Exploring the Pathways Through Which the Digital Economy Drives Common Prosperity in the Context of Sustainable Development

Abstract

The digital economy, as a major economic form after the agricultural and industrial economies, has become a new driving force in the development of the national economy, and it may provide opportunities for rural economic development through new businesses such as the platform economy and live e-commerce. However, there may also be a risk of a digital divide, and the mechanism of its impact on shared prosperity needs to be scientifically verified. Based on the panel data of 2243 counties in China from 2011 to 2021, the article empirically examines how the digital economy promotes common prosperity among regions and the spatial spillover effects of the digital economy. The findings suggest that, first, the geographic distance matrix reveals a positive spatial relationship between the digital economy and shared prosperity, and the phenomenon of geo-graphic agglomeration is observed, which manifests itself as a high-high-low aggregation. Second, the digital economy has had an impact that transcends space, enabling counties to both “expand the cake” and “share the cake” more equitably. Third, the coordinated, inclusive, and structurally optimizing effects of the digital economy help counties achieve common prosperity by upgrading the level of public services and promoting the upgrading of industrial structure. Ultimately, the digital economy promotes the common prosperity and long-term development of county economies through innovation-driven and optimized resource allocation.

1. Introduction

The United Nations has proposed that eradicating all forms of poverty is the first of the 17 goals of the 2030 Agenda for Sustainable Development [1]. From 1990 to 2014, the world made remarkable progress in reducing extreme poverty, with more than 1 billion people lifted out of extreme poverty [2]. However, the 2019 coronavirus disease pandemic has led to an increase in extreme poverty in the world [3]. Poverty is a major challenge for every country. China has achieved a comprehensive victory in the battle against poverty, with more than 90 million rural poor lifted out of poverty, 800 poor counties removed from the poverty list, and 128,000 poor villages listed [4]. Despite China’s major success in overcoming absolute poverty, the problem of uneven distribution of income among the population still exists, and uneven and insufficient development is still highlighted. Eliminating poverty, improving people’s livelihoods and realizing common prosperity are the core concepts of socialism and the common goal of all people. The twentieth National Congress of the Communist Party of China emphasized that “modernization with universal affluence is Chinese-style modernization”. The Congress reaffirmed its commitment to ensuring that the quest for a better life for the people remains the cornerstone and objective of modernity, with a commitment to uphold social fairness and equity, advancing common prosperity, and preventing excessive disparities. Structurally, common prosperity encompasses two key dimensions: “sharing” and “prosperity” [5]. In the pursuit of this goal, it is crucial to balance efficiency with fairness, ensuring both the expansion of wealth creation (“making the cake bigger”) and its equitable distribution (“dividing the cake well”). Therefore, it is of positive significance for the world’s sustainable development to construct an assessment system oriented to the realization of the goal of common prosperity and to explore the path of realizing common prosperity in China.

Sustainable development emphasizes stable and inclusive economic growth, requiring not only the enhancement of overall economic strength but also the narrowing of regional, urban–rural, and social disparities to ensure that all people share in the dividends of progress [6]. Developing the digital economy represents a strategic move to capitalize on the opportunities presented by emerging advancements in science and technology, alongside industrial transformation. It aligns with the goal of encouraging common prosperity via superior development and holds significant advantages in driving sustainable economic growth [7]. In the past few years, emerging technologies such as artificial intelligence and a huge volume of data have empowered industries across the board, accelerating the digital transformation of traditional sectors toward high-end, intelligent development. The increasing affordability and ease of use of digital tools help bridge the “capability gap” among users, enabling broader participation in the digital economy [8]. The circulation and utilization of data elements unlock new possibilities for “equal opportunities”, offering diverse innovation entities equal access to emerging industries and value creation. With the high penetration of digital technology across both urban and rural areas, its widespread application provides essential technical support for integrated urban–rural development [9], enhances fairness in primary income distribution, and contributes to “dividing the cake well”. Furthermore, digital technology plays a pivotal role in promoting regional coordinated development [10]. By driving industrial digitalization in central and western regions, it fosters economic growth, mitigates regional imbalances, enhances overall economic resilience, and lays the foundation for long-term, stable development [11].

In this context, integrating the concept of sustainable development into the digital economy development strategy—where economic growth and fair distribution complement each other—is essential. However, existing articles mainly use provincial and municipal macro data to explore the direct impact of digital economy on economic growth or the urban–rural income gap, which lays the foundation for the research design of this paper, but there are still the following deficiencies that need to be supplemented. First, the existing articles explore the mechanism of the digital economy on economic growth or urban–rural income gap, and few articles consider the impact of the digital economy on the common wealth from the perspectives of both economic growth and fair distribution at the same time. Secondly, the data reported in existing literature mainly come from provincial or municipal statistical yearbooks, and the research object is not specific enough. Finally, as a digital technology and means to break through time and space limitations, the impact on neighboring regions cannot be ignored. The following are this study’s possible marginal contributions: First, this study quantifies the impact of the digital economy on “making the cake bigger” and “sharing the cake” equitably. Second, by moving beyond the reliance on conventional data sources such as statistical yearbooks, EPS, and Wind databases, this study utilizes web crawler technology to collect data from the official TianYanCha website, creating a micro dataset that covers the business conditions of enterprises across 2243 districts and counties from 2011 to 2021. This approach provides more accurate data support for policy evaluation. Third, this study examines whether advanced digital economy regions at the level of county can foster common prosperity in lagging areas through spatial spillovers, and to what extent lagging regions can effectively leverage these spillovers.

