The Impact of Digital Financial Inclusion on the Construction of Agricultural Anti-Risk Capacity: Based on a Sample Analysis of 46 Prefecture-Level Cities in the Huaihe River Basin

Abstract

Digital financial inclusion plays an important role in promoting the structure of the agricultural sector and increasing agricultural anti-risk capacity. This paper takes panel data of 46 prefecture-level cities in the main grain-producing areas of the Huaihe River Basin from 2011 to 2020 as the research sample and adopts a two-way, fixed-effect model to empirically analyze the impact of digital financial inclusion on the development of agricultural anti-risk capacity. The results show that digital financial inclusion promotes the development of agricultural anti-risk capacity by 14% on average. And it is further found that digital financial inclusion is more favorable to agricultural anti-risk capacity when the scale of operation is larger, the level of industrial structure is higher, and the penetration of digital financial inclusion is deeper. In addition, the spatial spillover effect of digital financial inclusion on agricultural anti-risk capacity is nonlinear. In the future, the scale of land operation should be expanded, the industrial structure needs to be optimized, and the growth of digital financial inclusion ought to be enhanced in order to deepen the impact of digital financial inclusion on the risk resistance capacity of agriculture in different regions.

1. Introduction

With global warming intensifying agricultural production risks, food security is endangered to a certain extent [1]. According to the World Health Organization, the number of hungry people in the world has reached 820 million due to climate change, of which 63% are in Asia. According to data released by the World Bank in 2021, China’s arable land area is 108,862,000 ha, ranking fourth in the world. At the same time, as the largest developing country, China’s per capita grain output in 2023 was 492 kg, far exceeding the world average and about 25% higher than the internationally recognized food security line. China has a key part to play in global food security and economic stabilization [2]. However, in recent years, rising annual mean temperatures and the disproportionate distribution of precipitation in China [3] have adversely affected agricultural productivity. Due to dynamic changes in climate conditions, food crop yields are at risk of significant decline. For example, in West Africa, climate warming has led to an increase in the frequency of high temperatures and extreme rainfall, reducing rice and sorghum production by 10–20% and 5–15%, respectively [4]. However, food security issues may lead to a series of problems such as violent conflicts, group differentiation, social chaos, and economic fragility [5]. Meanwhile, the smallholder economy itself is characterized by small-scale operations, low human capital, low income, difficulty in borrowing, and weak social networks. Smallholder farmers do not have sufficient resources to resist or buffer against external shocks, and their agricultural anti-risk capacity is low [6]. In such a context, the likelihood of farmers returning to poverty is increased, and farmers may be unable to withstand the risks of the market and the natural environment, leading to farmland idling or abandonment, affecting food security. Improving anti-risk capacity building in agriculture has become an important part of China’s efforts to ensure global food security. In recent years, with the development of digital financial inclusion, the organic combination of Internet technology and financial inclusion has increased the coverage and penetration of digital inclusive finance [7]. This plays a positive role in improving the status quo of low income, difficult borrowing, and weak social networks of farmers, promoting the use of agricultural production technology, and improving the efficiency of total factor production in agriculture. Therefore, digital financial inclusion may become an effective tool to promote the development of agricultural anti-risk capacity. It is of great significance to clarify the mechanism of digital financial inclusion in the development of agricultural anti-risk capacity for China to reduce agricultural risks, build agricultural power, and even ensure international food security.

The Huaihe River Ecological Economic Belt, located in China’s north–south transition zone, is China’s main grain production area. At the same time, the Huaihe River Basin is also a typical disaster-prone area in China due to extreme weather [8]. Agricultural development is seriously affected by natural disasters, coupled with the rising cost of agricultural resources in recent years, the lack of capital sources for investment by small farmers, weak resistance, and strong instability factors in agricultural production, which affect food security. In 2023, the Chinese government proposed to promote the high-quality development of rural digital financial inclusion, broaden financing channels for agriculture-related subjects, and strengthen financial support for agricultural development. How to play the positive role of digital financial inclusion in the development of agricultural anti-risk capacity is an important direction to alleviate agricultural disaster and stabilize the grain output in the Huaihe River Basin. Therefore, based on the panel data of 46 prefecture-level cities in the Huaihe River Basin from 2011 to 2020, this paper investigates the impact of digital financial inclusion on the development of agricultural anti-risk capacity, which is representative to a certain extent. The existing research generally believes that digital financial inclusion has a positive effect on agricultural production. For example, Liu et al. [9] argued that digital financial inclusion promoted the agricultural production of rural households in China. Fang et al. [10] believe that digital financial inclusion can promote agricultural trade by restoring agricultural supply chains to achieve post-disaster recovery of agriculture. Hong et al. [11] proposed that digital financial inclusion could improve the mismatch of agricultural factors by reducing the distortion of factor prices of agriculture-related enterprises to achieve agricultural development. A study by Tang et al. [12] found that for every 1% increase in the digital financial inclusion index, agricultural total factor productivity increased by about 0.06%. A study by Liu [13] found that the average value of China’s agricultural total factor productivity was 1.072 in 2011–2018, and that after controlling for the effects of other factors, for every 1-unit increase in the level of digital financial inclusion development, the regional agricultural total factor productivity increased by an average of 4.8%. According to the data, China’s total factor productivity in agriculture was 1.045 from 2011 to 2020, with an average annual growth rate of 4.5% [14], which is somewhat similar to the stage of development of China’s digital financial inclusion. However, there have been no studies on the impact of digital financial inclusion on the development of agricultural resilience. This paper makes an effort in this regard.

The probable marginal contributions of this article lie in the following: First, this paper takes Huaihe River Basin, a major grain-producing area with frequent natural disasters in China, as the research object; defines the concept of agricultural anti-risk capacity; constructs an evaluation index system; reviews the impact mechanism of digital financial inclusion on the development of agricultural anti-risk capacity from the perspective of advanced industrial structure; and carries out an empirical test. The discussion of how to improve the development of agricultural anti-risk capacity from the perspective of digital financial inclusion has certain reference significance for other countries with strong agricultural production vulnerability in the world. Second, this paper discusses the different scales of agricultural land management and the development stage of digital financial inclusion. Digital financial inclusion has different impacts on the development of agricultural anti-risk capacity. Third, this paper further analyzes the nonlinear effect of digital financial inclusion on the development of agricultural anti-risk capacity from the perspective of space. Although digital financial inclusion loosens loan constraints and expands the scope of beneficiaries, the development of digital financial inclusion is prone to producing new digital divides and digital inequalities, especially when the surrounding areas lack advanced digital infrastructure and technology; large amounts of capital and labor flow out, accelerating the “urban shadow”, resulting in “the weak are weaker, and the strong are stronger”, or the “Matthew effect”, thus hindering the development of agricultural anti-risk capacity. This makes the spatial effect appear as a U-shaped trend. The remainder of this paper is structured as follows: Section 2 formulates the research hypotheses and reviews the body of existing literature. Section 3 describes the data sources and econometric model of the paper. Section 4 reports the empirical tests of digital financial inclusion affecting agricultural anti-risk capacity at different time stages and for different farm sizes, and it explores the spatial spillover effects of digital financial inclusion on agricultural anti-risk capacity. Section 5 is the conclusion and discussion.

