The Impact of Government Intervention in Rural Tourism Development on Residents’ Income: A Quasi-Natural Experiment from China

Abstract

The examination of government intervention in rural tourism within developing countries remains a critical area of academic inquiry. This study investigates the relationship between government intervention in rural tourism and the income growth of rural residents by utilizing a Difference-in-Differences method based on county-level data from the China County-Level Statistical Yearbooks from 2006 to 2022. The findings indicate that government-supported rural tourism development significantly promotes income growth among rural populations. This effect can be attributed to three key mechanisms: stimulation of entrepreneurial activity, promotion of related industrial development, and optimization of resource allocation. Heterogeneity analysis reveals that the income-enhancing effect is more pronounced in counties with stronger economic foundations, more developed agricultural sectors, and favorable geographic conditions. However, the intervention has not significantly reduced the urban–rural income gap or reversed the declining trend in the labor income share, suggesting that more targeted and inclusive strategies are needed. These findings offer important policy implications for developing countries aiming to foster rural revitalization through industrial policy instruments.

1. Introduction

Enhancing resident income and creating employment opportunities are the core objectives of rural tourism development [1], which is also one of the United Nations Sustainable Development Goals (SDGs). Over time, many developing countries have pursued urban-centric development strategies, further exacerbating the urban–rural development gap and resulting in a pronounced dichotomy. Developing countries have large rural populations, single-sector industry structures, lagging infrastructures, outdated production techniques, fragile ecological environments, and pressing issues with rural development and income growth [2,3]. Given these constraints, the avenues for promoting rural development and increasing rural resident income are limited. Owing to its characteristics of employment creation, income generation, and regional development stimulation [4,5], rural tourism is considered an integral part of rural development strategies [6] and a key means of addressing rural development and income enhancement in rural areas [7,8]. As more people seek respite from urban life [9], market demand for rural tourism is expanding, thereby highlighting the role of rural tourism in promoting rural development. Therefore, exploring the relationship between rural tourism development and rural resident income is of great significance and may provide practical pathways for narrowing the urban–rural income gap and achieving the SDGs.

However, the impact of rural tourism development on the income growth of rural residents still faces challenges. First, rural tourism development involves multiple stakeholders, including local and foreign enterprises, local governments, local residents, third -party organizations, etc. It may also lead to non-local residents exploiting tourism development opportunities, resulting in the loss of benefits for the local community [10], with local rural households receiving minimal benefits. Therefore, the involvement of local stakeholders is crucial for the revitalization of rural areas [5] and sustainable rural tourism development [11]. Second, rural areas in developing countries focus primarily on traditional agriculture. Although rural tourism overlaps with agriculture in many respects, those engaged in agriculture and unrelated to the tourism industry may not benefit from tourism development [12]. During the development of the tourism industry, rural residents need to adapt to a new identity distinct from that of agricultural laborers [13] and may face constraints related to skills and age. A lack of knowledge about the tourism industry and capital may also prevent rural residents from participating in tourism development [14]. In such cases, they may not necessarily benefit from rural tourism development. Thus, the impact of rural tourism development on rural residents’ income remains unclear.

Government intervention is a common means of promoting rural tourism, including special funds, tax incentives, subsidies, etc. [15]. For developing countries, whether government intervention in rural tourism development can overcome the disadvantages faced by rural residents in tourism development and increase their income or not requires further research and examination. Some scholars who oppose government intervention have argued that rural development processes should empower local people and reduce unnecessary dependence on national institutions [16,17]. On the contrary, some other scholars believe that rural tourism development in developing countries faces challenges such as weak infrastructure, severe capital constraints, and a lack of professional tourism institutions [2]. Under these circumstances, relying solely on market forces may not lead to sustainable rural tourism development. The development of tourism in rural areas is the government’s responsibility [18] and requires government participation in unified planning and management. Government intervention in rural tourism development is likely to alter the allocation of factors across regions and industries. Therefore, the impact of this intervention on rural resident income requires further examination [15]. In developing countries, can government intervention in rural tourism overcome the disadvantages faced by rural residents and increase their income? What are the underlying mechanisms through which such interventions operate, and what are the potential outcomes?

To address these questions, this study utilizes a quasi-natural experiment based on China’s “National Demonstration Counties for Leisure Agriculture and Rural Tourism” program. Implemented by the Chinese government over eight consecutive years since 2010, this program provides an ideal empirical context for policy evaluation. Drawing on county-level panel data spanning from 2005 to 2021, we apply a multiple-period Difference-in-Differences (DID) estimation strategy to identify the causal impact of government intervention in rural tourism development on rural residents’ income.

Furthermore, we explore the underlying channels of influence through mechanism testing, focusing on three key transmission mechanisms: (1) the entrepreneurial spirit stimulus effect, (2) the driving effect of related industrial development, and (3) the resource allocation optimization effect. We also conduct a heterogeneity analysis to assess how these effects vary across regions with different levels of economic development, agricultural development, and topographical characteristics. Beyond income effects, we further examine whether government intervention in rural tourism contributes to narrowing urban–rural income disparities and improving the labor income share in rural areas—measured as the proportion of rural residents’ income relative to total regional economic output. The findings provide important policy implications for developing countries aiming to promote rural revitalization through industrial policy tools. This study highlights the potential of targeted government intervention to stimulate rural income growth while also pointing to areas where policy refinement is necessary. In particular, when designing and implementing similar interventions, policymakers in developing economies should not only consider how to stimulate income growth among rural populations, but also address broader structural challenges—specifically, how to reduce urban–rural income inequality and enhance the labor income share in rural economies to ensure inclusive and sustainable development.

