Digital Economy, Rural E-Commerce Development, and Farmers’ Employment Quality

Abstract

Employment is the most significant source of livelihood and the foundation of social stability. The rapid development of the digital economy and rural e-commerce has continuously injected new kinetic energy into the comprehensive revitalization of the countryside and provided new possibilities for farmers’ employment. Comprehensively improving the quality of farmers’ employment is an important tool for promoting farmers’ income and solving the problems of rural development at this stage. Using panel data from 30 provincial-level administrative regions in China (2011–2020), this paper examines the impact of rural e-commerce development on rural employment quality in the context of the digital economy and analyzes its underlying mechanisms. The findings show that the development of rural e-commerce can promote the employment quality of farmers by improving the industrial structure. The digital economy plays a negative role in the process of the development of rural e-commerce promoting the employment quality of farmers, but the education level of farmers plays a positive role in it. Therefore, it is recommended to start with farmers’ education by gradually improving their literacy, enhancing their internal drive, and then promoting the positive effect of rural e-commerce development on the quality of farmers’ employment, which is more effective than relying on the external support of the digital economy. In addition, the development of rural e-commerce has a significant positive impact on the employment quality of farmers in the eastern, middle, and western regions of China, and has a greater impact on the middle and western regions than on the eastern region. The possible contribution of this paper lies in the systematic study of the relationship between the digital economy, rural e-commerce development, and the employment quality of farmers and the underlying mechanism. Moreover, this study also analyzes the unique influence and boundary conditions of China’s reality, which provides important reference and empirical evidence for promoting the development of rural e-commerce, improving the quality of rural employment, and promoting rural revitalization.

1. Introduction

Employment is the most significant source of livelihood and the basis of maintaining social stability. Globally, the issue of employment has always been a focus of attention for governments and society, especially in rural areas where the problems of low employment quality, high proportion of informal labor, and unstable incomes are particularly prominent. The downturn in the economy, rising global uncertainty, and the impact of unforeseen events have exacerbated the employment situation [1]. The rural labor force is at a disadvantage in terms of access to employment opportunities, and unemployment and underemployment are particularly pronounced compared to the formal sector [2]. Comprehensively improving the quality of farmers’ employment has become an important tool for promoting farmers’ income and solving rural development problems. As the global digitization process continues to advance, the digital economy, as an important engine of economic growth and social development, is profoundly changing the shape of the economy in rural areas. In this context, the development of rural e-commerce is gradually changing the structure of the rural economy and society, and constantly affecting the quality of employment for farmers. The statistical data collected in 2023 shows that China’s overall e-tailing in 2023 reached RMB 15.42 trillion, with an increase of 11.8% over the previous year, and rural e-tailing turnover was correspondingly boosted to RMB 2.5 trillion, with a growth rate of 12.9%. The rapid development of the digital economy and rural e-commerce has continuously injected new kinetic energy into the comprehensive revitalization of the countryside and provided new possibilities for farmers’ employment.

For a long time, relevant scholars have explored various aspects of the digital economy, rural e-commerce development, and farmers’ employment quality. The level of rural e-commerce development varies significantly in different regions, and the impact on the quality of farmers’ employment is also complex and diverse. Studies have shown that e-commerce promotes the efficient flow of information and inter-industry cooperation through the construction of sustainable local value chains, thereby enhancing market transparency and optimizing resource allocation, and ultimately significantly increasing the income level of rural households [3,4]. In addition, e-commerce provides a stable and reliable path for farmers’ income growth by enhancing the added value and potential returns of agricultural products and shifting farmers from the traditional mode of obtaining producer prices to direct access to consumer prices [5]. On the one hand, the rise of rural e-commerce enables farmers to directly participate in sales, logistics, warehousing, and other links through online platforms, which reduces reliance on intermediaries and improves sales efficiency and profit margins, thus increasing farmers’ income [5,6], and to a certain extent, contributes to the quality of farmers’ employment [7,8]. On the other hand, digital skills have a significant impact on the employment choices of rural workers, increasing their employment opportunities in non-agricultural and employment sectors while reducing the proportion of informal employment [9,10]. In addition, as an important engine for high-quality economic development, the digital economy is gradually integrating with the rural logistics industry. This trend is contributing to making rural logistics a fundamental, strategic, and pioneering industry [11]. The digital economy has promoted the improvement in rural logistics efficiency and stimulated farmers’ initiative to participate in e-commerce. With the empowerment of the digital economy, farmers can integrate into the market more efficiently, thus realizing farmers’ income increases [12].

Taken together, although the rapid development of rural e-commerce has created new opportunities for farmers’ employment, its actual impact on the quality of employment—such as the employment environment, labor remuneration, and labor protection—remains unclear. Existing studies focus more on the impact of rural e-commerce development on farmers’ income and rural economic development, and there is a relative lack of research on the relationship between the digital economy, rural e-commerce development, the quality of farmers’ employment, and the role of the mechanism behind it. Moreover, the conclusions of the small number of existing studies on the impact of the digital economy on the quality of farmers’ employment have not been agreed upon, and the quality of farmers’ employment is mostly treated as an intermediate part of the study of the development of rural economy, without the study of the quality of rural economic development. In the few existing studies on the impact of the digital economy on the quality of farmers’ employment, the conclusions are not unanimous, and the quality of farmers’ employment is mostly treated as an intermediate link in the study of rural economic development, which has not been further analyzed, and there is a lack of in-depth analyses of the unique impacts of China’s reality. Based on this, the potential contribution of this paper lies in systematically examining the relationship between the digital economy, rural e-commerce development, and farmers’ employment quality, as well as its underlying mechanisms. It also analyzes the unique impacts and boundary conditions within the Chinese context, providing important references and empirical evidence for promoting rural e-commerce development. Given the above, this paper systematically investigates the impact of rural e-commerce development on rural employment quality and its path of action based on an empirical study of panel data from 30 provincial-level administrative regions in China from 2011 to 2020, and tries to answer the following questions: (1) the impact of rural e-commerce development on the employment quality of farmers; (2) the influence mechanism and action path of industrial structure rationalization and digital economy on the relationship between rural e-commerce development and the employment quality of farmers; (3) the different influences of rural e-commerce development on farmers’ employment quality in the east, middle, and west regions of China and the reasons behind them. Compared with the existing studies, the possible contribution of this paper lies in the systematic study of the relationship between the digital economy, rural e-commerce development, and the employment quality of farmers and the underlying mechanism. This paper also analyzes the unique influence and boundary conditions of China’s reality, which provides an important reference and empirical evidence for promoting the development of rural e-commerce to improve the quality of rural employment and promote rural revitalization.

