Do Entrepreneurial Village Cadres Improve Rural Subjective Well-Being? Empirical Evidence from China

Abstract

Improving the well-being of rural residents remains a major policy challenge in developing countries. Previous studies have largely neglected the role of village leadership in influencing residents’ well-being. This study addresses this gap by examining the relationship between entrepreneurial village cadres (EVCs), defined as village leaders with entrepreneurial experience, and the subjective well-being (SWB) of rural residents in China. Using nationally representative data from the 2022 China Rural Revitalization Survey (CRRS), we found that EVCs significantly improve rural residents’ SWB. These results are robust to a series of identification strategies, including instrumental variable estimation and propensity score matching. Mechanism analysis reveals that EVCs exert their positive influence through three key channels: promoting income growth, enhancing democratic governance, and improving public services. Further heterogeneity analysis suggests that the effects of EVCs on SWB are more pronounced among non-poor households and in villages with external financial support. These findings enrich the literature on grassroots governance and well-being economics, while also offering practical implications for aligning leadership recruitment with broader goals of inclusive rural development.

1. Introduction

Improving human well-being has consistently been a central concern for scholars and policymakers alike, as it represents the ultimate goal of progress in modern society [1,2]. Subjective well-being (SWB), a key indicator of individual welfare, refers to one’s emotional experiences and cognitive evaluations of life. It encompasses multiple dimensions, including perceived happiness, emotional balance, a sense of accomplishment, and life satisfaction, providing a comprehensive reflection of an individual’s overall psychological state [3]. SWB plays a vital role in personal life, enabling individuals to reach their full potential, pursue important goals with confidence, and maintain the motivation and energy needed to persist through life’s challenges. As a general measure of individual happiness, SWB is also widely used in evaluating government policies and assessing the quality of public goods provision [4,5]. Goal 3 of the 2030 Sustainable Development Agenda—“Good Health and Well-Being”—explicitly aims to enhance the SWB of individuals across the globe.

In the rural regions of many developing countries, economic fragility and a heavy reliance on government assistance pose unique challenges to enhancing residents’ well-being [6]. These areas typically confront systemic obstacles such as scarce resources, underdeveloped infrastructure, and inadequate market integration, which further exacerbate their vulnerability. Nevertheless, the well-being of rural residents is closely linked to the overall level of national development and sustainability. Existing research generally asserts that well-being is influenced by both individual and household characteristics as well as by broader social factors—including economic, political, social, and ecological elements [7,8,9]. Scholars have extensively explored ways to enhance well-being among rural residents through approaches such as poverty intervention policies, residential environment improvements, and the development of rural e-commerce [10,11,12].

However, these studies have not incorporated the role of local leaders into their analytical frameworks. In fact, local leaders have long been indispensable participants and managers in community development across extensive rural areas, and their involvement is closely tied to the effectiveness of rural social governance [13,14,15]. They serve as concrete implementers of national policies and local government mandates, assuming the dual roles of state agents and representatives of rural residents [16]. Research indicates that variations in the individual competencies of local leaders influence their decision-making approaches and governance styles, which in turn profoundly shape village outcomes in areas such as economic development, public affairs management, and environmental governance [17,18,19,20]. This propagation effect of grassroots governance quality ultimately has a profound relationship with the SWB of rural residents. Existing studies also indicate that local governance and leadership are important determinants of SWB [21,22,23].

This paper examines a growing phenomenon in China with notable international relevance: the integration of entrepreneurial elites into local political leadership. In recent years, policy reforms in rural China have actively encouraged individuals with business experience to assume village leadership positions. These “Entrepreneurial Village Cadres” (EVCs) combine entrepreneurial skills with a public service orientation and are expected to leverage their innovation capacity, social networks, and risk-taking willingness to promote village development [24]. This transformation reflects a hybrid governance model that merges market logic with public authority at the grassroots level. Chen’s survey of four provinces in China showed that in half of the villages, the village party secretary or head had previously served as a manager in a state-owned enterprise or as the owner of a private business [25]. In Hengshui, Hebei Province, the EVC program has been continuously promoted, with over 2000 successful and highly skilled entrepreneurs returning to serve as village officials, leading rural development and enhancing villagers’ well-being (http://www.sx-dj.gov.cn/dylt/tszs/1740577839712092162.html, accessed on 15 September 2025). Similar leadership models have been documented in other countries, where rural leaders with business or economic backgrounds are seen as key drivers of local development strategies. Kusmulyono et al., using a qualitative research approach, found that rural leaders with entrepreneurial competence in Indonesia are better able to foster rural resilience and enhance community participation [26]. Moscardo, drawing on cases from 47 rural tourism destinations worldwide, demonstrated that community-based tourism leaders who combine entrepreneurial spirit with public leadership can significantly improve the overall well-being of destination communities [27].

Although existing research has explored the economic impacts of EVCs [28,29], little attention has been paid to their effect on residents’ SWB. In fact, rural residents are the ultimate beneficiaries of rural governance, and their SWB serves as a metric for assessing the effectiveness of village cadres’ management. Only when rural residents experience an improvement in their well-being can they become the main driving force for further rural development. Meanwhile, some studies have pointed out that within village governance, economic talents may exhibit elite capture [30]. This phenomenon is manifested through malfeasance, corruption, and the monopolization or misappropriation of public interests [31,32], as well as the pursuit of personal gains at the expense of ordinary citizens’ interests, the dominance of participatory development projects, and the monopolization of local decision-making [33,34], thereby posing potential risks to rural development and residents’ well-being. This highlights the underexplored relationship between EVCs and rural well-being, along with persistent doubts about whether such economically elite cadres truly serve collective rather than personal or political interests.

To address these gaps in the literature, we draw on the authoritative and nationally representative 2022 China Rural Revitalization Survey (CRRS) to empirically examine the impact of EVCs on rural residents’ SWB. We further explore the underlying mechanisms and assess the heterogeneity of these effects across different types of residents and villages. Our findings aim to advance the discourse on rural governance innovation and contribute to the broader literature on the economics of well-being in developing countries, offering robust empirical evidence for improving rural SWB and informing leadership selection strategies that promote inclusive development.

