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Article

Digital Rural Construction and Rural Household Entrepreneurship: Evidence from China

by
Yunwen Zhou
,
Zhijian Cai
* and
Jie Wang
College of Economics and Management, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(19), 14219; https://doi.org/10.3390/su151914219
Submission received: 23 August 2023 / Revised: 11 September 2023 / Accepted: 20 September 2023 / Published: 26 September 2023
Review Reports Versions Notes

Abstract

:
Promoting rural entrepreneurship is an important approach to achieving rural revitalization, accelerating the construction of a new development pattern, and enhancing the well-being of farmers. Based on the County Digital Rural Index (CDRI) and the China Household Finance Survey (CHFS), we empirically examine the impact and role of digital rural construction (DRC) on rural household entrepreneurship. Our findings are as follows: (1) DRC plays a vital role in facilitating entrepreneurial behaviors (EB) and entrepreneurial performance (EP) among rural households. (2) DRC indirectly fosters rural household entrepreneurship by facilitating resource acquisition and opportunity identification. (3) Our heterogeneity analysis reveals that DRC’s promotion effect is stronger among local entrepreneurs and individuals with risk-averse tendencies. Additionally, DRC has a more pronounced effect in stimulating EB within lower-income families, while its impact on EP shows the opposite trend. Furthermore, DRC’s influence on rural household entrepreneurship is particularly significant in regions with more advanced digital rural development. (4) Additionally, we observe a significant positive impact of the four dimensions of DRC on rural household entrepreneurship, further affirming the role of DRC in driving rural household entrepreneurship. In the digital economy era, this study provides empirical evidence to promote the integration of digital technology and rural entrepreneurship, offering valuable insights for advancement in this domain.

1. Introduction

Currently, the most prominent development imbalance in China continues to be the widening urban–rural gap, with the most significant deficiency still being the underdevelopment of rural areas (Yin et al. [1]). Certain research indicates that rural entrepreneurship catalyzes rural economic development, contributing to job creation and narrowing the urban–rural development gap (Kushalaksh and Raghurama [2]; Stephens et al. [3]; Korsgaard et al. [4]; Steiner and Atterton [5]). In this context, rural households, as individual economic entities within society, participate in entrepreneurial endeavors at the family level, constituting a crucial element of rural entrepreneurship. Rural household entrepreneurship encounters several challenges, including information asymmetry, limited access to financial services, inadequate rural infrastructure, and a mismatch between available financial resources and services (Li et al. [6]; Zhao et al. [7]). Enhancing the rate of rural household entrepreneurship and improving entrepreneurial performance are pressing issues that both academia and the government must urgently tackle.
In China, the “No. 1 central document” of 2018 marked the formal introduction of the digital rural construction (DRC) strategy. Subsequently, a series of documents were released, including the Outline of Digital Village Development Strategy, Digital Village Construction Guide 1.0, and Digital Village Development Action Plan (2022–2025). These documents collectively outline the vision, direction, and specific action plan for the implementation of DRC. DRC represents a model that harnesses the power of the digital economy and utilizes digital technology as a conduit to achieve the digital transformation of rural production, lifestyle, and governance (Mei et al. [8]; Cui et al. [9]). In the context of rural China, DRC plays a pivotal role in the development strategy aimed at establishing digital villages. DRC yields a diverse range of functional benefits, essentially crafting a parallel ‘digital realm’. It empowers rural governance, fortifies production capacities, and elevates the overall quality of life. Simultaneously, it acts as a bulwark against uncertainty in rural areas while substantially reducing transaction costs associated with commercial activities. This cost reduction, in turn, kindles the innovative spirit of farmers and fosters entrepreneurial dynamism. An equally remarkable facet of DRC lies in its ability to transcend geographical and temporal boundaries. Its delocalization features break down the physical confines, thereby activating key entrepreneurial elements such as identifying market opportunities, mobilizing dormant capital, and tapping into the reservoir of rural talent firmly rooted in these areas. DRC facilitates the optimal allocation and innovative amalgamation of diverse elements, thus catalyzing a transformation from the current scenario characterized by feeble agricultural competitiveness and resource-intensive practices in China. This strategic approach not only propels China’s agriculture towards high-quality development but also cultivates an ecosystem conducive to farmers’ entrepreneurial endeavors. It signifies a significant shift towards a more vibrant, sustainable, and economically prosperous rural landscape.
Existing research has extensively explored the reasons for the low rural entrepreneurship rate, which can be classified into two main categories. At the macro level, the primary factors include political conditions (such as national policies, laws, and market incentives), the geographical environment (including the terrain, soil, hydrology, and climate), the social culture (encompassing language, religion, and values), and the overall entrepreneurial atmosphere (Kotey [10]; Vessey [11]; Gaddefors et al. [12]; Wang et al. [13]). On the micro level, key factors encompass the characteristics of entrepreneurial individuals (such as gender, age, and personality), family attributes (such as income, assets, and social networks), and specific entrepreneurial traits (Ajayi et al. [14]; Kangogo et al. [15]; Wang et al. [16]). The absence of macro-environmental and micro-level conditions may lead to inadequate entrepreneurial motivation in rural areas. The renewability, external economy, and increasing marginal benefits of the digital economy play a vital role in solving these entrepreneurial problems (Camero and Alba [17]; Nambisan et al. [18]; Sahut et al. [19]). A favorable digital environment can empower and optimize other production factors through seamless integration and embedding (Tauscher and Laudien [20]). Over the past few years, many scholars have been actively discussing a wide range of topics related to DRC. For instance, Dillon et al. [21] confirmed that rural infrastructure indeed effectively boosts agricultural production. Furthermore, rural governance and rural living directly impact farmers’ input capacity. By constructing rational interest-sharing mechanisms, Zhang et al. [22] discovered that rural governance enhances farmers’ collective action capacity and achieves income growth and material capital accumulation in rural areas. Zhou et al. [23] found that an improved rural governance system, through providing basic public services and enhanced rural welfare, partially reconstructs rural social capital, reinforces incentives for farmers’ education investment, and influences the accumulation of rural human capital. Popkova and Sergi [24] proposed that digital villages can promote diversification in agricultural products and services, stimulating consumption.
Overall, the extensive literature does not explore the relationship between DRC and rural household entrepreneurship. Additionally, according to the CDRI, the scope of DRC encompasses multiple dimensions, including digital rural infrastructure (DRI), digital rural economy (DRE), digital rural governance (DRG), and digital rural lifestyle (DRL). However, few studies have comprehensively analyzed the impact and mechanisms of DRC on rural household entrepreneurship from a multidimensional perspective. This study empirically examines the relationship between DRC and rural household entrepreneurship using county-level digital rural index data and the 2019 CHFS. The findings indicate that DRC promotes rural household entrepreneurship. It achieves this by indirectly facilitating resource acquisition and opportunity identification for rural entrepreneurship. Heterogeneity analysis reveals that DRC has a stronger promotion effect on local and risk-averse individuals’ entrepreneurship. Furthermore, DRC has a more pronounced effect in stimulating EB within lower-income families, while its impact on EP shows the opposite trend. The promotion effect of DRC on rural household entrepreneurship is notably significant in regions with a higher level of digital village development but less pronounced in regions with a lower level of digital village development.
This study’s contributions are primarily reflected in three aspects: First, we innovatively matched the county-level digital rural index with the data from the CHFS, providing a novel analysis of the impact of DRC on rural household entrepreneurship at the province level. This enriches the research content concerning the relationship between DRC and rural entrepreneurship. Second, we specifically targeted rural household entrepreneurial activities and developed a theoretical framework to comprehend the driving forces behind rural household entrepreneurship. Our objective was to provide practical methods and strategies to assist individual rural households in overcoming entrepreneurial challenges. Third, through a multidimensional analysis of DRC and a heterogeneous analysis across different levels, this study delves deeper into uncovering the variations in the influence of DRC on rural household entrepreneurship. The empirical investigation explores the potential pathways through which digital rural development affects rural household entrepreneurship, focusing on resource acquisition and opportunity identification.
The remainder of the paper is structured as follows. Section 2 presents the theoretical framework underlying this study, while Section 3 describes the data we use in detail and the empirical model. Section 4 provides the results and discussion of the main findings. Following that, Section 5 investigates the impact of the subdimension of DRC on rural household entrepreneurship. Section 6 concludes the paper.

