Study on Farmers’ Employment Transformation and the Realization of Common Wealth under Digital Rural Construction

At present, the fundamental goal of the construction of socialism with Chinese characteristics is to eliminate poverty, improve people’s livelihoods and gradually achieve common prosperity, in order to make up for the short board of development, the 19th National Congress put forward the strategy of rural revitalization in accordance with the actual situation of rural development in the new period, rural revitalization should be based on the rural labor force born in the countryside and grown up in the countryside in the final analysis [1–3]. However, the current situation is that agricultural income is generally low, and most young and middle-aged laborers often choose to leave the countryside in search of employment opportunities, in order to achieve the revitalization of the rural economy, the development of human capital is the key. Let farmers out of the primary production of agriculture with low added value, liberate farmers from traditional agricultural production, realize the transformation of diversified employment not only relying on the land, and play the role of the main force of the majority of farmers to let farmers rooted in the countryside in order to better solve the problem of employment of farmers, and gradually realize the common wealth and the revitalization of the countryside [4–8].

In the new era, digital rural construction is a powerful aid to realize rural revitalization. On the one hand, digital rural construction integrates information technology into agricultural production and operation, based on data resources, integrates information, so as to realize the sharing of rural data resources, and then through the analysis of agricultural big data on agricultural production activities in accordance with the sub-division of the way to carry out rational planning, which is conducive to improving the efficiency of agricultural production [9–12]. On the other hand, with the rapid development of the Internet, mobile data and other information technologies, digital information breaks through the boundaries of time and space, and the speed of dissemination is greatly improved. Information technology such as big data and cloud computing empowers the networked sales of agricultural products, and the rapid development of rural e-commerce, live webcasting with goods and other industries in recent years has broken the geographical limitations of the countryside, broadened the channels of agricultural products for sale, enhanced the economic returns of agricultural products, and increased the income of farmers [13–16]. In summary, the construction of digital countryside can provide new ideas for the transformation of farmers’ employment and the realization of common wealth, and at the same time, it can expand the ways to revitalize the rural strategy and enrich the existing theoretical research [17–19].

According to the connection between digital rural construction, farmers’ employment transformation and common wealth, two hypotheses are proposed: digital rural construction can directly promote farmers’ common wealth, and digital rural construction promotes farmers’ common wealth through farmers’ employment transformation. Relevant variables are selected to construct the baseline model and the mediation effect model. Before the empirical analysis begins, the correlation between variables is verified through gray correlation analysis, and the overall fitness of the model is tested according to the chi-square degrees of freedom ratio. The relevant hypotheses are validated through benchmark regression analysis and mediation effect regression analysis. Heterogeneity analysis is also conducted to indicate whether there is regional heterogeneity and heterogeneity at the construction level in the effect of digital village construction on farmers’ common prosperity.

2

Theoretical models and research hypotheses

The direct impact of digital village construction on the common prosperity of farmers

Digital rural construction can revitalize all kinds of production factors in the countryside and give the countryside dynamic energy for development, and its direct impact on the common prosperity of farmers is reflected in the following aspects: first, digital rural construction promotes the digital transformation of agriculture, enlarges the “cake” for the common prosperity of farmers and raises the overall level of the common prosperity of farmers. Digital village construction relies on network and information service infrastructure to integrate agricultural science and technology into the entire process of farmers’ agricultural production, providing power to new farming tools for common wealth. In the production and decision-making process, digital rural construction is used to promote agricultural development from subjective experience to objective data support type. Such as the use of intelligent agriculture system real-time monitoring of soil, climate, pests and other data and integrated analysis, the formation of reliable agricultural planting recommendations, farm managers can adjust the allocation of factors of production accordingly, to achieve the goal of maximizing the output of the farmers to increase their income at the same time to improve the common wealth of the financial increment. Secondly, the construction of digital villages has created a new production and business model for rural development. First, digital villages have opened up channels for inclusive finance to the countryside, bringing financial resources to rural development and improving the availability and convenience of funds for farmers. Secondly, the e-commerce platform relies on digital technology to optimize the whole process of agricultural production, circulation and sales, and stimulate rural economic vitality on the basis of improving product profitability. Finally, digital village construction can not only enhance the farmers’ life satisfaction from the material conditions, but also give them a sense of happiness from the spiritual pursuit, share the “cake” for balanced sharing, and improve the level of common prosperity of farmers. Digital village construction for rural areas, especially in remote mountainous areas of the countryside to open up the infrastructure of the “last kilometer”, these digital infrastructure at a lower marginal cost to achieve a range of services to share, optimize the efficiency of the allocation of public service resources, improve the standard of public services. More importantly, the popularization of digital networking has promoted the diversity of public cultural and recreational activities in the countryside, the formation of unique cultural values in the countryside, the cultural vitality of the countryside has been stimulated, the diversified spiritual needs of the majority of peasants have been satisfied, and the sense of well-being of the peasants has been enhanced, which in turn is conducive to the realization of the common wealth of the peasants. Accordingly, this paper proposes:

H1: The construction of digitalized villages can directly contribute to the common prosperity of farmers.

2.2

Indirect effects of the transformation of peasant employment

Digital rural construction has had a profound impact on the development of rural industries and farmers’ lives by empowering rural construction through the use of digital technology. However, it should be noted that the key factor determining the level of common prosperity of farmers is the transformation of their employment. In the process of urbanization and modernization, many rural residents commute between the city and the countryside, and their status has changed from agricultural producers to self-employed small businessmen or workers. Behind the identity alternation, the living conditions of farmers have not been fundamentally changed, farmers want to get rich is not an easy thing, relying on a single farming and planting rich farmers are few and far between, farmers can change the traditional concept of employment and employment methods, through the employment transition to achieve the goal of living a rich life [20]. First, changing the direction of employment. When choosing jobs, farmers can increase their incomes through industrialized and market-oriented channels, by starting their own businesses, or by going to cities to work or run their own businesses. Farmers can also use local agricultural products as raw materials for further processing, which can solve the problem of agricultural product sales for farmers as well as increase their own incomes, for example, sweet potato starch processing, canned fruit processing, and so on. Independent entrepreneurship, employment and innovative action by farmers is an effective way of realizing the goal of “living in prosperity”, and farmers must exercise their subjective initiative and take positive action in order to obtain material income and spiritual upliftment, thereby realizing the ultimate goal of living in prosperity. Secondly, the development of specialty planting and breeding. Farmers’ agricultural incomes are relatively low, and it is difficult to obtain higher incomes by relying solely on agricultural cultivation; farmers can only increase their incomes by making efforts to seek new and refined products, such as opening farms, breeding farms, picking gardens, agricultural product processing plants and handicraft experiences. Modern people advocate healthy diet, put safety in the first place, farmers can use the rural land resources to play their own planting skills to develop special planting, planting green food or health ingredients, rural areas are large, you can breed green poultry. Farmers cannot use additives such as food and grass-fed to carry out cottage planting and farming, in order to create their own reputation and characteristics. Third, the development of e-commerce. Now that network information technology has entered the countryside and families, farmers can sell agricultural and sideline products unique to their villages on the Internet, because they are familiar to farmers, so they can control the quality of the products very well, because the cost of inputs in the countryside is relatively low, so it has a price advantage. Farmers can showcase their daily rural life to the outside world by broadcasting live sales of agricultural products with special characteristics. However, this requires farmers to strengthen learning to improve their skills and take the initiative to participate in training programs for related knowledge. In addition, farmers can also expand their labor skills, for example by developing agricultural machinery and transportation, obtaining a driver’s license to acquire a skill, expanding their employment, and increasing new employment opportunities. Therefore, the impact of digital village construction on farmers’ common wealth can also be realized through the transformation of farmers’ employment. Accordingly, this paper proposes:

H2: Digital village construction promotes the common prosperity of farmers through their employment transformation.

3

Models and variables

3.1

Selection of variables

1)

Explanatory variables: digital village construction (DV)

The evaluation index system of digital village construction is shown in Table 1, this paper refers to the relevant literature, constructs the comprehensive evaluation index system of digital village construction from the three levels of information infrastructure, rural digital economic development, and agricultural digitalization application, and uses entropy weight TOPSIS method to measure.

