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Article

Impact and Mechanisms of Digital Inclusive Finance in Relation to Farmland Transfer: Evidence from China

by
Ziqin Xu
†,
Hui Niu
†,
Yuxuan Wei
,
Yiping Wu
* and
Yang Yu
*
College of Economics and Management, Henan Agricultural University, Zhengzhou 450046, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2024, 16(1), 408; https://doi.org/10.3390/su16010408
Submission received: 4 December 2023 / Revised: 23 December 2023 / Accepted: 25 December 2023 / Published: 2 January 2024
Browse Figures
Figure 1
<p>Theoretical framework.</p> "> Figure 2
<p>Path diagram of the mediating effects. “**”, and “***” indicate significance at the statistical levels of 5%, and 1%.</p> ">
Versions Notes

Abstract

:
Land use efficiency is primarily limited by the fragmentation of land management. China’s fragmented farmland poses a significant threat to the country’s food security and rural revitalization. Therefore, promoting land transfer to establish large-scale operations is a significant solution. With digital technology’s advancements, digital inclusive finance (DIF) has permeated rural regions to provide financial assistance for farmers’ livelihood and rural development. However, it remains unclear if and how DIF can incentivize land transfer. Therefore, this paper aims to establish an econometric model to analyze the impact of digital inclusive finance on land transfer. Additionally, a chain mediation effect model is established to analyze how DIF affects land transfer through an exploration of the mechanisms of farmers’ livelihood capital and the use of digital information. Therefore, the findings from the analysis of data from 3165 farmers demonstrate that DIF has the potential to notably facilitate land transfer and work through the chain mediation channel. Moreover, the impact of DIF on land transfer is even more pronounced in economically developed regions. Consequently, this paper’s results hold the potential to inform policy making by offering insight into three viable paths—digital inclusive financial support, livelihood capital, and digital information—as means to promote land transfer.

