# Whether Digital Financial Inclusion Can Improve Capital Misallocation or Not: A Study Based on the Moderating Effect of Economic Policy Uncertainty

Authors: Guohua He; Lu Shen
Journal: Discrete Dynamics in Nature and Society
 (2021)

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## Abstract

This paper discusses the impact of digital financial inclusion on regional capital’s turn from the fictitious to the substantial economy. The continuous decline of the capital efficiency of the real economy is an important reason for the misallocation of financial capital, such as the financialization of real enterprises. Development of the digital financial inclusion helps to relieve small and micro businesses from financing constraints, encourage civilian consumption, and improve services concerning issues of agriculture, rural areas, and farmers. Yet, its financial features also indicate potential systematic risks, manifested as the capital’s departure from its intended purpose of serving the substantial economy, given some beneficiaries’ investment in the fictitious economy. Based on the provincial panel data between 2011 and 2019, this paper constructs an index describing capital’s diversion from the fictitious to the substantial economy. This paper then analyzes the impact of digital financial inclusion on such a diversion of the regional capital, investigating the regulatory effects caused by the uncertainty in economic policies. Empirical study reveals that digital financial inclusion has an evident positive effect on regional capital’s diversion from the fictitious to the substantial economy but without any spatial spillover effect. Among the three subdimensions of digital financial inclusion-scope of coverage, depth of usage, and level of digitalization, the scope of coverage has the strongest positive effect, and digitization level, the weakest. The positive correlation between digital financial inclusion and capital diversion from the fictitious to the substantial economy is under negative regulation due to economic policy uncertainty. In other words, increasing uncertainty in the economic policy would weaken digital financial inclusion’s support of the substantial economy.


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## Body

## 1. Introduction

The Chinese economy has progressed from the stage of high-speed growth to a high-quality one, and the importance of sustainable economic development cannot be overemphasized for the achievement of economic transformation. Finance is the spine of economic development. Due to its tendency to pursue profits and avoid risks, financial capital provides support and services only to a selected few, whereas those in urgent need of capital—for instance, small and micro businesses as well as individual businesses—are marginalized in the financial market. Such a severe mismatch of financial resources leads to the fund-raising difficulty of the social economy. To solve this issue, and in particular the problem of inaccessible funding in remote areas, the Chinese government has been advocating the construction of financial inclusion since 2015, lowering the threshold for some enterprises and individuals to obtain financial services. Despite its achievements, such financial inclusion is still implemented by traditional financial institutions, whose low efficiency, high costs, and limited coverage fail to meet the capital demands of the long-tail population. The rise of digital technology in recent years, represented by the Internet, big data, and cloud computing, has given birth to a new form of digital financial inclusion. G20 High-Level Principles for Digital Financial Inclusion issued at the 2016 G20 summit became the cornerstone for China’s initiation and participation of globalized digital economic actions [1]. According to the Report on the Development of Chinese Financial Technology and Digital financial inclusion (2019) published by Zhongguancun Internet Finance Institute, services of China’s digital financial inclusion are mostly provided by financial institutions (including banks and nonbank ones), Internet giants, and enterprises of financial technology. Their customers are small and micro businesses seeking funding, developer companies of urban construction, and service providers for agriculture, rural areas, and farmers. The infrastructure of digital financial inclusion comprises the payment system, the credit system, and the property transaction market, and they are protected by an institutional ecology comprising policies and laws [2]. In comparison with financial inclusion, digital financial inclusion can mend information asymmetry between the fundraiser and loan borrower, reduce transaction costs, and extend financial services to disadvantaged groups [3]. As it breaks free from temporal and spatial constraints, digital financial inclusion meets the government’s expectation for financial development-—high-quality growth and common prosperity.Earlier studies of digital financial inclusion focus on its concept, features, and advantages. Since the publication of The Peking University Digital Financial Inclusion Index of China in 2016 by two organizations holding massive data-Peking University’s Institute of Digital Finance and Ant Group, quantitative analysis began to gain momentum. Many scholars have analyzed the impact of digital financial inclusion on innovation from the perspectives of “inclusiveness” and “benefits”. They discovered that digital financial inclusion can promote regional and entrepreneurial technology innovation through the following means: improve the fund-raising environment for small and medium enterprises and individual businesses [4], encourage innovative investment and producer services industry [5], reduce the fund-raising costs and restraints for small and micro businesses to get loans [6], and alleviate the financial mismatch for enterprises [7]. Additionally, some scholars conclude that digital financial inclusion can accelerate high-quality economic growth [1, 8]. Since the 19th National Congress of the Chinese Communist Party, the CPC has paid close attention to issues of financial risks, expressing its determination to defend against systematic financial risks. History has testified that financial diversion from the substantial to the fictitious economy is the most severe financial risk, bringing maximum shock and destruction to the substantial economy. Due to its financial features, digital financial inclusion is not free from potential financial risks. Therefore, some scholars have investigated whether digital financial inclusion can curb substantial economy and enrich enterprises’ financialization. They unanimously concluded that digital financial inclusion is beneficial for the development of the substantial economy [9, 11] and reduces the level of entrepreneurial financialization. Yet, the accuracy of the above conclusions may be affected by three inadequacies. First, the choice of index is far from perfect. For substantial economy, the chosen data is regional GDP, exclusive of property, and financial industry’s output value. Yet, as both the Chinese economy and digital financial inclusion are in their respective rising phase, they present a statistically speaking “false positive correlation”. The corporate financialization index may have been distorted by the fluctuations in the capital market and the ever-growing property price in recent years. Furthermore, neither indexes truly reflect the capital’s departure from the fictitious to the substantial economy. Second, spatial effects have been neglected. Unlike traditional finance whose physical branches are subject to spatial restraints, digital finance is free from such restraints. For instance, Alipay, with its headquarter in Hangzhou, can serve nationwide customers. Therefore, any discussions concerning digital financial inclusion’s impact on substantial and fictitious economies alike should take into consideration the spatial spillover effect. Finally, existing literature has failed to take into account the external environment’s regulatory impact. It assumes that the relationship between digital financial inclusion and economic variants is linear and remains steady for a long period. But in reality, the “digital” and “inclusive” features of digital financial inclusion indicate that its impact on economic and financial activities is complicated and easily prone to external changes.Therefore, this paper has adopted provincial panel data for analyzing the impact of digital financial inclusion on capital’s diversion from the fictitious to the substantial economy. Compared with existing literature, it has made three aspects of improvement. Firstly, it uses innovative input and fluctuation in property price to respectively represent the substantial and the fictitious economy. The relative speed increase of research and development (R&D) and housing price is used for evaluating the capital’s diversion from the fictitious to the substantial economy, thereby avoiding the proxy deviation of a unilateral index. Secondly, it takes into consideration digital financial inclusion’s spatial spillover effect over capital’s diversion from the fictitious to the substantial economy. Thirdly, the paper pays special regard to the uncertainty of economic policies and examines its regulatory effects over the relationship between digital financial inclusion and capital’s diversion from the fictitious to the substantial economy. The results provide a reference for further study on the relationship between digital financial inclusion and fictitious economy, as well as how to keep away and deal with financial systematic risks. They also provide inspirational ideas for digital finance to better serve the substantial economy and reduce external economic policies’ interference. Hence, this paper is of realistic significance for promoting the healthy growth of digital financial inclusion, given the new age’s emphasis on high-quality development. The remaining sections are arranged as follows. Section2 analyzes the theoretical mechanism and raises assumptions. Section 3 constructs the model and selects variables. Section 4 is the empirical results and analysis. Section 5 concludes and proposes policy recommendations.


## 2. Theoretical Mechanism Analysis and Research Hypotheses

### 2.1. Impact Mechanism of Digital Financial Inclusion’s Development on Capital’s Diversion from the Fictitious to the Substantial Economy

