Can Data Elements Promote the High-Quality Development of China’s Economy?

Abstract

In recent years, the digital economy, with data as the key production factor, has developed rapidly and become the driving force of China’s high-quality economic development. The relationship between data factors and high-quality economic development has gradually become a key topic of academic research. Based on the panel data of 30 provincial-level regions in China from 2014–2021, this study first constructs the proposed data factors and economic high-quality development evaluation indexes, respectively, by using the entropy weight method, and empirically analyzes whether data factors can promote economic high-quality development by using regression models. The results demonstrate the following: (1) Data elements play an important driving role in high-quality economic growth, and they have become an important engine for sustainable economic growth. (2) The development level of data elements has a stable growth rate and obvious regional differences, showing a pattern of “strong in the east and weak in the west, strong in the south and weak in the north”. (3) The degree of influence of data elements on different dimensions of high-quality economic development is inconsistent, with a greater influence on coordinated development, green development, and innovation development, while the effect on open development is not obvious. The above findings provide a reference and scientific basis for countries to formulate relevant policies to fully release the vitality of data elements and promote high-quality economic development.

1. Introduction

The modern definition of factors of production originates from the neoclassical economic viewpoint and is described as resource inputs used in the production of goods and service provision. The global economy is currently in the process of a digital revolution. With the construction of a new generation of digital infrastructure and the development of digital technology, data elements are being collected, stored, and used for production on a large scale, contributing increasingly to economic growth and people’s lives. As the first production factor of the digital economy, data elements are the production factors of many online services, production processes, logistics, intelligent products, and artificial intelligence, especially the competitiveness of digital platform enterprises, which is increasingly dependent on the ability to acquire data and control data in a timely manner at low cost [1]. The exploitation of data elements will change the traditional economic growth, accelerate the digital transformation of the original industries, and give rise to new digital industries, which will become an important driver of high-quality economic development. Based on this, it can be assumed that data elements have become the key production factors of the digital economy.

As a key factor of production in the digital economy, the data factor presents significantly different characteristics compared to traditional factors of production, such as land, labor, and capital:

(1) Renewability. According to traditional economic theory, various factors of production are not inexhaustible. In order to meet human consumption needs, people are required to allocate non-renewable factors of production such as land, capital, and labor rationally. Unlike traditional factors of production, data is widely available in the process of human participation in various socio-economic activities. Moreover, data is renewable and can be reused, and it adds value by expanding the scope of sharing.

(2) Non-exclusivity (shareability). Exclusivity means that a factor of production cannot be used simultaneously with others. Based on the energy property of ownership, traditional factors of production such as land, labor, and capital cannot be shared, and the transfer of the right to use them must be compensated by corresponding benefits. Data elements, on the contrary, are partly “public goods” and can be reproduced infinitely, and they can be used by multiple entities at the same time with the help of mobile storage devices and digital infrastructure. In addition, the different data collection and processing methods do not interfere with or exclude each other.

(3) Immediacy. Immediacy is a significant technical and economic characteristic of data elements in the digital economy era that distinguishes them from traditional elements. The application of mobile Internet, block chain, 5G communication, and other new-generation information technology makes the cost of acquiring data elements drop rapidly, and also improves the speed of data generation (collection), transmission, and storage. The development of large-scale computing power and intelligent algorithms ensures that data can be processed and analyzed in real time, and feedback information can be obtained in a timely manner. In addition, feedback data can dynamically respond to the different needs of consumers and provide fast and flexible supply to meet their diverse needs.

(4) Permeability. Compared with traditional factors of production, data elements have a strong ability for integration and permeability. With their immediacy and reproducibility, data elements can be radiated to various industries and cover every link of the value chain creation process, such as R&D, design, production, and sales of enterprises. Relying on the breakthrough innovation development of digital technology and the construction of the “information superhighway”, data elements can realize cross-temporal flow, break the original production boundaries, complement each other with traditional elements, change traditional production methods, improve resource allocation efficiency, amplify the exponential effect of data elements on economic growth, and inject new momentum into economic development.

The development of the digital economy has changed the arrangement of the importance of the production factor system from the source. Data, as the core production factor in the digital economy era, has broken through the scarcity of traditional production factors in labor-intensive and resource-intensive economic models and become an important driving force for stable economic growth [2]. As data plays a fundamental and crucial role in production activities in the digital economy era, it embodies the nature of a factor of production, making it a universally accepted new generation of factors of production. Chadeiaux [3] was the first to point out that the data factor is a means of production like other factors of production, such as labor and capital. Varian [4] proposed that multiple subjects are necessarily involved in the generation and transmission of data, which makes the data elements naturally non-exclusive. Coupled with the easy replicability of data elements and the physical characteristics of data elements that can be easily disseminated in cyberspace, this allows data elements to be applied by more subjects, further strengthening the non-exclusivity of data elements. Ghasemaghaei et al. [5] stated that data elements need to be processed and analyzed to improve the efficiency of employees and, in turn, the productivity of the company. Additionally, it is because data has the characteristics of accurate distribution and a wide range of uses that companies can use the means of data analysis to obtain information from data [6]. Yu et al. [7], in the context of the digital economy, argued that the factors of production are no longer limited to land, labor, capital, etc. Still, data also belong to a key factor of production by arguing the law of change of the factors of production and the extended form of the production function. Dai et al. [8] analyzed the reasons why data factors have become key factors of production and argues that the further evolution of production methods driven by technological progress, and industrial change will inevitably lead to changes in critical factors of production. Qi et al. [9] considered that changes in consumer demand and technological advances are also external conditions for the factorization of data. The significance of treating data as a factor of production is that data factors can participate in distribution separately, which is conducive to improving the distribution system at the current stage of China and is an inevitable choice to promote the development of productivity in the new era [10].

