The Nexus of Digitalization, Talent, and High-Quality Development: How Clusters Foster Sustainable Economic Growth

Abstract

In the context of the digital economy reshaping the global competitive landscape, digital industry clusters have become the key driving force to overcome the diminishing returns of traditional inputs and realize sustainable economic development in the digital era. However, the internal mechanisms and spatial effects through which digital industrial clusters drive high-quality development and thereby foster sustainable regional economic growth remain unclear. Based on China’s provincial panel data from 2012 to 2023, this study constructs time-fixed spatial Durbin model and mediation effect model to systematically examine the impact mechanism of digital industry clusters on high-quality economic development, and to analyze their direct effects, spatial spillover effects and mediation transmission effects. The following effects have been found: (1) digital industry clusters can directly promote the high-quality development of the region’s economy (0.070), and can also significantly promote the high-quality development of the region’s economy through the mediating effect of innovative talent agglomeration (0.021); (2) the spatial spillover effect of digital industry clusters consists of the negative siphoning effect of innovative talent and positive technology diffusion and driving effect, which makes the total effect of digital industry clusters on neighboring regions uncertain; (3) Technology-intensive areas, as well as the eastern and northeastern regions, have effectively transformed the advantages of digital industry clusters into momentum for high-quality economic development, whereas central and western regions have not yet fully unleashed the driving effect of digital industry on the high-quality development of the economy, due to the constraints of the industrial structure, innovation factors and infrastructure. Based on the empirical results, the article suggests accelerating the construction of digital industry innovation hubs, establishing cross-regional technology sharing platforms, constructing a negative externality compensation mechanism for talent loss areas, and implementing differentiated regional development strategies. The study addresses a gap in existing research by analyzing the spatial mediation effects of digital industrial agglomeration on high-quality economic development. It extends theoretical insights into industrial clustering within the digital economy and offers actionable policy pathways for developing countries to promote sustainable economic growth through digital industrial clusters.

1. Introduction

To tackle structural challenges such as the waning demographic dividend, diminishing marginal returns on capital, and imbalanced regional development, China has adopted high-quality development as a core national strategy under the guiding principles of “innovation, coordination, green development, openness, and shared benefits” [1]. This strategic paradigm shift aims to transition toward a more balanced, inclusive, and sustainable model of economic and social development. The rapid development of digital technologies, especially the latest advances in areas such as big data, cloud computing and artificial intelligence, has made the digital economy an important force of change in increasing economic productivity. To advance digital economic development, China has implemented systematic policy frameworks through the 14th Five-Year Plan for Digital Economy Development and the Overall Layout Plan for Digital China Construction, establishing a comprehensive strategic framework that integrates digital infrastructure advancement, industrial digital transformation, and digital governance system optimization. Against this backdrop, China’s digital industry has progressively developed an integrated “four-chain convergence” ecosystem—encompassing innovation, industrial production, capital, and talent chains—which fosters synergistic evolution among digital manufacturing, technological application innovation, and data factor market cultivation, giving rise to innovation-driven digital industrial cluster ecosystems [2]. This study investigates the intrinsic mechanisms and spatial effects through which digital industrial clusters drive high-quality economic development. By extending the analytical framework of industrial clustering and labor factor agglomeration to the digital economy context, it provides strategic policy leverage points for policymakers in developing countries to foster sustainable economic growth through digital transformation of industries.

The theoretical study of industrial clusters can be traced back to the pioneering work of British economist Alfred Marshall in the late 19th century. Marshall observed the phenomenon of early industrial agglomeration in the United Kingdom, for the first time put forward the concept of Industrial District, defined as a spatial organization of geographically proximate and interrelated enterprises, institutions or industries [3]. At the end of the 20th century, American scholar Michael Porter further developed the theory of industrial clusters and put forward the famous “Diamond Model”, which systematically explains the formation mechanism and development dynamics of industrial clusters [4]. According to related research, industrial clusters can significantly improve economic efficiency by reducing internal friction, transaction costs, and social costs [5]. In addition, industrial clusters exert multiple positive effects on regional economic development, including knowledge spillovers, intensified competition, economies of agglomeration, market expansion, and specialized division of labor. These effects collectively contribute to optimizing regional industrial layouts and promoting sustainable economic growth.

The concept of digital industry as an important part of digital economy can be traced back to Tapscott’s (1996) early definition of digital economy [6]. The digital industry is born out of the information economy and the traditional information industry [7], and further evolves under the framework of digital economy to form an industrial system centered on the software and information technology service industry, the internet industry, the telecommunication industry, and the electronic information manufacturing industry [8], etc. From the perspective of factor structure, the digital industry takes digital technology as the cornerstone of development, establishes data as the key production factor, and relies on modern information networks to realize industrial interconnection [9]. In addition, the industry has significant knowledge-intensive characteristics, and its development is highly dependent on core elements such as patented technologies and high-end talents [10]. Existing studies have systematically explored the development characteristics of China’s digital industry, its spatial evolution law, and its driving mechanism, and the main findings can be summarized in the following three dimensions. First, in terms of industry attributes, China’s digital industry presents the typical qualities of broad industrial adaptability, strong economic permeability, prominent technology intensity, and both strategic leadership and development uncertainty [11]. These characteristics together constitute the essential difference in digital industry from traditional industry. Secondly, from the perspective of spatial pattern evolution, China’s digital industry layout presents four significant orientations: industrial cluster orientation, land price and cost orientation, innovation ecology orientation, and technology development orientation [12]; these drive the distribution of the digital industry to go through the dynamic evolution process of “urban agglomeration—suburban diffusion—re-agglomeration”, forming a multi-center agglomeration pattern with city clusters and central cities as the core. This has led to a dynamic evolution of the distribution of digital industries through the process of “urban agglomeration—suburban diffusion—re-agglomeration”, forming a polycentric agglomeration pattern with urban clusters and central cities as the core [8]. This geographical distribution pattern emerges primarily due to disparities in regional socioeconomic development levels, information infrastructure advancement, intercity innovation collaboration capacity, and R&D investment intensity [10]. Third, at the level of influence mechanism, the digital industry promotes sustainable economic development through two primary mechanisms. First, it accelerates technological advancement [13] while mitigating regional disparities, with innovation capacity, economic scale, and workforce competencies exhibiting both direct impacts and spatial spillovers [14]. Second, data capital and technology diffusion serve as critical mediators [15], catalyzing digital industrial agglomeration. This cascade effect successively drives corporate digital transformation [9], upgrades industrial structures, reduces carbon emissions [16], enhances urban green economic efficiency, and ultimately facilitates high-quality development and sustainable economic growth.