Compared to previous studies, our work makes the following contributions:

First, we innovatively examine the impact of EVCs on the well-being of rural community residents, thereby broadening the research perspective on SWB. As far as we are aware, this is the first empirical study to investigate how EVCs influence the SWB of rural residents. This reveals the multifaceted effects of grassroots governance on residents’ well-being, enriching our understanding of happiness and life satisfaction. Our findings contribute novel insights and robust empirical support for improving the well-being of rural populations.

Second, this study extends the predominantly case-based literature by offering a rigorous quantitative analysis based on the 2022 CRRS, a nationally representative dataset collected by the Chinese Academy of Social Sciences. The CRRS provides valuable and comprehensive information on grassroots leadership within the context of China’s rural revitalization strategy. To ensure rigorous causal identification, we adopt an instrumental variable strategy, which enables us to draw reliable conclusions about how EVCs influence the SWB of rural residents.

Finally, our research carries substantial practical significance. The findings indicate that there is no misalignment between an economically elite-driven development model and the welfare expectations of grassroots populations, addressing public concerns over whether EVCs might prioritize personal or political interests over collective well-being. Our study also extends the application of entrepreneurial experience to the field of rural public governance, demonstrating that EVCs are effective in addressing the challenge of improving rural well-being. This perspective offers important policy insights for the selection and appointment of local leaders in other countries facing rural decline and aiming to enhance the happiness and welfare of rural populations.

The structure of this study is outlined as follows: Section 2 presents the theoretical framework and research hypotheses. Section 3 outlines the dataset and variables. Section 4 details the empirical findings. Section 5 conducts the mechanism and heterogeneity analyses. And finally, Section 6 summarizes the study, offers policy recommendations, and discusses its limitations.

2. Theoretical Analysis and Research Hypothesis

In this section, we discussed the theoretical mechanisms through which EVCs influence the SWB of rural residents. This effect operates primarily through three key channels: promoting income growth, enhancing democratic governance, and improving public services. Figure 1 illustrates the theoretical framework of this study.

2.1. Promoting Income Growth

As dual agents in grassroots governance and market activity, EVCs play a pivotal role in driving economic development through their distinctive entrepreneurial spirit. Entrepreneurial spirit is also recognized as a key factor of production [35,36,37], and is considered one of the most critical driving forces behind sustained economic growth [38]. Entrepreneurs are capable of overcoming severe resource constraints, leveraging their entrepreneurial talent to combine business strategies with social principles in order to create value and achieve significant economic and social transformation.

Specifically, EVCs promote economic growth by breaking down institutional rigidities, resource fragmentation, and information barriers in traditional rural economies. They reconstruct local development models and activate social capital networks in the process. Drawing on entrepreneurial qualities such as innovativeness, risk-taking, and the ability to identify market opportunities [39,40], they are able to leverage local resource endowments such as ecological agriculture, intangible cultural heritage crafts, and rural tourism to cultivate specialized industries. This promotes industrial agglomeration and branded operations, ultimately forming economic growth poles characterized by a “one village, one product” model. At the same time, the introduction of modern business practices such as e-commerce operations and brand marketing helps overcome geographical constraints and reduce information asymmetry. These approaches enable producers to better grasp market demand and respond to price fluctuations, facilitating the integration of smallholder farmers into broader markets and expanding the sales channels for agricultural products [41]. Furthermore, EVCs hold dual roles as local governors and external linkers. They are able to utilize social capital to attract external investment and reactivate idle resources. By bringing in development projects and funding, they revitalize the endogenous assets of relatively poor villages, and improve the material conditions necessary for economic development.

These economic initiatives have enhanced the added value of agricultural products for rural residents, generated local employment opportunities, and expanded non-agricultural income channels. As a result, they have improved material well-being among rural residents and directly influenced their SWB [42]. At the same time, these measures have helped narrow the urban-rural income gap and improve relative income levels. In addition, these initiatives have significantly advanced the development of the collective economy. The inclusive distribution of benefits has alleviated feelings of relative deprivation and suppressed the negative emotions associated with social comparison [43], thereby further enhancing the SWB of rural residents.

2.2. Enhancing Democratic Governance

Existing research on well-being suggests that democratic governance exerts a systematic influence on subjective welfare by enhancing individuals’ sense of control over development processes, improving the efficiency of public resource allocation, and strengthening community-based social capital [44,45]. In traditional rural governance, the lack of transparency in decision-making often leads to elite capture and information asymmetries. These issues not only intensify villagers’ sense of political alienation but also undermine institutional trust at the grassroots level due to imbalanced public resource allocation and rent-seeking behaviors, thereby exerting a negative impact on SWB [46]. In response to these challenges, EVCs combine their entrepreneurial mindset with innovative governance strategies. They introduce market-oriented thinking into local administration and, through institutional innovation and technological empowerment, establish a dynamic mechanism through which democratic governance enhances administrative efficacy and translates into improved well-being for rural residents.

On the one hand, the capacity of EVCs for modern digital governance, combined with their technological acuity, enables them to transcend traditional governance approaches. They integrate modern tools with participatory governance by establishing multi-stakeholder platforms, such as online consultation systems, which significantly reduce the time and geographical constraints of conventional participatory methods. These mechanisms broaden channels for public involvement [47], incorporate stakeholders into decision-making processes, and shift villagers from symbolic presence to substantive participation. Such participatory empowerment fosters a stronger sense of procedural fairness [48] and enhances villagers’ identification with decisions by reducing information asymmetries.

On the other hand, modern corporate management practices emphasize transparency and procedural governance. These principles help establish standardized workflows that enhance administrative efficiency. In the context of village governance, transparency helps restore institutional trust. For example, open platforms are used to document key processes such as land transfers and project bidding, thereby ensuring data integrity. In addition, through online and transparent village governance systems, financial revenues and expenditures are disclosed in real time, thereby reducing opportunities for rent-seeking and enhancing villagers’ trust in local cadres. The combined effect of participatory empowerment and transparent governance satisfies residents’ need for procedural justice [49], lowers the cost of social governance, and improves the efficiency of public goods provision.