Currently, China’s economic development level has shifted to the stage of high-quality development, and the development of the digital economy has provided a new engine for China’s economic dynamics. On the whole, high-quality development is reflected in some characteristics of economic development, such as globalization, inclusiveness, financialization, and technologization [11], and such quality is specified in two aspects of high development and wide scope, mainly by improving supply efficiency and promoting the co-development and coordination of ecological civilization and human modernization [12]. From a development perspective, the key to high-quality development lies in improving the efficiency of matching supply and demand in the market and creating overall synergy between total factor productivity in society [13]. From the perspective of input-output and supply demand, in general, three requirements need to be met namely high quality of resource allocation and structural needs, high efficiency of output and income distribution behind output, and high matching between the supply and demand sides [14]; removing obstacles on the development path, such as uneven urban–rural development, high major risks, insufficient enterprise independent innovation capacity, and poor ecological environment, are current important requirements for high-quality development and a problem-oriented approach to development [15]. Therefore, high-quality development is the development that reflects the new development concept: innovation becomes the first driving force, coordination becomes the endogenous feature, green becomes the universal form, open becomes the necessary path, and sharing becomes the fundamental purpose [16].

In summary, scholars have reached a consensus on data becoming a key factor of production in the context of the digital economy, extensively discussed high-quality economic development, and formed a rich research result. However, the mechanism of the role of data elements in high-quality economic development was rarely fully considered in previous studies, and the degree of impact of data elements on high-quality economic development also needs further empirical testing. The purpose of this study is to empirically analyze the mechanism of the role of data factors in high-quality economic development. This study makes the following marginal contributions to extend the existing research. Firstly, it discusses the influence mechanism of data elements on high-quality economic development from five aspects: innovation, coordination, green, openness, and sharing, taking into account the connotation of high-quality economic development. Secondly, the evaluation index system of data elements and economic quality development in China’s provincial regions has been established. The evaluation index system is proposed from the perspective of “input–output” and fitted by the entropy method. Thirdly, the mechanism of data elements on economic quality development is empirically examined and analyzed, and the specific effects of data elements on various dimensions of economic quality development are revealed. These findings provide a new perspective for understanding the relationship between data elements and economic high-quality development, as well as a theoretical basis for China and other countries to formulate and implement economic high-quality development strategies.

2. Impact Mechanism Analysis

The digital economy has become an important support for high-quality economic development, and the core element of the digital economy is data. In this paper, the mechanism of the data element for high-quality economic development from five aspects is analyzed: innovative development, coordinated development, green development, open development, and shared development, to ensure the integrity and logic of the mechanism analysis.

2.1. Mechanism of the Role of Data Elements in Driving Innovation Development

Data factors promote innovation development by enriching the types of technological innovation factors, improving the efficiency of technological innovation, and optimizing the allocation of technological innovation factors. Firstly, as a key production factor in the digital economy, data factors are more easily combined with other innovation factors through the development of digital infrastructure and digital information technology, which makes the combination form of innovation factors more diversified and complex and enriches the types of technology innovation factors [17]. Secondly, compared with traditional factors of production, data factors are replicable at low cost, non-competitive, and shareable, which makes the use of data factors show the characteristics of increasing payoffs of scale. According to Metcalfe’s law, data factors will generate multiplier utility in the process of use, which will promote knowledge spillover, alleviate information asymmetry, and reduce technology innovation costs. By reshaping and reconstructing the innovation process, data elements accelerate the development of technological innovation activities, thus promoting high-quality economic development. Again, the immediacy and shareability of data elements make them shareable across time and regions, breaking the limitations of geographic space and time, opening up the flow and allocation scope of innovation factors, and accelerating the flow of innovation resources through the flow of data in all aspects of social production and life [18]. In addition, data elements can be combined with other traditional elements to generate innovative synergy utility and realize the sharing and optimal allocation of resources required for innovation development.

2.2. Mechanism of the Role of Data Elements in Driving Coordinated Development

Data factors enhance coordinated development through regional coordination, narrowing the urban–rural gap, and rural revitalization. Firstly, with the help of data information processing technology, the traditional factors are weakened by the restrictions of physical space. The reproducibility and permeability of data factors can break the dependence of economic development on traditional production factors, weakening the influence of geopolitical factors on economic growth. It also can create favorable conditions for the economic rise of the central and western regions. Secondly, through the cross-regional circulation of data factors, it realizes the mutual integration and common development of advantageous industries and traditional industries, extends the advanced industrial chains such as data resources and artificial intelligence from developed regions to relatively underdeveloped regions, and realizes the industrial upgrading of late-developing regions [19]. Thirdly, the new business model generated by the platform economy and other digital elements has a significant role in stimulating consumption demand in the central and western regions and further narrowing the regional gap [20]. Moreover, as digital industries slowly penetrate into agricultural and rural areas, rural revitalization has also ushered in new opportunities in the digital economy, and the income level of rural residents has significantly increased, further narrowing the urban–rural gap.

