Evaluation of the Mechanism and Effectiveness of Digital Inclusive Finance to Drive Rural Industry Prosperity

Abstract

Digital inclusive finance is an important policy for promoting rural industrial development in China. Can digital inclusive finance effectively contribute to the development of rural industries? What is its driving mechanism? Clarifying these questions can: (1) Release the value of digital inclusive finance; (2) Strengthen its driving role in rural industrial development; and (3) Implement the concept of inclusive development to give rural industrial subjects a greater sense of access and well-being. Based on the interpretation of the connotation of rural industrial prosperity, this paper constructs a theoretical framework of “digital inclusive finance—alleviating financing constraints—rural industrial prosperity”. Using the panel data of 31 provinces in China from 2011 to 2020, the study adopts the entropy weight method to measure the rural industry prosperity index; at the same time, it constructs a panel regression model to conduct benchmark tests, mediating effect tests and heterogeneity tests on the mechanisms and effects of digital inclusive finance to promote rural industrial prosperity. The article finds that digital inclusive finance effectively drives rural industrial prosperity; the alleviation of financing constraints plays a fully mediating role; the effect is significant in less economically developed regions. Therefore, this article proposes policy recommendations to advance rural digital construction, promote the digital transformation of traditional financial institutions, strengthen the regulation of internet-inclusive finance, and enhance the financial literacy of inclusive groups.

1. Introduction

China is currently deploying comprehensive rural revitalization. The “Opinions on Implementing the Rural Revitalization Strategy” in 2018 emphasized that industrial prosperity is the focus of rural revitalization. Furthermore, “Opinions on doing a good job in promoting the key work of rural revitalization in 2022” emphasizes “focusing on industry to promote rural development.” Therefore, industrial prosperity has become the top priority of China’s rural revitalization strategy. However, the rural industry is facing a series of problems such as insufficient vitality of industrial factors, short industrial chain and weak industrial infrastructure. Finance is the core of modern economy. However, it is an indisputable fact that rural industries, especially small, medium and micro enterprises in agriculture, are subject to ‘financial discrimination’ in practice.

Digital inclusive finance, as a new industry where digital technology and inclusive finance merge, is the highest stage of inclusive finance development. It enables broad and unhindered access to financial services for all segments and groups of society [1]. With the comprehensive advancement of the digital economy, since 2015, digital inclusive finance has gained rapid growth in China, providing a large amount of savings, investment and capital aggregation opportunities for economic agents [2], which has penetrated various financial fields, such as payment, financing, investment and risk management, and has become the mainstream of current inclusive financial development. The China County Digital Inclusive Finance Development Index Report, 2022 shows that the overall development level of China’s digital inclusive finance continues to increase and is becoming an effective supplement to county rural finance, which is released by the Institute of Rural Development of the Chinese Academy of Social Sciences. Chinese governments at all levels have made digital inclusive finance an important institutional arrangement to promote the development of rural industries. Can digital inclusive finance effectively empower rural industrial development? If so, what are the specific driving mechanisms? Is there regional heterogeneity? Study on these issues can provide a theoretical basis for the formulation of policies related to rural industrial prosperity, highlight the inclusive spirit of digital inclusive finance, and give rural industrial operators a greater sense of access and well-being.

In order to clarify the above problems, based on the interpretation of the connotation of rural industrial prosperity, this paper combs the theoretical mechanism of digital inclusive finance driving the prosperity of rural industries. Then, it conducts a benchmark test, mediating effect test and heterogeneity test on the effect of digital inclusive finance on the prosperity of rural industries. Additionally, it puts forward relevant policy recommendations. The possible contribution of this article is to comprehensively and systematically evaluate the impact of digital inclusive finance on rural industrial prosperity. Specifically, first, based on the financial institution level, the mechanism of digital inclusive finance on rural industrial prosperity is systematically analyzed from the perspective of alleviating financing constraints, constructing a “digital inclusive finance-financing constraint-rural industrial prosperity” theoretical framework. Second, in the evaluation dimension of rural industrial prosperity, it has certain innovation and constructs the index system of China’s rural industrial prosperity index. Third, the study found that digital inclusive finance actively drives rural industrial prosperity through the complete mediating effect of alleviating financing constraints. This effect is significant in economically underdeveloped areas. The reasons for forming heterogeneity effects are explained from the perspectives of underdeveloped areas’ emphasis on rural industrial prosperity, digital rural construction, rural industry’s financing needs, and China’s financial market structure characteristics.

2. Literature Review

Scholars mainly conduct research on the effect of digital inclusive finance on rural industry development in terms of influencing mechanisms, overall effects, and heterogeneity effects.

