Impact of Telecommunications Infrastructure Construction on Innovation and Development in China: A Panel Data Approach

Abstract

This paper empirically studies the impact of telecommunications infrastructure construction on economic and social innovative development using panel data from 31 provinces in China spanning from 2009 to 2022. The research findings indicate that telecommunication infrastructure significantly promotes innovation in terms of R&D investment, knowledge output, and application output. In addition, at various stages of telecommunication technology development, the impact on innovative development varies. Iterative updates in telecommunication technology drive higher R&D expenditures, facilitating better utilization of innovation outcomes in industries. Moreover, there are regional disparities in the influence of telecommunications infrastructure on economic and social innovative development. In the eastern regions, telecommunications infrastructure construction primarily promotes mobile communication, with clear spillover effects. In contrast, in western regions, it mainly facilitates fixed communication networks. Thus, further strengthening telecommunications infrastructure construction provides a new impetus for social innovative development and long-term sustainability. It is essential to persistently advance the coordinated construction of mobile and fixed communication infrastructure to achieve regional development coordination.

1. Introduction

Telecommunications infrastructure, as a vital component of new infrastructure, is at the forefront of the technological revolution. Traditional infrastructure construction has seen diminishing marginal utility and returns over the years, while new infrastructure, driven by technological innovation, is playing an increasingly critical role in the new economy, represented by the digital economy. Consequently, the relationship between telecommunications infrastructure development and the level of innovation development has become a widely discussed topic in the academic community. Both central and local governments attach great importance to telecommunications infrastructure construction and provide robust policy support. In 2021 and 2022, China issued the “14th Five-Year National Informatization Plan” and the “14th Five-Year Digital Economy Development Plan”, emphasizing that the next 3–5 years would represent a crucial period for the large-scale development of 5G technology in terms of expanding the breadth and depth of 5G applications, empowering various industries, and driving the intelligent and digital development of the digital economy. The number of telephone users in China surged from only 1.93 million households in 1978 to 1.73 billion households by 2023. The mobile phone penetration rate increased from less than half a unit per hundred people in 1978 to 122.51 units per hundred people in 2023. In this context, how does the development of telecommunications infrastructure affect innovation development, and what differential effects does it have on innovation development in different regions? Research on these questions holds significant theoretical and practical significance for the formulation and implementation of relevant national policies.

Mobile network construction varies significantly at different stages of telecommunications development. In the 3G development stage, mobile telecommunication technology began to support data transmission. During this period, mobile phones could support SMS and voice services, enhance multimedia services, and enable users to browse web pages and send/receive emails directly on their mobile phones, ushering in the era of mobile internet. After China’s official commercialization of 4G in 2014, there was large-scale development of mobile internet. In 2015, peak investment in mobile telecommunications infrastructure was reached, with a total investment of 438.62 billion CNY, mainly used for the construction of 4G telecommunication network infrastructure. The period from 2020 to 2023 heralded the deployment and construction of 5G telecommunications networks, and investment in the telecommunications industry gradually increased. In addition, the total number of mobile telecommunications base stations in the country increased from 3.51 million in the 3G era to 9.31 million in the 4G era, and further to 11.62 million in the 5G era. During this period, the development of telecommunications infrastructure allowed the mobile internet to penetrate into various aspects of the economy and society, changing people’s ways of production and living.

In terms of fixed telecommunications infrastructure, China has been actively promoting the “Broadband China” strategy, enhancing the level of broadband infrastructure and accelerating the pace of broadband network upgrading. From 2009 to 2013, the number of broadband access ports increased from 138.36 million to 359.72 million. After the implementation of the “Broadband China” strategy, fixed telecommunications became a strategic public infrastructure for the country, and from 2014 to 2023, the number of broadband access ports increased significantly from 401.05 million to 1140.00 million. The implementation of this strategy demonstrates the nation’s determination to improve network infrastructure construction. fixed telecommunications networks, as essential information infrastructure, support the development of innovative industries.

Concerning the impact of the location and layout of mobile and fixed telecommunications infrastructure on innovation development, due to the higher construction and maintenance costs of mobile telecommunications networks compared to fixed telecommunications network facilities [1], mobile telecommunications base stations are located with the aim to cover more population [2] and focus on densely populated areas. Because mobile telecommunications network construction is optimized and improved based on population distribution, areas with population densities exceeding 2000 people per square kilometer are prioritized. Data from the “Statistical Bulletin of National Economic and Social Development” were used to calculate the population density of each province in China. It can be observed that provinces and cities with higher population densities have a greater distribution of mobile telecommunications base stations. In contrast, the western regions in China have a more scattered population distribution. For the coverage of industrial parks and commercial buildings, broadband access is a cost-effective way to achieve better network coverage.

Compared to non-high-tech industries, telecommunications infrastructure possesses the characteristics of advanced technology, high investment, and high penetration. It plays a crucial role in promoting innovation development upstream and downstream of the industry chain. The development of innovation is reflected in three aspects, which are innovation R&D investment, innovation knowledge output, and innovation application output. In the article, the R&D expenses of large-scale industrial enterprises are used to measure innovation R&D investment; the number of patent applications is used to measure innovation knowledge output; and the industrial output value of high-tech enterprises is used to measure innovation application output.

There are three primary aims of this study: (1) This thesis will connect telecommunications infrastructure construction with the level of innovative development and measure the impact of telecommunications infrastructure on R&D investment, knowledge output, and application output from the dimensions of mobile telecommunications infrastructure and fixed telecommunications infrastructure. This will provide a new perspective for understanding how to enhance the level of innovative development. (2) This study will analyze the different impacts of telecommunications infrastructure on innovation at different stages of telecommunications technology development. (3) This study will analyze regional disparities in the impact of telecommunications infrastructure on economic and social innovative development. These findings offer guidance for policy regarding the role of fixed telecommunications infrastructure in ensuring the foundation for technological and convergent innovation.

The rest of this paper is organized as follows. Section 2 reviews recent findings regarding telecommunications and innovation. Section 3 describes the analytical framework and research hypotheses. Section 4 elaborates on the econometric methods and data. Section 5 of this paper presents the discussion of the estimated results. This paper is concluded in Section 6.

2. Literature Review

Most scholars believe that the development of information and communication technology has positive external effects on economy and innovation. Scholars studying the role of the internet in promoting total factor productivity have argued that the internet, as an information highway, possesses powerful network effects. Due to these significant network effects, the internet has a substantial positive impact on total factor productivity. Kong believes that the application of telecommunications technology can lead to innovation in industry products, production processes, and business models, with significant economic spillover effects [3]. In [4], Hulten focused on the Indian manufacturing industry and found a positive relationship between the development of Indian telecommunications infrastructure and total factor productivity. Domestic scholars hold similar views. Alice believes that the three types of infrastructure, transportation, energy, and information, all have spillover effects on China’s total factor productivity [5,6]. David conducted a study on the spatial spillover effects of telecommunications infrastructure and found that local telecommunications infrastructure has a positive impact on local technical efficiency and technological progress within the region [7]. Furthermore, telecommunications infrastructure in other regions has a positive impact on local technical efficiency, leading to significant positive spatial spillover effects on total factor productivity [8]. The existence of spatial spillover effects requires local governments to consider the actions of other regions when formulating economic policies. While maintaining the optimal scale of telecommunications infrastructure, they must make proactive adjustments to economic policies to ensure the continuous growth of total factor productivity. Han believes that the rapid development of the internet has significantly improved the efficiency of regional innovation in China [9]. The rapid development of the internet not only promotes the efficiency of regional innovation but also accelerates financial development, industrial upgrading, and human capital accumulation, indirectly benefiting regional innovation efficiency [10,11].

Additionally, scholars have explored the threshold values at which the external effects of telecommunications infrastructure come into play. Koutroumpis argued that the network effects of the internet in European Union countries began to manifest when the internet penetration rate reached 20% [12]. For OECD countries, the critical threshold was found to be when the fixed telephone penetration rate reached 40% [13]. In China, when the broadband penetration rate reached 10%, network effects became evident. Among China’s 31 provinces and cities, 40% were found to have a broadband penetration rate exceeding 10%, indicating that network effects were already present in terms of total factor productivity [14].

In summary, the literature lacks comprehensive research on the direct impacts of telecommunications infrastructure construction on innovative development. Most domestic and international studies either analyze this from the perspective of the vertical structure of the telecommunications infrastructure industry or focus on industries such as the internet, information technology, and intelligence. Both approaches overlook the fundamental role of telecommunications infrastructure in facilitating these industries. Furthermore, research on the economic impact of telecommunications infrastructure, both domestically and internationally, primarily concentrates on its role in economic growth. However, innovation is the driving force behind economic growth, and as a high-tech industry, telecommunications infrastructure promotes innovative development, which, in turn, stimulates economic growth. Therefore, further research on the direct impact of telecommunications infrastructure on innovative development is needed.

3. Analytical Framework and Research Hypotheses

With the improvement in telecommunications infrastructure and its penetration into various industries, the level of social innovative development has undergone significant changes. The deep integration and development of the information and telecommunications industry have provided guarantees for the innovation of production methods in other industries. This paper analyzes the underlying mechanisms through which telecommunications infrastructure impacts the level of social innovative development through the transmission mechanisms and proposes several important hypotheses.