G20 High-Level Principles for Digital Financial Inclusion has defined digital financial inclusion as any behavior promoting financial inclusion via digital means. Through digital technology, it lends financial services at a relatively low cost to those previously inaccessible to such services. Judging by the composition of the digital financial inclusion index created by Peking University, digital financial inclusion comprises three aspects: scope of coverage, depth of usage, and level of digitalization. Here, the scope of coverage refers to the number or ratio of customers receiving financial services via the Internet or mobile network tools like Alipay. The depth of usage means the frequency at which people use Internet financial services. The level of digitization refers to the convenience and efficiency of a region’s digital finance [13]. Theoretically, it is believed that digital finance can effectively make up for the insufficiency of traditional finance, alleviate small and micro enterprises’ fund-raising restraints, stimulate corporate innovation, and subsequently strengthen the development of the substantial economy [4, 6, 7]. But as digital finance stimulates substantial economy’s development through its resource allocation and innovation effects, is it also responsible for credit capital’s flow into the fictitious economy and capital’s subsequent diversion from the fictitious to the substantial economy? Li et al. [2] pointed out that digital financial inclusion raises the difficulty of financial regulation. Some enterprises and individuals use their loan funds to invest in the housing and stock market, and their behavior threatens financial security. Cheng et al. [12] also believed that, during financial transformation, declining profits of the substantial economy and the flourish of financial and housing departments may deprive digital financial inclusion of the chance to perform services as a functional department. Capital’s pursuit of profits speeds up its disconnection from the substantial economy.The development of digital finance helps to reduce the consequences of financial system deficiencies, and the development of digital finance helps increase the availability of credit for private enterprises and small enterprises. Digital finance improves the efficiency of financial allocation by reducing information asymmetry. Digital finance has enhanced the information-gathering capabilities of financial institutions. Digital finance reduces the cost of deposit-taking. Compared with traditional deposit-taking businesses, digital finance has expanded the coverage of financial institutions due to its advantages in breaking through geographic restrictions and absorbing the investors’ funds.This paper holds that, as a significant tool supplementing traditional finance and promoting high-quality economic growth, digital financial inclusion has far more encouragement for the substantial economy than for the fictitious economy. Its support for the fictitious economy is latent and may even prove to be a restraint rather than encouragement. Eventually, it achieves to redirect capital from the fictitious to the substantial economy. Encouragement for the substantial economy comprises two aspects. On the one hand, digital financial inclusion, with its big data technology and convenient operation, greatly reduces small and micro businesses’ costs for getting loans. Thus, it solves their fund-raising difficulties and provides capital support for corporate innovation, in service of substantial economy’s development. Existing literature also provides proof from the two aspects of innovation and substantial economy. On the other hand, currently, the most prominent substantial service provided by digital financial inclusion is to stimulate and increase civilian consumption. The biggest difference between digital financial inclusion and traditional finance lies in the former’s high efficiency, which enables those in need of money to get loans for consumption anywhere at any time. Cheng et al. [9] pointed out that digital financial inclusion alleviates the constraints on consumption mobility and enables a more convenient transformation of savings and investment returns into consumption. The rise in consumption then translates into further development of the substantial economy. Digital financial inclusion’s impact on the fictitious economy is also twofold. First, most enterprises increase their financialized investment out of a “reservoir effect” preventative motive [11]. In other words, they hope to avoid investment opportunities in the future in case they meet restraints of external capital. Along with the development of digital financial inclusion, it becomes much easier for businesses to gain funding, hence the reduction of financialized behavior. This view has been supported by scholars’ studies [11, 12]. Second, although the development of digital finance increases the difficulty for supervision, and some borrowers misuse their loans for stock and housing investment, digital technology does prove to be an effective means to stop capital’s flow from the substantial to the fictitious economy since it helps diminish the information asymmetry concerning fund usage among financial institutions, nonfinancial institutions, and loan borrowers. Digital financial inclusion’s course of development is evidently policy-based and targeted. Through big data and artificial intelligence algorithms, it can monitor the capital flow of micro, small, and medium businesses’ loans, thereby curbing the capital’s pursuit of profits [10]. Nonetheless, some opinions hold that only a limited number of digital finance beneficiaries would redirect their funding toward sectors of the fictitious economy. For example, at present, civilian housing loans are mainly borrowed from commercial banks, and only a minority would choose digital finance for capital turnover. Therefore, digital financial inclusion has little impact on the fictitious economy, displaying no evident role in the capital allocation between the substantial and the fictitious economy. Based on the above analysis, this paper proposes its first set of hypotheses:(1)
H1a: digital financial inclusion plays an evidently encouraging role in regional capital’s diversion from the fictitious to the substantial economy.(2)
H1b: the three dimensions of digital financial inclusion (scope of coverage, depth of usage, and level of digitalization) evidently encourage regional capital’s diversion from the fictitious to the substantial economy.


### 2.2. Digital Financial Inclusion’s Spatial Effects on Capital’s Diversion from the Fictitious to the Substantial Economy

The networking feature of digital financial inclusion allows it to break free from temporal and spatial limits and provide funding to those who reside in other regions and are in urgent need of support. This is the “inclusive” feature of digital financial inclusion. Digital finance can break through the limitations of traditional geographic space and realize low-cost financial resource allocation in remote areas. Therefore, the development of digital finance in a certain area should not have a significant correlation with the geographical location of the area and the level of digital finance development in surrounding areas. Compared with traditional finance, digital financial inclusion is not restricted by the distance between outlets and space. It can greatly reduce the daily cost of physical outlets. Regional digital financial inclusion has a positive spatial autocorrelation; that is, the development of digital financial inclusion between adjacent areas has similar attributes. The stronger the positive autocorrelation, the stronger the intensity of agglomeration. Guo et al. [13] discovered that, generally speaking, Chinese digital financial inclusion manifests strong regional convergence and space gathering. Therefore, digital financial inclusion’s influence over capital’s diversion from the fictitious to the substantial economy may hold some spatial effects. On the one hand, beneficiaries of local digital finance—once they have obtained the funding—could purchase commodities from other regions and promote the growth of the substantial economy in that region. On the other hand, if digital financial inclusion could inhibit capital flow into the housing and capital market, price fluctuation in advanced regions would be transmitted to neighboring regions. This is a de facto improvement of the local substantial economy’s increment compared with that of the fictitious economy, and the capital thereby leaves the fictitious economy for the substantial. It follows that there may be a positive spatial spillover effect for digital financial inclusion’s impact over capital’s diversion from the fictitious to the substantial economy in other regions. However, while digital financial inclusion stimulates resources to flow from (excessively) abundant regions to remote regions (in need), it could also mobilize various factors of production. For instance, enterprises in backward regions, upon receiving funding for production expansion, may adsorb labor forces from other regions. This would weaken the substantial economic growth in other regions and create negative spatial spillover for other regions’ capital diversion from the fictitious to the substantial economy.It follows that the spatial effects of digital financial inclusion over capital’s diversion from the fictitious to the substantial economy depend on the relative size of the positive and the negative spillover effects. Based on the above analysis, this paper proposes its second set of hypotheses:1
H2a: there is a positive spatial spillover effect for digital financial inclusion’s impact over regional capital’s diversion from the fictitious to the substantial economy in other regions. In other words, one region’s digital financial inclusion growth can encourage other regions’ capital diversion from the fictitious to the substantive economy.(2)
H2b: there is a negative spatial spillover effect for digital financial inclusion’s impact over regional capital’s diversion from the fictitious to the substantial economy in other regions. In other words, one region’s digital financial inclusion growth can discourage other regions’ capital diversion from the fictitious to the substantive economy.


### 2.3. Moderating Effect Mechanism of Economic Policy Uncertainty

China is currently undergoing an economic transformation. Regulation of market economy with the government’s “visible hand” can enhance the efficiency of resource allocation, strengthen the motivation for social innovation, and promote high-quality economic growth. Yet, in the current economic downturn, market demand is weak, insufficient investment, and overcapacity problems are prominent. In order to adapt to the new normal of Chinese economic development, the government frequently adjusts economic policies to stimulate economic development. This will inevitably increase the degree of uncertainty in economic policies, which in turn will affect the investment decisions of enterprises. Starting from the principal-agent problem, the information asymmetry caused by frequent policy adjustments and fluctuations will enhance the self-interested motivation of managers. At the same time, in order to avoid risks, management has made more cautious decisions, making investment decisions more difficult, and the efficiency of corporate capital allocation and investment levels have declined. Since micro businesses cannot anticipate the government’s policy orientation, repeated updates of industrial economic development policies would inevitably increase the policy-caused uncertainty in the external environment [14] and hence affect corporate decisions on investment and operation. Firstly, from the business perspective, rising uncertainty of economic policies would escalate fluctuations in a company’s external environment. To avoid the risks, corporate managers would reduce substantial investment and R&D spendings [15]. Funding raised via digital financial inclusion would be temporarily invested into the financial sector for enhanced utilization efficiency of the capital. This behavior would impair digital financial inclusion from performing its effects on the substantial economy. Ya et al. [16] believed that the rising uncertainty of economic policies is accompanied by an increase in the management circle’s self-serving motive. Managers would tend to allocate financial assets of rich returns and strong mobility and reduce R&D innovation activities of high uncertainty, long cycle, and irreversibility. Secondly, financial institutions like commercial banks would pay attention to the industrial uncertainty of their loan borrowers, since it would directly affect the credit risks. Once the uncertainty of economic policy escalates, business management becomes more complicated, and financial institutions accordingly shrink their quota of loans for enterprises [17]. As the costs for and restraints on the fund-raising rise, physical investment willingness drops low. Meanwhile, financial institutions’ support for the agriculture, rural areas, and farmers sectors, as well as livelihood, remain unaffected by policies. As a result, the positive correlation between digital financial inclusion and capital’s diversion from the fictitious to the substantial economy weakens. Finally, judging from the perspective of financial assets, the capital market is much affected by policy uncertainty. With the rising uncertainty of industrial policy, some investors may withdraw to avoid policy risks, and it leads to the fluctuating drop of financial asset prices. A drop in the financial investment profits would cause enterprises to reduce financial investment.Some scholars have examined the impact of economic policy uncertainty on corporate investment decisions. Both Liu et al. [18] and Xu et al. [19] discovered that financial policy uncertainty is of negative correlation with corporate physical investment and positive correlation with financial investment. Obviously, there is a close connection between financial investment and economic policy uncertainty. The analysis of Guo Yinhan et al. (2020) [20] reveals that rising uncertainty of economic policy would force enterprises to increase their financial asset holding, thereby evidently increasing the level of corporate diversion from the substantial to the fictitious economy. The study of Ya et al. [16] demonstrates that economic policy uncertainty increases the crowding-out effect of corporate financial asset allocation on innovative investment. It indicates enterprises’ tendency to avoid industrial risks and preference for finance. Generally speaking, economic policy uncertainty would accelerate corporate financialization and reduce innovative investment, thereby strengthening the capital diversion from the substantial to the fictitious economy. The dropping returns of financial assets, caused by clouded policies, may discourage an enterprise from financial investment. However, it will not encourage an enterprise’s industrial investment and is therefore incapable of reverting the capital diversion from the substantial to the fictitious economy. Based on the above analysis, this paper proposes its third hypothesis:1
H3: economic policy uncertainty has a negative moderating effect on the positive correlation between digital financial inclusion and capital diversion from the fictitious to the substantial economy.