The existing research on digital industry and its spatial agglomeration has achieved rich results, but through systematic combing of the existing literature, this paper finds that there are still the following three aspects of research deficiencies: First, from a research perspective, existing literature primarily focuses on analyzing the development characteristics of China’s digital industry, such as the evolution of its spatial structure, the interconnectivity of technological innovation, and the factors influencing it. However, there is insufficient exploration of the intrinsic mechanisms through which digital industry clusters promote high-quality economic development, particularly the intermediary transmission effects of talent aggregation. This lack of research fails to adequately explain the unprecedented aggregation of innovative talent in the digital age and its impact on sustainable and high-quality economic development. In the process of advancing economic digital transformation, neglecting the important role of innovative talent aggregation may result in a lack of key levers to achieve the policy objectives of high-quality economic development, thereby affecting the quality and sustainability of economic development. Second, in terms of spatial dimensions, digital industrial clusters may generate positive synergistic development effects through channels such as technology diffusion or may form negative siphoning effects on surrounding regions through the flow of talent, capital, and technology. However, existing research is largely limited to case studies of individual provinces or cities, lacking empirical testing of the spatial interactions between different regions in China and within them, resulting in inconclusive findings regarding spatial spillover effects. Spatial spillover effects have important policy implications for formulating policies to promote balanced and coordinated regional development. If spatial interactions are ignored, the outcomes of related economic policies may deviate from the goals of sustainable and high-quality development. Based on this, this study innovatively constructs a dynamic spatial Durbin mediation effect model using provincial panel data from China from 2012 to 2023. By comprehensively employing spatial autocorrelation analysis, spatial effect and mediation effect decomposition, and multiple robustness tests, the study not only reveals the direct promotional role of digital industrial clusters in high-quality economic development but also, for the first time, verifies the key mediating role of innovative talent aggregation, thereby addressing the shortcomings in the existing literature regarding the analysis of mediation transmission mechanisms. Meanwhile, the study overcomes the limitations of traditional single-region case analyses by using spatial econometric models to reveal that its effects exhibit significant local effects, limited spatial spillovers, and prominent regional heterogeneity, empirically testing the existence of cross-regional spatial interaction effects. These innovative findings provide novel empirical evidence for understanding the spatial interaction effects of digital industrial clusters and deepens the insights into how such clusters—together with the agglomeration of innovative talent—contribute to high-quality economic development. They further offer theoretical underpinnings and policy pathways for developing countries to achieve sustainable economic growth through digital industrial cluster initiatives.

2. Theoretical Analysis and Research Hypotheses

This study will systematically examine the impact mechanism of digital industrial clusters on the high-quality development of regional economies from four dimensions: direct effects, spatial spillover effects, mediating mechanisms, and regional heterogeneity. It proposes that digital industrial clusters not only directly promote the high-quality development of regional economies but also drive economic high-quality development through the key pathway of innovation talent aggregation, exhibiting significant local promotion effects, complex spatial spillover effects, and prominent regional heterogeneity.

2.1. The Direct Effect of Digital Industry Clusters in Promoting High-Quality Economic Development

As a new engine for regional economic growth, digital industry clusters directly promote high-quality economic development through four core mechanisms. (1) Intelligent transformation and improved production efficiency. As an innovative industry cluster, digital industry clusters can promote the transformation of traditional manufacturing into intelligent production systems, invest resources into high-productivity digital industry-related departments to improve total factor productivity (TFP) [17], and thereby promote high-quality regional economic development. (2) Network synergy and transaction cost reduction. The highly networked and synergistic characteristics of digital industry clusters can effectively alleviate the problem of information asymmetry, reduce the cost of market information search and resource matching costs [18], and give rise to “digital + new forms of business”, such as smart agriculture and industrial internet platform economy [19]. Information technology has become the core medium of cross-industry empowerment, industrial cross-fertilization to form a virtuous cycle of value-added expansion–innovation, which promotes the in-depth integration of talent, capital, and innovation factors with the real economy, optimizes the allocation of factors of production, and reduces the transaction costs through the whole process of intelligent supervision. (3) Technology spillover and innovation-driven growth. Digital industry clusters rely on modern information technology to break geographical barriers and enhance resource mobility [20]. Digital sharing lowers R&D costs while facilitating the convergence of industrial, innovation, talent, and capital chains, thereby creating synergistic innovation effects that shift economic growth from factor-driven to innovation-driven paradigms. (4) Digital industrial clusters demonstrate infrastructure connectivity effects. Through cross-regional digital infrastructure—including industrial internet systems, 5G networks, and arithmetic centers—these clusters enable efficient factor mobility, establish regional data markets, reduce technology diffusion barriers, and foster coordinated regional economic development [21]. Based on the above analysis, this paper proposes the following:

Hypothesis 1 (H1).

Digital industry clusters have a significant positive role in promoting the high-quality development of the region’s economy.

2.2. Spatial Spillover Effects of Digital Industry Clusters in Promoting High-Quality Economic Development

The spatial spillover effect of digital clusters on high-quality economic development is twofold; it promotes regional synergistic development through the positive radiation effect [22] but also exacerbates regional imbalance through the negative siphoning effect. From the perspective of positive effects, digital industry clusters play a role through multiple mechanisms, such as factor flow, technological innovation and knowledge dissemination, industrial synergy, and economies of scale. High-efficiency information and communication networks reduce cross-regional transaction costs, promoting the free flow of factors of production and policy synergies [23]; frequent interactions between enterprises in the cluster accelerate knowledge spillover, spread new technologies and management models to the surrounding area, enhance regional innovation capacity, and promote technological innovation in the surrounding region; industry chain gradients promote technological innovation in neighboring regions; the gradient transfer of industrial chain promotes the extension of industrial ecology, strengthens upstream and downstream collaboration, and forms the mechanism of industrial linkage and regional synergistic development; the scale effect reduces the production cost, drives the transformation and upgrading of industries in neighboring regions, and promotes the economy of scale and economy of scope. However, the negative effects are also significant: the siphoning effect of the core region on high-end talents and technologies leads to the “hollowing out of talents” [24] and weakening of innovation capacity in the surrounding areas, resulting in the siphoning effect of resources and factors; the core–periphery division of labor pattern locks the surrounding areas in the low-value-added links, forming the “dependent” development pattern, resulting in the locking of the industrial chain division of labor hierarchy; the differences in the basis of innovation make it difficult for the peripheral regions to effectively absorb the technological spillover, and on the contrary, exacerbate the “polarization of innovation”, resulting in the asymmetry of the knowledge spillover. This double effect essentially reflects the “polarization-trickle” dynamic equilibrium in regional economic development [25], and its net effect depends on the relative strength of various mechanisms. Based on this, this paper proposes the following:

Hypothesis 2 (H2).

The spatial spillover effect of digital industry clusters on the high-quality development of the neighboring regional economy has uncertainty, which stems from the complex interaction between the positive radiation effect and the negative siphon effect.

2.3. Regional Heterogeneity of Digital Clusters Affecting High-Quality Economic Development

The impact of digital industry clusters on the high-quality development of the economy is characterized by significant regional variability, which is due to the following: (1) The existence of large differences in technological factor endowments in different regions of China [26]. Technology-intensive regions are highly compatible with the factor conditions required for the development of digital industry clusters by virtue of their strong talent reserves, well-developed research infrastructures, and active innovation ecologies. Enterprises in such regions usually have stronger technological innovation capabilities and can more effectively realize the value transformation of digital technologies and data elements. In contrast, non-technology-intensive regions are limited by constraints such as insufficient investment in innovation and weak R&D capabilities of enterprises, which make it difficult to fully absorb and transform the technological spillover effects generated by digital clusters, thus weakening their role in promoting high-quality economic development. (2) Different regions in China have development gradient differences [27]. In terms of digital infrastructure, benefiting from early-mover advantages, the eastern coastal area has established an extensive, high-speed digital infrastructure network, creating an optimal technical environment for corporate digital transformation. Meanwhile, central and western regions struggle with delayed infrastructure deployment and slower urban intelligence upgrades. In terms of industrial structure, the eastern region’s economy is driven by modern services and advanced manufacturing—sectors inherently conducive to digital integration. By contrast, central/western and northeastern regions rely more heavily on traditional manufacturing and resource-based industries, resulting in steeper challenges during digital transition. In terms of innovation factor agglomeration, the eastern region has leveraged the persistent “talent siphon” effect to consolidate its innovation factor agglomeration advantage, further solidifying its dominance in digital technology R&D and implementation. In contrast, prolonged talent outflows in central and western regions have hindered the pace of digital technology adoption, delaying progress toward high-quality economic growth [28]. Based on the above analysis, this paper proposes the following:

Hypothesis 3 (H3).

