The Influence of the Internet on Regional Economic Development—An Empirical Study Based on China’s Provincial Panel Data

Abstract

With the rapid development of the digital economy, the level of Internet development has become increasingly important to the national economy. The development and application of the Internet have brought new impetus to economic development. At present, China’s economic growth is slowing down. Finding new support points to maintain high-quality economic growth has become the focus of current research. Therefore, at the moment of Internet economic development, it is of great significance to study whether the Internet development level can promote economic development, how to promote economic development and the size of its influence on economic development. Based on this, this paper constructs an indicator system for evaluating the Internet development level from four dimensions: Internet popularization level, information infrastructure development level, scientific and cultural development level, and industrial development and application level. With the Internet development level as the core explanatory variable, this paper establishes a fixed effect model based on the Hausman test by analyzing China’s provincial panel data from 2010 to 2019. The research results show that the Internet development level in all regions of China has risen from 2010 to 2019. The Internet development level has a promoting effect on economic growth, and there are regional differences. The promoting effect on the eastern region is higher than that in the central and western regions. The Internet development level has a significantly higher role in promoting the per capital GDP in the eastern region than in the central and western regions. It is expected to provide valuable ideas for the government to fully harness the role of the Internet in promoting economic development.

1. Introduction

According to the public data released in the 47th Statistical Report on China’s Internet Development, as of December 2020, the number of Internet users in China has reached 989 million, an increase of 85.4 million over March 2020, and the Internet penetration rate has reached 70.4%, an increase of 5.9 percentage points over March 2020. With the rapid development of the digital economy in recent years, the impact of the Internet on the national economy is becoming more critical. China’s economic growth has slowed, and the economy’s downward pressure has increased. In the new round of scientific and technological revolution and industrial reform, the search for new kinetic energy, new driving force, new technology, new industry, new business form, and a new economic and social development model has become increasingly prominent. The positive role of the Internet as an essential medium has been brought into full play. The work report of the China’s government in 2020 pointed out that online e-commerce shopping, online services, and other new formats have played an essential role in fighting the COVID-19 epidemic. It is necessary to continue introducing support policies, comprehensively promote the “Internet plus,” and create new advantages in the digital economy.

The Internet is linked with economic development inextricably. The impact of Internet development on the regional economy has become an important research topic. Jiménez and Martinez [1] found that Internet access significantly affected economic growth. Salahuddin and Gow [2] studied the impact of Internet usage on economic growth and found that it has a significant positive impact. Han and Zhang [3] found that Internet development can directly improve regional resource mismatch, thus improving resource allocation efficiency. Yan, Yuan, and Xu [4] found that proper government intervention can promote the effect of the Internet on economic growth.

Economic development has always been a hot issue in economic research. Especially under the normalization of COVID-19 epidemic prevention and control, studying the influence of the Internet on China’s regional economic development is of great significance to improving the efficiency and quality of China’s economic development. Therefore, this paper links the Internet with economic development, providing a theoretical basis for analyzing the impact of the Internet on economic development, thus giving new ideas for sustained and stable economic growth.

2. Literature Review

2.1. Can Internet Development Promote Economic Development

Jiménez and Martinez [1] used the Douglas production function to study the impact of Internet access on economic growth, including the development level of the Internet. They concluded that it significantly affects economic growth through empirical analysis. Shao and Lin [5] used the Malmquist Productivity Index (MPI) as an index and the method of Stochastic frontier analysis (SFA) to evaluate and measure the IT technology output performance of the Organization for Economic Co-operation and Development (OECD). They concluded that the productivity of the IT service industry increased by 7.4%. Salahuddin and Gow [2], Czernich, Falck, and Kretschmer et al. [6], Alfaro Cortes and Alfaro Navarro [7], Mallick [8], Lin [9], and Yousefi [10] have empirically studied the relationship between Internet development and economic growth from perspectives such as Internet penetration, Internet development indicators, and Internet resource index. Moreover, they all agreed that the Internet has a promoting effect on economic development.

Ding [11] believed that the rapid development of the Internet has not only brought about changes in China’s economic structure but also had a significant impact on the traditional logistics, communication, manufacturing, and catering industries, promoting the development of China’s national economy. Xie and Gao [12] demonstrated the role of Internet development in stimulating the economy from the perspective of Internet technology development in changing business models. Dai, Fan, and Liu [13] showed the part of the Internet in promoting the economy from the perspective of the added value of the Internet to the tertiary industry. Zhang, Cheng, and Pan [14] constructed an index system of Internet development based on Internet penetration rate and mobile phone penetration rate. Empirical analysis shows that the Internet can promote economic development. Through a practical model, Han and Zhang [3] found that Internet development can directly improve the mismatch of resources and indirectly encourage the rational distribution of market resources through marketization and opening up. Yang, Zhao, and Ping [15] believed that the Internet creates economic value by reshaping production relations and value networks and integrating with the real economy. Hu, Wang, and Duan [16] pointed out that the development of the Internet has brought significant changes to production factors, production methods, and industrial organizations and injected vitality into economic development. Bai and Li [17] believe that the Internet can warn the macro-economy of risks and fluctuations in financial markets and play a role in slowing down changes and stabilizing the economy. Yu and Hao [18] found that the Internet platform affects production efficiency based on technological innovation, industry competition, and entrepreneurial spillover effect. From the digital economy perspective, Gong and Wang [19] pointed out that the digital economy released the vitality of the market economy and realized the optimal allocation of resources. Yi, Chen, and Wei [20] pointed out that the digital economy uses big data to reduce economic transaction costs and improve resource allocation efficiency through digitalization and intelligence. Liu [21] proposed explosive growth of the digital economy and deep integration with the real economy to promote high-quality development of the China’s economy. Xia, Wang, and Zhang et al. [22] believed that the digital economy improved GDP and found that the employment impact of the technology-intensive manufacturing industry was more substantial than those of labor-intensive and capital-intensive.

On the other hand, some scholars believe that Internet development has no significant effect on economic growth or restraining influence. Some early scholars pointed out no apparent correlation between enterprises’ investment in information technology and return on investment. The Internet has no significant impact on regional economic growth, a “productivity paradox”. One study [23] took crowd-sourcing as an example to analyze the influence of the Internet on distributed creativity in detail. Internet level, represented by domain names and netizens, has no noticeable effect on promoting economic growth. Alexandre [24] indicated that the contribution of ICT to economic growth is quite limited in developing countries. Noh and Yoo [25] found that the popularity of the Internet hurts countries with a large gap between rich and poor, and the digital divide will hinder economic development.

