The impact of digital village development on farmers’ subjective wellbeing in rural China: the role of core capabilities and income inequality

Abstract

Digital village development has emerged as a key strategy for advancing comprehensive rural revitalization. However, it remains uncertain whether this initiative promotes equitable benefits for all farmers or exacerbates disparities. This study integrates survey data from rural areas in five provinces with the Peking University Digital Village Index to investigate the impact of digital village development on subjective wellbeing (SWB), with particular attention to capability enhancement and equity concerns. Benchmark regression results show that digital village development enhances rural households’ wellbeing significantly. Specifically, digital infrastructure, economic digitization, governance digitization, and life digitization each contribute meaningfully to improving SWB, with these effects remaining robust across various assessments. Further analysis of the mediation effect model indicate that the digitization of rural governance and daily life substantially increases income and capabilities, thereby enhancing wellbeing by fostering self-improvement and social affiliation. Nevertheless, digital infrastructure and economic digitization also appear to intensify internal inequality, ultimately diminishing overall SWB. Arguably, the development of digital rural areas accelerates income growth among high-income farmers, while offering limited benefits to low-income farmers. Heterogeneity analysis further reveals that the positive impact of digital rural development on SWB is significantly more substantial among regions with higher levels of digital infrastructure, high-income households, and individuals with higher human capital. Collectively, these findings offer theoretical insights into the mechanisms through which digital village development enhances farmers’ subjective wellbeing, while also offering practical guidance for creating equitable and inclusive digital village policies.

1 Introduction

The pursuit of happiness for its citizens has long been a central policy objective of the Chinese government. The 19th National Congress of the Communist Party of China emphasized the importance of meeting the growing needs of the people for a better life, ensuring that their sense of gain, happiness, and security becomes more fulfilling, reliable, and sustainable. Building on this, the 20th National Congress further proposed to the goal “comprehensively build a modern socialist country” and emphasized that “realizing the people’s aspirations for a better life should serve as the starting point and anchor of modernization efforts,” while also acknowledging that “material and spiritual prosperity are fundamental requirements of socialist modernization.”

Within this context, the digital village development strategy has emerged as a key technical policy for promoting rural revitalization. The “Outline of the Digital Village Development Strategy,” issued in 2019, emphasizes a people-centered approach by promoting a development model that aligns with the knowledge structure of the rural residents. It prioritizes addressing the immediate and practical interests of farmers to continuously enhance their sense of happiness. The “Outline” further defines digital village areas as “the endogenous process of agricultural and rural modernization and transformation that accompanies the application of networking, informatization, and digitization in the economic and social development of agriculture and rural areas, as well as the improvement of farmers’ modern information skills.” Accordingly, the construction of a digital village is characterized by reliance on advancements in digital technology and a focus on improving the farmers’ digital skills (Li and Li, 2024).

Existing research has primarily examined the role of digital village development from the perspectives of income and inequality. First, the development of digital villages—through the digitization of infrastructure, the economy, and daily life—has been shown to exert significant income-enhancing effects (Liu and Lai, 2024; Chen and Wang, 2024). Specifically, the growth of digital villages can enhance wage income, business income, property income, and entrepreneurial income (Hu et al., 2024; Tian and Zhang, 2024). In addition, digital villages development contributes to reducing income inequality among residents (Zhan et al., 2024; Si and Wang, 2024), largely through fostering entrepreneurial opportunities and boosting non-agricultural employment (Li, 2024). However, other evidence indicates a more complex relationship. Li and Li (2024) argue that the development of digital villages has a U-shaped influence on the income gap between urban and rural, initially narrowing the gap before widening it again, with the influence of digital infrastructure in the expanding stage. Similarly, the broader digital economy is increasingly recognized as a critical driver of income inequality (Wan et al., 2024), leading to disparities in labor rates and unequal distribution of human capital across different industries, which not only intensify the urban–rural income gap (Hong et al., 2024) but may also undermine people’s wellbeing. Third, digital villages not only improve farmers’ incomes and daily convenience (Wu and Hu, 2024) but also foster regional innovation, entrepreneurship, and grassroots political trust, thereby enhancing farmers’ sense of gain and happiness (Liu et al., 2024; Zhang and Yi, 2023).

On the other hand, individual subjective wellbeing is also significantly influenced by capabilities and income inequality. First, capability is an important factor in understanding individuals’ subjective wellbeing. Building on a critique of utilitarianism, Sen (1985, 1999) introduced the capability approach, offering fresh theoretical insights into the determinants of wellbeing. Sen (1999) emphasizes that capabilities refer to the opportunities and freedoms to achieve functionings under given social and economic conditions. Functionings, in this sense, denote the states of being and doing that people have reason to value, such as nourishment, shelter, health, social participation, self-respect, happiness, and security. Binder (2014) argues that this approach effectively addresses the issue of happiness adaptation, often referred to as the “helpless beggar” dilemma. Casini et al. (2021) highlight that basic capabilities—such as health, social relationships, and cultural skills, are critical determinants of individual wellbeing. Similarly, Steckermeier (2021) examined the interplay between personal autonomy and abilities, finding that greater autonomy mitigates the impact of factors like health, respect, and friendship on wellbeing. In contrast, deprivation of capabilities significantly reduces wellbeing (Suppa, 2015, 2021).

Secondly, as key core capabilities, both affiliation capability and self-development capability are profoundly influenced by emerging digital technologies. Nussbaum (2009) points out that affiliation refers to the ability to engage in various forms of social interaction and to show concern for others. Literature reviews conducted by Kato et al. (2018) and Bartolomei et al. (2024) suggest that affiliation generally encompasses social communication, participation, freedom from discrimination, and self-respect. Accordingly, this study conceptualizes affiliation as the opportunities individuals possess for social participation and social connection. Existing studies indicate that information and communication technologies (ICT) can play a beneficial role in enhancing individuals’ social communication capabilities (Bartolomei et al., 2024). Moreover, smartphones constitute the leading and most important technology in realizing participants’ communication capabilities in Israel (Marciano et al., 2024). In the context of rural China, research has shown that the development of digital villages contributes to the expansion of both intra-village and inter-village social networks (Luo and Tian, 2025). However, other studies argue that digital village construction does not significantly improve farmers’ opportunities for social participation (Song and Fu, 2025). Overall, the existing literature has not yet thoroughly examined the potential mediating role of affiliation capability in linking digital village development to farmers’ subjective wellbeing.

