The Road to Common Prosperity: Can the Digital Countryside Construction Increase Household Income?

Abstract

With the rapid development of China’s digital economy, promoting the digital countryside construction has become the strategic focus and priority development direction of rural revitalization and common prosperity. This paper combines the county-level digital countryside index with the data from the China Household Finance Survey to empirically analyze the impact and mechanism of the digital countryside construction on household income. The results show that the digital countryside construction can significantly improve the level of household income. After the endogenous analysis and the robustness test, this core conclusion is still valid. From the perspective of mechanism test, the digital countryside construction can increase household income by promoting household entrepreneurship and nonagricultural employment. Heterogeneity analysis shows that there is no significant difference in the income-increasing effect of the digital countryside construction, whether urban or rural households, or households with different human capital and social relations, which means that the digital countryside development has the characteristics of inclusiveness and sharing. Under the background of unswervingly taking the road of common prosperity for all Chinese people, this paper provides some micro-evidence for how the digital economy can contribute to household income growth, as well as provides a useful reference for the implementation of the digital China strategy.

1. Introduction

In today’s world, the new generation of information technology is deeply integrated with the real economy. It has gradually become a global consensus to develop the digital economy, promote economic and social transformation, and cultivate new drivers of economic growth. With the vigorous development of digital economy, new products, new forms of business, new occupations, and new models continue to emerge and show strong resilience and vitality [1]. According to the China Digital Economy Development Report (2022) released by the China Academy of Information and Communications Technology (CAICT), new breakthroughs were made in the development of China’s digital economy in 2021, with the scale of digital economy reaching 45.5 trillion CNY, more than double that of 2016, accounting for 39.8% of the GDP, an increase of 9.6 percentage points compared with 2016 [2]. There is no doubt that the digital economy is playing a more stable and supportive role in the national economy. In particular, China’s deployment to achieve common prosperity coincides highly with the rapid development of the digital economy, the development of which is also a viable path to achieve common prosperity. In this process, the spillover, synergistic, and inclusive effects caused by the digital economy have provided a sharing mechanism for balanced development and promoted the whole society to share the dividends of the digital economy. The Sixth Plenary Session of the 19th CPC Central Committee reiterated the need to “strengthen the real economy and develop the digital economy” and “unswervingly follow the path of common prosperity for all people” [3]. Therefore, under China’s grand blueprint of actively promoting rural revitalization and common prosperity, as well as building a well-off society in an all-round way, it is of great significance to explore the effects and mechanisms of enabling households to increase income through the development of the digital economy.

As a hot topic in academia, some scholars have explored the role of the digital economy in increasing income at a micro level. For example, Zhang et al. [4] combined the digital financial inclusion index at the urban level with the data of China Family Panel Studies (CFPS) and concluded that the development of digital finance only increased rural household income by promoting rural household entrepreneurship, but had no significant impact on urban household entrepreneurship and income growth. He and Song [5,6] also used this data to confirm that the development of digital finance can promote non-agricultural employment, but did not reflect the effect of income growth. Ma and Hu [7] found that digital financial inclusion can promote labor mobility through the income effect and employment effect. In addition, some scholars have focused on “e-commerce”, a development perspective of the digital economy, confirming that the development of e-commerce is conducive to the increase in farmers’ income [8,9,10,11]. For example, Qiu and Zhou [9] combined China’s e-commerce development index at the provincial level with CFPS data to prove that e-commerce is conducive to rural households’ income from the perspective of demand and supply, and that families with a high level of education will benefit more. Qin et al. [10] also drew a similar conclusion using rural e-commerce data, but their heterogeneity analysis showed that there was no significant difference in the effect of e-commerce development on households with different education levels. However, Couture et al. [12], based on the first e-commerce expansion plan in China, concluded that e-commerce did not increase rural household income by combining a randomized controlled trial with micro-survey data. In summary, most of the existing studies have affirmed the positive role of the digital economy in economic and social development, but there are still differences and unreasonable conclusions on promoting income growth; the data used are also not micro enough, and the problem of homogeneity is prominent.

As a part of the digital economy, the digital countryside is a new form of innovative development that relies on emerging information technologies such as the Internet, cloud computing, big data, and artificial intelligence to promote the comprehensive and deep integration of digitalization and agriculture, rural areas, and farmers’ production and life, and to boost rural revitalization with the digital transformation of rural economy and society. In view of this, the Chinese government has introduced a series of policies to facilitate the digital countryside construction. For example, in January 2018, the CPC Central Committee and the State Council issued the Opinions on Implementing the Rural Revitalization Strategy, which explicitly proposed the implementation of the digital countryside strategy for the first time [13]. In 2019, the General Office of the Central Committee and the General Office of the State Council issued the Outline of Digital Countryside Development Strategy, which put forward new requirements for exerting the diffusion effect of information technology innovation, the spillover effect of information and knowledge, and the inclusive effect of digital technology release [14]. In the same year, the Ministry of Agriculture and Rural Affairs and the Office of the Central Network Security and Information Commission issued the Digital Agriculture Rural Development Plan (2019–2025) [15]. In January 2022, 10 departments, including the Cyberspace Administration of China and the Ministry of Industry and Information Technology, jointly issued the Action Plan for Digital Countryside Development (2022–2025), which emphasizes the need to liberate and develop digital productivity and constantly stimulate the endogenous driving force of rural revitalization [16]. In summary, these policies have made it clear that empowering rural development with digital technology is an important part of building digital China, realizing rural revitalization and common prosperity. In fact, in addition to China, other countries have emphasized the importance of the digital countryside construction [17] and introduced relevant policies to support information and communication technology to promote agricultural and rural development [18].

