The Effect of the Use of Digital Technology on the Impact of Labor Outflow on Rural Collective Action: A Social–Ecological Systems Perspective

Abstract

The rapid development of urbanization has led to a continuous migration of rural labor to cities, while also facilitating the widespread adoption of digital technologies in both urban and rural areas. The existing literature predominantly focuses on the negative impact of labor outflow on rural collective action, with insufficient research addressing how to mitigate these adverse effects. By using the social–ecological systems framework, and based on survey data from 131 villages across 14 cities in Guangxi, China, this study finds that digital technologies can alleviate the negative impact of labor outflow on irrigation collective action. The relationship between labor outflow, irrigation collective action, and the use of digital technologies is particularly evident in villages located in non-plain regions, those with distinctive cultural resources, high collective economic income, and restructured planning, and where technological advancements have been promoted. The findings of this study highlight a beneficial relationship between the phenomena of labor outflow and the diffusion of digital technologies, both of which are consequences of urbanization. This suggests that issues arising from urbanization can also be addressed and resolved through urbanization itself. The conclusions offer a new perspective for understanding the interactions between variables in social–ecological systems and provide a reference for developing countries to find suitable paths for combating rural decline and achieving sustainable rural development amidst rapid urbanization.

1. Introduction

Research on the governance of commons suggests that collective action encompasses four key components. (1) Groups of individuals [1]: this refers to individuals who are interdependent, where these individuals form the fundamental decision-making units of collective action, and the group they form acts as the vehicle for such action. (2) Common interests [2]: these serve as the driving force behind collective action and the primary purpose for the formation of groups. (3) Collective decision-making [3]: this refers to the process by which individuals within the group negotiate on the achievement of common interests. (4) Institutional arrangements [2]: these represent the specific mechanisms through which collective action is carried out. In the context of this article, rural collective action refers to the process in which villagers, who share interdependent relationships, engage in negotiations on action issues related to public affairs that they collectively face, and implement institutional arrangements to ensure the provision of public goods, thereby advancing common interests. Consequently, rural collective action capacity refers to the ability of villagers to collectively adhere to institutional arrangements, thereby enabling them to organize collective action. The greater the compliance with these arrangements, the higher the level of participation in collective action, which in turn enhances the organization of the action and strengthens the capacity for collective action in rural areas.

The capacity for collective action in common-pool resource management is a decisive factor in establishing effective rural governance. The strength of collective action is often manifested in the adaptability of rural communities in responding to complex and changing external environments. In the face of internal and external pressures such as natural disasters and economic fluctuations, consistent and unified collective action ensures that rural governance actors make timely and informed decisions, allocate resources effectively, and respond to unexpected challenges at critical junctures [4], such as in land use projects and management [5]. However, in recent decades, the capacity for collective action in rural areas has been reducing, particularly in developing countries [6]. This decline is a key factor driving the degradation of the rural ecological environment, the stagnation of production and livelihoods, the erosion of local culture, limited organizational capacity, difficulties in villager cooperation, and the failure of management systems [7]. These issues have significantly hindered the processes of agricultural modernization, the achievement of common prosperity, and sustainable rural development, presenting a substantial challenge to the socio-economic progress of developing countries.

Among the many factors contributing to this decline, large-scale rural labor out-migration is a key driver of the widespread decline in collective action capacity in rural areas in developing countries [6]. Urbanization is a primary driver of large-scale rural labor out-migration in developing countries. By 2022, the global urbanization rate reached 56.9%. China, as the largest developing country, experienced a rapid increase in its urbanization rate, from 17.92% in 1978 to 66.16% in 2023. This rapid urbanization has resulted in the continuous migration of a large rural workforce. Over the half-century from 1960 to 2016, rural populations in emerging economies such as Brazil, Russia, India, and China decreased by 73%, 44%, 18%, and 47%, respectively (source: https://data.worldbank.org/ (accessed on 11 March 2025)). The sustained outflow of rural labor has significantly promoted large-scale land management, increased agricultural mechanization, and greatly liberated and developed rural productivity, creating conditions for rapid rural economic development [8]. However, as mentioned earlier, large-scale rural labor out-migration has also led to a decline in rural collective action capacity, which in turn hinders the socio-economic development of developing countries [6].

In this context, the emergence of digital technologies offers a promising avenue for addressing the decline in rural collective action. Over the past decade, digital technologies have been increasingly adopted in rural areas of developing countries, transforming traditional agricultural practices, increasing access to information, and improving rural livelihoods. Digital technologies have significantly improved production efficiency and resource utilization through precision agriculture, data-driven decision-making, and agricultural mechanization [9]. Simultaneously, e-commerce platforms, mobile payments, and real-time market information have improved farmers’ market access and income levels [10]. Furthermore, digital technologies have enhanced farmers’ capabilities and knowledge sharing through mobile learning platforms, online communities, and remote advisory services, and have promoted sustainable rural development and resilience through improved infrastructure, disaster management tools, and climate adaptation strategies [4,11]. It is evident that digital technologies have significantly transformed the impoverished and backward conditions of rural areas, offering hope for developing countries to leverage digital technology applications to mitigate the negative impacts of labor out-migration.

This study aims to investigate whether digital technologies can mitigate the impact of large-scale rural labor migration on collective action for irrigation in rural areas. Previous research on the effects of rural labor migration on common-pool resource governance has yielded varied results depending on the context. In certain regions, such as Nepal, rural out-migration has been linked to an increase in forest area, suggesting that labor migration may have a positive effect on the governance of forest common-pool resources [12]. In contrast, for irrigation common-pool resources, the impact of labor migration on collective action tends to be negative [6,13,14]. This is particularly evident in rural China, where key factors influencing irrigation collective action—such as leadership, social capital, community awareness, resource dependency, and economic heterogeneity—are all affected by large-scale labor migration, leading to failures in irrigation governance on multiple fronts [6].

In the context of rural labor outflow, irrigation collective action tends to exhibit a negative effect. However, in the case of irreversible and sustained rural labor outflow, the factors that could counterbalance the decline in irrigation collective action remain unclear. To date, little research has focused on the potential of digital technologies to foster collective action. Therefore, a detailed exploration of the interaction between labor outflow and digital technology usage on irrigation collective action may provide valuable insights into addressing the decline of collective action under urbanization’s impact. Moreover, it could offer references for China and other developing countries in identifying strategies to combat rural decline and achieve sustainable rural development during rapid urbanization. Given the limited attention in the traditional literature to the interplay between population migration and digital technology usage, investigating the relationship between rural labor outflow, digital technologies, and irrigation collective action could open up new theoretical and empirical pathways for collective action research.