3.1. Analysis of the Impact of Telecommunications Infrastructure Construction on Innovative Development

First, telecommunications infrastructure affects innovation development in upstream industries. Telecommunications technology patents provide fundamental support for the entire industry chain and are at the core of the information industry. The telecommunications industry has a closed nature, requiring high standards for patent technology and standardization. The development of telecommunications infrastructure construction to some extent drives the R&D of telecommunications technology [15,16]. In the history of the development of the telecommunications industry, electronic equipment companies often occupy the commanding heights of the telecommunications industry chain by mastering telecommunications technology patents [17]. In telecommunications infrastructure construction, the R&D of telecommunications technology must have a high level of innovation to effectively support it. This includes the development of core technology patents, chip manufacturing, and component equipment research [18]. High-quality and diverse telecommunications technology capabilities are conducive to improving telecommunications infrastructure applications [19]. All of these aspects place higher demands on R&D departments, prompting them to continuously develop new telecommunications technologies, build new R&D platforms, and enhance their telecommunications technology service capabilities and innovation efficiency, thereby promoting innovation spillover effects in R&D departments.

Second, telecommunications infrastructure construction and development have spillover effects on downstream industries. telecommunications infrastructure must have high technical capabilities and network support to provide effective telecommunications service capabilities to downstream industries. This includes providing advanced telecommunications networks for the restructuring of the industry terminal market, driving investment in cloud computing and data centers, and advancing exploration in the field of industrial internet [20]. High-quality and diversified telecommunications service capabilities facilitate the transformation of innovation methods and the emergence of innovation in downstream industries [21].

Finally, as telecommunications networks penetrate various industries, the innovative value of the telecommunications infrastructure empowering various industries gradually emerges. First, it provides crucial support for the effective integration and rapid transmission of information. Information and telecommunications technology can improve the way information is obtained by reducing time and space barriers, enhancing information sharing, and enabling the entire society to accumulate knowledge through the process of information acquisition, understanding, analysis, and reprocessing during information dissemination and sharing. This is conducive to improving the knowledge level of the workforce and accelerating the process of technological innovation. On the other hand, the foundation of digital transformation and network transformation in various industries is the rapid acquisition and efficient management of information. Therefore, the low latency and high bandwidth of telecommunications networks can meet the demand for real-time information transmission, covering the information range in various production and management stages, enhancing the matching of information, improving the speed and quality of production and management activities, and promoting the efficiency of innovation. Second, telecommunications infrastructure helps reduce production and transaction costs. Regarding production costs, in traditional industries, production and manufacturing processes often require manual on-site operations. Telecommunications networks have a high transmission efficiency and reliability, and when combined with artificial intelligence, they make remote control and unmanned operations possible, thereby reducing labor costs in production and manufacturing [22]. On the other hand, for complex industries, as the production process becomes more specialized, the resulting execution costs and transaction costs increase. This leads to a lower organizational efficiency [23]. Information and telecommunications technology, combined with big data, identifies, calculates, and performs process and step recognition during the production organization process, thereby improving production efficiency [24]. In terms of transaction costs, high-tech industries have high R&D costs, large capital investment, and rapid product updates. The information network established by telecommunications infrastructure helps high-tech companies capture changes in market demand and changes between upstream and downstream links of the supply chain, reducing market frictions and lowering transaction costs. It reduces the risk of mismatch between high-tech R&D and the market, increases the targeted allocation of innovation resources, and enhances innovation efficiency [25]. Based on this, this paper proposes the following research hypothesis:

Hypothesis 1.

Given the completeness of telecommunications infrastructure and its deep integration into its own upstream and downstream industry chains and other industries, it has a positive effect on the level of social innovative development. That is, telecommunications infrastructure promotes innovative development.

3.2. Analysis of Effects of Telecommunications Infrastructure Construction on Innovation Development in Different Time Stages and Regions

The previous analysis examined the general effects of telecommunications infrastructure on innovation development without considering cross-sectional differences. Previous studies have found differences in the impact of telecommunications infrastructure in different construction stages on R&D intensity, innovation knowledge output, and innovation application output, as well as different responses to external environmental shocks, such as economic conditions and market competition in different regions.

Regarding the different stages of telecommunications infrastructure construction, Scherngell suggested that mobile telecommunications infrastructure construction affects the level of data transmission in the mobile internet, and innovation efficiency is sensitive to data information transmission speed [26]. Concerning the different levels of telecommunications infrastructure in different regions, the widespread diffusion and effective utilization of telecommunications infrastructure construction in regional innovation systems cannot be achieved without matching differences in inputs such as technology, organization, and institutions. Due to differences in resource endowments, investment in regional innovation systems and their subsystems varies, resulting in different spillover effects of telecommunications infrastructure in regional innovation systems [27]. In addition, differences in factors such as economic development level, innovation capability, science, and technology policies can affect the innovation spillover of telecommunications infrastructure in regional innovation systems. Based on these considerations, this paper proposes the following research hypothesis:

Hypothesis 2.

The innovation spillover effects of telecommunications infrastructure construction exhibit certain heterogeneity characteristics in different time stages and regions.

4. Methodology

4.1. Econometric Methods

This study analyzes panel data samples from 31 provinces in China from 2009 to 2022. The samples may include unobservable factors that vary by province. To address the common problem of unobservable individual effects in panel data, two different estimation methods are available: if unobservable individual effects are correlated with some explanatory variables, the fixed effects model is used; if unobservable individual effects are uncorrelated with all explanatory variables, the random effects model is used [28,29]. This paper uses the Hausman test to make this determination. Based on the Hausman test, we rejected the hypothesis that the random effects model is the most appropriate, and we concluded that the fixed effects model is the most appropriate [30]. Thus, we use the fixed effects model for the analysis in this paper.

Previous research has shown a significant correlation between telecommunications infrastructure and innovation [31,32]. Building on these research findings, this paper constructs a panel data model including the R&D expenses of large-scale industrial enterprises, the number of patent applications, the industrial output value of high-tech enterprises, the number of mobile telecommunications base stations, the number of broadband access ports, and other control variables. The basic econometric model is as follows:

$$\begin{gathered} \ln {Innov}_{it}={\alpha }_i+{\beta }_1\ln {Station}_{it}+{\gamma }_1\ln {Pgdp}_{it}+{\gamma }_2{Urb}_{it} \\ +{\gamma }_3{Grlabor}_{it}+{\gamma }_4{Edugao}_{it}+{\gamma }_5{Trade}_{it}+{\epsilon }_{it} \end{gathered}$$

(1)

$$\begin{array}{cc}\hfill \ln {Innov}_{it}={\alpha }_i& +{\beta }_1\ln {Broadband}_{it}+{\gamma }_1\ln {Pgdp}_{it}+{\gamma }_2{Urb}_{it}\hfill \\ & +{\gamma }_3{Grlabor}_{it}+{\gamma }_4{Edugao}_{it}+{\gamma }_5{Trade}_{it}+{\epsilon }_{it}\end{array}$$

(2)

In addition, mobile telecommunications may have both complementary and substitution effects on fixed telecommunications, and the impact of one type of fixed telecommunications on innovation development may be influenced by the other type. To account for this, interaction terms for mobile telecommunications and fixed telecommunications are included in the model, as shown below:

$$\begin{array}{cc}\hfill \ln {Innov}_{it}={\alpha }_i& +{\beta }_1\ln {Station}_{it}+{\beta }_2\ln {Broadband}_{it}\hfill \\ & +{\beta }_3\ln {Station}_{it}\times \ln {Broadband}_{it}+{\gamma }_1\ln {Pgdp}_{it}\\ & +{\gamma }_2{Urb}_{it}+{\gamma }_3{Grlabor}_{it}+{\gamma }_4{Edugao}_{it}+{\gamma }_5{Trade}_{it}\\ & +{\epsilon }_{it}\end{array}$$

(3)

where ${Innov}_{it}$ is the level of innovation development in province i in year t. Innovation development is measured in three dimensions: R&D expenditure, which is ${R\&D}_{it}$; patent applications, which is ${Patentsl}_{it}$; and the industrial output value of high-tech enterprises, which is ${Techoutput}_{it}$. ${Station}_{it}$ is the number of mobile telecommunications base stations, and ${Broadband}_{it}$ is the number of broadband access ports, which is fixed telecommunications. ${\epsilon }_{it}$ is an error term. Our hypotheses are that the effects of telecommunications ${\beta }_1$ and ${\beta }_2$ are positive, and ${\beta }_1$ is larger for mobile telecommunications than fixed telecommunications.

In our econometric models, we have added GDP per capita, ${Pgdp}_{it}$, as a control variable to measure the level of economic development. According to Girmay’s findings [9], even though innovative technologies only account for a small portion of GDP, there is a significant correlation between innovative technologies and economic development. We also included the second control variable, ${Urb}_{it}$. ${Urb}_{it}$ is the proportion of urban population to the total population in each province, and it is used to reflect the level of urbanization. China has redirected substantial resources from rural to urban areas. Urban areas now house a greater number of industrial enterprises than rural areas [33]. Thus, urban regions are home to the majority of innovative technology research and development [34]. We also added ${ Grlabor}_{it}$ as the third control variable.${ Grlabor}_{it}$ is the growth rate of the labor force. The population aged 15 to 64 years, is used to represent labor force growth. The response of technology increases the available supply of productive workers in the economy [35,36]. We also added ${Trade}_{it}$ as the fourth control variable. ${Trade}_{it}$ is the ratio of the total import and export value of operating units to GDP, and it is used to measure the intensity of foreign trade. Global trade is a catalyst for innovation and technology, and the closeness among trading partners ensures the rapid development of innovation and technology [37].