2.3. Improving Public Services

The level of basic public service provision is closely related to the SWB of local residents [50,51]. The efficiency and quality of the public service system directly affect the convenience of daily life, the availability of development opportunities, and the sense of social security among residents [52]. However, transforming and upgrading the public service system often faces bottlenecks on the supply side, such as fiscal resource shortages and a lack of specialized talent. Traditional village cadres may lack sufficient incentives, resources, or capacities, leading to inefficiencies in the provision of public goods. Bureaucratic inertia under hierarchical systems often leads to passive, reactive governance. Public service decisions are constrained by top-down performance evaluation mechanisms, leading to a structural mismatch between resource allocation and local needs. Grassroots officials are more inclined to implement visible, rigid indicators while overlooking the actual needs and effectiveness of public service delivery.

In contrast, the entrepreneurial spirit of EVCs drives them to focus on performance and the optimization of resource allocation. They recognize the importance of improving public services for the long-term sustainable development of the village. Their market-oriented operational capabilities enable them to creatively integrate resources. For instance, they may secure construction funds through land swaps or leverage business negotiation skills to obtain corporate donations. Furthermore, the social network capital accumulated through entrepreneurial activities can serve as a bridge, helping the village access external resources, diversifying funding sources and improving the quality of public goods provision [53]. Finally, local governments often prefer to concentrate resources on exemplary villages with better economic conditions and stronger development capacities, transforming these villages into key performance projects. The policy execution capabilities brought by the individual abilities of EVCs make it easier for them to gain favor with higher-level governments. As a result, they are more likely to receive targeted fiscal transfers or policy support, which helps optimize the level of public goods provision.

Based on the above theoretical analysis, we propose the following hypotheses.

H1. 

EVCs can significantly enhance the SWB of rural residents.

H2. 

EVCs enhance rural residents’ SWB by promoting income growth.

H3. 

EVCs improve rural residents’ SWB by enhancing democratic governance.

H4. 

EVCs enhance rural residents’ SWB by improving public services.

The three mechanisms through which EVCs influence rural residents’ well-being, namely income growth, democratic governance, and improved public services, may interact and reinforce each other. Increased household income can empower residents to participate more actively in local governance, while enhanced governance and transparency can improve the efficiency of public service delivery. Likewise, better public services can facilitate income growth, thereby reinforcing the material and social foundations of residents’ well-being.

3. Data and Methodology

3.1. Data Resource

The data used in this study come from the 2022 China Rural Revitalization Survey (CRRS) conducted by the Institute of Rural Development at the Chinese Academy of Social Sciences. This biennial rural survey was launched in 2020. However, since the 2020 questionnaire did not include items related to EVCs, only the 2022 wave is used in this analysis. The survey collected relevant data on sample villages and rural residents for the year 2021 from 10 provinces across Mainland China via questionnaires. The sampling procedure of the survey was as follows. First, taking into account economic levels, regional locations, and basic agricultural production conditions, questionnaire surveys were conducted in 10 provinces—Zhejiang, Shandong, Guangdong, Anhui, Henan, Guizhou, Sichuan, Shaanxi, Ningxia, and Heilongjiang—selected from Eastern, Central, Western, and Northeastern Mainland China. Second, within each province, all counties were ranked by per capita GDP and divided into five groups. One county was randomly selected from each group, ensuring spatial coverage across the province; if selected counties fell within the same prefecture-level city, a county with similar GDP from a different city was chosen. This yielded five counties per province, totaling 50 sample counties. Third, towns within each county were ranked by per capita GDP and divided into three groups (high, medium, low), with one town randomly selected from each group, considering location, industrial layout, and other relevant factors. This resulted in three towns per county, totaling 150 towns. Fourth, villages were classified, with the help of township governments, into two groups—relatively well-developed and less-developed—due to the unavailability of village-level GDP data. One village was randomly selected from each group, considering location, industrial layout, and other indicators, resulting in two villages per town and 300 villages in total. Fifth, within each village, 12 to 14 households were systematically and randomly selected from the household register, yielding 3662 rural resident observations.

The 2022 CRRS is particularly well-suited for this research as it contains detailed modules on village demographics and organizational structures, individual characteristics and well-being of rural residents, levels of economic development, agricultural production and management, village governance, and living environment. This comprehensive coverage provides a solid foundation for achieving the research objectives of this study. These data have been widely utilized in numerous studies, especially those addressing issues in Chinese rural development, and are recognized for their extensive credibility and influence [54,55,56].

We cleaned the sample data according to the following procedures. First, to avoid biased causal inference due to a short tenure of the current village secretary, we excluded village samples in which the current village secretary had served for less than one year. This exclusion ensures that the observed impact on rural residents’ SWB primarily stems from the actual governance of the current village secretary rather than their predecessor or other external factors, thereby enhancing the validity and robustness of the analysis. Second, we eliminated rural resident samples with missing data on key variables, ultimately obtaining 3030 valid rural resident sample observations.

3.2. Variable Descriptions

3.2.1. Subjective Well-Being

Subjective well-being (SWB) refers to an individual’s self-evaluation of their overall life circumstances. It is commonly measured through self-reported scales, where respondents are asked to report their satisfaction with life, thereby reflecting their level of happiness [1]. Although this method is relatively straightforward, previous studies have shown that such measures possess high validity and reliability in terms of both comparability and accuracy, effectively capturing individuals’ true feelings [2,57]. Based on this approach, we utilized a question from the CRRS questionnaire that asks, “Overall, how satisfied are you with your current life?”. Respondents rate their satisfaction on an integer scale from 0 to 10, with higher scores indicating stronger SWB. Unlike measures of momentary happiness, which capture short-term emotional states, life satisfaction reflects a more stable, long-term assessment of one’s overall life circumstances. It is worth noting that using life satisfaction as a proxy for SWB is consistent with the practice adopted by numerous scholars [58,59]. Therefore, in this study, we adopted life satisfaction as the primary measure of SWB, which primarily reflects individuals’ overall evaluation of their life rather than transient emotions.

