers earn more than traditional agricultural labor compensation for nonagricultural work, they are constrained by their education, vocational skills, and other factors and have never been able to exceed the urban labor force. With the rise in capital-intensive and knowledge-intensive industries, such as high-end manufacturing and finance, the labor force selection of the key links in producing the labor force is more stringent and professional. The matching wage level is higher than traditional labor compensation, so the gap between urban and rural labor force wage income persists for a long time. It is also higher than traditional labor compensation, the primary driver of urban economic growth. Therefore, the wage income gap between urban and rural areas has not only existed for a long time but is also gradually widening.

The development of the financial and real estate industries has given urban residents more investment choices, and investment consumption by urban households has risen, with medium- and long-term returns enriching the property income of urban households. However, the difference in asset stocks among urban members causes vertical disparities in investment returns. Those with high capital stocks are more sensitive to the fluctuations of the financial system, take more excellent initiative in investment returns, and are more inclined to market value appropriation and unbalanced allocation of wealth. On the contrary, ordinary investors are often subject to value exploitation due to price hedging and have to painfully cut off their meat to defend themselves against risks [52]. On the contrary, in rural residents, property income mainly comes from savings interest and the popularization of financial knowledge and financial institutions. A small number of rural residents hold securities to supplement their property income, in addition to the sale of housing, renting, mortgaging, and other ways to realize the asset-based income of rural households [53], but in the rural residents, the net income accounts for a low percentage of the income. Labor income and land operation income are still the primary sources of income. The development of moderate financialization provides financial support and more investment and financing channels for optimizing and upgrading rural industries. It also gives rural residents more convenient investment channels to increase their share of property income and help farmers increase their income. Urban and rural residents have enjoyed certain financial dividends due to popularizing and promoting financial services, and the difference in property income between the two tends to narrow. Accordingly, this paper proposes research Hypothesis 1 and Hypothesis 2:

Hypothesis 1:

As the process of economic financialization moves forward, the income gap between urban and rural residents is persistently widened.

Hypothesis 2:

The development of economic financialization has curbed the convergence of the wage income gap between urban and rural residents, but narrowed the property income gap between urban and rural residents.

According to the first law of geography proposed by Waldo Tobler, “Everything is related to everything else, and things in close proximity are more closely related”. Similarly, in an environment driven by increasing marginal rewards and imperfect competition in the market, economic activities will undergo spatiotemporal agglomeration, and the financial resources in one region will be clustered or overflowed to its neighboring regions in order to improve the efficiency of capital allocation and the profit gained [53]. Economic financialization means that industrial capital no longer depends on industrial production, and turns to the financial cycle. In contrast, financial development and innovation give financial capital agglomeration of strong externalities, accelerating the spatial flow of capital; on the one hand, the effect of financial wealth is no longer limited to a single region and accelerates the transmission of the inter-regional, is able to increase the efficiency of the allocation of financial capital in various regions, and helps to improve the financial development of the inter-regional disparities. On the other hand, the agglomeration and diffusion of financial capital will certainly trigger capital accumulation and value appropriation between different regions and groups, expanding the inter-regional labor volume differences, especially financial capital and labor, in pursuit of high returns and frequent cross-regional mobility and transfers, forming the spatial structural deviation of labor income and spatial and temporal gaps in the total amount of income. Therefore, when administrative regions deregulate the free flow of capital, labor, and other factors of production, the correlation between regions becomes closer and closer, coupled with economic financialization, which makes the financial capital present a heterogeneous aggregation between urban and rural areas, and the differentiation of financial profits between different regions will inevitably lead to the inter-temporal and spatial mobility of financial capital. This will aggravate the spatial imbalance of the evolution of urban and rural income distribution [54], and ultimately form the spatial structure bias of income and total amount of income of each geospatial dependence and spillover of regional economic financialization development and urban–rural residents’ income gap [55]. Accordingly, this paper proposes research Hypothesis 3:

Hypothesis 3:

Economic financialization has strong externalities that affect the income gap between urban and rural residents in neighboring regions through spatial effects.

4. Variable, Data and Model

4.1. Selection of Variables

4.1.1. Explained Variable

When measuring the income gap between urban and rural residents, researchers often use indicators such as the Gini coefficient, the urban-to-rural income ratio, and the income difference between urban and rural residents. However, each of these indicators has certain limitations. Firstly, the Gini coefficient assesses income inequality by grouping individuals based on their income shares. While it effectively measures income disparity, it is susceptible to changes among middle-income groups and does not adequately reflect variations within the lower- and upper-income brackets [56]. Secondly, both the urban-to-rural income ratio and the absolute income difference fail to identify the specific characteristics of “urban” and “rural” populations [57]. They also overlook the dynamic changes in urban and rural demographics [58], making it challenging to capture the distinctive features of China’s dualistic economic structure. Many studies have opted for the Theil Index to better understand the income gap and account for changes in the demographic structure of urban and rural households. The Theil Index was proposed by Theil in 1967 to measure the income gap between individuals or regions or the degree of inequality. The advantage of this index is that residents can be grouped to discuss the income gap, and the degree of contribution to the overall income gap can be calculated based on the income gap within each group and the income gap between each group. This index is preferred because it incorporates variations in the distribution of incomes among urban and rural populations over time. Furthermore, it is more responsive to changes in income structures, thereby providing a more accurate reflection of the evolving income gap between urban and rural residents [59].

In this section, the Theil Index was chosen to measure the income gap between urban and rural residents. Its basic form is

$$THEIL={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n\frac{y_i}{\overline{y}}}ln{\displaystyle \frac{y_i}{\overline{y}}}$$

(1)

where THEIL denotes the Theil Index, n is the total number of households, $y_i$ is the income level of the ith family household, and $\overline{y}$ denotes the average of the total income level of the society. The smaller the index, the smaller the difference in income between households, and vice versa.

In order to reflect the income gap between urban and rural residents under China’s dual economic structure, this paper refers to Wang and Ouyang. It decomposes the Theil Index into the income gap within and between urban and rural groups by replacing the total household income with the income of urban and rural residents, respectively, and introducing the total urban and rural population, at which point the Theil Index not only takes into account the changes in the absolute incomes of urban and rural residents, but also the corresponding changes in the urban–rural population structure. The evolution of the Theil Index takes the following form:

$$THEI{L}_{it}={\displaystyle \sum _{j=1}^2\frac{w_{ij,t}}{w_{it}}}ln\left({\displaystyle \frac{w_{ij,t}/w_{it}}{m_{ij,t}/m_{it}}}\right)$$

(2)

where j = 1 and j = 2 denote urban and rural areas respectively; w_ij,t denotes the total income of the residents of urban or rural areas in area i in year t; w_it denotes the total income of the residents of area i in year t; m_ij,t denotes the population living in urban or rural areas in area i in year t; and _it denotes the total population living in area i in year t. The larger the Theil Index, the more significant the gap between urban and rural residents’ income levels; conversely, the more significant the gap between urban and rural residents’ income levels.

4.1.2. Explanatory Variable

Combining the above analysis, this paper refers to a multi-dimensional economic financialization of an evaluation index system constructed by Hao and Chen from four factors: the degree of monetary capitalization, the degree of capital virtualization, the degree of virtual capital independence, and international financialization [60]. It draws on the political economy definition from Zhang and Zhang and an analysis of economic financialization to construct a comprehensive evaluation of the economic financialization development index to better reflect the characteristics and evolutionary degree of financialization of the economic development under the characteristics of the financial system with Chinese characteristics [61,62].