There is significant regional heterogeneity in the promotion of high-quality economic development by digital industry clusters, which is specifically manifested in the more prominent positive impact on technology-intensive regions and eastern regions.

2.4. The Mediating Effect of Digital Industry Clusters in Promoting High-Quality Economic Development

As an innovation-driven economic form, the core competitiveness of digital industry clusters essentially comes from the ability of continuous innovation, and the key to the construction of this ability lies in the agglomeration effect of innovative talents. According to Skill-Biased Technical Change (SBTC) [29], the essential characteristics of digital technology determines its strong demand for highly skilled personnel, forming a significant “Capital-Skill Complementarity” effect. Based on the human capital theory and new economic geography perspective, the concentration of innovative talents may play a key intermediary role between digital industry clusters and high-quality economic development. Specifically, this intermediary mechanism consists of three interrelated dimensions: First, the factor optimization mechanism. Digital industry clusters form a rigid demand for innovative talents due to their high technological threshold and R&D-intensive characteristics [30]. This demand leads to the spatial agglomeration of high-skilled labor, which in turn optimizes the spatial allocation efficiency of regional innovation factors through Labor Market Pooling. Second, the mechanisms of knowledge spillover and innovation network formation. The agglomeration of innovative talent substantially lowers the spatial and temporal barriers to technological collaboration, facilitating the exchange and sharing of tacit knowledge [31]. This concentration fosters diverse knowledge interactions, which—combined with scale economy effects—collectively improve the regional innovation system’s overall efficiency and accelerate breakthroughs in digital technology R&D. Third, the mechanism of industrial structure upgrading and total factor productivity enhancement. In the innovation talent concentration area, the “Learning by Doing” effect and Network Externality are mutually reinforcing, forming an “Information Hub” with positive feedback characteristics. (The “Learning by Doing” effect and Network Externality reinforce each other to form an “Information Hub” with positive feedback characteristics.) The agglomeration effect facilitates both rapid dissemination of digital technologies and substantial cost reduction via labor substitution (“machine for man”) [32], thereby boosting total factor productivity (TFP) and advancing economic quality. To sum up, by attracting innovative talent concentration, digital industrial clusters establish a complete transmission mechanism: “digital industry clusters → innovation talent concentration → high-quality economic development”. Based on the above theoretical analysis, this paper proposes the following:

Hypothesis 4 (H4).

Innovative talent agglomeration plays a significant positive intermediary role in the process of promoting economic high-quality development of digital industry clusters.

3. Research Design

3.1. Modeling

The spatial measurement model was constructed as follows [33]:

$${EQD}_{it}={\rho }_1{\sum }_j{W}_{ij}{EQD}_{jt}+{\alpha }_1{DIC}_{it}+{\theta }_1{\sum }_j{W}_{ij}{DIC}_{jt}+{\gamma }_1{X}_{it}+{\phi }_1{\sum }_j{W}_{ij}{X}_{jt}+d_{pro}+d_{year}+{\epsilon }_{it}$$

(1)

where $EQD$ denote the level of high-quality economic development; $DIC$ denote the level of digital industry clusters; $X$ denote the control variables; $d_{pro}$ and $d_{year}$ are the individual fixed effects and time-fixed effects, respectively; ${\rho }_1$ is the spatial autoregressive coefficient, which is used to measure the spatial lagged effects of the $EQD$; ${\alpha }_1$ and ${\gamma }_1$ are used to measure the direct effect of the $DIC$ and $X$, respectively; ${\theta }_1$ and ${\phi }_1$ are spatial interaction coefficients, used to measure the spatial lagged effects of $DIC$ and $X$, respectively; the subscript i and t for all variables denotes the province and the year, respectively; ${\epsilon }_{it}$ is the error terms; $W_{ij}$ is the spatial weighting matrix reflecting the spatial relationship of province i and province j. Geographic distance matrix ($W_d$), economic distance matrix ($W_e$), and economic-geographic nested matrix ($W_{ed}$) are used for estimation the model, respectively, and its matrix element $w_{ij}$ is defined as follows:

$$W_d:w_{ij}=\left\{\begin{array}{c}{\displaystyle \frac{1}{d_{ij}}}i\ne j\\ 0i=j\end{array}\right.$$

(2)

$$W_e:w_{ij}=\left\{\begin{array}{c}{\displaystyle \frac{1}{\left|\overline{{pgdp}_i}-\overline{{pgdp}_j}\right|}}i\ne j\\ 0i=j\end{array}\right.$$

(3)

$$W_{ed}:w_{ij}=\left\{\begin{array}{c}{\displaystyle \frac{1}{d_{ij}}}i\ne j\\ 0i=j\end{array}\right.+\left\{\begin{array}{c}{\displaystyle \frac{1}{\left|\overline{{pgdp}_i}-\overline{{pgdp}_j}\right|}}i\ne j\\ 0i=j\end{array}\right.$$

(4)

where $d_{ij}$ represents the distance between province i and province j (calculated based on latitude and longitude), and $\overline{pgdp}$ denote the mean GDP per capita.

In order to test Research Hypothesis 3 (H3), the mediation model three-step method [34] is introduced into the SDM of Equation (1) to reveal the mediation effect played by the agglomeration of innovative talents. The models are constructed as follows:

$${TAL}_{it}={\rho }_2{\sum }_j{W}_{ij}{TAL}_{jt}+{\alpha }_2{DIC}_{it}+{\theta }_2{\sum }_j{W}_{ij}{DIC}_{jt}+{\gamma }_2{X}_{it}+{\phi }_2{\sum }_j{W}_{ij}{X}_{jt}+d_{pro}+d_{year}+{\epsilon }_{it}$$

(5)

$${EQD}_{it}={\rho }_3{\sum }_j{W}_{ij}{EQD}_{jt}+{\alpha }_3{DIC}_{it}+{\theta }_3{\sum }_j{W}_{ij}{DIC}_{jt}+{\beta }_1{{TAL}_{it}+{\beta }_2{\sum }_j{W}_{ij}{TAL}_{jt}+\gamma }_3{X}_{it}+{\phi }_3{\sum }_j{W}_{ij}{X}_{jt}+d_{pro}+d_{year}+{\epsilon }_{it}$$

(6)

In Equation (5), $TAL$ denote the level of innovative talent clustering; ${\rho }_2$ denotes the spatial autoregressive coefficient of $TAL$; ${\alpha }_2$ is the effect of ${DIC}_i$ on ${TAL}_i$; ${\gamma }_2$ reflects the influence of $X_i$ on ${TAL}_i$; ${\theta }_2$ and ${\phi }_2$ are the spatial interaction coefficients; in Equation (6), ${\alpha }_3$ is the effect of ${DIC}_i$ on the dependent variable ${EQD}_i$; ${\beta }_1$ is the effect of ${TAL}_i$ on ${EQD}_i$, reflecting the mediation effect of ${TAL}_i$ between ${DIC}_i$ and ${EQD}_i$. If ${\beta }_1$ is significant and the value of ${\alpha }_3$ decreases (or is not significant) compared to the value of ${\alpha }_2$ in Equation (5), it indicates that the mediating effect exists. The meaning of the remaining symbols are the same as Equation (1).

