The Connotation of the Era, Long-term Obstacles and Path to Realization of Digital Enabling New Urbanization and Common Wealth

Common wealth is both the essential requirement of socialism and the key goal of Chinese-style modernization. Chinese-style modernization is the modernization of common wealth for all people, which is determined by the nature of the system of socialism with Chinese characteristics, and inevitably requires that the new type of urbanization must promote the realization of common wealth for urban and rural residents of different classes, statuses and regions in a coordinated manner [1–2]. Among many national strategies, new urbanization with people at its core plays an important role in enhancing the balance, coordination and inclusiveness of development, enlarging the size of middleincome groups, raising the income of urban and rural residents and the level of equalization of basic public services, and promoting the common wealth of people’s spiritual and cultural life [3–5].

Urbanization is a natural historical process of non-agricultural industries gathering in towns and cities and rural population transferring to towns and cities driven by industrialization, which is an important symbol of national modernization and an inevitable path [6–7]. Empowering Chinese-style new urbanization with digital economy is a landmark embodiment of the combination of China’s historical urbanization process and national conditions [8]. The goal of realizing the common prosperity of all people puts forward higher requirements for the construction of new urbanization with people at the core. On the one hand, the construction of new-type urbanization must be integrated to promote the economy to achieve effective qualitative improvement and reasonable quantitative growth, so as to realize common prosperity in high-quality development [9–11]. On the other hand, the construction of new urbanization should be organically combined with rural revitalization, and efforts should be made to solve the problem of unbalanced development between urban and rural areas, so that the fruits of development can benefit all people in a greater and fairer way [12–14]. It is necessary to actively and steadily promote urbanization, and promote the reasonable division of labor, complementary functions and synergistic development of large, medium and small cities and towns [15]. It is also necessary to simultaneously promote the strategy of rural revitalization, strengthen industry to feed agriculture, towns and cities to support rural areas, and realize the benign interaction between industrialization and urbanization, and the mutual coordination of urbanization and agricultural modernization [16].

Literature [17] analyzed the spatial evolution and influencing factors of the coupled coordination of new urbanization and common prosperity, and found that the coupled coordination degree of new urbanization and common prosperity has significant regional variability and spatial agglomeration effect, and the level of science and technology expenditure has the greatest influence on the evolution process. Literature [18] based on the results of empirical investigation and research elaborated the importance of digital economy to achieve common prosperity, digital economy can optimize resource allocation, enhance the level of technological innovation, promote the coordinated development of urban and rural areas, and ultimately achieve common prosperity. Literature [19] shows that the digital economy empowers rural revitalization to become a new direction for the coordinated development of urban and rural areas, and by promoting the in-depth reform of the digital economy, preventing and resolving potential risks in the process of development, gradually narrowing the development gap between urban and rural areas, and ultimately realizing common prosperity. Literature [20] elucidates the outstanding advantages of digital technology-enabled rural revitalization and the real problems faced in the development process, puts forward countermeasures and suggestions such as increasing the integration of industrial technology development and constructing a support mechanism for digital elements, so as to achieve the goal of common prosperity by promoting the high-quality development of the countryside. Literature [21] studied the impact and mechanism of digital countryside construction on urban-rural income gap, digital countryside construction on the one hand can narrow the rural industrial agglomeration, on the other hand, through the establishment of rural digital talent cultivation mechanism, through the introduction of digital talents, inhibited the further expansion of urban-rural income gap. Literature [22] addresses the digital divide problem in high-quality digital rural construction, proposes the TechnologyOrganization-Environment (TOE) framework for identifying the configuration paths required for digital rural construction, and finds that digital rural construction presents a comprehensive development pattern in coastal provinces, while presenting a technology-driven development pattern in inland provinces, which provides valuable insights for solving the digital divide problem.

This paper dismantles the digital empowerment of new urbanization construction, concentrates on exploring the relationship between digital inclusive finance, new urbanization and common wealth, and devotes itself to proposing the realization path for the long-term development of the three. Based on the role of digital inclusive finance in the development of urbanization, the model hypotheses of digital inclusive finance, new urbanization development, and common wealth are proposed respectively. The research model is built around the dependent variable of common wealth and the regulating variable of digital inclusive finance. Statistical data for each variable is used to carry out the benchmark regression test and the mediation effect test respectively. Using the spatial econometric model, the spatial correlation test of digital inclusive finance with the level of new urbanization and common wealth development is conducted to verify the research hypotheses.

2

Digital financial inclusion, new urbanization and shared prosperity

2.1

Logic of the Theory of Common Wealth

Common wealth is the essential requirement of socialist core values and an important feature of modernization. Common prosperity requires all people to strive to eliminate polarization and absolute poverty, achieve differential prosperity on the basis of universal prosperity, and ultimately achieve a high quality of life for all.