In this paper, eight indicators constitute the comprehensive evaluation index (FINZ) of the development level of economic financialization in four dimensions: capitalization of money, virtualization of capital, power of the profit-grabbing class, and pan-financialization of the market.

Table 1. Comprehensive evaluation index system for the development level of economic financialization.

Target Index	Indicators I	Indicators II	Measurement	Indicator Properties
Comprehensive evaluation index of the level of development of economic financialization.	Capitalization of money.	Percentage of loan balances of financial institutions.	Loan balance of financial institutions/GDP.	+
	Capital virtualization.	Percentage of total portfolio transactions.	Total portfolio transactions/GDP.	+
	The power of the profiteering class.	Share of national income from financial rental income of the profit-taking class.	(Financial profits of financial corporations + financial profits of nonfinancial corporations + household financial investment income)/GDP.	+
	Pan-financialization of markets.	Percentage of financial employees.	Number of financial employees/total employees.	+
		Density of financial institutions’ business outlets.	Number of financial institutions operating/area of area.	+
		Financial sector expansion.	Financial sector value added/GDP.	+
		Financialization of commodities.	Value added of real estate industry/GDP.	+
		Financialization of nonfinancial corporations.	Financial profit of industrial enterprises above scale/total profit of industrial enterprises above scale.	+

shows the specific composition of the indicators, their classification, and their calculation.

The key to measuring the development level of economic financialization lies in establishing an appropriate indicator system and determining the weights for those indicators. To minimize estimation errors that can arise from subjective human factors, this paper employs the entropy-weighted TOPSIS method to conduct an objective evaluation of the development level of economic financialization. The specific steps for calculating the comprehensive evaluation index of economic financialization are outlined as follows:

Indicators are normalized without dimension:

$$\mathrm{Positive} \mathrm{indicators}: Z_{ij}={\displaystyle \frac{x_{ij}-min(x_{ij})}{max(x_{ij})-min(x_{ij})}}$$

(3)

$$\mathrm{Negative} \mathrm{indicators}: Z_{ij}={\displaystyle \frac{min(x_{ij})-x_{ij}}{max(x_{ij})-min(x_{ij})}}$$

(4)

where x_ij represents the value of the jth indicator in the ith year; max(x_ij) and min(x_ij) represent the maximum and minimum values of the selected relevant indicators; Z_ij represents the value of the jth indicator in the ith year after the dimensionless normalization treatment to eliminate differences in indicator rigidity and order of magnitude in the original data.

$$\mathrm{Determine} \mathrm{indicator} \mathrm{weights}: P_{ij}={\displaystyle \frac{Z_{ij}}{{\displaystyle \sum _{i=1}^n{Z}_{ij}}}}$$

(5)

$$\mathrm{Calculate} \mathrm{information} \mathrm{entropy} \mathrm{of} \mathrm{each} \mathrm{indicator}: e_j=-{\displaystyle \frac{1}{lnn}}{\displaystyle \sum _{i=1}^n{P}_{ij}ln}{P}_{ij}$$

(6)

$$\mathrm{The} \mathrm{information} \mathrm{utility} \mathrm{value} \mathrm{of} \mathrm{each} \mathrm{indicator}: d_j=1-e_j$$

(7)

$$\mathrm{The} \mathrm{weight} \mathrm{of} \mathrm{each} \mathrm{indicator}: w_j={\displaystyle \frac{d_j}{{\displaystyle \sum _{j=1}^n{d}_j}}}$$

(8)

$$\mathrm{Calculate} \mathrm{the} \mathrm{weight} \mathrm{matrix}: h_{ij}=w_j{Z}_{ij}$$

(9)

where h_ij denotes elements in the normalized matrix H obtained by weighting normalized raw data matrix.

$$\mathrm{Calculate} \mathrm{the} \mathrm{Euclidean} \mathrm{distance}: D_i^{+}=\sqrt{{\displaystyle \sum _{j=1}^m{(H_j^{+}-h_{ij})}^2}}$$

(10)

$$D_i^{-}=\sqrt{{\displaystyle \sum _{j=1}^m{(H_j^{-}-h_{ij})}^2}}$$

(11)

where $H_j^{+}$ and $H_j^{-}$ are denoted as the positive and negative ideal solutions, representing the maximum and minimum values of each column element in the normalized matrix H; $D_i^{+}$ and $D_i^{-}$ are denoted as the distance between the evaluation object and the positive and negative ideal solutions.

$$\mathrm{Calculate} \mathrm{comprehensive} \mathrm{evaluation} \mathrm{index}: C_i={\displaystyle \frac{D_i^{-}}{D_i^{+}+D_i^{-}}}$$

(12)

where C_i represents comprehensive evaluation index, and 0 ≤ C_i ≤ 1; the composite index C_i approaches 1, indicating optimal evaluation, and vice versa.

4.1.3. Control Variable

The following control variables are selected for this paper. The quality of economic development is expressed as the real per capita GDP of the region at the 2000 price level, which reflects the quality of actual economic development. Urbanization level is expressed as the ratio of urban resident population to total resident population to reflect the urbanization development process. The local government fiscal expenditure ratio to regional GDP measures government expenditure. Education investment is represented by the ratio of education expenditure to GDP in each region. The industrial structure is represented by the ratio of value added of secondary and tertiary industries to GDP in each region. Each region’s total postal and telecommunications services ratio to GDP represents the informatization level. Openness to the outside world is represented by the total import and export trade ratio to regional GDP expressed in RMB. Inward investment is the total foreign direct investment ratio to regional GDP in RMB. The marketization degree reflects the actual development level in each region, using the marketization index derived by Fan Gang. Inflation uses the regional CPI index to reflect the actual level of inflation in each region. Residents’ savings are represented by the savings rate, which is the percentage of total disposable income per capita that is saved. The actual market capital stock reflects capital stock as a percentage of regional GDP. The explanation of all the variables involved in this paper is shown in

Table 2. Summary of variable selection and measurement approach.

Variable	Definition	Description	Measurement
THEIL	Income gap between urban and rural residents.	Theil Index.	Calculated by using the Theil Index formula.
FINZ	Degree of economic financialization.	Comprehensive evaluation index of economic financialization.	The entropy-TOPSIS method was used for calculation.
GDP	Quality of economic development.	Real GDP per capita.	GDP per capita expressed at the 2000 price level and taking natural logarithms.
URBAN	Urbanization level (of a city or town).	Urbanization rate.	Urban resident population/total resident population.
GOV	Government expenditure.	Fiscal expenditure as a percentage.	Local government fiscal expenditure/regional GDP with natural logarithm.
EDU	Investment in education.	Percentage of expenditure on education.	Regional education expenditure/regional GDP with natural logarithm.
STRUC	Industrial structure.	Percentage of industrial value added.	Value added of secondary and tertiary industries in the region/regional GDP and take natural logarithm.
INFOR	Informatization level.	Percentage of total post and telecommunications business.	Total regional postal and telecommunication business/regional GDP and take the natural logarithm.
TRADE	Open to the outside world.	Percentage of total import and export trade.	Total import and export trade of each region/regional GDP expressed in RMB and taking natural logarithms.
FDI	Foreign investment.	Percentage of total FDI.	Total FDI/regional GDP expressed in RMB and taking natural logarithms.
MARKET	Marketability.	Fan marketization index.	Calculated using the marketization index derived by Fan Gang and taking natural logarithms.
CPI	Inflation.	CPI index.	CPI index for each country and take natural logarithm.
SAVING	Resident savings.	Savings rate.	Savings/gross disposable income per capital with natural logarithms.
CAPITAL	Capital stock.	Percentage of capital stock.	Regional real capital stock/regional GDP with natural logarithm.