1. Introduction

Land is a vital resource for agricultural development and a critical factor in the lives of farmers. The household contract responsibility system implemented in China in the last century fully mobilized the enthusiasm of farmers for production, greatly promoted agricultural productivity [1,2], stabilized and promoted economic and social development, and made major contributions to China’s agricultural growth. However, because of the advancements in agricultural production technology and the shift in labor to secondary and tertiary industries, small-scale farming has led to low comparative benefits in agriculture and the abandonment of cultivated land. This phenomenon has become increasingly serious. This situation poses a serious threat to sustainable agricultural development and food security. The contradiction between China’s agricultural modernization goal of improving agricultural productivity and the current situation is increasingly evident. Agricultural land management on a large scale has the potential to decrease the cost of land operations, establish economies of scale [3], and support the sustainable growth of agriculture. Therefore, the Chinese government proposed the “appropriate scale management of agriculture” and the land reform policy of the “separation of three rights” (namely, ownership, contract rights, and management rights) to enhance land resource efficiency, comparative agricultural benefits, total factor productivity, and, ultimately, sustainability in agriculture.
Subsequently, the Chinese government actively promoted and enhanced the land transfer system to achieve these goals. Relevant policies and regulations have been implemented to facilitate the transfer of agricultural land. Under strong promotion by the Chinese government, the scale of farmland transfer in China has grown rapidly. Farmland transfer has gradually become an effective means of solving the problem of abandoned cultivated land, an important way to realize large-scale agricultural management, and an inevitable trend in the high-quality development of agriculture. However, the marketability of agricultural land in China is low compared to the urgent need for intensive land use [4], which has restricted the scale and efficiency of farmland transfer to some extent. Therefore, finding new driving forces to promote rural land transfer is the main focus of future research. Existing scholars have studied various aspects of rural land transfer, including the willingness to grow nonfood crops [5], property rights systems [6], agricultural green total factor productivity [7], agricultural labor [8], agricultural socialized services [9], agricultural resource endowments [10], land tenure clarification [11], financial literacy [12], subsidy policies [13], land lease agreements [14], agriculture-related loans [15], and agricultural household start-ups [16]. In fact, the most important factor limiting farmland transfer is the shortage of disposable funds for farmers. The main sources of funding are endogenous family income and exogenous financial support. Endogenous factors have a significant impact on farmland transfer [17], while exogenous financial support, which is reflected in credit availability, also has a significant impact on farmland transfer [18]. The development of rural finance can provide financial support to farmers, increase farmers’ disposable funds, and promote farmland transfer, thereby achieving large-scale agricultural management. At the same time, financial development promotes improvements in people’s living standards and agricultural production efficiency, and promotes economic growth [19,20], thus achieving the goal of agricultural and rural modernization, comprehensively improving various indicators of farmers’ production and living standards. The coupling coordination relationship between financial development and farmland transfer [21] is of great significance for rural economic growth.
Before defining the concept of digital inclusive finance, it is essential to clarify the concept of inclusive finance. As part of financial development, the term inclusive finance was formally introduced by the United Nations in 2005, referring to a financial system that can effectively and comprehensively provide services to all strata of society [22]. Regarding the definition of digital inclusive finance, this study adopted the definition in the 2016 Global Partnership for Financial Inclusion (GPFI) report by the G20 [23]: Digital inclusive finance refers to all actions that use digital financial services to promote inclusive finance. It specifically includes the use of digital technology to provide a range of formal financial services to groups that cannot access or lack financial services. The financial services provided meet their needs and are delivered in a responsible, affordable manner that is sustainable for service providers. Digital inclusive financial services encompass financial products and services, including credit, payments, insurance, and wealth management. These products and services are realized through digital technologies, such as electronic currencies, payment cards, or traditional bank accounts. In the traditional framework of conventional finance, the high risk in agricultural and rural areas has created obstacles to accessing external financing for farmers. In comparison, digital inclusive finance has two advantages. Firstly, it has the advantage of being low cost. The development of digital inclusive finance allows both the supply and demand sides to exchange and transact relevant information through the Internet, further increasing the degree of information openness on the basis of providing selectivity for both parties, which can effectively avoid the transaction costs caused by information asymmetry. Secondly, the coverage of digital inclusive finance is broader. By using new communication tools, such as the Internet, digital inclusive finance can break through the geographical limitations faced by traditional financial industries. Traditional financial service institutions mostly set up relevant businesses in densely populated areas with good economic development, making it difficult in many areas to access the needed financial services. The emergence and popularization of digital inclusive finance have further expanded the scope of services that financial institutions can provide, overcoming geographical and spatial limitations, and expanding their coverage for users. Therefore, digital inclusive finance is a combination of digital communication technology and traditional finance. Through the widespread use of tools such as the Internet and big data, the development of digital inclusive finance effectively alleviates the problem faced by farmers of “difficulty in financing and high financing costs” with traditional finance, allowing credit loans to flow to farmers and rural areas, thereby providing financial support for farmers’ land transfer. In addition, the rapid development of internet technology in China has also provided tremendous help in the promotion of digital inclusive finance. In 2020, China’s internet penetration rate reached 70.4%, and the broadband network coverage rate in poor villages reached 98%. Therefore, digital inclusive finance is a novel financial system that integrates digital technology, leveraging the Internet, big data, and artificial intelligence, with traditional financial systems. The resulting system fosters inclusivity, making financial services accessible to a wider population. It offers appropriate financial services to low-income communities thanks to digital technology, serving as a valuable starting point for the Chinese government’s efforts to implement its rural revitalization strategy, promote the modernization of agriculture and rural regions, and achieve sustainable agricultural development.
In terms of research on digital inclusive finance, the existing literature mainly focuses on the impact of digital inclusive finance on agricultural green total factor productivity [24], farmers’ consumption structure [25], urban–rural income gap [26,27,28], food security [29], labor mobility [30], rural tertiary industries [31], and economic growth [32]. In terms of research on finance and farmland transfer, Shen found that digital inclusive finance promotes agricultural green total factor productivity by promoting farmland transfer [22]. Lei believes that the use of digital finance significantly increases the possibility and proportion of farmland transfer-out [4]. Liu argues that the farmland transfer system has a greater impact on the rural financial development system through the coupling coordination of the farmland transfer system and the rural financial development system, suggesting that the government should increase support for rural financial institutions [21]. Cai et al. tested the impact of digital inclusive finance on farmland transfer from a macro perspective using provincial panel data [33]. Zhang analyzed the impact of digital inclusive finance on farmland transfer from a micro perspective [34].
For farmers, livelihood capital is the foundation for engaging in livelihood activities, serving as a fundamental condition for adjusting livelihood strategies and improving living standards. The sustainable livelihood framework developed by the UK’s Department for International Development (DFID) is relatively typical [35]. The development of digital inclusive finance not only provides a source of funding and credit guarantee for farmers in transferring land but also has multidimensional impacts on farmers’ livelihood capital. This framework categorizes livelihood capital into five types: human capital, natural capital, physical capital, financial capital, and social capital. It describes how households, in the face of risk environments created by markets, institutional policies, and natural factors, utilize a variety of assets, rights, and strategies to enhance their livelihoods. It reflects the interrelated changes and interactions among farmers’ livelihood capital structure, livelihood processes, and livelihood goals. Wang et al. empirically analyzed the impact and mechanism of digital finance’s use on farmers’ diversification upgrades using fractional probit models, and they found that the use of digital finance significantly improves the diversification level of farmers’ livelihoods [36]. At the same time, farmers’ livelihood capital also has a significant impact on farmland transfer [37].
In summary, existing studies have not conducted a comprehensive analysis of the impact of digital inclusive finance on rural land transfer from the perspectives of farmers’ livelihood capital and the digital information effect. Especially in the context of rural revitalization, common prosperity, the construction of a digital China, and the modernization of agriculture and rural areas, the urgency of analyzing the impact and mechanisms of digital inclusive finance on rural land transfer in China becomes increasingly apparent. Therefore, this paper focuses on farmers’ livelihood capital and the digital information effect to study the impact and mechanisms of digital inclusive finance related to rural land transfer. The aim is to provide a new channel for studying the impact path of digital inclusive finance on rural land transfer and, consequently, offer policy recommendations for government departments, financial institutions, and other relevant fields.
On the basis of the Digital Inclusive Finance Index, compiled by Peking University (DIF), and microsurvey data from the China Rural Revitalization Survey, conducted by the Rural Development Institute at the Chinese Academy of Social Sciences (CRRS) in 2020, this paper examined the relationship between digital inclusive finance and rural land transfer from both a theoretical and empirical perspective.
The potential incremental value of this study resides in two areas. Initially, it examines the correlation between digital inclusive finance and rural land transfer. Earlier research has given scant attention to this association. Fundamentally, the influence of digital inclusive finance on the flow of agricultural land is rudimentary. After obtaining loans through digital inclusive finance, farmers primarily involved in agricultural work tend to increase the amount of agricultural land they transfer. This is due to limitations related to knowledge, literacy, and other factors causing them to be more willing to transfer additional land [2]. Farmers who are primarily engaged in nonagricultural employment, on the other hand, show greater interest in transferring their land. Additionally, previous studies focused primarily on farmers’ behaviors or certain aspects while disregarding farmers’ unique endowment conditions. As a result, this approach fails to fully and clearly reveal the internal mechanism and may result in biased results. Digital inclusive finance provides financial support to farmers, and digital technology has created new channels for farmers to access information about production and daily life. Therefore, this study examined the impact and mechanisms of digital inclusive finance related to farmland transfer by focusing on farmers’ livelihood capital and the digital information effect. The aim was to fill the gap in knowledge that exists in the literature. Additionally, previous studies have been unable to provide a clear explanation of the internal impact mechanism of digital inclusive finance related to rural land transfer, and multiple mechanisms may be at play. Therefore, this paper integrated two intermediary mechanisms, the livelihood capital effect and the digital information effect, into a cohesive analytical framework. This expands the multiple action mechanisms between digital inclusive finance and rural land transfer, resulting in a novel approach. The chain intermediary model clarifies the potential connections among various mechanisms, improving the impact mechanism of digital inclusive finance on rural land transfer and making the intermediary channels clearer.
The rest of this paper is arranged as follows: Section 2 provides the theoretical analysis and research hypotheses. Section 3 introduces the data sources, model construction, and index construction for the data, followed by descriptive statistics. Section 4 presents the empirical results and a discussion of the research. Section 5 offers the research conclusions and policy recommendations.

2. Theoretical Analysis and Research Hypotheses

2.1. Direct Impact of Digital Financial Inclusion on Farmland Transfer

Because of the real factors of a large population and, thus, less land in China, as well as the land system, most of China’s farmland is used in a small-scale and fragmented manner by farmers, which limits large-scale agricultural management and restricts the development of modern agriculture [38]. Over the past forty years, China’s farmland transfer system has not been perfect. The focus of China’s farmland system reform is to “stabilize land property rights” and “promote farmland transfer” [39]. With the reform and improvement in the land system, the scale of farmland transfer in China has gradually increased. By 2020, the contracted farmland area of family farming households in China was 1,561,662,400 mu, and the number of farming households under contract was 220,409,800 (source: Department of Policy and Regulation, Ministry of Agriculture, and Rural Affairs). In the large-scale transfer of land, the rural financial market has a significant impact on the willingness of farmers to participate in farmland transfer [40]. Because of the generally low credit rating of farmers, high loan risks, lack of mortgage assets, and other reasons, it is difficult to obtain support in the agricultural sector from traditional financial institutions, but the internal financing of agriculture plays a very important role [41], which is an indispensable part of agricultural development. Digital inclusive finance based on digital technology can greatly improve the efficiency of the agricultural sector’s fund matching and the availability of financial services at a low cost [42]. Specifically, as an information channel, digital inclusive finance can expand the scope and reach of financial services by fully exerting its role in improving the efficiency and expanding the scope of information dissemination, thereby promoting the reform and innovation of traditional financial institutions [43,44] and improving the efficiency of the financial industry [45]. Using a digital inclusive financial platform, farmers can obtain credit funds based on their specific conditions. This increases their disposable funds and investment in agriculture [46]. The platform also boosts the likelihood of land transfer out of rural nonagricultural employment [47] and enhances the willingness to transfer agricultural land.
On the basis of the above analysis, this paper proposes the following research hypothesis:
H1. 
Digital inclusive finance has a significant influence on farmland transfer.