## 2.1. Impact Mechanism of Digital Financial Inclusion’s Development on Capital’s Diversion from the Fictitious to the Substantial Economy

G20 High-Level Principles for Digital Financial Inclusion has defined digital financial inclusion as any behavior promoting financial inclusion via digital means. Through digital technology, it lends financial services at a relatively low cost to those previously inaccessible to such services. Judging by the composition of the digital financial inclusion index created by Peking University, digital financial inclusion comprises three aspects: scope of coverage, depth of usage, and level of digitalization. Here, the scope of coverage refers to the number or ratio of customers receiving financial services via the Internet or mobile network tools like Alipay. The depth of usage means the frequency at which people use Internet financial services. The level of digitization refers to the convenience and efficiency of a region’s digital finance [13]. Theoretically, it is believed that digital finance can effectively make up for the insufficiency of traditional finance, alleviate small and micro enterprises’ fund-raising restraints, stimulate corporate innovation, and subsequently strengthen the development of the substantial economy [4, 6, 7]. But as digital finance stimulates substantial economy’s development through its resource allocation and innovation effects, is it also responsible for credit capital’s flow into the fictitious economy and capital’s subsequent diversion from the fictitious to the substantial economy? Li et al. [2] pointed out that digital financial inclusion raises the difficulty of financial regulation. Some enterprises and individuals use their loan funds to invest in the housing and stock market, and their behavior threatens financial security. Cheng et al. [12] also believed that, during financial transformation, declining profits of the substantial economy and the flourish of financial and housing departments may deprive digital financial inclusion of the chance to perform services as a functional department. Capital’s pursuit of profits speeds up its disconnection from the substantial economy.The development of digital finance helps to reduce the consequences of financial system deficiencies, and the development of digital finance helps increase the availability of credit for private enterprises and small enterprises. Digital finance improves the efficiency of financial allocation by reducing information asymmetry. Digital finance has enhanced the information-gathering capabilities of financial institutions. Digital finance reduces the cost of deposit-taking. Compared with traditional deposit-taking businesses, digital finance has expanded the coverage of financial institutions due to its advantages in breaking through geographic restrictions and absorbing the investors’ funds.This paper holds that, as a significant tool supplementing traditional finance and promoting high-quality economic growth, digital financial inclusion has far more encouragement for the substantial economy than for the fictitious economy. Its support for the fictitious economy is latent and may even prove to be a restraint rather than encouragement. Eventually, it achieves to redirect capital from the fictitious to the substantial economy. Encouragement for the substantial economy comprises two aspects. On the one hand, digital financial inclusion, with its big data technology and convenient operation, greatly reduces small and micro businesses’ costs for getting loans. Thus, it solves their fund-raising difficulties and provides capital support for corporate innovation, in service of substantial economy’s development. Existing literature also provides proof from the two aspects of innovation and substantial economy. On the other hand, currently, the most prominent substantial service provided by digital financial inclusion is to stimulate and increase civilian consumption. The biggest difference between digital financial inclusion and traditional finance lies in the former’s high efficiency, which enables those in need of money to get loans for consumption anywhere at any time. Cheng et al. [9] pointed out that digital financial inclusion alleviates the constraints on consumption mobility and enables a more convenient transformation of savings and investment returns into consumption. The rise in consumption then translates into further development of the substantial economy. Digital financial inclusion’s impact on the fictitious economy is also twofold. First, most enterprises increase their financialized investment out of a “reservoir effect” preventative motive [11]. In other words, they hope to avoid investment opportunities in the future in case they meet restraints of external capital. Along with the development of digital financial inclusion, it becomes much easier for businesses to gain funding, hence the reduction of financialized behavior. This view has been supported by scholars’ studies [11, 12]. Second, although the development of digital finance increases the difficulty for supervision, and some borrowers misuse their loans for stock and housing investment, digital technology does prove to be an effective means to stop capital’s flow from the substantial to the fictitious economy since it helps diminish the information asymmetry concerning fund usage among financial institutions, nonfinancial institutions, and loan borrowers. Digital financial inclusion’s course of development is evidently policy-based and targeted. Through big data and artificial intelligence algorithms, it can monitor the capital flow of micro, small, and medium businesses’ loans, thereby curbing the capital’s pursuit of profits [10]. Nonetheless, some opinions hold that only a limited number of digital finance beneficiaries would redirect their funding toward sectors of the fictitious economy. For example, at present, civilian housing loans are mainly borrowed from commercial banks, and only a minority would choose digital finance for capital turnover. Therefore, digital financial inclusion has little impact on the fictitious economy, displaying no evident role in the capital allocation between the substantial and the fictitious economy. Based on the above analysis, this paper proposes its first set of hypotheses:(1)
H1a: digital financial inclusion plays an evidently encouraging role in regional capital’s diversion from the fictitious to the substantial economy.(2)
H1b: the three dimensions of digital financial inclusion (scope of coverage, depth of usage, and level of digitalization) evidently encourage regional capital’s diversion from the fictitious to the substantial economy.


## 2.2. Digital Financial Inclusion’s Spatial Effects on Capital’s Diversion from the Fictitious to the Substantial Economy

The networking feature of digital financial inclusion allows it to break free from temporal and spatial limits and provide funding to those who reside in other regions and are in urgent need of support. This is the “inclusive” feature of digital financial inclusion. Digital finance can break through the limitations of traditional geographic space and realize low-cost financial resource allocation in remote areas. Therefore, the development of digital finance in a certain area should not have a significant correlation with the geographical location of the area and the level of digital finance development in surrounding areas. Compared with traditional finance, digital financial inclusion is not restricted by the distance between outlets and space. It can greatly reduce the daily cost of physical outlets. Regional digital financial inclusion has a positive spatial autocorrelation; that is, the development of digital financial inclusion between adjacent areas has similar attributes. The stronger the positive autocorrelation, the stronger the intensity of agglomeration. Guo et al. [13] discovered that, generally speaking, Chinese digital financial inclusion manifests strong regional convergence and space gathering. Therefore, digital financial inclusion’s influence over capital’s diversion from the fictitious to the substantial economy may hold some spatial effects. On the one hand, beneficiaries of local digital finance—once they have obtained the funding—could purchase commodities from other regions and promote the growth of the substantial economy in that region. On the other hand, if digital financial inclusion could inhibit capital flow into the housing and capital market, price fluctuation in advanced regions would be transmitted to neighboring regions. This is a de facto improvement of the local substantial economy’s increment compared with that of the fictitious economy, and the capital thereby leaves the fictitious economy for the substantial. It follows that there may be a positive spatial spillover effect for digital financial inclusion’s impact over capital’s diversion from the fictitious to the substantial economy in other regions. However, while digital financial inclusion stimulates resources to flow from (excessively) abundant regions to remote regions (in need), it could also mobilize various factors of production. For instance, enterprises in backward regions, upon receiving funding for production expansion, may adsorb labor forces from other regions. This would weaken the substantial economic growth in other regions and create negative spatial spillover for other regions’ capital diversion from the fictitious to the substantial economy.It follows that the spatial effects of digital financial inclusion over capital’s diversion from the fictitious to the substantial economy depend on the relative size of the positive and the negative spillover effects. Based on the above analysis, this paper proposes its second set of hypotheses:1
H2a: there is a positive spatial spillover effect for digital financial inclusion’s impact over regional capital’s diversion from the fictitious to the substantial economy in other regions. In other words, one region’s digital financial inclusion growth can encourage other regions’ capital diversion from the fictitious to the substantive economy.(2)
H2b: there is a negative spatial spillover effect for digital financial inclusion’s impact over regional capital’s diversion from the fictitious to the substantial economy in other regions. In other words, one region’s digital financial inclusion growth can discourage other regions’ capital diversion from the fictitious to the substantive economy.


## 2.3. Moderating Effect Mechanism of Economic Policy Uncertainty

China is currently undergoing an economic transformation. Regulation of market economy with the government’s “visible hand” can enhance the efficiency of resource allocation, strengthen the motivation for social innovation, and promote high-quality economic growth. Yet, in the current economic downturn, market demand is weak, insufficient investment, and overcapacity problems are prominent. In order to adapt to the new normal of Chinese economic development, the government frequently adjusts economic policies to stimulate economic development. This will inevitably increase the degree of uncertainty in economic policies, which in turn will affect the investment decisions of enterprises. Starting from the principal-agent problem, the information asymmetry caused by frequent policy adjustments and fluctuations will enhance the self-interested motivation of managers. At the same time, in order to avoid risks, management has made more cautious decisions, making investment decisions more difficult, and the efficiency of corporate capital allocation and investment levels have declined. Since micro businesses cannot anticipate the government’s policy orientation, repeated updates of industrial economic development policies would inevitably increase the policy-caused uncertainty in the external environment [14] and hence affect corporate decisions on investment and operation. Firstly, from the business perspective, rising uncertainty of economic policies would escalate fluctuations in a company’s external environment. To avoid the risks, corporate managers would reduce substantial investment and R&D spendings [15]. Funding raised via digital financial inclusion would be temporarily invested into the financial sector for enhanced utilization efficiency of the capital. This behavior would impair digital financial inclusion from performing its effects on the substantial economy. Ya et al. [16] believed that the rising uncertainty of economic policies is accompanied by an increase in the management circle’s self-serving motive. Managers would tend to allocate financial assets of rich returns and strong mobility and reduce R&D innovation activities of high uncertainty, long cycle, and irreversibility. Secondly, financial institutions like commercial banks would pay attention to the industrial uncertainty of their loan borrowers, since it would directly affect the credit risks. Once the uncertainty of economic policy escalates, business management becomes more complicated, and financial institutions accordingly shrink their quota of loans for enterprises [17]. As the costs for and restraints on the fund-raising rise, physical investment willingness drops low. Meanwhile, financial institutions’ support for the agriculture, rural areas, and farmers sectors, as well as livelihood, remain unaffected by policies. As a result, the positive correlation between digital financial inclusion and capital’s diversion from the fictitious to the substantial economy weakens. Finally, judging from the perspective of financial assets, the capital market is much affected by policy uncertainty. With the rising uncertainty of industrial policy, some investors may withdraw to avoid policy risks, and it leads to the fluctuating drop of financial asset prices. A drop in the financial investment profits would cause enterprises to reduce financial investment.Some scholars have examined the impact of economic policy uncertainty on corporate investment decisions. Both Liu et al. [18] and Xu et al. [19] discovered that financial policy uncertainty is of negative correlation with corporate physical investment and positive correlation with financial investment. Obviously, there is a close connection between financial investment and economic policy uncertainty. The analysis of Guo Yinhan et al. (2020) [20] reveals that rising uncertainty of economic policy would force enterprises to increase their financial asset holding, thereby evidently increasing the level of corporate diversion from the substantial to the fictitious economy. The study of Ya et al. [16] demonstrates that economic policy uncertainty increases the crowding-out effect of corporate financial asset allocation on innovative investment. It indicates enterprises’ tendency to avoid industrial risks and preference for finance. Generally speaking, economic policy uncertainty would accelerate corporate financialization and reduce innovative investment, thereby strengthening the capital diversion from the substantial to the fictitious economy. The dropping returns of financial assets, caused by clouded policies, may discourage an enterprise from financial investment. However, it will not encourage an enterprise’s industrial investment and is therefore incapable of reverting the capital diversion from the substantial to the fictitious economy. Based on the above analysis, this paper proposes its third hypothesis:1
H3: economic policy uncertainty has a negative moderating effect on the positive correlation between digital financial inclusion and capital diversion from the fictitious to the substantial economy.