2.2. Whether There Are Regional Differences between Internet Development and Economic Development

Scholars have researched the above problems and concluded that Internet development would cause the “digital gap” and regional differences in economic development. Chakraborty and Bosman [26] measured the digital divide of American states in terms of race, income, and personal computer ownership. Howell [27] discussed the digital gap between urban and rural areas in New Zealand. Schleife [28] found that there is also a digital divide in Germany, mainly affected by the degree of network construction.

China’s scholars generally believe that the Internet has apparent differences in regional economic growth. Sun, Zhang, and Wang et al. [29] obtained the data of 16 indicators in seven categories, including Internet, economy, and population in 87 countries, calculated by the Lorentz curve and multiple regression method. They found that the spatial pattern of Internet information flow in the world presents a “center and two wings” type, with the United States as the center and the European Union and East Asia as the two wings. Zhang [30] thought that due to the difference in Internet development levels between regions, such as mobile Internet penetration rate and human capital, the Internet would promote the eastern region more than the western region. Jin and Li [31] believed that there are differences in the scale and penetration rate of Internet users between the eastern, central, and western provinces and between urban and rural areas in China, and the east part is higher than the part of the west, and the gap is increasing with time. Wang and Qiu [32] studied the growth of Internet users in three regions: east, central, and west, and found that the Internet has apparent differences in regional economic development. From a spatial perspective. Qiu and Wang [33] found remarkable differences in Internet penetration rates among the eastern, central, and western regions. Chang, Xue, and Wu [34] evaluated Internet innovation in China and found that the eastern region was significantly higher than the west and central regions. Cheng, Zhang, and Wang [35] found that regional network competitiveness presents a ladder-like development pattern of “East > Central > West” by constructing the index system of network competitiveness, and the digital divide still exists in various places.

To sum up, international scholars have made some meaningful research conclusions about the impact of the Internet on regional economic development, which laid a good foundation for further research. However, among some scholars who focus on the related research Internet on economic growth, they only conduct general investigations on the aspects of internationalization and technology, and few directly study the relationship between them. However, in the only studies that pay attention to the relationship between the two, some measuring the development level of the Internet only choose a single index, such as the Internet penetration rate or the netizen scale for analysis. There are few systematic, comprehensive studies. Therefore, this paper estimates the Internet development level in different regions of China by constructing an index system for evaluating the Internet development level and then compares and studies the regional differences in the impact of the Internet development level on China’s economic development through empirical analysis.

3. China’s Internet Development Status and Characteristics

This paper studies Internet development from the perspective of the Internet economy, mainly from the four dimensions of infrastructure, research, development investment, industrial technology development, and application level, to study the status and characteristics of its development.

3.1. Status and Characteristics of Internet Infrastructure

As can be seen from Table 1, China’s Internet infrastructure is constantly improving. The continuous construction of information resources, among which the length of long-distance optical cable lines, the number of Internet access ports, the number of domain names, and the number of IPv4 are generally on the rise, which to a certain extent reflects the continuous improvement of China’s Internet infrastructure and lays the foundation for the rapid development of China’s economy.

With the continuous development and improvement of infrastructure and information resources, such as the length of long-distance optical cable lines, the number of Internet access ports, the number of domain names, and the number of IPv4. The full name of IPv4 is (Internet Protocol version 4), also known as the fourth version of Internet communication protocol. IPv4 assigns a unique 32-bit identifier to each interface of each host (or router) on the Internet. The number of Internet users has been increasing (Figure 1). Therefore, expanding the Internet penetration rate, significantly improving the Internet level, and digitizing economic activities have become indispensable and essential in promoting economic development.

Table 2 shows the index and distribution of new digital economy infrastructure in China’s provinces in 2019. We constructed a comprehensive evaluation system for digital economic development from four levels of digital economic infrastructure, digital industrialization, industrial digitization, and digital governance using the entropy weight method for comprehensive scoring. Hence, a new digital economic infrastructure index is calculated. Since this part is quoted from the public data of the White Paper on China’s Digital Economy Development Index in 2020, we did not calculate it in person.

As shown in Table 2, except for Tianjin and Hainan, the eastern part of China is in the first and second gradients, especially in Beijing, Guangzhou, Jiangsu, and Zhejiang, which are far higher than the national average of 31.5 (the average value of China’s new digital economy infrastructure index). However, most central and western regions in the third and fourth gradients are lower than the national average. The above reflects the large gap between the east and the west and central parts of China and the need to strengthen the Internet infrastructure further. Figure 2 presents the image of Table 2.

It can be seen from Figure 2 that the distribution of the new digital economy infrastructure index in China’s provinces is divided into four echelons in the order of high to low. The red area represents the first echelon (the new digital economic infrastructure index is 45–60), including Guangdong, Beijing, Jiangsu, Zhejiang, and Shandong. The blue area represents the second tier (the new digital economic infrastructure index is 32–45), including Fujian, Shanghai, Henan, Sichuan, Hebei, Liaoning, Hunan, and Hubei. The yellow area represents the third echelon (the new digital economic infrastructure index is 25–32), including Anhui, Jiangxi, Shaanxi, Shanxi, Chongqing, Guangxi, Tianjin, and Heilongjiang. Green areas represent the fourth echelon (the new digital economic infrastructure index is below 25), including Yunnan, Jilin, Guizhou, Inner Mongolia, Gansu, Hainan, Xinjiang, Ningxia, Qinghai, and Tibet.

3.2. The Investment in Research and Development of Internet Enterprises Continues to Rising

According to the 2020 Internet society of China Industry Research Institute Report data, the total investment in research and development of the China’s top five Internet companies reached 137 billion yuan in 2020, which is enough to show that many Internet companies are paying more and more attention to investment in scientific and technological innovation. According to the financial data of major Internet companies in China, Alibaba ranked first with 32.8 billion in research and development investment in the first two quarters of 2020, Tencent ranked second with 17.9 billion, and Baidu, JD.COM, Meituan, and Pinduoduo were 9.3 billion, 6.9 billion, 4.7 billion and 3.1 billion, respectively. Internet companies’ technology research and development investment has continuously increased in the past few years.

