How Does the Digital Economy Drive High-Quality Regional Development? New Evidence From China

Abstract

The digital economy, which boasts general technology, intense penetration, platform ecology, and low marginal cost, is a product of advanced digital technology. This new engine has become a driving force for high-quality economic development. From the three aspects of development momentum, efficiency, and structure, this paper profoundly explores internal mechanisms to lead the high-quality growth of the regional economy. By constructing an econometric model, the influence effect and means of the digital economy on the high-quality development of the regional economy are empirically tested. The digital economy and its three sub-dimensions can significantly promote the high-quality development of the regional economy. However, industrial digitalization has the most vital role in promoting it. The digital economy has shown a more vital promotion role in the central and western regions and provinces with low total factor productivity, and it can indirectly impact high-quality economic development by promoting dynamic, efficient, and structural changes.

Keywords

digital economy total factor productivity mediation effect high-quality development

Introduction

Since the reform and opening up, China’s economy has proliferated for over 40 years, and its gross domestic product (GDP) currently ranks second globally. However, behind this rapid growth is extensive development at the cost of high input and consumption, and the overall quality of economic development could be higher. With the disappearance of the demographic dividend, the obstruction of “imitation innovation” technological progress, and the weak demand in the international market, China’s economic growth rate has slowed significantly in recent years. In light of current circumstances, economic progress in China must focus on enhancing the quality of growth, increasing development efficiency, and optimizing the economic structure. These measures will help alleviate the effects of slowed growth and achieve high-quality economic development Wu (2015). The rise of a new generation of information technology has paved the way for a digital economy, which is a key driver for high-quality development Litvinenko (2020). This phenomenon has permeated every aspect of human life and has produced various new formats and models, including mobile internet, big data, artificial intelligence, live streaming, sharing economy, and intelligent manufacturing. From paperless offices to online shopping, digital travel, and digital government, these digital phenomena have become commonplace, providing new growth opportunities for economic and social development and making life more convenient. The Chinese government has recognized the importance of the digital economy and has implemented significant strategic measures to promote it that have been included in the government work report five times since 2017. While the impact of the digital economy on high-quality economic development has been observed at a practical level and has received attention at the policy level, a comprehensive academic studies are still lacking.

Research on the digital economy and high-quality development primarily focuses on three aspects. First, it is based on various research perspectives. Scholars have studied the impact of the digital economy on high-quality development from micro, meso, and macro perspectives on economic development. At the macro level, optimizing resource allocation, enriching factor sources, deepening capital, and improving total factor productivity through technological progress can promote economic growth and encourage high-quality economic development Thi (2023) Shahbaz (2022). The development of the digital economy has effectively promoted the reform of industrial technology and industrial structure at the meso level and accelerated the integration of digital technology and the traditional economy Martin (2016) Jahanger (2023) Bai et al., 2023. At the micro level, the digital economy, as a new engine, helps businesses accelerate the decline of marginal costs, enrich product categories, and meet the diverse needs of customers. This approach achieves economies of scale and scope as well as long-tail economies while improving the efficiency of factor allocation and reducing transaction costs Bai et al., 2023 Elberse (2006) Yan (2022). Second, based on the development of the digital economy, scholars have conducted research on different dimensions. They have studied the impact of digital infrastructure construction, digital industrialization, and digital industrial development on high-quality development driven by the digital economy. According to Chao and Xue Chao (2020), the construction of new digital infrastructure such as big data, 5G networks, artificial intelligence, the internet of Things, and the industrial internet can promote the high-quality development of China’s economy at three levels: kinetic energy conversion, structural optimization, and efficiency improvement. Ren and Li Ren (2018) suggested that the deep integration and development of digital technology and the real economy driven by the growth of digital industries such as big data, artificial intelligence, and blockchain is a vital force for the high-quality development of China’s economy. However, Liu (2018) noted that technical bottlenecks must be resolved in the process. The integration and penetration of digital technology and the three major industries can be continuously promoted by building a world-class digital industry agglomeration and a primary digital platform. It is also important to establish institutional and legal guarantees for the development of the digital economy, realize a diversified co-governance mechanism, and create an excellent ecological environment for the development of the digital economy. The deep integration of the digital economy and the real economy plays a significant role in enhancing the innovation capacity of the real economy and expanding the space for its development Guo (2020), promoting the revitalization of the real economy and industrial transformation and upgrading, and driving the high-quality development of the economy Li (2020). Finally, based on the implementation path, relevant studies have been conducted. According to Ren Ren (2020), the digital economy drives high-quality development through three key mechanisms: quality, efficiency, and power change. Guo and Lian Guo (2020) emphasized that building a high-quality real economy is crucial to achieving this goal. By integrating the digital economy and the real economy, we can enhance innovation, expand development opportunities, optimize the institutional environment, promote green transformation, and improve international competitiveness, leading to high-quality economic development. Additionally, Shi Shi (2020) suggested that the digital economy can drive the high-quality development of urban economies by improving digital development infrastructure, optimizing the digital development environment, and establishing a digital governance system.

