Research on the Role Mechanism and Path of Digital Technology Empowering Farmers’ Common Prosperity

Abstract

Achieving common prosperity for farmers represent a key objective of rural revitalization and plays a crucial role in narrowing the urban–rural divide and advancing high-quality agricultural development. As a catalyst of modern development, digital technology fosters agricultural and rural progress, contributing significantly to the realization of farmers’ common prosperity. Using panel data from 30 Chinese provinces spanning 2012 to 2023, this study constructs econometric models to examine the direct, indirect, and nonlinear effects of digital technology on farmers’ common prosperity. Heterogeneity is also analyzed, endogeneity is addressed, and robustness tests are conducted. The results indicate that digital technology significantly enhances farmers’ common prosperity by 1%. This effect remains robust and displays both regional and dimensional heterogeneity. Additionally, digital technology indirectly fosters common prosperity by increasing farmers’ income and decreasing the urban–rural income gap, exhibiting a double-threshold nonlinear relationship. Policy recommendations include enhancing digital technology support, improving farmers’ digital literacy, and expanding income channels.

1. Introduction

Today, common prosperity is a central objective when promoting high-quality development. The 20th Party Congress re-emphasized the importance of advancing common prosperity and significantly improving individual wellbeing. By the first decade of the 21st century, China had made notable achievements in the pursuit of common prosperity, laying a robust foundation for continued development. Since the 18th Party Congress, residents’ per-capita disposable income has risen from 16,510 CNY to 39,218 CNY. The national GDP reached 126.06 trillion CNY in 2023, representing a growth rate increase of 2.2 percentage points compared with the previous year. The gross domestic product (GDP) of the eastern region amounted to 6520.84 billion CNY in 2023, an increase of 5.4% over the previous year; the GDP of the central region amounted to 2698.98 billion CNY, an increase of 4.9%; the GDP of the western region amounted to 2693.25 billion CNY, an increase of 5.5%; and the GDP in the northeast region was 5962.4 billion CNY, reflecting a 4.8% increment, with regional disparities narrowing. The ratio of disposable income per capita for urban and rural residents was 2.39, a decrease of 0.06 compared with the previous year. Common prosperity represents a fundamental social principle that promotes equitable resource distribution across society, enabling access to both material and spiritual wellbeing. It reflects not only economic equality and fairness but also a broader social ethos and development, aiming to realize social justice, stability, and a sustainable development orientation. Thus, social justice, stability, and sustainable development can be effectively realized. In the Chinese context, modernization is measured through the lens of achieving common prosperity, with development—particularly the growth and improvement of the national “cake”—as a prerequisite. This notion of “common” reflects justice, which implies that the “cake” should be both expanded and fairly distributed. Thus, the dialectical relationship between efficiency and fairness becomes crucial. From a practical perspective, China, as one of the largest developing countries, faces its most arduous development challenge in rural areas. Sustained and healthy economic development forms the bedrock for achieving common prosperity. Therefore, efforts should be directed toward strengthening agriculture-based scientific and technological innovation, enhancing the efficiency and quality of agricultural production, promoting agricultural modernization, and raising farmers’ income level. It is also essential to deepen reforms in the rural land system, safeguard farmers’ rights and interests, optimize rural land use, diversify income sources, and further expand the agricultural industrial system. These measures collectively contribute to the realization of common prosperity for farmers.

Digital technology, as the engine of the new era, can crucially promote the development of agriculture and rural areas, and the common prosperity of farmers [1]. The 2024 Central “No. 1 Document” highlighted the in-depth implementation of the digital village development action and the promotion of R&D activities for digital application scenarios. The Ministry of Agriculture and Rural Affairs and the Office of the Central Committee for Network Security and Informatization jointly issued the “Digital Agriculture and Rural Development Plan (2019–2025),” which greatly emphasized the construction of the digital countryside, application of digital technology in agriculture and rural areas, enhancement of digital productivity, and promotion of high-quality development of agriculture. Furthermore, the National Development and Reform Commission and the National Data Administration jointly issued the “Implementation Plan for Promoting Common Prosperity through the Digital Economy”, which outlines a detailed and feasible roadmap for the development of the rural digital economy. For infrastructural development, the plan emphasizes the continuous advancement of universal telecommunication services, the deepening of network coverage in rural areas, and the acceleration of the “Broadband Frontier” initiative. These efforts aim to bolster communication infrastructure in rural and remote areas, thereby constructing a robust “information bridge” that integrates digital technologies into rural settings. With these advancements, farmers can more easily access digital networks and overcome the challenges of information isolation. In the domain of industrial digital transformation, the plan actively encourages the development of smart agriculture by promoting the application of intelligent agricultural technologies and equipment tailored to regional and scale-specific needs.

Digital technology refers to the technical methods for acquiring, processing, and utilizing information through digital processing, storing, and application. As information technology continues to advance and proliferate, digital technology has become indispensable across all sectors of society. Its rapid development provides novel opportunities for realizing the common prosperity of farmers. The integration of digital technology and traditional industries accelerates industrial development, enhances total factor productivity, and drives high-quality and efficient growth—thereby providing robust support for promoting farmers’ common prosperity.

Digital technology and farmers’ common prosperity are critical to the strategy of rural revitalization and serve as important mechanisms for advancing agricultural modernization and increasing farmers’ income (AI). Public and private sector collaboration in digital technological investment and application within the agricultural sector is essential. Such efforts create favorable conditions for farmers to expand their income and wealth, foster a high-quality agricultural economy, support comprehensive rural progress, and drive the modernization of agriculture and rural communities. Thus, the common prosperity of farmers can be progressively realized. Although policy directives have defined a strategic pathway, the integration of digital technology into agriculture and rural sectors remains exploratory. A comprehensive theoretical understanding of how digital technology facilitates common prosperity among farmers remains nascent. Based on national panel data from 30 provincial-level regions from 2012 to 2023, this study constructs an econometric model to systematically examine the direct, indirect, and nonlinear effects of digital technology on farmers’ common prosperity. Mechanisms and heterogeneity are analyzed in detail. This research not only addresses the gap in the mechanistic studies on the relationship between digital technology and farmers’ common prosperity but also provides theoretical support and practical references for implementing the central government’s “Digital Rural” strategy and formulating targeted and effective policy measures. In doing so, digital technology can be better leveraged under policy direction to promote the sustainable realization of farmers’ common prosperity.