Self-development capability refers to the developmental potential possessed by individual farmers, which is primarily reflected in the knowledge, skills, and health necessary to achieve their own development (Zuo et al., 2021; Yang et al., 2025). As Sen (2012) emphasizes, self-development capability is not only determined by individual endowments but, more importantly, by the social opportunities accessible in areas such as education, healthcare, pension provision, and public safety. Therefore, this study defines self-development capability as the public service opportunities available to farmers in terms of serious illness medical insurance, pension security, children’s education, public facilities, and community safety. Empirical research has found that deeper use of the internet significantly enhances farmers’ endogenous development capacity, with particularly positive effects for groups such as non-registered poor households and farmers without migrant work experience (Yang et al., 2025). Nevertheless, the extent to which self-development capability mediates the relationship between digital village construction and farmers’ subjective wellbeing remains an important question that warrants further exploration.

Apart from capability-related factors, income inequality has also been identified as a critical determinant of subjective wellbeing (SWB). When individuals are provided with equal opportunities, they are better able to develop their capabilities and pursue subjectively optimal lifestyles (Zwierzchowski and Panek, 2020). Conversely, income inequality tends to foster a sense of relative deprivation, thereby exerting a negative influence on the wellbeing of farmers (You and Ma, 2023). For example, relocated farmers in lower-income circumstances are more likely to experience a diminished sense of gain and heightened relative deprivation (Wu and Li, 2024). Moreover, inequality disproportionately undermines the enjoyment of individuals with limited mobility (Li and Zhang, 2023).

In summary, existing literature provides a robust theoretical foundation for examining the intrinsic relationship between digital village development and farmers’ wellbeing.

Nevertheless, several important issues warrant further investigation. First, digital village development in China is multidimensional, encompassing digital infrastructure, digital rural economy, digital governance, and digital rural life. A key question that remains unresolved is which specific dimensions exert a significant impact on improving farmers’ SWB. Second, while the influence of capability on wellbeing and the role of digital technologies in shaping capabilities have attracted considerable scholarly attention, little is known about whether the two core capabilities—affiliation and self-development—serve as positive or negative mediators in the relationship between digital village development and farmers’ SWB. Third, prior studies have highlighted the effects of digital village development on income inequality, as well as the suppressing impact of income inequality on wellbeing. However, it remains unclear whether income inequality functions as a significant mediating mechanism in the relationship between digital village development and farmers’ SWB, and if so, whether this mediating effect is positive or negative. These questions constitute the core focus of the present study.

Focusing on fairness and capability perspectives, this article develops theoretical hypotheses on how digital village development influences farmers’ subjective wellbeing (SWB). The objective is to elucidate the mechanisms through which digital village initiatives impact farmers’ wellbeing. This study makes three potential contributions. First, it applies the theory of human needs to connect the fulfillment of a better life with farmers’ wellbeing, demonstrating how digital village development affects SWB through its four dimensions. Second, drawing on Sen’s capability approach, the article introduces core concepts of belonging ability and development ability, explaining how digital villages enhance these capabilities to empower the farmers’ wellbeing. Third, from an equality perspective, the article examines income inequality as a mediating variable, revealing how digital villages may exacerbate internal relative deprivation, thereby suppressing the farmers’ wellbeing. By extending the application of competency-based frameworks in SWB research (Casini et al., 2021; Steckermeier, 2021), this study provides valuable insights. In conclusion, the study enhances our understanding of how digital village construction affects farmers’ wellbeing and offers empirical evidence to guide government efforts in optimizing digital village initiatives.

2 Theoretical analysis and research hypotheses

In the practice of county-level digital village development, a digital village is defined as a new model of rural revitalization driven by the deep integration of emerging information technologies—such as the Internet, cloud computing, and big data—into rural production and daily life, thereby facilitating the digital transformation of rural economies and societies (Digital Rural Development Project Team, Institute for New Rural Development, Peking University, 2018). A digital village development consists of four core dimensions: digital infrastructure, digital rural economy, digital governance, and digital rural life (Digital Rural Development Project Team, Institute for New Rural Development, Peking University, 2020). First, digital infrastructure includes information, financial, and commercial infrastructure as well as agricultural product service platforms. It provides the essential physical and technological foundation for the digitalization of the rural economy, governance, and everyday life, enabling farmers to adopt digital technologies more effectively in both production and household activities. Second, the digital rural economy encompasses digitalized production, supply chain management, marketing, and finance. This dimension serves as a crucial driver of high-quality rural economic development and the sustained improvement of farmers’ wellbeing. Third, digital governance primarily refers to the digital transformation of rural social governance mechanisms. It enhances governance efficiency, promotes equal access to public services, and contributes to improving farmers’ subjective wellbeing. Fourth, digital rural life involves the digitalization of consumption, culture, tourism, education, healthcare, and everyday services. This dimension directly shapes farmers’ living standards and plays a critical role in enhancing their wellbeing.

Building on this foundation, the present study develops an analytical framework of “Digital Village—Core Capabilities/Income Inequality—Farmers’ Subjective Wellbeing,” as illustrated in Figure 1. Specifically, drawing on need satisfaction theory, the study examines how digital village development directly promotes farmers’ subjective wellbeing by meeting their multidimensional living needs, with particular attention to the role of the four digital village dimensions in improving farmers’ quality of life. With regard to the underlying mechanisms, the study focuses on empowerment and equity pathways. On the one hand, the capability approach (Sen, 1999; Casini et al., 2021; Steckermeier, 2021) posits that capabilities are the core micro-level determinants of subjective wellbeing. This study emphasizes two capabilities closely associated with digital village development—belonging capability and self-development capability—and conceptualizes digital village construction as an external policy-related conversion factor that shapes these capabilities. From this perspective, the analysis explores how digital governance and digital life indirectly enhance farmers’ wellbeing by improving their core capabilities.