Firstly, research on the impact mechanism of digital inclusive finance on rural industries can be divided into analyses based on financing and industry perspectives; (1) In the financing perspective, it can be further divided into two levels of research: financial institutions and inclusive groups. Research at the financial institution level suggests that digital inclusive finance, relying on digital technology to provide “hard information” [3] on the creditworthiness of economic entities, to a certain extent, solves the problem of information asymmetry between borrowers and lenders [4], improves the efficiency of financial institutions’ risk management, reduces transaction costs and collateral requirements [5], overcomes the spatial limitations of traditional financial institutions, realizes precise and convenient access to customers, effectively improves the accessibility and inclusiveness of financial services, provides opportunities for capital aggregation for economic entities, and solves the problem of financing difficulties in the development of digital agriculture [6]. Research at the inclusive group level suggests that improving financial literacy increases the accessibility of digital financial products and services, enhances the possibility of financial access, and thus, contributes to the welfare of inclusive finance and the value of financial innovation [7]; (2) In the industry perspective, research suggests that digital inclusive finance, through promoting technological innovation, improving the level of agricultural modernization, and establishing risk-sharing mechanisms [8], has opened up the “last mile” of financial services to agriculture and rural areas in a low-cost, convenient, and sustainable manner [9], providing sufficient capital elements for agricultural development [10], realizing the integration of digital technology and agricultural production [11]. In addition, the empirical verification of the impact mechanism focuses on variables such as technological innovation [8] and the difference between non-agricultural economic activity efficiency and agricultural production efficiency [12].

Secondly, regarding the overall effect of digital inclusive finance on rural industries, two types of data can be identified based on digital characteristics: microdata from the China Household Finance Survey [7] and the Rural Inclusive Finance Survey from China Agricultural University [13], as well as provincial panel data [10,14,15]. With regard to empirical models, scholars have selected diverse models that match the research objectives and data characteristics, including the difference-in-differences model [10], linear probability model [11], spatial econometric model and threshold model [15], panel regression model [16] and the SARAR model [17]. In terms of empirical results, most scholars’ research shows that digital inclusive finance has good inclusiveness effects, such as positively driving rural tertiary industry integration [8], effectively enhancing rural entrepreneurial activity [13], improving agricultural mechanization [15] and directly reducing carbon intensity [17]. However, Matthews [7] questions the research hypothesis of the financial inclusiveness vision of digital finance, arguing that about one billion of the world’s poorest adults are excluded from formal finance due to cultural and digital limitations, and therefore, cannot access digital finance. Thus, he believes that the agricultural benefits of digital inclusive finance are extremely limited.

Thirdly, regarding the study of the heterogeneous effects of digital inclusive finance driving agricultural development, scholars generally agree that the spatial heterogeneity of the effects of digital inclusive finance driving agricultural development is caused by the differences in education, digital infrastructure, and traditional financial development [17]. Some scholars believe that digital inclusive finance has a stronger inclusive effect on relatively strong inclusive groups and regions, such as provinces and cities with higher levels of integration of the third industry in rural areas [8] and economically developed regions in eastern China [14,15]. However, another group of scholars holds a different view, arguing that relatively weak inclusive groups, such as those with lower human, material and social capital [13], agricultural backward counties, plain counties and agricultural counties [15] and families in third- and fourth-tier cities [18], are more likely to benefit from digital inclusive finance.

In summary, the current research provides good theoretical and methodological insights for this article, but it does not directly address the relationship between digital inclusive finance and rural industrial prosperity, leaving room for further expansion. Firstly, although the article discusses the important role of alleviating financial constraints in driving rural industrial development through digital inclusive finance, it lacks systematicity. Secondly, there is some controversy over the empirical research conclusions on the effects of digital inclusive finance on rural industrial development, whether it is overall effects or heterogeneity effects that need further verification.

3. Theoretical Framework and Research Hypothesis

3.1. Interpretation of the Connotation of the Prosperity of Rural Industries

Relevant studies on the connotation of rural industry [19] concluded that agriculture is the basic industry of the countryside, and the root of industrial prosperity lies in agricultural prosperity [20]. Scholars generally agree that the core of rural industrial prosperity is to focus on agricultural development and to form a new pattern of rural industrial development with a well-developed industrial support system, a perfect industrial structure and significantly improved industrial functions. This development is divided into the following aspects.

First, the industrial support system is developed. The industrial support system consists of elements such as support subjects, production factors and environment [20]. Support subjects can be divided into two categories: government and market. Among them, the government mainly focuses on government industrial policies and financial inputs [21], and makes efforts on the top-level design and layout of talent, capital, science, technology and other institutional levels to form institutional and policy advantages; in the market, the main point is to focus on market access [22], capital investment, talent and business model innovation, deepen the degree of agricultural scale and organization and industrial cluster development [23] and form industrial advantages. Therefore, the rural industrial prosperity support system is a system of agricultural research and development, talent and technological innovation, as well as other factor inputs and innovation driven by the government and the market.

Second, the industrial structure is upgrading. Industrial prosperity requires rural industrial structure to develop from a low-level state to a high-level state, and realize the rationalization and advanced industrial structure [24], focusing on the integration of three rural industries and the extension of the agricultural industry chain [25]. Among them, the integration of three rural industries is relying on agricultural resources to promote the cross-fertilization and penetration of one, two and three rural industries, and to give birth to new agricultural models and new business models. The extension of the agricultural industry chain refers to the expansion of the whole agricultural industry chain and the enhancement of the agricultural value chain.

Third, the industrial function is enhanced. The value of prosperous rural industries lies in the in-depth excavation of multiple functions of agriculture. As human society leaps from agricultural civilization to industrial civilization and then to ecological civilization, modern agriculture inherits and innovates traditional agricultural functions and breaks through the traditional agricultural pursuit of food function and raw material function. Therefore, the economic, social and ecological functions of rural industrial prosperity in helping industrial poverty alleviation, promoting agricultural innovation and improving agricultural ecology are increasingly prominent [26]. This paper argues that rural industrial function enhancement lies in realizing the comprehensive enhancement of agricultural ecological, economic and social benefits.

