Research on the Impact of Digital Finance on the Industrial Structure Upgrading of the Yangtze River Economic Belt from the Perspective of R&D Innovation

Abstract

According to the panel data of 11 provinces (including two municipalities) in the Yangtze River Economic Belt (YREB) for 2011–2020, we empirically test the influence of digital finance on the structural upgrade of industries of the YREB using the least squares dummy variable estimation (LSDV) of the fixed effect model (FEM). R&D innovation is taken as the mediating variable to explore the mediating effect of R&D innovation in digital finance and industrial structural upgrading of the YREB. The empirical results indicate that the advancement of digital finance is able to advance upgrading the industrial structure of the YREB, and its promotion effectiveness has regional heterogeneity. Digital finance plays the largest role in accelerating industrial structural upgrading downstream of the YREB, followed by the midstream. The upstream plays the smallest role; R&D innovation has some mediating effects in digital finance, promoting structural upgrading of industries of the YREB. Therefore, we propose accelerating the development of digital finance, improving the digital financial system, and promoting regional coordinated regional development. Moreover, we propose to raise the R&D innovation level, further advance the industrial upgrading of YREB, advance the coordinated development of the YREB, and achieve common prosperity.

1. Introduction

In the era of rapid economic development, upgrading industrial structures has become increasingly important. Financial development, R&D innovation, and improving human capital are important factors for upgrading industrial structures. Financial development can advance industrial upgrading through R&D innovation. However, due to such problems as high innovation risk, long R&D cycle, large funds required, and technology, traditional finance can no longer provide good support for R&D innovation, which requires an efficient new financial model. Therefore, the integration of traditional finance and information technology has been constantly strengthened, leading to the emergence of digital finance [1]. Digital finance is the utilization of various new technologies based on traditional finance. It is a replenishment to traditional finance and can advance industrial upgrading [2]. Digital finance has overcome the shortcomings of traditional finance, lowered the threshold of access, improved inclusiveness, and enabled small-scale businesses to become investors, increasing market participants and thus promoting the improvement of the industrial structure [3,4]. Digital finance is conducive to broadening funds sources, can take advantage of big data technology to assess consumers’ credit, and reduce information asymmetry, thereby reducing risks and promoting industrial structure upgrading [5]. In addition, digital finance can enhance the financial inclusion of emerging and developed economies and promote sustained and stable economic growth [6].

The Yangtze River Economic Belt (YREB) includes 11 provinces and cities: Chongqing, Sichuan, Guizhou, Yunnan, Jiangxi, Hubei, Hunan, Shanghai, Jiangsu, Zhejiang, and Anhui (Figure 1). It is one of the “three strategies” for China’s regional economic development, accounting for 21% of China’s total land area and raising over 40% of the population and economic aggregates of China [7]. However, in recent years, the economic growth of provinces and cities in the YREB has slowed down gradually, and breakthroughs need to be sought [8]. At present, the YREB is making great efforts to develop digital finance, reduce carbon emissions, and improve innovation capabilities [9]. Therefore, the development level of digital finance, R&D innovation, and industrial structure of provinces and cities in the YREB have been improved, but there is still much room for improvement. The YREB stretches across three parts of China’s east, center and west. The economic development of each region is unbalanced, and the level of digital finance development and industrial structure of each province and city is different [10]. Therefore, it is of great significance to research the influence of digital finance on upgrading industrial structures in the YREB to advance high-quality regional economic growth.

2. Literature Review

2.1. Digital Finance Evolution and Upgrading Industrial Structure

Many academicians have researched the link between digital finance growth and upgrading industrial structures. Most believe that the digital finance evolution is capable of upgrading industrial structures. Developing digital finance is the main factor influencing industrial structure upgrading [11,12]. In addition, it enables the optimization of resource allocation and economic development and has a great promotion effect on upgrading industrial structures [13,14]. Liu et al. (2017) empirically tested the interaction between digital finance development and industry structure upgrading from the viewpoint of spatial heterogeneity and found that there is spatial heterogeneity in regional industry structure upgrading due to the regional disparities in digital finance growth levels. Furthermore, digital finance growth contributes to the structural upgrade of industry [15]. Ge et al. verify the significant contribution of digital finance to tertiary industry integration and industry structure upgrading, with regional heterogeneity in its promotion [16]. There is a non-linear relationship between digital finance and upgrading the industry structure; when the impact of financial development on technological innovation enters the stage of diminishing marginal returns, its positive influence on industrial structure transformation is an inverted “U” shape [17].

2.2. R&D Innovation and Structural Upgrade of Industry

Capital growth and labor force are the main factors for industrial upgrading, and innovation enables upgrading industrial structures. As such, numerous scholars have researched the connection between R&D innovation and the structural upgrade of industry, and most of them believe that R&D innovation is capable of promoting the upgrading of industrial structures. Wang et al. used the panel fixed effect and random effect models to study the promotion of innovation on the rationalization and structural upgrade of industry and discovered that innovation has an important role in promoting industry structure rationalization and growth [18]. There is regional heterogeneity in R&D innovation in facilitating a structural upgrade of industry [19]. Wu and Liu (2021) found that the direct impact of innovation on the structural upgrade of industries is apparent, but the indirect impact is inconspicuous [20]. Zameer et al. found that innovation and financial development positively impact industrial upgrading and poverty alleviation efficiency [21]. Innovation enables the upgrade of industrial structures, and its impact will continue to deepen over time. At the same time, the impact is mutual, and the upgrading of the industrial structure also promotes R&D innovation. Ge et al. believe that innovation has made a noteworthy contribution to industrial restructuring and found through heterogeneity analysis that innovation significantly contributes to the structural upgrade of industry in eastern China [12].