2. Literature Review and Research Hypothesis

2.1. Literature Review

Relevant studies on the impact of digital financial inclusion on the development of agriculture anti-risk capacity mainly focus on three aspects: the influencing factors of agricultural resilience, the relationship between industrial structure upgrading and economic resilience, and the impact of digital financial inclusion on agricultural resilience. The literature on the first aspect mainly analyzes the factors affecting agricultural resilience. For example, Zhao and Xu [15], based on measuring the resilience level of the agricultural economy, found that digital economy can promote the improvement of agricultural economic resilience by promoting the optimization of industrial structure (involving the upgrading and rationalization of industrial structure). Zhou et al. [16] found that rural industrial integration makes an important contribution to enhancing the resilience of the agricultural economy. These studies laid a good research foundation for this paper but failed to further analyze the impact of digital financial inclusion on the development of agricultural anti-risk capacity. The second aspect of the literature studies the relationship between industrial structure upgrading and economic resilience. For example, Tan et al. [17] argued that industrial diversification has a positive impact, while specialization tends to weaken economic resilience. Feng et al. [18] think that regional integration and economic resilience are substantially moderated by industrial structure. The literature agrees that the upgrading of industrial structure has an important impact on economic resilience. The third aspect of the literature analyzes the relationship between digital financial inclusion and the resilience of the agricultural economy. For example, Gao et al. [19] found that digital financial inclusion enhanced the resilience of the agricultural economy by promoting the integration of rural industries, based on the provincial panel data of 30 provinces in China from 2012 to 2021. The existing literature established an index system of economic resilience and agricultural economic resilience and further analyzed the relationship between digital financial inclusion and agricultural economic resilience, industrial structure, and agricultural economic resilience, laying the research foundation for this paper, but failed to further study the relationship between digital financial inclusion and the development of agricultural anti-risk capacity.

2.2. Research Hypotheses

In fact, the agricultural production cycle is long and susceptible to extreme weather such as drought, flood, low temperatures and freezing damage, hail, etc. In addition, in the context of globalization, the agricultural product market of various countries is affected by the international market, and the price fluctuates greatly. Furthermore, the technical application level of agricultural production in developing countries led by China is limited, agricultural production technology is complex, infrastructure is weak, and storage and transportation conditions are underdeveloped, which increases the probability of agricultural risks. Improving farmers’ ability to resist risks is an important part of improving agricultural competitiveness and increasing farmers’ income [20]. Risk management theory originated in the United States. In 1931, the American Management Association first advocated risk management, and from 1955 to 1964, modern academic and professional risk management was born. Since then, the study of risk management has gradually become systematic and professional, and risk management has gradually become an independent discipline. In the late 1980s, knowledge of risk management began to enter China. In this process, the development of anti-risk capacity originates from organizational management theory, which refers to the ability of enterprises to adapt to external environment changes (external causes) under different network relationship characteristics (internal causes), called enterprise anti-risk capability [21]. The ability of firms to respond to crises can measure the health and capital status of an organization or enterprise, as well as industry conditions, and so on. Subsequently, the measurement system can gradually be applied to other fields. Agricultural production is vulnerable to various risks, such as the exposure of open-air fruit farming in China to extreme weather such as hail. Through the construction of hailstorm nets, the disaster rate of apples can be reduced, and the anti-risk capacity of apple farmers can be improved, which plays an important role in stabilizing farmers’ income.

However, due to insufficient funds, small farmers cannot purchase the corresponding means of production to improve their ability to resist risks. Breaking the narrow source of small farmers’ funds has become the key to improving the development of small farmers’ anti-risk capacity. In recent years, with the increase in the penetration rate of digital financial services such as the Internet, mobile banking, Alipay, and WeChat in rural areas, digital financial inclusion has enhanced the accessibility of digital financial services. On this basis, the availability of digital financial inclusion has improved. Leveraging the universality, timeliness and inclusiveness of digital technology, it has realized the integration of information technology and financial services. And through mobile payment and credit exemption and other ways to improve the efficiency of resource allocation, reduce the cost of information collection, to meet a wide range of market demand. Compared with traditional financial service institutions, financial providers are more willing to offer services when digital financial inclusion is implemented, enhancing farmers’ ability to obtain information resources, expanding the scope and target of financial services [22], and bringing beneficial financial support to relatively disadvantaged populations, industries, and regions [23]. More farmers with low levels of human and material capital will also have the opportunity to receive financial support. This provides the financial basis for smallholder farmers to acquire the necessary means of production. In addition, the contents of agricultural insurance and inclusive credit, which are included in digital financial inclusion, help to improve the development of agricultural anti-risk capacity. Based on this, the following hypothesis is proposed:

H1. 

Digital financial inclusion improves the development of agricultural anti-risk capacity.

The economic structure represents the economic profile of a region, which can be broken down into several interrelated factors, such as economic openness, export scale/level, industrial structure, productivity, technological level, economic linkages, and policy systems [24]. Among them, industrial structure has long been considered the most basic factor of resilience. Industrial structure is important not only for resilience but also for the development of agricultural anti-risk capacity because of its ability to mitigate when the regional economy is directly exposed to external shocks [25]. On the one hand, in the process of the growth of agricultural products, due to the influence of seasonal and fragile agriculture and the instability of consumer demand and other factors, agricultural production and supply chains are prone to breaking, resulting in instability of the agricultural product market and market risks. Compared with traditional finance, digital inclusive finance can reduce information asymmetry and moral hazards in financial services through scenarios, data, information, and innovation [26], while the advanced industrial structure can provide resources such as capital, talent, and technology for the agricultural industry [27]. The upgrading of the agricultural industrial structure promotes the deep integration of agriculture with major links such as agricultural production, processing, and storage by building the whole agricultural industry chain, and it realizes the vertical integration of agriculture [28]. This increases the scope of agricultural operations, enhances the development of agricultural anti-risk capability, and successfully addresses the issues of low production efficiency, transportation challenges, high processing costs, and low agricultural product profitability. On the other hand, in rural areas, an advanced industrial structure dictates greater infrastructure and public social service requirements, which reduces interference with agriculture and improves the development of agricultural anti-risk capacity. For instance, based on the rural industry integration development project created by agriculture, the government and businesses should create new business models like leisure farming, vacation agriculture, agricultural tourism, and rural e-commerce. The requirements should be based on higher standards for infrastructure construction [29]. This new business model has enabled the improvement of rural agricultural infrastructure such as transportation, water, electricity and communication facilities. In addition, it improves farmers’ ability to obtain information, reduces the natural risks and market risks of agricultural production, and contributes to the development of anti-risk capacity [30].

H2. 

The effect of digital financial inclusion on the growth of agricultural anti-risk capacity becomes more obvious when the industrial structure advanced index is more higher.

3. Materials and Methods

3.1. Construction of Index System

(1) Explained variable: agricultural anti-risk capacity. This work makes reference to Zhao and Zhao’s research [31,32,33]. Combining the connotations of agricultural anti-risk capacity and the scientific nature and availability of data selection, the entropy method is used to measure agricultural anti-risk capacity (agr) from two dimensions: risk resistance ability and risk support ability. See Table 1 for specific indicators.