2)

Explained variable: farmers’ common wealth (RCP)

The evaluation index system of common prosperity of farmers is shown in Table 2. The first principle of constructing the common prosperity evaluation index system is to measure the two aspects of “making a bigger cake” and “sharing a better cake”, i.e., to measure the overall degree of prosperity and the sharing of development fruits, and at the same time, according to the overall performance of farmers’ common prosperity, it is categorized into the following: (1) the fairness of income distribution and the equalization of public services. At the same time, according to the overall performance of the common prosperity of farmers, it is divided into fair income distribution and equalization of public services. The evaluation index system of common prosperity of farmers constructed in this paper is measured using the entropy weight TOPSIS method.

3)

Mediating variable: transformation of rural employment (RET)

This paper takes the agricultural labor productivity, the proportion of non-agricultural employment, the ratio of agricultural to non-agricultural output, the number of migrant workers, the education level of migrant workers, the age structure of migrant workers, the gender ratio of migrant workers, the income level of migrant workers, the distribution of migrant workers’ employment industries, and the regional distribution of migrant workers’ employment in each province. Specific data can be obtained through the monitoring survey report of migrant workers released by the China Statistics Bureau.

4)

Control variables:

Level of financial support for agriculture (CGE): the level of financial support for agriculture is closely related to farmers’ life, measured by the proportion of expenditure on agriculture, forestry and water affairs in the total financial expenditure of each province. Regional economic growth (PGDP): measured by GDP per capita (10,000 yuan per person). Foreign Direct Investment (FDI): measured using total foreign investment in each province. Rural Market Size (RMP): measured using the share of total rural retail sales of consumer goods in total retail sales of consumer goods.

Table 1.

Digital rural construction evaluation index system

Primary index	Secondary index	Tertiary index	Attribute
Digital village construction	Rural network infrastructure	Rural Internet penetration	Forward
		Rural mobile number	Forward
		Rural delivery route length	Forward
	Rural digital economic development	Digital financial coverage span	Forward
		Digital financial usage depth	Forward
		The degree of digitisation of puhui finance	Forward
	Rural digital application scene	Quantity of farm machinery	Forward
		Rural electricity consumption	Forward
		E-commerce development	Forward

Table 2.

Farmers’ common rich evaluation index system

Primary index	Secondary index	Tertiary index	Attribute
Farmers share the same wealth	Income distribution fairness	Income level	Forward
		Income distribution	Forward
		Output efficiency	Forward
	Public service equalization	Social security	Forward
		Medical health	Forward
		Spiritual affluence	Forward
		Culture education	Forward

3.2

Modeling

To test the proposed hypothesis 1, the following baseline model was set up: 1 $R C P_{i t} = α_{o} + α_{1} D V_{i t} + α_{2} C o n t r o l s_{i t} + μ_{i} + ν_{t} + ε_{i t}$

Where i is the province, t is the year, RCP_it represents the level of common prosperity of farmers, DV_it denotes the level of digitalized rural construction, Controls_it is the ensemble of control variables, μ_i and ν_t denote the individual fixed effects and time fixed effects, respectively, and ε_it denotes the random perturbation term.

The mediating effect model is further constructed to verify whether digital village construction promotes the increase of farmers’ common wealth level through farmers’ employment transformation. The details are as follows: 2 $M_{i t} = β_{o} + β_{1} D V_{i t} + β_{2} C o n t r o l s_{i t} + μ_{i} + ν_{i} + ε_{i t}$ 3 $R C P_{i t} = γ_{o} + γ_{1} D V_{i t} + γ_{2} M_{i t} + γ_{3} C o n t r o l s_{i t} + μ_{i} + ν_{t} + ε_{i t}$

Where M is the mediator variable of farmers’ employment transition. If the estimated coefficient β₁ in equation (2) and the estimated coefficient γ₂ in equation (3) are both significantly positive, it means that the digitalized village construction promotes the common prosperity of farmers by acting on their employment transition.

3.3

Data sources

This paper uses panel data for 30 provinces excluding Tibet and Hong Kong, Macao and Taiwan for the period 2016-2023, which are mainly from the China Statistical Yearbook, China Rural Statistical Yearbook, and the official website of the China Bureau of Statistics.