2.3. Development of Digital Finance and R&D Innovation

Most researchers believe digital finance facilitates R&D innovation as R&D innovation is costly and digital finance provides the necessary funds for R&D innovation, which promotes the development of R&D innovation [22]. Digital finance can increase the intensity of innovation and improve the level of digitalization [23]. Digital finance is the key factor of innovation, and a country cannot achieve rapid economic growth without improving its innovation level and other factors [24]. Countries are improving their economic performance through economic transformation, intensifying the competition among enterprises, thus enhancing enterprises’ R&D innovation capabilities [25]. Financial development and innovation jointly affect economic development, and the impact of effectiveness has regional heterogeneity [26,27]. The continuous development of financial integration and innovation is contributed to the country’s economic development [28,29]. Institutional quality, financial development, and R&D investment are important determinants of innovation [30,31,32]. A good system can promote R&D innovation, and financial growth has a catalytic function for R&D innovation. R&D investment and innovation positively correlate, and marginal utility decreases [33]. Dendramis et al. believe that digital finance can reduce information asymmetry, reduce risks, and promote R&D innovation [5]. Law et al. found an inverted U-shaped non-linear relationship between financial development and R&D innovation [25].

2.4. Development of Digital Finance, R&D Innovation, and Industrial Structure Upgrading

Digital finance growth positively supports R&D innovation and industrial upgrading. Digital finance, institutional quality, and industrial structure are positively correlated with R&D innovation [34]. According to Feng et al., financial development was an important driving force for innovation, and digital financial advancement has a significant contribution to R&D innovation, which was mainly brought about by the reduction in financing constraints of enterprises, the upgrading of industrial structures and the development of the manufacturing industry [35]. Digital finance can promote R&D innovation and industrial structure upgrading by improving the efficiency of resource allocation [36]. By building a mediating effect model, Ge et al. discover digital finance can facilitate innovation, improve the integration efficiency of the tertiary industry and promote the upgrading of industrial structures [16]. Tang et al. found that digital finance can enhance industrial structure by supplementing funds, reducing risks, and promoting innovation to realize a society’s sustainable development [37].

The existing literature on the association between digital finance, R&D innovation, and the structural upgrade of industry has been relatively rich, which helps us comprehend the relationship between digital finance, R&D innovation, and industrial structure. Moreover, the research is mostly conducted at the national level, which helps us understand the digital finance, R&D innovation, and industrial structure level of the whole country. Furthermore, it provides policy suggestions for the government and promotes economic development. However, the existing literature is mostly about any two of the above three, and the research on the relationship between all three is relatively small. Research about the association between digital finance and structural upgrade of the industry using R&D innovation as a mediating variable is rare, which is not beneficial to social coordination. Moreover, existing studies focus on the influence of digital finance on upgrading industrial structures, with more research on global issues and less on local and regional issues. The Yangtze River Economic Belt is essential to the growth of China’s overall economic growth. It is of great significance to explore the influence of digital finance on the industrial structure of the YREB. For this reason, we focus on the YREB and take it as the research object. This paper uses benchmark regression and mediating effect models. It takes R&D innovation as the mediating variable to research the influence of digital finance on industrial structure upgrading of the YREB from the perspective of R&D innovation. The goal of this paper is to facilitate the structural upgrade of industry and promote the coordinated development of the YREB and high-quality economic development by studying the influence of digital finance on industry structure upgrading based on a perspective of R&D innovation. The possible benefits of the article are as follows: First, using current research results for reference, we measure the industrial structure level and innovation level of the YREB; secondly, we study the effect of digital finance on the upgrade of industry structure of the YREB from both the overall effect and the local effect to advance industry structure upgrading and high-level economic advancement.

3. Data and Variables

3.1. Samples and Data Sources

We analyzed the panel data of 11 provinces of the YREB for 2011–2020. The data of proxy variables such as R&D level, industrial structure level, government expenditure level, education and technology level, and economic development level are from the National Bureau of Statistics. The data of the digital financial development index is from The Peking University Digital Financial Inclusion Index of China (2011–2020). The data of proxy variables of foreign direct investment level and traditional financial development level are derived from each provincial and municipal statistical yearbook over the years. The data of proxy variables of the outward foreign direct investment’s level are obtained by statistical bulletins of China’s outward foreign direct investment.

3.2. Variables

3.2.1. Interpreted Variable

Industry Structure Level ($\mathrm{SL}$). Industry structure upgrading refers to the course or trend of changing industry structure from a lower form to a higher form due to technological upgrading [38]. Industry structure upgrading mainly refers to the reduction in the rate of the first industry and the increase in the rate of the third industry. In contrast, the role of the secondary industry cannot be ignored [13]. The industrial structure should be dominated by the tertiary industry, so the following indicators are used to measure the industry structure improvement and upgrade level of the YREB. The calculation formula of $\mathrm{SL}$ is: $\mathrm{SL}={\displaystyle \sum }_{\mathrm{i}=1}^3\mathrm{i}\times {\mathrm{x}}_{\mathrm{i}}$, where ${\mathrm{x}}_{\mathrm{i}}$ represents the added value of the ith industry to GDP rate as a percentage. The higher the value of $\mathrm{SL}$, the higher the level of regional industrial structure.