In terms of empirical research, current empirical research on the digital economy mainly focuses on the impact of digital economy development on technological innovation, industrial structure, outward direct investment, and resource allocation Zhang (2022) Buckley (2018) Dunning (2008). Zhao et al. Zhao (2014) used the internet development and digital inclusive financial index to measure the development level of the city’s digital economy, and their empirical research showed that the digital economy can promote high-quality economic development by increasing entrepreneurial activity. Li et al. Li (2020) found that the development of the digital economy effectively expanded industrial boundaries, reduced transaction costs, and triggered the transfer of value distribution and the deepening of demand, which have become important driving forces for the digital economy to promote the transformation and upgrading of the manufacturing industry. Yang and Jiang Yang (2021) used provincial panel data to conduct empirical research. They noted that the development of the digital economy can significantly improve total factor productivity, but the intensity of its effect is different in different regions. In addition, developing a digital economy can indirectly promote total factor productivity growth through human capital and industrial structure upgrading. Zhang Teng et al. Zhang (2021) constructed an evaluation index system for high-quality economic development from economic, social, ecological, and other dimensions. These authors empirically tested the role of the digital economy in promoting China’s high-quality economic development by constructing a spatial econometric model. Li and Yang (2021) used panel data for Chinese cities from 2011 to 2018 to conduct empirical research. They concluded that the development of the digital economy significantly promoted the growth of urban total factor productivity and indirectly affected total factor productivity through technological innovation and factor allocation efficiency. Based on the impact of national big data comprehensive pilot zones for the development of the digital economy on total factor productivity in various cities in China from 2013 to 2017, Qiu and Zhou Qiu (2021) found that the construction of extensive data pilot zones significantly improved total factor productivity.

The digital economy has emerged as a powerful force that drives high-quality economic growth. As a result, scholars are giving significant attention to the digital economy and its role in promoting high-quality development. On the one hand, some literature discusses the theoretical logic and implementation path by which the digital economy drives high-quality economic development. However, there is no unified research framework for research on the mechanism for this effect. In addition, due to the delay in data updates, it is impossible to truly evaluate the effect of the digital economy, and the existing empirical research has yet to comprehensively analyze the mechanism of the digital economy to promote high-quality economic development. Consequently, to contribute to the high-quality development of regional economies, this paper analyzes the internal theoretical framework of the digital economy. This study examines three crucial factors: development momentum, efficiency, and structure. Thirty provincial-level administrative regions in mainland China (excluding Tibet) from 2011 to 2019 are regarded as research objects. An econometric model is constructed to empirically test the effect of the digital economy on the high-quality development of the regional economy. The findings of this paper will serve as a valuable guide for policy-makers.

The rest of this paper is organized as follows. Section 2 presents the hypotheses and tests and analyzes the direct mechanism, indirect mechanism, and technological innovation that mediate the effect of the digital economy on high-quality economic development. Section 3 describes the data sources, methodology, and variable definition. Section 4 provides the empirical analysis and results Finally, Section 5 concludes the paper and provides policy recommendations.

Theoretical Analysis and Research Hypotheses

China’s economy has transitioned from a phase of rapid growth to a stage of high-quality development, resulting in a significant slowdown in economic growth. In the past, overreliance on numerous factor inputs for quick economic growth posed numerous challenges to sustainable economic development, including low production efficiency, inadequate development momentum, and an unbalanced industrial structure. However, in the present era, China’s economic progress demands quality and efficiency, necessitating dynamic, efficient, and structural changes. The digital economy undoubtedly plays a crucial role in driving high-quality economic development, reshaping growth momentum, enhancing development efficiency, and optimizing economic structure, as depicted in Figure 1.