2. Literature Review

In recent years, academic research on the relationship between digital technology and farmers’ common prosperity has been deepening, and rich theoretical results and practical experiences have been documented. Most studies on common prosperity focus on connotation, measurement, and path selection. As a crucial component of the theoretical system of socialism with Chinese characteristics, common prosperity is the core goal of realizing social justice and high-quality economic development. It is not only the essential requirement and value pursuit of socialism but also the core mission of the Communist Party of China (CPC) [2]. Common prosperity is usually perceived as the process of eliminating poverty and social inequality and realizing shared wealth, which involves material upliftment, spiritual satisfaction, and the improvement of social wellbeing [3]. This is highly consistent with the value objectives of “reducing inequality” (SDG 10) and “eradicating poverty” (SDG 1) outlined in the United Nations Sustainable Development Goals (SDGs). The United Nations Development Program states that promoting inclusive growth and equitable resource allocation is a key path to achieving global sustainable development [4]. When measuring common prosperity, most scholars apply the establishment of an indicator system to measure the level of common prosperity among farmers. Common indicators include the Gini coefficient, Engel’s coefficient [5], and Terrell’s index, which measure the degree of inequality especially for income or wealth; subjective feelings such as happiness and satisfaction are also applied as indicators for evaluating the degree of common prosperity [6]. Common prosperity implies realizing balanced economic growth and a reasonable distribution of resources. Thus, every member of society can enjoy sufficient material wealth and resources, promote poverty eradication, and improve their living standards. Achieving common prosperity requires the joint efforts of all parties, including the government, enterprises, social organizations, and each citizen. Eliminating poverty and realizing common prosperity is the CPC’s unchanging original intention and mission [7]. The term “common” mainly refers to what all members of society should accomplish together, and “affluence” primarily implies the accumulation of a form of social wealth. Luo believes that the problem of income disparity between urban and rural residents in China is a significant cause of social inequality, and effectively solving this problem is a critical requirement for harmonious development, which is signified by efficiency, fairness, and the promotion of common prosperity [8]. Although the income distribution gap has improved in recent years, there are still some problems causing an imbalanced income distribution, and common prosperity cannot be reached in the short term [9,10].

China is gradually transiting into the digital era. The future of common prosperity should therefore be based on digital technology to promote the “broadband China” initiative, enabling the formation of a “digital countryside”. As digital technology continues to extend to rural areas, the manner of production and living patterns of farmers are being profoundly altered [11]. Academic research on digital technology mainly focuses on interpreting connotations, measuring indicators, and setting empowerment paths. Digital technology mainly embodies the digitization of information, the in-depth application of computer technology, and the networked transmission of information. Through digital technology, individuals can process, store, and transmit information more efficiently and conveniently, thus promoting the societal informatization process [12]. Digital technology has been comprehensively assessed and measured through various indicators, such as digital informatization, digital internetization, and the number of patents related to the technology. The results of these assessments provide a decision-making basis for policymakers and reference and guidance for enterprises and individuals [13,14,15]. Digital technology can significantly facilitate the emergence of novel technologies, business forms, and models. It can effectively promote green technological innovation, improve green total factor productivity, and lower energy intensity, etc. [16]. With its strong innovation ability, penetration ability, and coverage ability, this form of technology has become a novel impetus for economic growth; it is a fulcrum for transforming and upgrading the traditional manufacturing industry and a crucial driving force for constructing modern economic systems [17]. Scholars such as Wang [18] and Qin [19] reveal that, currently, a new generation of digital technologies, represented by the Internet of Things, big data, and artificial intelligence, is accelerating the development of China’s agriculture and rural areas; they note that these technologies have had a far-reaching impact on the production and lifestyle of farmers and have become an effective guarantee for the promotion of farmers’ income and the realization of common prosperity. International studies also support this trend. Li and Zang [20] empirically note that digital technological adoption can significantly enhance farmers’ entrepreneurial willingness and thus improve their livelihoods and reduce poverty. Xiong, Guo and Yang [21] further propose that digitally inclusive finance facilitates the realization of common prosperity for all farmers by promoting land transfer and capital accumulation. This can realize high-quality agricultural development and farm household income improvement. In the macro perspective, Wang et al. [22], through statistical data and typical case studies, indicate that the digital transformation of China’s agriculture can address the over-application of chemical fertilizers and irrigation water, the reduction in carbon emissions, and challenges associated with climate change challenges to promote green agricultural practices and inclusive development.

Notably, despite the intense penetration and innovation of digital technologies, the distribution of their applications remains significantly uneven. Van Dijk [23] notes that the “digital divide” is manifested not only at the level of hardware access but also in the ability to apply information and social cognitive differences. Hilbert [10] further posits that the digital development process may exacerbate existing structural inequalities and that the lack of policy guidance can lead to the monopolization of the “digital dividend” by minority groups. However, existing studies mostly focus on the static description of the digital divide and lack an in-depth analysis of how it affects the dynamic process of common prosperity through the income distribution mechanism. In recent years, the discussion on the inclusiveness and empowerment of digital technology has been strengthened, and the challenge of realizing the fair embedding of digital technology through institutional design has become a key issue in promoting the common prosperity of farmers. By constructing a transmission model of “digital technology–income gap–common prosperity”, this study embeds the impact of the digital divide into the nonlinear framework of farmers’ income growth, and it reveals the variation characteristics of the divide effect at different development stages.

The platform economy, as a crucial carrier of digital technological implementation, has become a research hotspot in regard to its empowering effect on rural areas. Srnicek (2016) [24] proposed the theory of “platform capitalism”, which indicates that e-commerce platforms may form new monopolies by restructuring the circulation chain. Thus, especially in regard to value distribution, farmers find themselves in a disadvantaged position Zeng et al. (2022) conducted a study based on the Taobao Village case, which revealed that the platform economy can increase the proportion of farmers sharing profits in the circulation process by 15–20 percentage points [25]. The controversial point for the two types of research lies in the “double-edged sword” effect of platform empowerment: on the one hand, the development of e-commerce has indeed expanded the sales channels of agricultural products [26]; on the other hand, the platform’s skewed traffic algorithm may lead to the “Matthew effect”, where only 10% of the top farmers hold over 70% of the online market share.

On the whole, existing studies mainly explain the impact of digital technology on different levels of rural revitalization in China at the theoretical level, whereas studies focusing on how digital technology empowers farmers to share prosperity, as well as its mechanism of action and internal transmission mechanism, should be supplemented. The application of digital technology in rural areas can improve farmers’ productivity, increase their income, and narrow the income gap between urban and rural areas, thus promoting the common prosperity of farmers. However, the unbalanced popularization of digital technologies remains a significant challenge. Thus, the government, enterprises, and all sectors of society should collaborate to promote the popularization and application of digital technologies and ensure that farmers can comprehensively benefit from the development of the digital era. This study incorporates the concepts of the digital divide and platform economy into a unified analytical framework, addressing the limitations of existing research, which tends to interpret the impacts of technology in a fragmented manner. It systematically examines the direct effects of digital technology on farmers’ common prosperity, as well as its indirect effects via farmers’ income and the urban–rural income gap, while also identifying potential threshold effects. The study aims to provide an empirical reference for advancing farmers’ common prosperity. Furthermore, due to regional disparities, this study proposes differentiated development paths—optimizing the platform ecosystem in eastern regions and reinforcing policy support mechanisms in western regions—thereby offering micro-level evidence to support the “targeted policy implementation” requirement outlined in the Digital Agriculture and Rural Development Plan (2019–2025).

3. Theoretical Analysis and Research Hypothesis

3.1. The Direct Effect of Digital Technology on the Common Prosperity of Farmers

As a novel impetus for promoting industrial development, digital technology can considerably affect the realization of common prosperity, especially for farmers. The continuous innovation and development of digital technology can induce development opportunities, which can comprehensively revitalize China’s agricultural economy and society, and it is also a crucial method for promoting the common prosperity of rural areas and farmers. China’s application of digital technology in rural areas has been deepening, and the institutional guarantee system has been continuously refined to lay a robust foundation for the common prosperity of rural farmers. This promotes the transformation and upgrading of industrial structure, leading to the formation of a modern industrial system, and stimulates the creation and accumulation of wealth, forming a new impetus for economic development. Thus, a robust guarantee of common prosperity is provided. Therefore, the following hypothesis is proposed:

H1. 