This article is structured as follows: the research theories and theoretical analysis are presented in Section 2; Section 3 presents the Description of variables; the empirical study of spatial spillover effects is presented in Section 4; and Section 5 presents the empirical testing of mechanisms of action; the findings and policy recommendations are presented in Section 6.

2. Literature Review

2.1. Common Prosperity

Scholars have carried out a wealth of research on common prosperity, mainly focusing on the definition of the concept of common prosperity and the construction of the evaluation index system and other related contents. Regarding the definition of the connotation of common prosperity, as the essential requirement of socialism with Chinese characteristics, the theoretical foundation of common prosperity can be traced back to the socialist distribution theory of Marxism, which emphasizes the realization of “distribution according to needs” through the liberation of productive forces [12]. Modern academic definitions of common wealth generally emphasize the dual attributes of “common” and “affluence”; the dimension of “affluence” refers to the comprehensive improvement of material wealth and spiritual life, including the “affluence” dimension, which refers to the comprehensive improvement of material wealth and spiritual life, covering economic growth, income level, public services, ecological environment, and other areas of development [13,14,15]. The “common” dimension emphasizes the universality of the fruits of development, and aims to achieve social equity by narrowing the gap between urban and rural areas, regions, and groups [16]. In terms of common prosperity measurements, comprehensive measurement and single-indicator measurement dominate. Comprehensive measurement is mainly conducted through the construction of an evaluation index system to evaluate the level of common wealth in China. Scholars have built such evaluation index systems based on three aspects of common wealth: development, sharing, and accessibility [17,18]. These systems are combined with panel data and spatial measurement methods to quantify the regional synergistic effects of common wealth [19]. Some scholars have also utilized big data technology to construct the “common wealth index”, integrating non-traditional indicators such as e-commerce consumption and satellite night light data [20]. Single-indicator measurements mainly measure common wealth from the perspectives of economic inequality, multidimensional poverty, and public services [21,22,23,24]. The Gini coefficient, the urban–rural income ratio, and the regional GDP per capita difference index are widely used to measure the wealth distribution gap [25,26]. Other scholars assess the fairness of the distribution of public goods from the coverage of medical resources and the index of compulsory education equalization.

2.2. Digital Economy

The term “digital economy” was firstly proposed by Tapscott [27]. Economic activities supported by digital technology began to appear in the 1950s, and since the emergence of the international financial crisis in 2008, foreign countries have launched more in-depth research on digital economy [28,29]. With the rapid development and application of cutting-edge information technology, such as cloud computing, big data, artificial intelligence, and the Internet of Things, the digital economy is developing rapidly [30]. Research on the digital economy mainly focuses on theoretical and concept definition, measurement methods, and on the analysis of the influencing factors. When measuring the level of development of the digital economy, academics usually adopt different indicator systems for assessment. Measurement methods can be carried out at the macro, meso, and micro levels. At the national or regional macro level, the development of the digital economy is usually measured by indicators such as ICT investment, digital infrastructure construction (e.g., 5G network penetration), the degree of digital financial development, and the proportion of value added by the digital industry to GDP [31,32,33,34]. In addition, spatial econometric models are used to assess the spatial spillover effects of the digital economy on regional economic growth [35]. At the industry meso level, the development impact of digital economy can be measured by indicators such as the level of industry digitization and the degree of e-commerce penetration and transformation [36,37]. Research shows that the digital economy not only promotes the upgrading of industrial structures, but also enhances the synergistic development of regional industries and promotes the rise of new industries [38,39]. At the micro level of enterprises and individuals, the measurement of digital economy usually involves indicators such as the degree of digital transformation of enterprises, the application of data-driven decision-making, and the level of digital skills of employees [40,41,42,43]. Studies have found that the digital economy has a profound impact on the economic behaviors of micro-entities by enhancing labor productivity, optimizing enterprise management models, and improving market transparency [44,45,46].

2.3. Digital Economy and Common Prosperity

Based on the above review of the literature on the digital economy and common wealth, it is evident that the digital economy will have a greater impact on common wealth, but the existing literature has not formed a unified view on the relationship between the two. Some scholars believe that the digital economy will significantly promote common wealth, and scholars have explored the direct effect of the digital economy on common wealth through the construction of relevant models [47,48]. Some scholars also believe that the digital divide is very easy to appear in the process of digital economic development. The digital divide is essentially an unequal opportunity phenomenon in the process of digital development, which mainly refers to the opportunity gap between individuals from different socio-economic backgrounds in accessing and using ICT technology to carry out a variety of activities [49]. The creation of the digital divide will make it difficult for farmers to share in the dividends of digital development. This will make the distribution of benefits related to the digital economy unbalanced and will hinder the realization of common prosperity [50].

3. Theoretical Analysis and Study Hypothesis

3.1. The Regional Diffusion Effects of the Digital Economy on Common Prosperity

With the widespread adoption of digital technologies, traditional industries are now integrating deeply with digital systems, leading to the emergence of innovative business models and company structures [51]. Additionally, the employment structure has been optimized, partly due to the rise of the digital economy [52]. On one hand, the digital economy has created new job opportunities in fields such as data analysis and artificial intelligence. On the other hand, it has reshaped employment patterns in traditional industries, enabling workers to adapt more easily to market changes and pursue career development. Digital technology also helps governments deliver public services more effectively, such as online education and telemedicine, thus improving access to quality resources. Concurrently, digital technology supports government initiatives such as poverty alleviation and social security [53], helping to reduce social inequality.