The Direct Mechanism of the Digital Economy in Driving High Economic Quality

The general technicality of the digital economy provides further momentum for high-quality economic development. Based on emerging technologies, the digital economy has the characteristics of available technology. Consequently, it can be widely used in various fields, producing technological spillover effects on economic development to improve the efficiency and quality of financial operations. Furthermore, the widespread adoption of the digital economy facilitates the integration and advancement of traditional industries, leading to new industries, innovative formats, and novel models. This, in turn, fuels the growth of high-quality economic innovation and development. The digital economy can steer the transition from conventional production, exchange, and business methods, triggering a ripple effect across the entire industry chain. Ultimately, it can guide the digital transformation and development of the whole industry chain, significantly boosting efficiency and quality. The platform ecology of the digital economy improves economic operations by breaking traditional limitations of time and space, facilitating exchanges and cooperation between financial entities, and enhancing efficiency. It connects economic subjects and production factors, reduces information asymmetry and enables accurate resource matching. Digital platforms create a value network where financial entities cooperate and compete, leading to value co-creation and sharing. This ensures efficiency, fairness, and high-quality economic development. Due to its low marginal cost characteristics, the digital economy can help economic agents achieve economies of scale and employ the vast user base to produce and sell diversified products to form economies of scope. At the same time, powerful information processing capabilities based on digital platforms help to meet the personalized needs of consumers and promote the formation of a long-tail economy. The era of the digital economy has high inclusiveness and provides a good environment for the survival and development of a large number of small and medium-sized enterprises and the further realization of the spiraling growth of the economy.

• Hypothesis 1 (H1): The digital economy plays a significant role in promoting the high-quality development of the regional economy.

The Indirect Influence of the Digital Economy in Driving High Economic Quality

This paper outlines the requirements to achieve high-quality economic development in terms of three dimensions: micro, meso, and macro. At the micro level, the theory of technological innovation serves as the foundation for realizing power conversion. At the macro level, enhancing development efficiency is crucial for attaining high-quality economic development. At the meso level, reproduction theory stresses the importance of structural optimization. However, China’s economic growth has yet to be fully realized in the three aspects of power conversion levels, efficiency improvement, and structural optimization. The digital economy, represented by a new generation of information technology such as big data, 5G networks, artificial intelligence, and the internet of Things, can promote the high-quality development of China’s economy by facilitating kinetic energy conversion, efficiency improvement, and structural optimization. Significantly, the digital economy plays a vital role in driving high-quality regional economic growth.

• Dynamic mechanisms. Technological innovation can overcome limited resource constraints to achieve sustained, high-quality economic development Stern (2011). This is a critical factor in transforming the momentum of China’s economic growth Chen (2018). The digital economy has advantages in general technology, intense penetration, and rich and shareable data elements, which guarantee technological innovation. The digital economy improves innovation conditions, quality, and transformation to promote high-quality economic development. The general technology and platform ecology characteristics of the digital economy provide the necessary knowledge for technological innovation and financial guarantees. The development of a new generation of digital technology has led innovation organizations to become more open and allowed different subjects to participate in innovation activities while providing the possibility for product co-creation. The digital co-creation platform has effectively alleviated the information asymmetry between research and experimental development (R&D) personnel and consumers, improving the match between innovation activities and actual needs, and, ultimately, the quality of innovation. Digital platforms have spread rapidly among enterprises, industries, and stakeholders, which has increased user acquisition and speed up the transformation of scientific research into productivity, which can help society adapt. However, with the launch of innovative results, demand continues to evolve, requiring R&D personnel to innovate continuously to meet market needs. By optimizing the innovation process and direction, innovation capability is enhanced. Overall, the digital economy fosters technological innovation by improving innovation conditions and quality and promoting achievement transformation to achieve high-quality regional development.

Hypothesis 2 (H2): The digital economy can impact the high-quality development of regional economies by enhancing technological innovation.

• Efficiency mechanisms. Efficiently creating more use value is the cornerstone of high-quality development, and reducing the impact of nontechnical factors on the economic system is of utmost importance. China’s economy faces critical challenges in terms of resource misallocation and transaction inefficiency in the high-quality development stage. The digital economy is a crucial player in solving these problems. By leveraging emerging digital technologies, the digital economy ensures the free flow of traditional and digital elements, improves resource allocation efficiency, and enables coordinated development across different regions, industries, and enterprises. Furthermore, the digital economy makes production factors more abundant and facilitates the accurate matching of factor resources between supply and demand through digital technologies and platforms. The increased competitiveness brought about by the digital economy drives the development of resources into more efficient economic agents and promotes the free flow of factors and the accurate matching of elements. This can improves transaction efficiency and unleash the potential of inefficient resources.

Digital technology has numerous benefits, such as speeding up communication and information transmission, breaking information asymmetry, reducing search and processing costs, and enhancing transaction efficiency. Furthermore, the digital economy offers an ideal trading model based on digital platforms and allows entities to find relevant information on the entire network and achieve the best match. This promotes market openness and integration and significantly lowers transaction costs. The decline in transaction costs brought about by the digital economy is conducive to achieving regional economies of scale and scope as well as long-tail economies. In turn, this promotes the division of labor and specialization, leading to sustainable and high-quality development in the local economy.

Hypothesis 3 (H3): The digital economy has an impact on high-quality economic development by improving resource allocation and transaction efficiency.