Digital technology can directly promote the common prosperity of farmers.

3.2. The Indirect Effect of Digital Technology in Empowering Farmers’ Common Prosperity

Digital technology has promoted the intelligent development of agricultural production, realizing the information management of the entire agricultural industry chain through technologies such as the Internet of Things, big data, and artificial intelligence. This intelligence improves the efficiency of agricultural production, reduces the labor burden of farmers, and promotes an improvement in the quality and increase in the yield of agricultural products. First, digital technology can directly provide advanced technical equipment, and can realize scientific management and intelligent management through the adoption of cloud computing, Internet of Things, and smart computers. It can also provide farmers with novel technologies, enable them to access agricultural information and obtain relevant agricultural knowledge and technology promptly, and improve the efficiency of agricultural production. Thus, their income is increased, and common prosperity is promoted. The “Smart Agriculture on the Cloud” app of the Ministry of Agriculture and Rural Affairs offers customized courses tailored to farmers’ planting varieties and regional climate; meanwhile, the short video platform provides technology education through intuitive field practice videos. Farmers in remote western areas are gradually mastering scientific planting methods by imitating short video content, such as “fruit tree pruning” and “pest control,” to improve their income. Second, digital technology can help release rural labor and increase the non-farm employment population. With the implementation of projects that include advanced sensor technology, agricultural remote monitoring, and integrated agricultural irrigation, the number of individuals engaged in agricultural activities continue to decrease, and some young farmers choose to migrate to urban areas to find work, which increases their income and promotes the realization of common prosperity among farmers [27]. Digital technology facilitates the integration of agriculture with industries such as rural tourism and health and wellness services. By leveraging traceable agricultural products enabled by Internet of Things (IoT) monitoring, farmers can develop diversified rural business models, including homestays and pick-your-own farms. Third, digital technology has facilitated the emergence of rural e-commerce, providing farmers with low-cost entrepreneurial opportunities in service sectors and diversified sales channels for agricultural products—such as rural logistics agency services and agricultural product testing. These developments have made it easier for agricultural products to enter the market, increased farmers’ sources of income, boosted sales and profits, and contributed to the realization of common prosperity. Fourth, the development of digital technology has prompted farmers to engage with new agricultural technologies and management methods. Through digital tools including online training and mobile applications, farmers can access the latest agricultural knowledge, improve their professionalism, and better respond to market demand, thus increasing their income and promoting the common prosperity of farmers.

In the market circulation process, digital technology has opened up the “digital arteries” connecting urban and rural markets. Platforms such as rural e-commerce and live-streaming sales have broken through the barriers of middlemen’s markups in traditional circulation, allowing farmers to directly connect with urban consumers and share the profit gains from the circulation process. Meanwhile, big data analysis accurately captures changes in urban consumer demand, guiding farmers to develop contract farming and customized agriculture, which significantly increases farmers’ sales income. From a long-term perspective, digital technology narrows the human capital gap by improving rural public service levels. “Internet + Education” and “Internet + Healthcare” enable rural areas to share high-quality urban resources. Farmers learn how to use modern agricultural technology and perform e-commerce operations, and gain other knowledge, through online courses. The improvement of health levels and skill quality directly translates into higher labor productivity, forming a virtuous cycle of “ability improvement–income growth,” which fundamentally narrows the urban–rural development gap. If rural areas lag behind in network coverage, digital equipment availability, and farmers’ digital literacy, the concentration of technological dividends in urban areas becomes inevitable. Therefore, it is necessary to apply policy measures to improve rural digital infrastructure and perform targeted digital skills training to ensure that digital technology truly becomes an “equalizer” for urban–rural income distribution. This enables farmers to equitably share the proceeds of development in the digital era, ultimately promoting the continuous narrowing of income gaps and leading all farmers towards common prosperity. Therefore, the following hypothesis is proposed:

H2a. 

Digital technology promotes farmers’ common prosperity by increasing agricultural income.

H2b. 

Digital technology promotes farmers’ common prosperity by reducing the urban–rural income gap.

3.3. The Nonlinear Effect of Digital Technology in Empowering Farmers’ Common Prosperity

The introduction of digital technology has prompted the development of agriculture from the traditional mode to modernization and intelligence. Through intelligent agricultural technology, farmers can realize precise agricultural management and improve the yield and quality of farm products, thus affecting farmers’ income and the urban–rural income gap nonlinearly. Digital technology empowers farmers to share wealth. Initially, there may be a significant increase in the role of digital technology. As the development of digital technology progresses, it may not have a nonlinear relationship with its impact. At the initial stage of digital technology, especially the introduction of smart agricultural technology, there may be a significant nonlinear increase, which improves the production efficiency of agricultural products, affecting farmers’ income and reducing the urban–rural income gap. However, with the continuous development of digital technology, there may be a diminishing effect or a tendency towards saturation, a trend that may stem from the complete penetration of digital technology in agriculture and the gradual weakening of the initial innovation effect. At this point, to continue to achieve nonlinear farmer income growth, deeper technological innovations or a combination of developments in other fields may be required to further expand the impact of digital technologies on agriculture and farmers. Therefore, realizing the sustainable development of digital technology and ensuring its nonlinear effect on farmers’ common prosperity requires continued policy support and social innovation development. Thus, a more comprehensive and deeper development of digital technology in agriculture can be promoted. Therefore, the following hypothesis is proposed:

H3a. 

The impact of digital technology-enabled shared prosperity has a nonlinear relationship with farmers’ income.

H3b. 

The impact of digital technology-enabled farmers’ shared prosperity has a nonlinear relationship depending on the urban–rural income gap.

The mechanism of digital technology empowering farmers to share wealth is depicted in Figure 1.

4. Research Design

4.1. Model Construction

4.1.1. Benchmark Regression Model

To test the impact of digital technology on farmers’ common prosperity, based on the previous analysis of the theoretical mechanism, a multiple linear regression model is constructed as illustrated in Equation (1):

$${CP}_{it}=\gamma +\alpha {DT}_{it}+\beta {Control}_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{it}$$

(1)

${\mathrm{C}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ represents the level of farmers’ common prosperity among in province $\mathrm{i}$ in year $\mathrm{t}$; ${\mathrm{D}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$ signifies the level of digital technological development in province $\mathrm{i}$ in year $\mathrm{t}$; and ${\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}$ denotes the control variables for province $\mathrm{i}$ in year $\mathrm{t}$, including the degree of openness, level of economic development, labor force level, human capital level, and industrial structure. $\mathrm{\gamma }$ indicates the constant term;$\mathrm{\alpha }$ and $\mathrm{\beta }$ denote the coefficients to be estimated; ${\mathrm{\mu }}_{\mathrm{i}}$ represents individual fixed effects; ${\mathrm{\delta }}_{\mathrm{t}}$ signifies time fixed effects; and ${\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$ corresponds to the random disturbance term.