2. Background and Theoretical Hypotheses

2.1. DRC and Rural Household Entrepreneurship

Rural household entrepreneurship, as defined in the relevant literature, involves rural families relying on family organizations or creating new entities to pursue economic benefits. This includes expanding the production scale, venturing into new activities, or initiating new businesses by investing productive capital in rural areas (Pato and Teixeira [25]; Wong et al. [26]). The market is the driving force behind farmer entrepreneurship (Boppart [27]). However, in the context of the modern digital economy, DRC acts as a booster to further stimulate rural residents’ entrepreneurial development.
Recent developments in digital technology have prompted scholars to explore the impact of digitalization on rural household entrepreneurship. The existing literature primarily examines e-commerce, digital finance, and Internet usage. For instance, Barnett et al. [28] found that smartphone and Internet use positively influence entrepreneurship through social networking and information access channels. Tang et al. [29] focused on rural tourism, empirically testing the positive impact of the digital economy on rural entrepreneurial behavior at the micro level. Kim and Orazem [30] examined the effect of Internet use on the location of rural household entrepreneurship in the U.S., showing that broadband availability positively influences the location decisions of new businesses in rural areas. Additionally, Mack et al. [31] found that the Internet allows entrepreneurial subjects to identify more opportunities before starting a business and enhances productivity after the business’s launch.
The concept of the digital village possesses both relative significance and novelty. However, the meaning of digital village is slightly different in different countries. In the Chinese context, “digital village” encompasses the indigenous progression and evolution of agricultural and rural modernization. It involves the integration of networking, informatization, and digitalization into the economic and social development of rural areas, along with the enhancement of farmers’ proficiency in modern information technologies. This concept not only serves as a strategic pillar for rural revitalization but also constitutes a pivotal aspect of China’s broader ambition to construct a digitally empowered nation (Zhang et al. [32]; Jiang et al. 2022 [33]). Within the European Union, the term “digital village” pertains to rural areas and communities that leverage their existing strengths and assets while concurrently exploring novel prospects. In a digital village, both traditional and modern networks and services undergo enhancement through the application of digital and telecommunication technologies, innovation, and improved knowledge utilization. This transformation ultimately yields benefits for residents and businesses alike (Zavratnik et al. [34]). In short, the implementation of digital villages must adapt to social, cultural, and environmental conditions. Therefore, in rural areas, digitalization needs to adapt to corresponding concepts and solutions, and efforts must be made to improve the well-being of the rural population. The first hypothesis is proposed as follows:
H1. 
DRC significantly positively affects rural household entrepreneurial behavior (EB) and entrepreneurial performance (EP).

2.2. The Mediating Role of Resource Acquisition and Opportunity Identification

Entrepreneurship is not only the process of identifying and exploiting entrepreneurial opportunities, but also the process of moving from opportunity identification to coordinating resources and thus forming market competitiveness, and this view has also been recognized by more scholars (Jenkins and McKelvie [35]; Shane and Venkataraman [36]). Thus, entrepreneurship is an activity involving opportunity identification and resource acquisition.
Firstly, DRC facilitates rural household entrepreneurship by simplifying opportunity identification. The digital economy facilitates knowledge spillover, bridging cognitive gaps for entrepreneurs to identify opportunities and mitigate risks from information asymmetry (Cutolo and Kenney [37]; Lodefalk and Tang [38]). When viewed through the lens of the entrepreneurial process, the identification of entrepreneurial opportunities marks the very inception of the entire entrepreneurial journey. It constitutes a critical juncture in the value creation process and serves as a pivotal determinant in an entrepreneur’s decision to embark on a business venture (Shepherd et al. [39]). Referring to the relevant literature, entrepreneurial opportunities exist within the entrepreneurial environment (Shane [40]; Alvarez et al. [41]). During the opportunity identification process, entrepreneurs should harness their proactive instincts to the fullest and continuously gather, assess, and creatively enhance valuable information (Eller et al. [42]). Research on opportunity identification in rural settings, particularly among rural household entrepreneurs, is limited compared to its focus on enterprises and start-ups. The existing literature has primarily concentrated on entrepreneurial alertness, experience, social networks, and human capital as key factors influencing opportunity identification (Kirzner [43]; Ardichvili and Cardozo [44]). In summary, our research contends that farmers’ entrepreneurship opportunity identification involves farmers discovering new goods, technologies, and market trends by perceiving essential entrepreneurial resources, leading to value creation through entrepreneurship.
Secondly, DRC promotes rural household entrepreneurship by enabling resource acquisition, which is crucial for their success. As the entrepreneurial process advances, resource requirements shift from basic to more advanced resources. Farmers often face significant resource risks in the early stages due to low income, limited savings, and outdated information (Madestam [45]). However, the adoption of innovative financing by farming households can be facilitated through formal credit, social capital, and organic fertilizers when traditional funding sources are inadequate for agricultural activities (Appiah et al. [46]). As a result, the lack of resources poses a significant obstacle for farmers to start and grow their entrepreneurial ventures. The foundation of entrepreneurial competence is entrepreneurial resources. Entrepreneurs’ entrepreneurial ability is primarily reflected by their ability to transform resources (Muller and Korsgaard [47]). DRC also plays a crucial role in the stages of business idea generation and entrepreneurial survival for farmer entrepreneurs. It enables them to efficiently acquire financial, technical, and other resources while continuously developing their entrepreneurial abilities to gain competitive advantages. The Timmons and Wycombe models emphasize resource acquisition and opportunity identification and align with this perspective. Based on the above entrepreneurship theory, the rapid expansion of the digital economy drives the digitalization of production elements, aligning with the transformation of entrepreneurial activities into digital entrepreneurship. Rural entrepreneurs can achieve entrepreneurial development by identifying opportunities, mitigating market risks, and acquiring entrepreneurial resources. The following hypotheses are derived from the previous findings:
H2. 
DRC promotes rural household entrepreneurship by facilitating opportunity identification.
H3. 
DRC promotes rural household entrepreneurship by facilitating resource acquisition.

