Research on the Mechanism of Digital Skills for Enhancing Farmers’ Participation in Formal Financial Markets

Abstract

Exploring the factors and mechanisms influencing farmers’ participation behavior in formal financial markets is of great significance for improving the rural financial service system and comprehensively promoting the development of rural inclusive finance. Based on the data from the 2020 China Rural Revitalization Survey (CRRS), this paper explores the impact and mechanism of digital skills on farmers’ participation in formal financial markets through the theories of feasibility information, social capital, and technology acceptance. The research results show that digital skills significantly promote farmers’ likelihood and extent of participation in formal financial markets, including stocks, bonds, and wealth management products. This is because digital skills enhance farmers’ information acquisition and online transaction capabilities and broaden their social networks. Heterogeneity analysis reveals that digital skills exert a greater influence on both the likelihood and extent of participation of farmers with lower educational level, farmers in the middle-aged and elderly cohorts, and farmers with low income in formal financial markets. Further research reveals that participating in formal financial markets can significantly increase farmers’ annual income. Therefore, training should be strengthened to enhance farmers’ digital skills. Open information platforms should be established to broaden channels, gradually enabling farmers to freely acquire information, reducing the cost of maintaining social networks for farmers, and improving the convenience of farmers’ online transactions. In addition, efforts should be made to promote the development of inclusive finance, focus on the differentiation issues of farmers in information and wealth, and thereby more widely enhance farmers’ participation in formal financial markets.

1. Introduction

With the comprehensive advancement of rural revitalization efforts, the economic conditions of rural residents have improved, and significant progress has been made in the process of coordinated urban-rural development [1]. According to data released by the National Bureau of Statistics, the ratio of per capita disposable income between urban and rural residents has decreased from 2.88:1 in 2012 to 2.39:1 in 2023, indicating that the imbalance in urban-rural development is gradually being alleviated. However, it is worth noting that while urban households enjoy the benefits brought by financial development, rural households still show a clear aversion to risky assets [2]. Their financial asset allocation remains highly dependent on traditional savings deposits, and their participation in formal financial markets is insufficient [3]. In recent years, to address this issue, the state has formulated and implemented a series of financial system policies targeting rural areas and has comprehensively reformed rural formal financial institutions and markets. The Implementation Opinions on Promoting High-Quality Development of Inclusive Finance issued by the State Council in 2023 clearly stated that efforts should be made to increase financial support for rural infrastructure and public services [4]. While the 2023 policy on promoting high-quality inclusive finance (issued by the State Council) aligns closely with the core focus of this study—namely, enhancing farmers’ participation in formal financial markets and alleviating rural financial exclusion—its relatively short implementation period means that direct assessment of its long-term effects remains unfeasible at this stage. Instead, the 2020 CRRS dataset serves a critical benchmarking role: it captures the baseline status of rural households’ participation in formal financial markets prior to the policy’s rollout, providing a foundational reference for future studies to track how the policy influences changes in participation over time. Against this backdrop, addressing the pressing challenge of advancing inclusive finance, overcoming the multi-faceted dilemmas in rural finance, and shifting rural residents’ traditional mindset of financial exclusion has become an urgent priority for both academic research and policy practice [5].

Currently, the digital economy system is developing vigorously. The rise in Internet technology and the widespread popularity of mobile electronic devices have greatly lowered the threshold for people to access information and knowledge, becoming an important driving force for China’s economic development. Data shows that as of June 2024, the Internet penetration rate in rural areas of China has reached 63.8%. The deep integration of digital technology and financial services has accelerated the transformation and upgrading of financial products and services. Digital finance represented by online payment and mobile banking has also been widely promoted in rural areas. Research has found that the development of digital technology has a significant optimizing effect on household financial asset allocation, and its impact on the rural formal financial market cannot be ignored [3].

Many domestic and foreign scholars have conducted extensive and in-depth research on the multi-dimensional factors influencing participation in formal financial markets. Stango et al. [6] explored the intrinsic connection between payment bias and household financial outcomes, revealing that exponential growth bias would prompt households to reduce savings and improve their participation in formal financial markets. Agarwal et al. [7] examined the impact of fintech in the household finance field, particularly in payment, lending, and investment portfolio decisions, finding that fintech has positive effects on households by promoting consumption and lending activities; Coibion et al. [8] conducted a randomized experiment, providing different expectations about future economic growth to the subjects, and found that higher macroeconomic uncertainty would make households more inclined to reduce their participation in formal financial markets. In addition, household heterogeneity, as a key factor influencing investment portfolio choices, has also been widely discussed. Education level, financial knowledge [9,10], cognitive ability [11], social networks [12,13], and social preferences [14] have been shown to significantly influence households’ participation in formal financial markets, which further enriches our understanding of household financial decision-making behavior. Digital finance, as an important influencing factor in family financial asset allocation, has also received extensive attention. The rapid development of the digital-finance integration model has significantly boosted household income growth through lowering information search costs [15], stimulating entrepreneurial activities [16,17], and expanding access to financial markets [18].

The aforementioned research findings establish a foundation for the in-depth exploration in this study. However, several research dimensions remain underdeveloped. First, most of the existing literature takes a macro perspective and focuses on factors such as digital technology [19] and digital finance [20,21]. Wanzala et al. conducted an empirical analysis to examine the positive impact of the development of digital services in the Kenyan region on local financial inclusiveness [22], while Saeed et al. used cross-sectional data from 132 countries to analyze the impact of communication technology access on financial development [23]. Although recent studies have started to focus on farmers’ intrinsic capabilities, investigating the positive effects of digital skills on rural households’ property income [24] and e-commerce engagement [25], there is a notable lack of empirical evidence regarding the role of digital skills in formal financial market participation. Second, current research often treats urban and rural populations as homogeneous groups in aggregate analyses. However, rural residents exhibit substantial heterogeneity in resource endowments, cognitive frameworks, and information acquisition channels compared to their urban counterparts, raising concerns about the generalizability of existing findings to rural populations [5]. Third, the causal mechanisms through which digital skills influence farmers’ formal financial market participation require further rigorous validation. Although digital skills may expand rural households’ access to formal financial markets and incentivize asset allocation optimization, constraints such as limited human capital and institutional barriers could hinder their ability to utilize digital technologies effectively. These limitations may perpetuate information asymmetries, potentially fostering misconceptions about formal financial markets and suppressing willingness to participate. Thus, a critical re-examination of the impacts of digital skills on farmers’ participation in formal financial markets is warranted.