4

Empirical results and analysis

4.1

Gray correlation analysis

In this paper, the relationship between the variables is further verified, and in the case of determining the correlation between the variables, it establishes the foundation for the following path analysis. Gray correlation analysis is used to verify the relationship between the variables, and the specific analysis steps are as follows. 1)

Determine the analytic series: set the reference column to Y = {Y(k) | k = 1, 2, ⋯, n}. Comparison column to X_i = {X_i(k)|k = 1, 2, ⋯, n}, i = 1,2.

2)

Dimensionlessness of variables: In this paper, the homogenization method is used to obtain the homogenized sequences y(k) and x_i(k).

3)

Calculate the correlation coefficient: 4 $η_{i} (k) = \frac{\min_{i} \min_{k} | y (k) - x_{i} (k) | + ρ \max_{i} \max_{k} | y (k) - x_{i} (k) |}{| y (k) - x_{i} (k) | + ρ \max_{i} \max_{k} | y (k) - x_{i} (k) |}$ where ρ∈(0,∞), called the resolution factor, is usually taken as ρ = 0.5.

4)

Calculation of correlation: the mean value of the correlation coefficients at each moment of the two compared sequences is used to represent the correlation of these two sequences, i.e.: 5 $γ_{i} = \frac{1}{n} \sum_{k = 1}^{n} η_{i} (k)$

Correlations are ranked in order of magnitude and are generally considered γ_i = 0 for no correlation, 0 to 0.35 for weak correlation, 0.35 to 0.65 for moderate correlation, 0.65 to 0.85 for higher correlation, 0.85 to 1 for high correlation, and equal to 1 for complete correlation [21].

Based on the previous description of the variables, the gray correlation analysis of digital village construction (DV), farmers’ employment transformation (RET), the level of financial support for agriculture (CGE), regional economic growth (PGDP), foreign direct investment (FDI), the size of the rural market (RMP) and the farmers’ common wealth (RCP) is carried out in turn. The results of the gray correlation of each variable are shown in Figure 1, which shows a high degree of correlation between each variable, among which the correlation coefficients of digital village construction with farmers’ common wealth and farmers’ employment transformation are 0.888 and 0.892, respectively, which are highly correlated. Therefore, further impact paths can be explored.

4.2

Overall model fitness test

The overall model fit test is shown in Table 3. The chi-square degrees of freedom ratio of this paper is 1.922, indicating that the model fit to the sample data is more than satisfactory. The asymptotic residual mean square and square root (RMSEA), fitness index (GFI), comparative fitness index (CFI), incremental fitness index (IFI), and canonical fit index (NFI) are all within the criteria for fit, indicating that the model has a better fit, and all of them are in line with the evaluation criteria [22]. In summary, the path analysis model for digital village construction, farmers’ employment transformation, and farmers’ common prosperity established in this paper can fit the sample data and can be applied for empirical analysis.

Table 3.

Overall model suitability test

	Fit index	Reference standard			Numerical value	Model suitability judgment
	Fit index	Satisfaction	Acceptability	Inadequacy	Numerical value	Model suitability judgment
Absolute fitting	Calorie ratio	<3	3~5	>5	1.922	Yes
	RMSEA	<0.05	0.05~0.08	>0.1	0.053	Yes
	GFI	>0.90	0.80~0.90	<0.85	0.997	Yes
Relative fitting	CFI	>0.90	0.80~0.90	<0.80	0.996	Yes
	IFI	>0.90	0.80~0.90	<0.80	0.998	Yes
	NFI	>0.90	0.80~0.90	<0.80	0.996	Yes

4.3

Analysis of measurement results

4.3.1

Benchmark regression analysis

The results of the estimation of digital village construction on rural common wealth are shown in Table 4. ***, ** and * indicate significant at the 1%, 5% and 10% levels, respectively (below). Where column (1) represents the mixed regression results and column (2) is the regression results controlling for the effect of year and province. The results show that the regression coefficient of digitalized rural construction is significantly positive at the 1% level, indicating that digitalized rural construction is conducive to the common prosperity of farmers, which verifies Hypothesis H1. Columns (3) to (6) show the results of the gradual incorporation of the control variables into the regression model. Column (6) shows that the estimated coefficient of digital village construction is 0.1172 and significant at the 1% level, indicating that the impact of digital village construction on common wealth has not changed significantly after the addition of control variables in the model, which further confirms hypothesis H1. Digital village construction solves the problem of digital divide between urban and rural areas, and with the construction of the digital villages, the farmers’ lifestyles are also undergoing a changed, enriching the material and spiritual life of farmers and enhancing their sense of well-being.