With the accelerated embedding of digital technologies into many fields of rural production and life, the digital countryside construction has gradually gained the attention of scholars. Given the dual attributes of the relative importance and novelty of the digital countryside, and limited by the availability of data, the existing studies have mostly explored the impact of digital countryside construction from the theoretical level [17,18,19], while few scholars have conducted systematic empirical analysis from the county level, which is the main battlefield of the digital countryside construction. In particular, taking the county as the basic unit to promote the digital countryside construction not only has the overall advantages of economic, political, cultural, social, and ecological integration, but also has the convenient advantages of directly reaching the grassroots level and efficiently obtaining detailed and comprehensive information. In addition, the Key Tasks of New Urbanization and Urban–Rural Integration Development in 2022 issued by the National Development and Reform Commission and the Opinions on Promoting Urbanization Construction with County as an Important Carrier issued by the General Office of the CPC Central Committee and the General Office of the State Council both stressed the importance of rural revitalization with county as the strategic subject [20,21].

Under the background of vigorously promoting the construction of digital China, can the digital economy represented by the digital countryside construction at the county level promote an increase in household income? If there is a causal relationship, what is the mechanism? In addition, is the digital countryside construction inclusive? Will it contribute to rural revitalization? Clarifying the above issues not only helps to deeply understand the internal relationship between the digital economy and household income increase, but also has important theoretical value and practical significance for exploring the social benefits brought by the digital economy. Compared with the existing literature, this study has the following contributions: first, unlike the previous research perspectives at the provincial or urban level, this paper makes a new interpretation of the income-increasing effect of digital economy development at the county level. Second, in terms of data use, this paper, for the first time, combines the county-level digital countryside index jointly released by the Institute for New Rural Development of Peking University and the Ali Research Institute with the sample data of the China Household Finance Survey to investigate the impact of the digital countryside construction on household income from the micro level. Third, this paper not only explores the micro-mechanism of the digital countryside construction to increase household income, but also analyzes the heterogeneous effect of income-increasing effect from the perspective of inclusive sharing. This not only enriches the research on digital economy, but also helps to further explore the value and potential of digital economy in the process of promoting common prosperity. Moreover, it also has important strategic significance for promoting the high-quality development of the digital economy, consolidating the foundation of an all-round well-off society and realizing the common prosperity of all the people.

2. Theoretical Analysis and Research Hypothesis

The G20 Digital Economy Development and Cooperation Initiative adopted at the G20 Hangzhou Summit and the White Paper on the Development of China’s Digital Economy (2017) released by the CAICT pointed out that the digital economy is a new economic form that uses digital knowledge and information as the key production factors, digital technology innovation as the core driver, and modern information networks as an important carrier to continuously improve the digitalization and intelligence of traditional industries through the deep integration of digital technology and the real economy, and to accelerate the restructuring of economic development and government governance models [22,23]. In addition, the Digital Economy Report 2019 issued by the United Nations Conference on Trade and Development emphasized that the digital economy mainly includes digital industrialization and industrial digitalization [24]. The former is the basic part of the digital economy, namely, the information industry, including the electronic information manufacturing industry, communication industry, and software service industry. The latter is called the integration part of the digital economy, i.e., the process of digital upgrading, transforming, and reengineering of the upstream and downstream industrial chain, which mainly involves the improvement of production efficiency brought by the application of digital technology in traditional industries. In fact, the relevant research on the digital economy has also focused on whether and how digital technology can affect economic activities. Goldfarb and Tucker [25] pointed out in their review that digital technology can reduce five different economic costs related to digital economic activities: search cost, replication cost, transportation cost, tracking cost, and verification cost. On the one hand, digital technology itself has the characteristics of repeated calls and queries at low cost. In the era of the digital economy, with the decline in search costs, the breadth and depth of search have been increasing. On the other hand, with the help of information and communication technology, digital information transmission makes the transportation cost close to zero. In addition, compared with traditional industries, the new model derived from the development of the digital economy makes goods and services easier to produce, track, and verify, which also helps to reduce the corresponding costs. Specifically, in this paper, a higher level of development of digital countryside in a region results in a lower cost of information acquisition and job search, a lower cost of commodity transportation and tracking, and a lower cost of product replication and production for the residents of that region, which will help to improve the probability of entrepreneurship and nonagricultural employment of family members in the region, thus helping to achieve household income growth.

For entrepreneurs, capturing business opportunities, conducting business, and developing markets are inseparable from the role of information [26]. Digital countryside construction reduces the search cost of entrepreneurial information, provides more learning and entrepreneurial opportunities, broadens the information channels of entrepreneurship, and enriches entrepreneurial resources, thus improving the activity of entrepreneurship and the probability of entrepreneurial success. Specifically, residents can not only understand the trend of economic development and identify entrepreneurial opportunities through the Internet, but also make full use of the huge Internet online learning platform to reduce the cost of obtaining educational resources, optimize entrepreneurial learning methods, and improve the level of human capital [27]. This will not only help residents to carry out entrepreneurial learning [28], but also effectively improve individual entrepreneurial ability and ultimately contribute to the growth of family productivity [29]. At the same time, mobile Internet technology reduces the communication cost between residents and improves the convenience and effectiveness of communication, which not only strengthens the original social relations, but also helps to develop new social networks, thus helping to increase the social capital of entrepreneurs [30]. Social capital can indirectly bring material capital, experience, technology, and emotional support to entrepreneurs, and can improve their risk-taking ability. These resources are equally important for family entrepreneurship [31,32]. In addition, with the development of logistics network and sales network, as well as the decline in transportation and tracking costs, the opportunities for residents to open offline retail stores and online stores have increased significantly [33], and households can increase their income by selling industrial products and regional characteristic products. Lastly, as an important part of the digital countryside construction, digital financial inclusion helps to realize the rational allocation of resources, broaden and improve the scope and penetration of financial services, and ease the credit constraints of potential entrepreneurs, thus contributing to family entrepreneurship and inclusive growth [4,34].