The digital economy has a substantial spatial spillover effect, promoting prosperity across regions through its powerful network and diffusion effects. The continuous improvement of communication networks and rapid development of digital technologies have enabled the digital economy to spread quickly across regions. Information, knowledge, and resources flow more freely between areas, thanks to the adoption of technologies such as cloud computing, big data, and artificial intelligence. This diffusion effect enhances local economic development while also benefiting neighboring regions through spatial networks, thereby fostering synergistic interregional growth.

Moreover, regional economic activities interact and link in ways that further drive spatial spillovers. The digital economy breaks down the spatial barriers of traditional economies, allowing economic activities in different regions to become interconnected. Through e-commerce platforms, digital supply chains, and other tools, businesses can collaborate and compete across regions, leading to upgrades and optimization of industrial chains. Additionally, the digital economy facilitates the exchange of information and resources between consumers, producers, and service providers, reinforcing regional economic linkages [54]. These interactions not only enhance local efficiency but also promote the economic development of nearby regions through geographic spillover effects. This cross-regional sharing of resources reduces disparities in economic growth, supporting more equitable development. This logical relationship is shown in Figure 1.

H1: 

The digital economy generates a spatial spillover effect, simultaneously encouraging counties to “expand the cake” and “share the cake”.

3.2. Digital Economy, Public Services, and Common Prosperity

The inclusive effect of the digital economy can effectively encourage the equalization of public services and enhance the matching efficiency and standards of public services [55]. As the digital and physical economies become more intertwined, the mode of production of human society is moving into a new era, characterized by the digital economy. Although rural and urban areas have already achieved “the same network and the same speed” in terms of network coverage, the financial implications of the digital economy in advancing the equality of essential public services reveal structural aberrations and misalignments because of the inconsistent regional growth, the weak innovation capacity of the core industries of the digital economy in many counties, and the relatively insufficient digital literacy of rural households, thus hindering the method via which everyone can achieve common prosperity [56,57]. As an emerging kinetic energy, the digital economy, with its excellent resource integration capability, dynamic feedback mechanism, and data mining and sharing capacity, can encourage the growth of fundamental public services in the direction of intelligence, refinement, and universality, increasing the pace at which fundamental public services are being equalized [58].

As an important feature of today’s era, the digital economy, with its deep penetration into the countryside, is effectively promoting the redistribution of social resources with its powerful technology-driven force and fully demonstrating its inclusive effect, which promotes the vigorous development of digital village construction [59,60]. This process not only enhances the level of informatization in rural areas, but also promotes the optimal allocation of social resources at a deeper level. Through advanced technologies with cloud computing and large data, the limitations of traditional resource allocation have been broken, enabling resources to flow more precisely and efficiently to the countryside, somewhat increasing the relative wages of people living in remote areas, diminishing the gap between urban and rural areas [61,62].

The digital economy encourages everyone to share public services, enabling people in rural areas to enjoy the same public service entitlements as urban residents. Digitization has, on the one hand, greatly increased the quality of public services and broadened their reach. This not only gives people equal access to public services, but further promotes the diversification of public goods and the high-quality development of public services. Everyone has access to opportunities for fair participation, fair development and fair enjoyment, and social justice is better realized. The digital economy also has actively contributed to promoting the deepening of digital financial inclusion [63]. Through digitalization, financial services in rural areas have been greatly improved, and the coverage of financial services has been successfully expanded in both breadth and depth. This has not only eased the financing constraints of enterprises and enhanced the ease and effectiveness of financial assistance for the growth of the rural sector, but also improved the weakest areas, such as rural medical care and education through digital construction, reducing the disparity between public services in urban and rural areas even more [64]. The logical relationship is shown in Figure 1.

H2: 

The digital economy affects the expansion and distribution of the cake by influencing public services.

3.3. Digital Economy, Industrial Structure, and Common Prosperity

The digital economy promotes the gradual upgrading of the industrial structure from a lower to a higher level and enhances the long-term ability to achieve shared prosperity [65]. Amidst the advancements in scientific and technological innovation and other related progress, and with the rapid rise of advanced technology sectors, labor-intensive industries are undergoing a process of transformation and substitution to high technology content. In this process, the policy orientation is clearly inclined to urbanization and development, resulting in the incubation and growth of new technologies and emerging industries often taking precedence in large cities. This phenomenon further exacerbates the existing gap in human capital development and employment access among urban and rural populations. As the industrial structure continues to be optimized and upgraded, i.e., moving to a higher level, the urban–rural divide among residents caused by differences in education levels, skill acquisition, and access to information is likely to widen further, posing a more serious challenge to the promotion of balanced development of society as a whole [66].

Although the overall size of the economy has continued to expand, just as the “cake” is getting bigger and bigger, the pattern of its distribution has become more and more unbalanced, showing a significant inequality in distribution [67]. The substantial income gap, which persists both in urban versus rural settings and among different administrative regions, mirrors a chasm-like divide [68]. This divide not only hinders interregional complementarity and integration but also deepens the gap and exclusion between advanced and relatively lagging regions, reinforcing a stark segmentation dynamic [69]. A balanced distribution of resources and peaceful regional coexistence are required in order to overcome this phenomenon, which poses a serious challenge to the coordinated growth of the regional economy. Furthermore, the structure of industries reveals a clear disconnect. The input–output chains within industries have been severely disrupted by the growing income disparity between different sectors [70]. On one hand, the demand for high-quality intermediate inputs in frontier industries is often difficult to meet effectively from lagging industries, causing the supply and demand to diverge. On the other hand, the disparity in development and opportunity between industries is exacerbated when workers in less-developed industries are unable to take advantage of the positive spillover effects of technical innovation and industrial upgrading in frontier industries.