This study also closely aligns with the United Nations Sustainable Development Goals (SDGs): Firstly, SDG 1 emphasizes increasing the income of low-income groups through financial inclusion. The transmission path verified in this study provides micro-level evidence for poverty reduction; Secondly, SDG 8 advocates for sustainable rural economic development. The promotion effect of digital skills on farmers’ financial participation can optimize the allocation structure of rural assets and enhance the resilience of rural economies; Thirdly, SDG 10 focuses on the gap between urban and rural areas. This study focuses on the improvement of rural digital skills, which can alleviate financial exclusion caused by the digital financial gap between urban and rural areas. At the same time, the conclusions of this study also provide a reference for developing countries on rural financial inclusion and sustainable development based on Chinese experience.

This study makes three primary theoretical contributions. First, this study develops an integrated analytical framework that synthesizes the capability approach theory, social capital theory, and technology adoption theory to elucidate the mechanisms through which digital skills influence farmers’ participation in formal financial markets. This theoretical integration enriches the existing framework of mechanisms for promoting farmers’ engagement in formal financial markets. By leveraging the 2020 China Rural Revitalization Survey (CRRS) data, this paper focuses on rural residents and elaborately explores the impact of digital skills on rural residents’ formal financial markets participation behavior, aiming to provide new perspectives and empirical support for research in this field. At the same time, this paper employs 2SLS, IV-Probit, and IV-Tobit methods to effectively address endogeneity concerns. Additionally, it uses Propensity Score Matching Method and the Heckman two-step method for robustness checks, thereby enhancing the accuracy and reliability of the research conclusions. Second, from multiple dimensions such as different educational levels, ages, and annual income levels, this paper deeply analyzes the heterogeneity characteristics of the impact of digital skills on farmers’ formal financial market participation behavior by applying the human capital theory, life cycle theory, and financial exclusion theory. It provides practical and feasible reference suggestions for the comprehensive promotion of digital inclusive finance in rural areas. Third, the impact of farmers’ participation in formal financial markets on their annual income was further studied, thereby broadening the exploration of the relationship between digital skills and farmers’ economic welfare.

2. Theoretical Analysis and Research Hypothesis

2.1. Digital Skills and Farmers’ Participation in Formal Financial Markets

According to the classification of United Nations Educational, Scientific, and Cultural Organization (UNESCO), digital skills fall into three main categories: basic practical digital skills, general digital skills, and advanced digital skills that can bring about transformative usage patterns. Based on the above classification, this paper defines digital skills from four dimensions: e-learning, online business, chat and social, and network entertainment skills [24]. First of all, purchasing financial products such as stocks, bonds, and funds requires a high level of financial knowledge and technical proficiency [10]. Farmers with stronger digital skills can better learn financial knowledge, understand the basic information of financial products, and conduct transactions through various financial platforms [2,7,25]. The increase in financial knowledge will promote households’ participation in the financial market and increase the allocation of financial assets [9]. Second, the higher the digital skills are, the greater the possibility that farmers will participate in online transactions such as online shopping and online payments. Farmers can accumulate rich experience in online transactions and are thus more likely to participate in the financial market [26,27]. Third, farmers with higher levels of digital skills demonstrate greater proficiency in utilizing social media for communication and participating in online entertainment. Using the Internet can enhance farmers’ social interaction, thus increasing the probability of their participation in formal financial markets. At the same time, farmers can more quickly and comprehensively capture the latest market dynamics and in-depth policy interpretations through online interaction platforms. These information elements have a significant impact on their decisions to participate in the financial market. In contrast, offline social interaction is restricted by physical space and immediacy, and the scope of information dissemination is relatively limited, making it difficult for farmers to obtain comprehensive and detailed market information in a timely manner. The impact of online social interaction on households’ participation in the financial market is significantly greater than that of offline social interaction [28]. Finally, operating various financial transaction software and completing stock selection and trading requires strong technical skills, which are necessary skills for participating in formal financial markets. Farmers with digital skills have an advantage in participating in formal financial markets. They are more likely to make decisions to participate in formal financial markets and have a higher degree of participation. The theoretical analysis framework of the impact of digital skills on farmers’ participation in formal financial markets is shown in Figure 1. The theoretical analysis framework encompasses the specific mechanisms through which digital skills influence farmers’ participation in formal financial markets and the economic effects of farmers’ participation in formal financial markets, presenting the research hypothesis of this article. Based on the above analysis, this paper proposes:

Hypothesis 1. 

Digital skills have a positive impact on farmers’ likelihood and extent of participation in formal financial markets.

2.2. The Mechanism of Digital Skills in Enhancing Farmers’ Participation in Formal Financial Markets

Digital skills consist of four dimensions: e-learning, online business, chat and social, and network entertainment skills [24]. According to Amartya Sen’s capability approach theory, farmers, as information subjects, can achieve information acquisition freedom by enhancing their digital skills and thereby participate in formal financial markets. First, digital skills can enhance farmers’ information acquisition capabilities. Digital skills expand farmers’ information acquisition channels [25], reduce their information search costs, and thus alleviate the information asymmetry problem they face [24]. At the same time, farmers with digital skills can use Internet technology to obtain more extensive and comprehensive information, thereby effectively alleviating their long-term information disadvantage [5]. The effective acquisition of information affects an individual’s resource allocation ability [29] and is a key factor driving farmers to actively integrate into formal financial markets. When farmers can fully obtain the necessary financial information, they can make more reasonable allocations of financial products such as stocks and bonds based on their own needs, and thus participate more actively in financial market activities. According to the CRRS, the information acquisition in this paper represents farmers’ ability to obtain the information they need through the Internet. Whether farmers participate in formal financial markets largely depends on the participation costs, which specifically include the cost of obtaining financial information and transaction costs. It is worth noting that different information channels have differences in obtaining financial information and related transaction costs [30]. Farmers with digital skills can efficiently obtain information through Internet channels, reduce information acquisition costs, and increase their willingness to accept financial products. Compared with traditional media such as newspapers and periodicals, the Internet not only provides farmers with more convenient information acquisition channels but also greatly enriches information sources, effectively alleviating the information asymmetry in the transaction process. This information advantage gives farmers with a more comprehensive market insight, changes their perception of financial products and their risks, and thereby enhances their willingness to participate in financial markets and increases their participation extent [26,31]. Existing research shows that farmers who obtain information through new information channels such as the Internet show higher enthusiasm in participating in formal financial markets compared to those through traditional information channels [30]. Based on the above analysis, this paper proposes:

Hypothesis 2. 

Digital skills influence farmers’ likelihood and extent of participation in formal financial markets by enhancing their information acquisition capabilities.