3. Data and Variables

3.1. Data and Sampling Procedure

The data used consist of two parts. The first part consists of macro data on DRC in 2019 at the provincial level, sourced from the CDRI, created by the Institute for New Rural Development of Peking University. The CDRI is a collaborative effort between the Institute of New Rural Development of Peking University and the Ali Research Institute. We fill in the application information online by registering accounts on the websites of Peking University New Rural Development Research Institute and Ali Research Institute. The CDRI comprises the overall digital village index (DRC), four primary indicators (DRI, DRE, DRG, and DRL), and several secondary indicators. The second part of the data is derived from the CHFS in 2019. This database was created by the China Household Finance Survey and Research Centre of Southwest University of Finance and Economics, utilizing a stratified, three-stage sampling method proportional to the size measure. The data are updated through field visits and quarterly telephone calls, ensuring the sample data’s high scientific accuracy. However, in the CHFS database, specific names and national standard codes of sample cities, counties, towns/streets, and communities are not disclosed, which prevents matching with the CDRI data at the county level. As a result, this study calculates the DRC index at the provincial level, instead. For our analysis, we have chosen cross-sectional data from 2019. We have retained only the rural household sample and excluded the non-labor-force population (rural households with the head of household younger than 16 or older than 65). Additionally, we have removed rural households from the sample with missing values in the key variables.

3.2. Variable Definitions and Descriptive Statistics

The descriptive statistics for rural household entrepreneurship as independent variables and all the explanatory variables including the DRC are presented in Table 1.

3.2.1. Rural Household Entrepreneurship

EB of the rural household differs significantly from that of large enterprises, as it heavily relies on group and family support. Unfortunately, farmers face disadvantages in policy, institutions, management, finance, and technology compared to their larger counterparts. Firstly, rural household entrepreneurship, as defined in this study, encompasses industrial and commercial activities pursued by farmers in the non-farm sector. It includes private enterprises and self-employment, specifically falling under the category of non-farm entrepreneurship. Secondly, drawing from previous research (Wang et al. [48]; Scott and Bruce [49]) and data availability, we relied on the respondents’ answers to a specific questionnaire: “Is your household currently involved in commercial and industrial production and business activities, including self-employment, renting, transportation, online shops, running a business, etc.?” Responses were assigned a value of 1 for “yes” and 0 for “no.” Measuring rural household EP can be challenging due to their small-scale businesses, diverse household characteristics, and predominantly self-employed nature. Therefore, we have selected the logarithm of annual business income from their entrepreneurial projects as an indicator to measure EP. This logarithmic approach allows for a more practical and standardized representation of their EP.

3.2.2. Digital Rural Construction (DRC)

The CDRI published by the Institute of New Rural Development of Peking University is used as a proxy variable for DRC. The CDRI is a comprehensive index comprising 21 indicators from Alibaba Group, its business partners, and eco-partners, along with eight indicators derived from national statistics and web crawling. The index is standardized using the logarithmic efficacy function method and aggregated from the bottom to the top. It includes the total digital village index, four primary indicators, and several secondary indicators. Due to some data used in this paper coming from Alibaba Group, which may have vested interests in promoting the development of DRC, there may be negative effects of data deviation, which needs to be considered in future research. To derive the core independent variable for our study, we calculate the average digital village index for each province based on the CDRI data. Each province’s average digital village index serves as the key Independent variable in our analysis.

4. Model and Regression Results

4.1. Estimation Model

The primary focus of this paper is to investigate the influence of DRC on rural household EB. Referring to the research methods of Liu and Feng (2019) [50] and Luo et al. (2021) [51], given that the dependent variable, rural household EB, is binary in nature, generally speaking, the Probit or Logit model can be selected as an empirical strategy. However, there is little difference in the estimation results between the two models. In order to keep consistent with the endogenous processing model in the future, this paper uses the Probit binary selection model to estimate. The model expression is as follows:
Entrepreneuri = β0 + β1 Digitali + β2 Controlsi + εi
In this model, “Entrepreneuri” represents the EB of the ith household. When “Entrepreneuri” is equal to 1, it indicates that the ith household has chosen to start a business. On the other hand, if “Entrepreneuri” is equal to 0, it means that the ith household has not chosen to start a business. In the model, “Digitali” represents the level of DRC development in the province where the ith household is located. “Controlsi” represents a set of control variables. “β0” is the constant term, and “εi” is the residual term. The main focus of this paper is on the coefficient β1. A significant and positive value of β1 indicates that the DRC promotes rural household EB. Conversely, a significant and negative value of β1 suggests that DRC inhibits rural household EB. If the effect of DRC is found to be statistically insignificant, it implies that DRC does not have a significant impact on rural household EB.
On this basis, the paper further examines the impact of DRC on rural household EP. Because only entrepreneurial farmers have entrepreneurial performance, this situation is often regarded as data interception. The Tobit regression model can transform this truncated data into a probability model and then make a statistical analysis of the truncated data. In the Tobit regression model, the dependent variable is usually assumed to be continuous and truncated in a certain interval, such as between 0 and positive infinity. Therefore, the Tobit model is used for analysis, and the model expression is as follows:
Performancei = β0 + β1 Digitali + β2 Controlsi + εi
Performancei is the EP of the ith household. Digitali is the level of development of the DRC in the province where the ith household is located. Controlsi represents a set of control variables. β0 is the constant term, and εi is a residual item. This paper focuses on β1. If the effect of the DRC is significant and the value of β1 is positive, it means that DRC promotes rural household EP; if the effect of DRC is significant and the value of β1 is negative, it means that DRC inhibits rural household EP; and if the effect of DRC is statistically insignificant, it means that there is no significant impact of DRC on rural household EP.
To establish the impact of DRC on rural household entrepreneurship, this study tackles two potential endogeneity issues. Firstly, there is the problem of omitted variables. Both DRC and rural household entrepreneurship could be affected by various observable and unobservable factors, which may lead to a correlated relationship. Secondly, the issue of reverse causality arises. Rural households with stronger entrepreneurial intentions are more likely to adopt digital technology, contributing to the overall improvement of the DRC level in the region. To address these endogeneity concerns, the study adopts appropriate instrumental variables and employs the Conditional Mixed Process (CMP) estimation method for validation and treatment. Based on seemingly unrelated regression (SUR), the CMP method utilizes recursive equations to achieve maximum likelihood estimation of two- or multi-stage regression models. It can incorporate various regression models, including IV-Probit. The CMP method involves a two-stage estimation process, with interpretation occurring in two steps: In the first step, the correlation between the selected instrumental variables and the endogenous explanatory variables is estimated. If the coefficient is significant, it confirms that the instrumental variable satisfies the relevance condition. The second step incorporates the first-stage results into the baseline regression equation. The study determines whether the explanatory variable is endogenous by testing the significance level of the endogeneity test parameter. If the parameter significantly differs from zero, it confirms that the explanatory variable is endogenous, and the estimation results from the CMP method are considered reliable.

4.2. Baseline Regression

Table 2 presents the regression results for the impact of DRC on rural household entrepreneurship. In the first to the third columns and the fourth to the sixth columns, control variables are gradually added without altering the significance of the estimated coefficients of DRC. This indicates a certain level of robustness in the model. The results in the third and the sixth columns demonstrate that DRC has a positive and statistically significant impact on rural household EB and EP at a significance level of 1%. This is consistent with the research conclusions of Soluk et al. [52] and Chen et al. [53]. The marginal coefficient of DRC on rural household EB is 0.015, and for EP, it is 0.137, demonstrating some economic significance. Therefore, Hypothesis 1 has been preliminarily validated.
After conducting tests using various instrumental variables, the paper calculates the average communication cost for each province and uses the logarithm of this variable as the instrumental variable in the CMP regression. The regression results are shown in Table 3. Two main justifications support this choice: Firstly, the provincial average communication cost strongly correlates with the province’s DRC, making it a fitting instrument to tackle endogeneity concerns. Secondly, it effectively addresses bidirectional causality issues, ensuring the exogeneity of the instrument. The high F-test value, exceeding 10, further validates the appropriateness of this approach and dismisses concerns about weak instrumental variables. The first-stage regression results in the first and third columns indicate a positive and statistically significant correlation between the instrumental variables and the level of DRC at the 1% level. The second-stage regression in the second and fourth columns confirms the significance of the endogeneity test parameter, rejecting the null hypothesis of exogenous variables for DRC. These results provide robust evidence for the suitability of the chosen instrumental variables method in this study. The instrumental variable regression results solidify that DRC has a statistically significant positive impact on rural household entrepreneurship, as reflected in both EB and EP, at the 1% significance level. These findings further support Hypothesis 1, underscoring that the development of DRC indeed fosters and promotes entrepreneurship within rural households.