2. Theoretical Analysis and Research Hypothesis

2.1. Impact of Rural E-Commerce Development on the Quality of Farmers’ Employment

At the level of comparative advantage theory, the development of rural e-commerce enables farmers to more effectively utilize their comparative advantage to sell local specialty agricultural products through the Internet platform, thereby increasing their income and improving the quality of employment. Information asymmetry theory states that information asymmetry in the market leads to inefficient resource allocation. The development of rural e-commerce enables farmers to better understand market demand and prices through the Internet platform, reducing information asymmetry and thus optimizing production and sales. The synergy effect holds that the overall effect produced by the interaction between different elements or sectors is greater than the sum of the individual effects of each part. The development of rural e-commerce can lead to the synergistic development of rural infrastructure, logistics services, financial services, and other aspects, thus creating more employment opportunities. The rise of rural e-commerce and the linked development of related industries have created a large number of jobs for the rural labor force, especially the agricultural surplus labor force so that the agricultural surplus labor force can realize employment in the non-agricultural field [13]. In addition, the implementation of the policy of e-commerce in the village has a significant catalytic effect on the promotion of achieving endogenous development. It motivates rural residents to actively participate in non-agricultural employment by inducing the emergence of a large number of local non-agricultural employment opportunities, thus laying the foundation of human resources for the comprehensive revitalization of solid villages [5]. In light of the preceding discussion, the following research hypotheses are formulated:

H1: 

Rural e-commerce development can contribute to the quality of farmers’ employment.

2.2. The Mediating Role of Industrial Structural Upgrading

The theory of industrial clusters emphasizes the economic benefits that can be derived from the concentration of related firms and institutions within the same geographical area. Rationalization of industrial structure usually implies a shift of economic activities to higher value-added and high-technology industries. Kuznets [14] pointed out that there is a significant correlation between industrial structure and employment structure, although the changes between the two are not always strictly synchronized. However, industrial structure has a decisive impact on the size of employment [15] and the structure of employment role [16]. Mei [17] further confirmed that the optimization of employment structure effectively promotes the improvement in employment quality; at the same time, Zhao [18] argued that China should accelerate the adjustment of industrial structure to give full play to the key role of the tertiary industry in the process of urbanization in improving the quality of employment. E-commerce promotes the optimization and upgrading of the rural industrial chain by fostering the synergistic development of supporting industries such as production, processing, warehousing, and logistics, thereby accelerating the revitalization of rural industries [5]. As an important channel for farmers in developing countries to share the dividends of economic development, e-commerce significantly improves farmers’ economic conditions by adjusting the industrial structure, increasing non-farming employment opportunities [19,20], and boosting the wage income of rural households. From a distribution perspective, e-commerce empowers small farmers to directly connect with consumers, significantly reducing intermediate links and enabling traditionally disadvantaged groups to benefit from digital economic development [21]. This not only changes farmers’ disadvantaged position in the industrial chain and market structure but also effectively reduces the risk of product stagnation. Furthermore, it optimizes the structure of rural industries by lowering transaction regulation costs and enhancing the counter-cyclicality of agricultural products [22]. The promotion of rural e-commerce industry clustering on the allocation of market factor resources and the upgrading of industrial structure has become increasingly significant [23]. From the perspective of industrial evolution, the development of rural e-commerce directly promotes the increase in the proportion of rural residents engaged in local employment in the non-farm sector by boosting the vitality of rural enterprises and the transformation and upgrading of economic structure [5]. The upgrading of the industrial structure has led to an increase in rural employment opportunities and enhanced the added value of agricultural products, which is conducive to the release of economies of scale in the agricultural industry, thus improving the quality of farmers’ employment [24]. Accordingly, this paper proposes the following research hypothesis:

H2: 

Rural e-commerce development affects the quality of farmers’ employment by promoting the rationalization of industrial structure.