Digital skills can effectively expand farmers’ social networks and enhance their social capital. According to the theory of social capital, as a collection of resources embedded in social networks, social capital can increase farmers’ willingness to participate in formal financial markets by improving the availability of financial information and reducing transaction costs. First, digital skills enable farmers to break free from spatial constraints, changing the way social members communicate. This thereby reduces the time cost that farmers previously spent maintaining social relationships, effectively expanding the boundaries of farmers’ social interactions, and enhances the interaction and communication between farmers and social members [32]. At the same time, social networks can enhance and maintain farmers’ social capital [33]. Social capital represents the quantity of resources within the social network that farmers can utilize. In this paper, the number of relatives and friends from whom farmers can borrow 5000 yuan or more is used to represent farmers’ social networks. Secondly, social networks can significantly reduce household financial vulnerability by lowering liquidity constraints, facilitating information transmission, and sharing risks [34]. This effect is more pronounced in households with lower wealth and insufficient human capital. Rural residents have this characteristic compared with urban residents. Therefore, social networks can effectively reduce the financial vulnerability of farmers’ households and thereby increase the probability of their participation in formal financial markets. Finally, social networks can reduce the possibility of household financial exclusion [33]. Participation in financial markets requires adequate financial knowledge and information. Social networks can effectively enhance farmers’ financial knowledge, improve their ability to obtain financial information and resist risks, and change farmers’ financial risk preferences, thereby promoting farmers’ participation in formal financial markets [35,36]. Based on the above analysis, this paper proposes:

Hypothesis 3. 

Digital skills influence farmers’ likelihood and extent of participation in formal financial markets by expanding their social networks.

Digital skills can facilitate farmers’ participation in online transactions [25]. Purchasing financial products such as stocks, funds, and bonds has relatively high transaction thresholds. Farmers need to collect financial information and be familiar with trading software. Therefore, the ability to conduct online transactions is the foundation for farmers to participate in formal financial markets [26]. According to the technology adoption theory, online transactions can enhance farmers’ familiarity with digital finance, reduce their psychological distance from participating in formal financial markets, and alleviate financial exclusion issues caused by rural growth experiences, thus promoting farmers’ participation in formal financial markets. First, engaging in business and conducting online transactions require farmers to have operational skills for various trading platforms and software and the ability to collect relevant product information. Farmers with digital skills can learn through the internet, master the operation processes of trading products on various platforms, and use various platforms to search for the information and data needed for transactions. Second, this paper uses “whether they operate products through online transactions” to represent online transactions. Compared with online shopping, operating products and conducting online transactions require higher technical and information collection capabilities. Purchasing financial products such as stocks, funds, and bonds requires farmers to have the ability to operate financial software, collect information on stock trends and company financial indicators, and conduct online transactions. The rich software operation skills and information collection capabilities accumulated in online transactions are necessary conditions for participating in formal financial markets. Finally, online transactions can enhance farmers’ online social interaction. This can increase investors’ attention to the financial market and thereby increase their likelihood of participation in formal financial markets [37]. Based on the above analysis, this paper proposes:

Hypothesis 4. 

Digital skills influence farmers’ likelihood and extent of participation in formal financial markets by enhancing their online transaction capabilities.

2.3. Economic Effects of Digital Skills on Farmers’ Participation in Formal Financial Markets

Farmers’ participation in formal financial markets such as stocks, funds, and bonds can optimize asset allocation and yield higher returns compared to risk-free assets like deposits, thereby increasing their income. Firstly, digital inclusive finance can enhance households’ willingness to participate in financial markets, thereby raising the overall income level of families [38]. However, rural upbringing can significantly reduce participation in the stock market [39]. Due to their mindset and personality, farmers are unable to profit from financial markets, which has a negative impact on their wealth growth. After acquiring digital skills, farmers can access broader and more comprehensive information, thereby overcoming financial exclusion [40]. This effectively enhances the diversity of their asset allocation and increases their income. Secondly, farmers with digital skills can invest through digital financial tools [24], reducing the barriers to entry in financial markets caused by distance. Finally, digital skills can address issues such as low investment efficiency, high participation thresholds, and lack of financial information in traditional investment processes for farmers. They can choose financial products that match their asset levels and risk tolerance, thus increasing their income. Based on the above analysis, this paper proposes:

Hypothesis 5. 

Participation in formal financial markets can increase farmers’ annual income.

3. Research Design

3.1. Data Sources

This paper selects data from the 2020 China Rural Revitalization Survey (CRRS) conducted by the Rural Development Institute, Chinese Academy of Social Sciences across the country to study the impact of digital skills on farmers’ participation in formal financial markets. The research team adopted the method of systematic random stratified sampling. The sample covered 10 provinces, a total of 50 counties (cities, districts), 156 towns (townships), and 308 administrative villages. The survey covered the comprehensive agricultural production capacity and the development status of multiple fields in rural areas. Its research scope involved farmers’ income and social welfare, rural residents’ consumption, rural governance, and rural comprehensive reform, etc., ensuring the comprehensiveness and representativeness of the sample. Considering that this paper focuses on analyzing the impact of digital skills on farmers’ participation in formal financial markets, the missing values of the relevant data were excluded. Finally, 2895 valid farmer samples were obtained. The specific methods of questionnaire distribution and investigation, as well as the questions in the questionnaire regarding the explanatory variable and explained variables, are presented in Appendix A.

3.2. Model Specification

The explained variables in this paper are the farmers’ likelihood and extent of participation in formal financial markets. Firstly, the likelihood of participation is a binary choice variable. Therefore, this paper selects the binary Probit model, which is expressed as follows:

$$P({FMLP}_i=1)=\Phi \left({\beta }_0+{\beta }_1{Digi}_i+{\delta C}_i+{\mu }_i\right)$$

(1)

where $P({FMLP}_i=1)$ represents the probability that the i-th farmer participates in formal financial markets; $\Phi \left(.\right)$ is the cumulative distribution function of the standard normal distribution; ${Digi}_i$ is the digital skills variable; $C_i$ are the control variables; ${\beta }_1$ is the coefficient of digital skills; $\delta$ is the coefficient of the control variable; ${\beta }_0$ is the constant term of Formula (1); ${\mu }_i$ is the random error term of Formula (1).