3.3. Status and Characteristics of Internet Application Level

As shown in Figure 3, the scale of e-commerce transactions in China has continued to expand, reaching 34.81 trillion in 2019, accounting for 35.29% of the GDP. The rapid development of electronic commerce has provided the impetus and added vitality to China’s economic growth.

The development of the Internet has dramatically facilitated the lives of consumers. As seen in Table 3, instant messaging plays a leading role in the application and consumption of netizens. The number of users is constantly increasing, and netizens’ usage rate is also growing yearly. Take WeChat as an example. With the continuous increase and refinement of WeChat applets, its functions cover all aspects of people’s daily necessities, greatly facilitating people’s lives.

To sum up, the development level of the Internet in China has rapidly improved. The continuous improvement of infrastructures such as websites, domain names, and broadband access ports has laid a good communication foundation for developing the Internet. The continuous increase in research and development funds and investment of research and development personnel has provided funds and technical support for the development of the Internet. The constant expansion of e-commerce transactions and the diversification of consumers’ Internet application levels provide a good consumption environment for the development of the Internet. The overall development level of the Internet in China ranks second in the world. Compared with the United States, it can be seen that there is still a big gap between the development level of the Internet in China and that of the United States. Moreover, there are apparent differences in Internet development between provinces in China, and the digital divide phenomenon is still prominent.

4. Empirical Analysis

4.1. The Establishment and Calculation of the Index System of Internet Development Level

4.1.1. Establishment of Index System

Scholars’ evaluation of the Internet development level is a hot issue of concern, but there is no official unified standard. The indicators by which scholars measure the level of Internet development can be roughly divided into three categories. First, use of the Internet penetration rate (such as He and Xu [36], Chang and Zhong [37], Wang and Cao [38]) or use the Internet penetration rate and mobile phone penetration rate (e.g., Zhang and Cheng [14], Zhang [30]) to measure the level of Internet development. Second, websites and netizens are used as indicators to measure the level of Internet development. Such as Zhang, Shi, and Li et al. [39] used the number of web pages to measure the level of Internet development. Sun et al. [29] took CN domain names and netizens as the measurement indicators of Internet resources. To calculate the regional Internet’s development level, Hu [40] divided the number of websites in each region by the number of legal entities. Third, build the Internet index system. For example, Qiu and Wang [33] have created the Internet time-space difference index system from the regional economic development level, scientific and technological culture level, regional opening degree, regional information infrastructure development level, regional non-agricultural level, and geographical location characteristics. Han, Song, and Li [41] constructed the index system of Internet development level from five aspects: Internet popularization, Internet infrastructure, Internet information resources, Internet business application, and Internet development environment. Huang, Yu, and Zhang [42] chose four dimensions: Internet penetration rate, Internet-related employees, Internet-related output, and the number of mobile Internet users to build an index system to measure the development level of the Internet. Yu, Zhou, and Zhong [43] and He, Lai, and Liao [44] selected scientific and cultural development levels, industrial development, and application levels to build an indicator system of Internet development.

In terms of index selection, based on following the principles of data availability, comprehensiveness, and independence, combined with the research results of current scholars, refer to “China Informatization Development Index Statistical Monitoring Annual Report”, “China Statistical Yearbook” and “Internet Development Statistical Report”, this paper constructs four first-level index systems including the level of Internet popularization, the level of information infrastructure development, the level of scientific and cultural development, and the level of industrial development and application, and uses 14 second-level indicators as the index system for Internet development. See Table 4.

The popularity of the Internet is an essential indicator of the level of Internet development in a region. The popularity of mobile phones and the Internet represents the acceptance of Internet users and indicates the potential of Internet development.

The development level of information infrastructure is an essential pillar in supporting Internet development in a region. The story of the Internet needs the support of hardware conditions. Therefore, the length of long-distance optical cable lines and the number of Internet access ports represent the development level of Internet infrastructure in the modern information industry. The development of information resources is also an important supporting factor for developing the Internet in a region. This paper uses the number of domain names and websites to express that the number of domain names is a relatively limited resource. The number of websites represents the level of internationalization, reflecting the number of Internet resources in a region.

The influence of scientific and cultural development levels on the development of the Internet is also significant. The result of the Internet needs particular computer application ability and a consistent cultural level with information knowledge to understand. At the same time, the state’s investment in science and technology culture can also promote the development of the Internet. Therefore, this paper uses average years of education, investment in education, investment in research and development funds, R&D full-time equivalent, and the number of three patent applications to represent the development level of science and technology culture.

The industry’s development and application level also represent the Internet’s development status. The industry relies on the continuous innovation of Internet information technology to give birth to new modes and formats of industry. Therefore, this paper uses the gross output value of the post and telecommunications industry, the gross output value of the telecommunications industry, and the income of software and information technology services to represent the industrial development level.

4.1.2. Establishment of Index Weight

In this paper, SPSS 26.0 statistical analysis software is used, and the principal component analysis method is used to determine the weight of the Internet development level system. The dimension reduction method concentrates on information, and multiple single indicators are transformed into several comprehensive hands. Due to the lack of data in Tibet and Qinghai Province, this paper selects 29 provinces and autonomous regions except for Hong Kong, Macao, and Taiwan as the research objects. It analyzes the Internet development level of 29 regional autonomous regions in China from 2010 to 2019.

• (1). KMO Test and Bartlett Test

As shown in Table 5, KMO is 0.841, more significant than 0.7, proving that the partial correlation between variables is strong enough and the significance is less than 0.05. The assumption that variables are independent is rejected, indicating a strong correlation between variables, indicating that principal component analysis is supported.

• (2). Total Variance Explanation

As shown in Table 6, according to the principle that the eigenvalue is greater than 1, three common factors are extracted, and the cumulative variance contribution rate is 77.752%.

4.1.3. Measurement of Internet Development Level

• (1). Common Factor Expression F1, F2, and F3 (Table 7).