Marx and Engels linked common prosperity to the highest goal of socialism, and profoundly analyzed that “modern bourgeois private ownership is based on class antagonism, on the exploitation of some by others”. He emphasized that the future society would be a “collective association based on the common possession of the means of production”. Marx pointed out that the realization of socialist common wealth to eliminate class antagonisms “presupposes the tremendous growth and high development of the productive forces”. Only by realizing a high degree of development of the productive forces in a socialist country can the effective supply of social resources be fully ensured. After all people have achieved great material prosperity, they will seek the free and comprehensive development of human beings, realizing “the leap of mankind from the Kingdom of Necessity into the Kingdom of Freedom”. The core essence of common wealth is to focus on sharing the fruits of development and fairness and justice. The effective deployment of the means of production under public ownership is guaranteed, as is the right of the people to acquire wealth and become rich together, ultimately realizing the principle of “distribution according to need according to the best of one’s ability”.

The core idea of common wealth is mainly embodied in the aspects of system guarantee, economic foundation, main force, goal orientation and ultimate purpose. Marx and Engels believed that the socialist public ownership of the means of production is the institutional foundation of common wealth, and that the common wealth of all can be realized only under the leadership of the proletariat. Through the analysis of the productive forces and production relations, it can be seen that the common wealth takes advantage of the highly developed productive forces and extremely rich material resources of socialism as its economic foundation. The common wealth relies on the people as the main force, and aims to realize the fundamental interests of the people and to share the fruits of development with the people, so as to promote the all-round development of human beings and to achieve a high degree of unity between the spiritual realm and the material world.

2.2

New urbanization

The core concept of new-type urbanization is people-oriented, not at the expense of agriculture and food, ecology and the environment, focusing on farmers and covering rural areas, realizing the integration of urban and rural infrastructure and the equalization of public services, promoting economic and social development and achieving common prosperity. New urbanization is urbanization that integrates urban and rural areas, complements urban and rural areas, is ecologically livable and develops harmoniously, and is urbanization in which cities, towns, and rural communities develop in a coordinated manner and promote each other’s progress [23].

According to existing literature, the concept of new urbanization is enhanced by the following five aspects: population urbanization, social urbanization, life urbanization, environmental urbanization, and economic urbanization.

2.3

Digital financial inclusion

Inclusive finance means that appropriate and effective financial services can be provided to all classes and groups with financial needs, unlike traditional finance, which mostly serves rich groups with a certain amount of assets, and financial institutions, which are mostly built in economically developed metropolitan areas. Inclusive finance focuses on small and micro enterprises, farmers, urban low-income people, and other special groups. The creation of inclusive finance has benefited more groups and extended the breadth of coverage, and only by vigorously developing inclusive finance can we promote the sustained and balanced development of the financial industry and enhance social equity and harmony.

3

Variable selection and modeling

3.1

Theoretical analysis and research hypothesis

3.1.1

Digital financial inclusion affects the development of new urbanization

The development of digital inclusive finance can not only integrate disadvantaged groups into the formal financial service system, but also rely on the use of digital technology to establish a new credit model and financial system, which in turn has a direct or indirect impact on the construction of new towns and cities, as follows: 1)

Raising residents’ income and alleviating the gap between the rich and the poor

In order to alleviate the gap between urban and rural income levels, so that each group can effectively and fairly obtain the corresponding financial products and services, digital inclusive finance has become a bridge to introduce funds into rural areas.

2)

Optimize resource allocation and promote industrial upgrading

As the positive effect brought by digital inclusive finance for industrial upgrading helps to promote the advanced production technology and production capacity within the scope of industrial investment to form a gathering momentum in towns, thus upgrading the industrial structure is the key driving force to promote the development of new urbanization.

3)

Alleviating Financing Constraints and Assisting Enterprise Growth

By alleviating the financing constraints of small and medium-sized enterprises, digital inclusive finance not only helps enterprises to increase R&D investment to change the traditional production and working methods and improve the profitability of enterprises, but also attracts more labor, expands the scale of production and operation, and ultimately increases the government’s financial income and drives economic development, which in turn improves the level of new urbanization.

4)

Enhance environmental awareness and promote green development

Digital inclusive finance is in line with the country’s green development concept, and to a certain extent, it can promote the development of green economy, make better use of the external parts of environmental regulation, fully promote the construction of environmentally friendly society, and help the high-quality development of new urbanization.

Therefore, by combining the literature review and theoretical analysis conducted earlier, this paper formulates the following research hypotheses:

Hypothesis H1: Digital inclusive finance can positively promote the comprehensive development level of new urbanization.

3.1.2

Digital financial inclusion for shared prosperity

The development of inclusive finance is a crucial method for achieving common prosperity. The development of digital inclusive finance can realize the universality of financial development, help alleviate the problem of unbalanced and insufficient financial development, and enable market players and the people to share the fruits of financial development, which is one of the important means to promote common prosperity. The development of digital inclusive finance can rationally allocate more financial resources to key areas and weak links, thus consolidating the material foundation of common prosperity. Through the development of digital inclusive finance, the coordination and balance of development can be improved. It can narrow the gap between the rich and the poor and serve rural revitalization, thus promoting the comprehensive integration of urban and rural development.

