How to Achieve Government-business Collaboration in Information Technology Industry Policy? From the Perspective on Cost of Equity Capital

How to Achieve Government-business Collaboration in Information Technology Industry Policy? 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From the Perspective on Cost of Equity Capital Qiqing Huang, Ying Zou, Yaxuan Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4203006/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper examines the impact and the mechanism of the information technology industry policy on the cost of equity capital from the perspective of the government-business collaboration based on the externality theory and soft budget constraint theory. This research selected the Chinese A-share non-financial listed companies from 2012 to 2019 as examples and empirically tests the cost of equity capital effect of the implementation of "Broadband China" policy based on the difference-in-difference (DID) model. The main test shows that the "Broadband China" policy has significantly reduced the cost of equity capital. The mechanism test reveals the function of "Broadband China" policy in optimizing the return, the configuration and the distribution of factors, and reflecting the government-business collaboration effect. Furthermore, the regional economic development, the industry competitiveness and the supply chain conflicts are proved to strengthen the above mechanisms respectively. The heterogeneity analysis shows that the effect of the "Broadband China" policy on the cost of equity capital is realized through the equity investment, positively affected by government-business relationship, and shows spatial aggregation effect. Actually, the digital transformation of the economy, led by the new generation of information technology, now provides the government and companies with opportunities for coordinated development. Therefore, for government, we recommend it to improve the support systems related to the information technology industry and give additional impetus to the development of the digital economy by using the benevolent regulatory measures. For companies, we also expect them to take advantage of the digital economy to achieve sustainable development. JEL Classification : G12；G32；G38 Government-business Collaboration Cost of Equity Capital Broadband China Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction As Chinese economy enters a new normal stage, the "high-speed growth" model has become now of the pattern in Chinese economy instead of "high-quality development" model. In the document named " Opinions of the Central Committee of the Communist Party of China and the State Council on Accelerating the Construction of a National Unified Market " jointly issued by the Central Committee of the Communist Party and the State Council of China on April 10, 2022, a goal of " comprehensively promote the transformation of Chinese market from large to strong, thereby providing strong support for the construction of a high-standard market system and a high-level socialist market economic system " had become an important guiding ideology, reflecting the strategic transformation of Chinese government from "big to strong". Presently, there is a consensus among various sectors of Chinese society, that is to reverse the past inertial thinking of extensive economic growth based on investment, resources and external demand, and to efficiently utilize limited resources and capital for refined operations to achieve the goal of sustainable economic development. For companies, the capital cost runs through the 3 major financial policies of investment, financing and dividends, that can reflect the efficiency of resource configuration and the comprehensive competitiveness. Especially in the context of interest rate control, the Chinese listed companies have a general preference for equity financing. Therefore, it is no doubt saying that the cost of equity capital is of great significance to the wealth realizations of the shareholders, the companies and even the wider stakeholders. The deep integration of the information technology and the substantial economy has received a lot of attention as one of the strategic focuses of the Chinese economic transformation in recent years. The new generation information technology, represented by big data, cloud computing, the Internet and blockchain, is now regarded as one of the key roads to improve the economic distribution and optimize the value creation model. Different from the western liberal market, the Chinese current economic system was born out of the planned economy institution. Since the much of the resources is controlled by government, the "visible hand" constitutes an important part of the Chinese economy still then and the administrative interventions such as industrial policies, financial subsidies often make senses on the strategic decisions of the companies (Lin et al., 2004). Therefore, exploring the impact and the mechanisms of information technology industry policies on the decision-making of the equity investors constitutes an important research topic under the new normal development of the Chinese economy. Information technology industrial policy has the dual attributes of industrial policy and information technology, which should be viewed in two parts. For one, as an industrial policy, how effective is the policy implementation? For another, as an information technology, how can it help to realize the value creation in companies? When it comes to the first question, the “century debate” between Lin and Zhang on industrial policy seems to be still fresh. Lin, as the representation of the new structural economics school, supports the guiding role of the government and believes that the sustainable development of the national economy only relies on the "effective markets" to have certain limitations, and that market failures such as information spillover and public goods need to be reconciled through "proactive government" (Lin, 2017). While Zhang, holds the core views of the Austrian School of Economics, emphasizes the unpredictability of the industrial innovation, and advocates a perfectly competitive market mechanism to determine the success or failure of technology, industry or company. As for the second question, since the emergence of the Solow's information technology paradox in 1980s, the doubts of the relationship between the information technology and the productivity has never disappeared with the time goes by. With the aim of answering the above questions, this paper uses the soft budget constraint theory and externality theory, takes the A-share listed companies from 2012 to 2019 as the research examples, and analyze the cost of equity capital effect of the information technology industry policy based the quasi-natural experiment of the "Broadband China". The possible contributions of our study are as follows. First of all, in terms of research content, the previous literature on the driving factors of the cost of equity capital mostly focused on the micro level and rarely involved the analyses of macro policies, let alone the consideration of industrial policies. Actually, the cost of capital not only serves as a benchmark for various financial decisions of the companies, but is also crucial for government regulation and public project investment analysis, and is the value link between macro and micro economies. Ignoring the impact of industrial policy on the cost of capital, the research may be one-sided and unfair. Based on the background of the popularization of the new generation information technology in recent years and the deep integration with the substantial economy, we focus on the cost of equity capital effect from the perspective of the "Broadband China" policy, so that to provide useful supplement for both of the studies of the macro-influencing factors focused on the cost of equity capital and the research to the microeconomic consequences of the information technology industry policy. Secondly, in terms of research perspective, the existing research on the economic consequences of the "Broadband China" policy has been relatively limitation——either examining the macro and micro economic consequences of the "Broadband China" policy in isolation, or ignoring the fact that the information technology industrial policy has the dual attributes of industrial policy and information technology. This paper takes the perspective of government-business collaboration and analyzes the path by which the "Broadband China" can help realize the shareholder wealth, including improving the positive externalities (factors return), reducing the strategic burdens (factors configuration), and alleviating the social burdens (factors distribution). Not only the research enriches the connotation of the information technology industrial policy and the cost of equity capital, but it also broadens the application scope of the budget soft constraint theory and the externality theory. Finally, in terms of practical value, with the sweeping wave of new generation information technologies such as the Internet and big data in recent years, Chinese social market economic entities, including the government, companies and equity investors, have begun to participate in the corporate governance. In the process, it is inevitable to get involved in the consideration of information technology. Therefore, this paper can, firstly, provide incremental information for the "visible hand" of the government; secondly, provide appropriate enlightening suggestions for the listed companies to achieve the high-quality and sustainable development; thirdly, provide guidance for the equity investors to scientize and rationalize their investment decisions. The remainder of this article is organized as follows. The second part is the institutional background and literature review; the third part is the theoretical analysis and hypothesis deduction; the fourth part is the research design; the fifth part is the empirical results and analysis; the sixth part is the research conclusion and inspiration. 2. Institutional background and literature review 2.1 Institutional background Chinese Internet era began in 1994. In this year, China launched the construction of public computer Internet and achieved integration with the international Internet, marking the beginning of the development of Chinese information technology industry. In the past 20 years since then, China had made great breakthroughs in the construction of information technology infrastructure. However, problems such as high cost, low speed, shallow coverage and uneven development were still prominent, which had brought challenges to the deep integration of industrialization and informatization. For meeting the actual needs of Chinese information technology industry and ignite new engines of Chinese economic development such as informatization, digitization, and intelligence, the State Council of China drafted and announced the implementation draft of "Broadband China" strategy in September 2012 in conjunction with eight ministries and commissions including the Ministry of Industry and Information Technology and the National Development and Reform Commission. In August 2013, the " Notice of the State Council on Issuing the 'Broadband China' Strategy and Implementation Plan " was released, which elevated the public infrastructure construction of broadband networks to the national strategic level for the first time and clarified the overall development goals during the "12th Five-Year Plan" and “13th Five-Year Plan” period. In 2014, 2015 and 2016, the Ministry of Industry and Information Technology and the National Development and Reform Commission jointly led the construction of three batches of "Broadband China" demonstration cities, and a total of 120 cities were commended. Demonstration cities involved in the list of "Broadband China" policy would achieve all-round improvements in broadband user scale, network speed, coverage and economic services through a construction period of about 3 years. 2.2 Literature review In view of the close connection with the digital economy, scholars focused much attention on the "Broadband China" policy and launched multiple discussions on the macro and micro economic consequences in recent years, with most of the relatively positive evaluations. From the macro perspective, the "Broadband China" policy was proven to promote urban economic growth (Zhao et al., 2020), assist economic transformation (Wen and Zhang, 2023), optimize the industrial structure (Liu and Ma, 2 020), improve export quality (Li and Cui, 2022), release innovation potential (Zhong et al., 2022), correct labor factor configuration distortions (Niu and Cui, 2022) and so on. From the perspective of micro, studies mostly focused on innovation and production efficiency. In terms of innovation, Xue et al. (2020) found that the "Broadband China" policy promoted the technology diffusion of parent and subsidiary companies and technological cooperation between companies; the research of Li et al. (2022) showed that the "Broadband China" policy improved all aspects of innovation input, output and efficiency in companies. In terms of production efficiency, Luo et al. (2022) showed that the "Broadband China" policy optimized production capacity utilization by inhibiting excessive investment and improving management efficiency. Sun and Chen (2021) supported the positive impact of the "Broadband China" policy on the total factors productivity of companies. In addition, a few of studies focused on the optimization effect of the "Broadband China" policy to the information environment in companies (Li and Yun, 2023). The cost of equity capital reflects the expectations of the shareholders to the operating conditions and the future risk levels in companies, and it is one of the essential indicators of the realization of the maximizing shareholder wealth. Since Modigliani and Miller (1958) proposed the MM theory, scholars had conducted rich and in-depth research on the cost of equity capital from macro and micro aspects. In the macro, many scholars interpreted it from the perspective of the economic resilience and found that factors such as the trade friction (Cheng et al., 2021), the economic policy uncertainty (Guo and Sun, 2021) and the impact of the COVID-19 (Ke, 2022) constituted an important reason for the high cost of equity capital in recent years. In terms of macro policy, such as the "value added tax retention and refund" reform (Wu et al., 2021) and the market access control (Wang et al., 2019) was proved to reduce the cost of equity capital. Studies of the micro mainly focused on the information disclosure and the corporate governance. In terms of information disclosure, high-quality information disclosure (Yang et al., 2023) and active investor information interaction (Cai et al., 2022) were regarded as important measures to alleviate information asymmetry between companies and investors as well as reduce the cost of equity capital. In terms of corporate governance, internal control (Luo, 2018), core competitiveness (Qi et al., 2021) and big data technology (Zou et al., 2022) also played a positive role in optimizing corporate governance and reducing the cost of equity capital. In addition, a small number of scholars also discussed the invisible governance mechanisms of cost of equity capital, such as confucian traditional culture (Wang and Tan, 2022) and social trust (Zhao, 2020). To sum up, as a pilot policy at the city level, "Broadband China" has received full attention in the researches of macroeconomic consequences. However, the researches of the microeconomic consequences are relatively concentrated and rarely involves the discussion of the impact on corporate asset pricing. At the same time, existing literatures pay less attention to the institutional background of China, and still fail to effectively build a bridge for the "government-business" interactive relationship. From the perspective of the researches on the cost of equity capital, the past literatures focus more on the micro influencing factors and insufficient attention on the macro industrial policies, let alone the discussions on the information technology industry policies. In the context of the economic transformation toward intelligence, informatization, and digitalization, how to effectively use the "visible hand" of the government to empower shareholders to create wealth is a practical issue that needs to be discussed urgently. Therefore, we will analyze the impact and the mechanisms of the "Broadband China" policy on the cost of equity capital based on the perspective of the government-business collaboration, with the help of the soft budget constraint theory and the externality theory. 3. Theoretical analysis and hypothesis deduction 3.1 Theoretical analysis 3.1.1 Motivation of shareholders to lower the expected returns The cost of capital is the rate of return that investors expect from a company and is closely related to value creation. According to the EVA (economic value added) theory proposed by Miller and Modigliani in the 1960s, the Eq. ( 1 ) is as followed. Among them, eva is the economic value added, nopat is the net operating profit after tax, wacc is the weighted average cost of capital, and ta is the total invested capital. $$eva=nopat-wacc\times ta$$ 1 Based on the circumstance of the limited liability system, shareholders legally have the right to claim residual income after distribution shared by stakeholders. Therefore, EVA can be regarded as the wealth of shareholders retained in the company. The one of distribution to stakeholders partly is reflected in the net operating profit after tax, such as salaries paid to management and employees, taxes paid to the government and so on. The other part is reflected in the distribution to investors such as creditors and shareholders. The Eq. ( 1 ) can be rewritten as the Eq. ( 2 ). Among them, ogp is the operating gross profit, and dsoti represents the distribution to stakeholders other than investors. $$eva=ogp-dsoti-wacc\times ta$$ 2 The Eq. ( 2 ) can be rewritten into the Eq. ( 3 ). Among them, reva is the rate of the economic value added (= economic added value/total invested capital), rogp is the rate of operating gross profit to the invested capital (= operating gross profit/total invested capital), rdsoti is the rate of distribution to stakeholders other than investors (= distribution to stakeholders other than investors /total invested capital). $$reva\times ta=rogp\times ta- rdsoti\times ta-wacc\times ta$$ 3 Divide both sides of the Eq. ( 3 ) by the total invested capital to get the Eq. ( 4 ): $$reva=rogp-rdsoti-wacc$$ 4 Based on the perspective of production factors, rogp can be regarded as the value function consisted by the return rate ( return ) and the configuration efficiency ( configuration ) of different factors. Therefore, Eq. ( 5 ) is as followed: $$reva=F\left(return,configuration\right)-rsdsoti-wacc$$ 5 The Eq. ( 5 ) can be rewritten into the Eq. ( 6 ). Among them, rd is the cost of debt capital, lev is the leverage, and re is the cost of equity capital. $$reva=F\left(return,allocation\right)-rdsoti-rd\times lev-re\times (1-lev)$$ 6 Assuming that investment projects can be replicated infinitely, the rate of economic value added can be regarded as the retained wealth of shareholders, and the cost of equity capital can be regarded as the realized wealth of shareholders. The two combined constitute the value surplus enjoyed by shareholders in the company. The cost of equity capital can be regarded as the value trade-off of shareholders between retained wealth and realized wealth. Since Chinese debt market keeps long strict interest rate controls, the credit capital has never been a domination in the financial resources configuration until now. Most listed companies in China have obviously preference for the equity financing, and with the stable capital structure (Zhou and Xu, 2012). Therefore, from Eq. ( 6 ), if it is also assumed that rdsoti , return and configuration remain unchanged, due to the existence of lev , for 1 unit decrease in re , reva will increase by less than 1 unit. In other words, decrease of realized wealth of shareholders will lead to a smaller increase in the wealth retained of the companies. Two birds in the bush are worse than one bird in the hand, and two birds in the hand are better than one bird in the bush. If the reduction of shareholders' realized wealth only leads to a smaller increase in the wealth retained of the companies, then shareholders, as the rational economic men, have no incentive to lower their return requirements for the companies. Firstly, the reduction of the cost of equity capital leads to a decrease in the total amount of realized and retained wealth. Secondly, the same retained wealth contains more risks than the realized one. Thirdly, the retained wealth will realize economic benefits in the future, which will lead to a loss in the monetary time value. Therefore, if assuming the shareholders have incentives to reduce re , the increase in reva needs to be greater than the decrease of the former. The additional increases in reva comes from the reduction of rdsoti as well as the optimism of return and configuration . 3.1.2 Drivers for companies to undertake the political tasks Based on the EVA model, "maximizing shareholder wealth" is the ultimate financial goal and shareholders are regarded as the core subjects of the value creation. However, when stakeholders participate in corporate governance, companies may deviate from the orientation of "maximizing shareholder wealth" due to the possible differences in the interest demands among various value entities. In the context of Chinese transitional economic system, the government is an important part of the stakeholders and often internalizes its own political goals into the production and operation process of the companies. For government, besides the goal of short-term economic growth, it also shoulders the diversified functions such as maintaining the international competitiveness of industries and improving social welfare. Generally, when no entity within the companies benefits, the companies as a whole will tend to avoid the political tasks. It can be seen that the existence of policy beneficiaries is an important prerequisite for positively responds of companies to political tasks. Therefore, we summarize the drivers for companies to undertake political tasks into the following 3 aspects. Firstly, the political promotion incentives of executives. After the reform and opening, economic construction became the basic line of the primary stage of socialism in China, and the selection criteria for the local officials subsequently shifted from political indicators to economic performance assessment. Among them, local GDP growth had long played a dominate role in the official “promotion tournament” (Li and Zhou, 2005), focused more on “scale-oriented” rather than “value-oriented”. Compared with the non-state-owned executives, executives of the state-owned companies are not purely professional managers and have the characteristic of "quasi-officials" (Yang et al., 2013). For increasing the political promotion chips, executives in the state-owned companies tend to respond more actively to the political tone of the economic growth and implement radical financial policies to expanse, such as paying high M&A (merger and acquisition) considerations (Chen Shihua et al., 2015), high-leverage financing (Zhao Yu et al., 2019), over-investment (Liu Li et al., 2020), and so on. Secondly, tactical rent-seeking to maintain the competitive advantages of the companies. In the transitional economic period, the government still plays a leading role in the economic activities and has strong control over the economic resources such as taxation, credit, and subsidies (Le, 2011). Coupled with limited resource constraints, companies are motivated to take the initiative to undertake political tasks for financial support such as government subsidies and low-interest loans (Krueger, 1974). Especially in China, the state-owned has long occupied a large proportion. Thus, the government will naturally give more resources to state-owned out of "paternalism" (Kornai, 1986), causing private companies to fall into financing constraints and thereby breeding the tactical rent-seeking behaviors. For example, Yu et al. (2010) found that private companies would obtain government subsidies by building political connections with local governments. Tian and Fan (2018) confirmed that private companies obtain tax avoidance by bribing tax collection personnel. Yang et al. (2022) also found that the cost of obtaining funds for private companies was higher than that of state-owned and foreign-funded, and had stronger financing rent-seeking motives. Thirdly, correct the company's own strategic positioning deviation. Represented by David Ricardo's theory of comparative advantage, the global economy has laid the main tone of the liberal market since the second industrial revolution. However, the debate surrounding the relationship between government and market remains unresolved. It is particularly true in transition economies. In 2016, Lin and Zhang launched the "century debate" based on the industrial policy. The former advocated "good government", while the latter focused on the "free market". Correspondingly, in the studies of Chinese transitional economic system, though most of literatures regard the political tasks undertaken by companies as the policy burdens, there are also some facts of the government's corrective function in "disorderly markets". As an example, for the strategic emerging industries represented by electronic components, the development in early stage requires large amounts of capital investment and will go through a long period of time for returns. Driven solely by the individual tendency of "shareholders' value maximization", some companies may fall into the trap of "short-termism" due to the survival pressure (Wang et al., 2023). Under the circumstance, the “hand of the government” can play a guiding role in company innovation strategies. In short, for companies, improving their strategic planning and achieving sustainable development is another motivation to undertake political tasks (Li and Zhou, 2022). 