Reconfiguring Urban–Rural Systems Through Agricultural Service Reform: A Socio-Technical Perspective from China

Abstract

The transition toward integrated urban–rural development represents a complex socio-technical challenge in post-poverty alleviation China. This study examines how the reform of agricultural service systems—especially the rollout of full-process socialization services—reshapes urban–rural integration by embedding new institutional, technological, and organizational structures into rural production. Drawing on staggered provincial pilot programs, we apply a double machine learning framework to assess the causal impact of service reform on the urban–rural income gap, labor reallocation, and agricultural productivity. Results show that agricultural socialization services enhance systemic efficiency by reducing labor bottlenecks, increasing technology diffusion, and fostering large-scale coordination in agricultural operations. These effects are most pronounced in provinces with stronger institutional capacity and higher levels of mechanization. The findings highlight agricultural service reform as a systemic intervention that alters resource allocation logics, drives institutional change, and fosters structural convergence across urban and rural domains. This research contributes to the understanding of agricultural modernization as a systems-engineered solution for regional inequality.

1. Introduction

Following the official eradication of extreme poverty, the restructuring of China’s urban–rural relationship continues to face deep-seated institutional and structural challenges. While rural living standards have steadily improved, substantial disparities in income levels, infrastructure development, and access to public services persist between urban and rural areas. These disparities pose a substantial barrier to the pursuit of common prosperity. According to official statistics, in 2024, the per capita disposable income of urban residents in China was RMB 54,188, compared to RMB 23,119 for rural residents, yielding an urban–rural income ratio of 2.34:1. In Beijing, the ratio stood at 2.32:1, underscoring the continued significance and structural nature of the income divide.

One of the central barriers to integrated urban–rural development lies in the disconnection between the dominant smallholder-based agricultural production model and the institutional requirements of a modern agricultural system. Smallholder agriculture in China is commonly characterized by fragmented production, weak organizational capacity, and limited access to technology, all of which constrain agricultural productivity and rural income growth. In response, since 2013, China has adopted a centrally coordinated, provincially piloted approach to gradually establish a comprehensive agricultural socialized service system. This system seeks to restructure rural production through standardized operations, mechanization, and platform-based coordination. These services span the entire agricultural process—from plowing and planting to plant protection, harvesting, processing, and distribution—and aim to enhance production efficiency, reduce transaction costs, and strengthen the connection between smallholders, markets, and technologies.

This study conceptualizes agricultural socialization services as an institutional mechanism for the restructuring of socio-technical systems. Rather than being merely an efficiency-enhancing tool on the production side, these services function as a strategic pathway through which institutional intervention, organizational innovation, and resource reallocation are mobilized to restructure urban–rural relations and optimize the patterns of income distribution. As such, the agricultural service system not only reshapes the technological infrastructure of rural areas but also fundamentally transforms the institutional boundaries and factor mobility between urban and rural domains.

It is important to emphasize that the reform of agricultural socialization services is not an isolated policy intervention, but rather a core component of China’s broader state-led strategy for spatial reorganization and the optimization of population mobility. In recent years, the Chinese government has actively promoted the reform of the hukou (household registration) system, the construction of a national territorial spatial planning framework, and the advancement of a new urbanization strategy. These efforts form part of an integrated set of institutional instruments—including agricultural policies, land systems, and migration mechanisms—through which the state seeks to guide the orderly restructuring of socio-spatial configurations and establish a more balanced regional development pattern with optimized factor allocation [1]. Against this backdrop, the institutionalization and policy expansion of agricultural socialization services should be understood as a critical element within China’s overarching system of state-led socio-spatial restructuring.

Against this background, this study makes three key contributions to the fields of rural development and income inequality research. Theoretically, it reconceptualizes agricultural socialization services as an institutionalized mechanism for restructuring socio-technical systems in rural China, rather than merely an instrumental tool for enhancing production efficiency. By introducing the perspectives of platform governance and institutional embeddedness, the study demonstrates how the organic integration of state intervention, technological deployment, and organizational coordination reshapes rural production relations and agricultural governance structures, effectively bridging institutional divides between urban and rural areas. This framework expands the institutional boundaries of agricultural modernization research and enriches the theoretical understanding of state capacity in driving rural transformation. Methodologically, this study applies the Double Machine Learning (DML) approach to identify the causal effects of agricultural service expansion under staggered policy pilot implementation. Compared with traditional methods such as Propensity Score Matching-Difference-in-Differences (PSM-DID) or linear fixed effects models, the DML framework offers significant advantages in addressing endogeneity, high-dimensional covariates, and nonlinear confounding variables. It enhances the robustness and credibility of causal inference in complex policy environments and provides a replicable empirical strategy for evaluating spatially targeted interventions. Empirically, the paper investigates three mediating mechanisms—technological diffusion, agricultural scale consolidation, and human capital mobility—through which agricultural socialization services affect urban–rural income disparities. In addition, it explores heterogeneity across regions with differing functional roles, institutional foundations, and service capacities. Rather than limiting the analysis to average treatment effects, the study emphasizes how, why, and under what conditions these services reduce inequality. This approach yields policy-relevant insights into the poverty-reduction and income-enhancing potential of agricultural service reform across diverse regional and institutional contexts.

2. Literature Review

2.1. Advancements in Research on Agricultural Socialization Services

Agricultural socialization services refer to agricultural-related social organizations providing comprehensive production services, including planting, breeding, processing, and transportation, to agricultural producers based on market demand. The emergence and development of such services have become a focal point for scholars both domestically and internationally, with research primarily focusing on their impact on farmers’ income, production efficiency, and ecological benefits. Existing studies indicate that agricultural socialization services have significantly increased farmers’ income. Based on national micro-survey data, Several scholars found that mechanization services notably increased per capita income among farm households, with the most pronounced benefits observed among low- and middle-income groups. These services also contributed to a reduction in intra-rural income inequality [2]. In addition, other studies have confirmed that mechanization services improve farmers’ income and production incentives by substituting labor and reducing production costs [3,4,5,6,7]. Research conducted by scholars from the Chinese Academy of Sciences further indicates that with the expansion of the socialized service market and the implementation of agricultural machinery purchase subsidies, the number and coverage of such service organizations have significantly increased nationwide. This expansion has facilitated the transfer of rural labor to non-agricultural sectors, thereby boosting farm household incomes [2].

In terms of enhancing agricultural production efficiency, agricultural socialization services have optimized resource utilization and improved output quality. Based on empirical econometric modeling, Several scholars found that agricultural socialization services effectively improved the ecological efficiency of farmland by reducing pollution emissions and input consumption, while maintaining stable output levels. The impact of hired labor services was particularly significant in major grain-producing regions, whereas the effect of machinery rental services in non-grain areas appeared to be relatively limited. Mechanism analysis further revealed that these services promoted a more specialized division of labor within the agricultural sector, which contributed approximately 11.4% to improvements in ecological efficiency. Accordingly, the expansion of large-scale service provision and the promotion of green mechanization are conducive to advancing sustainable agricultural development [8].

Moreover, agricultural socialization services play a critical role in promoting the transition to green production. Studies have shown that such services encourage farmers to adopt green technologies and inputs by replacing manual labor with mechanization and standardizing production processes. This transformation facilitates a shift in agricultural production toward energy conservation, emission reduction, and environmentally friendly practices [8]. Tao and Zhao (2024) noted that the pilot program of full-process socialization services contributed to ensuring food security by reducing production costs, promoting the development of new agricultural business entities, and increasing farmers’ incomes [9]. Related research based on provincial panel data in China confirmed that socialization services significantly enhance the stability of grain yields, with more pronounced effects observed in major grain-producing and agriculturally developed regions. Taken together, existing studies have strongly affirmed the role of agricultural socialization services in improving agricultural efficiency, output, and farm household income, thereby creating favorable conditions for urban–rural integration and common prosperity.

Recent studies emphasize that agricultural socialization services also serve as an important institutional vehicle for integrating smallholder farmers into organized production networks and transforming both production relations and resource allocation. Service-oriented agricultural reforms have not only advanced agricultural specialization, scale, and digitalization but have also effectively facilitated the linkage between “small-scale production” and the “large-scale market” [10]. Furthermore, empirical research on service-driven technology diffusion suggests that the “service–technology–benefit” cycle not only enhances agricultural ecological efficiency but also constitutes a critical mechanism through which agricultural socialization services indirectly narrow the urban–rural income gap via technological progress [11].

2.2. Advancements in Research on the Urban-Rural Income Gap

Research on the urban–rural income gap has primarily focused on measurement methods, influencing factors, and dynamic evolution. Since the launch of the reform and opening-up policy, income levels for both urban and rural residents in China have increased, though the income gap initially widened before gradually narrowing. Thanks to targeted poverty alleviation and rural revitalization strategies, the urban–rural income ratio has declined steadily in recent years, with rural incomes experiencing continuous growth. According to reports from the relevant authorities, the overall urban–rural income gap has continued to narrow; however, regional disparities and intra-rural inequalities remain pronounced. Scholars generally agree that structural factors, institutional arrangements, and unequal resource allocation are the root causes of the urban–rural income gap. The persistence of the dual urban–rural system, industrial structural divergence, and imbalanced factor endowments all contribute to the existing income distribution pattern. Additional critical factors include low levels of rural human capital, weak education and infrastructure systems, limited industrial spillover effects, and disparities in financial and social security access between urban and rural areas. Labor migration to cities, land system reform, regional coordinated development, and the expansion of agricultural socialization services have all been identified as effective approaches to mitigating urban–rural inequality [12,13]. Utilizing a double machine learning approach, Lu et al. (2025) found that farmland transfer not only facilitated urban–rural integration but also significantly reduced income disparities [14]. The structure of urban–rural income distribution is jointly shaped by multiple variables, including labor mobility, land transfer, government subsidies, and socialized service provision.