Secondly, the farmers’ extent of participation includes a large number of samples with a value of 0. However, farmers with zero participation are still observed in the sample, while the part of their observed extent of participation ${FMPL}_i$ that is below zero is unobservable. This belongs to the data with a censored (left-censored) at zero. Therefore, this paper selects the Tobit model, and the formula is as follows:

$$\begin{gathered} {{FMEP}_i}^{*}={\beta }_0+{\beta }_1{Digi}_i+\delta C_i+{\mu }_i \\ {FMEP}_i=\left\{\begin{array}{c}0,{{FMEP}_i}^{*}\le 0\\ {{FMEP}_i}^{*},{{FMEP}_i}^{*}>0\end{array}\right. \end{gathered}$$

(2)

where ${FMEP}_i$ represents the observed farmers’ extent of participation; ${{FMEP}_i}^{*}$ represents the censored farmers’ extent of participation; ${Digi}_i$ is the digital skills variable; $C_i$ are the control variables; ${\beta }_1$ is the coefficient of digital skills; $\delta$ is the coefficient of the control variable; ${\beta }_0$ is the constant term of Formula (2); ${\mu }_i$ is the random error term of Formula (2).

3.3. Variable Selection

3.3.1. Explained Variable

According to the family financial asset status section of the CRRS questionnaire, the dependent variables in this paper include the likelihood and extent of participation in formal financial markets. Given that the formal financial market mainly consists of risky financial assets and risk-free financial assets [13], risky financial assets are related to investment decisions, while risk-free assets, mainly composed of bank deposits, have no participation threshold. Referring to existing studies [9,26], this paper defines the likelihood of participate in formal financial markets as the purchase of one or more risky financial assets such as stocks, bonds, and wealth management products by the household. This variable is a dummy variable, with 1 indicating participation in formal financial markets and 0 indicating no participation. The extent of participation in formal financial markets is defined as the ratio of household risky financial assets—including stocks, bonds, and wealth management products—to total household assets. Household assets include the sum of WeChat Wallet balance, Alipay balance, cash, bank deposits, money lent out, stocks, bonds, and wealth management products. The average participation rate of farmers in formal financial markets is only 2.3%, which is consistent with the phenomenon of limited participation in China’s financial market. Affected by factors such as network, transportation and income, the participation rate of farmers in the financial market is lower than that of urban residents. Therefore, the participation rate in the descriptive statistics is reasonable. The ratio of risky financial assets to household assets ranges from 0 to 1. Variable definitions and descriptive statistics are presented in

Table 1. Variable Definitions and Descriptive Statistics.

Variable Names	Variable Definitions and Assignments	Mean	Standard Deviation
Explained variables
Likelihood of participation in formal financial market	Whether to participate in formal financial markets, 0 = No, 1 = Yes.	0.023	0.151
Extent of participation in formal financial market	The ratio of financial assets to household assets	0.009	0.073
Explanatory variable
Digital skills	Comprehensive level of digital skills	1.285	0.769
Mechanism variables
Information acquisition	Acquisition situation: 1 = relatively difficult, 2 = sometimes possible, 3 = completely possible	2.319	0.801
Social networks	Number of relatives and friends from whom one can borrow 5000 yuan or more	7.524	13.652
Online transactions	Whether one operates products through online transactions, 0 = No, 1 = Yes.	0.073	0.260
Control variables
Gender	0 = Male, 1 = Female	0.069	0.253
Age	Age	54.075	10.554
Age Squared	The square of the age divided by 100	30.355	11.505
Education level	1 = Never attended school, 2 = Primary school, 3 = Junior high school, 4 = Senior high school, 5 = Secondary technical school, 6 = Vocational high school, 7 = Junior college, 8 = Bachelor’s degree or above.	2.853	1.101
Health status	1 = Very poor, 2 = Poor, 3 = Average, 4 = Good, 5 = Very good.	3.616	0.996
Party membership	Whether a Party member: 0 = No, 1 = Yes	0.238	0.426
Cadre status	0 = Non-village cadre, 1 = Village cadre	0.165	0.371
Training experience	Parameter for skills training: 0 = No, 1 = Yes	0.110	0.313
Household size	Number of people	4.136	1.505
Paved roads	Whether the road between the village and the group (the internal units of a village) is paved, 0 = No, 1 = Yes	0.941	0.235
Number of households with broadband	Number of households with broadband in the entire village	558.187	1060.931
Village economic conditions	Logarithm of per capita disposable income of the village in 2019	9.421	0.721
Village transportation conditions	Distance from the village committee to the county government (kilometers)	23.843	17.263

.

3.3.2. Explanatory Variable

Based on existing research [24,25], this paper adopts “comprehensive digital skills level” as the criterion for evaluating farmers’ digital skills. This level consists of four abilities: e-learning, online business, social chatting, and network entertainment. E-learning refers to farmers’ use of mobile phones to access and learn educational resources; online business refers to farmers’ use of mobile phones to conduct transactions such as selling or purchasing products; social chatting is manifested as farmers’ online communication and sharing of daily life through mobile phone applications; network entertainment involves farmers’ use of mobile phones for video, music, and other entertainment and leisure activities. To quantitatively assess farmers’ digital skills, this paper makes a judgment based on the average daily usage time of farmers on specific mobile phone functions. If a farmer’s average daily usage time for a certain function ranks among the top 3 in the interview sample, it is considered that they possess the corresponding digital skill. Among the four digital skills, farmers who do not possess any digital skills score 0 points and are considered to have no digital skills, those with one digital skill score 1 point and are considered to have beginner skills, those with two digital skills score 2 points and are considered to have intermediate skills, and those with three digital skills score 3 points and are considered to have advanced skills.

3.3.3. Mechanism Variables

According to existing research [24,25], this paper measures farmers’ information acquisition ability by “whether farmers can obtain information that meets their own needs through the Internet” in the CRRS data; “the number of relatives and friends from whom farmers can borrow an amount of 5000 yuan or more” is used to measure farmers’ social networks; “whether farmers operate products that are traded through the Internet” is used to measure farmers’ online transaction capabilities.

3.3.4. Control Variables

Based on existing research [24,25], control variables were selected from both the household head’s personal and village levels. Personal-level control variables for the household head include gender, age, age squared, educational attainment, health status, party membership, cadre status, training experience, and household size. Village-level control variables include the number of paved roads, the number of households with broadband access, village transportation conditions, and village economic conditions.

4. Empirical Analysis Results

4.1. Analysis of the Impact of Digital Skills on Farmers’ Participation in Formal Financial Markets

This paper employs a binary Probit regression (Model 1 in

Table 2. Regression Results of Digital Skills on Farmers’ Participation in Formal Financial Markets.