3.1.3 Conditions for the government-business collaboration As the mentioned above, the cost of equity capital is the result of shareholders' value trade-off between realized wealth and retained wealth, indicating the long-term value orientation. In comparison, there are more diversified drivers for companies to undertake political tasks. The inconsistency of economic goals between shareholders and the government is an important reason for conflicts between government and companies. When companies undertake political tasks that are consistent with their long-term value creation, the government can effectively guide the development of companies and correct the disorderly market. Based on the perspective of the cost of equity capital, we hold the view that the government-business collaboration effect of "Broadband China" policy depends on the impact to return, configuration, and distribution of factors. 3.2 Hypothetical deduction 3.2.1 "Broadband China" policy, factors return and cost of equity capital As far as a single production factor is concerned, the "Broadband China" policy has led to the transformation of information production factor (Guo et al., 2023), specifically reflected in the technologies, concepts, and applications. In terms of the technology, traditional information technology relies on computer hardware and software, such as personal computers, servers, database management systems and so on, causing certain limitations in processing large-scale and multiple types of data. In contrast, the new generation information technology focuses on the innovation of underlying technologies, driving a wealth of technological derivatives, including big data, virtualization, cloud computing, artificial intelligence and so on (Qi and Xiao, 2020). For example, big data technology can process large amounts of data, process data at high speed and support diverse data types. In terms of the concept, traditional information technology mainly assists business processes and is usually regarded as a tool to support business operations. In contrast, the new generation information technology emphasizes its leading role in value creation. The application of new generation information technology is not only to improve efficiency, but also to change business models, create new products and services, and prompt companies to rethink their business and innovation strategies to better adapt to rapidly changing market needs (Wu et al., 2021). In terms of the application, traditional information technology is mainly limited to specific fields or tasks. Companies using traditional information technology may focus more on internal data management and business processing. In contrast, the new generation information technology has a richer application scenario layout. In fields of marketing, financial analysis, and healthcare, the new generation information technology can enable companies to better understand customer needs, make strategic decisions, and provide personalized services (Nambisan et al., 2017). Similarity of the traditional information technology, the new generation information technology still faces the "Solow Paradox" controversy. Numbers of scholars regard the new generation information technology as an advanced production factor with increasing returns to scale and believe that it has strong economies of scale and scope (Prufer and Schottmmller, 2021). While some scholars also believe that it has diminishing returns to scale (He et al., 2017; Varian, 2019) and cannot provide companies with sustainable competitive advantages (Lambrecht and Tucker, 2017). Wang et al. (2022) took big data technology as an example and summarized 5 specific impact mechanisms of the returns to scale, including data quality, economies of scale, external economies, learning effects and quality ladders. Based on the theoretical framework, the phenomenon of increasing returns to scale of big data often appears in the complex problem situations, such as understanding human behavior, optimizing search engine algorithms, and improving the accuracy of autonomous driving. Therefore, it is no doubt saying that the returns to scale effect of new generation information does not only depend on itself, but also rely on institutional design, economic foundation and productivity. From the perspective of specific economic scenarios, Chinese economic transformation and upgrading in recent years has provided objective conditions for the returns to scale effect of new generation information technology. Firstly, Chinese institutional environment is constantly improving. Based on a series of the reforms and policy measures, Chinese government has continuously improved marketization and legalization, strengthened intellectual property protection, and promoted fair competition. For example, in the field of the intellectual property rights, Chinese government has improved legal protection, enhanced the legal enforcement and increased the awareness of innovators to intellectual property protection in recent years. In addition, reforms to streamline administration, delegate powers, and optimize the business environment have also provided enterprises with more autonomy and convenience for technological innovation and application (Wang et al., 2022). Secondly, Chinese economic structure is gradually optimized in the process of transformation and upgrading, shifting from reliance on resources and low-end manufacturing to high-tech and high-quality development. The transformation trend provides broad market space and demand for the application of new generation information technology (Qi et al., 2020). Furthermore, Chinese consumer market continues to expand, and the demand of residents for high-quality, personalized products and services is increasing rapidly for the time, creating more opportunities for companies to apply new generation information technology. Furthermore, China is full of labor resources. Especially in the context of education reform represented by "college enrollment expansion", Chinese high-level human capital has continued to expand, providing strong support for the efficient application of the new generation information technology (Huang et al., 2023). In short, we believe that the "Broadband China" policy can encourage companies to invest in the new generation information technology elements, and reduce the cost of equity capital by increasing the returns to scale. 3.2.2 "Broadband China" policy, factors configuration and cost of equity capital In the process towards the stage of high-quality economic development, factors configuration cannot be ignored. Compared with comparative advantage theory of David Ricardo's, the factor endowment theory represented by Heckscher (1919) and Ohlin (1933) expand product production from a single production factor to multiple production factors, emphasizing the importance of rational factors configuration under the same technical conditions and thus explaining the reasons for the formation of the international trade comparative advantage. Although the factor endowment theory is originally used in research in the field of international trade, the relevant concepts are also applicable to explain the laws of domestic regional economic exchange and the production efficiency of companies. For example, Jiang and Meng (2021) analyzed the interactive relationship between the internal and the external circulation of the Chinese economy based on the factor endowment theory, and pointed out that the two-wheel-driven development model of the internal and external circulation was an important reason for Chinese economy to maintain rapid growth for a long time. Shirley and Winston (2004) found that the investment in highway infrastructure can effectively adjust the production factor ratio of companies, reduce their inventory reserve costs, and thereby enhance economic returns. Based on Chinese institutional background, the "visible hand" of the government is regarded as an important reason for the factors’ inefficient configuration. For example, "traditional national system", as a legacy of the "planned economy" during the transitional economy, uses administrative intervention as the core of resource configuration and puts more emphasis on the administrative power of the government. It emphasizes "political goals" and neglects "economic benefits", which can easily lead to low factors mobility (Kong et al., 2022), single factor configuration scope (Zhang and Luan, 2022) and distort factor price (Wang and Li, 2018), accompanied by the deviation of strategic decisions from the optima. In the opening market competition, it is especially reflected in the strategic burden borne by some state-owned, that is, they rely one-sidedly on the development strategy choices of government and overinvest in capital-intensive industries without comparative strengths, leading to the loss of viability (Lin and Tan, 1999; Lin et al., 2004). The new generation information technology brought about by the "Broadband China" policy can help break down the above-mentioned obstacles in the production factors configuration. Firstly, the new generation information technology can help individual companies understand economic, industry and operating environment, make clearer judgments about prospects, clarify production needs, and scientifically formulate budgets (Zhong et al., 2017). At the same time, blockchain encryption technology can provide a safer trading environment for both supply and demand parties of the production factors (Liu, 2022), and facilitate the liberalized market flow of the production factors. As a result, there are subjective and objective feasibility conditions for companies to rationally allocate their basic production factors. Secondly, the new generation information technology can break the shackles of time and space, attract advanced production factors such as knowledge, technology, and management, and provide opportunities for independent innovation of the new business formats, models and technologies (Ma et al., 2023). Furthermore, using the new generation information technology, the supply and demand side of production factors can realize the exchange of price information, achieve accurate docking, reduce transaction costs caused by information asymmetry, and achieve optimal Pareto configuration (Wu and Deng, 2023). In addition, the one should not be ignored is that the new generation information technology, as an advanced production factor, when it integrates with traditional production factors, can improve the total factor productivity (Feng, 2022), bringing the value effect of amplifying, superimposing and multiplying. In short, we believe that the "Broadband China" policy can improve the efficiency of production factors configuration, optimize strategic decisions, and thereby reduce the cost of equity capital. 3.2.3 "Broadband China" policy, factors distribution and cost of equity capital A variety of goals are involved in the finance management of companies, such as "maximizing shareholder wealth", "maximizing company wealth", and "maximizing stakeholder wealth". The service objects of the above 3 financial management objectives are expanded in sequence, from the shareholders to the shareholders and the creditors, and then to a wider range of the internal and external stakeholders including the employees, customers, suppliers, and governments. The financial goals of companies can be viewed as the result of trade-offs among stakeholders who control different factors of the production. In the traditional production factor structure, companies follow the "input-output" business model, and the value creation is regarded as the "value surplus" after the input of traditional production factors such as capital, labor, and land. When the financial management of a company focuses on "maximizing shareholder wealth", there are serious conflicts in the distribution of residual value between the shareholders and other stakeholders. In other words, when the returns and configuration of the factors are given, the realization of shareholder wealth maximization depends on the relative competitive strengths of shareholders over other production factor entities, at the expense of the interests of other production factor entities. The phenomenon above has become common around the world since the boom of the western capital markets in the 1970s (Zhang, 2019). In addition, compared with other production factors, capital factor is more aggregated. It also led to the prevalence of the concept of "capital hiring labor" in the era of industrial economy (Feng and Li, 2013), ensuring the core position of shareholders in distribution. For reconciling the distribution conflicts between the shareholders and other stakeholders, the government will take the measures to achieve the redistribution of social wealth, such as tax adjustment. However, it often leads to the loss of economic benefits, which turns into a social burden for the companies (Zhang et al., 2021). With the investment in new generation information technology under the "Broadband China" policy, the underlying logic of corporate financial management may change. Different from traditional production factors such as labor, capital, and land, the new generation information technology is with the characteristics of replicable, renewable and lower marginal costs, and can transfer among various stakeholders. Furthermore, the input and output of information elements are usually difficult to clearly define (Dong, 2022), and the value creation of the new generation information technology usually requires the linkage of multiple value subjects. In other words, all of stakeholders can be the first responsible person in the value network. As the different stakeholders move towards value synergy, the distribution conflicts between yhe shareholders and other stakeholders may be alleviated. It provides more feasibility for the market-based mechanism to participate in factors distribution, and may thereby reduce the Pareto efficiency loss caused by government administrative intervention (Cai, 2021). In short, we believe that the "Broadband China" policy can improve the distribution of residual value of production factors, ease the value conflict between the shareholders and the stakeholders, and thereby reduce the cost of equity capital. Based on the above analysis, the Hypothesis 1 is as follows: Hypothesis 1 "Broadband China" policy can reduce the cost of equity capital. 4. Research design 4.1 Sample selection and data sources This paper takes Chinese A-share entity listed companies from 2012 to 2019 as research samples. After excluding samples with missing data, delisted, special treated (ST, * ST, PT, etc.), listed in 2019, and financial industry samples, 20,492 samples are involved. Among them, the pilot cities for the "Broadband China" policy are selected from the official website of the Ministry of Industry and Information Technology, and other main data comes from the CSMAR database. 4.2 Variable definition 4.2.1 "Broadband China" policy implementation ( bic ) This paper uses a difference-in-difference(DID) model to test the effect of "Broadband China" policy to the cost of equity capital. Specifically, once a sample company is registered in a "Broadband China" pilot city and is in the year of policy implementation, bic takes a value of 1, otherwise take 0. Among them, considering that the 4 batches of pilot cities for "Broadband China" were all announced in the second half of the year, the year following the announcement of the pilot cities will be used as the first year of policy implementation. 4.2.2 Cost of equity capital ( re ) Among the various methods for the cost of equity capital estimation, the internal rate of return method is widely used for its loose assumptions and accurate estimation results (Mao et al., 2012). Therefore, we also use the internal rate of return method to estimate the cost of equity capital, including Gordon model, MPEG model, PEG model and OJ model. Forecast earnings is the core of the internal rate of return method. Judging from the existing literature, the main measurement methods of the forecast earnings include the actual future earnings data (Lu et al., 2014), analyst forecast data (Kross et al., 1990), the HVZ regression model prediction data (Hou et al., 2012) and the RI regression model prediction data (Li and Mohanram, 2014). Due to the consideration of the missing data and subjectivity of the analysts’ earnings forecasts (Mao et al., 2012; Zhang, 2020), we use actual future earnings data, HVZ model forecast data and modified RI model forecast data to evaluate the forecast earnings. The cost of equity capital estimation in this paper mainly includes the following 3 steps. Firstly, calculate the cost of equity capital using the above 4 types of internal rate of return models based on the 4 sources of forecast earnings. Secondly, calculate the average cost of equity capital for 4 types of models under the 3 forecast earnings calibers. Thirdly, take the average of the 4 types of models under 3 forecast earnings calibers, as a proxy variable for the cost of equity capital ① . 4.2.3 Control variables Referring to Lu et al. (2014) and Zhang (2020), we select 19 control variables from 3 aspects, including market environment, governance structure and corporate finance, as shown in Table 1 . Table 1 Control variables Definition type symbol definition market environment beta Rolling regression for monthly returns of individual and market mb Total assets divided market value of equity size Add 1 to the market value and take the natural logarithm oversea If the tradable shares were issued in the current year, take 1; otherwise take 0 governance structure hhi1 Square of the largest shareholder equity ratio hhi2-5 Sum for the squares of 2nd to 5th largest shareholders equity ratio duality If chairman serves as CEO, take 1, otherwise take 0 board Add 1 to the number of directors and take the natural logarithm location If the independent directors are in the registered location, take 1; otherwise take 0 sup Add 1 to the number of supervisors and take the natural logarithm mshare Executive shareholding ratio salary Add 1 to the salary of the top three executives and take the natural logarithm corporate finance profit Net profit divided average total assets in current year lev Total Liabilities divided total assets operation Operating income divided average current assets in current year growth operating income growth rate compared to the last year liquid Current assets divided total assets cash Cash and cash equivalents divided total assets cfo Cash received from selling goods and providing services divided operating income 4.3 Regression Model We use the Eq. ( 7 ) to verify the relationship between "Broadband China" policy and cost of equity capital: $$re=\gamma +{\delta }_{1}bc+controls+year+industry+city+company+{\epsilon }_{ }$$ 7 In the above equation, controls represent the 19 control variables in Table 1 ; year , industry , city and company represent year, industry, city and company dummy variables respectively. The manufacturing industry is controlled by the industry secondary code of China Securities Regulatory Commission in 2012. We use the robust standard errors calculation by annual and company clustering to control the heteroskedasticity and autocorrelation bias. For alleviating the impact of the sample extreme values on regression results, we winsorize for all the continuous variables at the level of 1% and 99% quantiles. 5. Empirical results 5.1 Descriptives The descriptive statistics of the main variables are shown in Table 2 . Among them, the mean and median re of the sample company are 9.7% and 8.8% respectively; the maximum and minimum are 3.7% and 25.3% respectively; the standard deviation is 4.0%. The range and standard deviation of re are close to Zhang (2020) in recent. The mean of bic is 45.0%, indicating that the most companies were not registered in the "Broadband China" pilot cities during the sample period, but sample distribution of this variable is relatively even. The descriptive statistical results of each control variable are not significantly different from the exists, and will not be described again here. In addition, the unlisted univariate test results show that the absolute values of the partial correlation coefficients among the main variables are all less than 0.5, indicating that there is no serious multicollinearity in the regression model. Table 2 Descriptive statistics (N = 20492) variable mean median min max std re 0.097 0.088 0.037 0.253 0.040 bic 0.450 0.000 0.000 1.000 0.498 beta 1.125 1.102 -0.410 3.215 0.426 mb 4.264 2.129 0.143 57.007 7.464 size 22.195 22.133 20.065 25.151 1.024 oversea 0.026 0.000 0.000 1.000 0.158 hhi1 0.137 0.102 0.007 0.543 0.114 hhi2-5 0.019 0.011 0.000 0.105 0.022 duality 0.277 0.000 0.000 1.000 0.447 board 2.242 2.303 1.792 2.773 0.176 location 0.455 0.000 0.000 1.000 0.498 sup 1.490 1.386 1.386 2.079 0.190 mshare 0.073 0.001 0.000 0.615 0.140 salary 14.367 14.342 12.708 16.328 0.688 profit 0.038 0.037 -0.285 0.214 0.067 lev 0.430 0.419 0.055 0.936 0.211 operation 1.253 1.001 0.114 5.537 0.951 growth 0.182 0.100 -0.593 3.317 0.485 liquid 0.565 0.579 0.093 0.958 0.203 cash 0.183 0.147 0.015 0.634 0.129 cfo 0.989 1.010 0.438 1.685 0.200 5.2 Main test Table 3 lists the regression results of Hypothesis 1 . The column ( 1 ) of Table 3 shows that when only controlling year , industry and city , the regression coefficient of re on bic is -0.002, significantly negative at the 5% level. In the column ( 2 ) of Table 3 , company fixed effects are further controlled, and the coefficient of re on bic is -0.004, significantly positive at the 1% level. The Column ( 3 ) of Table 3 further involves the control variables, and the regression coefficient of bic is -0.004, still significantly positive at the 1% level. In the view of economic implications, re decreased by 4.4% on average from the control to experimental group (0.004/0.097), with a strong economic significance. The coefficient direction of the main control variables including beta , mb , size , lev and so on in the column ( 3 ) of Table 3 is also consistent with exists. In addition, the adjusted R-square in the column ( 3 ) of Table 3 is 41.8%, providing strong explanations for the regression model. Therefore, the Hypothesis 1 has been confirmed in preliminary. Table 3 Main test variable ( 1 ) ( 2 ) ( 3 ) re re re bic -0.002** -0.004*** -0.004*** (0.001) (0.001) (0.001) beta 0.003*** (0.001) mb 0.000*** (0.000) size -0.006*** (0.001) oversea -0.014* (0.008) hhi1 -0.009 (0.007) hhi2-5 -0.097*** (0.031) duality -0.000 (0.001) board -0.009*** (0.003) location _ -0.000 (0.001) sup -0.004 (0.005) mshare -0.013*** (0.005) salary -0.005*** (0.001) profit -0.111*** (0.009) lev 0.065*** (0.003) operation 0.004*** (0.001) growth 0.005*** (0.001) liquid 0.007* (0.004) cash 0.020*** (0.004) cfo 0.004* (0.002) cons 0.098*** 0.099*** 0.292*** (0.001) (0.001) (0.022) Year FE yes yes yes Industry FE yes yes yes City FE yes yes yes Company FE no yes yes N 20492 20492 20492 Adj-R 2 0.175 0.345 0.418 F 4.349 12.823 50.121 Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively, and the standard errors are in parentheses. The same below. 