The development of the digital economy has optimized the business environment and enhanced the vitality of regional entrepreneurship, which has not only generated the demand for jobs and expanded the scale of employment, but also reduced the cost of nonagricultural employment. Specifically, the massive application of digital technology has improved the efficiency of production and capital accumulation, helped to expand the scale of production, and brought about the increase of employment [35]. In addition, the digital countryside construction can promote the extension of the industrial chain, thus deriving new industries, new business models, and flexible employment needs, as well as creating more nonagricultural employment opportunities. This, to some extent, has alleviated the problem of job losses in traditional economic fields caused by factors such as industrial restructuring, capacity clearance, and market competition, and a large number of people who were forced to lose their jobs due to the substitution effect of new technologies were reabsorbed by the new industry [36]. For example, the development of e-commerce has driven the development of subsectors such as logistics, warehousing, and packaging, increased employment demand, and promoted the transfer of agricultural surplus labor to nonagricultural employment [37], thus increasing household income. At the same time, the rapid development of the platform economy has also provided low-skilled workers with new service jobs such as takeaway riders and online taxi drivers. According to the Research Report on Digital Economy and Chinese Women’s Employment and Entrepreneurship in 2021, the digital economy has lowered barriers to employment and entrepreneurship for female workers in rural and remote areas, created 57 million jobs for women in fields such as digital trade, e-commerce, and live broadcasting, and expanded the value of women in the labor market [38]. In addition, the Internet platform provides a large amount of employment information, which helps job seekers to obtain job information at a lower cost and have more employment options and opportunities [39]. Similarly, in the era of digital economy, the use of the Internet also helps to reduce the search and communication costs of jobs, thus improving the matching efficiency of labor and jobs, as well as reducing frictional unemployment [40].

On the basis of the above analysis, this paper proposes the following two hypotheses:

Hypothesis 1.

The digital countryside construction is conducive to increasing household income.

Hypothesis 2.

The digital countryside construction can increase household income by promoting household entrepreneurship and nonagricultural employment.

At present, although the problem of unbalanced and inadequate development between urban and rural areas in China is still prominent, the policy of urban–rural integration and coordinated development has been implemented continuously. In addition, human capital and social capital have been proven to affect household income [4,9,10,41]. Under the background of the inherent requirements of achieving common prosperity and the widespread popularization and application of digital technology, can the digital economy “share the cake” while “making the cake bigger”? Does the digital economy really have the characteristics of inclusiveness and sharing? Does it help to promote “sharing of wealth” between urban and rural areas and households with different types of human and social capital? Relevant theoretical research points out that the digital economy has obvious inclusive and spillover effects. It not only cultivates a large number of small and medium-sized enterprises and solves the threshold problem for small and micro-enterprises to enter the market, but also provides development opportunities for remote, poor, and underdeveloped areas, and it allows low-skilled workers, social vulnerable groups, and other market entities to have equal access to technology and services, participate in economic activities fairly, and share the development dividend of the digital economy, thus contributing to balanced growth [42,43,44]. However, the results of existing empirical studies are controversial. For example, Zhang et al. [4] pointed out that digital financial inclusion only significantly improved the income level of rural households. Cui et al. [45] believed that e-commerce development has a stronger role in increasing income for families with high education level. Zeng et al. [41] showed that a richer social capital leads to a greater income-increasing effect caused by e-commerce, but Qin et al. [10] believed that the income-increasing effect of e-commerce development does not differ between different social relations. It can be seen that the existing conclusions are not consistent, and they cannot fully reflect the advantages of inclusive sharing of the digital economy. Therefore, in order to demonstrate this problem, this paper analyzes the income-increasing effect of different households from a new perspective of county-level digital countryside construction. On this basis, this paper proposes the third research hypothesis:

Hypothesis 3.

The digital countryside construction has the characteristics of inclusiveness and sharing. Urban and rural households, as well as households with different human and social capital, can all achieve “sharing of wealth”.

3. Research Design

3.1. Model Setting

In order to explore the relationship between the digital countryside construction and household income, this paper constructs the regression model shown below.

$$\ln (Incom{e}_{ij})={\alpha }_0+{\alpha }_1\ln (Inde{x}_j)+{\alpha }_{2}In{d}_i+{\alpha }_{3}Fa{m}_i+{\alpha }_{4}Cou{n}_j+{\epsilon }_{ij},$$

(1)

where i denotes the family, and j represents the county where family I is located. The dependent variable $\ln (Incom{e}_{ij})$ is the logarithm of the total income of family i, and the core independent variable $\ln (Inde{x}_j)$ is the logarithm of the digital countryside index of the county j where family i is located, whose coefficient ${\alpha }_1$ measures the overall impact of the digital countryside construction on household income. $In{d}_i$, $Fa{m}_i$, and $Cou{n}_j$ represent the control variables at the head of household, family, and county levels, respectively. ${\epsilon }_i$ is a random error term. In addition, since this paper analyzes the relationship between the digital countryside construction and household income at the county level, the standard errors are clustered to the county level to avoid the influence of correlation among households within the county on the estimation results.

3.2. Variable Definition

3.2.1. Dependent Variable

As this paper examines the effect of the digital countryside construction on household income, the authors use the total household income (CNY) as the dependent variable. In the empirical study, this paper applies the logarithm, expressed as ln(Income).

3.2.2. Independent Variable

The core independent variable is the development level of the digital countryside. In this paper, the county-level digital countryside index is used to measure it, which is expressed as ln(Index). The index is based on 21 indicators from Alibaba Group and its business and eco-partners, and eight indicators from national statistics and web crawling, which are standardized using the logarithmic efficacy function method and aggregated from the bottom to the top.