This paper presents two potential contributions. Firstly, while the existing literature predominantly addresses the negative effects of labor outflow on rural collective action, there is a lack of research on mechanisms to mitigate these effects. This study introduces digital technology as a moderating variable within the analytical framework, emphasizing its crucial role in alleviating the negative consequences of labor outflow. In doing so, it fills the gap in current research on collective action by addressing the role of emerging technologies and expands the technological dimension of collective action research within commons governance. Secondly, this paper offers a dialectical perspective on the challenges posed by urbanization, arguing that such challenges can be addressed through the natural progression of urbanization itself. This perspective challenges the linear mindset underlying the pessimistic narrative that “urbanization inevitably leads to rural decline”, offering theoretical support for the synchronized development of urban and rural areas in developing countries.

2. Literature Review

2.1. Factors Affecting Collective Action

Research on the factors influencing rural collective action, both domestically and internationally, has made significant progress. These studies have explored the key factors and mechanisms affecting rural collective action from various theoretical perspectives and practical dimensions, leading to a rich body of literature. Institutional theorists generally argue that any specific action scenario is shaped by three sets of exogenous variables: natural geographical features, economic and social attributes, and general systems and rules [15]. Building on this theoretical framework, scholars Wang and Shu (2021) conducted a comprehensive study that delves into how these three sets of exogenous variables influence the mechanisms of rural collective action, providing important theoretical insights for understanding this phenomenon [16]. First, natural geographical features are considered critical factors affecting participation in collective action. For instance, the degree of terrain ruggedness directly impacts interdependence among rural groups. Complex terrain may weaken the connections between groups, thereby reducing the likelihood of collective action [17]. Additionally, factors such as proximity to markets and the degree of resource scarcity have been shown to significantly influence collective action [18]. These factors shape rural economic activities and social structures, thereby influencing the emergence and development of collective action. Second, at the level of economic and social attributes, several core factors affecting collective action have been identified. These include demographic characteristics, such as age, gender, and education level, which influence individuals’ values, lifestyles, cognitive abilities, and access to resources. These elements, in turn, affect their willingness, motivation, and methods of participating in collective action [19]. Group size also plays a role, as it can influence collective action patterns by adjusting social interaction and coordination costs [20]. Labor mobility, a dynamic process, can alter the socio-economic structure of rural areas, thereby stimulating or inhibiting the drive for collective action [21]. The collective income of the village and the personal income levels of villagers directly relate to their economic capacity and willingness to engage in collective action [22]. Trust among villagers is an essential social bond that propels collective action [23]. The leadership style and abilities of village leaders are also crucial, as they play a key role in organizing and mobilizing collective action [18]. Finally, heterogeneity, or socio-economic differences within the village, can have complex and far-reaching effects on collective action [23]. Finally, general systems and rules influence collective action, primarily through the levels of social rewards, perceived fairness, and the effectiveness of monitoring and sanction mechanisms. Social rewards and fairness perceptions directly affect villagers’ motivations to participate in collective action [24,25]. Monitoring and sanction mechanisms help ensure the effectiveness of collective action by regulating villagers’ behavior and maintaining social order [26]. Furthermore, Su et al. (2025) demonstrated that land trusteeship can facilitate collective action by reinforcing village leadership and fostering the development of cooperative organizations [27].

2.2. Research on Labor Migration

Scholars generally agree that the drivers of labor out-migration are multifaceted, encompassing economic, political, and social factors. The Harris–Todaro model (Todaro, 1969) posits that the primary driver is the disparity in expected income between urban and rural areas [28]. Cai (2000) similarly emphasizes the widening urban–rural income gap as the fundamental cause of rural-to-urban labor migration [29]. Furthermore, Li (2007) demonstrates a strong correlation between rural labor out-migration and constraints imposed by rural land tenure systems, low agricultural productivity, and increased urban employment opportunities [30]. The impact of labor out-migration on both sending and receiving regions is complex. On the one hand, it can alleviate employment pressure in sending regions, increase household income, and stimulate economic growth. On the other hand, it can also lead to human capital loss in sending regions, negatively affecting local economic and social development. For example, Massey et al. (1993) highlight that while labor out-migration increases economic income in sending regions, it also results in societal structural changes, such as altered family structures and decreased community cohesion [31]. Li (2015) points out that while rural labor out-migration improves rural household incomes, it also leads to wasted rural land resources and the emergence of empty-nest elderly and left-behind children [32]. To address the challenges posed by labor out-migration, international scholars have proposed various policy recommendations. Bhagwati (1976) suggests using tax policies to regulate labor out-migration and mitigate the negative impact of talent loss on sending regions [33]. Xu and Xi (2012) advocate for a comprehensive approach, including developing modern agriculture, enhancing labor skills, promoting land transfer, improving financial services, building agricultural infrastructure, promoting agricultural technology, and strengthening rural social services [34]. This integrated strategy aims to achieve balance between agricultural production and labor migration, ensuring farmers’ income growth and sustainable agricultural development. Furthermore, Su et al. (2025) found that adequate human and social capital are crucial factors in the return migration of labor [35].