5.3 Robustness test 5.3.1 Parallel trend test In order to eliminate the impact of the difference in annual development trends between the experimental and control group samples, parallel trend test is proposed as follows. Among them, bic_1- bic_5 respectively represent the impact of 1–5 years before the policy time, bic0 represents the impact of the policy in the current year, and bic1- bic4 respectively represent the impact of 1–4 years after the policy time. We use the year before the "Broadband China" policy as the base year to control the multicollinearity. As is shown in Fig. 1 and Fig. 2, wherever the control variables are introduced, the regression coefficients of bic_2- bic_5 are not significant, while the regression coefficients of bic0 and bic1- bic4 are all significantly negative, indicating that the difference between the experimental and control groups in the cost of equity capital are generally stable and supports the parallel trend hypothesis. In addition, what can be seen in the following figures is that the coefficient is still significantly negative in the 3rd and 4th years after the policy, though the "Broadband China" policy is with about 3 years construction period. Therefore, we can reasonably affirm the sustainability of the positive impact from the "Broadband China" policy to the cost of equity capital. (no control variables introduced) (introduction of control variables) 5.3.2 Placebo test While the above test supports the parallel trend hypothesis, some variables that vary with individuals are still difficult to observe and control, causing regression bias. Therefore, we conduct a placebo test. Specifically, we try to assume that the "Broadband China" policy is randomly piloted among sample companies. When the sample company is planned to be put into the experimental group, let the value of bic_placebo take 1, otherwise take 0. Based on the above setting, we conduct 1,000 repeated random experiments. As is shown in the Fig. 3 and Fig. 4, the regression coefficients of bic _placebo are concentrated near 0, with most of the p value greater than 0.1. In addition, the real coefficient of bic in Table 3 are abnormal compared to the placebo test results shown in following figures. Therefore, the impact of missing variables can be further eliminated. (no control variables introduced) (control variables introduced) 5.3.3 Propensity score matching The locations of sample companies may be also affected by the differences in company-level characteristics, causing variable selection bias. Therefore, we choose the propensity score matching to alleviate the above problem. Specifically, we take the 19 control variables in the above-mentioned regression model as the covariates, use a 1:1 non-replaceable nearest neighbor matching method, adopt a logit model regression, and sets the caliper radius to 0.005. Finally, we obtain 11,492 matched observations, including 5,746 samples from the experimental and control groups for each. The balance test results shows that the absolute values of the standardized deviation of the characteristic variables between the experimental and control groups are less than 5%, and the t-test results between the groups does not reject the 0 hypothesis at the 10% level. The regression results in column ( 1 ) and column ( 2 ) of Table 4 show that the coefficients of bic before and after the introduction of control variables are both still significantly negative, supporting the Hypothesis 1 . 5.3.4 Instrumental variable method The randomness is one of the major assumptions in the DID model application. However, the selection of "Broadband China" demonstration cities may also be affected by macroeconomics such as economic development and industrial structure, which does not fully comply with the exogeneity assumption, specifically resulting in the reverse causation bias. Therefore, we attempt to take a two stage regression test for the instrumental variable method. The instrumental variable selected is the historical postal and telecommunications business volume in the policy cities. Considering that Chinese Internet was in line with international standards in 1994, we select the postal and telecommunications business volume in 1993 as a proxy. Given that historical postal and telecommunications business volume is cross-sectional data, we multiply the above proxy by the rate of national Internet penetration in the previous year to construct the instrumental variable. The column ( 3 ) and column ( 4 ) of Table 4 show the two-stage regression results of the instrumental variable method. Among them, the column ( 3 ) of Table 4 shows that the regression coefficient of history on bic is 0.097, significantly positive at the 1% level, indicating that the choose of "Broadband China" policy city is generally determined by the historical development of telecommunications infrastructure. column ( 4 ) of Table 4 brings the predicted value of bic that obtained from the column ( 3 ) into the main testing model. The result shows that the coefficient of re on bic is -0.032, still significantly negative at the 1% level. In addition, the LM and Wald statistics are both significant at the 1% level, refusing the assumption of insufficient identification and weak instrumental variable. 5.3.5 Controlling more macro missing variables Since "Broadband China" is a time-varying policy at the city level, the registration locations of sample companies may differ in each year. Therefore, while city fixed effect is controlled, it is still necessary to consider some missing variables with time and city varying simultaneously. Referring to Xue et al. (2020), we further control the total population ( population ), the gross domestic product ( gdp ) and the proportion of the tertiary industry ( structure ) in city level. The column ( 5 ) of Table 4 shows that the coefficients of bic on population , gdp and strucutrue are 7.716, 0.321 and 0.907 respectively, all significantly positive at the 1% level, indicating the necessity of alleviating the self-selection bias. After including bic and 3 macroeconomic variables into the regression model, as is shown in the column ( 6 ) of Table 4 , the coefficient of bic is -0.003, still significantly negative at the 5% level, supporting the Hypothesis 1 . Table 4 Robustness tests variable ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) re re bic re bic re PSM PSM IV-2SLS IV-2SLS Full sample Full sample bic -0.003* -0.003* -0.032*** -0.003** (0.002) (0.001) (0.004) (0.0011) history 0.097*** (0.002) population 7.716*** 0.029 (0.691) (0.112) gdp 0.321*** -0.008*** (0.010) (0.002) structure 0.907*** -0.008 (0.122) (0.014) controls no yes yes yes yes yes FE yes yes yes yes yes yes N 11492 11492 19640 19640 16448 16448 Adj-R 2 0.312 0.385 0.752 0.418 0.744 0.432 F 3.112 24.788 _ _ 114.782 50.844 74.261 36.962 5.3.6 Other robustness test Some more robustness tests are given. Firstly, further controlling the cross-fixed effects of the year and the industry as well as the year and the province. Secondly, using the samples with the 2.5% and 97.5% quantiles, the 5% and 95% quantiles, and the 10% and 90% quantiles winsorized. Thirdly, eliminating the municipal samples. Fourthly, using the CAPM model, Fama and Franch model of 3 and 5 factors to estimate the cost of equity capital. The test results unlisted robustly support the Hypothesis 1 . 5.4 Mechanism test Based on the previous analysis, the "Broadband China" policy may affect the cost of equity capital through 3 aspects: factors return, factors configuration and factors distribution. With the aim to explore the specific effects of the above mechanisms, we take the following examinations. 5.4.1 Mechanism 1: factors return The digitization is an important indicator of new generation information technology the investment (Chen and Zhang, 2023). Based on the background, we incorporate the digital transformation of companies ( digital ) into the analysis framework to explore the returns to scale effect of the "Broadband China" policy to the cost of equity capital. The digital transformation is defined as "the characters ratio of digital transformation text to the management discussion and analysis text (MD&A) in annual report" ( digital ). The column ( 1 ) of Table 5 shows that the coefficient of digital on bic is 0.017, significantly positive at the 1% level, indicating that the implementation of the "Broadband China" policy has upgraded the digital transformation of companies within the policy cities. The column ( 2 ) of Table 5 shows that the coefficient of re on digital is -0.003, significantly negative at the 1% level, supporting the positive effect of the digital transformation to the cost of equity capital. When bic and digital are included in the same regression model that affects re , as is shown in the column ( 3 ) of Table 5 , the coefficients of bic and digital are still significantly negative at the 1% level. The Sobel test shows that the mediating effect of digital accounts for 2.7%, significant at the 5% level. Therefore, it is confirmed that the implementation of the "Broadband China" policy can reduce the cost of equity capital by increasing the returns to scale of new generation information technology. Table 5 Mechanism 1: factors return variable ( 1 ) ( 2 ) ( 3 ) digital re re bic 0.017*** -0.004*** (0.006) (0.001) digital -0.003*** -0.003*** (0.001) (0.001) controls yes yes yes FE yes yes yes N 20389 20389 20389 Adj-R 2 0.759 0.415 0.415 F 8.795 47.617 46.239 Sobel -2.373** 5.4.2 Mechanism 2: factors configuration In the transitional economies, the loss of factors configuration efficiency caused by government regulation will appear as a strategic burden for companies (Bai and Zhang, 2022). Therefore, we take strategic burden as a perspective to examine the factors configuration effect of the "Broadband China" policy on the cost of equity capital. Considering that companies are more inclined to operate in capital-intensive industries and thereby increase the investment of fixed assets when undertaking the national strategic development tasks, we use “the proportion of fixed assets to total assets” as a proxy (Lin et al., 2004). The column ( 1 ) of Table 6 shows that the coefficient of stb on bic is -0.007, significantly negative at the 1% level, indicating that the implementation of the "Broadband China" policy can reduce the strategic burden on the companies in policy cities. The column ( 2 ) of Table 6 shows that the coefficient of re on stb is 0.020, significantly positive at the 1% level, indicating that the strategic burdens will increase the cost of equity capital. The column ( 3 ) of Table 6 shows that the sign and significance of bic and stb have no substantial change when the above two variables are both involved in the regression model that affects re . The Sobel test also confirmed the mediating effect of strategic burden. Therefore, we conclude that the implementation of the "Broadband China" policy can reduce the cost of equity capital based on the efficient factors configuration, such as lower the investment of high-density assets. Table 6 Mechanism 2: factors configuration variable ( 1 ) ( 2 ) ( 3 ) stb re re bic -0.007*** -0.004*** (0.002) (0.001) stb 0.020*** 0.019*** (0.005) (0.005) controls yes yes yes FE yes yes yes N 20492 20492 20492 Adj-R 2 0.863 0.418 0.419 F 98.200 49.795 48.187 Sobel -3.575*** 5.4.3 Mechanism 3: factors distribution Once the value conflict is between the shareholders and the stakeholders, some public welfare expenditures may turn into the social burdens of companies (Luo and Liu, 2018). We use "income tax expenses divided operating income" to measure the social burden and explore the factors distribution effect of the "Broadband China" policy, for which can reflect the role of government in factors distribution. The column ( 1 ) of Table 7 shows that the coefficient of sob on bic is -0.001, significantly negative at the 5% level, indicating that the implementation of "Broadband China" policy can reduce the social burden. The column ( 2 ) of table (7) shows that the coefficient of re on sob is 0.260, significantly positive at the 1% level, indicating that the social burden will increase the cost of equity capital. The column ( 3 ) of Table 7 shows that after both bic and sob are included in that affects re , the coefficients are − 0.004 and 0.258 respectively, both of which are still significant at the 1% level. The Sobel test results also support the partial mediating effect of social burden. The above regression results confirm that with the implementation of the "Broadband China" policy, the function of taxation in factors distribution has been weakened, thereby reduced the necessary rate of return required by shareholders. Table 7 Mechanism 3: factors distribution variable ( 1 ) ( 2 ) ( 3 ) sob re re bic -0.001** -0.004*** (0.000) (0.001) sob 0.260*** 0.258*** (0.023) (0.023) controls yes yes yes FE yes yes yes N 20492 20492 20492 Adj-R 2 0.615 0.426 0.427 F 39.313 55.726 53.837 Sobel -2.929*** 5.5 Further analysis In the mechanism test, the "Broadband China" policy is proved to reduce the cost of equity capital by taking advantage of the increasing returns to scale effect of the new generation information technology, improving the production factors configuration, and optimizing the factors distribution. With the aim to deepen the above conclusions, we put some further analyses based on the mentioned paths. 5.5.1 Further analysis of the factors return effect: based on the regional life cycle As pointed out in the theoretical analysis above, the returns to scale effect of new generation information technology may be affected by the institutional environment, legal environment and marketization process. Actually, relevant mentioned often depend on the stage of macroeconomic development. According to the regional life cycle theory of Thompson's (1966), the regional economy is similar to the industrial development and the company growth, and will go through the stage of initial, growth, maturity and decline. In this process, the factors flow and the industrial structure of the company match its life cycle. In China, the economic development in eastern is currently arrived at a relatively mature stage, with a high degree of modernization and sufficient productivity to support the application of information technology. What's more, the eastern region is also facing more urgent needs for industrial transfer and upgrading (Dai et al., 2023). Based on the above deduction, we believe that the increasing returns to scale effect of the "Broadband China" policy is mainly reflected in the eastern region where the economic cycle is relatively mature. Table 8 conducts a group mediation effect test based on the regional life cycle. Among them, if the registered cities of the sample companies are located in 12 provinces (or municipalities) including Beijing, Shanghai, Tianjin, Guangdong, Jiangsu, Zhejiang, Shandong, Fujian, Hebei, Liaoning, Guangxi and Hainan, let east take the value of 1, otherwise take 0. The regression results show that the "Broadband China" policy reduces the cost of equity capital by improving the degree of digital transformation of companies only in the eastern city groups. Therefore, the above speculation is confirmed. Table 8 Further analysis of the factors return effect variable ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) digital re re digital re re east = 1 east = 1 east = 1 east = 0 eas = 0 eas = 0 bic 0.019** -0.005*** 0.003 -0.000 (0.008) (0.001) (0.009) (0.002) digital -0.003** -0.003** -0.003 -0.003 (0.001) (0.001) (0.003) (0.003) controls yes yes yes yes yes yes FE yes yes yes yes yes yes N 14502 14502 14502 5887 5887 5887 Adj-R 2 0.756 0.422 0.424 0.763 0.406 0.406 F 7.074 32.175 31.923 3.592 18.906 18.008 Sobel -1.889* -0.732 5.5.2 Further analysis of the factors configuration effect: based on the industry competitiveness The essence of positive factors configuration effect of the "Broadband China" policy is to enhance the viability of enterprises. From the behaviors of companies undertaking strategic burdens to the economic consequences of the loss of economic competitiveness, the institutional background of the transitional economy should not be ignored. The 15th National Congress of the Communist Party of China established a market economic system for social entities with "public ownership as the main and multiple ownership economies coexisting", forming the current pattern of restrictive industries and competitive industries coexisting. In the competitive industries, while some companies still undertake national strategic development tasks, they do not occupy an absolute dominant position and face competitiveness impacts from other market-oriented economic entities, which may aggravate the tragedy of factors configuration in competitive industries. Based on the above analysis, we believe that the factors allocation effect of the "Broadband China" policy mainly exists in competitive industries. The Table 9 conducts a group mediation effect test based on the competitiveness of the industry in which the sample companies are located. Following the ideas of Xin and Tan (2009), we distinguish the restricted and competitive industries based on the 2012 China Securities Regulatory Commission industry classification code. Among them, restricted industries include: ( 1 ) mining industry (code B); ( 2 ) petroleum processing, coking and nuclear fuel processing industry (code C25); ( 3 ) ferrous metal smelting and rolling processing industry (code C31); ( 4 ) non-ferrous metals Metal smelting and rolling processing industry (code C32); ( 5 ) electric power and heat production and supply industry (code D). When the sample belongs to a restricted industry, let regulation take the value 1, otherwise take 0. The regression results in Table 9 show that only when the sample company belongs to a competitive industry, the "Broadband China" policy significantly reduces the strategic burden, thereby reducing the cost of equity capital. The regression results confirm the above inference. Table 9 Further analysis of the effect of factors configuration variable ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) stb re re stb re re regulation = 1 regulation = 1 regulation = 1 regulation = 0 regulation = 0 regulation = 0 bic -0.003 -0.009** -0.007*** -0.004*** (0.006) (0.004) (0.002) (0.001) stb 0.007 0.007 0.022*** 0.021*** (0.016) (0.015) (0.005) (0.005) controls yes yes yes yes yes yes FE yes yes yes yes yes yes N 1908 1908 1908 18584 18584 18584 Adj-R 2 0.871 0.416 0.418 0.845 0.426 0.427 F 15.424 11.074 10.856 87.014 40.201 38.702 Sobel − 0.113 -3.729*** 5.5.3 Further analysis of the factors distribution effect: based on the supply chain conflicts The core of the "Broadband China" policy to improve the factors distribution is to achieve the value synergy among the stakeholders of companies. The supply chain is an important link in the value network of companies. According to the 5 forces model in the competitive advantage theory of Porter (Porter, 1980), buyers and suppliers are 2 of the 5 types of competitive forces. For manufacturing companies that account for the majorities of the samples, the business processes are relatively complex, going through a complete supply, production and marketing stage in the business operations. Therefore, those of whom are easily restricted by supply chain. Once the unequal supply chain relationships hinder the fair distribution of social wealth, the government is naturally more motivated to maintain the market order through the administrative means. Based on the above analysis, we conduct that the factors distribution effect of the "Broadband China" policy will be more obvious in companies faced with more serious supply chain conflicts. Supply chain concentration reflects the bargaining power of companies relative to supply chain companies. The higher the concentration of the customers (or the suppliers) is, the stronger bargaining power do the supply chain companies have (Chang et al., 2014). Therefore, when the customer or supplier concentration of a company is less than the 25% sample quantile or greater than the 75% sample quantile (that is, the bargaining power gap between the companies and the supply chain companies is large), we define it as a company with serious supple chain conflicts, otherwise define it as a company with relatively harmonious supply chain relationships. Table 10 conducts a group mediation effect test based on the dimensions of customers and suppliers respectively. The regression results of the two dimensions draw a similar conclusion, that is, only when the bargaining power gap between the companies and the customers (or the suppliers) is large ( customer / supplier = 1), can the "Broadband China" policy alleviate the social burden and thereby reduce the cost if equity capital, indicating the exist of the factor distribution effect. Table 10 Further analysis of the factors distribution effect Panel A: Based on the customer dimension variable ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) sob re re sob re re customer = 1 customer = 1 customer = 1 customer = 0 customer = 0 customer = 0 bic -0.001** -0.004** -0.001 -0.003* (0.001) (0.002) (0.001) (0.002) sob 0.201*** 0.199*** 0.287*** 0.287*** (0.041) (0.041) (0.035) (0.035) controls yes yes yes yes yes yes FE yes yes yes yes yes yes N 9687 9687 9687 9687 9687 9687 Adj-R 2 0.600 0.459 0.459 0.652 0.444 0.444 F 16.696 27.989 27.019 16.736 22.014 21.109 Sobel -2.340** -1.613 Panel B: Based on the supplier dimension variable ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) sob re re sob re re supplier = 1 supplier = 1 supplier = 1 supplier = 0 supplier = 0 supplier = 0 bic -0.001** -0.004** -0.000 -0.001 (0.001) (0.002) (0.001) (0.002) sob 0.277*** 0.275*** 0.215*** 0.215*** (0.043) (0.043) (0.038) (0.038) controls yes yes yes yes yes yes FE yes yes yes yes yes yes N 8744 8744 8744 8744 8744 8744 Adj-R 2 0.629 0.464 0.465 0.599 0.449 0.449 F 11.727 21.145 20.261 12.368 18.589 17.732 Sobel -1.915* -0.710 5.6 Heterogeneity analysis The "Broadband China" policy has the dual attributes of the information technology and the industrial policy, taking cities as the execution unit. Therefore, we further discuss the heterogeneous economic consequences of the "Broadband China" policy based on the perspectives of the information technology, the government-business relation, and the spatial effect. 5.6.1 Heterogeneity analysis 1: based on the information technology Equity investment and industrial investment are two choices for companies to realize the technological innovations (Wu and Yan, 2023). The former focuses on the extensive expansion, while the latter emphasizes the connotative revolution. Combining the investment methods of new generation information technology, we take the following heterogeneity analysis. Specifically, if the digital investment projects are disclosed in the notes to long-term equity investment of the sample company's annual report, and the net increase in the year is greater than 0, it is considered that the company has adopted the equity investment in new generation information technology application, and let equity take value of 1, otherwise take 0. In the same way, if a sample company discloses digital investment projects in the notes to fixed assets, intangible assets or development expenditures in the annual report, and the net increase for the year is greater than 0, it is deemed that the company has introduced a new generation information technology through industrial investment, let entity take 1, otherwise take 0. As is shown in Table 11 , while the industrial investment is currently more common than the equity investment in the application field of new generation information technology, only the latter can effectively amplify the inhibitory effect of the "Broadband China" policy on the cost of equity capital. It means that at least for the present, the "borrowingism" is more suitable for the new generation information technology wave in Chinese companies led by the implementation of the "Broadband China" policy. Table 11 Heterogeneity analysis 1: based on the Information Technology variable ( 1 ) ( 2 ) ( 3 ) ( 4 ) re re re re equity = 1 equity = 0 entity = 1 entity = 0 bic -0.017** -0.004*** -0.005*** -0.005** (0.006) (0.001) (0.001) (0.002) controls yes yes yes yes FE yes yes yes yes N 1025 19467 14025 6467 Adj-R 2 0.514 0.417 0.442 0.403 F 2.807 47.682 30.833 13.783 Fisher 0. 