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Article

The Impact of Digital Capabilities on Peasants’ Wage Growth: Evidence from Chinese Farmer Entrepreneurs

by
Shanhu Zhang
1,2,
Jinxiu Yang
2,*,
Yun Shen
2 and
Zhuoli Li
2
1
College of Economics, Sichuan University of Science & Engineering, Yibin 644005, China
2
College of Economics, Sichuan Agricultural University, Chengdu 611130, China
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(10), 1765; https://doi.org/10.3390/agriculture14101765
Submission received: 24 August 2024 / Revised: 23 September 2024 / Accepted: 3 October 2024 / Published: 6 October 2024
(This article belongs to the Special Issue Agricultural Policies toward Sustainable Farm Development)
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Abstract

:
The gradual integration of digital technology into traditional Chinese villages has triggered a shift in income distribution from labor to capital, posing challenges to the wage growth of employed peasants. Based on the theory of empowerment, this paper explores the mechanisms of credit availability and talent loss in the interplay between digital capabilities and wage augmentation among employed peasants. This study empirically examines or validates the mechanism of digital capabilities on wage growth for employed peasants through the entropy weight method, the OLS linear model, the mediation effect model, and propensity score matching while using survey data from 490 farmer entrepreneurs as samples. The findings are as follows. (1) The digital capabilities of farmer entrepreneurs have a significant positive impact on the wage growth of employed peasants, and this result remains robust after a series of robustness checks. In terms of hierarchical effects, digital foundational capabilities > digital application capabilities > digital innovation capabilities. (2) Credit availability and talent loss mediate the relationship between digital capabilities and wage growth for employed peasants. (3) The digital capabilities of farmer entrepreneurs who are young, highly educated, and have a low family-dependency ratio exert a more pronounced influence on the wage growth of employed peasants. Additionally, lower policy uncertainty enhances the effect of digital capabilities on wage growth for employed peasants. The study uncovers the empowerment mechanism of digital advancements embedded during the entrepreneurial journey, enriches research on digital capabilities and common prosperity, and provides a feasible path for governments to formulate reasonable entrepreneurship and digital promotion policies.

1. Introduction

Chinese farmer entrepreneurs make up an important group that drives nearby rural labor employment, increases peasants’ wages, and promotes common prosperity. After the Chinese government issued the “Outline of the Digital Rural Development Strategy” in 2019, the Chinese central government departments successively released a series of policy documents, including the “Digital Rural Development Action Plan (2022–2025)” and the “Guiding Opinions on the Construction of a Digital Rural Standard System”, indicating that the Chinese government highly values digital rural construction. With the gradual improvement of digital infrastructure, China’s rural entrepreneurial ecosystem has changed, with the old world gradually deconstructed and a new world of digital twins established [1]. The original business model has changed, with the internet connecting farmers to large markets and connecting rural areas to cities [2]. Through diversified logical forms and low-cost digital means, spatial barriers are eliminated, trade barriers are broken down, a multi-level network-connected ecosystem is created, and the relationship between producers and consumers is reshaped [3], triggering new agricultural factor allocation and the organizational division of labor [4], leading to a new rural production and operation system. In addition, digital technology has also led to a shift in income distribution from labor to capital [5], with backward production capacity being eliminated and the average wages of low-skilled labor even declining [6], further exacerbating income inequality. However, rural areas are based on a rural acquaintance society formed by blood and geography. Whether the improvement of digital capabilities brought about by digital technology among farmer entrepreneurs will impact the continued growth of small farmers’ wages remains to be further verified and answered.
Firstly, as digital countryside construction progresses, digital infrastructure is gradually improving. The internet facilitates the effective dissemination of information, enhances the demonstration effect of successful entrepreneurship, and promotes entrepreneurial behavior among rural residents [7,8]. Digital empowerment can also increase employment opportunities for labor with high human capital by expanding economic scale and improving management efficiency [9]. Concurrently, digital technology accessibility significantly promotes rural labor outflow [7]. Given the coexisting reality of increased demand for labor with high human capital and rural labor outflow, further investigation is needed to understand how the wages of employed peasants in rural areas will change. Secondly, the nature of locality and regionality in the digital age is evolving, with the boundaries of different ecosystems being transcended through digitization [10]. Digital transformation in enterprises plays a vital role in alleviating financing constraints [11]. The depth of digital inclusive finance (DIF) usage is crucial to the quality of rural entrepreneurship, and a supportive business environment strengthens this relationship [12]. Enhancing digital financial capabilities can disproportionately benefit disadvantaged groups, particularly those in rural or underdeveloped areas [13].
Meanwhile, those living in poverty, without secondary education, and in poor health are more vulnerable to digital exclusion [14]. Digital capabilities, characterized as dynamic capabilities [15], struggle to reasonably explain such contrasting realities. The dynamic capabilities of enterprises enable them to respond to the ever-changing market environment and maintain sustainable competitive advantages [16]. Different from enterprise digital capabilities, the digital capabilities of farmer entrepreneurs aim to effectively acquire and utilize information, improve agricultural production efficiency, and broaden sales channels, hoping to change the existing production and operation status, improve social status, and restore social functions. The enterprise digital capabilities representing dynamic capability characteristics cannot cover the social functions of farmer entrepreneurs’ digital capabilities. Therefore, empowerment theory presents new opportunities for research development. The core essence of “empowerment” revolves around bolstering the effectiveness of agents and furnishing them with pathways, instruments, or prospects for the accomplishment of their objectives. How digital capabilities based on empowerment theory can benefit employed peasants has not yet garnered significant attention from researchers.
This study embeds the empowerment theory into the entire process of farmer entrepreneurship and integrates it with the three stages of rural entrepreneurial practice: startup, expansion, and self-renewal. It views the digital capabilities of farmer entrepreneurs as multidimensional and dynamically evolving. By learning and utilizing digital tools, they attain novel competencies to enhance entrepreneurial excellence and value generation, thus realizing the digitization or digital transformation of marketing methods, products, and services [17]. In turn, this drives rural economic development, increases farmers’ income, and narrows the urban–rural gap. In the research on the development of digital technology, the study of digital capabilities has consistently garnered the attention of researchers, leading to a series of research achievements. However, the focus has primarily been on enterprises’ digital capabilities, with a noticeable lack of literature examining the impact mechanism of farmer entrepreneurs’ digital capabilities on the growth of peasants’ wages and, particularly, the absence of a systematic theoretical framework and empirical testing. Therefore, this study has three objectives. First, it aims to investigate the direct impact of farmer entrepreneurs’ digital capabilities on the wage growth of employed peasants. Second, the objective of this study is to delve into the consequences of farmer entrepreneurs’ digital capabilities on both credit accessibility and the phenomenon of talent loss; furthermore, it endeavors to validate the intermediary function hypothesis pertaining to credit availability and talent loss, ultimately elucidating the intricate mechanism through which farmer entrepreneurs’ digital capabilities influence the wage augmentation of rural employees. Third, the study aims to further explore the heterogeneity effects brought by farmer entrepreneurs’ groups and policy uncertainty.
In this light, leveraging survey data from 490 farmer entrepreneurs across 21 cities (prefectures) in Sichuan Province, the study employs the entropy weight method, the OLS linear model, the mediation effect model, and propensity score matching to unravel the mechanism underpinning the influence of farmers’ entrepreneurial digital capabilities on the sustained growth of employed peasants’ wages. Compared to existing research, the key contributions of this paper lie in elucidating the intricate dynamics between farmer entrepreneurs’ digital capabilities and their multifaceted impacts, specifically addressing the enhancement of credit accessibility, the mitigation of talent exodus, and the subsequent influence on wage growth among employed peasants. By adopting a nuanced approach, this research offers fresh insights into the intricate mechanisms at play, enriching the understanding of the digital transformation’s role in rural economic development. Firstly, it constructs a theoretical analysis framework linking digital capabilities to the wages of employed peasants, revealing the impact mechanisms of credit accessibility and talent loss on these wages, thereby broadening the scope of the empowerment theory. Secondly, it examines the heterogeneous effects of different levels of digital competence, heterogeneity within farmer entrepreneur groups, and policy uncertainty on the wage growth of employed peasants, clarifying the intricate relationships between digital competence and various farmer entrepreneur groups and policy environments. Thirdly, it enriches research on digital competence and common prosperity, providing quantitative evidence and empirical insights for refining digital promotion strategies and guiding entrepreneurial policies for different entrepreneurial groups in digital countryside construction.
The structure of the remainder of this paper is as follows. Section 2 presents a literature review on digital competence, wages of employed farmers, credit accessibility, and talent drain. Based on relevant theories, research hypotheses have been proposed. Section 3 elaborates on the research methodology, including data collection processes, variable measurement, and estimation strategies. Section 4 of this paper showcases the empirical findings derived from the study. Section 5 delves into a comparative analysis, examining the congruence, deviations, or supplementary nature of these discoveries in relation to the extant research corpus. Ultimately, Section 6 concludes the research endeavor by summarizing the key insights and outlining their implications for policy formulation and future research avenues.