4.1.2. Mediating Effect Model

To reveal the relationship between the mediating variables in the core explanatory variables and the explanatory variables, this study constructs the mediating effect model, taking the farmers’ income and the urban–rural income gap as the mediating variables, and it adopts a step-by-step test of the significance of the regression coefficients to test the mediating effect; see Equations (2)–(4) for details:

$${CP}_{it}=c{DT}_{it}+\beta {Control}_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{it}$$

(2)

$$M_{it}=a{DT}_{it}+\beta {Control}_{it}+{{\mu }_i+{\delta }_t+\epsilon }_{it}$$

(3)

$${CP}_{it}=c^{’}{DT}_{it}+b{M}_{it}+\beta {Control}_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{it}$$

(4)

$M_{it}$ represents the mediating variable, which refers to the farmers’ income and the urban–rural income gap (Theil index) in province $\mathrm{i}$ in year $\mathrm{t}$; in Equation (2), the coefficient $c$ represents the total effect of digital technology on the common prosperity of farmers. In Equation (3), the coefficient $a$ represents the effect of digital technology on rural income and the urban–rural income gap. In Equation (4), the coefficient $c^{’}$ represents the direct effect of digital technology on the common prosperity of farmers after controlling for the mediating variable, and coefficient $b$ represents the effect of rural income and the urban–rural income gap on the common prosperity of farmers after controlling for digital technology. The term $a\times b$ represents the mediating effect of digital technology on farmers’ income and the urban–rural income gap in the process of promoting common prosperity for farmers. The total effect is decomposed as $c=c^{’}+ab$. ${\epsilon }_{it}$ denotes the random disturbance term, ${\mu }_i$ signifies the individual fixed effect, and ${\delta }_t$ indicates the time fixed effect.

4.1.3. Threshold Effect Model

The level of farmers’ income and urban–rural income gap impacts the degree of common prosperity among farmers empowered by digital technology, respectively, with farmers’ income and the urban–rural income gap as the threshold variables, to construct the threshold fixed-effects model; see Equation (5) for details:

$${CP}_{it}={\sigma }_0+{\sigma }_1{DT}_{it}{I}_{it}\left(Z_{it}<{\theta }_1\right)+{\sigma }_1{DT}_{it}{I}_{it}\left({\theta }_1{\le Z}_{it}<{\theta }_2\right)+{\sigma }_2{DT}_{it}{I}_{it}\left(Z_{it}\ge {\theta }_2\right)+{\sigma }_c{X}_{it}+{\epsilon }_{it}$$

(5)

$Z_{it}$ signifies the threshold variable, namely, farmers’ income or the urban–rural income gap; $\theta$ indicates the threshold value; $I\left(·\right)$ denotes the indicator function, whose value is either 0 or 1; and ${\epsilon }_{it}$ represents the random perturbation term.

4.2. Variable Selection

The detailed treatment description of various variables in this study is explained as follows:

4.2.1. Explained Variable: Common Prosperity of Farmers

Common prosperity includes two aspects, namely, “common” and “prosperity”. “Common” does not refer to the affluence of a part of the population or region; it signifies the realization of comprehensive, coordinated, and balanced development, with no one being left behind. For “affluence”, the first step entails promoting high-quality development in all areas, continuing to make the “cake” bigger, creating greater value and wealth, and driving residents to increase their income Additionally, in realizing common prosperity, the coordinated development of the economy and ecology should be integrated, and economic gains should not be achieved at the expense of ecological damage. “Commonality” focuses on fairness, reflecting the development balance between urban and rural areas, regions, and groups; “Prosperity” is closely linked to development, reflecting the improvement of residents’ material living standards; “Infrastructure” focuses on sustainable development capabilities, and infrastructure and public services are important supports for common prosperity; the “ecological environment” integrates the concept of green development, and common prosperity requires considering economic and ecological coordination; “Development quality” focuses on endogenous growth momentum; and innovation is the key to achieving sustainable prosperity. For secondary indicators, the length of long-distance optical cable lines and the number of Internet broadband ports are infrastructure monitoring indicators clearly defined in the “14th Five-Year Plan for Digital Economy Development”; the digital inclusive finance index is widely applied to measure the penetration of digital finance in rural areas; the number of patent applications and the transaction volume of technology contracts are commonly utilized indicators for technological innovation in academia; the Gini coefficient and the Theil index are internationally applied income distribution inequality measurement tools; per-capita disposable income and the Engel coefficient are widely utilized to measure residents’ living standards; and the public service facilities per 10,000 people refers to the requirement of “equalization of basic public services in urban and rural areas” in the “Rural Revitalization Strategy Plan”, which has policy consistency. Based on the preceding analysis, this study constructs the indicator system from five aspects, namely, common degree, affluence degree, infrastructure, ecological environment, and development quality, as illustrated in

Table 1. Farmers’ common prosperity indicator system construction.

Primary Indicators	Secondary Indicators	Unit	Attribute
Common Degree	Gini coefficient	/	-
	Income multiplier for urban and rural residents	/	-
	Urbanization rate	% of urbanization rate	+
Affluence	Per-capita disposable income	CNY/person	+
	Per-capita consumption expenditure	CNY/person	+
	Engel’s coefficient	/	-
Infrastructure	Average years of education	Years/person	+
	Public library collection per capita	Books/person	+
	Number of beds in medical institutions per 10,000 people	Per bed	+
	Number of practicing assistant physicians per 10,000 people	Persons	+
	Public transportation vehicles per 10,000 people	Units	+
	Public restrooms per 10,000 people	Units	+
	Proportion of social security and employment expenditure	%	+
	Labor productivity of the whole society	CNY/person	+
Ecological Environment	Forest coverage	%	+
	Carbon emission intensity	Million tons/billion CNY	-
Quality of Development	R&D investment intensity	%	+
	Green patent authorization number takes logarithm	Units	+

+ and - means indicator attribute, indicating whether the impact on common prosperity or digital technology is promoted or inhibited.

, and the entropy value method is applied to calculate the level of farmers’ common affluence.

4.2.2. Core Explanatory Variables: Digital Technology

Promoting the construction of digital villages is currently a key issue facing rural areas. The Outline of the Fourteenth Five-Year Plan for the National Economic and Social Development of the People’s Republic of China and the Visionary Goals for 2035 proposed the accelerated construction of digital villages. It states that we should solve the problems of the “Three Rural Issues” using digital technology. Thus, digital technology can be closely integrated with the development of agriculture, rural governance, and farmers’ lives, laying a robust foundation for promoting agricultural modernization and high-quality development [28]. Digital technology mainly integrates information and communication technology into the current agricultural development, promotes the quality and efficiency of agriculture and rural areas, realizes high-quality development, high-efficiency enhancement, and high-quality output, and promotes agricultural income, rural prosperity, and common prosperity of farmers [29]. This study constructs the indicator system from four aspects, namely, digital infrastructure, digital industrialization, industrial digitization, and digital innovativeness, as depicted in

Table 2. Digital technology indicator system construction.

Primary Indicators	Secondary Indicators	Unit	Attribute
Digital Infrastructure	Number of Internet broadband access ports	10 thousand	+
	Number of Internet broadband access users	10 thousand households	+
	Mobile phone penetration rate	Number of mobile phones per 100 people	+
	Length of long-distance fiber optic cable lines	10 thousand kilometers	+
Digital Industrialization	Information technology service income to GDP	% of GDP	+
	Number of legal person units in information transmission, software, and information technology service industry	People	+
	Percentage of employed persons in information software industry	%	+
Industry Digitization	Digital inclusive finance index	/	+
	Websites per million enterprises	Number	+
	Share of enterprises with e-commerce transactions	%	+
Digital Innovation	No. of patents applied for and granted	Number	+
	Total no. of technology contract transactions	million CNY	+
	Total post and telecommunications business/GDP	%	+
	R&D expenditure of enterprises above scale	million CNY	+

. It adopts the entropy value method to calculate the level of digital technology.

