Effect of the Digital Economy on Farmers’ Household Income: County-Level Panel Data for Jilin Province, China

Abstract

Based on the County-Level Digital Rural Index and county-level panel data for Jilin Province (2018–2020), this study used a fixed-effect model to investigate the effect of the digital economy on Chinese farmers’ income. The results show that the digital economy can positively affect farmers’ disposable income and that there is regional heterogeneity. Specifically, digital economy development has better effects on farmers’ income in the eastern region of Jilin Province, but there is still room for development in the central and western regions. Accordingly, from the perspectives of urbanization, industrial structure, government support for agriculture, digital-inclusive finance, and agricultural digital talent, suggestions have been made to promote the coordinated development of the digital economy and farmers’ livelihoods in various regions.

1. Introduction

Based on high-tech advancements such as the Internet, big data, and cloud computing, digital economy has become a new driving force for social and economic development, and people are increasingly enjoying the dividends of its development. Aiming to build a digital China and modernize agriculture and rural areas, China has adopted strategies such as the Strategic Plan for Rural Revitalization (2018–2022), Outline of the Strategy for Digital Rural Development, and Action Plan for Digital Rural Development (2022–2025). Such measures aim to promote the application of agriculture-related digital technologies for rural revitalization, facility construction, economic development, social governance, environmental protection, and farmers’ livelihoods. Against this background, it is of practical significance to investigate whether the digital economy can increase farmers’ incomes, how such increases differ by region, and how to best promote farmers’ incomes through the digital economy. Clearly studying these issues will not only help to deepen the understanding of the relationship between the economy and farmers’ income growth but also help to provide a theoretical reference and practical basis for the decision-making and practice departments of digital village construction and rural revitalization strategies; additionally, it will help to provide a reference for the formulation of policies to improve the digital economy and promote farmers’ income growth, helping agriculture, rural areas, and farmers keep up with the wave of social digital development.

In recent years, researchers have paid increasing attention to the digital economy’s effect on farmers’ income. The digital economy is based on using information and communication technology as its core support (Guan et al., 2020) [1]. By applying digital technology (Feng and Zhu, 2013) [2] with digital platforms as the medium (Xu and Zhang, 2020) [3], transactions, exchanges, and cooperation can be successfully digitalized, thus promoting social and economic development. The digital economy can be organically integrated with traditional agriculture. As new production factors of digital agriculture, digital agricultural information, technology, and talent run through the whole process of agricultural production, operation, and management. This can help farmers develop production and sales plans according to local and temporal conditions, utilize digital visual expression, and achieve intelligent management. Digitalization can therefore help alleviate poverty among farmers in developing countries (Zhou, Fan, Chen, 2021) [4]. Informatization is an important engine for innovation-driven rural modernization, which can improve agricultural efficiency and increase farmers’ incomes (Xiong, Liu, Gong, 2021) [5]. Digital technology is especially beneficial for rural and remote areas. Integrated urbanization and digital economy development can transform and upgrade the industrial structure while also having an urbanization threshold effect (Zuo, Jiang, Chen, 2020) [6]. Moreover, the digital economy can improve the income gap and promote the overall growth of farmers’ incomes (Li and Ke, 2021) [7]. In this regard, the income-increasing effect for low-income farmers is significantly greater than it is for high-income farmers (Wang and Liu, 2022) [8]. There is a U-shaped relationship between the digital economy development and the income gap between urban and rural residents; that is, the urban–rural income gap is reduced in the early stages of digital economy development but will widen with further development, resulting in a “digital divide” (Chen and Wu, 2021) [9]. The digital economy’s income-increasing effect on farmers is affected by education level and age, and there is individual heterogeneity (Hu and Lu, 2019) [10]. Digital economy development will facilitate the construction of a “digital countryside”, which will promote e-commerce development and increase farmers’ incomes with the support of policies (Tang et al., 2020) [11] while also having spillover effects on non-e-commerce operators (Qin, Wang, Xu, 2022) [12]. Meanwhile, studies have confirmed that digital-inclusive finance can narrow the urban–rural income gap, diversify rural household income, and ultimately increase rural income; however, it is heterogeneous among different income groups (Lu, Yuan, Wu, 2022) [13]. Digital-inclusive finance has the greatest effect on the wage income of rural households and the weakest effect on transfer income (Zhang, 2021) [14]; it can also stimulate the entrepreneurial behavior of rural households. In this regard, there is a difference in the income-increasing effect between subsistence entrepreneurship and opportunistic entrepreneurship (Zhang, Guo, Li, 2021) [15]. Zhang et al. studied the impact of the development of digital finance on inclusive growth and found that digital finance is particularly helpful in promoting entrepreneurial behavior among households with low physical or social capital [16]. Yue et al. studied the impact of digital finance on households and showed that the widespread use of digital finance increased participation in the credit market while, on the other hand, increased the risk of households falling into the debt trap [17]. Wang Zheng argued that deepening rural reform through the construction of digital economy in counties is an important initiative, and the development of digital economy as a driving force for county development can promote high-quality rural economic development and increase the income of farmers [18]. Li Xiang believes that digital industry is an important driver for farmers to increase their income; it is both an important part of the modernized rural industry and a conversion medium for urban–rural integration and the integration of the three industries. Through the use of “digital content + communication”, “digital creativity + products”, “digital platform + cultural tourism”, and “digital tools + creators” in the digital culture industry, the rural economy can be revitalized and farmers’ entrepreneurial behavior and income generation motivation can be stimulated [19].