In terms of measurement methods, both domestic and international studies primarily employ indicators such as the Gini coefficient, the Theil index, and the urban–rural income ratio [13,15]. Among these, the Theil index and the urban–rural income ratio are particularly effective in capturing both absolute and relative structural disparities. The study by Wang and Ouyang integrated the income ratio with the Theil index to evaluate the income gap and further analyzed its implications for economic growth [15]. These measurement tools facilitate a multi-dimensional understanding of distributional imbalances.

The urban–rural income gap is not merely a structural phenomenon; rather, it is closely tied to institutional arrangements and resource misallocation. As argued by Peng et al. (2022), the integration of agricultural socialization services helps dismantle the barriers imposed by traditional production relations and optimizes resource distribution, thereby supporting the theoretical view that institutional design lies at the root of urban–rural income disparities [16]. Furthermore, a growing body of empirical research has identified technological progress, land scale operations, and human capital accumulation as key intermediary channels through which policy reforms influence income distribution [2,17,18,19,20,21,22,23,24,25]. For example, Lu et al. (2025) found that farmland transfer policies promote urban–rural integration by facilitating scale operations and labor mobility [14], while Yang et al. (2023) demonstrated that human capital upgrading accelerates the transfer of rural labor to non-agricultural sectors, thereby indirectly narrowing the income gap [26].

In summary, the existing literature has laid a solid foundation for uncovering the multifaceted causal mechanisms underlying the urban–rural income gap, while also highlighting the importance of an integrated analysis that considers institutional, technological, and organizational factors—an approach that aligns closely with the empirical framework adopted in this study.

2.3. Research on the Impact of Agricultural Socialization Services on the Urban–Rural Income Gap

In recent years, an increasing number of scholars have focused on how agricultural socialization services influence the urban–rural income gap through multiple mechanisms. Existing studies have not only highlighted the direct effects of such services but also systematically traced the causal pathways through which they operate—particularly via technological progress, scale operations, and labor reallocation. The following section reviews and evaluates the current literature from these three perspectives, with the aim of laying a solid foundation for the theoretical framework and empirical analysis presented in subsequent sections.

From the perspective of the technological progress mechanism, a large body of research has shown that agricultural socialization services significantly enhance rural production efficiency by accelerating the diffusion and application of agricultural technologies, thereby indirectly narrowing the urban–rural income gap. Several scholars found that socialization services serve as vital carriers for the dissemination of new agricultural technologies, helping smallholder farmers overcome barriers related to knowledge and financial constraints. These services effectively improve total factor productivity (TFP), which in turn raises rural household incomes [8]. From a theoretical standpoint, Peng et al. (2022) further argued that agricultural socialization services can facilitate a transformation in agricultural production relations through technological advancement, leading to a more optimal allocation of production factors [16]. Moreover, with the growing adoption of digital and smart farming technologies, the role of socialization services in driving technological progress has become even more pronounced. This transformation enables rural labor to be freed from repetitive manual tasks and redirected toward higher value-added activities, thus creating additional momentum for narrowing the income gap between urban and rural areas [2]. Technological advancement has, therefore, emerged as one of the core pathways linking agricultural socialization services to improvements in the urban–rural income distribution.

With respect to the scale operation mechanism, agricultural socialization services play a critical role in narrowing the urban–rural income gap by promoting the large-scale consolidation of land and other production factors. Empirical evidence from Ma et al. (2023) indicates that the introduction of socialization services accelerates farmland transfer, enabling more concentrated land use, which in turn leads to economies of scale and improvements in both per-unit output and farmers’ incomes [10]. In addition, service organizations—through entrusted management and cooperative arrangements—have enhanced farmers’ bargaining power in the market, thereby facilitating a deeper integration of smallholders into formal market systems [16]. Using a double machine learning approach, Several scholars confirmed that scale operation serves as a key mediating variable through which agricultural socialization services affect the structure of urban–rural income distribution, with particularly pronounced effects in major grain-producing and highly mechanized regions [14]. The scale mechanism is reflected not only in the consolidation of land and service functions but also in the intensification and specialization of the service organizations themselves, further improving production efficiency in rural areas.

At the labor force mechanism level, agricultural socialization services provide an important channel for narrowing the urban–rural income gap by releasing rural labor and facilitating its diversified mobility. The widespread adoption of socialization services has reduced the time and skill barriers associated with agricultural work, enabling a large number of rural laborers to shift into non-agricultural sectors or engage in entrepreneurial activities, thereby expanding income sources for rural households [2]. Changes in variables such as labor mobility and land transfer indicate that socialization services not only enhance the skill levels of the rural labor force but also optimize the allocation of labor between urban and rural areas [26]. In line with the mechanisms discussed above, the labor force pathway functions as a crucial intermediary link in the relationship between socialization services and income inequality, underscoring the practical importance of human capital improvement and structural optimization in promoting common prosperity.

In summary, the existing literature has provided a relatively comprehensive account of the causal pathways through which agricultural socialization services influence the urban–rural income gap, particularly through mechanisms of technological progress, scale operations, and labor mobility. However, research that systematically integrates these mechanisms and conducts rigorous empirical quantification remains limited. Building upon the theoretical framework and mechanism analysis outlined in this study, we aim to further investigate the specific impact channels of agricultural socialization services on the urban–rural income gap through multi-path mediation tests and machine learning techniques, thereby contributing to both theoretical advancement and empirical enrichment in the relevant research domain.

2.4. Dual Machine Learning in Economic Research

Double Machine Learning (DML) is a recent approach in causal inference, introduced by Chernozhukov et al. [27]. This approach integrates high-dimensional machine learning with traditional semiparametric estimation to robustly estimate causal parameters, including policy effects, by effectively managing the bias of high-dimensional covariates through orthogonalization and cross-sample partitioning techniques. Chernozhukov et al. (2018) were the pioneers in introducing this framework within the economics domain for evaluating treatment effects and structural parameters [27]. Yang et al. (2020) utilized the gradient boosting method within a dual machine learning model, effectively addressing the modeling challenges associated with large datasets and attaining favorable outcomes in audit quality [28]. Empirical economics researchers have employed DML to analyze policy effects and derive conclusions regarding causal relationships across various issues.

In empirical economic studies, Double Machine Learning (DML) has been increasingly utilized by researchers for causal inference and policy effect evaluation across a variety of contexts. For instance, Zhang and Li (2023) employ a dual machine learning methodology to analyze the impact of network infrastructure on inclusive green growth and regional development disparities, highlighting the significance of infrastructure investment in coordinating regional development [29]. Existing studies have compared traditional policy evaluation methods such as the Double Machine Learning (DML) model and the Difference-in-Differences (DID) approach, highlighting the advantages of the DML model over previous policy evaluation methods. The DML model employs machine learning algorithms to model high-dimensional covariates and nonlinear relationships, thereby eliminating confounding biases and yielding more accurate estimates of treatment effects. In contrast, the DID model relies on linear assumptions and cannot adequately control for the nonlinear and interactive effects of high-dimensional covariates, potentially leading to bias [27,30]. Conventional linear regression may introduce specification bias and produce less robust estimators, whereas DML leverages the strengths of machine learning algorithms in handling nonlinear data, effectively avoiding model misspecification issues. The DML model utilizes machine learning algorithms to automatically fit high-dimensional and complex nonlinear relationships. The DID model, relying on traditional regression techniques, struggles to handle high-dimensional data and nonlinear terms, making it susceptible to model misspecification errors [27,28]. The machine learning algorithms in DML employ cross-validation and regularization techniques to mitigate the excessive influence of model misspecification on results. In contrast, DID’s heavy reliance on linear assumptions renders it more prone to model instability [27]. The DML method does not depend on the parallel trends assumption because it adjusts for covariate effects through machine learning models and directly estimates treatment effects, thereby exhibiting lower reliance on parallel trends [27]. Additionally, based on the concepts of instrumental variable functions, two-stage predictive residual regression, and sample-splitting fitting, DML can alleviate the “regularization bias” present in machine learning estimates, ensuring an unbiased estimation of treatment effect coefficients even in small samples [29]. For instance, Lu et al. (2025) utilizing a DML-based causal identification method to examine the impact of China’s land transfer policy on urban-rural integration, conducted a comparative analysis of regression results identified by continuous DID and DML models. Their findings revealed that across six different identification strategies, the coefficients of explanatory variables in DML exhibited greater statistical significance with smaller standard errors in terms of robustness and significance, whereas DID exhibited model instability when controlling for complex relationships. When comparing complex settings involving fixed effects and high-dimensional control variables, DML utilized machine learning algorithms to automatically fit high-dimensional and complex nonlinear relationships, while DID demonstrated limited adaptability to complex models. In terms of model specification robustness, DML’s estimation results remained consistent across different strategies (e.g., incorporating fixed effects or high-dimensional covariates), whereas DID’s estimation results were highly sensitive to model specifications, with coefficients fluctuating substantially across strategies [14].

These studies demonstrate that DML can adeptly manage high-dimensional covariates and nonlinear relationships, thereby mitigating the omitted variable bias often present in conventional regression methods, so significantly bolstering the trustworthiness of empirical conclusions in economics. The utilization of dual machine learning techniques in economics is advancing, offering a robust instrument for evaluating the effects of intricate policies, including agricultural social services.

In conclusion, previous research has explored the multifaceted effects of agricultural socialization services, the sources and evolution of the urban–rural income gap, and the application of double machine learning (DML) methods in causal inference. However, existing studies have rarely integrated these perspectives into a unified analytical framework.

This study advances the literature in several important ways. First, it reconceptualizes agricultural socialization services as a socio-technical system reform mechanism that connects institutional intervention with structural transformation in rural China. Second, it innovatively applies a DML framework to a staggered provincial pilot policy context, improving the credibility of causal identification by mitigating high-dimensional confounding and endogeneity. Third, it incorporates a mechanism-oriented approach by empirically testing three distinct pathways—technological advancement, agricultural scale expansion, and human capital mobility—through which services affect urban–rural income distribution patterns. These theoretical and methodological innovations collectively enable a more precise empirical examination of the redistributive effects of agricultural socialization services, providing new explanatory pathways for how agricultural modernization functions as an institutional lever for inclusive development.