In order to investigate the lagged effects of mobile and broadband on innovation development, we introduced a time lag term ($\ln {Innov}_{i,t-1}$) into the fixed effects model to make the regression structure more realistic and selected fixed effects in the dynamic panel model to ensure the robustness of the regression results.

The model is specified as follows:

$$\begin{array}{cc}\hfill \ln {Innov}_{it}={\alpha }_i& +{\beta }_0\ln {Innov}_{i,t-1}+{\beta }_1\ln {Station}_{it}+{\gamma }_1\ln {Pgdp}_{it}\hfill \\ & +{\gamma }_2{Urb}_{it}\\ & +{\gamma }_3{Grlabor}_{it}+{\gamma }_4{Edugao}_{it}+{\gamma }_5{Trade}_{it}+{\epsilon }_{it}\end{array}$$

(4)

$$\begin{array}{cc}\hfill \ln {Innov}_{it}={\alpha }_i& +{\beta }_0\ln {Innov}_{i,t-1}+{\beta }_1\ln {Broadband}_{it}+{\gamma }_1\ln {Pgdp}_{it}\\ & +{\gamma }_2{Urb}_{it}+{\gamma }_3{Grlabor}_{it}+{\gamma }_4{Edugao}_{it}+{\gamma }_5{Trade}_{it}\\ & +{\epsilon }_{it}\end{array}$$

(5)

$$\begin{array}{cc}\hfill \ln {Innov}_{it}={\alpha }_i& +{\beta }_0\ln {Innov}_{i,t-1}+{\beta }_1\ln {Station}_{it}+{\beta }_2\ln {Broadband}_{it}\\ & +{\beta }_3\ln {Station}_{it}\times \ln {Broadband}_{it}+{\gamma }_1\ln {Pgdp}_{it}\\ & +{\gamma }_2{Urb}_{it}+{\gamma }_3{Grlabor}_{it}+{\gamma }_4{Edugao}_{it}+{\gamma }_5{Trade}_{it}\\ & +{\epsilon }_{it}\end{array}$$

(6)

where $\ln {Innov}_{i,t-1}$ is the one-year lagged logarithm of the level of innovation development in the province. We expect ${\beta }_0$ to be positively related with $\ln {Innov}_{it}$.

4.2. Data Sources

The data used in this paper have been collected from a number of different sources. Data on mobile telecommunications base stations and broadband access ports were retrieved from the China Telecommunications Statistics Yearbook Report and the annual reports of Chinese telecommunications operators and are available for the years 2009–2022. The data consist of the mobile telecommunications infrastructure, expressed as a the number of mobile telecommunications base stations, including 3G (e.g., WCDMA), 4G (e.g., LTE), and 5G mobile network stations. The data consists of the fixed telecommunications infrastructure, expressed as the number of broadband access ports.

Data on GDP, urban population, and import and export value were retrieved from the China Statistical Yearbook. The China Statistical Yearbook publishes different GDP series. This paper uses a measure where the levels of GDP have been constructed based on multiple PPP benchmark years and therefore corrects for changing prices between these benchmarks. Data on the labor force were retrieved from the China Labor Statistical Yearbook. The labor force is measured in terms of the population aged 15 to 64 years old.

Data on innovation development were retrieved from the Statistical Compilation of China’s Sixty Years and Statistical Yearbook of the Ministry of Science and Technology. The innovation development was constructed using the R&D expenses of large-scale industrial enterprises, the number of patent applications, and the industrial output value of high-tech enterprises. Descriptive statistical analyses for the major variables provide mean, standard deviation, median, minimum, and maximum values (

Table 1. Descriptive statistics of main variables.

Symbols	Observations	Mean	Std. Dev.	Min	Max
$\ln R\&D$	372	4.797	1.704	−1.832	7.973
$\ln Patentsl$	372	10.367	1.662	5.088	13.795
$\ln Techoutput$	372	18.667	2.709	7.362	22.797
$\ln Station$	372	11.435	1.021	8.381	13.715
$\ln Broadband$	372	6.927	1.159	2.753	9.141
$\ln Pgdp$	372	10.686	0.515	9.289	12.141
$Urb$	372	0.562	0.138	0.222	0.942
$Grlabor$	372	0.005	0.027	−0.074	0.232
$Edugao$	372	0.159	0.038	0.039	0.252
$Trade$	372	0.266	0.283	0.011	1.398

).

Additionally, based on the socioeconomic conditions of different regions and references to the regional development policies of the State Council and the National Development and Reform Commission, as well as the consideration of regional differences in the popularization of telecommunications technology applications, the 31 provinces and municipalities are classified into three regions: Eastern, Central, and Western China. Among these 31 provinces, there are 11 provinces in the Eastern region, 8 provinces in the Central region, and 12 provinces in the Western region in China.

5. Results and Discussion

5.1. Baseline Regressions

In this study, we analyzed panel data samples from 31 provinces in China for the years 2009–2022. The Hausman test was employed to make a determination. The null hypothesis of the Hausman test assumes that unobservable individual effects are unrelated to explanatory variables. The alternative hypothesis assumes that unobservable individual effects are related to explanatory variables. The Hausman test results in this study rejected the null hypothesis, implying that a fixed effects model should be used for the analysis.

The dependent variable in the regression analysis is the R&D expenditure of industrial enterprises above a certain scale, as shown in

Table 2. The effects of telecommunications infrastructure on log R&D expenditure of industrial enterprises.

Independent Variables	Dependent Variable: R&D Expenditure of Industrial Enterprises
	(1)	(2)	(3)	(4)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	1.361 *** (0.042)		0.466 *** (0.157)	0.138 (0.198)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		1.158 *** (0.0342)	0.780 *** (0.132)	0.264 (0.234)
$\ln {\mathrm{Station}}_{\mathrm{it}}\times \ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$				0.048 *** (0.018)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.729 *** (0.135)	0.942 *** (0.129)	0.856 *** (0.131)	0.790 *** (0.132)
$\mathrm{U}\mathrm{r}\mathrm{b}$	1.010 ** (0.492)	−0.949 ** (0.458)	−0.221 (0.515)	0.174 (0.531)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−2.071 (1.297)	−1.295 (1.257)	−1.409 (1.244)	−1.535 (1.234)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	5.304 *** (1.239)	4.114 *** (1.207)	4.299 *** (1.196)	4.227 *** (1.186)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.353 ** (0.161)	0.322 ** (0.156)	0.318 ** (0.154)	0.223 (0.157)
Constant	−6.199 *** (1.278)	0.371 (1.227)	−1.849 (1.425)	2.082 (2.048)
Observations	372	372	372	372
R-squared	0.872	0.881	0.884	0.886

Note: Variables with ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

. In Table 2, the first column presents the Log of the number of mobile telecommunications base stations as the core explanatory variable, and the second column uses the Log of the number of broadband access ports. The regression coefficients in both columns are positive and statistically significant at the 1% level. In the third column, both the number of mobile telecommunications base stations and the number of broadband access ports are included as explanatory variables for R&D expenditure, testing the robustness of the previous results. The results show that the regression coefficients for both variables remain significantly positive at the 1% level, confirming the conclusions of the first and second columns. The fourth column presents the regression results after introducing the interaction term between mobile base stations and broadband access. The coefficient is 0.048, and it is statistically significant at the 1% level. This result suggests that mobile and fixed telecommunications have a significant positive impact on R&D expenditure, indicating a complementary effect between the two. These results are consistent with one strand of the literature. With the improvement in mobile telecommunications network capabilities, the development of associated infrastructure is synchronized [10], enhancing the capabilities of fixed telecommunications networks and thereby promoting an increase in R&D expenditure [27,33].

The results for the number of patent applications as the dependent variable are shown in

Table 3. The effects of telecommunications infrastructure on the log number of patent applications.