000*** 0.398 Note: Fisher is the empirical p value obtained by the self-extraction method 500 times. The same below. 5.6.2 Heterogeneity analysis 2: based on the government-business relationship The effect of policy is largely affected by the government-business relationship. For example, in the transition economies, policy burdens are mostly seen on the state-owned (Lin et al., 2004); the closer the geographical distance to the government, the easier it is for companies to obtain government subsidies and thereby respond to the government’s call for innovation; once the senior executives have a political background, they are more will to take participate in the “green governance” (Zhuang et al., 2022). By reviewing of the exists, we divide the government-busniess relationship into 3 dimensions, including governance, company and management. The government-business relationship in the dimension of governance focuses on the view of ownership. The column ( 1 ) and column ( 2 ) of Table 12 conduct a grouped regression based on the nature of property rights ( soe ) of the sample companies. The test results show that the coefficients of bic are − 0.007 and − 0.003 respectively in the state-owned group ( soe = 1) and the non-state-owned group ( soe = 0), and with a 1% level of significance in intergroup coefficient difference. It can be concluded that the positive impact of the "Broadband China" policy to the cost of equity capital is more obvious in the state-owned companies. Government-business relationship in the dimension of company focuses on the geographical distance. The column ( 3 ) and column ( 4 ) of Table 12 conduct a grouped regression based on the geographical distance ( distance ) between government and companies. Among them, the geographical distance between government and companies refers to the research of Wei and Su (2013), and is measured by the spherical distance calculated from the longitude and latitude of the registered address of the sample companies and the local government of the city to which it belongs. The regression results show that no matter how far ( distance = 1) or close ( distance = 0) the spherical distance between the sample companies and the local government is, the implementation of the "Broadband China" policy will help reduce the cost of equity capital, but the coefficient of the latter is lower than the former at the 1% level. Therefore, we can conclude that the relatively close geographical distance between government and companies strengthens the inhibitory effect of the "Broadband China" policy on the cost of equity capital. Government-business relationship in the dimension of management revolves around the political background of core executives. The column ( 5 ) and column ( 6 ) of Table 12 present the grouped regression according to the political background of core executives ( pc ). Among them, the political background of core executives is measured by the government tenure of the chairman or the CEO of the sample companies. When the chairman or CEO of a company currently serves or has served in the government, let pc take the value 1, otherwise take 0. Judging from the test results, the core executives with political background obviously strengthens the positive effect of "Broadband China" policy to the cost of equity capital at the 1% level. In short, the conclusions based on the above heterogeneity analyses in 3 dimensions of the government-business relationship are relatively consistent, showing the positive adjustment effects of the government-business relationships. For companies with closer government-business relationships, they are full of smoother communication channels between government and companies, and therefore can understand the spirit of policies correctly and comprehensively (Zheng et al., 2022). In other words, a well understanding of the policy may be an important prerequisite for effective implementation. Table 12 Heterogeneity analysis 2: based on the government-business relationship variable ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) re re re re re re soe = 1 soe = 0 distance = 1 distance = 0 pc = 1 pc = 0 bic -0.007*** -0.003*** -0.003* -0.006*** -0.007*** -0.003** (0.002) (0.001) (0.001) (0.001) (0.002) (0.001) controls yes yes yes yes yes yes FE yes yes yes yes yes yes N 7055 12773 10126 10126 6818 13674 Adj-R 2 0.421 0.434 0.425 0.433 0.451 0.431 F 19.247 28.249 20.490 26.811 13.329 33.273 Fisher 0. 000*** 0.004*** 0.000*** 5.6.3 Heterogeneity analysis 3: based on the spatial effect The new geographical economic theory represented by Krugman (1991) focuses on the inter-regional flow of production factors and believes that the "center-periphery" regional spatial structure can easily lead to the agglomeration and the diffusion of inter-regional economic resources. Based on the theory, we examine the spatial effects of the "Broadband China" policy. For the spatial aggregation effect, we conduct a grouped regression of based on the average geographical distance among a sample company and other listed companies in the same registered location. If the distance above is greater than the sample median, let inside take the value 1, otherwise take 0. The column ( 1 ) and column ( 2 ) of Table 13 show the regression results. Among them, in the longer average geographical distance group, the coefficient of re on bic is -0.002 with no statistical significance; in the shorter aggregation distance group, the coefficient of re on bic is -0.006 with the significance of 1% level. Furthermore, the difference in regression coefficients between the two groups is significant at the 1% level. Therefore, we confirm the existence of the spatial aggregation effect in the “Broadband China” policy. For the spatial spillover effect, we retain the samples that are not in the "Broadband China" policy pilot cities, and conduct a grouped regression based on the average geographical distance among a sample company and the "Broadband China" pilot cities (outside). As is shown in column ( 3 ) of Table 13 , the coefficient of re on outside is -0.002 with no statistical significance, indicating that the "Broadband China" policy is hard to radiate to listed companies in nearby cities. Table 13 Heterogeneity analysis 3: based on the spatial Effect variable ( 1 ) ( 2 ) ( 3 ) re re re inside = 1 inside = 0 bic = 0 bic -0.002 -0.006*** (0.001) (0.002) outside -0.002 (0.014) controls yes yes yes FE yes yes yes N 10302 9396 4724 Adj-R 2 0.426 0.434 0.458 F 21.352 21.894 10.724 Fisher 0.000*** 6. Research Conclusions and Enlightenments Based on externality theory and soft budget constraint theory, we take the Chinese non-financial listed companies in A-share from 2012 to 2019 as the examples, test the effect of the "Broadband China" policy to the cost of equity capital, and therefore clarify the specifical roads of government-business collaboration in information technology industry policy. The main conclusions are as follows: ( 1 ) the "Broadband China" policy has reduced the cost of equity capital; ( 2 ) return, configuration and distribution of factors constitute the important mechanisms, specifically reflected in the higher degree of digital transformation as well as the lower strategic and social burden; ( 3 ) the above-mentioned mechanism only exists in the eastern region with mature regional economic development, competitive industries, and companies with serious supply chain conflicts; ( 4 ) the effective implementation of the "Broadband China" policy relies on the equity investment, closed to the government-business relationship, and with a certain spatial aggregation effect. There are some enlightenments. Firstly, the "Broadband China" policy. Based on the previous empirical studies, we believe that the implementation of the "Broadband China" policy has achieved the expected effects as a whole: not only assisting the digital transformation, but also reducing two types of policy burdens, indicating the adhere to the laws of market economy for Chinese government. However, just like the previous technological innovations, the following strategy achieved a more direct value-added effect than the leading strategy in the early stage of new generation information technology application. Based on the background of Sino-US trade war and anti-globalization trend, the international flow of core production factors has slowed down, reflecting the potential dangers in relying on the outsourcing to achieve technological innovation. Therefore, besides of the economic benefits, we also recommend Chinese government to strengthen the maintenance of independent innovation capabilities in companies, encourage the originals, and avoid falling into the "borrowingism" trap. Secondly, the cost of capital. Based on the above tests, it is not difficult to conclude that the capital cost can comprehensively reflect the return, the configuration and the distribution of production factors. However, in the macro, the cost of capital has not received enough attention. For example, in the 2019 version of the "Detailed Rules for the Assessment of Economic Value Added" and the "Measures for the Assessment of Business Performance of Persons in Charge of Central Enterprises", the cost of capital in central companies are set at 3 fixed values of 4.1%, 5% and 5.5%, ignoring the heterogeneity other than the asset liquidity and the leverage, obviously contrary to the laws of the market. Therefore, in the process of factor marketization reform, we call for the consideration of deepening the estimation methods of the cost of capital and giving corresponding weight. From the perspective of the financial management concepts behind the cost of capital, while scholars of much currently question the "maximizing shareholder wealth", due to the availability of economic data, it is still effective to conduct the analyses. Especially when it involves of the digitalization, the wealth demands of shareholders and other stakeholders are consistent in gradual, giving more comprehensive perspectives for the economic analyses based on the cost of capital cost. Finally, government-business relationship. As Lin et al. (2004) pointed out, the contradiction between the government and the market in transitional economies cannot simply be attributed to institution. The political vision of government is to achieve the "maximizing social welfare", different from the "maximizing shareholder wealth" for business owners. Once the value demands of the two are inconsistent, it will lead to the inefficiency of the factor's configuration and distribution. How to play the role of the "visible hand", cooperate with the laws of the market economy, and achieve Pareto improvement based on the way of value collaboration is an urgent issue that needs to be solved. Fortunately, the digital transformation of the economy led by the new generation information technology is now giving the government and enterprises opportunities for coordinated development. For government, we recommend it to improve the supporting systems related to the information technology industry and give additional impetus to the development of the digital economy by using the benign regulation measures. 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NBER Working Paper, No. 24839 Wei H, Su Y (2013) The Effect of Institutional Proximity in Non-local University-industry Collaborations: an Analysis Based on Chinese Patent Data [J]. Res Policy 42(2):454– Yang M, Yan L, Dayong D (2023) Strategic Information Disclosure and the Cost of Equity Capital: Evidence from China [J]. Finance Res Lett 51:103418 Zhong R, Xu X, Lotz E, Newman S (2017) Intelligent Manufacturing in the Context of Industry 4.0: a Review [J]. Engineering 3(5):616– Footnotes The estimate methods for cost of equity capital comes from "Capital Cost: Theory and Estimation", published by Economic Management Press in 2018, author: Wang Ping. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4203006","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286867882,"identity":"3ddad400-01e4-4822-8dc9-a78eada1b457","order_by":0,"name":"Qiqing Huang","email":"","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Qiqing","middleName":"","lastName":"Huang","suffix":""},{"id":286867883,"identity":"16d242e3-606a-49f9-8d4c-d797045efda4","order_by":1,"name":"Ying Zou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3PsWvCQBTH8RcOrsuVbPIkkr/h5CCT4L9yD+EmawWXDA4BSxwaO/tndHQ8OMh00tXR/Ad2c7HY3RLTzeG+8/v84AGEQg/YU8GsPeWYxnEVHXW+vE+E5dRs/Uj1t57Jo6+7EKHUc2lI2invN2+sA+nZDAV3Guy+zqngEK/fdTtJtEEUbhatPsyBdgNAv/9sJeNE1yjRLRj47ECeg8SXdiISKlFLRyVMszmVrAuZMGm1oeqXQEdioqawI4XoFWpfi7u/iOT15C4/mI6/quH3OV+m8XrTTm43/nceCoVCoT+7Ah5mTDQIKps5AAAAAElFTkSuQmCC","orcid":"","institution":"Capital University of Economics and Business","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zou","suffix":""},{"id":286867884,"identity":"f8fc6a9a-950a-488e-81d5-deea6470f674","order_by":2,"name":"Yaxuan Zhao","email":"","orcid":"","institution":"Capital University of Economics and Business","correspondingAuthor":false,"prefix":"","firstName":"Yaxuan","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-04-02 00:29:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4203006/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4203006/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54135116,"identity":"30677a9e-3d50-42bb-8ad5-25984eea6bd7","added_by":"auto","created_at":"2024-04-05 06:11:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19811,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic effect\u003c/p\u003e\n\u003cp\u003e(no control variables introduced)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4203006/v1/c1f48f909a8b53bdc85007d6.jpg"},{"id":54135115,"identity":"197ab588-e8be-4cfe-ba0e-f4cff2ba4f72","added_by":"auto","created_at":"2024-04-05 06:11:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21949,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic effect\u003c/p\u003e\n\u003cp\u003e(introduction of control variables)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4203006/v1/0228379a3942ad37d679765a.jpg"},{"id":54135117,"identity":"879b2a02-c192-416b-9af7-5bc62f2f1a6c","added_by":"auto","created_at":"2024-04-05 06:11:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17944,"visible":true,"origin":"","legend":"\u003cp\u003ePlacebo test\u003c/p\u003e\n\u003cp\u003e(no control variables introduced)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4203006/v1/42aa8e3acf4a1bfdc4044268.jpg"},{"id":54135118,"identity":"ef86da19-c9b2-48f3-bd0e-431c093c397c","added_by":"auto","created_at":"2024-04-05 06:11:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":19147,"visible":true,"origin":"","legend":"\u003cp\u003ePlacebo test\u003c/p\u003e\n\u003cp\u003e(control variables introduced)\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4203006/v1/d5ddbb0c898eec85ba4abdfc.jpg"},{"id":54220942,"identity":"86a4710d-0ea1-4935-a104-e460d8c23b06","added_by":"auto","created_at":"2024-04-06 19:07:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981511,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4203006/v1/f03513a1-8c17-4960-bcfa-0a642b5e8c2f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How to Achieve Government-business Collaboration in Information Technology Industry Policy? From the Perspective on Cost of Equity Capital","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs Chinese economy enters a new normal stage, the \"high-speed growth\" model has become now of the pattern in Chinese economy instead of \"high-quality development\" model. In the document named \"\u003cem\u003eOpinions of the Central Committee of the Communist Party of China and the State Council on Accelerating the Construction of a National Unified Market\u003c/em\u003e\" jointly issued by the Central Committee of the Communist Party and the State Council of China on April 10, 2022, a goal of \"\u003cem\u003ecomprehensively promote the transformation of Chinese market from large to strong, thereby providing strong support for the construction of a high-standard market system and a high-level socialist market economic system\u003c/em\u003e\" had become an important guiding ideology, reflecting the strategic transformation of Chinese government from \"big to strong\". Presently, there is a consensus among various sectors of Chinese society, that is to reverse the past inertial thinking of extensive economic growth based on investment, resources and external demand, and to efficiently utilize limited resources and capital for refined operations to achieve the goal of sustainable economic development. For companies, the capital cost runs through the 3 major financial policies of investment, financing and dividends, that can reflect the efficiency of resource configuration and the comprehensive competitiveness. Especially in the context of interest rate control, the Chinese listed companies have a general preference for equity financing. Therefore, it is no doubt saying that the cost of equity capital is of great significance to the wealth realizations of the shareholders, the companies and even the wider stakeholders.\u003c/p\u003e \u003cp\u003eThe deep integration of the information technology and the substantial economy has received a lot of attention as one of the strategic focuses of the Chinese economic transformation in recent years. The new generation information technology, represented by big data, cloud computing, the Internet and blockchain, is now regarded as one of the key roads to improve the economic distribution and optimize the value creation model. Different from the western liberal market, the Chinese current economic system was born out of the planned economy institution. Since the much of the resources is controlled by government, the \"visible hand\" constitutes an important part of the Chinese economy still then and the administrative interventions such as industrial policies, financial subsidies often make senses on the strategic decisions of the companies (Lin et al., 2004). Therefore, exploring the impact and the mechanisms of information technology industry policies on the decision-making of the equity investors constitutes an important research topic under the new normal development of the Chinese economy.\u003c/p\u003e \u003cp\u003eInformation technology industrial policy has the dual attributes of industrial policy and information technology, which should be viewed in two parts. For one, as an industrial policy, how effective is the policy implementation? For another, as an information technology, how can it help to realize the value creation in companies? When it comes to the first question, the \u0026ldquo;century debate\u0026rdquo; between Lin and Zhang on industrial policy seems to be still fresh. Lin, as the representation of the new structural economics school, supports the guiding role of the government and believes that the sustainable development of the national economy only relies on the \"effective markets\" to have certain limitations, and that market failures such as information spillover and public goods need to be reconciled through \"proactive government\" (Lin, 2017). While Zhang, holds the core views of the Austrian School of Economics, emphasizes the unpredictability of the industrial innovation, and advocates a perfectly competitive market mechanism to determine the success or failure of technology, industry or company. As for the second question, since the emergence of the Solow's information technology paradox in 1980s, the doubts of the relationship between the information technology and the productivity has never disappeared with the time goes by.\u003c/p\u003e \u003cp\u003eWith the aim of answering the above questions, this paper uses the soft budget constraint theory and externality theory, takes the A-share listed companies from 2012 to 2019 as the research examples, and analyze the cost of equity capital effect of the information technology industry policy based the quasi-natural experiment of the \"Broadband China\". The possible contributions of our study are as follows. First of all, in terms of research content, the previous literature on the driving factors of the cost of equity capital mostly focused on the micro level and rarely involved the analyses of macro policies, let alone the consideration of industrial policies. Actually, the cost of capital not only serves as a benchmark for various financial decisions of the companies, but is also crucial for government regulation and public project investment analysis, and is the value link between macro and micro economies. Ignoring the impact of industrial policy on the cost of capital, the research may be one-sided and unfair. Based on the background of the popularization of the new generation information technology in recent years and the deep integration with the substantial economy, we focus on the cost of equity capital effect from the perspective of the \"Broadband China\" policy, so that to provide useful supplement for both of the studies of the macro-influencing factors focused on the cost of equity capital and the research to the microeconomic consequences of the information technology industry policy.\u003c/p\u003e \u003cp\u003eSecondly, in terms of research perspective, the existing research on the economic consequences of the \"Broadband China\" policy has been relatively limitation\u0026mdash;\u0026mdash;either examining the macro and micro economic consequences of the \"Broadband China\" policy in isolation, or ignoring the fact that the information technology industrial policy has the dual attributes of industrial policy and information technology. This paper takes the perspective of government-business collaboration and analyzes the path by which the \"Broadband China\" can help realize the shareholder wealth, including improving the positive externalities (factors return), reducing the strategic burdens (factors configuration), and alleviating the social burdens (factors distribution). Not only the research enriches the connotation of the information technology industrial policy and the cost of equity capital, but it also broadens the application scope of the budget soft constraint theory and the externality theory.\u003c/p\u003e \u003cp\u003eFinally, in terms of practical value, with the sweeping wave of new generation information technologies such as the Internet and big data in recent years, Chinese social market economic entities, including the government, companies and equity investors, have begun to participate in the corporate governance. In the process, it is inevitable to get involved in the consideration of information technology. Therefore, this paper can, firstly, provide incremental information for the \"visible hand\" of the government; secondly, provide appropriate enlightening suggestions for the listed companies to achieve the high-quality and sustainable development; thirdly, provide guidance for the equity investors to scientize and rationalize their investment decisions.\u003c/p\u003e \u003cp\u003eThe remainder of this article is organized as follows. The second part is the institutional background and literature review; the third part is the theoretical analysis and hypothesis deduction; the fourth part is the research design; the fifth part is the empirical results and analysis; the sixth part is the research conclusion and inspiration.