3.2.3. Control Variables

Referring to the existing literature [4,8,9,10,11,46,47,48], this paper tries to control as many factors as possible that affect household income. The characteristics of the head of household include the head’s gender, age, education level, party member or not, health condition, and marital status. Among them, gender is a dummy variable, where male is assigned 1, and female is assigned 0. The level of education is measured by the number of years of education corresponding to the household head’s qualifications, specifically, 0 years for no schooling, 6 years for primary school, 9 years for junior high school, 12 years for high school/technical secondary school/vocational high school, 15 years for college/higher vocational education, 16 years for bachelor’s degree, 19 years for master’s degree, and 23 years for doctorate. If the head of the household is a party member, the value is 1; otherwise, it is 0. This is because, in China, if you are a party member, you have more advantages in the choice or employment of some professions, such as civil servants, and it is also easy to obtain special wages and sideline opportunities [46]. Therefore, we have reason to believe that, if the head of household is a party member, this will help to increase household income. Health condition is defined according to the self-rated health of the householder, which is given a value of 1 for “good” and “very good”, and 0 otherwise. If the head of the household is married, then marital status is assigned a value of 1; otherwise, it is 0. The variables at the family level include family size, employment, child dependency ratio, and elderly dependency ratio. Among them, the family size is measured by the number of family members, while the employment is calculated by the number of people with jobs in a family. The child dependency ratio and the elderly dependency ratio are the proportion of the number of members under the age of 16 and over the age of 60 to the number of working-age members, respectively. In this paper, the working age is set at 16–59 years old. The control variables at the county level are the proportion of the secondary industry, the proportion of the tertiary industry, the proportion of fiscal expenditure, and the population density. Among them, the proportions of the secondary industry and tertiary industry are measured by the proportion of the added value (10,000 CNY) of secondary industry and tertiary industry in GDP (10,000 CNY), respectively. The proportion of fiscal expenditure is measured by the proportion of local general public budget expenditure (10,000 CNY) to GDP (10,000 CNY). The population density is expressed by the ratio of the registered population (10,000 people) to the land area (square kilometers) of the administrative area.

3.3. Data Sources and Descriptive Statistics

The data used in this paper mainly come from three main sources. Firstly, the digital countryside index comes from the county-level digital countryside index database jointly established by the Institute for New Rural Development of Peking University and the Ali Research Institute. These data break through the existing evaluation model of the digital economy index with cities or regions as the main evaluation object. It takes the county as the basic unit for the first time, comprehensively combines the digital content and specific representation of rural infrastructure, rural economy, rural life, rural governance, and other aspects, takes into account the perspective of producers and consumers to select specific representation indicators, and fully considers the new digital phenomenon in the current rural development. The actual development level of the digital countryside in 1880 counties (excluding 970 municipal districts and one special zone) in China was comprehensively evaluated. The data are also accompanied by a research report called County-level Digital Countryside Index (2018), which introduces the indicator system and calculation method in detail and shows the development status of China’s digital countryside from different perspectives. Secondly, the data of total household income, head of household, and family characteristics are from the China Household Finance Survey (CHFS) in 2019. The survey is a nationwide sampling survey conducted by the China Family Survey and Research Center of Southwestern University of Finance and Economics, aiming to collect relevant information on the micro level of families. In 2019, CHFS covered 345 districts and counties in 29 provinces (autonomous regions and municipalities directly under the Central Government), with a sample size of 34,643 households. The data are representative at both the national and the provincial levels. Lastly, the added value of the secondary and tertiary industries, GDP, local fiscal expenditure, the registered population, and the administrative land area are from the 2019 China County Statistical Yearbook (County and City Volume). In the actual research, this paper matches the county-level digital countryside index (2018 data), CHFS2019 (household income is 2018 data), and county-level statistical data (2018 data). It should be noted that, due to the limitations of the data itself, only 1 year of data could be matched for research. After eliminating the samples with missing variables, a total of 14,917 households were finally obtained. The descriptive statistical results of variables are shown in

Table 1. Descriptive statistics of variables.

Type of Variable	Variable	Obs	Min	Max	Mean	SD
Dependent variable	ln(Income)	14,917	0	15.8777	10.2858	1.5168
Independent variable	ln(Index)	14,917	2.671	4.4751	3.9684	0.2041
Householder characteristics	Gender	14,917	0	1	0.8102	0.3921
	Age	14,917	18	101	55.5958	13.1849
	Education level	14,917	0	23	8.1720	3.8822
	Party	14,917	0	1	0.1501	0.3572
	Health condition	14,917	0	1	0.3698	0.4828
	Marital status	14,917	0	1	0.8601	0.3469
Family characteristics	Family size	14,917	1	15	3.4427	1.7221
	Employment	14,917	0	8	1.7304	1.1209
	Child dependency ratio	14,917	0	0.8333	0.1192	0.1711
	Elderly dependency ratio	14,917	0	1	0.3295	0.3969
County characteristics	Secondary industry	14,917	0.0492	0.8402	0.3957	0.1357
	Tertiary industry	14,917	0.1313	0.8294	0.4189	0.0913
	Fiscal expenditure	14,917	0.0133	2.8638	0.2822	0.2804
	Population density	14,917	0.00003	0.3079	0.0371	0.0280

.

4. Results

4.1. Main Results

Table 2. Basic regression results.