## 3. Econometric Model and Data Specifications

### 3.1. Samples and Data Source

Drawing from the panel data of 31 provinces in China, this paper analyzes the impact of digital financial inclusion on the capital diversion from the fictitious to the substantial economy as well as its spatial effects. The time frame is set between 2011 and 2019. The term “digital financial inclusion” comes from The Peking University Digital Financial Inclusion Index of China (2011–2020) edited by Guo et al. [21]. The term “economic policy uncertainty” derives from Baker et al.’s [22] policy uncertain index, calculated based on the statistics of keywords (“China”, “economy”, “uncertainty”, “policy”) in South China Morning Post. The primary data of capital’s diversion from the fictitious to the substantial economy and of other controlled variables come from the National Bureau of Statistics’ database and each province’s statistic annals.


### 3.2. Model Construction

The following model is constructed to test digital financial inclusion’s impact on regional capital’s diversion from the fictitious to the substantial economy.(1)etdit=α0+α1×difiit+∑jβjXj,it.In formula (1), etdit represents the level of capital diversion from the fictitious to the substantial economy in i province of the year t. difiit stands for digital financial inclusion, including its overall development level as well as its three subdimensions-scope of coverage, depth of usage, and degree of digitalization. Xj represents it is no. j is a controlled variable.Considering the possibility that there may be spatial spillover effect in digital financial inclusion’s impact on capital’s diversion from the fictitious to the substantial economy, we then construct a spatial panel data model:(2)etdit=α0+ρW×etdit+α1×difiit+α2W×difiit+∑jβjXj,it.In formula (2), W is the spatial weight matrix. ρ reflects how the current region’s capital diversion from the fictitious to the substantial economy is spatially affected by that diversion in other regions. α2 represents the spatial spillover effect of this process.To examine the moderating effect of economic policy uncertainty, we introduce interaction variables to formulas (1) and (2).(3)etdit=α0+α1×difiit+λ×difi∗epuit+β×epuit+∑jβjXj,it,(4)etdit=α0+ρW×etdit+α1×difiit+α2W×difiit+∑jβjXj,it+λ1×d+λ2W×d+β1×epuit+β2W×epuit,epu represents economic policy uncertainty. difi∗epu is the multiplicative interaction of digital financial inclusion and economic policy uncertainty, reflecting the moderating effect of economic policy uncertainty on digital financial inclusion and regional capital’s diversion from the fictitious to the substantial economy.


### 3.3. Choice of Variables

#### 3.3.1. Capital’s Diversion from the Fictitious to the Substantial Economy

From Table1, we can observe that academic evaluation of the two diversions—“from the substantial to the fictitious economy” and “from the fictitious to the substantial economy”—tends to adopt a unidirectional index. Both Wang et al. [10] and Cheng et al. [9] adopted the development level of substantial economy (regional GDP with housing and financial industries’ added value deduced) as a proxy. The rise of the substantial economy would be considered as an improvement of capital diversion “from the fictitious to the substantial economy”. Cheng et al. [12] and Chen et al. [11] adopted the fluctuation of corporate financialization level for evaluation. The rise of corporate financialization level represents the capital’s diversion “from the substantial to the fictitious economy”. This paper believes that since both the economic growth and corporate financial investment in China are in the rising phase, unilateral fluctuation cannot truly represent how the substantial economy and the fictitious economy counterbalance against each other’s development. Taking both aspects into consideration, this paper evaluates the degree of capital’s diversion from the fictitious to the substantial economy with the discrepancy between the R&D input growth rate of regional enterprise above designated size and regional housing price’s fluctuation rate. The higher the discrepancy, the more evident diversion from the fictitious to the substantial economy, indicating the social preference for a substantial (innovative) economy. The ratio, rather than the discrepancy between these two figures, is used for the robustness test.Table 1
Variable definition.
variable typeVariable nameSignEvaluation indexDependent variableCapital’s diversion from the fictitious to the substantial economyEtd (%)The discrepancy between regional R&D investment growth rate and housing price fluctuation rateIndependent variableDigital financial inclusionDifiGuo et al.’s [21] assessment of provincial digital financial inclusion total indexDigital financial inclusion’s scope of coverageCoverageProvincial digital financial inclusion’s scope of coverage indexDigital financial inclusion’s depth of usageUsageProvincial digital financial inclusion’s depth of usage indexDigital financial inclusion’s digitization levelDigitizationProvincial digital financial inclusion’s digitization level indexModerating variableEconomic policy uncertaintyEpuAnnual average of Baker et al.’s (2016) calculation of China’s monthly EPU indexControlled variableEconomic growth rateGdp (%)Actual growth rate of regional GDPGovernment’s fiscal expenditureFinanceThe ratio between government’s technology and education spendings and regional GDPForeign direct investmentlnfdiNatural logarithm of foreign direct investment


#### 3.3.2. Digital Financial Inclusion

All the existing empirical literature on digital financial inclusion has adopted Peking University’s digital financial inclusion development index, and this paper follows suit. This index is measured and quantified by Guo et al. [21], covering three subdimensions: digital financial inclusion’s scope of coverage, depth of usage, and degree of digitalization.


#### 3.3.3. Economic Policy Uncertainty

Policy uncertainty can be displayed by either the frequency of policy issuance or the policy density of various industries. China has so many policies—national, regional, and industrial ones—that it is hard to make an accurate calculation. Therefore, this paper has referred to the approach adopted by Baker et al. [22], defining the level of Chinese economic policy uncertainty through the ratio between keyword numbers (based on statistics of South China Morning Post) and the total number of articles in a given month. The primary data of this index is monthly, based on which we can calculate the annual average. It should be noted that this index takes an equal value of all provinces.


#### 3.3.4. Controlled Variables

They include three indexes: regional economic growth rate, government fiscal expenditure, and foreign direct investment.


## 3.1. Samples and Data Source

Drawing from the panel data of 31 provinces in China, this paper analyzes the impact of digital financial inclusion on the capital diversion from the fictitious to the substantial economy as well as its spatial effects. The time frame is set between 2011 and 2019. The term “digital financial inclusion” comes from The Peking University Digital Financial Inclusion Index of China (2011–2020) edited by Guo et al. [21]. The term “economic policy uncertainty” derives from Baker et al.’s [22] policy uncertain index, calculated based on the statistics of keywords (“China”, “economy”, “uncertainty”, “policy”) in South China Morning Post. The primary data of capital’s diversion from the fictitious to the substantial economy and of other controlled variables come from the National Bureau of Statistics’ database and each province’s statistic annals.


## 3.2. Model Construction

The following model is constructed to test digital financial inclusion’s impact on regional capital’s diversion from the fictitious to the substantial economy.(1)etdit=α0+α1×difiit+∑jβjXj,it.In formula (1), etdit represents the level of capital diversion from the fictitious to the substantial economy in i province of the year t. difiit stands for digital financial inclusion, including its overall development level as well as its three subdimensions-scope of coverage, depth of usage, and degree of digitalization. Xj represents it is no. j is a controlled variable.Considering the possibility that there may be spatial spillover effect in digital financial inclusion’s impact on capital’s diversion from the fictitious to the substantial economy, we then construct a spatial panel data model:(2)etdit=α0+ρW×etdit+α1×difiit+α2W×difiit+∑jβjXj,it.In formula (2), W is the spatial weight matrix. ρ reflects how the current region’s capital diversion from the fictitious to the substantial economy is spatially affected by that diversion in other regions. α2 represents the spatial spillover effect of this process.To examine the moderating effect of economic policy uncertainty, we introduce interaction variables to formulas (1) and (2).(3)etdit=α0+α1×difiit+λ×difi∗epuit+β×epuit+∑jβjXj,it,(4)etdit=α0+ρW×etdit+α1×difiit+α2W×difiit+∑jβjXj,it+λ1×d+λ2W×d+β1×epuit+β2W×epuit,epu represents economic policy uncertainty. difi∗epu is the multiplicative interaction of digital financial inclusion and economic policy uncertainty, reflecting the moderating effect of economic policy uncertainty on digital financial inclusion and regional capital’s diversion from the fictitious to the substantial economy.


## 3.3. Choice of Variables

### 3.3.1. Capital’s Diversion from the Fictitious to the Substantial Economy

From Table1, we can observe that academic evaluation of the two diversions—“from the substantial to the fictitious economy” and “from the fictitious to the substantial economy”—tends to adopt a unidirectional index. Both Wang et al. [10] and Cheng et al. [9] adopted the development level of substantial economy (regional GDP with housing and financial industries’ added value deduced) as a proxy. The rise of the substantial economy would be considered as an improvement of capital diversion “from the fictitious to the substantial economy”. Cheng et al. [12] and Chen et al. [11] adopted the fluctuation of corporate financialization level for evaluation. The rise of corporate financialization level represents the capital’s diversion “from the substantial to the fictitious economy”. This paper believes that since both the economic growth and corporate financial investment in China are in the rising phase, unilateral fluctuation cannot truly represent how the substantial economy and the fictitious economy counterbalance against each other’s development. Taking both aspects into consideration, this paper evaluates the degree of capital’s diversion from the fictitious to the substantial economy with the discrepancy between the R&D input growth rate of regional enterprise above designated size and regional housing price’s fluctuation rate. The higher the discrepancy, the more evident diversion from the fictitious to the substantial economy, indicating the social preference for a substantial (innovative) economy. The ratio, rather than the discrepancy between these two figures, is used for the robustness test.Table 1
Variable definition.
variable typeVariable nameSignEvaluation indexDependent variableCapital’s diversion from the fictitious to the substantial economyEtd (%)The discrepancy between regional R&D investment growth rate and housing price fluctuation rateIndependent variableDigital financial inclusionDifiGuo et al.’s [21] assessment of provincial digital financial inclusion total indexDigital financial inclusion’s scope of coverageCoverageProvincial digital financial inclusion’s scope of coverage indexDigital financial inclusion’s depth of usageUsageProvincial digital financial inclusion’s depth of usage indexDigital financial inclusion’s digitization levelDigitizationProvincial digital financial inclusion’s digitization level indexModerating variableEconomic policy uncertaintyEpuAnnual average of Baker et al.’s (2016) calculation of China’s monthly EPU indexControlled variableEconomic growth rateGdp (%)Actual growth rate of regional GDPGovernment’s fiscal expenditureFinanceThe ratio between government’s technology and education spendings and regional GDPForeign direct investmentlnfdiNatural logarithm of foreign direct investment


### 3.3.2. Digital Financial Inclusion

All the existing empirical literature on digital financial inclusion has adopted Peking University’s digital financial inclusion development index, and this paper follows suit. This index is measured and quantified by Guo et al. [21], covering three subdimensions: digital financial inclusion’s scope of coverage, depth of usage, and degree of digitalization.