Source: China Statistical Yearbook, Statistical Yearbooks of Provinces (Autonomous Regions and Municipalities Directly under the Central Government), China Population and Employment Statistical Yearbook.

.

4.2. Data Source

The data in this paper come from the 2003–2022 China Statistical Yearbook and China Financial Yearbook, the Statistical Yearbook and Financial Yearbook of each province (autonomous region and municipality directly under the central government), and the China Population and Employment Statistical Yearbook, and all the observations of the sample variables were computed and organized by the author. Considering that Tibet has a lot of missing data and is not included in the data sample, this paper adopts the panel data of 30 provinces (autonomous regions and municipalities directly under the central government) in mainland China from 2003 to 2022 as the primary research sample. The results of the statistical description of each variable are shown in

Table 3. Summary statistics.

Variable	Obs.	Mean	St. Dev.	Min	Max
THEIL	600	0.118	0.057	0.020	0.270
FINZ	600	0.082	0.087	0.021	0.805
GDP	600	8.717	0.737	7.102	10.410
URBAN	600	50.180	15.290	24.360	88.890
GOV	600	−1.639	0.435	−2.482	−0.412
EDU	600	1.595	0.267	1.109	2.269
STRUC	600	4.474	0.074	4.236	4.601
INFOR	600	1.623	0.508	0.361	3.159
TRADE	600	−1.677	0.997	−3.427	0.469
FDI	600	0.500	1.083	−4.534	2.794
MARKET	600	1.783	0.327	0.932	2.386
CPI	600	4.628	0.019	4.588	4.677
SAVING	600	3.159	0.238	2.443	3.579
CAPITAL	600	0.718	0.230	0.343	1.399

.

4.3. Modeling Setup

This part examines the impact of economic financialization on the functional income structure of residents and the income gap between urban and rural residents. Then, it examines the impact of economic financialization on the scale income gap and functional income obliquity of urban and rural residents. In order to ensure that the variables are smooth and reduce the adverse effects of extreme value fluctuations, this part takes the natural logarithm of each variable in the model. It performs 1% two-sided tailing and constructs a benchmark model (13):

$$THEI{L}_{it}={\alpha }_0+{\alpha }_{1}FIN{Z}_{it}+{\displaystyle {\sum }_j{\alpha }_{j}CONTRO{L}_{jit}}+{\lambda }_t+{\mu }_i+{\epsilon }_{it}$$

(13)

where i denotes province; t denotes time; THEIL_it denotes the proxy variable for the income gap between urban and rural residents in each region; FINZ_it denotes the degree of economic financialization in each region; α₁ denotes the degree of influence of economic financialization process on the income gap between urban and rural residents, respectively; CONTROL_jit denotes the control variable; α_j denotes the coefficient of influence of control variable; j denotes the jth control variable; λ_t and μ_i denote the time control effect and individual control effect, respectively; ε denotes the model random perturbation term; and α denotes the constant term. λ_it and μ₀ represent the time control effect and individual control effect, respectively; ε denotes the random perturbation term of the model; and α denotes the constant term.

5. Empirical Analysis

5.1. Baseline Regression Analysis

Referring to the existing literature and combining the data samples, after passing the Hausman test, this part selects the fixed-effects model for estimation. Panel data often face significant challenges, such as autocorrelation and heteroskedasticity, which the FE (fixed-effects) model struggles to address effectively. These problems could lead to distorted estimation results that undermine the reliability of the analysis. To tackle these issues, we will employ an FE-SCC (fixed-effects with robust standard error correction) model in this section. This advanced model would provide technical advantages and excel in correcting the problems of heteroskedasticity and autocorrelation in panel data, ensuring that our estimation results are both accurate and comprehensive compared to the traditional FE model [29].

Table 4. Benchmark regression.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
FINZ	0.170 ***	0.135 ***	0.134 ***	0.135 ***	0.128 ***	0.126 ***	0.151 ***
	(5.101)	(7.330)	(6.389)	(5.430)	(5.282)	(6.390)	(5.607)
GDP		0.001	0.001	−0.001	0.002	0.002	0.004
		(−0.038)	(−0.057)	(−0.232)	(0.410)	(0.861)	(1.484)
URBAN		−0.001 ***	−0.001 ***	−0.001 ***	−0.001 ***	−0.001 ***	−0.001 ***
		(−3.217)	(−3.232)	(−3.567)	(−2.860)	(−3.498)	(−2.956)
GOV			0.019	0.02	0.029 *	0.029 **	0.022 *
			(1.133)	(1.242)	(1.870)	(2.544)	(1.923)
EDU			−0.006	−0.007	−0.010	−0.011	−0.018 ***
			(−0.569)	(−0.725)	(−1.134)	(−1.452)	(−2.848)
STRUC				0.023	0.071 **	0.071 **	0.079 **
				(0.549)	(2.084)	(2.095)	(2.073)
INFOR				0.001	0.002	0.001	0.005
				(0.211)	(0.315)	(0.153)	(0.708)
TRADE					−0.015 ***	−0.015 ***	−0.014 ***
					(−15.248)	(−16.255)	(−9.300)
FDI					−0.001	−0.001	−0.001
					(−0.587)	(−0.648)	(−0.915)
MARKET						0.001	0.002
						(0.123)	(0.197)
CPI						0.176 ***	0.209 ***
						(3.074)	(3.699)
SAVING							−0.016 ***
							(−3.135)
CAPITAL							0.018 ***
							(3.222)
Constant	0.108 ***	0.159 **	0.205 **	0.113	−0.132	−0.948 ***	−1.122 ***
	(64.452)	(2.625)	(2.228)	(0.439)	(−0.640)	(−5.089)	(−5.810)
Obs.	600	600	600	600	600	600	600
Adjusted R2	0.764	0.778	0.780	0.780	0.798	0.800	0.808
Time Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Province Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Note: t-statistics are in parentheses; ***, **, and * denote significance test levels of 1%, 5%, and 10%, respectively.

reports the results of the FE-SCC estimation of the economic financialization process on the income gap between urban and rural residents. The results show that for the baseline estimation, gradually controlling for other economic factors, economic financialization consistently maintains a 1% significant positive effect on the income gap between urban and rural residents, i.e., as the process of economic financialization advances and develops, the sizeable income gap between urban and rural residents is also consistently widened by its influence, and Hypothesis 1 is confirmed. The difference in financial development between urban and rural areas generates different feedback mechanisms on the income structure and income scale of urban and rural residents. Specifically, the construction of the financial system in the urban sector is relatively sound, and financial credit and securities provide a variety of financial channels for urban groups to invest and finance and for enterprises to expand reproduction. This guarantees the enhancement of the income of urban residents and enables the continuation of urban residents’ wage income. With the decline in the marginal profit rate of the production sector, the investment of capital declines, and the high returns in the financial sector drive the acceleration of surplus capital transfer, resulting in the systematic separation of capital and the real economy. This, in turn, accelerates the financial penetration and the rise of commodity financialization and economic virtualization. The financial wealth effect increases, and ultimately strengthens the ability of residents to preserve and increase the value of their assets; the structure of the residents’ income shifts to property income, and the scale of income due to the retention of capital and the financial profits claimed is raised. On the contrary, in the rural sector, due to the relative backwardness of its financial system construction, the financial wealth effect is too complex to form a scale, and the rural financial efficiency is weak. Therefore, the rural assets are mostly taken from agricultural and nonagricultural labor generated by the accumulation of labor income. The lack of profitable assets hinders the scale of income, and then the development of economic and financial development of the rural residents of the net income of the wealth effect is limited. The result is that the income gap between urban and rural residents of scale has little effect.