Variable	Dependent Variable: ln(Income)
	(1)	(2)	(3)	(4)
ln(Index)	0.6228 ***	0.4788 ***	0.5380 ***	0.5179 ***
	(0.1309)	(0.1394)	(0.1466)	(0.1633)
Gender		0.1352 ***	0.2232 ***	0.2228 ***
		(0.0344)	(0.0336)	(0.0334)
Age		0.0150 **	−0.0239 ***	−0.0240 ***
		(0.0072)	(0.0066)	(0.0067)
(Age)2		−0.0003 ***	0.0002 ***	0.0002 ***
		(0.0001)	(0.0001)	(0.0001)
Education level		0.0801 ***	0.0894 ***	0.0927 ***
		(0.0051)	(0.0052)	(0.0043)
Party		0.2761 ***	0.2881 ***	0.2775 ***
		(0.0358)	(0.0353)	(0.0352)
Health condition		0.3596 ***	0.3480 ***	0.3399 ***
		(0.0278)	(0.0272)	(0.0270)
Marital status		0.4932 ***	0.2406 ***	0.2508 ***
		(0.0428)	(0.0461)	(0.0441)
Family size			0.0705 ***	0.0672 ***
			(0.0121)	(0.0119)
Employment			0.2767 ***	0.2823 ***
			(0.0180)	(0.0175)
Child dependency ratio			−0.1926 *	−0.1933 *
			(0.1000)	(0.0993)
Elderly dependency ratio			−0.2790 ***	−0.2734 ***
			(0.0527)	(0.0517)
Secondary industry				0.7364 **
				(0.3097)
Tertiary industry				0.6941
				(0.4250)
Fiscal expenditure				0.2524 **
				(0.1167)
Population density				−0.3328
				(1.0561)
Constant	7.8144 ***	7.3867 ***	7.3665 ***	6.7661 ***
	(0.5188)	(0.6109)	(0.6379)	(0.6626)
Observations	14,917	14,917	14,917	14,917
R2	0.0070	0.1615	0.2210	0.2237

Note: the values in brackets are robust standard errors clustered to the county level; *** p < 0.01, ** p < 0.05, * p < 0.10.

reports the basic regression results of the impact of digital countryside construction on household income. In this table, column (1) only considers the univariate relationship between the digital countryside index and the total household income, while columns (2)–(4) show the regression results of gradually adding the control variables at the level of household head, household, and county. We found that the coefficient of the digital countryside index was significantly positive at the level of 1% in all regressions, which indicates that the construction and development of the digital countryside is conducive to increasing household income; that is, the digital countryside construction has a significant income-increasing effect. Therefore, Hypothesis 1 is verified.

In addition, from the results of the control variables, a male head of household, a higher number of years of education of the head of household, the head of household being a party member, the head of household being healthy, and the head of household being married all contributed to the increase in household income; these results are also more consistent with the reality. In particular, after controlling the family characteristics, the primary term coefficient on the age of the household head was significantly negative, and the secondary term coefficient was significantly positive. One possible explanation is that, as the age of the household head increases, the family burden gradually increases, and income becomes low. However, as age continues to increase, the children are basically independent, the working population of the family increases, and the family burden gradually decreases, thus increasing the total household income. In terms of family characteristics, there was a significant positive correlation among family size, family employment, and household income, while higher child and elderly dependency ratios tended to reduce household income, which is consistent with the research conclusion of Zhang et al. [4]. In addition, the coefficient of the proportion of the tertiary industry was not significantly positive, while the proportion of the secondary industry was significantly positive, which may be because the secondary industry still plays a vital role in economic growth in the county-level administrative regions of nonmunicipal districts. The increase in local fiscal expenditure is conducive to optimizing the allocation of resources and ensuring people’s livelihood, thus helping to increase household income. Lastly, because the population density of the county is relatively low, it is unable to enjoy the economies of scale brought by population agglomeration.

4.2. Endogenous Analysis

This paper examines the impact of county-level digital countryside construction on household income, as well as controls the influencing factors at different levels as much as possible, which alleviates the endogenous effect to some extent. However, household income may also counteract the development of digital countryside; that is, for a county with a higher level of economic development, digital rural construction is also better, and household income and digital countryside construction may be affected by unobservable factors at the same time. To better identify causal effects, this paper attempts to mitigate the endogeneity problem using the instrumental variable (IV) method. Drawing on the ideas of Huang et al. [49], this paper uses the proportion of the number of fixed-line telephone users at the county level in the total number of households at the end of the year in 2000 as the IV of the digital countryside index. From the perspective of relevance, the regions with a higher penetration rate of fixed-line telephones in history are also the regions with a higher penetration rate of Internet in the future to a large extent. Theoretically, these should also be the regions with a high level of digital development in the future. From the perspective of externality, with the rapid development of digital technology, the impact of the number of fixed-line telephone users in history on household income has gradually disappeared, and it has less of an effect on the current household income; that is, historical conditions have given this aspect the advantage of being nearly exogenous [50].

Table 3. Results of IV regression.

Variable	First Stage	Second Stage
	(1)	(2)
ln(Index)		2.3260 ***
		(0.6154)
Fixed-line telephone (IV)	0.2777 ***
	(0.0565)
Householder characteristics	Yes	Yes
Family characteristics	Yes	Yes
County characteristics	Yes	Yes
Observations	14,917	14,917
F value/R2	24.17	0.1934

*** p < 0.01.

reports the regression results of IV method. It can be seen from the first stage of regression that there was a significant positive correlation between the proportion of fixed-line telephone users and the digital countryside index at the level of 1%, i.e., the higher the penetration rate of fixed-line telephone in a region in history, the higher the level of digital construction in that region today. In addition, the F value in the first stage of regression was 24.17, which is greater than the critical value of 16.38 [51] at the 10% error level, indicating that there was no problem of weak IV. The results from the second stage of regression show that the coefficient of the digital countryside index was significantly positive at the 1% level, thus confirming the reliability of the findings from the basic regression.

4.3. Robustness Test

To ensure the credibility of the results, this paper conducts robustness tests from the aspects of specifically including replacing the core independent variable, using the sub-dimensional indicators of the digital countryside index, replacing the estimation model, and changing the processing method of the core independent variable. The corresponding results are shown in

Table 4. Robustness test.