3. Justification and Research Hypothesis

3.1. Theoretical Framework

The urban-rural income gap, as per Marxist political economics, arises from the inequitable allocation of resources and production relations. Agricultural socialization services have integrated dispersed small farmers into a cohesive production service network, transformed the agricultural production model through organization, specialization, and scale of service delivery, and effectively dismantled the information and transaction barriers that traditionally separated “small-scale agricultural production” from participation in broader market systems, thereby promoting the transformation of production relations and the optimal distribution of agricultural resources [17]. This mechanism has fundamentally enabled the synergistic development of agricultural productivity enhancement and farmers’ income growth, thereby laying an institutional and practical foundation for narrowing the urban–rural income gap. A systematic basis and a pragmatic approach for diminishing the income gap between rural and urban areas, effectively facilitating the synergistic growth of enhanced agricultural revenue and better agricultural efficiency.

Agricultural socialization services refer to a production service system led by specialized service organizations, providing full-process services for farmers—including ploughing, planting, pest control, harvesting, processing, storage, and marketing [31]. Through the advancement of policies and the progressive enhancement of market mechanisms, the agricultural services market has exhibited a trend towards diversification of service types, comprehensive coverage of service recipients, and digitalization of the service process, progressively achieving a transition from “gap-filling services” to “systematic services”. The Ministry of Agriculture and Rural Affairs, in collaboration with the Ministry of Finance, executed the “Pilot Socialization Services for the Entire Agricultural Production Process” initiative, significantly enhancing the accessibility and efficiency of agricultural services. The services include the operation of agricultural machinery, integrated pest and disease prevention and control, advancement of agricultural technology, coordination of production and marketing, and the development of digital platforms.

This study reconceptualizes agricultural socialization services as a systemic and institutionalized mechanism for socio-technical restructuring, rather than merely an instrumental tool for improving agricultural productivity. With the progressive expansion of agricultural service coverage, an increasing number of scholars have begun to reassess its functional mechanisms from the perspective of institutional-technical integration. First, from the perspective of institutional embeddedness and platform governance, agricultural socialization services—represented by coordinated mechanization and digital service platforms—are emerging as a new mode of governance embedded within both central and local state structures. For example, Xia (2025) argues that under the digital empowerment paradigm, such services not only enhance total factor productivity but also become embedded in the national governance framework through resource allocation and market connectivity, thereby promoting agricultural restructuring and the integration of smallholders [32]. This model of “embedded governance” aligns closely with the socio-economic interdependence logic proposed by Granovetter and provides a new analytical approach to rural institutional transformation.

Second, agricultural socialization services function as a platform for socio-technical system reconstruction. Several scholars using provincial panel data, demonstrate that these services provide organizationally coordinated technical support, mechanized assistance, and information services. Their role extends beyond enhancing production efficiency to restructuring rural production organizations and facilitating the integration of smallholder farming into the modern agricultural system [33]. This perspective resonates with our framing of agricultural socialization services as a vehicle for socio-technical restructuring, highlighting the synergy of institutions, technologies, and organizations in driving rural transformation.

Third, from the standpoint of mechanism diversity and environmental sustainability, Wang et al. (2023) find that agricultural socialization services significantly promote the adoption of sustainable agricultural practices (SAPs) and further advance green agricultural development through pathways of scaled production [34]. These findings offer empirical support for the mechanisms of “technological diffusion” and “scale expansion” proposed in this study, while also extending their relevance to the domain of environmental governance.

3.2. Mechanism of Action Analysis

While outlining the logic of the agricultural socialization services mechanism in this section, it is also necessary to briefly highlight how it serves as an extension of the state’s territorial restructuring objectives in rural spaces. In advancing strategic initiatives such as agricultural and rural modernization and rural revitalization, China has extended public services and modern agricultural production factors to rural areas to achieve the integration of modernization pathways with national territorial governance objectives [1,35]. In this context, agricultural socialization services are not merely market-oriented or technical support but also constitute an important mechanism for the “decentralization of national governance structures to rural areas”, including the following: first, the embedding of public service functions. The State Council’s 14th Five-Year Plan explicitly extends basic public service systems such as modern agricultural equipment, Internet+, and agricultural technology information to rural areas to address development shortcomings and achieve equitable public services between urban and rural areas. The agricultural socialization services system, through channels such as technology promotion, machinery deployment to rural areas, and agricultural technology guidance, effectively assumes the government’s direct role in rural public affairs, strengthening the state’s service and management functions in rural areas. Second, the reproduction of institutional governance networks. Under the dual-tiered operational system of “unified management and decentralized operations”, the state encourages village collective organizations to participate in socialization services to enhance grassroots governance capabilities. Taking Changji and other pilot counties as examples, agricultural socialization services platforms are led by village party organizations, with collective economic organizations assuming responsibilities for integrating elements and allocating resources, effectively promoting the alignment of rural organizational structures with the national governance system. Third, the coordination of technical-policy spatial configuration. With the introduction of technologies such as agricultural machinery, smart irrigation, and digital agriculture in rural areas, the implementation of agricultural socialization services is equivalent to the state promoting modernization and ecological planning within rural spaces. This process not only adjusts the agricultural production structure but also embeds it within the state’s ecological civilization and rural revitalization planning framework, forming a governance network that connects with urban spaces. The following section analyzes the specific impact mechanisms. In addition to the textual analysis, this paper also presents a diagram illustrating the transmission mechanisms (see Figure 1).

3.2.1. Direct Income Effect

The most direct method to modify the urban-rural income gap is through agricultural socialization services, which enhance farmers’ income by improving agricultural production efficiency via specialized labor division and economies of scale. Tao and Zhao (2024) assert that provincial panel data indicate a positive association between the extent of agricultural socialization services and farmers’ incomes, with an expansion of these services resulting in increased per capita disposable income in rural regions [9]. Chen et al. (2022) employed a quasi-natural experiment methodology and further demonstrated that the full-process agricultural socialized service pilot significantly increased net income for farm households, with particularly pronounced effects in central, western, and impoverished rural regions [23]. By integrating services to lower agricultural production costs, stabilize marketing channels for agricultural products, and mitigate production risks, service organizations have markedly enhanced the market adaptability of smallholder farmers, consequently elevating their income levels and diminishing the urban-rural divide.

Based on the above theoretical analysis, this study proposes the following hypothesis:

H1. 

Agricultural socialization services can mitigate the urban-rural economic disparity. Enhancing agricultural production efficiency, augmenting farmers’ income, and broadening income-generating avenues, agricultural socialization services play a crucial role in income adjustment and redistribution, hence facilitating a convergence impact on the urban-rural income gap [9].

3.2.2. Effects of Technological Advancement

Agricultural socialization services serve as a “institutional vehicle” for advanced agricultural technology, promoting a shift toward technology-intensive agricultural production through the “service–technology–benefit” transmission mechanism. Agricultural socialized services exhibit significant technology spillover effects in terms of reducing fertilizer application and increasing green yields, thereby promoting the enhancement of agricultural green total factor productivity [36]. The mechanization of agricultural services has markedly enhanced grain yields and diminished reliance on human labor, thus boosting productivity and land returns in rural regions. The implementation of agricultural socialization services has diminished the income and productivity gap between rural and urban regions, while enhancing the marginal output of agricultural technology through a transition from fragmented and ineffective individual technology adoption to structured and process-oriented outsourcing [37].

Based on the above theoretical analysis, this study proposes the following hypothesis:

H2. 

Agricultural socialization services mitigate the urban-rural income gap through technological progress. Agricultural socialization services facilitate the dissemination of innovative technologies and enhance agricultural mechanization, thereby augmenting the total factor productivity and marginal returns in agriculture, which in turn elevate rural incomes and narrow the urban-rural income gap [36,37].

3.2.3. Effects of Agricultural Scale

Land is the most fundamental input in agricultural production, yet its fragmentation has long been a major barrier to improving agricultural efficiency. By enabling consistent operations and stringent control, agricultural socialization services may liberate centralized land management from its limitations, resulting in the “service-driven scale and scale-driven reinforcement service” coupling mechanism. Several scholars have indicates that the utilization of agricultural equipment services significantly improved the efficiency and extent of land management [16]. Furthermore, in the principal grain-producing areas, Ma et al. (2025) observed that social service organizations have augmented agricultural land ownership through cooperative cultivation and trusteeship [10]. The economic gap between urban residents and rural inhabitants has diminished due to advancements in land consolidation and extensive agriculture, which have enhanced the market power of agricultural enterprises and boosted both output and profitability.

H3. 

Agricultural socialization services mitigate the income gap between urban and rural regions via the agricultural scale effect.

Agricultural socialization services such as land pooling, trusteeship, cooperative farming, and joint planting strengthen farmers’ capacity to withstand risks and adapt to market fluctuations while augmenting their operating revenue and yield per acre. This subsequently enables rural inhabitants to increase their income [10,16].

3.2.4. Effects of Human Capital

Agricultural socialization services have markedly liberated the rural labor force and enabled the transition of labor to secondary and tertiary industries, as well as the emerging rural economy, serving as a crucial indirect mechanism influencing the urban-rural income gap. The proportion of family income derived from non-agricultural sources has increased, and services associated with agricultural equipment have significantly facilitated employment opportunities in non-agricultural sectors for young and middle-aged individuals residing in rural areas [26]; Conversely, land trusteeship and service outsourcing have diminished the time farmers allocate to agricultural inputs and have effectively facilitated their engagement in novel job avenues, including self-employment and entrepreneurship in their rural hometowns [38]. Alongside enhancing agricultural vocational skills and developing a workforce of service-oriented farmers, agricultural socialization services refine income structures, facilitate the accumulation of rural human capital, and are pivotal in advancing equitable development between urban and rural regions through the “education—training—employment” chain effect.

H4. 

