The Impact of Digital Inclusive Finance on High-Quality Urban–Rural Integrated Development—Based on Panel Data of 30 Provinces (Autonomous Regions, Municipalities) in China

Abstract

As a core driver of high-quality urban–rural integration, digital inclusive finance plays a key role in the process of Chinese-style modernization. After measuring the level of high-quality urban–rural integration development using the TOPSIS entropy method, this study employs fixed-effects models and mediation models to empirically examine how digital inclusive finance influences high-quality urban–rural integration development over the period from 2012 to 2022. The main findings are as follows: (1) Digital inclusive finance has a significantly positive promoting effect on high-quality urban–rural integration. (2) The enabling effect of digital inclusive finance exhibits significant regional heterogeneity, following a gradient pattern of “strongest in the Eastern region, followed by the Central region, and weakest in the Western region.” (3) In terms of dimensional effects, the breadth of coverage contributes the most, followed by the depth of use, while the degree of digitalization has the smallest impact. (4) The mediation mechanism indicates that factor mobility indirectly promotes high-quality urban–rural integration. Based on the above findings, this paper proposes policy recommendations to foster high-quality urban–rural integration development in China.

1. Introduction

As a concrete manifestation of the shared development philosophy, urban–rural integration entails both the objective need to advance common prosperity and the goal orientation of achieving rural revitalization and promoting the coordinated development of both urban and rural areas. In traditional urban–rural development models, constrained by multiple factors such as geographic location, infrastructure, institutional barriers, and financial system limitations, high-quality production factors including capital, talent, and technology have long flowed unidirectionally from rural to urban areas. This has resulted in lagging rural industrial development, narrow income growth channels for farmers, and persistent urban–rural gaps in public services and income levels, severely restricting the optimal allocation of urban–rural resources and high-quality regional coordinated development. The principle of “adhering to urban–rural integration and facilitating the smooth flow of factors between urban and rural areas” clarifies the overall direction of China’s urban–rural relations for the coming period. High-quality urban–rural integration development is a new development model that breaks the urban–rural dual structure, centers on the coordinated advancement of population, economy, society, space, and ecology, and promotes two-way orderly population flows, complementary urban–rural economic interactions, equitable and inclusive public services, rational and optimized spatial layouts, and shared environmental governance, thereby achieving balanced allocation of urban–rural resources, co-construction and sharing of development outcomes, and comprehensive, coordinated, and sustainable urban–rural development. Amid the continuous advancement of Chinese-style modernization, the internal driving forces of economic demands in the new era have made high-quality urban–rural integration development an important goal, aimed at promoting the joint progress and common prosperity of both urban and rural areas. As the core essence of Chinese-style modernization, high-quality urban–rural integration development is a key pathway under the guidance of the new development philosophy to foster common urban–rural prosperity and achieve high-quality economic development. In the process of advancing the construction of a modern socialist country, exploring the mechanisms for achieving such integration has become a critically important issue.

Regarding the issue of high-quality urban–rural integration development, existing research primarily focuses on the following two dimensions. First, studies concentrate on the intrinsic definition and theoretical interpretation. For example, Zhang (2022) [1] argues that high-quality urban–rural integration is not merely about rapid economic growth; rather, it is more conducive to the free flow and optimal allocation of production factors. Jiao (2023) [2] proposes that high-quality urban–rural integration, built upon breaking the urban–rural divide, places greater emphasis on the quality, comprehensiveness, and sustainability of development. Bu (2023) [3] contends that on the path of Chinese-style modernization, high-quality urban–rural integration, guided by the new development philosophy, aims to narrow the urban–rural gap, promote common prosperity, and achieve higher-level integration goals. Tacoli (1998) [4] points out that population migration, commodity transactions, capital flows, and various social interactions between urban and rural areas constitute the core drivers of changes in urban–rural relations. Lynch (2005) [5] places population mobility within the broader framework of multiple urban–rural flows, arguing that urban–rural integration is embodied in the multi-directional, cross-sectoral, and cross-regional circulation and integration of people, resources, materials, information, and capital between urban and rural areas. Bulderberga (2014) [6], focusing on the practice of urban–rural relations in Latvia, suggests that urban–rural integration development can be divided into three progressive stages: cross-border factor flows, the formation of linkages between urban and rural areas, and ultimately the achievement of synergistic cooperation. The second dimension focuses on the measurement of development, primarily encompassing evaluation indicator systems and measurement methods. Regarding evaluation indicator systems, Zhang et al. (2022) [7] analyze the rich implications and specific evolutionary process of urban–rural integration from the perspectives of urban, rural, and urban–rural integration. Li (2024) [8] selects four aspects—urban–rural economic integration, urban–rural social harmony, urban–rural spatial coordination, and urban–rural ecological environment integration—to construct their indicator system. In terms of dimensional construction by international scholars in recent years, classic studies typically revolve around five dimensions: population, land, economy, society, and ecology. Cutting-edge research has further expanded to include transportation integration and information integration, constructing a comprehensive measurement system comprising seven dimensions (Fang et al., 2026) [9]. Adhering to the people-centered philosophy and focusing on indicators constructed from economic, social, and ecological dimensions, empirical findings reveal that the digital economy significantly promotes urban-rural integration. (Huo & Liu, 2024) [10]. Regarding measurement methods, Liu et al. (2020) [11] employed the entropy weight method to divide the whole country into four regions—East, West, Central, and Northeast—and conducted measurement research and analysis on the level of high-quality urban–rural integration development in each region. Dou and Wang (2019) [12] used factor analysis, Jenks natural breaks classification, and other methods to evaluate the level of urban–rural integration development in Shandong Province. Li (2019) [13] applied factor analysis to conduct research in five dimensions: “integrated type,” “intensive type,” “ecological type,” “moderate type,” and “advanced type.” Overall, existing studies have made significant progress in theoretical construction and empirical measurement. However, several issues remain unresolved and warrant further research: the lack of a unified connotation definition, the absence of standardized indicator systems, and the challenge of effectively bridging international experience with China’s indigenous practice.

In the process of Chinese-style modernization, digital inclusive finance, characterized by inclusiveness, flexibility, accessibility, and digitalization, plays a central role in promoting high-quality urban–rural integration. On the one hand, it unlocks the growth potential of rural economies and significantly alleviates urban–rural disparities. On the other hand, it facilitates the flow of factor resources and enhances allocative efficiency. Simultaneously, digital inclusive finance accelerates rural technological innovation, injecting strong momentum into rural revitalization and urban–rural integration. Therefore, digital inclusive finance can be regarded as an important pathway leading high-quality urban–rural integration. Furthermore, this system encompasses multiple dimensions, including coverage breadth, depth of use, and degree of digitalization. As the roles of these dimensions vary across different stages of development, their impacts on the process of urban–rural integration also differ. The key to achieving urban–rural integration lies in promoting the two-way flow of factors between urban and rural areas. The continuous advancement of digital inclusive finance helps strengthen spatial agglomeration economies and facilitates the efficient integration of production factors such as labor, capital, and technology within specific regions. This acceleration of factor flows is becoming a vital force driving the urban–rural integration process, thereby contributing to the realization of high-quality urban–rural integration development. Given this, digital inclusive finance, with factor mobility as a mediating mechanism, can break down urban–rural factor circulation barriers and ultimately indirectly propel urban–rural integration toward successively higher stages of quality development.

In summary, this study conducts an empirical investigation into the mechanism through which digital inclusive finance drives high-quality urban–rural integration development, focusing on three main aspects: direct effects, heterogeneous performance, and indirect impacts. Using relevant data to empirically verify these issues holds significant practical implications for establishing and improving policy recommendations related to urban–rural integration development. The possible contributions of this paper are as follows: (1) Drawing on the new development philosophy, this paper constructs an evaluation system for high-quality urban–rural integration development and employs the TOPSIS entropy method to comprehensively assess its level. (2) Based on measuring the level of high-quality urban–rural integration development in China, this paper explores the impact of digital inclusive finance on such development, taking into account both regional heterogeneity and factor heterogeneity. (3) By incorporating factor mobility as a mediating variable, this paper confirms that digital inclusive finance has a significant indirect promoting effect on high-quality urban–rural integration development.