3.2.2. Entrepreneurial Village Cadres

In China’s rural grassroots governance system, village cadres serve as the primary leaders of local administration, among whom the Party secretary—holding the highest authority within the village’s two committees—occupies the core leadership position [60]. Positioned between the government and local residents, the Party secretary is responsible for policy implementation, order maintenance, social mobilization, and public service delivery. Therefore, this study focuses on village Party secretaries as the unit of analysis to examine the effect of Entrepreneurial Village Cadres (EVCs) on rural residents’ SWB.

To identify EVCs, we used responses from village cadres to the CRRS question: “Does the village Party secretary have experience in founding or managing a business?”. A response of “yes” is coded as 1, indicating an EVC, while a response of “no” is coded as 0. This measure is considered credible, as the CRRS interviews both village officials and local residents within each village, allowing for informal cross-verification of background information at the village level.

3.2.3. Control Variables

Drawing on existing studies [54,61,62], we included control variables at both the individual and village levels in our analysis of SWB. At the individual level, we controlled for age, gender, marital status, and educational attainment. At the village level, we accounted for the village’s distance from the county government, the registered population, and the topography.

3.2.4. Mechanism Variables

To investigate the underlying mechanisms behind the observed effects, we selected a set of mechanism variables based on items from the CRRS questionnaire.

Variables measuring the “promoting income growth”: We selected the average per capita disposable income and the per capita income from the rural collective economy, both measured in 2021.

Variables measuring the “enhancing democratic governance”: We selected two questions as proxies: whether the village has an “Internet + government services” platform and whether the village’s collective assets are managed through information technology.

Variables measuring the “improving public services”: We selected two questions as proxies: whether the village has an employment and entrepreneurship service station and whether the village has an e-commerce service station.

Table 1. Variable definitions and descriptive statistics.

Variable Type	Variable Name	Definition	N	Mean	SD	Min	Max
Dependent variable	SWB	Ranges from 0 to 10, with higher values indicating greater well-being	3030	7.856	1.786	0	10
Independent variable	EVCs	The village Party secretary has experience in founding or operating a business (Yes = 1, No = 0)	3030	0.473	0.499	0	1
Control variable	Age	Individual age	3030	57.182	11.068	23	98
	Gender	Male = 1, female = 0	3030	0.933	0.249	0	1
	Marriage	Married = 1, otherwise = 0	3030	0.906	0.292	0	1
	Edu1	Illiterate = 1, otherwise = 0	3030	0.042	0.200	0	1
	Edu2	Preschool education = 1, otherwise = 0	3030	0.022	0.148	0	1
	Edu3	Primary school education = 1, otherwise = 0	3030	0.331	0.471	0	1
	Edu4	Junior high school education = 1, otherwise = 0	3030	0.435	0.496	0	1
	Edu5	Senior high school education = 1, otherwise = 0	3030	0.118	0.323	0	1
	Edu6	Secondary vocational school = 1, otherwise = 0	3030	0.016	0.126	0	1
	Edu7	Technical/vocational high school = 1, otherwise = 0	3030	0.004	0.065	0	1
	Edu8	Associate degree = 1, otherwise = 0	3030	0.026	0.159	0	1
	Edu9	Bachelor’s degree or above = 1, otherwise = 0	3030	0.005	0.070	0	1
	Distance	Distance from the village committee to the county government (kilometers)	3030	23.396	17.766	1	125
	Village size	Registered population of the village	3030	736.690	458.732	68	2484
	Topography1	Plain area = 1, otherwise = 0	3030	0.390	0.488	0	1
	Topography2	Hilly area = 1, otherwise = 0	3030	0.267	0.443	0	1
	Topography3	Mountainous area = 1, otherwise = 0	3030	0.318	0.466	0	1
	Topography4	Semi-mountainous area = 1, otherwise = 0	3030	0.024	0.152	0	1
Mechanism Variable	Income	Per capita disposable income last year (10,000 yuan)	3030	1.627	0.878	0.240	6.000
	Collective Economy	Per capita collective economic income last year (10,000 yuan)	3030	0.230	1.423	0.000	19.188
	EGOV	Village has an “Internet + government services” platform (Yes = 1, No = 0)	3030	0.558	0.497	0	1
	CADM	Village collective assets are digitally managed (Yes = 1, No = 0)	2945	0.846	0.361	0	1
	EESS	Village has an employment and entrepreneurship service station (Yes = 1, No = 0)	3030	0.411	0.492	0	1
	ECSS	Village has an e-commerce service station (Yes = 1, No = 0)	3030	0.506	0.500	0	1

Note: Yuan is Chinese currency, 1 yuan is approximately equal to 0.14 US dollars.

presents the detailed definitions and descriptive statistics of all variables.

3.3. Baseline Regression Model

The baseline regression model employed in this study is specified as Equation (1):

$${\mathit{SWB}}_i=\alpha +\beta {\mathit{EVCs}}_i+\gamma {\mathit{Controls}}_i+{\epsilon }_i$$

(1)

where ${\mathit{SWB}}_i$ denotes the dependent variable, representing the SWB score of respondent i. A higher score indicates a greater level of perceived well-being. ${\mathit{EVCs}}_i$ represents the core explanatory variable of this study, indicating whether the village Party secretary is an Entrepreneurial Village Cadre. ${\mathit{Controls}}_i$ denotes a set of control variables at both the village and individual levels, and ${\epsilon }_i$ is the random error term.

It is worth emphasizing that the measure of SWB employed in this study is an ordinal variable. When the dependent variable has five or more categories and the model is properly specified, linear OLS and nonlinear Oprobit models yield comparable coefficient estimates and significance levels [63]. Given that OLS estimation results are more intuitive and easier to interpret, and that this method has been widely applied in studies on residents’ well-being [11], we used OLS estimation in our baseline model and employed the Oprobit model as a robustness check.

4. Results and Discussion

4.1. Baseline Regression Analysis

Table 2. Baseline regression results.