4.2.3. Mediating Variables

Farmers’ income: Farmers’ income can be a crucial indicator for measuring the economic benefits of digital technology in the field of agriculture. Through the application of digital technology, the yield and quality of agricultural products have been improved. The production process has been optimized, thus directly affecting farmers’ income levels. The increase in farmers’ income can reflect the social equity and common prosperity induced by digital technology in rural areas. Because disposable income per capita usually reflects the average level of the country or region as a whole, so the logarithm of disposable income per capita is utilized to measure the level of farmers’ income.

Rural–urban income gap: The popularization and application of digital technology can reduce the rural–urban income gap, promote the development of the rural economy, and improve the living standards of farmers. The Tel Index is a critical indicator for measuring the urban–rural income gap, and it considers population change factors, increasing its reliability, as depicted in Equation (6):

$${Theil}_t={\sum }_{i=1}^2\left({\displaystyle \frac{I_{it}}{I_t}}\right)·\ln \left[{\displaystyle \frac{I_{it}}{I_t}}/{\displaystyle \frac{P_{it}}{P_t}}\right]$$

(6)

$i$ represents two economic groups, namely, rural and urban; $I_t$ denotes the total income in year $t$; $I_{it}$ indicates the total income of an urban or rural area in year $t$; $P_t$ signifies the total population in the year $t$; and $P_{it}$ corresponds to the resident population of an urban or rural area in year $t$.

4.2.4. Control Variables

There are many other factors affecting the common prosperity of farmers. This study, which combines the relevant literature, includes the following as control variables in the research model to exclude the impact of these factors on the measurement results. Drawing on the works of other scholars [30,31,32], the total amount of imported and exported goods as a proportion of GDP is utilized to measure the degree of openness to the outside world (FDI); the universally recognized per-capita GDP is applied to measure the level of economic development (PCGDP); the number of employed persons is taken as a natural logarithm to indicate the level of the labor force (LFL); the number of students enrolled in tertiary education (in 10,000)/total population is utilized to indicate the human capital (LHC); and the value added of tertiary industry/value added of secondary industry measures the industrial structure (IS).

4.3. Data Sources and Descriptive Statistics

Data was mainly obtained from the China Statistical Yearbook, the China Science and Technology Statistical Yearbook, the 7th Population Census, and provincial statistical yearbooks. As there are a many missing values in Tibet, considering the large number of indicators required herein and the difficulty of data acquisition, this study selects the panel data of 30 provinces except Tibet, Hong Kong, Macao, and Taiwan from 2012 to 2023, and the linear interpolation method is applied to supplement some of the missing data. The descriptive statistics of each variable are depicted in

Table 3. Descriptive statistics.

Variables	(1)	(2)	(3)	(4)	(5)
	Obs.	Mean	SD	Min	Max
DT	360	0.162	0.113	0.0266	0.646
CP	360	0.307	0.110	0.119	0.684
AI	360	10.13	0.418	9.187	11.35
Theil	360	0.0804	0.0368	0.0159	0.197
FDI	360	0.264	0.265	0.00757	1.354
PCGDP	360	10.94	0.453	9.849	12.21
LFL	360	7.589	0.781	5.545	8.864
LHC	360	0.0221	0.00810	0.00638	0.114
IS	360	1.406	0.767	0.611	5.690

.

5. Analysis of Empirical Results

5.1. Benchmark Regression Test

The results of the benchmark regression model calculated according to Equation (1) are illustrated in

Table 4. Benchmark regression test results of digital technology empowering farmers’ common prosperity.

	(1)	(2)	(3)	(4)	(5)
	CP	CP	CP	CP	CP
DT	0.613 ***	0.053 ***	0.044 **	0.045 **	0.054 ***
	(11.2175)	(2.7047)	(2.1273)	(2.1690)	(2.5932)
FDI			−0.021	−0.024 *	−0.030 **
			(−1.5709)	(−1.7873)	(−2.4916)
PCGDP				0.024 *	0.058 ***
				(1.8332)	(3.7900)
LFL					−0.061 ***
					(−3.3629)
LHC					−0.087
					(−1.2091)
IS					0.017 ***
					(2.8073)
_cons	0.208 ***	0.494 ***	0.518 ***	0.247	0.270
	(25.1552)	(73.0571)	(34.2529)	(1.6448)	(1.3806)
Year fe	No	Yes	Yes	Yes	Yes
City fe	No	Yes	Yes	Yes	Yes
N	360	360	360	360	360
r2_a	0.397	0.987	0.987	0.987	0.988
F	125.833	729.351	792.821	821.581	914.493

t statistics in parentheses * p < 0.1, ** p < 0.05, and *** p < 0.01.

. Column (1) depicts the regression results of digital technology on farmers’ common prosperity without fixing the time and province; digital technology positively and significantly affects farmers’ common prosperity, specifically, at the 1% significance level, each 1% increase in digital technology leads to an average increase in farmers’ common prosperity by 0.613 on average. Column (2) indicates that digital technology also positively affects farmers’ common prosperity after fixing time and province at the 1% significance level, where each 1% increase in digital technology leads to an average increase in farmers’ common prosperity by 0.053. Columns (3–5) depict the regression results after adding the control variables stepwise, fixing time and province, respectively. From Table 4, it can be noted that digital technology still significantly and positively affects farmers’ common prosperity after adding each control variable; meanwhile, digital technology positively affects farmers’ common prosperity at the 1% significance level after adding all control variables, and every 1% increase in digital technology on average leads to a 0.054 increase in farmers’ common prosperity, which is consistent with the results of the previous study. Thus, Hypothesis H1 is valid.

5.2. Endogeneity Test

To address the impact of the endogeneity problem on the research findings, this study draws on the research ideas of related scholars [33], sets the one-period lagged digital technical indicator (l.DT) as an instrumental variable, and adopts the two-stage least squares (2SLS) method for parameter estimation. This method effectively mitigates the possible endogeneity bias problem of the model through the reasonable selection of instrumental variables and the organic combination of two-stage regression, and the results are depicted in

Table 5. Endogeneity test results.

	(1)	(2)
	DT	GTFP
L.DT	1.0020 ***
	(31.7717)
DT		0.0499 **
		(2.4487)
FDI	0.0254 *	−0.0134
	(1.7072)	(−1.2140)
PCGDP	0.0123	0.0584 ***
	(0.7778)	(3.6086)
LFL	−0.0274	−0.0686 ***
	(−1.6147)	(−4.2750)
LHC	−0.0057	−0.0595
	(−0.0665)	(−0.8774)
IS	0.0090	0.0203 ***
	(1.5528)	(3.9148)
_cons	0.0103	0.2311
	(0.0547)	(1.1563)
N	330	330
R2	0.9872	0.9899
Adj. R2	0.9852	0.9883

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with robust t-values in parentheses.

. The results indicate that the test results of the LM statistic value and the Wald F statistic both present significant positive values, comprehensively confirming the instrumental variables’ validity. The findings show that digital technology significantly impacts farmers’ common prosperity, with a regression coefficient of 0.0499. This finding further confirms the validity of Hypothesis H1 and enhances the credibility of the results of the empirical analysis.