2. Prior Literature Review and Hypothesis

2.1. Digital Capabilities and the Growth of Employed Peasants’ Wage

In 1976, the term “empowerment” was first used in Barbara Solomon’s book “Black Empowerment: Social Work in Oppressed Communities”, where empowerment was defined as a process that aids marginalized groups in enhancing their sense of worth and dignity, ultimately aiming to improve living conditions for both current and future generations. Solomon [18] regarded empowerment theory as a supplement to existing theories, emphasizing that empowerment is not a privilege for the few but a fundamental force for survival. Through social policies, individuals can be empowered with the belief to control their lives and gradually regain their footing. From a management perspective, methods for achieving empowerment include education, leadership, organization, provision, guidance, and implementation [19], all of which are correlated with performance [20]. Joseph [21] critically analyzed empowerment theory using the Theoretical Evaluation Scale (TES), demonstrating its suitability as an ideal framework for mixed-methods research paradigms as a theoretical model. During China’s economic transformation and development, while achieving economic prosperity and remarkable accomplishments, it has also led to the emergence of vulnerable groups, particularly laid-off workers, landless farmers, and rural left-behind populations due to social changes. These groups struggle to sustain their lives through their own means, forming impoverished segments of society. In this context, introducing empowerment theory can reignite the life aspirations of vulnerable groups. Yue Xiaowenxu et al. [22] proposed a new endogenous development mechanism for rural China centered on self-empowerment of capable individuals, organizational empowerment, community empowerment, and empowerment of collective members. Therefore, based on empowerment theory, farmer entrepreneurs’ digital capabilities should serve as a catalyst for their own effectiveness in transforming existing production and operation status, altering adverse social positions, and restoring societal functional deficiencies.
This paper regards farmer entrepreneurs as capable actors who, through learning and utilizing digital tools empower themselves with control and coping skills in daily life and production. This empowerment does not solely stem from the mere possession of digital tools; rather, it encompasses a multi-faceted and sequential process [23]. The digital capabilities of farmer entrepreneurs are influenced by multiple factors, with existing research mainly focusing on the quality of digital infrastructure, public policies (such as broadband subsidies), the educational level of rural residents, and their digital skill levels [24,25,26]. Researchers have concluded that digital capability is a multidimensional concept, involving proficiency in utilizing software applications or operating digital instruments and cognitive complexities, emotional factors, and interpersonal abilities [27,28], which can be measured through micro-survey databases or questionnaire data.
A standard system of measurement indicators for digital capabilities has not yet been established. The existing research primarily constructs digital capability indices from three dimensions: digital access, digital usage, and digital transformation [29]. In small and medium-sized enterprises, digital capabilities positively impact employees’ digital performance [30]. In farmer enterprises, digital capabilities not only enhance farmers’ household resilience by encouraging entrepreneurship and deepening their involvement in financial markets [31] but also promote herders’ income growth by elevating human capital levels, facilitating the adoption of production technologies, strengthening bargaining power, and reducing information search costs [5]. Farmers’ wage income is a crucial component of their overall income growth, influenced by their personal qualities and intimately tied to their economic environment [32]. Building upon previous research [33,34] and integrating the realities of farmers’ entrepreneurial practices, this paper categorizes farmers’ entrepreneurs’ digital capabilities into three tiers: digital basic capabilities, digital application capabilities, and digital innovation capabilities [35,36]. In addition, it proposes the following research hypotheses:
H1. 
Digital capabilities positively contribute to the wage growth of employed peasants.
H1a. 
Digital foundational capabilities positively contribute to the wage growth of employed peasants.
H1b. 
Digital application capabilities positively contribute to the wage growth of employed peasants.
H1c. 
Digital innovative capabilities positively contribute to the wage growth of employed peasants.

2.2. The Mediating Role of Credit Availability (CA)

The fundamental reason for farmers’ difficulty obtaining formal credit lies in information asymmetry, and particularly, the lack of effective collateral accepted by banks [37]. Coupled with the characteristics of farmers’ financing needs, such as large amounts, long terms, and low interest rates [38], this further exacerbates the challenge. To address this issue, prior research has unearthed that the engagement of large-scale farmers within agricultural and industrial supply chain networks can effectively mitigate transaction expenses stemming from informational disparities and contractual imperfections inherent in their interactions and formal financial institutions, thereby enhancing their credit availability [39]. Additionally, the confirmation of rural land rights has significantly increased the credit availability of high-income farmers [40]. Meanwhile, various factors influence farmers’ credit availability, including the household head’s age, gender, education level, farming experience, farm size, and social networks [41,42]. Digital technology plays a pivotal role among these influencing factors. It reduces transaction costs, enhances operational capabilities, and mitigates the impact of under investment [43], but it also increases credit availability, promotes income growth, and narrows income gaps, thereby alleviating poverty [44].
As digital technology continues to infiltrate traditional rural life, data footprints generated by mobile devices can effectively alleviate information asymmetry, elevate individual risk preferences, strengthen social trust and interaction, and significantly increase the probability of household credit availability [45]. The emergence of digital inclusive finance has notably impacted the credit availability of farmers with borrowing needs, labor capabilities, and learning abilities [46]. Digital capabilities can enhance farmers’ agricultural entrepreneurial capabilities by increasing production credit [47], providing favorable conditions for farmers’ entrepreneurship [48]. Therefore, this paper proposes the following research hypothesis:
H2. 
Credit availability mediates the relationship between digital capabilities positively and the wage growth of employed peasants.

2.3. The Mediating Role of Talent Loss (TL)

The migration of rural populations to cities has led to a trend of population contraction in rural settlements [49], objectively plunging rural China into the predicaments of “hollowing out”, “aging”, and “empty-nest syndrome”. Faced with the coexistence of multiple hollowing-out phenomena, including industrial hollowing, cultural hollowing, and social fragmentation, the Chinese government has made strategic adjustments and path innovations [50]. Factors influencing brain drain encompass overt factors such as work environment, compensation, and career platforms [51], and psychological factors akin to regional identity, among which psychological factors are the most significant [52]. By integrating local elites, labor, and funds, the Chinese government guides migrant workers to seek employment locally, restoring the family’s elderly care and education functions, thereby addressing significant social issues stemming from rural decline and imbalances in urban-rural development [53]. In the process of modernization, rural and urban areas enjoy equal rights to development. Driven by both urbanization and marketization, the revitalization of rural areas and villages is imminent [54].
In rural areas where agriculture is the dominant industry, returnees tend to engage in agricultural work [55]. The emergence of rural e-commerce has facilitated the coordinated development of regional economies. Smartphones, as crucial tools for digital technology access, influence flexible employment through information acquisition and social activities [56]. Digital infrastructure promotes employment structural transformation by fostering internet development, digital inclusive finance, and entrepreneurial growth channels [57]. Digital technology enables flexible workers with low security and income to access more stable and higher-quality employment opportunities, fostering a high concentration of digital technology within formal employment [58]. Digital empowerment increases the proportion of highly skilled labor, in turn, this leads to an optimization of the employment skillset configuration within the labor force, enhancing its overall competence and efficiency [59]. Corporate digital transformation positively impacts the scale of labor employment and salary levels [60], enhancing the salaries of both management and ordinary employees [61]. It is evident that the higher the digital capability of rural entrepreneurs, the higher the employment skill requirements for labor. Given the continuous mobility of the rural population, rural entrepreneurs may attract highly skilled talent by increasing the wages of employed farmers. Therefore, this paper proposes the following research hypothesis:
H3. 
The degree of talent loss mediates the relationship between digital capabilities and the wage growth of employed peasants.

2.4. The Theoretical Model

Based on the empowerment theory, this paper regards farmer entrepreneurs as capable subjects and actors who can develop digital capabilities through digital tools, thereby transforming existing production and business methods, reversing unfavorable social status, and restoring social functions. The availability of formal credit and the extent of talent loss are crucial factors for farmer entrepreneurs to expand their business scale, enhance the market competitiveness of their products, and determine whether they can provide higher wages and benefits to employed peasants. On this basis, from the perspectives of credit availability and talent loss, this paper delves into the intricate mechanisms and pathways that underpin the relationship between digital capabilities and the sustainable augmentation of employed peasants’ wage growth. Please refer to Figure 1 for specific contents.