2.3. The Relationship Between Labor Migration and Collective Action

Several studies have shown that labor migration has a significant negative impact on the collective action capacity of rural areas. Baker (1997), using the example of the Himachal Pradesh region of India, found that an increase in non-agricultural employment reduces the number of participants in irrigation collective action, exacerbates caste conflicts, leads to a loss of leadership, and alters planting patterns, thereby reducing the demand for irrigation [13]. Ostrom (2000) argued that labor migration reduces farmers’ dependence on agricultural resources, which in turn decreases their willingness to participate in rural public affairs [36]. This reduction in resource dependence weakens farmers’ motivation to engage in collective action. Adhikari and Lovett (2006) suggested that labor migration exacerbates economic heterogeneity among villagers, leading to a divergence in their demands for rural public affairs, thereby increasing the difficulty of collective action [37]. Rudel (2011) pointed out that labor migration due to globalization can have a disruptive impact on collective action [38]. Regional openness and high labor returns trigger labor migration, which increases the likelihood of collective action failure. Wang et al. (2016), in the context of large-scale rural labor migration in China, conducted an empirical study using the IAD framework and found that labor migration is an important cause of the decline in collective action capacity in rural China [6]. They analyzed five key mechanisms: first, labor migration reduces rural public leadership, leading to a lack of organization for collective action; second, it undermines the social capital accumulated by villagers, reducing enthusiasm for collective action; third, it diminishes people’s sense of belonging to the village, reducing opportunities to engage in collective action; fourth, it reduces people’s dependence on agricultural resources, decreasing the demand for irrigation collective action; and fifth, it increases economic heterogeneity among villagers, making it difficult to organize irrigation collective action. In conclusion, labor migration has had a significant impact on rural irrigation collective action. Although this impact may vary in different regions and social contexts, the overall trend is negative.

2.4. Research on the Relationship Between Digital Technology and Rural Development

The continuous advancement of science and technology has placed the application of digital technologies in rural development at the forefront of academics’ and policy-makers’ attention. Studies demonstrate that digital technologies significantly contribute to comprehensive rural development across multiple sectors, including agricultural modernization, rural governance, and cultural revitalization. In the agricultural sector, digital technologies provide a powerful impetus for development. Zhang (2023) argues that the digital economy is a crucial foundation for modern agriculture, and its deep integration with agriculture can drive the transformation of rural agriculture toward intensification, industrialization, and digitalization [39]. Larisa Hrustek (2020) shows through research that the digital transformation of agriculture effectively promotes sustainable agricultural development [40]. In the realm of rural governance, digital technologies offer modern technological tools for grassroots governance. Ma and Zhang (2020) demonstrate through analysis that the application of digital technologies effectively enhances the professionalization of rural governance and promotes the implementation of the rural revitalization strategy [41]. In the cultural sphere, digital technologies serve as a significant driving force for rural cultural revitalization. Li and Liang (2024) analyze the three dimensions of the integration, dissemination, and development of rural culture, highlighting the capacity of digital technologies to enable systematic cultural integration, three-dimensional dissemination, and chain-like development [42].

2.5. Literature Commentary

Overall, the existing literature has thoroughly examined the factors influencing collective action, providing important references and insights for a deeper understanding of the complexity and diversity of rural collective action. However, there are still two areas in which further improvement is needed in current research: On one hand, few scholars have investigated the impact of digital technologies on collective action, and the relationship between the use of digital technologies and collective action has yet to be explored in depth. On the other hand, while much of the existing literature focuses on the negative effects of labor outflow on rural collective action, there is a lack of further research and exploration on how to mitigate these negative impacts. In particular, there has been no exploration of how digital technologies can alleviate the negative effects of labor outflow. Therefore, this paper aims to investigate the relationship between the use of digital technologies and rural collective action in the context of labor outflow, hoping to further extend and supplement the conclusions of existing research.

3. Theoretical Analysis and Research Hypothesis

Over the past half-century, the international academic community has produced a wealth of research on the formation of effective collective action, resulting in the development of three generations of collective action theories [43]. This study primarily draws upon the theoretical advancements of the second generation of collective action theories. A key contribution of the second-generation collective action theory is the development of a systematic diagnostic framework for collective action [44]. Currently, the most widely adopted analytical frameworks for diagnosing human collective action are the Institutional Analysis and Development (IAD) framework and the social–ecological systems (SES) framework. Both the IAD and SES frameworks offer structural approaches to studying complex social–ecological systems [45]. This paper uses the SES framework to develop a theoretical model for understanding how labor migration affects rural collective action. One key feature of the SES framework is its decomposability. In line with the principles of institutional complexity and diversity, the decomposability of the SES framework offers theoretical insights for examining collective action issues in diverse contexts. Figure 1 illustrates the internal structure of the SES framework [46]. In this study, labor migration and the use of digital technologies are treated as background variables representing economic and social development. This approach allows for an exploration of the phenomena arising from these two forms of economic and social progress and their impact on collective action within the social–ecological system. Moreover, the heterogeneity among the resource system, resource unit subsystems, governance subsystems, and actor subsystems necessitates further analysis of the interactions between labor migration and digital technologies across various contexts.

3.1. The Impact of Labor Migration on Collective Action

The large-scale migration of labor from rural areas to urban centers has become a widespread phenomenon. This migration process has not only profoundly altered the socio-economic structure of rural areas, but has also had a significant negative impact on the collective action capacity of rural communities [6]. As shown in Figure 1, the impact of labor migration on subsystems or specific variables within the SES system can primarily be manifested in the following four ways: First, labor migration significantly affects the level of leadership in villages. It leads to a reduction in individuals with leadership abilities and influence within the village, thereby decreasing the village’s capacity for organization and mobilization in collective action [47]. Second, labor migration dilutes social capital, causing the social network within the village to loosen, trust levels to decrease, and social capital to diminish, which in turn hinders the effective implementation of collective action [48]. Third, labor migration reduces the sense of community belonging. Migrant villagers, working away from home for extended periods, gradually weaken their ties with the local community, reducing their sense of attachment and identification with the village, thereby lowering their willingness to engage in collective action [49]. Finally, labor migration decreases resource dependency. As labor shifts to non-agricultural employment, households become less dependent on agriculture, and their focus on collective actions closely related to agricultural production (such as irrigation and disaster prevention) also declines [36]. In conclusion, the following research hypothesis is proposed:

Hypothesis 1.

There is a significant negative effect of labor outflow on rural collective action.