5.3. Robustness Test

This study draws on the lagged core explanatory variables commonly applied by scholars [34], excluding some regions [35], and shrinking the tail 1% treatment [36], etc., to validate the stability of the model in order to avoid errors occasioned by chance factors, and the test results are illustrated in the

Table 6. Robustness test results.

Variable	One-Period Lag Treatment	Excluding Some Areas	Shrinking 1% Treatment
	CP	CP	CP
DT		0.073 ***	0.054 **
		(3.1264)	(0.021)
L.DT	0.050 **
	(0.022)
FDI	−0.012	−0.012	−0.025 **
	(0.012)	(−0.6793)	(0.012)
PCGDP	0.059 ***	0.059 ***	0.054 ***
	(0.018)	(3.3719)	(0.016)
LFL	−0.070 ***	−0.071 ***	−0.061 ***
	(0.018)	(−4.0854)	(0.018)
LHC	−0.060	−0.110	−0.034
	(0.073)	(−0.5395)	(0.208)
IS	0.021 ***	0.020 **	0.015 **
	(0.006)	(2.1502)	(0.007)
_cons	0.414 *	0.107	0.313
	(0.223)	(0.5360)	(0.205)
N	330	288	360
adj. R2	0.988	0.979	0.987

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

. Even after lagging the core explanatory variable digital technology by one period, excluding six regions, namely, Beijing, Shanghai Municipality, Inner Mongolia Autonomous Region, Guangxi Zhuang Autonomous Region, Ningxia Hui Autonomous Region, and Xinjiang Uygur Autonomous Region, and shrinking the tail of all the variables by 1%, digital technology significantly promotes farmers’ common prosperity. This indicates that the model is robust and the results have a certain degree of reliability.

5.4. Conduction Mechanism Test

According to Equations (2)–(4), Stata17.0 software was applied to measure the results of the transmission mechanism test between digital technology and farmers’ common prosperity, as illustrated in

Table 7. The transmission mechanism test of digital technology empowering farmers’ common prosperity.

	(1)	(2)	(3)	(4)
	AI	AI	Theil	CP
DT	0.118 ***	0.042 **	0.043 ***	0.037 *
	(3.9127)	(2.1243)	(5.6378)	(1.8572)
FDI	0.107 ***	−0.041 ***	−0.038 ***	−0.015
	(5.5973)	(−3.2250)	(−7.8164)	(−1.1244)
PCGDP	0.306 ***	0.026	−0.030 ***	0.070 ***
	(13.2274)	(1.4069)	(−5.1679)	(4.5712)
LFL	0.084 ***	−0.070 ***	−0.016 ***	−0.055 ***
	(3.6091)	(−4.6412)	(−2.6435)	(−3.6747)
LHC	0.140	−0.102	−0.090 *	−0.052
	(0.6792)	(−0.7818)	(−1.7274)	(−0.3975)
IS	0.007	0.016 ***	0.011 ***	0.013 **
	(0.9166)	(3.3920)	(5.7867)	(2.5139)
AI		0.106 ***
		(2.9570)
Theil				0.389 ***
				(2.7460)
_cons	6.226 ***	−0.460	0.490 ***	0.008
	(22.3299)	(−1.6179)	(6.9435)	(0.0438)
N	360	360	360	360
r2_a	0.998	0.988	0.983	0.988

t statistics in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

. Columns (1–2) report the test results of digital technology affecting farmers’ common prosperity through farmers’ income. From Table 4, it can be observed that digital technology has a significant positive impact on farmers’ common prosperity. Drawing on Wen [37], the significance of the coefficient a in Equation (3) and the coefficient b in Equation (4) is sequentially tested, and it is found that both coefficients are significant; therefore, the indirect effect is significant, i.e., there is also a mediating effect. Consequently, the researchers continued to test the significance of the coefficient c′ in Equation (4) and found that it is also significant; therefore, the direct effect is also significant. Furthermore, this study determined whether ab and c′ have the same value. Table 7 in columns (1–2) indicates that the two have the same value, therefore belonging to the partially mediated effect; i.e., in the process determining the impact of digital technology on the farmers’ common prosperity, the mediating effect of the farmer’s income is significant. Therefore, Hypothesis H2a holds. Columns (3–4) indicate the results of the test seeking to determine whether digital technology affects farmers’ common prosperity through the urban–rural income gap. Similarly, this study finds that the coefficients a and b are significant. Therefore, the indirect effect is also significant, there is also a mediation effect, and the coefficients c′ are also significant. Consequently, the direct effect is also significant. The study further finds that ab and c′ share the same value, as depicted in columns (3–4) of Table 7. Therefore, the existence of a partially mediated effect is confirmed, and Hypothesis H2b is valid.

5.5. Nonlinearity Test

To further test the nonlinear effect of digital technology on farmers’ common prosperity, this study applied Stata17.0 software, as depicted in Equation (5). Simultaneously, it applied the self-help method of repeated sampling (number of iterations: 300 times), obtaining the test results when the income level and urban–rural income gap are the threshold variables, as illustrated in

Table 8. Farmers’ income threshold effect test.

CP	Coefficient	std. err.	t	p > t	[95% Conf.]	[Interval]
FDI	0.029	0.023	1.260	0.219	−0.018	0.075
PCGDP	0.190	0.008	23.520	0.000	0.174	0.207
LFL	−0.125	0.030	−4.160	0.000	−0.187	−0.064
LHC	0.240	0.293	0.820	0.420	−0.359	0.839
IS	0.032	0.007	4.670	0.000	0.018	0.046
_cat#c.DT
DT (AI < 10.3963)	−0.148	0.023	−6.560	0.000	−0.194	−0.102
DT (10.3963 ≤ AI < 10.1140)	−0.076	0.023	−3.250	0.003	−0.124	−0.028
DT (AI ≥ 10.1140)	0.040	0.022	1.820	0.080	−0.005	0.086
_cons	−0.875	0.264	−3.320	0.002	−1.414	−0.335
sigma_u	0.104
sigma_e	0.017
rho	0.975	(fraction of variance due to u_i)

and

Table 9. Threshold effect test for the urban–rural income gap.

Coefficient	Coefficient	std. err.	t	p > t	[95% Conf.]	[Interval]
FDI	0.028	0.028	1.230	0.228	−0.018	0.074
PCGDP	0.196	0.008	23.380	0.000	0.179	0.213
LFL	−0.135	0.029	−4.630	0.000	−0.194	−0.075
LHC	0.107	0.218	0.490	0.628	−0.339	0.552
IS	0.024	0.008	3.040	0.005	0.008	0.040
_cat#c.DT
DT (Theil < 0.05 67)	0.071	0.027	2.600	0.015	0.015	0.127
DT (0.0567 ≤ Theil < 0.0722)	−0.030	0.023	−1.300	0.205	−0.077	0.017
DT (Theil ≥ 0.072 2)	−0.115	0.022	−5.140	0.000	−0.161	−0.069
_cons	−0.858	0.269	−3.180	0.003	−1.409	−0.307
sigma_u	0.109
sigma_e	0.016
rho	0.978	(fraction of variance due to u_i)