3.2. The Driving Mechanism of Digital Inclusive Finance for Rural Industry Prosperity

Based on the consideration that digital inclusive finance has digital and inclusive characteristics, this paper analyzes its theoretical mechanism of driving the prosperity of rural industries.

3.2.1. Inclusive Innovation Eases Rural Industry Financing Constraints

Digital inclusive finance is a comprehensive concept that extends the “breadth” and “depth” of traditional finance [27]. The “breadth” is the availability of digital inclusive finance, which measures the coverage of digital inclusive financial services. The “depth” is the degree of diversification of financial resources and financial industry, such as financial institutions and financial products. Digital inclusive finance eases the financing constraints of rural industries by expanding the breadth and depth of digital financial inclusion. First, there are innovations in internet-inclusive finance. Many internet-inclusive financial products with wide coverage, variety, low cost and convenience, such as P2P online lending, internet insurance and crowdfunding, were born, expanding the coverage of digitally inclusive financial services [28]. Second, traditional financial institutions are inclusive of innovation. Digital financial models relying on digital technologies indirectly increase the cost of bank credit funding, exacerbate commercial bank risks, intensify competition among traditional financial institutions, and drive them to enhance business upgrading and transformation [29,30]. The inclusive innovation of the internet and traditional finance together strengthen the breadth and depth of digital inclusive finance to meet the multi-level and diversified financial needs of rural industries.

3.2.2. Digital Innovation Eases Rural Industry Financing Constraints

Relying on digital technology innovations such as artificial intelligence, big data, distributed technologies and blockchain, financial institutions have been able to mine and analyze massive amounts of data at a lower cost, leading the digital transformation of financial inclusion and improving the total factor productivity of digital financial inclusion [31]. This is manifested as follows: first, digital innovation establishes a dynamic credit assessment and risk monitoring system, improves risk management capabilities, addresses information asymmetry [32], circumvents adverse selection and moral hazard [33], and enhances financial institutions’ willingness to supply finance. Second, these technologies enable the matching of inclusive finance with industrial projects [34] and the precise placement of financial information and financing methods needed for the prosperity of rural industries. Third, it shrinks several costs of inclusive financial services for traditional financial institutions, such as reducing the reliance of financial institutions on cash and paper records, which in turn reduces the operating costs of financial institutions [35] and enables them to provide low-cost financial services to rural industries.

In summary, the internet and traditional financial inclusion innovations work together to strengthen the breadth and depth of digital financial inclusion and meet the multi-level and diversified financial needs of rural industries. Inclusive and digital innovations in digital inclusive finance enhance the willingness of financial supply, promote accurate matching of inclusive finance to rural industries, and reduce costs for financial institutions. At the same time, digital inclusive finance eases the financing constraint of rural industries, provides financial driving force for talent, technology, capital and other factors to gather in rural industries, and drives the advanced and rationalization of rural industries. These features help rural industries to realize their functions, and eventually, realize the prosperity of rural industries. Accordingly, this paper constructs a driving mechanism framework of “digital inclusive finance - alleviation of financing constraints - rural industrial prosperity” (Figure 1).

In view of the above theoretical analysis, this paper proposes the following research hypotheses:

Hypothesis 1 (H1).

Digital financial inclusion has actively driven rural industrial prosperity.

Hypothesis 2 (H2).

Alleviating financing constraints is a mediating factor for digital inclusive finance to drive the prosperity of rural industries.

3.3. Digital Financial Inclusion Drives Rural Industrial Prosperity with Regional Economic Heterogeneity

Practical and empirical theoretical research of digital inclusive finance shows significant heterogeneity in the effects of digital inclusive finance in promoting rural consumption upgrading, innovation development and economic growth [36]. Regions with high levels of economic development have better rural industrial and digital foundations. In addition, rural industrial agents in regions with high levels of economic development have richer financial knowledge and better digital literacy. The combination of these two factors makes it easier for rural industries in regions with high economic development to access digital inclusive financial services, which in turn leads to a stronger driving effect of digital inclusive finance in the region. Accordingly, Hypothesis 3 is presented as follows:

Hypothesis 3 (H3).

The effect of digital inclusive finance to empower rural industries to thrive is stronger in areas with high economic levels than in areas with low economic development.

4. Research Design

4.1. Model Construction

In order to study the impact of digital inclusive finance on the prosperity of rural industries and verify the existence of the driving mechanism of “digital inclusive finance—alleviation of financing constraints—prosperity of rural industries”, Panel Regression Model (1) is constructed based on the theoretical model in Figure 1 to investigate the direct influence of digital inclusive finance on the prosperity of rural industries.

$$Industr{y}_{it}=a_0+a_{1}Inclusiv{e}_{it}+Contro{l}_{it}+{\theta }_i+{\mu }_i+{\epsilon }_{it}$$

(1)

where $Industr{y}_{it}$ denotes the rural industry prosperity index of region i in year t; $Inclusiv{e}_{it}$ denotes the number of region i in year t. $Contro{l}_{it}$ denotes the control variable; ${\theta }_i$and ${\mu }_i$ denote region and year fixed effects, respectively, and ${\epsilon }_{it}$ is used as the random error term.