5.2. Endogeneity Test

The FE-SCC fixed-effects estimation of the benchmark regression reveals that the positive relationship between the process of economic financialization and the income gap between urban and rural residents is significant, but endogeneity problems such as observation errors of the model’s proxies, unobservable omitted variables, and other endogeneity problems may cause bias in the model estimation results; therefore, this part of the design of instrumental variable regression analysis is designed to alleviate the bias of regression coefficients of economic financialization due to endogeneity problems.

This section takes the share-movement instrumental variable approach to construct Bartik-IV [63]. Bartik-IV, first proposed by Timothy Bartik in 1991, is constructed using an interaction term between regional industry-level shares and national industry-level growth rates [64]. This approach captures how regions or industries respond differently to the same exogenous shock. Borusyak et al. developed Bartik-IV by analyzing the relationship between labor supply (measured by employment growth rates) and wages (measured by wage growth rates) [65]. They created Bartik-IV, also known as the Shift-Share Instrumental Variable (Shift-Share-IV), by calculating the inner product of the employment share of a region’s industries and the national-level employment growth rate of those industries in the initial year [65]. The use of Bartik-IV addresses the endogeneity problem that arises when the residual term ε in the estimated equation for labor supply elasticity is correlated with the regional industry-level employment growth rate. After appropriately controlling for region, industry, and year-fixed effects, Bartik-IV remains uncorrelated with other residual factors that might influence wage growth in regional industries. At the same time, it shows a strong correlation with actual employment levels. In essence, Bartik-IV leverages the initial share composition of individuals and the aggregate growth rate to simulate estimates over time. The resulting estimates closely align with actual values but not with other residual factors. Thus, the Bartik-IV effectively addresses endogeneity issues resulting from omitted variables and reverse causation. It helps to mitigate the impact of unobservable factors, thereby avoiding biased estimations of regression coefficients and providing consistent estimation results [66].

The Bartik-IV in this section is constructed from the product of the lagged first-order economic financialization index FINZ_it−1 and the first-order difference of economic financialization index at that time ΔFINZ_t,t−1, i.e.,

$$Bartik-IV=FIN{Z}_{it-1}\cdot \mathsf{\Delta }FIN{Z}_{t,t-1}$$

(14)

This section selects Bartik-IV and lagged first-order economic gilt index as instrumental variables. Two-stage least squares estimation (2SLS) is carried out, and the estimation results are shown in

Table 5. Endogeneity test.

	Phase I	Phase II	Phase I	Phase II
	Economic Financialization	Theil Index	Economic Financialization	Theil Index
	(8)	(9)	(10)	(11)
Bartik-IV	18.293 ***		17.747 ***
	(5.174)		(5.156)
L.FINZ	0.885 ***		0.838 ***
	(14.439)		(13.195)
FINZ		0.213 ***		0.181 ***
		(10.181)		(6.855)
Control Variable	No	No	Yes	Yes
Obs.	570	570	570	570
Adjusted R2	0.901	0.784	0.904	0.833
Time Fixed Effect	Yes	Yes	Yes	Yes
Province Fixed Effects	Yes	Yes	Yes	Yes
Under-identifiability Test	22.683		29.822
Weak Instrumental Variables Test	1004.976		536.192
Over-Identification Test	0.845		0.747

Note: t-statistics are in parentheses; *** denotes significance test levels of 1%.

. The estimated coefficients of the instrumental variables in the first stage (columns (8) and (10)) are statistically significant and different from zero. The estimated coefficients of the economic gold melt index in the second stage (columns (9) and (11)) remain significantly positive. The results of the nonidentification test and the test of weak instrumental variables reject the original hypotheses, which indicates that the instrumental variables are nonidentifiable. The possibility of weak instrumental variables is relatively small, and, at the same time, the test of instrumental variables over-identification is accepted. Meanwhile, the test of over-identification of instrumental variables accepts the original hypothesis of “instrumental variables are strictly exogenous”, and the above tests indicate that the instrumental variables selected in this part are exogenous and effective. Therefore, it can be seen that after considering the endogeneity problem, the development of economic financialization still significantly inhibits the convergence of the income gap between urban and rural residents, which means that the conclusion of the baseline regression is robust and reliable, and further confirms the accuracy of Hypothesis 1.

5.3. Robustness Test

This part conducts robustness tests in the following four aspects, and the test results are shown in

Table 6. Robustness test.

	Substitution of Explained Variables	Substitution of Explanatory Variables	Excluding Municipalities	Correcting for Outliers
	(12)	(13)	(14)	(15)
	Ratio of Disposable Income of Urban and Rural Residents	Theil Index	Theil Index	Theil Index
FINZ	0.576 ***		0.176 ***	0.214 ***
	(6.215)		(3.181)	(8.812)
FINZ(PCA)		0.007 ***
		(7.720)
Constant	−0.729	−1.180 ***	−0.571 **	−1.240 ***
	(−0.753)	(−6.890)	(−2.652)	(−6.804)
Control Variable	Yes	Yes	Yes	Yes
Obs.	600	600	520	600
Adjusted R2	0.752	0.802	0.823	0.808
Time Fixed Effect	Yes	Yes	Yes	Yes
Province Fixed Effects	Yes	Yes	Yes	Yes

Note: t-statistics are in parentheses; *** and ** denote significance test levels of 1% and 5%, respectively.

.

(1)

Replacement of explained variables. This section selects the difference in disposable income between urban and rural residents as a proxy for the Theil Index to measure the relative gap between their incomes. Column (12) reports the corresponding test results. The results indicate that the coefficient of economic financialization continues to have a positive impact, suggesting that the baseline regression results remain robust after substituting the explained variables.

(2)

Replacement of explanatory variables. This part uses principal component analysis (PCA) to measure the development of economic financialization. PCA is designed to convert high-dimensional data into lower-dimensional forms via linear transformation. This process preserves as much variability as possible, eliminates redundant information, and reduces correlations among variables, leading to a more stable dataset. Column (13) reports the corresponding estimation results. The results indicate that the coefficient on economic financialization has a significant positive influence, suggesting that the baseline regression results are robust even after applying PCA to measure economic financialization.

(3)

Excluding the sample of municipalities. Beginning with the data, the categorization is adjusted based on various criteria. We test whether the results remain significant after making these adjustments. Next, we exclude municipalities and utilize data from the remaining provinces and autonomous regions to assess the robustness of the benchmark regression and ensure that the estimated results are universality. Column (14) reports the regression results after excluding the municipality sample. The results show that the regression results of the baseline model are unchanged after excluding the municipality samples.

(4)

Correcting for outliers. Outliers are data points that differ significantly from other observations, potentially leading to bias and distortion in estimation results. Correcting for outliers can eliminate their impact on these results. In this section, all the explanatory variables and the core explanatory variables are individually 1 per cent deflated, and this completes the estimation. Column (15) reports the corresponding results. The results show that after correcting for outliers, the coefficient of economic financialization is significantly positive, indicating that the effect of economic financialization on the income gap between urban and rural residents remains unchanged and the benchmark regression results are robust.

5.4. Heterogeneity Analysis

Given that geographic location factors and the quality of the actual economic development of the region may affect the effect of the development of economic financialization on the income gap between urban and rural residents, this part examines and tests the heterogeneity of the process of economic financialization affecting the income gap between urban and rural residents from the perspective of geographic regions and the quality of economic development, and the results of the test are shown in

Table 7. Heterogeneity analysis.