3. Materials and Methods

3.1. Sample and Data Collection

Sichuan Province, a pivotal exporter of migrant labor, possesses an extensive pool of empirical exemplars. Capitalizing on the momentum of fostering rural industrial rejuvenation leaders in Sichuan in 2022, this study embarked on a questionnaire survey spanning from early March to late May 2023. Our investigative objectives transcend the conventional focus on migrant workers repatriating for entrepreneurial endeavors, embracing a broader spectrum that encompasses returning entrepreneurs, university graduates, military veterans, and technical specialists—a collective termed “famer entrepreneurs”. For a more detailed sampling method of the questionnaire, please refer to the published research of our research team [35].
The survey was structured into two distinct phases: an initial phase utilizing paper questionnaires, followed by a subsequent phase employing electronic questionnaires. The electronic version mirrored the content of the paper counterpart, ensuring consistency. During the first phase, participants received clarifications on pertinent matters and precautions pertaining to questionnaire completion. The majority of questionnaires were completed independently, while personalized, one-on-one interviews were arranged on-site for those encountering difficulties. Proceeding to the second phase, each paper questionnaire underwent meticulous individual verification for logical inconsistencies, inaccuracies, omissions, and other potential issues. To address these, follow-up interviews were conducted either via telephone or by sending revised electronic questionnaires for completion and additional information gathering. Detailed information is provided in Table 1.
A total of 650 questionnaires were disseminated, with subsequent screening to exclude invalid responses featuring incomplete data, uncorrectable logical inconsistencies, and uniform responses across key metrics. This process yielded 490 valid samples, translating to an effective response rate of 75.38%. The sample composition is predominantly male, with females comprising a relatively modest 20% of the total. Age-wise, the majority falls within the 31–40 and 41–50 age brackets, constituting 31.22% and 45.92%, respectively. For a comprehensive breakdown, please refer to Table 2.

3.2. Variable Measurement

3.2.1. Dependent Variable

The dependent variable is the wages of hired peasants. To reduce the influence of regional economic development level, rural labor force quantity, and other relevant factors on the core dependent variable, this paper takes the degree of conformity of agricultural entrepreneurs’ self-evaluation of “peasants’ wages are not lower than those of peer enterprises” as the core dependent variable and adopts the Likert 5-point scoring method for valuation. The higher wage of peasants hired by farmer entrepreneurs means that the entrepreneurial project is in the development or maturity stage, and requires more labor support; additionally, it indicates that the supply and demand situation in the labor market is shifting towards the supply side. Agricultural entrepreneurs must offer higher wages and benefits to attract more peasants to work.

3.2.2. Independent Variable

The independent variable is digital capabilities (DC), which includes three dimensions: digital foundational capabilities, digital applicational capabilities, and digital innovative capabilities, with 12 measurement items in total. For the specific item content and the results of reliability and validity tests of the questionnaire, please refer to the published research of our research team [35].

3.2.3. Mechanism Variables

Credit availability (CA) and talent loss (TL) are the two mechanism variables. (1) CA: Based on the dummy variable of “whether credit is obtained” [62], this paper uses the Likert 5-point scale to measure farmer entrepreneurs’ evaluation of the extent to which “it is now easy to borrow from banks to support entrepreneurship” is true. As the score increases, so does the availability of credit, indicating a positive correlation. (2) TL: This paper still uses the Likert 5-point scale to measure farmer entrepreneurs’ evaluation of the extent to which “there are difficulties in recruitment or serious TL” is true. The higher the score is, the more severe the TL is.

3.2.4. Control Variable

This paper controls two variables that may affect the wage growth of employed farmers [63]: (1) Characteristics of entrepreneurial projects: family farms, investment scale, and product quality. (2) Regarding the external environment, dummy variables are utilized for specific locations, thereby mitigating the potential impact of geographical factors, technological progress, and policy uncertainty. The definitions of the main variables and the descriptive statistics are shown in Table 3.

3.3. Approach and Identification Techniques

3.3.1. Benchmark Model Construction

To test the direct impact of DC of farmer entrepreneurs on the continuous growth of employed peasants’ wages, the model is constructed as follows:
Wi = α + βXi + γControli + εi
In this model, Wi represents the numerical value of the wage paid by farmer entrepreneur i to employed peasants relative to the peer level, X represents the DC or DFC or DAC or DIC of farmer entrepreneur i, Controli represents relevant control variables, εi is the random error term, and α is the constant term. β is the estimated coefficient of the core explanatory variable. If this coefficient is significantly greater than 0, it indicates that the improvement of farmer entrepreneurs’ DC is conducive to the continuous growth of employed peasants’ wages; otherwise, it has a negative impact.

3.3.2. Mediation Analysis Model

To further explore the influence mechanism of farmer entrepreneurs’ DC on the continuous wage growth of employed peasants, this study employs a methodology akin to Jiang Ting’s approach [64]. The precise configuration of the model is outlined below:
Mi= ω0 + ω1 Xi + ω2 Controli + εi
wherein Mi represents the mediating variables, including credit CA and TL; ω0 is constant terms, and ω1 represents the estimated coefficients.

4. Results

4.1. Fundamental Regression Analysis

Prior to executing the foundational regression analysis framework, we conducted the Breusch–Pagan (BP) and White tests at a 95% confidence threshold to ascertain the presence of heteroscedasticity, subsequently applying robust standard errors for adjustment. The peak variance inflation factor (VIF) observed for an individual explanatory variable was 1.42, while the model’s mean VIF stood at 1.17, both comfortably below the threshold of 5, attesting to the absence of multicollinearity among the variables chosen in this study. Furthermore, the Ramsey RESET test and Link test outcomes upheld the null hypothesis (H0), signifying no omitted nonlinear terms within the model, thereby justifying the adoption of a linear model.
See Table 4 for details. DC, DFC, DAC, and DIC all positively affect the wages of employed peasants at a 1% confidence level. Moreover, the effect of DFC is higher than that of DAC, and the effect of DAC is higher than that of DIC. The possible explanations are as follows. First, with the continuous advancement of digital rural construction, farmer entrepreneurs can obtain digital technology support at extremely low costs, making it easy to enhance their DFC. By connecting to larger markets through the internet, they can expand production scales while increasing the wages of employed peasants to attract more labor force. Second, the continuous integration of new formats and scenarios in smart agriculture provides a platform for peasants to participate in the multifunctional market operation of agriculture and rural entrepreneurship, significantly promoting local rural employment and increasing peasants’ wages. The above results indicate that Hypotheses H1, H1a, H1b, and H1c are verified.

4.2. Robustness Test

4.2.1. Three Aspects of Robustness Test

To fortify the reliability of the regression analysis outcomes, validation was undertaken from three distinct angles: altering the examination approach, substituting the computational method utilized for the key dependent variables, and exchanging the independent variables. The results are shown in Table 5. First, the test method was altered by treating the explained variable, originally a continuous numerical value, as an ordered discrete value and estimating it using the ordered Probit model. Under the ordered Probit model, Model (1) produced a coefficient linking digital capabilities (DC) to employed peasants’ wages that was in alignment with the baseline model and achieved statistical significance at the 5% confidence threshold, thereby demonstrating the model’s robustness. Second, the calculation method was replaced with the core dependent variables. Using the entropy weight TOSIS method, the DC values were recalculated. Model (2) produced a DC coefficient that was generally consistent in direction with the benchmark model and passed the 1% significance test, demonstrating the model’s robustness. Third, the independent variable was substituted by replacing the farmer entrepreneurs’ self-assessed hired peasants’ wages with the logarithm of the average monthly wage of farmers (base 10). Model (3) exhibited a coefficient for digital capabilities (DC) that concurred with the standard regression model and was statistically significant at the 5% confidence level, thereby reinforcing the model’s robustness. In summary, the constructed OLS regression model passed the tests in all three aspects, confirming its robustness. This underscores the benchmark regression model employed in this paper as maintaining a consistent and steadfast interpretation of the evaluation outcomes, thereby ensuring their reliability.