2.3. Mechanism of the Role of Data Elements in Driving Green Development

The data element enhances green development by promoting the transformation of kinetic energy and improving technical support, as well as energy conservation and efficiency [21]. The traditional economic growth method has an obvious dependence on natural resources and often achieves economic growth based on the consumption of large amounts of resources. With the consumption of resources, the expansion of the total economic volume will inevitably cause great pressure on the environment. The renewable and non-exclusive nature of data elements can break the resource constraint and make up for the drawbacks of low efficiency and high energy consumption in the original development model. By combining with traditional production factors, data elements can accelerate the transformation of economic development patterns, cultivate new economic growth points characterized by low carbon emissions, and adjust and transform traditional high-pollution, high-energy consumption industries. Key emphasis is placed on developing emerging industries, such as new energy and environmental protection, to promote the transformation of green development momentum [22]. In addition, green production regards ecological carrying capacity and resource carrying capacity as important conditions for economic activities, focusing on promoting energy efficiency and effectiveness in circulation, consumption, production, and construction, which contributes to ecological environment protection and a harmonious coexistence between humans and nature.

2.4. Mechanism of the Role of Data Elements in Driving Open Development

Data elements promote open development by promoting the development of cross-border e-commerce and expanding digital trade. With the popularity of the Internet and the rapid development of digital technology, digital trade has become a new engine of world economic development and a new mode of international trade. As data elements continue to circulate, the integration of digital technology and industries becomes increasingly tight. The rapid development of information and communication technology has promoted the penetration of key digital technologies such as blockchain, big data, artificial intelligence, and the Internet of Things in international trade, leading to the continuous expansion of the scale of digital trade. As an important part of digital trade, cross-border e-commerce will promote the arrival of the global digital trade era. With the instantaneous and shareable nature of data elements, relying on trade models, platform functions, and information technology advantages, cross-border e-commerce can effectively reduce trade costs, expand import and export trade boundaries, realize the efficient circulation of commodities and data, technology, capital, and other factor resources in domestic and international markets, and provide different countries [23,24]. The online communication between buyers and suppliers in the supply chain provides great convenience and realizes a controllable process of production, supply, and sales, which can not only effectively stimulate domestic demand and expand foreign demand, but also deepen international division of labor and cooperation and promote the formation of a development pattern of mutual promotion of domestic and foreign double cycles.

2.5. Mechanism of the Role of Data Elements in Driving Shared Development

Data elements achieve shared development by improving the matching of supply and demand and promoting the equalization of public services. The non-exclusivity and immediacy of data elements determine that the digital economy itself has the characteristic of inclusive sharing, which is one of the important logics of resource allocation and sustainable economic development, and also an important mechanism for enhancing social welfare. Data elements can realize the separation of ownership and usage rights, reduce transaction costs, realize the long-tail effect and scale effect, use information off-domain and new credit mechanisms, and rely on multiparty market platforms to realize the integration of demand, supply, and matching mechanisms. The sharing economy essentially covers the major life scenarios, and the main business models include the redistribution of goods, tangible products, and services; the collaborative sharing of non-tangible resources; and open collaborative sharing. In the context of the digital economy, with the development of digital technology and digital platforms, the level of employment and public services has increased the coverage of long-tail users who cannot be covered by traditional economy, enabling them to better enjoy the fruits of economic development. Relying on digital government platforms, government organizations have significantly improved operational efficiency, significantly reduced government costs, and significantly improved service levels and efficiency; at the same time, the equalization of public services such as education, healthcare, and culture, coordinated regional development, and the sharing of high-quality educational resources are also inseparable from the important role played by data elements.

3. Empirical Test of Data Elements to Promote High-Quality Economic Development

3.1. Model Construction

As one of the most important factors of production in the era of the digital economy, data factors can bring typical and direct multiplier effects on economic development through multiple channels, paths, and business modes. Data factors generally improve total factor productivity through combination with other factors [25]. To verify the impact of data factors on high-quality economic development, based on the above analysis, this paper constructs the basic econometric model as follows:

$$\mathrm{H}de{v}_{it}={\beta }_0+{\beta }_{1}data{f}_{it}+{\beta }_{2}tech{f}_{it}+{\beta }_{3}cap{f}_{it}+{\beta }_{4}lab{f}_{it}+{\beta }_{5}Co{n}_{it}+{\epsilon }_{it}$$

(1)

where, i denotes a region, t denotes time (year), Hdev is the level of high-quality economic development, dataf represents the data factor, techf denotes the technology factor, capf is the capital factor, labf indicates the labor factor, Con denotes the control variable, and ε represents the random error term. Based on the characteristics of the data used, we will use a fixed effects model for the analysis.

3.2. Data Sources

The data in this paper are mainly from the National Bureau of Statistics, China Statistical Yearbook, China Science and Technology Statistical Yearbook, China High Technology Industry Statistical Yearbook, and other relevant statistics. As the official data yearbooks and reports on individual indicators do not publish relevant data until 2014, this paper selects panel data from 2014–2021 for 30 provincial regions except for Tibet and Hong Kong, Macao, and Taiwan.

3.3. Definition of Indicators

3.3.1. Explanatory Variables: Constructing a Data Element Development Indicator System

Based on the specificity and complexity of data elements, it is difficult to accurately represent the characteristics of data elements by selecting a single variable, and the effective information contained in a single variable is relatively small, which cannot accurately measure the development level of data elements [26]. Therefore, this paper adopts the method of constructing an indicator system to measure the development level of data factors as the core explanatory variables based on the perspective that data factors are input into the production process and eventually transformed into output [27].