Agricultural socialization services mitigate the economic disparity between rural and urban areas through the enhancement of human capital. Agricultural socialization services facilitate the shift of rural labor toward higher value-added sectors, invest in rural human capital, provide skill training, and optimize employment structures, thereby broadening income sources for rural residents and diminishing income disparity between rural and urban populations [26,38].

4. Research Methodology

4.1. Study Area

This study encompasses all provinces in mainland China, excluding Tibet, Hong Kong, Macao, and Taiwan, utilizing 30 of the 31 administrative units as its primary research region. Tibet is excluded from the analysis due to prolonged data deficiencies, inconsistent data quality, and the absence of a regularly executed agricultural socialization service system. This analysis similarly excludes Hong Kong, Macao, and Taiwan as they are not relevant to the policy context or statistical framework of agricultural socialization services.

The selection of provincial administrative units as the research unit is predicated on three primary considerations:

There is a significant correlation between the degree of policy implementation and regional disparities. The policy of agricultural socialization services, particularly the “full range of agricultural socialization services pilot”, is primarily administered by the central government and executed at the local level, with significant variation in implementation across provincial units. For example, while certain western provinces such as Gansu and Qinghai are in the nascent phases of service systems, major agricultural provinces like Hebei, Shandong, and Henan have pioneered the development of agricultural production service supply, financial assistance, and the establishment of digital agricultural platforms. The regional heterogeneity makes the provincial level a suitable analytical unit for illustrating the relationship between the expansion of agricultural services and the income discrepancy between urban and rural areas [18,26].

Analyzing information on services in conjunction with data regarding income inequality between urban and rural regions. Provincial-level statistics regarding China’s urban-rural income gap, encompassing rural residents’ net household income, the urban-rural income ratio, and per capita disposable income for both urban and rural populations, are often disseminated by the National Bureau of Statistics. The primary statistical indicators of agricultural socialization services, including service area, service type coverage, and number of service subjects, are predominantly aggregated at the provincial level and disseminated through the annual work report of the Ministry of Agriculture and Rural Development and local agricultural yearbooks. Utilizing provincial administrative units as the research unit enhances data accuracy, alignment, and facilitates a horizontal comparison analysis of critical variables.

Lastly, it facilitates the implementation of spatial identification and the investigation of regional heterogeneity. The provincial-level analysis facilitates a more accurate identification of regional disparities in agricultural service provision, farmer engagement capability, and urban-rural dynamics. This also facilitates the integration of a subregional heterogeneity test into the forthcoming extended study, aiding in the comprehension of the influence of agricultural socialization services across various regions and their respective magnitudes [39]. Moreover, the scale exhibits robust academic comparability and practical applicability, evidenced by the recurrent utilization of provincial panel data in empirical studies of agricultural services conducted by researchers such as Li et al. (2024) and Sun et al. (2023) [40,41].

This study analyzes 30 provincial administrative units in mainland China, excluding Tibet, Hong Kong, Macao, and Taiwan. This selection aligns with the actual dynamics of policy implementation and service system development, while also satisfying the requirements for data on urban-rural income gap and agricultural service indicators. It establishes a comprehensive data repository and a basis for regional comparison, facilitating the application of advanced causal inference techniques, including dual machine learning.

4.2. Modelling

This study will employ the previously delineated theoretical analytical framework to experimentally investigate the impact of agricultural socialization service pilots on the urban-rural income disparity. Challenges related to sample selection bias, data structure limitations, and rigid premise assumptions hinder conventional policy evaluation techniques (such as propensity score matching, synthetic control, and difference-in-differences methods) from delivering an accurate representation of causality in complex policy environments. The Double Machine Learning (DML) model was introduced to capture nonlinear interactions among variables using nonparametric methods. This approach enables an impartial evaluation of the policy disposition effect under limited sample conditions by eliminating the influence of high-dimensional confounding variables in a quasi-natural experiment and utilizing the orthogonalization framework [27]. The model concurrently uses the “instrumental variable method” to mitigate regularization bias in machine learning estimates and establish a robust statistical basis for causal inference. Current research in causal inference relies on these studies, which lend methodological validity to the DML model by illustrating its superiority in managing high-dimensional data, nonlinear relationships, and endogeneity challenges [27,28,29].

Referring to previous research [27], this paper constructs a partial linear model using double machine learning (for the specific model design, please refer to Appendix A). To enhance the stability and reliability of the model estimates, this paper employs a 5-fold cross-validation method to process the regression samples and adjusts the results by taking the median of 101 repeated samples, with the final estimate being the median value.

4.3. Variable Selection

4.3.1. Explained Variable

The Urban-Rural Income Gap (URIG) functions as the principal explained variable. The urban-rural income ratio, the Gini coefficient, and Theil’s index are the three predominant methodologies for quantifying urban-rural income difference. This research uses Theil’s index as the principal explained variable, supplemented by the urban-rural income ratio (inc) and the Gini coefficient (gini) to perform a robustness test. This aims to ensure the reliability and robustness of the study’s conclusions, considering elements including data availability, decomposition capability, and measurement precision. Theil’s index quantifies income inequality by the entropy weight principle, offering great additivity, effective decomposability, and insensitivity to outliers, making it particularly apt for analyzing structural disparities among various locations. The Thiel index, utilizing China’s pronounced urban-rural split, may analyze the income gap between urban and rural areas and assess the impact of social service programs on income distribution [15]. The Theil’s index is less reliant on sample size and does not necessitate quantile data, making it ideal for assessing and comparing cross-sections of provincial yearly panel data. Research conducted by scholars Zheng Jun and Yi Huanhuan (2023) presents a measurement paradigm that serves as a reference; it utilizes the Thiel index as the principal explained variable to investigate the impact of institutional instruments, such as agricultural insurance, on income inequality within the context of urban-rural integration and development [13].

The urban-rural income ratio is the most definitive and clear indicator of inequality, as it compares the disposable income per capita of urban and rural residents, providing the most direct evidence of disparity. This indicator solely represents the disparity in average values, neglects alterations in the income distribution structure, is unresponsive to marginal enhancements and acute poverty, and exhibits a specific “mean trap”. Integrating data within the urban-rural dichotomy presents hurdles, despite the Gini coefficient’s prevalent application in assessing overall income inequality. The Gini coefficients for urban and rural areas in China have been reported using diverse units, standards of measurement, and calculation methodologies over the years. Nonetheless, a considerable volume of data is absent at the provincial level, complicating subregional attribution analysis and hindering its application in continuity studies. Consequently, URG serves as the primary explanatory variable in the principal regression of this paper. The robustness test employs Inc and Gini, along with the urban-rural income ratio and urban-rural income, to evaluate if the primary conclusions remain valid across various measurement methodologies.

4.3.2. Core Explanatory Variable

Agricultural socialization services (AGSS). Referring to Zhou et al. (2024) and Zhou et al. (2023) [42,43], a staggered difference-in-differences approach is employed to analyze the impact of policy pilot programs on regional rural income disparities among farmers. This approach was founded on two policy documents: the 2013 Guidance for Implementing the Pilot Work on the Socialization of the Entire Agricultural Production Process and the 2016 Notice of the Pilot Work on the Socialization of the Entire Agricultural Production Process. The policy is represented by the policy dummy variable (Treat × Time), which equals 1 if the sample province is a pilot province and 0 if it is not. Time is represented as a year dummy variable; however, due to the staggered implementation of the policy by provincial governments, it is assigned a value of 1 in the year the policy was implemented in the respective province and in the years thereafter, and 0 otherwise.

4.3.3. Control Variable

This article also considers supplementary variables that may influence rural and urban prosperity, enabling an accurate evaluation of policy outcomes with the currently available data. Due to its regularization methodology, double machine learning proficiently manages multidimensional control variables. Below is a compilation of factors that can be regulated to affect the extent of agricultural and regional development: (1) Industrialization Level (IDL) measured by the ratio of industrial added value to regional GDP; (2) Informationization Level (ILS), measured by the ratio of total postal and telecommunication services to regional GDP; (3)The Human Capital Level (HCL), measured by the ratio of students enrolled in general higher education institutions to the resident population at year-end. (4) Transportation Infrastructure Level (TIS), measured by the natural logarithm of total road mileage; (5) Soil Farmland quality (SFQ), represented by the area under soil erosion control measures; (6) Irrigation and water conservancy facilities (IWF), measured by the proportion of irrigated farmland to total cultivated land; (7) Agricultural Disaster Affected Degree (ADA), measured by the total area of farmland affected by disasters.

4.3.4. Mediator

This research develops a dual machine learning causal model to examine the effect of agricultural socialization services on the urban-rural income gap. The model considers the impacts of technological advancement, agricultural scale, and human capital, and incorporates three types of mediating variables to elucidate this link. Research underscores the bifunctional nature of agricultural socialization services. They improve the efficiency of agricultural production. Conversely, they indirectly influence the urban-rural income distribution by facilitating the optimal deployment of agricultural resources and enhancing farm family patterns [44,45].

The initial aspect is the influence of technological progress, measured by agricultural total factor productivity (ATFP) and the level of agricultural mechanization (AMS). Agricultural socialization services facilitate the spread and application of agricultural technology and represent a crucial aspect of modern agricultural factor organization. By standardizing operations, utilizing new agricultural machinery, and advocating for digital agricultural platforms, professional service organizations can enhance the technological intensity and labor productivity of agricultural output. On the other hand, socialization services can assist smallholder farmers in surmounting their deficiencies in knowledge, capital, and technology, hence enhancing their production technology standards. “Agricultural total factor productivity” (ATFP) denotes the extent to which technological progress and optimal resource allocation influence agricultural output. Utilizing the green total factor productivity metrics for regional agriculture established by Li et al. (2024) [45]. The proportion of mechanized cultivated area to total cultivated area serves as an indicator of the level of agricultural mechanization (AMS), based on data from the China Agricultural Mechanization Yearbook and several provincial statistical yearbooks. Increased rates of mechanization boost the ability of service businesses to export technology and accelerate technological penetration.