, respectively. Notably, a double-threshold effect can be observed in both variables. Table 8 and Figure 2 indicate the test results when farmers’ income (AI) is the threshold variable, when farmers’ income decreases by 0.148 for every 1% increase in farmers’ income, when, farmers’ income decreases by 0.076 for every 1% increase in farmers’ income, and when farmers’ income increases by 0.040 for every 1% increase in farmers’ income. When farmers’ income crosses different thresholds, the degree of influence of digital technology on farmers’ common prosperity changes from negative to positive and gradually increases. With the rapid development of digital technology, its digitization degree is constantly increasing, and the degree of influence on farmers’ common prosperity is constantly increasing. For low-income regions, it is necessary to implement “lightweight technology subsidies”, prioritize supporting low-cost digital tools, and lower the initial investment threshold; for middle-income regions, governments focus on cultivating the “technology + industry” integration model, centrally purchase IoT equipment through cooperatives, and develop contract agriculture to share costs; and for high-income regions, they should encourage digital technology to extend to the high end of the value chain, support deep processing of agricultural products, rural tourism, and other business forms, and enhance product premiums through data empowerment. Table 9 and Figure 3 depict the test results when the urban–rural income gap is the threshold variable; when for every 1% increase in digital technology, the common prosperity of farmers increases by 0.071; when for every 1% increase in digital technology, the common prosperity of farmers decreases by 0.030; and when for every 1% increase in digital technology, the common prosperity of farmers decreases by 0.115. As the urban–rural income gap grows, the effect of its digital technology on farmers’ common prosperity gradually decreases. For low-gap regions, stakeholders should promote the “integration of urban and rural digital facilities” and share urban cold chain logistics and cloud computing centers; for medium-gap regions, they should establish a “benefit distribution adjustment mechanism” and require e-commerce platforms to give concessions to farmers; and for high-gap regions, they should strengthen the “digital anti-monopoly” and limit the monopoly of urban capital over rural e-commerce channels. In summary, Hypotheses H3a and H3b are valid.

5.6. Heterogeneity Test

Regional heterogeneity test: Because the resource endowment and economic and social development level of China’s regions differ significantly, the development level of digital technology in the different regions is also different. Therefore, for the promotion of common prosperity of farmers, the country is characterized by significant regional heterogeneity. Herein, 30 provinces are divided into eastern, central, and western regions, as illustrated in columns (1–3) of

Table 10. Heterogeneity test results.

Variable	Regional Heterogeneity Test			Dimensional Heterogeneity Test
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	East	Central	West	Digital Infrastructure	Digital Industrialization	Industrial Digitization	Digital Innovativeness
DT	0.004	0.049	−0.202 ***	0.032 **	0.066 ***	−0.012	0.034 **
	(0.1382)	(0.7810)	(−3.0203)	(2.0473)	(3.3809)	(−0.5752)	(1.9988)
FDI	−0.003	0.030	−0.053	−0.039 ***	−0.027 **	−0.036 ***	−0.030 **
	(−0.1661)	(0.4121)	(−1.1798)	(−3.3599)	(−2.2697)	(−3.1019)	(−2.4358)
PCGDP	0.066 **	0.060 **	0.116 ***	0.056 ***	0.059 ***	0.059 ***	0.059 ***
	(2.3389)	(2.0062)	(3.1721)	(3.6096)	(3.8934)	(3.8612)	(3.7780)
LFL	0.033	−0.060 ***	−0.133 *	−0.055 ***	−0.069 ***	−0.052 ***	−0.057 ***
	(0.8338)	(−3.3029)	(−1.9719)	(−3.0291)	(−3.7425)	(−2.8413)	(−3.1616)
LHC	−0.037	0.545	−0.307	−0.106	−0.147 *	−0.112	−0.071
	(−0.6879)	(1.2254)	(−0.3155)	(−1.4631)	(−1.9400)	(−1.5261)	(−0.9886)
IS	0.001	0.016	0.050 ***	0.019 ***	0.017 ***	0.019 ***	0.017 ***
	(0.1449)	(1.2651)	(2.9521)	(3.0308)	(2.8585)	(3.1715)	(2.8340)
_cons	-0.498	-0.010	0.006	0.261	0.309	0.211	0.239
	(−1.4804)	(−0.0308)	(0.0096)	(1.3351)	(1.5761)	(1.0946)	(1.2336)
Year fe	Yes	Yes	Yes	Yes	Yes	Yes	Yes
City fe	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	132	96	132	360	360	360	360
r2_a	0.992	0.980	0.965	0.988	0.988	0.987	0.988
F	1128.087	271.593	218.670	941.214	929.902	932.887	898.085

t statistics in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01. Note: The 30 provinces are divided as follows: the eastern region comprises 11 provinces and municipalities, namely, Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Shandong, Guangdong, Hainan, Fujian, and Liaoning; the central region comprises 8 provinces and municipalities, namely, Henan, Hunan, Hubei, Anhui, Jiangxi, Shanxi, Heilongjiang, and Jilin; and the western region comprises 11 provinces and municipalities, namely, Sichuan, Chongqing, Guizhou, Yunnan, Guangxi, Xinjiang, Qinghai, Ningxia, Inner Mongolia, Shaanxi, and Gansu.

. For the western region, the promotion effect of digital technology on farmers’ common prosperity is negative at the 1% significance level. This is essentially a concentrated manifestation of regional development imbalance, imperfect digital ecology, and lagging supporting policies in the western rural areas. This difference lies in the imbalance of the regional development foundation, digital infrastructural layout, and technological application conditions. The eastern region exhibits a robust economic foundation, with a GDP of 65,208.4 billion CNY in 2023, accounting for 51.7% of the national total. Its industrial structure is mainly composed of high-tech industries and modern service industries, and rural areas are closely linked to urban economies. Digital technology can rapidly penetrate into agricultural production, and agricultural product circulation when the industrial chain is sound. The GDP of the western region is 2.69325 trillion CNY, and the economy is mainly based on traditional agriculture and resource-based industries. The fragmented characteristics of the rural economy are apparent, making the formation of a virtuous cycle of “technology input–benefit output” difficult. The promotion of digital technology may even manifest short-term negative effects due to high-cost initial equipment procurement and expensive technical training. Due to the peculiar geographic environment, demographic structure, and economic foundation of the western region, it manifests more significant “access gap”, “skill gap”, and “ecological deficiency” problems than the eastern region, especially in the process of digital technological penetration. Thus, it becomes difficult to release the technological dividend, and the loss of short-term benefits inevitably occurs.

Dimensional heterogeneity test: This study measures the heterogeneous impact of digital technology on farmers’ common prosperity from four dimensions: digital infrastructure, digital industrialization, industrial digitization, and digital innovativeness [38]. Columns (4–7) of Table 10 indicate that digital infrastructure, digital industrialization, and digital innovativeness have significant positive impacts on farmers’ common prosperity, and digital industrialization manifests the greatest contribution, followed by digital innovativeness and digital infrastructure. From the perspective of leading digital industrialization, the development of digital technology has promoted an increase in legal entities tailored to information transmission, software, and information technology services. This has led to the formation of a holistic digital industry ecosystem that can provide mature technological solutions for rural areas and directly drive farmers’ income growth. The regional agglomeration of digital innovation power is significant, with the R&D investment intensity in the eastern region being 3.2 times that of the western region, and with the number of authorized patent applications authorized accounting for 72% of the country’s total. Although, in this region, the diffusion of digital technological innovation achievements to rural areas is faster, there is a lack of innovative resources and insufficient technological adaptability. For the lag in industrial digitization, rural industrial digitization relies on large-scale agricultural operations and industrial chain integration. However, in China’s rural areas, small-scale farmers are the majority, and it is difficult for them to bear the fixed costs of digital transformation, leading to slower penetration of industrial digitization applications such as digital inclusive finance and e-commerce. This indicates that digital industrialization injects new vitality into farmers’ common prosperity, facilitating the attainment of common prosperity.