Table 4.

The influence of digital rural construction on rural common prosperity

	(1)	(2)	(3)	(4)	(5)	(6)
DV	0.4132***	0.1099***	0.1569***	0.0984***	0.1163***	0.1172***
CGE	-	-	0.0081***	0.0051***	0.0058**	0.0055***
RET	-	-	-	0.0044***	0.0013***	0.0013***
RMP	-	-	-	-	0.0176**	0.0159**
FDI						-0.0012**
Constant	0.2314	0.2655***	0.2341***	0.1874***	0.1289***	0.1347***
Year	-	Control	Control	Control	Control	Control
Province	-	Control	Control	Control	Control	Control
N	300	300	300	300	300	300
adj.R²	0.1239	0.3159	0.4158	0.5596	0.5741	0.5963

4.3.2

Analysis of intermediation effects

The results of the regression of the mediating effect of digital village construction on the common prosperity of farmers by increasing their employment transition are shown in Table 5. Column (1) represents the direct effect, which shows the effect of digital village construction on farmers’ common wealth after considering the factor of farmers’ employment transition. Column (2) demonstrates the indirect effect by demonstrating the role of farmers’ employment transition between digital village construction and their common wealth. In the mediation effect analysis, it is crucial to test whether the indirect effect exists, whether it is significant, and what its share is in the total effect. Obviously, in the empirical test of this paper, the transformation of farmers’ employment has a significant mediating effect, in which the share of mediating effect is 19%. It shows that the construction of digitalized countryside can promote the common prosperity of farmers by improving the transformation of farmers’ employment, which verifies hypothesis H2.

Table 5.

The intermediary effect of farmer’s employment transformation

Variable	(1)	(2)	(3)
Variable	Direct effect	Indirect effect	Total effect
DV	0.3019***	0.0452**	0.3428***
RET	0.1152**	-	0.1158**
Sobel-p	[0.0266]
Delta-p	[0.0435]
Monte Carlo-p	[0.0579]
Sample size	300
Mediation effect	19%

4.3.3

Heterogeneity analysis

1)

Regional Heterogeneity

In this paper, we group the regions to which different provinces belong, and based on the division of the National Bureau of Statistics, the 30 provinces are divided into two groups, one group is the eastern provinces, and the other group is the central and western provinces. The results of the heterogeneity test are shown in Table 6. It can be seen that the coefficients of digital village construction in eastern provinces and central and western provinces are 0.0722** and 0.2541* respectively, which are both significantly positive. Further analysis shows that the regression coefficient of digital village construction in central and western provinces is larger than that in eastern provinces, indicating that digital village construction to promote the common prosperity of farmers is more prominent in central and western regions.

2)

Heterogeneity of different construction levels

The results of using panel quartiles to test the impact of the level of digital rural construction on common wealth at different quartiles are presented in Table 6. The analysis finds that there are significant differences in the impact of different levels of digitalized rural construction on farmers’ common wealth, and the estimated coefficients of digitalized rural construction are 0.1863***, 0.1524***, 0.1136***, 0.0874***, which are all significantly positive at the 10%, 25%, 50%, 75% and 90% quartiles of rural construction levels, respectively, but with the increase of the quantile points, the impact coefficient of digitalized village construction becomes smaller. This indicates that there is both regional and construction-level heterogeneity in the effect of digital village construction on farmers’ common prosperity.

Table 6.