### 3.3.3. Economic Policy Uncertainty

Policy uncertainty can be displayed by either the frequency of policy issuance or the policy density of various industries. China has so many policies—national, regional, and industrial ones—that it is hard to make an accurate calculation. Therefore, this paper has referred to the approach adopted by Baker et al. [22], defining the level of Chinese economic policy uncertainty through the ratio between keyword numbers (based on statistics of South China Morning Post) and the total number of articles in a given month. The primary data of this index is monthly, based on which we can calculate the annual average. It should be noted that this index takes an equal value of all provinces.


### 3.3.4. Controlled Variables

They include three indexes: regional economic growth rate, government fiscal expenditure, and foreign direct investment.


## 3.3.1. Capital’s Diversion from the Fictitious to the Substantial Economy

From Table1, we can observe that academic evaluation of the two diversions—“from the substantial to the fictitious economy” and “from the fictitious to the substantial economy”—tends to adopt a unidirectional index. Both Wang et al. [10] and Cheng et al. [9] adopted the development level of substantial economy (regional GDP with housing and financial industries’ added value deduced) as a proxy. The rise of the substantial economy would be considered as an improvement of capital diversion “from the fictitious to the substantial economy”. Cheng et al. [12] and Chen et al. [11] adopted the fluctuation of corporate financialization level for evaluation. The rise of corporate financialization level represents the capital’s diversion “from the substantial to the fictitious economy”. This paper believes that since both the economic growth and corporate financial investment in China are in the rising phase, unilateral fluctuation cannot truly represent how the substantial economy and the fictitious economy counterbalance against each other’s development. Taking both aspects into consideration, this paper evaluates the degree of capital’s diversion from the fictitious to the substantial economy with the discrepancy between the R&D input growth rate of regional enterprise above designated size and regional housing price’s fluctuation rate. The higher the discrepancy, the more evident diversion from the fictitious to the substantial economy, indicating the social preference for a substantial (innovative) economy. The ratio, rather than the discrepancy between these two figures, is used for the robustness test.Table 1
Variable definition.
variable typeVariable nameSignEvaluation indexDependent variableCapital’s diversion from the fictitious to the substantial economyEtd (%)The discrepancy between regional R&D investment growth rate and housing price fluctuation rateIndependent variableDigital financial inclusionDifiGuo et al.’s [21] assessment of provincial digital financial inclusion total indexDigital financial inclusion’s scope of coverageCoverageProvincial digital financial inclusion’s scope of coverage indexDigital financial inclusion’s depth of usageUsageProvincial digital financial inclusion’s depth of usage indexDigital financial inclusion’s digitization levelDigitizationProvincial digital financial inclusion’s digitization level indexModerating variableEconomic policy uncertaintyEpuAnnual average of Baker et al.’s (2016) calculation of China’s monthly EPU indexControlled variableEconomic growth rateGdp (%)Actual growth rate of regional GDPGovernment’s fiscal expenditureFinanceThe ratio between government’s technology and education spendings and regional GDPForeign direct investmentlnfdiNatural logarithm of foreign direct investment


## 3.3.2. Digital Financial Inclusion

All the existing empirical literature on digital financial inclusion has adopted Peking University’s digital financial inclusion development index, and this paper follows suit. This index is measured and quantified by Guo et al. [21], covering three subdimensions: digital financial inclusion’s scope of coverage, depth of usage, and degree of digitalization.


## 3.3.3. Economic Policy Uncertainty

Policy uncertainty can be displayed by either the frequency of policy issuance or the policy density of various industries. China has so many policies—national, regional, and industrial ones—that it is hard to make an accurate calculation. Therefore, this paper has referred to the approach adopted by Baker et al. [22], defining the level of Chinese economic policy uncertainty through the ratio between keyword numbers (based on statistics of South China Morning Post) and the total number of articles in a given month. The primary data of this index is monthly, based on which we can calculate the annual average. It should be noted that this index takes an equal value of all provinces.


## 3.3.4. Controlled Variables

They include three indexes: regional economic growth rate, government fiscal expenditure, and foreign direct investment.


## 4. Empirical Results and Analysis

### 4.1. Descriptive Statistics

Table2 illustrates the simple descriptive statistics of all variables. The discrepancy between regional R&D investment growth rate and housing price growth rate is used for evaluating the capital’s diversion from the fictitious to the substantial economy. The average value is 4.015%, indicating that the growing speed of industrial R&D investment is higher than that of housing price. This index does vary a lot in different regions and years. For instance, in 2017, Tianjin’s R&D growth rate is 41.2% lower than its housing price’s growth rate, whereas, in 2011, Hainan’s R&D growth rate is 36.8% higher than its housing price’s growth rate. The average value of digital financial inclusion is 202.3, and there is obvious heterogeneity among regions. The development level of digital financial inclusion is evidently higher in economically advanced regions than in backward regions. The same features can be observed in the three subdimensions. Among the controlled variables, the average regional GDP growth rate is 8.5%; the average strength of the government’s fiscal spendings on technology and education is 18.3%; the natural logarithm for average foreign direct investment is 12.683.Table 2
Descriptive statistics of the variables.
VariableObserved valueAverage valueStandard deviationMinimum valueMaximum valueEtd (%)2794.01513.111−41.19336.839Difi279202.34891.64716.220410.281Coverage279182.25190.4741.960384.656Usage279197.01891.4566.760439.912Digitization279278.400117.6737.580462.228Epu279312.767204.263113.897791.874gdp(%)2798.4822.3970.50016.400Finance2790.1830.0320.1060.253lnfdi27912.6831.8066.10015.090


