1. Introduction
For a long time, poverty and income inequality have hindered sustainable global economic and social development. After eliminating absolute poverty, China has set easing income inequality and achieving common prosperity as part of its next stage of development goals. And rural areas are the most arduous key to achieving this goal. Household differentiation, rural labor migration, and regional development imbalances have exacerbated the rural income gap [
1,
2]. The per capita disposable income of low-income families and high-income families in rural China will be CNY 5264 and CNY 50,136, respectively, with a ratio of 1:9.52, which is compared to 1:6.47, as the income gap has been widening, and the Gini coefficient of rural residents’ income has increased from 0.333 in 2003 to 0.365 in 2023 (data source: National Bureau of Statistics of the People’s Republic of China), showing a trend of growth. The increasing income gap will result in numerous adverse effects, including diminished happiness, worsening health conditions, consumption constraints, class stratification, and other issues [
3]. These effects hinder the construction of rural rejuvenation and rural modernization. Therefore, how to narrow the FIG and form a new pattern of reasonable and orderly income distribution has emerged as a critical issue that needs to be addressed to foster sustainable rural development.
As information technology penetrates rural areas, the EC is emerging as a new catalyst for sustainable development in rural areas [
4]. EC is a typical approach to transform the traditional real economy by digital technology and form an integrated rural industry support system by combining Internet resources with traditional rural elements.
Figure 1 shows the overall growth trend of Taobao villages and sales. EC reduces disparities in information, enables low-income populations to connect to enormous markets, introduces employment and business opportunities, and boosts income [
5].
More and more scholars are paying attention to the income effect of EC, with a particular focus on two core issues: income effects and income distribution. The current study predominantly investigates (1) the influence of EC on farmers’ income and (2) its contribution to urban–rural income inequalities. Scholars generally recognize the role of EC in increasing farmers’ income and believe that EC can increase farmers’ operating income and wage income. EC using information technology offers farmers efficient means to access product markets, enabling them to acquire timely market information, enhance product distribution channels, and alter their previous disadvantageous status as price recipients [
6]. EC can improve bargaining power by shortening transaction links and costs [
7], thereby augmenting operational income [
8,
9]. It also enables farmers’ seamless access to factor markets, serving as a crucial conduit for enhancing farmers’ revenue development [
10]. For example, Qiu et al. [
11] pointed out that the Internet technology used in EC can effectively break the information asymmetry, improve the transaction efficiency of farmers, alleviate the financing constraints of farmers, and improve the operating income of farmers regarding three aspects: improving the convenience of information acquisition, reducing the cost of EO, and improving financial services. Meanwhile, the development of EC can provide many job opportunities, as well as increase farmers’ wage income [
12,
13,
14]. Specifically, the development of EC requires and can lead to the development of supporting industries, such as processing, logistics, and other related industries [
15], which creates more jobs for farmers and increases their wage income [
16]. In addition, current research indicates that EC exhibits three correlations with the urban–rural income disparity: “widening gap”, “narrowing gap”, and “inverted U-shaped relationship”, which have yet to yield a definitive result [
17]. Certain scholars contend that the “digital divide” results in urban areas possessing more comprehensive digital infrastructure and social environments than rural regions, facilitating access to the benefits of EC and consequently exacerbating the urban–rural income disparity [
18]. When it comes to the digital divide, Bowen and Morris [
19] find that farmers and rural farms not only lack access to information technology, but also lack digital literacy and the ability to use it, which makes it difficult for farmers to grasp the dividends brought by EC, and the gap between farmers and urban residents is widening. Some experts contend that the extensive dissemination of digital technology and the ongoing policy investments have effectively bridged the “digital divide”. Moreover, several experts contend that the influence of EC development on the urban–rural income disparity is non-linear. Initially, urban inhabitants have had larger advantages; however, as the development of EC occurs, rural inhabitants will also obtain benefits, with an inverted U-shaped influence trend [
20]. Li et al. [
21] explained that the EC elements are initially concentrated in cities, which leads to the widening of the digital divide between urban and rural areas and the widening of the income gap. After the development of cities matures, the feedback effect on rural areas will be formed through factor spillover, market expansion, and policy guidance, and the income gap between urban and rural areas will be narrowed.