4.2.2. Addressing Endogeneity Issues

To more accurately evaluate the net effect of DC on the sustained wage growth of employed farmers, a propensity score matching method was applied to process the samples [65], aiming to address the issue of “sample selection bias”. Taking the median value of DC among farmer entrepreneurs as the threshold, the entire sample was divided into a treatment group and a control group. Those with values greater than the median were assigned to the treatment group (coded as 1), while those with values less than the median were assigned to the control group (coded as 0). The specific results are shown in Table 6. It can be observed that the average treatment effects (ATTs) from the three methods, namely, K-nearest neighbor matching (n = 4), radius matching, and kernel matching, were all significant at the 1% confidence level. Furthermore, the average of these three ATTs was 0.1546, lower than the pre-matching value of 0.3429, indicating that without controlling for sample selection bias, the role of farmer entrepreneurs’ digital capabilities in the wage growth of employed peasants would be overestimated. A balance test was conducted on the variables to ensure the accuracy of the results. After matching, the absolute values of the standard deviations were significantly reduced, which met the relevant standards of the propensity score matching method for weakening the systematic differences of variables. Therefore, the selection bias of the samples was eliminated. It can be seen that the positive values of the average treatment effects (ATTs) are consistent with the direction of the benchmark model coefficients, suggesting that the results of the benchmark model in this paper are relatively stable.

4.3. Mechanism Analysis

4.3.1. Path Test of CA

The results obtained from the mediation test model are shown in Model (1) in Table 7, indicating that DC significantly enhances CA for farmer entrepreneurs at the 5% significance level. Specifically, the DC of farmer entrepreneurs first bridges the digital divide, promotes entrepreneurial activities by alleviating financing constraints and improving CA, and positively impacts farmer household income [66]. Second, enhancing CA positively affects adopting agricultural technologies [67], helping farmer producers increase their yields and incomes, thereby reducing poverty, improving food security, and promoting inclusive development [68]. Third, providing credit to credit-constrained farmers can achieve better welfare outcomes [69]. Consequently, it is conducive to increasing the wage income of employed farmers, verifying H2.

4.3.2. Path Test of TL

As shown in Model (2) in Table 7, a positive correlation exists between entrepreneurs’ DC and their perceived severity of TL at the 10% significance level. As farmer entrepreneurs’ DC escalates, their standards for employee quality correspondingly intensify, thereby exacerbating their perception of TL. Farmer entrepreneurs can only attract talents who would otherwise work outside the local area to work nearby by increasing wages and benefits. High-quality talents are always crucial for farmer entrepreneurs to achieve sustainable development. For instance, appointing college graduates as village officials aims to transfer high-quality talents from big cities to rural areas. These talents can contribute to the increase in transfer income, property income, and wage income in rural areas, and they have a more substantial effect on boosting the income of disadvantaged groups in rural areas, thereby promoting common prosperity in rural regions [70]. Therefore, H3 is verified.

4.4. Differentiated Analysis of Variations

4.4.1. Differential Analysis of Agricultural Entrepreneurs’ Related Characteristics

The heterogeneity analysis of the different impacts of agricultural entrepreneurs’ age, education level, and family-dependency ratio on the wage growth of employed peasants is presented in Table 8. First, according to the United Nations World Health Organization (WHO), youth are defined as individuals under 44 years old, while those over 45 are considered middle-aged and elderly [71]. Based on this criterion, this paper categorizes farmers into two groups, young entrepreneurs (aged 18–40) and middle-aged entrepreneurs (over 40), for regression analysis. Models 1 and 2 demonstrate a statistically significant and positive influence of DC on the wage augmentation of farmers employed by youthful entrepreneurs, reaching a 1% level of statistical significance. However, its effect on the wage growth of peasants employed by middle-aged entrepreneurs is insignificant. The possible reason is that youthful entrepreneurs exhibit an elevated level of eagerness and cognitive prowess in acquiring digital technology skills and have stronger receptivity to new knowledge, thereby achieving continuous income growth and increasing wages for employed peasants. Influenced by historical factors, the traditional mindset of middle-aged individuals in rural China is deeply entrenched, making it difficult for them to comprehend and accept new knowledge, which limits the significance of digital competence in boosting the wages of employed peasants.
Secondly, as the primary decision-makers driving the development of entrepreneurial projects, agricultural entrepreneurs’ education level is crucial to the success of these projects. This paper adopts high school education as the dividing line [70] and conducts a grouped regression analysis of agricultural entrepreneurs. As shown in Models 3 and 4, DC significantly and positively affects the wage growth of peasants employed by entrepreneurs with education levels above high school at the 5% statistical level. However, its effect on those employed by entrepreneurs with education levels below high school is not significant. This can be attributed to the fact that more educated agricultural entrepreneurs can integrate modern management concepts with information technology equipment, innovate entrepreneurial business models, enhance operational management efficiency, and, ultimately, generate better entrepreneurial performance, thereby lifting the wages of employed peasants.
Lastly, China’s population structure is undergoing rapid transformation. On the one hand, the birth rate is declining rapidly, while on the other hand, the average life expectancy of Chinese residents is increasing, accelerating the aging process of the age structure and resulting in an increased family-dependency ratio. A higher family-dependency ratio for agricultural entrepreneurs translates into heavier family responsibilities, implicitly creating a crowding-out effect on entrepreneurial time. For regression analysis, this paper classifies agricultural entrepreneurs into two groups based on their family-dependency ratio: low (below 0.5) and high (above 0.5). Models 5 and 6 reveal that DC significantly and positively affects the wage growth of peasants employed by entrepreneurs with a low family-dependency ratio at the 10% statistical level, with a coefficient greater than that of entrepreneurs with a high family-dependency ratio. This indicates that a higher family-dependency ratio exerts a more substantial crowding-out effect on entrepreneurial time, adversely impacting entrepreneurship and the wage growth of employed peasants. A robust social security system can alleviate the dependency burden in such scenarios.

4.4.2. Heterogeneity Analysis of Policy Uncertainty

Policy uncertainty refers to the difficulty for farmer entrepreneurs to accurately predict whether the government will alter existing economic policies [72]. In today’s volatile and ever-changing world, farmer entrepreneurs face a significant rise in economic PU. This paper divides the sample into two groups based on a threshold value of 3 for PU: a group with high PU (value > 3) and a group with low PU (value ≤ 3). The analysis compares whether PU exerts heterogeneous effects on the wage growth of employed peasants. As shown in Table 9, PU affects employed peasants’ wage growth. When PU is low, DC significantly and positively impacts wage growth at the 5% statistical level, with a higher coefficient and significance than in the high PU group. At different levels, when PU is low, DIC significantly and positively affects wage growth at the 10% statistical level. In contrast, when PU is high, both DFC and DAC significantly and positively impact wage growth at the 5% statistical level, which may be attributed to high PU exposing target groups participating in benefit distribution to different rules, leading to speculative behavior and structural short-term actions in pursuit of benefits. In turn, this makes it difficult for farmer entrepreneurs to predict market changes, creating entrepreneurial risks that ultimately affect the wage growth of employed peasants.

5. Interpretation and Implications

This study delves into the pragmatic implications of issues surrounding the relationship between digital competence and wage growth among employed farmers. Employing survey outcomes garnered from 490 agricultural entrepreneurs spanning 21 municipalities (or prefectures) within Sichuan Province, it uses methods such as the entropy weight method, the OLS linear model, the mediation model, and propensity score matching to specifically address the mechanisms and pathways through which DC promotes wage growth among employed farmers. The results signify that DC exerts a direct and affirmative influence on wage advancement, while also exerting an indirect effect through the mediation of either CA or TL mechanisms. The present investigation uncovers the facilitating mechanisms of digital technology integration within rural entrepreneurial collectives and procedures, thereby broadening the horizons of empowerment theory-based research endeavors. Furthermore, it provides quantitative evidence and empirical insights for achieving sustained wage growth among employed peasants. The subsequent sections provide a nuanced examination and clarification of the concordance, disparities, and mutual reinforcement between our research findings and the extant scholarly works.
(1) DC positively affects wage growth among employed peasants. The testing of H1, H1a, H1b, and H1c indicates that Research Objective 1 has been achieved. The significant positive impact of peasant entrepreneurs’ digital capability on the wage growth of employed peasants is consistent with the prior findings of Wang et al. (2024), who identified that corporate digital capability positively influences employees’ digital performance [30]. This discovery implies that peasant entrepreneurs’ digital capability is crucial in promoting local employment of rural labor and increasing their income. Consequently, the government should strengthen the cultivation of peasant entrepreneurs’ digital capability, focusing on the needs of agricultural digitization, which can be achieved by integrating mobile application usage, live streaming sales, rural e-commerce, and smart agriculture into training programs tailored to local talents’ comprehension, learning ability, and free time. By combining online and offline training, as well as short-term and long-term approaches, the government can simplify theoretical training and enhance practical, interactive, and engaging content, which will enable continuous improvement in peasant entrepreneurs’ digital capabilities.
(2) CA and TL mediate the relationship between DC and wage growth among employed peasants. The results of testing H2 and H3 indicate that Research Objective 2 has been achieved. DC plays a vital role in influencing CA [43], and access to formal credit facilitates the successful development of entrepreneurial activities [47], subsequently impacting the wages of employed peasants. TL is a real dilemma in China’s rural development [50]. The emergence of rural e-commerce promotes regional coordinated development [56], and digital technology enables employees to engage in more stable and higher-quality jobs [58], ultimately positively affecting wage growth among employed peasants. This finding underscores the importance of enhancing DC and CA in addressing rural TL, offering valuable lessons for policymakers. Policy makers ought to enhance comprehensive strategic planning, expedite the development of rural digital infrastructure, and tackle challenges including inadequate broadband coverage, network instability, and elevated tariffs in select rural regions, thereby fostering a conducive business ecosystem. This will enable farmers’ entrepreneurs to access financial loans, job opportunities, and other information through the internet, simultaneously fostering continuous improvement in DC.
(3) The DC enhancement of young, highly educated farmers’ entrepreneurs with low family-dependency ratios has a more pronounced effect on wage growth among employed peasants. Additionally, lower PU correlates with a larger and more significant coefficient of DC impact on wage growth. Through a heterogeneous analysis of peasant entrepreneurs’ groups and policy uncertainty, Research Objective 3 has been achieved. These findings suggest that farmers’ entrepreneurs must possess a certain human capital to adapt to the digital era’s changes and maintain a competitive edge in the market. Excessive caregiving burdens can negatively impact entrepreneurial development. Comprehensive social security systems provided by policymakers can alleviate caregivers’ pressures. A stable policy environment means the government adheres to its commitments in long-term interactions with farmers’ entrepreneurs, enhancing policy compliance, government credibility, and authority, which reduces speculative and structured short-term behaviors in pursuit of benefits, mitigating entrepreneurial risks.