Variable	SWB	SWB	SWB
	OLS	OLS	Oprobit
	(1)	(2)	(3)
EVCs	0.209 ***	0.197 ***	0.108 ***
	(0.065)	(0.065)	(0.038)
Age		0.016 ***	0.008 ***
		(0.003)	(0.002)
Gender		−0.022	−0.015
		(0.142)	(0.081)
Marriage		0.184	0.082
		(0.127)	(0.072)
Edu2		−0.144	−0.086
		(0.277)	(0.165)
Edu3		−0.134	−0.086
		(0.178)	(0.104)
Edu4		0.083	0.040
		(0.179)	(0.104)
Edu5		0.145	0.061
		(0.192)	(0.112)
Edu6		0.140	0.069
		(0.301)	(0.179)
Edu7		−0.204	−0.138
		(0.536)	(0.317)
Edu8		0.612 **	0.322 **
		(0.246)	(0.149)
Edu9		0.809 **	0.393 *
		(0.324)	(0.216)
Distance		−0.003 *	−0.002 *
		(0.002)	(0.001)
Village size		−0.000	−0.000*
		(0.000)	(0.000)
Topography2		−0.093	−0.048
		(0.081)	(0.048)
Topography3		−0.112	−0.064
		(0.083)	(0.049)
Topography4		−0.275	−0.164
		(0.226)	(0.118)
Observations	3030	3030	3030

Note: Robust standard errors are presented in parentheses. Significance levels are: * p < 0.10, ** p < 0.05, *** p < 0.01.

reports the baseline estimation results for the causal impact of EVCs on the SWB of rural residents in China. Column (1) includes only the core explanatory variable. The coefficient on EVCs is found to be significantly positive at the 1% level, suggesting that village governance led by individuals with entrepreneurial experience is positively associated with higher levels of SWB among rural residents. In Column (2), after incorporating controls for both individual and village characteristics, the coefficient remains robustly positive and statistically significant at the 1% level. Specifically, EVCs increase the happiness score by approximately 0.197 points, representing a 2.51% increase relative to the sample mean of 7.856. Column (3) employs an Oprobit model for robustness checks, and the core findings remain substantively unchanged. Therefore, Hypothesis H1 is supported.

These results are consistent with previous findings that EVCs can positively contribute to rural development [28,29]. However, this study extends the existing evidence by showing that their influence also enhances the SWB of rural residents, highlighting a broader impact of entrepreneurial leadership in village governance.

4.2. Robustness Tests

4.2.1. Instrumental Variables Regression

The instrumental variable selected for this study is the number of Jinshi in each county during the Ming dynasty (1368–1644) and the Qing dynasty (1644–1912). Existing studies have demonstrated that historical institutions can be passed down through cultural genes, influencing the ideologies and behaviors of contemporary populations across generations [64]. In the context of China, Confucianism is the most influential and far-reaching cultural symbol, constituting the mainstream of traditional Chinese culture [65]. It represents not only the most enduring and significant force in Chinese philosophical thought and values but also a long-standing moral code and action guide that has been widely respected by individuals and organizations [66].

As products of the imperial examination system, Jinshi epitomize the level of regional human capital accumulation and the depth of Confucian cultural penetration in the pre-industrial era. The imperial examination during the Ming and Qing dynasties consisted of three stages: the provincial exam (Xiangshi), the metropolitan exam (Huishi), and the palace exam (Dianshi). Only those who succeeded in all three stages were awarded the prestigious Jinshi title. As the most outstanding scholars among Confucian literati, Jinshi not only represented the local elite class but also served as practitioners and disseminators of Confucian values. A higher number of Jinshi in a given region indicates a deeper historical embedding of Confucian cultural norms and socialization processes [67].

From a mechanistic perspective, Confucian culture exerts a lasting influence through three main pathways. First, the imperial examination system fostered literacy and governance skills, forming a human capital foundation that later supported commercial endeavors. Some Confucian scholars transitioned into the business world, giving rise to a tradition of Confucian entrepreneurs (Rushang), whose legacy continues to shape China’s private sector today [68]. Second, Jinshi elites built cross-regional networks through kinship clans and native-place associations, which evolved into important channels for transmitting business information and credit during China’s modernization. These networks significantly reduced transaction costs for entrepreneurs. Third, the Confucian emphasis on the moral obligation to “cultivate oneself, regulate the family, govern the state, and bring peace to the world”, along with the ideal of the Shishen spirit—where social responsibility is integrated into elite behavior—suggests that economic elites are expected to balance personal gain with collective well-being [69,70]. This ethical framework strongly resonates with the behavioral logic of entrepreneurs who return to rural areas to serve as village cadres.

The number of Jinshi during the Ming and Qing dynasties serves as a historical variable from the pre-industrial era. Its spatial distribution was shaped by institutional factors such as the civil examination system, the prevalence of academies, and geographic location—factors unrelated to contemporary residents’ SWB. Therefore, this instrument satisfies both the relevance and exclusion restriction conditions.

We first used the two-stage least squares (2SLS) method for estimation. As reported in Column (1) of

Table 3. Instrumental variables regression results.

Variable	EVCs	SWB	EVCs	SWB
	2SLS	2SLS	CMP	CMP
	(1)	(2)	(3)	(4)
Jinshi	0.001 ***		0.002 ***
	(0.000)		(0.000)
EVCs		3.493 ***		1.421 ***
		(1.155)		(0.374)
Control variables	YES	YES	YES	YES
F	16.63
Atanhrho_12			−0.436 ***
			(0.133)
Observations	3030	3030	3030	3030

Note: Robust standard errors are presented in parentheses. Significance level is: *** p < 0.01.

, the first-stage regression yields an F-statistic of 16.63 for the Stock-Yogo weak instrument test, which exceeds the commonly used Stock-Yogo critical value for weak instruments at the 10% maximal IV size. The second-stage regression results in Column (2) show that, after accounting for endogeneity, the coefficient of EVCs on SWB is 3.493, statistically significant at the 1% level. Compared to the baseline OLS estimates, the magnitude of the effect increases, suggesting that correcting for endogeneity not only preserves but also strengthens the positive and significant impact of EVCs on rural residents’ SWB.