The second is the agricultural scale effect, quantified through land-scale management (LSM) and service-scale management (SSM). Agricultural social services offer a feasible avenue for small farmers to integrate into contemporary agricultural systems via a “service scale” that progresses to “land scale”. Socialization services reduce barriers for farmers to access the market and engage in large-scale operations, facilitating the intensive management of fragmented arable land. On the other hand, the evolving nature of service entities indicates specialization and conglomeration, resulting in a multiplier effect of “organization + scale” [10]. Large Scale Management (LSM): The ratio of total cultivated area to the total number of individuals employed in agriculture serves as one indicator. Monitoring of Land Transfers: The Ministry of Agriculture and Rural Development and the China Rural Statistics Yearbook are two data sources. Service Scale Management (SSM) is a methodology for evaluating the density and distribution of service organizations in agricultural output. The assessment is predicated on the production value of professional and ancillary activities in agriculture, forestry, animal husbandry, and fisheries, together with the workforce size in these sectors.

Third, the human capital effect is assessed through rural innovation and entrepreneurship (RIE) and rural labor transfer (RLT). Agricultural social services enhance rural human capital accumulation and mobility by liberating agricultural labor and enabling factor reallocation, thus ameliorating structural income disparities between urban and rural regions. A beneficial outcome of service intervention is that it reduces the time and skill demands of farm households’ productive activities, hence creating greater options for non-farm work or entrepreneurship. Conversely, service-oriented innovation in rural regions is propelled by the unrestricted exchange of knowledge, technology, and capital, which enhances the framework of rural human capital [13,26]. A secondary indicator of the amount of rural innovation and entrepreneurship (RIE) is the number of farmers’ cooperatives and people engaged in agriculture, forestry, animal husbandry, and fisheries. This indicator relies on data obtained from statistical bulletins of local agriculture and rural offices. By utilizing data from the National Bureau of Statistics’ Labor Force Survey Yearbook and provincial statistical yearbooks, we may ascertain the amount of rural labor transfer (RLT) as the ratio of those employed in the primary industry to those engaged in the secondary and tertiary industries.

4.4. Source of Data

This analysis examines the period from 2005 to 2022, excluding Tibet, Hong Kong, Macao, and Taiwan, utilizing currently available data. The information presented in this article primarily derives from official Chinese sources, such as the China Statistical Yearbook, China Rural Statistical Yearbook, and China Rural Management Statistical Yearbook, among others. The data were interpolated to address any gaps. The descriptive statistics of the main variables are shown in

Table 1. Descriptive statistics of data.

Variable	Variable Definition	Variable Calculation Definition	Mean	SD	Min	Max	N
URIG	Urban–rural income gap	Calculated following the method proposed by Zheng Jun and Yi Huanhuan (2023) [13]	0.105	0.055	0.017	0.281	540
ATFP	Agricultural total factor productivity	Calculated based on the regional green total factor productivity (GTFP) index of agriculture constructed by Li Xiaohui et al. (2024) [45]	0.911	0.295	0.327	2.524	540
AMS	Agricultural mechanization level	The ratio of mechanized cultivated area to total cultivated area.	91.411	39.118	4.993	224.997	540
LSM	Land scale management	Total sown area of crops/ Number of employees in the primary industry	7.033	4.120	0.127	29.362	540
SSM	Service scale management	Output value of specialized and auxiliary activities in agriculture, forestry, animal husbandry, and fishery/ Number of employees in agriculture, forestry, animal husbandry, and fishery	0.210	0.141	0.027	0.955	540
RIE	Rural innovation and entrepreneurship	Number of farmers’ cooperatives/ Number of employees in agriculture, forestry, animal husbandry, and fishery	47.537	43.172	0.183	176.344	540
RLT	Rural labor transfer	(Number of employees in the secondary industry + Number of employees in the tertiary industry)/ Number of employees in the primary industry	4.127	7.199	0.299	63.143	540
IDL	Industrialization level	Industrial value added/ Regional gross domestic product	0.353	0.086	0.100	0.574	540
ILS	Informatization level	Total volume of postal and telecommunication services/Regional gross domestic product	0.065	0.046	0.009	0.290	540
HCL	Human capital level	Number of students enrolled in regular institutions of higher education/ Year-end resident population	0.019	0.007	0.006	0.043	540
TIS	Transportation infrastructure level	Ln(Total road mileage)	2.380	0.872	−0.211	3.715	540
SFQ	Soil Farmland quality	Area of soil erosion control	4057.134	3083.055	28.360	16,678.700	540
IWF	Irrigation and water conservancy facilities	Irrigated cultivated area/ Total cultivated area	47.144	23.337	12.467	107.919	540
ADA	Agricultural disaster impact level	Area of affected farmland	957.933	940.156	2.000	7394.000	540

.

5. Empirical Findings

5.1. Baseline Regression

Irrespective of the employed model, the benchmark regression results indicate that the disparity in income between urban and rural areas is considerably and consistently influenced by the extent of agricultural social service (AGSS) development (see

Table 2. Benchmark Regression.

	(1)	(2)	(3)	(4)
VARIABLES	theil	theil	theil	theil
AGSS	−0.014 ***	−0.015 ***	−0.014 ***	−0.022 ***
	(0.004)	(0.004)	(0.005)	(0.004)
Province FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Constant	−0.000	−0.000	−0.001	−0.000
	(0.002)	(0.002)	(0.002)	(0.001)
Observations	540	540	540	540
(5)	(6)	(7)	(8)	(9)
theil	theil	theil	theil	theil
−0.031 ***	−0.015 ***	−0.010 **	−0.014 **	−0.016 ***
(0.005)	(0.005)	(0.005)	(0.006)	(0.006)
YES	YES	YES	YES	YES
YES	YES	YES	YES	YES
0.000	−0.000	−0.000	−0.000	−0.000
(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
540	540	540	540	540

Robust standard errors in parentheses *** p < 0.01, ** p < 0.05.

). The urban-rural Theil index, representing the income gap, significantly diminishes with each unit increase in AGSS, as evidenced by the negative and statistically significant coefficient of AGSS in the first model that accounts solely for province and year fixed effects (column (1) in the table). By methodically incorporating control variables such as industrialization level (IDL), information technology level (ILS), and human capital level (HCL) (columns (2) to (4)), as well as transport infrastructure level (TIS), soil quality of farmland (SFQ), water conservation facilities (IWF), and the degree of agricultural disaster (ADA) (columns (5) to (8)), the absolute value of the AGSS coefficient fluctuates yet remains consistently negative and statistically significant at the 1% or 5% levels. This indicates that while the marginal effect of AGSS on urban-rural disparity slightly adjusts with the inclusion of other controls, its negative direction and statistical significance in reducing the income gap remain unchanged. The coefficients’ order of magnitude exhibits minimal variation, with the AGSS coefficient being consistently negative and significant even after the inclusion of all control variables and associated quadratic terms in column (9). The robustness of these results indicates that the negative relationship between AGSS and the urban-rural income gap is a reliable finding across multiple model specifications and not driven by omitted variable bias.

Importantly, beyond statistical significance, the AGSS coefficient also demonstrates notable economic significance. Specifically, in the fully controlled model (column (9)), the coefficient of –0.016 implies that a one-unit increase in the AGSS index—representing either an expansion in service coverage or a significant policy shift toward full-process agricultural services—is associated with a 1.6 percentage point decrease in the urban-rural Theil index, ceteris paribus. Considering that the mean value of the Theil index is approximately 0.105 (see Table 1), this represents a reduction of over 15% in the urban-rural income disparity, a non-negligible improvement in income equity. This finding indicates that agricultural socialized services are not only statistically relevant but also economically meaningful in reshaping rural income structures and enhancing distributive justice between urban and rural regions.

AGSS enhances production efficiency and resource allocation, hence reducing the rural-urban income gap. AGSS has significantly enhanced agricultural production efficiency and output by implementing production aspects such as new farming equipment, technological extensions, and training services. Agricultural socialization services (AGSS have demonstrated significant improvements in the technical efficiency of smallholder farmers in numerous empirical investigations. Cai et al. (2024) found that ASS significantly improved the technical efficiency of rice production by supplying contemporary production methods, resources, and expertise [19]. Enhanced efficiency results in greater outputs per unit of input, hence yielding increased revenues for farmers. Liu et al. (2025) empirically demonstrate that the statewide extension of agricultural social services can result in a substantial increase in total food output (about 54.4 percent), indicating a considerable rise in the production and income of farming households [46]. Agricultural social services substantially influence agricultural labor productivity, resulting in an increase in total output and, consequently, additional revenue streams for rural inhabitants. The income gap between rural and urban residents has diminished due to these effects, which have increased the per capita income of rural populations.

Secondly, agricultural social services embody the economic principles of specialized division of labor and labor substitution. When labor is scarce or labor expenses increase in rural regions, specialized service providers emerge as a more economical alternative for farmers. Social services enable farmers to delegate certain farming tasks to specialized teams, thus realizing economies of scale and cost efficiencies. Research indicates that farmers can leverage economies of scale and labor division via agricultural socialization services, enabling them to employ external professionals for specific facets of agricultural production [46]. This division of labor enhances operational efficiency and mitigates the production risk for farmers. Empirical research from Sang et al. (2023) indicates that agricultural mechanization services substantially enhance rural household income and contribute to the mitigation of the income gap in rural regions [2]. In summary, enhancing the total productivity of the rural sector has significantly helped low-income groups in rural areas, successfully diminishing income distribution inequities. The enhancement of efficiency liberates excess rural labor, allowing greater involvement in non-agricultural work or entrepreneurship, hence augmenting non-farm incomes. Sang et al. (2023) found that agricultural mechanization services significantly reduced the non-farm income gap among rural households, in addition to enhancing farm revenues [2]. This implies that rural incomes are indirectly increased and the urban-rural income gap is narrowed.

In summary, agricultural socialization services can narrow the income gap between urban and rural areas. By improving agricultural production efficiency, increasing farmers’ income, and expanding income-generating channels, agricultural socialization services possess significant income regulation and redistribution functions, thereby producing a convergence effect on the income gap between urban and rural areas. Hypothesis H1 is thus validated.