(2) Explanatory variable: the digital financial inclusion index. In this paper, standardized values (dfi) of The Peking University Digital Financial Inclusion Index of China [34] are used for regression. The development degree of digital financial inclusion in different regions is described by using the Digital Financial Inclusion Index at the prefecture level.

(3) Control variables: Concerning the studies of Gao et al. [35], Lin et al. [36], and Zeng [37], this paper focuses on four dimensions: government expenditure, development of agricultural business entities, social development, and financial support. The relative values of government education budget expenditure (gov), agricultural modernization degree (mod), city size (scale), and deposit balance of financial organizations at the year’s end (save) were selected as control variables.

Resilience is a set of economic concepts related to the development of anti-risk capacity. They have both similarities and differences. In common, they emphasize the ability of the system to cope with external risks, while the difference is that resilience puts more emphasis on the ability of the system to recover to its initial state after suffering internal or external shocks [38]. In contrast, the development of agricultural anti-risk capacity focuses more on the resistance of the agricultural economy to external shocks, the enhancement of external support to internal capacity, and the risk resistance and risk recovery capacity brought by the resistance of the agricultural system. Agricultural resilience refers to the ability of agricultural systems to maintain productivity and stability in the face of various stresses and challenges [39].

Most existing studies have constructed indicator systems from the aspects of resistance, adaptation and adjustment, and transformation and innovation [15,40]. Among these, risk resistance ability and risk support ability are reflections of the strain that sudden disruptions and disruptive events—which are external risks—place on agricultural economic systems. The adaptability adjustment ability refers to the adaptability adjustment of the regional agricultural economic system in the face of external shocks and environmental changes and its ability to restore a stable operation state, which provides a good research basis for the development of an agricultural anti-risk capacity index system. Agricultural anti-risk capacity building refers to the resilience of the agricultural economic system to external shocks, specifically including the risk resistance and risk support capacity resulting from the internal resilience of the agricultural system. Based on this concept and drawing on existing research, this paper attempts to construct an index system of agricultural anti-risk capacity from the two perspectives of risk resistance ability and risk support ability [41]. Among them, the risk resistance ability refers to the pressure caused by sudden disturbances and destructive events of external risks on the agricultural economic system, and the risk support ability refers to the ability of the regional agricultural economic system to construct a stable operating state when facing the impact of external risks. As risk resistance indicators, scholars mostly use grain production, employment in primary industry, fertilizer application [31], value added by primary industry as a proportion of GDP, surface water resources, and the total power of agricultural machinery [32] to characterize the resistance of the agricultural system. Since the level of agricultural infrastructure is mainly captured by electricity consumption, agricultural electricity consumption was chosen to characterize its level of infrastructure development. On the other hand, risk support ability refers to the ability of the agricultural system to mobilize the flow of relevant factors and effectively mitigate shocks after being hit by external shocks. Most of the existing studies construct the risk support ability of the agricultural system from the indicators of disposable income; per capita consumption expenditure; agricultural insurance amounts; financial expenditure on agriculture, forestry, and water [31]; total retail sales of consumer goods; the number of mobile phones; and the number of beds in health institutions [33]. Since the demand for grain lies mainly in urban areas, this paper chose the highway mileage, urbanization rate, and rural–urban income ratio as indicators to measure factor circulation. The details of each indicator can be found in Table 1.

3.2. Model Setting

To investigate the effect and mechanism of digital financial inclusion on the anti-risk ability of agriculture, the following benchmark regression model was constructed according to the above theoretical framework:

(1)$y_{it}={\alpha }_0+{\alpha }_1{x}_{it}+{\alpha }_n{C}_{it}+{\delta }_i+{\phi }_t+{\epsilon }_{it}$

where i represents the city, t is the year, y represents the agricultural risk resistance, x is the value after standardization of the digital financial inclusion index, C is the logarithm of the relevant control variables, ${\delta }_i$ represents the fixed effect of the city, ${\phi }_t$ represents the fixed effect of time, and ${\epsilon }_{it}$ is the random error term.

Further, based on the impact mechanism of the digital economy on the construction of agricultural anti-risk capacity, a mediation effect model was constructed:

(2)$y_{it}={\beta }_0+{\beta }_1{x}_{it}+{\beta }_2{M}_{it}+{\beta }_3{x}_{it}\times M_{it}+{\delta }_i+{\phi }_t+{\epsilon }_{it}$

where y represents the agricultural anti-risk capacity, x is the value after standardization of the digital financial inclusion index, ${\gamma }_0$ is a constant term, M is the intermediary variable, ${\delta }_i$ represents the city fixed effect, ${\phi }_t$ represents the time fixed effect, and ${\epsilon }_{it}$ is the random disturbance term.

Spatial metrology models usually include SDM, SAR, SEM, and SAC, and their optimal models can be identified by Wald and LR tests. To investigate the spatial spillover effect of the digital economy on agricultural risk resistance ability, a spatial Durbin model can be further constructed:

(3)$y_{it}={\theta }_0+\rho W{y}_{it}+{\theta }_1{x}_{it}+W{x}_{it}{\theta }_2+{\theta }_{n}W{C}_{it}+{\delta }_i+{\epsilon }_{it}$

where y represents the agricultural anti-risk capacity, x is the value after standardization of the digital financial inclusion index, ${\theta }_0$ is a constant term, ${\theta }_1$ is the coefficient of x, $\rho$ is the spatial autoregressive coefficient, W is the spatial adjacency (0–1) spatial weight matrix, ${\delta }_i$ represents the urban fixed effect, and ${\epsilon }_{it}$ is the random disturbance term.

3.3. Study Area and Data Source

The Huaihe River Basin spans the Henan, Hubei, Anhui, Jiangsu, and Shandong provinces (because the Huaihe River Basin in Hubei province covers an area of only 1410 square kilometers, it is generally said that the Huaihe River Basin spans the Henan, Anhui, Jiangsu, and Shandong provinces). As one of the seven major river basins in China, the Huaihe River Basin is not only the main agricultural population-gathering area in China but also an important main grain-producing area, occupying an important position in the overall situation of China’s social and economic development. However, the Huaihe River Basin is located in the climate transition zone of North and South China, and the climate in the basin is complicated and changeable. Take precipitation as an example: Precipitation varies greatly from year to year, and the distribution within the year is highly uneven. The Huaihe River Basin is in the rainy season from June to September every year, and the precipitation accounts for 60–70% of the annual total. Winter precipitation accounts for only 8% of the total [42]. The uneven distribution of water resources throughout the year has a great impact on agricultural development in the Huaihe River Basin. With the frequent occurrence of extreme climate events in recent years, the development of agricultural anti-risk capacity in the Huaihe River Basin has become an urgent practical problem to be solved. In this paper, the Henan, Anhui, and Jiangsu provinces in the Huaihe River Basin were selected as research areas. First of all, the three provinces are located in the area where the mainstream of the Huai River flows through, while Shandong is located in the area of Yi, Shu, and Sihe River systems and the Shandong Peninsula, which indicates that Henan, Anhui, and Jiangsu are in a similar geographical environment. Secondly, the average annual rainfall in Henan, Anhui, and Jiangsu is 800–1000 mm, while the average annual rainfall in Shandong is about 700 mm. The data show that Henan, Anhui, and Jiangsu have similar climatic conditions. Lastly, the Henan, Anhui, and Jiangsu provinces are on the plains, whereas Shandong province has a lot of terrain variation, including the Shandong hills. The plain terrain is gentler, has advantages for mechanization and other scale management, and is better suited for investigating how scale management affects agricultural anti-risk capacity in the context of digital financial inclusion. When the entropy method is used to calculate the agricultural anti-risk capacity, the final index is biased by the single positive or negative influence of climate indicators such as temperature and rainfall. Therefore, climate-related indicators were not included in the development of agricultural anti-risk capacity. However, the climate index is also an important factor affecting the agricultural risk resistance ability, so this paper controlled for climate indicators to conduct a robustness test. Finally, the Henan, Anhui, and Jiangsu provinces were selected as research areas based on their similar geographical locations, climatic environment, and terrain environment.