Figure 1

On the other hand, relative deprivation theory suggests that an unequal income distribution weakens individual wellbeing by intensifying perceptions of relative deprivation. Income inequality therefore serves as another critical mediating variable linking digital village development to farmers’ wellbeing. Evidence indicates that digital infrastructure and the digital rural economy help to narrow income disparities (Wang et al., 2024). However, differences in digital literacy and uneven fiscal resources across regions produce heterogeneous effects across income groups, education levels, and digital literacy cohorts (Hua and Pan, 2024; Wang, 2021). Moreover, the negative network externalities of digital technologies may allow higher-income groups to capture greater benefits, thereby exacerbating inequality (Daud et al., 2021; Antinyan et al., 2019).

2.1 Overall impact of digital village development on the subjective wellbeing of farmers

The theory of quality of life posits that an individual’s wellbeing is derived from the positive emotions generated by the continuous satisfaction of their needs. For farmers, essential needs that enhance wellbeing in the context of digital village development include material needs (He, 2017), social security (Yin et al., 2021), social participation (Huo et al., 2021), and basic psychological needs (Liu et al., 2013).

The digitization of the rural economy plays a pivotal role in meeting farmers’ material needs by providing “more satisfactory income” and improving their overall wellbeing. Specifically, the rapid expansion of rural e-commerce, particularly through the “Digital Commerce Promotes Agriculture” project initiated in 2022, has optimized various aspects of rural production, supply, marketing, and service sectors. Digital agricultural production technologies have improved operational efficiency, while digitization of agricultural supply chains and marketing have enhanced the circulation efficiency of agrarian products (Li and Li, 2024). Furthermore, the development of digital inclusive finance provides crucial financial support for farmers’ entrepreneurial activities (Zhang et al., 2019). As a result, digital initiatives in the rural economy enable farmers to achieve substantial gains in agriculture, employment, and entrepreneurial income, thereby continuously fulfilling their material needs.

Digital infrastructure and the digitization of daily life are equally critical in enriching farmers’ social needs and enhancing their SWB. The construction of digital infrastructure, supported by the “Wide China strategy” and the “Broadband Frontier” program, has substantially improved connectivity in rural areas. This advancement reduces information transmission costs, accelerates knowledge creation and sharing, and promotes equal opportunities (Zhan et al., 2024). Enhanced access to government services has further addressed farmers’ developmental needs, including improved education, healthcare, and social security (Pang et al., 2023). Meanwhile, the digitization of daily life supports both social security and participation needs. It includes various aspects of rural life, such as consumption, culture, tourism, education, healthcare, and essential services. Digital consumption improves the convenience of purchasing goods and services for farmers, while developments in digital culture and tourism enrich social participation opportunities. Additionally, digital education and healthcare provide high-quality social security services, making essential resources more accessible. In summary, the development of digital rural areas continuously meets farmers’ social needs—particularly in terms of participation in digital life and leisure tourism (Wei and Jiang, 2023)—thereby significantly enhancing their wellbeing.

The digitization of rural governance plays a critical role in enhancing farmers’ social participation and fulfilling their basic psychological needs, improving their wellbeing. On the one hand, the promotion of digital rural governance encourages farmers to engage more actively in rural revitalization and governance, enriching their social interactions in areas such as community relations and public affairs (Chen and Yang, 2024). On the other hand, the advancement of digital rural governance addresses farmers’ basic psychological needs, such as social identity and a sense of belonging (Liu et al., 2013; Zhao and Fu, 2017). The development of rural e-government improves the transparency and accessibility of village government information, which, in turn, bolsters farmers’ trust and fosters a sense of collective identity and belonging to their local governance (Zhang and Yi, 2023).

Based on these findings, this article proposes research:

Hypothesis 1: Digital village development can significantly enhance the wellbeing of farmers.

2.2 Capability enhancement: digitalization of rural governance and rural life can enhance social belonging and self-development capabilities

Based on the capability approach, Binder (2014) proposes a theoretical framework for wellbeing, suggesting that wellbeing operates as a function of individual capability. “Capability” refers to the opportunities and freedoms individuals have to achieve their desired functions, encompassing all the potential functions they can choose from. “Functioning” represents aspects of life that people value and have reason to cherish, emphasizing the pursuit of happiness (Sen, 1985). Farmers utilize internal and external conversion factors to transform their available income, goods, and services into functions related to wellbeing. Internal conversion factors include personal attributes such as age, gender, and education level, while external transformation factors encompass social policies, social norms, cultural contexts, and natural environments. In this sense, capability emerges as a critical determinant of individual wellbeing (Yeung and Breheny, 2016). Empirical studies have confirmed that access to educational opportunities, safety (Casini et al., 2021), respect, leisure, and social connections (Steckermeier, 2021) significantly enhance individual wellbeing.

From a macro perspective, social policies also play a significant role in shaping wellbeing. Increased public expenditure by governments aimed at improving healthcare and medical insurance (Tran et al., 2017), as well as the implementation of reasonable secondary distribution policies, can contribute to enhancing wellbeing (Deaton, 2018). Conversely, adverse social conditions, such as income inequality (Cheung, 2018) and government corruption (Li and An, 2020), can significantly diminish wellbeing. Furthermore, some studies have explored the role of social transformation factors in influencing the relationship between capability and wellbeing. Broader contextual elements—such as the country’s economic conditions, civil liberties, and societal values —affect key capabilities, including financial security, leisure, and social recognition, thereby influencing overall wellbeing (Steckermeier, 2021).

In this study, digitalization of rural governance and digitalization of rural life are conceptualized as two critical dimensions of digital village development. They are incorporated into the existing capability framework as social conversion factors to examine how they influence farmers’ subjective wellbeing through two core capabilities: belonging capability and self-development capability.