6. Conclusions

6.1. Study Conclusions

Understanding the impact of farmer entrepreneurs’ digital capabilities on the wages of employed peasants contributes to China’s goal of achieving common prosperity. Theoretically, this study empirically demonstrates the critical driving factors behind the wage growth of employed peasants, thereby expanding the empowerment theory within the context of digital rural development. Our study demonstrates that an increase in farmer entrepreneurs’ DC fosters a significant advancement in the wage growth of employed peasants. This result remains valid after a series of robustness tests, namely, H1 holds. In terms of effect, DFC > DAC > DIC, namely, H1a, H1b, H1c holds. Furthermore, this study addresses the research gap mentioned in previous chapters. It proposes a theoretical framework for understanding how enhancing farmer entrepreneurs’ digital capabilities can promote wage growth among employed peasants. DC can positively affect the wage increase of employed peasants through CA and TL, namely, H2, H3 holds. This theoretical framework provides avenues for future researchers to expand their research models.
Practically, this study contributes to entrepreneurs and policymakers in related fields by emphasizing the impact of cultivating farmer entrepreneurs’ digital capabilities and credit availability on the wage growth of employed peasants. The differentiated analysis revealed that the wage augmentation among employed peasants is notably more pronounced under the leadership of young farmer entrepreneurs, in comparison to their middle-aged counterparts, and the wage growth of employed peasants under entrepreneurs with higher education is more significant than that under entrepreneurs with lower education. Entrepreneurs with a lower family-dependency ratio have a greater effect on the wage growth of employed peasants than those with a higher family-dependency ratio. These research findings can encourage peasant entrepreneurs to recognize that starting businesses earlier, improving educational attainment, and controlling the family-dependency ratio can all positively influence entrepreneurship, thereby providing possibilities for peasant entrepreneurs to enhance the quality of their businesses. The lower the uncertainty in the policy environment, the greater the effect of DC on the wage growth of employed peasants, reminding policymakers of the importance of maintaining policy stability.

6.2. Limitations and Future Research Directions

Given the author’s inherent limitations in energy and cognition, coupled with the external constraints posed by research funding, human resources, and operational conditions, there remains ample scope for further exploration on the theme of DC and wage growth among employed peasants. Future endeavors can aim to enrich and refine the following aspects:
Firstly, research content could be broadened. Future studies might endeavor to conduct an in-depth exploration of the nuances of DC levels and their contributing factors or determinants among farmer entrepreneurs, encompassing variations in industries, business models, agricultural product types, geographical terrains, and entrepreneurial stages. Such a granular analysis would offer profound insights into the localized mechanisms underpinning DC.
Secondly, transitioning from cross-sectional data to longitudinal panel data is crucial to enhance the study’s robustness. The current findings tend to emphasize short-term effects, overlooking potential lags between DC and sustainable entrepreneurial income growth. With adequate resources and opportune timings, conducting at least two rounds of data collection and analysis would provide a more holistic view of the influence exerted by digital technology on sustained income or long-run earnings.
Thirdly, expanding the sample survey’s geographical scope would yield richer data. The current focus on Sichuan Province limits the generalizability of findings to other regions. Enlarging the sample to encompass provinces like Anhui, which are also significant labor exporters, would facilitate comparative analyses, ultimately yielding more comprehensive and reliable conclusions.
Lastly, the absence of a standardized DC measurement framework poses challenges for inter-study comparisons. While the 5-point Likert scale is prevalent, it may not fully encapsulate the intricacies of the analyzed phenomena. Thus, there is a need for more detailed investigations into specific facets of DC.

Author Contributions

The conceptual framework and methodological approach were devised by S.Z. and Y.S. The tasks of data curation, systematic analysis, examination, and confirmation were undertaken collectively by S.Z., J.Y. and Z.L. The writing responsibilities were primarily shouldered by S.Z. and J.Y. All authors have read and agreed to the published version of the manuscript.

Funding

The current study was financially supported by the National Social Sciences Foundation of China (grant number 22XJY015;23BJL103), Sichuan Natural Science Foundation (2023NSFSC0522) and Sichuan Social Science Fund (SCJJ23ND170), ensuring its robust execution and analysis.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The participants in this research have granted their informed consent, ensuring ethical compliance and respect for their autonomy.

Acknowledgments

We express our gratitude towards the invaluable support rendered by all the esteemed reviewers.

Conflicts of Interest

The authors affirm the absence of any potential conflicts of interest pertaining to this study.