There are many factors affecting the agroecological environment. Each factor has different effects and degrees on the ecological environment, and among which, the most important influencing factors are accumulated temperature and precipitation. In terms of cumulative temperature, the active cumulative temperature (≥10 °C) in Jilin Province is 1900–3200 °C; Siping, Shuangliao, and Ji’an are above 3000 °C, Changchun and Baicheng are 2500–2900 °C, and Changbai and Dunhua are about 1900–2200 °C. In summary, Jilin Province shows a pattern of high temperatures in the east and west and low temperatures in the center, which is a situation that is related to the topography and latitude. In terms of precipitation, the average annual precipitation in Jilin Province ranges from 320 to 730 mm, with a maximum (Tianchi) of 738 mm and a minimum (Zhenlai) of 355 mm and with the main trend being a gradual decrease from southeast to northwest. Corn is one of the major crops in Jilin Province, and its suitable planting area is mainly concentrated in the central and southern part of Jilin Province; the second most suitable area is concentrated in most of the northwestern and eastern parts of Jilin Province.

Jilin Province is a large agricultural province in China and an important commodity grain base. As such, its agricultural development is important for local economies as well as national food security. In 2018, the Third Plenary Session of the 11th Jilin Provincial Party Committee adopted the “Opinions on Accelerating the Transformation of Old and New Driving Forces and Promoting High-Quality Development under the Guidance of Digital Jilin Construction” (Jifa (2018) No. 19). In this document, digital rural construction was taken as the strategic direction of rural revitalization, which is aimed at promoting the digital transformation and informatization of agriculture in Jilin Province (Zhang, 2021) [20]. Meanwhile, during the 14th Five-Year Plan period, breakthroughs were made in the construction of “digital Jilin” and “digital agriculture”, with related improvements in terms of networked and intelligent socioeconomic operations. Big data, cloud computing, and artificial intelligence became important supports for agricultural transformation, and digital dividends were achieved in rural areas. Moreover, governments at all levels attached importance to developing farmers’ incomes. Indeed, the per capita disposable income of Jilin’s rural residents increased from CNY 13,748 in 2018 to CNY 16,067 in 2021, indicating a clear improvement in rural living standards. Nevertheless, in 2020, the per capita disposable income of Jilin’s rural residents was CNY 16,067, which was still CNY 1064 lower than the national average of CNY 17,131; the gap was even larger between Jilin and more economically developed provinces (China Statistical Yearbook, 2021) [21]. In this regard, while “digital dividends” have great potential for increasing farmers’ incomes, agricultural digitalization in Jilin Province remains weak. Data resources are scattered, and data acquisition ability is limited. There is insufficient data development and application, inadequate integration and sharing, and weak management service support (Agriculture and Rural Affairs Department of Jilin Province, 2018) [22]. In the process of integrating the whole agricultural industrial chain, there is a lack of matching digital technology supply, digital economy innovation, platform transformation, and supporting conditions, which inhibits the digital economy’s ability to increase farmers’ incomes.

Existing studies suggest that while the “digital countryside” is an important part of rural reform, it faces many challenges, such as inadequate development in various fields, unbalanced regional development, and insufficient cultivation of digital talent. Few studies have specifically investigated the relationship between digital economy development and farmers’ incomes in Jilin Province. Most existing studies only focus on the effect of digital economy development on the overall economy or agricultural economy of Jilin Province. There remains a need for a more in-depth investigation. This study, therefore, considers the county-level digital economy development in Jilin Province with a focus on farmers’ household incomes. By using the County-Level Digital Rural Index, which was jointly issued by the Peking University New Rural Development Institute and Ali Research Institute, as well as by using county-level panel data for Jilin Province from 2018 to 2020, this study uses a fixed-effect model to study the digital economy’s effect on farmers’ incomes. This work is important for clarifying development trends in the rural digital economy, identifying weaknesses and deficiencies, and optimizing related policies. It can provide a reference to help national and local governments improve digital rural development policies, increase rural residents’ incomes, and accelerate rural revitalization.

The contribution of this paper is as follows. Seven segmented indicators are selected to construct an econometric model of the impact of the level of development of the digital economy on farmers’ income. The 38 county-level administrative units in Jilin Province were divided into three regions, and the impact of the level of digital economy development on farmers’ income was analyzed. Based on the results of the calculation of the weights of each influencing factor, suggestions were made to reduce regional differences in the digital economy and promote farmers’ income and prosperity.

2. Theoretical Mechanism and Hypotheses

Digital technology creates new growth points for economic development. It is a new driving force in China’s economic development and a key support for building new development patterns. With regard to agricultural production, the digital economy enhances its management efficiency, production efficiency, and social benefits; it also provides information support for agriculture and facilitates scientific farmland management, thus improving agricultural product quality as well as allowing for the precise identification, marketing, and supply of products. Moreover, digital economy improves efficiency, saves costs, and integrates agricultural production. China, therefore, should aim to promote agricultural technology innovation, upgrade agricultural product quality, optimize the rural industrial structure, and increase farmers’ incomes.