4.2. Variable Selection

4.2.1. Dependent Variable

Based on the previous interpretation of the connotation of rural industrial prosperity, this paper refers to the relevant research results of rural industrial development, and at the same time, considers the availability of data; we construct a rural industrial prosperity index system from the three dimensions of the developed rural industrial support system, industrial structure upgrading and industrial function (see

Table 1. Rural industrial prosperity evaluation index system.

Dimension	Principle Indicators	Secondary Indicators	Indicator Description
Rural industrial support system development	Technology	Agricultural R&D input intensity	Agricultural R&D expenditure/regional agricultural output
	Finance	The total amount of investment in fixed assets completed by the local agricultural reclamation	Total completed investment in fixed assets of agricultural reclamation
	Human resources	Proportion of the population with a university degree or higher to the population over 6 years of age	Population with university degree or above/Population over 6 years old
Agricultural industry structure upgrade	Integration of three rural industries	The added value of tertiary industry in each region/the added value of secondary industry in each region	The added value of tertiary industry in each region/the added value of secondary industry in each region
		The increased value of the tertiary industry in each region + the added value of the secondary industry in each region the added value of the primary industry in each region of farming and reclamation	The increased value of the tertiary industry in each region + the added value of the secondary industry in each region/the added value of the primary industry in each region of farming and reclamation
		Forestry tertiary industry output value ratio	Forestry tertiary industry output value/regional total forestry output value
	Agricultural industry chain extension	The proportion of forest economy output value	Forest economy output value/regional total forestry output value
		The proportion of forest tourism output	Forest tourism output value/regional total forestry output value
Agricultural function enhancement	Economic benefits	Total output of agriculture–forestry–stockbreeding–fishery	Output of agriculture–forestry–stockbreeding–fishery
		Per capita disposable urban of rural residents	Disposable urban of rural residents/resident population of rural households
	Social benefits	Number of rural self-employed	Total population employed in rural self-employment
		Number of the most beautiful leisure countryside	China’s beautiful leisure villages announced by China’s Ministry of Agriculture and Rural Development
	Ecology benefits	Forest cover	Forest area/total land area
		Agricultural plastic film use	Increased use of agricultural plastic film will lead to increased “white pollution”, which is a negative indicator
		Agricultural diesel use	Increased use of agricultural plastic film can lead to increased agricultural pollution and is a negative indicator
		Amount of pesticides used	Increased use of pesticides leads to increased agricultural pollution and is a negative indicator

). In this study, the entropy weight method [37] is applied to measure the rural industrial prosperity index (see Figure 1 and Figure 2) as the explained variable ($Industry$) to objectively and accurately reflect the degree of rural industrial prosperity.

Data on the time difference of rural industry prosperity in Figure 2 shows that since 2011, the overall level of rural industry prosperity in both the country and various regions has exhibited a volatility of ups and downs. Since 2014, it has shown a steady upward trend. However, since 2019, the increase has been somewhat reduced due to the impact of the new crown epidemic.

This article compares the per capita GDP of each province from 2011 to 2020 with the national average level. The economic development level divides the sample into two categories: economically developed regions and economically underdeveloped regions. Regions with per capita GDP higher than China’s average level are considered economically developed regions, and other regions are considered economically underdeveloped regions. The spatial differences in the prosperity level of rural industries are examined. Figure 3 shows that, in terms of the overall level of rural industry prosperity, economically developed regions are better than economically underdeveloped regions; in terms of the level of developed industrial support systems, economically underdeveloped regions are better than economically developed regions; in terms of the level of upgrading the agricultural industry structure, economically developed regions are better than economically underdeveloped regions; in terms of the level of improving agricultural functions, economically developed regions are better than economically underdeveloped regions.

4.2.2. Major Explanatory Variables

Referring to the study by Xun Zhang et al. [38] and others, the digital inclusive finance index is used as the core explanatory variable ($Inclusive$), which is measured by the Digital Finance Group of Peking University. The index contains three categories, which are as follows: digital inclusive finance coverage, usage depth and digitization degree. In this article, the digital inclusive financial coverage and depth of use are the indicators reflecting its inclusive characteristics, and the degree of digitalization is the indicator reflecting its digital characteristics.

4.2.3. Control Variables

In order to control other influencing factors of the prosperity of rural industries as much as possible, this paper sets the following control variables with reference to current research results: first, infrastructure is the support for the prosperity of rural industries, measured by road mileage (road); second, openness to the outside world, measured by the ratio of the amount of actual foreign capital used to regional GDP (open); third, the level of financial development, measured by the ratio of financial institutions’ year-end (finance); the fourth is the level of government intervention, measured by the ratio of local fiscal expenditures on affairs of agriculture, forestry and water to regional GDP (govern), and the fifth is the urbanization process, measured by the urban–rural ratio (urban).

4.3. Data Sources

This paper chooses panel data from 2011 to 2020, covering 31 provinces (provinces, autonomous regions and municipalities directly under the central government) in the Chinese mainland. The data are mainly obtained from the National Bureau of Statistics, China Agricultural Statistical Yearbook, China Rural Statistical Yearbook and China Financial Statistical Yearbook. The linear interpolation method is used to add the lost data of some indexes in some years in some regions. Since the original values of the data are too large, the original finance data are divided by 10,000 and other variables are handled as logarithms.