3.2. The Mitigating Effect of Digital Technology on the Negative Impact of Labor Outflow

The large-scale migration of labor from rural areas to cities has undoubtedly had a profound impact on the socio-economic structure of rural communities and their collective action capacity. However, under the wave of digital technology, this challenge is gradually being transformed into new opportunities. Digital technology, with its unique advantages, is becoming an important force in mitigating the negative impact of labor outflow on collective action in rural communities. As shown in Figure 1, the use of digital technology plays a key role in alleviating the negative effects of labor outflow on specific subsystems or variables within the socio-economic system (SES) in the following ways. First, the use of digital technology provides a new platform for leadership development and display in rural communities. Research by Avolio et al. (2014) found that several types of AITs, such as the internet and Google Glass, can be used to accelerate leadership development within organizations [50]. This helps to enhance the ability of leaders to demonstrate appropriate leadership behavior in various situations. Additionally, through online communities and social media tools, potential leaders can showcase their talents and leadership across geographic boundaries, compensating for the leadership gaps caused by labor outflow to some extent. Second, digital technology facilitates the rapid dissemination and sharing of information, strengthening the connections and interactions between community members. Scholars such as Geng et al. (2023), who matched data from the 2018 China County Digital Rural Index with the China Household Tracking Survey, found that digital rural construction can significantly improve social trust among rural residents [51]. Even when far from home, villagers can stay in close contact through video calls, social media groups, and other means, enhancing trust and cooperation, and thus increasing the social capital of the community. Third, digital technology provides rural communities with platforms for common concerns and activities. Digital platforms, such as communication channels, strengthen public rationality in the daily lives of villagers, fostering a collective consciousness and public action, which in turn helps to enhance the sense of identity and belonging among community members [52], thus mitigating the weakening of community awareness caused by labor outflow. Fourth, digital technology contributes to the diversification and inclusive development of the rural economy. Through e-commerce platforms, internet finance, and other tools, villagers can more easily access market information and resources and participate in diverse economic activities [53,54], thus alleviating the increased economic heterogeneity caused by labor outflow to some extent. Finally, digital technology provides rural communities with more flexible and diverse means of resource acquisition. Through smart agriculture, remote monitoring, and other technological methods, villagers can manage agricultural resources more effectively and reduce their dependence on traditional labor resources. Meanwhile, the level of digital infrastructure development can promote the growth of urban–rural mobile network penetration, enabling urban and rural residents to enjoy the conveniences brought by digitalization. This is conducive to closing the “digital divide” between urban and rural areas, achieving high-quality urban–rural economic development, narrowing the urban–rural economic gap, and empowering common prosperity for both [55]. In conclusion, the following research hypothesis is proposed:

Hypothesis 2.

The use of digital technology can mitigate the negative impact of labor outflow on collective action in rural communities.

4. Data Sources, Variable Selection, and Methods

4.1. Data Sources

Guangxi, located in southern China, is one of the five autonomous regions in the country for ethnic minorities. The permanent resident population of Guangxi Zhuang Autonomous Region was 50.13 million at the end of 2024. The total land area is 237,600 square kilometers, and it also features approximately 7000 square kilometers of sea area. Its economy is primarily supported by agriculture, light industry, and border trade. Positioned to the south of the Beibu Gulf and sharing a border with Vietnam, Guangxi plays a crucial role as a frontier and gateway for China’s cooperation with ASEAN, holding significant strategic importance. The region’s terrain is varied, featuring mountains, hills, and plains, with a warm and humid climate conducive to the growth of a wide range of crops. Guangxi stands out due to its diverse climate, complex topography, supportive policies, and unique geographical location, all of which contribute to its distinct regional characteristics in terms of land resources, cultural diversity, population demographics, and agricultural production models. According to data from Guangxi’s third national land survey, the region boasts an arable land area of 3.3076 million hectares, with generally high-quality soil. Some areas have fertile soil and ample water resources, making them ideal for the cultivation of various crops. Additionally, Guangxi is home to a multi-ethnic population with a long history, which has fostered a rich and diverse cultural landscape. Over time, these ethnic groups have developed unique cultural traditions. Guangxi is bordered by Guangdong Province to the east, Beibu Gulf to the south, Hainan Province across the sea, Yunnan Province to the west, Guizhou Province to the northwest, and Hunan Province to the northeast. Its strategic location and convenient transportation networks have made it a major labor-exporting province, with large numbers of workers migrating annually for employment. In 2022, 2.7565 million people from poverty-stricken areas of Guangxi migrated for work (Guangxi Daily). Agriculture in Guangxi is primarily focused on labor-intensive crops such as rice. As a result, the region’s agricultural sector heavily depends on irrigation systems and collective irrigation efforts. The majority of Guangxi’s farmland is located along rivers or in areas with abundant water resources, which provides a natural advantage for collective irrigation practices. Finally, in alignment with the major strategic plans of the CPC Central Committee and the State Council, Guangxi has made significant strides in digital transformation. This comprehensive digital strategy supports economic and social upgrades, furthers the development of Digital Guangxi, and promotes the deeper integration of digital technologies into the agricultural sector. In summary, Guangxi provides an ideal context for studying the interactions between labor migration, digital technology, and collective action.

This study utilizes data from the “Hundred Villages, Thousand Households” survey conducted by the School of Public Administration, Guangxi University, in 2023. A research team meticulously recruited and trained 79 surveyors who completed a questionnaire survey from September 2023 to February 2024 across 14 cities in Guangxi. The survey comprised village-level and household-level questionnaires, investigating key aspects of village and household conditions, land use, infrastructure, digital technology applications, rural tourism, natural disasters, social capital, rural governance, rural education, and environmental management over the past three years. A multi-stage stratified random sampling method was employed, proceeding from city to county to township to village to household. Specifically, 14 cities across Guangxi were selected, followed by the random selection of 1–2 counties from each city, 6–8 townships from each county, 2–3 villages from each township, and 7–11 households from each village. A member of the village committee familiar with village affairs represented each village in the village-level questionnaire survey, while one household member represented each selected household in the household-level survey. The research team completed data collection in December 2023. During the questionnaire survey, households were screened based on irrigation system usage. Surveyors initially asked respondents whether they used an irrigation system; only households who answered affirmatively were included in the subsequent data collection. Consequently, the sample households in this study are all users of irrigation systems. The final sample consists of 447 irrigation-dependent households from 114 villages across 14 cities in Guangxi, China. Figure 1 displays the geographical distribution of the study sample.