2.3. The Moderating Role of the Digital Economy

The development of the digital economy provides opportunities for rural e-commerce in many ways, but its impact on the relationship between rural e-commerce development and farmers’ employment quality needs to be further explored and tested. Human capital theory emphasizes the important role of education and training in enhancing worker productivity and employment quality. The development of the digital economy requires workers to possess certain digital skills, while rural areas often lack corresponding training resources. This makes it difficult for rural residents to obtain high-quality employment opportunities, and many can only engage in low-skill jobs that lack stability and security, which increases workers’ job insecurity and affects the improvement in employment quality. From the perspective of modern competition theory, the development of the digital economy has made the market more open and transparent, and agricultural products can directly face the national or even global market through e-commerce platforms. While this has expanded the market, it has also brought more intense competition. Some agricultural products and farmers with smaller scales and low brand awareness may be at a competitive disadvantage. Although their sales channels have increased, their actual earnings may not have been significantly boosted, leading to a squeeze on profits and thus affecting the quality of employment. The development of the digital economy has led to an increase in the overall economic level, providing a better macroeconomic environment for rural e-commerce development to promote the improvement in the quality of farmers’ employment, but it may also have a certain weakening effect on the process of rural e-commerce development affecting the quality of farmers’ employment. The rapid development of the digital economy has led to an oversupply of highly skilled personnel, a situation that restricts the optimization and enhancement of labor relations [25], which in turn has a certain impact on the improvement in employment quality. On the other hand, the booming development of the digital economy has boosted the demand for skills, which may lead to a rise in the employment threshold, further expanding the employment inequity phenomenon and posing a potential challenge to the improvement in overall employment quality [26]. The expansion of the digital economy may lead to increased employment volatility, prompting frequent career changes for workers, thus posing a potential threat to the stability and quality of employment [27]. In addition, some scholars have empirically explored the association between China’s digital economy and the quality of employment, and the results show that the expansion of the digital economy has produced negative effects in terms of enhancing employment stability, salary levels, and labor security [28].

Farmers’ educational level directly affects their ability to accept and adapt to e-commerce platforms and digital technology. Better-educated farmers are more likely to understand and master the basic requirements of e-commerce operations, marketing techniques, and customer service, and to integrate more quickly into the e-commerce ecosystem. Education improves farmers’ cognitive and learning abilities and enables them to use e-commerce platforms more efficiently, thus improving their employment quality [29]. And, theoretically, education promotes individual human capital, and the enhancement of human capital is the basis of improving employment quality. The education received by farmers not only enhances their skills and knowledge reserves in the process but also enhances their ability to innovate and respond to challenges. With the continuous development of rural e-commerce, market competition intensifies, and thus more educated farmers are more likely to take advantage of their knowledge in the fierce market competition to get a place, so as to achieve higher-quality employment.

The conceptual explanation of the quality of farmers’ employment is presented in Section 3.2.1.

Accordingly, this paper proposes the following research hypotheses:

H3a: 

The digital economy plays a negative moderating role in the impact of rural e-commerce development on the improvement in farmers’ employment quality.

H3b: 

The education level of farmers plays a positive moderating role in the impact of rural e-commerce development on the improvement in farmers’ employment quality.

3. Research Design

3.1. Sample Selection and Data Sources

The data in this paper come from the China Statistical Yearbook, China Population and Employment Statistical Yearbook, China Rural Statistical Yearbook, China Urban Statistical Yearbook, and China Labor Statistical Yearbook. Moreover, the authors were given restricted access to data from the Tibet Autonomous Region, Hong Kong, Macao, and Taiwan. From the standpoint of guaranteeing the completeness of the data, these regions are not included in the scope of this paper’s investigation. In this paper, Stata 17.0 software was used for data analysis. Based on panel data from 30 provincial-level administrative regions in mainland China (2011–2020, excluding Tibet, Hong Kong, Macao, and Taiwan), this study examines how rural e-commerce development influences farmers’ employment quality in the digital economy era.

3.2. Definition of Variables

The definitions of the main variables in this paper are given in

Table 1. Definition of main variables.

Variable Type	Variable Symbol	Variable Name	Variable Definition
Explained variable	Employment	Level of farmers’ employment quality	Level of quality of farmers’ employment as measured by the entropy method
Core explanatory variable	Commerce	Level of rural e-commerce development	Level of rural e-commerce development measured by entropy method
Mediation variable	Te	Industrial structure rationalization index	By means of the formula: $SR={\displaystyle \sum _i^3}{\displaystyle \frac{Y_i}{Y}}\left|{\displaystyle \frac{L{P}_i}{LP}}-1\right|$
Moderator variable	Economic	Level of development of the digital economy	Level of development of the digital economy as measured by the entropy method
	E-Level	The education level of farmers	Through the formula to calculate the average years of education farmers
Control variables	Agri	Level of agricultural contribution	Value added of primary sector/GDP
	Urban	Urbanization level (of a city or town)	The ratio of urban population to total population
	Edu	Level of human capital	Number of students enrolled in higher education/total population
	Invest	Level of foreign investment	Actual utilization of foreign investment/GDP
	Open	Degree of openness to the outside world	Amount of exports and imports of goods/GDP
	Consumption	Social consumption level	Gross retail sales of consumer goods/GDP
	Govern	Intensity of government intervention	Fiscal expenditure/GDP

.

3.2.1. Explained Variable

The quality of farmers’ employment is a multidimensional concept, and this paper finely constructs an evaluation system of farmers’ employment quality, based on the evaluation system of China’s employment quality indicators [30]. The evaluation system covers three dimensions: employment environment, labor remuneration, and labor protection. Specifically, the secondary indicator of employment environment focuses on the economic development dynamics and employment structure characteristics of each province; the secondary indicator of labor remuneration examines the average income level of each province; and the secondary indicator of labor protection is evaluated based on the number of trade union organizations and the data of work injury recognition in each province. The final results are measured by the entropy value method, and the specific indicators are shown in

Table 2. System of indicators of the quality level of farmers’ employment.

General Level	Indicator Layer	Interpretation and Direction of Indicators	Weights
Level of farmers’ employment quality	Employment environment (0.617)	Value added of tertiary sector (+)	0.269
		Share of tertiary output in total output (+)	0.093
		Years of schooling per capita (+)	0.066
		Tertiary employment (+)	0.189
	Labor remuneration (0.142)	Disposable income per rural resident (+)	0.142
	Labor protection (0.241)	Number of grass-roots trade union organizations (+)	0.217
		Number of persons recognized as injured at work (−)	0.024

A “+” sign indicates that the indicator has a positive impact on the level of quality of employment of farmers, while a “−” sign indicates that the indicator has a negative impact on the level of quality of employment of farmers.