2.1. The Digital Economy Helps Save Costs and Increases Efficiency in Agricultural Production

Coase’s theory of transaction costs proposes that transaction costs comprise information search costs, negotiation costs, contracting costs, and contract-monitoring costs. Transaction costs can be lowered by relying on institutional organizations, contracts, and policies, as well as by using standardized weights and measures [23]. China has made use of digital tools in its agricultural production. Such tools include remote-sensing technologies used to monitor crop growth and climate change, GIS and GPS devices installed on intelligent agricultural machinery, and remote control of operations being achieved using Internet of Things technology. Such digital production modes can reduce manual labor intensity, lower production costs (e.g., saving water, fertilizer, and manpower by more than 20%), and improve production efficiency (increasing production by about 30%) [24]. In the circulation of agricultural products, China uses 5G networks, big data, cloud computing, and other information technologies to build platforms that integrate commodity and information flows, reduce asymmetry in supply and demand information, shorten product circulation time, reduce information collection and transaction costs, and promote the two-way flow of commodities and factors between urban and rural areas. Such technologies therefore support the formation of an integrated urban–rural commodity market and factor market (Xie and Han, 2022) [25].

2.2. The Digital Economy Improves the Quality and Efficiency of Agricultural Production

Classical economist David Ricardo suggested that intensive farming was the embodiment of labor-force increase and capital accumulation; neoclassical economists also emphasized the importance of intensive production [26]. According to classical Marxist theory, graded land rent inhibits the development of agriculture; that is, land rent is affected by factors such as geographical location, land fertility, and distance from the market. Owners of good agricultural land have higher-than-average profits, while those who own poor land have below-average profits [27]. At present, superior land resources are limited, and the scale and quality of agricultural production are heterogeneous. Moreover, land quality and yield are greatly affected by the amount of chemical fertilizers and pesticides applied. If the applied amount exceeds the actual need, it will lead to soil deterioration, lowered agricultural product quality, increased farming costs, and adverse environmental effects. In China, the regional economic development is unbalanced, and the western region has lagged behind the eastern region for a long time. In 2019, China’s Ministry of Science and Technology and the UK Precision Agriculture Innovation Engineering Center jointly built a smart farm using digital technologies to provide a dynamic management system for agricultural producers. The system can collect and monitor information about trace elements in the soil in real time using land sensors and soil monitors; it can also analyze soil composition and determine the types of crops suitable for cultivation, thus providing a data basis for improving the soil environment and soil quality. AI technologies such as robots and drones are used for intelligent planting, irrigation, fertilization, and harvesting. The application of such digital technologies can promote intensive agricultural production, improve farmers’ production efficiency, improve agricultural quality, and change the form of graded land rent.

2.3. The Digital Economy Helps Integrate and Improve Agricultural Production

US economist Theodore Schulz noted the following:

“Developing countries should pay attention to the transformation from traditional agriculture to modern agriculture in economic development and introduce new modern agricultural production factors: First, establish a system suitable for the transformation of traditional agriculture. The second step is to create conditions for the introduction of modern production factors from both supply and demand. Third, human capital investment should be made into farmers [28]”.

The digital transformation of agriculture not only provides a new impetus for modern agricultural development but also promotes the integrated development of agriculture, industry, and services. This is mainly reflected in two aspects. First, it promotes the development of new industries, business forms, and models, such as rural e-commerce, digital cultural tourism, sightseeing agriculture, leisure farming, rural themed B&Bs, and farming experience; it constructs the entire agriculture industry chain, attracts foreign investment and consumption, promotes the integrated development of primary, secondary, and tertiary rural industries, and enables farmers to share more industrial value-added benefits. Second, the development of new business forms creates new jobs, provides opportunities for surplus rural labor to start businesses and find jobs, broadens the channels for farmers to increase their income, and helps more farmers to become the users, beneficiaries, and promoters of digital technologies.

2.4. Hypotheses

Based on the above, we proposed the two hypotheses:

Hypothesis 1 (H1).

The digital economy plays a positive role in increasing farmers’ incomes. Being a new economic model based on network technology, big data, and artificial intelligence, the digital economy can solve problems in agricultural production using data and information technologies. It can improve farmland management, enhance agricultural production capacity, increase crop yield, improve agricultural supply chains, improve the processing efficiency of agricultural products, guarantee the quality of agricultural products, and help farmers sell their products more effectively, thus having a positive effect on income.

Hypothesis 2 (H2).

There is regional heterogeneity in the digital economy. As an emerging economic structure, digital economy development might be affected by geographical location, policy environment, and industry environment. For example, in certain rapidly developing regions, factors such as government policy support and sound technological foundations have enabled the digital economy to develop faster. In less developed regions, meanwhile, owing to backward technologies, unfavorable policies, and other factors, digital economy development is slow, thus giving rise to regional heterogeneity.