• Structural mechanisms. For China to achieve high-quality economic development in this new era, it must break free from the limitations of low-end industries, optimize and adjust its economic structure, and upgrade its financial systems. Industrial structure plays a crucial role in the economic system. This paper examines how the digital economy impacts high-quality development through the lens of industrial structure transformation and upgrading. Kuznets’ theory of economic growth suggests that traditional industrial structures have hindered economic growth due to differences in labor productivity across sectors. The digital economy, powered by emerging technologies, can aid in the efficient allocation of resources from inefficient industries to efficient ones, driving China’s industrial structure toward the middle and high end. This, in turn, promotes higher-quality development at a systemic level. Integrating the digital economy with traditional industries enables the transformation and upgrading of China’s industrial structure, leading to structural growth in the digital economy era.

Hypothesis 4 (H4): The digital economy impacts the high-quality development of regional economies by optimizing the industrial structure.

Methodology and Materials

Model Construction

This paper aims to assess the impact of the digital economy on the high-quality development of the regional economy. To achieve this, a benchmark regression model is constructed that is driven by the facilitation of the high-quality development of the regional economy through the digital economy. It can be expressed as

T F P_{i t} = α_{0} + α_{1} D E I_{i t} + α_{c} C V_{i t} + ε_{i t},

(1)

where TFP_it is total factor productivity, that is, the variable to be explained, while DEI_it denotes the digital economy index, which is the core explanatory variable of this paper; CV_it represents the control variable, and ɛ_it denotes the random disturbance term. The subscripts i and t represent the region (province) and time (year), respectively.

Next, to verify theinternal mechanism by which the digital economy affects the regional economy’s high-quality development, this paper examines the dynamic, efficiency, and structural means. The panel regression model of the mediating effect can be formulated as

M e d_{i t} = β_{0} + β_{1} D E I_{i t} + β_{c} C V_{i t} + ε_{i t},

(2)

T F P_{i t} = γ_{0} + γ_{1} D E I_{i t} + γ_{2} M e d_{i t} + γ_{c} C V_{i t} + ε_{i t},

(3)

where Med_it is the mediating variable, including the three aspects of the dynamic, efficiency, and structural mechanisms. The rest of the variables are introduced in equation (1).

Variable Setting and Measurement

• Explained variables. This paper uses total factor productivity as a proxy variable to assess the high-quality development of provinces and cities in China. Liu et al. Liu (2020) and He et al. He (2018) employed the Malmquist index method based on data envelopment analysis (DEA) for 30 areas in China between 2011 and 2019.

• Core explanatory variable. To accurately represent the digital economy and its development on a provincial level, a comprehensive evaluation index system is constructed. This system is based on the definition of the digital economy and the framework of “infrastructure-digital industrialization-industrial digitalization,” with data science and availability as the premise. Table 1 displays this index system, which is measured using the TOPSIS complete evaluation model based on the entropy weight value.

Table 1.

Comprehensive Evaluation Index System for China’s Provincial Digital Economy Development.

Primary indicators	Secondary indicators	Measuring indicators
Digital infrastructure	Internet penetration	Number of internet broadband access ports
	Mobile internet penetration	Number of mobile phone subscribers per 100 people
	Information communication capabilities	Length of optical cable lines
Digital industrialization	The scale of basic telecommunications industry	The primary business income of telecommunications
	The scale of electronic information manufacturing industry	The primary business income of the electronic information manufacturing industry
	The scale of the software industry and information services industry	The primary business income of software and information services
Industrial digitalization	Level of digital transaction	The index of digital financial inclusion

• Mediating variables. The previous analysis showed that the digital economy indirectly impacts the high-quality development of the economy through power, efficiency, and structural mechanisms. Consequently, mediating variables are established at three levels as follows.

Dynamic Mechanism

In this paper, technological innovation is chosen as the driving mechanism and is measured by the number of accepted patent applications as a representation of innovative achievements. The level of technological innovation is expressed by the number of patent applications received.

Efficiency Mechanism

According to the mechanism analysis above, two variables are selected to reflect the efficiency level.

Resource Allocation Efficiency

To gauge resource allocation efficiency, we use the degree of distortion of the factor market as a proxy variable. To construct the distortion indicators, we follow the method outlined by Lin and Du Lin (2013), which involves measuring the relative gap between the factor market’s degree of development and the highest degree of development in each region, which can be expressed by

R A_{i t} = \frac{[\max (f a c t o r_{i t}) - f a c t o r_{i t}]}{\max (f a c t o r_{i t})} \times 100,

(4)

where factor_it represents the factor market development index. Fan et al. Wang (2019) provide data on the degree of factor market development. Drawing on studies by Ma et al. Ma (2015), data on the level of growth in the factor market are derived by using the average growth of the index over the years due to insufficient data from 2017 to 2019.