### 4.2. Impact of Financial Mismatch’s Correction on Industrial Transformation and Upgrade

Table3 illustrates formula (1)’s estimation of digital financial inclusion’s impact on capital diversion from the fictitious to the substantial economy. In column (1), the gross index coefficient of digital financial inclusion is 0.049, statistically evident under 1% probability. It suggests that digital financial inclusion has an evident positive impact on capital’s diversion from the fictitious to the substantial economy, and the development of digital financial inclusion can encourage social capital to flow from the fictitious economy to the substantial economy. In the last three columns, digital financial inclusion’s scope of coverage, depth of usage, and level of digitization are all evidently positive, confirming that they can all promote capital diversion from the fictitious to the substantial economy. Judging by the size of their coefficients, the scope of coverage has the largest positive impact, while the digitization level has the smallest. Among the controlled variables, the coefficient for a region’s GDP growth rate is evidently above 0. As for the coefficient of fiscal expenditure on technology and education as well as the coefficient of foreign direct investment, neither of them are evident, suggesting that a more flourishing economy would be favorable for the capital’s diversion from the fictitious to the substantial economy, reducing the probability of capital flow into the housing industry and financial assets. Results in Table 3 prove hypotheses H1a and H1b.Table 3
Impact of digital financial inclusion on capital’s diversion from the fictitious to the substantial economy.
Dependent variableetdetdetdetdColumn(1) difi(2) coverage(3) usage(4) digitizationDifi0.049∗∗∗0.050∗∗∗0.039∗0.032∗∗∗(0,017)(0,018)(0,016)(0,012)Gdp (%)2.275∗∗∗2.311∗∗∗1.839∗∗∗1.995∗∗∗(0,655)(0,667)(0,594)(0,615)Finance51.09249.78349.65961.530(40,440)(40,486)(40,714)(40,436)lnfdi−0.042−0.015-0.1970.197(0,730)(0,730)(0,739)(0,733)Constant−34.068∗∗∗−33.690∗∗∗−25.767∗∗∗−35.705∗∗∗(10,989)(10,938)(9,878)(11,809)RegionControlledControlledControlledControlledEstimation methodRandom effectRandom effectRandom effectRandom effectN279279279279Note. The bracketed figure is a standard deviation; ∗∗∗, ∗∗, and ∗ represent that the figure is evident at 1%, 5%, and 10% levels, respectively.To determine whether digital financial inclusion holds any spatial effect over capital diversion from the fictitious to the substantial economy, first, we need to ascertain, through Moran’s I Index, whether there is any spatial correlation between digital financial inclusion and such capital diversion. Here, we adopt geographically adjacent space weight and economic distance’s space weight for calculation. (The economic distance is measured according to 2015 regional per capita GDP in each province.) Table4 illustrates the results testify to the absence of any continuously positive spatial correlation in both capital diversion from the fictitious to the substantial economy and digital financial inclusion. This is especially true for the capital diversion variable, where there are more spatially irrelevant years than relevant ones. It means that capital flow within regions is not always identical and may be related to heterogeneous factors like a region’s industrial structure, level of capital adequacy, and government regulatory policies.Table 4
Spatially relevant coefficients.
WeightGeographical adjacencyEconomic distanceVariableEtdDifiEtdDifi2011−0.0280.219∗∗∗−0.1160.0482012−0.183∗0.272∗∗∗−0.144∗0.267∗∗∗2013−0.0220.0810.144∗∗∗0.03920140.164∗∗∗−0.0170.148∗∗∗0.02320150.0800.437∗∗∗−0.030−0.06320160.245∗∗∗0.0640.0100.101∗20170.0690.031−0.1290.090∗2018−0.0380.529∗∗∗−0.0610.210∗∗∗2019−0.0950.486∗∗∗−0.0360.269∗∗∗Note. The bracketed figure is a standard deviation. ∗∗∗, ∗∗, and ∗ represent that the figure is evident at, respectively, 1%, 5%, and 10% levels.Table5 lists the estimated results under two kinds of the spatial weight matrix. We can see that, under these two kinds of the spatial weight matrix, digital financial inclusion’s variable coefficients remain evidently above 0, indicating the evidently positive correlation between digital financial inclusion and regional capital diversion from the fictitious to the substantial economy. This is consistent with the conclusion of Table 3. The Wxetd statistics is not conspicuous among spatial variables, suggesting the absence of spatial autocorrelation in capital diversion from the fictitious to the substantial economy. In other words, for regions of high capital diversion from the fictitious to the substantial economy, they are neither geographically adjacent nor share the same level of regional GDP per capita. Nor is the Wxdifi variable coefficient a conspicuous one. It demonstrates that for geographically neighboring regions, or regions of close economic distance, their digital financial inclusion development has nothing to do with the current region’s capital diversion from the fictitious to the substantial economy. In other words, digital financial inclusion does not have any evident spatial spillover effect on regional capital’s diversion from the fictitious to the substantial economy. This result supports neither hypothesis H2a nor hypothesis H2b. This thesis believes that the major mechanism behind digital financial inclusion’s promotion of capital diversion from the fictitious to the substantial economy lies in its relief of corporate fund-raising restraints and encouragement of corporate investment enthusiasm. The development of local enterprises is most beneficial for the local substantial economy and has limited effects on the external economy. Meanwhile, in the context of population migration and the supply and demand conflict of housing, urban property prices do not rise universally. There is a rapid appreciation of property values in first-and-second tier cities, but the growth is much slower in fourth-and-fifth-tier cities. Consequently, there may not be any obvious transference of property value fluctuations among cities, nor does digital financial inclusion have any obvious spatial spillover effect on the fictitious economy.Table 5
Digital financial inclusion’s spatial effects on capital’s diversion from the fictitious to the substantial economy.
Dependent variableetdetdetdetdetdWeightGeographical adjacencyEconomic distanceColumn(1) difi(2) coverage(3) usage(4) digitization(5) difiDifi0.053∗∗∗0.059∗∗∗0.048∗∗∗0.040∗∗∗0.052∗∗∗(0, 015)(0, 012)(0, 013)(0, 015)(0, 016)Gdp (%)2.287∗∗∗2.352∗∗∗1.854∗∗∗2.048∗∗∗2.253∗∗∗(0, 651)(0, 662)(0, 590)(0, 628)(0, 649)Finance51.32354.06045.08760.33652.469(40, 763)(40, 930)(41, 402)(40, 893)(41, 175)lnfdi0.1410.1370.0070.2330.113(0, 758)(0, 736)(0, 802)(0, 733)(0, 812)Constant−36.824∗∗∗−37.309∗∗∗−27.950∗∗∗−36.770∗∗∗−35.781∗∗∗(11, 403)(11, 253)(10, 472)(12, 122)(13, 101)W × etd0.000−0.0010.0140.014−0.178(0, 174)(0, 174)(0, 175)(0, 174)(0, 204)W × difi0.0550.0630.0320.0250.030(0, 065)(0, 052)(0, 053)(0, 065)(0, 089)Estimation methodRandom effectRandom effectRandom effectRandom effectRandom effectN279279279279279Note. Under the economic distance weight, the three subdimensions of digital financial inclusion have the same kind of impact on capital’s diversion from the fictitious to the substantial economy. It shall not be repeated here for lack of space. The bracketed figure is a standard deviation. ∗∗∗, ∗∗, and ∗ represent that the figure is evident at, respectively, 1%, 5%, and 10% levels.


### 4.3. Moderating Effect of Economic Policy Uncertainty

Previous analysis has confirmed that digital financial inclusion has no spatial effect over capital diversion from the fictitious to the substantial economic policy uncertainty. Therefore, analysis of economic policy uncertainty’s moderating effect adopts only formula (3) for estimation. From Table 6, we can observe that digital financial inclusion’s general development index and three subdimensions are still evidently positive. The size and evidentness of subdimension coefficients decrease in order, again testifying to the positive effect of digital financial inclusion over regional capital’s diversion from the fictitious to the substantial economy and the fact that scope of coverage holds the maximum positive effect and level of digitization holds the minimum. In column (1), the interaction coefficient of digital financial inclusion and economic policy uncertainty is below 0 under the 10% probability. It means that rising economic policy uncertainty would weaken the positive correlation between digital financial inclusion and capital’s diversion from the fictitious to the substantial economy. In the last three columns, all three interaction coefficients are below 0, and only the figures in column (4) remain inconspicuous. Generally speaking, economic policy uncertainty has a reverse moderating effect over the positive correlation between digital financial inclusion and capital’s diversion from the fictitious to the substantial economy. The stronger the positive correlation, the higher the level of moderation. Besides, the single economic policy variable is evidently negative, suggesting that economic policy uncertainty may inhibit corporate investment’s preference for finance or housing rather than substantial industries. Thus, economic policy uncertainty has an evidently negative moderating effect on the positive correlation between digital financial inclusion and capital diversion from the fictitious to the substantial economy. Hypothesis H3 is proved valid.Table 6
Moderating effect of economic policy uncertainty.
Dependent variableetdetdetdetdColumn(1) difi(2) coverage(3) usage(4) digitizationDifi0.082∗∗∗0.086∗∗∗0.066∗0.046∗(0, 031)(0, 032)(0, 030)(0, 026)Difi × epu−0.016∗−0.017∗−0.014∗−0.009(0, 010)(0, 009)(0, 008)(0, 011)Epu−0.053∗−0.051∗−0.046∗−0.042(0, 030)(0, 028)(0, 025)(0, 041)Gdpr2.500∗∗∗2.526∗∗∗2.098∗∗∗2.294∗∗∗(0, 670)(0, 683)(0, 610)(0, 642)Finance49.02248.18247.89258.036(40, 402)(40, 441)(40, 717)(40, 475)lnfdi0.2400.2060.1740.403(0, 749)(0, 743)(0, 770)(0, 750)Constant−49.733∗∗∗−48.340∗∗∗−41.529∗∗∗−48.281∗∗∗(14, 133)(13, 508)(13, 031)(16, 213)RegionControlledControlledControlledControlledEstimation methodRandom effectRandom effectRandom effectRandom effectN279279279279Note. Considering the differences of variable magnitude, we divide the EPU index (representing economic policy uncertainty) by 100 upon introducing the interaction values. The bracketed figure is a standard deviation. ∗∗∗, ∗∗, and ∗ represent that the figure is evident at, respectively, 1%, 5%, and 10% levels.


### 4.4. Robustness Test

To verify the validity of the above conclusions, we conduct a robustness test from two perspectives. The first is to evaluate the level of capital’s diversion from the fictitious to the substantial economy through the ratio between regional R&D input growth rate and housing price’s fluctuation rate. The second is to adopt instrumental variables of system GMM for estimation due to the innate issues of the model: allocation of corporate capital on the substantial and the fictitious economy may reversely affect the development of digital financial inclusion. Results in Table7 and 8 support the above conclusions. For digital financial inclusion and economic policy uncertainty, their respective relationship with capital’s diversion from the fictitious to the substantial economy is consistent with the previous description, verifying our results to be dependable.Table 7
Robustness test of capital’s diversion from the fictitious to the substantial economy index.
Dependent variableetd2etd2etd2etd2Column(1) difi(2) coverage(3) usage(4) digitizationDifi0.020∗∗∗0.021∗∗∗0.016∗∗∗0.012∗∗∗(0, 002)(0, 002)(0, 002)(0, 002)Difi × epu−0.001∗−0.001∗−0.001∗−0.001∗(0, 001)(0, 001)(0, 001)(0, 001)Epu−0.004∗−0.004∗−0.003∗−0.008∗∗∗(0, 002)(0, 002)(0, 002)(0, 003)Gdpr0.138∗0.146∗∗∗−0.0170.069(0, 055)(0, 056)(0, 051)(0, 053)Finance2.3212.3330.0025.061(3, 705)(3, 659)(3, 899)(3, 641)lnfdi−0.109−0.111−0.134∗−0.005(0, 072)(0, 070)(0, 077)(0, 069)Constant−3.537∗∗∗−3.213∗∗∗−0.563−4.816∗∗∗(1, 182)(1, 137)(1, 148)(1, 308)RegionControlledControlledControlledControlledEstimation methodRandom effectRandom effectRandom effectRandom effectN279279279279Note: The bracketed figure is a standard deviation. ∗∗∗, ∗∗ and ∗ represent that the figure is evident at, respectively, 1%, 5%, and 10% levels.Table 8
Robustness test based on system GMM estimation.
Dependent variableetdetdetdetdColumn(1) difi(2) coverage(3) usage(4) digitizationDifi0.031∗∗0.063∗∗∗0.051∗∗∗0.025∗∗∗(0, 014)(0, 019)(0, 009)(0, 008)Difi × epu−0.004∗−0.011∗∗∗−0.005∗∗−0.001(0, 002)(0, 002)(0, 002)(0, 003)Epu−0.013∗−0.028∗∗∗−0.015∗∗−0.010(0, 007)(0, 007)(0, 007)(0, 011)Gdp (%)0.7450.7701.638∗∗∗1.135∗∗∗(0, 601)(0, 802)(0, 231)(0, 242)Finance44.558∗301.637∗∗∗128.663∗∗∗225.993∗∗∗(24, 970)(26, 093)(22, 000)(41, 941)lnfdi0.350−5.332∗∗∗−2.288∗∗∗−3.419∗∗∗(0, 630)(0, 807)(0, 563)(0, 751)Constant−21.826∗∗−3.517−15.391∗∗−12.645∗(9, 532)(14, 021)(6, 554)(6, 398)AR(1) prob0.0070.0080.0060.007AR(2) prob0.2190.2070.2230.210Sargan test1.0001.0001.0001.000N279279279279Note. The bracketed figure is a standard deviation. ∗∗∗, ∗∗, and ∗ represent that the figure is evident at, respectively, 1%, 5%, and 10% levels.