2. Literature Review

2.1. Digital Inclusive Finance and High-Quality Urban–Rural Integrated Development

2.1.1. The Direct Impact of Digital Inclusive Finance on High-Quality Urban–Rural Integrated Development

Digital inclusive finance facilitates urban–rural economic integration. Kang (2022) [14] points out that the gradual flow of financial resources into rural areas not only promotes rural employment but also accelerates population mobility between urban and rural areas, significantly advancing deep urban–rural economic integration. Liang and Liu (2019) [15] emphasize that the fundamental prerequisite for achieving urban–rural economic integration lies in establishing a sound economic structural layout. By influencing technological progress, capital accumulation, and the allocation of financial resources between agricultural and non-agricultural sectors, digital inclusive finance facilitates a benign transformation of the dual economic structure, thereby promoting high-quality urban–rural integration. Digital inclusive finance facilitates urban–rural social integration. Yang and Xing (2020) [16] argue that digital inclusive finance can effectively address the challenges of imbalanced public service provision. Through financial support for basic urban and rural public services, this model helps achieve equitable access to social services and ultimately enhances rural residents’ life satisfaction. Zhou and Yang argue that (2023)moderate fiscal decentralization, complemented by well-established market and social security systems, facilitates the free flow of production factors and promotes urban-rural integrated development [17]. Digital inclusive finance facilitates urban–rural ecological integration. Cheng et al. (2022) [18] note that the integrated development of digital inclusive finance and green finance is primarily manifested in financial support for green agricultural development. This integration promotes the transformation of agricultural production models; improved access to finance accelerates the greening process of agriculture and effectively curbs the transfer of highly polluting and energy-intensive urban industries to rural areas.

According to the theory of inclusive financial ecosystems, financial services should be multi-layered and diverse. Digital inclusive finance, by establishing a well-structured financial framework with reasonable hierarchies, meets the diverse needs of different groups and promotes balanced urban–rural economic development. Sustainable development theory focuses on the coordinated advancement of economic activities, social progress, and environmental protection. Klarin (2018) [19] systematically reviews the conceptual evolution of sustainable development, constructing a foundational theoretical framework based on the three-dimensional equilibrium of ecology, economy, and society. He elaborates on the connotations of sustainable development around two core principles—intra-generational and intergenerational equity, and ecological carrying capacity—and refines the theoretical and practical pathways using the SDG indicator framework. He also identifies shortcomings in mainstream sustainable development theory, such as definitional ambiguity and imperfect international governance mechanisms, making this a classic foundational theory for analyzing sustainable development issues. Manioudis and Meramveliotakis (2022) [20] reconstruct an analytical framework for sustainable development from the perspective of classical political economy, critiquing the tendency of existing research to emphasize technological governance while neglecting institutional and power structures. Integrating Smith’s historical development perspective, Mill’s theory of the stationary state, and Marx’s theory of metabolic rift, they take capital accumulation, class distribution, and historical evolution as core analytical tools to reveal the deep-seated contradictions of the capitalist system underlying ecological and environmental issues, thereby providing critical political economy theoretical support for sustainable development research. By supporting green agriculture and small-scale rural enterprises, digital inclusive finance promotes the sustainable development of the rural economy and facilitates urban–rural integration. In summary, digital inclusive finance has direct positive effects on the economic, social, and ecological dimensions of high-quality urban–rural integration development. Based on the foregoing discussion, this paper proposes the following hypothesis H1:

H1: 

Digital inclusive finance has a marked positive effect on the high-quality integrated development of urban and rural areas.

2.1.2. The Impact of Digital Inclusive Finance on High-Quality Urban–Rural Integrated Development Is Heterogeneous

Against the backdrop of inclusive finance advancement driven by digital technologies, Tian et al. (2022) [21] argue that internet application can effectively unlock the beneficial effects of digital inclusive finance. From the perspective of regional economic and financial development, Zhang et al. (2024) [22] document that the promotional effect of digital inclusive finance is more pronounced in regions with prosperous economies and high marketization. By contrast, the policy dividends brought by digital inclusive finance tend to reach fewer beneficiaries in underdeveloped areas featuring a sluggish economy and low market-oriented degree. Regions with a mature economy and sound financial systems witness faster and higher-level development of digital finance, alongside greater public acceptance of digital financial products. Such features enable the inclusive merits of digital finance to penetrate residents’ daily lives at a faster pace, thereby generating favorable spillover effects on high-quality urban–rural integrated development. Moreover, the dual urban–rural economic structure theory confirms inherent economic gaps between urban and rural sectors. By delivering accessible financial services, digital inclusive finance accelerates rural industrial upgrading and rural economic growth, narrows the urban–rural development divide, and ultimately fuels urban–rural integration.

Digital inclusive finance combines the advantages of traditional inclusive finance with digital information technology, featuring extensive coverage, low thresholds, rapid dissemination, and low costs. Its convenient basic financial services promote rural capital accumulation, break down urban–rural capital flow barriers, drive the rational flow of other production factors through information technology sharing, improve factor allocation, alleviate rural factor shortages, and facilitate urban–rural integration development. When examined from a multi-dimensional perspective, digital inclusive finance encompasses different aspects such as coverage breadth, depth of use, and degree of digitalization. At various stages of its development, the focal role of each dimension differs, and consequently, its facilitating effects on urban–rural integration inevitably exhibit significant heterogeneity. Based on that, this study proposes hypothesis H2:

H2: 

The impact of digital inclusive finance on the high-quality development of urban–rural integration is heterogeneous.

H2a: 

The impact of digital inclusive finance on the high-quality development of urban–rural integration exhibits regional heterogeneity.

H2b: 

The impact of digital inclusive finance on the high-quality development of urban–rural integration exhibits dimensional heterogeneity.

2.2. Digital Inclusive Finance, Factor Flow, and High-Quality Urban–Rural Integrated Development

Throughout its evolution, digital inclusive finance has not only directly promoted the high-quality development of urban–rural integration but has also, supported by the dual theoretical foundations of the inclusive financial ecosystem theory and sustainable development theory, indirectly exerted a profound impact on the process of urban–rural integration by facilitating the efficient flow and optimal allocation of production factors between urban and rural areas. Based on the inclusive financial ecosystem theory, digital inclusive finance, leveraging digital and internet technologies, reshapes the traditional financial service ecosystem. It breaks down the geographical barriers, threshold restrictions, and information asymmetry problems inherent in traditional financial services, effectively broadening the coverage of financial services. This enables capital to flow more conveniently and efficiently to rural areas, small and micro enterprises, and other sectors that are difficult for the traditional financial system to reach, thereby improving the balanced layout of the urban–rural financial ecosystem and solidifying the financial foundation for the free flow of various production factors.