F1 = −0.043X₁ − 0.026X₂ + 0.044X₃ + 0.034X₄ − 0.204X₅ + 0.030X₆ − 0.079X₇ + 0.231X₈ + 0.199X₉ + 0.140X₁₀ + 0.214X₁₁ + 0.153X₁₂ + 0.084X₁₃ + 0.129X₁₄(1)

F2 = 0.267X₁ + 0.274X₂ − 0.269X₃ − 0.082X₄ + 0.019X₅ + 0.133X₆ + 0.316X₇ − 0.039X₈ + 0.003X₉ − 0.112X₁₀ − 0.027X₁₁ − 0.006X₁₂ − 0.075X₁₃ + 0.113X₁₄(2)

F3 = 0.084X₁ + 0.038X₂ + 0.244X₃ + 0.343X₄ + 0.734X₅ + 0.085X₆ − 0.006X₇ − 0.223X₈ − 0.156X₉ + 0.037X₁₀ − 0.179X₁₁ − 0.051X₁₂ + 0.175X₁₃ − 0.106X₁₄(3)

• (2). Calculate the coefficient U in a linear combination. The formula is

(4) $U_i^j=\frac{F_i^j}{{\mu }^j}$

Among them, $U_i^j$ are the linear combination coefficient corresponding to the i-th index of the j-th component, $F_i^j$ the component matrix value corresponding to the i-th index of the j-th element, and ${\mu }^j$ the square root of the characteristic value of the j-th component. i = 1, 2, 3……14; j = 1, 2, and 3. The specific calculation results are shown in Table 8.

• (3). According to the coefficient of each component, calculate the comprehensive score Q.

(5) $Q_i=\frac{U_i^1\ast R^1+U_i^2\ast R^2+U_i^3\ast R^3}{R^1+R^2+R^3}$

Among them, Q is the coefficient in the comprehensive score model, which is the coefficient of the I-th index $U_i^1$, $U_i^2$, $U_i^3$, and a linear combination of components 1, 2, and 3, respectively. R¹, R², and R³ are the variance of the first, second, and third principal components, respectively.

Comprehensive model

Y = 0.0324X₁ + 0.0462X₂ + 0.0.3788X₃ + 0.5086X₄ + 0.3256X₅ + 0.2292X₆ + 0.1859X₇ + 0.3411X₈ + 0.3499X₉ + 0.2821X₁₀ + 0.6041X₁₁ + 0.3941X₁₂ + 0.9243X130.8709X₁₄(6)

After that, each index is normalized, and the specific calculation method is:

(7)$P_i=\frac{Q_i}{ \Sigma Q_i}$

• (4). Internet Development Level

Multiply the weight of each factor and the corresponding weight to get the HLM of the Internet development level of each region. The specific calculation formula is:

(8)$HLW={\Sigma }_{i=1}^{14}{P}_i\ast W_i$

From 2010 to 2019, the Internet development level of all provinces in China dramatically improved (Table 9), showing an upward trend. Through horizontal comparison, we can find that Internet development in China is unbalanced, and there are apparent differences between east and west. Except for some provinces (cities) in the eastern region, the Internet development level of other sections (municipalities) is far higher than the national average, while the Internet development level of the central and western provinces (cities) is below the national average.

4.2. Impact of the Internet on China’s Regional Economic Development

4.2.1. Construction of Theoretical Model

This paper uses the production function to construct the output function to estimate the impact of the Internet on China’s regional economic development, as shown in the Formula (9).

(9)$Y_i=A_i{K}_i^{\alpha }{L}_i^{\beta }$

In the above formula, Y_t represents the output in year t, K_t and L_t represent the capital and labor input in year t, α, β represents the output elasticity of capital and labor, respectively, and A is the comprehensive technical level.

Since the comprehensive technical level is affected by the Internet development level and R&D intensity, the Internet development level and R&D intensity are included in the model. Considering that the activity of the private economy, the level of urbanization, and the dependence on foreign trade also have a particular impact on the development of the regional economy, they are included in the model. Meanwhile, this paper deals with the index logarithmically to eliminate the influence of heteroscedasticity, and the modified model is shown in the Formula (10).

lnPGD_i,t = c + β1lnK_i,t + β2lnL_i,t + β3lnHLW_i,t + β4lnRDI_i,t + β5lnPE_i,t + β6lnUR_i,t + β7lnTRA_i,t − β8lnPEO_i,t + ε_i,t(10)

In the above formula, PGD_i,t is the per capital GDP of region i in year t. K_i,t and L_i,t are the capital input and labor input of area i in the t-the year, respectively. Since not all innovation outputs are only created by R&D personnel and R&D funds in the statistical sense, the Internet has the characteristics of openness and sharing. Therefore, this paper is expressed by the capital investment and the number of employees in each region. HLW_i,t is the Internet development level of area i in the t-the year. RDI_i,t is the R&D intensity in the t-the year of region i. PE_i,t is the activity of private economy of part i in the t-the year. UR_i,t is the urbanization level of area i in the t-the year. TRA_i,t is the foreign trade dependence of region i in the t-the year. PEO_i,t is the total number of people in the t-the year of region i. c is a constant. β1, β2, β3……β8 is the coefficient. ε_i is a random error. i is 1, 2, 3…29, respectively, representing 29 provinces, cities, and autonomous regions in China. t is 2010, 2011…2019.

4.2.2. Variable Selection and Data Source

• (1). Variable selection

Capital investment: capital is necessary for economic development and greatly promotes regional economic development. Capital investment should be included in the model as a factor to measure economic development. This paper uses the perpetual inventory method to estimate capital stock. Namely, K_i,t = k_i(t−1)(1 − δ_i,t) + I_i,t/P_i,t.

In the above formula, K_i,t represents the actual capital stock in the t-the year of region i. I_i,t represents the total fixed-asset investment in the t-the year of region i. P_i,t is the fixed asset investment price index in the t-the year of region i. δ is the depreciation rate, the total fixed capital formation is divided by 10% as the initial capital stock of the province, drawing on the practice of Zhang, Wu, and Zhang [45].

Labor force: labor input is expressed by the number of employed people in each province.

Internet development level: use the Internet index obtained in the above research to represent the Internet development level.

R&D intensity: R&D investment is an essential factor in promoting economic development. The increase in R&D investment means the addition of scientific and technological achievements. The expansion of scientific and technical achievements drives the improvement of technological innovation and then drives economic growth. This paper uses “R&D investment/GDP” to express R&D intensity.

Urbanization: urbanization is an important symbol of the level of modernization. The urbanization process is accompanied by the aggregation of talents, capital, technology, and other factors brought by population migration, which has become an important variable affecting China’s economic development. This paper uses urban population/total population to express.