2.2. Indirect Impact of Digital Financial Inclusion on Farmland Transfer

This section discusses the indirect impact of digital inclusive finance on farmland transfer from the two aspects of livelihood capital and the digital information effect, and discuss the relationship between them.
Firstly, with livelihood capital, the development of digital inclusive finance helps improve farmers’ level of livelihood capital, thereby promoting the participation of farmers in farmland transfer. The development of digital inclusive finance alleviates the financial exclusion of farmers [48], improves the availability of credit for farmers [49], increases farmers’ disposable funds, and effectively improves their own financial capital. An improvement in financial capital will affect farmers’ mechanization level [50] and likelihood of participating in land transfer [51]. However, improvements in farmers’ financial capital may also lead to the transfer of more land [52], the establishment of businesses [53,54], or gaining nonagricultural employment [55]. This is in addition to farmers’ human capital, as the processes of recognizing, identifying, and, ultimately, acquiring digital inclusive finance—especially whether it can be effectively combined with other types of livelihood capital after obtaining it and participating in farmland transfer—largely depend on the level of farmers’ human capital. Moreover, farmers can also increase investment in human capital using funds obtained through digital inclusive finance [56]. Farmers with more human capital will use more advanced production technologies and tools, thus generating demand for land transfer. The enhancement of farmers’ human capital will provide them with a competitive edge in nonagricultural management. Consequently, they may shift their focus toward land use and delve into nonagricultural management practices. The development of digital inclusive finance can also increase the accumulation of material capital by expanding the financial scale [57], encouraging farmers to purchase durable consumer goods, such as agricultural production machinery and tools, or real estate to improve their material capital, thereby improving farmers’ labor efficiency and agricultural production efficiency, motivating farmers to transfer land. The natural capital owned by farmers is also an important factor affecting whether farmers participate in farmland transfer. The more natural capital a farmer has, the more likely that farmer is to adopt agricultural production as their main livelihood; influenced by digital inclusive finance, farmers will be more inclined to transfer land to obtain benefits of scale [58]. If farmers have less natural capital, they will be more inclined to transfer land for nonagricultural operations to increase income. Finally, the development of digital inclusive finance also increases the number of “weak relationships” by promoting the development of network interaction models, improving farmers’ social capital, providing information that supports farmers to participate in farmland transfer, increasing farmers’ employment and entrepreneurship channels, and encouraging farmers to leave agriculture management [59]. At the same time, it also enables farmers to obtain more information about farmland transfer, promoting land transfer.
On the basis of the above analysis, this paper proposes the following research hypothesis:
H2. 
Digital inclusive finance can significantly impact farmers’ livelihood capital, thereby promoting land circulation.
Secondly, with the digital information effect, digital inclusive finance can encourage farmers to participate in farmland transfer through the effects of digital information. The purpose of this paper was to explore the impact of digital information on farmers’ productivity and quality of life. Specifically, we investigated how farmers access and utilize online resources to meet their production and personal needs. Some studies have found that internet use can have a significant impact on farmland transfer. First, internet use can affect farmland transfer-out via three paths—promoting nonagricultural employment and its stability, broadening information channels, and enhancing social interaction [60]—as well as affecting farmland transfer-out by reducing the risks of unemployment for the nonagriculturally employed in transfer-out households [61]. Secondly, it can also have a significant impact on farmland transfer-in [62], expanding the scale of farmers’ land management [63]. Digital inclusive finance achieves the accurate delivery and push of information by relying on internet and big data technologies. This improves farmers’ convenience when obtaining information, as well as its accuracy; effectively alleviates information asymmetry between market entities; and removes the geographical restrictions related to the “acquaintance society”, enabling the smooth transmission of land transfer information in the farmland transfer market, and further improving the farmland transfer system, which is conducive to the marketization of farmland transfer. Farmers obtain the information they need for their production and lives through the use of the Internet, thus gaining access to and collecting more information on farmland transfer and engaging in online interactions through the network, promoting the flow of land transfer information, thereby reducing transaction costs for farmers, alleviating information asymmetry, and increasing the probability of farmers participating in farmland transfer activities. In summary, the development of digital inclusive finance will drive regional digital transformation [64], increase Internet penetration, and, thus, promote farmers’ use of the Internet to meet their production and living needs, ultimately affecting farmers’ participation in farmland transfer.
On the basis of the above analysis, this paper proposes the following research hypothesis:
H3. 
Digital inclusive finance can encourage farmers to participate in farmland transfer through digital information effects.
Thirdly, there may be a mutual influence between the above two mechanisms. Of these two mechanisms, livelihood capital is, undoubtedly, the decisive factor for whether farmers participate in farmland transfer. A large number of existing studies have proven that internet use can optimize the allocation of farmers’ livelihood capital [65]. The digital information effect has facilitated farmers in meeting their financial needs by easing credit constraints [66,67,68] and improving credit availability [69] while augmenting their financial capital. Additionally, digital information channels enable farmers to acquire production tools, durable consumer goods, and real estate or homesteads, thus enhancing their material capital. We can improve our human capital by utilizing the network more efficiently for learning and training to enhance agricultural production skills or nonagricultural management levels. The Internet acts as a social medium that enables farmers to maintain their “acquaintance network” by keeping in touch with friends and relatives, thus increasing the accumulation of social resources and enhancing the social capital of farmers [70,71,72]. On the other hand, in the process of farmers using the Internet to obtain information, the expansion of farmers’ social boundaries promotes the development of weak contact networks, which is conducive to farmers obtaining more extensive social capital. Therefore, the digital information effect can enhance farmers’ livelihood capital and further strengthen the intermediary role of livelihood capital in the impact of digital inclusive finance on farmers’ participation in farmland transfer behavior; that is, there is a chain intermediary effect between the digital information effect and livelihood capital.
Based on the above analysis, this paper proposes the following research hypothesis:
H4. 
Digital inclusive finance can impact farmers’ livelihood capital through the digital information effect, promoting further participation in agricultural land transfer. This creates a chain intermediary role.
Based on the above theoretical analysis, the theoretical framework of this study is shown in Figure 1.