In addition to the 2SLS estimation, we also adopted the Conditional Mixed Process (CMP) method to address potential endogeneity. As shown in Columns (3) and (4) of Table 3, the endogeneity test statistic Atanhrho_12 is significant at the 1% level, confirming the presence of endogeneity in the model and supporting the appropriateness of using the CMP estimation method. The results in Column (4) demonstrate that, after accounting for endogeneity using the CMP model, the estimated effect remains consistent with the baseline findings.

4.2.2. Propensity Score Matching Method

To address potential sample selection bias in impact evaluation, we employed the Propensity Score Matching (PSM) method to construct a counterfactual framework for assessing the impact of EVCs on rural residents’ SWB. This method serves as a robustness check for the baseline regression results. Specifically, we defined villages with EVCs as the treatment group and those without as the control group. Three matching techniques were implemented: nearest-neighbor matching (n = 4), kernel matching, and caliper matching (caliper = 0.001).

The results of the covariate balance test based on kernel matching are reported in

Table 4. Results of the balance test.

Variable	Matching Status	Mean		% Bias	% Reduct. Bias	t-Test
		Treated	Control			t	p > t
Age	Unmatched	56.819	57.507	−6.2	78.4	−1.71	0.088
	Matched	56.830	56.979	−1.3		−0.36	0.720
Gender	Unmatched	0.934	0.933	0.3	−78.1	0.08	0.938
	Matched	0.935	0.936	−0.5		−0.14	0.892
Marriage	Unmatched	0.918	0.895	8.1	94.9	2.22	0.027
	Matched	0.918	0.919	−0.4		−0.12	0.906
Edu2	Unmatched	0.020	0.024	−2.8	98.6	−0.78	0.438
	Matched	0.021	0.020	0.0		0.01	0.991
Edu3	Unmatched	0.297	0.361	−13.8	84.7	−3.79	0.000
	Matched	0.301	0.310	−2.1		−0.57	0.569
Edu4	Unmatched	0.456	0.416	8.2	93.2	2.25	0.024
	Matched	0.460	0.457	0.6		0.15	0.882
Edu5	Unmatched	0.134	0.105	9.1	74.1	2.50	0.012
	Matched	0.130	0.123	2.4		0.61	0.542
Edu6	Unmatched	0.013	0.019	−4.4	97.5	−1.20	0.229
	Matched	0.013	0.014	−0.1		−0.03	0.974
Edu7	Unmatched	0.006	0.003	5.7	89.8	1.59	0.112
	Matched	0.004	0.005	−0.6		−0.15	0.877
Edu8	Unmatched	0.034	0.019	9.6	98.3	2.66	0.008
	Matched	0.033	0.033	−0.2		−0.04	0.969
Edu9	Unmatched	0.006	0.004	1.7	34.8	0.47	0.639
	Matched	0.006	0.005	1.1		0.29	0.774
Distance	Unmatched	22.965	23.782	−4.6	80.2	−1.26	0.207
	Matched	23.164	23.002	0.9		0.25	0.805
Village size	Unmatched	768.580	708.070	13.2	72.7	3.63	0.000
	Matched	764.590	748.080	3.6		0.93	0.354
Topography2	Unmatched	0.256	0.277	−4.8	82.8	−1.32	0.186
	Matched	0.259	0.263	−0.8		−0.22	0.824
Topography3	Unmatched	0.320	0.317	0.5	−58.1	0.13	0.900
	Matched	0.321	0.318	0.7		0.19	0.847
Topography4	Unmatched	0.034	0.015	12.0	97.7	3.34	0.001
	Matched	0.025	0.025	−0.3		−0.07	0.942

. As shown in the table, prior to matching, there were significant differences in several covariates between the treatment and control groups. However, after matching, the standardized mean differences for all variables fall below the 10% threshold, and t-tests fail to reject the null hypothesis of no systematic differences between the two groups. Moreover, most standardized differences are substantially reduced. These findings indicate that the PSM procedure effectively minimizes the systematic differences in individual characteristics between the treatment and control groups, thereby approximating the conditions of a randomized experiment.

Table 5. Results of the PSM method.

Variable	Matching Method	Treated	Controls	ATT	S.E.	t
SWB	Unmatched	7.967	7.758	0.209	0.065	3.22
	Nearest neighbor matching (n = 4)	7.961	7.806	0.156	0.076	2.05
	Kernel matching	7.961	7.762	0.199	0.066	3.01
	Caliper matching (caliper = 0.001)	7.970	7.755	0.215	0.091	2.37

presents the estimated effects of EVCs on rural residents’ SWB based on three different matching methods. The results show that the Average Treatment Effect on the Treated (ATT) is statistically significant at least at the 5% level (t > 1.96) across all matching approaches, with the lowest ATT estimate being 0.156. These findings are consistent with our previous results and provide robust causal evidence that EVCs significantly enhance rural residents’ well-being.

4.2.3. Additional Robustness Tests

First, to verify the robustness of our findings, we conducted a series of robustness checks by modifying the measurement of the dependent variable and applying both OLS and Oprobit estimation methods. We began by replacing the original SWB variable with respondents’ satisfaction with overall village development (VillageDev_Satisfaction). The estimation results in columns (1) and (2) of

Table 6. Robustness test results.