4.3. Robustness Test

This paper employs a series of robustness tests to enhance the credibility of our primary findings. Given the Internet’s rapid advancement in certain regions, external environmental factors can disrupt the digital activities of rural households, potentially causing deviations in estimation results independent of micro-level decisions. Moreover, we bolster our study’s robustness by substituting both explained and core explanatory variables, ensuring the consistency and effectiveness of previous estimations. The regression results are shown in Table 4.

4.3.1. Replacement of Key Variables

Firstly, we replaced the independent variable. The level of DRC was categorized into low, medium, and high based on the digital village index of each province. The regression results, as shown in the first and second columns of Table 4, indicated a significant and positive influence of DRC on both the EB and EP of rural households. Secondly, we replaced the dependent variable. The study used the average level of rural household entrepreneurship in the same province as a proxy variable for entrepreneurship and conducted traditional OLS regression. The results, reported in the third column of Table 4, confirmed a significantly positive impact of DRC on the average level of entrepreneurship, supporting the findings from the baseline regression and strengthening the overall conclusions.

4.3.2. Eliminate Areas with High Digital Levels

This study conducted a regression analysis by excluding the sample of farmers in the top five provinces (Guangdong, Beijing, Zhejiang, Jiangsu, and Shanghai) with the highest level of Internet development according to the China Internet Development Report (CDRI). This step was taken to ensure that the promotion of entrepreneurship by DRC was not driven solely by outliers from regions with higher levels of Internet development. The results, presented in the fourth and fifth columns of Table 4, show that DRC positively and significantly impacts both EB and EP at the 1% statistical level even after excluding the subsample from the top five provinces. This finding further confirms the robustness of the baseline regression. It strengthens the conclusion that DRC has a positive impact on rural household entrepreneurship, regardless of the variation in Internet development levels across provinces.

4.4. Mechanism Analysis

Building upon the theoretical framework of “DRC—resource acquisition/opportunity identification—rural household entrepreneurship”, this research paper investigates how DRC influences rural household entrepreneurship. Referring to Shi and Shi [54] and McDermott et al. [55], the paper narrows its investigation to three crucial categories of entrepreneurial resources: financial resources, information resources, and technological resources. Financial resources are represented by the formal and informal credit generated by entrepreneurial projects in 2019, measured by its logarithm. Information resources are reflected through the communication costs of household heads, indicating the ease of obtaining entrepreneurship-related information. Lower communication costs signify more accessible and rapid access to relevant information. Technological resources are assessed using the Internet sales revenue of entrepreneurial projects in 2019, serving as a proxy for the level of technical resources. Online business activities indicate the farmers’ access to and utilization of technical resources. Understanding how these three categories of entrepreneurial resources mediate the influence of DRC can provide valuable insights into the underlying mechanisms driving rural entrepreneurship in the context of digital rural development. The regression results are shown in Table 5. The first and third columns display the impact of DRC on resource acquisition, while the fourth column demonstrates the effect of DRC on opportunity identification. The results reveal that DRC significantly and positively affects the acquisition of entrepreneurial financial information and technological resources, as well as opportunity identification, all at the 1% statistical level. This corresponds with the conclusions drawn by He et al. [56], who demonstrated a positive correlation between opportunity-based entrepreneurship and the environmental quality of sustainable development, particularly in terms of resources. This underscores the vital role played by resources and opportunities in fostering entrepreneurial development. As a result, hypotheses 2 and 3 are confirmed through the empirical analysis.

4.5. Heterogeneity Analysis

This paper conducts a heterogeneity analysis to explore the varying impact of DRC on rural household entrepreneurship based on different characteristics. The analysis considers indicators from three aspects: household head, household, and region. Specifically, it examines the household head’s labor experience and risk attitude, the total household income, and the level of DRC in the region. The results of the analyses are summarized in following three tables.

4.5.1. Heterogeneity Analysis at the Household-Head Level

This study examines the impact of DRC on two distinct groups of entrepreneurs: returning entrepreneurs, who have gained experience from working outside their home village, and local entrepreneurs, who are embedded in local social networks. The goal is to understand how DRC and technology affect their entrepreneurial activities differently. The results, presented in Table 6, indicate that DRC positively influences the EB and EP of both returning and local entrepreneurs. However, the impact is more significant for local entrepreneurs. This is attributed to the fact that digital technology helps alleviate resource constraints for local entrepreneurs, and their deep understanding of the local social network enables them to identify entrepreneurial opportunities more effectively. Furthermore, the study explores the impact of DRC on farmers with different risk attitudes. It is found that DRC strongly promotes EP of both risk-preferring and risk-averse farmers. However, for EB, DRC has a significant positive effect on risk-averse farmers but not those without risk-averse tendencies. This difference in impact is likely due to how risk attitudes influence farmers’ ability to use resources effectively. Risk-averse farmers may be more receptive to adopting digital technology and have better access to entrepreneurial information, which partially offsets the facilitating effect of DRC on their EB. In summary, DRC has a stronger impact on promoting entrepreneurship among local entrepreneurs and risk-averse farmers. Targeting these specific groups with DRC initiatives can significantly affect the overall entrepreneurship in rural areas. In addition, at the household-head level, we also performed a heterogeneity analysis of gender, age, and education. However, the results show that the entrepreneurial promotion effect of DRC on the two groups of samples is significant at the same statistical level, and the coefficients are not much different.

4.5.2. Heterogeneity Analysis at the Household Level

Table 7 presents the results of group regressions based on total household income, where sample farmers are divided into high-income and low-income groups. The regression results show that DRC has a significant positive effect on the EB of both groups, but the impact is more pronounced for low-income rural households. This may be due to DRC’s role in facilitating access to financial resources and reducing entry barriers to entrepreneurship, which is particularly beneficial for farmers with lower incomes. Additionally, the regression results indicate that DRC promotes the EP of high-income households more significantly than that of low-income households. This difference could be attributed to the time required for DRC’s effects to become visible, as certain aspects of DRC implementation, such as the construction of e-commerce service outlets and industrial parks, the dissemination of e-commerce knowledge, and the training of human resources, may take time to yield tangible results. Moreover, given the lower overall education level of rural residents and their limited familiarity with Internet technology, the improvement of EP relies more heavily on the entrepreneurial abilities of individuals.