## 4.1. Descriptive Statistics

Table2 illustrates the simple descriptive statistics of all variables. The discrepancy between regional R&D investment growth rate and housing price growth rate is used for evaluating the capital’s diversion from the fictitious to the substantial economy. The average value is 4.015%, indicating that the growing speed of industrial R&D investment is higher than that of housing price. This index does vary a lot in different regions and years. For instance, in 2017, Tianjin’s R&D growth rate is 41.2% lower than its housing price’s growth rate, whereas, in 2011, Hainan’s R&D growth rate is 36.8% higher than its housing price’s growth rate. The average value of digital financial inclusion is 202.3, and there is obvious heterogeneity among regions. The development level of digital financial inclusion is evidently higher in economically advanced regions than in backward regions. The same features can be observed in the three subdimensions. Among the controlled variables, the average regional GDP growth rate is 8.5%; the average strength of the government’s fiscal spendings on technology and education is 18.3%; the natural logarithm for average foreign direct investment is 12.683.Table 2
Descriptive statistics of the variables.
VariableObserved valueAverage valueStandard deviationMinimum valueMaximum valueEtd (%)2794.01513.111−41.19336.839Difi279202.34891.64716.220410.281Coverage279182.25190.4741.960384.656Usage279197.01891.4566.760439.912Digitization279278.400117.6737.580462.228Epu279312.767204.263113.897791.874gdp(%)2798.4822.3970.50016.400Finance2790.1830.0320.1060.253lnfdi27912.6831.8066.10015.090


## 4.2. Impact of Financial Mismatch’s Correction on Industrial Transformation and Upgrade

Table3 illustrates formula (1)’s estimation of digital financial inclusion’s impact on capital diversion from the fictitious to the substantial economy. In column (1), the gross index coefficient of digital financial inclusion is 0.049, statistically evident under 1% probability. It suggests that digital financial inclusion has an evident positive impact on capital’s diversion from the fictitious to the substantial economy, and the development of digital financial inclusion can encourage social capital to flow from the fictitious economy to the substantial economy. In the last three columns, digital financial inclusion’s scope of coverage, depth of usage, and level of digitization are all evidently positive, confirming that they can all promote capital diversion from the fictitious to the substantial economy. Judging by the size of their coefficients, the scope of coverage has the largest positive impact, while the digitization level has the smallest. Among the controlled variables, the coefficient for a region’s GDP growth rate is evidently above 0. As for the coefficient of fiscal expenditure on technology and education as well as the coefficient of foreign direct investment, neither of them are evident, suggesting that a more flourishing economy would be favorable for the capital’s diversion from the fictitious to the substantial economy, reducing the probability of capital flow into the housing industry and financial assets. Results in Table 3 prove hypotheses H1a and H1b.Table 3
Impact of digital financial inclusion on capital’s diversion from the fictitious to the substantial economy.
Dependent variableetdetdetdetdColumn(1) difi(2) coverage(3) usage(4) digitizationDifi0.049∗∗∗0.050∗∗∗0.039∗0.032∗∗∗(0,017)(0,018)(0,016)(0,012)Gdp (%)2.275∗∗∗2.311∗∗∗1.839∗∗∗1.995∗∗∗(0,655)(0,667)(0,594)(0,615)Finance51.09249.78349.65961.530(40,440)(40,486)(40,714)(40,436)lnfdi−0.042−0.015-0.1970.197(0,730)(0,730)(0,739)(0,733)Constant−34.068∗∗∗−33.690∗∗∗−25.767∗∗∗−35.705∗∗∗(10,989)(10,938)(9,878)(11,809)RegionControlledControlledControlledControlledEstimation methodRandom effectRandom effectRandom effectRandom effectN279279279279Note. The bracketed figure is a standard deviation; ∗∗∗, ∗∗, and ∗ represent that the figure is evident at 1%, 5%, and 10% levels, respectively.To determine whether digital financial inclusion holds any spatial effect over capital diversion from the fictitious to the substantial economy, first, we need to ascertain, through Moran’s I Index, whether there is any spatial correlation between digital financial inclusion and such capital diversion. Here, we adopt geographically adjacent space weight and economic distance’s space weight for calculation. (The economic distance is measured according to 2015 regional per capita GDP in each province.) Table4 illustrates the results testify to the absence of any continuously positive spatial correlation in both capital diversion from the fictitious to the substantial economy and digital financial inclusion. This is especially true for the capital diversion variable, where there are more spatially irrelevant years than relevant ones. It means that capital flow within regions is not always identical and may be related to heterogeneous factors like a region’s industrial structure, level of capital adequacy, and government regulatory policies.Table 4
Spatially relevant coefficients.
WeightGeographical adjacencyEconomic distanceVariableEtdDifiEtdDifi2011−0.0280.219∗∗∗−0.1160.0482012−0.183∗0.272∗∗∗−0.144∗0.267∗∗∗2013−0.0220.0810.144∗∗∗0.03920140.164∗∗∗−0.0170.148∗∗∗0.02320150.0800.437∗∗∗−0.030−0.06320160.245∗∗∗0.0640.0100.101∗20170.0690.031−0.1290.090∗2018−0.0380.529∗∗∗−0.0610.210∗∗∗2019−0.0950.486∗∗∗−0.0360.269∗∗∗Note. The bracketed figure is a standard deviation. ∗∗∗, ∗∗, and ∗ represent that the figure is evident at, respectively, 1%, 5%, and 10% levels.Table5 lists the estimated results under two kinds of the spatial weight matrix. We can see that, under these two kinds of the spatial weight matrix, digital financial inclusion’s variable coefficients remain evidently above 0, indicating the evidently positive correlation between digital financial inclusion and regional capital diversion from the fictitious to the substantial economy. This is consistent with the conclusion of Table 3. The Wxetd statistics is not conspicuous among spatial variables, suggesting the absence of spatial autocorrelation in capital diversion from the fictitious to the substantial economy. In other words, for regions of high capital diversion from the fictitious to the substantial economy, they are neither geographically adjacent nor share the same level of regional GDP per capita. Nor is the Wxdifi variable coefficient a conspicuous one. It demonstrates that for geographically neighboring regions, or regions of close economic distance, their digital financial inclusion development has nothing to do with the current region’s capital diversion from the fictitious to the substantial economy. In other words, digital financial inclusion does not have any evident spatial spillover effect on regional capital’s diversion from the fictitious to the substantial economy. This result supports neither hypothesis H2a nor hypothesis H2b. This thesis believes that the major mechanism behind digital financial inclusion’s promotion of capital diversion from the fictitious to the substantial economy lies in its relief of corporate fund-raising restraints and encouragement of corporate investment enthusiasm. The development of local enterprises is most beneficial for the local substantial economy and has limited effects on the external economy. Meanwhile, in the context of population migration and the supply and demand conflict of housing, urban property prices do not rise universally. There is a rapid appreciation of property values in first-and-second tier cities, but the growth is much slower in fourth-and-fifth-tier cities. Consequently, there may not be any obvious transference of property value fluctuations among cities, nor does digital financial inclusion have any obvious spatial spillover effect on the fictitious economy.Table 5
Digital financial inclusion’s spatial effects on capital’s diversion from the fictitious to the substantial economy.
Dependent variableetdetdetdetdetdWeightGeographical adjacencyEconomic distanceColumn(1) difi(2) coverage(3) usage(4) digitization(5) difiDifi0.053∗∗∗0.059∗∗∗0.048∗∗∗0.040∗∗∗0.052∗∗∗(0, 015)(0, 012)(0, 013)(0, 015)(0, 016)Gdp (%)2.287∗∗∗2.352∗∗∗1.854∗∗∗2.048∗∗∗2.253∗∗∗(0, 651)(0, 662)(0, 590)(0, 628)(0, 649)Finance51.32354.06045.08760.33652.469(40, 763)(40, 930)(41, 402)(40, 893)(41, 175)lnfdi0.1410.1370.0070.2330.113(0, 758)(0, 736)(0, 802)(0, 733)(0, 812)Constant−36.824∗∗∗−37.309∗∗∗−27.950∗∗∗−36.770∗∗∗−35.781∗∗∗(11, 403)(11, 253)(10, 472)(12, 122)(13, 101)W × etd0.000−0.0010.0140.014−0.178(0, 174)(0, 174)(0, 175)(0, 174)(0, 204)W × difi0.0550.0630.0320.0250.030(0, 065)(0, 052)(0, 053)(0, 065)(0, 089)Estimation methodRandom effectRandom effectRandom effectRandom effectRandom effectN279279279279279Note. Under the economic distance weight, the three subdimensions of digital financial inclusion have the same kind of impact on capital’s diversion from the fictitious to the substantial economy. It shall not be repeated here for lack of space. The bracketed figure is a standard deviation. ∗∗∗, ∗∗, and ∗ represent that the figure is evident at, respectively, 1%, 5%, and 10% levels.