4.4. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	N	Mean	SD	Min	p50	Max
Industry	310	0.648	0.366	0.248	0.559	4.373
Inclusive	308	5.213	0.679	2.786	5.412	6.252
Breadth	306	5.062	0.924	0.673	5.284	10.541
Depth	304	5.188	0.654	1.911	5.312	6.192
Digi	303	5.517	0.689	2.026	5.779	6.136
road	310	2.466	0.846	0.191	2.746	4.578
Open	310	0.109	1.513	0.000	0.003	25.725
finance	310	0.331	0.220	−0.079	0.320	3.832
govern	310	0.039	0.040	0.003	0.026	0.284
Urban	310	2.610	0.430	1.272	2.557	5.591
network	310	263.702	289.497	−1.433	138.650	1416.400
business	310	5.005	10.911	−122.700	6.000	44.900
constraint	279	3.127	2.692	0.041	2.283	16.799

shows that there is a regional imbalance in the development of digital inclusive finance and the prosperity of rural industries. Among them, the regional differences in digital inclusive finance are even smaller. It can be seen that with the in-depth promotion of the Digital China strategy, the digital infrastructure, on which digital inclusive finance relies, is improving, and the digital divide between economically developed regions and backward regions is shrinking.

5. Empirical Results and Analysis

5.1. Benchmark Inspection

5.1.1. Benchmark Regression

Considering that the random disturbance term may be related to the explanatory variables, it is difficult to ensure the consistency of the estimated results using a random effects model. Therefore, this article uses a fixed effects model (within-group estimation method) for estimation. The consequences of the benchmark regression of digital inclusive finance on rural industrial prosperity are shown in

Table 3. Benchmark regression.

	(1)	(2)	(3)	(4)	(5)	(6)
	Industry	Industry	Industry	Industry	Industry	Industry
inclsive	0.104 ***	0.102 ***	0.101 ***	0.099 ***	0.097 ***	0.087 **
	(0.034)	(0.033)	(0.033)	(0.033)	(0.034)	(0.039)
road		0.023	0.024	0.023	0.022	0.023
		(0.065)	(0.066)	(0.065)	(0.065)	(0.065)
open			0.006 ***	0.006 ***	0.006 ***	0.006 ***
			(0.001)	(0.001)	(0.001)	(0.001)
finance				−0.039 **	−0.034 *	−0.032
				(0.018)	(0.020)	(0.019)
govern					0.477	0.455
					(0.686)	(0.678)
urban						−0.064
						(0.054)
_cons	0.105	0.060	0.061	0.086	0.079	0.298
	(0.176)	(0.236)	(0.236)	(0.232)	(0.230)	(0.321)
N	308	308	308	308	308	308
F	9.571	4.802	986.346	790.410	3838.789	3908.681
r2	0.063	0.063	0.064	0.065	0.066	0.068

Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

. Column (1) shows that the regression coefficient of digital inclusive finance index on rural industrial prosperity index is 0.104, and passes the significance test at the 1% level. Columns (2)–(6) show the regression results after adding five control variables step by step, indicating that digital inclusive finance has actively driven the prosperity of rural industries. H1 is verified. digital inclusive finance effectively alleviates the financing constraints of rural industries, expands the breadth and depth of digital inclusive financial services for rural industries and actively promotes the prosperity of rural industries. This finding has important reference value for releasing the inclusive value of digital inclusive finance and strengthening its driving role in the prosperity of rural industries.

5.1.2. Robustness Tests

To ensure the robustness of the baseline regression results, systematic GMM and OLS tests are used in this paper, and the breadth, depth and digitization of digital inclusive finance are used to replace the digital financial inclusion index in the OLS test, as well as the addition of control variables, and the regression results are reported in

Table 4. Robustness test results.

	Industry (GMM)	Industry (OLS)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Inclusive	0.074 ***
	(0.027)
Breadth		0.074 ***	0.057 **
		(0.022)	(0.023)
Depth				0.116 ***	0.085 *
				(0.034)	(0.042)
Digi						0.097 ***	0.060 *
						(0.030)	(0.032)
L.y	0.797 ***
	(0.073)
road	0.057 *		0.054		0.146		0.281
	(0.035)		(0.078)		(0.341)		(0.294)
Open	0.008		0.007 ***		0.007 ***		0.007 ***
	(0.026)		(0.001)		(0.001)		(0.000)
finance	0.334 **		−0.037 *		−0.032*		−0.032 *
	(0.159)		(0.019)		(0.019)		(0.017)
govern	−2.326 **		0.507		0.315		0.547
	(0.916)		(0.635)		(0.756)		(0.657)
Urban	0.130		−0.081 *		−0.069		−0.075
	(0.105)		(0.047)		(0.042)		(0.049)
_cons	−0.713 *	0.275 **	0.433	0.048	0.027	0.114	−0.188
	(0.411)	(0.114)	(0.272)	(0.176)	(0.728)	(0.167)	(0.692)
N	277	306	306	304	304	303	303
r2	-	0.056	0.063	0.067	0.072	0.057	0.070
ar2p	0.268	-	-	-	-	-	-
hansenp	0.242	-	-	-	-	-	-

Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

. The regression coefficient is 0.074 and passes the 1% significance test. The OLS test results in Columns (2)–(3) show that despite controlling for variables or not, the breadth and depth of digital inclusive finance have a positive and significant impact on the prosperity of rural industries. It indicates that the results of the benchmark regression analysis are robust.