3. Data and Methodology

3.1. Variable Selection

3.1.1. Explained Variable

In this paper, the transfer of agricultural land (LAND) is taken as the dependent variable. Agricultural land transfer encompasses both the inflow and outflow of land, considering any action by farmers involving the transfer of land, whether it be acquisition or disposition, as constituting the occurrence of agricultural land transfer. Farmland transfer includes farmland transfer-in and farmland transfer-out. Therefore, this paper used the following items in the questionnaire to measure “whether the farmer transferred in or out land in 2019”. If so, it is assigned a value of 1. Otherwise, it is assigned a value of 0.

3.1.2. Core Explanatory Variable

For digital inclusive finance (DIF), this paper selected the Digital Inclusive Finance Index of China, compiled by Ant Group and Peking University Digital Finance Research Center, as the core explanatory variable. This index has wide coverage, a comprehensive indicator selection, includes typical institutions in China’s digital finance field as samples, and conducts research on massive amounts of microdata to construct the complete “Peking University Digital Inclusive Finance Index of China”. It covers 31 provinces, 337 prefecture-level cities, and approximately 2,800 counties and districts from 2011 to 2020, accurately and scientifically depicting the real development status of digital inclusive finance in China, and providing data that support relevant research fields; it is widely used by many scholars [73,74,75,76].

3.1.3. Mediating Variable

First, the digital information effect (DIE), in this paper, refers to farmers obtaining the information needed for their production and life through the use of the internet. Specifically, we investigated how farmers access and utilize online resources to meet their production and personal needs. On this basis, this paper used “whether network information can meet the needs of production and life” to measure the DIE, which was divided into the following five levels: 5 = completely satisfied; 4 = basic satisfaction; 3 = general; 2 = not very satisfied; and 1 = completely dissatisfied.
Second, this paper selected livelihood capital (LC) as the mediating variable. On the basis of existing research by the UK’s Department for International Development (DFID) [35] and scholars [77,78,79], this paper divided farmers’ livelihood capital into five aspects—human capital, social capital, financial capital, and physical capital—according to the DFID Sustainable Livelihood Framework. Fourteen indicators were selected for measurement, with specific choices as follows: (1) human capital—represented by the age of the household head, the education level of the household head [80,81], and the number of family laborers; (2) physical capital—measured by the number of productive tools owned by the family, the quantity of livestock raised [82], and the area of residential land; (3) financial capital—represented by the family’s annual income, credit availability, and the total value of cash and deposits [65,83]; (4) social capital—characterized by expenditures and income from interpersonal relationships [65,83] and the degree of trust in relatives and neighbors; and (5) natural capital—represented by the total area of cultivated land and the number of land plots [84]. Evaluating farmers’ livelihood capital is crucial in studying the impact of digital inclusive finance on land transfer. The entropy weighting method is used to reduce subjective influence, ensuring scientific and accurate reasoning during the process. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was employed to calculate the comprehensive value of farmers’ livelihood capital. Therefore, drawing on relevant studies [85,86,87] and combining existing methods for measuring livelihood capital [83], this paper objectively assigned weights to the above indicators using the entropy-weighting TOPSIS method. Subsequently, the weighted average method is employed to determine the comprehensive values of farmers’ livelihood capital in various dimensions, reflecting the level of possession of various types of livelihood capital by farmers.

3.1.4. Control Variable

To ensure the reliability of the results and control for the impact of other factors on land transfer, this paper, referencing existing studies [4,38], controlled for farmers’ personal characteristics and farmers’ family characteristics. The personal characteristics adopted “household head gender (GEN), household head health status (HEA), marital status (MAR), whether they joined cooperatives (COOP), and whether it is a specialized family farm” (SPE); the family level is represented by “the number of family members” (MEM) due to the relevant variables contained in farmers’ livelihood capital.

3.1.5. Instrumental Variable

In order to solve the endogeneity problem caused by reverse causality in the study, this paper used an instrumental variable method for inspection. The selection of instrumental variables needs to meet the following two conditions: exogeneity and correlation. Existing research commonly utilizes pertinent indicators, such as per capita mobile phone ownership [88], as instrumental variables for digital finance. However, these indicators frequently correlate with the level of local economic development, rendering them endogenous variables. In contrast, terrain features wield more significant externalities. First, topographical features are purely geographical factors, and they do not directly affect economic variables in the model, so they can meet the exogeneity conditions. Secondly, DIF is affected by the development level of internet and communication infrastructure [25], while topographical features often affect infrastructure construction, so the relevance condition is met. Therefore, referring to research by Falck et al. [89] and Ivus et al. [90], the relief degree of land surface (RDLS) [91] was chosen to serve as a proxy for topographical characteristics and used as an instrumental variable to test for endogeneity in DIF. The village where the farmer is located is “plain”, and it is assigned a value of 1; otherwise, it is assigned a value of 0.

3.2. Model Setting

3.2.1. Baseline Regression

Because the dependent variable used in this paper, “land transfer”, is a binary dummy variable, the probit model, which is a binary classification variable model that assumes the probability of an event occurring, follows the cumulative normal distribution function. It assumes that each entity faces a binary choice, and this choice depends on discernible characteristics, aiming to find the relationship between a set of characteristics describing the individual and the probability of a particular choice made by that individual. Therefore, this paper established a probit model to explore the impact of digital inclusive finance on farmers’ land transfer:
L A N D i = α 0 + α 1 D I F + α 2 C o n t r o l + ε 1
where I represents farmland transfer, L A N D i = 1 means farmland transfer, L A N I i = 0 means no land transfer, D I F represents the Digital Inclusive Finance Index; C o n t r o l represents control variables, and ε 1 represents random disturbance term.

3.2.2. Livelihood Capitals Assessment

The entropy weighting method is used to reduce subjective influence, ensuring scientific and accurate reasoning during the process. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was employed to calculate the comprehensive value of farmers’ livelihood capital. This method consists of the entropy value method and the TOPSIS algorithm, employing entropy theory for weighting. It has high reproducibility and credibility, effectively reducing the impact of subjectivity on evaluation results. The TOPSIS algorithm sorts the closeness of a finite number of objects for comparison to the idealized target and conducts a relative evaluation of the merits and demerits among the existing objects. Combining the two can objectively derive a comprehensive score. The basic steps are as follows:
X i j ′ is the value of the j_th indicator of the i_th sample, P i j is the proportion of the j ( j = 1,2 , … , n ) indicator of the i ( i = 1,2 , … , m ) sample indicator under the j_th indicator, e j is the entropy value of the j_th indicator, and ω j ( a ) is the entropy weighted right of the j_th indicator, as shown in Formula (2).
P i j = X i j ′ ∑ i = 1 m X i j ′ , e j = − 1 l n m ∑ i = 1 m P i j l n P i j ,   ω j a = 1 − e j ∑ j = 1 n 1 − e j
The Livelihood Capital index was measured as shown in Formula (3).
I j + = max ⁡ y 1 j , y 2 j , … , y n j I j − = min ⁡ y 1 j , y 2 j , … , y n j d i + = I 1 + − y i 1 2 + I 2 + − y i 2 2 + … + I m + − y i m 2 d i − = ( I 1 + − y i 1 ) 2 + ( I 2 + − y i 2 ) 2 + … + ( I m + − y i m ) 2 C i = d i − d i − + d i +
In Formula (3), I j + and I j − are the optimal and worst solutions for the evaluated object respectively; d i + and d i − are the Euclidean distances between the evaluated object and the optimal and worst solutions; and C i is the comprehensive livelihood capital value of the i_th farmer’s livelihood capital.