## 4.3. Moderating Effect of Economic Policy Uncertainty

Previous analysis has confirmed that digital financial inclusion has no spatial effect over capital diversion from the fictitious to the substantial economic policy uncertainty. Therefore, analysis of economic policy uncertainty’s moderating effect adopts only formula (3) for estimation. From Table 6, we can observe that digital financial inclusion’s general development index and three subdimensions are still evidently positive. The size and evidentness of subdimension coefficients decrease in order, again testifying to the positive effect of digital financial inclusion over regional capital’s diversion from the fictitious to the substantial economy and the fact that scope of coverage holds the maximum positive effect and level of digitization holds the minimum. In column (1), the interaction coefficient of digital financial inclusion and economic policy uncertainty is below 0 under the 10% probability. It means that rising economic policy uncertainty would weaken the positive correlation between digital financial inclusion and capital’s diversion from the fictitious to the substantial economy. In the last three columns, all three interaction coefficients are below 0, and only the figures in column (4) remain inconspicuous. Generally speaking, economic policy uncertainty has a reverse moderating effect over the positive correlation between digital financial inclusion and capital’s diversion from the fictitious to the substantial economy. The stronger the positive correlation, the higher the level of moderation. Besides, the single economic policy variable is evidently negative, suggesting that economic policy uncertainty may inhibit corporate investment’s preference for finance or housing rather than substantial industries. Thus, economic policy uncertainty has an evidently negative moderating effect on the positive correlation between digital financial inclusion and capital diversion from the fictitious to the substantial economy. Hypothesis H3 is proved valid.Table 6
Moderating effect of economic policy uncertainty.
Dependent variableetdetdetdetdColumn(1) difi(2) coverage(3) usage(4) digitizationDifi0.082∗∗∗0.086∗∗∗0.066∗0.046∗(0, 031)(0, 032)(0, 030)(0, 026)Difi × epu−0.016∗−0.017∗−0.014∗−0.009(0, 010)(0, 009)(0, 008)(0, 011)Epu−0.053∗−0.051∗−0.046∗−0.042(0, 030)(0, 028)(0, 025)(0, 041)Gdpr2.500∗∗∗2.526∗∗∗2.098∗∗∗2.294∗∗∗(0, 670)(0, 683)(0, 610)(0, 642)Finance49.02248.18247.89258.036(40, 402)(40, 441)(40, 717)(40, 475)lnfdi0.2400.2060.1740.403(0, 749)(0, 743)(0, 770)(0, 750)Constant−49.733∗∗∗−48.340∗∗∗−41.529∗∗∗−48.281∗∗∗(14, 133)(13, 508)(13, 031)(16, 213)RegionControlledControlledControlledControlledEstimation methodRandom effectRandom effectRandom effectRandom effectN279279279279Note. Considering the differences of variable magnitude, we divide the EPU index (representing economic policy uncertainty) by 100 upon introducing the interaction values. The bracketed figure is a standard deviation. ∗∗∗, ∗∗, and ∗ represent that the figure is evident at, respectively, 1%, 5%, and 10% levels.


## 4.4. Robustness Test

To verify the validity of the above conclusions, we conduct a robustness test from two perspectives. The first is to evaluate the level of capital’s diversion from the fictitious to the substantial economy through the ratio between regional R&D input growth rate and housing price’s fluctuation rate. The second is to adopt instrumental variables of system GMM for estimation due to the innate issues of the model: allocation of corporate capital on the substantial and the fictitious economy may reversely affect the development of digital financial inclusion. Results in Table7 and 8 support the above conclusions. For digital financial inclusion and economic policy uncertainty, their respective relationship with capital’s diversion from the fictitious to the substantial economy is consistent with the previous description, verifying our results to be dependable.Table 7
Robustness test of capital’s diversion from the fictitious to the substantial economy index.
Dependent variableetd2etd2etd2etd2Column(1) difi(2) coverage(3) usage(4) digitizationDifi0.020∗∗∗0.021∗∗∗0.016∗∗∗0.012∗∗∗(0, 002)(0, 002)(0, 002)(0, 002)Difi × epu−0.001∗−0.001∗−0.001∗−0.001∗(0, 001)(0, 001)(0, 001)(0, 001)Epu−0.004∗−0.004∗−0.003∗−0.008∗∗∗(0, 002)(0, 002)(0, 002)(0, 003)Gdpr0.138∗0.146∗∗∗−0.0170.069(0, 055)(0, 056)(0, 051)(0, 053)Finance2.3212.3330.0025.061(3, 705)(3, 659)(3, 899)(3, 641)lnfdi−0.109−0.111−0.134∗−0.005(0, 072)(0, 070)(0, 077)(0, 069)Constant−3.537∗∗∗−3.213∗∗∗−0.563−4.816∗∗∗(1, 182)(1, 137)(1, 148)(1, 308)RegionControlledControlledControlledControlledEstimation methodRandom effectRandom effectRandom effectRandom effectN279279279279Note: The bracketed figure is a standard deviation. ∗∗∗, ∗∗ and ∗ represent that the figure is evident at, respectively, 1%, 5%, and 10% levels.Table 8
Robustness test based on system GMM estimation.
Dependent variableetdetdetdetdColumn(1) difi(2) coverage(3) usage(4) digitizationDifi0.031∗∗0.063∗∗∗0.051∗∗∗0.025∗∗∗(0, 014)(0, 019)(0, 009)(0, 008)Difi × epu−0.004∗−0.011∗∗∗−0.005∗∗−0.001(0, 002)(0, 002)(0, 002)(0, 003)Epu−0.013∗−0.028∗∗∗−0.015∗∗−0.010(0, 007)(0, 007)(0, 007)(0, 011)Gdp (%)0.7450.7701.638∗∗∗1.135∗∗∗(0, 601)(0, 802)(0, 231)(0, 242)Finance44.558∗301.637∗∗∗128.663∗∗∗225.993∗∗∗(24, 970)(26, 093)(22, 000)(41, 941)lnfdi0.350−5.332∗∗∗−2.288∗∗∗−3.419∗∗∗(0, 630)(0, 807)(0, 563)(0, 751)Constant−21.826∗∗−3.517−15.391∗∗−12.645∗(9, 532)(14, 021)(6, 554)(6, 398)AR(1) prob0.0070.0080.0060.007AR(2) prob0.2190.2070.2230.210Sargan test1.0001.0001.0001.000N279279279279Note. The bracketed figure is a standard deviation. ∗∗∗, ∗∗, and ∗ represent that the figure is evident at, respectively, 1%, 5%, and 10% levels.


## 5. Conclusions and Recommendations

Along with the rise of digital technology, the rapid growth of digital financial inclusion in China lends momentum to the relief of fund-raising constraints over the small and micro businesses, promotion of civilian consumption, and assurance of serving the agriculture, rural areas, and farmers. It meets the demands made on financial development by the two great missions—high-quality development and common prosperity. Yet, due to its financial nature, digital financial inclusion is inevitably entangled with—however latent—financial risks. Some beneficiaries of digital finance invest their funding into the fictitious economy, resulting in the capital’s diversion from the substantial to the fictitious economy. So, is the growth of digital financial inclusion beneficial for capital diversion from the fictitious to the substantial economy, or is it the reason why capital flows from the substantial to the fictitious economy instead? Based on the provincial panel data between 2011 and 2019, this paper constructs an index describing capital’s diversion from the fictitious to the substantial economy. The paper then analyzes the impact of digital financial inclusion on such a diversion of the regional capital, investigating the regulatory effects caused by the uncertainty in economic policies. Empirical study leads to the following discoveries. (1) Digital financial inclusion holds an evident positive effect over regional capital’s diversion from the fictitious to the substantial economy. In other words, the development of digital financial inclusion is beneficial for such capital diversion. Among the three subdimensions of digital financial inclusion, the scope of coverage holds the strongest positive effect, the level of digitization has the weakest, and the depth of usage is in-between. (2) At the provincial level, there is no evident spatial correlation between digital financial inclusion and the level of capital diversion from the fictitious to the substantial economy, nor does the former hold any spatial spillover effect on the latter. (3) Economic policy uncertainty has a negative moderating effect on the positive correlation between digital financial inclusion and capital diversion from the fictitious to the substantial economy. It suggests that rising economic policy uncertainty would weaken digital financial inclusion’s support of the substantial economy.Based on the above findings, this paper proposes the following policy recommendations:(1)
Accelerate the development of digital financial inclusion, bringing benefits to more populations marginalized by traditional finance. The popularity of smartphones in China has created much room for further development of digital finance. Meanwhile, most of the Chinese small, medium, and micro enterprises, as well as individual businesses, face different levels of fund-raising constraints. The development of digital financial inclusion could help solve these subjects’ funding issues, reduce a company’s financial costs, stimulate the growth of the substantial economy, and encourage enterprises’ innovative eagerness. Due to the differences of individual needs for funding and regional development structure, we may adopt the strategy of “prioritizing scope over depth” in developing digital financial inclusion, so that its positive effects as the inclusive economy could be maximized, guide banks to establish a special working mechanism for online financing services, use financial technology to promote online processing, guide banks to apply financial technology to customer analysis, analyze customers through big data, and improve the level of precision marketing.(2)
Establish a fund usage monitoring mechanism of advanced strictness, precision, and intelligence, to avoid loans being used for fictitious economy. Pursuit of profits is innate to capital. China is undergoing an economic transformation in recent years; the equity return rate of the substantial industry is so low that some capital may be prompted to flow into the housing industry and stock market. In other words, the capital is diverted from the substantial to the fictitious economy. Therefore, in our vigorous promotion of digital financial inclusion, we should also strengthen “postloan” supervision of beneficiaries’ funding usage. The fund’s application should be consistent with the purpose for which it is lent; individuals and enterprises misusing the loans for property and stock speculation would be marked for poor credit. With digital finance, funding application becomes evasive and frequent; then, it requires intelligent supervision for accuracy and efficient regulation. We reasonably allocate financial resources, focus on improving the financial service environment in underdeveloped areas, accelerate the construction of financial infrastructure, and improve a diversified and extensive institutional system.(3)
Reduce overdense industrial policies and raise enterprises’ policy expectations. Currently, China is undergoing an economic transformation. To fully mobilize available resources and enhance their allocation efficiency, the government has issued a series of economic policies to guide industrial development. Some of these industrial policies are instructional and some are prescriptive. They have undoubtedly increased the uncertainty of the external environment for enterprises to run business in, motivating them to avoid risks and delay investment, even to the extent of corporate financialization. Therefore, the government should be fore-sighted when making and issuing economic policies. It should publish detailed agenda of industrial development at once, thereby reducing the policy update frequency and stabilizing entrepreneurs’ policy expectations. For small, medium, and micro businesses, the government should adopt subsidies (to award standard enterprises) rather than punitive measures (to punish nonstandard enterprises) and offer full protection of corporate rights.



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*Source: 4912836-2021-12-17.xml*