3. Research Design

3.1. Model Design

To study the effect of the county-level digital economy development level (DEDL) on farmers’ income in Jilin Province, we constructed the following econometric model:

(1)$FI{L}_{it}={\beta }_0+{\beta }_1*DED{L}_{it}+{\beta }_2*S_{it}+u_{it}$

where I is the county region, t is the year, FIL_it is the per capita household disposable income of farmers in 38 county-level administrative units in year t, DEDL_it is the digital economy index of i county in year t, and S_it is the control variable. S_it mainly includes gross domestic product per capita, industrial structure, urbanization level, government finance level, financial industry development level, and education level. u_it is a random disturbance item, β₁ is used to measure the overall effect of the development level of the digital economy on farmers’ income, and β₂ is used to measure the effect of the control variables on farmers’ income.

3.2. Data Sources and Variables

3.2.1. Explained Variables

In official state statistics, the disposable income of rural residents is an index used to express the income level of farmers in a year; these statistics can be considered rigorous and scientific. In this study, based on data from the China County Statistical Yearbook, Jilin Province Statistical Yearbook, and each county statistical yearbook, we obtained the per capita disposable income of rural residents to measure farmers’ income levels.

3.2.2. Core Explanatory Variables

Using the County-Level Digital Rural Index jointly released by the Peking University New Rural Development Institute and Ali Research Institute, we selected the digital economy index of 38 county-level administrative units in Jilin Province from 2018 to 2020 to measure county-level digital economy development in Jilin Province.

3.2.3. Control Variables

With reference to the literature and based on the actual situation in addition to digital rural development level as the core explanatory variable, we also selected six control variables that might affect rural household income: gross domestic product (GDP) per capita, industrial structure, urbanization level, government finance level, financial industry development level, and education level. Table 1 explains the specific variables and their calculation methods.

3.2.4. Regional Divisions and Descriptive Statistics of the Variables

Because of obvious differences in geomorphic morphology, the terrain of Jilin Province inclines from southeast to northwest; namely, it is high in the southeast and low in the northwest, as shown in Figure 1a. With the central Great Black Mountains as the boundary, it can be divided into the two major landforms of the eastern mountains and the central and western plains. According to differences in resource endowments and the actual situation, Jilin Province is divided into eastern, central, and western regions. The administrative division of Jilin Province is shown in Figure 1b. The eastern region is close to the Changbai Mountains and is rich in various mountain products, wood, and mineral resources. It has 16 county-level administrative units, including Tonghua (Ji’an City, Meihekou City, Tonghua County, Liuhe County, Huinan County), Baishan (Linjiang City, Jingyu County, Fusong County, Changbai Korean Autonomous County), and Yanbian Korean Autonomous Prefecture (Tumen City, Dunhua City, Longjing City, Helong City, Hunchun City, Antu County, Wangqing County). The central region is located in the plains, with developed industries in corn, sorghum, rice, and other traditional planting; it has 14 county-level administrative units, including Changchun (Yushu City, Dehui City, Princess Ling City, Nongan County), Jilin (Huadian City, Panshi City, Jiahe City, Shulan City, Yongji County), Liaoyuan (Dongfeng County, Dongliao County), and Siping (Double Liao City, Lishu County, Yitong Manchu Autonomous County). The plains of the western region have particularly outstanding geomorphic features, with obvious advantages for grassland animal husbandry. It has eight county-level administrative units, including Baicheng (Taonan City, Da’an City, Zhenlai County, Tongyu County) and Songyuan (Fuyu City, Gan’an County, Changling County, former Guoerrose Mongolian Autonomous County).

Table 2 shows the descriptive statistics of the main variables. For the whole province, the standard deviation of the explained variable, the disposable income of rural residents (FIL), is 0.194, indicating that farmers’ incomes fluctuate little among counties in Jilin Province. The minimum value of the digital rural index is 36.9, and the maximum is 63.2. This reflects that digital economy development in Jilin Province is different among counties, which is more obvious in the regional test. Compared with the central and western regions, the digital rural index in the eastern region is the highest, but the standard deviation within the region is the largest. This indicates that although the level of digital economy development is better in the eastern region, development among counties is imbalanced. In addition, the GDP per capita (GDPPC), industrial structure (IS), urbanization level (U), and financial industry development level (FD) of the eastern region are all higher than the provincial average. Farmers in the central region have a higher income level (FIL) and education level (EDU) than in the other two regions. The western region has the largest proportion of government financial support for agriculture (GE).

4. Empirical Analysis

4.1. Model Selection

We used panel data for 38 county-level administrative units in Jilin Province from 2018 to 2020 for the empirical analysis. Owing to the difference between the intercept term and slope, there are three alternative models for panel data: the mixed model (POOL), fixed-effect (FE) model, and random effect (RE) model. We first conducted a model test to find the optimal model.

In Table 3, the F test presents a significance of F(13,21) = 5.911 at the 5% level, p = 0.000 < 0.05. This means that when compared with the POOL model, the FE model was better. The BP test showed a 5% level of significance, chi(1) = 5.489, p = 0.010 < 0.05, indicating that the RE model is better than the POOL model. The Hausman test showed a 5% level of significance, chi(7) = 52.144, p = 0.000 < 0.05, indicating that the FE model was better than the RE model. Based on this analysis, the FE model was selected for the investigation.