\u003c/p\u003e"},{"header":"2. Institutional background and literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Institutional background\u003c/h2\u003e \u003cp\u003eChinese Internet era began in 1994. In this year, China launched the construction of public computer Internet and achieved integration with the international Internet, marking the beginning of the development of Chinese information technology industry. In the past 20 years since then, China had made great breakthroughs in the construction of information technology infrastructure. However, problems such as high cost, low speed, shallow coverage and uneven development were still prominent, which had brought challenges to the deep integration of industrialization and informatization. For meeting the actual needs of Chinese information technology industry and ignite new engines of Chinese economic development such as informatization, digitization, and intelligence, the State Council of China drafted and announced the implementation draft of \"Broadband China\" strategy in September 2012 in conjunction with eight ministries and commissions including the Ministry of Industry and Information Technology and the National Development and Reform Commission. In August 2013, the \"\u003cem\u003eNotice of the State Council on Issuing the 'Broadband China' Strategy and Implementation Plan\u003c/em\u003e\" was released, which elevated the public infrastructure construction of broadband networks to the national strategic level for the first time and clarified the overall development goals during the \"12th Five-Year Plan\" and \u0026ldquo;13th Five-Year Plan\u0026rdquo; period. In 2014, 2015 and 2016, the Ministry of Industry and Information Technology and the National Development and Reform Commission jointly led the construction of three batches of \"Broadband China\" demonstration cities, and a total of 120 cities were commended. Demonstration cities involved in the list of \"Broadband China\" policy would achieve all-round improvements in broadband user scale, network speed, coverage and economic services through a construction period of about 3 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Literature review\u003c/h2\u003e \u003cp\u003eIn view of the close connection with the digital economy, scholars focused much attention on the \"Broadband China\" policy and launched multiple discussions on the macro and micro economic consequences in recent years, with most of the relatively positive evaluations. From the macro perspective, the \"Broadband China\" policy was proven to promote urban economic growth (Zhao et al., 2020), assist economic transformation (Wen and Zhang, 2023), optimize the industrial structure (Liu and Ma, 2 020), improve export quality (Li and Cui, 2022), release innovation potential (Zhong et al., 2022), correct labor factor configuration distortions (Niu and Cui, 2022) and so on. From the perspective of micro, studies mostly focused on innovation and production efficiency. In terms of innovation, Xue et al. (2020) found that the \"Broadband China\" policy promoted the technology diffusion of parent and subsidiary companies and technological cooperation between companies; the research of Li et al. (2022) showed that the \"Broadband China\" policy improved all aspects of innovation input, output and efficiency in companies. In terms of production efficiency, Luo et al. (2022) showed that the \"Broadband China\" policy optimized production capacity utilization by inhibiting excessive investment and improving management efficiency. Sun and Chen (2021) supported the positive impact of the \"Broadband China\" policy on the total factors productivity of companies. In addition, a few of studies focused on the optimization effect of the \"Broadband China\" policy to the information environment in companies (Li and Yun, 2023).\u003c/p\u003e \u003cp\u003eThe cost of equity capital reflects the expectations of the shareholders to the operating conditions and the future risk levels in companies, and it is one of the essential indicators of the realization of the maximizing shareholder wealth. Since Modigliani and Miller (1958) proposed the MM theory, scholars had conducted rich and in-depth research on the cost of equity capital from macro and micro aspects. In the macro, many scholars interpreted it from the perspective of the economic resilience and found that factors such as the trade friction (Cheng et al., 2021), the economic policy uncertainty (Guo and Sun, 2021) and the impact of the COVID-19 (Ke, 2022) constituted an important reason for the high cost of equity capital in recent years. In terms of macro policy, such as the \"value added tax retention and refund\" reform (Wu et al., 2021) and the market access control (Wang et al., 2019) was proved to reduce the cost of equity capital. Studies of the micro mainly focused on the information disclosure and the corporate governance. In terms of information disclosure, high-quality information disclosure (Yang et al., 2023) and active investor information interaction (Cai et al., 2022) were regarded as important measures to alleviate information asymmetry between companies and investors as well as reduce the cost of equity capital. In terms of corporate governance, internal control (Luo, 2018), core competitiveness (Qi et al., 2021) and big data technology (Zou et al., 2022) also played a positive role in optimizing corporate governance and reducing the cost of equity capital. In addition, a small number of scholars also discussed the invisible governance mechanisms of cost of equity capital, such as confucian traditional culture (Wang and Tan, 2022) and social trust (Zhao, 2020).\u003c/p\u003e \u003cp\u003eTo sum up, as a pilot policy at the city level, \"Broadband China\" has received full attention in the researches of macroeconomic consequences. However, the researches of the microeconomic consequences are relatively concentrated and rarely involves the discussion of the impact on corporate asset pricing. At the same time, existing literatures pay less attention to the institutional background of China, and still fail to effectively build a bridge for the \"government-business\" interactive relationship. From the perspective of the researches on the cost of equity capital, the past literatures focus more on the micro influencing factors and insufficient attention on the macro industrial policies, let alone the discussions on the information technology industry policies. In the context of the economic transformation toward intelligence, informatization, and digitalization, how to effectively use the \"visible hand\" of the government to empower shareholders to create wealth is a practical issue that needs to be discussed urgently. Therefore, we will analyze the impact and the mechanisms of the \"Broadband China\" policy on the cost of equity capital based on the perspective of the government-business collaboration, with the help of the soft budget constraint theory and the externality theory.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Theoretical analysis and hypothesis deduction","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Theoretical analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Motivation of shareholders to lower the expected returns\u003c/h2\u003e \u003cp\u003eThe cost of capital is the rate of return that investors expect from a company and is closely related to value creation. According to the EVA (economic value added) theory proposed by Miller and Modigliani in the 1960s, the Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is as followed. Among them, \u003cem\u003eeva\u003c/em\u003e is the economic value added, \u003cem\u003enopat\u003c/em\u003e is the net operating profit after tax, \u003cem\u003ewacc\u003c/em\u003e is the weighted average cost of capital, and \u003cem\u003eta\u003c/em\u003e is the total invested capital.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$eva=nopat-wacc\\times ta$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBased on the circumstance of the limited liability system, shareholders legally have the right to claim residual income after distribution shared by stakeholders. Therefore, EVA can be regarded as the wealth of shareholders retained in the company. The one of distribution to stakeholders partly is reflected in the net operating profit after tax, such as salaries paid to management and employees, taxes paid to the government and so on. The other part is reflected in the distribution to investors such as creditors and shareholders. The Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) can be rewritten as the Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among them, \u003cem\u003eogp\u003c/em\u003e is the operating gross profit, and \u003cem\u003edsoti\u003c/em\u003e represents the distribution to stakeholders other than investors.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$eva=ogp-dsoti-wacc\\times ta$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) can be rewritten into the Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among them, \u003cem\u003ereva\u003c/em\u003e is the rate of the economic value added (=\u0026thinsp;economic added value/total invested capital), \u003cem\u003erogp\u003c/em\u003e is the rate of operating gross profit to the invested capital (=\u0026thinsp;operating gross profit/total invested capital), \u003cem\u003erdsoti\u003c/em\u003e is the rate of distribution to stakeholders other than investors (=\u0026thinsp;distribution to stakeholders other than investors /total invested capital).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$reva\\times ta=rogp\\times ta- rdsoti\\times ta-wacc\\times ta$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDivide both sides of the Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) by the total invested capital to get the Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e):\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$reva=rogp-rdsoti-wacc$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBased on the perspective of production factors, \u003cem\u003erogp\u003c/em\u003e can be regarded as the value function consisted by the return rate (\u003cem\u003ereturn\u003c/em\u003e) and the configuration efficiency (\u003cem\u003econfiguration\u003c/em\u003e) of different factors. Therefore, Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) is as followed:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$reva=F\\left(return,configuration\\right)-rsdsoti-wacc$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) can be rewritten into the Eq.\u0026nbsp;(\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among them, \u003cem\u003erd\u003c/em\u003e is the cost of debt capital, \u003cem\u003elev\u003c/em\u003e is the leverage, and \u003cem\u003ere\u003c/em\u003e is the cost of equity capital.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$reva=F\\left(return,allocation\\right)-rdsoti-rd\\times lev-re\\times (1-lev)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAssuming that investment projects can be replicated infinitely, the rate of economic value added can be regarded as the retained wealth of shareholders, and the cost of equity capital can be regarded as the realized wealth of shareholders. The two combined constitute the value surplus enjoyed by shareholders in the company. The cost of equity capital can be regarded as the value trade-off of shareholders between retained wealth and realized wealth.\u003c/p\u003e \u003cp\u003eSince Chinese debt market keeps long strict interest rate controls, the credit capital has never been a domination in the financial resources configuration until now. Most listed companies in China have obviously preference for the equity financing, and with the stable capital structure (Zhou and Xu, 2012). Therefore, from Eq.\u0026nbsp;(\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), if it is also assumed that \u003cem\u003erdsoti\u003c/em\u003e, \u003cem\u003ereturn\u003c/em\u003e and \u003cem\u003econfiguration\u003c/em\u003e remain unchanged, due to the existence of \u003cem\u003elev\u003c/em\u003e, for 1 unit decrease in \u003cem\u003ere\u003c/em\u003e, \u003cem\u003ereva\u003c/em\u003e will increase by less than 1 unit. In other words, decrease of realized wealth of shareholders will lead to a smaller increase in the wealth retained of the companies.\u003c/p\u003e \u003cp\u003eTwo birds in the bush are worse than one bird in the hand, and two birds in the hand are better than one bird in the bush. If the reduction of shareholders' realized wealth only leads to a smaller increase in the wealth retained of the companies, then shareholders, as the rational economic men, have no incentive to lower their return requirements for the companies. Firstly, the reduction of the cost of equity capital leads to a decrease in the total amount of realized and retained wealth. Secondly, the same retained wealth contains more risks than the realized one. Thirdly, the retained wealth will realize economic benefits in the future, which will lead to a loss in the monetary time value. Therefore, if assuming the shareholders have incentives to reduce \u003cem\u003ere\u003c/em\u003e, the increase in \u003cem\u003ereva\u003c/em\u003e needs to be greater than the decrease of the former. The additional increases in \u003cem\u003ereva\u003c/em\u003e comes from the reduction of \u003cem\u003erdsoti\u003c/em\u003e as well as the optimism of \u003cem\u003ereturn\u003c/em\u003e and \u003cem\u003econfiguration\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Drivers for companies to undertake the political tasks\u003c/h2\u003e \u003cp\u003eBased on the EVA model, \"maximizing shareholder wealth\" is the ultimate financial goal and shareholders are regarded as the core subjects of the value creation. However, when stakeholders participate in corporate governance, companies may deviate from the orientation of \"maximizing shareholder wealth\" due to the possible differences in the interest demands among various value entities. In the context of Chinese transitional economic system, the government is an important part of the stakeholders and often internalizes its own political goals into the production and operation process of the companies. For government, besides the goal of short-term economic growth, it also shoulders the diversified functions such as maintaining the international competitiveness of industries and improving social welfare. Generally, when no entity within the companies benefits, the companies as a whole will tend to avoid the political tasks. It can be seen that the existence of policy beneficiaries is an important prerequisite for positively responds of companies to political tasks. Therefore, we summarize the drivers for companies to undertake political tasks into the following 3 aspects.\u003c/p\u003e \u003cp\u003eFirstly, the political promotion incentives of executives. After the reform and opening, economic construction became the basic line of the primary stage of socialism in China, and the selection criteria for the local officials subsequently shifted from political indicators to economic performance assessment. Among them, local GDP growth had long played a dominate role in the official \u0026ldquo;promotion tournament\u0026rdquo; (Li and Zhou, 2005), focused more on \u0026ldquo;scale-oriented\u0026rdquo; rather than \u0026ldquo;value-oriented\u0026rdquo;. Compared with the non-state-owned executives, executives of the state-owned companies are not purely professional managers and have the characteristic of \"quasi-officials\" (Yang et al., 2013). For increasing the political promotion chips, executives in the state-owned companies tend to respond more actively to the political tone of the economic growth and implement radical financial policies to expanse, such as paying high M\u0026amp;A (merger and acquisition) considerations (Chen Shihua et al., 2015), high-leverage financing (Zhao Yu et al., 2019), over-investment (Liu Li et al., 2020), and so on.\u003c/p\u003e \u003cp\u003eSecondly, tactical rent-seeking to maintain the competitive advantages of the companies. In the transitional economic period, the government still plays a leading role in the economic activities and has strong control over the economic resources such as taxation, credit, and subsidies (Le, 2011). Coupled with limited resource constraints, companies are motivated to take the initiative to undertake political tasks for financial support such as government subsidies and low-interest loans (Krueger, 1974). Especially in China, the state-owned has long occupied a large proportion. Thus, the government will naturally give more resources to state-owned out of \"paternalism\" (Kornai, 1986), causing private companies to fall into financing constraints and thereby breeding the tactical rent-seeking behaviors. For example, Yu et al. (2010) found that private companies would obtain government subsidies by building political connections with local governments. Tian and Fan (2018) confirmed that private companies obtain tax avoidance by bribing tax collection personnel. Yang et al. (2022) also found that the cost of obtaining funds for private companies was higher than that of state-owned and foreign-funded, and had stronger financing rent-seeking motives.\u003c/p\u003e \u003cp\u003eThirdly, correct the company's own strategic positioning deviation. Represented by David Ricardo's theory of comparative advantage, the global economy has laid the main tone of the liberal market since the second industrial revolution. However, the debate surrounding the relationship between government and market remains unresolved. It is particularly true in transition economies. In 2016, Lin and Zhang launched the \"century debate\" based on the industrial policy. The former advocated \"good government\", while the latter focused on the \"free market\". Correspondingly, in the studies of Chinese transitional economic system, though most of literatures regard the political tasks undertaken by companies as the policy burdens, there are also some facts of the government's corrective function in \"disorderly markets\". As an example, for the strategic emerging industries represented by electronic components, the development in early stage requires large amounts of capital investment and will go through a long period of time for returns. Driven solely by the individual tendency of \"shareholders' value maximization\", some companies may fall into the trap of \"short-termism\" due to the survival pressure (Wang et al., 2023). Under the circumstance, the \u0026ldquo;hand of the government\u0026rdquo; can play a guiding role in company innovation strategies. In short, for companies, improving their strategic planning and achieving sustainable development is another motivation to undertake political tasks (Li and Zhou, 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Conditions for the government-business collaboration\u003c/h2\u003e \u003cp\u003eAs the mentioned above, the cost of equity capital is the result of shareholders' value trade-off between realized wealth and retained wealth, indicating the long-term value orientation. In comparison, there are more diversified drivers for companies to undertake political tasks. The inconsistency of economic goals between shareholders and the government is an important reason for conflicts between government and companies. When companies undertake political tasks that are consistent with their long-term value creation, the government can effectively guide the development of companies and correct the disorderly market. Based on the perspective of the cost of equity capital, we hold the view that the government-business collaboration effect of \"Broadband China\" policy depends on the impact to return, configuration, and distribution of factors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Hypothetical deduction\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 \"Broadband China\" policy, factors return and cost of equity capital\u003c/h2\u003e \u003cp\u003eAs far as a single production factor is concerned, the \"Broadband China\" policy has led to the transformation of information production factor (Guo et al., 2023), specifically reflected in the technologies, concepts, and applications. In terms of the technology, traditional information technology relies on computer hardware and software, such as personal computers, servers, database management systems and so on, causing certain limitations in processing large-scale and multiple types of data. In contrast, the new generation information technology focuses on the innovation of underlying technologies, driving a wealth of technological derivatives, including big data, virtualization, cloud computing, artificial intelligence and so on (Qi and Xiao, 2020). For example, big data technology can process large amounts of data, process data at high speed and support diverse data types. In terms of the concept, traditional information technology mainly assists business processes and is usually regarded as a tool to support business operations. In contrast, the new generation information technology emphasizes its leading role in value creation. The application of new generation information technology is not only to improve efficiency, but also to change business models, create new products and services, and prompt companies to rethink their business and innovation strategies to better adapt to rapidly changing market needs (Wu et al., 2021). In terms of the application, traditional information technology is mainly limited to specific fields or tasks. Companies using traditional information technology may focus more on internal data management and business processing. In contrast, the new generation information technology has a richer application scenario layout. In fields of marketing, financial analysis, and healthcare, the new generation information technology can enable companies to better understand customer needs, make strategic decisions, and provide personalized services (Nambisan et al., 2017).\u003c/p\u003e \u003cp\u003eSimilarity of the traditional information technology, the new generation information technology still faces the \"Solow Paradox\" controversy. Numbers of scholars regard the new generation information technology as an advanced production factor with increasing returns to scale and believe that it has strong economies of scale and scope (Prufer and Schottmmller, 2021). While some scholars also believe that it has diminishing returns to scale (He et al., 2017; Varian, 2019) and cannot provide companies with sustainable competitive advantages (Lambrecht and Tucker, 2017). Wang et al. (2022) took big data technology as an example and summarized 5 specific impact mechanisms of the returns to scale, including data quality, economies of scale, external economies, learning effects and quality ladders. Based on the theoretical framework, the phenomenon of increasing returns to scale of big data often appears in the complex problem situations, such as understanding human behavior, optimizing search engine algorithms, and improving the accuracy of autonomous driving. Therefore, it is no doubt saying that the returns to scale effect of new generation information does not only depend on itself, but also rely on institutional design, economic foundation and productivity.\u003c/p\u003e \u003cp\u003eFrom the perspective of specific economic scenarios, Chinese economic transformation and upgrading in recent years has provided objective conditions for the returns to scale effect of new generation information technology. Firstly, Chinese institutional environment is constantly improving. Based on a series of the reforms and policy measures, Chinese government has continuously improved marketization and legalization, strengthened intellectual property protection, and promoted fair competition. For example, in the field of the intellectual property rights, Chinese government has improved legal protection, enhanced the legal enforcement and increased the awareness of innovators to intellectual property protection in recent years. In addition, reforms to streamline administration, delegate powers, and optimize the business environment have also provided enterprises with more autonomy and convenience for technological innovation and application (Wang et al., 2022). Secondly, Chinese economic structure is gradually optimized in the process of transformation and upgrading, shifting from reliance on resources and low-end manufacturing to high-tech and high-quality development. The transformation trend provides broad market space and demand for the application of new generation information technology (Qi et al., 2020). Furthermore, Chinese consumer market continues to expand, and the demand of residents for high-quality, personalized products and services is increasing rapidly for the time, creating more opportunities for companies to apply new generation information technology. Furthermore, China is full of labor resources. Especially in the context of education reform represented by \"college enrollment expansion\", Chinese high-level human capital has continued to expand, providing strong support for the efficient application of the new generation information technology (Huang et al., 2023).