3.2. Variable Measurement and Description

3.2.1. Explained Variable

Economic high-quality development ($EQD$). High-quality economic development refers to economic growth that places greater emphasis on innovation-driven development, resource allocation, people’s well-being, ecological environment, and sustainability, thereby enhancing the quality and efficiency of economic development. Based on the new development philosophy, and considering the availability and accuracy of data, this study adopts the high-quality development evaluation framework proposed by Li J.C. (2019) [35], selecting 12 secondary indicators and 21 tertiary indicators across five dimensions: innovation, coordination, green development, openness, and shared prosperity. In terms of calculation methods, the entropy method is used for objective weighting to construct an economic high-quality development index, reflecting the comprehensive level of economic development across regions. The weight distribution is shown in

Table 1. Comprehensive evaluation index system for high-quality economic development.

Primary Indicators	Secondary Indicators	Indicator Definitions	Directions	Indicator Weights
Innovative development	GDP growth rate	Regional GDP growth rate	+	0.005
	R&D investment intensity	R&D expenditure of industrial enterprises above Scale/GDP	+	0.070
	Investment efficiency	Investment rate/regional GDP growth rate	−	0.048
	Technological transaction activity	Technology transaction turnover/GDP	+	0.287
Coordinated development	Demand structure	Total retail sales of consumer goods/GDP	+	0.015
	Urban–rural structure	Urbanization rate	+	0.043
	Government debt burden	Government debt balance/GDP	−	0.013
	Industrial structure	Tertiary industry output/GDP	+	0.055
Green development	Energy consumption elasticity coefficient	Energy consumption growth rate/GDP growth rate	−	0.007
	Wastewater per unit of output	Wastewater emissions/GDP	−	0.015
	Wasted gases per unit of output	Sulfur dioxide emissions/GDP	−	0.009
Open development	Dependence on foreign trade	Total imports and exports/GDP	+	0.155
	Share of foreign investment	Total foreign investments/GDP	+	0.158
	Marketization degree	Marketization index	+	0.042
Shared development	Proportion of labor remuneration	Compensation of laborers/GDP	+	0.029
	Elasticity of growth of residents’ income	Disposable income per capita growth rate/GDP growth rate	+	0.016
	Urban–rural consumption gap	Consumption expenditure per urban resident/consumption expenditure per rural resident	−	0.016
	Share of fiscal expenditure on people’s livelihood	(Expenditure on housing security + expenditure on health care + expenditure on financial education + expenditure on social security and employment)/local budget expenditure	+	0.019

, where the innovation dimension (weight 0.410) and openness dimension (weight 0.355) have higher proportions, reflecting the intrinsic requirements of China’s economic high-quality development.

3.2.2. Core Explanatory Variables

Digital industry clusters ($DIC$). The construction of indicators for digital industrial clusters must fully consider their characteristics of technology-driven development, cross-industry integration, and network collaboration. Based on industrial agglomeration theory, innovation ecosystem theory, and network effect theory, and drawing on the research findings of Wang J.H. (2021) [11] and Love J.H. (2001) [36] and other research findings, combined with the stage-specific characteristics of China’s digital economic development, and referencing the authoritative standards of the National Bureau of Statistics of China’s “Statistical Classification of the Digital Economy and Its Core Industries (2021)”, we have selected four key sectors: digital manufacturing, software and information technology services, telecommunications, and the internet industry. By incorporating the unique data-driven characteristics and industrial integration traits of the digital economy, we have constructed an evaluation system for digital industrial clusters that can comprehensively and objectively reflect their development level (see

Table 2. Digital industry indicator system.

Primary Indicators	Indicator Description	Unit	Direction	Weighting
Digital Manufacturing	Full-time Equivalent of R&D Personnel in Digital Manufacturing	person–years	+	0.136
	R&D Expenditures/GDP in Digital Manufacturing	%	+	0.057
	Number of New Product Development Projects in Digital Manufacturing	item	+	0.116
Software and Information Technology Services	Revenue from Software Business	billion yuan	+	0.132
	Revenue from Software Products	billion yuan	+	0.130
	Revenue from IT Services	billion yuan	+	0.131
Telecommunications Industry	Mobile Phone Penetration	part/hundred person	+	0.043
	Total Telecommunications Business	billion yuan	+	0.047
Internet Industry	Number of Web Pages	million	+	0.165
	Internet Broadband Access Users	million households	+	0.043

).

A comprehensive measurement method combining entropy values with locational entropy is employed to scientifically assess the development level of regional digital industry clusters. First, based on the entropy value method, multiple indicators across four dimensions—digital manufacturing, software and information technology services, telecommunications, and the internet industry—are objectively weighted to calculate the comprehensive index of digital industry development. This effectively overcomes the bias associated with subjective weighting, ensuring the scientific validity of the evaluation results. Second, drawing on the research findings of Yuan G.H. (2023) [15], Xin L.L. (2023) [16], and Li X.W. (2022) [37], the location entropy index is introduced to quantify the spatial agglomeration degree of the digital industry, accurately capturing its spatial agglomeration characteristics. The formula for calculating it is

$${DIC}_{it}={\displaystyle \frac{{DIG}_{it}/{GDP}_{it}}{{\sum }_{i=1}^{30}{DIG}_{it}/{\sum }_{i=1}^{30}{GDP}_{it}}}$$

(7)

where $DIG$ indicates the level of digital technology development; $GDP$ denotes the level of industrial development; the rest of the symbols have the same meaning as in Equation (1).

3.2.3. Mediating Variable

Innovation talent agglomeration ($TAL$). Innovative talent aggregation refers to the spatial clustering phenomenon formed by talent with innovative thinking, professional skills, and R&D capabilities through geographical concentration and industry-related connections. Its core lies in promoting knowledge sharing, technological cooperation, and collaborative innovation through increased talent density. In terms of indicator construction, drawing on the research findings of Jiang R. (2023) [38] and Cui X.M. (2022) [39], based on knowledge spillover theory and innovation ecosystem theory, we aggregate the total number of employees across the following sectors: information transmission, software, and information technology services; finance; leasing and business services; scientific research and technical services; education; and culture, sports, and entertainment. This sum represents the scale of innovation talent. We then divide this figure by the total population at year-end to calculate innovation talent density, serving as a metric for measuring the concentration level of innovation talent.

3.2.4. Control Variables

To avoid omitting key variables and ensure the validity of model estimates, based on endogenous economic growth theory and drawing on the empirical research frameworks of Liu X.Z. (2022) [40] and Zhang Z.D. (2023) [41], we focused on controlling five key factors: first, we use the “fiscal expenditure ratio” indicator (government fiscal expenditure/GDP × 100%) to capture the role of local governments in resource allocation; second, we used the “per capita GDP” to represent the foundation of regional economic development; third, we used the “number of large-scale industrial enterprises” to represent level of industrial development; fourth, we used the “education level” (number of undergraduate and graduate students/permanent residents × 100%) to represent human capital reserves; finally, we used the “population density” (people per square kilometer) to represent the degree of factor agglomeration. This system of control variables takes into account both traditional economic growth factors and spatial agglomeration characteristics, providing a reliable basis for accurately identifying the net effects of core explanatory variables.

3.3. Data Sources and Descriptive Statistics

This research employs panel data covering China’s 30 provincial regions (2012–2023), sourced from authoritative publications including the China Statistical Yearbook (https://www.stats.gov.cn/sj), China High-Tech Industry Statistical Yearbook (https://www.zgtjnj.org/navibooklist-n3025012914-1.html), and China Population and Employment Statistical Yearbook (http://www.tjcn.org), supplemented by provincial/municipal statistical reports (http://www.tjnjw.com/diqu/) and the China Research Data Service Platform (https://www.cnrds.com). The relevant websites were accessed on 10 August 2025. Linear interpolation was employed to address sporadic missing values. Descriptive statistics of the variables are shown in

Table 3. Descriptive statistics of variables.