First, employment opportunities in certain traditional agricultural sectors have declined as capital and technology have intensified, and the demand for highly skilled labor increases. If the education and skill levels of rural residents cannot meet the requirements of emerging industries, they may struggle to secure high-paying jobs [71]. Coupled with intensified market competition and policy adjustments, rural residents may face greater competitive pressure in the job market. Additionally, as the industrial structure advances, resources may move more frequently to cities in order to facilitate the growth of new industries. This shift may result in relatively slower development of infrastructure and rural public services, further limiting the opportunities and income levels of rural residents. In contrast, urban areas are developing rapidly because they are supported by more resources, and urban residents tend to have greater access to high-quality instruction and practice, which facilitates their adaptation to the demands of emerging industries and their acquisition of lucrative employment opportunities [72]. Rural residents may have relatively lower levels of education and skills, limiting their competitiveness in the high-skilled labor market and further increasing economic disparity [73,74]. This logical relationship is shown in Figure 1.

H3: 

The digital economy affects the expansion and distribution of the cake by influencing the industrial structure.

4. Description of Variables

4.1. Explanatory Variable

The establishment of digital economy indicators is based on four factors: the digital environment, industrial digitalization, the digital infrastructure, and digital industrialization [75,76]. The digital economy is built upon the foundation of digital infrastructure. It refers to the infrastructure system that transmits data and computing power, with communication networks as the carrier, driven by a new generation of data and information [77]. This infrastructure is essential to the spread of digital culture, in addition to providing a strong basis for the expansion of the digital economy. The digitization of industries stands as a fundamental pillar within the digital economy. It involves the application of digital technologies and data resources to traditional industries, aiming to enhance output and efficiency. This approach accelerates the translation cycle of research and technical advancements and fosters creativity and cross-sector convergence in conventional industries, injecting fresh momentum into their pursuit of superior-quality growth. The superior-grade growth of the digital economy is largely dependent on digital industrialization. It outlines the approach of leveraging digital and IT solutions to drive the modernization and transformation of traditional industries toward intelligent, digitized, and service-oriented models [78]. The digital environment, with its three main characteristics of digitization, embeddedness, and relational connectivity, allows for the fusion and re-innovation of huge amounts of data and improves the ability to analyze data, while information technology helps to realize intelligent living by embedding digital technologies into social life. In addition, the huge amount of data stored in the digital environment allow for unlimited possibilities of development within social life [79]. The digital economy indicator system is constructed as shown in

Table 1. Development of assessment metrics for the digital economy.

First-Level Indicators	Second-Level Indicators	Indicator Explanation
Digital infrastructure	Mobile phone penetration	Number of mobile phone subscribers/year-end household count (%)
	Fixed-line telephone penetration	Number of fixed-line subscribers/year-end household count (%)
	Internet penetration	Number of broadband access subscribers/year-end household count (%)
Digitalization of the industry	Number of agricultural robot enterprises	Number of agricultural robot enterprises (units)
	The number of microblogging companies	The number of companies that have opened Weibo (units)
	Number of smart irrigation enterprises	Number of smart irrigation enterprises (units)
Digital industrialization	Number of AI companies	Number of AI companies (units)
	Vitality of the electronic information service industry	Number of new enterprises in the software, information technology, and information transfer services sectors (units)
Digital environment	Per capita electricity consumption in rural areas	Total rural electricity consumption/total number of rural people (kWh/per person)
	Innovative outputs	The quantity of patents at the district and county levels of the State Intellectual Property Office (units)
	County-level entrepreneurial vitality	Number of newly registered businesses (units)

.

4.2. Explained Variable

This paper argues that the income levels of rural inhabitants compared to their urban counterparts, along with the disparity in earnings between these regions, can act as pivotal metrics to assess the progress toward common prosperity [80]. To fully realize the common prosperity strategy, it is essential to focus on the earnings of rural populations relative to urban ones, reducing the income disparity between city and countryside, preventing extreme wealth division, fostering balanced regional growth, and guaranteeing that the fruits of progress are fairly distributed among everyone.

4.3. Control Variables

In this paper, we mainly choose the following control variables. Financial Development Level (FDL) is calculated by dividing the total amount of loans from financial institutions by the GDP of the county at the year’s end [81]. Enterprise Development Level (EDL) is determined by dividing the total industrial output value of enterprises above the authorized size by the county’s gross domestic product [82]. Real Estate Development Investment (RDI) is the amount of money invested in real estate development at the county level [83]. Environmental Quality (PMA) is measured by annual average PM2.5 [84]. Economic Development Level (ED), which reflects local economic activity, is determined by dividing the county’s GDP by the permanent population [85]. Social Welfare Level (SWN) is quantified based on the count of social welfare establishments.

4.4. Mechanism Variables

(1) Level of public services.