Heterogeneity test results

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Variable	East	Midwest	10%	25%	50%	75%	90%
DV	0.0722**	0.2541*	0.2049***	0.1863***	0.1524***	0.1136***	0.0874***
Control variable	Control	Control	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control	Control	Control
Province	Control	Control	Control	Control	Control	Control	Control
N	86	184	300	300	300	300	300

5

The transformation of peasant employment and the path to shared prosperity

1)

Deepening institutional reforms to enhance the fairness of initial distribution

The primary distribution system directly affects the order and outcome of distribution, and has a direct and important impact on the realization of common prosperity. The first is to rationalize the proportions of labor and fiscal revenues in national income, to give full play to the role of fiscal and financial resources, and to steadily increase the proportion of residents ’ incomes in the distribution of national income. The second is to improve the mechanism for the formation and growth of wages, raise the proportion of labor remuneration in the initial distribution, and create a social culture in the whole society that respects labor and works hard to become rich. Third, we are expanding residents’ income channels and increasing their property-based income by improving the environment for entrepreneurship, developing a multi-level capital market, and introducing diversified financial management tools. Fourth, strengthening the regulation and adjustment of high incomes, protecting legal incomes in accordance with the law, reasonably adjusting excessive incomes, cleaning up and regulating unreasonable incomes, rectifying the order of income distribution, resolutely outlawing illegal incomes, protecting property rights and intellectual property rights, protecting lawful wealth, promoting the standardized and healthy development of various types of capital, and narrowing the gap between the incomes of residents. Fifthly, it is raising the proportion of middle-income groups, studying and implementing a plan to double the number of middle-income groups, consolidating and expanding the results of poverty eradication, promoting common prosperity among farmers and rural areas, and comprehensively advancing the revitalization of the countryside, so as to push more low-income people into the middle-income bracket.

2)

Increase policy support for modern agriculture and improve farmers’ production conditions and income levels

The development of modern agriculture aims to improve the production conditions and income levels of farmers, reduce labor conditions and income gaps between industry and agriculture, and cultivate professional farmers. Compared to traditional agriculture, modern agriculture is more capital-intensive, and the development of modern agriculture cannot be separated from policy support such as fiscal and financial support. The high-risk nature of agriculture weakens the market-oriented financial service dynamics, and policy-based financial services become a compensatory choice. After the implementation of policy-based agricultural insurance, the government should further expand policy-based financial services to the credit field, and improve the level of agricultural credit and reduce the cost of financing by means of guarantees, interest subsidies and other means.

3)

Further strengthening vocational education and training for farmers and fostering skillful professional farmers

The transformation of farmers’ employment requires the professional training of agricultural labor. Sustainable vocational education and training for farmers should be implemented. Formulate and implement strategies for farmers’ vocational education and training. Make use of agricultural leisure time, fully utilize the education and training functions of agricultural vocational colleges and universities, and organize farmers to participate in agricultural vocational education. Develop specialized training menus that allow farmers to choose their own training programs according to their needs and specialties. The establishment of a monitoring and inspection system for farmers’ vocational education and training is necessary to ensure that funds are used in a standardized manner and that education and training are effective.

6

Conclusion

Based on the panel data of 30 provinces and cities in China from 2016 to 2023, this paper empirically examines the driving effect, mechanism of action and heterogeneity characteristics of digital rural construction on farmers’ common prosperity by constructing a comprehensive indicator system of digital rural construction and farmers’ common prosperity. Research findings: 1)

The results of the baseline regression analysis show that the coefficient of digital village construction is 0.1172 and is significant at the 1% level. The mediating effect analysis results show that the mediating effect of farmers’ employment transition accounts for 19%. It shows that digital rural construction significantly promotes farmers’ common wealth, and the transition of farmers’ employment is an important path for digital rural construction to promote farmers’ common wealth.

2)

There is regional heterogeneity and construction level heterogeneity in the promotion effect of digitalized rural construction on farmers’ common prosperity, and the effect of digitalized rural construction on farmers’ common prosperity is more obvious in the central and western provinces and provinces with a lower level of rural construction.

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Study on Farmers’ Employment Transformation and the Realization of Common Wealth under Digital Rural Construction

Jingyu Chen

Published Online: Mar 19, 2025

Received: Oct 21, 2024

Accepted: Jan 30, 2025

DOI: https://doi.org/10.2478/amns-2025-0493

KeywordsDigitization, Regression analysis, Gray correlation, Common wealth

© 2025 Jingyu Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Digitization, Regression analysis, Gray correlation, Common wealth