Variable Type	Variable Name	Symbol	Unit	Mean Value	Standard Deviation	Minimum Value	Maximum Value
Explained Variables	High-quality economic development	$EQD$	/	0.244	0.098	0.137	0.582
Core Explanatory Variables	Digital industry clusters	$DIC$	/	0.898	0.653	0.255	4.171
Mediating Variables	Clustering of innovative talents	$TAL$	/	0.033	0.026	0.016	0.169
Control Variables	Fiscal expenditure ratio	$FER$	%	0.258	0.110	0.105	0.758
	Level of economic development	$EDL$	million yuan	6.487	3.212	1.971	20.028
	Level of industrial development	$IDL$	million	1.325	1.460	0.034	7.197
	Human capital reserves	$HCR$	%	0.022	0.006	0.009	0.044
	Population density	$DEN$	people/km2	477.105	713.340	7.905	3950.794

.

4. Empirical Analysis

4.1. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis is used to investigate the spatial dependence characteristics of digital industry clusters and high-quality economic development. Calculating Global Moran’s I and Geary’s C under the geographic distance matrix (

Table 4. Moran’s I and Geary’s C for the economic distance matrix.

Year	${\mathit{E}\mathit{Q}\mathit{D}}_{\mathit{i}\mathit{t}}$				${\mathit{D}\mathit{I}\mathit{C}}_{\mathit{i}\mathit{t}}$				${\mathit{T}\mathit{A}\mathit{L}}_{\mathit{i}\mathit{t}}$
	$\mathbf{M}\mathbf{o}\mathbf{r}\mathbf{a}\mathbf{n}\mathbf{’}\mathbf{s}\mathbf{I}$	Z-Value	$\mathbf{G}\mathbf{e}\mathbf{a}\mathbf{r}\mathbf{y}\mathbf{’}\mathbf{s}\mathbf{C}$	Z-Value	$\mathbf{M}\mathbf{o}\mathbf{r}\mathbf{a}\mathbf{n}\mathbf{’}\mathbf{s}\mathbf{I}$	Z-Value	$\mathbf{G}\mathbf{e}\mathbf{a}\mathbf{r}\mathbf{y}\mathbf{’}\mathbf{s}\mathbf{C}$	Z-Value	$\mathbf{M}\mathbf{o}\mathbf{r}\mathbf{a}\mathbf{n}\mathbf{’}\mathbf{s}\mathbf{I}$	Z-Value	$\mathbf{G}\mathbf{e}\mathbf{a}\mathbf{r}\mathbf{y}\mathbf{’}\mathbf{s}\mathbf{C}$	Z-Value
2012	0.256 ***	5.796	0.670 ***	−4.869	0.109 ***	3.081	0.815 **	−2.496	0.045 ***	3.627	0.850	−1.502
2013	0.254 ***	5.75	0.674 ***	−4.824	0.126 ***	3.321	0.802 ***	−2.782	0.088 ***	4.283	0.806 **	−2.032
2014	0.258 ***	5.809	0.673 ***	−4.876	0.149 ***	3.846	0.778 ***	−3.073	0.093 ***	4.236	0.800 **	−2.124
2015	0.262 ***	5.865	0.672 ***	−4.903	0.151 ***	3.944	0.775 ***	−3.064	0.095 ***	4.355	0.799 **	−2.123
2016	0.259 ***	5.804	0.678 ***	−4.814	0.155 ***	4.057	0.772 ***	−3.086	0.097 ***	4.432	0.798 **	−2.131
2017	0.247 ***	5.575	0.681 ***	−4.753	0.138 ***	3.804	0.785 ***	−2.828	0.101 ***	4.523	0.796 **	−2.17
2018	0.240 ***	5.43	0.687 ***	−4.682	0.104 ***	3.804	0.815 **	−2.402	0.103 ***	4.475	0.793 **	−2.211
2019	0.240 ***	5.45	0.689 ***	−4.624	0.114 ***	3.365	0.801 **	−2.541	0.128 ***	4.496	0.768 ***	−2.631
2020	0.242 ***	5.43	0.688 ***	−4.709	0.121 ***	3.755	0.794 **	−2.516	0.117 ***	4.337	0.780 **	−2.46
2021	0.240 ***	5.341	0.692 ***	−4.735	0.108 ***	3.514	0.805 **	−2.34	0.125 ***	4.354	0.770 ***	−2.627
2022	0.235 ***	5.194	0.699 ***	−4.703	0.103 ***	3.373	0.811 **	−2.279	0.127 ***	4.338	0.765 ***	−2.698
2023	0.235 ***	5.194	0.699 ***	−4.703	0.106 ***	3.502	0.807 **	−2.315	0.123 ***	4.359	0.768 ***	−2.633

Note: *** and ** represent 1% and 5% significance levels, respectively.

). It is found that Moran’s I statistic value is significantly positive, and Geary’s C statistic value is significantly less than 1, indicating that there are significant spatial positive autocorrelation characteristics of ${EQD}_{it}$, ${DIC}_{it}$, and ${TAL}_{it}$, and the regions with similar development levels show significant spatial agglomeration characteristics in space. This lays a basis for the subsequent use of spatial econometric models to accurately capture the spatial characteristics of the data [33].

4.2. Benchmark Regression Results

According to the spatial econometrics testing process, the applicability of the spatial Durbin model (SDM), spatial lag model (SAR), and spatial error model (SEM) was compared, firstly, through the Lagrange multiplier test (LM) and its robust form (Robust LM) [42]. The test results show that both LM_err (11.206) and Robust LM_err (18.101) reject the original hypothesis at the 1% significance level, which means the SDM should not degenerates into the SEM; while LM_lag (0.016) is not significant, Robust LM_lag (6.911) can reject the hypothesis at the 1% significance level, which means that the SDM should not degrade to the SAR model. So, we chose to construct the spatial Durbin model (SDM). Secondly, the fixed effects and random effects of the model were screened by Hausman test, and it was found that the Hausman statistic was 67.76 (p < 0.01), which significantly rejected the original hypothesis of “the random effects are better”, indicating that the model should be selected as the SDM fixed effects model. Finally, comparing the individual fixed effects, time-fixed effects, and double fixed effects models by the information criteria (AIC and BIC) and the goodness-of-fit (R2), it was found that the SDM with time-fixed effects had the smallest AIC (1666.1) and the smallest BIC (1627.2) and the highest goodness-of-fit (0.931). Based on the above systematic tests, the spatial Durbin model (SDM) with time-fixed effects was finally selected as the benchmark model to scientifically decompose the spatial interaction effects of the explanatory variables and ensure the validity and reliability of the estimation results.

Table 5. Spatial measurement benchmark model regression results.