5.2. Sensitivity Analysis

This research conducts sensitivity studies on variable substitution, study area, and temporal variation, and modifications to model design to assess the robustness of the baseline regression results. The data in the table indicate that the influence of AGSS on the urban-rural income gap is significant across many situations, indicating strong robustness of the study’s conclusions.

5.2.1. Replacement of Variables

This research substitutes the explanatory variables with the income ratio (inc) of urban and rural populations and the urban-rural Gini coefficient (gini) to assess the impact of the income gap quantification method on the results. The agricultural socialization service significantly reduces the urban-rural income ratio and diminishes the income gap between urban and rural areas, as indicated in Column (1) of the table, where the coefficient of AGSS is −0.153 and is statistically significant at the 1% level. The results are corroborated by the explanatory variable in Column (2), the urban-rural Gini coefficient, which has a coefficient of −0.011 (significant at the 5% level), demonstrating the robustness of the conclusions (see

Table 3. Regression results with alternative variables.

	(1)	(2)	(3)	(4)
VARIABLES	inc	gini	1%	5%
AGSS	−0.153 ***	−0.011 **	−0.014 **	−0.015 **
	(0.034)	(0.005)	(0.006)	(0.006)
Province FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Constant	−0.001	−0.000	−0.000	−0.000
	(0.006)	(0.002)	(0.001)	(0.001)
Observations	540	540	540	540

Robust standard errors in parentheses *** p < 0.01, ** p < 0.05.

).

Secondly, this research employs 1% and 5% winsorization to evaluate the robustness, as the results may be influenced by the extreme values of the variables. The results are unaffected by the extreme value configuration, as demonstrated in columns (3) and (4), where the AGSS coefficients are −0.014 and −0.015, respectively, both significant at the 5% level (see Table 3).

5.2.2. Change of Study Area and Time

The results are presented in Column (1); the AGSS coefficient is −0.011, and the significance level remains at 1%, indicating that the results are not influenced by the municipalities’ distinctive economic structures. This is conducted to determine whether the results are influenced by specific regions. Beijing, Shanghai, Tianjin, and Chongqing were the four municipalities that were excluded from the regression (see

Table 4. Regression results with altered study regions and time periods.

	(1)	(2)	(3)
VARIABLES	theil	theil	theil
AGSS	−0.011 ***
	(0.004)
AGSS_fake_2008		−0.007
		(0.006)
AGSS_fake_2010			−0.005
			(0.004)
Province FE	YES	YES	YES
Year FE	YES	YES	YES
Constant	−0.000	0.000	0.002
	(0.001)	(0.001)	(0.002)
Observations	468	540	540

Robust standard errors in parentheses *** p < 0.01.

).

Additionally, this article conducts a counterfactual test to determine whether the timing of policy implementation affects causal identification. The objective of this test is to ascertain whether the policy pilot continues to have a significant impact on the urban-rural income gap. The policy pilot is scheduled to operate virtually from 2008 to 2010 [47]. As illustrated in columns (2) (fake 2008) and (3) (fake 2010), the coefficients of the AGSS dummy variables are −0.007 and −0.005, respectively. These values are not statistically significant, which implies that the policy time setting is valid and that the model is not substantially impacted by the issue of omitted variables (see Table 4).

5.2.3. Replacement Model Design

The robustness test in this research is conducted using three algorithms: Gradient Boosting (gradboost), Neural Network (nnet), and Support Vector Machine (svm). The impact of various machine learning techniques on the estimate of DML (Double Machine Learning) is considered. The AGSS coefficient is −0.015 under the gradboost model (column 1), −0.019 under the neural network model (column 2), and −0.044 under the Support Vector Machine (column 3), as indicated by the results. At the 1% level, each of the three models is statistically significant. This is additional evidence that agricultural socialization services substantially reduce the rural-urban income gap, as the estimated results remain consistent across a variety of algorithm parameters (see

Table 5. Regression results with revised model design.

	(1)	(2)	(3)	(4)	(5)
VARIABLES	gradboost	nnet	svm	K = 3	K = 8
AGSS	−0.015 ***	−0.019 ***	−0.044 ***	−0.019 ***	−0.011 **
	(0.004)	(0.004)	(0.003)	(0.006)	(0.005)
Province FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
Constant	0.000	−0.000	−0.016 ***	−0.000	−0.000
	(0.001)	(0.002)	(0.003)	(0.001)	(0.001)
Observations	540	540	540	540	540

Robust standard errors in parentheses *** p < 0.01, ** p < 0.05.

).

Lastly, to assess the sensitivity of the results to the choice of cross-validation folds, the parameter K is adjusted from 5 to 3 and 8. The results are presented in columns (4) and (5), and the AGSS coefficients are −0.019 and −0.011, respectively. These coefficients satisfy the significance test, further demonstrating that the model’s results are insensitive to the sample splitting settings (see Table 5).

5.2.4. Using Traditional Models

To further verify the robustness of the main model’s identification results, this study supplements the Double Machine Learning (DML) approach with two widely used causal inference strategies—namely, the traditional Difference-in-Differences (DID) and the Propensity Score Matching Difference-in-Differences (PSM-DID) methods—for comparative analysis. The regression results are presented in the table below (see

Table 6. Comparison of Regression Results across Three Models.

	(1)	(2)	(3)
VARIABLES	DML	did	Psm-did
AGSS	−0.016 ***	−0.015 ***	−0.012 ***
	(0.006)	(0.003)	(0.003)
IDL		0.191 **	0.109
		(0.075)	(0.082)
ILS		0.109	−0.075
		(0.192)	(0.227)
HCL		−11.252 ***	−14.042 ***
		(1.279)	(1.842)
TIS		0.014 *	−0.000
		(0.008)	(0.012)
SFQ		−0.000	0.000 ***
		(0.000)	(0.000)
IWF		−0.001 ***	−0.002 ***
		(0.000)	(0.000)
ADA		0.000 **	0.000 ***
		(0.000)	(0.000)
IDL2		−0.330 ***	−0.252 **
		(0.119)	(0.122)
ILS2		0.035	0.708
		(0.563)	(0.677)
HCL2		184.074 ***	244.866 ***
		(26.486)	(41.770)
TIS2		0.000	0.001
		(0.002)	(0.003)
SFQ2		0.000	−0.000 ***
		(0.000)	(0.000)
IWF2		0.000 ***	0.000 ***
		(0.000)	(0.000)
ADA2		−0.000 ***	−0.000 ***
		(0.000)	(0.000)
Constant	−0.000	0.227 ***	0.281 ***
	(0.001)	(0.029)	(0.033)
Observations	540	540	456
R-squared		0.767	0.737

Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

).

All three methods consistently indicate that Agricultural socialization services (AGSS) have a significant negative effect on the urban–rural income gap, suggesting that such services contribute to reducing income inequality between urban and rural areas. Among them, the DML model demonstrates greater robustness in controlling for high-dimensional covariates and capturing nonlinear structures. Nevertheless, the direction and significance of the main effect remain consistent even in the more constrained DID and PSM-DID models, thereby confirming the credibility and robustness of the policy effect from multiple analytical perspectives.

Furthermore, to verify whether the DID model satisfies the parallel trends assumption, this study employs an event-study framework to plot the dynamic policy effects, as shown in the Figure 2 below. The horizontal axis represents the relative event time of policy implementation, while the vertical axis indicates the dynamic policy effects on the urban–rural income gap. The graph shows that the estimated coefficients during the pre-treatment period (t = −5 to t = −1) fluctuate around zero, with confidence intervals largely covering zero. This suggests the absence of significant pre-treatment trends, thereby confirming the validity of the parallel trends assumption. In contrast, during the post-treatment period (t = 1 and beyond), the urban–rural income gap gradually narrows, and the policy effect becomes more pronounced—demonstrating the reform’s sustained impact on improving income distribution. In summary, while the DML model provides greater theoretical rigor by better addressing high-dimensional confounders and nonlinearities, the consistency of effect direction and significance across all models—including DID and PSM-DID—combined with the validation of the DID model’s assumptions via the event study approach, provides strong evidence for the robustness and internal validity of the study’s main findings.

In summary, the previous empirical findings are entirely corroborated by the robustness of the negative impact of agricultural social services on the urban-rural income gap, which is evident in a variety of variable settings, sample regions, policy time settings, and machine learning models.

5.3. Mechanism Testing and Policy Pathways

This paper expands upon the causal mediation effect analysis method introduced by Jiang Ting (2022) within the dual machine learning (DML) estimation framework to investigate the mechanism by which agricultural socialization services (AGSS) influence the urban-rural income divide [48]. It introduces a variety of potential mediating variables and develops mechanism tests based on the effects of technological advancement, agricultural scale, and human capital, respectively (see in

Table 7. Conduction Mechanism Checklist.

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	CRS	AMS	LSM	SSM	RIE	RLT
AGSS	13.856 ***	8.603 **	0.847 **	0.057 ***	14.543 ***	1.482 ***
	(0.065)	(3.727)	(0.411)	(0.018)	(4.996)	(0.436)
Constant	14.944	−0.368	0.055	−0.003	−0.183	0.072
	(14.318)	(0.893)	(0.051)	(0.006)	(0.773)	(0.121)
Observations	540	540	540	540	540	540

Robust standard errors in parentheses *** p < 0.01, ** p < 0.05.

).