Firstly, the data transportation environment is the basis of data application and data value transformation. Based on the characteristics of data elements, high circulation is the difference between data elements and traditional factors of production, and data that cannot flow is just a pile of code stored in the medium without any value. Only through the smooth channel of data transportation can a connected data network be formed, so that data can be involved in various economic activities and the vitality of data elements can be released. The development of data elements must be based on the level of data transportation. In addition, the generation of data and the final destination of data are directed to enterprises or individuals, people in daily life, enterprises in production and trading, every use of data is constantly generating new data, updating the old data, and constantly bursting out new value results. Based on the integration of data, the application process of data elements represents the combination process of data elements with labor, capital, technology, and other production factors, and the application of data by multiple subjects is an important prerequisite for releasing the value of data. Therefore, in this paper, we start from the perspective of the transport environment of data elements and select Internet broadband access ports, the number of websites owned by enterprises, mobile Internet access traffic, mobile telephone exchange capacity, and the number of computers used per 100 people for measurement.

Secondly, the value of data factors is different from the value of general production factors. Previous scholars have used wages to measure the value of labor factors and interest to measure the value of capital factors, but it is difficult to obtain the value of data factors directly because China has not yet built a perfect data factor market, and the issues of data factor tenure system and the fair price of data factors are still in the stage of exploration. Therefore, the text starts from another perspective, that is, data factors are invested into production activities as a production factor and, like other production factors, will eventually be transformed into output as part of economic growth, so this paper chooses to indirectly measure the value of data factors by industrial output that has a high dependence on data factors. Strictly speaking, the value transformation process of data permeates all industries, but based on the current accounting framework, it is difficult for most industries to separate the value of data elements from that of other factors of production. According to the current industrial operation mode, this paper selects four industries that have a strong reliance on data elements, namely, the software industry, e-commerce, the telecommunication industry, and the express delivery industry. Without the support of data elements, these four industries can barely operate effectively and create value, so it can be presumed that the value of data elements occupies an extremely high proportion of their output value, and their output not only represents the value of data elements, but also represents the value transformation ability of data elements. Therefore, this paper selects the software industry, e-commerce, telecommunication industry, express delivery industry and other industries with high reliance on data elements to indirectly measure the value of data elements, and selects the indicators of total telecommunication business, software business income, e-commerce sales, information technology service income, and express delivery business income to measure the application value of data elements. The specific indicator system is shown in

Table 1. Indicator measurement system for the level of development of data elements.

Secondary Indicators	Tertiary Indicators	Units	Indicator Attributes
Data elements Transport environment	Internet broadband access ports	Ten thousand Items	Positive
	Number of websites owned by enterprises	Items	Positive
	Mobile Internet access traffic	Ten thousand GB	Positive
	Number of corporate owned websites	Ten thousand Items	Positive
	Number of computers per 100 people	Items	Positive
Data elements Value of applications	Total telecommunication business	Billions	Positive
	Software business revenue	Million	Positive
	E-commerce sales	Billions	Positive
	Revenue from information technology services	Million	Positive
	Income from express delivery business	Million	Positive

Note: The raw data are sourced from the China Statistical Yearbook.

.

Drawing on Yang et al. [28], the entropy method (EEM) is used to measure relevant indicators comprehensively. The entropy method is a way to objectively assign weights to indicators by the size of information entropy and to evaluate multiple indicators comprehensively: the smaller the information entropy, the greater the dispersion; and the more information, the greater the weight assigned.

The specific steps are as follows:

(1) Assume that there are r years, n provincial regions, and m indicators; $x_{ijk}$ denotes the value of the ith year, the jth region, and the kth indicator;

(2) Raw data processing. The data may be of different orders of magnitude due to different types of data indicators, which may have negative values and may have both positive indicators (the larger the better) and negative indicators (the smaller the better), which requires the data to be standardized first. Data standardization is performed by separating positive and negative indicators using different formulas.

The normalization process of positive indicators:

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

(2)

Negative indicators are normalized:

$$x_{ijk}^{\prime }=\frac{x_{\max }-x_{\mathrm{ij}}}{x_{\max }-x_{\min }}$$

(3)

(3) Determine the indicator weights:

$$y_{ijk}=\frac{x_{ijk}^{\prime }}{{\displaystyle {\sum }_i{\sum }_j{x}_{ijk}^{\prime }}}$$

(4)

(4) Determine the entropy value of the kth indicator:

$${\mathrm{e}}_k=-\frac{1}{\ln (rn)}{\displaystyle {\sum }_i{\sum }_j{y}_{ijk}}\ln (y_{ijk})$$

(5)

(5) Calculate the information utility value of the kth indicator:

$$g_k=1-e_k$$

(6)

(6) Calculate the weight of the indicator:

$$w_k=\frac{g_k}{{\displaystyle {\sum }_k{g}_k}}$$

(7)

(7) Calculate the final score for the level of development of the data element:

$${\mathrm{S}}_{\mathrm{ij}}={\displaystyle {\sum }_{\mathrm{k}}{w}_k}{x}_{ijk}^{\prime }$$

(8)

Comparing the measured “data element development level” of each provincial-level region with the “China Big Data Development Level Assessment Report” of the China Electronics Information Development Institute, the results are consistent, indicating that it is scientific and reasonable to measure the development level of data elements by using this as a proxy indicator.

According to the data element evaluation index system constructed in the previous section, the data element development level scores of each provincial-level region in China were obtained after fitting by the entropy weight method, and

Table 2. Descriptive statistics of the level of development of data elements.