4.2. Variable Selection

There are two methods commonly used to measure collective action in irrigation. The first is the process method, which assesses collective action by examining its procedural aspects. For example, the number of successfully organized collective activities (such as collective lobbying or maintenance of irrigation channels) can be used to gauge the effectiveness of collective action [56]. This method places more emphasis on observing the organizational process, participation levels, and outcomes of activities related to irrigation collective action. The second method is the output method, which evaluates collective action based on its outcomes. For instance, the condition of irrigation infrastructure can be used to assess the success of collective action [57]. This approach focuses on observing the physical state of the irrigation system or facilities and how well they perform their intended functions. Referring to the study by Fujiie et al. (2005), which used the degree of farmer participation as a measure of collective action, we have used the question “In the past five years, has your household participated in collective maintenance of the village irrigation channels?” (0 = No, 1 = Yes) as a dependent variable to measure the capacity for collective action in irrigation [56]. Research on collective action often uses irrigation maintenance as a key indicator (see Ostrom, 1990; Bardhan, 1993; Araral, 2009; Wang and Wu, 2018; Wang et al., 2019) [2,58,59,60,61]. In rural China, irrigation serves as the foundation of agricultural production, and the irrigation system is a widespread public facility. The competitive nature of the resource units and the non-exclusivity of the system require farmers to collaborate in order to maintain the effective functioning of the irrigation infrastructure. Consequently, a variety of factors influencing collective action come into play during the irrigation maintenance process, and these factors are central to research on collective action [62]. The core independent variables of this study are the use of digital technologies and labor migration. On one hand, this study measures the use of digital technologies by the indicator “Do you use the internet to obtain agricultural production information?” The internet, as one of the most prominent digital technology platforms today, serves as an effective representation of the extent to which farmers accept and apply digital technologies, based on its usage frequency and scope. On the other hand, this study measures labor migration using the indicator “The ratio of the number of individuals in the respondent’s village who worked outside for more than six months in 2022 to the total labor force of the village.” The number of individuals who worked outside for over six months directly reflects the extent of labor migration in rural areas, and serves as a critical indicator of changes in rural demographic structures.

In addition to labor migration and the use of digital technologies, rural collective action is primarily shaped by natural geographic characteristics, the economic and social characteristics of the involved actors, and the institutional rules within the governance system (GS) [63,64]. To account for the effects of these factors, it is necessary to incorporate them into the regression model. Specifically, this study draws on the research of McGinnis and Ostrom (2014), Wang et al. (2016), and Zang et al. (2019), using control variables based on three categories of factors that influence the formation of collective action: natural geographical features, economic and social attributes, and general systems and rules [6,14,46]. Additionally, we include province indicator variables to control for the impact of provincial differences on the empirical results.

Table 1. Selection of observed variables and descriptive statistics (data source: data collected through research conducted by the author’s team).

Variables	Description	Mean	SD
Dependent variable	N = 447
PCM	In the past five years, has interviewee’s household participated in the collective maintenance of village irrigation canals? 0 = No; 1 = Yes (Binary variable)	0.586	0.493
Core independent variable
PWO	Proportion of the population working outside the village or county for more than six months (Continuous variable)	0.409	0.292
VUS	Does interviewee use the internet to obtain agricultural production information? 0 = No; 1 = Yes (Binary variable)	0.924	0.265
Natural conditions
OT	Village longitude (Continuous variable)	102.041	23.495
AT	Village latitude (Continuous variable)	30.824	23.917
FL	Does interviewee’s farmland lie on flat land? 0 = No; 1 = Yes (Binary variable)	0.803	0.398
SL	Does interviewee’s farmland lie on sloped land? 0 = No; 1 = Yes (Binary variable)	0.454	0.498
KT	Is the village located in a karst landscape? 0 = No; 1 = Yes (Binary variable)	0.121	0.326
Economic and social attributes
GR	Gender of interviewee 0 = Male; 1 = Female	0.611	0.488
VA	Age of interviewee (Continuous variable)	48.781	11.696
VPI	Per capita net income of the village at the end of 2022 (Unit: RMB) (Continuous variable)	1.425	0.883
VFI	Total income of interviewee’s household in 2022 (Unit: RMB) (Continuous variable)	7.956	10.780
CE	Collective economic income of the village in 2022 (Unit: 10,000 RMB) (Continuous variable)	14.345	121.340
institutional rules
SORR	Does interviewee monitor others’ compliance with village rules and regulations? 0 = No; 1 = Yes (Binary variable)	0.794	0.405
LRPN	To what extent is interviewee familiar with local government policies on rural environmental management? 1 = Not very familiar; 5 = Very familiar (Ordinal variable)	3.468	1.075
IWF	How fair is the distribution of irrigation water for farmland in the village? 1 = Not very fair; 5 = Very fair (Ordinal variable)	4.018	1.096
SDR	How willing is interviewee to actively stop others from damaging the environment? 1 = Not very willing; 5 = Very willing (Ordinal variable)	4.179	0.984
ROR	In the past five years, has the village undergone reconstruction or restoration? 0 = No; 1 = Yes (Binary variable)	0.729	0.445

outlines the selection of variables and presents the results of the descriptive statistical analysis.

5. Results

5.1. Baseline Regression Results

This study utilizes Stata 15.0 software and applies an ordered probit regression model to investigate whether the use of digital technologies can mitigate the negative impact of labor outflow on collective action. In order to assess this, the interaction term between labor outflow and the use of digital technologies, represented as LO*VUS (Labor Outflow * Use of Digital Technologies), is incorporated into the model. The estimated results are presented in

Table 2. Selection of observed variables and descriptive statistics.