Digital inclusive finance can improve the lives of the poor and enhance the overall well-being of society by providing more financial services and resources. It can help poor people obtain more financial services, improve their quality of life, and raise their income levels, thus promoting common prosperity.

In addition, government enterprises can make better use of the power of science and technology with the help of digital inclusive finance to improve the efficiency and quality of financial services, improve the accessibility of financial services, and government enterprises can make better use of big data and artificial intelligence technology to improve the safety and reliability of financial services, thus promoting common prosperity. Therefore, this paper proposes the following hypotheses:

Hypothesis H2: Digital financial inclusion can promote common wealth.

3.1.3

New urbanization for common prosperity

As a core part of the “New Four Harmonizations”, the construction of new urbanization is a powerful means to break the major social contradictions of the new era and solve the problem of unbalanced and insufficient development, as well as an important driving force for the construction of a socialist modernized country and the realization of common prosperity for the entire population. On the one hand, by accelerating the optimization and upgrading of industrial structure, expanding market investment and consumption demand, upgrading the capacity of urban public services, and stimulating the vitality of social innovation and entrepreneurship, the construction of new urbanization has led to the full liberation of social productive forces, shifted to high-quality development of the economy, and provided a material guarantee for the realization of common prosperity. On the other hand, the construction of new urbanization has promoted the complementary development of urban and rural areas by accelerating the flow of urban and rural factors and optimizing the allocation of urban and rural resources. At the same time, the positive externalities of agglomeration enable rural areas to share the advanced productivity of cities, effectively promote the development of agricultural modernization, enhance farmers’ income, and narrow the gap between urban and rural development and income. Accordingly, this paper proposes:

Hypothesis H3: The construction of new urbanization has a positive impact on the high-quality development of the economy and the narrowing of the urban-rural development gap, and can effectively promote common prosperity.

3.1.4

Spatial spillover effects of new urbanization on shared prosperity

Everything has a certain degree of correlation, with the closer the distance, the stronger the correlation, and the further the distance, the weaker the correlation. Neighboring regions usually have similar economic foundations, political backgrounds, and cultural environments. Based on similar development conditions, the convergence process of neighboring regions shows the characteristics of convergence.

The theory of agglomeration and diffusion points out that agglomeration can lead to the aggregation of resources and produce spatial spillover effects and externalities. Diffusion, on the other hand, can produce diffusion effect and realize the flow of resources to the surrounding areas. The construction of new urbanization realizes the agglomeration of human, material and financial resources, and when the agglomeration of resources exceeds its own carrying capacity, part of the resources will diffuse to the surrounding areas, thus generating spatial spillover effects. Accordingly, this study proposes the hypothesis.

Hypothesis H4: The spatial spillover effect generated by new urbanization will affect the common prosperity process in the surrounding adjacent areas.

3.2

Study design

3.2.1

Model setup

1)

Benchmark regression model

In order to verify the impact effect of digital inclusive finance on new urbanization and validate hypothesis H1. And ensure the smoothness of the data, so that the residuals of the model show random characteristics and alleviate the impact of covariance and heteroskedasticity on the empirical results, all variables are logarithmized. This paper constructs the model as follows: 1 $l n N E W_{i t} = β_{0} + β_{1} l n D I F_{i t} + \sum β_{j} C o n t r o l_{i t} + μ_{i} + v_{t} + ε_{i t}$

InNEW denotes the New Urbanization Composite Index and InDIF denotes the Digital Inclusive Finance Index. i and t denote city and year, respectively. β denotes estimated parameters, μ_i denotes unobservable area effects. ν_t denotes unobservable time effects, and ε_it is a random perturbation term. Control denotes a control variable.

In order to verify the promotion effect of digital financial inclusion on common wealth and test hypothesis H2, the following econometric model is constructed: 2 $C O P_{i, t} = α_{0} + α_{1} D i f_{i, t} + α_{k} C o n t r o l s_{i, t} + μ_{i} + δ_{t} + ε_{i, t}$

In order to study the impact of new urbanization on common wealth and to test hypothesis H3. This paper uses a two-way fixed effect model for regression analysis: 3 $C P_{i t} = α + β U R_{i t} + δ X_{i t} + μ_{i} + λ_{t} + ε_{i t}$ where i and t denote province and year respectively. CP_it is the common wealth index of province i and t, UR_it is the new urbanization level of province i in year t, and X_ut represents a series of control variables. μ_i denotes region fixed effects, λ_t denotes year fixed effects. ε_i is a random disturbance term, and β is the coefficient to be estimated, which is predicted to be significantly positive.