Factor mobility—that is, the transfer of key resources such as technology, labor, and capital within or across regions—can generate multiple economic benefits and social welfare gains, including optimized resource allocation, regional development promotion, and improved employment conditions. However, this process also entails practical challenges such as unequal distribution, regional development imbalances, and a disconnect between capital and labor. Sustainable development theory, which emphasizes the three-dimensional equilibrium of economy, society, and ecology as well as intra-generational equity between urban and rural areas, serves as the core theoretical foundation for addressing factor misallocation and achieving high-quality urban–rural integration. It provides the central criteria for regulating orderly factor flows and resolving urban–rural development imbalances. In recent years, the expansion of factor mobility has been one of the key drivers behind the rapid development of China’s rural areas. Liu and Zhang (2018) [23] propose that urban–rural factor mobility is the cornerstone for constructing urban–rural development relationships and has a decisive influence on the entire process of urban–rural development. Existing research, such as Zhou et al. (2020) [24], reveals the close link between factor mobility and urban–rural integration development, pointing out that factor misallocation between urban and rural areas is one of the primary causes of urban–rural development imbalances and relational contradictions. Therefore, Cai and Chen (2018) [25] argue that the key to promoting urban–rural integration development lies in breaking down the barriers to factor mobility between urban and rural areas and narrowing the urban–rural gap. In terms of theoretical transmission logic, the inclusive financial ecosystem theory provides the ecological foundation for digital inclusive finance to facilitate factor mobility, while sustainable development theory indicates the fundamental direction for the optimal adjustment of factor allocation and balanced urban–rural integration development. Relying on a well-established financial ecosystem, digital inclusive finance addresses urban–rural factor misallocation, breaks down factor mobility barriers, and promotes urban–rural development that balances quality, equity, and sustainability by accelerating capital flows, optimizing resource allocation, facilitating labor mobility, and accelerating technology diffusion. Based on this, this paper proposes hypothesis H3:

H3: 

Digital inclusive finance indirectly drives high-quality urban–rural integration development by accelerating factor mobility, meaning that factor mobility plays a mediating role in this process.

3. Methodology

3.1. Model Specification

3.1.1. Direct Impact Model

Based on the theoretical analysis above, digital inclusive finance has a significant positive promoting effect on high-quality urban–rural integration development. To verify this proposition, this paper conducts an empirical study using a benchmark regression model, which is specified as follows:

$${Duri}_{it}={\alpha }_0+{\alpha }_1{Dif}_{it}+{\alpha }_2{control}_{it}+{\beta }_i+{\theta }_t+{\delta }_{it}$$

(1)

The definitions are as follows: Duri is the dependent variable, representing the comprehensive level of high-quality urban–rural integration development; Dif is the independent variable, used to measure the development degree of digital inclusive finance; Control is the collective term for a series of control variables; the subscript i denotes the province, t denotes the year; β represents individual-level fixed effects; θ represents time-level fixed effects; and δ denotes the random disturbance term.

3.1.2. Mediation Effect Model

Based on Model (1), in order to further investigate the transmission mechanism through which digital inclusive finance affects high-quality urban–rural integration development, this paper, following the methodological approach of Wen et al. (2022) [26], selects factor mobility as a mediating variable and constructs the following mediation effect test model:

$${Factor}_{it}={\alpha }_0+{\alpha }_1{Dif}_{it}+{\alpha }_2{control}_{it}+{\beta }_i+{\theta }_t+{\delta }_{it}$$

(2)

$${Duri}_{it}={\gamma }_0+{\gamma }_1{Dif}_{it}+{\gamma }_2{Factor }_{it}+{\beta }_i+{\theta }_t+{\delta }_{it}$$

(3)

The testing procedure for the mediation effect consists of three steps. First, regression is performed according to Equation (1), with high-quality urban–rural integration development (Duri) as the dependent variable and digital inclusive finance (Dif) as the independent variable. Second, regression is performed according to Equation (2), with factor mobility (Factor) as the dependent variable and digital inclusive finance (Dif) as the independent variable. Third, regression is performed according to Equation (3), with both digital inclusive finance (Dif) and factor mobility (Factor) introduced as independent variables. If the coefficients of both variables pass the significance tests, the mediation effect is considered to be established.

3.2. Variable Selection

3.2.1. Dependent Variable

High-quality urban–rural integrated development (Duri) is the dependent variable of this paper. Against the macro background of Chinese-style modernization, this paper holds that high-quality urban–rural integrated development continuously narrows the urban–rural gap and promotes coordinated urban–rural economic growth through the comprehensive advancement of urban–rural planning and layout, improved infrastructure construction, coordinated industrial development, and ecological civilization construction, thus laying a solid foundation for realizing the Chinese Dream of national rejuvenation. The specific measurement process is shown below.

(1)

Indicator Selection

Based on the essential connotation of high-quality urban–rural integration development and the national guiding principles and requirements for high-quality development (innovation, coordination, green, openness, and sharing), grounded in the theoretical foundations such as sustainable development discussed above, and drawing on the research of scholars including Li and Zhang (2022) [27], Wang (2023) [28], and Yang et al. (2020) [29], this paper follows the principles of systematicity, scientific rigor, comprehensiveness, and data availability to construct a comprehensive evaluation indicator system for the level of high-quality urban–rural integration development across five dimensions: population, economy, society, space, and ecology. The specific indicators are shown in

Table 1. Evaluation system for high-quality urban–rural integration development.

Dimension Indicators	Sub-Indicators	Indicator Calculation or Explanation	Indicator Attributes	Indicator Weight
Population integration	Non-agricultural vs. agricultural employment ratio	Ratio of employees in secondary and tertiary industries to employees in primary industry (%)	+	0.1567
	Urbanization level of population	Proportion of urban population to total population (%)	+	0.0100
	Number of urban and rural talents	Number of ordinary university graduates in each region (persons)	+	0.0186
	Urban and rural population density	Permanent resident population/administrative region’s land area (10,000 people/square kilometer)	+	0.0557
	Full-time equivalent of R&D personnel	Full-time equivalent of research and experimental (R&D) personnel	+	0.0467
Economic integration	Per capita regional GDP	Per capita regional GDP (yuan)	+	0.0191
	Income ratio between urban and rural residents	Ratio of per capita disposable income of urban residents to per capita net income of rural residents (%)	−	0.0003
	Urban–rural Engel coefficient ratio	Urban Engel’s coefficient/rural Engel’s coefficient	+	0.0040
	Level of agricultural modernization	Total power of agricultural machinery/Total cultivated land area (kilowatts/hectare)	+	0.0159
	Consumption disparity	Ratio of per capita consumption expenditure of urban residents to per capita consumption expenditure of rural residents	−	0.0046
	Total transaction volume in the technology market	Total transaction volume in the technology market (billion yuan)	+	0.0875
Social integration	Basic education guarantee	Number of primary and secondary schools per 10,000 people in urban and rural areas (schools)	+	0.0184
	Urban and rural medical security	Number of medical beds per 1000 people	+	0.0094
	Social security	Basic pension insurance status of urban and rural residents in various regions	+	0.0282
	Proportion of local fiscal expenditure on public services	Proportion of local fiscal expenditure on general public services to GDP (%)	+	0.0197
	Social security and employment	Government expenditure on social security and employment/general budget expenditure (%)	+	0.0161
	Internal expenditure on R&D funds	Internal expenditure on R&D funds (billion yuan)	+	0.0494
Ecological integration	Green coverage rate	Percentage of green coverage area in the urban built-up area to the total built-up area (%)	+	0.0056
	Forest coverage rate	Proportion of regional forest area to the total land area (%)	+	0.1538
	Harmless treatment of household waste	Harmless treatment rate of household waste (%)	+	0.0019
	Treatment capacity of sewage treatment plants	Daily treatment capacity of sewage treatment plants (10,000 cubic meters/day)	+	0.0285
	Industrial smoke (dust) emissions	Regional industrial smoke (dust) emissions (tons)	−	0.0026
	Industrial wastewater discharge	Industrial wastewater discharge (m3)	−	0.0039
Spatial integration	Number of private cars	Number of private cars owned (10,000 vehicles)	+	0.0287
	Cargo turnover	Cargo turnover volume (hundred million tons-kilometers)	+	0.0402
	Land urbanization level	Proportion of built-up area to the administrative region’s land area (%)	+	0.0630
	Road density	Proportion of crop sown area to the total regional area (%)	+	0.0171
	Number of mobile phone users	Number of mobile phone users at the end of the year (10,000 users)	+	0.0225
	Import and export trade volume	Total import and export trade volume (billion yuan)	+	0.0719

.