4.5.3. Heterogeneity Analysis at the Regional Level

When farmers have strong entrepreneurial abilities but lack adequate digital infrastructure, their entrepreneurial potential may be limited. For instance, inadequate e-commerce logistics and digital financial outlets can hinder their success. To unlock greater opportunities for rural entrepreneurship, addressing these digital gaps is crucial, ensuring that farmers have access to the right technological tools to complement their entrepreneurial skills effectively. This paper investigates the heterogeneity of DRC on rural household entrepreneurship based on different levels of digital village development. The DRC index is divided into high- and low-level groups using the mean value as a threshold. The results of the subsample regression are presented in Table 8. The regression results in the first and third columns represent the high-level group, while the second and fourth columns show the results for the low-level group. In areas with high levels of digital village development, the Probit model indicates that DRC significantly promotes rural household entrepreneurship. However, in areas with low levels of digital village development, the effect of DRC is not significant. This suggests that the impact of DRC on rural entrepreneurship is more pronounced in regions with a higher level of digital village development, where there are more opportunities for farmers to access and utilize digital resources for entrepreneurial activities. On the other hand, in regions with lower levels of digital village development, the limited availability of digital resources may hinder the promotion of rural household entrepreneurship through DRC.

5. Further Discussion

The regression results presented in Table 9 reveal that all four dimensions of DRC significantly impact rural household entrepreneurship. Notably, the dimensions of DRI and DRE exhibit higher marginal coefficients, indicating a stronger promotion effect on rural entrepreneurship. This finding suggests that the digitization of rural infrastructure and the development of the digital rural economy play a crucial role in fostering entrepreneurship in rural areas. This result is also consistent with the conclusion of Salemink et al. [57]. There are persistent and expanding differences in the quality of data infrastructure and economic growth between urban and rural areas. However, the obstruction of technology dissemination and the low average level of entrepreneurship in rural areas have had a negative impact on adoption and use. Paradoxically, rural communities need to improve their digital connectivity the most to make up for their remoteness. Therefore, future research should focus on rural communities with poor connectivity and digital exclusion and provide “customized policies” for entrepreneurial activities in rural areas.

6. Conclusions

6.1. Research Conclusions

Thanks to the rapid development and widespread application of digital technology in recent years, isolated locations are no longer isolated, and closed sectors are no longer closed. The Internet has brought all types of amenities for rural enterprises in the digital age, opening up new chances for farmers. This research empirically examines the relationship between DRC and rural household entrepreneurship using the 2019 CHFS database of rural families active in commerce and industry and obtains the following findings: (1) DRC has a significant positive impact on rural household entrepreneurship, and the four dimensions of DRC also significantly contribute to fostering entrepreneurial activities in rural areas. (2) DRC indirectly promotes rural household entrepreneurship by facilitating resource acquisition and opportunity identification. (3) DRC’s promotion effect is stronger among local entrepreneurs and individuals with risk-averse tendencies. Additionally, DRC has a more pronounced effect in stimulating EB within lower-income families, while its impact on EP shows the opposite trend. Furthermore, DRC’s influence on rural household entrepreneurship is particularly significant in regions with more advanced digital rural development.

6.2. Policy Recommendations

Based on the empirical research findings presented earlier, we propose the following policy recommendations:
(1)
Promoting DRC: With the aim of fostering entrepreneurship among rural households, we recommend the implementation of a top-down approach to facilitate DRC. Tailored digital village development plans should be formulated, taking into account the local resource endowments and development foundations. This entails strengthening the infrastructure for digital information, enhancing digital governance capabilities, and systematically cultivating digital literacy and entrepreneurship skills among farmers in the domains of digital rural life and financial services. These efforts aim to elevate the quality of entrepreneurial endeavors among rural households.
(2)
Special attention to areas with lower digitalization levels: Government initiatives should prioritize regions with lower levels of digitalization, which often coincide with lower economic development. In these areas, rural entrepreneurs often engage in fewer entrepreneurial projects, even if they have the willingness to do so, primarily due to resource constraints and limited opportunities. To address these challenges, we recommend a targeted approach that focuses on bolstering essential resources in these regions. This entails providing critical material support to a larger number of farmers and delivering diverse entrepreneurial guidance. Moreover, we encourage learning from advanced digital village planning experiences in developed regions, such as the eastern parts of the country.
(3)
Multi-stakeholder participation: DRC requires the active involvement of various stakeholders. It is especially important to promote data sharing between grassroots organizations and villagers, leveraging digital platforms to facilitate reciprocal data connectivity. Utilizing digital technologies to converge multiple stakeholders within the same governance space can help circumvent the traditional “island effect” often associated with rural governance. We strongly encourage the exploration of novel collaborative models in digital rural governance.

6.3. Limitations

This paper focuses on promoting the entrepreneurial rate and quality of rural households through DRC, but some data used in this paper come from Alibaba Group, which may have vested interests in promoting the development of DRC, so there may be negative effects of data deviation, which needs to be considered in future research. In addition, due to the limitations of data, there may be some limitations in studying the conclusion that DRC in China has an impact on rural household entrepreneurship at the provincial level. Since rural communities exhibit substantial variations across multiple dimensions, including resource endowment, economic conditions, geographic factors, and social challenges, they cannot be viewed as uniform or homogeneous entities. The entrepreneurial dynamics observed at the provincial level may not necessarily translate accurately when examined at the community level. Consequently, future research endeavors should be directed toward a more granular examination of rural communities.

Author Contributions

Conceptualization, Y.Z. and Z.C.; methodology, Y.Z., Z.C. and J.W.; software, Y.Z.; validation, Y.Z., Z.C. and J.W.; formal analysis, Y.Z.; investigation, Y.Z.; resources, Y.Z.; data curation, Y.Z.; writing—original draft preparation, Y.Z.; writing—review and editing, Y.Z., Z.C. and J.W.; visualization, Y.Z., Z.C. and J.W.; supervision, Y.Z. and Z.C.; project administration, Y.Z., Z.C. and J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

No new data were created.