2)

Mediating effects model

Mediating effect refers to exploring the ways or means through which the independent variable affects the dependent variable. In order to further analyze through which paths the independent variable of digital financial inclusion affects the dependent variable of new urbanization, the mediation effect model is established as follows: 4 $l n M E D_{i t} = γ_{0} + γ_{1} l n D I F_{i t} + \sum γ_{j} C o n t r o l_{i t} + μ_{i} + v_{t} + ε_{i t}$

lnMED_it denotes the mediating variable and InDIF denotes the Digital Inclusion Index. i and t denote city and year, respectively, γ denotes estimated parameters, μ_i denotes unobservable area effects. ν_t denotes unobservable time effects, and ε_it is the random disturbance term.Control denotes the control variable. i.e: 5 $l n N E W_{i t} = t_{0} + t_{1} l n D I F_{i t} + t_{2} l n M E D_{i t} + \sum t_{j} C o n t r o l_{i t} + μ_{i} + v_{t} + ε_{i t}$

InNEW denotes the composite index of new urbanization, InDIF denotes the digital financial inclusion index, lnMED_it denotes the mediator variable, i and t denote the city and year, respectively, t denotes the estimated parameter, μ_i denotes the unobservable area effect, ν_t denotes the unobservable time effect, ε_it is a random perturbation term, and Control denotes the control variable, which consists of the share of actual utilized foreign capital (InNEW), degree of fiscal decentralization (LnNEW), and financial scale (InFIN). The size of the mediation effect can be calculated according to Equation (4) and Equation (5), and the mediation effect is calculated as: 6 $M e d i a t i o n e f f e c t = \frac{γ_{E s t i m a t i o n p a r a m e t e r} t_{y e a r}}{t_{E s t i m a t i o n p a r a m e t e r}}$

3)

Spatial measurement model

(1)

Spatial measurement weight setting

The spatial inverse distance weight matrix is a matrix that reflects the inverse proportionality between distance and spatial effect. The closer the distance between two prefectures, the stronger the spatial effect between them, and conversely, the farther the distance between two prefectures, the weaker the spatial effect between them. The formula for the spatial inverse distance weight matrix is as follows: 7 $W = {\begin{matrix} d_{i j}^{- a} β_{i j}^{b} & i \neq j \\ 0 & i = j \end{matrix}$ where a and b denote the exogenous distance friction coefficients and boundary sharing effect coefficients, respectively. d_ij represents the distance between spatial sections a and b. β_ij represents the ratio of the length of the two shared boundaries to the total boundary length of sample i.

(2)

Spatial correlation test

In order to test whether there is spatial correlation of the explanatory variables or the core explanatory variables, the Moran’sl index is usually used, and the Moran’sl index is divided into GlobalMoran’sl index and LocalMoran’sl index, and the calculation formula is as follows: 8 $Global Moran's I = \frac{\sum_{i = 1}^{n} \sum_{j = 1}^{n} W_{i j} (Y i - \bar{Y}) (Y j - \bar{Y})}{S^{2} \sum_{i = 1}^{n} \sum_{j = 1}^{n} W_{i j}}$ 9 $Local Mora n^{'} s I = \frac{(Y i - \bar{Y}) \sum_{j = 1}^{n} W_{i j} (Y j - \bar{Y})}{S^{2} / n}$ where $S^{2} = \sum_{i = 1}^{n} {(Y i - \bar{Y})}^{2}$ , $Y = \frac{1}{n} \sum_{i = 1}^{n} Y i$ , Yi denotes observations for city i, and Y_j denotes observations for city j, and W_ij is the spatial inverse distance matrix.

The value of Moran’s index is between -1 and 1. If the value is significantly positive, it means positive spatial autocorrelation, if the value is significantly negative, it means negative spatial autocorrelation, and if the value is zero, it means no spatial autocorrelation exists. The larger the absolute value of Moran’s index is, the larger the spatial correlation is [24–25].

(3)

Spatial measurement modeling

The common spatial measurement models are spatial autoregressive model (SAR), spatial error model (SEM) and spatial Durbin model (SDM). Spatial autoregressive models (SAR) are used to measure the magnitude of endogenous interaction effects by including the lagged terms of the explanatory variables. Spatial error models (SEM) are used to measure the interaction effect between different errors by including the spatial lag of the error term. The Spatial Durbin Model (SDM), which incorporates the lagged terms of both the explanatory and explanatory variables, is used to measure the magnitude of all interaction effects simultaneously.

The spatial autoregressive model was constructed as follows: 10 $l n N E W_{i t} = β_{1} l n D I F_{i t} + \sum β_{j} C o n t r o l_{i t} + η W l n N E W_{i t} + ε_{i t}$

lnNEW denotes the comprehensive index of new urbanization, InDIF denotes the digital financial inclusion index, β denotes the general regression coefficient, e denotes the random error term, n is the spatial autoregressive coefficient, W is the spatial inverse distance weight matrix, and Control denotes the control variable.