Based on panel data from 2011 to 2020, this study took the Jiangsu, Anhui, and Henan provinces in the Huaihe River Basin as the research object to examine the impact of digital financial inclusion on agricultural anti-risk capacity. The data utilized in this research came from the relevant statistical yearbooks of the Jiangsu, Anhui, and Henan provinces; the China Statistical Yearbook; the statistical bulletin of national economic and social development; the EPS database; the China Economic Network database; etc. The digital financial inclusion data came from The Peking University Digital Financial Inclusion Index of China, and the missing values were supplemented by the mean value. For the very few missing data, the interpolation method was used to make up the difference. Although the maximum value of agricultural risk resistance was 0.6034, the average value was 0.1849, which is closer to the minimum value. This indicates that the overall level of agricultural anti-risk capacity is still low. In addition, the standard deviation of the digital financial inclusion index was 71.3920, indicating that there are still differences in the penetration of digital financial inclusion in different regions. This paper mainly used stata18 for empirical analysis.

4. Results

4.1. Benchmark Regression Results of the Impact of Digital Financial Inclusion on Agricultural Risk Resistance

This paper mainly investigated the impact of digital financial inclusion on agricultural anti-risk capacity and used a panel two-way, fixed-effect model to estimate it. The benchmark regression results are shown in Table 2, which shows that digital financial inclusion can significantly improve agricultural anti-risk capacity. A variance inflation factor (VIF) analysis was also conducted for all variables to prevent estimation bias caused by multicollinearity; the findings indicated that there was no significant multicollinearity among the variables, with the maximum VIF value of 6.82 being below the threshold of 10. Panel regression was used in equation (1), and the findings indicated that for each additional unit of digital financial inclusion, the agricultural anti-risk capacity increased by 10.28%. To eliminate the interference of government, city size, and other characteristics on the results, based on column (1) in Table 2, columns (2)–(5) successively introduced control variables, year fixed effects, regional fixed effects, and two-way fixed effects and adopted robust standard errors for regression. From the estimated results, the coefficients of digital financial inclusion were significantly positive. This shows that from 2011 to 2020, the application of digital financial inclusion significantly improved the development of agricultural anti-risk capacity. Thus, hypothesis 1 is verified.

Further, the regression results of columns (3) and (5) show that after controlling for the year fixed effect, the promotion effect of digital financial inclusion on agricultural risk resistance was strengthened. This may be because, in the process of the development of digital inclusive finance from 2011 to 2020, there was a downward trend in the resource support of the macro market economy for agriculture due to profit-seeking tendencies (Table 3).

Digital financial inclusion can effectively alleviate the disadvantages of the small-scale peasant economy, such as appropriately expanding the scale of farmers’ operation through the use of large-scale machinery and equipment, improving their education level and thus improving their human capital, and strengthening their social networks with the help of digital technology. This may be an important reason why digital financial inclusion contributes to agricultural anti-risk capacity.

4.2. Robustness Check

4.2.1. Instrumental Variable Method

Although digital financial inclusion was found to has a significant positive effect on agricultural risk resistance, this result may have been affected by endogenous problems: Firstly, potential reverse causality affects the research conclusion of this paper; for example, regions with higher agricultural anti-risk capacity have a higher possibility of using digital financial inclusion. Secondly, the result may face the problem of missing variables. Based on this, the existence of Ming Dynasty post stations in relevant prefecture-level cities can be taken as an instrumental variable of digital financial inclusion. The reason for this is that, as the main transmission channel of information and materials in history, post stations are more accessible, making it easier to form a good economic foundation for the region, and often correspond to regions with a good level of information infrastructure and relatively developed logistics services [43]. The development of digital financial inclusion depends on the past level of economic development in the region and the local information infrastructure. Therefore, Ming Dynasty post stations are closely related to digital inclusive finance. However, as a historical variable, Ming Dynasty post stations have no obvious correlation with agricultural anti-risk capacity. Therefore, Ming Dynasty post stations can satisfy both the correlation and homogeneity conditions of instrumental variables, making them an effective instrumental variable. At the same time, by referring to the treatment method of Li [44] for instrumental variables, the multiplication of the number of Ming Dynasty post stations and the number of Internet access ports was introduced into the model as an instrumental variable to overcome the data dimension limitation of sectional instrumental variables.

Table 4 reports the regression results of the instrumental variable method. According to the regression results of the first stage, there was a significant positive correlation between the instrumental variables and digital financial inclusion at the level of 1%, indicating that the instrumental variables met the correlation conditions; that is, to a certain extent, the existence of Ming Dynasty post stations has accelerated the development of digital financial inclusion in the area. The F-value of the first stage was 44.4, indicating that there was no weak instrumental variable problem. It can be seen from columns (1–2) in Table 4 that the Cragg–Donald Wald F statistic of 2SLS exceeded the critical value of 10%, further proving the validity of the instrumental variable. According to the results of the second-stage regression, the coefficient value and significance level of agricultural anti-risk capacity are basically consistent with the direction of the baseline regression, which indicates that after the use of an instrumental variable method to deal with the endogenous problem, digital financial inclusion still has a significant promoting effect on agricultural anti-risk capacity, and hypothesis 1 is further verified.

4.2.2. Robustness Test

There is a corresponding lag in the adoption and popularity of digital inclusive finance due to China’s uneven regional economic development and the disparities in infrastructure and humane characteristics across regions. Therefore, a robustness test was carried out by using lagging variables and excluding other interfering factors. At the same time, the data were re-evaluated by replacing the model, which further strengthened the reliability of the data.

• Core independent variable lagged one phase: Considering the possible lag effect of the development of digital financial inclusion, the digital financial inclusion index can be processed with a lag of one phase, that is, using the digital financial inclusion with a lag of one phase to re-estimate the benchmark regression. Because the impact of digital financial inclusion on agricultural anti-risk capacity is sustained over time, it may have an impact on the subsequent agricultural anti-risk capacity through the optimal allocation of resources and the improvement of human capital. The results reported in model (1) in Table 5 show that the current development of digital financial inclusion is conducive to the development of agricultural anti-risk capacity in the next phase.