Regions	Mean	Standard Deviation	Minimum	Maximum
Guangdong	0.5307	0.2143	0.2707	0.8131
Jiangsu	0.3395	0.1125	0.2024	0.4844
Beijing	0.3082	0.1328	0.1631	0.5318
Zhejiang	0.2851	0.1090	0.1486	0.4271
Shanghai	0.2664	0.0968	0.1525	0.4209
Shandong	0.2357	0.0775	0.1349	0.3451
Sichuan	0.1762	0.0694	0.0961	0.2711
Henan	0.1325	0.0534	0.0674	0.2049
Fujian	0.1220	0.0370	0.0738	0.1654
Hubei	0.1150	0.0409	0.0635	0.1671
Hebei	0.1145	0.0489	0.0578	0.1819
Liaoning	0.1074	0.0188	0.0846	0.1358
Anhui	0.1031	0.0422	0.0509	0.1597
Hunan	0.0972	0.0450	0.0482	0.1614
Shaanxi	0.0953	0.0436	0.0427	0.1525
Chongqing	0.0812	0.0347	0.0407	0.1276
Tianjin	0.0755	0.0284	0.0415	0.1174
Guangxi	0.0706	0.0430	0.0255	0.1304
Yunnan	0.0705	0.0337	0.0351	0.1212
Jiangxi	0.0636	0.0306	0.0258	0.1050
Guizhou	0.0600	0.0328	0.0242	0.1087
Heilongjiang	0.0533	0.0179	0.0295	0.0781
Shanxi	0.0502	0.0246	0.0235	0.0839
Jilin	0.0479	0.0167	0.0256	0.0695
Neimenggu	0.0478	0.0198	0.0233	0.0737
Xinjiang	0.0440	0.0197	0.0230	0.0736
Gansu	0.0351	0.0190	0.0123	0.0619
Hainan	0.0303	0.0103	0.0170	0.0431
Ningxia	0.0177	0.0078	0.0070	0.0284
Qinghai	0.0176	0.0086	0.0069	0.0286

shows the descriptive statistics of the data element development level of each provincial-level region from 2014 to 2021, listing the mean, standard deviation, and minimum and maximum values of the data element development level of different regions, and the results are sorted according to the mean index as shown in Table 2.

From Table 2, it can be found that, firstly, there are obvious regional development differences in data elements, and most of the top ten provinces are in the eastern coastal region, with six provinces and cities in Guangdong, Jiangsu, Beijing, Zhejiang, Shanghai, and Shandong leading the country.

Secondly, there is an obvious pattern of agglomeration development of data elements in which the Pearl River Delta region has obvious development advantages, with Guangdong’s development level far ahead of other provinces, in the Yangtze River Delta region, Jiangsu, Zhejiang, and Shanghai, ranking among the top five in the country.

3.3.2. Explanatory Variables: Constructing the Index System of High-Quality Economic Development

Through the review of previous literature, it can be found that different scholars have different focuses on high-quality development, and the constructed index systems are not exactly the same. Based on the new concept of high-quality economic development, this paper constructs a system of indicators for measuring high-quality economic development at the provincial level in China based on five dimensions: innovative development, coordinated development, green development, open development, and shared development.

(1) Innovation development focuses on solving the problem of development momentum; innovation is the flexibility of a nation’s progress and the inexhaustible power of a country’s prosperity; throughout the history of human development, through several industrial revolutions, every progress of the economy and society is inseparable from innovation, and innovation provides an important engine for high-quality economic development. In this paper, based on the research of Zhang [13] and Guo [25], we describe the indicators of innovation development from two aspects: output and input. The output of scientific and technological achievements mainly includes the number of patent applications received and technology market turnover, while the input of scientific and technological achievements includes R&D personnel and R&D expenditure investment. In combination with the reality of China, R&D activities mainly occur in industrial enterprises above the scale, therefore, this paper selects the full-time equivalent of R&D personnel and R&D expenditure of industrial enterprises above the scale as the measurement indicators.

(2) Coordinated development focuses on solving the problem of unbalanced development. For a long time, due to natural reasons, historical reasons, policy reasons, etc., the economic development of China’s eastern coast has been ahead of the central and western regions. The difference between urban and rural areas is large, and the income gap between residents is high; therefore, coordinating regionally coordinated development, breaking the dual structure between urban and rural areas, and narrowing the income gap between residents have become important features of high-quality development. Drawing on Zhou [16] and Yang [28], in terms of regional coordination, we chose to measure the average wage of urban units employed and local fiscal tax revenue, and in terms of urban–rural disparity, we chose to measure the ratio of disposable income of rural and urban residents and the ratio of per capita consumption expenditure of rural residents.

(3) Green development focuses on solving the problem of harmony between humans and nature, which is interdependent, interconnected, and at the same time mutually constrained, and the existence and development of human beings are predicated on the existence and development of nature, which requires human beings to protect the natural environment while carrying out social production, to put the construction of ecological civilization on the same level of importance as economic development, and to make green development a universal form of high-quality development. In this paper, two aspects of the green environment and environmental governance are considered based on the principles of indicator selection and the studies of Chang [21] and Long [22]. The green environment aspect is indicated by the greening coverage rate of the built-up area and the park green area per capita; for environmental governance, we chose the harmless domestic waste treatment rate and the completed investment in industrial pollution treatment to measure.

(4) Open development focuses on solving the problem of internal and external linkages in development. As an important part of the world economy, China is shouldering more international responsibilities and expectations, and open development has become a necessary path for China’s high-quality development. According to Zhou [16] and Karine Y [23], we measure China’s openness and ability to participate in international trade using indicators such as total imports and exports of foreign-invested enterprises, foreign direct investment to GDP ratio, new product export sales revenue of industrial enterprises above the scale, and total imports and exports of domestic destinations and sources.