5.1.3. Endogeneity Test

The possible reverse causality between the explanatory variables and the explained child, the possible correlation between digital inclusive finance and rural industrial prosperity due to a third-party variable and the existence of omitted variables cause possible endogeneity problems in the causal identification process of the model. Therefore, this paper adopts the digital inclusive finance development index with a three-period lag as the explanatory variable with the usage of the instrumental variable method to solve the endogeneity problem.

First, the two-stage least squares (2SLS) estimation of the panel regression model is performed using the digital financial inclusion index ($Inclusiv{e}_{it-3}$), with three lags as the instrumental variable. The results are presented in Columns (1)–(2) of

Table 5. Endogeneity test results.

	Y (Benchmark)	Y (Lag)	X (Instrumental)	Y (Instrumental)
	(1)	(2)	(3) Stage One	(4) Stage Two
X (x = network business)	0.087 **			0.212 ***
	(0.039)			(0.066)
L3.x		0.093 ***
		(0.023)
network			0.001 ***
			(0.000)
business			0.023 ***
			(0.006)
road	0.023	0.021	0.953	−0.165
	(0.065)	(0.027)	(0.612)	(0.130)
Open	0.006 ***	0.006 *	0.001	0.005 ***
	(0.001)	(0.003)	(0.006)	(0.002)
finance	−0.032	−0.204	−0.076	−0.003
	(0.019)	(0.195)	(0.190)	(0.047)
govern	0.455	0.467	3.608	−0.084
	(0.678)	(0.558)	(2.699)	(0.976)
Urban	−0.064	−0.065	−0.874 ***	0.103
	(0.054)	(0.065)	(0.263)	(0.094)
_cons	0.298	0.373 *
X (x = network business)	(0.321)	(0.217)
N	308	216	-	308
r2	0.068	0.288	-	0.014
jp	-	-	-	0.170
widstat	-	-	-	21.105 (11.59)

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

and are not significantly different from the results of the previous benchmark regression.

Second, because the degree of digitization [39] and e-business [40] not only improve the technological content of rural industries, but also enable the digital transformation of digital financial inclusion, this paper uses rural broadband access subscribers (network) and the share of e-commerce transaction activities (business) as instrumental variables to measure the degree of digitization and the degree of e-commerce, respectively, for endogeneity tests. The test results are shown in Columns (3)–(4) of Table 5. It can be seen that the coefficient results are not significantly different from the baseline regression results after the inclusion of instrumental variables.

In summary, after accounting for endogeneity, the estimates remain reasonable and reliable, i.e., digital financial inclusion still significantly drives rural industrial prosperity.

Overall, digital inclusive finance can effectively drive the prosperity of rural industries. This conclusion is in line with the views of most scholars [8,13,15]. During the “13th Five-Year Plan” period, China lifted nearly 100 million rural poor people out of poverty. In addition, the China Rural Revitalization Survey (CRRS) published the “2021 Report on China Rural Revitalization Survey”, which shows that the majority of the labor force in rural China aged 15–64 have a junior high school education. Therefore, China’s rural labor force has the economic ability and basic literacy to accept digital inclusive finance. Therefore, this paper takes China’s digital inclusive finance and rural industry development as its research object, and there is no problem with the research hypothesis raised by Matthews [7]. Therefore, the authors of this paper believe that digital inclusive finance effectively alleviates the financing constraints of rural industries, expands the breadth and depth of digital inclusive finance services for rural industries, and actively promotes the prosperity of rural industries. This conclusion has important reference value for releasing the inclusive value of digital inclusive finance and strengthening its driving role in rural industrial prosperity.

5.2. Mechanism Analysis

The baseline regression results indicate that digital inclusive finance can actively drive the prosperity of rural industries, and this paper further explores its mechanism of action. According to the theoretical analysis, digital inclusive finance drives rural industrial prosperity by alleviating rural industrial financing constraints. To test this hypothesis, Model (2) is developed with the alleviation of financing constraints as the explanatory variable by referring to Li [7] to examine the effect of the level of development of digital inclusive finance in alleviating the financing constraints of rural industries; the level of development of digital inclusive finance and the alleviation of financing constraints are simultaneously included in Model (3) to investigate whether the alleviation of financing constraints exists as a mediator in the relationship between digital inclusive finance and rural industrial prosperity:

$$M_{it}=b_{0}Industr{y}_{it}+b_{1}Inclusiv{e}_{it}+Contro{l}_{it}+{\theta }_i+{\mu }_i+{\epsilon }_{it}$$

(2)

$$Industr{y}_{it}=c_0+c_{1}Inde{x}_{it}+c_2{M}_{it}+c_{3}Contro{l}_{it}+{\theta }_i+{\mu }_i+{\epsilon }_{it}$$

(3)

where M_it denotes the mediating variable, and in this paper, the balance of agriculture-related loans (constraint) is selected as the proxy variable with the alleviation of financing constraints as the mediating variable. Other variables are consistent with the previous paper.

The regression results in

Table 6. Mediating effects.