On the one hand, traditional rural governance has long been constrained by problems such as information asymmetry, fragmented public services, and limited villager participation. To overcome these challenges and advance rural governance modernization, governments have actively promoted digital governance transformation. For instance, platforms such as WeChat, Alipay, Alibaba Rural Ding, and China Telecom’s Village-to-Village Service have been increasingly embedded into grassroots party building, farmer training, village affairs disclosure, financial management, and democratic supervision. These initiatives alleviate information asymmetry, diversify villager participation channels (Su et al., 2023), and enhance villagers’ sense of identity and emotional attachment (Ding, 2021), thereby strengthening their belonging capability. In addition, provincial-level digital governance platforms streamline public service processes and improve service efficiency, thereby expanding farmers’ access to high-quality education, healthcare, and elderly care services, which in turn enhances their self-development capability.

On the other hand, digitalized rural life at the regional level encompasses digital consumption, digital education, digital tourism, digital culture, and digital healthcare. As an important social conversion factor, it exerts a significant impact on farmers’ core capabilities. Specifically, digitalized rural life broadens channels for social participation and interaction (e.g., online communities, digital cultural and tourism activities), fostering social integration and emotional belonging. Furthermore, digital education enhances farmers’ knowledge and skills, while digital consumption and digital tourism expand their horizons and information access, thereby empowering them to achieve personal development. Overall, these environmental factors reinforce farmers’ development capability and foster a stronger sense of accomplishment in self-development.

Consequently, we propose the following research:

Hypothesis 2: The digitalization of governance and daily life will enhance social belonging and self-development capabilities, leading to an improvement in the wellbeing of farmers.

2.3 Equality mechanism: digital infrastructure and digital development of rural economy will expand the impact on relative deprivation

The relative deprivation theory posits that environments characterized by income inequality can intensify the sense of “relative deprivation” among groups, thereby diminishing individuals’ overall wellbeing. Helsper (2017) argues that citizens assess their social equality status based on their emotional perception of relative disadvantage within a group. When individuals perceive their living conditions to be inferior to those of their peers and believe they deserve a better quality of life, they are likely to experience negative emotions such as anger and resentment (Smith and Pettigrew, 2014). One perspective suggests that digital rural construction can help alleviate relative deprivation in rural areas. Digital infrastructure and a digital economy can foster non-agricultural employment, enhance agricultural productivity, improve information accessibility, and develop human capital (Si and Wang, 2024; Wang et al., 2024). Conversely, another viewpoint argues that such digital initiatives may fail to reduce relative deprivation. The marginal agglomeration effect, combined with the concentration of digital technologies and financialization, enables high-income individuals to access financial products and digital technologies at lower costs, thereby widening income inequality (Daud et al., 2021). Furthermore, the digital divide and the expansion of social networks may further exacerbate relative deprivation further (Liu and Luo, 2023; Antinyan et al., 2019; Zhang and Sun, 2024).

This article argues that digital infrastructure and economic digitization can contribute to the Matthew effect of welfare inequality. While these advancements can raise incomes for farmers overall, resource-rich individuals are more likely to benefit disproportionately from the digital dividend, resulting in a faster rate of income growth compared to the less affluent groups. At the same time, the widening digital divide is expected to exacerbate the income gap between resource-rich and resource-poor farmers. Therefore, we propose the following research:

Hypothesis 3: The digitization of digital infrastructure and the rural economy will intensify relative deprivation among farmers, leading to a decline in their overall wellbeing.

3 Data source and research methodology

3.1 Data source

This study employed two matched datasets. The first dataset comprises county-level digital village development data, sourced from the County Digital Village Index (2018 and 2019), jointly released by the Peking University New Rural Development Research Institute and the Alibaba Research Institute. This dataset provides insights into the status of digital countryside initiatives across various Chinese counties.

The second dataset is derived from a rural household survey conducted by the research team between July and August 2020. The survey employs a stratified random sampling method. Provinces were first selected based on regional economic development levels. Within each selected province, five counties (or districts) were chosen, followed by one village per county. Finally, households were randomly sampled within each village. After integrating Peking University’s Digital Rural Dataset, a total of 666 valid farmer responses from 24 counties across five provinces were retained for analysis. The distribution of the matched samples is presented in Figure 2, and the main characteristics of the three surveyed regions are summarized in Table 1. In eastern China, Zhejiang Province represents the relatively developed region, with a per capita GDP exceeding 100,000 RMB and rural per capita disposable income of 29,876 RMB in 2019. A total of 187 valid samples were collected from six sites: Xianju, Yuyao, Jingning, Jiangshan, Pan’an, and Suichang. In central China, Hubei and Jiangxi illustrate moderate development, with per capita GDPs of 77,387 RMB and 53,164 RMB, and rural incomes about 54% of Zhejiang’s level. Together, 224 samples were collected, including 107 from Hubei and 117 from Jiangxi. In western China, Guizhou and Yunnan are less developed, with per capita GDP levels below 50,000 RMB and rural incomes just above 10,000 RMB—roughly 36–40% of Zhejiang’s level. A total of 255 samples were obtained, 125 from Guizhou and 130 from Yunnan.

Figure 2

Notably, within Zhejiang Province, Jingning and Pan’an are relatively underdeveloped, with rural per capita incomes of 20,005 RMB and 19,461 RMB, comparable to those of several central counties such as Tianmen (18,138 RMB), Yicheng (20,154 RMB), Gong’an (19,784 RMB), and Wuning (18,086 RMB). A total of 60 samples were collected from these two sites. Considering this, the overall sample size is broadly consistent with the proportional distribution of the rural population across the surveyed regions.

3.2 Variable construction

• (1) The subjective wellbeing (SWB) of farmers serves as the dependent variable in this study. Life satisfaction is a widely utilized measure of individual wellbeing (Casini et al., 2021; Li, 2024; Li and Zhang, 2023), and we use farmers’ ratings of life satisfaction as a key indicator of SWB. Zhang and Bao (2022), in their study on the subjective wellbeing of employees in state-owned forest farms, emphasized that life confidence represents an important dimension of subjective wellbeing. Building on this insight, we conduct a robustness check by incorporating farmers’ self-assessed confidence in daily interactions as an alternative dependent variable in our robust regression analysis. Both indicators are measured on a scale from 1 to 5, with higher scores reflecting greater life satisfaction or social confidence among farmers.