3.2.2. Explanatory Variables

By drawing on the available literature, we adopt the Peking University Digital Inclusive Finance Index to represent digital finance’s growth level in the YREB [39].

Total Digital Financial Development Index $\left(\mathrm{FI}\right)$. The index system and the specific calculation method of the index can be found in The Peking University Digital Financial Inclusion Index (2011–2020). The indicator constructs the digital finance index system from three aspects: digital finance coverage width, digital finance use depth, and digital degree. Specifically, the current digital inclusive financial index includes the above three dimensions and 33 specific indicators in total and describes the development level of digital finance in China from multiple dimensions. The specific meanings of three sub-indexes of digital finance are as follows:

Digital financial coverage width $\left(\mathrm{Width}\right)$. This indicator uses the amount of Alipay customers per 10,000 people, the percentage of Alipay card-tied users, and the average amount of bound bank cards per Alipay account to indicate the coverage level of digital finance.

Digital finance use depth $\left(\mathrm{Depth}\right)$. This indicator measures digital financial usage depth in terms of digital financial services’ actual usage, including the type and use of financial services.

Digital degree $\left(\mathrm{Digital}\right)$. This indicator shows the degree of digitalization of digital finance from four aspects: mobility, materialization, credit, and facilitation.

3.2.3. Mediating Variable

R&D Innovation level $\left(\mathrm{RD}\right)$. Referring to published literature, we find that digital finance can enhance industry structure upgrading and R&D innovation in the YREB, which can promote industry structure upgrading. Digital finance may promote upgrading the industrial structure of the YREB by improving the R&D innovation capability. Therefore, this paper takes R&D innovation as a mediating variable to study the impact of digital finance on the structural upgrade of industry of the YREB. We use the R&D funds-to-GDP ratio of industrial enterprises above the designated size in each province and city to represent $\mathrm{RD}$ in the YREB.

3.2.4. Control Variables

Referring to the existing literature, it is found that foreign direct investment, government expenditure level, traditional financial development level, etc., affect industry structure upgrading [40]. Therefore, we take foreign direct investment $\left(\mathrm{FDI}\right)$, government expenditure level $\left(\mathrm{Gov}\right)$, outward foreign direct investment $\left(\mathrm{OFDI}\right)$, and traditional financial development level $\left(\mathrm{TF}\right)$ as control variables. We use the actual foreign investment used to GDP ratio to indicate $\mathrm{FDI}$. The regional general budget expenses to GDP ratio is expressed as $\mathrm{Gov}$. The outward foreign non-financial direct investment to GDP ratio indicates $\mathrm{OFDI}$. Financial organizations’ total various deposits and credits to GDP ratio indicate the $\mathrm{TF}$ of each province and city. In addition, education provides an important guarantee for the quality of human resources and is an important factor affecting innovation and structural upgrades of industry. The higher quality of human resources, the more they can promote innovation and the structural upgrade of industry, and they play an essential role in innovation and industrial structure [20]. Therefore, this paper also uses education technology level ($\mathrm{ET}$) as a control variable. We take the ratio of the sum of local financial expenses on education technology of each province and city to the general financial budget expenditure to express $\mathrm{ET}$. The main variables are depicted in

Table 1. Main variables.

	Variable Name	Variable Description
$\mathrm{SL}$	Industrial Structure Level	${\displaystyle \sum }_{\mathrm{i}=1}^3\mathrm{i}\times {\mathrm{x}}_{\mathrm{i}}$ where ${\mathrm{x}}_{\mathrm{i}}$ represents value added of the first industry to GDP rate as a percentage
$\mathrm{FI}$	Total Digital Financial Development Index	Total digital financial development index.
$\mathrm{Width}$	Digital financial coverage width	Digital financial development subindex.
$\mathrm{Depth}$	Digital finance use depth	Digital financial development subindex.
$\mathrm{Digital}$	Digital degree	Digital financial development subindex.
$\mathrm{RD}$	R&D Innovation level	$\mathrm{R}\&\mathrm{D} \mathrm{expenditure} \mathrm{of} \mathrm{industrial} \mathrm{enterprises} \mathrm{above} \mathrm{the} \mathrm{scale}∕\mathrm{GDP}$ (%)
$\mathrm{FDI}$	Foreign Direct Investment	$\mathrm{Amount} \mathrm{of} \mathrm{foreign} \mathrm{investment} \mathrm{actually} \mathrm{used}\times \mathrm{Exchange} \mathrm{rate} \mathrm{of} \mathrm{the} \mathrm{current} \mathrm{year}∕\mathrm{GDP}$ (%)
$\mathrm{OFDI}$	Outward Foreign Direct Investment	$\mathrm{Outward} \mathrm{foreign} \mathrm{non}-\mathrm{financial} \mathrm{investment}\times \mathrm{Exchange} \mathrm{rate} \mathrm{of} \mathrm{the} \mathrm{current} \mathrm{year}∕\mathrm{GDP}$ (%)
$\mathrm{Gov}$	Government Expenditure Level	$\mathrm{General} \mathrm{budget} \mathrm{expenditure} \mathrm{of} \mathrm{local} \mathrm{finance}∕\mathrm{GDP}$ (%)
$\mathrm{ET}$	Education and Science and Technology Level	$\mathrm{Sum} \mathrm{of} \mathrm{local} \mathrm{fiscal} \mathrm{expenditure} \mathrm{on} \mathrm{education} \mathrm{and} \mathrm{science} \mathrm{and}$ $\mathrm{technology}∕\mathrm{General} \mathrm{budget} \mathrm{expenditure} \mathrm{of} \mathrm{local} \mathrm{finance}$
$\mathrm{TF}$	Traditional Financial Development Level	$\mathrm{Sum} \mathrm{of} \mathrm{balances} \mathrm{of} \mathrm{deposits} \mathrm{and} \mathrm{loans} \mathrm{of} \mathrm{financial}$ $\mathrm{institutions}∕\mathrm{GDP}$

.