Independent Variables	Dependent Variable: Number of Patent Applications
	(1)	(2)	(3)	(4)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	1.523 *** (0.041)		0.548 *** (0.154)	0.458 ** (0.196)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		1.294 *** (0.033)	0.848 *** (0.129)	0.708 *** (0.232)
$\ln {\mathrm{Station}}_{\mathrm{it}}\times \ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$				0.0133 (0.0182)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.661 *** (0.134)	0.900 *** (0.127)	0.798 *** (0.129)	0.780 *** (0.131)
$\mathrm{U}\mathrm{r}\mathrm{b}$	2.943 *** (0.488)	0.747 * (0.451)	1.603 *** (0.505)	1.710 *** (0.526)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	1.179 (1.286)	−0.324 (1.24)	−0.458 (1.22)	−0.492 (1.222)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	0.355 (1.228)	−0.956 (1.191)	−0.738 (1.173)	−0.758 (1.174)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.26 (0.16)	0.228 (0.153)	0.222 (0.151)	0.197 (0.155)
Constant	−15.88 *** (1.267)	−8.535 *** (1.21)	−11.14 *** (1.398)	−10.07 *** (2.027)
Observations	372	372	372	372
R-squared	0.891	0.898	0.902	0.902

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

. The first and second columns of Table 3 show that the regression coefficients for the number of mobile telecommunications base stations and the number of broadband access ports are 1.532 and 1.294, respectively, and both are statistically significant at the 1% level. This implies that for every 1% increase in the number of mobile telecommunications base stations, there is a 1.532% increase in the number of patent applications, and for every 1% increase in the number of broadband access ports, there is a 1.294% increase in the number of patent applications. The third column includes both the number of mobile telecommunications base stations and the number of broadband access ports as variables, and the results show that their regression coefficients remain significantly positive at the 1% level, confirming the robustness of the previous results. The fourth column shows the regression results after introducing the interaction term between mobile base stations and broadband access. The coefficient is 0.048, and it is statistically significant at the 1% level. As mentioned in the literature review, this indicates that mobile and fixed telecommunications have a significant positive impact on the number of patent applications, but there is no significant complementary effect between them [3,7]. These results further support the idea of Daron’s findings [38], which showed that the efficiency properties of telecommunications infrastructure and creation of new technologies will help patent applications to save on costs.

The regression results for the total industrial output value of high-tech enterprises are shown in

Table 4. The effects of telecommunications infrastructure on the log total industrial output of high-tech enterprises.

Independent Variables	Dependent Variable: Total Industrial Output of High-Tech Enterprises
	(1)	(2)	(3)	(4)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	1.631 *** (0.043)		0.438 *** (0.156)	−0.243 (0.191)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		1.394 *** (0.034)	1.039 *** (0.131)	−0.0317 (0.225)
$\ln {\mathrm{Station}}_{\mathrm{it}}\times \ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$				0.101 *** (0.017)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.313 ** (0.140)	0.563 *** (0.129)	0.482 *** (0.131)	0.343 *** (0.127)
$\mathrm{U}\mathrm{r}\mathrm{b}$	3.713 *** (0.508)	1.390 *** (0.455)	2.073 *** (0.513)	2.892 *** (0.512)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−3.170 ** (1.338)	−2.180 * (1.251)	−2.288 * (1.239)	−2.548 ** (1.188)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	6.645 *** (1.278)	5.132 *** (1.201)	5.306 *** (1.191)	5.155 *** (1.141)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	−0.149 (0.166)	−0.191 (0.155)	−0.196 (0.153)	−0.392 *** (0.151)
Constant	−20.29 *** (1.318)	−12.41 *** (1.221)	−14.49 *** (1.420)	−6.332 *** (1.971)
Observations	372	372	372	372
R-squared	0.897	0.911	0.913	0.920

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

. The first and second columns of Table 4 show that the regression coefficients for the number of mobile telecommunications base stations and the number of broadband access ports are 1.631 and 1.394, respectively, and both are statistically significant at the 1% level. This implies that for every 1% increase in the number of mobile telecommunications base stations, there is a 1.631% increase in the total industrial output value of high-tech enterprises, and for every 1% increase in the number of broadband access ports, there is a 1.394% increase in the total industrial output value of high-tech enterprises. The third column shows the joint test of the number of mobile telecommunications base stations and the number of broadband access ports, and both coefficients are statistically significant at the 1% level, indicating the robustness of the results in the first and second columns. The fourth column shows that the interaction term between the number of mobile telecommunications base stations and the number of broadband access ports is significantly positive at the 1% level, indicating a complementary effect between mobile and fixed telecommunications that promotes the increase in the total industrial output value of high-tech enterprises.

The above research results indicate the following. First, in terms of economic and social innovation development measured based on the dimensions of innovation R&D input, innovation knowledge output, and innovation application output, all the dimensions are significantly positively affected by the improvement in telecommunications infrastructure. These results are in line with those of previous studies [3,10,11]. The regression coefficients for mobile telecommunications networks are generally higher than those for telecommunications networks. Second, compared to other factors, mobile telecommunications network capability has the largest regression coefficient for the total industrial output value of high-tech enterprises, followed by the number of patent applications and R&D expenditure. Telecommunications network capabilities exhibit similar results. This indicates that the supporting function of telecommunications infrastructure for applying innovative technology to economic industries needs further improvement.

Additionally, from Table 2, Table 3 and Table 4, it can be observed that other control variables also play roles: (1) Economic development level promotes economic and social innovation activities. In all three regression models, the regression coefficients for per capita GDP, representing the economic development level, are positive and statistically significant at the 1% and 5% levels. This result suggests that the economic development level lays the foundation for innovation R&D input, innovation knowledge output, and innovation application output. Economic development is an essential accelerator for innovation development. This finding broadly supports the work of other studies in this area linking economy with innovation development [6,8]. (2) The level of urbanization contributes positively to the promotion of innovation development. The aforementioned regression results indicate that urbanization, in most cases, significantly enhances innovation R&D investment, the generation of innovative knowledge, and the advancement of innovation application. A possible explanation for this might be that higher levels of urbanization represent more comprehensive urban infrastructure and public service provisions, particularly in the development of digital infrastructure, which can significantly bolster innovation efforts by businesses and research institutions. These explanations corroborate the ideas of Roller’s findings [13]. (3) The significance of labor force supply for the impact of economic and social innovation development varies. This outcome is contrary to that of Maestas [35] and Daron Acemoglu et al. [38], who found that the supply of the labor force can potentially result in stronger economies of scale, as individuals accumulate experience while simultaneously producing goods and providing services, thereby contributing to increased production efficiency.

Table 5. The effects of telecommunications infrastructure on the log R&D expenditure of industrial enterprises and lagged effects (including lagged dependent variable).

Independent Variables	Dependent Variable: R&D Expenditure of Industrial Enterprises
	(1)	(2)	(3)	(4)
$\ln {\mathrm{R}\&\mathrm{D}}_{\mathrm{i},\mathrm{t}-1}$	0.556 *** (0.040)	0.552 *** (0.041)	0.556 *** (0.041)	0.528 *** (0.042)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	−0.091 * (0.053)		−0.091 (0.061)	−0.199 ** (0.077)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		−0.046 *** (0.053)	0.001 (0.057)	−0.162 ** (0.092)
$\ln {\mathrm{Station}}_{\mathrm{it}}\times \ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$				0.017 *** (0.008)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.695 *** (0.106)	0.647 *** (0.112)	0.695 *** (0.116)	0.704 *** (0.116)
$\mathrm{U}\mathrm{r}\mathrm{b}$	−0.442 (0.559)	−0.829 (0.518)	−0.444 (0.577)	−0.722 (0.586)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	0.073 (0.311)	0.081 (0.314)	0.073 (0.313)	0.116 (0.312)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	1.352 * (0.751)	1.474 * (0.750)	1.352 * (0.752)	1.141 (0.753)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.195 (0.149)	0.303 ** (0.131)	0.195 (0.150)	0.298 * (0.155)
Constant	−4.216 *** (0.710)	−4.262 *** (0.878)	−4.21 *** (0.877)	−3.030 *** (1.015)
Observations	310	310	310	310
R-squared	0.879	0.878	0.879	0.882

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

,

Table 6. The effects of telecommunications infrastructure on the log number of patent applications and lagged effects (including lagged dependent variable).

Independent Variables	Dependent Variable: Number of Patent Applications
	(1)	(2)	(3)	(4)
$\ln {\mathrm{P}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{s}\mathrm{l}}_{\mathrm{i},\mathrm{t}-1}$	0.714 *** (0.045)	0.687 *** (0.044)	0.688 *** (0.044)	0.651 *** (0.042)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	0.107 *** (0.067)		−0.016 (0.075)	0.310 *** (0.090)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		0.252 *** (0.063)	0.260 *** (0.129)	0.728 *** (0.105)
$\ln {\mathrm{Station}}_{\mathrm{it}}\times \ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$				−0.049 *** (0.008)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.332 *** (0.132)	0.155 (0.131)	0.164 (0.137)	0.277 *** (0.131)
$\mathrm{U}\mathrm{r}\mathrm{b}$	−0.451 (0.715)	−1.062 (0.644)	−0.992 (0.715)	−0.001 (0.696)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	0.996 ** (0.392)	0.876 ** (0.384)	0.875 ** (0.385)	0.776 ** (0.364)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	−1.145 (0.940)	−1.116 (0.913)	−1.137 (0.919)	−0.371 (0.878)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	−0.049 (0.188)	−0.005 (0.160)	−0.025 (0.184)	−0.327 * (0.181)
Constant	−1.216 (0.858)	0.748 (0.994)	0.756 (0.997)	−3.689 *** (1.214)
Observations	310	310	310	310
R-squared	0.928	0.932	0.932	0.939

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

and

Table 7. The effects of telecommunications infrastructure on the log total industrial output of high-tech enterprises and lagged effects (including lagged dependent variable).