\u003c/p\u003e \u003cp\u003eIn short, we believe that the \"Broadband China\" policy can encourage companies to invest in the new generation information technology elements, and reduce the cost of equity capital by increasing the returns to scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 \"Broadband China\" policy, factors configuration and cost of equity capital\u003c/h2\u003e \u003cp\u003eIn the process towards the stage of high-quality economic development, factors configuration cannot be ignored. Compared with comparative advantage theory of David Ricardo's, the factor endowment theory represented by Heckscher (1919) and Ohlin (1933) expand product production from a single production factor to multiple production factors, emphasizing the importance of rational factors configuration under the same technical conditions and thus explaining the reasons for the formation of the international trade comparative advantage. Although the factor endowment theory is originally used in research in the field of international trade, the relevant concepts are also applicable to explain the laws of domestic regional economic exchange and the production efficiency of companies. For example, Jiang and Meng (2021) analyzed the interactive relationship between the internal and the external circulation of the Chinese economy based on the factor endowment theory, and pointed out that the two-wheel-driven development model of the internal and external circulation was an important reason for Chinese economy to maintain rapid growth for a long time. Shirley and Winston (2004) found that the investment in highway infrastructure can effectively adjust the production factor ratio of companies, reduce their inventory reserve costs, and thereby enhance economic returns.\u003c/p\u003e \u003cp\u003eBased on Chinese institutional background, the \"visible hand\" of the government is regarded as an important reason for the factors\u0026rsquo; inefficient configuration. For example, \"traditional national system\", as a legacy of the \"planned economy\" during the transitional economy, uses administrative intervention as the core of resource configuration and puts more emphasis on the administrative power of the government. It emphasizes \"political goals\" and neglects \"economic benefits\", which can easily lead to low factors mobility (Kong et al., 2022), single factor configuration scope (Zhang and Luan, 2022) and distort factor price (Wang and Li, 2018), accompanied by the deviation of strategic decisions from the optima. In the opening market competition, it is especially reflected in the strategic burden borne by some state-owned, that is, they rely one-sidedly on the development strategy choices of government and overinvest in capital-intensive industries without comparative strengths, leading to the loss of viability (Lin and Tan, 1999; Lin et al., 2004).\u003c/p\u003e \u003cp\u003eThe new generation information technology brought about by the \"Broadband China\" policy can help break down the above-mentioned obstacles in the production factors configuration. Firstly, the new generation information technology can help individual companies understand economic, industry and operating environment, make clearer judgments about prospects, clarify production needs, and scientifically formulate budgets (Zhong et al., 2017). At the same time, blockchain encryption technology can provide a safer trading environment for both supply and demand parties of the production factors (Liu, 2022), and facilitate the liberalized market flow of the production factors. As a result, there are subjective and objective feasibility conditions for companies to rationally allocate their basic production factors. Secondly, the new generation information technology can break the shackles of time and space, attract advanced production factors such as knowledge, technology, and management, and provide opportunities for independent innovation of the new business formats, models and technologies (Ma et al., 2023). Furthermore, using the new generation information technology, the supply and demand side of production factors can realize the exchange of price information, achieve accurate docking, reduce transaction costs caused by information asymmetry, and achieve optimal Pareto configuration (Wu and Deng, 2023). In addition, the one should not be ignored is that the new generation information technology, as an advanced production factor, when it integrates with traditional production factors, can improve the total factor productivity (Feng, 2022), bringing the value effect of amplifying, superimposing and multiplying.\u003c/p\u003e \u003cp\u003eIn short, we believe that the \"Broadband China\" policy can improve the efficiency of production factors configuration, optimize strategic decisions, and thereby reduce the cost of equity capital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 \"Broadband China\" policy, factors distribution and cost of equity capital\u003c/h2\u003e \u003cp\u003eA variety of goals are involved in the finance management of companies, such as \"maximizing shareholder wealth\", \"maximizing company wealth\", and \"maximizing stakeholder wealth\". The service objects of the above 3 financial management objectives are expanded in sequence, from the shareholders to the shareholders and the creditors, and then to a wider range of the internal and external stakeholders including the employees, customers, suppliers, and governments. The financial goals of companies can be viewed as the result of trade-offs among stakeholders who control different factors of the production. In the traditional production factor structure, companies follow the \"input-output\" business model, and the value creation is regarded as the \"value surplus\" after the input of traditional production factors such as capital, labor, and land. When the financial management of a company focuses on \"maximizing shareholder wealth\", there are serious conflicts in the distribution of residual value between the shareholders and other stakeholders. In other words, when the returns and configuration of the factors are given, the realization of shareholder wealth maximization depends on the relative competitive strengths of shareholders over other production factor entities, at the expense of the interests of other production factor entities. The phenomenon above has become common around the world since the boom of the western capital markets in the 1970s (Zhang, 2019). In addition, compared with other production factors, capital factor is more aggregated. It also led to the prevalence of the concept of \"capital hiring labor\" in the era of industrial economy (Feng and Li, 2013), ensuring the core position of shareholders in distribution. For reconciling the distribution conflicts between the shareholders and other stakeholders, the government will take the measures to achieve the redistribution of social wealth, such as tax adjustment. However, it often leads to the loss of economic benefits, which turns into a social burden for the companies (Zhang et al., 2021).\u003c/p\u003e \u003cp\u003eWith the investment in new generation information technology under the \"Broadband China\" policy, the underlying logic of corporate financial management may change. Different from traditional production factors such as labor, capital, and land, the new generation information technology is with the characteristics of replicable, renewable and lower marginal costs, and can transfer among various stakeholders. Furthermore, the input and output of information elements are usually difficult to clearly define (Dong, 2022), and the value creation of the new generation information technology usually requires the linkage of multiple value subjects. In other words, all of stakeholders can be the first responsible person in the value network. As the different stakeholders move towards value synergy, the distribution conflicts between yhe shareholders and other stakeholders may be alleviated. It provides more feasibility for the market-based mechanism to participate in factors distribution, and may thereby reduce the Pareto efficiency loss caused by government administrative intervention (Cai, 2021).\u003c/p\u003e \u003cp\u003eIn short, we believe that the \"Broadband China\" policy can improve the distribution of residual value of production factors, ease the value conflict between the shareholders and the stakeholders, and thereby reduce the cost of equity capital.\u003c/p\u003e \u003cp\u003eBased on the above analysis, the Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is as follows:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003e\"Broadband China\" policy can reduce the cost of equity capital.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Research design","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Sample selection and data sources\u003c/h2\u003e \u003cp\u003eThis paper takes Chinese A-share entity listed companies from 2012 to 2019 as research samples. After excluding samples with missing data, delisted, special treated (ST, * ST, PT, etc.), listed in 2019, and financial industry samples, 20,492 samples are involved. Among them, the pilot cities for the \"Broadband China\" policy are selected from the official website of the Ministry of Industry and Information Technology, and other main data comes from the CSMAR database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Variable definition\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 \"Broadband China\" policy implementation (\u003cem\u003ebic\u003c/em\u003e)\u003c/h2\u003e \u003cp\u003eThis paper uses a difference-in-difference(DID) model to test the effect of \"Broadband China\" policy to the cost of equity capital. Specifically, once a sample company is registered in a \"Broadband China\" pilot city and is in the year of policy implementation, \u003cem\u003ebic\u003c/em\u003e takes a value of 1, otherwise take 0. Among them, considering that the 4 batches of pilot cities for \"Broadband China\" were all announced in the second half of the year, the year following the announcement of the pilot cities will be used as the first year of policy implementation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Cost of equity capital (\u003cem\u003ere\u003c/em\u003e)\u003c/h2\u003e \u003cp\u003eAmong the various methods for the cost of equity capital estimation, the internal rate of return method is widely used for its loose assumptions and accurate estimation results (Mao et al., 2012). Therefore, we also use the internal rate of return method to estimate the cost of equity capital, including Gordon model, MPEG model, PEG model and OJ model.\u003c/p\u003e \u003cp\u003eForecast earnings is the core of the internal rate of return method. Judging from the existing literature, the main measurement methods of the forecast earnings include the actual future earnings data (Lu et al., 2014), analyst forecast data (Kross et al., 1990), the HVZ regression model prediction data (Hou et al., 2012) and the RI regression model prediction data (Li and Mohanram, 2014). Due to the consideration of the missing data and subjectivity of the analysts\u0026rsquo; earnings forecasts (Mao et al., 2012; Zhang, 2020), we use actual future earnings data, HVZ model forecast data and modified RI model forecast data to evaluate the forecast earnings.\u003c/p\u003e \u003cp\u003eThe cost of equity capital estimation in this paper mainly includes the following 3 steps. Firstly, calculate the cost of equity capital using the above 4 types of internal rate of return models based on the 4 sources of forecast earnings. Secondly, calculate the average cost of equity capital for 4 types of models under the 3 forecast earnings calibers. Thirdly, take the average of the 4 types of models under 3 forecast earnings calibers, as a proxy variable for the cost of equity capital\u003csup\u003e①\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Control variables\u003c/h2\u003e \u003cp\u003eReferring to Lu et al. (2014) and Zhang (2020), we select 19 control variables from 3 aspects, including market environment, governance structure and corporate finance, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eControl variables Definition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003etype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003emarket environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ebeta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRolling regression for monthly returns of individual and market\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emb\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal assets divided market value of equity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdd 1 to the market value and take the natural logarithm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eoversea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIf the tradable shares were issued in the current year, take 1; otherwise take 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003egovernance structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ehhi1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSquare of the largest shareholder equity ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ehhi2-5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum for the squares of 2nd to 5th largest shareholders equity ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eduality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIf chairman serves as CEO, take 1, otherwise take 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eboard\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdd 1 to the number of directors and take the natural logarithm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003elocation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIf the independent directors are in the registered location, take 1; otherwise take 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esup\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdd 1 to the number of supervisors and take the natural logarithm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emshare\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExecutive shareholding ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esalary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdd 1 to the salary of the top three executives and take the natural logarithm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ecorporate finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eprofit\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNet profit divided average total assets in current year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003elev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Liabilities divided total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eoperation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperating income divided average current assets in current year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003egrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eoperating income growth rate compared to the last year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eliquid\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent assets divided total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ecash\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCash and cash equivalents divided total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ecfo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCash received from selling goods and providing services divided operating income\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Regression Model\u003c/h2\u003e \u003cp\u003eWe use the Eq.\u0026nbsp;(\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) to verify the relationship between \"Broadband China\" policy and cost of equity capital:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$re=\\gamma +{\\delta }_{1}bc+controls+year+industry+city+company+{\\epsilon }_{ }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the above equation, \u003cem\u003econtrols\u003c/em\u003e represent the 19 control variables in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; \u003cem\u003eyear\u003c/em\u003e, \u003cem\u003eindustry\u003c/em\u003e, \u003cem\u003ecity\u003c/em\u003e and \u003cem\u003ecompany\u003c/em\u003e represent year, industry, city and company dummy variables respectively. The manufacturing industry is controlled by the industry secondary code of China Securities Regulatory Commission in 2012. We use the robust standard errors calculation by annual and company clustering to control the heteroskedasticity and autocorrelation bias. For alleviating the impact of the sample extreme values on regression results, we winsorize for all the continuous variables at the level of 1% and 99% quantiles.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Empirical results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Descriptives\u003c/h2\u003e \u003cp\u003eThe descriptive statistics of the main variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among them, the mean and median \u003cem\u003ere\u003c/em\u003e of the sample company are 9.7% and 8.8% respectively; the maximum and minimum are 3.7% and 25.3% respectively; the standard deviation is 4.0%. The range and standard deviation of \u003cem\u003ere\u003c/em\u003e are close to Zhang (2020) in recent. The mean of \u003cem\u003ebic\u003c/em\u003e is 45.0%, indicating that the most companies were not registered in the \"Broadband China\" pilot cities during the sample period, but sample distribution of this variable is relatively even. The descriptive statistical results of each control variable are not significantly different from the exists, and will not be described again here. In addition, the unlisted univariate test results show that the absolute values of the partial correlation coefficients among the main variables are all less than 0.5, indicating that there is no serious multicollinearity in the regression model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics (N\u0026thinsp;=\u0026thinsp;20492)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003estd\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebeta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003emb\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eoversea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ehhi1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ehhi2-5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eduality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eboard\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003elocation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esup\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003emshare\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esalary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eprofit\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003elev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eoperation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003egrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eliquid\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecash\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecfo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Main test\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e lists the regression results of Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) of Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that when only controlling \u003cem\u003eyear\u003c/em\u003e, \u003cem\u003eindustry\u003c/em\u003e and \u003cem\u003ecity\u003c/em\u003e, the regression coefficient of \u003cem\u003ere\u003c/em\u003e on \u003cem\u003ebic\u003c/em\u003e is -0.002, significantly negative at the 5% level. In the column (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cem\u003ecompany\u003c/em\u003e fixed effects are further controlled, and the coefficient of \u003cem\u003ere\u003c/em\u003e on \u003cem\u003ebic\u003c/em\u003e is -0.004, significantly positive at the 1% level. The Column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e further involves the control variables, and the regression coefficient of \u003cem\u003ebic\u003c/em\u003e is -0.004, still significantly positive at the 1% level. In the view of economic implications, \u003cem\u003ere\u003c/em\u003e decreased by 4.4% on average from the control to experimental group (0.004/0.097), with a strong economic significance. The coefficient direction of the main control variables including \u003cem\u003ebeta\u003c/em\u003e, \u003cem\u003emb\u003c/em\u003e, \u003cem\u003esize\u003c/em\u003e, \u003cem\u003elev\u003c/em\u003e and so on in the column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) of Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is also consistent with exists. In addition, the adjusted R-square in the column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) of Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is 41.8%, providing strong explanations for the regression model. Therefore, the Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e has been confirmed in preliminary.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebeta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003emb\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.006***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eoversea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.014*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ehhi1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ehhi2-5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.097***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eduality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eboard\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.009***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003elocation _\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esup\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003emshare\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.013***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esalary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.005***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eprofit\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.111***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003elev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eoperation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003egrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eliquid\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecash\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecfo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econs\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.099***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.292***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIndustry FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCity FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCompany FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively, and the standard errors are in parentheses. The same below.