Variable	Dependent Variable: ln(Income)
	(1)	(2)	(3)	(4)	(5)
ln(Financial inclusion)	1.9240 ***
	(0.4353)
ln(Economic digitalization)		0.5323 ***
		(0.1906)
ln(Life digitalization)			0.3803 ***
			(0.1016)
ln(Governance digitalization)				0.0817 *
				(0.0421)
ln(Index)					0.5172 ***
					(0.1633)
Householder characteristics	Yes	Yes	Yes	Yes	Yes
Family characteristics	Yes	Yes	Yes	Yes	Yes
County characteristics	Yes	Yes	Yes	Yes	Yes
Observations	14,709	14,917	14,917	14,917	14,917
R2/ Pseudo R2	0.2274	0.2238	0.2251	0.2220	0.0686

*** p < 0.01,* p < 0.10.

.

4.3.1. Replacing the Independent Variable

The research of Zhang et al. [4] showed that the development of digital financial inclusion is conducive to the increase of farmers’ income. In addition, as there is a certain relationship between digital financial inclusion index and the digital countryside index, this paper also uses the county-level digital financial inclusion index jointly compiled by the Digital Finance Research Center of Peking University and Ant Financial Services Group for regression. It can be seen from column (1) of Table 4 that the estimated coefficient of the digital financial inclusion index was significantly positive at the level of 1%, which shows that the conclusion that “digital countryside construction is conducive to increasing household income” is robust.

4.3.2. Using the Sub-Dimensional Indicators

The digital countryside index is composed of four sub-dimensional indicators: countryside digital infrastructure index, countryside economic digitalization index, countryside governance digitalization index and countryside life digitalization index. In line with the research ideas of Ma and Hu [7], this paper tests the estimated results of these four first-level indicators, and the results are shown in columns (2)–(4) of Table 4. Although the countryside digital infrastructure index did not pass the significance test (not shown in the table), the core conclusion of this paper was still relatively robust on the whole.

4.3.3. Changing the Processing Method

Since the agricultural production and operation, as well as industrial and commercial production and operation, in the composition of total household income are calculated as net income, the family has a negative income due to operating losses. However, this paper takes the logarithm of the household income and then carries out the regression, which limits its value range. In view of this, this paper uses the Tobit model to solve the problem of a limited dependent variable. The results in column (5) of Table 4 show that the coefficient of the digital countryside index was still significantly positive, which shows that the above conclusion is reliable.

4.3.4. Other Robustness Tests

On the one hand, this paper draws on the idea of Qiu and Zhou [9] to divide the digital countryside index by 100 and then regress it. On the other hand, in order to reduce the impact caused by the differences in regional economic development, this paper further controls the dummy variables in the eastern, central, and western regions. Lastly, this paper replaces the dependent variable with the logarithm of per capita household income for regression. The estimated results shown in

Table 5. Other robustness test.

Variable	ln(Income)		ln(Per Income)
	(1)	(2)	(3)
Index/100	1.3580 ***
	(0.3300)
ln(Index)		0.4460 ***
		(0.1697)
ln(Index)			0.5025 ***
			(0.1638)
Householder characteristics	Yes	Yes	Yes
Family characteristics	Yes	Yes	Yes
County characteristics	Yes	Yes	Yes
Regional dummy variables	No	Yes	No
Observations	14,917	14,917	14,917
R2	0.2257	0.2240	0.1787

*** p < 0.01.

once again support the core conclusions of this paper.

4.4. Mechanism Analysis

As mentioned above, the digital countryside construction may increase household income by affecting household entrepreneurship and nonagricultural employment. In order to identify whether these two pathways of action exist, this paper uses the method of mediating effect test of Wen and Ye [52] for reference, and sets the following recursive model on the basis of Equation (1) to test the possible mediating effect:

$$M_i={\beta }_0+{\beta }_1\ln (Inde{x}_j)+{\beta }_{2}In{d}_i+{\beta }_{3}Fa{m}_i+{\beta }_{4}Cou{n}_j+{\epsilon }_{ij},$$

(2)

$$\ln (Incom{e}_{ij}) = {\gamma }_0 + {\gamma }_1\ln (Inde{x}_j)+{\gamma }_2{M}_i + {\gamma }_{3}In{d}_i + {\gamma }_{4}Fa{m}_i + {\gamma }_{5}Cou{n}_j + {\epsilon }_{ij},$$

where $M_i$ refers to the mediating variable, which is expressed by household entrepreneurship and the level of nonagricultural employment in the household. Regarding household entrepreneurship, this paper draws on the practice of Yin et al. [53]. If the household is engaged in industrial and commercial business projects (including self-employed households, leasing, transportation, online stores, WeChat businesses, purchasing on behalf of others, and operating companies), the value is 1; otherwise, the value is 0. It was proven above that digital countryside construction is conducive to the increase of household income. On this basis, Equation (2) is regressed to verify the effect of digital countryside construction on the mediating variables. Lastly, the digital countryside index and the mediating variable are included in the model for the regression of household income, i.e., the regression of Equation (3). Provided that ${\beta }_1,{\gamma }_1$, ${\gamma }_2$ are all significant, if ${\beta }_1\times {\gamma }_2$ has the same sign as ${\gamma }_1$ and the absolute value of ${\gamma }_1$ is less than the that of ${\alpha }_1$, then it can be shown that digital countryside construction can increase household income through affecting household entrepreneurship and household nonagricultural employment. If ${\beta }_1 \times {\gamma }_2$ is of opposite sign to ${\gamma }_1$, this implies a masking effect.

The regression results of column (1) in

Table 6. Mediating effect test.