• Excluding other interfering factors: Because the agricultural anti-risk capacity is greatly affected by climate, the estimated results of this paper are affected by these disturbing factors, and the clean causal effect cannot be identified. To control for the influence of climate factors, we controlled for the temperature and precipitation in columns (2) and (3) of Table 5. According to the results, after controlling for the impact of climate factors, digital financial inclusion still has a significant positive promoting effect on agricultural anti-risk capacity. The coefficients after controlling for temperature and precipitation are 0.1426 vs. 0.1433, respectively. This is essentially no different from the baseline regression coefficients after controlling for the variables, indicating that the impact of climate does not affect the identification of causality in this paper. It shows that the findings of this paper are relatively robust.

• Replacing the model: Since the agricultural anti-risk capacity (explained variable) measured in this paper is between 0 and 1, which complies with the conditions of the limited dependent variable model, the Tobit model can be used for reassessment, and the fixed effect is still selected here. The parameters are estimated by maximum likelihood estimation with high estimation accuracy and reliability. By referring to the practice of Hao et al. [45] and comparing the regression results of the Tobit model and OLS (Table 5), it was found that after replacing the model, the influence direction of the estimated coefficient of digital financial inclusion remained unchanged. The contribution of digital financial inclusion to agricultural anti-risk capacity averaged 5.34% and was significant at the 1% level, suggesting that the benchmark result and the predicted result following model replacement were still compatible.

4.3. Heterogeneity Analysis

Given the large differences in resource endowments across regions, the impact of digital financial inclusion on agricultural anti-risk capacity may vary significantly. Thus, using three dimensions, this study further investigated the diverse impacts of digital financial inclusion: agricultural operation characteristics, stage of digital financial inclusion development, and industrial structure. The estimation results are shown in Table 6.

4.3.1. Scale of Agricultural Land Operation

Referring to Hua et al. [46], the digitalization of agriculture can alleviate the mismatch of farmland resources and promote the scale operation of farmland. To study the difference in the impact of digital financial inclusion on cultivated land of different sizes, this paper divided the per capita agricultural operation scale into two regressions according to the per capita seeding scale (seeding area/number of employees in the primary industry) (Table 6). It was found that the positive and significant impact of digital financial inclusion on agricultural anti-risk capacity is mainly reflected in agricultural operation areas with a large per capita cultivated land scale. As shown in Column 1 of Table 6, the average contribution of digital financial inclusion to agricultural anti-risk capacity is 17.3% in the case of large farmland operations, but the effect of digital financial inclusion on agricultural anti-risk capacity is not significant in the case of small farmland operations. Based on this, this paper argues that the scale of agricultural land management can affect the effect of digital financial inclusion on the development of agricultural anti-risk capacity. A possible reason for this is that the larger the scale of per capita management, the more willing farmers are to improve the efficiency of cultivated land use by improving agricultural production conditions. For small-scale farmers, the limitation of cultivated land resource endowment determines that farmers will minimize costs for the purpose of farming and lack sufficient incentives to use digital financial inclusion to increase productive investment. This is the same as the conclusion of Yang et al. [47] that smallholder farmers are at a disadvantage in terms of government support, urbanization, planting structure, etc., and have limited access to digital finance. In contrast, large-scale farmers have widely used and benefited from digital financial inclusion.

4.3.2. Stages of Development

Concerning The Peking University Digital Financial Inclusion Index of China, this paper divided the research period into the rapid development stage (2011–2015) and the intensive development stage (2016–2020) and constructed a segmentation panel data model for regression analysis. The results are shown in Table 6, and the coefficient becomes positive and significant only after 2016. This indicates that the positive effect of digital financial inclusion on cultivated land use efficiency in the intensive development stage is stronger than that in the rapid development stage. A possible reason for this is that the rapid development of digital financial inclusion failed to reach rural areas, resulting in low participation of farmers. With the improvement of the coverage breadth of policies and digital financial inclusion, the acceptance degree of farmers has gradually increased, thus improving the agricultural anti-risk capacity. This is similar to the conclusion of Quan et al. [48] that the agricultural risk resistance ability of eastern China was low before 2015 and then benefited from the development of economic, technological, and other resources to make agriculture show higher resilience.

4.3.3. Industrial Structure

Referring to the study by Fu [49], this paper constructs the angle value of industrial structure upgrading by taking the proportion of the increase in different industries to the proportion of GDP as a vector to measure the degree of industrial structure upgrading. Its calculation formula is as follows:

(4)$w_j=arccos\left[{\displaystyle \frac{\sum _{i=1}^3(Q_{i,j}\times Q_{i,0})}{(\sum _{i=1}^3{\left(Q_{i,j}^2\right)}^{\frac{1}{2}}\times \sum _{i=1}^3{\left(Q_{i,0}^2\right)}^{\frac{1}{2}})}}\right]$

(5)$ind=\sum _{k=1}^3\sum _{j=1}^k{w}_j=3w_1+2w_2+w_3$

The three variables $Q_1$, $Q_2$, and $Q_3$ represent the three industry vectors, where $Q_1$ = (1,0,0), $Q_2$ = (0,1,0), and $Q_3$ = (0,0,1). $Q_0$ is the spatial vector component of the ratio of the added value of the three industries to the GDP. $w_1$, $w_2$, and $w_3$ represent the respective angles between the industry vectors and $Q_0$, and ind represents the industry upgrading index.

The higher the degree of industrial upgrading, the stronger the role of digital financial inclusion in enhancing agricultural anti-risk capacity. The upgrading of industrial structure is characterized by a sequential increase in the proportion of the three industries. In the process of upgrading industrial structure, agriculture is the primary industry, and its lack of financial capital hinders the construction of its anti-risk capacity. The development of digital financial inclusion can lead to the flow of factors from other industries to the agricultural industry, thus contributing to the development of agricultural anti-risk capacity. It can be seen from the results in Table 6 that digital financial inclusion can promote agricultural anti-risk capacity through industrial upgrading. Thus, hypothesis 2 is verified. Similarly to the study by Gao et al. [50], this study suggests that through digital financial inclusion, rural industry integration is essential to boosting the agricultural economy’s resilience. An advanced industrial structure can promote the circulation of factors, upgrade the industrial structure, and promote the construction of agricultural anti-risk capacity. In regions with a low index of advanced industrial structure, the application of digital financial inclusion is poor due to the depth and breadth of its use by agriculture-related entities. The existence of the digital divide hinders the development of some farmers with low human capital who do not have digital skills, and the positive impact of digital financial inclusion on the development of agricultural risk resistance is limited. With the development of advanced industrial structure and the gradual closing of the digital divide, the promotion of factor flows and the enhancement of the capacity of agricultural operation entities by digital financial inclusion are gradually emerging, and its marginal benefits are gradually increasing.

Figure 1 shows the degree of impact of digital financial inclusion on agricultural anti-risk capacity under different heterogeneity conditions. The results show that when the industrial structure is advanced, the scale of agricultural operation and the development stage of digital inclusive finance are different, and the degree of influence of digital financial inclusion on the agricultural anti-risk capacity is gradually reduced.

4.4. Further Analysis

The agricultural economic system is not only subject to the influence of the time scale but also subject to corresponding geographical scale characteristics. This part mainly analyzes the spatial characteristics of digital financial inclusion on the development of agricultural anti-risk capacity. A spatial autocorrelation test of the model found that the values of the Moran’s I index of agricultural anti-risk capacity were all positive and passed the significance level test of at least 5%, indicating the existence of positive spatial dependence and the significant agglomeration of agricultural anti-risk capacity in neighboring cities, so the spatial econometric model could be used for testing. The results showed that there was a positive spatial spillover effect on agricultural anti-risk capacity (Table 7).