Independent Variables	Dependent Variable: Total Industrial Output of High-Tech Enterprises
	(1)	(2)	(3)	(4)
$\ln {\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t}}_{\mathrm{i},\mathrm{t}-1}$	0.467 *** (0.046)	0.468 *** (0.045)	0.468 *** (0.046)	0.467 *** (0.046)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	0.092 (0.107)		0.011 (0.121)	−0.020 (0.151)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		0.166 * (0.098)	0.161 (0.112)	0.115 (0.173)
$\ln {\mathrm{Station}}_{\mathrm{it}}\times \ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$				0.004 (0.014)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.459 ** (0.195)	0.340 * (0.204)	0.335 (0.213)	0.328 (0.214)
$\mathrm{U}\mathrm{r}\mathrm{b}$	−0.251 (1.098)	−0.589 (1.002)	−0.636 (1.128)	−0.728 (1.160)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−1.008 (0.618)	−1.094 * (0.617)	−1.092 * (0.619)	−1.079 * (0.621)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	1.714 (1.468)	1.701 (1.455)	1.715 (1.466)	1.639 (1.484)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.215 (0.295)	0.214 (0.255)	0.228 (0.295)	0.259 (0.308)
Constant	4.268 *** (1.217)	−12.41 *** (1.531)	5.622 *** (1.534)	6.048 *** (1.962)
Observations	310	310	310	310
R-squared	0.762	0.764	0.764	0.764

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

show that one year lagged coefficients remain highly significant. It also indicates that our results are still robust, while introducing dynamic specifications and a lagged dependent variable. Including a lagged dependent variable can cause bias in the estimation of lagged dependent variable coefficients. The bias is inversely proportional to the length of the panel. It will gradually disappear as the panel becomes longer. Table 5, Table 6 and Table 7 show that after introducing the lagged dependent variable in the regression, our estimated values are small but still highly significant.

5.2. Estimation Results of the Development Phases of Telecommunications Infrastructure

To investigate the impact of telecommunications infrastructure development on innovation R&D expenditure, innovation knowledge output, and innovation application output, this study constructs dummy variables for different periods, representing the years 2009–2013 and 2014–2022. The period from 2009 to 2013 marked the era of 3G mobile telecommunications technology application in China, which presented an improvement in voice and data transmission speeds and met the basic demands for mobile internet access. The years from 2014 to 2022 witnessed the construction of 4G and 5G telecommunications networks, which provided higher-speed data telecommunications capabilities, significantly enhanced data upload and download speeds, and fostered the development of mobile payments and short video applications.

The regression results for the R&D expenditure of large-scale industrial enterprises as the dependent variable are shown in

Table 8. Relationship between telecommunications infrastructure development in different development phases and R&D expenditure of industrial enterprises.

Independent Variables	Dependent Variable: R&D Expenditure of Industrial Enterprises
	2009–2013			2014–2022
	(1)	(2)	(3)	(4)	(5)	(6)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	1.287 *** (0.074)		0.241 (0.393)	1.415 *** (0.049)		0.089 (0.283)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		1.112 *** (0.058)	1.564 ** (0.600)		1.208 *** (0.041)	−0.195 (0.394)
$\ln {\mathrm{Station}}_{\mathrm{it}}\times \ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$			−0.0441 (0.053)			0.077 ** (0.032)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	1.258 ***	1.437 ***	1.556 ***	0.584 ***	0.812 ***	0.633 ***
	(0.301)	(0.276)	(0.319)	(0.139)	(0.135)	(0.136)
$\mathrm{U}\mathrm{r}\mathrm{b}$	−0.727	−2.946 ***	−3.393 ***	1.723 ***	−0.056	1.262 **
	(1.022)	(0.921)	(1.197)	(0.523)	(0.492)	(0.562)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−0.903 (2.427)	0.922 (2.278)	0.83 (2.298)	−1.673 (1.551)	−0.438 (1.527)	−1.35 (1.486)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	4.628 ** (2.204)	3.524 * (2.058)	3.352 (2.081)	6.061 *** (1.422)	4.925 *** (1.409)	5.309 *** (1.358)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.374 (0.254)	0.343 (0.236)	0.428 * (0.258)	0.251 (0.206)	0.22 (0.203)	0.064 (0.205)
Constant	−8.365 *** (2.658)	−2.042 (2.499)	−5.477 (4.959)	−6.852 *** (1.385)	−0.145 (1.335)	3.464 (3.121)
Observations	155	155	155	217	217	217
R-squared	0.845	0.866	0.867	0.903	0.907	0.915

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

. According to the results in Table 8, innovation R&D expenditure increases with the expansion of telecommunications infrastructure. (1) Specifically, during the period from 2009 to 2013, the regression coefficients for mobile telecommunications and fixed telecommunications are 1.287 and 1.112, respectively, both significant at the 1% level. The interaction term between mobile telecommunications and fixed telecommunications is not significant. (2) In the period from 2014 to 2022, the regression coefficients for mobile telecommunications and fixed telecommunications are 1.415 and 1.208, respectively, both significant at the 1% level. The interaction term between mobile telecommunications and fixed telecommunications is significantly positive. Based on the above results, two observations can be made. First, the development of fixed telecommunications network infrastructure during different periods has a significant positive impact on innovation R&D expenditure, with this impact becoming more pronounced after 2014 when new-generation telecommunications technologies were widely adopted. Second, from 2014 to 2022, with the extensive commercialization of new-generation telecommunications technologies and the implementation of the Broadband China strategy, the synergistic effects of mobile telecommunications and fixed telecommunications on innovation R&D expenditure significantly increased.

For the dependent variable of patent applications, models (1) to (3) are re-estimated, as shown in

Table 9. Relationship between telecommunications infrastructure development in different development phases and the number of patent applications.

Independent Variables	Dependent Variable: Number of Patent Applications
	2009–2013			2014–2022
	(1)	(2)	(3)	(4)	(5)	(6)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	1.546 *** (0.071)		−0.123 (0.369)	1.493 *** (0.052)		0.625 ** (0.310)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		1.326 *** (0.054)	0.500 (0.564)		1.270 *** (0.044)	0.520 (0.431)
$\ln {\mathrm{Station}}_{\mathrm{it}}\times \ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$			0.063 (0.050)			0.014 (0.035)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.676 ** (0.288)	0.905 *** (0.261)	0.688 ** (0.299)	0.654 *** (0.149)	0.896 *** (0.146)	0.754 *** (0.149)
$\mathrm{U}\mathrm{r}\mathrm{b}$	3.147 *** (0.978)	0.452 (0.870)	1.502 (1.124)	2.862 *** (0.559)	0.973 * (0.533)	2.046 *** (0.615)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−2.162 (2.322)	−0.121 (2.151)	−0.134 (2.158)	0.182 (1.660)	1.465 (1.654)	0.882 (1.626)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	−0.596 (2.109)	−1.821 (1.942)	−1.497 (1.953)	1.313 (1.522)	0.164 (1.526)	0.429 (1.486)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.222 (0.243)	0.193 (0.222)	0.070 (0.242)	0.298 (0.221)	0.268 (0.220)	0.237 (0.224)
Constant	−15.45 *** (2.544)	−7.926 *** (2.359)	−4.027 (4.656)	−16.15 *** (1.483)	−9.073 *** (1.446)	−11.33 *** (3.415)
Observations	155	155	155	217	217	217
R-squared	0.884	0.902	0.904	0.897	0.898	0.905

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

. According to the results in Table 9, the growth of telecommunications infrastructure stock consistently promotes innovation knowledge output, but this promotion does not increase with the development of new-generation telecommunications technologies. (1) Specifically, during the period from 2009 to 2013, the regression coefficients for mobile telecommunications and fixed telecommunications infrastructure with respect to the number of patent applications were 1.546 and 1.326, respectively, both significant at the 1% level. The regression results for the interaction term between the two are not significant. (2) In the period from 2014 to 2022, the regression coefficients for the number of mobile telecommunications base stations and broadband port quantities were 1.493 and 1.270, respectively, both significant at the 1% level. The regression results for the interaction term between them are not significant. Summarizing the regression analysis results above, two main points can be made. First, for innovation knowledge output, both mobile telecommunications and fixed telecommunications networks have significant positive effects. However, the progress in fixed telecommunications technology after 2014 did not enhance this impact. Second, for the number of patent applications, there are no complementary effects between mobile telecommunications and fixed telecommunications for innovation knowledge output.

For the dependent variable of the total industrial output value of high-tech enterprises, models (1) to (3) are re-estimated, as shown in

Table 10. Relationship between telecommunications infrastructure development in different development phases and the total industrial output value of high-tech enterprises.