	Regional Heterogeneity in Geographic Distribution			Regional Heterogeneity in Quality of Economic Development
	Eastern	Central	Western	Leading	Catching-Up	Disadvantaged
	(16)	(17)	(18)	(19)	(20)	(21)
FINZ	0.081 ***	0.344 ***	−0.275 ***	0.085 ***	0.097	−0.181 ***
	(4.617)	(5.972)	(−7.277)	(4.002)	(1.318)	(−4.092)
Constant	−0.1	−0.071	−1.692 ***	−0.05	−0.167	−1.038 **
	(−0.123)	(−0.233)	(−12.202)	(−0.075)	(−0.807)	(−3.899)
Control Variable	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	220	160	220	200	280	120
Adjusted R2	0.795	0.918	0.939	0.798	0.919	0.953
Time Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes
Province Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes

Note: t-statistics are in parentheses; *** and ** denote significance test levels of 1% and 5%, respectively.

.

5.4.1. Regional Heterogeneity

China’s regional economic development has shown a significant trend of increasing from west to east, and the construction and development of regional financial systems are at different stages due to differences in the quality of economic development. One of the reasons for the disparity between urban and rural financial development is that the heterogeneity of urban–rural dualistic economic development has prompted a large concentration of financial capital in the urban sector, presenting a mismatch of local financial capital and financial speculative markets. Thus, economic financialization exhibits regional heterogeneity in the income gap between urban and rural residents. Columns (16)–(18) in Table 7 report the estimation results of economic financialization on urban–rural residents’ income gap in each region, and it can be found that economic financialization in the eastern region and the central region still maintains a significant expanding effect on the urban–rural residents’ income gap. The extent of the influence in the central region is more significant than that in the eastern region.

In contrast, in the western region, the development of economic financialization has been flipped, and it shows a significant inhibitory effect. Possible reasons are that the eastern and central provinces in the development of the financial industry are advanced, and the eastern region is relatively saturated with financial development, diminishing marginal returns on financial investment; the degree of expansion of the income gap tends to slow down; the central region has become the “front-runner” of the new development, the full utilization of financial resources to achieve the rapid growth of profit income. There is a structural change in the income of the residents, which is eventually reflected in the income gap. Structural changes are ultimately reflected in the widening income gap; western provinces are affected by location factors, resulting in a relative lag in the construction of infrastructure and financial systems. The process of economic financialization is in its early stages; the regional economic development of its increasing sensitivity to the urban and rural residents plays a pulling role in the total amount of income and promotes the convergence of the urban and rural residents of the western region’s income gap.

5.4.2. Heterogeneity in the Quality of Economic Development

In this section, we refer to the study by Deng and Cao on the analysis of regional differences in the level of high-quality economic development. This study divides the country into regions that are leading in high-quality economic development, regions that are catching up in high-quality economic development, and regions that are lagging in high-quality economic development [67]. Compared with the geographic division, the division by the quality of economic development is more conducive to reflecting the “club convergence” effect of regional economic development, and the test results are reported in columns (19)–(21) of Table 7. The results show that the coefficient of economic financialization’s impact is positive in leading and catching-up regions. However, the effect of financialization on the economy in catching-up regions is not significant. The possible reasons are that the leading regions are primarily developed eastern provinces, the financial service construction is relatively developed, and the financial speculation activities are frequent. In contrast, the catching-up region contains some provinces in the western region, and the actual economic development of these provinces, such as infrastructure construction and industrial production cycle, needs the financial system to promote the role of the financial system in order to be realized. The financial assets are more involved in the actual production than financial speculation. Therefore, the distributional effect of economic financialization is not apparent, but there is a potential expansionary effect. From the test results, the development of economic financialization in the backward regions shows significant inhibition, and the construction and popularization of financial services in the backward provinces are lagging. The industries are dominated mainly by primary and secondary industries, which leads to a lack of sensitivity to the development of the financial system between urban and rural areas. Therefore, once economic financialization has been promoted, the region’s production cycle and investment in assets can help the urban and rural residents to increase their income and reduce the income disparity between the urban and rural residents.

5.5. Further Analysis

Examining the effect of economic financialization on the functional income gap between urban and rural residents will help to explore the income distribution effect of economic financialization and its impact on the income gap between urban and rural residents at a deeper level. This part refers to the Theil Index measurement process described in the previous section. It calculates the wage income Theil Index and the property income Theil Index to measure the wage income gap between urban and rural residents and the property income gap between urban and rural residents, respectively, and the test results are shown in

Table 8. Functional income gap test.

	Wage Income Theil Index	Wage Income Theil Index	Property Income Theil Index	Property Income Theil Index
	(22)	(23)	(24)	(25)
FINZ	1.517 ***	1.096 ***	−0.345 ***	−0.245 **
	(9.831)	(5.957)	(−10.427)	(−2.090)
Constant	0.314 ***	0.324	0.218 ***	4.813 ***
	(40.341)	(0.098)	(130.440)	(3.502)
Control Variable	No	Yes	No	Yes
Obs.	600	600	600	600
Adjusted R2	0.593	0.689	0.362	0.434
Time Fixed Effect	Yes	Yes	Yes	Yes
Province Fixed Effects	Yes	Yes	Yes	Yes

Note: t-statistics are in parentheses; *** and ** denote significance test levels of 1% and 5%, respectively.

. As can be seen from the estimation results in Table 8, economic financialization shows a significant expansionary effect on the wage income gap between urban and rural residents, which implies that the industrial cycle and the production of value are more easily affected by the process of economic financialization, mainly when the penetration and spread of financial capital have boosted the prosperity and rise of the financial industry and even the pan-financial industry, which has directly led to the wage income of financial practitioners. At the same time, the monetization and borrowing of industrial capital have compressed the profits of industrial capitalists and the income of laborers, while the nonfarm income of rural laborers is still at a low level so that the wage income gap between urban and rural residents has been widened overall. On the contrary, the development of economic financialization on the property income gap between urban and rural residents is in the opposite direction, showing a significant inhibitory effect, which indicates that economic financialization has accelerated the development of the financial industry and the promotion of financial services and broadened the channels of financial investment for urban and rural residents, especially in the environment of high-speed development of information technology. The popularization of the Internet has made financial services more convenient and popular and alleviated asymmetric information, which has contributed to the increase in financial services between urban and rural residents and the increase in financial investment channels between urban and rural residents. Information asymmetry prompts the property income of urban and rural residents to be fully accumulated, ultimately narrowing the property income gap. However, the absolute impact of economic financialization on the property income gap (0.245) is relatively weaker than the effect on the wage income gap (1.096). In short, the impact of economic financialization on the income gap between urban and rural residents does not all stem from the property income gap. The structural gap in wage income plays a decisive role in the income distribution chain, and the above results verify the establishment of Hypothesis 2.

5.6. Spatial Effects Analysis

The external effects of financial development promote the inter-temporal flow of financial resources, improving the asset allocation efficiency of the financial cycle and providing space for the monopolistic accumulation of financial capital and the plundering and appropriation of financial profits. Therefore, examining the spatial spillover effect of economic financialization and testing whether the development of economic financialization has a spatial spillover effect on the income gap between urban and rural residents is crucial to verifying the mechanism of the income distribution effect of economic financialization.