It can be seen that, firstly, most of the existing studies on the discussion and measurement of rural EC focus on the macro level, that is, the impact of EC policies, and the research results have been relatively perfect. Second, when discussing income inequality and income disparity, most of the existing studies focus on the problem of urban–rural income disparity, but the widening FIG is also a major obstacle to alleviating relative poverty and improving people’s happiness, which has a relatively broad space for exploration and high exploration value. Therefore, this paper chooses to observe the individual EO behavior of farmers from a micro perspective, and the overall goal of the research is to investigate the impact of EOs on the FIG, and the specific content is carried out around three issues. First of all, can EO narrow the FIG, and if so, what is the impact mechanism? Secondly, is there any difference in the impact of EO on the FIG under different geographical locations, different agricultural functional zones, and different terrain conditions? Finally, is there any difference in the impact of different EO scales and types on the FIG?
The marginal contributions of this study are as follows: First, the economic effect of EC is observed by using micro-individual variables. Compared with macro policies, which can reflect more overall economic activities, micro-behavior variables are more in-depth, which allows them to not only explore the economic effects of EO behaviors but also examine the differences in economic effects brought on by different models of EO behaviors, which are organically complementary to macro policy research and have targeted guiding significance for further exploring EC, the digital economy, and related policy formulation and improvement. The second is to expand the boundaries of the analysis of the economic effect of EO, place the research perspective on the FIG, and explore new breakthroughs in alleviating relative poverty and income inequality by using an income indicator in addition to the income gap between urban and rural areas. Thirdly, the heterogeneous impact of EO on the FIG is discussed, and the development potential of EC in different scenarios and regions is considered, which provides useful empirical insights for the implementation of targeted policies. Fourth, the impact of different EO conditions on the FIG is further distinguished, and a feasible path for the inclusive development of EC is proposed so as to make full use of the characteristics and advantages of EC to promote rural development.
5. Discussion
The vigorous development of the Internet and digital information technology has given birth to many new business forms. Driven by the “Internet plus agriculture” model, EC platforms provide a viable path for farmer employment and agricultural product sales, helping to alleviate income inequality. This study discusses the effect of farmers’ EO behavior on narrowing the FIG and carries out heterogeneity analysis from different perspectives. On this basis, it further discusses the effect difference of different EO scales and EO models, providing new theoretical support for a better understanding of the income distribution effect of rural EC.
Specifically, this paper finds that farmers’ EO can significantly reduce the FIG, which is consistent with the conclusions of previous scholars [
27]. However, different from related articles, when studying the relationship between EO and the FIG, scholars often start from the income increase effect of EO, and further verify the effect of EO in reducing the FIG through quantile regression. This paper uses the RIF model to establish a direct relationship between income inequality and EO and uses quantile regression to support it, hoping to provide a more intuitive and richer supplement for exploring the influencing factors of EO’s economic effect and income inequality. In explaining the influencing mechanism, this paper hypothesizes that EO can narrow the income gap of farmers by narrowing the difference in farmers’ labor endowment and uses the part-time employment of older farmers or female farmers to measure the difference in labor endowment among farmers, and the hypothesis is finally verified. Contrary to the findings of Niu et al. [
48], who found that part-time employment widened the FIG, the difference is that this paper focused on part-time employment between older and female farmers. The more active their part-time work behavior is, it indicates that the lack of labor endowment has eased their work constraints, reflecting the improvement of the ability of rural disadvantaged groups and low-income farmers to increase their income, thus having a positive impact on narrowing the FIG.