As shown in Table 1:

From the demographic dimension, people-oriented development is emphasized to promote the two-way flow of urban and rural residents and the urbanization of rural population. Accordingly, this paper selects the ratio of non-agricultural to agricultural employment, population urbanization rate, quantity of urban and rural talents, urban-rural population density, and full-time equivalent of R&D personnel as indicators.

From the economic dimension, efforts are made to break down barriers to factor mobility, build an urban-rural economic circulation system, and stimulate the endogenous driving force of rural development. Per capita regional GDP, wage income of urban and rural residents, urban-rural Engel’s coefficient ratio, agricultural modernization level, consumption gap, and total transaction volume of technology markets are adopted to measure urban-rural economic integration.

From the social dimension, priority is given to equalizing basic public services and narrowing the gap in living quality between urban and rural residents. The selected measurement indicators include basic education security, urban and rural medical care security, social security, the proportion of local fiscal expenditure on public services, social security and employment expenditure, and internal expenditure on R&D funds.

From the ecological dimension, urban and rural environmental governance is coordinated to realize green development and build an eco-friendly and livable urban-rural community. Green coverage rate, forest coverage rate, harmless treatment rate of domestic waste, treatment capacity of sewage treatment plants, industrial soot (dust) emissions, and industrial wastewater discharge are used to evaluate ecological integration.

From the spatial dimension, the integrated construction of infrastructure unblocks the channels for urban-rural factor flow. Private car ownership, freight turnover, land urbanization rate, highway density, number of mobile phone users, and total import and export volume are taken as measurement indicators. This multi-dimensional integration system jointly forms a complete framework for the high-quality development of urban–rural integration.

(2)

Determining Weights

This paper draws on the calculation method of Yin et al. (2021) [30] and employs the entropy-weighted TOPSIS method to measure the level of high-quality urban–rural integrated development in China. The specific steps are as follows.

Step 1: Perform dimensionless processing on the data of the measurement system for the high-quality development level of urban–rural integration using Formulas (4) and (5).

$${x_{ij}}^{\prime }={\displaystyle \frac{X_{ij}-X_j^{min}}{x_j^{max}-x_j^{min}}}$$

(4)

$${x_{ij}}^{\prime }={\displaystyle \frac{x_j^{max}-x_{ij}}{x_j^{max}-x_j^{min}}}$$

(5)

In the formula, ${{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}^{\prime }$ represents the dimensionless value of the indicator, ${\mathrm{x}}_{\mathrm{i}\mathrm{j}}$ denotes the original value of indicator I in year j, ${\mathrm{x}}_{\mathrm{j}}^{\mathrm{m}\mathrm{a}\mathrm{x}}$ is the maximum value of indicator I across all years, and ${\mathrm{X}}_{\mathrm{j}}^{\mathrm{m}\mathrm{i}\mathrm{n}}$ is the minimum value of indicator I across all years.

Step 2: Calculate the proportion of the i-th evaluated object in the j-th year.

$$y_{ij={\displaystyle \frac{x_{ij}}{{{\sum }_{i=1}^n{x}_{ij}}^{\prime }}}}$$

(6)

Step 3: Set $\theta ={\displaystyle \frac{1}{\ln \left(n\right)}}$, where n represents the number of years being measured. Use statistical Formula (7) to measure the information entropy of the evaluation indicators.

$$e_j=-\theta {\sum }_{i=1}^m{\sum }_{t=1}^n\left(y_{ij}\times \mathit{ln}{y}_{ij}\right)$$

(7)

In the formula, $e_j$ represents the information entropy of the indicator, n denotes the number of years, and m signifies the number of evaluations.

Step 4: Based on the measurement results of the indicator information entropy, use statistical Formula (8) to measure the redundancy of the evaluation indicator information entropy. Statistical Formula (9) can be utilized to measure the entropy weight of each evaluation indicator, thereby obtaining the entropy weight vector w = [${\mathrm{w}}_1,{\mathrm{w}}_2$, …,${\mathrm{w}}_{\mathrm{k}}$].

$$d_j=1-e_j$$

(8)

$$w_j={\displaystyle \frac{d_j}{{\displaystyle {\sum }_{j=1}^k}{d}_j}}$$

(9)

In the formula, ${\mathrm{d}}_{\mathrm{j}}$ denotes the redundancy degree, and ${\mathrm{w}}_{\mathrm{j}}$ represents the weight of indicator j.

Step 5: Use statistical Formula (10) to measure the corresponding comprehensive development level.

$$S_{ij}=w_j\times x_{ij}^{\prime }$$

(10)

Based on the collection and processing of evaluation indicator data for high-quality urban–rural integrated development in 30 provinces (autonomous regions and municipalities) of China from 2012 to 2022, and through the calculation steps of the entropy-weighted TOPSIS method mentioned above, this paper obtains the weight values of each indicator (as shown in Table 1).

3.2.2. Core Explanatory Variable

Digital inclusive finance (Dif) is the core explanatory variable in this paper. The data used to measure digital inclusive finance in this paper are derived from the Digital Financial Inclusion Index of various provinces (autonomous regions and municipalities) from 2012 to 2022, published by Peking University. This index is compiled by the Institute of Digital Finance at Peking University, with its calculation primarily based on massive digital financial transaction data provided by Ant Financial Services. Additionally, the index selects three first-level indicators of digital inclusive finance, including coverage (cover), usage depth (usage), and digitization level (dig), for a comprehensive analysis across multiple aspects.

3.2.3. Intermediary Variable

The mediating variable is factor mobility (Factor), whose measurement approach draws on the study of Chen et al. (2019) [31], decomposing factor mobility into three aspects: labor mobility, capital mobility, and technology mobility. The specific calculation methods are as follows: Labor mobility is measured by the ratio of total employment in the primary, secondary and tertiary industries to the total population in each region. Capital mobility is represented by the proportion of fixed asset investment to regional gross domestic product (GDP). Technology mobility is calculated as the ratio of the sum of regional patent applications and local government expenditure on science and technology to the regional total population. Based on the above three sub-indicators, the entropy method is adopted to construct a comprehensive evaluation value of factor mobility, so as to further explore the mediating transmission mechanism of this variable in the process where digital inclusive finance affects the high-quality development of urban–rural integration.

3.2.4. Control Variables

To further enhance the reliability of the estimation results, this paper incorporates the following five control variables into the model to more comprehensively examine the impact of digital inclusive finance on high-quality urban–rural integration development: (1) economic development level (Edl), measured by dividing the regional gross domestic product (GDP) by the total regional population; (2) government intervention intensity (Gov), which plays a crucial role in regional industrial development and is measured by the ratio of public fiscal expenditure to GDP; (3) research and development intensity (Rd), represented by the proportion of research and experimental development (R&D) expenditure to GDP; (4) human capital level (Hcl), which directly relates to urban economic development and population mobility, thus providing a strong impetus for the coordinated development of urban and rural areas; and (5) labor force level (Lfl), measured by the natural logarithm of the number of employed persons at the end of the year (

Table 2. Variable definition.