4.2. Analysis of Regression Results

As shown in Table 4, the regression coefficients of the county-level digital rural index in the whole province are significantly positive at the level of at least 0.01. Every 1% increase in the county-level digital rural index will increase the per capita disposable income of rural residents by 0.015 percentage points. This means that there is a significant positive mechanism between the DEDL and FIL in the whole province. This verifies Hypothesis 1 (i.e., digital economy has a positive promoting effect on farmers’ income). In other words, the higher the development level of the digital economy, the higher the income level of farmers. Digital economy development thus plays a positive role in promoting farmers’ incomes.

Among the six control variables, the coefficient of per capita GDP is 0.06. This indicates that improved GDPPC can expand production demand, promote consumption, and directly or indirectly improve rural residents’ income.

Industrial structure (IS) has a significant negative correlation with FIL. For every 1% increase in the proportion of the GDP of secondary and tertiary industries in the regional GDP, farmers’ incomes will decrease by 1.418 units. That is, in all regions of the province, the proportion of the GDP of the primary industry in the regional GDP is higher, and the FIL is higher. The GDP of the secondary and tertiary industries accounts for a higher proportion of the regional GDP, and the income level of rural households is lower. The coefficient of EDU is negative, indicating that the higher the education level, the greater the pressure to retain the young labor force in rural areas; thus, there is a possible negative effect on farmers’ income. The regression coefficients of U, GE, and FD are all positive but are not significant. This indicates that the three control variables have limited effects on increasing farmers’ disposable income. Specifically, the effect of urbanization on rural household income might be divided into two types. One effect is that improved urbanization levels could lead to an outflow of the rural labor force. If the digitalization and production and management efficiency of rural households are not improved in time, the output and quality per unit area will decline, directly affecting the rural households’ income. Second, counties with high urbanization rates have large urban population bases and a relatively large demand for agricultural products. However, the rural population is small, and the supply of agricultural products is also small, which pushes up the price of agricultural products and indirectly affects the FIL. Special financial expenditure in agriculture is often used for infrastructure construction for agriculture, forestry, animal husbandry, and fishery purposes. Theoretically, the improvement of the government financial level not only provides a basic guarantee for farmers’ living standards but also solves the problem of fund shortages and employment in farmers’ production and operation processes, which helps increase of farmers’ incomes. However, our empirical test results showed that the regression coefficient of the GE is not strong. That is, in the process of the “digital countryside” development in Jilin Province, financial support for agriculture in each county is unbalanced, and the income-increasing effect on farmers is limited. The regression coefficient of FD has no obvious significance in model (1). Loans are an effective way of promoting the construction of rural facilities and the development of the “digital countryside.” However, unbalanced agricultural loans across the province lead to an insignificant effect of loans on the growth of farmers’ income, which is basically in line with rural reality. That is, rich rural areas are willing to borrow money to expand construction and increase their scale, while poor areas are reluctant to take loan risks, which restricts the growth of farmers’ income.

According to the regression results for each region, the influence of the EDU on the FIL is more significant in the eastern region than in the central and western regions. A possible reason is that the eastern region is affected by factors related to society, history, the natural environment, minority languages, and teachers, where rural education is relatively backwards. However, the urbanization rate of the eastern region ranks first in the whole province, indicating that the educated population in the eastern region flows into the cities in large numbers. The rural areas lack high-quality agricultural and technical talent, which affects the income of rural households. The central region is located in the middle of the Songliao Plain in Northeast China; it is the main grain-producing area of Jilin Province and is also its main supporting area for economic development. According to the regression results, only the DEDL and the IS have a significant influence on the income level of farmers in the central region. The regression results for other variables are all within a reasonable range. The western region is the main area for the national strategies of increasing grain production by 50 million tons and 100 million tons, and it has clear advantages in land resources and agricultural population, which accounts for the vast majority of the region. The per capita area of cultivated land, grassland, water surface, and reeds ranks first in the province. The per capita area of cultivated land reaches 4.6 mu (nearly 7 mu for agricultural population), which is 3.5 times that of the country and 1.5 times that of the province. In recent years, the economy of the western region has rapidly developed, and the GDP of the region has increased to one-fifth of the whole province. Therefore, compared with the eastern and central regions, the GDPPC, U, and FIL in the western region have a more significant influence. The effects of the GE and FD on the FIL are not significant in the three regions. This verifies Hypothesis 2 (i.e., the digital economy has regional heterogeneity). In other words, in the whole province, the implementation effect of the digital economy differs in each region due to differences in resource endowment.

4.3. Robustness Test

4.3.1. Logarithmic Robustness Test

To reduce the influence of heteroscedasticity and test for the robustness of regression results, we took the logarithm of the explanatory variable DEDL for the regression analysis to see whether there were significant changes in the regression results. Column (1) in Table 5 shows the regression results. According to the results, the digital rural index is still significant for farmers’ incomes at the level of 0.01. Moreover, the logarithm can be better explained in the economic sense. When the digital countryside index increases by 1%, the per capita disposable income of rural residents increases by 0.695%. These test results are consistent with the regression results, indicating that model (1) is robust.