3.2.3. Chain Mediation Effect

To test the mediating effect of digital inclusive finance in promoting farmland transfer, this paper set up the following metric model:
D I E = γ 0 + γ 1 D I F + γ 2 C o n t r o l + ε 2
L C = β 0 + β 1 D I F + β 2 D I E + β 3 C o n t r o l + ε 3
L A N D = τ 0 + τ 1 D I F + τ 2 D I E + τ 3 L C + τ 4 C o n t r o l + ε 4
where D I E represents digital information effect, and L C represents Livelihood Capital Index.

3.3. Data Sources and Descriptive Statistics

This paper mainly used “China Rural Revitalization Survey (CRRS)” data from 2020 and the Digital Inclusive Finance Index, released by the Financial Research Center of Peking University, to study the impact and mechanisms of digital inclusive finance in relation to farmland transfer [92]. The calculation of the Digital Inclusive Finance Index is based on traditional inclusive finance indicators proposed in existing literature and by international organizations. It combines the new trends and features of digital financial services with the availability and reliability of data. The index is constructed according to three dimensions: breadth of digital financial coverage, depth of digital financial usage, and degree of inclusiveness in financial digitization. Specifically, the current Digital Inclusive Finance Index includes a total of 33 specific indicators across these three dimensions. The CRRS is a nationwide microsurvey providing data based on the “Comprehensive Survey on Rural Revitalization and China Rural Survey Database Project”, which covers the major economic and social survey projects of the Chinese Academy of Social Sciences [93]. According to the random stratified sampling principle, the CRRS data cover 10 provinces, 50 counties (cities and districts), 150 townships (towns), and 300 administrative villages across the country. The project’s team has implemented a rigorous sampling strategy to bolster sample representativeness. Initially, the team thoroughly assessed economic development, geographical location, and other determining factors. They selected 10 provinces, namely, Zhejiang, Shandong, Guangdong, Anhui, Henan, Guizhou, Sichuan, Shaanxi, Ningxia, and Heilongjiang, in line with a one-third proportion of the total number of provinces, covering the eastern, central, western, and northeastern regions. Secondly, to ensure uniform geographical representation, the counties, cities, and districts in each province were classified into five groups based on their per capita GDP levels. From each group, one county was then randomly selected, resulting in the selection of five counties, cities, and districts from each province. Thirdly, in each county or district, three townships were randomly selected based on their high, medium, and low levels of economic development. Then, one administrative village with good economic development and one with poor economic development were chosen from each of the selected townships, considering their level of economic development. Finally, the project team’s researchers used the isometric sampling method to randomly select 12–14 farmers from the administrative village list in order to investigate the state of rural comprehensive development and agricultural production. Given the paper’s focus on farmers’ livelihood capital and farmland transfer behavior, 3165 farmer samples were acquired through a process of compiling necessary research data, completing missing variable features, and removing abnormal data.
The descriptive statistics of the main variables in this paper are shown in Table 1.

4. Estimated Results and Heterogeneity Test

4.1. Baseline Regression Analysis Results

Table 2 reports the benchmark estimation results of the impact of digital inclusive finance on farmland transfer. Columns (1) to (7) provide the results after sequentially adding control variables. Although the estimation coefficients of digital inclusive finance fluctuate, they remain significantly positive, indicating that digital inclusive finance will significantly promote farmland transfer, hypothesis 1 is supported. The rapid development of digital technology and its popularity have promoted the digitalization of inclusive finance, which has provided inclusive financial support to farmers, thereby promoting farmland transfer. In terms of control variables, household head gender, whether they joined cooperatives, and whether it is a family farm passed the 1% significance test, indicating that these control variables can significantly promote farmland transfer. Household head gender will affect the management decisions of the family, thereby promoting farmland transfer. Whether farmers join cooperatives or operate as family farms is an indicator of their level of specialization. Cooperatives offer farmers the opportunity to enhance their market position, minimize transaction costs, expand their sales channels for agricultural products, and facilitate the transfer of agricultural land. Additionally, cooperatives allow farmers to earn wage or dividend income through land shares. This will also encourage the transfer of agricultural land owned by farmers to a certain extent. The advantages of scale that come with managing family farms will promote the expansion of the scale of agricultural land management and lead to achieving effective scale management. The declining role of agricultural labor, along with the rise in mechanization, is the primary reason for why the health status of a household’s head has no significant effect on the transfer of agricultural land. The transfer of agricultural land is negatively affected by the number of family members and marital status, which is likely due to livelihood pressures that lead to changes in farmers’ strategies [94]. These adaptations rely more heavily on agricultural output, thus further contributing to the negative effect on land transfer.

4.2. Robustness and Endogenous Test

4.2.1. Robustness Test

To ensure the robustness of the benchmark regression results, this paper adopted the following two methods for robustness test: taking the logarithm of the explanatory variable and replacing the estimation method.
To avoid heteroscedasticity, this paper first took the logarithm of the Digital Inclusive Finance Index. The regression model, at this time, is as follows:
L A N D i = α 0 + α 1 l n D I F + α 2 C o n t r o l + ε 1
Table 3 shows the regression results after taking the logarithm of the Digital Inclusive Finance Index. Column (1) shows that when the explanatory variable takes the logarithm, digital inclusive finance can significantly affect land transfer at the 1% significance level, and the significant increase compared to the benchmark regression results, to some extent, indicates that the benchmark research conclusions of this paper are trustworthy.
Second, in terms of the estimation method’s replacement, the probit model was used to examine the impact of digital inclusive finance on farmland transfer. Since the explained variable, farmland transfer, is a binary dummy variable, this paper also used the logit model for the robustness test. Column (2) of Table 3 reports the logit regression results. The results show that digital inclusive finance can significantly affect land transfer at the 1% significance level, which also increased significantly compared to the benchmark regression results, further proving that the regression results are reliable.
The above robustness test results all Indicate that digital inclusive finance can significantly promote farmland transfer, which verifies Hypothesis 1 again.