5.6.1. Spatial Correlation Test

Moran’s I index is the most commonly used method to test the spatial autocorrelation of variables, specifically reflecting the spatial clustering of the observed variables as a way to identify the spatial autocorrelation properties and spatial dependence characteristics of the variables. Moran’s I index is defined as

$$Moran\text{'}s I={\displaystyle \frac{n}{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{w}_{ij}}}}}{\displaystyle \frac{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}}}{{\displaystyle \sum _{i=1}^n{\left(x_i-\overline{x}\right)}^2}}}$$

(15)

where x_i represents the ith regional observation, w_ij represents the spatial weight matrix, and n is the total number of regions. When Moran’s I ≥ 0, there is spatial positive autocorrelation of the observed variable; when Moran’s I ≤ 0, there is spatial negative autocorrelation of the observed variable; if the value of Moran’s I index is close to 0, it means that there is no spatial correlation of the observed economic variable and the variable is spatially randomly distributed.

Table 9. Economic financialization and income gap between urban and rural residents Moran’s I index.

Year	Economic Financialization		Income Gap Between Urban and Rural Residents
	Moran’s I Index	Z-Value	Moran’s I Index	Z-Value
2003	0.199 ***	4.643	0.134 ***	4.150
2004	0.203 ***	4.693	0.146 ***	4.530
2005	0.225 ***	5.146	0.157 ***	4.758
2006	0.225 ***	5.136	0.165 ***	4.904
2007	0.216 ***	4.959	0.164 ***	5.012
2008	0.222 ***	5.108	0.161 ***	5.036
2009	0.227 ***	5.205	0.177 ***	5.255
2010	0.236 ***	5.391	0.191 ***	5.447
2011	0.227 ***	5.191	0.190 ***	5.521
2012	0.229 ***	5.252	0.191 ***	5.429
2013	0.232 ***	5.299	0.192 ***	5.434
2014	0.231 ***	5.276	0.193 ***	5.467
2015	0.234 ***	5.340	0.189 ***	5.405
2016	0.234 ***	5.345	0.193 ***	5.438
2017	0.241 ***	5.521	0.171 ***	5.207
2018	0.240 ***	5.496	0.160 ***	5.089
2019	0.239 ***	5.466	0.166 ***	5.154
2020	0.238 ***	5.451	0.172 ***	5.186
2021	0.238 ***	5.458	0.172 ***	5.176
2022	0.239 ***	5.508	0.168 ***	5.213

Note: *** denotes significance test levels of 1%.

lists Moran’s I index to test the spatial clustering of economic financialization and urban–rural income disparity in 30 provinces in China. As can be seen from the table, the Moran’s I index of economic financialization and urban–rural residents’ income gap from 2003 to 2022 passes the 10% confidence level, which indicates that there is a significant positive spatial correlation between economic financialization and urban–rural residents’ income gap in each province of China, so it is necessary to consider the spatial spillover effect to explore the impact of economic financialization on the income gap between urban and rural residents.

5.6.2. Spatial Weight Matrix Selection

(1)

Neighbor weight matrix W₀₁

The neighbor weight matrix W₀₁ indicates the spatial correlation between two units, i and j, based on their adjacency. If the two spatial units are adjacent, the value in the matrix is 1, signifying that there is a spatial correlation between them. Conversely, if the two units are not adjacent, the value is 0, indicating a lack of spatial correlation. The neighbor weight matrix W₀₁ is as follows:

$$W_{01}=\left\{\begin{array}{c}1\\ 0\end{array}\right.\begin{array}{c}\\ \end{array}\left(i\ne j\right)$$

(16)

(2)

Geographic distance weight matrix W_geo

The geographic distance weight matrix W_geo is a spatial weight matrix created by taking the reciprocal of the geographic distance d_ij between two spatial units, i and j. The geographic distance between these two locations directly impacts the spatial correlation between them. When the geographic distance is greater, the weight assigned to that distance becomes smaller, indicating a lower degree of spatial correlation. Conversely, when the distance is shorter, the weight is higher, indicating a stronger spatial correlation. The geographic distance weight matrix W_geo is as follows:

$$W_{geo}=\left\{\begin{array}{cc}\frac{1}{d_{ij}}& i\ne j\\ 0& i=j\end{array}\right.$$

(17)

(3)

Economic distance nesting matrix W_eg

The economic distance nesting matrix, referred to as W_eg, is a spatial weight matrix that combines economic and geographic distance weights. This matrix takes into account the economically relevant attributes and the spatial-geographic distance characteristics between two spatial units, labeled as i and j. The economic distance nesting matrix W_eg is as follows:

$$W_{eg}=\left\{\begin{array}{cc}\left(\frac{1}{\left|PGD{P}_i-PGD{P}_j+1\right|}\right)\times e^{-d_{ij}}& i\ne j\\ 1& i=j\end{array}\right.$$

(18)

In the formula, PGDP_i and PGDP_j represent the GDP per capita of spatial units i and j, respectively, while d_ij indicates the spatial geographic distance between units i and j.

5.6.3. Spatial Effects Model Selection

When the spatial autocorrelation between economic variables is determined by the spatial lag term of the dependent variable, the spatial autoregressive model (SAR) is chosen. When the spatial autocorrelation between economic variables is transmitted through the omitted variables, the spatial correlation between the variables is represented by the error perturbation term, in which case the spatial error model (SEM) is used. When testing whether the financialization of the economy in a province has a spatial spillover effect on the urban–rural income gap in its neighboring provinces, a spatial Durbin model (SDM) is considered.

In order to select an appropriate spatial model to analyze the spatial transmission relationship between economic financialization and the urban–rural income gap, this paper will conduct the LM test, LR test, and Wald test, respectively. The results of the spatial effect test are shown in

Table 10. Spatial effects test.

LM Test	W01	Wgeo	Weg
Spatial Error:
Moran’s I	5.907 ***	38.917 ***	28.596 ***
Lagrange Multiplier	496.619 ***	1137.623 ***	642.252 ***
Robust Lagrange Multiplier	421.647 ***	829.402 ***	466.232 ***
Spatial Lag:
Lagrange Multiplier	79.821 ***	318.993 ***	183.103 ***
Robust Lagrange Multiplier	4.849 **	10.773 ***	7.082 ***
LR Test	W01	Wgeo	Weg
H0: SAR is better than SDM	58.060 ***	74.560 ***	77.950 ***
H0: SEM is better than SDM	71.680 ***	99.630 ***	91.840 ***
Wald Test	W01	Wgeo	Weg
H0: SDM is degradable to SAR and SEM	290.160 ***	321.380 ***	330.610 ***
Hausman Test	W01	Wgeo	Weg
H0: Individual effects and explanatory variables are not correlated	86.700 ***	758.65 ***	98.450 ***

Note: *** and ** denote significance test levels of 1% and 5%, respectively.

, which show that the LM-Error and LM-Lag tests are significant, and the LR test and Wald test reject the original hypotheses, so the SDM model should be selected. Further, the Hausman test rejects the original hypothesis that “individual effects and explanatory variables are not correlated” at the 1% level, so the fixed-effects model is chosen. Given the above results, this paper selects the fixed-effects SDM model as the spatial effect model.