Independent Variables	Dependent Variable: Total Industrial Output of High-Tech Enterprises
	2009–2013			2014–2022
	(1)	(2)	(3)	(4)	(5)	(6)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	1.497 *** (0.065)		−0.473 (0.320)	1.739 *** (0.057)		−0.0229 (0.317)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		1.296 *** (0.047)	0.437 (0.489)		1.498 *** (0.045)	0.123 (0.441)
$\ln {\mathrm{Station}}_{\mathrm{it}}\times \ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$			0.084 * (0.043)			0.081 ** (0.035)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.105 (0.266)	0.310 (0.227)	0.083 (0.260)	0.410 ** (0.161)	0.682 *** (0.149)	0.515 *** (0.152)
$\mathrm{U}\mathrm{r}\mathrm{b}$	3.922 *** (0.903)	1.346 * (0.759)	2.182 ** (0.975)	4.125 *** (0.607)	1.987 *** (0.543)	3.209 *** (0.629)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−4.783 ** (2.144)	−2.630 (1.876)	−2.448 (1.872)	−0.198 (1.802)	1.395 (1.686)	0.520 (1.663)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	5.111 *** (1.948)	3.808 ** (1.694)	4.135 ** (1.694)	7.881 *** (1.652)	6.286 *** (1.556)	6.651 *** (1.520)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.045 (0.224)	0.008 (0.194)	−0.152 (0.210)	−0.347 (0.240)	−0.396 * (0.224)	−0.559 ** (0.229)
Constant	−15.54 *** (2.348)	−8.170 *** (2.058)	−1.537 (4.040)	−23.86 *** (1.609)	−15.63 *** (1.474)	−11.29 *** (3.494)
Observations	155	155	155	217	217	217
R-squared	0.895	0.922	0.924	0.911	0.922	0.927

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

. The output of innovative applications increases with the growth of fixed telecommunications infrastructure stock, and there is a significant complementary effect between mobile telecommunications infrastructure and broadband infrastructure. (1) Specifically, during the period from 2009 to 2013, an increase of one percentage point in the number of mobile telecommunications base stations leads to an increase of 1.497 percentage points in the total industrial output value of high-tech enterprises, while an increase of one percentage point in the number of broadband access ports results in an increase of 1.296 percentage points, both significant at the 1% level. The interaction term is significant at the 10% level. (2) In the period from 2014 to 2022, the regression coefficients for the numbers of mobile telecommunications base stations and broadband access ports increase to 1.739 and 1.498, respectively, both significant at the 1% level. The significance of the interaction term increases to the 5% level. The above regression results indicate that after 2014, when the fixed telecommunications industry entered a mature phase, a complementary effect between mobile telecommunications and fixed telecommunications emerged, increasing the total industrial output value of high-tech enterprises. This suggests that in the process of transforming innovative knowledge into innovative output, the better integration of mobile telecommunications and fixed telecommunications, along with their complementary advantages and collaboration, promote innovation development.

5.3. Estimation Results of Regional Disparities in Telecommunications Infrastructure

In terms of economic development level, economic structure, and openness, there are significant differences between the eastern coastal areas and inland regions in China. Domestic infrastructure investment is highly unevenly distributed across regions, so the level of infrastructure development has different effects on economic growth. This is of great significance for analyzing the key aspects of telecommunications infrastructure construction investment.

The regional regression results for R&D expenditure reveal significant differences in the effects of telecommunications infrastructure construction on the growth of R&D expenditure for large-scale industrial enterprises across the eastern, central, and western regions, as shown in

Table 11. Relationship between telecommunications infrastructure development in different regions and R&D expenditure of industrial enterprises.

Independent Variables	Eastern			Central			Western
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	1.706 *** (0.092)		−0.593 (0.473)	1.248 *** (0.146)		−0.173 (0.584)	1.328 *** (0.048)		−0.086 (0.223)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		1.505 *** (0.073)	−0.103 (0.511)		1.026 *** (0.137)	−1.549 (1.051)		1.216 *** (0.040)	0.025 (0.277)
Ln Stationit × Ln Broadbandit			0.080 *** (0.019)			0.156 * (0.0813)			0.123 *** (0.045)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	−0.082 (0.246)	0.259 (0.219)	0.169 (0.224)	2.306 *** (0.192)	2.200 *** (0.211)	2.156 *** (0.191)	−0.729 *** (0.215)	−0.560 *** (0.200)	−0.668 *** (0.189)
$\mathrm{U}\mathrm{r}\mathrm{b}$	4.287 *** (0.889)	2.464 *** (0.777)	3.688 *** (0.863)	0.0569 (0.846)	−2.324 *** (0.715)	−0.240 (0.821)	8.390 *** (0.818)	6.261 *** (0.758)	7.268 *** (0.782)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−2.289 (2.186)	−1.777 (2.022)	−3.197 (2.014)	2.835 (1.793)	4.932 ** (1.872)	2.406 (1.797)	−1.113 (1.971)	−0.801 (1.832)	−0.966 (1.709)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	−0.838 (2.970)	−1.242 (2.740)	−3.157 (2.778)	−6.804 *** (1.247)	−2.059 (1.377)	−6.087 *** (1.544)	8.319 *** (1.960)	5.281 *** (1.853)	6.092 *** (1.764)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.772 *** (0.260)	0.667 *** (0.241)	0.612 ** (0.234)	−1.273 (0.916)	−2.654 *** (0.930)	−2.792 ** (1.105)	0.217 (0.511)	−0.939 * (0.486)	−1.003 ** (0.483)
Constant	−16.39 *** (2.179)	−9.977 *** (2.019)	−1.331 (4.382)	−32.27 *** (2.483)	−23.391 *** (2.182)	−15.993 ** (7.757)	−8.565 *** (2.185)	−1.797 (1.951)	1.541 (2.449)
Observations	132	132	132	96	96	96	144	144	144
R-squared	0.848	0.870	0.881	0.908	0.896	0.918	0.950	0.957	0.963

Note: 1. Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses. 2. ${ln Station}_{it}$ represents the Log of the number of mobile telecommunications base stations, $\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$ represents the Log of the number of broadband access ports, and ${ln Station}_{it}\times {ln Broadband}_{it}$ represents the product of the Log of the number of mobile telecommunications base stations and the Log of the number of broadband access ports.

. The regression coefficients for mobile telecommunications infrastructure are higher than those for broadband access in all three regions. In particular, the regression coefficients for mobile telecommunications base stations and broadband access in the eastern region are 1.706 and 1.505, respectively; the highest among the three regions. Furthermore, the interaction term between mobile telecommunications and broadband access in the western region has a regression coefficient of 0.123, which is significant at the 1% level. These empirical results indicate the following. First, for all regions, both mobile telecommunications networks and fixed telecommunications networks have significant positive effects on R&D expenditure, with mobile telecommunications networks having a slightly larger impact. Second, in terms of the regression coefficients, the eastern region has the highest values, followed by the western and central regions. This suggests that in the eastern region, the impact of mobile telecommunications network infrastructure development on R&D expenditure is more significant. Third, in the western region, the interaction term between mobile telecommunications and broadband access is most significant, reaching the 1% level. This indicates that the western region exhibits the most pronounced complementary effect between mobile telecommunications and fixed telecommunications, and the synergistic role of mobile telecommunications and fixed telecommunications in promoting R&D expenditure is more significant in this region.

The regression results for innovation knowledge output (

Table 12. Relationship between telecommunications infrastructure development in different regions and the number of patent applications.

Independent Variables	Eastern			Central			Western
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	1.506 *** (0.066)		0.620 * (0.372)	1.425 *** (0.207)		0.830 (0.866)	1.405 *** (0.072)		−0.615 * (0.331)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		1.275 *** (0.061)	−0.056 (0.402)		1.124 *** (0.195)	−0.112 (1.559)		1.324 *** (0.056)	1.781 *** (0.412)
Ln Stationit × Ln Broadbandit			0.048 (0.036)			0.039 (0.121)			0.005 (0.028)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.947 *** (0.177)	1.312 *** (0.183)	1.043 *** (0.177)	2.302 *** (0.273)	2.207 *** (0.299)	2.198 *** (0.284)	−0.018 (0.322)	0.197 (0.280)	0.250 (0.281)
$\mathrm{U}\mathrm{r}\mathrm{b}$	2.797 *** (0.639)	0.965 (0.649)	2.579 *** (0.679)	3.240 *** (1.202)	0.371 (1.014)	2.969 ** (1.218)	1.252 (1.227)	−1.014 (1.059)	−1.912 (1.164)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−0.743 (1.571)	−0.365 (1.687)	−1.106 (1.584)	−1.929 (2.548)	0.516 (2.655)	−1.722 (2.666)	−1.166 (2.958)	−0.458 (2.560)	−0.510 (2.541)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	1.347 (2.135)	0.603 (2.286)	0.438 (2.185)	−19.071 *** (1.772)	−13.79 *** (1.952)	−17.73 *** (2.292)	9.545 *** (2.941)	5.706 ** (2.588)	4.673 * (2.624)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.376 ** (0.187)	0.342 * (0.201)	0.314 * (0.184)	−0.967 (1.301)	−2.609 * (1.319)	−1.501 (1.639)	0.404 (0.767)	−0.968 (0.679)	−1.430 ** (0.718)
Constant	−19.064 *** (1.566)	−13.532 *** (1.684)	−13.217 *** (3.447)	−28.517 *** (3.528)	−18.254 *** (3.095)	−23.068 ** (11.51)	−7.926 ** (3.279)	−1.285 (2.726)	2.303 (3.642)
Observations	132	132	132	96	96	96	144	144	144
R-squared	0.848	0.870	0.881	0.908	0.896	0.918	0.950	0.957	0.963

Note: 1. Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses. 2. ${ln Station}_{it}$ represents the Log of the number of mobile telecommunications base stations, ${ln Broadband}_{it}$ represents the Log of the number of broadband access ports, and ${ln Station}_{it}\times {ln Broadband}_{it}$ represents the product of the Log of the number of mobile telecommunications base stations and the Log of the number of broadband access ports.