.

3.2.2. Core Explanatory Variables

The level of rural e-commerce development is a core explanatory variable. Based on the evaluation of the rural e-commerce development level [31], this paper is constructed from the three aspects of industry development, facility construction, and development status quo, taking into account a variety of factors and data availability, in which the industry development is measured by the average population served by each postal outlet in rural areas and e-commerce sales, respectively. Facility construction is determined by the Internet. The development of the industry is measured by the average population served by each postal outlet in rural areas and the sales of e-commerce. The construction of facilities is determined by the density of broadband access ports and the number of cell phone subscribers per 100 households in rural areas. The development status is determined by the number of Taobao villages and the proportion of administrative villages that have been connected to the postal service. The final results are measured by the entropy method. The specific indicators are shown in

Table 3. Indicator system of rural e-commerce development level.

General Level	Indicator Layer	Interpretation and Direction of Indicators	Weights
Level of rural e-commerce development	Industry development (0.251)	Average population served per postal outlet in rural areas (+)	0.064
		E-commerce sales (+)	0.187
	Facilities construction (0.238)	Internet broadband access port density (+)	0.219
		Cell phone subscriptions per 100 rural households (+)	0.019
	Development status (0.511)	Number of Taobao villages (+)	0.508
		Percentage of administrative villages with postal access to all administrative villages (+)	0.003

The “+” sign indicates that the indicator has a positive influence on the level of rural e-commerce development.

, and the specific calculation process of the entropy method is the same as described above.

3.2.3. Mediation Variable

The industrial structure rationalization index is the mediation variable. This paper adopts the industrial structure rationalization index model [7], which is formulated as follows: $SR=\sum _i^3{\displaystyle \frac{Y_i}{Y}}\left|{\displaystyle \frac{L{P}_i}{LP}}-1\right|$, where $i$ represents the three industries in each region ($i$ = 1, 2, 3); ${ Y}_i$ and $Y$ represent the output value and total output value of the first $i$ and the total output value of the industry in each region, respectively; and $L{P}_i$ and $LP$ are the labor productivity and total labor productivity of the first industry in each region, respectively.

3.2.4. Moderator Variable

The level of the digital economy is one moderator variable. In this paper, we adopt the methodology in [32], which specifically assesses four key indicators, namely the Internet penetration rate, the proportion of Internet-related employees, the Internet-related output, and the number of mobile Internet users. The substantive connotations of these indicators are, respectively, the number of Internet users per 100 people, the proportion of employees in the computer services and software industries in the employment of urban units, the total amount of telecommunication services per person, and the number of cell phone users per 100 people. All raw data are available from the China Urban Statistical Yearbook. For the development status of digital finance, this paper adopts the China Digital Inclusive Finance Index, which is jointly compiled by the Digital Finance Research Center of Peking University and Ant Gold Service Group [28]. The final result uses the entropy value method to measure the comprehensive development level of the digital economy, and the specific calculation process of the entropy value method is the same as above. The specific indicators are shown in

Table 4. Indicator system for the comprehensive development index of the digital economy.

General Level	Indicator Layer	Interpretation and Direction of Indicators	Weights
Comprehensive development index of the digital economy	Internet penetration	Internet users per 100 population (+)	0.116
	Number of Internet-related workers	Percentage of employees in computer services and software (+)	0.306
	Internet-related outputs	Total telecommunications per capita (+)	0.404
	Number of mobile Internet users	Cell phone subscribers per 100 population (+)	0.083
	Digital finance for inclusive development	China Digital Inclusive Finance Index (+)	0.091

A “+” sign indicates that the indicator has a positive influence on the comprehensive development index of the digital economy.

.

The average degree of education of farmers is the other moderating variable, which is measured by the average years of education of farmers. The formula is derived from the method of Luan [33].

3.2.5. Control Variables

The quality of farmers’ employment is affected by many factors, such as socio-economic factors, policy and institutional factors, and market environment factors. In this paper, we set Agri, Urban, Edu, Invest, Open, Consumption, and Govern as control variables.

3.3. Model Design

3.3.1. Impact of Rural E-Commerce Development on the Quality of Farmers’ Employment

Given the above analysis, this paper aims to deeply explore the impact of rural e-commerce development on the quality of farmers’ employment, and to this end, this paper adopts a two-way fixed-effects model for empirical analysis. The specific model design is as follows:

$$\begin{array}{l}{Employment}_{it}={\alpha }_0{+\alpha }_1{Commerce}_{it}+{\alpha }_2{Agri}_{it}+{\alpha }_3{Urban}_{it}+{\alpha }_4{Edu}_{it}+{\alpha }_5{Invest}_{it}+{\alpha }_{6}Open\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\alpha }_{7}Consumption+{\alpha }_8{Govern}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}\end{array}$$

(1)

where Employment denotes the level of farmers’ employment quality; Commerce denotes the level of rural e-commerce development; Agri denotes the level of agricultural development; Urban denotes the level of urbanization; Edu denotes the level of human resources; Invest denotes the level of foreign investment; Open denotes the level of opening up to the outside world; Consumption is the level of social consumption; Govern is the level of government intervention; ${ \mu }_i$ is an individual fixed effect;${\lambda }_t$ is the time-fixed effect; and ${\epsilon }_{it}$ is a random error term.