As the social security system has become more developed and sophisticated, its share of the national economy has increased, which has significantly promoted the optimization of China’s income distribution pattern [86]. Specifically, the comprehensive social security system, encompassing minimum living standards, pension schemes, health coverage, unemployment benefits, and educational support, has demonstrated a beneficial impact on adjusting the national income distribution structure and has significantly fostered the balanced integration of social equity with economic growth. This shift not only reflects the country’s high priority on people’s livelihood and well-being, but also serve as an important cornerstone for building a more just and sustainable society [87]. Therefore, combined with the findings of current research, the extent of fundamental public service provision is assessed from three perspectives: social welfare, healthcare and medical, and education. The system of indicators for public service levels is constructed as shown in

Table 2. System of indicators for public service levels.

First-Level Indicators	Second-Level Indicators	Indicator Explanation
The degree of public service equality	Protection	Number of adoptive beds for various social welfare institutions
	Medical coverage	Number of beds in healthcare facilities
	Educational guarantees	Overall student enrollment within the county/population of the county

.

(2) Optimization of industrial structure:

$$UIS=S_1\ast 1+S_2\ast 2+S_3\ast 3$$

(1)

where S₁, S₂, and S₃ represent contributions from the primary, secondary, and tertiary sectors, illustrating the progression of the industrial framework from basic to advanced levels and from less to more efficient operations.

4.5. Data Source

There are three main types of data sources: (1) statistical data, gathered mainly from the China City Statistical Yearbook, the statistical yearbooks of various provinces, cities and counties, the statistical development bulletin, and the data from the Chinese Academy of Social Sciences and the State Intellectual Property Office; (2) dynamic web pages containing data such as the number of businesses using agricultural robots, the number of artificial intelligence enterprises, the vitality of the electronic information service industry, and other data are crawled with the help of Python language, respectively. The data are crawled from Enterprise Search platforms and prefecture-level municipal government portals; (3) remote sensing data: PM2.5 environmental quality data come from the PM2.5 raster data published by Dalhousie University. Finally, the research sample consisted of panel data from 2243 Chinese districts and counties from 2011 to 2021.

5. Empirical Analysis of Spatial Spillover Effects

5.1. Model Settings

5.1.1. Spatial Matrix Settings

To ensure a comprehensive and objective examination of how common prosperity affects different regions at the county level, a geographic matrix was developed utilizing the attributes of physical distance between locations. The geographical matrix was constructed according to the physical proximity among various districts and counties, as well as the spatial relationship between cities, where closer proximity indicates a stronger connection. It can be inferred from this that the high level of common prosperity in a specific region may be affected by neighboring cities.

$${\mathrm{W}}_{ij}=\left\{\begin{array}{l}{\displaystyle \frac{1}{d_{ij}}},\hspace{1em}{d}_{ij}\ne 0\\ 0,\hspace{1em}{d}_{ij}=0\end{array}\right.$$

(2)

$$d_{ij}=\left\{\begin{array}{l}R\times ar\cos [\cos (Y_i)\times \cos (Y_j)\times \cos (X_i-X_j)+\sin (Y_i)\times \sin (Y_j)],i\ne j\\ 0 \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em},i=j\end{array}\right.$$

(3)

5.1.2. Spatial Econometric Model

The spatial lag model (SAR) is centered on the analysis of spatial spillovers between dependent variables, i.e., exploring how changes in the dependent variable in a neighboring geographic unit may have a ripple effect about the target region’s dependent variable [88]. The spatial error model not only considers those spatial propagation factors that are difficult to observe directly, but also explores how the error terms (or disturbance terms) in neighboring regions indirectly affect the dependent variables in a specific region through spatial interactions. The SDM, as a more comprehensive version of SAR, constructs a more comprehensive and refined spatial econometric analysis framework by introducing the spatially lagged component of the independent variable, i.e., by simultaneously taking into account the spatial dimension’s spillover effects of the independent and dependent variables.

$$\begin{array}{c}y=\alpha +\rho W_{1}y+\beta X+\theta W_{2}X+\mu \\ \mu =\lambda W_3\mu +\epsilon \\ \epsilon ~N(0,{\sigma }^2{I}_n)\end{array}$$

(4)

For the dependent variable, $y$ is its n-dimensional vector; the n × k-dimensional matrix of the independent variables is denoted as $X$; $W_1$, $W_2$, and $W_3$ represent n × n-dimensional spatial weight matrices, which may consist of distinct or identical matrices; $\rho$ represents the dependent variable’s spatial autocorrelation coefficient; the correlation parameters of the independent variables are represented by the vector $\beta$; $\theta$ represents the coefficient of spatial autocorrelation for the independent variables; $\lambda$ signifies the coefficient of spatial error; $\alpha$ is a constant term; the random error terms are $\mu$ and $\epsilon$; and the n-order unitary matrix is denoted by $I_n$. SAR models are those in which $\rho \ne 0$, $\theta =0$, and $\lambda =0$; SEM models are those in which $\lambda \ne 0$, $\rho \ne 0$, and $\theta =0$; and SDM models are those in which $\rho \ne 0$, $\theta \ne 0$, and $\lambda =0$.

5.2. Testing the Internet Economy’s Effect on Common Prosperity

5.2.1. Spatial Autocorrelation Test

As shown in

Table 3. Global Moran index measurement results.

	Relative Income of Rural Residents	Urban–Rural Income Disparity	Digital Economy
2011	0.118 ***	0.065 ***	0.085 ***
2012	0.126 ***	0.068 ***	0.089 ***
2013	0.120 ***	0.067 ***	0.089 ***
2014	0.121 ***	0.068 ***	0.083 ***
2015	0.121 ***	0.065 ***	0.082 ***
2016	0.119 ***	0.066 ***	0.080 ***
2017	0.118 ***	0.069 ***	0.076 ***
2018	0.117 ***	0.071 ***	0.079 ***
2019	0.116 ***	0.072 ***	0.077 ***
2020	0.112 ***	0.071 ***	0.080 ***
2021	0.101 ***	0.070 ***	0.076 ***

Standard errors in parentheses; *** p < 0.01.