• (2) Explanatory Variables. The development of digital villages serves as the primary explanatory variable in this study. We employ the “County Digital Village Index,” released by the New Rural Development Research Institute of Peking University, to assess the level of digital countryside development. To mitigate potential endogeneity issues arising from 2020 farmers’ data, we utilize the logarithmic mean of the county-level digital village index from 2018 and 2019. Given the absence of a digital rural index in some surveyed counties, we supplement the data using a multiple imputation method. For the missing data on the county-level digital rural index, we apply this imputation technique to enhance the dataset. The “County Digital Village Index” is constructed across four dimensions— digital infrastructure, digitalization of the rural economy, digitalization of rural governance, and digitalization of rural life. Specifically, it incorporates 21 indicators sourced from Alibaba Group and its partners, as well as eight indicators derived from national statistical data and web scraping. The index is standardized using the logarithmic function method (Zhao et al., 2017). Taking 2018 as the benchmark year, indicator scores are normalized to range from 0 to 100, whereas 2019 scores may exceed 100 or fall below 0, reflecting fluctuations relative to the benchmark year (Tian and Zhang, 2024).

Specifically, rural digital infrastructure serves as the material foundation for digital village construction, encompassing five key aspects: information infrastructure, digital financial infrastructure, digital commercial hubs, and essential digital resources. The digitization of the rural economy forms the core of digital village construction, including four dimensions: digital production, digital supply chains, digital marketing, and digital finance. The digitization of rural governance functions as a crucial mechanism for advancing digital government and rural transformation, comprising four governance indicators. Lastly, the digitization of rural life represents the fundamental objective of digital village construction, covering six essential areas: digital consumption, digital culture, digital education, digital healthcare, digital tourism, and digital life services.

• (3) Mechanism Testing Variables. To investigate the capability-enhancement mechanism through which digital village construction affects the SWB of farmers, this study adopts the capability approach, emphasizing two interrelated core abilities: social belonging ability and self-development ability.

First, digital village development enhances social belonging by providing farmers with more excellent opportunities for social participation, strengthening their social connections, and reshaping their identity with traditional cultural concepts. These aspects of social participation and connections are critical in fostering a sense of social belonging (Nussbaum, 2009). The social belonging profoundly influences farmers’ sense of identity and belonging (Suppa, 2021), significantly contributing to overall life satisfaction (González et al., 2021). In this study, social belonging is measured by indicators such as mutual assistance between neighbors and the practice of daily social gifts.

Sen (2012) emphasizes that social opportunities are crucial for achieving freedom, which includes government-provided public services such as education, healthcare, and elderly care. In this context, the construction of digital rural areas enhances the quality and accessibility of these essential public services through digital governance and improved digital living conditions. Such development is instrumental in strengthening farmers’ self-development abilities. A more substantial development capability correlates with a heightened sense of personal advancement among farmers, which in turn increases their wellbeing (Zhu and Li, 2024).

Sen (2012) emphasizes the importance of social opportunities in achieving freedom, which encompasses access to government-provided public services such as education, healthcare, and elderly care. In this regard, the construction of digital rural areas significantly improves the quality and accessibility of these essential services through digital governance and enhanced digital living conditions. Such developments are crucial for strengthening the farmers’ self-development abilities. A more substantial development capability fosters a heightened sense of personal advancement among farmers, leading to increased wellbeing (Zhu and Li, 2024). To empirically capture farmers’ development capabilities, this study analyses their subjective evaluations of several key areas, including major illness medical security, elderly care security, children’s education, village public facilities, and community safety.

To explore the equality mechanism through which digital rural construction impacts farmers’ wellbeing, this study draws on relevant research (Wu and Li, 2024; Hua and Pan, 2024) and employs the Kakwani inequality index to assess the relative sense of deprivation experienced by farmers. This index is introduced as a mediating variable. If digital rural development successfully reduces relative deprivation among farmers, it enhances social (income) equality, leading to higher wellbeing. Conversely, if digital rural development exacerbates relative deprivation, it diminishes social (income) equity, resulting in lower wellbeing.

• (4) Control Variables. Based on existing research (Suppa, 2015; Tran et al., 2017; Li and Zhang, 2023), this article identifies several control variables that may influence the relationship between digital village construction and farmers’ wellbeing. At the household level, these variables include the age of the head of household, education level, health status, family land area, loan, social capital, livelihood risks, technical training, internet use, trust in community cadres, among others. In addition, we also controlled for variables at the county level, including industrial structure, government support, human capital, and medical level. Descriptive statistics for all variables are presented in Table 2.

3.3 The construction of the empirical model

To investigate the impact of digital village development on the wellbeing of farmers, this study constructs a benchmark linear regression model.

In Equation 1, represents the dependent variable, representing the wellbeing of the i-th farmer. denotes the core explanatory variable, indicating the level and dimensions of digital village development in the county where the ith farmer resides. Correspondingly, represents the estimated parameter value of the explanatory variable. refers to a series of control variables that capture various personal and family characteristics of the respondent, with indicating the estimated parameter values associated with these control variables. Finally, represents the random error term.

Given that the traditional three-step mediation method is unable to confirm causal relationships (Wen and Ye, 2014), Jiang (2022) proposed a mediation approach that assesses the significance of the relationship between the core explanatory variable and the mechanism variable, as well as the significance of the relationship between the mechanism variable and the dependent variable (Jiang, 2024). This method has gained increasing attention in the field (Sun and Han, 2024; Song and Fu, 2025). In this article, we construct a mechanism model represented by equations 2, 3. Here, denotes the mechanism variable, which includes factors such as the income inequality of the i-th farmer, as well as the social belonging ability and self-development ability of the i-th farmer. represents the impact of digital rural development on the mechanism variables, while denotes the effect of the mechanism variable on farmers’ wellbeing. If both and are found to be significant in equations 2, 3, this indicates the presence of significant mechanistic effects.