The spatial error model is constructed as follows: 11 $l n N E W_{i t} = β_{1} l n D I F_{i t} + \sum β_{j} C o n t r o l_{i t} + ε_{i t} ε_{i t} = λ W ε_{i t} + μ_{i t}$

InNEW denotes the composite index of new urbanization, InDIF denotes the digital financial inclusion index, β denotes the general regression coefficient, ε denotes the random error term, λ is the spatial autocorrelation coefficient, μ is the stochastic perturbation term, and W is the spatial inverse distance weight matrix.Control denotes the control variables.

The spatial Durbin model is constructed as follows: 12 $l n N E W_{i t} = β_{1} l n D I F_{i t} + \sum β_{j} C o n t r o l_{i t} + θ_{1} W l n D I F_{i t} + \sum θ_{j} W C o n t r o l_{i t} + η W l n N E W_{i t} + ε_{i t}$

lnNEW denotes the new urbanization composite index and InDIF denotes the digital financial inclusion index. β denotes general regression coefficient, ε denotes random error term. η is the spatial autoregressive coefficient, θ is the spatial regression coefficient. W is the spatial inverse distance weight matrix. Control denotes control variables.

3.2.2

Data Measurement and Description

1)

Explained variable: level of shared affluence development

This study measures the level of common affluence from two dimensions: the level of overall affluence and the level of shared affluence. The overall level of affluence is used to characterize the overall living standard of the entire country, which includes material prosperity, spiritual and cultural affluence, and a livable ecological environment. The level of shared prosperity is used to identify the differences in the process of shared prosperity, particularly those related to regional and urban-rural differences, with the smaller the differences, the higher the level of shared prosperity. The system of indicators for measuring shared affluence is shown in Table 1, including 5 first-level indicators, 10 second-level indicators, and 14 third-level indicators.

2)

Core explanatory variables: new urbanization level

The evaluation index system of new urbanization is constructed from four aspects: economy, population, space and sustainable development, and the evaluation index system of new urbanization is shown in Table 2. The entropy value method is used to measure the new urbanization level of each province (Ur).

3)

Mediating variable: digital financial inclusion (Dif)

This paper chooses the digital inclusive finance index of 30 provinces during 2011-2022 to measure the development level of digital inclusive finance. It also uses the breadth of coverage, depth of use, and degree of digitization of digital inclusive finance to study the impact of different dimensions of digital inclusive finance on the level of common wealth.

Table 1.

The common rich measure system

Index category	Primary indicator	Secondary indicator	Tertiary index
Overall affluence	Material life is rich	Income level	The per capita disposable income of rural residents
		Income level	The per capita disposable income of urban residents
		Household consumption level	Engel coefficient
	Spiritual culture	Cultural level	Cultural utilities account for fiscal spending
	Spiritual culture	Cultural level	Per capita public library
	Ecoenvironment	Ecological quality	Per capita green space
	Ecoenvironment	Ecological quality	Construction zone greening coverage
Share the wealth	Regional difference	Regional income difference	The per capita disposable income of rural residents is more than the per capita disposable income ratio of rural residents
		Regional income difference	The per capita disposable income of urban residents is compared to the per capita disposable income of the national urban residents
		Regional education differences	Average junior high school students spend more than the average junior high school students
		Regional medical differences	The per capita hospital bed number is compared with the national per capita hospital bed number ratio
		Regional public facilities differences	The average person has the area of the road and the area ratio of the country
	Urban and rural difference	Urban and rural income differences	The per capita disposable income ratio of urban and rural residents
	Urban and rural difference	Urban and rural consumption differences	Urban and rural residents consume spending ratios

Table 2.

New urbanization evaluation index system

Target layer	Index layer	Indicator
New type of urbanization	Economy	The added value of the second and third industries is the proportion of GDP.
		Total retail retail (million yuan)
		General budget income (100 million yuan)
		Energy consumption of unit GDP (000 metric tons of standard coal/million yuan)
	Population	Urban population accounts for a total population of the population.
	Population	Urban population density (man/square kilometer)
	Space	The city’s construction area accounts for a total area of the city.
	Sustainable development	Forest coverage(%)
	Sustainable development	Per capita (volume/person) of public library
		Education spending is a proportion of GDP

3.2.3

Data sources

The data in this paper comes from the 2011-2022 Statistical Yearbook, the official website of the National Bureau of Statistics, and the Digital Finance Research Center of Peking University. Based on the availability of data, this paper selects the relevant panel data for 30 provinces from 2011 to 2022, and for individual missing data, linear interpolation is used to supplement.

The variables are summarized as shown in Table 3.

Table 3.