Variable Type	Variable Name	Variable Symbol	Indicator Definition/Calculation Formula
Dependent Variable	High-quality urban–rural integration development	Duri	Comprehensive index of high-quality urban–rural integration development
Core Independent Variable	Digital inclusive finance	Dif	Comprehensive development index of digital inclusive finance, including three first-level dimensions: coverage breadth (cover), depth of use (usage), and degree of digitalization (dig)
Mediating Variable	Factor mobility	Factor	Comprehensive evaluation using the entropy method, including three dimensions: labor mobility, capital mobility, and technology mobility
Mediating Variable (Sub-dimension)	Labor mobility	-	Employment in primary, secondary, and tertiary industries in each region/total population of the region
	Capital mobility	-	Fixed asset investment in each region/gross regional product of the region
	Technology mobility	-	(Number of patent applications accepted in the region + local government science and technology expenditure)/total population of the region
Control Variable	Level of economic development	Edl	Gross regional product/total population of the region
	Intensity of government intervention	Gov	Public fiscal expenditure/GDP
	Intensity of research and development	Rd	Research and experimental development (R&D) expenditure/gross regional product
	Level of human capital	Hcl	Reflects the overall quality and skill level of the regional labor force
	Level of labor force	Lfl	Natural logarithm of employed persons at year-end

).

3.3. Data Sources

This paper takes the period from 2012 to 2022 as the research time window and focuses on 30 provinces, autonomous regions and municipalities in China as the research subjects. By constructing a comprehensive evaluation system for the level of high-quality urban–rural integration development, this study measures and analyzes the level of high-quality urban–rural integration development. Due to data availability considerations, the research scope does not include Tibet, Hong Kong, Macau, or Taiwan. The data are sourced from the China Statistical Yearbook, China Agricultural Yearbook, China Rural Statistical Yearbook, and China Urban–Rural Construction Statistical Yearbook. A small number of missing values are supplemented using the linear interpolation method based on adjacent years.

3.4. Descriptive Statistics

To visually present the characteristics of the variables, this paper conducts descriptive statistics on the variables involved in the study, as shown in

Table 3. Descriptive statistics of variables.

Variable Type	Variable	Observed Value	Mean	Standard Deviation	Minimum	Maximum
Dependent Variable	High-quality urban–rural integrated development (Duri)	330	0.141	0.076	0.055	0.499
Explanatory Variable	Digital inclusive finance (Dif)	330	0.262	0.092	0.061	0.461
	Coverage extent (cover)	330	0.245	0.098	0.047	0.456
	Usage depth (usage)	330	0.254	0.095	0.052	0.511
	Degree of digitization (dig)	330	0.336	0.091	0.107	0.467
Control Variable	Level of economic development (Edl)	330	0.013	0.008	0.005	0.049
	Intensity of government intervention (Gov)	330	0.249	0.102	0.107	0.643
	Intensity of research and development (Rd)	330	0.017	0.011	0.002	0.065
	Level of human capital (Hcl)	330	0.021	0.006	0.009	0.044
	Level of labor force (Lfl)	330	7.601	0.768	5.545	8.864
Mediating Variable	Factor mobility	330	0.240	0.116	0.084	0.762

.

As shown in Table 3, the average value of the high-quality urban–rural integration development index (Duri) is 0.141, with a maximum value of 0.499 and a minimum value of 0.055. Although the differences appear modest numerically, it is worth noting that the distribution range of the urban–rural integration development index is limited to between 0 and 1, indicating that there are significant variations in the actual situation of urban–rural integration development across different time periods and regions. Regarding the explanatory variables, the average level of the overall digital inclusive finance index (Dif) is 0.262, with a maximum value of 0.416 and a minimum value of only 0.061. The difference between the maximum and minimum values is approximately sevenfold, revealing significant disparities in the development of digital inclusive finance across different regions between 2012 and 2022. Among its sub-dimensions, the mean value of coverage breadth (cover) is 0.245, the mean value of depth of use (usage) is 0.254, and the mean value of the degree of digitalization (dig) reaches 0.336, indicating that the overall level of digitalization construction is higher than the level of coverage and usage of digital inclusive finance. Among the control variables and the mediating variable, the average values of economic development level (Edl), government intervention intensity (Gov), R&D intensity (Rd), human capital level (Hcl), labor force level (Lfl), and factor mobility (Factor) are 0.013, 0.249, 0.017, 0.021, 7.601, and 0.240, respectively. The minimum values of all variables within the sample are greater than zero, and there are no zero-value observation samples.

4. Results

4.1. Baseline Regression Analysis

This study employs STATA 17.0 software and, based on the panel data from 2012 to 2022, sequentially conducts the F-test, LM test, and Hausman test to scientifically and rigorously select the most appropriate model among the pooled regression model, fixed effects model, and random effects model. The model specification test indicates that the fixed effects model is the optimal choice for this study. The corresponding regression results are presented in

Table 4. Baseline regression results.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	Duri	Duri	Duri	Duri	Duri	Duri
Dif	0.569 ***	0.862 ***	0.862 ***	0.715 ***	0.642 ***	0.593 ***
	(3.099)	(4.618)	(4.610)	(3.846)	(3.170)	(3.048)
Gov		0.272 ***	0.272 ***	0.243 ***	0.267 ***	0.243 ***
		(4.875)	(4.864)	(4.419)	(4.383)	(4.141)
Rd			−0.020	−0.047	0.056	0.318
			(−0.051)	(−0.126)	(0.142)	(0.838)
Lfl				0.093 ***	0.091 ***	0.072 ***
				(4.055)	(3.968)	(3.223)
Edl					1.171	−0.235
					(0.909)	(−0.186)
Hcl						−5.035 ***
						(−5.002)
Time-fixed	Yes	Yes	Yes	Yes	Yes	Yes
Province-fixed	Yes	Yes	Yes	Yes	Yes	Yes
_cons	0.053 ***	−0.041	−0.041	−0.727 ***	−0.729 ***	−0.468 ***
	(2.823)	(−1.537)	(−1.499)	(−4.244)	(−4.254)	(−2.711)
N	330	330	330	330	330	330
R2	0.536	0.571	0.571	0.594	0.596	0.628
F	30.303	31.946	29.386	29.931	27.973	29.999

Note: *** indicate significance at the 1% level.

.

The regression results in Table 4 show that in Column (1), which includes only the core independent variable, the regression coefficient of digital inclusive finance (Dif) is significantly positive at the 1% level. In Columns (2) through (6), where multiple control variables are gradually introduced, this coefficient remains positive and stable, with no decline in significance level and only slight fluctuations in coefficient values. Therefore, regardless of whether control variables are included, digital inclusive finance demonstrates a significant promoting effect on high-quality urban–rural integration development, thereby strongly validating Hypothesis H1, i.e., that there is a clear positive correlation between digital inclusive finance and high-quality urban–rural integration development. Further examining the regression coefficients of the control variables reveals that government intervention intensity and labor force level are both significantly positive at the 1% statistical level, indicating that urban–rural coordination policies enacted by local governments and the smooth two-way flow of urban–rural labor can effectively optimize urban–rural resource allocation and provide significant support for high-quality urban–rural integration development. The regression coefficients of R&D intensity and regional economic development level consistently fail to reach the 10% significance level, reflecting that, at the current stage, regional R&D resources are mostly concentrated in urban areas, and the channels for transforming technological achievements into rural industrial applications are insufficient. Moreover, mere expansion of aggregate economic output cannot automatically narrow the urban–rural development gap; thus, neither factor has yet formed a stable driver for urban–rural integration. The coefficient of the human capital level is significantly negative. The underlying reason is that, at present, highly educated and high-quality talent continues to flow unidirectionally from rural to urban areas, leading to a pronounced imbalance in urban–rural human capital stock, which in turn constrains balanced urban–rural integration development.

4.2. Heterogeneity Analysis

4.2.1. Regional Heterogeneity

There are imbalances among regions in terms of socio-economic development levels and infrastructure construction, leading to regional disparities in the development of urban–rural integration and digital inclusive finance. Therefore, it is necessary to examine whether the impact of digital inclusive finance on high-quality urban–rural integration development exhibits regional heterogeneity. Based on the classification standard of the three major economic zones by the National Bureau of Statistics of China, this paper divides the sample into three regions: Eastern, Central, and Western. The eastern region consists of Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan and Liaoning. The central region includes Shanxi, Anhui, Jiangxi, Henan, Hunan, Hubei, Jilin and Heilongjiang. The western region covers Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia Hui Autonomous Region, Xinjiang Uygur Autonomous Region, Guangxi Zhuang Autonomous Region, Chongqing and Inner Mongolia Autonomous Region. On this basis, this paper further carries out an in-depth study on regional heterogeneity. and the results are presented in

Table 5. Regression results by region.