Transaction Efficiency

Transaction efficiency, as defined by Yang and Zhang Yang (1999), refers to the ratio of the amount of goods obtained by a buyer to the total units of goods purchased. In this context, transaction costs are the difference between the total units of goods purchased and the amount received by the buyer. Shi’s “price index method” is used to measure transaction costs Shi (2020), and Lu’s approach is adopted in the specific calculation process Lu (2009). The measurement process involves using the retail price index of eight categories of goods (food, beverages, tobacco and alcohol, clothing, footwear, daily necessities, sports and entertainment goods, Chinese and Western medicines, books, newspapers and magazines, and fuel) across 30 provinces in China (excluding Tibet) from 2011 to 2019.

Structural Mechanism

To measure structural change, the proxy variable chosen is the level of optimization and upgrading in the industrial structure. This measurement method is based on the approach used by Sun et al. Sun (2022). The industrial structure optimization and upgrading coefficient are obtained through the weighted average of the proportion of the tertiary industry. The specific calculation process is as follows

I S = \frac{(I S_{1} + 2 I S_{2} + 3 I S_{3})}{100},

(5)

here, the proportions of added value from the primary, secondary, and tertiary industries to GDP are represented by IS₁, IS₂, and IS₃, respectively. IS refers to the industrial structure upgrading coefficient. The closer IS is to 1, the lower the level of industrial structure optimization and upgrading. Conversely, the closer IS is to 3, the higher the level of industrial structure optimization and upgrading.

• Control variables. To ensure the reliability of the regression results, opening up, fiscal intervention, the education level, and the transportation base are also selected as control variables. Foreign Direct Investment (FDI): The proportion of foreign direct investment in GDP is selected to measure the degree of openness of each province. Government intervention (Gov): The proportion of local government fiscal expenditure to GDP is selected to indicate the degree of local government intervention in the economy. Education Level (Edu): The proportion of education expenditure to GDP is selected to measure the education level of each province. Transportation Basics (Trans): The sum of railway operating and highway mileage per square kilometer is selected to express this variable.

Data Sources and Descriptive Statistics

This paper analyzed panel data from 30 provincial-level administrative regions in mainland China (excluding Tibet) between 2011 and 2019. The sample consisted of 270 data points. The data sources included the China Statistical Yearbook, China Information Yearbook, Wind Database, Peking University Digital Financial Inclusion Index (2011–2019), and the China Marketization Index by Province compiled by Fan et al. Wang (2019). Descriptive statistics for the primary variables are provided in Table 2.

Table 2.

Descriptive Statistics for Variables.

			Obs	Mean	Std	Min	Max
Explained variable	TFP	Total factor productivity	270	1.004	.0536	.730	1.209
Core variable	DEI	Digital economy index	270	.205	.144	.0305	.904
Mediating variable	TE	Technological innovation	270	.106	.0141	.066	.136
	RA	Resource allocation	270	.636	.170	.0001	.983
	TC	Transaction costs	270	.0186	.0157	.003	.126
	IS	Industrial structure	270	2.367	.129	2.166	2.832
Control variables	FDI	Foreign direct investment	270	.0197	.0152	.0001	.0796
	Gov	Government intervention	270	.249	.103	.110	.628
	Edu	Education level	270	.0397	.0142	.0211	.0907
	Trans	Transportation basics	270	.971	.520	.092	2.188

Empirical Test of the Impact of the Digital Economy on High-Quality Economic Development

Analysis of Benchmark Regression

Prior to conducting the regression analysis, we tested for multicollinearity between the main explanatory variables. The variance inflation factor (VIF) was found to be between [1.38, 5.38], indicating no multicollinearity. We also used the auxiliary regression method and determined that a fixed-effect model was appropriate. Table 3 displays the coefficients of the core explanatory variables, which were significantly positive, suggesting that the development of the digital economy can dramatically enhance total factor productivity. This finding supports Hypothesis 1. In Table 3, Columns (2), (3), and (4) demonstrate how the three subdimensions of the digital economy impact total factor productivity. The results show that all three subdimensions have a significantly positive impact, indicating that developing digital infrastructure, digital industrialization, and industrial digitalization can lead to high-quality economic development. Among these three dimensions, industrial digitalization has the most significant impact coefficient on total factor productivity, implying that it has the largest promotion effect.

Table 3.

Benchmark Regression Results of the Digital Economy and Total Factor Productivity.