All the Moran’s I index values of digital financial inclusion from 2011 to 2020 were positive and passed the significance test of at least 5%, indicating that the development of digital financial inclusion in prefecture-level cities had a significant positive spatial correlation and showed an obvious clustering phenomenon in geographical space. This manifested as a spatial distribution of cluster (Table 8).

The degree of spatial correlation between digital financial inclusion and agricultural anti-risk capacity is shown in Figure 2. The Moran’s I values of both digital financial inclusion and agricultural anti-risk capacity were significant at least at the 5% level and characterized by an aggregated distribution. However, the spatial correlation of digital financial inclusion was more obvious compared to that of agricultural anti-risk capacity.

To explore the spatial effect of digital financial inclusion and agricultural anti-risk capacity, this paper used the LR test and Hausman test and then selected the spatial–temporal fixed-effects model. The spatial spillover effect of digital financial inclusion on the development of agricultural anti-risk capacity is divided into the influence of local digital financial inclusion on agricultural anti-risk capacity and the influence of surrounding areas’ digital financial inclusion on local agricultural anti-risk capacity. The two may have different action trends.

In terms of the impact of digital financial inclusion on the agricultural anti-risk capacity of local agriculture, although digital financial inclusion has a promoting effect on the agricultural anti-risk capacity in the early stage of development, this effect may present a situation of diminishing marginal returns. At the same time, due to the base level of agricultural anti-risk capacity being relatively low, its role may be more obvious. In summary, digital financial inclusion has a linear positive impact on agricultural anti-risk capacity.

In terms of the spatial spillover of digital financial inclusion on agricultural anti-risk capacity, on the one hand, it may have a siphon effect. Although digital financial inclusion has relaxed the loan constraints and expanded the scope of beneficiaries, this development can easily lead to new digital divides and digital inequalities. Especially when the surrounding areas lack advanced digital infrastructure and technology due to differences in the human capital of beneficiaries and so on. Especially when the surrounding areas lack advanced digital infrastructure and technology, large amounts of capital, labor, and other outflows accelerate the occurrence of the “urban shadow”. This, in turn, results in the Matthew effect of “the poor get poorer and the rich get richer”, thus hindering the development of agricultural anti-risk capacity. On the other hand, digital financial inclusion may accelerate the diffusion of production factors such as capital and technology from developed regions to relatively rural regions by reducing transaction costs, thus achieving the diffusion effect and promoting the improvement of agricultural risk resistance. The spatial spillover effect of digital financial inclusion in the surrounding areas is determined to some extent by the development degree of the local area relative to the surrounding area. Therefore, the impact of the development of digital financial inclusion in the surrounding areas on the local agricultural anti-risk capacity is nonlinear.

This study calculated the estimated results of the SDM model. According to column (2), the coefficient of digital financial inclusion is 0.044, and the coefficient of digital financial inclusion quadratic (dif²) is 0.300, which is significant at the 1% level. This indicates that the impact of the development of digital financial inclusion on the local agricultural anti-risk capacity is linear as shown in H1, and digital financial inclusion promotes the development of regional agricultural anti-risk capacity.

In column (3), the spatial impact coefficients of digital financial inclusion and its quadratic term are −0.208 and −0.187, respectively, which are significant at least at the 5% level. This indicates that digital financial inclusion in the surrounding areas also has a significant impact on the agricultural anti-risk capacity of the region, and its impact is inverted U-shaped. When the development degree of digital financial inclusion in the surrounding areas is higher than the critical value, the impact of digital financial inclusion on the regional agricultural anti-risk capacity is a siphon effect. When the development degree of digital financial inclusion in the surrounding areas is less than the critical value, the impact of digital financial inclusion on the regional agricultural anti-risk capacity is manifested as a diffusion effect. This may be due to the fact that when the level of digital financial inclusion in the neighboring regions is low, on the one hand, the growth rate is relatively fast due to the low base of digital financial inclusion, which may be shown by a significant decrease in transaction costs, as well as a significant reduction in asymmetric information barriers. On the other hand, as digital financial inclusion grows, the boundaries of social networks among farmers expand. This may drive the building of agricultural anti-risk capacity in the region by digital financial inclusion in neighboring regions. When the degree of development of digital financial inclusion in the neighboring region is high, the impact of digital financial inclusion on agricultural anti-risk capacity gradually manifests itself in the form of supply chain integration capabilities and relative advantages. On the one hand, when the degree of development of digital financial inclusion is high, the supply chain of farmers is basically formed, and the business entities and regions with higher bargaining power have a stronger ability of appropriation for the distribution of benefits. At the same time, the existence of relative advantages causes the relevant supply chain development gap to further expand. This is manifested in the negative impact of digital financial inclusion in neighboring regions on the development of agricultural anti-risk capacity in the region (Table 9).

5. Discussion and Conclusions

Anti-risk capacity, resilience, and sustainable development are a group of related economic concepts that have both commonalities and differences, the commonality being that they all emphasize the ability of a system to maintain an entity, outcome, or process [51]. The difference is that resilience puts more emphasis on the ability of the system to maintain its own stability and return to its initial state and innovation path after suffering from endogenous or external shocks [38]. In recent years, sustainable development, in addition to emphasizing the process of the system’s maintenance of its own development [52], has put more emphasis on maintaining a balanced development of environmental sustainability, social sustainability, and economic sustainability. Especially in recent years, the concept of sustainable development has been getting closer to environmental sustainability, as the worsening of the environment has not stopped. In recent years, with the increase in population, the demand for food has begun to increase. In order to improve food production, more and more chemical fertilizers and pesticides are put into the agricultural production process, which affects soil fertility and, in the long run, on the contrary, reduces the productivity of the land. This has a serious impact on the sustainable development of agriculture and also increases the chances of agricultural risks occurring. Thus, improving agricultural anti-risk capacity also puts demands on the capacity for sustainable agricultural development. Compared with agricultural resilience and sustainable development, agricultural anti-risk capacity building emphasizes more on the strike resistance of the agricultural economy in the face of external shocks and focuses more on the role of external support in enhancing intrinsic capacity, as well as the risk resistance and risk support brought about by strike resistance within the agricultural system. Based on this concept, this paper further discussed the impact of digital financial inclusion on building agricultural anti-risk capacity.