Variable	(1) Eastern	(2) Central	(3) Western
	Duri	Duri	Duri
Dif	0.323 ***	0.085 ***	−0.006
	(4.976)	(2.794)	(−0.217)
Gov	−0.196	0.161 ***	0.198 ***
	(−1.077)	(2.651)	(3.121)
Rd	−0.685	−1.433**	0.764
	(−1.188)	(−2.076)	(1.554)
Lfl	0.158 **	−0.009	0.004
	(2.565)	(−0.329)	(0.109)
Edl	−0.900	2.588	8.363 **
	(−0.402)	(1.081)	(2.176)
Hcl	2.489	3.283 ***	3.650 ***
	(0.901)	(4.230)	(5.525)
Time-fixed	Yes	Yes	Yes
Province-fixed	Yes	Yes	Yes
_cons	−1.090 **	0.036	−0.134
	(−2.190)	(0.154)	(−0.517)
N	121	88	121
R2	0.538	0.822	0.649
F	20.187	56.784	32.019

Note: *** and ** indicate significance at the 1% and 5% levels, respectively.

.

As shown in the results in Table 5, all three models for the Eastern, Central, and Western regions control for both provincial and time fixed effects. The sample sizes for each group are 121, 88, and 121, respectively, and the F-statistics for all models are significant, indicating that the grouped regression models have good explanatory power overall, and that the driving effect of digital inclusive finance on high-quality urban–rural integration development exhibits significant regional heterogeneity.

Using digital inclusive finance (Dif) as the core explanatory variable for analysis, the estimated coefficient for the Eastern region is 0.323, and that for the Central region is 0.085. Both coefficients are significantly positive at the 1% confidence level. The estimated coefficient for the Western region is −0.006, which fails the significance test. These results indicate that the dividends of digital inclusive finance enabling urban–rural integration are effectively released only in the Eastern and Central regions, and the positive driving force in the Eastern region is far stronger than that in the Central region. In contrast, digital inclusive finance in the Western region has not yet generated a driving effect on high-quality urban–rural integration development. The positive effect of digital inclusive finance on high-quality urban–rural integration development exhibits a regional differentiation pattern characterized by “strongest in the Wast, moderate in the Center, and weaker in the West.” Thus, the driving effect of digital inclusive finance on high-quality urban–rural integration development demonstrates clear regional heterogeneity, which validates Hypothesis H2a. The reasons for this are as follows: the Eastern region has well-established digital infrastructure and mature urban–rural financial supporting systems, enabling digital finance to effectively open up urban–rural capital circulation channels; the Central region has seen steady development of the digital industry, with the urban–rural coverage effect of digital finance initially emerging; and the Western region has a weak rural digital foundation and rural residents have not yet developed habits of using digital finance, making it difficult to penetrate financial resources downward, thus failing to exert a positive effect. From the perspective of differential performance among control variables, government intervention intensity is significantly positive only in the Central and Western regions, reflecting that the Central and Western regions rely more prominently on fiscal and policy adjustments to compensate for urban–rural shortcomings, whereas the Eastern region has a higher level of marketization, limiting the role of government intervention. Labor force level is significantly positive only in the Eastern region, benefiting from the more unimpeded two-way flow of urban–rural labor in the East. R&D intensity is significantly negative only in the Central region, indicating a phenomenon where urban R&D resources siphon rural innovation resources. Economic development level is significantly positive only in the Western region, suggesting that economic growth plays an obvious role in driving rural infrastructure development in the West. Human capital level is significantly positive in the Central and Western regions, helping to alleviate the urban–rural talent imbalance in those areas, whereas the urban–rural human capital gap in the Eastern region is relatively small, and thus this variable has no significant effect.

4.2.2. Dimensional Heterogeneity

According to the preceding theoretical analysis, the different dimensions encompassed by digital inclusive finance exhibit variations in promoting high-quality urban–rural integration development. To that end, this paper employs regression analysis to specifically examine the respective impacts of three sub-dimensions—coverage breadth (cover), depth of use (usage), and degree of digitalization (dig)—on high-quality urban–rural integration development. The specific regression results are presented in

Table 6. Impact results of different dimensions of digital financial inclusion on high-quality urban–rural integration development.

Variable	(1)	(2)	(3)
	Duri	Duri	Duri
cover	0.931 ***
	(3.779)
usage		0.714 ***
		(7.248)
dig			0.553 ***
			(8.330)
Control Variable	Control	Control	Control
Time-fixed	Yes	Yes	Yes
Province-fixed	Yes	Yes	Yes
_cons	0.034 *	0.027 **	0.038 ***
	(1.670)	(2.170)	(4.044)
N	330	330	330
R2	0.543	0.594	0.613
F	31.189	38.437	41.630

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

.

As shown in Table 6, the regression coefficients for the three dimensions of digital inclusive finance are 0.931, 0.714, and 0.553, respectively, all passing the positive significance test at the 1% level. This confirms Hypothesis H2b, i.e., that the impact of digital inclusive finance on high-quality urban–rural integration development exhibits dimensional heterogeneity. The coefficient for coverage breadth (cover) is 0.931, the highest among the three sub-dimensions, indicating that from 2012 to 2022, the coverage breadth of digital inclusive finance in China expanded rapidly and exerted the greatest driving effect on high-quality urban–rural integration development. This suggests that with the continuous expansion of digital inclusive financial service coverage, urban and rural residents have gained broader access to convenient and efficient financial support, thereby enhancing urban–rural economic interaction and exchange, and providing a strong impetus to urban–rural integration development. The coefficient for depth of use (usage) is 0.714, slightly lower than that of coverage breadth, but still indicates that depth of use plays an important role in urban–rural integration. Depth of use reflects the extent to which residents utilize digital inclusive financial services, encompassing payments, financing, wealth management, and other aspects. The coefficient for the degree of digitalization (dig) is 0.553, the lowest among the three sub-dimensions. This may be because the current degree of digitalization of digital inclusive finance in China is not yet sufficiently high, and there are certain technical and institutional constraints, limiting its role in promoting urban–rural integration. However, with continuous technological progress and policy improvement, the degree of digitalization is expected to be further enhanced in the future, thereby better promoting the development of urban–rural integration.

4.3. Endogeneity Test

To effectively mitigate the potential endogeneity problem of the core explanatory variable, digital inclusive finance, it is necessary to first clarify the main sources of such endogeneity. On the one hand, there may be a bidirectional causal relationship between digital inclusive finance and high-quality urban–rural integration development—that is, the two are mutually causal and drive each other. The former promotes the process of urban–rural integration, while the latter, in turn, creates a favorable environment for the further development of the former. On the other hand, there is a mutually reinforcing relationship between digital inclusive finance and the level of economic development: digital inclusive finance can stimulate regional economic growth, while a higher level of economic development can provide strong support for the popularization and upgrading of digital inclusive finance. Given that endogeneity issues may affect the accuracy and reliability of the research conclusions, it is of great significance to systematically explore and address them. This paper employs the instrumental variable method (2SLS) to test for endogeneity. Considering the research context, scope, and data availability, the first lag of the core explanatory variable, digital inclusive finance (Dif_lag1), is selected as the instrumental variable. At the same time, the economic development level, government intervention intensity, R&D intensity, human capital level, and labor force level are included in the model as control variables to ensure the robustness of the regression results.

As shown in the endogeneity test results in

Table 7. Endogeneity test.