4.3.2. Robustness Test of Alternative Variables

When considering the effect of government financial expenditure on farmers’ income, the variable selected above is the proportion of expenditure on agriculture, forestry, and water affairs in the local general public budget expenditure. To test the robustness of the regression results, we replaced the variable with the proportion of local general public budget expenditure in the regional GDP for the regression analysis. Column (2) in Table 5 shows the regression results. We can see that after replacing the variables, the coefficient of the DEDL is 0.016, which is still significant at the level of 0.01. The regression results for other variables are consistent with the above, indicating that the regression results are robust.

4.3.3. Replace Variables and Test the Robustness of Logarithms

Combined with the first two robustness tests, we replaced the GDP per capita, which measures the control variable GDPPC, with the GDP; furthermore, we used the logarithm for the regression analysis. Column (3) in Table 5 shows the results. GDPPC still has a significant effect on farmers’ income at the level of 0.01 with a coefficient of 0.014, which once again verifies the robustness of the regression results.

5. Discussion

Increasing farmers’ income is the focus of China’s “three rural” work, which is the central task of the rural revitalization strategy and the key point for achieving common prosperity [29]. With the development of the digital economy, the Internet and agriculture-related digital technologies have been deeply integrated into the lives and work of farmers. The popularity of rural Internet has given rise to new businesses such as rural e-commerce and rural inclusive finance, which make a significant contribution to the income growth of farm households [30]. Most scholars believe that the application of the Internet and the development of the digital economy can improve the farm and non-farm incomes of farmers [31]. Through the use of digital technology and digital machinery, farmers can break the barriers barring the free flow of various factors and optimize the efficiency of their own resource allocation [32]. However, some scholars hold a different view that the development of the digital economy does not have a significant impact on the income of farm households [33]. This conclusion is mostly due to the low Internet coverage, poor economic base, weak infrastructure development, slow Internet speeds, and unstable Internet connections in the sample of rural areas they studied. These problems can reduce the effectiveness of digital technologies, which become meaningless for rural economic growth and have little impact on the income of farm households [34]. Second, farmers lack the skills to match the application of digital technology due to their age, education level, and occupational factors, and even if they can solve the problem of Internet access, they still have problems using the Internet. Therefore, the method, extent, and skill level of farmers with respect to using the Internet determines the income they may earn [35].

In summary, the digital economy’s effect on farmers’ income differs in different regions of Jilin Province. It is necessary, then, to pay attention to the coordinated development of the digital economy and farmers’ income. When promoting digital economy construction at the county level, the objective factors and actual development needs of each region should be fully considered. Regional differences in the digital economy should be reduced as much as possible according to the local conditions in terms of economic development, urbanization level, industrial structure, financial support for agriculture, and education level; this will help improve farmers’ incomes. Based on the above, we made the following suggestions.

5.1. Improve the Rural Digital Infrastructure and Promote the Integrated Development of the Digital Economy and Urbanization

There have been phased achievements in digital economy development and urbanization in Jilin Province. However, Internet acceptance, digital mechanization, and production efficiency are not high in some rural areas, which restricts the development of urbanization. In particular, rural household income in western China is greatly affected by urbanization. Therefore, the integration of the digital economy and urbanization should be promoted in the whole province, especially in the western region. Furthermore, digital technology should be widely applied in the construction of modern cities and towns so that cities can better conduct digital economy development. Specifically, it is necessary to accelerate the improvement of digital infrastructure construction in rural areas, popularize mobile phones and other mobile devices, expand Internet coverage, build e-commerce sites and logistics systems, and connect the Jinong Cloud, Jinong Code, and Sannong application service platforms to Jilin’s digital information platform. Achieve the management and sharing of agricultural data, leverage “digital dividends”, and improve farmers’ levels of production and management. Develop agricultural producer services (e.g., digital agricultural market information, smart agricultural machinery operation and maintenance, and e-commerce marketing) to improve the per capita per acre productivity, attract farmers to return to their villages and start businesses, and improve the incomes of regional farmers.

5.2. Improve the Rural Industrial Structure and Promote the Integrated Development of the Digital Economy and Industries with Rural Characteristics

Studies have shown that when the GDP of the primary industry accounts for an increase in the proportion of the regional GDP, farmers’ planting and breeding income increases accordingly. As a large agricultural province, the Jilin Province’s primary sector of industry is its pillar of economic development. To ensure the steady development of basic agriculture, China should make full use of the advantages of the geographical resources and promote a new primary sector of industry with specialized, clustered, and regional characteristics, taking cash crops and characteristic breeding as the main means of revenue to improve the crop value per unit area and regional output value. Regarding the farmers’ income structure, development of the secondary and tertiary sectors of industry will increase farmers’ property income and wage income. Therefore, it is necessary to further optimize the rural industrial structure in the following ways: broaden the development horizon of rural industry in the dimensions of industrial technology, industrial function, and industrial value; integrate digital technology resources such as the Internet, big data, cloud computing, and blockchain; actively promote the integration of three sectors of industry; and tap the potential value of the rural industrial chain in terms of leisure tourism, food and folk customs, cultural communication, and education reform. Give full play to collective economic organizations in rural industry optimization. Moreover, increase support for establishing township enterprises and developing rural leisure, tourism, culture, healthcare, and other regional industries to better optimize and transform the provincial economic structure. Using these strategies, farmers will gain more value-added benefits from the primary, secondary, and tertiary industries, thereby indirectly increasing rural household incomes.