, the rural citizens’ relative income, the rural–urban economic disparity, and the digital economy’s Moran’s I were statistically significant between 2011 and 2021 at the 1% level and larger than 0. Accordingly, there was a positive spatial association and agglomeration effect between the digital economy and common prosperity.

5.2.2. Spatial Overflow Test

As shown in

Table 4. Spatial spillover effect test.

	(1)	(2)	(3)	(4)	(5)	(6)
	RRI	URI	RRI	URI	RRI	URI
	SAR		WITHOUT		SDM
DG	−0.0099	−0.0535 *	0.0618 ***	−0.0495 *	0.0583 ***	−0.0579 *
	(0.0170)	(0.0289)	(0.0174)	(0.0297)	(0.0173)	(0.0297)
lnPMA	0.0421 ***	−0.0714 ***	0.0408 ***	−0.1331 ***	0.0238 ***	−0.0835 ***
	(0.0027)	(0.0045)	(0.0038)	(0.0064)	(0.0041)	(0.0070)
lnFDL	0.0407 ***	−0.0199 ***	0.0421 ***	−0.0260 ***	0.0377 ***	−0.0308 ***
	(0.0027)	(0.0046)	(0.0028)	(0.0048)	(0.0028)	(0.0048)
lnEDL	0.0107 ***	−0.0103 ***	0.0134 ***	−0.0127 ***	0.0128 ***	−0.0149 ***
	(0.0012)	(0.0020)	(0.0012)	(0.0020)	(0.0012)	(0.0020)
lnED	0.0183 ***	−0.0104 ***	0.0320 ***	−0.0105 ***	0.0322 ***	−0.0189 ***
	(0.0013)	(0.0022)	(0.0014)	(0.0024)	(0.0014)	(0.0025)
lnRDI	−0.0025 ***	−0.0003	−0.0007	0.0012	−0.0001	0.0005
	(0.0005)	(0.0008)	(0.0005)	(0.0009)	(0.0005)	(0.0009)
lnSWN	0.0003	0.0019 ***	0.0001	0.0015 *	−0.0000	0.0015 **
	(0.0004)	(0.0007)	(0.0004)	(0.0008)	(0.0004)	(0.0008)
W×X	yes	yes	yes	yes	yes	yes
R	4.1494 ***	2.7366 ***			4.2005 ***	4.2015 ***
	(0.0071)	(0.0061)			(0.0203)	(0.0194)
L			3.2969 ***	2.7329 ***
			(0.0222)	(0.0071)
Direct effects	−0.0086	−0.0561 *			0.0674 ***	−0.0744 **
	(0.0162)	(0.0317)			(0.0186)	(0.0315)
Indirect effects	0.0116	0.0863 *			0.7120 ***	−1.5023 ***
	(0.0218)	(0.0488)			(0.0720)	(0.1260)
Total effect	0.0030	0.0302 *			0.7795 ***	−1.5768 ***
	(0.0056)	(0.0171)			(0.0742)	(0.1293)
N	24,673	24,673	24,673	24,673	24,673	24,673
R2	0.0111	0.0008	0.0362	0.0019	0.0295	0.0181

Standard errors in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

, both urban–rural disparity in income and the relative income of rural inhabitants have considerably positive spatial autoregressive coefficients (ρ). This indicates that there is significant spatial dependence across regions, meaning that the spatial factor cannot be overlooked when exploring the implications of the digital economy for mutual prosperity. Based on the regression findings of the spatial Durbin model, the effect decomposition approach is used to separate the direct and indirect impacts of the independent variable’s overall influence on the dependent variable. Specifically, when the region’s digital economy grows by 1%, rural people’ relative income rises by 0.0674%, and the urban–rural income gap narrows by 0.0744%. Meanwhile, through spatial spillover effects, advancements in the digital economy indirectly increase the relative income of rural residents in nearby areas by 0.712%, reducing differences in income between urban and rural areas by 1.5023%. Finally, nationwide improvements in the digital economy lead to a 0.7795% increase in the relative income of rural residents and a 1.5768% reduction in the income disparity between urban and rural areas. These results demonstrate that the digital economy furthers the attainment of common prosperity by influencing nearby regions through geographical impacts in addition to fostering common wealth within a region. Therefore, the digital economy’s spatial spillover impact contributes not only to a “bigger cake” within the region but also aids neighboring regions in achieving a “fair distribution of the cake”. This largely validates Hypothesis 1.

The main reasons for this may lie in the following aspects. On the one hand, from the perspective of industrial development, the growth of the digital economy has given rise to new rural industry models in the region. The rise of rural e-commerce has broken the geographical restrictions on the sale of traditional agricultural products and reduced transaction costs. At the same time, rural e-commerce has also driven the development of rural logistics, packaging and other related industries, providing more employment opportunities and sources of income for rural residents, raising the relative income level of rural residents, and thus narrowing the urban–rural income gap. On the other hand, the spread of knowledge and technology is an important factor. When a region’s digital economy develops, its accumulated knowledge of digital technology and advanced business models will spread to neighboring regions through personnel mobility and information sharing. Rural residents in neighboring regions can learn the e-commerce operation skills of developed regions and return to their local areas to launch e-commerce businesses, improving the efficiency and prices of agricultural products, and thus increasing their incomes. Moreover, rural enterprises in the surrounding areas can also learn from the digital management experience of developed regions to optimize business operations and improve productivity, which in turn will drive the income growth of rural residents and narrow the income gap between urban and rural areas.