4.2.2. Endogenous Test

There are many factors affecting farmers’ farmland transfer, and it is difficult to avoid endogenous problems caused by missing variables even after the strictest possible control of some variables. Therefore, to avoid estimation errors due to reverse causality and missing variables, as well as other endogeneity problems, and to ensure the robustness of the results, this paper adopted the instrumental variable method for two-stage least squares regression (2SLS).
First, according to the above analysis, this paper selected terrain roughness (RDLS) as the instrumental variable in this paper. Secondly, the instrumental variable was tested to verify whether it met the conditions of weak correlation and exogeneity. It can be seen from the results in Table 4 that the F statistic of the weak instrumental variable was 70.4384, rejecting the hypothesis of a weak instrumental variable, indicating that the instrumental variable is not weakly correlated with the explanatory variable. Relief degree of land surface, as an instrumental variable, appears to be a suitable selection.
Finally, two-stage least squares regression was performed. It can be seen from the first-stage regression results that the regression coefficient of terrain roughness on the Digital Inclusive Finance Index was significant at 8.4060. Terrain roughness has a significant positive effect on digital inclusive finance. In the second-stage regression results, the coefficient of digital inclusive finance on farmland transfer was significant at 0.0263, indicating that digital inclusive finance can still significantly promote farmland transfer, and the correlation coefficient is significantly greater than the correlation coefficient of the benchmark regression. This test result shows that there is no problem of endogeneity between digital inclusive finance and farmland transfer, so it can be considered that the benchmark regression results are robust; that is, digital inclusive finance can significantly promote farmland transfer.

4.3. Mechanism Analysis

The baseline regression results show that digital inclusive finance can significantly promote farmland transfer. On this basis, this paper further explored the inherent mechanism of digital inclusive finance’s effect on farmland transfer. Using the previous parameter settings for the intermediary effect model, the results are shown in Table 5. In column (1), we examine the digital information effect as the explained variable. The correlation coefficient of digital inclusive finance shows a significant positive impact at the 1% confidence interval, with a coefficient of 0.0037. This suggests that the growth of digital inclusive finance has a substantial effect on the digital information effect. We should, therefore, consider digital inclusive finance as a critical factor in the development of digital information. Column (2) takes livelihood capital as the explained variable, and the correlation coefficients of digital inclusive finance and digital information effect are both significantly positive at the 1% confidence interval, with correlation coefficients of 0.0025 and 0.0100, respectively. Column (3) takes farmland transfer as the explained variable, and the correlation coefficients of digital inclusive finance and livelihood capital are both significantly positive at the 1% confidence interval, with correlation coefficients of 0.0025 and 0.1165, respectively. The digital information effect is significantly positive at the 5% confidence level, with a correlation coefficient of 0.0141. The results from columns (1), (2), and (3) demonstrate that digital inclusive finance has a significant impact on agricultural land circulation through the digital information effect and the intermediary channel of livelihood capital. Thus, Hypotheses 2 and 3 are confirmed.
To validate Hypothesis 4, we used a path diagram (Figure 2) to illustrate the mediating effects and conducted a Bootstrap test to analyze the chained multiple mediating effects (Table 6). The results in Figure 2 and Table 6 indicate that all mediating effects were significant at the 1% level. This study suggests that digital inclusive finance has the potential to facilitate agricultural land transfer not only through the digital information effect and independent intermediary channel of livelihood capital, but also through the chain intermediary channel of digital inclusive finance. This process involves the following sequence: digital information effect → livelihood capital → agricultural land transfer. Digital information effect is found to have independent intermediary and chain intermediary effects. Hypothesis 4 is supported.

4.4. Heterogeneity Analysis

On the basis of the per capita GDP level of each province [95], this paper conducted a regional heterogeneity analysis of digital inclusive finance on farmland transfer. In this paper, the 10 provinces in the survey sample were divided into economically developed regions and economically underdeveloped regions according to the median of the per capita GDP in 2019 (divided according to the median of the per capita GDP in 2019, the economically developed regions were higher than the median, including Guangdong, Shaanxi, Anhui, Sichuan, Shandong, and Zhejiang; economically underdeveloped regions were below the median, including Henan, Guizhou, Heilongjiang, and Ningxia), and inspections are carried out separately.
The results of the estimation of DIF on LAND by region are shown in Table 7. The estimated coefficient of digital inclusive finance was found to be significantly positive and passes the significance test at the 1% level in economically developed areas. Conversely, it was negative and passed the significance test at the 10% level in economically underdeveloped areas. Furthermore, the correlation coefficient between the two is significantly different from the numerical value. This shows that digital inclusive finance can significantly promote farmland transfer in economically developed regions while inhibiting farmers’ farmland transfer in economically underdeveloped regions. The reason may be that economically developed regions have higher levels of economic development, more complete infrastructure, better resource endowments, higher levels of digital inclusive finance development, and higher acceptance of digital inclusive finance by farmers, thus promoting farmland transfer, while economically underdeveloped regions have lower economic levels, incomplete infrastructure, lower levels of digital inclusive finance development, and a generally lower level of financial literacy for farmers [96], leading to farmers’ lack of understanding of relevant policies of digital inclusive finance, thus affecting farmers’ unwillingness to transfer land in economically underdeveloped regions. Additionally, the scarcity of nonagricultural job opportunities in underdeveloped regions, coupled with low nonagricultural earnings for farmers, results in heightened agricultural investment which further impedes the transfer of agricultural land.
Table 8 reports the results of the Bootstrap intermediary effect test by region. In economically developed regions, the digital information effect, independent intermediary effect of livelihood capital, and chain intermediary effect of the digital information effect on livelihood capital were all significantly positive. This finding further supports the significant positive impact of digital inclusive finance on the circulation of agricultural land in economically developed regions. In economically underdeveloped regions, the independent intermediary effects of digital information effects and livelihood capital, as well as the chain intermediary effect of digital information effects → livelihood capital were negative, of which the impacts of livelihood capital and chain intermediary effects were not significant, and the digital information effect had significantly negative effects, which also verifies the results in Table 8. The reasons may be as follows: there are fewer nonagricultural employment opportunities in less developed areas, and farmers’ production and lives mainly revolve around agriculture. When digital information support is available, farmers are more likely to increase their productive investment in agriculture to enhance crop yields and thwart the transfer of farmland.

5. Conclusions and Recommendations

5.1. Conclusions

This paper theoretically and empirically analyzed the role of digital inclusive finance in farmland transfer, and the main conclusions are as follows: (1) This paper constructed an indicator system based on farmers’ livelihood capital and measured farmers’ livelihood capital using the entropy TOPSIS method. (2) A mechanism framework of digital inclusive finance → digital information effect → livelihood capital → farmland transfer was constructed. (3) This paper first tested the impact of digital inclusive finance on farmland transfer, and the results were highly significant. (4) The test results of the mechanism demonstrate that digital inclusive finance has the potential to markedly boost agricultural land transfer through the effects of digital information and livelihood capital. The findings also confirm that digital inclusive finance not only reinforces agricultural land transfer through the digital information effect but also through the independent intermediary effect of livelihood capital, and this will have an impact through the intermediary chain of digital inclusive finance → digital information effect → livelihood capital → agricultural land transfer. (5) The heterogeneity test shows that digital inclusive finance has a positive and effective impact on economically developed regions.