	(1)	(2)	(3)	(4)
	TFP	TFP	TFP	TFP
DEI	.231*** (4.57)
DEI1		.064* (2.00)
DEI2			.058** (2.72)
DEI3				.078*** (3.47)
FDI	.911* (1.80)	.812 (1.56)	.744 (1.39)	.959* (1.81)
Gov	−.700** (−2.18)	−.531 (−1.59)	−.457 (−1.38)	−.814** (−2.67)
Edu	−2.220** (−2.28)	−2.539** (−2.51)	−2.591** (−2.60)	−1.943* (−2.03)
Trans	.029 (.58)	.106** (2.16)	.141** (2.71)	.014 (.24)
_cons	1.173*** (14.57)	1.103*** (14.10)	1.063*** (13.88)	1.215*** (14.96)
N	270	270	270	270
R2	.235	.191	.184	.222
FE	YES	YES	YES	YES

Notes: (1) Robust standard errors are in parentheses, (2) asterisks indicate significance levels: ***p < .01, **p < .05, *p < .1.

Table 3 demonstrates that opening up has a significantly positive impact on total factor productivity. This suggests that foreign investors bring technological spillover effects and are absorbed in the investment process, which in turn promotes total factor productivity growth. Conversely, government intervention significantly negatively impacts total factor productivity, as many scholars have noted Shi (2013) Mao and Xu (2016). Excessive or inefficient intervention in economic activity can lead to economic efficiency losses that negatively impact total factor productivity. Furthermore, the impact of education level on total factor productivity is significantly negative. This indicates that despite educational resource input, the local labor level has not improved, resulting in labor loss and a lack of improvement in total factor productivity. Additionally, talent cultivated by education may not match social needs, leading to a limited role in promoting economic efficiency growth. It is worth noting that transportation has a positive but insignificant impact on total factor productivity. This could be due to the diminished role of traditional transportation infrastructure in promoting total factor productivity in the digital economy era. Therefore, emphasis should be placed on the role of new infrastructure in achieving significant results.

Robustness Test

A robustness test using tool variables, winsorize, and replacement DEI measurement. The results are presented in Table 4. The tool variables include the number of mobile SMSs and fixed phones. The number of mobile SMSs is expressed per capita in each province, while the number of fixed phones is defined as the number of selected telephone users per 100 people in 1984. This study used the method proposed by Nunn and Qian Nunn (2014) to multiply the number of fixed phones with the first-order lag term of internet penetration at the national level to construct the panel tool variable.

Table 4.

Regression Results of Robustness Test.

	(1)	(2)	(3)
	2SLS	Winsorize	Replacement DEI measurement
DEI	.357*** (4.30)
DEI_trh		.434*** (3.72)
DEI_NEW			.266*** (4.35)
FDI	1.010* (1.73)	.965 (1.48)	1.009* (1.74)
Gov	−.840*** (−3.23)	−.912*** (−3.25)	−.858*** (−3.23)
Edu	−1.984** (−2.14)	−1.686* (−1.78)	−1.982** (−2.13)
Trans	−.039 (−.72)	−.048 (−.76)	−.042 (−.78)
N	270	241	270
R ²	.218	.234	.224
FE	YES	YES	YES

Notes: (1) Robust standard errors are in parentheses, (2) asterisks indicate significance levels: ***p < .01, **p < .05, *p < .1.

The estimated coefficient of the digital economy index increased to .357 after endogenous control using instrumental variables, indicating an endogenous effect. However, the regression results did not produce a significant difference, and the baseline model results still held. The study also found that the sample of abnormal points did not substantially impact the estimation results. After changing the core explanatory variable estimation method, the estimated coefficients of the digital economy were consistent with the regression results of the benchmark model, demonstrating robustness.

Analysis of Heterogeneity

To investigate the different effects of the digital economy on total factor productivity (TFP), this paper empirically examined the differences in the impact of the digital economy from the perspective of heterogeneity, such as the TFP level and geographical location.

• TFP level. The impact of the digital economy on total factor productivity in the benchmark regression model is tested through its average effect and dynamic law at the 25%, 50%, and 75% levels of total factor productivity using the quantile method. After analyzing the regression results in Table 5, it can be found that the DEI estimation coefficients are significantly positive across the three quantiles of TFP. At the 25% quantile level, TFP has the largest DEI estimation coefficient, whereas, at the 75% quantile level, it has the smallest. This shows that areas with high TFP levels have more room for improvement compared to areas with already high TFP levels. The digital economy plays a crucial role in promoting the development of undevelopment areas with insufficient factor endowments and a lack of natural resources. By providing necessary resources for economic development, the digital economy based on digital technology can ultimately impact economic growth.