(5) Shared development focuses on solving social equity and justice issues. Shared development refers to the fact that through high-quality development, people can enjoy public services equally and improve their own living standards and quality of life, and public services include medical services, education levels, administrative services, pension insurance, and other aspects. According to Li [14], the number of health technicians per 10,000 people, the average number of students in higher education per 100,000 population, the number of units of autonomous organizations, and the number of urban and rural residents’ social pension insurance participants are selected in this paper to measure the level of public services under shared development.

Based on the previous analysis, from the connotation of high-quality economic development, we construct a high-quality development indicator system suitable for the current national conditions, which contains five dimensions of innovation development, coordinated development, green development, open development, and shared development, with a total of 20 indicators. To comprehensively evaluate the level of high-quality economic development in each provincial region, the entropy value method is used to measure the data of the above indicators, and the specific steps are the same as above.The economic quality development index system is shown in

Table 3. Measurement system of China’s high-quality economic development indicators.

Second-Level Indicators	Third-Level Indicators	Measurement Index Unit	Indicator Attributes
Innovative Development	Number of patent applications received	Items	Positive
	Technology market turnover	Billion	Positive
	Full-time equivalent amount of R&D personnel of industrial enterprises above the scale	Person/year	Positive
	R&D expenditure of industrial enterprises above the scale	Ten thousand CNY	Positive
Coordinated Development	Average wage of employed persons in urban units	RMB	Positive
	Local fiscal tax revenue	Billion	Positive
	Ratio of disposable income of rural and urban residents	%	Positive
	Ratio of per capita consumption expenditure of rural residents	%	Positive
Green Development	Greening coverage rate of built-up areas	%	Positive
	Harmless treatment rate of domestic waste	%	Positive
	Completed investment in industrial pollution control	Ten thousand CNY	Positive
	Per capita park green area	Square meters/person	Positive
Open Development	Total import and export of foreign-invested enterprises	thousand dollars	Positive
	Export sales revenue of new products of industrial enterprises above the scale	Ten thousand CNY	Positive
	Ratio of foreign direct investment to GDP	%	Positive
	Total import and export of domestic destinations and sources of goods	Thousand dollars	Positive
Shared Development	Number of health technicians per 10,000 people	Person	Positive
	Average number of students in higher education schools per 100,000 population	Person	Positive
	Number of units of autonomous organizations	Items	Positive
	Number of urban and rural residents’ social pension insurance participants	Ten thousand people	Positive

.

3.3.3. Control Variables

There are many factors affecting high-quality economic development. In order to prevent unobservable factors from interfering with the results, the level of marketization, the level of financial development, and the level of urbanization are used as control variables, drawing on the academic views of Deng et al. [29,30]. The level of marketization is represented by the ratio of employees of non-state enterprises; the level of financial development is represented by the ratio of the value added of the financial sector to the regional GDP; and the level of urbanization is measured by the ratio of the urban population to the total population at the end of the year. To unify the criteria, the control variables are all synthesized using the entropy method.

There are more factors affecting high-quality economic development, and this paper controls the main factors affecting high-quality economic development at the level of production factors and factor use environment, respectively. In terms of factors of production, the specific measurements of the technology factor, capital factor, and labor factor mainly refer to the research results of Yao [29] and He et al. [30]. In terms of factor use environment, to prevent unobservable factors from interfering with the results, the level of marketization, the level of financial development, and the level of urbanization are used as control variables, drawing on the academic views of Deng et al. [31,32]. The level of marketization is represented by the ratio of employees of non-state enterprises; the level of financial development is represented by the ratio of the value added of the financial sector to the regional GDP; and the level of urbanization is measured by the ratio of the urban population to the total population at the end of the year. To unify the criteria, the control variables are all synthesized using the entropy method.

3.4. Descriptive Statistics

The results of descriptive statistics related to each variable are shown in

Table 4. Descriptive statistics for the main variables.

Variable	Sample Size	Mean	Standard Deviation	Minimum	Maximum
Hdev	240	0.135	0.112	0.0199	0.664
dataf	240	0.126	0.131	0.00691	0.813
techf	240	0.0952	0.143	0.000272	0.957
capf	240	0.127	0.124	0.00362	0.725
labf	240	0.247	0.170	0.00565	0.841
marl	240	0.131	0.0676	0.00003	0.300
urbl	240	0.126	0.0657	0.00289	0.289
finl	240	0.111	0.0699	0.00004	0.411

.

4. Empirical Results

To analyze the relationship between the level of data factor development and the level of high-quality economic development, Stata16 is used to draw scatter plots and fitted curves for the data factor development index measured in this paper and the high-quality economic development index, as shown in Figure 1.

As seen in Figure 1, there is a clear trend of quantitative correlation between the level of data factor development and the level of economic quality development. As the level of data factor development increases, the level of economic quality development increases, indicating a positive correlation between the two. The positive slope of the fitted curve of the scatter plot also indicates a positive correlation between the two. No outliers were found in the scatter plot, indicating that individual salient values do not affect the accuracy of the conclusions of the qualitative analysis.

4.1. Basic Model Regression Results

To avoid the existence of multicollinearity among the variables, the basic econometric model was regressed stepwise. The specific regression results are shown in

Table 5. Influence of data elements on high-quality economic development (stepwise regression results).