5.2. Recommendations

On the basis of the above research conclusions, this paper believes that the positive role of digital inclusive finance in rural land transfer should be valued, and the development level of digital inclusive finance should be improved in the following five aspects, so that inclusive finance can benefit land transfer. Firstly, promote the construction of digital infrastructure in rural areas and accelerate the advancement of the digital countryside strategy. The construction of digital infrastructure is the basic element for the development of digital inclusive finance. However, the digital foundation of most rural areas in China is still relatively weak, especially in economically underdeveloped areas. Therefore, more attention should be paid to the construction of digital infrastructure in underdeveloped areas, and the construction of infrastructure such as internet communication, big data, and the Internet of Things in rural areas should be accelerated. In response, government departments should increase investment in the construction of infrastructure in underdeveloped areas, such as mobile communication, fiber optics, and IoT projects, and use means such as transfer payments and fiscal subsidy policies to promote coordinated development in these regions. At the same time, government departments should actively introduce social capital into the construction of digital infrastructure and establish partnerships through regulation, cooperation, and other means to improve resource allocation efficiency. Finally, government departments should collaborate with social enterprises to jointly develop more efficient and secure financial technology to enhance customer trust [97].
Secondly, it is important to fully activate the inclusive capacity of digital finance to ensure it can benefit all aspects of farmers’ production and daily life. Increasing the promotion of digital inclusive finance and accelerating the digital transformation of traditional financial institutions in rural areas can help improve financial accessibility for farmers. Encouraging banks and rural credit unions to use digital technology to launch financial products and expand their area of business, as well as customizing their financial products for agriculture and rural farmers, aligns with the strategy for the revitalization of the countryside. This will enable financial investment to benefit farmers in a significant way, allowing for the full impact of financial support on farmers’ livelihood capital. This will enable financial investment to benefit small- and medium-scale farmers, as well as new business entities in large numbers.
Thirdly, improve the market-oriented farmland transfer system and innovate farmland transfer transaction methods. First, utilize various relevant transaction service venues, institutions, and platforms to improve the rural property rights transfer transaction system. Second, clearly define entry transaction conditions; enrich entry transaction varieties; improve rules for various transactions; and standardize transaction applications, entrusted acceptance, information announcements, transfer acceptance, organized transactions, transaction termination, organized signing, transaction (i.e., contract) certification, and document management procedures. Third, guide transaction service institutions to strengthen construction of internal controls, strictly regulate financial service behavior, strengthen information system risk prevention and control, and ensure the standardized conduct of rural property rights transfer transactions. Finally, take the development of digital inclusive finance and the market-oriented development of rural land transfer as an opportunity. Establish a big data financial service platform and a land transfer transaction platform, create a long-term mechanism for the organic integration of digital inclusive finance and market-oriented rural land transfer, and promote financial innovation and land transfer system innovation, thereby effectively promoting the development of digital inclusive finance and increasing the rate of rural land transfer.
Fourthly, improve farmers’ financial literacy. It is recommended that the government, educational institutions, and the financial sector design new financial systems and innovative inclusive policies to integrate individual farmers into the financial service system, aiming to cultivate financial literacy among farmers [98]. Additionally, efforts should be intensified to promote digital inclusive finance, conduct policy advocacy, and enhance awareness and accessibility of digital inclusive financial services by lowering the entry barriers for farmers, simplifying the thresholds for using financial products, and encouraging active participation among farmers. By establishing collaborative relationships between government departments and educational institutions, developing a sustainable mechanism for financial literacy education, assisting farmers in learning financial knowledge, fostering financial awareness, and improving financial literacy, this will encourage the use of digital inclusive finance among farmers and, subsequently, drive land transfer.
Fifthly, improve the digital inclusive financial supervision system. The supervision methods for traditional finance should be innovatively adapted to the development of digital inclusive finance, strictly controlling the access qualifications of financial institutions, strengthening technical supervision, improving the supervisory system, and establishing a real-time and dynamic long-term supervision mechanism. Therefore, it is crucial to strengthen the supervision of digital inclusive finance to achieve sustainable development, improve farmers’ living standards, promote rural industries’ prosperity, enhance agricultural and rural areas’ sustainable development, and, ultimately, achieve the rural revitalization strategy.
Finally, in terms of recommendations for management, integrate digital inclusive finance with rural finance effectively. Firstly, guide financial institutions to provide financial services to farmers through new communication tools, such as the Internet and mobile terminals, promoting the orderly development of financial technology and rural finance. Provide innovative financial products and service methods for farmers, forming an effective management service system combining digital inclusive finance with rural finance. Secondly, construct a digital inclusive finance management system based on the four levels of city, county, township, and village. The acquisition of agricultural loans should reduce management levels, optimize loan processes, and establish a standardized service system for farmers’ credit loans, mortgage loans, pledge loans, and other businesses. Finally, implement quantitative assessments of agricultural credit scales for state-owned banks and local banks, promoting the increase in agricultural credit scales through incentives and regulations.
While the results of this study suggest that digital inclusive finance plays an important role in facilitating rural land transfer, it is important to note that these findings are preliminary and that there are certain limitations to this study. The study only used the Digital Inclusive Finance Index to measure digital inclusive finance and its impact on land transfer. Future research should explore the influence of digital inclusive finance on land transfer comprehensively, considering the three dimensions of digital financial coverage, depth of digital financial usage, and the degree of inclusiveness in financial digitization. Future research should explore the influence of digital inclusive finance on land transfer comprehensively, considering the three dimensions of digital financial coverage, depth of digital financial usage, and the degree of inclusiveness in financial digitization. Future research should explore the influence of digital inclusive finance on land transfer comprehensively, considering the three dimensions of digital financial coverage, depth of digital financial usage, and the degree of inclusiveness in financial digitization. This study only included data from farmers and the Digital Inclusive Finance Index from 2020. Future research, using more recent data, could conduct comparative analyses and explore the nuanced impact of digital inclusive finance on land transfer. Finally, this study found that digital inclusive finance has a chain-mediated effect on land transfer through digital information and livelihood capital. However, further exploration is needed to identify other potential mediating variables. Therefore, we will search for potential variables through additional theoretical analyses and empirical investigations.