The activity of the private economy: as an essential part of the national economy, a private economy can improve regional production capacity, sustainable development, innovation ability, etc. This paper uses the total sales output value of private enterprises/industrial sales output value above the Designated Size to represent the activity of the private economy.

Dependence on foreign trade: through an opening to the outside world, China carries out trade with countries all over the world, absorbs the technology diffusion and knowledge diffusion of developed countries, learns advanced technology and management experience, makes full use of foreign capital and expands the market scope of trade. This paper uses the total import and export/GDP to measure the openness of foreign exchange.

Per capital GDP: the total value of social final products and services produced by a country according to the average population in a certain period, accurately measuring economic growth.

• (2). Data source

This paper selects the relevant data from 29 provinces, cities, and autonomous regions in China from 2010 to 2019. The per capital GDP, labor force employment, private economic activity, and foreign trade dependence are calculated from the data of the China Statistical Yearbook from 2009 to 2020. The development level of the Internet is calculated according to the principal component analysis method. The data on R&D intensity comes from the statistical yearbook of China’s science and technology. The level of urbanization comes from the statistical yearbook of China’s population and employment. Descriptive statistics of main variables are shown in Table 10.

4.2.3. Empirical Analysis

• (1). Stationarity Test

When conducting empirical analysis on panel data, the stationarity of the data should be considered. Otherwise, it may lead to pseudo-regression. Therefore, this paper first tests the stationarity of the data. The results are shown in Table 11, and the data are stable.

• (2). Regression Analysis

We used stata26.0 software to conduct an empirical analysis of Equation (10). At the same time, considering the differences in the impact of the Internet on regions, 29 provinces and municipalities in China are divided into two sub-samples: eastern, and central and western.

According to the model I in Table 12 and Table 13, the increase in China’s per capital GDP mainly depends on capital investment, and the contribution of the labor force is small. Compared with the east, the labor force in the central and western regions has no significant effect on the per capital GDP. This is because the human capital stock in the central and western regions is lower than in the east. Taking 2019 as an example, the number of people receiving high school and university degrees in the east generally accounts for more than 50%, while that in the central and western regions is generally less than 50%.

From model II in Table 12 and Table 13, the improvement of Internet development level can significantly promote the per capital GDP, but there are pronounced regional differences. The effect of the Internet development level in the east is 0.089, and that in the central and western regions is 0.062. Therefore, the promotion effect of the Internet development level on the eastern part is more significant than that in the west and central areas. R&D intensity also positively impacts per capital GDP, but R&D intensity has a significant effect on per capital GDP in the eastern region. The impact on the central and western areas is lower than in the east. There are also differences in the effects of private economic activity and urbanization development on the per capital GDP in eastern, central, and western regions. Overall, the coefficient in the east is significantly higher than in the west and significant areas. In the list of the top 500 private manufacturing industries, Guangdong and Zhejiang rank first and second, respectively, and the number and ranking of the eastern region are much higher than those of the central and western areas.

Table 12 and Table 13 show that with the addition of variables, as shown in models II, III, and IV, the coefficient of Internet development level on per capital GDP is becoming more and more significant, and the promotion effect is becoming more and more powerful and is generally on the rise. The Internet development level has gradually become essential to China’s economic growth.

5. Conclusions and Suggestions

5.1. Conclusions

This paper sets up an Internet index system from four levels: Internet popularity level, information infrastructure development level, scientific and cultural development level, and industrial development and application level, and uses the principal component analysis method to measure the Internet development level of 29 provinces, cities and autonomous regions in China. It empirically analyzes the impact of the Internet on China’s economic growth in the east and central and western areas. The conclusions are as follows:

• (1). China’s provinces, cities, and autonomous regions have significantly improved their Internet development level, but regional differences exist. The Internet development level in the eastern part is higher than that in the central and western regions. On the one hand, the technology in the central and western regions is relatively backward, and the network cost is high, which limits the efficiency of local people in using the Internet to some extent. On the other hand, the backward economic development level and poor resource endowment in the central and western regions lead to the relatively backward network infrastructure in the central and western regions. All these have led to the failure of the central and western regions to fully play the role of the Internet in promoting economic development.

• (2). The level of Internet development has a promoting effect on China’s economic growth. At the same time, there are differences in the impact on economic growth between the eastern, central, and western regions. The effect coefficient in the east is more significant than in the west and central areas. The level of Internet development has a significantly higher lifting effect on the per capital GDP in the eastern region than that in the central and western areas. This is because the role of the Internet development level in economic growth depends to some extent on the size of the R&D intensity. After the R&D intensity is added, the effectiveness of the Internet development level has increased in both the eastern, and central and western regions. From the empirical analysis results above, we can see that the R&D intensity plays a significant role in the per capital GDP of the eastern region, while its effect on the central and western regions is lower than that of the eastern region. As R&D investment is an important factor in promoting economic development, the increase in R&D investment can drive the improvement of technological innovation levels and drive economic growth. Therefore, to some extent, the benefits of the Internet to economic development in the east are greater than those in the central and western regions.

• (3). The application and development of the Internet in many aspects have led to a rapid increase in the number of regional websites and Internet users, and the rapid construction of websites of government and other institutions, which has promoted the efficiency of people’s communication and office work. Therefore, relevant industries have begun to emerge at the historical moment, such as the emergence and rapid development of emerging industries such as e-commerce and Internet finance. While changing people’s consumption habits and payment methods has also weakened the industry’s requirements for location, many high-tech industries have few requirements for location and transportation. As long as the Internet is available and relevant technologies and equipment are built, operations and services can be carried out. Changes in product sales and transportation modes also directly stimulate people’s consumption levels, thereby promoting regional economic growth.

5.2. Suggestions

5.2.1. Further Improve the Internet Infrastructure

China’s Internet infrastructure has been significantly improved, but Internet development is unbalanced in different regions. There is a big gap between the eastern, central, and western areas, and the digital divide is evident, especially in the low network coverage in the west and major regions. Therefore, the China’s government should further increase investment in Internet infrastructure construction in the central and western areas, encourage talents to flow to the west and major regions, and expand the reserve of R&D talents in the west and the main areas. At the same time, we will introduce relevant policies to support the Internet industry in the west and the significant regions to improve the ability and level of attracting foreign investment.