Variable summary

Variable type	Variable name	Variable symbol	Variable definition
Explained variable	Common rich index	COP	Common rich index
Core interpretation variable	New level of urbanization	NURB	New level of urbanization
Core interpretation variable	The level of the new type of urbanization	CCD	The level of the new type of urbanization
Mediation variable	Digital puhui financial index	Dif	Digital puhui financial index
Threshold variable	Financial development	Fin	Total loan balance/GDP
	Government intervention	fiscal	Government expenditure
	Population density	density	The number of people per square kilometer
Control variable	Foreign investment level	FDI	Foreign direct investment/GDP
	Openness	open	Import and export total/GDP
	The degree of education	Edu	Average education
	Green technology innovation level	GTI	Green technology innovation level
	Manufacturing upgrade	MAU	Manufacturing upgrade
	Foreign trade dependency	OPE	Foreign trade dependency
	Marketable level	MAR	Marketable level

The descriptive statistics of each variable are shown in Table 4. It can be seen that the standard deviation of each variable is around 1, the data is relatively smooth and the mean value is closer, which can avoid the effect of too large range of the scale.

Table 4.

Descriptive statistics of variables

Variable name	Sample size	Mean value	Standard deviation	Minimum value	Maximum value
COP	432	0.316	0.186	0.057	0.812
Dif	432	0.242	0.669	0.024	0.465
Fin	432	3.519	1.043	1.643	8.692
fiscal	432	0.644	0.364	0.075	1.873
density	432	0.437	0.717	0.004	4.001
FDI	432	0.215	0.246	0.003	1.362
open	432	0.347	0.358	0.009	1.618
Edu	432	0.261	0.975	8.064	13.703

4

Empirical findings

4.1

Analysis of baseline regression results

In conducting the empirical study, this paper uses the method of adding control variables step by step to mitigate the impact of bias and other effects that omitted variables may have on the model results.

The analysis of the impact of digital financial inclusion on new urbanization is shown in Table 5. The table displays the estimation results for adding control variables step by step. Column (1) presents the estimation results when only time and area fixed effects are considered. Column (2) presents the estimation results when the level of technological innovation (TE) is introduced as a control variable. From column (3) to column (6), the results are then estimated by sequentially introducing the level of government intervention (Gov), industrial structure (Ind), regional development level (ln Pgdp) and financial development level (Fin) are estimated after being used as control variables.

Table 5.

The impact analysis of the new type of urbanization in digital puhui finance

Variable	(1) NURB	(2) NURB	(3) NURB	(4) NURB	(5) NURB	(6) NURB
DIF	0.082***	0.041***	0.035***	0.033***	0.032***	0.031***
DIF	(8.261)	(6.536)	(5.732)	(5.056)	(5.007)	(5.372)
TE		0.021***	0.022***	0.025***	0.022***	0.023***
TE		(8.635)	(7.004)	(7.607)	(7.114)	(7.036)
Gov			-0.068***	-0.059***	-0.054***	-0.056***
Gov			(-7.361)	(-5.996)	(-4.413)	(-4.018)
Ind				-0.025*	-0.017	-0.021
Ind				(-1.352)	(-1.324)	(-1.104)
InP gdp					0.025**	0.011**
InP gdp					(2.376)	(2.104)
Fin						-0.003
Fin						(-1.507)
Constant term	0.075***	0.085***	0.124***	0.137**	-0.005	0.006
Constant term	(12.635)	(25.369)	(24.797)	(21.094)	(-0.057)	(0.079)
Sample size	432	432	432	432	432	432
Time fixed effect	Control	Control	Control	Control	Control	Control
Regional fixation effect	Control	Control	Control	Control	Control	Control
Adjust R²	0.753	0.826	0.847	0.869	0.871	0.893

From the regression results in column (1), it can be observed that the estimated coefficient is 0.082 and presents significance at 1% significance level when the model includes only digital financial inclusion as its explanatory variable. This result indicates that digital financial inclusion plays a significant role in promoting urbanization.

The regression results in columns (2) and (3) show that the estimated coefficients of digital financial inclusion show a tendency to become smaller after adding the two control variables of the level of technological innovation and the degree of government intervention, which are 0.041 and 0.035, respectively, but they still remain significant at the 1% significance level. This indicates that in the process of analyzing new urbanization, the level of technological innovation and the degree of government intervention are two important factors that cannot be ignored and should be considered in the model. As the control variables are added to the model, the value of R² increases from 0.753 to 0.826, which indicates that the goodness of fit of the model has increased and it is reasonable to add these control variables to the model. In addition, it can be seen that the direction of the estimated coefficients for digital financial inclusion is always consistent and positive in the 6 columns of regression results. And they are always significant at the 1% significance level, which also indicates that the regression results are somewhat robust.

4.2

Intermediation test

The test results of the mediation effect are shown in Table 6. The financial sector development efficiency (dfp) factor is added to the model, where columns (1) to (3), the results indicate that digital financial inclusion has a positive role in promoting the construction of new urbanization. And column (3) financial inclusion index is significant at 10% significant level, but the development of new urbanization construction is not significant, so further sobel test is conducted. Sobel test result passes, mediation effect is significant. R² is 0.924, indicating that the digital financial inclusion has a significant correlation on common wealth.

Table 6.