Acknowledgments

I acknowledge the support given by all reviewers.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Yin, X.; Chen, J.; Li, J. Rural innovation system: Revitalize the countryside for a sustainable development. J. Rural Stud. 2022, 93, 471–478. [Google Scholar] [CrossRef]
  2. Kushalakshi, D.; Raghurama, A. Rural entrepreneurship: A catalyst for rural development. Int. J. Sci. Res. 2014, 3, 51–54. [Google Scholar]
  3. Stephens, H.M.; Partridge, M.D.; Faggian, A. Innovation, entrepreneurship and economic growth in lagging regions. J. Reg. Sci. 2013, 53, 778–812. [Google Scholar] [CrossRef]
  4. Korsgaard, S.; Müller, S.; Tanvig, H.W. Rural entrepreneurship or entrepreneurship in the rural–between place and space. Int. J. Entrep. Behav. Res. 2015, 21, 5–26. [Google Scholar] [CrossRef]
  5. Steiner, A.; Atterton, J. Exploring the contribution of rural enterprises to local resilience. J. Rural Stud. 2015, 40, 30–45. [Google Scholar] [CrossRef]
  6. Li, Y.; Westlund, H.; Liu, Y. Why some rural areas decline while some others not: An overview of rural evolution in the world. J. Rural Stud. 2019, 68, 135–143. [Google Scholar] [CrossRef]
  7. Zhao, W.; Liang, Z.; Li, B. Realizing a Rural Sustainable Development through a Digital Village Construction: Experiences from China. Sustainability 2022, 14, 14199. [Google Scholar] [CrossRef]
  8. Mei, Y.; Miao, J.; Lu, Y. Digital villages construction accelerates high-quality economic development in rural China through promoting digital entrepreneurship. Sustainability 2022, 14, 14224. [Google Scholar] [CrossRef]
  9. Cui, M.; Pan, S.L.; Newell, S.; Cui, L. Strategy, resource orchestration and e-commerce enabled social innovation in Rural China. J. Strateg. Inf. Syst. 2017, 26, 3–21. [Google Scholar] [CrossRef]
  10. Kotey, B. Enablers and barriers to exit of regional small business owners in Australia. Aust. Geogr. 2016, 47, 195–214. [Google Scholar] [CrossRef]
  11. Vessey, W.B.; Barrett, J.D.; Mumford, M.D.; Johnson, G.; Litwiller, B. Leadership of highly creative people in highly creative fields: A historiometric study of scientific leaders. Leadersh. Q. 2014, 25, 672–691. [Google Scholar] [CrossRef]
  12. Gaddefors, J.; Anderson, A.R. Entrepreneursheep and context: When entrepreneurship is greater than entrepreneurs. Int. J. Entrep. Behav. Res. 2017, 23, 267–278. [Google Scholar] [CrossRef]
  13. Wang, J.; Cai, Z. The Effect of Dependency Burden on Household Entrepreneurial Exit Behavior: Empirical Evidence from Chinese Households. Sustainability 2022, 14, 8933. [Google Scholar] [CrossRef]
  14. Ajayi, O.C.; Place, F.; Akinnifesi, F.K.; Sileshi, G.W. Agricultural success from Africa: The case of fertilizer tree systems in southern Africa (Malawi, Tanzania, Mozambique, Zambia and Zimbabwe). Int. J. Agric. Sustain. 2011, 9, 129–136. [Google Scholar] [CrossRef]
  15. Kangogo, D.; Dentoni, D.; Bijman, J. Adoption of climate-smart agriculture among smallholder farmers: Does farmer entrepreneurship matter. Land Use Policy 2021, 109, 105666. [Google Scholar] [CrossRef]
  16. Wang, J.H.; Chang, C.C.; Yao, S.N.; Liang, C. The contribution of self-efficacy to the relationship between personality traits and entrepreneurial intention. High. Educ. 2016, 72, 209–224. [Google Scholar] [CrossRef]
  17. Camero, A.; Alba, E. Smart City and information technology: A review. Cities 2019, 93, 84–94. [Google Scholar] [CrossRef]
  18. Nambisan, S.; Wright, M.; Feldman, M. The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Res. Policy 2019, 48, 103773. [Google Scholar] [CrossRef]
  19. Sahut, J.M.; Iandoli, L.; Teulon, F. The age of digital entrepreneurship. Small Bus. Econ. 2021, 56, 1159–1169. [Google Scholar] [CrossRef]
  20. Täuscher, K.; Laudien, S.M. Understanding platform business models: A mixed methods study of marketplaces. Eur. Manag. J. 2018, 36, 319–329. [Google Scholar] [CrossRef]
  21. Dillon, A.; Sharma, M.; Zhang, X. Estimating the impact of rural investments in Nepal. Food Policy 2011, 36, 250–258. [Google Scholar] [CrossRef]
  22. Zhang, Q.; Ye, C.; Duan, J. Multi-dimensional superposition: Rural collaborative governance in Liushe Village, Suzhou City. J. Rural Stud. 2022, 96, 141–153. [Google Scholar] [CrossRef]
  23. Zhou, X.; Cui, Y.; Zhang, S. Internet use and rural residents’ income growth. China Agric. Econ. Rev. 2020, 12, 315–327. [Google Scholar] [CrossRef]
  24. Popkova, E.G.; Sergi, B.S. A digital economy to develop policy related to transport and logistics. Predictive lessons from Russia. Land Use Policy 2020, 99, 105083. [Google Scholar] [CrossRef]
  25. Pato, M.L.; Teixeira, A.A. Twenty years of rural entrepreneurship: A bibliometric survey. Sociol. Rural. 2016, 56, 3–28. [Google Scholar] [CrossRef]
  26. Wong, C.Y.; Ng, B.K.; Azizan, S.A.; Hasbullah, M. Knowledge structures of city innovation systems: Singapore and Hong Kong. J. Urban Technol. 2018, 25, 47–73. [Google Scholar] [CrossRef]
  27. Boppart, T. Structural change and the Kaldor facts in a growth model with relative price effects and non-Gorman preferences. Econometrica 2014, 82, 2167–2196. [Google Scholar] [CrossRef]
  28. Barnett, W.A.; Hu, M.; Wang, X. Does the utilization of information communication technology promote entrepreneurship: Evidence from rural China. Technol. Forecast. Soc. Chang. 2019, 141, 12–21. [Google Scholar] [CrossRef]
  29. Tang, G.N.; Ren, F.; Zhou, J. Does the digital economy promote “innovation and entrepreneurship” in rural tourism in China. Front. Psychol. 2022, 13, 979027. [Google Scholar] [CrossRef]
  30. Kim, Y.; Orazem, P.F. Broadband internet and new firm location decisions in rural areas. Am. J. Agric. Econ. 2017, 99, 1–18. [Google Scholar] [CrossRef]
  31. Mack, E.A.; Marie-Pierre, L.; Redican, K. Entrepreneurs’ use of internet and social media applications. Telecommun. Policy 2017, 41, 120–139. [Google Scholar] [CrossRef]
  32. Zhang, Y.; Feng, M.; Fang, Z.; Yi, F.; Liu, Z. Impact of Digital Village Construction on Agricultural Carbon Emissions: Evidence from Mainland China. Int. J. Environ. Res. Public Health 2023, 20, 4189. [Google Scholar] [CrossRef] [PubMed]
  33. Jiang, S.; Zhou, J.; Qiu, S. Digital agriculture and urbanization: Mechanism and empirical research. Technol. Forecast. Soc. Chang. 2022, 180, 121724. [Google Scholar] [CrossRef]
  34. Zavratnik, V.; Kos, A.; Stojmenova Duh, E. Smart villages: Comprehensive review of initiatives and practices. Sustainability 2018, 10, 2559. [Google Scholar] [CrossRef]
  35. Jenkins, A.; McKelvie, A. What is entrepreneurial failure? Implications for future research. Int. Small Bus. J. 2016, 34, 176–188. [Google Scholar] [CrossRef]
  36. Shane, S.; Venkataraman, S. The promise of entrepreneurship as a field of research. Acad. Manag. Rev. 2000, 25, 217–226. [Google Scholar] [CrossRef]