Author Contributions

Conceptualization, Z.X., H.N. and Y.W. (Yiping Wu); Data curation, Z.X.; Formal analysis, Z.X. and Y.W. (Yuxuan Wei); Funding acquisition, Y.W. (Yiping Wu) and Y.Y.; Investigation, Z.X.; Methodology, Z.X. and Y.W. (Yiping Wu); Resources, Z.X.; Software, Z.X. and H.N.; Validation, Z.X., H.N. and Y.W. (Yuxuan Wei); Writing—original draft, Z.X.; Writing—review and editing, Y.W. (Yiping Wu) and Y.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the commissioned research project of high-standard farmland construction of Henan Provincial Department of Agriculture and Rural Affairs, “Research on the Construction, Operation, and Management of High-Standard Farmland and its Demonstration Area Under the Joint Participation of Multiple Capitals” (HNGBNT2023-16); the major project of the Philosophy and Social Sciences of Higher Educational Institutions in Henan Province in the year of 2022, “Research on the High-Quality Development of the Grain Industry in Henan by Promoting Path Research” (2022-YYDZ-08); and the 2022 Soft Science Project of Henan Province, “Research on the Mechanism of Realizing the Value of Ecological Products” (222400410201).

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 16 00408 g001
Figure 1. Theoretical framework.
Figure 1. Theoretical framework.
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Sustainability 16 00408 g002
Figure 2. Path diagram of the mediating effects. “**”, and “***” indicate significance at the statistical levels of 5%, and 1%.
Figure 2. Path diagram of the mediating effects. “**”, and “***” indicate significance at the statistical levels of 5%, and 1%.
Sustainability 16 00408 g002
Table 1. Variables’ statistical description.
Table 1. Variables’ statistical description.
VariableObs.MeanStd. Dev.Min.Max.
DIF3165324.480429.0235292.31387.49
LAND31650.59750.490501
DIE31653.94571.213915
LC31650.21240.20980.0010.77
HEA31653.59340.997401
SPE31650.01930.137501
GEN31650.85880.348301
MAR31650.89000.312901
COOP31650.23950.426801
MEM31654.08841.5900110
RDLS31650.51440.499901
Table 2. Benchmark regression results.
Table 2. Benchmark regression results.
Variable(1)(2)(3)(4)(5)(6)(7)
DIF0.0083 ***0.0083 ***0.0085 ***0.0078 ***0.0085 ***0.0084 ***0.0086 ***
Hea −0.0005−0.0015−0.0035−0.0010−0.0052−0.0055
Spe 0.7976 ***0.7872 ***0.6779 ***0.6703 ***0.6744 ***
Gen 0.3146 ***0.3703 ***0.3739 ***0.3803 ***
Mar −0.2812 ***−0.3057 ***−0.2706 ***
Coop 0.3041 ***0.3080 ***
Mem −0.0414 ***
Cons−2.4424 ***−2.4408 ***−2.4988 ***−2.5545 ***−2.5820 ***−2.5914 ***−2.5190 ***
N3165316531653165316531653165
R20.02440.02440.02910.03440.03740.04450.0464
“***” indicate significance at the statistical levels of 1%.
Table 3. Robustness test.
Table 3. Robustness test.
Explained VariableLANDLAND
Test MethodVariable Logarithmic Treatment (1)Replace Estimation Method (2)
LnDIF2.7810 ***
(9.51)
DIF 0.0139 ***
(9.59)
Cons−15.7870
(−9.48)		−4.0452 ***
(−8.67)
ControlYesYes
N31653165
“***” indicate significance at the statistical levels of 1%.
Table 4. Endogenous test: 2SLS regression.
Table 4. Endogenous test: 2SLS regression.
Estimation MethodTwo-Stage Least Squares
First StageSecond Stage
DIF 0.0263 ***
(7.57)
RDLS8.4060 ***
(8.39)
Cons287.8148
(105.63)		−7.2552 ***
(−7.06)
ControlYesYes
N31653165
R20.09810.0961
F 70.4384
“***” indicate significance at the statistical levels of 1%.
Table 5. Mediating effects.
Table 5. Mediating effects.
VariableDIELCLAND
(1)(2)(3)
DIF0.0037 ***
(4.96)		0.0025 ***
(20.44)		0.0025 ***
(7.71)
DIE 0.0100 ***
(3.45)		0.0141 **
(2.00)
LC 0.1165 ***
(2.67)
ControlYesYesYes
Cons2.8774 ***
(11.38)		−0.5921 ***
(−14.10)		−0.3438 ***
(−3.27)
N316531653165
Note: The values in brackets are t-values, the same below. “**”, and “***” indicate significance at the statistical levels of 5%, and 1%.
Table 6. Bootstrap mediation effect test results.
Table 6. Bootstrap mediation effect test results.
Mediating Effect TypeMediating ChannelEffect Size
Independent Mediating Effect 1DIF→DIE→LAND0.0029 ***
Independent Mediating Effect 2DIF→LC→LAND0.0027 ***
Chain Mediating EffectDIF→DIE→LC→LAND0.0020 ***
“***” indicate significance at the statistical levels of 1%.
Table 7. Estimation results of DIF on LAND by region.
Table 7. Estimation results of DIF on LAND by region.
VariableEconomically Developed RegionsEconomically Underdeveloped Regions
DIF0.1161 ***
(8.66)		−0.0052 *
(−1.65)
ControlYesYes
N19011264
R20.05110.0563
“*”, and “***” indicate significance at the statistical levels of 10%, and 1%.
Table 8. Bootstrap intermediary effect test results by region.
Table 8. Bootstrap intermediary effect test results by region.
Mediating Effect TypeMediating ChannelEffect Size
Economically Developed RegionsEconomically Underdeveloped Regions
Independent Mediating Effect 1DIF→DIE→LAND0.0036 ***−0.0019 ***
Independent Mediating Effect 2DIF→LC→LAND0.0032 ***−0.0018
Chain Mediating EffectDIF→DIE→LC→LAND0.0003 *−0.0053
“*”, and “***” indicate significance at the statistical levels of 10%, and 1%.
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Xu, Z.; Niu, H.; Wei, Y.; Wu, Y.; Yu, Y. Impact and Mechanisms of Digital Inclusive Finance in Relation to Farmland Transfer: Evidence from China. Sustainability 2024, 16, 408. https://doi.org/10.3390/su16010408

AMA Style

Xu Z, Niu H, Wei Y, Wu Y, Yu Y. Impact and Mechanisms of Digital Inclusive Finance in Relation to Farmland Transfer: Evidence from China. Sustainability. 2024; 16(1):408. https://doi.org/10.3390/su16010408

Chicago/Turabian Style

Xu, Ziqin, Hui Niu, Yuxuan Wei, Yiping Wu, and Yang Yu. 2024. "Impact and Mechanisms of Digital Inclusive Finance in Relation to Farmland Transfer: Evidence from China" Sustainability 16, no. 1: 408. https://doi.org/10.3390/su16010408

APA Style

Xu, Z., Niu, H., Wei, Y., Wu, Y., & Yu, Y. (2024). Impact and Mechanisms of Digital Inclusive Finance in Relation to Farmland Transfer: Evidence from China. Sustainability, 16(1), 408. https://doi.org/10.3390/su16010408

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