) show that the regression coefficients for mobile telecommunications infrastructure in the eastern, central, and western regions are all higher than the coefficients for broadband access. Specifically, in the eastern region, the regression coefficient for mobile telecommunications base stations is 1.506, the highest among the three regions. In the western region, the regression coefficient for broadband access is 1.781, which is also the highest among the three regions. However, the regression coefficients for the interaction terms between these two factors are not statistically significant. An analysis of the above regression results reveals the following conclusions. First, for all regions, both mobile telecommunications networks and fixed telecommunications networks have a significant positive impact on the number of patents granted. However, mobile telecommunications networks have a slightly greater impact than fixed telecommunications networks. Second, the regression coefficient for mobile telecommunications infrastructure is the highest in the eastern region, followed by the western and central regions. This suggests that in the eastern region, investments in mobile telecommunications networks have a more fixed impact on the number of patent acceptances, which is greater in the western region. This may be explained by the lower construction costs and easier implementation of fixed telecommunications infrastructure. In regions with lower economic development levels, such as the western region, meeting basic network fixed telecommunications needs is a priority. Therefore, the increase in patents granted, as a component of innovation knowledge output, depends more on the construction of fixed telecommunications infrastructure. Third, there is no significant complementary effect between mobile telecommunications and fixed telecommunications in terms of patents granted. This suggests that, in the context of innovation knowledge output, the synergistic role played by the combination of mobile and fixed telecommunications is not particularly strong at the theoretical and knowledge innovation levels.

The regression results for innovation application output (

Table 13. Relationship between telecommunications infrastructure construction in different regions and the total industrial output of high-tech enterprises.

Independent Variables	Eastern			Central			Western
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	1.411 *** (0.092)		−0.475 (0.487)	−0.350 (0.212)		0.102 (0.895)	1.267 *** (0.046)		0.242 (0.229)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		1.228 *** (0.076)	−0.470 (0.527)		−0.279 (0.188)	0.587 (1.610)		1.154 *** (0.039)	0.244 (0.284)
lnStationit × lnBroadbandit			0.122 ** (0.046)			−0.054 (0.125)			0.044 *** (0.019)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.587 ** (0.241)	0.889 *** (0.227)	0.767 *** (0.231)	2.496 *** (0.279)	2.521 *** (0.288)	2.537 *** (0.293)	0.297 (0.204)	0.453 *** (0.198)	0.365 * (0.194)
$\mathrm{U}\mathrm{r}\mathrm{b}$	1.161 (0.870)	−0.417 (0.805)	0.959 (0.890)	−7.024 *** (1.228)	−6.332 *** (0.979)	−6.955 *** (1.258)	4.742 *** (0.779)	2.714 *** (0.749)	3.727 *** (0.802)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−1.544 (2.141)	−1.413 (2.094)	−2.585 (2.078)	5.686 *** (2.603)	5.090 * (2.563)	5.890 ** (2.753)	−0.727 (1.877)	−0.492 (1.810)	−0.563 (1.752)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	6.091 ** (2.909)	5.637 ** (2.837)	3.807 (2.865)	−8.191 *** (1.810)	−9.497 *** (1.885)	−8.239 *** (2.367)	−2.821 (1.867)	−5.616 *** (1.830)	−4.653 ** (1.809)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.745 *** (0.254)	0.676 *** (0.250)	0.621 ** (0.241)	−8.461 *** (1.329)	−8.063 *** (1.273)	−7.960 *** (1.693)	2.715 *** (0.487)	1.637 * (0.480)	1.819 *** (0.495)
Constant	−5.734 *** (2.134)	−0.468 (2.090)	7.734 * (4.520)	3.010 (3.604)	0.490 (2.988)	−2.407 (11.890)	−1.678 (2.081)	4.861 ** (1.928)	5.101 ** (2.511)
Observations	132	132	132	96	96	96	144	144	144
R-squared	0.829	0.837	0.851	0.815	0.814	0.816	0.927	0.933	0.938

Note: 1. Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses. 2. ${ln Station}_{it}$ represents the Log of the number of mobile telecommunications base stations, ${ln Broadband}_{it}$ represents the Log of the number of broadband access ports, and ${ln Station}_{it}\times {ln Broadband}_{it}$ represents the product of the Log of the number of mobile telecommunications base stations and the Log of the number of broadband access ports.

) indicate that the regression coefficients for mobile telecommunications infrastructure and broadband access are all higher than those for innovation application output in the eastern, central, and western regions. Specifically, in the eastern region, the regression coefficients for mobile telecommunications base stations and broadband access ports are the highest, at 1.411 and 1.228, respectively. An analysis of these regression results reveals the following, as Figure 1 shows. First, both mobile and fixed telecommunications infrastructure have a significant positive impact on innovation application output in both the eastern and western regions. Mobile telecommunications have a greater impact on the increase in the industrial output value of high-tech enterprises. Second, mobile telecommunications network development has a slightly greater impact on innovation application output in the eastern region than in the western region. The impact of fixed-line fixed telecommunications network development is similar in both regions. This may be due to the lower construction costs of fixed-line fixed telecommunications infrastructure and its widespread development across the country, leading to similar spillover effects on innovation application output in different regions. In contrast, the weaker coverage of mobile telecommunications networks in the western region and the less-saturated mobile telecommunications market there result in smaller spillover effects on innovation application output.

5.4. Robustness Test

Based on the previous regression results, it is evident that both mobile and fixed telecommunications infrastructure construction and improvement play a significant role in promoting economic and social innovation development. To verify the reliability of the estimated results, this study constructs various alternative variables that measure fixed telecommunications network capabilities and conducts robustness analyses. Substituting variables that reflect telecommunications infrastructure capabilities for the previous explanatory variables is one approach.

First, the numbers of mobile and fixed-line users are used as explanatory variables, replacing the previous variables of the number of mobile telecommunications base stations and broadband access ports. The regression results are shown in

Table 14. Robustness test—alternative variables: Log of the number of mobile and fixed-line users.

Independent Variables	R&D Expenditure of Industrial Enterprises		Number of Patent Applications		Total Industrial Output of High-Tech Enterprises
	(1)	(2)	(3)	(4)	(5)	(6)
$\mathrm{l}\mathrm{n}$ mobile users	1.537 ***		1.432 ***		1.256 ***
	(0.036)		(0.040)		(0.040)
$\mathrm{l}\mathrm{n}$ broadband users		1.348 ***		1.260 ***		1.109 ***
		(0.032)		(0.034)		(0.035)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.614 ***	0.464 ***	1.003 ***	0.859 ***	1.048 ***	0.919 ***
	(0.121)	(0.122)	(0.133)	(0.132)	(0.135)	(0.133)
$\mathrm{U}\mathrm{r}\mathrm{b}$	2.423 ***	2.358 ***	1.657 ***	1.615 ***	−0.254	−0.279
	(0.432)	(0.433)	(0.478)	(0.471)	(0.485)	(0.475)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−1.906	−4.339 ***	1.134	−1.070	0.280	−1.614
	(1.427)	(1.415)	(1.579)	(1.539)	(1.602)	(1.553)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	4.067 ***	3.033 ***	−2.110 *	−3.144 **	3.134 **	2.181 *
	(1.139)	(1.152)	(1.260)	(1.253)	(1.278)	(1.264)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	−0.261 *	−0.172	0.105	0.184	0.259	0.325 **
	(0.145)	(0.145)	(0.161)	(0.158)	(0.163)	(0.160)
Constant	−15.99 ***	−10.34 ***	−12.41 ***	−7.129 ***	−2.248 *	2.396 *
	(1.143)	(1.151)	(1.265)	(1.252)	(1.283)	(1.263)
Observations	341	341	341	341	341	341
R-squared	0.927	0.926	0.897	0.900	0.877	0.882

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

. Second, mobile and fixed-line business revenues are used as explanatory variables, replacing the previous variables of the number of mobile telecommunications base stations and broadband access ports. The regression results are shown in

Table 15. Robustness test: alternative variable—telecommunications business revenue.