5.2.2. Create an Exemplary Network Environment

Since the outbreak of the new epidemic, the role of the Internet in economic development has become increasingly apparent. Therefore, on the one hand, China should further improve the Internet penetration rate, increase the number of network access ports, and enhance the network service capability. On the other hand, enterprises and users should be better aware of the Internet, actively implement the strategy of strengthening the country through the Internet, accelerate the in-depth integration of the Internet and the real economy, give full play to the role of the Internet in economic development, and actively create an exemplary network environment.

5.2.3. Improve the Level of Technological Innovation

Information and communication technology progress is an important driving force for China’s regional economic development. In recent years, the China’s government has gradually increased the investment of R&D funds in the Internet industry, and previous empirical findings show that R&D intensity has dramatically promoted China’s economic development. Therefore, the China’s government should continue to encourage enterprises to innovate independently and actively guide enterprises and universities to cooperate in depth by promoting the collaborative innovation of “Government and Industry University Research” to continuously enhance the collective innovation capability, promote the docking of production and demand, and integrate traditional industries with emerging industries, thus forming a new pattern of innovation-driven development.

5.2.4. Implement a Dynamic and Differentiated Regional Internet Development Strategy

The governments of all regions in China should take practical and effective measures to constantly improve Internet development and pay attention to the integrated development of the Internet and other industries, based on the local resource endowments and the actual situation of Internet development. The central and western regions can use the Internet open platform to integrate national and global industrial chain resources, break through the resource bottleneck, path dependence, and growth cycle of industrial development, accelerate the development of big data, Internet of Things, cloud computing, mobile Internet and other emerging industries, speed up Internet construction, actively learn from the advanced experience of the eastern region, and transform their resource advantages into the advantages of developing the Internet economy, to better promote the coordinated development of the eastern, central and western regions of China.

Author Contributions

Conceptualization, Y.W. and Z.W.; methodology, Y.W. and Y.D.; software, Y.W. and Y.D.; validation, Y.W., Y.D. and Z.W.; formal analysis, Y.D.; investigation, Y.W. and Z.W.; resources, Y.W.; data curation, Y.W.; writing—original draft preparation, Y.W.; writing—review and editing, Y.D.; visualization, Y.D.; supervision, Y.W.; project administration, Z.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

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Figure 1. The Internet Users and Penetration Rate in China from 2011 to 2019. Source: China Statistical Yearbook 2011–2019, 40th–47th China Internet Development Statistics Report.

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Figure 2. Index Distribution of New Digital Economy Infrastructure in China’s Provinces in 2019. Red: the new digital economic infrastructure index is 45–60; Blue: the new digital economic infrastructure index is 32–45; Yellow: the new digital economic infrastructure index is 25–32; Green: the new digital economic infrastructure index is below 25.

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Figure 3. China’s E-commerce Turnover from 2011 to 2019. Source: China National Bureau of Statistics (2020).

Table 1

The Status of Internet Basic Resources in China from 2010 to 2019.

Time	Long-Distance Optical Cable Line Length (km)	Number of Domain Names (10,000)	IPv4 (10,000)	Internet Access Ports (10,000)
2010	818,133	865.6	27,763.7	18,781.1
2011	842,341	774.8	33,044.0	23,239.4
2012	868,175	1341.2	33,053.5	32,108.4
2013	890,018	1843.6	33,030.8	35,945.3
2014	928,398	2059.6	33,198.8	40,546.1
2015	965,283	3101.4	33,652.0	57,709.4
2016	994,092	4227.6	33,810.3	71,276.9
2017	1,044,998	3848.0	33,870.5	77,599.1
2018	994,130	3792.8	33,892.5	86,752.3
2019	1,084,937	5094.2	33,909.3	91,578.0

Source: China Statistical Yearbook 2010–2020.

Table 2

Index and Distribution of New Digital Economic Infrastructure in Provinces of China in 2019.

Fraction	Provinces
First echelon (45–60)	Guangdong (59.1), Beijing (55.1), Jiangsu (47.9), Zhejiang (47.8), Shandong (45.2),
Second echelon (32–45)	Fujian (42.0), Shanghai (41.7), Henan (40.5), Sichuan (39.0), Hebei (35.2), Liaoning (33.9), Hunan (33.7), Hubei (32.9)
Third echelon (25–32)	Anhui (31.2), Jiangxi (30.0), Shaanxi (27.9), Shanxi (27.1), Chongqing (26.5), Guangxi (26.0), Tianjin (26.0), Heilongjiang (25.9)
The fourth echelon (below 25)	Yunnan (24.0), Jilin (23.3), Guizhou (22.8), Inner Mongolia (22.8), Gansu (22.3), Hainan (21.2), Xinjiang (20.5), Ningxia (18.0), Qinghai (17.1), Tibet (11.1)

Source: 2020 China Digital Economic Development Index White Paper.

Table 3

Size and Utilization Rate of Instant Messaging Users from 2017 to 2019.

	2017		2018		2019
	Size of Users(Million)	The Utilization Rate of Internet Users	Size of Users(Million)	The Utilization Rate of Internet Users	Size of Users(Million)	The Utilization Rate of Internet Users
Instant messaging	72,023	93.3%	79,172	95.6%	98,111	99.2%
Search engine	63,956	82.8%	68,132	82.2%	76,977	77.8%
Netnews	64,689	83.8%	67,472	81.4%	74,274	75.1%
online shopping	53,332	69.1%	61,011	73.6%	78,241	79.1%
Online delivery	34,338	44.5%	40,601	49.0%	41,883	42.3%
Online payment	53,110	68.8%	60,040	72.5%	85,434	86.4%

Source: the 44th–46th China Internet Development Statistics Report.

Table 4

Measurement System of Comprehensive Development Level of China’s Inter-provincial Internet.

Primary Index	Secondary Index
Internet popularity level	1. Mobile phone penetration rate (%)2. Internet penetration rate (%)
The development level of information infrastructure	3. Long-distance optical cable line length (thousands of miles)4. Number of Internet access ports (millions)5. Number of domain names (10,000)6. Number of websites (10,000)
Scientific and cultural development level	7. Average years of education (years)8. R&D full-time equivalent (person-year)9. Number of three kinds of patent applications authorized (10,000 pieces)10. Investment in education (100 million yuan)11. Research and development investment (100 million yuan)
Industrial development and application level	12. Gross output value of post and telecommunications industry (100 million yuan)13. Total output value of the telecommunications industry (100 million yuan)14. Software and information technology service revenue (100 million yuan)

Source: China Statistical Yearbook, Statistical Report on Internet Development in China.