5.2.3. Robustness Test

In 2021, Zhejiang Province became a pioneering region for common prosperity, taking the lead in exploring the path toward common prosperity. Meanwhile, Zhejiang province is a leader in the development of digital industrialization, a model for the digital transformation of industries, an innovator in digital economy mechanisms, and a globally influential center for digital science and technology innovation. This may lead to outcomes that differ from those of other regions. This uniqueness could potentially skew the research conclusions. To ensure the representativeness of the sample and eliminate any bias, county-level data from Zhejiang Province were excluded from the analysis. The results of the robustness test are shown in

Table 5. Robustness test results.

	(1)	(2)
	RRI	URI
DG	0.0419 **	−0.0473 **
	(0.0178)	(0.0312)
W×lndg	−1.3275 ***	4.6794 ***
	(0.2534)	(0.4499)
rho	2.8027 ***	4.0952 ***
	(0.0201)	(0.0170)
LR_Direct
lndg	0.0311 *	−0.0691 **
	(0.0187)	(0.0336)
LR_Indirect
lndg	0.6866 ***	−1.4233 ***
	(0.1327)	(0.1337)
LR_Total
lndg	0.7177 ***	−1.4924 ***
	(0.1333)	(0.1379)
Control variables	Yes	Yes
Individual fixed effects	Yes	Yes
Time fixation effect	Yes	Yes
N	23,716	23,716
R2	0.0081	0.0083

Standard errors in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

. The regression results after excluding these samples demonstrate that the coefficients’ direction and importance under the spatial model remain consistent with previous findings, thereby confirming the resilience of the conclusions.

6. Empirical Testing of Mechanisms of Action

6.1. Mechanism Modeling

To investigate the ways in which industrial structure and public service level impact shared wealth in the digital economy, the following mediation effect model is constructed:

$$M_{it}={\alpha }_0+{\alpha }_{1}D{G}_{it}+{\alpha }_2{C}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(5)

If $M$ serves as the mediating variable, $i$ stands for the county, and $t$ for the year, then the level of public services is the dependent variable, $DG$ is the explanatory variable, and $C$ denotes a series of control variables; individual effects are denoted by $\mu$, and time effects by $\lambda$.

6.2. Coordination of the Analysis of Inclusive Mechanisms

The influence of the digital economy on public service levels is examined using a two-way fixed-effects model, with public services serving as the mediating variable. Public service levels are the dependent variable, and the digital economy is the primary independent variable in the regression findings shown in column (3) in

Table 6. Coordination of inclusive mechanisms.

	(1)	(2)	(3)
	Urban–Rural Income Gap	Relative Income of Rural Inhabitants	Level of Public Services
DG	−0.27543 ***	0.10072 ***	0.01577 **
	(0.03553)	(0.01934)	(0.00633)
constant term (math.)	1.72216 ***	0.22743 ***	0.00371
	(0.04010)	(0.02183)	(0.00715)
control variable	Yes	Yes	Yes
individual fixed effect	Yes	Yes	Yes
time fixed effect	Yes	Yes	Yes
N	24,673	24,673	24,673
R2	0.882	0.954	0.809

Standard errors in parentheses; ** p < 0.05, *** p < 0.01.

. The results show that the coefficient of the digital economy is considerably positive at the 1% significance level, indicating that it plays an essential role in raising the standard of public services.

Existing literature highlights that the digital economy drives innovation in public service delivery, making services more accessible and efficient, especially in underdeveloped regions [89]. Digital platforms enable citizens to access government services online, overcoming traditional geographical and time barriers. Telemedicine and online education serve as prime examples, where digital technologies offer remote populations access to high-quality healthcare and education, thereby narrowing the urban–rural divide in service availability [90]. Furthermore, the use of digital technologies has enhanced the accuracy of public service delivery, enabling governments to more effectively assess and respond to public needs. This has facilitated the provision of more targeted and personalized services, promoted social justice, and addressed regional disparities [91]. Digital tools also improve efficiency and reduce errors in public service processes, thereby enhancing service quality and fostering a more equitable distribution of resources.

6.3. Analysis of Structural Optimization Mechanisms

According to the regression results in column (3) in

Table 7. Structural optimization mechanisms.

	(1)	(2)	(3)
	Urban–Rural Income Gap	Relative Income of Rural Inhabitants	Level of Industrial Structure
DG	−0.27543 ***	0.10072 ***	0.26526 ***
	(0.03553)	(0.01934)	(0.05074)
constant term (math.)	1.72216 ***	0.22743 ***	8.91296 ***
	(0.04010)	(0.02183)	(0.05726)
control variable	Yes	Yes	Yes
individual fixed effect	Yes	Yes	Yes
time fixed effect	Yes	Yes	Yes
N	24,673	24,673	24,673
R2	0.882	0.954	0.991

Standard errors in parentheses; *** p < 0.01.