References

  1. An, X. Digital Infrastructure: A Solid Foundation for The Digital Twin World. Inf. Commun. Technol. Policy 2020, 46, 5–9. [Google Scholar]
  2. Liu, H. Speed Up The Construction of Digital Agriculture and Add New Energy to Agricultural and Rural Modernization. Chin. J. Agric. Resour. Reg. Plan. 2017, 38, 1–6. [Google Scholar]
  3. Yin, H.; Huo, P.; Wang, S. Agricultural and Rural Digital Transformation: Realistic Representation, Impact Mechanism and Promotion Strategy. Reform 2020, 12, 48–56. [Google Scholar]
  4. Chen, G.; Wang, G. Perspective of Hema Villages from the Space of Flows: A Developmental Reconstruction of Modernization of Agriculture and Rural Areas Driven by Digital Agricultural Economy. Issues Agric. Econ. 2023, 517, 88–107. [Google Scholar] [CrossRef]
  5. Lu, J.; Qiu, H.; Su, L.; Liu, M. Do Digital Capabilities Enhance Herdsmen’s Income? Empirical Evidence from Typical Pastoral Areas. Chin. Rural Econ. 2023, 12, 85–105. [Google Scholar] [CrossRef]
  6. Lee, N.; Clarke, S. Do Low-Skilled Workers Gain from High-Tech Employment Growth? High-Technology Multipliers, Employment and Wages in Britain. Res. Policy 2019, 48, 103803. [Google Scholar] [CrossRef]
  7. Zhang, J.; Li, M. Digital Technology Access, Labor Market Behavior, and Income Inequality in Rural China. Heliyon 2024, 10, e33528. [Google Scholar] [CrossRef]
  8. Li, X.; Yang, G.; Shao, T.; Yang, D.; Liu, Z. Does Digital Infrastructure Promote Individual Entrepreneurship? Evidence from a Quasi-Natural Experiment on the “Broadband China” Strategy. Technol. Forecast. Soc. Change 2024, 206, 123555. [Google Scholar] [CrossRef]
  9. Qiu, L.; Duan, Y.; Zhou, Y.; Xu, F.; Zheng, H.; Cai, X.; Jiang, Z. Impact of Digital Empowerment on Labor Employment in Manufacturing Enterprises: Evidence from China. Heliyon 2024, 10, e29040. [Google Scholar] [CrossRef]
  10. Marquardt, L.; Harima, A. Digital Boundary Spanning in the Evolution of Entrepreneurial Ecosystems: A Dynamic Capabilities Perspective. J. Bus. Res. 2024, 182, 114762. [Google Scholar] [CrossRef]
  11. Gao, Y.; Yang, H.; Sun, X.; Tian, X.; Xu, J. Corporate Digital Transformation and Financing Constraints: The Moderating Effect of Institutional Investors. Heliyon 2024, 10, e33199. [Google Scholar] [CrossRef] [PubMed]
  12. Yu, W.; Wang, L.; Liu, X.; Xie, W.; Zhang, M. Can Digital Inclusive Finance Promote High-Quality Rural Entrepreneurship? A County-Level Analysis from China. Financ. Res. Lett. 2024, 67, 105820. [Google Scholar] [CrossRef]
  13. Luo, Y.; Peng, Y.; Zeng, L. Digital Financial Capability and Entrepreneurial Performance. Int. Rev. Econ. Financ. 2021, 76, 55–74. [Google Scholar] [CrossRef]
  14. Anrijs, S.; Mariën, I.; Marez, L.D.; Ponnet, K. Excluded from Essential Internet Services: Examining Associations between Digital Exclusion, Socio-Economic Resources and Internet Resources. Technol. Soc. 2023, 73, 102211. [Google Scholar] [CrossRef]
  15. Annarelli, A.; Battistella, C.; Nonino, F.; Parida, V.; Pessot, E. Literature Review on Digitalization Capabilities: Co-Citation Analysis of Antecedents, Conceptualization and Consequences. Technol. Forecast. Soc. Change 2021, 166, 120635. [Google Scholar] [CrossRef]
  16. Teece, D.J.; Pisano, G.; Shuen, A. Dynamic Capabilities and Strategic Management. Strateg. Manag. J. 1997, 18, 509–533. [Google Scholar] [CrossRef]
  17. Li, Y.; Shan, B.; Fei, Y.; Li, B. Digital Technology Entrepreneurship: Review and Prospect of Research Topics. RD Manag. 2021, 33, 65–77. [Google Scholar] [CrossRef]
  18. Lin, S.; Jiang, Y. Overview and Analysis of Entrepreneurship Research Progress. Mod. Manag. 2005, 06, 22–24. [Google Scholar]
  19. Rajotte, C.A. Empowerment as a Leadership Theory. Kans. Nurse 1996, 71, 1. [Google Scholar]
  20. Chen, G.; Kirkman, B.L.; Kanfer, R.; Allen, D.; Rosen, B. A Multilevel Study of Leadership, Empowerment, and Performance in Teams. J. Appl. Psychol. 2007, 92, 331–346. [Google Scholar] [CrossRef]
  21. Joseph, R. The Theory of Empowerment: A Critical Analysis with the Theory Evaluation Scale. J. Hum. Behav. Soc. Environ. 2020, 30, 138–157. [Google Scholar] [CrossRef]
  22. Yue, X.; Wang, X.; Han, X.; Zhou, L. How Does Empowerment Practice Promote Neo-Endogenous Development in Rural Areas: A Multi-Case Analysis Based on Empowerment Theory. Chin. Rural Econ. 2022, 05, 36–54. [Google Scholar]
  23. Mäkinen, M. Digital Empowerment as a Process for Enhancing Citizens’ Participation. E-Learn. Digit. Media 2006, 3, 381–395. [Google Scholar] [CrossRef]
  24. de Clercq, M.; D’Haese, M.; Buysse, J. Economic Growth and Broadband Access: The European Urban-Rural Digital Divide. Telecommun. Policy 2023, 47, 102579. [Google Scholar] [CrossRef]
  25. Salemink, K.; Strijker, D.; Bosworth, G. Rural Development in the Digital Age: A Systematic Literature Review on Unequal ICT Availability, Adoption, and Use in Rural Areas. J. Rural Stud. 2017, 54, 360–371. [Google Scholar] [CrossRef]
  26. Young, J.C. Rural Digital Geographies and New Landscapes of Social Resilience. J. Rural Stud. 2019, 70, 66–74. [Google Scholar] [CrossRef]
  27. Eshet-Alkalai, Y. Digital Literacy: A Conceptual Framework for Survival Skills in the Digital Era. J. Educ. Multimed. Hypermedia 2004, 13, 93–106. [Google Scholar]
  28. Reddy, P.; Sharma, B.; Chaudhary, K. Digital Literacy: A Review of Literature. Int. J. Technoethics 2020, 11, 65–94. [Google Scholar] [CrossRef]
  29. Du, F.; Zhao, X.; Zheng, J.; Zhang, M. How Digital Capabilities Promote Farmers to Adopt Carbon Sequestrationt Echnologies in Farmland?: Microscopic Evidence Based on Graingrowers in Shandong Province. Chin. J. Eco-Agric. 2024, 32, 932–943. [Google Scholar]
  30. Wang, G.; Niu, Y.; Mansor, Z.D.; Leong, Y.C.; Yan, Z. Unlocking Digital Potential: Exploring the Drivers of Employee Dynamic Capability on Employee Digital Performance in Chinese SMEs-Moderation Effect of Competitive Climate. Heliyon 2024, 10, e25583. [Google Scholar] [CrossRef]
  31. Tian, J.; Li, M.; Huang, M. The Impact of Digital Capability on the Development Resilience of Rural Households. Agric. Econ. Manag. 2024, 04, 49–61. [Google Scholar]
  32. Yu, Z.; Hu, X.; Wang, Y. Provincial Comparison of Evolution of Wage Income Structure of Farmers in China: Based on Deviation-Share Analysis. Agric. Econ. Manag. 2019, 06, 62–72. [Google Scholar]
  33. Wu, X.; Wang, H. Farmer’s Digital Literacy: Framework System, Driving Effects, and Cultivation Path: An Analytical Perspective of Competency Theory. E-Gov. 2023, 08, 105–119. [Google Scholar] [CrossRef]
  34. Liu, X.; Yang, Y.; Sun, Z. The Construction and Evolutionary Development of Enterprise Digital Capability: A Multi-Case Exploratory Study of Leading Digital Enterprises. Reform 2022, 10, 45–64. [Google Scholar]
  35. Zhang, S.; Yang, J.; Shen, Y.; Li, Z. How Do Digital Capabilities Impact the Sustained Growth of Entrepreneurial Income: Evidence from Chinese Farmer Entrepreneurs. Sustainability 2024, 16, 7522. [Google Scholar] [CrossRef]
  36. Zhang, S.; Yang, J.; Zeng, J. How Digital Empowerment Can Improve the Entrepreneurship Quality of Returning and Entering Rural Areas: An Explanation Based on Empowerment Theory. Rural Econ. 2024, 01, 102–111. [Google Scholar]
  37. Peng, K.; Zhang, L.; Qiu, Y. Has the Increase of Farmers’ Lncome Lmproved the Formal Credit Availability of Their Households? Financ. Trade Econ. 2017, 38, 49–65. [Google Scholar]
  38. Zhang, X.; Gao, S.; Dong, J. Farmer Credit: Demand Characteristics, Availability, and Influencing Factors: Based on a Survey of 922 Farmers in Shandong Province. Rural Econ. 2018, 02, 65–71. [Google Scholar]