5.3. Strengthen “Digital Countryside” Construction and Promote Fiscal Support for Agriculture and Digital-Inclusive Finance

The development of digital-inclusive finance is relatively slow in Jilin Province. Farmers in some rural areas have little understanding of digital-inclusive finance and a generally weak financial consciousness. Financial education offered by financial institutions generally has a poor educational effect, which seriously restricts the popularization and development of digital-inclusive finance in rural areas. We find that in the process of “digital countryside” development in Jilin Province, financial support for agriculture has a limited effect on farmers’ incomes, and implementation levels vary by region. China should therefore combine the development characteristics of different regions in Jilin Province and promote digital-inclusive finance in a scientific way according to local conditions. China should also encourage cooperation between enterprises and financial institutions to introduce innovative financial products that align with the realities of rural development and the needs of farmers. In the eastern and western parts of Jilin Province, for example, digital-inclusive finance education for rural households should be strengthened. In this regard, short video platforms such as Douyin, Kuaishou, and Video Number can be used to enrich the content and form of financial education activities, improve farmers’ understanding of digital-inclusive finance, and cultivate their general awareness of financial management and fraud prevention. Farmers can be guided to use mobile banking apps such as TenPay, Jingdong Financial, and Ant Financial to complete transactions and reduce time and transaction costs. In addition, market-oriented operations can improve the use of funds for supporting agriculture, give full play to the role of the market in agricultural development, and drive the transformation and upgrading of agricultural industry. In this way, the price levels of agricultural products can coordinate the interests of consumers and farmers, improve farmers’ enthusiasm for growing grain, and increase production and income.

5.4. Improve Personnel Training and Promote the Integrated Development of the Digital Economy and Digital Agriculture Personnel

There are large gaps in education levels between the rural and urban areas of Jilin Province. Agricultural management personnel and technical personnel are scarce. It is necessary, therefore, to cultivate more versatile digital talent who know and care about agriculture so that more digital scientific and technological achievements can be applied to the field. One way is to foster a new type of digital farmer. Training regarding the whole industrial chain of digital technology can be conducted for major farming households, family farm operators, leaders of farmers’ cooperatives, and leading enterprises. In this way, information technologies such as 5G networks, artificial intelligence, and cloud platforms can be applied to the whole process of agricultural production, and rural households’ digital literacy can be improved. Another approach is to train digital agricultural cadres. As a basic unit connecting urban and rural areas, the digital literacy of county cadres is closely related to the development of regional digital economies. Therefore, China should give full play to the role of the “first secretary”, college student village officials, and other Party cadres in villages, and promote intelligent agricultural and rural management services, digital industrialization, and industry digitalization. The third method is to strengthen the cultivation of digital agriculture professionals in colleges and universities. Agriculture-related colleges and universities can introduce artificial intelligence, smart agriculture, and other related majors; accelerate interdisciplinary integration, and formulate digital agriculture personnel training programs. This will help students understand the specific technologies of digital agriculture and put them into practice, thus supplying high-quality digital agriculture technical personnel for governments and enterprises.

6. Conclusions

This study investigates whether digital economy development can improve farmers’ incomes. To this end, we selected panel data for 38 county-level administrative units in Jilin Province, China, from 2018 to 2020; these data were combined with the County Digital Rural Index, and we used a fixed-effect model for analysis. We found that the digital economy has a significant effect on increasing farmers’ incomes in the whole province. The industrial structure has a significant negative effect on farmer’s income. Regarding subregions, although digital economy development has a positive effect on farmers’ income in all parts of Jilin Province, there is regional heterogeneity. Compared with other regions, digital economy development is better in the eastern region, although there are still gaps between counties. Industrial structure has the most significant effect on farmers’ incomes, and education level also has a significant negative effect on farmers’ incomes in the eastern region. The regression results for the central region are consistent with those for the whole province. In western China, the DEDL and GDP per capita positively correlate with rural household income, while industrial structure and urbanization level have significant negative correlations with rural household income.

Author Contributions

Writing–original draft, H.L.; Writing–review & editing, H.J. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figure and Tables

Figure 1. GIS representation of Jilin Province.

Figure 1. GIS representation of Jilin Province.

Table 1

Explanations of variables and their calculation methods.