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Robustness test\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1 Parallel trend test\u003c/h2\u003e \u003cp\u003eIn order to eliminate the impact of the difference in annual development trends between the experimental and control group samples, parallel trend test is proposed as follows. Among them, \u003cem\u003ebic_1- bic_5\u003c/em\u003e respectively represent the impact of 1\u0026ndash;5 years before the policy time, \u003cem\u003ebic0\u003c/em\u003e represents the impact of the policy in the current year, and \u003cem\u003ebic1- bic4\u003c/em\u003e respectively represent the impact of 1\u0026ndash;4 years after the policy time. We use the year before the \"Broadband China\" policy as the base year to control the multicollinearity. As is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;2, wherever the control variables are introduced, the regression coefficients of \u003cem\u003ebic_2- bic_5\u003c/em\u003e are not significant, while the regression coefficients of \u003cem\u003ebic0\u003c/em\u003e and \u003cem\u003ebic1- bic4\u003c/em\u003e are all significantly negative, indicating that the difference between the experimental and control groups in the cost of equity capital are generally stable and supports the parallel trend hypothesis. In addition, what can be seen in the following figures is that the coefficient is still significantly negative in the 3rd and 4th years after the policy, though the \"Broadband China\" policy is with about 3 years construction period. Therefore, we can reasonably affirm the sustainability of the positive impact from the \"Broadband China\" policy to the cost of equity capital.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(no control variables introduced) (introduction of control variables)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.3.2 Placebo test\u003c/h2\u003e \u003cp\u003eWhile the above test supports the parallel trend hypothesis, some variables that vary with individuals are still difficult to observe and control, causing regression bias. Therefore, we conduct a placebo test. Specifically, we try to assume that the \"Broadband China\" policy is randomly piloted among sample companies. When the sample company is planned to be put into the experimental group, let the value of \u003cem\u003ebic_placebo\u003c/em\u003e take 1, otherwise take 0. Based on the above setting, we conduct 1,000 repeated random experiments. As is shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;4, the regression coefficients of \u003cem\u003ebic _placebo\u003c/em\u003e are concentrated near 0, with most of the p value greater than 0.1. In addition, the real coefficient of \u003cem\u003ebic\u003c/em\u003e in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e are abnormal compared to the placebo test results shown in following figures. Therefore, the impact of missing variables can be further eliminated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(no control variables introduced) (control variables introduced)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.3.3 Propensity score matching\u003c/h2\u003e \u003cp\u003eThe locations of sample companies may be also affected by the differences in company-level characteristics, causing variable selection bias. Therefore, we choose the propensity score matching to alleviate the above problem. Specifically, we take the 19 control variables in the above-mentioned regression model as the covariates, use a 1:1 non-replaceable nearest neighbor matching method, adopt a logit model regression, and sets the caliper radius to 0.005. Finally, we obtain 11,492 matched observations, including 5,746 samples from the experimental and control groups for each. The balance test results shows that the absolute values of the standardized deviation of the characteristic variables between the experimental and control groups are less than 5%, and the t-test results between the groups does not reject the 0 hypothesis at the 10% level. The regression results in column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and column (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that the coefficients of \u003cem\u003ebic\u003c/em\u003e before and after the introduction of control variables are both still significantly negative, supporting the Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e5.3.4 Instrumental variable method\u003c/h2\u003e \u003cp\u003eThe randomness is one of the major assumptions in the DID model application. However, the selection of \"Broadband China\" demonstration cities may also be affected by macroeconomics such as economic development and industrial structure, which does not fully comply with the exogeneity assumption, specifically resulting in the reverse causation bias. Therefore, we attempt to take a two stage regression test for the instrumental variable method. The instrumental variable selected is the historical postal and telecommunications business volume in the policy cities. Considering that Chinese Internet was in line with international standards in 1994, we select the postal and telecommunications business volume in 1993 as a proxy. Given that historical postal and telecommunications business volume is cross-sectional data, we multiply the above proxy by the rate of national Internet penetration in the previous year to construct the instrumental variable.\u003c/p\u003e \u003cp\u003eThe column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and column (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) of Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show the two-stage regression results of the instrumental variable method. Among them, the column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) of Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the regression coefficient of history on \u003cem\u003ebic\u003c/em\u003e is 0.097, significantly positive at the 1% level, indicating that the choose of \"Broadband China\" policy city is generally determined by the historical development of telecommunications infrastructure. column (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) of Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e brings the predicted value of \u003cem\u003ebic\u003c/em\u003e that obtained from the column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) into the main testing model. The result shows that the coefficient of \u003cem\u003ere\u003c/em\u003e on \u003cem\u003ebic\u003c/em\u003e is -0.032, still significantly negative at the 1% level. In addition, the LM and Wald statistics are both significant at the 1% level, refusing the assumption of insufficient identification and weak instrumental variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e5.3.5 Controlling more macro missing variables\u003c/h2\u003e \u003cp\u003eSince \"Broadband China\" is a time-varying policy at the city level, the registration locations of sample companies may differ in each year. Therefore, while city fixed effect is controlled, it is still necessary to consider some missing variables with time and city varying simultaneously. Referring to Xue et al. (2020), we further control the total population (\u003cem\u003epopulation\u003c/em\u003e), the gross domestic product (\u003cem\u003egdp\u003c/em\u003e) and the proportion of the tertiary industry (\u003cem\u003estructure\u003c/em\u003e) in city level. The column (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) of Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the coefficients of \u003cem\u003ebic\u003c/em\u003e on \u003cem\u003epopulation\u003c/em\u003e, \u003cem\u003egdp\u003c/em\u003e and \u003cem\u003estrucutrue\u003c/em\u003e are 7.716, 0.321 and 0.907 respectively, all significantly positive at the 1% level, indicating the necessity of alleviating the self-selection bias. After including \u003cem\u003ebic\u003c/em\u003e and 3 macroeconomic variables into the regression model, as is shown in the column (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) of Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the coefficient of \u003cem\u003ebic\u003c/em\u003e is -0.003, still significantly negative at the 5% level, supporting the Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIV-2SLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV-2SLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFull sample\u003c/p\u003e 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\u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ehistory\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.097***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003epopulation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.716***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.112)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003egdp\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.321***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.008***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003estructure\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.907***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.788 _ _\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.3.6 Other robustness test\u003c/h2\u003e \u003cp\u003eSome more robustness tests are given. Firstly, further controlling the cross-fixed effects of the year and the industry as well as the year and the province. Secondly, using the samples with the 2.5% and 97.5% quantiles, the 5% and 95% quantiles, and the 10% and 90% quantiles winsorized. Thirdly, eliminating the municipal samples. Fourthly, using the CAPM model, Fama and Franch model of 3 and 5 factors to estimate the cost of equity capital. The test results unlisted robustly support the Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Mechanism test\u003c/h2\u003e \u003cp\u003eBased on the previous analysis, the \"Broadband China\" policy may affect the cost of equity capital through 3 aspects: factors return, factors configuration and factors distribution. With the aim to explore the specific effects of the above mechanisms, we take the following examinations.\u003c/p\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1 Mechanism 1: factors return\u003c/h2\u003e \u003cp\u003eThe digitization is an important indicator of new generation information technology the investment (Chen and Zhang, 2023). Based on the background, we incorporate the digital transformation of companies (\u003cem\u003edigital\u003c/em\u003e) into the analysis framework to explore the returns to scale effect of the \"Broadband China\" policy to the cost of equity capital. The digital transformation is defined as \"the characters ratio of digital transformation text to the management discussion and analysis text (MD\u0026amp;A) in annual report\" (\u003cem\u003edigital\u003c/em\u003e). The column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) of Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the coefficient of \u003cem\u003edigital\u003c/em\u003e on \u003cem\u003ebic\u003c/em\u003e is 0.017, significantly positive at the 1% level, indicating that the implementation of the \"Broadband China\" policy has upgraded the digital transformation of companies within the policy cities. The column (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the coefficient of \u003cem\u003ere\u003c/em\u003e on \u003cem\u003edigital\u003c/em\u003e is -0.003, significantly negative at the 1% level, supporting the positive effect of the digital transformation to the cost of equity capital. When \u003cem\u003ebic\u003c/em\u003e and \u003cem\u003edigital\u003c/em\u003e are included in the same regression model that affects \u003cem\u003ere\u003c/em\u003e, as is shown in the column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) of Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the coefficients of \u003cem\u003ebic\u003c/em\u003e and \u003cem\u003edigital\u003c/em\u003e are still significantly negative at the 1% level. The Sobel test shows that the mediating effect of digital accounts for 2.7%, significant at the 5% level. Therefore, it is confirmed that the implementation of the \"Broadband China\" policy can reduce the cost of equity capital by increasing the returns to scale of new generation information technology.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism 1: factors return\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003edigital\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003edigital\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.003***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSobel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-2.373**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e5.4.2 Mechanism 2: factors configuration\u003c/h2\u003e \u003cp\u003eIn the transitional economies, the loss of factors configuration efficiency caused by government regulation will appear as a strategic burden for companies (Bai and Zhang, 2022). Therefore, we take strategic burden as a perspective to examine the factors configuration effect of the \"Broadband China\" policy on the cost of equity capital. Considering that companies are more inclined to operate in capital-intensive industries and thereby increase the investment of fixed assets when undertaking the national strategic development tasks, we use \u0026ldquo;the proportion of fixed assets to total assets\u0026rdquo; as a proxy (Lin et al., 2004). The column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) of Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the coefficient of \u003cem\u003estb\u003c/em\u003e on \u003cem\u003ebic\u003c/em\u003e is -0.007, significantly negative at the 1% level, indicating that the implementation of the \"Broadband China\" policy can reduce the strategic burden on the companies in policy cities. The column (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the coefficient of \u003cem\u003ere\u003c/em\u003e on \u003cem\u003estb\u003c/em\u003e is 0.020, significantly positive at the 1% level, indicating that the strategic burdens will increase the cost of equity capital. The column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) of Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the sign and significance of \u003cem\u003ebic\u003c/em\u003e and \u003cem\u003estb\u003c/em\u003e have no substantial change when the above two variables are both involved in the regression model that affects \u003cem\u003ere\u003c/em\u003e. The Sobel test also confirmed the mediating effect of strategic burden. Therefore, we conclude that the implementation of the \"Broadband China\" policy can reduce the cost of equity capital based on the efficient factors configuration, such as lower the investment of high-density assets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism 2: factors configuration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003estb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003estb\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSobel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-3.575***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e5.4.3 Mechanism 3: factors distribution\u003c/h2\u003e \u003cp\u003eOnce the value conflict is between the shareholders and the stakeholders, some public welfare expenditures may turn into the social burdens of companies (Luo and Liu, 2018). We use \"income tax expenses divided operating income\" to measure the social burden and explore the factors distribution effect of the \"Broadband China\" policy, for which can reflect the role of government in factors distribution. The column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) of Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that the coefficient of \u003cem\u003esob\u003c/em\u003e on \u003cem\u003ebic\u003c/em\u003e is -0.001, significantly negative at the 5% level, indicating that the implementation of \"Broadband China\" policy can reduce the social burden. The column (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of table (7) shows that the coefficient of \u003cem\u003ere\u003c/em\u003e on \u003cem\u003esob\u003c/em\u003e is 0.260, significantly positive at the 1% level, indicating that the social burden will increase the cost of equity capital. The column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) of Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that after both \u003cem\u003ebic\u003c/em\u003e and \u003cem\u003esob\u003c/em\u003e are included in that affects \u003cem\u003ere\u003c/em\u003e, the coefficients are \u0026minus;\u0026thinsp;0.004 and 0.258 respectively, both of which are still significant at the 1% level. The Sobel test results also support the partial mediating effect of social burden. The above regression results confirm that with the implementation of the \"Broadband China\" policy, the function of taxation in factors distribution has been weakened, thereby reduced the necessary rate of return required by shareholders.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism 3: factors distribution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esob\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esob\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.260***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.258***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSobel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-2.929***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Further analysis\u003c/h2\u003e \u003cp\u003eIn the mechanism test, the \"Broadband China\" policy is proved to reduce the cost of equity capital by taking advantage of the increasing returns to scale effect of the new generation information technology, improving the production factors configuration, and optimizing the factors distribution. With the aim to deepen the above conclusions, we put some further analyses based on the mentioned paths.\u003c/p\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e5.5.1 Further analysis of the factors return effect: based on the regional life cycle\u003c/h2\u003e \u003cp\u003eAs pointed out in the theoretical analysis above, the returns to scale effect of new generation information technology may be affected by the institutional environment, legal environment and marketization process. Actually, relevant mentioned often depend on the stage of macroeconomic development. According to the regional life cycle theory of Thompson's (1966), the regional economy is similar to the industrial development and the company growth, and will go through the stage of initial, growth, maturity and decline. In this process, the factors flow and the industrial structure of the company match its life cycle. In China, the economic development in eastern is currently arrived at a relatively mature stage, with a high degree of modernization and sufficient productivity to support the application of information technology. What's more, the eastern region is also facing more urgent needs for industrial transfer and upgrading (Dai et al., 2023).\u003c/p\u003e \u003cp\u003eBased on the above deduction, we believe that the increasing returns to scale effect of the \"Broadband China\" policy is mainly reflected in the eastern region where the economic cycle is relatively mature. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e conducts a group mediation effect test based on the regional life cycle. Among them, if the registered cities of the sample companies are located in 12 provinces (or municipalities) including Beijing, Shanghai, Tianjin, Guangdong, Jiangsu, Zhejiang, Shandong, Fujian, Hebei, Liaoning, Guangxi and Hainan, let \u003cem\u003eeast\u003c/em\u003e take the value of 1, otherwise take 0. The regression results show that the \"Broadband China\" policy reduces the cost of equity capital by improving the degree of digital transformation of companies only in the eastern city groups. Therefore, the above speculation is confirmed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFurther analysis of the factors return effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003edigital\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003edigital\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eeast\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eeast\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eeast\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eeast\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eeas\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eeas\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003edigital\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSobel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-1.889*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e5.5.2 Further analysis of the factors configuration effect: based on the industry competitiveness\u003c/h2\u003e \u003cp\u003eThe essence of positive factors configuration effect of the \"Broadband China\" policy is to enhance the viability of enterprises. From the behaviors of companies undertaking strategic burdens to the economic consequences of the loss of economic competitiveness, the institutional background of the transitional economy should not be ignored. The 15th National Congress of the Communist Party of China established a market economic system for social entities with \"public ownership as the main and multiple ownership economies coexisting\", forming the current pattern of restrictive industries and competitive industries coexisting. In the competitive industries, while some companies still undertake national strategic development tasks, they do not occupy an absolute dominant position and face competitiveness impacts from other market-oriented economic entities, which may aggravate the tragedy of factors configuration in competitive industries.\u003c/p\u003e \u003cp\u003eBased on the above analysis, we believe that the factors allocation effect of the \"Broadband China\" policy mainly exists in competitive industries. The Table \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e conducts a group mediation effect test based on the competitiveness of the industry in which the sample companies are located. Following the ideas of Xin and Tan (2009), we distinguish the restricted and competitive industries based on the 2012 China Securities Regulatory Commission industry classification code. Among them, restricted industries include: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) mining industry (code B); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) petroleum processing, coking and nuclear fuel processing industry (code C25); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) ferrous metal smelting and rolling processing industry (code C31); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) non-ferrous metals Metal smelting and rolling processing industry (code C32); (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) electric power and heat production and supply industry (code D). When the sample belongs to a restricted industry, let \u003cem\u003eregulation\u003c/em\u003e take the value 1, otherwise take 0. The regression results in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e show that only when the sample company belongs to a competitive industry, the \"Broadband China\" policy significantly reduces the strategic burden, thereby reducing the cost of equity capital. The regression results confirm the above inference.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFurther analysis of the effect of factors configuration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003estb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003estb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eregulation\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eregulation\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eregulation\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eregulation\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eregulation\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eregulation\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.009**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003estb\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSobel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-3.729***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003e5.5.3 Further analysis of the factors distribution effect: based on the supply chain conflicts\u003c/h2\u003e \u003cp\u003eThe core of the \"Broadband China\" policy to improve the factors distribution is to achieve the value synergy among the stakeholders of companies. The supply chain is an important link in the value network of companies. According to the 5 forces model in the competitive advantage theory of Porter (Porter, 1980), buyers and suppliers are 2 of the 5 types of competitive forces. For manufacturing companies that account for the majorities of the samples, the business processes are relatively complex, going through a complete supply, production and marketing stage in the business operations. Therefore, those of whom are easily restricted by supply chain. Once the unequal supply chain relationships hinder the fair distribution of social wealth, the government is naturally more motivated to maintain the market order through the administrative means.