  37. Cutolo, D.; Kenney, M. Platform-dependent entrepreneurs: Power asymmetries, risks, and strategies in the platform economy. Acad. Manag. Perspect. 2021, 35, 584–605. [Google Scholar] [CrossRef]
  38. Lodefalk, M.; Tang, A. The impact of hiring top workers on productivity: What is the role of absorptive capacity. Appl. Econ. Lett. 2018, 25, 1402–1406. [Google Scholar] [CrossRef]
  39. Shepherd, D.A.; Wennberg, K.; Suddaby, R.; Wiklund, J. What are we explaining? A review and agenda on initiating, engaging, performing, and contextualizing entrepreneurship. J. Manag. 2019, 45, 159–196. [Google Scholar] [CrossRef]
  40. Shane, S. Prior knowledge and the discovery of entrepreneurial opportunities. Organ. Sci. 2000, 11, 448–469. [Google Scholar] [CrossRef]
  41. Alvarez, S.A.; Barney, J.B.; Anderson, P. Forming and exploiting opportunities: The implications of discovery and creation processes for entrepreneurial and organizational research. Organ. Sci. 2013, 24, 301–317. [Google Scholar] [CrossRef]
  42. Eller, F.J.; Gielnik, M.M.; Wimmer, H.; Thölke, C.; Holzapfel, S.; Tegtmeier, S.; Halberstadt, J. Identifying business opportunities for sustainable development: Longitudinal and experimental evidence contributing to the field of sustainable entrepreneurship. Bus. Strategy Environ. 2020, 29, 1387–1403. [Google Scholar] [CrossRef]
  43. Kirzner, I.M. Perception, Opportunity, and Profit; Chicago University Press: Chicago, IL, USA, 1983. [Google Scholar]
  44. Ardichvili, A.; Cardozo, R.N. A model of the entrepreneurial opportunity recognition process. J. Enterprising Cult. 2000, 8, 103–119. [Google Scholar] [CrossRef]
  45. Madestam, A. Informal finance: A theory of moneylenders. J. Dev. Econ. 2014, 107, 157–174. [Google Scholar] [CrossRef]
  46. Appiah-Twumasi, M.; Donkoh, S.A.; Ansah, I.G.K. Farmer innovations in financing smallholder maize production in Northern Ghana. Agric. Financ. Rev. 2020, 80, 421–436. [Google Scholar] [CrossRef]
  47. Müller, S.; Korsgaard, S. Resources and bridging: The role of spatial context in rural entrepreneurship. Entrep. Reg. Dev. 2018, 30, 224–255. [Google Scholar] [CrossRef]
  48. Wang, Y.; Cai, Z.; Wang, J. The Impact of Digital Technology Use on Passive Entrepreneurial Exit in Rural Households: Empirical Evidence from China. Sustainability 2022, 14, 10662. [Google Scholar] [CrossRef]
  49. Scott, S.G.; Bruce, R.A. Determinants of innovative behavior: A path model of individual innovation in the workplace. Acad. Manag. J. 1994, 37, 580–607. [Google Scholar] [CrossRef]
  50. Liu, C.Y.; Ye, L.; Feng, B. Migrant entrepreneurship in China: Entrepreneurial transition and firm performance. Small Bus. Econ. 2019, 52, 681–696. [Google Scholar] [CrossRef]
  51. Luo, Y.; Peng, Y.; Zeng, L. Digital financial capability and entrepreneurial performance. Int. Rev. Econ. Financ. 2021, 76, 55–74. [Google Scholar] [CrossRef]
  52. Soluk, J.; Kammerlander, N.; Darwin, S. Digital entrepreneurship in developing countries: The role of institutional voids. Technol. Forecast. Soc. Change 2021, 170, 120876. [Google Scholar] [CrossRef]
  53. Chen, W.; Wang, Q.; Zhou, H. Digital rural construction and farmers’ income growth: Theoretical mechanism and micro experience based on data from China. Sustainability 2022, 14, 11679. [Google Scholar] [CrossRef]
  54. Shi, X.; Shi, Y. Unpacking the process of resource allocation within an entrepreneurial ecosystem. Res. Policy 2022, 51, 104378. [Google Scholar] [CrossRef]
  55. McDermott, K.; Kurucz, E.C.; Colbert, B.A. Social entrepreneurial opportunity and active stakeholder participation: Resource mobilization in enterprising conveners of cross-sector social partnerships. J. Clean. Prod. 2018, 183, 121–131. [Google Scholar] [CrossRef]
  56. He, J.; Nazari, M.; Zhang, Y.; Cai, N. Opportunity-based entrepreneurship and environmental quality of sustainable development: A resource and institutional perspective. J. Clean. Prod. 2020, 256, 120390. [Google Scholar] [CrossRef]
  57. Salemink, K.; Strijker, D.; Bosworth, G. Rural development in the digital age: A systematic literature review on unequal ICT availability, adoption, and use in rural areas. J. Rural Stud. 2017, 54, 360–371. [Google Scholar] [CrossRef]
Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
VariablesDescriptionMeanStd. Dev.Number
EBStart a business: 1 = Yes; 0 = No0.0980.2988551
EPThe logarithm of operating income from entrepreneurial projects1.96424.5078551
DRCOverall digital village index53.8959.5788551
DRIDigital rural infrastructure index58.7387.4238551
DREDigital rural economy index54.36710.0688551
DRGDigital rural governance index51.65613.1718551
DRLDigital rural life index47.66913.5778551
GenderSex of farmer: 1 = Male, 0 = Female0.8610.3468551
AgeAge of the farmer in 2019 (years)52.3998.5698551
EducationYears of formal education of the farmer (years)7.7003.2178551
Marital status1 = married/cohabitation, 0 = else0.8960.3068551
Health statusOn a scale of 1 = very unhealthy to 5 = in perfect health3.1461.0258551
Household sizeNumber of household members (number)3.6271.6598551
Number of older peopleNumber of household members older than 65 (number)0.2550.5278551
Number of childrenNumber of household members younger than 16 (number)0.6280.9158551
Household assetsThe logarithm of total household assets12.1771.3618551
City level1 = first-line city/new first-line city, 2 = second-tier city, 3 = third-tier cities and below2.7050.6268551
Table 2. Main results on DRC and rural household entrepreneurship.
Table 2. Main results on DRC and rural household entrepreneurship.
VariableEBEP
(1)(2)(3)(4)(5)(6)
ProbitProbitProbitTobitTobitTobit
DRC0.017 *** (0.002)0.015 *** (0.002)0.015 ***(0.002)0.222 *** (0.028)0.214 *** (0.028)0.137 *** (0.027)
Sex 0.094 (0.064)0.078 (0.065) 1.018 (0.800)1.244 (0.757)
Age 0.038 * (0.020)0.029 (0.021) 0.518 * (0.280)0.481 * (0.274)
Age square −0.000 ** (0.000)−0.000 * (0.000) −0.006 ** (0.003)−0.005 (0.003)
Education 0.042 *** (0.007)0.032 *** (0.008) 0.089 (0.088)−0.191 ** (0.084)
Party 0.113 * (0.059)0.088 (0.061) 2.217 ** (0.879)1.164 (0.832)
Marital status 0.351 *** (0.085)0.165 * (0.088) −0.469 (0.905)−2.019 ** (0.893)
Health status 0.142 *** (0.020)0.125 *** (0.020) 1.139 *** (0.268)0.415 (0.254)
Household size 0.143 *** (0.019) 0.292 (0.260)
Number of old people −0.116 *** (0.040) 0.502 (0.532)
Number of children −0.087 *** (0.031) 0.655 (0.443)
Household assets 0.002 *** (0.000) 0.086 *** (0.003)
City level −0.015 (0.032) −0.986 ** (0.406)
Number855185518551855185518551
Wald chi269.57 ***232.50 ***359.86 ***
LR chi2 64.58 ***109.87 ***1085.41 ***
Log-likelihood−2708.880−2599.368−2470.549−39,454.780−39,432.133−38,944.361
Pseudo R20.0150.0540.1010.0010.0010.014