3.3.2. The Mediating Role of Industrial Structure

To further empirically test the mediating role of industrial structure on the impact of rural e-commerce development on the quality of farmers’ employment, this paper constructs a specific model designed as follows:

$${Te}_{it}={\delta }_0+{\delta }_1{Commerce}_{it}+{\delta }_n{Control}_{it}{+\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(2)

where $Te$ refers to the index of rationalization of industrial structure.

3.3.3. The Moderating Role of the Digital Economy Between Rural E-Commerce Development and Farmers’ Employment Quality

In order to further empirically test the moderating role of the digital economy between rural e-commerce development and farmers’ employment quality, this paper constructs a specific model designed as follows:

$$\begin{gathered} {Employment}_{it}=a_0+a_1{Commerce}_{it}+a_2{Economic}_{it}+a_3{Commerce}_{it}\times \\ {Economic}_{it}{+a_4{Control}_{it}+\mu }_i+{\lambda }_t+{\epsilon }_{it} \end{gathered}$$

(3)

where $Economic$ refers to the level of development of the digital economy.

$$\begin{gathered} {Employment}_{it}=a_0+a_1{Commerce}_{it}+a_2{E-level}_{it}+a_3{Commerce}_{it}\times \\ E-{Level}_{it}{+a_4{Control}_{it}+\mu }_i+{\lambda }_t+{\epsilon }_{it} \end{gathered}$$

(4)

where E-Level refers to the education level of farmers.

4. Empirical Results and Analysis

4.1. Analysis of Results of Descriptive Statistics

Table 5. Descriptive analysis of variables.

Variable Type	Variables	Average Value	Standard Deviation	Minimum Value	Maximum Value
Explained variable	Employment	0.281	0.139	0.065	0.806
Core explanatory variable	Commerce	0.081	0.089	0.021	0.629
Moderator variable	Economic	0.239	0.182	0.049	1
	E-Level	7.688	0.839	3.819	9.801
Mediation variable	Te	0.157	0.094	0.008	0.451
Control variables	Agri	0.099	0.053	0.003	0.258
	Urban	0.59	0.122	0.35	0.896
	Edu	0.02	0.005	0.008	0.041
	Invest	0.019	0.015	0	0.08
	Open	0.266	0.296	0.008	1.548
	Consumption	0.381	0.068	0.222	0.538
	Govern	0.25	0.103	0.11	0.643

presents the descriptive statistics of the main variables. Among them, the mean value of employment is 0.281 and the standard deviation is 0.139, indicating that the level of the quality of farmers’ employment in each province is relatively different; the mean value of commerce is 0.081 and the standard deviation is 0.089, indicating that the level of rural e-commerce development in each province is relatively not different. In addition, from the descriptive statistics results of regulating variables and mediating variables, there are some differences in the level of the digital economy development (Economy) in each province, and there is not much difference in the index of industrial structure rationalization in each province. From the descriptive statistics results of controlling variables, there are some differences in the level of agricultural development (Agri), urbanization (Urban), human capital (Edu), foreign investment (Invest), foreign openness to the outside world (Open), and the quality of employment of farmers in each province; the level of social consumption (Consumption) and the intensity of government intervention (Govern) are small.

4.2. Base Regression Analysis

In this section, a two-way fixed effects model is used to regress Equation (1). The regression results are shown in

Table 6. Regression of rural e-commerce development on farmers’ employment quality.

	(1)	(2)
	Employment	Employment
Commerce	0.315 ***	0.283 ***
	(15.047)	(12.093)
Agri		−0.309 **
		(−2.441)
Urban		0.065
		(0.541)
Edu		−0.345
		(−0.272)
Invest		0.137
		(0.901)
Open		−0.057 ***
		(−2.645)
Consumption		0.035
		(1.112)
Govern		−0.159 ***
		(−3.035)
Constant term	0.184 ***	0.231 ***
	(52.339)	(3.865)
Province fixed effect	Yes	Yes
Time fixed effect	Yes	Yes
R2	0.894	0.907

Note: Values in parentheses are t-values; **, and *** correspond to 5%, and 1% significance levels for significance tests, respectively, and the same below.

.

According to the regression results of the impact of rural e-commerce development on farmers’ employment quality in column (1) and column (2) of Table 6, the coefficients of rural e-commerce development level are significantly positive at the 1% level regardless of controlling for related variables. So there is a significant positive correlation between the development of rural e-commerce and the quality of farmers’ employment, which verifies H1 of this paper.

4.3. Robust Test

(1) The explanatory variables were determined using lagged one-period regression. In order to ensure the reliability of the regression results, lagged one-period regression of the rural e-commerce level indicators and control variables was selected to regress the farmers’ employment quality indicators again. According to the data in

Table 7. Robust test 1.

	(1)	(2)
	Employment	Employment
L. Commerce	0.322 ***	0.278 ***
	(13.482)	(10.455)
L. Agri		−0.308 **
		(−2.475)
L. Urban		0.055
		(0.438)
L. Edu		−1.297
		(−0.972)
L. Invest		0.158
		(1.064)
L.Open		−0.062 ***
		(−2.887)
L. Consumption		0.051 *
		(1.707)
L. Govern		−0.139 **
		(−2.383)
Constant term (math.)	0.207 ***	0.267 ***
	(61.847)	(4.217)
Province fixed effects	Yes	Yes
Time fixed effect	Yes	Yes
Sample size	270	270
R2	0.883	0.900

*, **, and *** correspond to 10%, 5%, and 1% significance levels for significance tests, respectively.

, the variable adjustment in the lagged period did not significantly change the results of the regression analysis of the quality of farmers’ employment, which is consistent with the findings of the original baseline regression, thus confirming the robustness of the research results. The endogeneity problem caused by reverse causality is avoided to a certain extent.