Variable Names	Model 1	Model 2
Digital skills	0.207 *** (0.072)	0.180 *** (0.062)
Gender	0.237 (0.173)	0.186 (0.147)
Age	0.054 (0.041)	0.047 (0.036)
Age squared	−0.050 (0.040)	−0.044 (0.035)
Education level	0.139 *** (0.044)	0.130 *** (0.040)
Health status	0.055 (0.055)	0.037 (0.048)
Party membership	0.045 (0.138)	0.048 (0.120)
Cadre status	−0.285 (0.176)	−0.250 (0.159)
Training experience	0.450 *** (0.134)	0.343 *** (0.107)
Household size	0.018 (0.036)	0.018 (0.033)
Paved roads	−0.175 (0.290)	−0.230 (0.269)
Number of households with broadband	0.0001 * (0.0001)	0.0001 * (0.00003)
Village economic conditions	0.409 *** (0.084)	0.363 *** (0.076)
Village transportation conditions	−0.0002 (0.004)	−0.0001 (0.003)
Constant term	−8.287 ***	−7.202 ***
Pseudo R2	0.1271	0.1224
Log pseudolikelihood	−271.48749	−272.59037
Observation	2895	2895

Note: Model 1 analyzed the impact of digital skills on farmers’ decisions to participate in formal financial markets through Probit regression; Model 2 analyzed the influence of digital skills on the degree of farmers’ participation in formal financial markets through Tobit regression. * indicates significance at the 10% level; *** indicates significance at the 1% level.

) to analyze the impact of digital skills on farmers’ likelihood of participation in formal financial markets, and simultaneously uses a Tobit regression (Model 2 in Table 2) to examine the influence of digital skills on the extent of farmers’ participation. Digital skills significantly enhance farmers’ likelihood and extent of participation in formal financial markets at the 1% significance level. We calculated the marginal effect by using the method of average marginal effect. The result shows that a one-unit increase in farmers’ digital skills leads to a 0.983% (standard error: 0.004; confidence interval of 95 percent: [0.0027957, 0.0168573]) increase in their likelihood of participation in formal financial markets and an 18.368% (standard error: 0.062; confidence interval of 95 percent: [0.0582436, 0.3024930]) increase in the extent of participation. This is consistent with the conclusion that digital skills improve farmers’ financial knowledge and information collection capabilities, increase their online transactions experience and online social interaction, thereby enhance their participation behavior in formal financial markets. It demonstrates that digital skills can significantly and positively influence farmers’ likelihood and extent of participation in formal financial markets, thus verifying Hypothesis 1.

4.2. Endogeneity Analysis

Digital skills can promote farmers’ participation in formal financial markets. During the process of purchasing stocks, funds, and bonds, farmers will engage in stock selection, trading, and other behaviors, which in turn promotes the improvement of digital skills. This indicates that there might be a reverse causality between digital skills and farmers’ participation in formal financial markets. To address the endogeneity problem caused by this, referring to existing research [36], this paper takes “the average level of comprehensive digital skills of other farmers in the same village except for oneself” as the instrumental variable for digital skills and conducts endogeneity tests through 2SLS, IV-Probit and IV-Tobit methods. The digital skill level of farmers is affected by the digital skills of other farmers in the same village, and the digital skill level of farmers in the same village does not directly affect individual farmers’ participation behavior in formal financial market. This meets the requirements that the instrumental variable is related to the explanatory variable and strictly exogenous to the error term.

First, the endogeneity issue was tested using the 2SLS method. In the instrumental variable regression results of digital skills on farmers’ likelihood of participation in formal financial markets, the first-stage regression (

Table 3. 2SLS Regression Results of Endogeneity Test.

Variable Names	Model 3	Model 4	Model 5	Model 6
	Digital Skills	Likelihood of Participation in Formal Financial Markets	Digital Skills	Extent of Participation in Formal Financial Markets
	First-Stage	Second-Stage	First-Stage	Second-Stage
Digital skills		0.072 ** (0.032)		0.031 ** (0.151)
Instrumental variables for digital skills	0.191 *** (0.328)		0.191 ** (0.029)
Control variables	Yes	Yes	Yes	Yes
Constant term	0.799 ***	−0.289 ***	0.799 ***	−0.106 ***
One stage F statistic	28.76 (p = 0.0000)		28.76 (p = 0.0000)
Wald test		87.39 (p = 0.0000)		55.99 (p = 0.0000)
Observation	2895	2895	2895	2895

Note: ** indicates significance at the 5% level; *** indicates significance at the 1% level.

, Model 3) shows that F = 28.76, which is greater than the critical value of 16.38, and p = 0.0000, rejecting the null hypothesis of “weak instrumental variable”. The second-stage regression (Table 3, Model 4) shows that, under the condition of controlling for endogeneity, digital skills have a significant positive impact on farmers’ likelihood of participation in formal financial markets. Second, in the instrumental variable regression of digital skills on farmers’ extent of participation in formal financial markets, the first-stage regression (Table 3, Model 5) shows that F = 28.76, which is greater than the critical value of 16.38, and p = 0.0000, rejecting the null hypothesis of “weak instrumental variable”. The second-stage regression (Table 3, Model 6) shows that, under the condition of controlling for endogeneity, digital skills have a significant positive impact on farmers’ extent of participation in formal financial markets. This indicates that Hypothesis 1 results are robust.

Secondly, the endogeneity was tested using the IV-Probit and IV-Tobit methods. In the instrumental variable regression results of digital skills on farmers’ likelihood of participation in formal financial markets, the first-stage regression (

Table 4. Results of IV-Probit and IV-Tobit Regression for Endogeneity Test.

Variable Names	Model 7	Model 8	Model 9	Model 10
	Digital Skills	Likelihood of Participation in Formal Financial Markets	Digital Skills	Extent of Participation in Formal Financial Markets
	First-Stage	Second-Stage	First-Stage	Second-Stage
Digital skills		1.257 ** (0.628)		1.099 ** (0.559)
Instrumental variables for digital skills	0.191 *** (0.029)		0.191 *** (0.029)
Control variables	Yes	Yes	Yes	Yes
Constant term	0.799 ***	−9.338 ***	0.799 **	−8.066 ***
One stage F statistic	28.76 (p = 0.0000)		28.76 (p = 0.0000)
Wald test		67.40 (p = 0.0000)		38.15 (p = 0.0005)
Observation	2895	2895	2895	2895

Note: Model 7 and Model 8 are IV-Probit regressions; Model 9 and Model 10 are IV-Tobit regressions. ** indicates significance at the 5% level; *** indicates significance at the 1% level.