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
dataf	0.382 *** (16.24)	0.244 *** (6.53)	0.160 *** (3.84)	0.162 *** (3.98)	0.159 *** (3.53)	0.128 *** (2.50)	0.134 *** (2.72)
techf		0.183 ** (2.75)	0.0477 (0.64)	0.0631 (0.80)	0.0666 (0.79)	0.0766 (0.96)	0.0751 (0.99)
capf			0.296 *** (9.00)	0.299 *** (9.48)	0.297 *** (9.08)	0.293 *** (8.92)	0.289 *** (8.70)
labf				−0.0827 (−1.68)	−0.0794 (−1.59)	−0.0768 (−1.44)	−0.0932 * (−1.78)
marl					0.0177 (0.46)	−0.0129 (−0.32)	−0.0297 (−0.74)
urbl						0.156 *** (3.21)	0.237 *** (3.62)
finl							−0.0702 *** (−3.06)
_cons	0.0863 *** (29.02)	0.0863 *** (34.32)	0.0724 *** (18.92)	0.0908 *** (9.18)	0.0879 *** (7.96)	0.0753 *** (6.32)	0.0790 *** (6.88)
N	240	240	240	240	240	240	240
R-sq	0.715	0.742	0.911	0.913	0.914	0.919	0.925
F	263.7	351.0	106.2	99.35	115.7	112.1	127.3

Note: Robust t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

.

In terms of the overall variables, the data factor has a positive effect on high-quality economic development at the 1% level of significance in all conditions. The technology factor, capital factor, and labor factor also all have a positive effect on the economic quality development at the 1% significant level, indicating that the selected variables are statistically significant. Regarding individual variables, model 1 is the basic model, and model 2, model 3, and model 4 are models with the addition of the technology factor, capital factor, and labor factor, respectively. The results show that adding traditional factors somewhat weakens the coefficient of influence of data factor on high-quality economic development. However, the data factor still plays a role in promoting the high-quality development of economy at the 1% significant level.

4.2. Discussion on the Endogeneity of Data Elements and Economic Quality Development

Regions with higher-quality economic development also tend to have higher levels of data factor development. To address the endogeneity problem caused by two-way causality, it is common practice to use fixed-effects models in regression models, but the control of endogeneity is not strict enough. Therefore, in this paper, a two-stage least squares regression using the instrumental variables method will be used to further alleviate the endogeneity problem.

According to the results of the benchmark regression, data elements significantly promote high-quality economic development, and in order to address the endogeneity issue and refer to the research method of Ni et al. [33], this paper mainly uses the lagged terms of explanatory variables (datal) as instrumental variables to mitigate the possible endogeneity between data elements and high-quality economic development. Theoretically, the current level of data elements affects the quality of economic development in the current period, but the current state of economic development cannot affect the level of data elements in the previous period. The regression results of the instrumental variables are shown in

Table 6. Impact of data elements on quality economic development (instrumental variables regression).

	Stage 1	Stage 2
dataf		0.169 *** (3.99)
datal	0.955 *** (12.12)
techf	0.116 (1.33)	0.0697 (1.11)
capf	−0.00314 (−0.28)	0.286 *** (14.25)
labf	−0.0924 (−0.85)	−0.100 * (−2.26)
marl	0.0781 (1.13)	−0.0531 (−1.61)
urbl	−0.106 (−0.89)	0.220 *** (3.62)
finl	0.132 * (2.47)	−0.0680 ** (−3.09)
N	210	210
Kleibergen–Paap rk LM statistic	12.049 *** (0.0005)
Cragg–Donald Wald F statistic	418.411
Kleibergen–Paap rk Wald F statistic	146.814

Note: Robust t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

.

The regression results from Table 6 show that the Kleibergen–Paap rk LM statistic is significant at the 1% level, rejecting the hypothesis of under-identification of instrumental variables, while the Cragg–Donald Wald F statistic is greater than the critical value of the Stock–Yogo weak instrumental variable identification F test at the 1% significance level, indicating that the hypothesis of weak instrumental variables is rejected, which also This means that the instrumental variables selected in this paper all pass the weak instrumental variable test and the over-identification test, i.e., the above instrumental variables selected in this paper are valid. The regression results of the lagged data elements show that the coefficients of the data elements are significantly positive at the 1% confidence level, which means that the data elements have a positive contribution to the economic quality development, which is essentially consistent with the results obtained from the benchmark regression, confirming that the core conclusions of this paper still hold.

4.3. Robustness Test

To ensure the reliability of the conclusions, this paper adopts the method of replacing the explanatory variables for robustness testing. In this paper, we use the entropy method to fit the economic quality development index to characterize the level of economic quality development in the basic regression. Here, the explanatory variables are replaced by the gross regional product and the value added of the tertiary industry. Then, the regression is performed again at the application level of the data elements.

Table 7. Effect of data elements on economic quality development (robustness test).

Variable	Original Explanatory Variable (Economic Quality Development)	Substitution of Explanatory Variables (Gross Regional Product)	Substitution of Explanatory Variables (Value Added of Tertiary Industry)
dataf	0.134 *** (2.72)	0.136 *** (4.33)	0.249 *** (6.86)
techf	0.0751 (0.99)	0.251 *** (3.76)	0.232 *** (3.80)
capf	0.289 *** (8.70)	0.00393 (0.23)	0.00884 (0.51)
labf	−0.0932 * (−1.78)	−0.0697 (−0.86)	−0.0619 (−0.64)
marl	−0.0297 (−0.74)	−0.0874 (−1.70)	−0.137 *** (−2.96)
urbl	0.237 *** (3.62)	0.525 *** (5.42)	0.489 *** (4.51)
finl	−0.0702 *** (−3.06)	−0.0504 * (−1.84)	−0.0154 (−0.81)
_cons	0.0790 *** (6.88)	0.0340 (1.57)	0.0258 (1.04)
N	240	240	240
R-sq	0.925	0.926	0.938
F	127.3	54.96	54.89

Note: Robust t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the results of the robustness test in this paper. Table 6 shows that after replacing the explanatory variables, the fitted values of the data elements are significantly positive, indicating that the data elements still have a significant contribution to high-quality economic development after the robustness treatment. Thus, it can be seen that the core findings of this paper are robust.