Intermediate test results

Variable	(1)	(2)	(3)
Variable	COP	NURB	COP
dif	0.796**	3.124***	0.767*
dif	(0.415)	(0.578)	(0.419)
ed	0.079***	-0.405***	0.093***
ed	(0.036)	(0.073)	(0.017)
Ind	0.052	-0.154	0.065
Ind	(0.074)	(0.395)	(0.183)
gov	0.835***	0.751***	0.714***
gov	(0.036)	(1.264)	(0.185)
dfp	-0.076***	-0.173*	-0.073***
dfp	(0.034)	(0.036)	(0.036)
Constant	-0.663**	7.899***	-0.869***
Constant	(0.241)	(1.427)	(0.373)
N	432	432	432
R²	0.893	0.783	0.924

In summary, it can be seen that although financial inclusion can promote the increase of common wealth level, it does not want to improve the level of new urbanization as expected. Both the level of common wealth and the development of new urbanization increased during the study period, indicating that the increase in the level of common wealth is not balanced.

4.3

Analysis of spatial spillover effects

4.3.1

Spatial correlation tests

The spatial correlation test was conducted between common wealth development levels, digital financial inclusion levels, and new urbanization levels. The reported global Moran index and test results are shown in Figure 1.

From 2011 to 2022, the common wealth development level of each province is always maintained in the range of 0.1 to 0.2. The global Moran index of the level of common wealth development and the level of digital financial inclusion and new urbanization in each province has a significant positive spatial correlation between 2011 and 2022, i.e., there is a significant spatial clustering phenomenon in both of them. This suggests that the promotion of common wealth development in this province will be influenced by the increase in common wealth development in neighbouring provinces. Correspondingly, an increase in the level of digital financial inclusion and new urbanization in neighboring provinces will promote the coordinated development of digital financial inclusion and new urbanization in this province.

4.3.2

Spatial measurement model tests

As the global Moran’s index is significantly positive for the level of common wealth development, digital financial inclusion, and new urbanization. It indicates that there is spatial correlation or heterogeneity, and the results of parameter estimation are biased or invalid, i.e., the traditional OLS regression model is no longer applicable. And the spatial econometric model using the great likelihood estimation method can avoid this problem to a certain extent. On this basis, this section conducts the LM test on the non-spatial OLS panel model to determine whether a spatial econometric model is needed. The results of the LM test are shown in Table 7.

Table 7.

LM test results

Statistical inspection		Statistic	P value
Spatial error	Moran’s I	1.5263	0.1417
	Lagrange multiplier	1.6539	0.2282
	Robust Lagrange multiplier	25.7743	0.00000
Spatial lag	Lagrange multiplier	1.6539	0.2282
Spatial lag	Robust Lagrange multiplier	25.7743	0.00000

As can be seen from the table, in the test for the spatial error model, the Robust Lagrange multiplier statistic passed the 1% significance test. In the test for the spatial lag model, the statistics of both the Lagrange multiplier and Robust Lagrange multiplier passed the 1% significance level. The results of the LM test rejected the OLS regression model and the assumption that the model is non-spatial, proving once again that a spatial econometric model should be used for empirical analysis.

Based on the use of spatial econometric models, the Hausman test was performed on the panel data. The spatial panel model can be divided into spatial fixed effects models and spatial random effects models. The Hausman test statistic is 59.17 with a p-value of 0. The Hausman test result passes the 1% significance test, therefore this paper chooses to use spatial fixed effects. The purpose of conducting the Wald test and LR test was to identify the specific type of spatial econometric model.

The results of the Wald and LR tests are shown in Table 8. As can be seen from the table, the statistics of Wald spatial lag, LR spatial lag, and Wald spatial error and LR spatial error all passed the 1% significance test, indicating that the spatial Durbin model should be selected.

Table 8.

Wald and LR test

Statistical inspection	Statistic	P value
Wald spatial lag	35.6241	0.00001
LR spatial lag	67.0015	0.00001
Wald spatial error	39.7500	0.00001
LR spatial error	72.1500	0.00001

Meanwhile, the maximum likelihood estimate of the spatial Durbin model with double fixed time and spatial effects is larger than the maximum likelihood estimate of the spatial Durbin model with only fixed time effects or only fixed spatial effects, so the spatial econometrics model should be fixed with both time and spatial effects. In summary, this paper uses the Durbin model for spatial panels with double fixed time and spatial effects.

4.3.3

Decomposition of spatial spillover effects

This section analyzes the spatial effects of digital financial inclusion and new urbanization on common wealth using a spatial panel Durbin model with double fixed time and spatial effects.

The decomposition of the spatial spillover effects of digital financial inclusion, new urbanization and common wealth is shown in Table 9. The direct, indirect and total effects of digital financial inclusion and new urbanization are all significantly positive, indicating that the increase in the level of digital financial inclusion and new urbanization has a significant contribution to the increase in the level of common wealth development in this province and the neighboring provinces, thus verifying Hypothesis 4. The estimated value of the coefficients of the explanatory variables is 1.5021. It is significantly positive at the 10% significance level, and the direct effect is 1.7965. Significantly positive at 10% level of significance, with an indirect effect of 2.8761. Significantly positive at 1% level of significance, with a total effect of 4.5104, significantly positive at 1% level of significance. This indicates that one unit increase in the level of digital financial inclusion and new urbanization can increase the level of common wealth development in the province by 1.7965 units. At the same time, it can increase the level of common wealth development of neighboring provinces by 2.8761 units, thus making the overall common wealth development level increase by 4.5104 units.