4. Model Settings

4.1. Benchmark Regression Model

To study how digital finance affects upgrading the industry structure of the Yangtze River Economic Belt, this paper establishes the following benchmark regression model:

$${\mathrm{SL}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\beta }\mathrm{FI}}_{\mathrm{i},\mathrm{t}-1}+{\displaystyle \sum }{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{Control}}_{\mathrm{i},\mathrm{t}-1}^{\mathrm{j}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(1)

where $\mathrm{SL}$ denotes the regional industry structure level, and $\mathrm{FI}$ represents the overall index of digital financial development. $\mathrm{Control}$ denotes a set of control variables: $\mathrm{FDI}$, $\mathrm{Gov}$, $\mathrm{OFDI}$, $\mathrm{ET}$ and $\mathrm{TF}$. ${\mathsf{\alpha }}_0$ is a constant term, the subscript $\mathrm{i}$ denotes provinces, $\mathrm{t}$ denotes the year, ${\mathsf{\mu }}_{\mathrm{i}}$ denotes individual fixed effect, and ${\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$ represents the random error term. The coefficient $\mathsf{\beta }$ reflects the impact of digital finance on the structural upgrade of the industry of the YREB. To reduce the endogeneity caused by the correlation between the explanatory variables and the contemporaneous error term, all explanatory variables are lagged by one period.

4.2. Mediating Effect Model

For further research on digital finance and R&D innovation on the structural upgrade of industry of the YREB through relevant theoretical analysis, this paper sets R&D innovation as an intermediate variable and investigates the mediating effect of R&D innovation on digital finance and industrial structure upgrading. Referring to Luo et al., we adopt the three-step regression method to examine the mediating utility of R&D innovation [41]. The model established is as follows:

$${\mathrm{SL}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_1+{\mathsf{\beta }\mathrm{FI}}_{\mathrm{i},\mathrm{t}-1}+{\displaystyle \sum }{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{Control}}_{\mathrm{i},\mathrm{t}-1}^{\mathrm{j}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\omega }}_{\mathrm{i},\mathrm{t}}$$

(2)

$${\mathrm{RD}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_2+{\mathsf{\phi }\mathrm{FI}}_{\mathrm{i},\mathrm{t}-1}+{\displaystyle \sum }{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{Control}}_{\mathrm{i},\mathrm{t}-1}^{\mathrm{j}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(3)

$${\mathrm{SL}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_3+{\mathsf{\gamma }\mathrm{FI}}_{\mathrm{i},\mathrm{t}-1}+\vartheta {\mathrm{RD}}_{\mathrm{i},\mathrm{t}-1}+{\displaystyle \sum }{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{Control}}_{\mathrm{i},\mathrm{t}-1}^{\mathrm{j}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\xi }}_{\mathrm{i},\mathrm{t}}$$

(4)

where $\mathrm{SL}$ denotes the regional industry structure level, $\mathrm{FI}$ represents the overall index of digital financial development, and $\mathrm{RD}$ is the intermediate variable of the regional R&D innovation level. $\mathrm{Control}$ denotes a set of control variables: $\mathrm{FDI}$, $\mathrm{Gov}$, $\mathrm{OFDI}$, $\mathrm{ET}$ and $\mathrm{TF}$. ${\mathsf{\alpha }}_1$, ${\mathsf{\alpha }}_2$ and ${\mathsf{\alpha }}_3$ are constant terms, the subscript $\mathrm{i}$ denotes provinces, $\mathrm{t}$ denotes the year, ${\mathsf{\mu }}_{\mathrm{i}}$ denotes the individual fixed effect, and ${\mathsf{\omega }}_{\mathrm{i},\mathrm{t}}$, ${\mathsf{\xi }}_{\mathrm{i},\mathrm{t}}$ represents the random error term. In order to reduce the endogeneity caused by the correlation between the explanatory variables and the contemporaneous error term, all explanatory variables are lagged by one period.

The factor $\mathsf{\beta }$ of model (2) indicates the total effect of the explanatory variable $\mathrm{FI}$ on the dependent variable $\mathrm{SL}$ of the YREB, and the coefficient $\mathsf{\phi }$ in the model (3) reflects the influence of the explanatory variable $\mathrm{FI}$ on the mediating variable $\mathrm{RD}$ of the YREB. Model (4) adds the mediating variable R&D innovation level based on model (2). The coefficient $\mathsf{\gamma }$ represents the direct effect of the explanatory variable $\mathrm{FI}$ on the dependent variable $\mathrm{SL}$ of the YREB after controlling the mediating variable. In contrast, the coefficient $\vartheta$ represents the impact of the mediating variable $\mathrm{RD}$ on $\mathrm{SL}$ of the YREB after controlling the explanatory variable. We compare the relative size and significance of the estimated values of regression coefficients $\mathsf{\beta }$, $\mathsf{\phi }$, $\mathsf{\gamma }$ and $\vartheta$, and then calculate the estimated value of $\mathsf{\phi }\vartheta /\mathsf{\beta }$ will enable to assess the intermediary effect of $\mathrm{RD}$ and the influence of $\mathrm{FI}$ on the $\mathrm{SL}$ of the YREB.