In addition, this paper explores the heterogeneity of EO effects in different geographical locations, agricultural functional zones, and topography. First, in terms of geographical location, the conclusions of this paper are consistent with those of most of the literature, and it is found that the effect of EO on poverty in the central and western regions is more significant, but not in the more economically developed eastern regions [
49]. The main reason for this is that the development of the central and western regions started late, and the original development channels were relatively simple; the introduction of EC can bring a higher marginal effect to the income of low-income farmers in these areas. Secondly, in terms of agricultural functional zoning, the results show that EO in the major grain-producing areas can better narrow the FIG. Previous studies have found that the establishment of major grain-producing areas increases farmers’ operating income and strengthens the tendency of finance to support agriculture [
25], which also provides a favorable explanation for the conclusions of this paper. The large scale of agricultural production in the major grain-producing areas helps farmers reduce costs, increase profits in the process of EO, and increase the operating income. Financial support for agriculture can promote the development of the agricultural industry, which is conducive to the expansion of the scale of EO, and further exerts the poverty alleviation and inclusiveness of EC. Finally, in terms of topography, EO can reduce the FIG in mountainous areas more than in plain areas. The relevant literature has also concluded that rural EC has a higher income increase effect in mountainous areas [
14], which is explained in this paper as the base of low-income groups in mountainous areas being larger, and the marginal effect of EC on their income increase is more significant, so it is conducive to weakening the FIG on a large scale. In addition, since this paper uses the data of individual farmers’ EO, it is convenient for us to explore the differences in the effects of EO after further subdividing the scale and mode of EO, and the results show that large-scale EO and platform EC can narrow the FIG. When the platform economy develops to a certain scale, it is conducive to achieving economies of scale and reducing marginal costs [
50], which not only brings greater profit margins to EC farmers but also creates more jobs for low-income groups, and the poverty alleviation effect of EC can be brought into play to a greater extent.
Overall, the theory of equal opportunity provides a basic logical framework for explaining the alleviation of income inequality by rural EC. The development of EC in rural areas can stimulate the vitality of surrounding industries, broaden the income channels of local residents, and bring economic opportunities to low-income rural households. At the same time, EC-related work is highly flexible, breaks through the restrictions of working hours and locations, and can be performed by farmers with lower skill levels, reducing the differences in labor endowments among rural households, all of which help to reduce income inequality caused by environmental factors. The theoretical contribution of this paper is to explore the direct relationship and mechanism between rural EC and FIG, verify the digital dividend and co-prosperity effect brought on by EC, and explore a new path of rural development. In addition, the differences in the effectiveness of EC under different conditions and contexts also help us to further infer that digital technology can bring greater development opportunities to developing countries and regions. All of these provide a theoretical reference for developing countries to gather the momentum of rural development, alleviate income inequality, and improve people’s satisfaction and happiness.
However, this study also has some specific shortcomings, which need to be improved in future studies. First, due to the limited availability of data, this paper only uses the data from the CRRS 2020 to study the impact of EO on the FIG, which inevitably have the limitations of cross-sectional data. The lack of timeliness of the data makes it difficult to fully reflect the long-term impact of EO on the FIG, and we will dynamically track and analyze the relationship between EO and the FIG after obtaining sufficient data and include more possible relevant factors. Second, in reality, EO will have an impact on the FIG in richer and more complex ways, and the discussion of its impact mechanism in this paper is still not comprehensive enough. For example, China’s rural areas are a typical “humane society”, according to the theory of social capital. Once farmers operate EC, their advanced technology, rich experience, and broad opportunities can be spread through social networks, benefiting more farmers and regulating the FIG. On the contrary, according to the “skill bias theory”, in the process of EO, the difference in labor skills between farmers will also show a large gap in their wage income, thereby widening the FIG. All these situations require us to take into account the actual circumstances in rural areas. Third, due to the complexity and volatility of income inequality within rural areas, the results of this study may be valid for specific time periods or specific scenarios, and the scope of replication is limited. Especially during the COVID-19 pandemic, the types of products and channels that residents consumed underwent major changes. The demand for fresh food and agricultural hoarding increased significantly, and the corresponding lockdown measures forced urban and rural residents to shift their consumption channels from offline to online, providing opportunities for the development of rural EC. However, at the same time, the spread of the COVID-19 epidemic restricted farmers from going out for non-farm employment, and there were also differences in the ability of rural households to resist risks under different levels of household economic resilience, and the income pattern of rural households changed accordingly. In the post-COVID-19 pandemic era, the new forms and models of rural industries spawned by the prosperity of the Internet economy have created new ways for farmers to find employment and start businesses, and the government has also vigorously strengthened the non-agricultural employment guarantee for rural women and older farmers. Whether rural EC can further release the dividend of poverty and whether low-income farmers who experience livelihood risks can grasp the pulse of the times and steadily increase their incomes still need to be further explored after obtaining more data. Therefore, in the future, we need to conduct a long-term analysis based on the actual data of EC business activities in different regions to more systematically reflect the dynamic impact of EO on FIG.