Table 5

KMO Test and Bartlett Test.

Scheme 0		0.841
Sphericity test of Bartlett	Approximate chi-square	4364.567
	df	91
	Sig	0.000

Table 6

Total Variance Explained.

Total Variance Explanation
Component	Initial Eigenvalue			Extract the Sum of Load Squares			The Square Sum of Rotational Load
	Total	Variance Percentage	Cumulative%	Total	Variance Percentage	Cumulative%	Total	Variance Percentage	Cumulative%
1	7.434	53.099	53.099	7.434	53.099	53.099	6.112	43.655	43.655
2	2.424	17.312	70.411	2.424	17.312	70.411	3.082	22.014	65.669
3	1.028	7.341	77.752	1.028	7.341	77.752	1.692	12.083	77.752
4	0.702	5.013	82.765
5	0.541	3.863	86.628
6	0.454	3.246	89.874
7	0.409	2.920	92.794
8	0.329	2.352	95.146
9	0.205	1.468	96.613
10	0.186	1.327	97.940
11	0.132	0.941	98.881
12	0.083	0.590	99.470
13	0.054	0.385	99.855
14	0.020	0.145	100.000

Extraction method: a principal component analysis.

Table 7

Component Score Coefficient Matrix.

Component Number	Component1	Component2	Component3
x1 Mobile phone penetration rate (department/100 people)	−0.043	0.267	0.084
x2 Internet penetration rate (%)	−0.026	0.274	0.038
x3 Long-distance optical cable line length (thousands of miles)	0.044	−0.269	0.244
x4 Internet access ports (millions)	0.034	−0.082	0.343
x5 CN Number of domain names (10,000)	−0.204	0.019	0.734
x6 number of websites (10,000)	0.030	0.133	0.085
x7 Average years of education (years)	−0.079	0.316	−0.006
x8 R&D ; the number of FTEs (person-years)	0.231	−0.039	−0.223
x9 Authorized number of three kinds of patent applications (10,000 pieces)	0.199	0.003	−0.156
x10 Investment in education (100 million yuan)	0.140	−0.112	0.037
x11 Investment in research and development funds (100 million yuan)	0.214	−0.027	−0.179
x12 Gross value of postal industry (100 million yuan)	0.153	−0.006	−0.051
x13 Total value of the telecom industry (100 million yuan)	0.084	−0.075	0.175
x14 software and information technology service revenue (100 million yuan)	0.129	0.113	−0.106

Extraction method: a principal component analysis. Rotation method: Caesar normalized maximum variance method. Component score.

Table 8

Coefficient of Linear Combination of Each Component Where the Index is located.

	U1	U2	U3
X1	−0.0158	0.0979	0.0308
X2	−0.0167	0.1760	0.0244
X3	0.4340	−0.2653	0.2407
X4	0.4058	−0.9787	0.4094
X5	−0.2774	0.0258	0.9979
X6	0.0445	0.1974	1.2615
X7	−0.1235	0.4941	−0.0938
X8	0.4027	−0.0680	−0.3888
X9	0.4395	0.0066	−0.3445
X10	0.3246	−0.2597	0.0858
X11	0.5890	−0.7432	−0.4927
X12	0.5311	−0.0208	−0.1770
X13	0.3615	−3.2275	0.7531
X14	0.9122	0.7990	−0.7495

Table 9

Internet Development Level in Eastern China, Central, and Western China from 2010 to 2019.

		2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Overall	The whole country	0.5139	0.5040	0.5032	0.5272	0.5384	0.5812	0.6419	0.6931	0.8622	1.1223
Eastern	Beijing	0.6170	0.6697	0.6433	0.6951	0.7360	0.8320	1.0222	1.0975	1.2846	1.5989
	Tianjin	0.3961	0.4111	0.4725	0.4634	0.4746	0.5147	0.5589	0.5865	0.6216	0.7816
	Hebei	0.3808	0.4235	0.3563	0.3184	0.2894	0.2799	0.3467	0.3221	0.5364	0.8448
	Liaoning	0.2937	0.3664	0.3162	0.3448	0.3359	0.3676	0.3407	0.2825	0.2903	0.4006
	Shanghai	0.3443	0.4350	0.3881	0.4457	0.4643	0.5357	0.6191	0.7146	0.7588	0.9486
	Jiangsu	0.6974	0.6788	0.8590	0.9859	1.1548	1.4738	1.6262	1.9261	2.4265	3.0167
	Zhejiang	0.3739	0.3280	0.4502	0.4976	0.5949	0.9744	1.0955	1.3414	1.9375	2.5642
	Fujian	0.2929	0.3565	0.3027	0.2830	0.2014	0.2549	0.3813	0.5008	0.6991	0.8940
	Shandong	0.3724	0.3209	0.3985	0.8549	0.7376	0.7951	0.9628	1.0915	1.4609	1.7883
	Guangdong	1.1260	0.8786	1.0976	1.3822	1.6176	2.0864	2.3474	2.8413	3.9246	4.9220
	Hainan	0.5713	0.6445	0.6813	0.6905	0.7135	0.7258	0.7313	0.7429	0.7642	0.7601
Central and Western	Shanxi	0.4908	0.5299	0.5147	0.4993	0.4523	0.4517	0.4306	0.3976	0.3250	0.4492
	Neimenggu	0.6147	0.6613	0.6501	0.6369	0.6558	0.6457	0.6725	0.6463	0.5785	0.6850
	Jilin	0.4111	0.3349	0.3979	0.4571	0.4798	0.4894	0.5345	0.5796	0.5924	0.5703
	Heilongjiang	0.4901	0.4165	0.4638	0.4827	0.4773	0.5000	0.5258	0.5631	0.5663	0.5373
	Anhui	0.4840	0.5009	0.4145	0.3581	0.3098	0.2401	0.2872	0.3069	0.4530	0.7219
	Jiangxi	0.3958	0.2479	0.3157	0.3880	0.4043	0.4998	0.5365	0.5640	0.6057	0.5443
	Henan	0.3848	0.3697	0.3351	0.2725	0.4738	0.2834	0.3999	0.4819	0.8689	1.2152
	Hubei	0.3896	0.4169	0.3544	0.2862	0.2301	0.1645	0.2122	0.1758	0.3294	0.5916
	Hunan	0.4180	0.4665	0.3948	0.3609	0.3016	0.2580	0.3082	0.3044	0.4598	0.7455
	Guangxi	0.5704	0.8663	0.5564	0.5756	0.4949	0.4350	0.4363	0.4046	0.4908	1.9107
	Chongqing	0.3801	0.2551	0.2766	0.3508	0.3896	0.4770	0.5411	0.5887	0.6675	0.6332
	Sichuan	0.4590	0.4910	0.3696	0.2905	0.3440	0.3656	0.4792	0.5661	0.9035	1.4705
	Guizhou	1.1755	0.7044	0.6439	0.5816	0.5787	0.5277	0.5214	0.4306	0.5080	0.6787
	Yunnan	0.6521	0.6984	0.6192	0.5766	0.5759	0.5197	0.5141	0.4448	0.5670	0.7688
	Shanxi	0.4600	0.4949	0.4462	0.4275	0.3387	0.2760	0.2417	0.2187	0.3292	0.5012
	Gansu	0.5150	0.4517	0.5606	0.5458	0.5412	0.5932	0.6048	0.6274	0.6617	0.6355
	Ningxia	0.6068	0.7404	0.7121	0.7070	0.7042	0.7337	0.7354	0.7506	0.7634	0.7650
	Xinjiang	0.5385	0.4558	0.6018	0.5313	0.5426	0.5547	0.6009	0.6022	0.6291	0.6036