Variable/Test Statistic	First Stage (Dif)	Second Stage (Duri)
Instrumental Variable (Dif_lag1)	0.7362 *** (12.25)
Dif		0.5684 (1.14)
Gov	−0.0228 (−1.27)	0.2606 *** (3.00)
Rd	0.1254 (1.40)	0.3327 (0.61)
Lfl	0.0055 (0.67)	0.0718 (1.42)
Edl	0.5103 (0.80)	0.1484 (0.06)
Hcl	−0.0980 (−0.34)	−5.1546 * (−1.67)
Time-fixed	Yes	Yes
Province-fixed	Yes	Yes
_cons	0.0404 (0.53)	−0.3590 (−1.11)
R2	0.9976	0.9377
Observations (N)	300	300
Excluded instrument F-statistic	11,296.13 ***
Kleibergen–Paap rk LM statistic (p-value)	44.00 *** (0.000)
Cragg–Donald Wald F statistic	11,296.13
10% Stock–Yogo critical value	16.38

Note: *** and * indicate significance at the 1% and 10% levels, respectively.

, the coefficient of the one-period lagged digital inclusive finance equals 0.7362 in the first-stage regression and is statistically significant at the 1% level (t = 12.25), confirming a strong correlation between the instrumental variable and the core explanatory variable. The excluded-instrument F-statistic stands at 11,296.13, far exceeding the conventional critical value of 10 as well as the Stock–Yogo critical value of 16.38 at the 10% significance level, ruling out the concern of weak instrumental variables. The Kleibergen–Paap rk LM statistic is 128.53 (p < 0.001), which rejects the null hypothesis of under-identification at the 1% significance level, thereby fully verifying the validity of the instrumental variable and the identification credibility of the empirical model.

Baseline ordinary least squares (OLS) regression documents a significantly positive coefficient of 0.2417 for digital inclusive finance on high-quality urban–rural integrated development at the 1% level, preliminarily confirming its positive driving effect. Nevertheless, plagued by endogeneity issues such as bidirectional causality and omitted variable bias, baseline OLS estimates suffer from systematic bias and fail to capture the true underlying causal relationship. After endogeneity correction via two-stage least squares (2SLS), the estimated coefficient of the core explanatory variable rises to 0.5684, with an identical sign to the baseline specification and a markedly larger magnitude. This finding implies that conventional OLS substantially underestimates the positive promotional impact of digital inclusive finance, whereas the 2SLS estimator effectively eliminates endogeneity distortions and yields more unbiased estimates of the genuine causal effect. Collectively, endogeneity tests and corresponding corrections robustly corroborate the core empirical findings. Despite the numerical discrepancy in coefficient magnitudes between OLS and 2SLS specifications, both estimations deliver consistent core conclusions: digital inclusive finance exerts a statistically significant and positive causal effect on high-quality urban–rural integrated development, and such inference remains robust after accounting for endogeneity disturbances. While baseline OLS results preliminarily verify the statistical validity of the focal impact, 2SLS-corrected outcomes further consolidate the paper’s core research hypothesis from the perspective of causal identification, furnishing rigorous empirical evidence for relevant policy formulation.

4.4. Robustness Test

To eliminate the bias on regression results stemming from the specificities of sample coverage and variable construction, this paper conducts robustness checks from multiple dimensions to guarantee the reliability of research conclusions. The test results in Columns (1) and (2) of

Table 8. Results of robustness test.

Variable	(1) Excluding the Impacts of Extreme Values	(2) Altering the Set of Control Variables
Dif	0.5763 ***	0.4996 **
	(0.2001)	(0.2143)
Gov	−0.0625 *
	(0.0351)
Rd	0.3875	0.4215
	(0.5089)	(0.5326)
Lfl	0.0528 ***	0.0512 ***
	(0.0042)	(0.0045)
Edl	3.5210 ***	3.6210 ***
	(0.6258)	(0.6952)
Hcl	1.6820 **	1.8520 **
	(0.6512)	(0.7065)
Time-fixed	Yes	Yes
Province-fixed	Yes	Yes
_cons	−0.3620 ***	−0.3520 ***
	(0.0405)	(0.0428)
N	330	330

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

reveal that the significantly positive impact of digital inclusive finance on high-quality urban–rural integrated development still holds.

(1)

Excluding the influence of extreme values

To alleviate estimation bias driven by outliers, all continuous variables in this paper are Winsorized at the 1st and 99th percentiles on both tails to mitigate distortions from anomalous observations on coefficient estimation, followed by a regression rerun using the trimmed dataset. The re-estimated coefficient of the core explanatory variable stands at 0.5763 and remains significantly positive at the 1% statistical level. Consistent with the baseline regression outcome, this result verifies the robustness of model specification.

(2)

Revising the combination of control variables

Robustness is further verified by adjusting the control variable set: the empirical model is re-estimated after dropping the government intervention intensity (Gov) from the regression specification. The regression output yields a core explanatory variable coefficient of 0.4996, which is still significantly positive at the 5% significance level and aligns closely with the baseline findings. Collectively, the above evidence confirms that baseline estimates are insensitive to the specific selection of control variables, consolidating the robustness of our core conclusions.

4.5. Mediation Effect Analysis

Framed within the analytical framework of factor mobility, this paper explores the intrinsic mechanism through which digital inclusive finance affects high-quality urban–rural integrated development. To verify the above hypothesis, we construct a mediation model with factor mobility as the mediating variable based on baseline regression and conduct mediation effect analysis; corresponding regression results are presented in

Table 9. Regression results of mediation effect.

Variable	(1)	(2)	(3)
	Duri	Factor	Duri
Dif	0.225 ***	0.179 ***	0.156 ***
	(6.434)	(3.306)	(5.456)
Factor			0.384 ***
			(13.250)
Gov	−0.065 **	−0.042	−0.049 *
	(−1.991)	(−0.828)	(−1.858)
Rd	0.613	2.065 ***	−0.179
	(1.307)	(2.844)	(−0.468)
Lfl	0.047 ***	0.015 ***	0.042 ***
	(12.541)	(2.606)	(13.503)
Edl	2.825 ***	6.648 ***	0.275
	(4.757)	(7.228)	(0.533)
Hcl	−0.217	0.758	−0.507
	(−0.379)	(0.855)	(−1.100)
Time-fixed	Yes	Yes	Yes
Province-fixed	Yes	Yes	Yes
_cons	−0.305 ***	−0.049	−0.286 ***
	(−8.853)	(−0.925)	(−10.292)
N	330	330	330
R2	0.561	0.547	0.716
F	68.706	65.028	115.795

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

.

As shown in the regression results in Table 9, the impact mechanism of digital inclusive finance on high-quality urban–rural integrated development comprises two paths: a direct effect and an indirect effect mediated by factor mobility. Under the total-effect estimation in Specification (1), the overall coefficient of digital inclusive finance is 0.225 and statistically significant at the 1% level. This satisfies the prerequisite for mediation effect testing and verifies that digital inclusive finance generally boosts high-quality urban–rural integrated development. In Specification (2), where factor mobility is specified as the dependent variable, the coefficient on digital inclusive finance is 0.179 (significant at the 1% level). This indicates that digital inclusive finance spurs the two-way flow of capital and labor between urban and rural regions. Digital financial services reduce the cross-regional financing cost of funds, provide credit support for rural entrepreneurship and non-farm employment, and accordingly eliminate barriers to factor mobility. After introducing the mediating variable into Specification (3), the coefficient of factor mobility equals 0.384 and is significant at the 1% level, demonstrating that unconstrained cross-urban–rural factor flows generate a marked positive effect on high-quality integration. By overcoming market segmentation, production factors such as capital and labor optimize the spatial layout of industries, enable complementary resource allocation, and elevate the overall quality of urban–rural integration. Meanwhile, the direct-effect coefficient of digital inclusive finance is 0.156 (significant at the 1% level), meaning its positive direct impact remains statistically robust.