5. Results

5.1. Descriptive Stats

The descriptive statistical results of the main variables are presented in

Table 2. Descriptive statistics of the main variables.

Variables	Sample Size	Mean	Std. Deviation	Minimum	Maximum
$\mathrm{SL}$	110	240.1036	10.8434	222.4913	274.9730
$\mathrm{FI}$	110	224.5697	100.5657	18.4700	431.9300
$\mathrm{Width}$	110	200.8319	98.7661	3.0600	395.2000
$\mathrm{Depth}$	110	231.6070	101.4792	27.5100	488.6800
$\mathrm{Digital}$	110	289.6935	125.2332	7.5800	462.2300
$\mathrm{RD}$	110	1.2231	0.5423	0.3143	2.3186
$\mathrm{FDI}$	110	2.0770	1.2783	0.1699	4.4991
$\mathrm{OFDI}$	110	0.5513	0.8281	0.0059	5.3703
$\mathrm{Gov}$	110	0.2224	0.0669	0.1206	0.4087
$\mathrm{ET}$	110	0.1927	0.0239	0.1337	0.2508
$\mathrm{TF}$	110	3.0739	0.9064	1.7329	6.2144

. From 2011 to 2020, digital finance in the YREB developed rapidly. From the descriptive statistical results of the sub-indicators of the digital financial evolution, it is clear that the mean value of $\mathrm{Digital}$ is the largest, the mean of $\mathrm{Depth}$ is the second, and the mean value of $\mathrm{Width}$ is the smallest. This shows that digital degree is the main source of the growth of the overall digital financial development index, the depth of digital finance use has a relatively small contribution to the growth of the overall digital financial development index, and the contribution of the digital financial coverage width is the smallest. In addition, the digital finance growth overall index and sub-index standard deviation are very large, indicating that the heterogeneity of the digital finance advancement level of the YREB is serious. The standard deviation of the industrial structure level is relatively large, indicating that the industrial structure of the YREB is developing unevenly.

5.2. Regression Result Analysis

5.2.1. Benchmark Regression Analysis

According to the benchmark regression model, we conducted the Hausman test, which indicates that $\mathrm{Prob}>\mathrm{chi}2=0.0000$ and the p-value was less than 5%. The test results indicate that we should select the fixed effects model. Therefore, we use LSDV of the fixed effect model to study how digital financial advancement affects the structural upgrade of industries of the YREB. At the same time, to make the results more reliable, this paper uses the clustering robust standard error. See

Table 3. Benchmark regression results.

Variables	(1)	(2)	(3)	(4)	(5)
$\mathrm{L}.\mathrm{FI}$	0.0447 *** (0.0049)	0.0430 *** (0.0047)
$\mathrm{L}.\mathrm{Width}$			0.0445 *** (0.0045)
$\mathrm{L}.\mathrm{Depth}$				0.0411 *** (0.0062)
$\mathrm{L}.\mathrm{Digital}$					0.0252 *** (0.0051)
$\mathrm{L}.\mathrm{FDI}$		0.2697 (0.5690)	0.1723 (0.5400)	0.1032 (0.5577)	−0.2327 (0.7227)
$\mathrm{L}.\mathrm{OFDI}$		−0.1828 (0.2225)	−0.2187 (0.2466)	−0.2167 (0.2735)	−0.2373 (0.4901)
$\mathrm{L}.\mathrm{Gov}$		53.9306 ** (20.6137)	69.1642 *** (21.3654)	52.0613 ** (20.3281)	23.5872 (21.6710)
$\mathrm{L}.\mathrm{ET}$		−5.9463 (21.5321)	4.4305 (18.6391)	−21.9248 (25.4965)	−4.6000 (30.3731)
$\mathrm{L}.\mathrm{TF}$		1.6294 (1.6877)	1.8549 (1.5679)	3.2832 (2.1297)	3.5509 (2.3694)
$\_\mathrm{cons}$	256.5690 *** (1.2840)	237.3625 *** (8.5300)	231.8131 *** (8.3473)	232.3975 *** (8.2574)	239.9542 *** (10.9884)
$\mathrm{Province}$	Yes	Yes	Yes	Yes	Yes
$\mathrm{N}$	99	99	99	99	99
${\mathrm{R}}^2$	0.9628	0.9693	0.9709	0.9660	0.9482

Notes: Clustering robust standard errors in parentheses; ** and *** indicate that the regression coefficients are significant at the 5% and 1% levels, respectively.

for regression results.

Columns (1) and (2) of Table 3 are regressions of $\mathrm{FI}$, and columns (3)–(5) are regressions for the three sub-indexes, where (1) indicates the regression results of adding only explained variables and explanatory variables without adding control variables after controlling for fixed effects of provinces, and (2) indicates the regression results of including control variables in model (1). The results indicate that the regression coefficients of explanatory variables are positive and significant at a 1% significance level whether control variables are added or not. This shows that the evolution of digital finance contributes significantly to industry structure upgrading in the YREB. Columns (3)–(5), respectively, show the regression results for $\mathrm{Width}$, $\mathrm{Depth}$, and $\mathrm{Digital}$. The regression coefficients are all positive with a 1% significance level, which proves that the evolution of digital finance facilitates the upgrade of the industrial structure in the YREB. In addition, the regression coefficients of (3) and (4), 0.0445 and 0.0411, are larger than those of column (5), 0.0252, indicating that the $\mathrm{Width}$ and $\mathrm{Depth}$ have a greater contribution to industrial structure upgrades in the YREB. In contrast, $\mathrm{Digital}$ has a relatively weak role in promoting the structural upgrade of industry. Moreover, most of the regression coefficients of $\mathrm{Gov}$ are positive, indicating that the government expenditure level contributed significantly to upgrading the industrial structure. The possible reason why digital finance can contribute to industrial structure upgrades in the YREB is that the digital finance depends on the advancement of digital technology. With the continuous development of digitizing technology, government and enterprises focus on digital management. The increasing degree of digitalization of various industries promotes digital finance’s continuous evolution and further promotes industrial structure upgrades.