Variant	${\mathit{E}\mathit{Q}\mathit{D}}_{\mathit{i}\mathit{t}}$
	${\mathit{W}}_{\mathit{d}}$	${\mathit{W}}_{\mathit{e}}$	${\mathit{W}}_{\mathit{e}\mathit{d}}$	${\mathit{W}}_{\mathit{d}}$	${\mathit{W}}_{\mathit{e}}$	${\mathit{W}}_{\mathit{e}\mathit{d}}$
	$\left(1\right)$	$\left(2\right)$	$\left(3\right)$	$\left(4\right)$	$\left(5\right)$	$\left(6\right)$
${DIC}_{it}$	0.123 *** (0.004)	0.107 *** (0.003)	0.106 *** (0.003)	0.091 *** (0.004)	0.062 *** (0.004)	0.080 *** (0.004)
${W\times DIC}_{it}$	0.112 *** (0.026)	0.124 *** (0.145)	0.245 (0.025)	0.110 *** (0.032)	0.001 (0.014)	0.028 (0.029)
Control variables	No	No	No	Yes	Yes	Yes
W × Control variables	No	No	No	Yes	Yes	Yes
Individual effects	No	No	No	No	No	No
Time effects	Fixed	Fixed	Fixed	Fixed	Fixed	Fixed
ρ	0.330 * (0.140)	−0.060 (0.078)	−0.195 (0.133)	−0.167 (0.211)	−0.159 * (0.090)	−0.136 (0.158)
σ2	0.002 *** (0.000)	0.001 *** (0.000)	0.001 *** (0.000)	0.001 *** (0.000)	0.000 *** (0.000)	0.001 *** (0.000)
${\mathrm{R}}^2$	0.790	0.837	0.862	0.942	0.917	0.915
N	360	360	360	360	360	360

Note: *** and * represent 1% and 10% significance levels, respectively. The standard errors are in parentheses.

demonstrates the estimation results of the spatial Durbin model (SDM) with time-fixed effects. Firstly, the results show that the regression coefficients of digital industrial clusters ($DIC$) are significantly positive (columns (1) to (3)) under the geographic distance matrix ($W_d$), economic distance matrix ($W_e$), and economic–geographic nested matrix ($W_{ed}$), which implies that the digital industrial clusters have a significant positive contribution to the high-quality development of the economy, verifying Research Hypothesis 1 (H1). After incorporating control variables and their spatial interaction terms to correct for biased estimates caused by omitted variables, the slope coefficient for digital industrial clusters ($\mathrm{D}\mathrm{I}\mathrm{C}$) decreased but remained significantly positive (Columns (4) to (6)). This finding aligns with studies by Liu X.Z. (2022) [40] and others that industrial agglomeration significantly promotes high-quality economic development. Both studies confirm that digital industrial clusters exert a significant positive driving effect on high-quality economic development, thereby validating Research Hypothesis 1 (H1) of this study. The direction and significance of the spatial autoregressive coefficients are unstable under different weight matrices, indicating that the digital industry agglomeration in this region does not have a significant impact on the quality of economic development in the neighboring regions and that the spatial spillover effect is uncertain, which verifies the Research Hypothesis 2 (H2). The error term variance (σ² = 0.000) is significantly non-zero, confirming that the introduction of spatial dependence and time-fixed effects in the model is necessary.

4.3. Effect Decomposition

Based on the estimation results of the spatial Durbin model (SDM), the impact effects of the explanatory variables were decomposed using the partial differentiation method (see

Table 6. Effect decomposition of dynamic SDM.

Variant	Direct Effects (Local Effects)			Indirect Effects (Spatial Spillovers)
	${\mathit{W}}_{\mathit{d}}$	${\mathit{W}}_{\mathit{e}}$	${\mathit{W}}_{\mathit{e}\mathit{d}}$	${\mathit{W}}_{\mathit{d}}$	${\mathit{W}}_{\mathit{e}}$	${\mathit{W}}_{\mathit{e}\mathit{d}}$
	$\left(1\right)$	$\left(2\right)$	$\left(3\right)$	$\left(4\right)$	$\left(5\right)$	$\left(6\right)$
${DIC}_{it}$	0.090 *** (0.004)	0.063 *** (0.004)	0.080 *** (0.004)	0.087 ** (0.036)	−0.009 (0.011)	0.017 (0.025)

Note: *** and ** represent 1% and 5% significance levels, respectively. The standard errors are in parentheses.

). The results show that the direct effects of the core explanatory variables are significantly positive under all three spatial weight matrices, indicating that the development of digital industry clusters can significantly drive the high-quality development of the local economy (p < 0.01). The indirect effect is significantly positive only under the matrix of $W_d$, but fails the significance test under $W_e$ and $W_{ed}$. This suggests that local digital industry cluster development has limited impact on the quality of economic development in neighboring provinces, which is consistent with the expectation of Research Hypothesis 2 (H2). This may stem from the simultaneous existence of positive technology diffusion effect and negative resource siphoning effect between regions, which cancel each other out and make the spatial spillover effect not significant [43].

4.4. Robustness Test

To ensure the reliability of the research conclusions, the following robustness test scheme is implemented with reference to the methodological framework of Fang H. (2021) [44] and Xu X.F. [45]: First, the core explanatory variables are replaced. The digital industry cluster ($DIC$) is replaced with the comprehensive development index of digital economy ($DIG$) to test the robustness of the model to the choice of indicators. Second, exclude the impact of epidemic. Considering that the COVID-19 epidemic may have a structural impact on the process of digital technology development, the 2020 observation sample is excluded to rule out the interference of the epidemic, an exogenous shock, on the research findings. Third, municipalities are excluded. Considering that the development foundation of municipalities is better than that of other provinces, the sample of municipalities is excluded and regressed on the remaining provinces. Fourth, the two-sided 10% shrinkage. In order to control the effect of extreme values, a 10% shrinkage on each side of all continuous variables is implemented to enhance the robustness of the estimation results. Since the fitting results with the matrix of $W_d$, $W_e$, and $W_{ed}$ are basically the same, and the model fit superiority and significance level with the matrix of $W_d$ are higher, the $W_d$ is adopted as the model in the robustness test, endogeneity treatment, mechanism test, and heterogeneity analysis. Columns (1) to (4) of

Table 7. Robustness and endogeneity tests.

Variant	Substitution Variables	Exclusion of Epidemic Impact	Eliminate Municipalities	Shrinking by 20%	Instrumental Variables (IV1)	Instrumental Variables (IV2)
	(1)	(2)	(3)	(4)	(5)	(6)
${DIC}_{it}$	0.216 *** (0.014)	0.090 *** (0.004)	0.068 *** (0.005)	0.068 *** (0.005)	0.108 *** (0.005)	0.106 *** (0.005)
${W\times DIC}_{it}$	−0.082 * (0.049)	0.107 *** (0.034)	0.120 *** (0.038)	0.181 *** (0.046)	0.173 *** (0.043)	0.126 *** (0.043)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
W × Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Individual effects	No	No	No	No	No	No
Time effects	Fixed	Fixed	Fixed	Fixed	Fixed	Fixed
ρ	−0.190 ** (0.085)	−0.168 (0.221)	−0.261 (0.226)	−0.301 (0.228)	−0.503 ** (0.249)	−0.522 ** (0.238)
σ2	0.000 *** (0.000)	0.001 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)	0.001 *** (0.000)
$\overline{{\mathrm{R}}^2}$	0.921	0.939	0.693	0.866	0.863	0.870
N	360	360	360	360	360	360

Note: ***, **, and * represent 1%, 5%, and 10% significance levels, respectively. The standard errors are in parentheses.

demonstrate the results of the robustness test. The results show that the positive promotion effect of digital industry clusters on the high-quality development of the local economy is statistically significant under different settings, and the core conclusions of the study are not affected by indicator measures, epidemic shocks, city grades, and extreme values, and are highly robust and reliable.