	(1)	(2)	(3)	(4)
	Industry	Constraint	Industry	Industry
Inclusive	0.087 **	1.167 ***		0.020
	(0.039)	(0.208)		(0.033)
loan			0.065 ***	0.061 ***
			(0.019)	(0.016)
road	0.023	0.073	0.038	0.017
	(0.065)	(0.258)	(0.063)	(0.054)
Open	0.006 ***	0.032 **	0.005 ***	0.005 ***
	(0.001)	(0.013)	(0.001)	(0.001)
finance	−0.032	−0.385 **	−0.019	−0.020
	(0.019)	(0.165)	(0.023)	(0.026)
govern	0.455	−20.024	−1.064	−1.458
	(0.678)	(13.129)	(1.078)	(1.321)
Urban	−0.064	0.292 *	−0.081 *	−0.061
	(0.054)	(0.152)	(0.041)	(0.051)
_cons	0.298	−2.956 **	0.596 ***	0.524 **
Inclusive	(0.321)	(1.185)	(0.170)	(0.246)
N	308	277	279	277
r2	0.068	0.383	0.093	0.094

Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01

show that Column (1) demonstrates that the regression results of digital inclusive finance on rural industrial prosperity are significantly positive, and Column (2) displays that the regression results of the digital inclusive finance index on agriculture-related loans are significantly positive. Column (3) shows that the regression results of farm-related loans on the rural industrial prosperity index are significantly positive. In Column (4), the regression coefficient of digital inclusive finance on rural industrial prosperity is 0.020; the regression coefficient of farm-related loans on the rural industrial prosperity index is 0.061, and it passes the significance test at 1% level, indicating that digital inclusive finance does not directly affect rural industrial prosperity, but drives rural industrial prosperity through a fully mediated mechanism of alleviating financing constraints. Hypothesis H2 is tested. This conclusion supports the research conclusion with the intermediary variable of alleviating financing constraints [35]. It shows that alleviating financing constraints is an important channel for the inclusive effect of digital inclusive finance. New digital inclusive finance formats and models should be designed around rural industrial financing issues.

6. Heterogeneity Test

According to the level of economic development, this paper divides the research samples into two categories of economically developed areas and economically underdeveloped areas, and examines the heterogeneity of the economic development level that digital inclusive finance drives regarding the prosperity of rural industries. Columns (1) and (2) in

Table 7. Heterogeneity test results.

	Industry
	(1) Less Developed	(2) Developed
inclvsive	0.111 ***	−0.003
	(0.030)	(0.098)
road	0.001	1.449
	(0.056)	(1.099)
open	0.006 ***	−9.936
	(0.001)	(12.600)
finance	−0.032 *	2.148
	(0.019)	(3.044)
govern	0.507	−24.930
	(0.617)	(15.433)
urban	−0.026	−0.259
	(0.042)	(0.183)
_cons	0.093	−1.776
	(0.279)	(2.454)
N	219	89
$Adj-R^2$	0.086	0.127
$Empirical-value$	0.004 ***

Standard errors in parentheses, * p < 0.1, *** p < 0.01, “Empirical-value” is used to test the significance of the differences in the index coefficients between groups, which is obtained through 1000 bootstraps. In addition, the differences in variable values between groups are also significant at the 1% level.

show the regression results controlling for individual fixed effects. The results show that the regression coefficient of digital inclusive finance in economically developed areas is not significant, while the coefficient in underdeveloped areas is significantly positive. The empirical p-value obtained by the bootstrap method shows a significant difference between the two regions. Moreover, all other control variables are significant at the 1% level, except for the inclusive finance index. The test results indicate significant heterogeneity between developed and underdeveloped regions.

According to the regression results in Table 7, digital inclusive finance has a significant positive impact on the prosperity of rural industries in economically underdeveloped areas. The empirical results reject Research Hypothesis 3 and support the view that relatively vulnerable inclusive groups are more likely to benefit from digital inclusive finance [13,15,18]. Differences in the boundaries of the study may be a possible reason for the different conclusions between this study and some other studies [8]. The meaning expressed by the integration of the three industries in rural areas and the level of economic development in rural areas still have certain differences. Ge (2022) [8] has a different interpretation of the connotation of the integration of the three industries in rural areas, the design of indicators, and the measurement of indexes from those described in this study. It is worth noting that Ge examined the heterogeneity effect of the digital inclusive finance industry integration based on the high and low percentiles of the three-industry integration efficiency, while this study examined the heterogeneity effect of the level of economic development of digital inclusive finance. In addition, the authors of this study believe that there are three reasons why digital inclusive finance has a significant positive impact on the prosperity of rural industries in economically underdeveloped areas. First, although rural industries are weak, they are the economic lifeline of underdeveloped areas. It can be seen from the fact that the support system for rural industries in underdeveloped areas is better than that in developed areas, so underdeveloped areas will pay more attention to the prosperity of rural industries and strengthen the innovation of digital inclusiveness and inclusiveness by embedding them in rural industries. Second, in recent years, the Chinese government has continued to strengthen the construction of digital rural areas, improving the quality and coverage of rural communication networks in underdeveloped areas, narrowing the “digital divide” between underdeveloped and developed areas, and enabling rural industries in underdeveloped areas to enjoy the dividends of digital inclusive finance development better. Third, the current financial needs of rural industries are mainly financing needs. In China’s financial market system, dominated by indirect financing, financing needs are concentrated on traditional bank credit demand. Traditional banks can directly connect with industrial financing subjects through physical outlets and customer managers, overcoming problems such as financial and digital divides.