5.2.2. Regional Heterogeneity Analysis

The degree of digital finance growth in different parts of the YREB might be different. To further explore how digital finance affects industry structure upgrading in different regions of the YREB, this paper conducts a regression analysis for the upstream (Chongqing, Sichuan, Guizhou, Yunnan), midstream (Jiangxi, Hubei, Hunan), and downstream (Shanghai, Jiangsu, Zhejiang, Anhui) regions of the YREB. The regression results are shown in

Table 4. Heterogeneity test results 1.

Variables	${{F}{I}}^{\prime }={F}{I}$			${{F}{I}}^{\prime }={W}{i}{d}{t}{h}$
	(1–1)	(2–1)	(3–1)	(1–2)	(2–2)	(3–2)
$\mathrm{L}.\mathrm{FI}\prime$	0.0308 ** (0.0090)	0.0489 ** (0.0078)	0.0398 *** (0.0038)	0.0302 ** (0.0089)	0.0489 ** (0.0079)	0.0436 *** (0.0052)
$\_\mathrm{cons}$	217.1454 *** (22.0974)	212.9970 *** (12.9470)	242.4388 *** (15.4794)	212.5970 *** (22.7396)	207.8195 *** (10.0563)	236.7343 *** (14.6936)
$\mathrm{Control}$	Yes	Yes	Yes	Yes	Yes	Yes
$\mathrm{Province}$	Yes	Yes	Yes	Yes	Yes	Yes
$\mathrm{N}$	36	27	36	36	27	36
${\mathrm{R}}^2$	0.8661	0.9340	0.9914	0.8652	0.9326	0.9927

Notes: Clustering robust standard errors in parentheses; ** and *** indicate that the regression coefficients are significant at the 5% and 1% levels, respectively.

and

Table 5. Heterogeneity test results 2.

Variables	${{F}{I}}^{\prime }={D}{e}{p}{t}{h}$			${{F}{I}}^{\prime }={D}{i}{g}{i}{t}{a}{l}$
	(1–3)	(2–3)	(3–3)	(1–4)	(2–4)	(3–4)
$\mathrm{L}.\mathrm{FI}\prime$	0.0406 *** (0.0068)	0.0465 ** (0.0062)	0.0297 *** (0.0045)	0.0058 (0.0099)	0.0371 ** (0.0062)	0.0236 *** (0.0035)
$\_\mathrm{cons}$	208.8356 *** (18.6010)	204.0040 *** (12.9584)	225.4627 *** (17.6091)	233.4331 *** (19.8773)	225.0550 *** (16.5795)	233.4372 *** (16.8697)
$\mathrm{Control}$	Yes	Yes	Yes	Yes	Yes	Yes
$\mathrm{Province}$	Yes	Yes	Yes	Yes	Yes	Yes
$\mathrm{N}$	36	27	36	36	27	36
${\mathrm{R}}^2$	0.8960	0.9219	0.9852	0.8246	0.8970	0.9861

Notes: Clustering robust standard errors in parentheses; ** and *** indicate that the regression coefficients are significant at the 5% and 1% levels, respectively.

.

The first three columns of data in Table 4 are the results of the influence of $\mathrm{FI}$ on $\mathrm{SL}$ in the upstream, midstream and downstream of the YREB, respectively. The last three columns of data are the findings of the effect of $\mathrm{Width}$ on $\mathrm{SL}$ in the upstream, midstream, and downstream of the YREB, respectively. The first three columns in Table 5 are the regression findings of the influence of $\mathrm{Depth}$ on $\mathrm{SL}$ in different parts of the YREB, and the last three columns are the regression findings of the influence of $\mathrm{Digital}$ on $\mathrm{SL}$.

The regression coefficients of explanatory variables in Table 4 and Table 5 are positive. In addition, the regression coefficients of columns (1–1)–(2–1) and (1–2)–(2–2) in Table 4 satisfy the 5% significance test, and the regression coefficient of column (2–1) is larger than that of column (1–1). Furthermore, the regression coefficients in columns (3–1) and (3–2) satisfy the 1% significance test. This means $\mathrm{FI}$ and $\mathrm{Width}$ have a more significant influence on $\mathrm{SL}$ downstream of the YREB, followed by the middle reaches and the least impact on the upstream. In Table 5, the regression coefficients in columns (2–3) satisfy the 5% significance test, the regression coefficients in columns (1–3) and (3–3) both pass the significance test of 1%, and the coefficient value in column (1–3) is larger than that in column (3–3). This shows that the $\mathrm{Depth}$ has the most significant influence on $\mathrm{SL}$ upstream of the YREB, followed by the downstream and the middle reaches. In Table 5, the regression coefficients in columns (2–4) are positive and significant at a 5% significance level, and those in columns (3–4) pass the significance test of 1%. This means that the $\mathrm{Digital}$ is more significantly influencing $\mathrm{SL}$ downstream of the YREB, followed by the middle reaches and the least impact on the upstream. In a word, there is heterogeneity in the impact of digital finance on industry structure upgrades in diverse parts of the YREB. The development of digital finance downstream of the YREB, where the degree of digital finance is the best, has the largest driving effect on industry structure upgrading, followed by the middle reaches and the upstream.