For the robustness checks, the ordered probit model is employed as an alternative estimation method. In addition, two-stage least squares regression with instrumental variables (IV-2SLS) is applied to address potential endogeneity. To further examine the mechanism of income inequality, quantile regression is conducted to capture the heterogeneous income effects of digital rural development across different quantiles of rural households.

4 Research results

4.1 Results of benchmark regression

Table 3 reports the benchmark regression results on the impact of county-level digital rural development on farmers’ wellbeing. Columns (1)–(5) present the OLS estimates for the core explanatory variables, while Columns (6)–(10) report the results with control variables. To ensure model validity, diagnostic tests are conducted. For example, in Model 6, the VIF values of the 17 explanatory variables range from 1.07 to 2.44, all below the conventional threshold of 5, indicating no multicollinearity. The Breusch–Pagan (BP) test yields an LM statistic of 0.87 (p = 0.351), suggesting no linear-form heteroscedasticity, whereas the White test reports an LM statistic of 209.87 (p = 0.01), confirming heteroscedasticity. Models 7–10 produce similar results. Accordingly, OLS estimation with robust standard errors is employed (see Table 3).

The findings indicate that, after accounting for individual, family, and regional characteristics, the estimated coefficient for digital rural development at the county level is significantly positive, indicating that digital rural initiatives enhance farmers’ wellbeing. From a dimensional perspective, the coefficients for rural digital infrastructure, the digitization of rural economies, rural governance digitization, and the digitization of rural life are all significantly positive, suggesting that each of these four dimensions of digital rural construction contributes to improving farmers’ wellbeing. Overall, the results confirm a positive correlation between the development of digital rural areas and farmers’ wellbeing, underscoring the role of digital rural construction in enhancing farmers’ happiness.

From the perspective of control variables, the coefficients for the health status of the household head, land area, social capital, social security, and political trust are all significantly positive. This indicates that higher levels of education among household heads are associated with greater wellbeing. Additionally, families with more material and social capital, along with better access to medical and educational security, and greater trust in community officials, tend to report higher wellbeing. Furthermore, the coefficient for Internet usage is significantly positive, suggesting that households with Internet access can utilize it to enhance farmers’ wellbeing. On the other hand, the coefficient for debt is significantly negative, indicating that substantial financial obligations, such as housing loans and educational loans for children, can significantly reduce life satisfaction. At the county level, government financial support demonstrates a significantly positive impact, suggesting that increased economic assistance from these authorities can effectively enhance farmers’ subjective wellbeing. In some models, the coefficients for the county-level industrial structure and healthcare levels are also significantly positive; however, these results lack robustness.

In conclusion, digital rural development and its four dimensions exert a significant positive impact on farmers’ subjective wellbeing. This suggests that, as a key strategy for rural revitalization, digital rural development can serve as an effective means of enhancing farmers’ subjective happiness.

4.2 Robustness test

To test the robustness of the regression results, alternative dependent variables, key explanatory variables, and estimation methods are employed. Table 4 reports the ordered probit estimates. Columns (1)–(5) present the results based on life satisfaction, measured on a 1–5 scale. The Pseudo R² values of Models 1–5 all exceed 0.1, indicating acceptable model fit. Compared with regressions excluding household- and county-level factors, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for Models 1–5 decline substantially, suggesting improved model performance. For example, the AIC and BIC of Model 1 are 1,437.835 and 1,532.362, respectively, which are markedly lower than the corresponding values (1,595.743 and 1,631.753) when household- and county-level factors are omitted.

Columns (6)–(10) report the regression results when the dependent variable is replaced with confidence in daily communication, while columns (11)–(15) present the estimated results after substituting the core explanatory variable with Peking University’s 2019 Digital Village Index. Across these robustness tests, the coefficients of digital rural development and its four dimensions remain significantly positive, providing further evidence that digital village development improves farmers’ life satisfaction. These findings are consistent with the baseline regression, thereby confirming the robustness of the study’s conclusions.

4.3 Endogeneity test

When analyzing the wellbeing of farmers from the perspective of county-level digital rural development, the main challenge lies in potential endogeneity arising from omitted variables and measurement errors. To mitigate these issues, this study employs instrumental variable methods and sensitivity analysis, thereby enhancing the robustness and credibility of the empirical results.

Drawing on Wei’s (2022) research and considering data availability, this study selects the number of fixed telephone users in each county and district in 2011 as the first instrumental variable for digital rural development. The number of fixed telephone users is closely linked to the development of digital rural areas but does not directly impact the farmers’ wellbeing. Additionally, following Zhang et al.’s (2019) approach, the spherical distance between the city where rural households reside, and Hangzhou is used as the second instrumental variable for digital rural development. The underlying data for the Digital Village Index primarily originates from Ant Group in Hangzhou, a city that is highly advanced in digital economy. The diffusion and spillover effects of digital technology from Hangzhou are influenced by geographical distance, making it strongly correlated with digital village development across regions. Importantly, this distance does not directly affect the subjective wellbeing of farmers (Hu et al., 2024).

Table 5 presents the results from the two-stage regression using instrumental variables. In the first-stage, both the under-identification test (LM) and the weak instrument test (Wald F) decisively reject the null hypothesis for both instrumental variables, confirming their validity. In the second stage, the coefficient for digital rural development at the county level remains significantly positive, thereby providing further support for the robustness of the benchmark regression results.

The sensitivity analysis method proposed by Cinelli et al. (2020) effectively addresses concerns related to potential omitted variables. Lin and Zhu (2022) adapted this approach to assess the minimum strength an omitted variable must have to invalidate previous research conclusions and conducted sensitivity tests for omitted variables. Table 6 presents the estimated results, with Parts A and B showing the findings without and with included comparison variables, respectively. The results for the four dimensions of the index similarly demonstrate robustness, though these are not included here due to space constraints.