6. Conclusions and Policy Implications

6.1. Conclusions

Based on the panel data of 30 provinces in China from 2012 to 2023, this study systematically researches the impact of digital technology on farmers’ common prosperity, and it systematically studies the direct effect, indirect effect, and nonlinear effect of digital technology on farmers’ common prosperity by constructing an econometric model. Finally, it clarifies the relationship between digital technology and farmers’ common prosperity, and it provides path choices for the former to empower farmers’ common prosperity. The results indicate that digital technology has a significantly direct and indirect role in promoting the common prosperity of farmers, and it validates the existence of heterogeneity. The impact of digital technology on the common prosperity of farmers has a nonlinear effect; it can be observed that farmers’ income and the urban–rural income gap have passed the double-threshold test.

6.2. Policy Implication

According to this study, the realization path of digital technology empowering farmers’ common prosperity is proposed:

(1)

Increase the policy support for digital technology to promote the common prosperity of farmers. First, there is apparent regional heterogeneity in the level of development of digital technology in China. The government can promote the level of digital technology in rural areas by increasing the promotion and support of digital technology in such areas. Thus, the urban–rural digital divide is narrowed, and coordinated regional development is realized. Second, it can increase investment in rural digital infrastructural construction (e.g., upgrade rural network coverage and strengthen the upgrading of digital technological equipment) to provide more favorable conditions for the application of digital technology in rural areas. The government can prioritize the deep coverage of 4G networks in remote rural areas in the west and the construction of 5G pilot projects in rural areas in the east, prioritize the construction of IoT base stations in smart agriculture demonstration zones and rural e-commerce industrial parks, and establish a long-term operation and maintenance model of “government subsidies + enterprise operation + farmer sharing”. Third, it can formulate targeted supportive policies such as offering tax incentives, subsidies, and rewards, increase support for social security, stimulate farmers’ motivation while providing basic protection, enhance their sense of access and happiness, and promote the widespread application of digital technology in rural areas. Thus, it can realize resource sharing and complementary advantages, and it can promote the common prosperity of farmers. Fourth, the development of digital technology can lead to the emergence of a “digital divide” between developed and underdeveloped regions, as well as urban and rural areas. The more underprivileged groups are unable to accept, understand, and effectively apply digital technology, the more uneven development becomes. Therefore, the central and western regions should promote the development of digital technological infrastructure by implementing sound policies.

(2)

Improve farmers’ digital literacy. First, provide farmers with necessary education and training, including agricultural technology, management knowledge, and market information to improve their comprehensive quality and skill level. Second, farmers can harness digital tools and techniques such as cell phones, the Internet, and on-site teaching to improve their knowledge of digital development. The government can also guide farmers to utilize modern scientific and technological means for enhanced production efficiency, and they can support farmers to develop specialty industries, increase the added value of agricultural products, improve the quality of agricultural products, and increase their incomes. Third, farmers should be encouraged to participate in cooperatives to improve their degree of organization and enhance collective strength and self-knowledge.

(3)

Expanding channels for farmers to increase their income. By adjusting the structure of the agricultural sectors and developing high-value-added and high-yield agricultural industries, farmers are guided to increase the planting and breeding of high-quality and high-yield agricultural products, and in the process of development, rural e-commerce is gradually being promoted to expand the sales channels for agricultural products, increase the market coverage of agricultural products, and raise the sales prices of agricultural products. Implement the “three exemptions and one subsidy” policy for rural e-commerce practitioners, free training, and entry into county-level e-commerce industrial parks, and free use of warehousing and logistics centers. Meanwhile, the government should improve the rural social security system, including rural pension insurance, and medical insurance, to improve the social security level of farmers, reduce the pressure on their lives, increase their disposable income, narrow the income gap between urban and rural areas, and promote rural development more optimally, which will inevitably increase the support for the rural economy. Encourage the development of rural industries and the employment and entrepreneurship of farmers. Promote the “mobile banking + industry chain finance” model, develop “agricultural machinery loans” for new types of agricultural business entities, and launch small-scale, high-speed loans for small farmers to ensure their economic development. Through the development of industries such as modern agriculture, rural tourism, and rural e-commerce, raise farmer’s income level and narrow the income gap between urban and rural areas. In regard to talent, the government should implement employment support policies, promote employment of the rural labor force, improve the quality of their employment and income level, strengthen vocational training to improve their skill level, and enhance their awareness. Thus, the income level of farmers can be improved, and the income gap between urban and rural areas can be reduced.

6.3. Further Discussion

Digital technology remains subject to multiple limitations in the process of promoting common prosperity for farmers. First, there is a significant urban–rural and regional gap in digital infrastructure. Insufficient network coverage and limited bandwidth in remote rural areas in the western region make it difficult to support the application of technologies such as smart agriculture and e-commerce live streaming. The high cost of hardware installation and maintenance have further exacerbated the imbalance of technological penetration. Second, farmers’ digital literacy varies. Due to factors such as age and educational background, the left-behind group manifests a weak ability to operate smart devices and analyze data information. Thus, the group cannot reasonably apply technical tools and even idle equipment. Third, there is a “last mile” obstruction in technological application. Agricultural digitalization solutions mostly rely on large-scale operations. Small farmers are unable to afford the initial investment required for IoT sensors, smart agricultural machinery, etc., due to insufficient funds and weak risk mitigation policies, and the supply of lightweight technologies adapted to the small peasant economy is insufficient. In addition, the imperfect rural digital ecology, lagging logistics distribution, and lack of after-sales service also restrict the sustainability and effectiveness of technological empowerment.

Although existing studies have revealed macro laws based on provincial panel data, the heterogeneity at the micro level remains unexplored. Future studies can collect micro information such as the frequency of digital technology use, skill improvement path, and income structure changes by conducting long-term follow-up surveys at the farmer level. Meanwhile, researchers can integrate e-commerce platform transaction data, IoT device monitoring data, and farmer questionnaire data to build a micro transmission chain of “technology application–production efficiency–income growth” and accurately identify the key nodes of digital technological empowerment. Different countries have followed different paths in promoting rural development with digital technology. By comparing the effects of rural digital infrastructure construction and small farmers’ digital support policies between China and emerging economies such as India and Brazil, we can refine technological empowerment models so they are suitable for developing countries. The “digital cooperative + order agriculture” model developed by China for small farmers is of reference for small-scale agricultural operations in regions such as Southeast Asia and Latin America. The iteration of technologies such as artificial intelligence and big data can reshape the rural development pattern, explore the lightweight application of artificial intelligence in small farmers’ production, and analyze the “double-edged sword” effect of big data platforms in matching the supply and demand of agricultural products. In the future, researchers can further reveal the deep-seated laws of how digital technology enables farmers to achieve common prosperity, and they may provide theoretical and practical references for global rural digital transformation and inclusive development.