5.2.3. Mediating Effect Analysis

This paper takes the R&D innovation level $\left(\mathrm{RD}\right)$ as the mediating variable and studies whether to push the YREB industry structure to upgrade by improving the R&D innovation level in the course of digital finance driving industry structure upgrades of the YREB. According to the mediating effect model established in Section 4.2, the results are shown in

Table 6. Mediating effect test results.

Variables	(1)	(2)	(3)
$\mathrm{L}.\mathrm{FI}$	0.0430 *** (0.0047)	0.0012 *** (0.0003)	0.0324 *** (0.0055)
$\mathrm{L}.\mathrm{RD}$			8.0837 * (4.0513)
$\_\mathrm{cons}$	237.3625 *** (8.5300)	1.2830 *** (0.4507)	229.3609 *** (7.0449)
$\mathrm{Control}$	Yes	Yes	Yes
$\mathrm{Province}$	Yes	Yes	Yes
$\mathrm{N}$	99	99	99
${\mathrm{R}}^2$	0.9693	0.9799	0.9730

Notes: Clustering robust standard errors in parentheses; * and *** indicate that the regression coefficients are significant at the 10% and 1% levels, respectively.

.

The regression coefficient of column (1) of the $\mathrm{FI}$ in Table 6 is significantly positive at a 1% significance level, which indicates the driving effect of $\mathrm{FI}$ on the $\mathrm{SL}$ of the YREB. The coefficient of column (2) of the $\mathrm{FI}$ is also significantly positive at a 1% significance level, indicating a facilitating effect of $\mathrm{FI}$ on $\mathrm{RD}$ in the YREB. Column (3) of the $\mathrm{FI}$ is positive at a 1% significance level, and the $\mathrm{RD}$ is markedly positive at a 10% significance level. It shows that both $\mathrm{FI}$ and $\mathrm{RD}$ contribute to the industry structure upgrading of the YREB. We calculate $\mathsf{\phi }\vartheta /\mathsf{\beta }=22.56\%$. That is, the intermediary role of $\mathrm{RD}$ in the promotion of $\mathrm{SL}$ in the YREB by digital finance is 22.56%. The regression results prove that R&D innovation has some mediating effects in the course of digital finance, contributing to the industry structure upgrade of YREB.

5.2.4. Robust Test

As the upgrade of the industrial structure of the YREB is influenced by many factors, we cannot consider all of them. In order to ensure the robustness of the research results, OLS regression and tail reduction (1% and 99% tail reduction) are adopted to test the robustness of the model. It is proved that digital finance is able to improve the industry structure upgrade of the YREB. The specific regression findings are given in

Table 7. Robustness test results: OLS regression.

Variables	(1)	(2)	(3)	(4)
$\mathrm{L}.\mathrm{FI}$	0.0317 *** (0.0054)
$\mathrm{L}.\mathrm{Width}$		0.0340 *** (0.0061)
$\mathrm{L}.\mathrm{Depth}$			0.0331 *** (0.0046)
$\mathrm{L}.\mathrm{Digital}$				0.0183 *** (0.0045)
$\_\mathrm{cons}$	227.5902 *** (8.4298)	227.9363 *** (8.5713)	227.6356 *** (8.3599)	228.1328 *** (8.1812)
$\mathrm{Control}$	Yes	Yes	Yes	Yes
$\mathrm{Province}$	Yes	Yes	Yes	Yes
$\mathrm{N}$	99	99	99	99
${\mathrm{R}}^2$	0.8753	0.8774	0.8773	0.8568

Notes: Clustering robust standard errors in parentheses; *** indicates that the regression coefficient is significant at the 1% level.

and

Table 8. Robustness test results: tailing treatment.

Variables	(1)	(2)	(3)	(4)
$\mathrm{L}.\mathrm{FI}$	0.0429 *** (0.0045)
$\mathrm{L}.\mathrm{Width}$		0.0442 *** (0.0043)
$\mathrm{L}.\mathrm{Depth}$			0.0407 *** (0.0062)
$\mathrm{L}.\mathrm{Digital}$				0.0251 *** (0.0049)
$\_\mathrm{cons}$	237.0046 *** (8.7988)	231.8236 *** (8.6279)	231.6572 *** (8.4116)	239.0186 *** (10.9312)
$\mathrm{Control}$	Yes	Yes	Yes	Yes
$\mathrm{Province}$	Yes	Yes	Yes	Yes
$\mathrm{N}$	99	99	99	99
${\mathrm{R}}^2$	0.9683	0.9697	0.9643	0.9471

Notes: Clustering robust standard errors in parentheses; *** indicates that the regression coefficient is significant at the 1% levels.

. The regression findings reveal this: the regression factors of $\mathrm{FI}$, $\mathrm{Width}$, $\mathrm{Depth}$ and $\mathrm{Digital}$ are significantly positive at a 1% significance level. It is in accordance with the benchmark regression findings that digital finance is capable of promoting industry structure upgrading of the YREB. Therefore, the empirical findings of this paper are robust.