, the upgrading of industrial structure is significantly and positively affected by the digital economy as the main explanatory variable at the 1% level. This indicates the importance of the digital economy in driving the transformation of the industrial system. The digital economy makes it easier for existing sectors to transform into networked, intelligent, and digitalized systems through the use of digital technologies and data resources. In addition to improving traditional industries, the digital economy has triggered the emergence of new areas, such as the Internet of Things, cloud computing, artificial intelligence, and big data. These fields have driven the rapid expansion of intra-industry structures and injected new dynamics into economic growth [92]. The digital economy has accelerated the evolution of the entire industrial landscape by encouraging innovation and supporting new industries. In addition, through the extensive application of ICT, the digital economy promotes the coordination and optimization of industrial structure. By optimizing the industrial structure, the digital economy helps to establish a modern industrial system that transcends the boundaries of industry and geography, facilitates collaboration upstream and downstream of the industrial chain, and promotes common prosperity.

7. Conclusions and Discussion

7.1. Conclusions

Using panel data from 24,673 counties between 2011 and 2021, a spatial econometric model was used to examine the relationship between the digital economy and common prosperity. The following deductions were made: (1) Positive geographical correlation: The digital economy and shared prosperity both exhibit positive geographical correlation and spatial aggregation, shown as high–high and low–low clustering in the geographic distance matrix. Regardless of whether the model is used for SAR, SEM, or SDM, the digital economy significantly increases the relative income of rural residents and reduces the economic gap between urban and rural regions. (2) Spatial spillover effect: The digital economy has an effect on the broader public’s prosperity. In addition to raising the relative income of local rural residents, it effectively closes the income gap between urban and rural communities. It also affects neighboring regions, reducing the economic gap between urban and rural populations and raising the relative income of rural residents in neighboring areas. Therefore, spatial factors should not be overlooked when analyzing the relationship between the two. This is consistent with the findings of Zhou and Guo, as well as Chen and Zhang [93,94]. (3) Coordinated and inclusive impact: The digital economy has a structural optimization impact in addition to a coordinated and inclusive effect. It helps counties achieve common prosperity by promoting the development of public service levels and advancing industrial structure.

7.2. Discussion

In addition to the above findings, this paper discusses the results in depth from the perspectives of theoretical contributions and policy extension. In terms of theoretical contributions, this study reveals spillover effects that may be ignored by traditional linear analysis by introducing the geographic distance matrix and the spatial measurement model. The digital economy, characterized by positive correlation and spatial agglomeration, may form growth poles through technology diffusion, labor mobility, or capital mobility, which aligns with the dynamic evolution of the “center-periphery” theory in the new economic geography [95], but the unique low marginal cost characteristic of the digital economy may accelerate this process. The path through which the digital economy promotes common wealth through industrial structure optimization can be interpreted under the framework of Schumpeter’s innovation theory—digitalization not only gives rise to new businesses [96], but also transforms and upgrades the traditional industries by means of digital technology, which may become a new mechanism for narrowing the gap between urban and rural areas. In terms of policy extension, this study finds that there is a significant spatial spillover effect of digital economy and suggests the construction of a cross-administrative digital synergistic development belt and the establishment of a regional compensation mechanism for the benefits of the digital economy, so as to avoid siphoning effects that exacerbate regional polarization. Based on the discussion of the findings, this paper offers the following recommendations.

First, it is essential to enhance the spatial connectivity between regions and release the digital dividends across areas. Strengthening regional linkages within the digital economy is crucial, with more developed regions playing a driving role. This makes cross-regional collaboration initiatives in the digital economy easier, encouraging resource sharing and the complementarity of regional assets. Through mechanisms such as technology transfer, cooperative research and development, and more, less developed regions can benefit from the advanced experience and technological advancements of regions with a high level of digital economy development in areas such as digital technology, big data analysis, and cloud computing. This will help the latter improve their technological capabilities.

Secondly, it is crucial to develop sound digital infrastructure and promote digital literacy education. To unlock the potential of digital economic development and build a bridge for its growth, the focus must first be on the hardware foundation. This includes accelerating the construction of digital infrastructure, expanding broadband coverage, coordinating the digital resources across regions, and ensuring the rational allocation of data resources. Additionally, lowering the cost of internet access will enable more households to afford connectivity. Following this, internet education and training programs should be introduced to encourage wider participation. By enhancing digital literacy and skills, individuals will be better equipped to navigate the digital world, thereby narrowing the digital divide.

Thirdly, creating a special plan for the digital economy is essential. In regions with a high level of digital economy growth, the primary objective should be to improve the present development framework. In order to consistently overcome current barriers, it is necessary to enhance the digital economy system and pertinent regulations, encouraging technical innovation and advocating for closer integration between the digital and real economies. It is crucial to create strategies that are specific to the economic features and present state of development of areas with comparatively low levels of digital economy growth. These policies should aim to retain regional resources and minimize resource loss to external areas. Cooperation with areas with a higher degree of digital economic development can also help to balance regional digital development, improve economic exchanges, and use the positive spillover effects of more developed regions to propel shared prosperity in less developed areas.

Future research should focus on utilizing micro-level primary data, including surveys of residents, enterprises, etc., to more realistically explore the effects of the digital economy on farm households. Additionally, scholars should explore the mechanisms through which the digital economy exerts spatial spillover effects on common wealth and explore in depth the mechanisms through which the digital economy impacts the common wealth of neighboring regions, so as to provide insights for provinces and cities to realize overall common wealth. Furthermore, researching the interactions between the expansion of the digital economy and other socioeconomic policies such as healthcare, education, and labor market reforms may yield important information for creating an all-encompassing plan to attain common prosperity.