5.6.4. Spatial Effects Model Estimation

This part chooses SDM to complete the test of the spatial spillover effect. The SDM takes the specific form:

$$\begin{array}{l}THEI{L}_{it}={\alpha }_0+\rho W\times THEI{L}_{it}+{\alpha }_{1}FIN{Z}_{it}+\delta W\times FIN{Z}_{it}\\ +{\displaystyle {\sum }_j{\alpha }_{j}CONTRO{L}_{jit}}+{\displaystyle {\sum }_j{\alpha }_{j}W\times CONTRO{L}_{jit}}+{\lambda }_t+{\mu }_i+{\epsilon }_{it}\end{array}$$

(19)

where W is the n-order proximity spatial weight matrix; W × THEIL_it is the weighted sum of the variables of the urban–rural income disparity in neighboring areas; $\rho$ is the spatial autocorrelation coefficient used to measure the effect of the spatial lag of the explanatory variables on the explanatory variables themselves; W × FINZ_it is the weighted sum of economic financialization of the neighboring regions. The definitions of the remaining variables are consistent with Equation (19).

This part not only completes the estimation of the SDM under different spatial weight matrices but also utilizes the method to decompose the total effect of SDM into direct and indirect effects to more clearly describe the spatial effect impact of economic and financial development. In this, the direct effect reflects the performance of the economic and financial development in affecting the income disparity between urban and rural residents of the region, and the indirect effect reflects the performance of the economic and financial development in affecting the income disparity between urban and rural residents of neighboring regions [68].

Table 11. SDM estimation results.

	Adjacency Weight Matrix		Geographic Distance Weighting Matrix		Economic Geography Nested Matrix
	(26)	(27)	(28)	(29)	(30)	(31)
FINZ	0.109 ***	0.094 ***	0.144 ***	0.115 ***	0.139 ***	0.106 ***
	(6.057)	(3.301)	(7.976)	(3.939)	(7.430)	(3.421)
W×FINZ	0.004 ***	0.008 **	0.006 ***	0.011 **	0.006 ***	0.011 **
	(2.908)	(1.980)	(3.169)	(2.011)	(3.687)	(2.142)
ρ	0.524 ***	0.480 ***	0.654 ***	0.591 ***	0.462 ***	0.444 ***
	(6.355)	(5.242)	(10.999)	(7.418)	(3.966)	(3.631)
Direct Effect	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***
	(6.142)	(7.857)	(5.644)	(8.180)	(5.883)	(8.992)
Indirect Effect	0.122 ***	0.107 ***	0.154 ***	0.123 ***	0.145 ***	0.112 ***
	(6.240)	(3.477)	(7.964)	(4.030)	(7.590)	(3.455)
ALL Effect	0.140 ***	0.134 ***	0.328 ***	0.252 ***	0.172 **	0.162 **
	(3.276)	(2.647)	(3.807)	(3.337)	(2.316)	(2.117)
Control Variable	No	Yes	No	Yes	No	Yes
Obs.	600	600	600	600	600	600
Adjusted R2	0.262	0.236	0.421	0.36	0.308	0.285
Time Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes
Province Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes

Note: t-statistics are in parentheses; *** and ** denote significance test levels of 1% and 5%, respectively.

reports the SDM estimation results and effect decomposition. The results show that by using different spatial weight matrices, economic financialization consistently has a significant positive impact of 1% on the income gap between urban and rural residents. The regression coefficient of economic financialization in neighboring regions maintains an expansionary effect above the significant 5% confidence level, suggesting that economic financialization development in neighboring or surrounding regions inhibits the convergence of the income gap between urban and rural residents in the region. Possible explanations are the following: economic financialization has changed the financial capital circulation pattern, giving the financial system more substantial external attributes and realizing the allocation of financial capital across time and space flows. This has resulted in the unequal development of inter-regional economic realities, followed by a large number of urban and rural nonagricultural laborers in the pursuit of high capital and high returns to the pursuit of the trans-regional influx of developed provinces to engage in the value of the production of work. This has increased the scale of urban income and improved the structure of income and the low return on investment of agricultural production, making the rural labor force engage in value production work. The low rate of return on investment in agricultural production makes it difficult for the rural labor force to form capital on a large scale, enlarging the differences in the volume of the labor force and the gap in the value of scale between regions and aggravating the unbalanced evolution of urban–rural income distribution. From the estimation results, we can also see that the spatial autocorrelation coefficient ρ is significantly positive under different spatial weight matrices. The urban–rural income gap exhibits significant spatial spillover effects. It means that trends often influence the widening or narrowing of the income gap in neighboring regions and provinces. This phenomenon leads to the “club convergence” concept concerning the income gaps between provinces.

From the estimation results of the spatial effect decomposition in Table 11, under different spatial weight matrices, the direct, indirect, and total effects of economic financialization on the income gap between urban and rural residents are all significantly positive. The indirect effect is greater than the direct effect, which means that economic financialization of the region pulls open the income gap between urban and rural residents in that region while significantly inhibiting the contraction of the income gap between urban and rural residents in the neighboring region or the surrounding provinces. Thus, Hypothesis 3 is confirmed by the test. Marginal investment returns in areas with a high concentration of financial capital have declined, leading to accelerated spatial and temporal “flight” of financial capital, and neighboring areas have begun to enjoy the regional economic development, industrial upgrading of towns and cities, and improvement of residents’ incomes. The possibility of income differentiation between urban and rural residents in neighboring regions is also magnified; compared with regions with a high concentration of financial capital, regions with a low concentration of financial capital do not have a high degree of economic financialization but have abundant financial profit margins, so the inter-temporal spillover effect of economic financialization will inevitably make the urban sector in financially backward regions give priority to obtaining external financial resources, promote local enterprises to absorb financial capital and complete the expansion of reproduction, and further absorb social surplus labor for nonagricultural labor. The level of laborers’ wages and incomes is raised, the functional income structure is improved, and the scale of incomes is significantly better than that of traditional agricultural production. The income gap between urban and rural residents is further widened.

6. Discussion

Our study reveals important findings about economic financialization and its effects on income distribution.

First, we measure economic financialization with comprehensive indicators, which include not only the actual operation of macro-financial development but also the meso financial industry development and micro-personal financial assets situation into the measurement of comprehensive indicators of economic financialization, reflecting the complexity of economic financialization, which coincides with the research methodology of Jiao and Chen [24].

Secondly, economic financialization presents a significant impact on the income distribution of urban and rural residents, manifested in that the sizeable income gap between urban and rural residents continues to widen as the degree of economic financialization deepens. The relatively sound construction of the financial system in the urban sector has made the industrial cycle in urban areas continue to run due to the injection of financial capital, which has guaranteed the stability of urban residents’ wage income and, at the same time, given urban residents more diversified financial investment channels and ways to improve their income. As the marginal profit rate of the production sector declines, capital investment declines, and the high return of the financial sector drives the accelerated transfer of surplus capital, resulting in the systematic separation of capital and the real economy. At this time, the wealth effect of the financial sector becomes a sought-after object of society. The structure of the resident’s income shifts to the property income, and the income scale is increased by the capital retention and the claim of the financial profit. Because financial capital is concentrated in the urban part, rural residents find it challenging to remove the relatively backward rural financial system and weak rural financial efficiency, which seriously impedes the generation and accumulation of rural financial capital and increases the difficulty in the formation of rural income scale, resulting in a large gap between urban and rural residents in the scale of income. This finding validates Goldman’s findings on the deprivation of rural capital by financialization [69]. Therefore, to keep the economy’s financialization within a reasonable range, we should also actively guide the introduction of healthy urban capital into rural areas and increase the stock of rural capital. This will have a positive effect on optimizing rural industries and increasing rural residents’ incomes.