Reduced sample cycle time: To exclude the effect of the epidemic, the data for 2019 and 2020 were excluded, and the baseline model was rerun to regress the results of the two-way fixed effects test. As can be seen in

Table 8. Robust test 2.

	(1)	(2)
	Employment	Employment
Commerce	0.377 ***	0.360 ***
	(11.998)	(9.884)
Agri		−0.362 ***
		(−2.689)
Urban		0.042
		(0.289)
Edu		−0.723
		(−0.451)
Invest		0.041
		(0.255)
Open		−0.031
		(−1.268)
Consumption		0.065 **
		(2.015)
Govern		−0.079
		(−0.948)
Constant term (math.)	0.181 ***	0.216 ***
	(52.132)	(2.803)
Province fixed effects	Yes	Yes
Time fixed effect	Yes	Yes
Sample size	240	240
R2	0.886	0.895

**, and *** correspond to 5%, and 1% significance levels for significance tests, respectively.

, the regression results obtained for the quality of farmers’ employment largely coincide with the original benchmark regression results after excluding the panel data for 2019 and 2020, thus confirming the robustness of this study’s results.

4.4. Mediating Effect Test

Given the problem of overuse and endogeneity bias of the traditional stepwise test of mediated effects, this paper follows the research proposal in [34] to focus on the reliability of the causal explanatory validity of the core explanatory variables on the explanatory variables, and at the same time adopts the same methodology to identify the causal associations between the core explanatory variables and the mediator variables to accurately reveal the influence mechanism. Based on Equation (2), the mediation effect model is constructed to test Hypothesis 2. Specifically, if the coefficients are both significantly positive, this indicates that the development of rural e-commerce can realize the enhancement of farmers’ employment quality by improving industrial structure. Column (2) of

Table 9. Analysis of the regression results of the mediating effect and the moderating effect.

	(1)	(2)	(3)	(4)
	Employment	Te	Employment	Employment
Commerce	0.283 ***	0.051 **	0.384 ***	−0.082
	(12.093)	(2.082)	(5.788)	(−0.699)
E-Level				−0.002
				(−0.881)
Economic			0.096 **
			(2.089)
Economic*Commerce			−0.257 *
			(−1.656)
E-Level*Commerce				0.046 ***
				(3.147)
Agri	−0.309 **	−1.601 ***	−0.293 **	−0.317 **
	(−2.441)	(−12.159)	(−2.293)	(−2.536)
Urban	0.065	−0.800 ***	−0.045	−0.015
	(0.541)	(−6.414)	(−0.348)	(−0.128)
Edu	−0.345	−6.369 ***	−0.831	−0.598
	(−0.272)	(−4.829)	(−0.648)	(−0.480)
Invest	0.137	−0.119	0.198	0.211
	(0.901)	(−0.749)	(1.280)	(1.406)
Open	−0.057 ***	0.020	−0.064 ***	−0.040 *
	(−2.645)	(0.884)	(−2.957)	(−1.896)
Consumption	0.035	−0.028	0.025	0.047
	(1.112)	(−0.861)	(0.785)	(1.541)
Govern	−0.159 ***	0.004	−0.146 ***	−0.155 ***
	(−3.035)	(0.073)	(−2.673)	(−3.023)
Constant term	0.231 ***	0.926 ***	0.272 ***	0.280 ***
	(3.865)	(14.940)	(4.287)	(4.624)
Province fixed effect	Yes	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes	Yes
R2	0.907	0.854	0.909	0.912

*, **, and *** correspond to 10%, 5%, and 1% significance levels for significance tests, respectively.

indicates the influence effect of the core explanatory variable rural e-commerce level on the mediator variable industrial structure rationalization index. The regression coefficient, which is 0.051 and is significant at a 5% level of significance, confirms that rural e-commerce has a positive role in promoting the upgrading of industrial structure. Therefore, H2 is established.

From the analysis of the actual situation, the development of rural e-commerce has made rural production not only limited to the sale of agricultural products but also led to the development of logistics and distribution, warehousing and processing, packaging design, and other related industries. In terms of logistics and distribution, the demand from e-commerce platforms has prompted logistics enterprises to expand their rural coverage and improve distribution efficiency, thereby driving the development of the logistics industry. In terms of warehousing and processing, the standardized requirements of e-commerce platforms for agricultural products have spurred the modernization of warehousing facilities and the advancement of processing technologies. In terms of packaging design, the e-commerce sales model demands more attractive and functional packaging for agricultural products, thus fostering the growth of the packaging design industry. The integration of the development of these industries with agriculture has formed a diversified industrial structure, providing farmers with more employment opportunities and sources of income. Moreover, with the upgrading and diversification of industrial structure, rural laborers need to master more skills and knowledge to be qualified for new jobs, which enables them to continuously improve their skill level and comprehensive quality. Therefore, the development of rural e-commerce provides more employment opportunities and sources of income in rural areas by promoting the rationalization of industrial structure, improving the skill level and comprehensive quality of the rural labor force, and improving the employment environment and living conditions in rural areas, thus promoting the enhancement of the quality of farmers’ employment.

4.5. Moderating Effect Test

Model (3) introduces the interaction term between the digital economy index and the level of rural e-commerce development based on model (1) as a moderating effect model. The results of the model regression are as follows: the coefficient of the interaction term between the digital economy index and the level of rural e-commerce development (Economic*Commerce) is significantly negative, indicating that the digital economy index can significantly inhibit the impact of the level of rural e-commerce development on the quality of employment of farmers. Therefore, H3a is valid.