, Model 7) shows that F = 28.76, which is greater than the critical value of 16.38, and p = 0.0000, rejecting the null hypothesis of “weak instrumental variable”. The second-stage regression (Table 4, Model 8) shows that, under the condition of controlling for endogeneity, digital skills have a significant positive impact on farmers’ likelihood of participation in formal financial markets. Second, in the instrumental variable regression of digital skills on farmers’ extent of participation in formal financial markets, the first-stage regression (Table 4, Model 9) shows that F = 28.76, which is greater than the critical value of 16.38, and p = 0.0000, rejecting the null hypothesis of “weak instrumental variable”. The second-stage regression (Table 4, Model 10) shows that, under the condition of controlling for endogeneity, digital skills have a significant positive impact on farmers’ extent of participation in formal financial markets. This further confirms that Hypothesis 1 results are robust.

4.3. Robustness Tests

This study conducted robustness tests on the model to enhance the reliability and accuracy of the model estimation results. Firstly, the Propensity Score Matching (PSM) method was used for robustness testing. The core strategy of the PSM method is to select one or more control units with highly similar characteristics and without intervention from the control group for each sample unit that received a specific intervention based on the propensity score. This enables the estimation of the counterfactual outcome of individuals in the intervention group without intervention using the results of the non-intervention samples, thereby effectively weakening the interference of confounding factors and more accurately assessing the intervention effect. When conducting regression analysis using this method, it is necessary to estimate the average treatment effect on the treated (ATT) of farmers with digital skills based on the propensity score; that is, the average effect difference that the intervention group gains due to mastering digital skills compared to the similar group without intervention. This paper adopted Nearest neighbor matching for empirical testing. The results are summarized in

Table 5. Estimation Results of the Participation decision in Formal Financial Markets Using PSM.

Variable Names	Sample	Treated	Controls	Difference	S.E.	T-Stat
Likelihood of participation in formal financial markets	Unmatched	0.026	0.004	0.022	0.007	2.98 ***
	ATT	0.026	0.007	0.019	0.006	3.31 ***

Note: *** indicates significance at the 1% level.

and

Table 6. Estimation Results of the Degree of Participation in Formal Financial Market Using PSM.

Variable Names	Sample	Treated	Controls	Difference	S.E.	T-Stat
Extent of participation in formal financial markets	Unmatched	0.010	0.002	0.007	0.003	2.12 **
	ATT	0.010	0.004	0.006	0.003	1.84 *

Note: * indicates significance at the 10% level; ** indicates significance at the 5% level.

. The analysis shows that the ATT coefficients obtained by the matching method present a significantly positive features, indicating that mastering digital skills has a significant positive impact on farmers’ likelihood and extent of participation in formal financial markets. Considering the self-selection bias, the positive effect of digital skills on farmers’ participation behavior in formal financial market still holds, further confirming the robustness of Hypothesis 1.

Second, the Heckman two-step method was used to test whether the original regression equation had endogeneity problems caused by self-selection bias. Since farmers’ acquisition of digital skills is not a random process and may be influenced by various potential factors such as ideological concepts and information processing capabilities, it is necessary to conduct in-depth discussions on self-selection bias. In the first-stage model, this paper referred to the research of Luo et al. [24] to construct a dummy variable for farmers’ acquisition of digital skills and included it as the dependent variable in the Probit model for estimation. At the same time, referring to the research of Jin et al. [41], the “average level of digital skills of non-interviewed farmers in a same village” was introduced as an identification variable. In the second-stage model, the inverse Mills ratio (IMR) generated by the first-stage model was included as a control variable in the model. The specific results are detailed in

Table 7. Heckman Two-step Estimation Results of Digital Skills on Formal Financial Markets Participation Behaviors.

Variables and Statistical Parameters	Model 11		Model 12
	Select Model Dummy Variables for Digital Skills	Regression Model Likelihood of Participation in Formal Financial Markets	Select Model Dummy Variable for Digital Skills	Regression Model Extent of Participation in Formal Financial Markets
Digital skills		0.00889 * (0.00379)		0.178 ** (0.0623)
Average level of digital skills	0.327 *** (0.0676)		0.327 *** (0.0676)
IMR1		−0.0278 (0.0616)
IMR2				0.253 (1.053)
Control variables	Yes	Yes	Yes	Yes
Constant term	0.550	−0.208 **	0.550	−7.275 ***
R2/Pseudo R2	0.1353	0.0321	0.1353	0.1225
Observation	2895		2895

Note: In Model 11, the first stage adopts Probit regression, while the second stage employs OLS regression; in Model 12, the first stage uses Probit regression and the second stage adopts Tobit regression. * indicates significance at the 10% level; ** indicates significance at the 5% level; *** indicates significance at the 1% level.

. The research results show that the IMR coefficient did not reach a significant level, indicating that the original regression model was not significantly affected by self-selection bias. The results obtained using the Heckman two-step model are not significantly different from those obtained using the original regression equation, further confirming Hypothesis 1’s robustness.

4.4. Mechanism Analysis

This paper employs OLS regression to analyze the impact of digital skills on information acquisition, social networks, and online transactions. The results show that digital skills positively influence farmers’ information acquisition ability at the 1% significance level (

Table 8. Results of Mechanism Test.

Variable Names	Model 13	Model 14	Model 15
	Information Acquisition	Social Networks	Online Transactions
Digital skills	0.244 *** (0.192)	0.629 ** (0.274)	0.026 *** (0.006)
Control variables	Yes	Yes	Yes
Constant term	1.138 ***	−10.019 ***	−0.158 ***
R2	0.2358	0.0310	0.0364
Observation	2895	2895	2895

Note: ** indicates significance at the 5% level; *** indicates significance at the 1% level.

, Model 13), which is consistent with the conclusion drawn from the analysis based on capability approach theory. Digital skills can enhance farmers’ information acquisition ability, promote the growth of their financial knowledge, reduce information search costs, and thereby encourage their participation in formal financial markets, thus verifying Hypothesis 2. Digital skills positively affect farmers’ social networks at the 5% significance level (Table 8, Model 14), which aligns with the conclusion based on the analysis of social capital theory. Digital skills can expand farmers’ social networks, increase their social capital, and thereby promote their likelihood and extent of participation in formal financial markets, thus verifying Hypothesis 3. Digital skills positively influence farmers’ online transaction ability at the 1% significance level (Table 8, Model 15), which is the same as the conclusion drawn from analysis based on theory of technology adoption. Digital skills can enhance farmers’ online transaction ability, reduce their psychological distance to participate in the financial market, effectively alleviate the problem of financial exclusion in rural areas, and improve their operational skills required for transactions as well as their ability to collect transaction information and data through platforms, thereby enhancing their likelihood and extent of participation in formal financial markets, thus verifying Hypothesis 4.