Variable	Entrepreneurship	ln(Income)	Nonagricultural	ln(Income)
	(1)	(2)	(3)	(4)
ln(Index)	0.3727 **	0.5080 ***	0.2980 ***	0.3312 **
	(0.1667)	(0.1616)	(0.1006)	(0.1410)
Mediating variable		0.1383 ***		0.6265 ***
		(0.0443)		(0.0217)
Householder characteristics	YES	YES	YES	YES
Family characteristics	YES	YES	YES	YES
County characteristics	YES	YES	YES	YES
Observations	14,917	14,917	14,917	14,917
R2/Pseudo R2	0.0946	0.2245	0.4543	0.3179

Note: in column (1), Equation (2) was estimated as the Probit model to examine the relationship between digital countryside construction and household entrepreneurship, and the regression results are reported as Pseudo R². *** p < 0.01, ** p < 0.05.

show that, when household entrepreneurship was taken as the mediating variable, the estimated coefficient of the digital countryside index was significantly positive at the level of 5%, which indicates that digital countryside construction is conducive to improving the probability of household entrepreneurship. The results in column (2) show that the estimated coefficient of household entrepreneurship on household income was significantly positive at the level of 1%; that is, household entrepreneurship is conducive to improving the level of household income. In addition, the coefficient of digital countryside index was significantly positive, and the absolute value was lower than that of column (4) of basic regression. According to the test principle of the mediating effect, household entrepreneurship was a partial mediating variable; that is, the digital countryside construction can increase household income by promoting household entrepreneurship. Similarly, when the level of household nonagricultural employment was taken as the mediating variable, the regression results in column (3) show that digital countryside construction is conducive to promoting household nonagricultural employment, which is consistent with the research results of He and Song [5], who also confirmed that the development of digital economy helps to promote the shift of labor force from agricultural sector to nonagricultural sector, thus realizing the optimal allocation of labor resources. By comparing the regression results in column (4) of Table 2 and column (4) of Table 6, we can see that household nonagricultural employment also plays a partial mediating role in the income-increasing effect of digital countryside construction. On the basis of the above analysis, Hypothesis 2 is verified.

4.5. Heterogeneity Analysis

4.5.1. Urban–Rural Heterogeneity

Under the background of urban–rural integration development, the grouping regression of urban–rural samples can evaluate whether the development of digital countryside is inclusive and shared, and whether it can achieve the shared prosperity of urban and rural households. In contrast to the conclusions of Zhang et al. [4], the results in columns (1)–(2) of

Table 7. Heterogeneity analysis.

Variable	Urban	Rural	High Educ	Low Educ	High Cap.	Low Cap.
	(1)	(2)	(3)	(4)	(5)	(6)
ln(Index)	0.4253 **	0.6684 ***	0.4417 **	0.5887 ***	0.4724 ***	0.5724 ***
	(0.1939)	(0.2207)	(0.1768)	(0.2047)	(0.1723)	(0.1762)
Householder characteristics	Yes	Yes	Yes	Yes	Yes	Yes
Family characteristics	Yes	Yes	Yes	Yes	Yes	Yes
County characteristics	Yes	Yes	Yes	Yes	Yes	Yes
Observations	7171	7746	8800	6117	8928	6024
R2	0.2009	0.2425	0.1766	0.1778	0.2027	0.2080

*** p < 0.01, ** p < 0.05.

show that digital countryside construction has a positive impact on the income of both urban and rural households, rather than only increasing the income of rural households. In addition, this paper also introduces the interactive term of digital countryside index and urban household dummy variable to test whether the coefficients of the inter-group are significantly different after the subsample regression. column (1) of

Table 8. Inter-group coefficient test.

Variable		ln(Income)
	(1)	(2)	(3)
ln(Index)	0.4346 **	0.4806 ***	0.4159 ***
	(0.1801)	(0.1804)	(0.1492)
ln(Index) × urban household dummy variable	0.2509
	(0.1792)
ln(Index) × high education dummy variable		0.0665
		(0.1623)
ln(Index) × high social capital dummy variable			0.1413
			(0.1249)
Urban household dummy variable	−0.4928
	(0.7157)
High education dummy variable		−0.2467
		(0.6421)
High social capital dummy variable			−0.1587
			(0.4925)
Householder characteristics	YES	YES	YES
Family characteristics	YES	YES	YES
County characteristics	YES	YES	YES
Observations	14,917	14,917	14,917
R2	0.2463	0.2237	0.2397

*** p < 0.01, ** p < 0.05.

shows that the coefficient of the interactive term was not significant. This means that there is no significant difference in the promotion effect of digital countryside construction on household income in urban and rural areas, nor does it further widen the urban–rural income gap at the county level. This may be because the digital countryside construction has promoted the integration of urban and rural industries and the reform of agricultural supply-side, optimized the industrial chain and supply chain, and created a large number of new forms of business and new jobs, thus promoting the coordinated development of urban and rural areas and sharing the dividend from the development of digital economy.

4.5.2. Heterogeneity of Human Capital

Human capital is an important factor influencing household income [54]. In general, a higher education level of individuals better enables them to use digital technology to gain benefits [55]. If the development of digital countryside helps more groups with advantages in human capital, it will exacerbate the income gap within the region. In this paper, with reference to the research of Yin et al. [56], according to the education level of the head of household, the total sample was divided into the high education group (junior high school and above) and low education group (below junior high school). The regression results in columns (3)–(4) of Table 7 show that there was no significant difference in the effect of digital countryside construction on increasing income between the high education group and low education group, which is similar to the findings of Hjort and Poulsen [57]. Similarly, the coefficient of the interactive term in column (2) of Table 8 also confirms this. The reason may be that the development of digital countryside provides some jobs with low educational requirements (such as logistics, warehousing, online taxi drivers, and takeaway riders), so that groups with low education can also benefit from it, which is conducive to rural revitalization and common prosperity.