Firstly, in terms of the impact of digital financial inclusion on agricultural anti-risk capacity, most existing studies focused on the impact of digital financial inclusion on anti-risk capacity. For example, Ma et al. [53] argued that digital literacy promotes livelihood resilience among livestock farmers. Yang et al. [47] also argued that digital finance improves agricultural economic resilience. This paper found a positive contribution of digital financial inclusion to agricultural anti-risk capacity. However, few articles further focused on agricultural anti-risk capacity. This paper is the first to construct an indicator system of agricultural anti-risk capacity and further explore the impact of digital financial inclusion on agricultural anti-risk capacity, which is different from the established research. In turn, this paper constructed an index system of agricultural anti-risk capacity and explored the impact of digital financial inclusion on agricultural anti-risk capacity. Secondly, from the perspective of the heterogeneity of digital financial inclusion’s effects on agricultural anti-risk capacity, this paper argues that the larger the scale of agricultural operations, the higher the level of industrial structure, and the deeper the penetration of digital financial inclusion, the more digital financial inclusion helps to promote agricultural anti-risk development. For example, Zeng et al. [54] suggested that digital technology significantly facilitates land transfer, expands the scale of land operations, and promotes the development of agricultural modernization. Hou et al. [55] suggested that digital economy can enhance rural environmental governance and achieve sustainable rural development by accelerating industrial structure optimization. However, these studies failed to further discuss the heterogeneity of the impacts of differences in the scale of agricultural operations, industrial structure, and degree of penetration of digital financial inclusion on agricultural anti-risk capacity based on the context of digital financial inclusion; this paper performed an extended study in this regard. Finally, in terms of spatial characterization, most of the existing literature focused on exploring the linear effect of digital financial inclusion on agricultural anti-risk capacity. For example, Qin et al. [56] suggested that there is a spatial spillover effect of digital green finance on agricultural green total factor productivity. And in this paper, considering the convenience of digital technology and the hindering nature of digital divides, the trend of the impact of digital financial inclusion showed a nonlinear impact. Therefore, this paper explored the nonlinear effect of digital financial inclusion on agricultural anti-risk capacity by adding a quadratic term. In addition, this paper chose the main grain-producing areas in the plains as the study area and selected three provinces with similar latitude and longitude in order to explore whether the scale of agricultural land operation, industrial structure, and development of digital financial inclusion in the plains affect the impact of digital financial inclusion on agricultural anti-risk capacity. This kind of region-specific study is more targeted than a large-scale national study, which is conducive to the subsequent proposal of relevant policies.

Based on the two-way, fixed-effect model, this paper analyzed the impact of digital financial inclusion on the development of agricultural anti-risk capacity in the Henan, Anhui, and Jiangsu provinces from 2011 to 2020 in the Huaihe River Basin. On the basis of a systematic theoretical framework, a system of agricultural anti-risk capacity indicators was constructed. Under this framework, we examined the impact of digital financial inclusion on agricultural anti-risk capacity. The results of this study show that digital financial inclusion promotes agricultural anti-risk capacity by an average of 14.35% after controlling for variables such as region and time. Considering the potential endogeneity and robustness issues of the results, we adopted an instrumental variable approach, and these consistent results also demonstrated that digital financial inclusion promotes agricultural anti-risk capacity. In the case of larger-scale agricultural operation, a higher level of industrial structure, and deeper penetration of digital financial inclusion, the promotion effects of digital financial inclusion on agricultural anti-risk capacity are 17.3%, 9.31%, and 28.01% on average, respectively. In the spatial econometric model, the impact of digital financial inclusion in the neighboring regions on the agricultural anti-risk capacity of the region shows an inverted U-shaped curve. With the development of digital inclusive finance, the effect of digital inclusive finance in neighboring regions gradually transforms from a diffusion effect to a siphon effect on the region. This study defined the concept of agricultural anti-risk capacity for the first time and constructed an evaluation index system, which lays a better research foundation for agricultural risk management-related research. In addition, using the Huaihe River Basin as a sample area, this study examined the impact of digital financial inclusion on agricultural anti-risk capacity at different stages of farmland operation scale and digital financial inclusion development, which is of great significance for other developing countries.

Based on this, there are the following policy implications: (1) The government should focus on supporting digital financial inclusion in improving farmers’ anti-risk capacity. For example, in terms of agricultural socialization services and the purchase of agricultural facilities, the corresponding digital inclusive the corresponding digital financial inclusion should be provided with convenience and preferential policies in terms of use with convenience and preferential policies in terms of use. (2) Appropriately relaxing land transfer-related policies to promote the expansion of the scale of agricultural land management. In turn, it will better utilize the positive role of digital financial inclusion in building agricultural anti-risk capacity. (3) The development of digital financial is a phased process that should not only pay attention to the speed of digital financial development but also promote the development of digital financial inclusion to the stage of intensive development to further enable the positive role of digital financial inclusion in promoting the development of agricultural anti-risk capacity. It should be noted that digital financial inclusion has a nonlinear impact on the development of agricultural anti-risk capacity. Effects on the local and surrounding agricultural anti-risk capacity also manifest in different influencing processes. Therefore, it is necessary to continuously improve the development of local digital infrastructure and improve the digital ability of farmers, so as to promote the inclusive role of digital financial inclusion in the development of agricultural anti-risk capacity. On the other hand, we should promote the flow of financial resources to the surrounding economically less developed areas and leverage the diffusion effects of digital financial inclusion.

Due to the limitations of data availability, it was not possible to further analyze the specific characteristics at the farmer’s level. Moreover, it was not possible to conduct a comparative study on the development of digital financial inclusion and the development of agricultural anti-risk capacity between China and other developing countries (such as India). As such, we will consider obtaining micro-data through field research to supplement this part of the study.

Author Contributions

Conceptualization, Y.C. and L.X.; writing—original draft preparation, Y.C.; writing—review and editing, Y.C. and Y.W.; methodology, Y.C. and S.X.; supervision, L.X. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data used in this article are available in a publicly accessible repository. The original data presented in the study are openly available in statistical yearbooks at https://data-cnki-net-443.webvpn2.sxnu.edu.cn/ (accessed on 3 December 2024), https://www.epsnet.com.cn/index.html#/Index (accessed on 12 November 2024), and https://db.cei.cn/jsps/Home (accessed on 6 October 2024) and on official government websites. The digital financial inclusion data came from The Peking University Digital Financial Inclusion Index of China.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:

agr	Agricultural risk resistance ability	Multidisciplinary Digital Publishing Institute
dfi	Digital financial inclusion index	Directory of open access journals
gov	Government expenditure	Linear dichroism
mod	Development of agricultural business entities
scale	Social development
save	Financial support

Footnotes

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Figures and Tables

Figure 1. Heterogeneity analysis of digital financial inclusion on agricultural anti-risk capacity.

Figure 2. Spatial correlation analysis of digital financial inclusion and agriculture anti-risk capacity from 2011 to 2020.

Table 1

Evaluation index system for agricultural risk resistance ability.

Indicator	Explanation	Indicator Properties
Risk resistance ability	Grain production/grain sown area	+
	Total power of agricultural machinery/grain sown area	+
	Employment in primary industry	+
	Surface water resources/grain sown area	+
	Fertilizer application amount/grain sown area	−
	Value added by primary industry as a proportion of GDP	+
	Rural electricity consumption	+
Risk support ability	Number of township divisions	+
	Rural disposable income	+
	Rural per capita consumption expenditure	+
	Total retail sales of consumer goods	+
	Highway mileage	+
	Agricultural insurance amount/grain sown area	+
	Rural–urban income ratio	−
	Urbanization rate	+
	Beds in health institutions	+
	Number of mobile phones	+
	Fiscal expenditure on agriculture, forestry, and water conservancy	+

Table 2

Variable definitions and descriptive statistical results.