In addition, we further explore the impact of economic financialization on the functional income gap between urban and rural residents. We find two different outcomes of economic financialization on the functional income gap between urban and rural areas. In particular, economic financialization significantly expands the wage income gap between urban and rural residents. In contrast, it acts in the opposite direction on the property income gap between urban and rural residents, showing a significant suppression effect. This finding is dissimilar to the findings of Karsten et al. who agreed that financialization can significantly affect the distribution of functional income, and, in particular, financial openness and household debt have a significant negative impact on the distribution of capital income, which is an important reason for exacerbating the income wage income gap [70]. However, Karsten et al. lack an analysis of the impact of financialization on the distribution of working-class property income, and our findings complement this research gap. Economic financialization has accelerated the development of the financial industry and the promotion of financial services, and financial inclusion has broadened the financial investment channels of the residents, which has contributed to the enrichment and accumulation of their property income and narrowed the inter-group property income gap. Rationally utilizing the characteristics of financialization in the economy and increasing the proportion of property-based income for residents will effectively alleviate the income gap between urban and rural areas.

Finally, we turn the research perspective to the spatial effect of economic financialization to verify the spatial impact of economic financialization on the income gap between urban and rural residents. As French et al. said, financialization is a profound spatial phenomenon, and both its generative process and economic impact are spatially related [71]. Through the spatial spillover effect, the widening or narrowing of the urban–rural income gap in a region is not only related to the degree of economic financialization in the region but also affected by the spillover effect of economic financialization in neighboring regions, with a certain degree of “club convergence”. We agree with French et al. on the spatial impact of financialization. By utilizing the spatial spillover effect, we can not only cool down the overheated region of economic financialization but also compress financial bubbles and reduce financial instability. Additionally, this effect allows for the transfer of financial resources to areas with lower levels of financialization, significantly meeting their financial demands. As a result, the industrial chain of the local economy is accelerated, leading to increased labor force remuneration due to expanded production. This process can help alleviate income inequality. At the same time, neighboring regions will also experience a more balanced level of financialization in their economies.

Although our study supplements the issue of the connotation of economic financialization and its income distribution effect and verifies the impact of economic financialization on the income gap between urban and rural residents through empirical distribution, there are certain shortcomings in this study. First, to facilitate the conceptual definition, measurement, and evaluation of economic financialization, this study takes the proportion of real estate added value to regional GDP as an indicator for measuring real estate financialization. However, real estate can be divided into available commercial housing and guaranteed housing according to its attributes. The former has financial attributes, and the latter does not, so there are errors in the measurement. Second, in analyzing the impact of economic financialization on the income gap between urban and rural residents, this study attempts to consider the impact of factors such as government behavior and incorporates government payments as a share of regional GDP in the empirical model to reflect actual government behavior. However, due to the limitations of the current methodology for quantifying institutional factors, this study fails to comprehensively consider the impact of institutional factors on the development of economic financialization when constructing the model for analysis. Also, it fails to consider how the institutional differences across regions play a role in the actual impact of economic financialization on the urban–rural residents’ income gap.

Therefore, future research should refine the internal characteristics of economic financialization in order to reveal the complexity of economic financialization to explore income distribution more comprehensively, especially within the framework of China’s urban–rural dual economic structure. Determining how to rationally use financialization to achieve fairness in income distribution between urban and rural areas will be the focus of future research.

7. Conclusions and Suggestions

7.1. Conclusions

Based on the logic and mechanism of political economy theory, this paper analyzes and summarizes the effect of economic financialization on the income gap between urban and rural residents. First, economic financialization is derived from the monetization and virtualization of industrial capital, and the essence of its income distribution is the division of surplus value and the plundering and appropriation of labor income. Secondly, economic financialization magnifies the disparity in gaming power and class conflicts between the profit-grabbing and working classes, with urban and rural conflicts particularly prominent. The main reason for economic financialization to widen the income gap between urban and rural residents is the gap in wage income between urban and rural residents. In contrast, the property income is relatively convergent. Third, empirical tests on China’s provincial panel data found that financialization of the economic development has curbed the convergence of the income gap between urban and rural residents, showing evident regional heterogeneity; economic financialization has significantly widened the wage income gap between urban and rural residents and also pushed up the consistency of urban and rural residents’ property incomes. Fourthly, there is a spatial spillover effect of economic financialization; the widening or narrowing of the income gap between urban and rural residents in the region is not only related to the degree of economic financialization in the region. However, it is also affected by the spillover effect of economic financialization in neighboring regions, and there is a specific “club convergence” effect.

7.2. Suggestions

This paper draws the following suggestions:

(1)

The supervision and control of economic financialization should be based on the careful consideration of the market currency circulation, the level of direct financial development, the debt level of the economic sector, the degree of capital aggregation, the degree of development of the financial market, and the size of the assets of the new profit-grabbing class. At the same time, there should be further deepening of the reform of the financial system, optimization of the financial innovations and the structure of the financial capital, curbing of the expansion of consumer credit and the financialization of the commodities, and limiting of the frequency of the financial capital’s departure. This would restrict the frequency of financial capital excursions, avoiding overheating of economic financialization, and continuing to achieve “de-virtualization to realism” or “slowing down virtualization to promote realism”, to guarantee the high-quality and sustainable economic development. To achieve high-quality financial development, operating with low leverage in the market is important. The focus should be on the quality and excellence of financial services, with a strong emphasis on serving the real economy. Stricter credit restrictions should be applied to high-risk areas such as real estate and nontargeted financing. Additionally, regulations should govern various financial activities to prevent the misuse of financial derivatives and other risky practices.

(2)

Full play should be given to the important role of the policy-based financial system in social distribution, including regulation of the mechanism of wealth accumulation, focusing on changes in the tilt of the functional income structure of the population, correctly guiding the financial capital for the development of the real economy, revitalizing the high-quality development of the real economy, optimizing the industrial structure and the industrial system, and clearing the resistance to industrial production. These will improve labor compensation in the industrial cycle, and guarantee the high-quality and sustainable development of the economy. Labor remuneration in the industrial cycle should be improved, residents’ labor income should be protected, the wealth accumulation mechanism should be optimized, and people’s well-being should be continually enhanced.

(3)

Inclusive rural financial services should be deepened, the construction of financial systems should be improved to support and assist agriculture, differentiated and distinctive rural financial services should be designed, concerning the actual needs of different regions, there should be a reduction in the cost of rural financing, and a focus on supporting the development of local industries. The enhancement of rural labor incomes should be taken as a long-term development plan, and the channels connecting rural wage incomes and property incomes should be opened up, along with raising the proportion of property incomes of rural households. Rural residents can increase their incomes, alleviate long-term class conflicts and economic frictions between urban and rural areas, and ensure the sustainability of a balanced distribution of residents’ incomes. For example, with the rural inclusion policy as the mainstay, the coverage of rural financial institutions (such as village banks and rural credit unions) has been expanded, and, based on big data analysis, special service loans for “rural revitalization” have been promoted to support the optimization and upgrading of the rural industry and to increase the income of rural residents from the agricultural economy while realizing the growth of the rural economy.

(4)

Full play should be given to the role of developed provinces and regions as “leaders”, correctly utilizing the financial spatial spillover effect, and actively guiding the input of financial resources to regions with a relative lack of financial services to guide the realization of a sustainable equilibrium state of economic and financial development at the spatial and temporal levels. This will promote the convergence of the income disparity between urban and rural residents, and sustainably promote the high-quality development of the regional economy and the people’s sense of well-being. By focusing on developed regions and utilizing innovative digital financial and information technology, we can extend valuable financial resources to neighboring areas that lack financial access. This approach aims to achieve a more balanced distribution of financial resources and to maximize the critical role of financial services in supporting the real economy. Ultimately, this strategy will contribute to national economic growth and promote a fairer income distribution among residents.