Variable	VillageDev_Satisfaction	VillageDev_Satisfaction	SWB_5	SWB_5	Eliminate Pandemic Impact	Eliminate Pandemic Impact
	OLS	Oprobit	OLS	Oprobit	OLS	Oprobit
	(1)	(2)	(3)	(4)	(5)	(6)
EVCs	0.211 ***	0.112 ***	0.091 ***	0.117 ***	0.175 **	0.097 **
	(0.065)	(0.039)	(0.031)	(0.040)	(0.074)	(0.045)
Control variables	YES	YES	YES	YES	YES	YES
Observations	3008	3008	3030	3030	2161	2161

Note: Robust standard errors are presented in parentheses. Significance levels are: ** p < 0.05, *** p < 0.01.

indicate that after replacing the dependent variable, the outcome variable still exhibits a significant positive effect at the 1% level. Additionally, we constructed an alternative measure of subjective well-being (SWB_5), categorizing the original dependent variable into five levels, ranging from 1 to 5, which correspond to varying degrees of life satisfaction from “very dissatisfied” to “very satisfied”. Higher scores reflect greater levels of well-being. The regression results are presented in columns (3) and (4) of Table 6. The coefficient of EVCs is significantly positive at the 1% level, which is consistent with the baseline regression results.

Second, we excluded respondents who had experienced major life shocks. The COVID-19 pandemic, as a global public health crisis, has had a widespread impact on rural economies and social psychology [71]. Its effects are exogenous and uneven, with certain rural residents who were more severely impacted by the pandemic potentially introducing estimation bias into this study. To address this, we excluded respondents who reported being heavily affected by the pandemic, accounting for 28.49% of the total sample. The regression results, shown in columns (5) and (6) of Table 6, indicate that the positive effect of EVCs on rural residents’ SWB remains statistically significant at the 5% level.

5. Mechanism and Heterogeneity Analysis

5.1. Mechanism Analysis

Based on the theoretical mechanisms discussed above, EVCs exert their influence through three paths: promoting income growth, enhancing democratic governance, and improving public services. This study uses the proxy variables for these three paths as the dependent variables for estimation, employing instrumental variables to address potential endogeneity issues, thus enhancing the robustness of the research conclusions. The results are presented in

Table 7. Mechanism test results: Promoting income growth.

Variable	Income	Income	Collective Economy	Collective Economy
	OLS	2SLS	OLS	2SLS
	(1)	(2)	(3)	(4)
EVCs	0.172 ***	13.819 ***	0.120 **	2.279 **
	(0.031)	(3.491)	(0.051)	(0.952)
Control variables	YES	YES	YES	YES
Observations	3030	3030	3030	3030

Note: Robust standard errors are presented in parentheses. Significance levels are: ** p < 0.05, *** p < 0.01.

,

Table 8. Mechanism test results: Enhancing democratic governance.

Variable	EGOV	EGOV	CADM	CADM
	Probit	IV-Probit	Probit	IV-Probit
	(1)	(2)	(3)	(4)
EVCs	0.086 *	2.202 **	0.113 *	3.784 **
	(0.047)	(0.938)	(0.059)	(1.621)
Control variables	YES	YES	YES	YES
Observations	3030	3030	2945	2945

Note: Robust standard errors are presented in parentheses. Significance levels are: * p < 0.10, ** p < 0.05.

and

Table 9. Mechanism test results: Improving public services.

Variable	EESS	EESS	ECSS	ECSS
	Probit	IV-Probit	Probit	IV-Probit
	(1)	(2)	(3)	(4)
EVCs	0.305 ***	4.080 ***	0.165 ***	4.892 ***
	(0.047)	(1.242)	(0.047)	(1.500)
Control variables	YES	YES	YES	YES
Observations	3030	3030	3030	3030

Note: Robust standard errors are presented in parentheses. Significance level is: *** p < 0.01.

.

Table 7 presents the estimation results for income growth. Since the first-stage estimates of the instrumental variables have already been discussed, only second-stage results are shown. Column (1) shows that EVCs significantly increase per capita income at the 1% significance level. The estimation results in Column (3) indicate that EVCs can significantly improve the level of rural collective economic development, with significance at the 5% level. Columns (2) and (4) confirm these findings remain robust after addressing potential endogeneity using the 2SLS approach. EVCs consolidate local resources and introduce modern business models such as e-commerce and branding. They boost rural residents’ non-farm incomes and product value while broadening collective benefits, improving material welfare and reducing relative deprivation to enhance SWB. Therefore, Hypothesis H2 is supported.

Table 8 provides empirical evidence for democratic governance enhancement. Columns (1) and (3) report average marginal effects based on a Probit model, whereas Columns (2) and (4) present second-stage coefficients from the IV-Probit estimation. The results in Columns (1) and (3) show that EVCs have a marginal effect of 0.086 on the development of “Internet + government services” platforms and 0.113 on the informatization of collective asset management, both statistically significant at the 10% level. Additionally, Columns (2) and (4) verify that these estimates are robust to endogeneity concerns. Our findings show that EVCs bring digital tools and corporate management into village governance—using online consultation and public information disclosure—to ensure real resident participation and oversight. This transparent, fair process rebuilds grassroots trust and strengthens rural residents’ ownership and belonging, boosting their security and well-being. Therefore, Hypothesis H3 is supported.

Table 9 presents the results related to improving public services. The results show that, compared with villages without EVCs, those with such cadres are 30.5% more likely to provide employment and entrepreneurship services (Column 1), and 16.5% more likely to offer rural e-commerce services (Column 3). Columns (2) and (4) confirm that these conclusions remain robust after addressing potential endogeneity issues. These findings provide strong evidence that EVCs involvement significantly enhances village public services. By leveraging broad social networks and policy connections, EVCs secure additional funding and projects to optimize service delivery in critical areas, enabling rural residents to enjoy greater convenience in daily life and ultimately elevating SWB. Therefore, Hypothesis H4 is supported.

5.2. Heterogeneity Analysis

Furthermore, we assessed whether this governance effect varies across different groups, with the aim of analyzing which types of rural residents and villages derive more SWB from the governance of EVCs.

First, we examined the effect differences between groups with different economic endowments. If the rural resident belongs to a poor household, the value is 1, otherwise, it is 0. An interaction term model was used for the heterogeneity analysis. The regression results in column (1) of

Table 10. Results of heterogeneity analysis.