4.4. Mechanism of Action Test

According to the previous analysis, to examine the effects of data elements on different dimensions of economic quality development, this paper refers to the mechanism testing strategy of Li et al. [34] and further tests the innovation effect, coordination effect, green effect, open effect, and sharing effect of data elements on economic quality development. Specifically, the indicators of each dimension of economic quality development are fitted separately according to the entropy method, and then a fixed-effects model is used to carry out the empirical test study. The results of the mechanism of action in this paper are shown in

Table 8. The effect of data elements on economic quality development (mechanism of action test).

Variables	(1) Innovation Effect	(2) Coordination Effect	(3) Green Effect	(4) Openness Effect	(5) Sharing Effect
dataf	0.393 *** (5.37)	0.550 *** (4.32)	0.323 ** (1.74)	−0.0326 (−0.56)	0.112 ** (1.58)
techf	0.512 *** (4.52)	−0.0669 (−0.55)	0.207 (1.84)	−0.147 (−1.31)	−0.0965 (−0.99)
capf	0.0286 (0.82)	0.111 *** (7.63)	−0.11 (−0.70)	0.580 *** (8.25)	0.0308 (0.81)
labf	−0.0656 (−0.79)	−0.00260 (−0.01)	0.207 (1.14)	−0.179 *** (−2.01)	−0.0146 (−0.11)
marl	−0.134 * (−2.03)	0.267 *** (2.85)	0.500 ** (1.86)	−0.102 (−1.61)	0.0617 (0.47)
urbl	0.0193 (0.22)	1.349 *** (6.34)	−0.363 (−0.86)	0.0806 (0.94)	1.078 *** (7.32)
finl	−0.0789 ** (−2.26)	−0.0527 (−1.00)	−0.0330 (−0.20)	−0.0692 *** (−2.23)	−0.0874 ** (−2.26)
_cons	0.0586 *** (4.05)	0.00755 (0.21)	0.241 *** (3.07)	0.0689 *** (3.36)	0.151 *** (3.37)
N	240	240	240	240	240
R-sq	0.949	0.908	0.105	0.837	0.601
F	496.1	128.2	2.805	20.34	12.73

Note: Robust t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

.

According to the results in Table 7, data elements have the most significant effect on coordinated development, followed by green development, innovation development, and shared development, among different dimensions of high-quality economic development. The significant level for open development is not high, which shows that data elements have the most prominent effect on regional coordination, rural revitalization, and narrowing the urban–rural gap. They also have the most significant effect on promoting the transformation of old and new dynamic energy, strengthening the ecological system. There are also obvious effects on protection and significant contributions to R&D innovation and equalization of public services, but the effects on cross-border e-commerce and international trade are not obvious, probably because, on the one hand, the macroeconomic environment in recent years, the global economic growth slowdown and trade protectionism overlap, coupled with the impact of the new crown epidemic, make the process of economic globalization emerge. On the other hand, China’s industrial chain and value chain in the international economic system are still in a relatively low position, independent innovation capacity is insufficient, high-tech fields are highly dependent on imports, and the key core technologies exist as hollow, short board, and blank phenomena.

5. Conclusions and Future Research

5.1. Conclusions

In this paper, by sorting out the mechanism of the role of data elements in high-quality economic growth, establishing a system of indicators for the development level of data elements, and further establishing a panel model to study the influence of data elements on different dimensions of high-quality economic growth, the following conclusions were obtained. Firstly, the results of panel regression analysis based on the measurement results of data elements level indicate that data elements play a significant role in promoting economic growth, which shows that after China’s economy entered the digital economy era, data elements have become an important engine for sustainable economic growth, and with their unique characteristics of integration, easy replication, and high-speed flow, have rapidly entered the modern economic system and become a powerful source of power. In the macroeconomic context, digital transformation can further increase the demand for human capital, which is a key factor for modern economic growth [35,36,37,38]. Secondly, the development level of data elements has a stable growth rate and significant regional differences. In this paper, we measured the data factor development level of 30 provincial administrative units from 2014 to 2021, and the results show that there is an obvious positive correlation between China’s high-quality economic development and the data factor development level in urban and rural areas, but there are significant differences in the data factor development level among provinces and cities, showing regional imbalance, showing a pattern of “strong in the east and weak in the west, strong in the south and weak in the north”. The pattern of “strong in the east and weak in the west, strong in the south and weak in the north”. Thirdly, the degree of influence of data elements on different dimensions of high-quality economic development is not consistent. According to the results of the empirical analysis, data elements have a greater influence on coordinated development, green development, and innovation development, followed by shared development. In contrast, the role of open development is not obvious.

5.2. Deficiencies and Future Research

Although this study has important theoretical and practical implications, some deficiencies should be addressed in future research. Firstly, the index system is not comprehensive enough. This paper constructs the index system of the data element development level from two dimensions: the data element transportation environment and the data element application level, but data elements must not only contain these two aspects and must also consider data security, data marketization, and other factors in theory. In the future, with the establishment of China’s data element market and the improvement of legislation on data rights, these factors need to be expressed in quantitative indicators to truly reflect the development level of data elements. Secondly, this paper mainly discusses the impact of data elements on economic quality development and has not discussed financial development in depth. It is a very important issue to give full play to the value and financialization characteristics of data elements, and to establish and improve the basic system for the coordination of the development of multi-level and multi-channel data elements and the financial industry, which is worth further study in the future.