First, the reason for this result is that the development of the digital economy is highly dependent on digital skills [35], including technologies such as big data, cloud computing, and artificial intelligence. While this provides new employment opportunities for some farmers, the majority of rural workers may lack the necessary skills and knowledge to adapt to these new jobs. In addition, when farmers use e-commerce platforms to sell their products, they often need to rely on the platform’s promotion and traffic. While the platform’s support will expand farmers’ sales channels, this may lead them to rely too much on the platform and lose their pricing power and market discourse, and the platform often charges high service fees or commissions, which further compresses farmers’ profits. Moreover, Internet infrastructure in rural areas is lagging behind compared to urban areas, and some rural areas lack local digital education programs. This has led to the marginalization of some farmers in the wave of the digital economy, preventing them from enjoying the employment dividend brought about by the digital economy, which may instead adversely affect the improvement in employment quality.

Second, with the rapid development of rural e-commerce, more and more farmers are joining the e-commerce industry, leading to increasingly fierce competition in the market [36,37]. Some farmers with a first-mover advantage or strong digital skills can quickly stand out with higher incomes and better employment opportunities, while farmers lacking these conditions may face declining incomes and unstable employment [38]. This income polarization may exacerbate intra-rural inequalities and harm the improvement in the quality of employment for some farmers.

Third, in the context of the digital economy, the problem of information asymmetry still exists and may harm the improvement in the quality of farmers’ employment [39,40]. On the one hand, farmers may have difficulty in accurately obtaining information about market demand [41], leading to a mismatch between production and sales; on the other hand, consumers may have doubts about the quality and reputation of rural e-commerce products, affecting the sales of agricultural products. This information asymmetry may not only lead to a decline in farmers’ income but also damage their reputation and credibility in the e-commerce sector, further affecting the improvement in their employment quality.

Model (4) is a moderating effect model introducing the interaction term between farmers’ education level and rural e-commerce development level. The results of the regression model are as follows: The E-Level*Commerce coefficient between the education level of farmers and the development level of e-commerce in rural areas is significantly positive. The results show that the education level of farmers can significantly promote the impact of the development of rural e-commerce on the employment quality of farmers. Therefore, it can be concluded that the comprehensive adjustment effect 1 is valid: the development of the digital economy and the education level of farmers reflect the external driving force and the internal driving force, respectively, which indicates that the employment quality of farmers should be improved through the development of rural e-commerce. A more effective way is to start with improving the education level of farmers in order to enhance their internal drive. Therefore, Hypothesis 3b is valid. This is due to the fact that better-educated farmers are more likely to acquire the knowledge and skills needed for e-commerce platforms in order to be able to effectively access and analyze market information and be more entrepreneurial and adaptable to new technologies. These advantages enable them to obtain higher income and more stable employment opportunities in the area of e-commerce, thus significantly improving the quality of employment. Therefore, improving the education level of farmers is the key to improving the quality of employment for farmers.

4.6. Heterogeneity Analysis

Given the differentiation of rural e-commerce development among Chinese provinces, rural e-commerce development shows diversity among geographies, and in order to deeply analyze the impact of rural e-commerce development on the regional heterogeneity of farmers’ employment quality, this paper divides the 30 provinces into three major regions, namely, the eastern, the middle, and the western regions, and independently examines the correlation between rural e-commerce development and the quality of farmers’ employment in each region. Specifically, the eastern region covers 11 provinces and municipalities, including Beijing, Tianjin, Hebei, and Shanghai; the middle region includes eight provinces, including Shanxi, Anhui, Hubei, and Henan; and the western region contains 11 regions, including Sichuan, Chongqing, Yunnan, and Guizhou. The test results are shown in

Table 10. Analysis of heterogeneity test.

	(1)	(2)	(3)
	Eastern Part	Middle Section	Western Part
	Quality of Employment of Farmers	Quality of Employment of Farmers	Quality of Employment of Farmers
Commerce	0.277 ***	0.960 ***	0.752 **
	(2.973)	(3.185)	(2.411)
Agri	1.163 *	0.285	−0.779 ***
	(1.710)	(1.605)	(−2.696)
Urban	0.463	1.775 ***	0.578
	(0.975)	(3.055)	(1.383)
Edu	3.133	−8.872 ***	−2.226
	(0.513)	(−3.290)	(−1.159)
Invest	−0.231	0.842 **	0.432
	(−0.548)	(2.571)	(0.579)
Open	−0.127	0.289 ***	0.064
	(−1.258)	(2.811)	(0.968)
Consumption	0.061	−0.035	0.010
	(0.579)	(−0.978)	(0.342)
Govern	−0.190	0.208	−0.071
	(−0.721)	(1.624)	(−1.198)
Constant term	−0.029	−0.561 *	−0.023
	(−0.100)	(−1.939)	(−0.146)
Province fixed effect	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes
Sample size	110	80	100
R2	0.988	0.9896	0.9871
Chow test		5.20
p-value		0.0017

*, **, and *** correspond to 10%, 5%, and 1% significance levels for significance tests, respectively.

.

Based on the data in columns (1), (2), and (3) of Table 10, it can be concluded that the impact of the development of rural e-commerce on the quality of farmers’ employment in each region exhibits significant heterogeneity characteristics. Overall, the degree of development of rural e-commerce demonstrates a significant positive impact on enhancing the quality of farmers’ employment, and its regression coefficients are all significant at the 1% significance level. Moreover, the Chow test indicates that there are marked regional contrasts in the capabilities of the eastern, middle, and western regions. In terms of the impact on the quality of farmers’ employment, the enhancement effect of rural e-commerce development is relatively strong in the middle and western regions, and the enhancement effect in the eastern regions is relatively weak. This phenomenon may stem from two key factors: first, the economic development and social progress of eastern cities are highly developed, and they have significant advantages in remuneration packages, institutional frameworks, and human resource introduction strategies, leading to a tendency to centralize high-quality resources, which makes the marginal effect of rural e-commerce on the improvement in the quality of farmers’ employment in the east relatively weak; second, the middle and western regions, especially in the west, are relatively lagging in terms of economic development. As an emerging business model, the promotion of rural e-commerce can optimize the employment structure and employment environment of these regions to a certain extent, so the positive impact of rural e-commerce on the employment quality of farmers in the middle and western regions is more obvious.