	(1)	(2)	(3)	(4)	(5)
	Probit	Probit	Probit	Probit	IV-Probit
Core independent variable
LO	−5.649 **	−5.720 **	−5.533 **	−5.719 **	−4.015 ***
	(2.540)	(2.660)	(2.708)	(2.645)	(1.490)
VUS	−0.582	−0.554	−0.621	−0.680	−0.682
	(0.455)	(0.474)	(0.485)	(0.471)	(0.296)
Interaction term
LO*VUS	3.094 **	3.099 **	3.061 **	3.158 **	2.124 ***
	(1.288)	(1.350)	(1.368)	(1.338)	(0.751)
Natural conditions
OT		0.032	0.040	0.042	0.023
		(0.047)	(0.048)	(0.049)	(0.019)
AT		0.022	0.030	0.031	0.020
		(0.048)	(0.048)	(0.049)	(0.019)
FL		−0.067	−0.108	−0.110	−0.040
		(0.161)	(0.163)	(0.165)	(0.062)
SL		−0.251	−0.252	−0.279	−0.110
		(0.205)	(0.207)	(0.212)	(0.079)
KT		0.352	0.399 *	0.372	0.109
		(0.234)	(0.239)	(0.235)	(0.090)
Economic and social attributes
GR			−0.048	−0.093	−0.039
			(0.143)	(0.145)	(0.053)
VA			−0.006	−0.004	−0.002
			(0.006)	(0.006)	(0.002)
VPI			−0.178 *	−0.153	0.122
			(0.093)	(0.094)	(0.075)
VFI			0.005	0.003	0.001
			(0.006)	(0.006)	(0.002)
CE			0.001	0.001	0.000 **
			(0.001)	(0.001)	(0.000)
Institutional rules
SORR				0.223	0.096
				(0.175)	(0.066)
LRPN				0.104	0.041
				(0.067)	(0.025)
IWF				0.006	−0.013
				(0.060)	(0.024)
SDR				0.028	0.023
				(0.073)	(0.028)
ROR				−0.075	−0.057
				(0.163)	(0.064)
Region	Control	Control	Control	Control	Control
Observations	447	447	447	447	447
p	0.000	0.000	0.000	0.000	0.000

Notes: Robust standard errors are shown in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

. Model 1 illustrates the effect of the interaction between digital technologies and labor outflow on collective action, while accounting for regional variations. The results reveal that the coefficient for labor outflow is negative and statistically significant, indicating that labor outflow has a substantial negative impact on collective action. Moreover, the interaction term between digital technologies and labor outflow is positive and statistically significant, suggesting that the use of digital technologies can alleviate the detrimental effect of labor outflow on collective action. Models 2, 3, and 4 display the effects of the interaction between digital technologies and labor outflow on collective action after sequentially adding control variables for natural geographical features, economic and social attributes, and general systems and rules. The results indicate that in all models, the coefficient for labor outflow remains negative and statistically significant, underscoring that labor outflow continues to exert a significant negative effect on collective action even when control variables are introduced. Furthermore, in all models, the interaction term between digital technologies and labor outflow remains positive and statistically significant, demonstrating that the use of digital technologies continues to mitigate the negative impact of labor outflow on collective action, even after adjusting for the control variables.

5.2. Endogeneity Issue Test

When dealing with ordered probability regression models, endogeneity issues, represented by omitted variables and reverse causality, may lead to biased estimation results. Specifically, on the one hand, there is a lack of control for changes in related ecosystems (ECO), such as changes in climate conditions (ECO1), which could have significant impacts. Previous studies have pointed out that changes in climate conditions not only affect the collective action capacity for irrigation within regions, but also increasingly become an important factor driving population movement between regions [65]. On the other hand, rural collective action capacity is closely related to the adoption and promotion of complex large-scale agricultural technologies [66]. Therefore, villages with lower collective action capacity are inevitably constrained by the insufficient development of modern agricultural production technologies, which results in generally lower incomes for farmers, making them more willing to migrate to places with higher wages. Thus, a village with a lower collective action capacity is more likely to experience labor outflow. To address the endogeneity issue arising from omitted variables and reverse causality, this study employs an instrumental variable method for further testing, aiming to eliminate the interference of endogeneity on the empirical results.

Wooldridge (2015) suggests that an effective instrumental variable must satisfy the following three conditions: first, it must be correlated with the independent variable; second, it must be independent of the dependent variable, meaning its effect should not be influenced by other confounding factors; third, it must affect the dependent variable only through its effect on the independent variable, without directly influencing the dependent variable [67].

To resolve the potential endogeneity issues in the baseline model, this paper constructs two instrumental variables for labor outflow: “(number of people working outside the county for more than six months/total labor force) * (distance from the village committee to the county government)” and “(number of people working outside the county for more than six months/total labor force) * (distance from the village committee to the municipal government)”. These two instrumental variables represent the average distance each labor force member in the village travels for work. This is because the distance traveled by laborers for work may be correlated with the proportion of laborers who migrate. Farther work destinations may lead workers to perceive the distance as too great, making it difficult to migrate, and thus they are less willing to leave. The distance traveled by laborers for work has no direct causal relationship with collective action within the village or with unobserved factors that may affect the outcome.

Table 2 presents the estimation results after the introduction of instrumental variables in Model 5. The test for weak instruments indicates that the Cragg–Donald Wald F statistic is 16.680, which adheres to the rule that the F-statistic should exceed 10 [67]. This suggests that the instrumental variables selected in this study are strongly correlated with the core independent variables, and there is no evidence of weak instruments. Furthermore, to examine whether the instrumental variables are uncorrelated with the random error term, an over-identification test was conducted. The results show that the p-value of the Sargan statistic is 0.830 (>0.1), meaning that we fail to reject the null hypothesis that the instrumental variables are uncorrelated with the random error term. Thus, it can be concluded that the chosen instrumental variables are unrelated to other unobserved factors. In Model (5), the coefficient for labor outflow is negative and statistically significant, indicating that labor outflow has a significant negative effect on collective action. Similarly, the interaction term between digital technology and labor outflow is positive and statistically significant, suggesting that the use of digital technology can mitigate the negative impact of labor outflow on collective action. Therefore, Hypotheses H1 and H2 are supported.

5.3. Robustness Test

We conduct a robustness test by substituting different dependent variables to further validate whether the use of digital technologies can mitigate the negative impact of labor outflow on collective action. Specifically, we use the “degree of villagers’ participation in collective discussions about farmland irrigation water distribution” and the “degree of participation in collective discussions regarding the maintenance of farmland irrigation facilities” as alternative dependent variables to re-examine the previous regression results. Additionally, we incorporate “the degree of villagers’ involvement in household wastewater treatment” to explore whether collective actions beyond those related to irrigation are also influenced by labor outflow and the use of digital technologies.

Table 3. Robustness test analysis of labor outflow.

Variables	(6)	(7)	(8)
	Irrigation Water Distribution	Irrigation Facility Repair	Domestic Wastewater Treatment
LO	−1.972 **	−2.372 ***	−2.755 ***
	(0.882)	(0.872)	(0.760)
VUS	−0.293	−0.399	−0.809
	(0.289)	(0.287)	(0.305)
LO*VUS	0.903 **	1.171 ***	1.165 ***
	(0.440)	(0.455)	(0.377)
Control variables	Controlled	Controlled	Controlled
Province control	YES	YES	YES
p	0.000	0.000	0.000
N	447	447	447

Notes: Robust standard errors are shown in parentheses; *** p < 0.01, ** p < 0.05.

presents the estimation results after accounting for regional differences and including relevant control variables.