4.5. Endogeneity Test

Considering the possible bidirectional causal relationship between digital industry clusters and high-quality economic development, this study adopts the instrumental variable approach to systematically assess the impact of endogeneity issues on the research results, with reference to Yang X. (2022) [46], Ivus O. (2015) [47], and Nunn N. (2014) [48]. In the selection of instrumental variables, the degree of terrain undulation directly affects the deployment and maintenance costs of digital infrastructure, while the average elevation indirectly contributes to the agglomeration level of digital industries by affecting the industrial structure; at the same time, there is a lack of direct theoretical correlation between these two types of geographic variables and the regional economic development, which satisfies the condition of exogeneity of the instrumental variables. Therefore, this study takes the interaction term between terrain undulation degree and digital industry clustering as the first instrumental variable (IV1), and the interaction term between average elevation and digital industry clustering as the second instrumental variable (IV2). The results of the instrumental variable validity tests indicate the following: (1) the Kleibergen–Paap rk Wald F-statistics for IV1 and IV2 are 57.452 and 56.437, respectively, both significantly exceeding the critical value (16.380) at the 10% level under the Stock–Yogo test, indicating no weak instrumental variable issues; (2) the Kleibergen–Paap rk LM statistics for IV1 and IV2 are 42.508 and 44.723, respectively, both rejecting the null hypothesis of “instrumental variables unidentifiable” at a high significance level, further supporting the validity of the instrumental variables. Columns (5) to (6) in Table 7 report the instrumental variable regression results. They show that after controlling for endogeneity issues, the research conclusions remain significantly valid. This indicates that endogeneity problems did not substantially affect the main findings, thereby enhancing the reliability of the research conclusions.

4.6. Heterogeneity Test

To examine regional disparities in the effects of digital industrial clusters on economic development quality and to improve the reliability and precision of the findings, a regional heterogeneity analysis was conducted based on Research Hypothesis 4, adopting the classification approach proposed by Zhou M.S. (2022) [49]. The results are presented in

Table 8. Heterogeneity test.

	Technology Intensive	Non-Technology Intensive	East	Central	West	Northeast
Variant	(1)	(2)	(3)	(4)	(5)	(6)
${DIC}_{it}$	0.120 *** (0.006)	0.054 *** (0.006)	0.094 *** (0.008)	0.030 (0.033)	−0.005 (0.008)	0.096 *** (0.038)
${W\times DIC}_{it}$	0.041 ** (0.021)	0.003 (0.041)	0.039 (0.024)	−0.132 (0.124)	0.018 (0.043)	0.130 ** (0.065)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
W × Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Individual effects	No	No	No	No	No	No
Time effects	Fixed	Fixed	Fixed	Fixed	Fixed	Fixed
ρ	−0.061 (0.174)	−0.763 *** (0.244)	−0.293 (0.185)	−0.622 ** (0.253)	−0.803 *** (0.279)	−0.184 (0.190)
σ2	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)	0.000 *** (0.000)
$\overline{{\mathrm{R}}^2}$	0.727	0.701	0.913	0.661	0.600	0.006
N	96	264	120	72	132	36

Note: *** and ** represent 1% and 5% significance levels, respectively. The standard errors are in parentheses.

. First, the sample was divided into two subsamples of technology-intensive regions (including eight innovation-driven provinces and municipalities such as Beijing, Tianjin, and Shanghai) and non-technology-intensive regions in order to examine the spatial heterogeneity of the economic effects of digital industry clusters under different degrees of technological intensity and gradient development patterns. The results show that the economic promotion effect of digital industry clusters is more significant for technology-intensive regions (${\alpha }_1$ = 0.120, p < 0.01), while the promotion effect for non-technology-intensive regions is relatively small (${\alpha }_1$ = 0.054, p < 0.01). This difference mainly stems from the fact that technology-intensive regions have better innovation ecosystems and higher quality human capital, which can more efficiently transform digital technologies into productivity [26], thus further verifying the positive facilitating effect of digital industry clusters on the quality of economic development. Second, the sample was divided into four subsamples of eastern, central, western, and northeastern regions according to the regional division criteria of the National Bureau of Statistics of China in order to examine the impact of the gradient development pattern on the research results. The results show that there is obvious regional heterogeneity in the economic effects of digital clusters: significant positive promotion effects are exhibited in the eastern region (${\alpha }_1$ = 0.094, p < 0.01) and the northeastern region (${\alpha }_1$ = 0.096, p < 0.01), while the effects in the central region (${\alpha }_1$ = 0.030, p > 0.1) and the western region (${\alpha }_1$ = −0.005, p > 0.1) are not significant. This difference reflects the structural characteristics of China’s digital economy development [27]. Relying on the core areas of digital economy such as Beijing–Tianjin–Hebei, Yangtze River Delta, and Guangdong–Hong Kong–Macao Greater Bay Area, the eastern region has formed a complete industrial chain and innovation network, which effectively promotes the high-quality development of the economy through the scale effect and technological spillover; the northeastern region shows a more obvious promotion effect, driven by the support of the digital industry policy and the digital transformation of traditional industries. In contrast, the central region faces the problems of lagging behind in the transformation of traditional industries and high dependence on foreign countries for key technologies; in the western region, despite the policy support of “counting in the east and counting in the west”, it is still constrained by the limitations of weak infrastructures and the initial development of the industry, which has resulted in the driving effect of the digital industry clusters not yet being fully manifested. The above results verify Research Hypothesis 3 (H3).

4.7. Mediating Effect Test

In order to test the mediating role of innovative talent agglomeration in the process of digital industry clusters promoting economic quality development, this study adopts a three-step approach to test the results, which are shown in

Table 9. Mechanism tests.

Variant	${\mathit{E}\mathit{Q}\mathit{D}}_{\mathit{i}\mathit{t}}$	${\mathit{T}\mathit{A}\mathit{L}}_{\mathit{i}\mathit{t}}$	${\mathit{E}\mathit{Q}\mathit{D}}_{\mathit{i}\mathit{t}}$
	(1)	(2)	(3)
${DIC}_{it}$	0.091 *** (0.004)	0.015 *** (0.001)	0.070 *** (0.004)
${TAL}_{it}$			1.400 *** (0.161)
${W\times DIC}_{it}$	0.110 *** (0.032)	−0.036 *** (0.011)	0.161 *** (0.037)
$W\times {TAL}_{it}$			1.970 ** (0.957)
Control variables	Yes	Yes	Yes
W × Control variables	Yes	Yes	Yes
Individual effects	No	No	No
Time effects	Fixed	Fixed	Fixed
$\rho$	−0.167 (0.211)	−1.298 *** (0.209)	−0.251 (0.222)
σ2	0.001 *** (0.000)	0.000 *** (0.000)	0.001 *** (0.000)
$\overline{\mathrm{R}}$	0.942	0.732	0.828
N	360	360	360

Note: *** and ** represent 1% and 5% significance levels, respectively. The standard errors are in parentheses.