This phenomenon can be explained from three aspects: regional development disparities, policy support, and labor market structure [34]. First, the central and western regions have lower levels of economic development and fewer traditional employment opportunities. E-commerce provides new employment channels and income sources for local communities, leading to a more significant improvement in employment quality. Second, as key regions for national rural revitalization, the central and western regions receive more policy support and resources. For example, the implementation of the “Comprehensive Demonstration of E-commerce in Rural Areas” policy has promoted the development of e-commerce infrastructure and increased employment opportunities. Finally, the labor market in the eastern region is relatively mature, with diversified employment opportunities, resulting in a lower marginal effect of e-commerce. In contrast, the labor market in the central and western regions is more homogeneous, and the introduction of e-commerce has significantly improved the employment structure and quality of employment.

5. Research Conclusions and Policy Recommendations

5.1. Research Conclusions

Drawing on China’s provincial-level panel data (2011–2020), this study explores the impact and action paths of rural e-commerce development on the quality of farmers’ employment in the context of the digital economy, culminating in the following conclusions: first, rural e-commerce development has a significant positive impact on the quality of farmers’ employment; second, rural e-commerce development can realize the improvement in farmers’ employment quality by improving the industrial structure quality, indicating that rural e-commerce development is conducive to promoting the rationalization of industrial structure, which plays a supportive role in realizing high-quality rural employment; third, it is recommended to start with farmers’ education by gradually improving their literacy, enhancing their internal drive, and then promoting the positive effect of rural e-commerce development on the quality of farmers’ employment, which is more effective than relying on the external support of the digital economy; and fourth, there is regional heterogeneity in the impact of rural e-commerce development on the quality of farmers’ employment. Rural e-commerce development has a significant positive impact on the employment quality of farmers in the eastern, middle, and western regions, with a greater role in promoting the employment quality of farmers in the western and middle regions and a smaller role in promoting the employment quality of farmers in the eastern region.

In summary, this study reveals the impact of rural e-commerce development on the quality of farmers’ employment and emphasizes that by enhancing farmers’ education levels and skill literacy, their endogenous motivation can be effectively strengthened, enabling them to better adapt to the evolving demands of the digital economy. This finding provides important insights into the sustainable development of rural e-commerce: by continuously improving farmers’ education and skills training, rural e-commerce can establish a virtuous cycle of endogenous drive, reduce reliance on external support, and enhance resilience to economic fluctuations and technological changes. This development model, centered on education and internal drive, not only improves the quality of farmers’ employment but also lays a solid foundation for the long-term stability and sustainable development of the rural economy.

5.2. Policy Recommendations

Based on the above findings, this paper proposes the following policy recommendations:

First, the government has increased policy support and financial subsidies for rural e-commerce enterprises to reduce operating costs and improve competitiveness. Moreover, the government has strengthened Internet infrastructure construction in rural areas, lowering the cost of Internet access to ensure network coverage and network quality and improve the operational efficiency of rural e-commerce. Relevant organizations have carried out training related to rural e-commerce to improve the e-commerce skills and entrepreneurial capabilities of rural residents. The government should increase investment in vocational training for farmers, particularly in skills related to the digital economy, such as e-commerce operations, logistics management, and the use of digital tools, to help farmers better adapt to the demands of rural e-commerce development. Additionally, the government should further improve rural infrastructure, including network coverage, logistics systems, and payment systems, to provide solid support for the growth of rural e-commerce. Second, the government encourages and supports the development of new industries such as deep processing of agricultural products, special agriculture, and rural tourism; optimizes the rural industrial structure; promotes the integration of agriculture with e-commerce, logistics, finance, and other industries; and forms a diversified and modernized rural economic system.

Third, the relevant departments have promoted the digital transformation of rural e-commerce enterprises, upgraded their digitalization in production, management, and sales, and established and improved the data-sharing platform for rural e-commerce to facilitate the flow of information and improve market transparency.

Fourth, for the middle and western regions, relevant rural e-commerce policy support, such as tax incentives and business start-up subsidies, should be increased to enhance the quality of employment for their farmers; for the eastern region, efforts should focus on enhancing e-commerce operational efficiency and service quality. Additionally, it is crucial to strengthen synergistic development among the eastern, central, and western regions, facilitating resource and information sharing to upgrade rural e-commerce development as a whole.

5.3. Limitations and Future Research Directions of This Study

Although this paper systematically explores the impact of rural e-commerce development on the quality of farmers’ employment in the context of the digital economy, there are still some limitations. Due to constraints in data availability, this study primarily relies on provincial-level panel data from China (2011–2020), failing to incorporate more granular county-level or village-level data, which may limit the ability to comprehensively reflect the micro-level impact of rural e-commerce development.

Future research can be further expanded in several directions. With the rapid growth of emerging models such as live e-commerce and community group purchasing, future studies could focus on the impact of these new models on the quality of farmers’ employment, as well as their similarities and differences compared to traditional models. Additionally, the relationship between human capital quality and e-commerce development represents another important research direction. In the future, as more relevant data from developing countries become publicly available and research advances, international comparisons could be conducted to further validate the generalizability of this study, explore the relevance of China’s experience for rural development in other developing countries, and provide more empirical evidence to support global rural revitalization efforts.