The results indicate that in models (6), (7), and (8), the coefficients for labor outflow are negative and statistically significant, suggesting that labor outflow significantly hinders collective action. Moreover, the interaction terms between digital technology use and labor outflow are positive and statistically significant, indicating that digital technologies help alleviate the negative impact of labor outflow on collective action.

5.4. Heterogeneity Analysis

In light of the confirmed negative impact of labor outflow on rural collectives and the mitigating effect of digital technology use, this study conducts a heterogeneity analysis to explore the variation in these effects under different circumstances, based on multi-dimensional characteristics. As illustrated in Figure 1, the heterogeneity scenarios are categorized as follows: In terms of natural geographic features, we analyze whether the area is characterized by plains. Regarding socio-economic factors, we consider the presence of distinctive cultural resources and the level of collective economic income as the basis for grouping. At the institutional level, we examine whether the village has undergone re-planning and whether it has implemented technology promotion as criteria for grouping.

Table 4. Heterogeneity analysis.

Variables	(1)		(2)		(3)		(4)		(5)
	With Plains	Without Plains	With Distinctive Cultural Resources	With Distinctive Cultural Resources	Collective Income: High	Collective Income: Low	Re-Planning	Not Re-planning	Promoting Technology	Not Promoting Technology
LO	−11.443	−3.328 ***	−4.566 *	−2.220	−3.898 ***	−2.209	−10.552 **	−0.530	−5.000 ***	2.847
	(14.792)	(1.291)	(2.499)	(1.795)	(1.424)	(4.620)	(4.228)	(0.581)	1.816	(3.472)
VUS	−1.674	−0.493 **	−0.552	−0.371	−0.762 **	−0.288	−1.341 **	−0.037	−0.845 **	−.0152
	(2.345)	(0.248)	(0.400)	(0.406)	(0.344)	0.674	(0.612)	(0.204)	0.385	0.331
LO*VUS	5.644	1.684 ***	2.386 *	1.205	1.934 ***	1.214	5.349 **	0.202	2.554 ***	−1.051
	(7.347)	(0.614)	(1.264)	(0.888)	(0.697)	(2.344)	(2.114)	(0.272)	0.877	1.672
N	129	318	190	257	250	197	340	107	345	102
Control variables	Controlled		Controlled		Controlled		Controlled		Controlled
Province	Controlled		Controlled		Controlled		Controlled		Controlled
p > chi-squared	0.000		0.000		0.000		0.000		0.000
N	447		447		447		447		447

Notes: Robust standard errors are shown in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

show the results of geterogeneity analysis. In the group of regions with or without plains, in the plain areas, the coefficient of labor outflow is negative but statistically insignificant, while the interaction term between digital technology and labor outflow is positive and statistically insignificant. In contrast, in non-plain areas, the coefficient of labor outflow is negative and statistically significant, indicating that labor outflow has a significant negative impact on collective action. Meanwhile, the interaction term between digital technology and labor outflow is positive and statistically significant, suggesting that the use of digital technology in non-plain areas can alleviate the negative effects of labor outflow on collective action. The reasons for these results may be as follows: Plain areas typically have open terrain and relatively developed transportation networks, which to some extent facilitate the natural dissemination of information and interaction among people. Even in the presence of labor outflow, the original, relatively convenient communication channels and close geographical ties may allow collective action to maintain a certain degree of operation without deep intervention from digital technology, thus making the role of digital technology in alleviating the negative effects of labor outflow on collective action less pronounced. In contrast, non-plain areas tend to have complex terrain and inconvenient transportation, which severely restricts traditional information exchange and interpersonal interaction. After labor outflow, the internal connections within villages become more fragile. The introduction of digital technology significantly compensates for geographical communication barriers, allowing outflow labor to participate in the decision-making, organization, and implementation of village collective action through online communication platforms, remote collaboration tools, and others, thus significantly alleviating the impact of labor outflow on collective action.

Among the four groups based on the presence of characteristic cultural resources, the level of collective economic income, whether the village has been re-planned, and whether the village has promoted technology, the results indicate that in the groups characterized by the presence of distinctive resources, high collective economic income, re-planned villages, and those that have implemented technological promotion, the coefficient for labor migration is negative and statistically significant. This suggests that labor migration has a significant negative effect on collective action. Furthermore, the interaction term between digital technology and labor migration is positive and statistically significant, indicating that the use of digital technology can mitigate the negative impact of labor migration on collective action. However, in the groups characterized by the absence of distinctive resources, low collective economic income, unplanned villages, and those where technology has not been promoted, both the coefficient for labor migration and the interaction term between digital technology and labor migration are not statistically significant. A possible explanation for this is that in these villages with poorer conditions, collective action faces more complex and diverse challenges, which diminishes the prominence of the effects of labor migration and digital technology. Additionally, in villages with more favorable conditions, the negative impact of labor migration on collective action is more pronounced, yet the use of digital technology can alleviate this negative effect. This suggests that the detrimental impact of labor migration on collective action emerges during the development process, while the adoption of digital technologies, which is also promoted during development, can help alleviate this social dilemma.

6. Discussion

The existing literature on the relationship between labor mobility and collective action predominantly highlights the significant negative impact of labor outflow on rural collective action [6]. The empirical results in this study, based on samples from underdeveloped regions in western China’s border areas, reinforce the validity of these findings. This further underscores that the labor outflow induced by urbanization has a far-reaching and profound impact on rural social governance in China. Such findings deepen our understanding of the challenges faced by developing and underdeveloped regions during the urbanization process.

While there is a general consensus in the literature regarding the negative effects of labor outflow on rural collective action, recent studies have also begun to explore ways to mitigate or even reverse these adverse effects. For instance, Su et al. (2020) demonstrated that moderate land transfer can help reduce the negative impact of labor outflow on rural collective action [7]. Overall, most of the limited studies in this area focus on traditional factors, such as land, to address the issue of labor outflow. In contrast, this study introduces the perspective of emerging factors, specifically digital technology, and shows that the adoption of such technologies can also alleviate the negative consequences of labor outflow.