Variable	SWB	SWB
	(1)	(2)
EVCs	0.216 ***	−0.053
	(0.071)	(0.108)
Poor Household	−0.063
	(0.136)
EVCs × Poor Household	−0.371 *
	(0.223)
External Financial Support		−0.108
		(0.096)
EVCs × External Financial Support		0.394 ***
		(0.136)
Control variables	YES	YES
Observations	2787	2993

Note: Robust standard errors are presented in parentheses. Significance levels are: * p < 0.10, *** p < 0.01.

show that the coefficient of EVCs × Poor Household is significantly negative, indicating that non-poor households derive more SWB from the governance of EVCs. The structural differences in resource acquisition capabilities are an important reason why non-poor households benefit more significantly. Village cadres with entrepreneurial backgrounds often create economic opportunities through industry projects, market-based cooperation, and other means, while non-poor households typically have stronger social capital, human capital, or initial assets, which allow them to more effectively access such resources. In contrast, poor households are constrained by limited information channels, weaker risk tolerance, and other factors, restricting their ability to participate in emerging economic activities.

Second, we explored the heterogeneity of villages from the perspective of external financial support. If a village had industry development support funds, it was coded as 1, otherwise, it was coded as 0. An interaction effect model was also used for the heterogeneity test. The empirical results in column (2) of Table 10 show that the coefficient of EVCs × External Financial Support is significantly positive, indicating that the governance actions of EVCs have a more significant effect on improving the happiness of villagers in those villages with industry development support funds. This result verifies the synergistic effect of “external financial empowerment” and “entrepreneurial capacity”. The governance of EVCs relies not only on their personal resource integration abilities but also on institutional capital injection to overcome resource bottlenecks, thereby amplifying the multiplier effect of human capital. In villages without financial support, in a context of capital scarcity, the entrepreneurial abilities of village cadres are likely forced to shift toward non-productive activities, such as competing for limited subsidies or lobbying for higher-level transfer payments, which leads to social resource misallocation and diminished collective trust. Moreover, resource constraints might trigger a negative selection in capable governance, causing loss of confidence due to the consumption of personal resources, thus making it difficult to sustain the improvement of villagers’ happiness.

6. Conclusions, Policy Implications, and Limitations

As highlighted by a substantial body of academic research, improving well-being, particularly in rural areas, remains a major concern for many countries worldwide. Existing literature has systematically examined the determinants of well-being, including individual characteristics such as education and health status, household socioeconomic conditions such as income level and asset ownership, and macro-level factors such as infrastructure accessibility and the provision of public services. This study focuses on a critical but relatively underexplored dimension by investigating whether village cadres with entrepreneurial experience can enhance residents’ well-being through more effective public governance.

Based on data from the 2022 China Rural Revitalization Survey (CRRS), we empirically examined the impact of EVCs on the SWB of rural residents. The study reveals that village cadres with entrepreneurial experience can enhance the SWB of residents in their respective villages. This finding remains robust even after a series of tests for robustness and endogeneity. Mechanism analysis indicates that this effect is achieved through three pathways: promoting income growth, enhancing democratic governance, and improving public services. Further analysis shows that the benefits of EVCs are more substantial among non-poor households and villages with external financial support.

Based on the findings of this study, several policy implications can be drawn. First, this research provides empirical evidence confirming the positive impact of EVCs on the SWB of rural residents. Therefore, encouraging entrepreneurs to return to their hometowns to assume village leadership roles is crucial for enhancing happiness in rural communities. In the selection process for village cadres, entrepreneurial experience should be incorporated as an evaluation criterion, and priority should be given to returnees with experience in entrepreneurship or market operations. Their capacity for resource integration and innovative thinking should be fully leveraged. At the same time, incentive policies that facilitate the return of rural elites should be explored, providing institutional support for entrepreneurial talent to participate in rural governance. It is also essential to establish clear boundaries of authority and responsibility to prevent excessive profit-driven behavior.

Second, our findings indicate that the positive effects of EVCs are more pronounced among non-poor households. Therefore, it is essential to establish complementary support mechanisms for vulnerable groups, such as customized skills training programs and risk compensation funds. The government should strengthen safety net policies to offset the welfare disparities potentially caused by market mechanisms. Moreover, performance evaluation metrics for EVCs should be optimized to encourage inclusive development, thereby reducing the perceived gap in governance benefits across different population groups. By incorporating such measures, well-being can be enhanced through a more inclusive approach, ensuring that the positive effects of governance by EVCs benefit all segments of society.

Third, our analysis shows that villages with external financial support are better able to leverage the advantages of EVCs in enhancing residents’ well-being. This suggests the need to advance both “external infusion” and “internal generation” in parallel for rural development. The government should avoid an overreliance on the personal capabilities of EVCs and instead develop a coordinated system that combines policy support, talent recruitment, and resource allocation. Strengthening external financial assistance is essential to support the sustainable development of rural communities.

This study has several limitations that warrant consideration. First, due to data constraints, we are unable to identify the specific types of enterprises operated by EVCs. In addition, it is difficult to capture the long-term effects of their activities on SWB. In future research, specially designed questionnaires and longitudinal surveys targeting EVCs are expected to address this issue. Second, consistent with previous studies, our measurement of SWB is based on self-reported responses from the CRRS questionnaire. This approach is susceptible to short-term emotions, social desirability bias, and survey context effects, inevitably introducing subjectivity and potential selection bias. Acknowledging this limitation, future research could explore more objective approaches to measuring SWB, such as incorporating verifiable indicators including household consumption levels, participation in healthcare, and education, to strengthen the validity of the findings. Third, although this study discusses the possibility that EVCs may not lead to elite capture, we are unable to empirically test this governance concern due to data limitations. Future research could integrate village-level governance data, elite network characteristics, and economic returns to further examine whether, and to what extent, EVCs help mitigate elite capture within rural governance structures. Finally, it is important to acknowledge that the findings of this study are embedded within China’s unique institutional and cultural context. The strong role of the state in grassroots governance, the collectivist orientation of rural communities, and the institutionalized village cadre system may influence how entrepreneurial leadership functions and how residents respond. As a result, caution is warranted when generalizing these findings to countries with different governance structures or cultural norms. Future research could examine whether similar mechanisms operate in alternative institutional and cultural settings to enhance the cross-country applicability of these insights.