Note: Table 2 shows the baseline regression. In columns (1)–(3), the explained variables are rural household EB, and in columns (3)–(4), the explained variables are rural household EP. The standard errors are clustered at the city level in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.
Table 3. The results of CMP regression.
Table 3. The results of CMP regression.
VariablesEBEP
(1) First Stage(2) Second Stage(1) First Stage(2) Second Stage
DRCEBDRCEP
DRC 0.066 *** (0.010) 0.068 *** (0.010)
Instrumental variable0.041 *** (0.003) 0.041 *** (0.003)
Control variablesControlledControlledControlledControlled
F-value58.05 *** 58.05 ***
atanhrho_12 −0.551 ** (0.137) −0.573 *** (0.137)
Number8551855185518551
Note: Table 3 shows the regression results of CMP. The instrumental variable is the average communication cost of the respondents’ province. ** p < 0.05, *** p < 0.01.
Table 4. The results of robustness test regression.
Table 4. The results of robustness test regression.
VariablesEBEPLevel of EbEBEP
(1)(2)(3)(4)(5)
ProbitTobitOLSProbitTobit
Level of DRC0.124 *** (0.026)1.077 *** (0.317)
DRC 0.003 *** (0.000)0.012 *** (0.003)0.077 *** (0.020)
Control variablesControlledControlledControlledControlledControlled
Number85518551855176327632
F-value 374.29 ***
Wald chi2342.58 *** 270.99 ***
LR chi2 1071.10 *** 233.66 ***
Log-likelihood−2483.016−38,951.517 −2109.390−31,053.024
Pseudo R20.0970.0140.0290.0850.004
Note: Table 4 shows the results of the robustness test. Columns (1)–(3) show the results of robustness tests by replacing key variables. Columns (4) and (5) present the results of robustness tests by excluding samples from regions with high levels of digital development. *** p < 0.01.
Table 5. Mechanism analysis.
Table 5. Mechanism analysis.
VariablesFinancial ResourcesInformation ResourcesTechnological ResourcesOpportunity Identification
(1)(2)(3)(4)
TobitTobitTobitTobit
DRC0.004 *** (0.001)0.710 *** (0.140)0.003 *** (0.001)0.004 *** (0.001)
Control variablesControlledControlledControlledControlled
Number8551855185518551
LR chi2266.50 ***728.08 ***148.97 ***623.72 ***
Log-likelihood−13,273.01−53,082.597−9698.262−5512.329
Pseudo R20.0100.0070.0080.054
Note: Table 5 shows the results of the mechanism analysis. Columns (1)–(3) show the results of the mediating role of resource acquisition. Column (4) presents the results of the mediating role of opportunity identification. *** p < 0.01.
Table 6. The heterogeneity at the household-head level.
Table 6. The heterogeneity at the household-head level.
VariablesEBEPEBEP
(1)(2)(3)(4)(5)(6)(7)(8)
ReturneeLocalReturneeLocalRisk-PreferringRisk-AverseRisk-PreferringRisk-Averse
DRC0.012 ** (0.005)0.014 *** (0.003)0.147 * (0.089)0.099 *** (0.026)0.007 (0.005)0.018 *** (0.003)0.191 ** (0.087)0.124 *** (0.026)
Control variablesControlledControlledControlledControlledControlledControlledControlledControlled
Number15626989156269891617693416176934
Wald chi2139.59 ***278.28 *** 67.98 ***295.19 ***
LR chi2 431.90 ***741.51 *** 278.73 ***795.98 ***
Log-likelihood−527.899−1926.608−7538.442−31,013.29−581.797−1874.20−7893.217−30,681.79
Pseudo R20.1170.101 0.0120.1030.1010.0170.013
Note: Table 6 shows the heterogeneity of work experience and risk attitude. Columns (1) and (3) are subsamples with returnee entrepreneurs, and columns (2) and (4) are subsamples with local entrepreneurs. Columns (5) and (7) are subsamples with risk-preferring attitudes, and columns (6) and (8) are subsamples with risk-averse farmers. * p < 0.10, ** p < 0.05, *** p < 0.01.
Table 7. The heterogeneity at the household level.
Table 7. The heterogeneity at the household level.
VariablesEBEP
High-IncomeLow-IncomeHigh-IncomeLow-Income
(1)(2)(3)(4)
DRC0.012 *** (0.003)0.018 *** (0.005)0.203 *** (0.045)0.053 ** (0.027)
Control variablesControlledControlledControlledControlled
Number4275427642754275
Wald chi2138.45 ***112.55 ***
LR chi2 844.57 ***97.35 ***
Log-likelihood−1599.502−847.705−20,295.413−17,819.923
Pseudo R20.0790.0830.0200.003
Note: Table 7 shows the heterogeneity of household income. Columns (1) and (3) are subsamples with high household income, and columns (2) and (4) are subsamples with low household income. ** p < 0.05, *** p < 0.01.
Table 8. The heterogeneity at the regional level.
Table 8. The heterogeneity at the regional level.
VariablesEBEP
High-LevelLow-LevelHigh-LevelLow-Level
(1)(2)(3)(4)
DRC0.004 *** (0.001)−0.007 (0.006)0.005 *** (0.001)−0.001(0.001)
Control variablesControlledControlledControlledControlled
Number4286426542864265
Wald chi2317.51 ***102.33 ***
LR chi2 587.61 ***149.35 ***
Log-likelihood−1349.201−1078.622 −983.282−306.141
Pseudo R20.1460.0570.2300.196
Note: Table 8 shows the heterogeneity of levels of digital village development. Columns (1) and (3) are subsamples with high levels of digital village development, and columns (2) and (4) are subsamples with low levels of digital village development. *** p < 0.01.
Table 9. Impact of the sub-dimension of DRC on rural household entrepreneurship.
Table 9. Impact of the sub-dimension of DRC on rural household entrepreneurship.
VariablesEBEP
(1)(2)(3)(4)(5)(6)(7)(8)
ProbitProbitProbitProbitTobitTobitTobitTobit
DRI0.014 ***
(0.003)		 0.145 ***
(0.034)
DRE 0.014 ***
(0.002)		 0.118 ***
(0.026)
DRG 0.008 ***
(0.002)		 0.096 ***
(0.020)
DRL 0.011 ***
(0.002)		 0.094 ***
(0.019)
Control variablesControlledControlledControlledControlledControlledControlledControlledControlled
Number85518551855185518551855185518551
Wald chi2333.45 ***352.00 ***354.04 ***381.12 ***
LR chi2 1077.26 ***1080.46 ***1083.17 ***1084.82 ***
Log-likelihood−2482.024−2471.956−2481.231−2464.744−38,948.44−38,946.84−38,945.48−38,944.66
Pseudo R20.0970.1010.0970.1030.0140.0140.0140.014
Note: Table 9 shows the impact of the sub-dimension of DRC on rural household entrepreneurship. Columns (1)–(4) are the impact of the sub-dimension of DRC on EB, and columns (5)–(8) are the impact of the sub-dimension of DRC on EP. *** p < 0.01.
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Zhou, Y.; Cai, Z.; Wang, J. Digital Rural Construction and Rural Household Entrepreneurship: Evidence from China. Sustainability 2023, 15, 14219. https://doi.org/10.3390/su151914219

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Zhou Y, Cai Z, Wang J. Digital Rural Construction and Rural Household Entrepreneurship: Evidence from China. Sustainability. 2023; 15(19):14219. https://doi.org/10.3390/su151914219

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Zhou, Yunwen, Zhijian Cai, and Jie Wang. 2023. "Digital Rural Construction and Rural Household Entrepreneurship: Evidence from China" Sustainability 15, no. 19: 14219. https://doi.org/10.3390/su151914219

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