Based on the observation that digital technology, as an emerging factor, can mitigate the negative effects of labor outflow, a more intriguing topic can be discussed. Studies by Wang et al. (2016) and Wang et al. (2022) suggest that labor outflow, driven by urbanization, negatively impacts rural collective action [6,68]. This implicitly points to a paradox: while urbanization, a key driver of labor outflow, significantly undermines rural collective action, it is also an essential process for economic growth and prosperity in developing countries. Given that urbanization is both irreversible and unavoidable, does this mean that the decline in rural collective action due to labor outflow is an inevitable fate that all developing nations must accept? This paradox is addressed in this paper through the lens of digital technology, which, like labor outflow, emerges from the process of urbanization. Existing research has established that labor outflow is a direct consequence of urbanization [69,70]. Furthermore, digital technology, as an emerging factor, is a result of the ongoing urbanization process and contributes to economic and social development [71]. Therefore, this paper’s finding—that digital technology can alleviate the negative impacts of labor outflow—holds significant implications. It suggests that developing countries can leverage further urbanization to address challenges that are themselves products of the urbanization process.

Nevertheless, the negative impact of labor outflow on rural collective action remains a critical issue. While digital technology holds promise in alleviating this challenge, the empirical results in this paper show that it only partially mitigates the negative effects of labor outflow, without fully reversing them. Therefore, further research should explore additional strategies to address the adverse effects of labor outflow, aiming to develop a comprehensive governance framework to strengthen rural collective action capacity.

The empirical findings in this paper also highlight a conclusion that warrants further exploration: in villages located outside the plains, or those with distinctive resources, relatively high collective economic income, undergoing village re-planning, and promoting new seed technologies, the influence of digital technology on collective action is also shaped by labor migration. Specifically, when labor migration is low, digital technology tends to have a negative effect on rural collective action. This may be due to the fact that villagers have long-established, stable collective action patterns and communication practices, relying on face-to-face interactions and traditional organizational structures. When digital technology is introduced, villagers may struggle to adjust to new digital communication platforms, leading to issues such as poor coordination, delayed responses, and miscommunication during the organization and implementation of collective actions. As a result, this can reduce the efficiency and effectiveness of collective action, demonstrating a negative impact [72]. In some cases, the time and effort required to adapt to new technologies may even disrupt the usual rhythm of collective action, further exacerbating negative outcomes. Another reason may be that, in collective actions like village re-planning, which involve the interests of many villagers, face-to-face communication is critical for building and maintaining trust. Digital communication, in contrast, lacks the depth of emotional exchange and immediate feedback necessary for fostering trust. This can lead to skepticism about the information conveyed through digital platforms or the collective action initiatives proposed, hindering the success of these efforts [73,74]. On the other hand, when labor migration reaches a higher level, the continuous outflow of workers significantly enhances the role of digital technology in facilitating collective action. This is primarily because, in areas with substantial labor migration, the erosion of traditional social structures and networks makes villagers more reliant on digital tools to establish new connections and cooperation mechanisms. Additionally, the widespread use of digital technology mitigates the geographical barriers to collective action, enabling villagers to organize more easily and address public affairs collectively [75]. In areas with higher levels of migration, digital media can efficiently communicate the goals, plans, and benefits of collective action to members who are dispersed across different regions. Furthermore, digital technology enables organizers to coordinate resources and personnel more effectively, as migrant members often have diverse skills, resources, and schedules. The use of digital tools allows organizers to better integrate these resources and allocate tasks appropriately. As the extent of labor migration increases, the geographic distribution of participants in collective action becomes more widespread, amplifying the network effects of digital tools. Finally, when labor migration is extensive, governments are more likely to support the promotion of digital technologies to strengthen connections with migrant populations, thus enhancing the frequency and effectiveness of digital technology use.

Relevant research indicates that the existence of coworking spaces in rural areas is expected to alleviate the negative impact of labor outflow on collective action, reflecting the role of physical space in addressing rural governance issues [76,77]. The conclusion of this paper, suggesting that digital technologies can also mitigate the negative effects of labor outflow on collective action, essentially extends the promoting function of physical space in rural governance to the online space. This, in turn, represents an expansion of the previous narrow understanding of coworking spaces in earlier studies, while also demonstrating that even without physical spaces for coworking, people can still establish low-cost and more convenient coworking E-spaces through the use of digital technologies, thus contributing to the resolution of rural governance challenges.

7. Conclusions, Implications and Limitations

This paper, using irrigation collective action as a case study, empirically demonstrates that rural labor migration negatively impacts collective action in rural areas. Through a thorough analysis of the moderating role of digital technology in the relationship between labor migration and rural collective action, this study finds that digital technology can, to a certain extent, mitigate the negative effects of labor migration on rural collective action. Based on previous research by scholars on the pathways through which labor migration affects irrigation collective action [68], it can be inferred that the use of digital technology enhances communication among villagers, thereby improving village leadership. Digital technology also helps accumulate social capital, strengthening the social networks within the village. Furthermore, it fosters a greater sense of belonging to the village, increases dependence on local resources, and reduces economic inhibitions among villagers, ultimately promoting the development of collective action.

From a policy perspective, in economically developed regions, the government should prioritize investments in digital infrastructure, such as broadband networks and mobile communication base stations, to expand the reach and application of digital technologies in rural areas. In contrast, in less economically developed regions, while addressing the complex challenges faced by rural communities, the government should also enhance efforts to promote and raise awareness about the use of digital technologies. This includes the development of digital platforms and applications tailored to rural needs, such as online education, telemedicine, and agricultural e-commerce platforms, all aimed at improving production efficiency and the quality of life in these areas. Furthermore, the government should utilize digital platforms, such as social media and online forums, to foster greater connectivity among rural residents, facilitate information sharing, and support collective decision-making, thus strengthening the collective action capacity of rural communities. It is also important to encourage increased collaboration among various stakeholders to jointly promote the digital transformation of rural areas and support the broader goal of rural revitalization.

This paper does have certain limitations. Due to resource constraints, this research is limited to the Guangxi Zhuang Autonomous Region of China. Future studies could build upon these findings by incorporating a more diverse set of data to further validate the conclusions drawn.