\u003c/p\u003e \u003cp\u003eBased on the above analysis, we conduct that the factors distribution effect of the \"Broadband China\" policy will be more obvious in companies faced with more serious supply chain conflicts. Supply chain concentration reflects the bargaining power of companies relative to supply chain companies. The higher the concentration of the customers (or the suppliers) is, the stronger bargaining power do the supply chain companies have (Chang et al., 2014). Therefore, when the customer or supplier concentration of a company is less than the 25% sample quantile or greater than the 75% sample quantile (that is, the bargaining power gap between the companies and the supply chain companies is large), we define it as a company with serious supple chain conflicts, otherwise define it as a company with relatively harmonious supply chain relationships. Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e conducts a group mediation effect test based on the dimensions of customers and suppliers respectively. The regression results of the two dimensions draw a similar conclusion, that is, only when the bargaining power gap between the companies and the customers (or the suppliers) is large (\u003cem\u003ecustomer\u003c/em\u003e/\u003cem\u003esupplier\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1), can the \"Broadband China\" policy alleviate the social burden and thereby reduce the cost if equity capital, indicating the exist of the factor distribution effect.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFurther analysis of the factors distribution effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ePanel A: Based on the customer dimension\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esob\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003esob\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ecustomer\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ecustomer\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ecustomer\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ecustomer\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ecustomer\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ecustomer\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esob\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.201***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.199***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.287***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.287***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.035)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSobel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-2.340**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-1.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ePanel B: Based on the supplier dimension\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esob\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003esob\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esupplier\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003esupplier\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003esupplier\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003esupplier\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003esupplier\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003esupplier\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esob\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.277***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.275***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.215***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.215***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSobel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-1.915*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-0.710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Heterogeneity analysis\u003c/h2\u003e \u003cp\u003eThe \"Broadband China\" policy has the dual attributes of the information technology and the industrial policy, taking cities as the execution unit. Therefore, we further discuss the heterogeneous economic consequences of the \"Broadband China\" policy based on the perspectives of the information technology, the government-business relation, and the spatial effect.\u003c/p\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003e5.6.1 Heterogeneity analysis 1: based on the information technology\u003c/h2\u003e \u003cp\u003eEquity investment and industrial investment are two choices for companies to realize the technological innovations (Wu and Yan, 2023). The former focuses on the extensive expansion, while the latter emphasizes the connotative revolution. Combining the investment methods of new generation information technology, we take the following heterogeneity analysis. Specifically, if the digital investment projects are disclosed in the notes to long-term equity investment of the sample company's annual report, and the net increase in the year is greater than 0, it is considered that the company has adopted the equity investment in new generation information technology application, and let \u003cem\u003eequity\u003c/em\u003e take value of 1, otherwise take 0. In the same way, if a sample company discloses digital investment projects in the notes to fixed assets, intangible assets or development expenditures in the annual report, and the net increase for the year is greater than 0, it is deemed that the company has introduced a new generation information technology through industrial investment, let \u003cem\u003eentity\u003c/em\u003e take 1, otherwise take 0. As is shown in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, while the industrial investment is currently more common than the equity investment in the application field of new generation information technology, only the latter can effectively amplify the inhibitory effect of the \"Broadband China\" policy on the cost of equity capital. It means that at least for the present, the \"borrowingism\" is more suitable for the new generation information technology wave in Chinese companies led by the implementation of the \"Broadband China\" policy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis 1: based on the Information Technology\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eequity\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eequity\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eentity\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eentity\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.017**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0. 000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Fisher is the empirical p value obtained by the self-extraction method 500 times. The same below.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section3\"\u003e \u003ch2\u003e5.6.2 Heterogeneity analysis 2: based on the government-business relationship\u003c/h2\u003e \u003cp\u003eThe effect of policy is largely affected by the government-business relationship. For example, in the transition economies, policy burdens are mostly seen on the state-owned (Lin et al., 2004); the closer the geographical distance to the government, the easier it is for companies to obtain government subsidies and thereby respond to the government\u0026rsquo;s call for innovation; once the senior executives have a political background, they are more will to take participate in the \u0026ldquo;green governance\u0026rdquo; (Zhuang et al., 2022). By reviewing of the exists, we divide the government-busniess relationship into 3 dimensions, including governance, company and management.\u003c/p\u003e \u003cp\u003eThe government-business relationship in the dimension of governance focuses on the view of ownership. The column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and column (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of Table \u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e conduct a grouped regression based on the nature of property rights (\u003cem\u003esoe\u003c/em\u003e) of the sample companies. The test results show that the coefficients of \u003cem\u003ebic\u003c/em\u003e are \u0026minus;\u0026thinsp;0.007 and \u0026minus;\u0026thinsp;0.003 respectively in the state-owned group (\u003cem\u003esoe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) and the non-state-owned group (\u003cem\u003esoe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0), and with a 1% level of significance in intergroup coefficient difference. It can be concluded that the positive impact of the \"Broadband China\" policy to the cost of equity capital is more obvious in the state-owned companies.\u003c/p\u003e \u003cp\u003eGovernment-business relationship in the dimension of company focuses on the geographical distance. The column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and column (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) of Table \u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e conduct a grouped regression based on the geographical distance (\u003cem\u003edistance\u003c/em\u003e) between government and companies. Among them, the geographical distance between government and companies refers to the research of Wei and Su (2013), and is measured by the spherical distance calculated from the longitude and latitude of the registered address of the sample companies and the local government of the city to which it belongs. The regression results show that no matter how far (\u003cem\u003edistance\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) or close (\u003cem\u003edistance\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0) the spherical distance between the sample companies and the local government is, the implementation of the \"Broadband China\" policy will help reduce the cost of equity capital, but the coefficient of the latter is lower than the former at the 1% level. Therefore, we can conclude that the relatively close geographical distance between government and companies strengthens the inhibitory effect of the \"Broadband China\" policy on the cost of equity capital.\u003c/p\u003e \u003cp\u003eGovernment-business relationship in the dimension of management revolves around the political background of core executives. The column (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and column (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) of Table \u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e present the grouped regression according to the political background of core executives (\u003cem\u003epc\u003c/em\u003e). Among them, the political background of core executives is measured by the government tenure of the chairman or the CEO of the sample companies. When the chairman or CEO of a company currently serves or has served in the government, let \u003cem\u003epc\u003c/em\u003e take the value 1, otherwise take 0. Judging from the test results, the core executives with political background obviously strengthens the positive effect of \"Broadband China\" policy to the cost of equity capital at the 1% level.\u003c/p\u003e \u003cp\u003eIn short, the conclusions based on the above heterogeneity analyses in 3 dimensions of the government-business relationship are relatively consistent, showing the positive adjustment effects of the government-business relationships. For companies with closer government-business relationships, they are full of smoother communication channels between government and companies, and therefore can understand the spirit of policies correctly and comprehensively (Zheng et al., 2022). In other words, a well understanding of the policy may be an important prerequisite for effective implementation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis 2: based on the government-business relationship\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esoe\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003esoe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003edistance\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003edistance\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003epc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003epc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0. 000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section3\"\u003e \u003ch2\u003e5.6.3 Heterogeneity analysis 3: based on the spatial effect\u003c/h2\u003e \u003cp\u003eThe new geographical economic theory represented by Krugman (1991) focuses on the inter-regional flow of production factors and believes that the \"center-periphery\" regional spatial structure can easily lead to the agglomeration and the diffusion of inter-regional economic resources. Based on the theory, we examine the spatial effects of the \"Broadband China\" policy. For the spatial aggregation effect, we conduct a grouped regression of based on the average geographical distance among a sample company and other listed companies in the same registered location. If the distance above is greater than the sample median, let \u003cem\u003einside\u003c/em\u003e take the value 1, otherwise take 0. The column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and column (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of Table \u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e show the regression results. Among them, in the longer average geographical distance group, the coefficient of \u003cem\u003ere\u003c/em\u003e on \u003cem\u003ebic\u003c/em\u003e is -0.002 with no statistical significance; in the shorter aggregation distance group, the coefficient of \u003cem\u003ere\u003c/em\u003e on \u003cem\u003ebic\u003c/em\u003e is -0.006 with the significance of 1% level. Furthermore, the difference in regression coefficients between the two groups is significant at the 1% level. Therefore, we confirm the existence of the spatial aggregation effect in the \u0026ldquo;Broadband China\u0026rdquo; policy.\u003c/p\u003e \u003cp\u003eFor the spatial spillover effect, we retain the samples that are not in the \"Broadband China\" policy pilot cities, and conduct a grouped regression based on the average geographical distance among a sample company and the \"Broadband China\" pilot cities (outside). As is shown in column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e, the coefficient of \u003cem\u003ere\u003c/em\u003e on \u003cem\u003eoutside\u003c/em\u003e is -0.002 with no statistical significance, indicating that the \"Broadband China\" policy is hard to radiate to listed companies in nearby cities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis 3: based on the spatial Effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ere\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003einside\u003c/em\u003e =\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003einside\u003c/em\u003e =\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eoutside\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003econtrols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Research Conclusions and Enlightenments","content":"\u003cp\u003eBased on externality theory and soft budget constraint theory, we take the Chinese non-financial listed companies in A-share from 2012 to 2019 as the examples, test the effect of the \"Broadband China\" policy to the cost of equity capital, and therefore clarify the specifical roads of government-business collaboration in information technology industry policy. The main conclusions are as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the \"Broadband China\" policy has reduced the cost of equity capital; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) return, configuration and distribution of factors constitute the important mechanisms, specifically reflected in the higher degree of digital transformation as well as the lower strategic and social burden; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the above-mentioned mechanism only exists in the eastern region with mature regional economic development, competitive industries, and companies with serious supply chain conflicts; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) the effective implementation of the \"Broadband China\" policy relies on the equity investment, closed to the government-business relationship, and with a certain spatial aggregation effect.\u003c/p\u003e \u003cp\u003eThere are some enlightenments. Firstly, the \"Broadband China\" policy. Based on the previous empirical studies, we believe that the implementation of the \"Broadband China\" policy has achieved the expected effects as a whole: not only assisting the digital transformation, but also reducing two types of policy burdens, indicating the adhere to the laws of market economy for Chinese government. However, just like the previous technological innovations, the following strategy achieved a more direct value-added effect than the leading strategy in the early stage of new generation information technology application. Based on the background of Sino-US trade war and anti-globalization trend, the international flow of core production factors has slowed down, reflecting the potential dangers in relying on the outsourcing to achieve technological innovation. Therefore, besides of the economic benefits, we also recommend Chinese government to strengthen the maintenance of independent innovation capabilities in companies, encourage the originals, and avoid falling into the \"borrowingism\" trap.\u003c/p\u003e \u003cp\u003eSecondly, the cost of capital. Based on the above tests, it is not difficult to conclude that the capital cost can comprehensively reflect the return, the configuration and the distribution of production factors. However, in the macro, the cost of capital has not received enough attention. For example, in the 2019 version of the \"Detailed Rules for the Assessment of Economic Value Added\" and the \"Measures for the Assessment of Business Performance of Persons in Charge of Central Enterprises\", the cost of capital in central companies are set at 3 fixed values of 4.1%, 5% and 5.5%, ignoring the heterogeneity other than the asset liquidity and the leverage, obviously contrary to the laws of the market. Therefore, in the process of factor marketization reform, we call for the consideration of deepening the estimation methods of the cost of capital and giving corresponding weight. From the perspective of the financial management concepts behind the cost of capital, while scholars of much currently question the \"maximizing shareholder wealth\", due to the availability of economic data, it is still effective to conduct the analyses. Especially when it involves of the digitalization, the wealth demands of shareholders and other stakeholders are consistent in gradual, giving more comprehensive perspectives for the economic analyses based on the cost of capital cost.\u003c/p\u003e \u003cp\u003eFinally, government-business relationship. As Lin et al. (2004) pointed out, the contradiction between the government and the market in transitional economies cannot simply be attributed to institution. The political vision of government is to achieve the \"maximizing social welfare\", different from the \"maximizing shareholder wealth\" for business owners. Once the value demands of the two are inconsistent, it will lead to the inefficiency of the factor's configuration and distribution. How to play the role of the \"visible hand\", cooperate with the laws of the market economy, and achieve Pareto improvement based on the way of value collaboration is an urgent issue that needs to be solved. Fortunately, the digital transformation of the economy led by the new generation information technology is now giving the government and enterprises opportunities for coordinated development. For government, we recommend it to improve the supporting systems related to the information technology industry and give additional impetus to the development of the digital economy by using the benign regulation measures. For companies, we also expect them to take advantage of the digital economy to improve their own value network to achieve sustainable development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQiqing Huang and Yaxuan Zhao wrote the main manuscript text, Ying Zou prepared the figures and provided the funders. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBai X, Zhang Z (2022) Can the mixed ownership reform effectively resolve the overcapacity of SOEs [J]. Economic Theory Bus Manage 42(09):21\u0026ndash;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai C, Ma L, Cai Y (2021) Cloud computing drives tax administration innovation [J]. 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Finance Res Lett 51:103418\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong R, Xu X, Lotz E, Newman S (2017) Intelligent Manufacturing in the Context of Industry 4.0: a Review [J]. Engineering 3(5):616\u0026ndash;\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe estimate methods for cost of equity capital comes from \"Capital Cost: Theory and Estimation\", published by Economic Management Press in 2018, author: Wang Ping.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Government-business Collaboration, Cost of Equity Capital, Broadband China","lastPublishedDoi":"10.21203/rs.3.rs-4203006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4203006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines the impact and the mechanism of the information technology industry policy on the cost of equity capital from the perspective of the government-business collaboration based on the externality theory and soft budget constraint theory. This research selected the Chinese A-share non-financial listed companies from 2012 to 2019 as examples and empirically tests the cost of equity capital effect of the implementation of \"Broadband China\" policy based on the difference-in-difference (DID) model. The main test shows that the \"Broadband China\" policy has significantly reduced the cost of equity capital. The mechanism test reveals the function of \"Broadband China\" policy in optimizing the return, the configuration and the distribution of factors, and reflecting the government-business collaboration effect. Furthermore, the regional economic development, the industry competitiveness and the supply chain conflicts are proved to strengthen the above mechanisms respectively. The heterogeneity analysis shows that the effect of the \"Broadband China\" policy on the cost of equity capital is realized through the equity investment, positively affected by government-business relationship, and shows spatial aggregation effect. Actually, the digital transformation of the economy, led by the new generation of information technology, now provides the government and companies with opportunities for coordinated development. Therefore, for government, we recommend it to improve the support systems related to the information technology industry and give additional impetus to the development of the digital economy by using the benevolent regulatory measures. For companies, we also expect them to take advantage of the digital economy to achieve sustainable development.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eJEL Classification\u003c/em\u003e: G12；G32；G38\u003c/p\u003e","manuscriptTitle":"How to Achieve Government-business Collaboration in Information Technology Industry Policy? From the Perspective on Cost of Equity Capital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 06:11:14","doi":"10.21203/rs.3.rs-4203006/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7e6a75a-6908-455e-8fe0-4f9e115880ff","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-06T18:59:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 06:11:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4203006","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4203006","identity":"rs-4203006","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}