5.3.1. Impact of Technological Advancement

Technological progress is regarded as a critical influencing mechanism behind changes in the urban-rural income gap. This study selects agricultural total factor productivity (ATFP) and the level of agricultural mechanization (AMS) as representative variables for technological progress. The impact of agricultural socialization services (AGSS) on ATFP is significantly positive, with a regression coefficient of 13.856 that holds at the 1% significance level, indicating that the service system has propelled improvements in agricultural production efficiency. Similarly, AGSS demonstrates a significant promoting effect on the level of agricultural mechanization (AMS), with a coefficient of 8.603 reaching the 5% significance level. The regression coefficient of 13.856 for ATFP implies that for each unit increase in AGSS, agricultural green total factor productivity will rise by approximately 13.86 percentage points. Given that the mean value of ATFP is merely 0.91 (see Table 1), this magnitude suggests a multiplicative leap in technological efficiency driven by AGSS. Likewise, the regression coefficient of 8.603 for AMS underscores that the advancement of AGSS will substantially elevate the mechanization rate of cultivated land, effectively alleviating the shortage of agricultural labor. Evidently, reforms in the agricultural service system not only exhibit statistical significance but also wield substantial structural transformation effects in economic terms, robustly fostering technological progress and efficiency transitions in rural areas. In summary, agricultural socialization services indeed contribute indirectly to narrowing the urban-rural income gap by enhancing agricultural total factor productivity and mechanization levels, thereby validating Hypothesis H2.

5.3.2. Agricultural Scale Effects

To investigate the mechanisms of agricultural scale operation, this study introduces two indicators: land scale management (LSM) and service scale management (SSM). AGSS (Agricultural socialization services) exhibits a significantly positive impact on LSM (coefficient = 0.847, significance level = 5%), indicating that enhancing land intensification levels can effectively narrow the urban-rural income gap. Along the SSM pathway, AGSS similarly significantly promotes service scale expansion (coefficient = 0.057, reaching 1% significance). The coefficient of 0.847 for LSM suggests that for each unit increase in AGSS, the sown area per agricultural laborer will rise by approximately 0.85 hectares on average, demonstrating a pronounced effect of land consolidation and centralized management. In regions with severe farmland fragmentation, such scale enhancement holds significant importance for achieving economies of scale. Meanwhile, the coefficient of 0.057 for SSM reflects a marked improvement in the production density and service efficiency of agricultural service organizations per unit of labor. Together, these findings indicate that AGSS facilitates the transformation of agriculture toward organizational and large-scale operations, enabling a more efficient allocation of land and service resources. Therefore, agricultural socialization services indirectly improve urban-rural income distribution by expanding the scale of land and service operations and elevating agricultural organizational levels, thereby validating Hypothesis H3.

5.3.3. Human Capital Effect

The human capital mechanism primarily manifests through agricultural socialization services (AGSS), enhancing rural labor efficiency and income by fostering rural entrepreneurship and facilitating rational labor mobility. This study employs two indicators for measurement: “Rural Innovation and Entrepreneurship Activity” (RIE) and “Rural Labor Transfer Intensity” (RLT). Regression results reveal a significant positive impact of AGSS on RIE (coefficient = 14.543, at the 1% significance level), indicating its strong mediating and moderating effects. Similarly, AGSS exerts a significantly positive influence on RLT (1.482, at the 1% significance level), which, while described as “negative impact” in the original text (likely a misstatement, as the positive coefficient implies enhanced mobility), actually underscores that increased human resource mobility contributes to alleviating urban-rural income disparities. The coefficient of 14.543 for RIE suggests that each unit increase in AGSS drives an average increase of approximately 14.5 participants in rural innovation and entrepreneurship activities. Meanwhile, the coefficient of 1.482 for RLT indicates that the degree of labor structural shift toward non-agricultural sectors rises by roughly 1.5 percentage points. These findings demonstrate that AGSS plays a pivotal role in unlocking rural human capital potential, not only by liberating agricultural labor to expand non-farm employment opportunities but also by constructing a rural innovation ecosystem that diversifies rural income structures and reduces reliance on the singular agricultural sector. Collectively, these results indicate that agricultural socialization services enhance the allocation efficiency of human resources in rural areas and promote the optimization of urban-rural labor structures, providing strong empirical support for Hypothesis H4.

The results of the mechanism tests suggest that among the three channels examined—technological advancement, agricultural scale operations, and human capital mobility—all exhibit statistically significant effects in mediating the relationship between agricultural socialization services and the urban–rural income gap. However, human capital mobility shows the strongest effect in both magnitude and breadth, indicating that policies facilitating labor reallocation and rural innovation ecosystems may deliver the most immediate and scalable distributional gains.

Furthermore, technological advancement—particularly through the expansion of mechanized services and total factor productivity improvement—demonstrates a foundational and compounding effect, suggesting it should serve as the long-term backbone of rural service system development.

Policies aimed at land consolidation and scale services, while effective, may face higher institutional and ecological constraints in fragmented or mountainous regions. Thus, from a policy sequencing perspective, facilitating human capital activation and technological access should be prioritized in national-level planning, while scale-based approaches may require more localized, adaptive governance mechanisms.

5.4. Heterogeneity Analysis and Policy Targeting Implications

This paper will further explore the issue of heterogeneity by examining the correlation between the effectiveness of agricultural development policies, agricultural resource endowment, and other factors. Notably, different regions of these factors often exhibit significant regional heterogeneity. This paper will examine heterogeneity from four perspectives:

5.4.1. Regional Positioning Development Differences

China has divided its provinces into three categories in accordance with the national food security plan: those responsible for cereal production, those responsible for marketing, and those responsible for maintaining a balance between the two. The functional allocation of agricultural socialization service programs is influenced by the distribution of agricultural resources and the assistance from policymakers, which varies among regions.

The regression results indicate that agricultural socialization services have a coefficient of −0.012, which is consistent with theoretical assumptions. This coefficient significantly mitigates the income gap between urban and rural areas. An abundance of agricultural resources in the primary producing regions, a high policy tendency, and a relatively well-established service supply system are all potential explanations that have the potential to increase the incomes of farmers and agricultural efficiency. The positive-signifying coefficient of 0.001 in the primary marketing region indicates that socialized agricultural services in that region may not only not contribute to the reduction of the income gap between city residents and rural residents, but may also exacerbate it. This may be due to the scarcity of agricultural laborers, the challenge of promoting socialization services on a large scale, and the limited agricultural resources in the primary marketing region. Area of balanced production and marketing: the coefficient is −0.00362, which is relatively low but consistent with expectations. The results of the strategy have not yet been fully realized, and it implies that the region with balanced production and commercialization may be located in the “middle ground” in terms of the endowment of agricultural resources and institutional support (see in

Table 8. Heterogeneity in functional localization.

	Major Grain-Producing Areas	Main Grain Sales Area	Grain Production and Marketing Balance Area
AGSS	−0.012 *	0.001	−0.004
	(0.005)	(0.008)	(0.005)
_cons	−0.001	0.000	−0.000
	(0.001)	(0.001)	(0.001)
N	234	126	180

Standard errors in parentheses * p < 0.05.

and Figure 3).

This collection of findings indicates that agricultural socialization services have a more substantial impact on the reduction of the urban-rural income gap in the primary production regions that possess robust agricultural resource endowments and robust supply systems.

5.4.2. Geographic Heterogeneity

There are significant differences among eastern, central, and western China in terms of economic development, agricultural foundations, and public service provision. These disparities may also influence the effectiveness of agricultural socialization services. The 0.009 coefficient, which is positively skewed, indicates that agricultural socialization services may not effectively reduce the urban-rural income gap and may potentially exacerbate it in the developed eastern region. This may be due to the fact that the agricultural socialization services have a minimal influence on farmers’ income, as there are numerous non-farm job alternatives in the east and a development gap between rural and urban regions. The middle region exhibits a highly significant coefficient of −0.018. This illustrates that agricultural socialization services substantially mitigate the income gap between urban and rural areas in the central region. This is likely attributable to the region’s robust agricultural base; however, rural development remains a challenge, and there is ample opportunity for services to intervene effectively. Furthermore, there is some significance at the coefficient level of −0.017 in the western region. The coefficient, which is −0.017, is also noteworthy. The evidence indicates that agricultural social services in the western region contribute to the reduction of the income gap between rural and urban areas. This is most likely due to the state’s investment in rural development and the strengthening of the agricultural subsidy system in the region (see in

Table 9. Geographic heterogeneity.

	Eastern	Central	Westward
AGSS	0.008	−0.018 ***	−0.0169 **
	(0.009)	(0.005)	(0.006)
_cons	−0.000	0.000	−0.000
	(0.001)	(0.001)	(0.001)
N	234	108	198

Standard errors in parentheses ** p < 0.01, *** p < 0.001.

and Figure 4).

In conclusion, agricultural socialization services are more effective in reducing the urban-rural income gap in the central and western regions, whereas there may be a “diminishing policy margin” in the eastern region.

5.4.3. Explore Differences in Service Organizations

The efficacy of socialization services in agriculture is significantly influenced by the scale and capabilities of service organizations. In addition to ensuring a consistent supply of services, professional co-ops that are operated by farmers also facilitate the integration of existing resources, the development of new technologies, and the exchange of knowledge. This paper employs the median number of farmers’ professional cooperatives in each province as a criterion for classification. Subsequently, the sample is divided into three categories: “high number of cooperatives”, “medium number of cooperatives”, and “low number of cooperatives”. The objective is to determine whether the efficacy of agricultural social services is influenced by variations in the number of cooperatives. Independently, regression estimates are conducted on the “high number of co-operatives” subsample, the “medium number of co-operatives” subsample, and the “low number of co-operatives” subsample. Agricultural socialization services have the most significant impact in regions with a low number of cooperatives, thereby reducing the income gap between urban and rural areas (coefficient = −0.022, p = 1%). Nevertheless, the impact of these services decreases as the number of cooperatives increases (coefficient = −0.017 in the intermediate group and −0.011 in the high group). −0.011, which is statistically significant despite being significantly less than zero (see in

Table 10. Heterogeneity of service organizations.

	Low Group	Medium Group	High Group
AGSS	−0.022 ***	−0.0167 ***	−0.011
	(0.005)	(0.005)	(0.010)
_cons	0.000	−0.000	−0.000
	(0.000)	(0.001)	(0.001)
N	180	180	180

Standard errors in parentheses *** p < 0.001.

and Figure 5). This could imply that agricultural socialization services, an alternative form of organization, may not have as significant an impact in regions with a small number of cooperatives. However, they still assist farmers by increasing their income, optimizing land utilization, and reducing the income gap between urban and rural areas. In contrast, regions with numerous cooperatives may experience lower policy returns and less efficient services as a result of potential duplication of resources and overlapping services.