7. Conclusions and Policy Recommendation

7.1. Conclusions

This paper conducts theoretical and empirical analysis on the effect of digital inclusive finance in driving the prosperity of rural industries, and the main conclusions are as follows: (1) Focusing on agricultural development, the connotation of rural industrial prosperity is interpreted from three dimensions: developed industrial support system, perfected industrial structure and significantly improved industrial function; the index system is constructed, and the entropy weight method is adopted to measure the rural industrial prosperity index; (2) The mechanism framework of “ digital inclusive finance—alleviation of financing constraints—rural industrial prosperity” is constructed; (3), the mechanism framework of “digital inclusive finance—alleviation of financing constraints—rural industrial prosperity” was established; (4) Overall, digital inclusive finance significantly contributes to the prosperity of rural industries, as shown by the digital inclusive finance index and its three sub-indices of digital inclusive finance coverage breadth, depth and digitization degree, which all have a positive and significant impact on the rural industry prosperity index; (5) Mechanism tests show that digital inclusive finance alleviates the financing constraints of rural industries through the innovation of digital inclusive finance and digitality, and then drives the prosperity of rural industries; (5) Heterogeneity tests show that digital inclusive finance has a positive and effective effect on the prosperity of rural industries in less developed areas, that is, areas with lower economic development level.

7.2. Policy Recommendation

Based on the above research conclusions, the authors of this article believe that we should attach importance to the driving role of digital inclusive finance in the process of rural industrial prosperity and improve the development level of digital inclusive finance from the following aspects, so as to make inclusive value benefit rural industrial prosperity.

Firstly, we need to promote the digitalization of rural areas. Data are the fundamental element of digital inclusive finance. However, the digital infrastructure in Chinese rural areas is still relatively weak, facing problems such as the need for improvement in the big data system of agriculture and rural areas and the governance of rural digitalization. We should focus on the resource endowment and characteristics of rural industries and layout new infrastructure such as artificial intelligence and the Internet of Things. We should integrate various types of data, such as industry and commerce, legal affairs, the internet, communications, and energy consumption, to achieve coordinated and linked rural resource planning. This will provide an intensive and efficient rural industry information guarantee for the development of digital inclusive finance. We can solve the problem of information asymmetry in financial markets, overcome moral risks and promote accurate matching between digital inclusive finance products and services and industrial demands.

Secondly, we need to promote the digital transformation of traditional financial institutions, drive banks and other traditional financial institutions to use financial technology to enter customer acquisition, risk control, marketing management business sectors, and build big data application platforms and financial sales service platforms. We need to carry out digital inclusive financial innovation, enhance the digital and inclusive nature of providing enterprise data services, post-loan risk monitoring, and smart marketing management functions.

Thirdly, we need to strengthen the regulation of inclusive finance in the internet sector. This is both due to the objective requirement of preventing the operational risks of financial institutions and the reality that rural residents generally lack financial knowledge, self-protection awareness and risk-bearing capacity [11]. It is necessary to strengthen the regulation of inclusive finance in the internet sector, guide the sustainable development of inclusive finance in the internet sector and drive the benefits of inclusiveness and digitalization to rural industries: (1) We need strict access control. We need to establish a main initiator mechanism and limit the qualifications of main initiators. Link the loan scale with the actual paid-in capital and improve the qualified investor mechanism; (2) We need to accelerate the construction of financial regulatory technology and enhance the regulatory capacity to distinguish financial innovation from financial fraud. We need to improve the non-on-site supervision system, promote the real-time connection between the internet financial institution’s background system and the non-on-site supervision system, and strengthen dynamic monitoring and early warning of the entire process and chain.

Fourthly, we should enhance the financial literacy of the inclusive group. Given that most Chinese people lack sufficient financial literacy [41], this paper puts forward the following suggestions: (1) Financial regulatory authorities should place more emphasis on financial consumer education, establish a mechanism for financial knowledge education and encourage extensive participation of financial institutions and consumers. At the same time, financial institutions should regulate the design and sale of inclusive financial products; (2) Financial institutions should establish a systematic popularization system for financial knowledge and adapt to the trend of financial digital transformation, enhance the pertinence, effectiveness, and accuracy of education, adopt differentiated education methods for the inclusive group and guide them to identify the characteristics of financial products and services; (3) The inclusive financial group should also enhance their awareness of financial literacy, actively learn financial knowledge and improve their ability to allocate financial assets and liabilities.

7.3. Limitations and Prospects

(1)

Based on the perspective of financial institutions, this paper analyzes the theoretical mechanism of digital inclusive finance driving the prosperity of rural industries. However, the alleviation of financing constraints in rural industries depends not only on financial institutions, but also on the characteristics of inclusive groups [13,41] and the rural financial ecological environment [8]. However, this article did not fully discuss these factors, and intends to conduct theoretical and empirical research on the role of factors such as the financial literacy of inclusive groups in driving the development of rural industries through digital inclusive finance in future research.

(2)

In order to control for other factors that may affect the prosperity of rural industries as much as possible, this article set up five control variables. However, there are still other omitted variables that affect the prosperity of rural industries. The main reason is that there are difficulties in obtaining data and problems with mismatched data structures. For example, the characteristics of inclusive groups, such as financial literacy, can only be obtained through analyzing the micro-level data of farmers, while the model used in this article adopts data at the provincial level. It is difficult to integrate them into one model for interpretation.

(3)

This article focuses on digital inclusive finance in China and its relationship with rural industry development, and does not fully consider the characteristics of inclusive groups and digital inclusive finance development in other countries and regions. Subsequent research should pay attention to the development relationship between digital inclusive finance and rural industry in other countries and regions outside of China, as well as the applicability of the conclusions of this study in other countries and regions.