• Geographical location. Further research is needed to determine whether there are regional variations in the impact of the digital economy on total factor productivity. In this regard, the provinces of China were classified into two regions - eastern and central/western, and a group analysis was conducted. The regression results are presented in Table 5. The DEI coefficients suggest that the development of the digital economy has a significant positive effect in both the eastern and central/western regions. Moreover, the estimated coefficient for the central and western regions is considerably higher than that for the eastern region, indicating a more pronounced effect of the digital economy’s promotion in the former. This may be due to the relatively lower level of economic development in these regions, where the digital economy has provided a much-needed impetus for economic growth. Furthermore, the development of the digital economy has fueled growth in the developed eastern regions, which have experienced a slowdown in recent years due to the declining demographic dividend. However, because China is still in its early stages of digital economy development, there is untapped potential for further growth. Therefore, all regions should work together to leverage the opportunities presented by the digital economy and drive high-quality economic development.

Table 5.

Descriptive Statistics for Variables.

a	(1)	(2)	(3)	(4)	(5)
a	TFP25	TFP50	TFP75	Eastern	Midwest
DEI	.256*** (4.05)	.233*** (5.00)	.207*** (3.29)	.190*** (5.22)	.335** (2.78)
FDI	1.652** (2.09)	.981* (1.66)	.207 (.26)	1.440* (2.19)	−.111 (−.07)
Gov	−1.029*** (−3.30)	−.732*** (−3.14)	−.389 (−1.25)	−.337 (−.42)	−.893** (−2.56)
Edu	−1.140 (−.90)	−2.118** (−2.27)	−3.244*** (−2.58)	−7.181** (−2.94)	−1.001 (−1.23)
Trans	.024 (.45)	.028 (.73)	.034 (.64)	.127 (.75)	−.047 (−.70)
N	270	270	270	270	270
FE	YES	YES	YES	YES	YES

Notes: (1) Robust standard errors are in parentheses, (2) asterisks indicate significance levels: ***p < .01, **p < .05, *p < .1.

Analysis of Mediating Effect

The mediating effect model empirically tests the mediating effect of technological innovation, resource allocation efficiency, transaction efficiency, and industrial structure optimization and upgrading. The specific regression results can be observed in Table 6.

Table 6.

Test Results of Mediation Model.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	TE	TFP	RA	TFP	TC	TFP	IS	TFP
Te		2.517** (2.29)
RA				−.067 (−1.32)
TC						.427** (2.18)
IS								.225** (2.22)
DEI	.033*** (4.27)	.148*** (3.00)	−.535*** (−5.26)	.195*** (3.61)	−.031* (−1.67)	.244*** (4.39)	.398*** (3.25)	.142** (2.45)
_cons	.070*** (10.73)	.996*** (8.79)	.860*** (8.49)	1.231*** (14.69)	−.009 (−.47)	1.177*** (21.40)	1.945*** (36.75)	.736*** (3.13)
N	270	270	270	270	270	270	270	270
R2	.697	.254	.288	.246	.027	.250	.749	.255
CV	YES	YES	YES	YES	YES	YES	YES	YES
FE	YES	YES	YES	YES	YES	YES	YES	YES
Sobel			Z = 1.783	p = .074

Notes: (1) Robust standard errors are in parentheses, (2) asterisks indicate significance levels: ***p < .01, **p < .05, *p < .1.

• Analysis of dynamic mechanism regression results. As shown in Table 6, the growth of the digital economy has a positive impact on technological innovation. The findings in Column (1) suggest that the development of the digital economy can enhance technological innovation. Furthermore, the results in Column (2) indicate that total factor productivity has a significant effect on technological innovation, with an estimated coefficient of 2.517. The estimated coefficient of total factor productivity of the digital economy in Column (2) is .148, which is at 1% and suggests a direct impact. Overall, these empirical results demonstrate that the digital economy plays a key role in improving technological innovation and advancing total factors.

• Analysis of efficiency mechanism regression results. Based on the data presented in Tables 6 and it is evident that resource allocation in the digital economy has a significant negative coefficient. This implies that the digital economy has the potential to reduce factors that cause distortion and enhance resource allocation. However, in Column (4), the coefficient for total factor productivity estimation is insignificant at −.067. We conducted a Sobel test and discovered that the indirect effect was significant. The coefficient for total factor productivity in Column (4) is .195, signifying a substantial positive direct impact. These empirical findings indicate that the digital economy can reduce resource misallocation, improve overall resource allocation, and boost total factor productivity. Therefore, the digital economy not only directly promotes high-quality economic growth but also indirectly encourages it by optimizing resource allocation and reducing distortion.