The analysis revealed that when omitted variables with a strength three times greater than the original were introduced, most variables exhibited R²dz values and R²yz dx values smaller than the robustness thresholds RV_q = 0.1364 and RV_qa = 0.0669, respectively. This indicates that the conclusions would only be overturned if the omitted variables were more than three times stronger than all included control variables. Therefore, this study concludes that the risk of significant bias from omitted variables in the model is extremely low, further reinforcing the reliability of the original model results.

In conclusion, the results derived from the instrumental variable approach and sensitivity analysis confirm that the finding—namely, that digital villages significantly improve farmers’ subjective wellbeing—is highly robust.

4.4 Mechanism research

4.4.1 The test results of the equality mechanism

The results in Table 6 show that the estimated coefficients for digital infrastructure and rural digital economy development on the mechanism variable related to the farmers’ income are significantly positive. Additionally, the regression coefficient of household income on farmers’ wellbeing is also significantly positive. This indicates that improvements in digital infrastructure and the rural digital economy enhance the farmers’ wellbeing by boosting their income. One possible explanation is that digital infrastructure creates a more supportive decision-making environment for farmers, reducing decision-making costs. It also expands employment opportunities and increases the farmers’ wage income through the benefits of digitalization (Liu and Lai, 2024). Furthermore, improved digital infrastructure provides technological and equipment support for agricultural production, helping farmers effectively manage risks such as diseases and pests, enhance production quality, and ultimately boost rural household operating income (Hu et al., 2024). Similarly, the digitization of the rural economy facilitates the upgrading of rural e-commerce, expands the distribution channels for agricultural products, enhances rural logistics systems, and reduces transportation costs for agricultural inputs and final products (Liu et al., 2024). This process further promotes the integration of rural tertiary industries (Hao, 2022), thereby generating additional employment and entrepreneurial opportunities for rural families. Collectively, these mechanisms increase farmers’ income levels, improve their living standards, and, consequently, enhance their subjective evaluations of living conditions (Bai and Wu, 2017).

Unfortunately, the results in Table 7 indicate that the estimated coefficient for rural digital economy development on income inequality among farmers is significantly positive, while the coefficient for income inequality on farmers’ wellbeing is significantly negative. This finding suggests that the development of the rural digital economy may exacerbate income inequality within the farming community, ultimately reducing their wellbeing. These findings support the view that the construction of digital rural infrastructure does not promote a fair social environment, rather, it may deepen the sense of relative deprivation among farmers, adversely impacting their subjective wellbeing. A possible explanation lies in the digital literacy gap among farmers, which can widen the income disparities between high-skilled and low-skilled farmers, further widening income inequality within the farming community in the context of the digital economy. This observation is consistent with findings from Tian et al. (2024).

Furthermore, an expanding income gap fosters a sense of relative deprivation, which ultimately lowers subjective wellbeing among individuals (You and Ma, 2023). In addition, income inequality can lead to an uneven distribution of social resources, reduce social public service security, decrease political participation rates, erode trust among individuals, and ultimately undermine residents’ wellbeing (Kawachi et al., 1997).

To reveal the differences in income growth among farmers with varying income levels due to the development of digital rural areas, Table 8 presents the quantile regression results for household income. The findings show that digital rural development has a more pronounced impact on high-income farmers than low-income farmers. At the 90th percentile, the estimated coefficient for digital rural development is 1.351, indicating that a 1% increase in the digital rural index corresponds to a 135.1% increase in income. Further analysis dividing respondents into high-income and low-income groups based on their average income, confirms that the income-increasing effect of digital rural development is significant for high-income farmers, but not for the low-income farmers. Moreover, the positive effect becomes increasingly pronounced at higher income levels, with the 90th percentile coefficient (1.054) exceeding those at the 20th and 40th percentiles. Counties were also classified into more developed and less developed groups according to the average level of digital rural development. In counties with more advanced digital rural areas, the income-increasing effect is significant, whereas in less developed areas, the effect is not significant. These findings underscore that the income-enhancing effects of digital rural development are primarily concentrated among high-income households and in regions with more advanced digital infrastructure.

Overall, the expansion of digital rural areas disproportionately benefits high-income farmers and those in digitally advanced regions, whereas low-income farmers and those in less developed areas experience relatively limited gains. Such disparities may substantially exacerbate existing income inequality.

4.4.2 Results of the capability enhancement mechanism

The findings in Table 9 reveal that the estimated coefficients for the digitalization of rural governance and rural life on the mechanism variable of farmers’ development capacity are significantly positive. Similarly, the regression coefficient of farmers’ development capacity on their subjective wellbeing is also significantly positive. These results suggest that the digitalizing of rural governance and life improves the farmers’ subjective wellbeing by enhancing their development capacity. A plausible explanation for this relationship is that digital governance functions as a channel effect (Zhang and Yi, 2023), mitigating geographical barriers to farmers’ access to public services. This facilitates farmers’ use of cultural and recreational spaces, strengthens village security, and satisfies their social participation needs. Moreover, the digitalization of rural life—especially in education, healthcare, and daily services—has enhanced the quality of public service delivery. Consequently, this improvement fosters more positive subjective evaluations of public services among farmers and effectively addresses their social security needs.

The estimated coefficients for the mechanism variable of social belonging ability associated with the digitalization of rural governance and life are significantly positive. Similarly, the regression coefficient of social belonging ability on farmers’ subjective wellbeing is also significantly positive. These findings suggest that the digitalization of rural governance and life enhances the farmers’ subjective wellbeing by fostering their sense of social belonging. Specifically, digital cultural platforms have broadened the channels for cultural dissemination, improved access to high-quality public cultural resources from urban areas, and enriched the cultural lives of farmers (Liu et al., 2024). Simultaneously, grassroots governments are leveraging digital technology to implement policies that strengthen farmers’ social identity (Peng et al., 2022). Furthermore, digital technology facilitates the exploration and promotion of high-quality rural cultural traditions, enhancing the appeal and dissemination of valuable traditional culture. In China’s current rural society, which remains an acquaintance-based society governed by personal norms (You and Ma, 2023), farmers’ recognition of and confidence in the cultural tradition, such as “rule by etiquette,” fosters emotional connections among the villagers and significantly enhance overall wellbeing.