6. Conclusions

We study how digital finance influences the industry structure upgrades of the YREB by building a fixed effect model and a mediating effect model, using panel data of provinces and cities from 2011 to 2020. The robustness of the empirical results is tested. The main research results include the following:

(1) The evolution of digital finance has a significant positive influence on industry structure upgrading of the YREB. That is, digital finance can promote industry structure upgrades of the YREB to some extent. Compared with previous studies, this paper selects the YREB as the research area and analyzes specific problems in detail to promote the development of the YREB and provide a reference for the development of digital finance and industrial structure in China. The regression findings of LSDV in the fixed effect model in this paper show that, for $\mathrm{FI}$,$\mathrm{Width}$,$\mathrm{Depth}$, and $\mathrm{Digital}$, the regression coefficients are all positive at a 1% significance level. This means that digital finance is capable of promoting industry structure upgrading of the YREB, in which the promotion of $\mathrm{Width}$ and $\mathrm{Depth}$ is greater, while the promotion of $\mathrm{Digital}$ is relatively weak. Therefore, to facilitate the structural upgrading of industries of the YREB, attention should be paid to the growth of digital finance as a means to drive structural upgrading of the industry.

(2) The utility of digital finance in promoting an enhanced industry structure of the YREB has regional heterogeneity. Similar to most previous studies, regional heterogeneity is also analyzed in this paper. The difference is that the conclusion of this paper is more aimed at the specific region of the YREB. Regional digital finance with a higher digital finance development level is more obvious in driving industry structure upgrading. This means that the promotion of digital finance for industry structure upgrading is the greatest downstream of YREB, next to midstream and upstream. Therefore, to improve the industrial structure of the YREB, we should promote the advancement of digital finance and also focus on continuously improving the overall comprehensive capacity of the region and promoting coordinated digital finance growth.

(3) Digital finance can contribute to the industry structure upgrading of the YREB by facilitating the R&D innovation level. Few previous studies have studied the impact of digital finance on industrial structure upgrading from the perspective of R&D innovation. We use the three-step regression method to test the mediating effect of R&D innovation in the utility of digital finance’s influence on the structural upgrade of industries of the YREB. The regression findings reveal the total digital financial index $\left(\mathrm{FI}\right)$ and R&D innovation level $\left(\mathrm{RD}\right)$ coefficients are remarkably positive. It shows the role of digital finance in driving the R&D innovation of the YREB, and both digital finance and R&D innovation can promote the industrial upgrading of the YREB. In addition, digital finance is capable of promoting R&D innovation levels of the YREB, thereby driving up the industry structure of the YREB. Through calculation, the mediating effect of the R&D innovation level is 22.56%. Therefore, to facilitate YREB’s industry structure upgrade, it is necessary to vigorously develop digital finance and increase R&D innovation to enhance the YREB industry’s digitalization level and thus enhance the industrial structure’s level.

Based on the above empirical analysis results, to push forward industry structure upgrading of the YREB, we propose the following suggestions:

• Accelerate digital finance growth and improve the construction of the digital financial system. Digital information technology should be widely applied to drive the shift from conventional finance to digital finance. The application of digital finance should be increased to drive the shift of industries in the YREB from primary to tertiary industries. At the same time, the supervision of digital finance should be strengthened to ensure the security and stability of digital finance to improve the public’s trust in it and promote its growth. In addition, the digital financial system’s construction of the YREB should be continuously improved to optimize the service efficiency and enhance the service function of the digital financial system. Accelerating the digital finance growth of the YREB will thereby promote industry structure upgrading of the YREB.
• Promoting coordinated regional development. Because of the unbalanced growth of digital finance and industrial structure in various regions of the YREB, it is necessary to raise the digital finance development level of each region based on local conditions, improve the promotion effect of digital finance on industry structure upgrading, and accelerate the development of the industry structure level in the upper reaches of the YREB with low development levels. In addition, it is necessary to improve the communication of digital finance in all regions of the YREB, reasonably guide the flow of digital financial resources from developed to less developed regions, form a sound digital financial transmission mechanism, and promote the coherent regional growth of digital finance. Meanwhile, the increase in public investment in the YREB will attract more high-tech industries, form a high-tech industry agglomeration, and raise the level of industrial structure.
• Pay attention to R&D innovation and enhance the R&D innovation level. Strengthen the training of talents [42], formulate relevant talent incentive policies, and improve the regional R&D innovation capacity. Improve the existing intellectual property protection mechanism, strengthen intellectual property protection, and provide a favorable environment for R&D innovation. Thus, improve the R&D innovation ability of the YREB, enhance the digital innovation ability of the digital finance industry, and try to make the best use of digital finance to promote R&D innovation. This will escort the transmission mechanism of “digital finance growth–R&D innovation– industrial structure upgrading”, making the industry structure upgrading in the YREB more reasonable and effective.

This paper studies digital finance’s influence on industry structure upgrading of the YREB in terms of R&D innovation. However, some shortcomings still exist: (1) We make a preliminary investigation of the association of digital finance and industry structure upgrades in the YREB. Still, it needs further exploration. (2) The variable metrics set in this paper may have defects, and the selected control variables are limited, so the regression results of the model may deviate from the actual situation. Finally, future research can investigate the influence of digital finance on industrial structure upgrading and its impact on economic resilience from a spatial perspective to better promote regional digital finance growth and the upgrade of industrial structure, improve economic resilience and facilitate the high-quality growth of the economy.