Table 10

Description and Statistics of Variables.

Variable	Meaning	Number of Observations	Mean Value	StandardDeviation	Minimum Value	Maximum Value
PGDP	Per capital GDP	290	5.425411	3.246518	0.1953394	26.82948
K	Capital stock	290	5.284691	3.478721	0.4615907	16.15026
L	Labor force	290	2769.189	1680.594	339.6	6766.86
HLW	Internet development level	290	0.6529297	0.5095777	0.1644691	0.904
RDI	R&D intensity	290	1.645414	1.117733	0.34	6.0137
PE	The activity of the private economy	290	27.22716	12.71557	3.006242	56.95609
UR	Urbanization level	290	57.29931	12.5962	33.81	89.6
TRA	Dependence on foreign trade	290	29.43899	39.76171	2.462994	486.7644
PEO	Inhabitant	290	4685.652	2668.173	633	11,521

Table 11

LLC stationary test results.

Variable	Adjusted t	p Value	Conclusion
LNPGDP	−1.3842	0.0831	Stable
LNK	−8.4370	0.0000	Stable
LNL	−10.3208	0.0000	Stable
NHL	−5.1055	0.0000	Stable
LORDI	−2.3968	0.0083	Stable
LNPE	−4.6012	0.0000	Stable
LAURA	−5.6128	0.0000	Stable
LNTRA	−6.3896	0.0000	Stable
LEO	−2.5255	0.0000	Stable

Table 12

Impact of Internet Development Level on National Per Capital GDP.

	I	II	III	IV	V
c	−4.104 ***(−1.27)	−3.819 ***(−1.19)	−3.708 ***(−1.16)	−10.274 ***(−3.13)	−17.496 ***(−2.42)
lnK	0.738 ***(9.39)	0.803 ***(1.29)	0.694 ***(6.31)	0.057 ***(0.47)	0.0043 ***(0.03)
lnL	0.587(1.38)	0.425(1.29)	0.543(1.28)	0.577 ***(1.87)	0.471 ***(1.46)
lnHLW		0.050 *(1.54)	0.078 *(1.57)	0.096 ***(3.12)	0.101 ***(3.23)
lnRDI			0.308 ***(1.54)	0.121 ***(0.93)	0.104 ***(0.80)
lnPE				0.061 ***(0.86)	0.073 ***(1.01)
lnUR				2.336 ***(5.38)	2.371 ***(5.45)
lnTRA				0.658 ***(19.96)	0.658 ***(19.94)
lnpPEO					0.970 **(1.12)
R2	0.8616	0.8674	0.8732	0.8732	0.9630
F	73.36	49.95	38.26	38.26	101.83
Hausman	0.0037	0.0017	0.0157	0.0000	0.0000

Note: *, **, *** represents significance at the level of 10%, 5%, and 1%, FE represents the fixed effect model, and the t value of variables in parentheses.

Table 13

Impact of Internet Development Level on Per Capita GDP in the Eastern, The Central, and Western.

	Eastern				The Central and Western
	I	II	III	IV	I	II	III	IV
c	−1.545 ***(−0.34)	−1.513 ***(−0.34)	−1.73 ***(−0.39)	−20.49 ***(−1.65)	−8.077 ***(−1.71)	−6.947 ***(−1.44)	−6.807 ***(−1.40)	−0.608 **(−0.11)
lnK	0.800 ***(8.04)	0.8136 ***(8.11)	0.703(5.35)	0.0146 ***(0.08)	0.687 ***(4.45)	0.763 ***(4.49)	0.671 **(2.98)	0.072 ***(0.65)
lnL	0.242 ***(0.41)	0.244 ***(0.41)	0.286 ***(0.48)	0.431 ***(0.91)	1.129 **(1.79)	0.970(1.50)	0.953(1.46)	−0.327 **(−1.16)
lnHLW		0.089 **(1.05)	0.090 ***(0.97)	0.099 ***(1.59)		0.062 *(1.08)	0.065 ***(1.10)	0.067 ***(3.16)
lnRDI			0.325 ***(1.28)	0.141 ***(0.75)			0.220 ***(0.63)	0.294 ***(2.46)
lnPE				0.080 ***(0.76)				0.042 *(0.72)
lnUR				2.376 ***(3.38)				0.664 ***(1.56)
lnTRA				0.587 ***(12.89)				0.929 ***(31.86)
lnPEO				1.3167(0.79)				0.680(1.23)
R2	0.8550	0.8550	0.8660	0.9071	0.8840	0.8301	0.8914	0.9520
F	44.02	44.02	22.80	46.46	30.24	72.40	85.36	225.76
Hausman	0.0084	0.0082	0.0070	0.0000	0.0037	0.0306	0.0324	0.0000

Note: *, **, *** represents significance at the level of 10%, 5%, and 1%, FE represents the fixed effect model, and the t value of variables in parentheses.

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