6. Conclusions and Implications
6.1. Research Conclusions
Based on the data of China Rural Revitalization Survey, this paper uses the RIF model and the mediation effect model to investigate the influence and mechanisms of EO on the FIG and discusses the heterogeneity of EO on the FIG. On this basis, this study further distinguishes the effects of different EO scales and EO models on the FIG and finally draws the following conclusions.
First, EO significantly narrows the FIG, alleviates income inequality within rural areas, and has characteristics of inclusive growth. This shows that in order to fully utilize the efficiency of EC and promote the sustainable development of farmers, it is necessary to increase the implementation of rural EC and optimize the business environment of EC.
Second, EO reduces the FIG by narrowing the difference in labor endowment. The “de-skilling” and flexibility of non-farm jobs related to EC make up for the lack of work skills and time of rural disadvantaged groups, such as the older labor force and women, reduce the difference in labor endowment among rural households, and narrow the FIG.
Third, the impact of EO on the FIG is heterogeneous in different geographical locations, agricultural function zones, and terrain conditions. Specifically, EO can narrow the FIG in the west, major grain-producing areas, and mountainous areas. It is mainly affected by factors such as the level of economic development, the scale of agricultural production, and policy inclination. It shows that regions with relatively backward economic levels need to grasp the digital dividends brought on by the digital economy and take advantage of the late-mover advantage to generate income and increase income, and the government also needs to introduce relevant policies to bridge the digital divide and ensure fair development opportunities for farmers.
Fourth, compared with small-scale EC and social EC, large-scale EC and platform EC are better able to narrow the FIG. EOs that reach a certain scale can create more jobs and play a greater role in supporting agriculture.
6.2. Research Implications
First, enhance support for rural EC, persist in advancing EC technology, managerial expertise, and capital to rural areas, further diminishing the technical, financial, and other barriers to EC operations. Utilize the convenience of the Internet to alleviate farmers’ information disadvantages, create a fairer and more convenient EC operating environment, and enable low-income groups to enjoy more EC benefits. Reduce income disparities by addressing inequalities caused by environmental factors.
Second, establish an EC support structure for rural vulnerable groups to elevate their EC skills. The government ought to enhance the EC capabilities of rural at-risk populations by facilitating EC training programs and conducting visits to EC demonstration sites. Motivate EC operators and platforms to create additional employment and development opportunities for vulnerable farmers, thereby maximizing the advantages of EC.
Third, bridge the gap in regional EC advancement. To address the slow development of EC sectors, governments ought to enhance policy preferences, motivate the integration of industry and resource endowments, pinpoint breakthroughs in EC development, and fully harness its potential. Additionally, establishing regional cooperation and benefit-sharing mechanisms will facilitate the high-level growth of rural EC and mitigate income disparity.
Fourth, facilitate the scaling and platforms of rural EC. Intensify the support strength in the rural EC sector, establish synergies within rural EC industrial clusters, extend the value chain of the rural EC industry, generate scale effects, create additional EC-related employment opportunities, and assist vulnerable farmers in improving their incomes.