According to the findings in Table 6, the digital economy can effectively reduce transaction costs between economic agents. This is evident from the negative estimated coefficient of transaction costs in Column (5), which is significant at the 10% level. The intermediary effect is substantial, as shown by the significant estimated coefficient of total factor productivity in Column (6), which is .427 at the 5% level. The direct effect of the digital economy on total factor productivity is essential, as indicated by the significant estimated coefficient of .244 in Column (6) at the 1% level. There is an obscuring effect caused by the distinct symbols of β₁γ₂ and γ₁. This effect may be attributed to the digital economy’s progress in lowering transaction costs, which encourages labor division and collaboration across regions. However, this progress also hinders the enhancement of local total factor productivity levels. As a result, local governments frequently safeguard their respective economies by implementing pricing and taxation mechanisms. According to the criterion of the mediating effect model, transaction costs play an intermediary transmission role in developing the digital economy and promoting high-quality economic development, but there is a masking effect. However, in theory, the development of the digital economy brings new technologies, new formats, and new models, which makes the flow of information between regions smoother, provides a convenient trading environment for the trading activities of the traditional economy, and promotes the continuous decline of transaction costs. As transaction costs continue to fall, the division of labor between regions strengthens. In the short term, this may inhibit the development of the local economy. However, in the long run, the intensification of competition, the survival of the fittest, and the deepening of the division of labor in this process promote the continuous improvement of the specialization level of local retained enterprises and improve local production efficiency.

• Analysis of structural mechanism results. As presented in Table 6, Column (7) shows that the growth of the digital economy has a positive impact on the industrial structure. This is demonstrated by the significantly positive estimation coefficient displayed and suggests that the industrial system is transformed and upgraded because of this growth. In the eighth column, the estimated coefficient for total factor productivity of industrial design is .225, which is a significant figure at the 5% level. When combined with the previous coefficient, it suggests that the intermediary effect is substantial. In addition, the digital economy’s estimated coefficient on total factor productivity is .142, which is significantly positive and indicates a significant direct effect. Overall, these empirical results demonstrate that the digital economy promotes the transformation and upgrading of the industrial structure, and the transformation and upgrading of industrial design can enhance the total factor improvement. This outcome implies that the digital economy can directly stimulate high-quality economic development. It can also indirectly promote it by facilitating the flow of factors from inefficient to efficient industries and deepening the integration with traditional industries, advancing the transformation and upgrading of China’s industrial structure.

Conclusion and Policy Recommendations

This paper analyzes panel data for 30 Chinese mainland provinces and cities (excluding Tibet) from 2011 to 2019 to determine the direct and indirect effects of the digital economy on high-quality regional economic development. By constructing a benchmark regression model and an intermediary model for the digital economy’s high-quality development, the study finds that the digital economy significantly promotes regional economic development. This effect is more pronounced in the central and western regions and in provinces with low total factor productivity. The study also identifies digital infrastructure, digital industrialization, and industrial digitalization as significant factors that drive high-quality economic development. Industrial digitalization has the greatest promoting effect. The integration of digital technology and the real economy has led to new business models such as the platform economy, sharing economy, and internet celebrity economy, which have injected new momentum into high-quality economic development. Moreover, the digital economy indirectly impacts economic development by promoting dynamic, efficient, and structural changes. It reduces transaction costs, improves transaction efficiency, and increases transaction exchanges between different regions, concealing its short-term inhibitory effect on the local economy. Consequently, this paper provides the following suggestions.

• To keep up with the demands of the digital economy, it is essential to prioritize the construction of digital infrastructure. This includes developing new digital infrastructure such as 5G, artificial intelligence, the internet of Things, and big data. It is crucial for the government to increase investment in digital infrastructure and implement relevant policies to support it. The coverage of digital infrastructure should also be expanded, and its quality must be improved to ensure the rapid development of the digital economy.

• The development of the digital economy should be accelerated in underdeveloped regions to promote the high-quality economic development of the entire region. The role of the digital economy in underdeveloped regions should be considered to alleviate the dilemma of a lack of natural resources in underdeveloped areas, drive the high-quality economic development of these regions, and address barriers to economic growth.

• The digital transformation of industries should be actively promoted to realize the integration of the digital economy and the real economy. The digital transformation of the real economy guarantee the integrated development of the digital and the real economy, maximizes advantages of China’s digital economy development, and applies modern emerging technologies to accelerate the digital transformation and development of agriculture, industry and service industries.

• The construction of big data platforms should be actively promoted to achieve resource sharing and integration. Digital technology should be used to share data resources, realize the free flow of resources, and reduce the misallocation of resources between regions, industries, and enterprises. At the same time, platform features should be used to enrich the types of innovation subjects and further enhance innovation capabilities.

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Footnotes

Acknowledgments

The authors gratefully acknowledge financial support from the Soft Science Project of Jiangsu Science and Technology Program (Grant Nos. BR2017047).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Soft Science Project of Jiangsu Science and Technology Program; (BR2017047), The Philosophy and Social Science Fund of Education Department of Jiangsu Province; (2018SJA1532).

ORCID iD

Ying Guo

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