Independent Variables	R&D Expenditure of Industrial Enterprises		Number of Patent Applications		Total Industrial Output of High-Tech Enterprises
	(1)	(2)	(3)	(4)	(5)	(6)
$\mathrm{l}\mathrm{n}$ mobile service income	1.643 ***		1.584 ***		1.378 ***
	(0.048)		(0.045)		(0.047)
$\mathrm{l}\mathrm{n}$ broadband service income		1.570 ***		1.555 ***		1.347 ***
		(0.054)		(0.047)		(0.048)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	−0.042	−0.160	0.339 **	0.180	0.478 ***	0.344 **
	(0.151)	(0.171)	(0.142)	(0.149)	(0.147)	(0.154)
$\mathrm{U}\mathrm{r}\mathrm{b}$	3.579 ***	0.703	2.942 ***	0.259	0.826	−1.517 ***
	(0.534)	(0.573)	(0.503)	(0.500)	(0.519)	(0.516)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−4.291 **	−3.604 *	−0.536	0.580	−1.310	−0.391
	(1.708)	(1.928)	(1.609)	(1.683)	(1.662)	(1.734)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	5.522 ***	9.630 ***	−1.307	2.323 *	3.963 ***	7.157 ***
	(1.366)	(1.494)	(1.288)	(1.304)	(1.330)	(1.343)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	−0.479 ***	−0.798 ***	−0.147	−0.502 ***	0.049	−0.254
	(0.177)	(0.202)	(0.167)	(0.177)	(0.173)	(0.182)
Constant	−21.44 ***	−16.38 ***	−17.71 ***	−12.88 ***	−6.847 ***	−2.637 *
	(1.393)	(1.548)	(1.313)	(1.351)	(1.356)	(1.392)
Observations	341	341	341	341	341	341
R-squared	0.893	0.866	0.892	0.883	0.865	0.855

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

. The regression analysis results indicate that different indicators reflecting telecommunications network capabilities continue to show similar significant results. Both mobile telecommunications networks and fixed telecommunications networks have a promoting effect on R&D investment, innovation knowledge output, and innovation application output. Specifically, fixed telecommunications network construction and improvement have the greatest impact on R&D investment, followed by innovation knowledge output and innovation application output. The regression coefficients for mobile telecommunications networks are still slightly higher than those for fixed telecommunications networks, indicating that mobile telecommunications play a greater role in promoting economic and social innovation development.

To further establish the causal relationship between telecommunications infrastructure and socioeconomic innovation development, this study needs to address the challenge of endogeneity. There exists reverse causality between the development and improvement of telecommunications infrastructure and innovation development. In other words, the more advanced the telecommunications network construction is, the higher the level of innovation it promotes. Conversely, higher innovation vitality in the economy and society also stimulates the development of telecommunications technology, making it more likely to provide technical support for telecommunications infrastructure construction. To tackle the issue of reverse causality, this study employs the method of instrumental variables, where effective instrumental variables need to satisfy the criteria of relevance and endogeneity [39].

The estimated results using the market share of telecommunications operators as instrumental variables for both the Log of the number of mobile telecommunications base stations [40] and the Log of the number of broadband access ports in the regression are shown in

Table 16. Endogeneity test: instrumental variable—telecom operator market share.

Independent Variables	R&D Expenditure of Industrial Enterprises		Number of Patent Applications		Total Industrial Output of High-Tech Enterprises
	(1)	(2)	(3)	(4)	(5)	(6)
$\ln {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	1.387 *** (0.300)		0.247 (0.534)		0.720 ** (0.358)
$\ln {\mathrm{B}\mathrm{r}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$		0.893 *** (0.216)		0.159 (0.354)		0.463 * (0.254)
$\ln \mathrm{P}\mathrm{g}\mathrm{d}\mathrm{p}$	0.467 * (0.281)	0.891 *** (0.233)	1.707 *** (0.501)	1.783 *** (0.382)	1.260 *** (0.336)	1.480 *** (0.274)
$\mathrm{U}\mathrm{r}\mathrm{b}$	2.819 **	−0.082	−2.177	−2.694 *	−1.592	−3.098 ***
	(1.309)	(0.860)	(2.329)	(1.413)	(1.564)	(1.014)
$\mathrm{G}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}$	−7.156 **	−9.531 ***	−14.21 **	−14.64 ***	−8.143 **	−9.377 **
	(3.429)	(3.341)	(6.103)	(5.487)	(4.097)	(3.937)
$\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{o}$	8.955 ***	11.46 ***	12.51 **	12.96 ***	11.11 ***	12.41 ***
	(3.166)	(2.997)	(5.635)	(4.921)	(3.783)	(3.531)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{e}$	0.0670	0.259	1.132 **	1.166 ***	0.812 **	0.912 ***
	(0.270)	(0.268)	(0.481)	(0.441)	(0.323)	(0.316)
Constant	−19.03 ***	−12.77 ***	−11.87 ***	−10.76 ***	−3.397 *	−0.148
	(1.698)	(1.620)	(3.022)	(2.660)	(2.029)	(1.908)
Instrumental variable p-value	0.004	0.000	0.004	0.000	0.004	0.000
First-stage F-statistic	21.4	30.87	21.4	30.87	21.4	30.87
Observations	341	341	341	341	341	341

Note: Variables with * represents significance at the 10% level, ** at the 5% level, and *** at the 1% level; standard errors are in parentheses.

. From the results in the first column, the third column, and the fifth column, it can be observed that the instrumental variable has a p value of 0.004, which is significant at the 1% level, indicating a strong correlation between the instrumental variable and the explanatory variable: the number of mobile telecommunications base stations. The first-stage F-statistic is 21.4, which is greater than 10, indicating that the null hypothesis of a “weak instrumental variable” can be rejected. Therefore, the market share of a certain telecommunications operator is suitable as an instrumental variable for the number of mobile telecommunications base stations. In the results of the second column, the fourth column, and the fifth column, the p value of the instrumental variable is 0.00, which is significant at the 1% level, indicating a strong correlation between this instrumental variable and the explanatory variable: the number of broadband access ports. The first-stage F-statistic of 30.87 is greater than 10, rejecting the null hypothesis of a “weak instrumental variable” and indicating the appropriateness of choosing the market share of a certain telecommunications operator as an instrumental variable for the number of broadband access ports.

In the second stage, when the explanatory variables—the Log of the number of mobile telecommunications base stations and the Log of the number of broadband access ports—are regressed against the dependent variables, the coefficients are consistent in sign and significant at the 1% level with the previous analysis. Thus, considering endogeneity issues, the construction and improvement of telecommunications infrastructure significantly promote R&D investment, innovation knowledge output, and innovation application output. This conclusion aligns with the findings of the basic regression analysis. The growth rate in the region is slightly lower than that in the western region, at 143.05%. On the other hand, the western region has advantages in terms of energy, geology, climate, etc., making it more suitable for the development of data centers based on broadband networks. Regions such as Ningxia, Guizhou, and Inner Mongolia actively deploy data centers to meet the demand for data center construction from eastern regions, accelerating the construction of telecommunications infrastructure [41].

6. Conclusions and Recommendations

Using provincial panel data from 2009 to 2022 in China and employing fixed effect econometric models, this paper considers the foundational role of telecommunications infrastructure and examines the impact of telecommunications infrastructure on the R&D expenditure of industrial enterprises, number of patent applications, and total industrial output of high-tech enterprises. A heterogeneity analysis was also conducted for different stages and regions. The main conclusions are as follows.

First, we can confirm the first hypothesis that, between 2009 and 2022, both mobile telecommunications infrastructure and telecommunications infrastructure had a significant promoting effect on the R&D expenditure of industrial enterprises, the number of patent applications, and the total industrial output of high-tech enterprises. The impact of mobile telecommunications infrastructure was slightly greater than that of telecommunications infrastructure [42]. The development of telecommunications network infrastructure had the most significant impact on R&D expenditure, followed by total industrial output of high-tech enterprises and number of patent applications. These conclusions still hold after robustness checks using alternative explanatory variables and endogeneity tests for instrument variables.

Second, the impact of telecommunications infrastructure on innovation exhibited different characteristics in different stages [43]. We tested all these hypotheses at the level of significance. Before 2014, during the 3G era, the construction of both mobile telecommunications and telecommunications infrastructure had a certain positive effect on the R&D expenditure, the number of patent applications, and the output of high-tech enterprises. After 2014, with the mass commercialization and popularization of new-generation telecommunications technologies, the promoting effect of telecommunications infrastructure on R&D expenditure and the number of patent applications significantly increased. Furthermore, the interaction terms between the two became more significant after 2014, indicating that the iteration and update of telecommunications technologies promoted the convergence of mobile and telecommunications, thereby driving companies to invest more in R&D expenditure, facilitating the better application of innovation outcomes in industries, and ultimately increasing the output of high-tech enterprises.

Finally, the impact of telecommunications infrastructure on economic and social innovation development varies by region. In the eastern region, mobile telecommunications network capabilities play a predominant role in all three innovation dimensions, while in the western region, the promoting effects of mobile and telecommunications are similar. This suggests that for innovation development in different regions, telecommunications infrastructure should leverage the different characteristics of mobile and telecommunications.

This study shows that the operation of telecommunication infrastructure construction plays a crucial and positive role in economic and social innovation development. Telecommunication infrastructure has a significant promoting effect on R&D expenditure, patent applications, and the output of high-tech enterprises. Furthermore, the impact of telecommunication infrastructure on innovation development exhibits different characteristics at different stages and regions. The iterative update of communication technology drives companies to invest more in R&D expenses, enabling innovation outcomes to be better applied in various industries.

A limitation of this study is that due to the late development of China’s telecommunication industry, the availability of data in this article is greatly limited. On a temporal level, the indicators related to mobile telecommunications and telecommunications infrastructure used in this article are only from 2009 to 2022, and it is difficult to obtain data on relevant indicators before 2009. At the regional level, this article uses panel data from 31 provinces in China, and it is difficult to obtain data on communication capabilities and services at the prefecture level.

Future studies should further expand the sample size of the empirical data. In addition to the variables already considered in this article, there are also a series of other factors that have a significant impact on innovation and development, such as marketization level, government intervention, and even cultural differences and other social and humanistic factors. This article has not comprehensively and carefully studied other factors, which is an important research direction for the next step.