In conclusion, the mechanism test results indicate that the digitalization of rural governance and the digitalization of daily life enhance rural households’ subjective wellbeing primarily by strengthening their self-development capabilities and sense of belonging. Looking ahead, the advancement of digital villages should place greater emphasis on cultivating the core capabilities of rural households, while continuously reinforcing their endogenous development potential and fostering a stronger sense of social belonging.

4.5 Further discussion

Due to substantial variations in the level of digital rural development and differences in household resources (such as income and social capital) across the surveyed regions, the happiness effects of digital rural development may vary significantly. Table 10 presents the results of the heterogeneity analysis. First, regarding the heterogeneity of digital rural development, counties were divided into more developed and less developed groups based on the average digital rural index. The results reveal that, compared to less developed counties, the impact of digital rural development on farmers’ SWB is greater and more significant in counties with more developed digital villages. This finding contrasts with the conclusions of Liu and Lai (2024), who suggested that digital rural development benefits farmers in underdeveloped areas more. Instead, the current results indicate that digital rural development has widened the welfare gap between farmers in developed and underdeveloped areas. Second, with respect to income heterogeneity, the sample was divided into high-income and low-income groups based on farmers’ average annual income. The results show that, compared to the low-income group, digital rural development exerts a more significant impact on the SWB of the high-income group. This suggests that digital rural development has not substantially improved the subjective wellbeing of low-income farmers but has had a more pronounced effect on high-income farmers. Third, regarding social capital heterogeneity, the sample was divided into high-level and low-level social capital groups based on farmers’ average social capital. The results demonstrate that digital rural development significantly impacts the SWB of both groups. However, the effect is markedly stronger for farmers with high social capital (1.045) than for those with low social capital (0.428).

In summary, resource conditions play a critical role in determining whether digital rural development can enhance farmers’ wellbeing. Higher levels of digital rural development, higher income, and higher social capital significantly increase the likelihood that farmers will derive tangible benefits from digital rural development, thereby achieving higher levels of subjective wellbeing.

5 Conclusion and policy implications

Based on the integration of the county-level digital village index with rural survey data from five provinces in China, this study explores the pathways through which digital village construction impacts the farmers’ wellbeing. It further examines the mediating effects of core competencies and relative deprivation.

The findings reveal that developing digital rural infrastructure significantly enhances farmers’ happiness. In particular, digital infrastructure, economic digitalization, governance digitalization, and life digitalization all contribute to improving the farmers’ SWB. Mediation analysis indicates that digital governance and the digitalization of daily life reinforce traditional cultural values in rural areas and enhance the farmers’ sense of social belonging. These factors also improve the quality of rural public services and strengthen farmers’ self-development capabilities, leading to higher wellbeing. Conversely, digital infrastructure and economic digitalization have not achieved equitable outcomes for farmers’ wellbeing. While they promote income growth for all farmers, they also exacerbate welfare inequality through the Matthew effect, intensifying income disparities and amplifying relative deprivation, ultimately reducing the farmers’ wellbeing. Heterogeneity analysis further highlights that the impact of digital rural development on farmers’ SWB follows a pattern of “the strong getting stronger.” Farmers in more developed digital rural areas, high-income farmers, and those with abundant social capital are more likely to benefit from these advancements. In summary, while the comprehensive promotion of digital rural construction offers significant income-boosting and empowerment effects, it also risks creating an unequal social environment and reinforcing the Matthew effect, ultimately undermining the SWB of farmers.

The policy implications can be summarized into four key aspects. First, to strengthen the farmers’ sense of social belonging and group identity, efforts should prioritize improving the digital efficiency of rural governance. This entails accelerating the development and implementation of the “Internet Plus Government Services” initiative, establishing incentive mechanisms for farmers to participate in rural governance, and enhancing their political trust and social belonging. Promoting the “Internet Plus Grid” governance model can further improve community security and satisfaction with public services. Second, to enhance the farmers’ self-development capabilities, digital technologies should be integrated into sectors such as education, healthcare, elderly care, and cultural activities. Increased government investment and policy support are essential for initiatives like “Internet Plus Education,” smart healthcare, smart community elderly care, digital libraries, and digital tourism. Narrowing the urban–rural gap in essential public services will expand farmers’ access to educational, healthcare, and elderly care resources, thus improving their capacity to meet these needs. Third, digital technology should be leveraged to optimize agricultural production and provide farmers with cost-effective, high-quality agrarian services. E-commerce platforms and live-streaming technologies can facilitate agrarian product sales, enhance brand recognition, and offer insights into market trends, driving income growth—particularly for the low-income farmers. Lastly, it is crucial to address gaps in digital infrastructure in remote rural areas, enhance farmers’ digital literacy, and reduce disparities in digital technology adoption. Accelerating the implementation of the “Wide China” strategy and the “Broadband Frontier” initiative will ensure equitable access to digital dividends. Concurrently, greater emphasis should be placed on promoting and training farmers in digital technologies, particularly in employment information, sales promotion, cultural engagement, and access to public services.

This study has several limitations. First, it relies on cross-sectional data, which constraints its ability to establish causal relationships between digital village development and farmers’ subjective wellbeing. Future research should employ panel data to enhance causal inferences. In addition, the Matthew effect observed in digital rural development raises critical questions about its long-term impact, in particular, whether it will diminish over time and eventually benefit low-income and resource-poor families. These aspects warrant further investigation. Second, the study is based on survey data from only five provinces in China. While informative, this scope may not fully reflect the diversity of a country with a large population and significant regional disparities in digital development. Third, the study utilizes the Digital Countryside Index developed by the Peking University research team. Although this index is valuable, it is not the only measure available, and other indices or alternative methodologies may provide complementary insights. Future research should consider conducting broader empirical analyses to address these limitations. Fourth, several important village-level factors, such as governance quality and social capital, were not incorporated into the analysis. Future studies should include these variables to provide a more comprehensive understanding of the effects of digital rural development.

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