  39. Zhou, Y.; Wang, Y.; Peng, Y. Agricultural Industrial Chain Organizations, Credit Transaction Cost and Credit Availability of Large-Scale Farmers. Chin. Rural Econ. 2019, 04, 41–54. [Google Scholar]
  40. Mi, Y.; Qian, Y.; Yang, T.; Xie, Y. Does Land Registration Expand the Gap between the Rich and the Poor in Credit Availability? Issues Agric. Econ. 2020, 05, 54–65. [Google Scholar] [CrossRef]
  41. Zheng, H.; Dang, Q.; Li, G. Impact of Credit Access on Rice Farmers’ Adoption of Water-Fertilizer Integration Technology. J. Agro-For. Econ. Manag. 2024, 23, 456–464. [Google Scholar] [CrossRef]
  42. Yeasmin, S.; Haque, S.; Adnan, K.M.M.; Parvin, M.T.; Rahman, M.S.; Rahman, K.M.; Salman, M.; Hossain, M.E. Factors Influencing Demand for, and Supply of, Agricultural Credit: A Study from Bangladesh. J. Agric. Food Res. 2024, 16, 101173. [Google Scholar] [CrossRef]
  43. Li, C.; Liu, J.; Liu, Y.; Wang, X. Can Digitalization Empowerment Improve the Efficiency of Corporate Capital Allocation: Evidence from China. Econ. Anal. Policy 2023, 80, 1794–1810. [Google Scholar] [CrossRef]
  44. Zhou, L.; Liao, J.; Zhang, H. Digital Financial Inclusion, Credit Availability and Household Poverty: Evidence from a Micro-Level Survey. Econ. Sci. 2021, 01, 145–157. [Google Scholar]
  45. Zhang, C.; Yin, Z. The Lmpact of Mobile Terminal Applications on Household Credit Availability in China. Res. Econ. Manag. 2023, 44, 17–36. [Google Scholar] [CrossRef]
  46. An, C. How Digital Finance Accurately Promotes Income Increase of Target Rural Households. J. Shenzhen Univ. (Humanit. Soc. Sci.) 2024, 41, 83–93. [Google Scholar]
  47. Cheng, C.; Gao, Q.; Ju, K.; Ma, Y. How Digital Skills Affect Farmers’ Agricultural Entrepreneurship? An Explanation from Factor Availability. J. Innov. Knowl. 2024, 9, 100477. [Google Scholar] [CrossRef]
  48. Chen, B.; Li, H.; Ren, J. The Village Adoption of Digital Finance, the Availability of Credit and the Entrepreneurial Behavior of Farmer Households. Financ. Res. 2023, 02, 70–79. [Google Scholar] [CrossRef]
  49. Shi, Y.; Huang, Y.; Xiao, L. Population Hollowing of Rural Settlements Based on Social Network and Lts Planning: Wutongmiao Village of Dazhou Sichuan As An Example. Urban Dev. Stud. 2023, 30, 121–132. [Google Scholar]
  50. Hu, X. Lmplementation of Village-Based Policies: A Trap of Hollow Rural Population Research on Transformation of Development Path. J. Soochow Univ. (Philos. Soc. Sci. Ed.) 2023, 44, 50–60. [Google Scholar] [CrossRef]
  51. Gu, H.; Wang, J.; Ling, Y. Economic Geography of Talent Migration and Agglomeration in China: A Dual-Driver Framework. China Econ. Rev. 2024, 86, 102180. [Google Scholar] [CrossRef]
  52. Shao, J.; Li, F. An Empirical Study on the Relationship between the Brain Drain of Core Talents and Degree of Regional Ldentity in the Underdeveloped Areas. Sci. Technol. Prog. Policy 2012, 29, 28–31. [Google Scholar]
  53. Hu, S.; Liang, F. Rural Hollowing, Three Local Capital and Rural Revitalization. J. Xi’Univ. Financ. Econ. 2019, 32, 52–59. [Google Scholar] [CrossRef]
  54. Ma, L.; Kang, Y. “Hollow Village” or “Empty Nest Village”? An Ldentification of the Existing Forms and Evolutionary Process of China’s Village Society. China Rural Surv. 2022, 05, 123–139. [Google Scholar]
  55. Chen, H.; Wang, X. Exploring the Relationship between Rural Village Characteristics and Chinese Return Migrants’ Participation in Farming: Path Dependence in Rural Employment. Cities 2019, 88, 136–143. [Google Scholar] [CrossRef]
  56. Yin, Z.; Qiu, H.; Gong, X. Smart Phone and Flexible Employment: Evidence from China Household Finance Survey. Manag. Rev. 2024, 36, 107–118. [Google Scholar] [CrossRef]
  57. Cheng, M.; Wang, Y. The Effects of Digital Infrastructure on the Transformation of Employment Structure. Inq. Into Econ. Issues 2024, 07, 133–153. [Google Scholar]
  58. Xie, Y.; Li, H.; Jiang, Z. Does the Use of Digital Technology Contribute More to the Income Growth of Flexible Workers: Based on the Perspective of the Second-Stage Digital Divide. Soft Sci. 2024, 1–15. Available online: https://link.cnki.net/urlid/51.1268.g3.20240715.1723.008 (accessed on 2 October 2024).
  59. Du, C.; Shu, S. Digital Empowerment, Skill-Biased Technological Progress and Wage Income. Mod. Econ. Res. 2024, 05, 1–14. [Google Scholar] [CrossRef]
  60. Song, J.; Wang, Y. Is Enterprise Digital Transformation an Opportunity or a Substitution Crisisfor Employment: Evidence from Text Analysis of Chinese Listed Companies. China Soft Sci. 2024, 04, 131–143. [Google Scholar]
  61. Yang, J.; Kuang, G. The Impact of Digital Transformation on Workforce Demand: Create or Disrupt? J. Harbin Univ. Commer. (Soc. Sci. Ed.) 2024, 02, 102–117. [Google Scholar]
  62. Ma, J.; Cui, Y.; Dong, C. Credit Availability, Water Rights Confirmation and Water-Saving Technology Investment: Evidence Based on Quasi-Natural Experiment in Water Rights Confirmation Pilots. Chin. Rural Econ. 2022, 08, 70–92. [Google Scholar]
  63. Wang, J.; Zhou, J.; Ren, M. Research on Income Effect of Green Production Factor Input Behavior of Agricultural Producers: Empirical Analysis of ESRM and UOR for Organic Fertilizer and Biopesticide. J. Northwest AF Univ. (Soc. Sci. Ed.) 2024, 24, 110–123. [Google Scholar] [CrossRef]
  64. Jiang, T. Mediating Effects and Moderating Effects in Causal Inference. China Ind. Econ. 2022, 05, 100–120. [Google Scholar] [CrossRef]
  65. Zhao, F.; Jiang, G. Digital Technology Enabling Labor Employment: Internal Mechanism and Micro Evidence: An Empirical Analysis Based on CFPS Data. Popul. J. 2023, 45, 99–112. [Google Scholar] [CrossRef]
  66. Yin, Z.; Jiang, J.; Yan, Y. Does the Digital Divide Affect Household Income? Financ. Trade Econ. 2021, 42, 66–82. [Google Scholar] [CrossRef]
  67. Shano, B.K.; Waje, S.S. Understanding the Heterogeneous Effect of Microcredit Access on Agricultural Technology Adoption by Rural Farmers in Ethiopia: A Meta-Analysis. Heliyon 2024, 10, e35859. [Google Scholar] [CrossRef]
  68. Jumpah, E.T.; Adams, A.; Ayeduvor, S. Estimating Yield and Income Effects of Formal Credit-Based Programme among Tomato Farmers in the Greater Accra Region of Ghana. Sci. Afr. 2020, 9, e00499. [Google Scholar] [CrossRef]
  69. Amanullah; Lakhan, G.R.; Channa, S.A.; Magsi, H.; Koondher, M.A.; Wang, J.; Channa, N.A. Credit Constraints and Rural Farmers’ Welfare in an Agrarian Economy. Heliyon 2020, 6, e05252. [Google Scholar] [CrossRef]
  70. Chen, S.; Luo, E. Does College Graduates Serving as Village Officers Policy Narrowed the Income Gap in Rural Areas: Based on Empirical Analysis of 1324 Villages Chinese Family Database in 2015 and 2017. South China J. Econ. 2022, 12, 82–98. [Google Scholar] [CrossRef]
  71. Lin, L.; Chen, C. The Characteristics Analysis and Policy Support Construction of Returning Home Entrepreneurial Youth: Based on the Field Survey of 995 Entrepreneurs in 75 Counties of 12 Provinces in China. China Youth Study 2018, 09, 53–61+10. [Google Scholar] [CrossRef]
  72. Yu, D.; Chen, H.; Zhang, H. Digital Transformation, Servitization Integration and Performance Improvement of Manufacturing Firms: On the Moderating Effect of Economic Policy Uncertainty. Financ. Trade Res. 2024, 35, 1–14+56. [Google Scholar] [CrossRef]
Agriculture 14 01765 g001
Figure 1. The relationship mechanism between DC and the growth of employed peasants’ wages.
Figure 1. The relationship mechanism between DC and the growth of employed peasants’ wages.
Agriculture 14 01765 g001
Table 1. Questionnaire survey schedule.
Table 1. Questionnaire survey schedule.
TimePhaseEventNumber of