4.5. Heterogeneity Analysis

The promoting effect of digital skills on farmers’ participation in formal financial markets may exhibit heterogeneous characteristics due to the internal differences among farmers. Referring to existing studies [21,24], this paper will conduct a heterogeneity analysis from three dimensions: educational attainment, age group, and household economic status. From the perspective of human capital theory, education constitutes the core element of human capital development and can effectively enhance an individual’s skill level and overall quality. After acquiring digital skills, farmers with different educational backgrounds may have varying intentions to participate in formal financial markets. Furthermore, according to life cycle theory, individuals formulate different consumption and savings strategies at various stages of their life cycle. Farmers of different age groups, after mastering digital skills, tend to adopt differentiated asset allocation strategies. Finally, based on the theory of financial exclusion, households with differing income levels often have distinct wealth accumulation and resources. Farmers with lower incomes lack access to financial institutions and encounter obstacles when using financial products [42], which in turn affects their participation in financial markets.

4.5.1. Heterogeneity Analysis Across Educational Levels

The promoting effect of digital skills on farmers’ participation in formal financial markets. This study classified farmers with junior high school education or below as having a low educational level and those with senior high school education or above as having a high educational level based on the educational levels of the farmers in the sample. When conducting the regression analysis, the control variable of farmers’ educational level was excluded. The analysis results show that digital skills can significantly enhance the likelihood and extent of participation in formal financial markets for farmers with a lower educational level at the 1% significance level, but have no significant impact on farmers with a higher educational level (

Table 9. Heterogeneity Analysis Results of Digital Skills on Farmers’ Formal Financial Market Participation Decisions.

Variable Names	Model 16		Model 17		Model 18
	Regression Results Across Different Educational Levels for Grouping		Regression Results Across Different Age Groups		Regression Results Across Different Income Levels Groups
	Low Educational Level	High Educational Level	Young Group	Middle-Aged and Elderly Group	Low-Income Level	High-Income Level
Digital skills	0.289 *** (0.078)	−0.111 (0.178)	0.054 (0.240)	2.40 *** (0.069)	0.311 *** (0.107)	0.129 (0.101)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Constant term	−9.264 ***	−14.378 ***	−10.225 ***	−6.565 ***	−8.710 ***	−7.905 ***
Pseudo R2	0.1238	0.2327	0.2006	0.1191	0.1972	0.0698
Observation	2415	480	283	2612	2094	801

Note: *** indicates significance at the 1% level.

, Model 16;

Table 10. Heterogeneity Analysis Results of Digital Skills on the Degree of Formal Financial Market Participation of Households.

Variable Names	Model 19		Model 20		Model 21
	Regression Results Across Different Educational Levels for Grouping		Regression Results Across Different Age Groups		Regression Results Across Different Income Levels Groups
	Low Educational Level	High Educational Level	Young Group	Middle-Aged and Elderly Group	Low-Income Level	High-Income Level
Digital skills	0.248 *** (0.071)	−0.071 (0.133)	0.078 (0.193)	0.208 *** (0.060)	0.370 *** (0.125)	0.074 (0.069)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Constant term	−7.869 ***	−12.925 ***	−9.261 ***	−5.789 ***	−10.353 ***	−4.864 ***
Pseudo R2	0.1127	0.2223	0.2006	0.1115	0.1834	0.0868
Observation	2415	480	283	2612	2094	801

Note: Models 19, 20 and 21 employ Tobit analysis to examine the impact of digital skills on the degree of farmers’ participation in formal financial markets under different conditions. *** indicates significance at the 1% level.

, Model 19). The possible reason is that farmers with a lower educational level have more limited channels to obtain information. The improvement of digital skills can broaden their information acquisition channels and enhance their financial knowledge reserves, thereby promoting their likelihood and extent of participation in formal financial markets.

4.5.2. Heterogeneity Analysis Across Age

According to the classification criteria of existing studies [42,43], this paper classifies farmers aged 18 to 40 as the young group and those aged 41 and above as the middle-aged and elderly group. In the regression analysis, the control variable of age and age squared was excluded. The analysis results show that digital skills can significantly enhance the likelihood and extent of participation in formal financial markets of middle-aged and elderly farmers at the 1% significance level, but have no significant impact on the young farmers’ likelihood and extent of participation in formal financial markets (Table 9, Model 17; Table 10, Model 20). The possible reason is that middle-aged and elderly farmers have a relatively poor ability to obtain information, and digital skills have a more significant impact on their information acquisition capabilities and financial knowledge reserves, thereby promoting their likelihood and extent of participation in formal financial markets.

4.5.3. Heterogeneity Analysis Across Annual Income Levels

This study classifies farmers with an annual income lower than or equal to the mean as having a low-income level, and those with an annual income higher than the mean as having a high-income level. In the regression analysis, the control variable of the total annual income of farmers was excluded. The results show that digital skills can significantly increase the likelihood and extent of participation in formal financial markets for low-income farmers at the 1% significance level, but have no significant impact on the likelihood and extent of participation in formal financial markets for high-income farmers (Table 9, Model 18; Table 10, Model 21). The possible reason is that the participation threshold for financial products such as stocks, funds, and bonds is relatively high, requiring sufficient financial information. After low-income farmers acquire digital skills, they effectively broaden their information acquisition channels to acquire the necessary financial knowledge and investment information to purchase financial products, thus enabling them to participate in formal financial markets.

5. Further Analysis

To further analyze the economic impact of participation in formal financial markets on farmers, we have constructed the following econometric model:

$${\mathrm{l}\mathrm{n}(Income}_i)={\beta }_0+{\beta }_1{FMLP}_i+\delta C_i+{\mu }_i$$

(3)

where ${\mathrm{l}\mathrm{n}(Income}_i)$ represents the annual income of the household, taking the logarithm; ${FMLP}_i$ represents farmers’ likelihood of participation in formal financial markets; $C_i$ represents the control variables; ${\beta }_1$ represents the coefficient of digital skills; $\delta$ represents the coefficient of control variables; ${\beta }_0$ represents the constant term in Formula (2); ${\mu }_i$ represents the random error term in Formula (3).

$${\mathrm{l}\mathrm{n}(Income}_i)={\beta }_0+{\beta }_1{FMEP}_i+\delta C_i+{\mu }_i$$

(4)

where $\ln \left({Income}_i\right)$ represents the annual income of the household, taking the logarithm of their income; ${FMEP}_i$ represents farmers’ extent of participation in formal financial markets; $C_i$ represents the control variables; ${\beta }_1$ represents the coefficient of digital skills; $\delta$ represents the coefficient of control variables; ${\beta }_0$ represents the constant term in Formula (2); ${\mu }_i$ represents the random error term in Formula (4).