Table 9.

Spatial panel dubin model estimation and spatial overflow effect decomposition

Variable	SDM (1)	Direct effect (2)	Indirect effect (3)	Total effect (4)
CCD	1.5021*	1.7965*	2.8761***	4.5104***
CCD	(1.04)	(1.53)	(2.67)	(2.23)
GTI	-0.0896	-0.0712	0.0241	-0.0766
GTI	(-1.17)	(-1.25)	(0.09)	(-0.34)
MAU	1.3426	0.6999	-8.0163	-7.1596
MAU	(0.69)	(0.56)	(-1.41)	(-1.24)
OPE	-0.9683	-0.6053	4.5296**	3.6119
OPE	(-1.17)	(-0.82)	(2.15)	(1.34)
MAR	-0.0569**	-0.0412	0.4007***	0.3105**
MAR	(-1.47)	(-1.22)	(3.42)	(2.97)
W*CCD	1.1243*
W*CCD	(1.77)
W*GTI	0.0528
W*GTI	(0.26)
W*MAU	-6.2199
W*MAU	(-1.57)
W*OPE	3.0169*
W*OPE	(-2.04)
W*MAR	2.9869**
W*MAR	(-2.03)
Regional effect	Control
Time effect	Control
Log-likelihood	136.9588

5

Research findings and policy recommendations

5.1

Conclusions of the study

This paper analyzes the relationship between digital inclusive finance, new urbanization and common wealth, establishes a research model, carries out regression tests, and verifies the mediating effect of digital inclusive finance in the development and construction of new urbanization and common wealth. Combined with the results of empirical analysis, the development path between digital inclusive finance and new urbanization is proposed, which is committed to the construction of common wealth.

The baseline regression model for the analysis of the impact of digital inclusive finance on new urbanization shows that the estimated coefficient is 0.082 and presents significance when only digital inclusive finance is included in the model as its explanatory variable. This result indicates that digital inclusive finance has a significant role in promoting new urbanization. The spatial correlation test of the development level of common wealth and digital inclusive finance with the level of new urbanization, and the analysis of spatial effect shows that there is a significant spatial clustering phenomenon in both. The decomposition of spatial spillover effects of digital inclusive finance, new urbanization, and common wealth shows that the direct, indirect, and total effects of digital inclusive finance and new urbanization levels are all significantly positive. A one-unit increase in the level of digital inclusive finance and new urbanization can increase the level of common wealth development in this province by 1.7965 units, and promote the increase in the level of common wealth development in the neighboring provinces to achieve the overall common wealth development and construction.

5.2

Policy recommendations

The construction of common wealth has been officially put on the agenda, and digital inclusive finance has shown strong potential in promoting common wealth in recent years. Today, with the rapid development of new urbanization construction, digital inclusive finance should be actively promoted. Whether it is in terms of technological development and infrastructure, it should grasp the historical opportunity and follow the historical trend to promote the high-quality construction of new urbanization and the development and construction of common wealth. 1)

Insist on promoting the development of digital inclusive finance

It is necessary to further promote the digitalization of backward areas, accelerate the construction of 5G and other infrastructures that play a supporting role in remote areas, and ensure that backward areas can enjoy high-quality digital services. To improve the efficiency of financial services, standardize the behavior of grassroots financial services, rationalize the distribution of financial outlets, and fill the gaps in financial services. Accelerate the development of digital technology, improve the credit collection system and user profiles to ensure that financial institutions can efficiently and accurately identify the credit risk status of borrowers, and control the risks faced by financial institutions while ensuring the function of inclusive finance.

2)

Strengthening the construction of digital infrastructure and expanding the coverage of digital inclusive finance. Digital infrastructure is the foundation for the development of digital inclusive finance and a requirement for promoting the development of the entire financial industry. Therefore, it is recommended that the government accelerate the construction of a perfect broadband network system, increase investment in digital infrastructure construction, especially for remote areas such as rural areas, narrow the digital divide between urban and rural areas, and gradually promote the development of common wealth building.

Lingua:: Inglese

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The Connotation of the Era, Long-term Obstacles and Path to Realization of Digital Enabling New Urbanization and Common Wealth

Weiwei Guo

Pubblicato online: 19 mar 2025

Ricevuto: 13 ott 2024

Accettato: 05 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0390

Parole chiaveSpatial spillover effect, Spatial Durbin model, Digital inclusive finance, New urbanization

© 2025 Weiwei Guo, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Spatial spillover effect, Spatial Durbin model, Digital inclusive finance, New urbanization