Variable Classification	Variable Name and Abbreviation	Variable Definition or Calculation Method
Explained variable	Farmer income level (FIL)	Per capita disposable income of rural residents (CNY 10,000/people)
Explanatory variable	Digital economy development level (DEDL)	County-level digital rural index (2018–2020)
Control variable	Gross domestic product per capita (GDPPC)	Per capita GDP (10,000 yuan/person)
	Industrial structure (IS)	Gross domestic product of secondary and tertiary industries/gross regional product
	Urbanization level (U)	Urban population/total population
	Government finance level (GE)	Expenditure on agriculture, forestry, and water conservancy affairs/expenditure from local general public budgets
	Development level of financial industry (FD)	Balance of loans to financial institutions at year-end/gross regional product
	Educational level (EDU)	(Middle school students + high school students)/total population

Table 2

Descriptive statistics of the main variables.

	Variable	FIL	DEDL	GDPPC	IS	U	GE	FD	EDU
Entire province	Observations	114	114	114	114	114	114	114	114
	Mean	1.373	49.189	3.085	0.773	0.966	0.234	1.074	0.056
	Std. Dev	0.194	5.478	1.11	0.123	0.967	0.062	0.471	0.042
	Min	0.89	36.9	1.462	0.446	0.194	0.109	0.275	0.023
	Max	1.707	63.2	6.28	0.953	4.75	0.366	2.635	0.482
Eastern region	Observations	48	48	48	48	48	48	48	48
	Mean	1.334	52.162	3.348	0.861	1.685	0.192	1.165	0.054
	Std. Dev	0.206	6.615	1.292	0.065	1.128	0.054	0.495	0.064
	Min	0.89	36.9	1.462	0.717	0.47	0.109	0.347	0.023
	Max	1.707	63.2	6.28	0.953	4.75	0.326	2.204	0.482
Central region	Observations	42	42	42	42	42	42	42	42
	Mean	1.486	48.312	2.866	0.711	0.435	0.237	0.96	0.057
	Std. Dev	0.123	2.74	0.885	0.127	0.228	0.031	0.447	0.008
	Min	1.1	41.4	1.807	0.446	0.209	0.167	0.275	0.044
	Max	1.67	53.24	5.806	0.897	1.019	0.316	2.635	0.076
Western region	Observations	24	24	24	24	24	24	24	24
	Mean	1.255	44.774	2.941	0.707	0.455	0.311	1.093	0.056
	Std. Dev	0.175	2.107	0.995	0.096	0.172	0.033	0.439	0.006
	Min	0.98	41.38	2.034	0.577	0.194	0.242	0.353	0.045
	Max	1.556	49.56	5.787	0.897	0.686	0.366	2.114	0.062

Table 3

Model test results.

Test	Result
F test	F(13,21) = 5.911, p = 0.000
BP test	χ2(1) = 5.489, p = 0.010
Hausman test	χ2(7) = 52.144, p = 0.000

Table 4

Overall regression and regional regression results of model (1).

Area	Entire Provinces	Eastern Region	Central Region	Western Region
Variable	FIL	FIL	FIL	FIL
DEDL	0.015 **	0.016 **	0.006 *	0.010 *
	(5.447)	(2.943)	(2.235)	(2.382)
GDPPC	0.060 **	0.096	0.011	0.052 **
	(2.686)	(1.784)	(0.606)	(4.383)
IS	−1.418 **	−4.082 **	−0.733 *	−1.286 **
	(−5.579)	(−5.589)	(−2.666)	(−7.038)
U	0.436	0.786	−3.696	−11.938 **
	(0.655)	(1.430)	(−1.695)	(−8.372)
GE	0.119	0.615	0.353	0.055
	(0.387)	(0.979)	(0.697)	(0.164)
FD	0.048	−0.184	0.065	−0.034
	(1.366)	(−1.711)	(1.995)	(−0.962)
EDU	−0.335 **	−0.332 *	−4.139	5.008
	(−5.592)	(−2.579)	(−0.381)	(0.895)
R 2	−4.219	−15.527	−47.865	−125.216
R2 (within)	0.653	0.735	0.692	0.966
Observations	114	48	42	24
Test	F(7,69) = 633.050,p = 0.000	F(7,25) = 1665.465,p = 0.000	F(7,21) = 33.065,p = 0.000	F(7,9) = 2183.888,p = 0.000

* p < 0.05; ** p < 0.01; t values are in parentheses.

Table 5

Robustness test results.

Observations	(1)	(2)	(3)
	FIL	FIL	FIL
DEDL	0.695 **	0.016 **	0.014 **
	−5.158	−5.745	−5.418
GDPPC	0.060 *	0.071 **	0.318 **
	−2.609	−3.306	−3.673
IS	−1.403 **	−1.290 **	−1.605 **
	(−5.509)	(−4.603)	(−6.083)
U	0.44	0.5	0.465
	−0.665	−0.825	−0.704
GE	0.118	0.296	0.169
	−0.383	−1.129	−0.567
FD	0.061	0.019	0.111 **
	−1.758	−0.43	−2.696
EDU	−0.344 **	−0.290 **	−0.345 **
	(−5.560)	(−5.118)	(−6.038)
R 2	−4.267	−6.979	−2.603
R2 (within)	0.65	0.663	0.677
Observations	114	114	114
Test	F(7,69) = 649.417, p = 0.000	F(7,69) = 833.644, p = 0.000	F(7,69) = 596.189, p = 0.000

* p < 0.05; ** p < 0.01; t values are in parentheses.

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