Variables	Mean	Std	Min	Max
Agricultural anti-risk capacity	0.1849	0.0786	0.0648	0.6034
Digital financial inclusion index	180.2504	71.3920	23.88	313.9
Industry upgrading index	6.5631	0.2922	5.7648	7.2697
Government expenditure	4.1187	0.6711	2.0894	5.9533
Development of agricultural business entities	0.2963	0.1102	0.0477	0.5848
Social development	0.0671	0.0327	0.0136	0.1667
Financial support	17.0523	0.9514	14.9321	19.7831

Table 3

Regression results of the impact of digital financial inclusion on the agricultural risk resistance ability.

Variables	(1)agr	(2)agr	(3)agr	(4)agr	(5)agr
dfi	0.1028 ***	0.0522 ***	0.1648 **	0.0561 ***	0.1435 *
	(0.0062)	(0.0076)	(0.0741)	(0.0108)	(0.0811)
gov		0.0367 ***	0.0383 ***	0.0314 ***	0.0294 ***
		(0.0085)	(0.0096)	(0.0094)	(0.00978)
mod		0.0996 ***	0.0710 ***	0.0920 ***	0.0604 ***
		(0.0207)	(0.0194)	(0.0228)	(0.0217)
scale		1.0862 ***	1.0867 ***	1.4831 ***	1.4288 ***
		(0.3291)	(0.3131)	(0.4436)	(0.421)
save		0.0090 **	−0.0015	0.0081 *	−0.0055
		(0.0041)	(0.0056)	(0.0045)	(0.00628)
Amount of observed data	460	460	460	460	460
$R-squared$	0.7886	0.8435	0.8623	0.8457	0.8644
Region fixed effect	NO	NO	NO	YES	YES
Year fixed effect	NO	NO	YES	NO	YES

Notes: *** p < 0.01, ** p < 0.05, * p < 0.10.

Table 4

Regression results using instrumental variable method.

Variables	First Stage	Second Stage
dfi		1.0111 ***
		(0.1749)
instrumental variables	1.3060 ***
	(0.1960)
gov	−0.0317 ***	0.0485 ***
	(0.0085)	(0.0105)
mod	0.0562 ***	−0.0164
	(0.0213)	(0.0287)
scale	0.1028	0.7691 ***
	(0.2414)	(0.2891)
save	0.1515 **	−0.0145 **
	(0.0061)	(0.0073)
Amount of observed data	460	460
Cragg–Donald Wald F	44.4
Region/year fixed effect	YES	YES

Notes: *** p < 0.01, ** p < 0.05.

Table 5

Regression results of robustness test.

Variables	(1)	(2)	(3)	(4)
	agr	agr	agr	agr
	Lagged Variable	Temperature Control	Precipitation Control	Replacement Model
dfi		0.1426 *	0.1433 *	0.0534 ***
		(0.0812)	(0.0811)	(0.00659)
L. dfi	0.163 **	0.0289 ***	0.0271 ***
	(0.0706)	(0.0097)	(0.0100)
gov	0.0346 ***	0.0622 ***	0.0521 **	0.0356 ***
	(0.0115)	(0.0218)	(0.0222)	(0.00563)
mod	0.0504 **	1.4432 ***	1.4296 ***	0.0979 ***
	(0.0227)	(0.4206)	(0.4203)	(0.0150)
scale	1.220 ***	−0.0054	−0.0067	1.156 ***
	(0.428)	(0.0063)	(0.0062)	(0.150)
save	−0.00341	0.1426 *	0.1433 *	0.00857 **
	(0.00583)	(0.0812)	(0.0811)	(0.00374)
temperature		controlled
precipitation			controlled
Amount of observed data	414	460	460	460
$R-squared$	0.8422	0.8647	0.8691
Region/year fixed effect	YES	YES	YES	YES

Notes: *** p < 0.01, ** p < 0.05, * p < 0.10.

Table 6

Regression results of economic characteristic heterogeneity.

Variables	(1)		(2)		(3)
	The Scale of Agricultural Land Is Large	The Scale of Agricultural Land Is Small	2011–2015	2016–2020	The Level of Industrial Structure Is High	The Level of Industrial Structure Is Low
dfi	0.173 *	0.00144	0.0248	0.0931 **	0.2801 **	−0.0645
	(0.101)	(0.0870)	(0.0557)	(0.0427)	(0.1043)	(0.0481)
gov	0.0211 *	0.0432 **	0.00771	0.0285 **	0.0269	0.0077
	(0.0119)	(0.0160)	(0.00623)	(0.0118)	(0.0182)	(0.0087)
mod	0.0235	0.0662 *	0.0826 **	0.0236	0.0577*	0.0269
	(0.0353)	(0.0365)	(0.0403)	(0.0284)	(0.0340)	(0.0255)
scale	2.234 **	1.247 ***	1.001 *	1.079 ***	1.2425 ***	0.7587
	(1.016)	(0.263)	(0.566)	(0.258)	(0.3242)	(0.5647)
save	−0.0141	0.0124 **	−0.00492	0.00440	−0.0086	0.0217 ***
	(0.00887)	(0.00572)	(0.0159)	(0.00363)	(0.0079)	(0.0058)
Amount of observed data	230	230	230	230	230	230
$R-squared$	0.8679	0.8789	0.8432	0.6933	0.8528	0.8998
Region/year fixed effect	YES	YES	YES	YES	YES	YES

Notes: *** p < 0.01, ** p < 0.05, * p < 0.10.

Table 7

Moran’s I of agricultural anti-risk capacity in China from 2010 to 2020.

Year	Moran’s I	Z-Value
	agr
2011	0.3101 ***	3.8443
2012	0.2775 ***	3.4787
2013	0.2656 ***	3.3458
2014	0.2581 ***	3.2598
2015	0.2552 ***	3.2174
2016	0.2654 ***	3.3702
2017	0.2320 ***	2.9333
2018	0.2323 ***	2.9351
2019	0.2145 **	2.7435
2020	0.2274 **	2.8841

Notes: *** p < 0.01, ** p < 0.05.

Table 8

Moran’s I of digital financial inclusion in China from 2010 to 2020.

Year	Moran’s I	Z-Value
	dfi
2011	0.5842 ***	6.5179
2012	0.5618 ***	6.2836
2013	0.5530 ***	6.2156
2014	0.6072 ***	6.7648
2015	0.5541 ***	6.2167
2016	0.4989 ***	5.6124
2017	0.5307 ***	5.9630
2018	0.5509 ***	6.1772
2019	0.5359 **	6.0113
2020	0.5437 **	6.0898

Notes: *** p < 0.01, ** p < 0.05.

Table 9

Decomposition of spatial effects based on geographic distance weight matrices.

	(1)	(2)	(3)	(4)
Variables	Main	Direct	Indirect	Total
dfi	0.055 *	0.044	−0.208 **	−0.163 *
	(0.031)	(0.032)	(0.085)	(0.095)
dfi2	0.312 ***	0.300 ***	−0.187 ***	0.113 ***
	(0.029)	(0.027)	(0.040)	(0.032)
Control variable	Controlled	Controlled	Controlled	Controlled
ρ	0.321 ***
	(0.060)
Log L	1539.104
Amount of observed data	460	460	460	460

Notes: *** p < 0.01, ** p < 0.05, * p < 0.10.

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