5.4.4. Exploring Differences in Mechanization Levels

The degree to which agriculture is mechanized is a critical indicator of agricultural modernization and a prerequisite for the effective implementation of agricultural socialization services. This paper classifies samples into “high mechanization group”, “medium mechanization group”, and “low mechanization group” based on the median total power of agricultural machinery in order to further investigate the policy impacts of agricultural socialization services on varying mechanization bases. The regression coefficients for the impacts of each group on the urban-rural income gap are subsequently estimated. The regression coefficients of the effects of three groups on the urban-rural income gap are estimated in this study: the “high mechanization group”, “medium mechanization group”, and “low mechanization group”, which are named after the median total power of agricultural equipment. According to the data, socialization services do not substantially reduce the urban-rural gap in regions with low agricultural mechanization (the coefficient is 0.024, which fails the significance test). Indeed, there is a positive effect, which may indicate that the service system is not functioning effectively in the absence of the requisite infrastructure. Socialization services begin to reduce the income gap between urban and rural residents, particularly in highly mechanized regions, in the middle group (coefficient = 0.002) and the high group (coefficient = −0.001) (see in

Table 11. Mechanization level heterogeneity.

	Low Group	Medium Group	High Group
AGSS	0.0235 ***	0.002	−0.001
	(0.006)	(0.003)	(0.003)
_cons	0.000	−0.000	−0.000
	(0.001)	(0.001)	(0.001)
N	180	180	180

Standard errors in parentheses *** p < 0.001.

and Figure 6). This is due to the fact that socialized agricultural services have a beneficial impact on the operation of agricultural apparatus, thereby increasing the efficiency of both agricultural production and factor allocation. This study further demonstrates that agricultural modernization infrastructure enhances the efficacy of policy.

The heterogeneity results underscore that agricultural socialization services are most effective in central and western regions, particularly in major grain-producing provinces and areas with mid-to-high mechanization levels. These areas combine agricultural potential, favorable institutional support, and increasing demand for service infrastructure, making them ideal targets for scaled-up policy intervention.

Conversely, in eastern regions or grain-marketing areas where non-agricultural income dominates or land fragmentation persists, the marginal effects of services may be lower or counterproductive if not accompanied by complementary reforms (e.g., land consolidation, fiscal transfers).

Additionally, the finding that regions with fewer farmer cooperatives experience stronger income equalization effects implies that agricultural socialization services may act as an effective substitute for cooperative-led integration in weakly organized areas. This further suggests that policymakers should, in light of regional disparities, develop a dual-track model in which farmer cooperatives take the lead while service platforms provide complementary support. Such an approach aims to ensure that agricultural service systems can be efficiently adapted and equitably extended across regions with varying institutional foundations.

6. Conclusions and Discussion

In the context of urban-rural integration and development, the academic and policy communities are currently profoundly concerned with the issue of utilizing agricultural policy instruments to reduce the income gap between urban and rural areas and foster shared prosperity. By employing China’s pilot program of comprehensive socialization services for agricultural production, this paper empirically determines the influence of agricultural socialization services on the urban-rural income divide. In order to conduct a more comprehensive analysis of the mechanism of action and the characteristics of heterogeneity, it then implements a dual machine learning (DML) approach with provincial panel data from 2005 to 2022.

6.1. Agricultural Socialization Services Significantly Reduce the Urban-Rural Income Gap

Primary discoveries of the investigation include the following:

The income gap between urban and rural areas is substantially diminished by agricultural socialization services. The benchmark regression results indicate that the pilot agricultural socialization services (AGSS) have a significant negative impact on the urban-rural income gap, even after accounting for a variety of control variables. Additionally, the core explanatory variable, the urban-rural Theil index (Theil), consistently exhibits a negative coefficient for AGSS. Variable shrinkage treatment, counterfactual tests, and a variety of machine learning models that incorporate sample division ratios, as well as sensitivity analyses of alternative explanatory variables, such as the urban-rural income ratio and the urban-rural Gini coefficient, substantiate the results. The reliability and validity of the findings are illustrated by these analyses.

6.2. Technological Progress, Scale Operations, and Human Capital Accumulation Are Important Pathways to Narrowing the Disparity

Technological advancements, the accumulation of human capital, and the implementation of large-scale operations are critical methods for reducing the income gap. In terms of mechanism analysis, this paper incorporates a mediation effect identification approach into the double machine learning model, examining the mechanisms from three dimensions: technological progress, scale effects, and human capital. The findings indicate that agricultural socialization services contribute to the reduction of the urban-rural income gap by promoting technological advancement—through improvements in agricultural total factor productivity (ATFP) and mechanization levels (AMS); enhancing scale efficiency—via the expansion of land scale management (LSM) and service scale management (SSM); and facilitating human capital accumulation—by promoting the transfer of rural labor to non-agricultural sectors (RLT) and stimulating rural innovation and entrepreneurship (RIE). This discovery offers additional evidence that agricultural socialization services are a critical instrument for increasing the efficacy of agricultural output and are also a fundamental component of the broader pattern of integrated urban-rural development.

6.3. The Impact on Urban—Rural Income Gap Exhibits Significant Regional Heterogeneity. According to Heterogeneity Studies, the Impact of Agricultural Socialization Services on the Urban-Rural Income Gap Is Significantly Influenced by Regional, Policy, and Resource—Endowed Factors

Initially, agricultural socialization services have a more significant effect on the reduction of poverty and the increase in income in the primary food-producing regions, as indicated by regional functional allocation. This may be due to the fact that the urban-rural income divide is more significantly influenced by social services, the service sector is generally well-developed, and a greater proportion of the workforce is employed in agriculture. In contrast, policies are enforced with greater vigor in the primary producing regions.

Secondly, the severity of the policy reaction differs between the East, the central, and the West regions when it is deconstructed by geography. The socialization services system is flawless in the eastern region, where agriculture is not as prevalent, illustrating the policy’s high-level marginal development. In the western and central regions, where there is a substantial agricultural population and limited opportunities for non-farm employment, socialization services are more critical in adjusting the income structure and reducing the urban-rural income divide.

Third, the urban-rural income gap is more significantly influenced by agricultural socialization services in regions with a high concentration of cooperatives, as evidenced by the number of farmers’ professional cooperatives. New agricultural management organizations, such as cooperatives, are well-suited to ensure the effective release of policy dividends, provide socialization services, and integrate services due to their strong organizational and coordination skills.

Lastly, the socialization services’ capacity to reduce the urban-rural income gap is most effective in highly mechanized regions, as indicated by the agricultural mechanization level subgroups. This suggests that the service system is more efficient at allocating production factors and has stronger intergenerational income spillovers when integrated into the technology system.

6.4. Policy Implications and Future Research

Based on the data presented above, the report concludes with the following policy recommendations:

The scope and availability of agricultural socialization services should be further expanded. The accessibility and accuracy of services may be enhanced through the implementation of financial inputs, service standardization, and public platform construction.

Agricultural socialization services continue to encounter challenges, such as fragmented supply and imbalanced regional growth. Encourage the integration of urban and rural areas by utilizing agricultural socialization services to the fullest. Institutional integration and factor circulation should be achieved through agricultural modernization, and socialization services should be integrated into rural industrial, ecological, and governance systems. This will facilitate the convergence of rural and urban production and lifestyles.

Synergy in the development of organizational systems and technological advancement is a priority. Foster agricultural science and technology social service organizations, enhance the agricultural machinery purchase subsidy policy, and encourage the construction of high-quality farmland. These are merely a few of the essential measures that must be taken to fortify the technical foundation of services and facilitate the systematic development of new, high-quality agricultural productivity.

Encourage the development of utility networks in the less developed western and central regions of China. By directing investments, enhancing property rights protection, and attracting talented individuals to rural areas, we can encourage interregional synergistic poverty reduction and direct agricultural socialization services to the less fortunate.

Given the pronounced regional disparities in the impact of agricultural socialization services on the urban-rural income gap, policy formulation should be tailored to local conditions and adopt differentiated strategies: In major grain-producing areas, where the agricultural service system is relatively well-established, continuous investments in technology and mechanical equipment should be made to advance the modernization of agricultural production, thereby sustaining their contribution to narrowing the urban-rural income gap; in major grain-consuming areas, efforts should be accelerated to build the service system, increase financial and technical support, and address the shortcomings of insufficient demand, so as to fully leverage the role of agricultural services in bridging the urban-rural income divide; in areas with balanced production and marketing, efficient service models can be cultivated based on actual conditions to promote the integrated production and circulation of agricultural products. For the eastern, central, and western regions, differentiated measures should be implemented according to their respective agricultural foundations and development levels: the eastern region can prioritize enhancing the quality and added value of agricultural services, encouraging efficient operations and branded development; the central and western regions need to strengthen infrastructure and institutional capacity building, reinforce the organization of cooperatives and the construction of mechanized service teams, so as to narrow the gap with the eastern region. Additionally, it is essential to enhance economies of scale in regions with a large number of cooperatives and provide policy support and technical training in areas with fewer cooperatives or low levels of mechanization. Through these differentiated policies that more precisely leverage agricultural socialization services, it is anticipated that the urban-rural income gap will be effectively narrowed, thereby facilitating the realization of the goal of common prosperity.

Finally, the findings presented in this study are subject to certain limitations. Initially, provincial panels are the primary data source at the data level, but they do not accurately represent individual variations or institutional nesting processes at a more micro level. Secondly, the DML framework is effective in recognizing the mediating impact; however, the mechanism’s complexity may still be deficient. Future research can explore the mechanism of policy effects and spatial spillover paths in agricultural socialization services for urban-rural integration by utilizing micro data from farm households, prefectures, and municipal levels, as well as spatial econometric or structural modeling methods. This will enhance the theoretical framework and policy system.