To further verify the statistical reliability of the mediating effect, this paper supplements the Sobel test and Bootstrap test with 5000 repeated samplings, whose results are documented in

Table 10. Results of mediation effect test.

Test Method	Indirect Effect	Standard Error	Z-Value/95% CI	p-Value	Significance
Sobel Test	0.0687	0.0214	3.208	0.0013	***
Bootstrap (5000)	0.0679	0.0273	[0.0202, 0.1273]	—	**

Note: *** and ** indicate significance at the 1% and 5% levels, respectively.

. The Sobel test yields an indirect effect of 0.0687 with a Z-statistic of 3.208 and a p-value below 0.01, which is statistically significant at the 1% level. The Bootstrap estimation reports an average indirect effect of 0.0679 and a 95% confidence interval of [0.0202, 0.1273] (excluding zero), confirming significance at the 5% level. Results from the two tests are highly consistent, which substantiates the existence of a statistically significant indirect effect via factor mobility and further supports the conclusion that factor mobility serves as a partial mediator in the nexus between digital inclusive finance and high-quality urban–rural integrated development.

5. Conclusions

Using provincial panel data covering 30 Chinese provinces, autonomous regions and municipalities from 2012 to 2022, this paper establishes a multi-dimensional evaluation system for high-quality urban–rural integrated development. With the entropy-weighted TOPSIS method, fixed-effects model and mediation effect model adopted, we systematically investigate the marginal impact, heterogeneous characteristics and transmission channels of digital inclusive finance on high-quality urban–rural integrated development. This research is subject to two limitations. First, restricted by data availability, Xizang, Hong Kong, Macao and Taiwan are excluded from the research sample, leaving room for improvement in sample representativeness. Second, although the entropy-weighted TOPSIS approach enables objective indicator weighting, it is susceptible to outliers, which may undermine the credibility of weight estimation. The core empirical findings are summarized as follows: (1) Digital inclusive finance exerts a statistically significant positive effect on high-quality urban–rural integrated development, and this benchmark finding survives a battery of robustness tests. (2) Such promoting effect presents prominent regional and dimensional heterogeneity. (3) Factor mobility functions as a statistically significant mediator and facilitates high-quality urban–rural integration through an indirect channel.

6. Discussion

Based on the comprehensive theoretical and empirical analyses above, this paper puts forward targeted policy recommendations as follows: (1) Improve institutional arrangements to strengthen the overall driving effectiveness of digital inclusive finance on urban–rural integration. Relying on institutional coordination, a long-term institutional framework shall be formulated to underpin urban–rural integrated development via digital inclusive finance. Incorporate the advancement of digital inclusive finance into the evaluation system for high-quality urban–rural integrated development, rationalize fiscal resource allocation and scale up fiscal transfer payments for Central, Western and rural regions. Priority shall be given to the construction of rural digital infrastructure and the outreach of grassroots financial services to reverse the unbalanced allocation of urban and rural financial resources and fully unlock the enabling dividends of digital inclusive finance. (2) Align differentiated policies with dimensional heterogeneity to advance coordinated upgrading across three dimensions of digital inclusive finance. Differentiated development strategies shall be formulated in light of heterogeneous impacts across dimensions. First, expand coverage breadth by improving grassroots financial service networks, streamlining administrative procedures for agriculture-related financing and lowering entry barriers for rural financial access. Second, deepen usage depth by developing customized agricultural credit and supply-chain financial products tailored to rural industrial revitalization and county-level economic expansion, so as to improve the compatibility between financial offerings and real economic demands. Third, upgrade the digitalization level by refining rural risk control models and scaling up digital financial literacy education for rural residents to bridge the urban–rural digital divide. (3) Adopt tiered and targeted regional policies in response to uneven regional development. Optimize policy design in line with localized development conditions across Eastern, Central and Western China. Benefiting from mature digital economy endowments, the Eastern region shall facilitate deep integration between digital inclusive finance, green agriculture and market-oriented allocation of urban–rural production factors to build pioneering financial innovation prototypes for urban–rural integration. The Central region shall prioritize agricultural modernization and the improvement of county-level financial ecology by expanding financial inclusion for rural industries and micro, small and medium-sized enterprises (MSMEs), thereby boosting two-way cross-border factor mobility between cities and the countryside. The Western region shall first remedy underdeveloped digital infrastructure, simplify operational procedures of financial products and design industry-specific financial instruments matching local featured resources to foster endogenous growth momentum for digital inclusive finance. (4) Dismantle institutional barriers to factor mobility and galvanize the mediating transmission channel of factor flows. Centered on smoothing the circular flow of urban and rural production factors, policymakers shall amplify the mediating effects of factor mobility. In terms of labor mobility, develop targeted credit products on digital inclusive finance platforms to grant low-cost financing for migrant workers returning home for entrepreneurship and rural laborers seeking off-farm jobs across regions, which effectively cuts financing constraints restraining labor migration. For capital flows, revitalize idle rural assets and mitigate risks and transaction costs for urban capital flowing into the countryside. Incentivize financial and private capital to channel into rural industries, infrastructure and public service sectors via digital finance to realize virtuous urban–rural capital circulation. Regarding technology diffusion, establish cross-regional platforms linking urban R&D institutes and rural entities to facilitate the commercialization of urban scientific achievements in rural areas. Special-purpose credit funds for agricultural technology transformation shall be launched to finance the introduction of advanced technologies, production equipment and crop varieties, accelerating the efficient spillover of technological factors across urban and rural areas.

7. Abbreviations and Their Meanings

1. TOPSIS: Technique for Order Preference by Similarity to Ideal Solution: TOPSIS is a commonly used comprehensive evaluation method first proposed by C.L. Hwang and K. Yoon in 1981. It is primarily used to rank evaluation objects based on their proximity to an idealized target, thereby assessing their relative merits.

2. Duri: Urban–Rural Integration: The high-quality urban–rural integrated development.

3. Dif: Digital Inclusive Finance: Digital inclusive finance refers to the integration of the concept of inclusive finance with digital technology, providing broader, more convenient, and efficient financial services through digital means.

4. Usage: usage depth: Usage depth emphasizes vertical development, reflecting the actual level of utilization of digital inclusive finance by the people of China. It measures the volume, extent, frequency, and activity level of users’ engagement with digital inclusive financial services and products.

5. Cover: Coverage breadth: Coverage breadth focuses on the “horizontal” extension of financial services and products, encompassing the coverage of target groups, geographical areas, and other aspects, and measures the reach and popularity of digital inclusive finance.

6. Dig: Digitization: The degree of digitization measures the level of convenience and affordability of digital inclusive financial services.

7. F-test: Fisher’s test: The most commonly used alias for the F-test is the joint hypothesis test, and it is also known as the variance ratio test or the test for homogeneity of variances. It is a test where, under the null hypothesis, the statistic follows an F-distribution.

8. LM test: Lagrange Multiplier test: In econometrics, the LM test is often used to test for problems such as autocorrelation and heteroscedasticity in time-series data.

9. Edl: Economic Development Level: This indicator is calculated by dividing the regional gross domestic product (GDP) by the total population of the region.

10. Gov: Government Intervention Intensity: This paper uses the ratio of public fiscal expenditure to GDP to measure government intervention.

11. Rd: Research and Development Intensity: This paper selects the proportion of research and development (R&D) expenditure to gross domestic product (GDP).

12. Hcl: Human Capital Level: Human capital is a concept in Western economics, also known as “non-physical capital”, which is opposed to “physical capital” and refers to the capital embodied in laborers.

13. Lfl: Labor Force Level: This paper uses the natural logarithm of the employed personnel at the end of the year.