This paper analyzed the impact of participation in formal financial markets on the annual income of farmers through OLS regression. The results show that both the likelihood and extent of participation in formal financial markets positively affect the annual income of farmers at the 1% significance level (see

Table 11. Regression Results of Formal Financial Market Participation Behavior on famers’ annual income.

Variable Names	Farmers’ Annual Income
	Model 22	Model 23
Likelihood of Participation in formal financial markets	0.632 *** (0.115)
Extent of participation in formal financial markets		0.763 *** (0.260)
Control variable	Yes	Yes
Constant term	5.592 ***	5.515 ***
R2	0.1283	0.1255
Observation	2895	2895

Note: *** indicates significance at the 1% level.

). This is consistent with the conclusion that digital skills can reduce the financial exclusion of farmers, promote their participation in formal financial markets, and optimize their financial asset allocation, thereby increasing their annual income. This verifies Hypothesis 5.

6. Discussion

The continuous updates and iterations of digital technology have profoundly influenced people’s lifestyles and cognitive patterns. However, the unevenness in its popularization, especially the disparity between urban and rural areas, remains an urgent issue to be addressed. The suggestions of this study include: (1) Enhancing farmers’ participation in formal financial markets through a dual-track strategy of strengthening digital financial education and establishing regional mutual assistance platforms. Local financial institutions should implement regular digital financial education and training programs to widely disseminate the theoretical framework and practical operation knowledge of internet finance to farmers. By deepening farmers’ understanding of the digital finance field through diversified channels, their willingness to participate in formal financial markets can be further enhanced. Additionally, establishing regional financial mutual assistance organizations can provide farmers with a platform for exchanging financial knowledge and sharing financial information, effectively leveraging the information dissemination capabilities of social networks to reduce the cost of obtaining financial information and stimulate their initiative and enthusiasm in family financial asset allocation. For farmers with low educational attainment and those in the middle-aged and elderly groups, “one-on-one practical training” is conducted; for young farmers, “advanced training” is provided. (2) Further expanding farmers’ channels for obtaining financial information and stimulating their intrinsic motivation to participate in online transactions. The primary task is to increase investment in digital information infrastructure in rural areas to enhance the coverage and depth of internet and mobile communication technologies in rural areas, thereby building a stable and efficient platform for farmers to obtain financial information and the village can integrate the resources of local rural commercial banks and supply and marketing cooperatives, and regularly release information on local financial products. In addition, regular thematic exchange meetings should be organized to promote in-depth interaction and knowledge sharing between digital technology experts and farmers, strengthening the leading role of digital technology experts in key areas such as digital finance and online transactions, and further expanding farmers’ cognitive scope and participation in digital technology. (3) Promoting the in-depth development of inclusive finance to alleviate the differentiation problems caused by “information asymmetry” and “financial exclusion”. For farmers with lower education levels and the elderly, targeted assistance mechanisms should be established, the participation process in the digital financial market should be simplified, and the entry threshold should be moderately lowered to narrow the cognitive gap between “information-poor” and “information-rich” individuals. Moreover, for low-income farmers, the supply and promotion of basic digital financial services and low-threshold financial products should be increased, providing practical and feasible paths for their participation in the financial market, and helping them better integrate into and benefit from the modern financial system.

The core finding of this study—that digital skills can significantly enhance participation in formal financial markets in rural areas—effectively aligns with existing research conclusions. For instance, Wang et al.’s study based on Chinese household survey data has confirmed the positive impact of digital capabilities on the risk financial asset allocation of rural households [44], providing cross-sample consistency support for the conclusion of this study.

At the intermediary mechanism level, the test results of the transmission paths regarding information acquisition, online transactions, and social networks in this paper have also been supported by both international and domestic related research: Hsueh et al. using the 2013–2019 Chinese Household Financial Survey (CHFS) data found that the popularization of digital payment can promote individual participation in financial markets [45], which is highly consistent with the mechanism logic of “online transaction ability promoting financial participation” in this study; Parvin et al. based on microdata from five cities in India further pointed out that social interaction can significantly enhance individuals’ willingness to participate in the stock market [46], and the verification of its “social network value” also corroborates with the empirical results of social network as an intermediary variable in this study.

From the perspective of application scope, the above conclusion holds certain universal significance in developing countries. Especially for those countries where common problems such as information asymmetry and weak digital infrastructure are prevalent in rural areas, the core logic of digital skills empowering financial participation has reference value. However, it should be noted that its direct application is still limited by the institutional differences among different developing economies. Therefore, when adopting this mechanism, it is necessary to make targeted adjustments based on the local institutional environment and the actual situation of rural development, so as to better leverage the role of digital skills in promoting participation in formal financial markets.

7. Conclusions

This paper systematically studies the mechanism of digital skills on farmers’ participation in formal financial markets through the theories of capability approach, social capital, and technology adoption, and conducts an empirical analysis based on the 2020 China Rural Revitalization Survey (CRRS) data. The research shows that digital skills can significantly enhance farmers’ likelihood and extent of participation in formal financial markets. The marginal effect analysis shows that for every 1 unit increase in digital skills, the likelihood of participation in formal financial markets would increase by 0.983%, and the extent of participation would increase by 18.368%Meanwhile, digital skills mainly promote farmers’ likelihood and extent of participation in formal financial markets by improving their information acquisition ability and online transaction ability, and expanding their social networks. The “online transactions—financial participation” mechanism discovered in this article is consistent with the international research conclusions. Misra et al.’s research in the Indian region pointed out that the use of electronic wallets can increase their likelihood of participating in the stock market, which is highly consistent with the mechanism described in this article [47]. Heterogeneity analysis indicates that the impact of digital skills on farmers’ participation in formal financial markets varies significantly among different groups. Digital skills have a greater impact on the likelihood and extent of participation of farmers with low education levels, middle-aged and elderly farmers, and low-income farmers, and all the impact is significant at 1% level; while the impact on more educated, young, and high-income farmers is not significant. Further analysis shows that participation in formal financial markets can significantly increase farmers’ annual income, generating an economic effect that promotes rural economic development.

This paper has the following shortcomings. In terms of the instrumental variable, this study uses “the average level of digital skills of other farmers in the same village” as the instrumental variable. Although it passed the weak instrumental variable test (F = 28.76), there are still potential limitations. The instrumental variable may be affected by unobserved variables such as “village-level digital infrastructure”, resulting in some endogeneity not being completely eliminated. In terms of data timeliness, this study uses the 2020 CRRS data, which cannot reflect the latest impact of the rapid development of rural digital finance after 2020. In the future, panel data can be used to track the dynamic changes in farmers’ digital skills and formal financial markets participation.