. The testing process and main findings are as follows: In the first step, the total effect of digital industry cluster ($DIC$) on the quality of economic development ($EQD$) is tested. From column (1) of Table 8, it can be seen that there is a significant positive total effect of digital industrial clusters on economic quality development (${\alpha }_1$ = 0.091, p < 0.01), and the coefficient of the spatial lag term is significantly positive, which satisfies the preconditions of the mediation effect test. In the second step, the effect of digital industry cluster ($DIC$) on innovative talent aggregation ($TAL$) is tested. Column (2) of Table 9 shows that digital industry clusters have a significant positive promotion effect on innovation talent agglomeration (${\alpha }_2$ = 0.015, p < 0.01), while the coefficient of the spatial lag term is significantly negative, indicating that digital industry clusters not only promote the agglomeration of local innovation talent but also produce a certain competition and siphoning effect on the talent of the neighboring regions. In the third step, digital industry clusters ($DIC$) and innovative talents agglomeration ($TAL$) are included in the model at the same time to test the joint effect of the two on the quality of economic development ($EQD$). Column (3) of Table 9 shows that the agglomeration of innovative talent not only contributes to high-quality economic development in the local region (${\beta }_1$ = 1.400, p < 0.01) but also exerts a significantly positive spatial spillover effect on the economic development of neighboring areas (${\beta }_2$ = 1.970, p < 0.05). This finding contrasts with the results reported by Jiang, R. (2023) [38], which indicated that the concentration of scientific and technological talent at the provincial level suppressed high-quality economic development in surrounding regions. The discrepancy may be attributed to enhanced industrial clustering effects within the region and the increasingly effective interregional coordination mechanisms for development. The discrepancy may stem from enhanced industrial cluster effects within regions and increasingly refined interregional coordination mechanisms. Furthermore, compared to Column (1) in Table 9, the slope coefficient for digital industrial clusters on high-quality economic development decreased to 0.070, while the spatial lag term became significantly positive. This indicates that innovation talent agglomeration indeed mediates part of the relationship [50], confirming the existence of a mediating transmission mechanism: “digital industrial clusters → innovation talent agglomeration → high-quality economic development.” Specifically, digital industry clusters attract and concentrate innovative talents by creating high-quality employment and optimizing the innovation ecosystem; these talents, in turn, promote the quality and efficiency of the local economy through knowledge spillover and technological innovation, and have positive spatial spillover effects on the surrounding areas. The above results confirm Research Hypothesis 4 (H4) and provide important empirical evidence for a deeper understanding of the positive mediating role of innovative talent agglomeration in promoting high-quality economic development, thereby offering policy insights for achieving more balanced, inclusive, and sustainable economic growth.

Based on the estimation results of the spatial Dobin mediation model, a path diagram illustrating the mediating effect of innovative talent agglomeration and spatial spillover effects is presented (Figure 1). Figure 1 shows that the sum of the direct effect of digital industry clusters on local high-quality economic development (0.070) and the local mediating effect generated through innovation talent agglomeration (0.015 × 1.400 = 0.021) is 0.091. This total effect is significantly positive and consistent with the estimation results in Column (1) of Table 8, indicating that digital industry clusters can effectively promote local high-quality economic development. This further validates Research Hypothesis 1 (H1). Regarding spatial spillover effects, the direct effect of digital industrial clusters on high-quality economic development in surrounding areas (0.161) combined with their indirect effect through the innovation talent agglomeration channel (−0.036 × 1.970 = −0.071) yields a total spatial spillover effect of 0.090. Although slightly lower than the total spatial spillover effect estimate (0.110) in Column (1) of Table 8, it remains significantly positive with consistent economic implications. This indicates that digital industrial agglomeration still exerts a net positive driving effect on the high-quality economic development of surrounding areas. Notably, while digital industry clusters attract innovative talent to local areas, they simultaneously exert a certain inhibitory effect on talent accumulation in surrounding regions through competition and the siphon effect. Consequently, while promoting high-quality local economic development, they generate negative impacts on surrounding areas via talent competition. This indicates that the ultimate effect of digital industry agglomeration on the high-quality economic development of surrounding regions depends on the balance between its positive spillover effects and negative siphon effects, with outcomes carrying a degree of uncertainty. This finding validates Research Hypothesis 2 (H2).

5. Conclusions and Discussion

5.1. Conclusions

This study systematically examines the impact mechanism of digital industrial clusters on high-quality economic development based on China’s provincial panel data (2012–2023), yielding three key findings: First, both digital industrial clusters and high-quality economic development demonstrate significant positive spatial autocorrelation. Local digital industrial clusters have been shown to effectively drive high-quality economic development within the same region. This empirical finding has been robustly supported by multiple robustness tests and endogeneity examinations and is consistent with the research conducted by Liu X.Z. (2022) [40]. It thus provides a solid empirical basis for encouraging and promoting the development of digital industrial clusters. Second, the overall spatial spillover effect of digital industrial clusters on high-quality economic development is uncertain. This uncertainty arises because the concentration of innovative talent plays a crucial mediating role. Consequently, the overall effect of digital industrial clusters on the economic development quality of surrounding areas comprises both a negative talent siphoning effect and a positive technology diffusion effect. When the absolute value of the negative siphoning effect is smaller than the positive spillover effect, a positive overall spatial spillover effect emerges. Conversely, it may yield insignificant or negative spatial spillover effects, offering a policy perspective for promoting coordinated regional development and mitigating spatial polarization and developmental inequality. Third, empirical model findings reveal regional heterogeneity in the impact of digital industrial clusters on economic development quality. Technology-intensive regions, eastern developed areas, and northeastern regions have more effectively transformed the advantages of digital industrial clusters into drivers of high-quality economic development, while central and western regions, constrained by factors such as industrial structure, innovation factors, and infrastructure, have yet to fully unleash the driving effect of digital industries on economic development quality. This suggests that central and western regions should address development shortcomings in a targeted manner, formulate differentiated regional digital industry development policies, and achieve simultaneous improvements in economic development quality and efficiency.

5.2. Recommendations

This paper proposes the following targeted policy recommendations: First, given the empirical finding that digital industrial clusters exert a significantly positive local effect on economic development quality, all regions are encouraged to actively promote the agglomeration of digital-related industries. Accelerating the development of digital innovation hubs will help drive high-quality regional economic growth. Second, in light of the uncertain spatial spillover effects associated with digital industrial clusters, it is advisable to establish cross-regional technology-transfer platforms and outcome-sharing mechanisms. Such initiatives would help amplify the positive technological diffusion and demonstrate effects of digital clusters on neighboring areas. At the same time, while refining talent cultivation, recruitment, and incentive mechanisms within clusters, a compensatory framework should be introduced to address negative externalities in regions experiencing talent outflows. This would help alleviate adverse impacts such as brain drain and resource competition on surrounding regions, thereby promoting shared prosperity and sustainable development. Finally, considering significant regional heterogeneity, differentiated development strategies should be implemented. Technologically intensive and economically advanced regions should focus on breakthroughs in frontier digital technologies and upgrading digital industrial chains, with continued improvements to innovation environments and talent policies. Less technology-intensive regions and those with relatively weaker economic foundations should prioritize digital transformation of traditional industries, accelerate the rollout of digital infrastructure, and strengthen innovation and R&D capacity, thereby supporting sustainable economic growth.

5.3. Limitations and Further Research

This study could be enhanced in the following aspects: First, refining the spatial scale. While provincial-level data were employed in this research to enhance the richness and representativeness of the indicator system, such a scale remains insufficient for fully capturing the spatial heterogeneity of economic development within provinces. Future studies could refine the spatial scale to the prefectural level, enabling a more precise identification of regional disparities. Second, improving the research methods. A mutually reinforcing and cumulative cyclical causal mechanism may exist between digital industrial clusters, agglomeration of innovative talent, and high-quality development. Although this study employed a time-fixed spatial Durbin mediation model to capture both spatial and mediation effects and addressed endogeneity concerns using an instrumental variable approach, the current methodology still falls short of fully characterizing such complex nonlinear feedback processes. Future research could incorporate methodological innovations such as system dynamics or coupling coordination models to analyze these comprehensively endogenous interaction mechanisms. Third, extending the research into normative analysis. Both high-quality development and sustainable economic growth are inherently characterized by normative economic attributes. The connotation of high-quality development further encompasses multiple objectives such as enhanced economic efficiency, structural optimization, transition of growth drivers, shared benefits, and green transformation. Although this study applied the entropy method to assign weights to the five dimensions and constructed a composite index, this approach may oversimplify the complex interrelationships—and even potential trade-offs—among these dimensions. Future studies could expand from empirical examination to normative analysis by introducing methodologies such as multi-criteria decision analysis or equity-efficiency framework, thereby enabling a more systematic and policy-oriented evaluation of overall developmental outcomes.