4.5.3. Heterogeneity of Social Capital

Social capital exists in interpersonal relationships and social organizations, and it has positive economic returns [58]. Generally speaking, the wider the interpersonal relationship of a household, the more organizations it participates in, the more social capital stock it has, and the more benefits it will eventually gain. Therefore, can the digital countryside construction alleviate the role of social capital and enable households with weak social relations to enjoy the income-increasing effect? On the one hand, it should be considered that the accumulation of social capital needs social capital investment. On the other hand, the availability of data should be taken into account. In line with the research of Zhang et al. [4], this paper uses whether the household has transferred expenditure (dummy variable) to measure social capital. The existence of transfer payment indicates that the household has close contact with the outside world and is richer in social capital. The results in columns (5)–(6) of Table 7 and column (3) of Table 8 confirm that there was no significant difference in the income-increasing effect of digital countryside construction among households with different levels of social capital. This paper argues that the development of digital countryside has further increased the market-oriented employment behavior, which can reduce the dependence of households on social networks.

To sum up, whether urban or rural households, or households with different human capital and social relations, there is no significant difference in the income-increasing effect of digital countryside construction. This shows that the development of digital countryside is inclusive and conducive to the realization of “shared prosperity”. On the basis of the above analysis, Hypothesis 3 is confirmed. The conclusions of this paper are different from those of Zhang et al. [4], Qiu and Zhou [9], and Zeng et al. [41]. On the one hand, this may be because they only considered a certain dimension of the digital economy, which cannot fully reflect the real development level of the digital economy. On the other hand, it may be because their independent variables were mostly based on the provincial and urban levels, whereby there would be large measurement errors when estimating their impact on household income. In addition, Zeng et al. [41] conducted a study based on whether households participate in e-commerce or not. However, in addition to directly participating in e-commerce, households can also be employed by households or enterprises engaged in the digital industry. Therefore, we think that this paper ignored the income-increasing effect of digital countryside construction due to the spillover effect.

5. Conclusions and Discussion

5.1. Conclusions

With the advancement of the fourth scientific and technological revolution marked by cloud computing, Internet, and artificial intelligence, the real economy has continued to expand using digital technology, economic costs have been significantly reduced, production efficiency has been significantly improved, and the form of industrial organization has been constantly reshaped. As a new economic form, the digital economy is becoming an important driving force to promote quality change, efficiency change, and power change in economic development. Taking the digital countryside construction as the support for rural revitalization and using the digital economy as a new engine for high-quality development in counties is of great significance for achieving an overall well-off society and common prosperity.

This paper combined the county-level digital countryside index released by the Institute for New Rural Development of Peking University and the Ali Research Institute with the CHFS data, and used the data of the China County Statistical Yearbook, based on the IV method and the mediating effect model, to explore the effect and mechanism of the digital countryside construction on household income. The results are as follows: first, the digital countryside construction significantly increases household income, and this core conclusion is still valid after endogenous and multiple robustness tests. Second, the mechanism test found that the digital countryside construction can promote household income by improving the probability of household entrepreneurship and increasing the number of nonagricultural employment of households. Third, the heterogeneity analysis pointed out that the digital countryside construction is not only conducive to the “sharing of wealth” between urban and rural households, but also has no significant difference in the income-increasing effect of households with different human capital and social capital, which reflects the inclusive and shared characteristics of the development of digital countryside to some extent.

5.2. Policy Implications

Firstly, local governments and relevant departments should actively promote the implementation of the Action Plan for Digital Countryside Development (2022–2025), constantly improve the system and mechanism design of county-level digital countryside construction from the aspects of government functions, market role, investment mechanism, incentive mechanism, assessment and evaluation mechanism, etc., issue special support plans, fully mobilize the strength of multiple social entities, and build a digital countryside development model of co-construction, co-governance, and sharing of multiple entities, thus improving the development speed of county-level digital countryside.

Secondly, while constantly improving the construction of countryside economic digitalization, countryside governance digitalization, and countryside life digitalization, attention should also be paid to improving the level of countryside digital infrastructure, such as broadening the coverage of information and financial infrastructure, and actively promoting the construction of data centers and service platforms, which will help to make up for the shortcomings of income-increasing effect of county-level digital countryside and realize the coordinated development of different fields of digital countryside.

Thirdly, in order to better realize the strategic goal of rural revitalization, rural and poverty-stricken areas need to make full use of the dividends brought about by the development of digital countryside. The government departments should also adopt more inclusive and equitable development strategies, increase the policy preference for the integration and development of digital technology and rural advantageous industries in such areas, promote the cross-regional and cross-urban–rural flow of data elements, human resources, capital, and technology, and expand the radiation-driven and spatial spillover effect of digital countryside construction, which will help to narrow the urban–rural income gap and achieve common prosperity.

Lastly, affected by the epidemic and other factors, local governments should strengthen the entrepreneurship assistance for vulnerable groups, implement the fiscal and tax preferential policies for small and micro-enterprises, and further give full play to the inclusive and shared characteristics of the digital economy. In addition, the employment and entrepreneurship promotion mechanism of digital countryside construction should be flexibly used to actively and reasonably guide the surplus labor force to engage in new forms of digital economy, such as e-commerce live broadcasting, logistics, and warehousing, so that more practitioners can make use of the dividend of digital economy to increase household income.

5.3. Limitations and Prospects

Of course, the analysis of this paper still had some limitations, which need to be further improved and expanded. Firstly, due to the limitation of the data, this study only used the cross-section data for analysis, which would have affected the accuracy of the results to some extent. However, as relevant data are updated, richer studies based on more detailed data can be conducted in the future. Secondly, this paper only explored the income-increasing effect of digital countryside construction from the perspective of common prosperity. In fact, common prosperity includes not only income, but also other aspects. In the future, we can try to build a comprehensive index of common prosperity, which will help to explore the huge dividends of the development of digital economy in a more comprehensive way. Lastly, this study can be further deepened. For example, mathematical models can be built to deeply analyze the role of digital countryside construction in increasing household income, and we can also further analyze how digital countryside construction affects household economic behaviors such as consumption and savings decision-making, employment choice, and household division of labor.