Questionnaires
March 2023–May 2023Phase IDistribute paper questionnaires418
May 2023–June 2023Phase IISupplement electronic questionnaires232
Table 2. Distribution of samples.
Table 2. Distribution of samples.
CategoryClassificationSample
Count
(n = 490)		Percentage
(%)
GenderMale39280.00
Female9820.00
Age18~2581.63
26~30346.94
31~4015331.22
41~5022545.92
Above 507014.29
EducationBelow Primary School81.63
Junior High School275.51
High School/Vocational School16934.49
Junior College19840.41
Bachelor’s Degree or Above8817.96
Entrepreneurship
Duration		0–3 year459.18
3–5 year6613.47
5–7 year10721.84
7–9 year9619.59
9 years and above17635.92
Table 3. Definition and descriptive statistics of major variables.
Table 3. Definition and descriptive statistics of major variables.
TypeVariableDescriptionMeanStandard
Deviation
Dependent VariableWages (W)Peasants’ wages are not lower than those in peer enterprises: 1 = Completely disagree, 2 = Somewhat disagree, 3 = Cannot say, 4 = Somewhat agree, 5 = Strongly agree.3.9180.941
Independent VariableDigital Capabilities (DC)Calculated by the entropy method.0.5130.258
Digital Foundational Competence (DFC)Calculated by the entropy method.0.0640.019
Digital Applicational Capabilities (DAC)Calculated by the entropy method.0.1250.058
Digital Innovative Capabilities (DIC)Calculated by the entropy method.0.3240.206
Mechanism VariablesCredit Availability (CA)It is easy to obtain bank loans to support entrepreneurship: 1 = Completely disagree, 2 = Somewhat disagree, 3 = Cannot say, 4 = Somewhat agree, 5 = Strongly agree.3.3061.245
Talent Loss (TL)There are difficulties in recruitment or serious talent loss: 1 = Completely disagree, 2 = Somewhat disagree, 3 = Cannot say, 4 = Somewhat agree, 5 = Strongly agree.3.3011.233
Control
Variables		Family Farm (FF)Family Farm Owner: 0 = No, 1 = Yes0.4780.500
Investment Scale (IS)Logarithm of the cumulative investment amount with base 10.2.4670.591
Product Quality (PQ)Compared to similar products, the quality of the products operated by this entrepreneurial project: 1 = Poor, 2 = Slightly Poor, 3 = Average, 4 = Good, 5 = Excellent.3.8610.966
Regional Dummy Variable (RDV)Whether it belongs to the Chengdu Plain Economic Zone: 0 = No; 1 = Yes0.3860.487
Technological Progress (TP)The rapid development of production technology provides significant opportunities for entrepreneurial projects: 1 = Completely Disagree, 2 = Somewhat Disagree, 3 = Neutral, 4 = Somewhat Agree, 5 = Strongly Agree.3.7451.016
Policy Uncertainty (PU)In the past year, there have been significant adjustments to economic policies and regulatory systems related to the entrepreneurial project: 1 = Completely Disagree, 2 = Somewhat Disagree, 3 = Neutral, 4 = Somewhat Agree, 5 = Strongly Agree.3.7331.091
Table 4. Key regression outcomes: digital capabilities facilitating sustained wage growth among employed peasants.
Table 4. Key regression outcomes: digital capabilities facilitating sustained wage growth among employed peasants.
VariableWage
Model 1Model 2Model 3Model 4
DC0.9355 ***
(0.1732)
DFC 10.7829 ***
(2.5202)
DAC 3.9377 ***
(0.7747)
DIC 1.0556 ***
(0.2174)
FF0.07220.04010.07080.0618
(0.0557)(0.0562)(0.0559)(0.0556)
IS−0.0538−0.0193−0.0476−0.0587
(0.0992)(0.0991)(0.1005)(0.0999)
PQ−0.0411−0.0772−0.0313−0.0419
(0.0466)(0.0477)(0.0471)(0.0470)
RDV0.2519 *0.2761 **0.21090.2703 *
(0.1442)(0.1378)(0.1433)(0.1446)
TP0.01330.0157−0.00680.0235
(0.1210)(0.1235)(0.1191)(0.1226)
PU−0.1475 *−0.1361−0.1557 *−0.1372
(0.0843)(0.0856)(0.0842)(0.0849)
Constant2.8794 ***2.6978 ***2.8126 ***3.0344 ***
(0.4098)(0.4371)(0.4210)(0.4079)
R20.1095 ***0.0936 ***0.1035 ***0.0981 ***
Number490490490490
Note: The values in parentheses are standard errors, and ***, **, and * indicate that the regression results pass the significance test at the 1%, 5%, and 10% confidence levels, respectively. The same applies to the following tables.
Table 5. The robustness test results.
Table 5. The robustness test results.
VariableModel (1)Model (2)Model (3)
WageWageLogarithm of Monthly Average Wage
DC0.5266 **0.4711 ***0.1546 **
(0.2222)(0.1815)(0.0621)
ControlYesYesYes
Constant 1.7295 ***3.1690 ***
(0.3135)(0.1063)
ThresholdOmit
Pseudo R20.0793 ***
R2 0.1875 ***0.0602 ***
Number490490490
Note: *** and ** indicate statistical significance at the 1% and 5% level.
Table 6. Propensity scores matching results of the impact of DC on the wage growth of employed peasants.
Table 6. Propensity scores matching results of the impact of DC on the wage growth of employed peasants.
VariableMatching MethodTreatment GroupControl GroupATTStandard ErrorT-Value
Before Matching4.08983.74690.3429 ***0.08374.10
WagesK-Nearest Neighbor Matching (n = 4)4.07693.90170.1752 *0.10001.75
Radius Matching4.07693.92000.1569 *0.09751.61
Kernel Matching4.07693.94510.1318 *0.09321.41
Note: *** and * indicate statistical significance at the 1% and 10% level.
Table 7. The results of the mechanism test on the impact of DC on the growth of employed peasants’ wages.
Table 7. The results of the mechanism test on the impact of DC on the growth of employed peasants’ wages.
VariableModel (1)Model (2)
CATL
DC0.5705 **0.4317 *
(0.2483)(0.2482)
ControlYesYes
Constant1.8320 ***2.7186 ***
(0.3855)(0.3914)
R20.1451 ***0.0458 ***
Number490490
Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level.
Table 8. Outcomes of disparate analysis on farmer entrepreneurs’ relevant characteristics.
Table 8. Outcomes of disparate analysis on farmer entrepreneurs’ relevant characteristics.
VariableYoungMiddle-Aged Below High SchoolAbove High SchoolLow Family
Dependency
Ratio		High Family
Dependency
Ratio
(1) Wage(2) Wage(3) Wage(4) Wage(5) Wage(6) Wage
DC0.9773 ***0.03260.14700.6236 **0.5143 *0.4015 *
(0.2552)(0.2264)(0.2507)(0.2424)(0.2991)(0.2092)
ControlYesYesYesYesYesYes
Constant2.1928 ***1.3899 ***2.1753 ***1.4635 ***1.9601 ***1.6340 ***
(0.4604)(0.4319)(0.5147)(0.3982)(0.6225)(0.3338)
R20.1987 ***0.2093 ***0.1321 **0.2496 ***0.1690 ***0.2234 ***
Number195295204286166324
Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level.
Table 9. Heterogeneity analysis results of policy uncertainty.
Table 9. Heterogeneity analysis results of policy uncertainty.
VariableLow PUHigh pu
(1) Wages(2) Wages(3) Wages(4) Wages(5) Wages(6) Wages(7) Wages(8) Wages
DC0.5913 ** 0.3754 *
(0.2922) (0.2044)
DFC 4.2732 6.6868 **
(4.3496) (2.7586)
DAC 2.0206 2.0087 **
(1.2600) (0.9159)
DIC 0.7137 * 0.3511
(0.3721) (0.2505)
ControlYesYesYesYesYesYesYesYes
Constant2.7029 ***2.5401 ***2.6151 ***2.7570 ***2.1646 ***1.9116 ***2.1312 ***2.1956 ***
(0.4850)(0.5026)(0.4838)(0.4882)(0.3580)(0.3788)(0.3634)(0.3581)
R20.0844 **0.0726 **0.0791 **0.0825 **0.1683 ***0.1772 ***0.1726 ***0.1642 ***
Number183183183183307307307307
Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level.
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Zhang, S.; Yang, J.; Shen, Y.; Li, Z. The Impact of Digital Capabilities on Peasants’ Wage Growth: Evidence from Chinese Farmer Entrepreneurs. Agriculture 2024, 14, 1765. https://doi.org/10.3390/agriculture14101765

AMA Style

Zhang S, Yang J, Shen Y, Li Z. The Impact of Digital Capabilities on Peasants’ Wage Growth: Evidence from Chinese Farmer Entrepreneurs. Agriculture. 2024; 14(10):1765. https://doi.org/10.3390/agriculture14101765

Chicago/Turabian Style

Zhang, Shanhu, Jinxiu Yang, Yun Shen, and Zhuoli Li. 2024. "The Impact of Digital Capabilities on Peasants’ Wage Growth: Evidence from Chinese Farmer Entrepreneurs" Agriculture 14, no. 10: 1765. https://doi.org/10.3390/agriculture14101765

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