2. Literature Review, Theoretical Framework, and Hypotheses

2.1. Rural Tourism and Government Intervention

Rural tourism refers to the phenomenon in which tourists are attracted to visiting rural areas because of their distinctive rurality, which is formed by unique resources and environments [3]. Over time, many countries have pursued urban-centric development strategies, resulting in the marginalization of rural areas and various issues such as income inequality, rural depopulation, aging populations, and community decline [19,20,21]. Owing to its potential to stimulate rural economies, create diverse employment opportunities, provide multiple income sources, and improve the natural and cultural environment, rural tourism is a key solution for revitalizing rural areas and addressing the challenges they face [8,22,23]. Therefore, promoting the development of rural tourism is crucial for changing the marginalized status of rural areas.

Similar to debates in economics, discussions on government intervention versus market mechanisms are inevitable when considering ways to drive rural tourism development. Scholars skeptical of government intervention argue that it often results in inefficient short-term actions and question its effectiveness due to a lack of sufficient knowledge [24].

Some scholars argue that rural tourism may not effectively address issues related to employment and new income source generation [20]. Conversely, proponents of government intervention argue that the tourism industry is susceptible to market failures and distortions, necessitating government intervention to address market failures in rural areas. Without government intervention, the tourism industry may struggle to survive [25]. Moreover, governments play roles in economic, socio-cultural, political, and environmental dimensions, particularly in developing countries [26]. In many countries, the central government is responsible for intervening in rural tourism development through industrial policies and aligning it with top-level strategies [18]. This is especially crucial in the rural areas of developing countries such as China, which often face severe challenges such as policy gaps, inadequate investment, weak infrastructure, and community decline [2,3]. Government intervention in rural tourism development is necessary in this context. It is worth noting that those who question government intervention are not necessarily skeptical of it per se but rather of its outcomes. One significant reason for this skepticism is that policymakers often prioritize the legitimacy of policymaking, but lack robust post-policy evaluation methods [20]. Consequently, the role of government intervention in rural tourism development has not been rigorously assessed, even in countries where the government plays a pivotal role in the national governance system, such as China [15]. Therefore, this study employs a multi-period Difference-in-Differences approach to rigorously assess the impact of government intervention on rural tourism development and the income of rural residents.

2.2. Theoretical Framework and Hypotheses

2.2.1. The Impacts of Government Intervention in Rural Tourism Development on Rural Residents’ Income

The core objectives of rural tourism development should revolve around increasing income and generating employment opportunities [1]. Rural tourism is a method of preserving rural resource integrity, promoting rural economies, and sustaining rural ways of life, and represents a significant source of income for rural areas [7,27]. Rural tourism does not replace traditional economic activities in rural areas but complements existing industries and enhances economic diversification. It provides diverse incomes and employment opportunities for rural areas with minimal investment, thereby increasing the total income in rural regions [28,29]. Rural tourism offers new income sources for families in remote rural areas [23]. In some developing countries, rural tourism is seen as a crucial means of improving rural economies and addressing income inequality between urban and rural areas and between different regions [1]. In developed countries, urban populations often relocate to rural areas and view rural tourism as a supplementary income source [30]. Rural tourism development not only increases agricultural income but also generates non-agricultural income [20]. Rural tourism is developed based on local tourism resources and agricultural development, maintaining a foundation in the agricultural industry, thereby avoiding the issue of rural tourism development competing with agricultural development. However, rural areas in developing countries often face challenges such as a lack of capital and talent. Even if these regions have abundant tourism resources, attracting sufficient capital and talent for development through market forces alone remains challenging. Government intervention in rural tourism development can mitigate conflicts among stakeholders and maintain local benefits, ultimately increasing resident income. Based on this analysis, we propose the following hypothesis:

Hypothesis 1. 

Government intervention in rural tourism development increases rural resident income.

2.2.2. Entrepreneurial Spirit Stimulus Effect

Rural tourism development can stimulate entrepreneurial spirit in rural areas, and unified government planning for rural tourism can attract more entrepreneurs and foster their entrepreneurial spirit. First, rural tourism offers local residents entrepreneurial opportunities [2]. Individual enterprises and entrepreneurs play significant roles [31], especially local rural tourism entrepreneurs, who often have strong emotional attachments to their communities and strive to provide employment opportunities for fellow residents while playing a major role in rural reception activities [32]. Therefore, entrepreneurs are crucial forces in sustainable rural tourism development [33]. Second, rural tourism development provides labor participation opportunities for vulnerable groups, such as women [34]. Rural tourism requires a significant labor force, and with appropriate skill training, more women can participate, significantly unleashing their potential [35]. Many women excel in tourism crafts and related areas, making them active participants in rural tourism [36]. Third, rural tourism development can attract external populations to innovate and initiate business in rural areas. Rural areas not only offer unique entrepreneurial opportunities and development potential, but also boast beautiful natural environments that attract many external visitors. Many newcomers engage in rural tourism and adopt it as a rural entrepreneurial lifestyle [30]. Additionally, the development of rural tourism not only increases agricultural income, but also generates non-agricultural income [20]. In summary, the development of rural tourism can provide diversified entrepreneurial opportunities in rural areas and ultimately increase the total income of rural residents [29]. Based on this analysis, we propose the following hypothesis:

Hypothesis 2. 

Rural tourism development stimulates entrepreneurial spirit in rural areas and ultimately increases rural resident income.

2.2.3. The Driving Effect of Related Industrial Development

Rural tourism has a strong driving effect on related industries, and government intervention through appropriate measures can enhance this effect. Rural tourism relies on a wide range of natural and cultural resources, related infrastructure and facilities, as well as a supply of accommodation, food, beverages, and commodities [6]. The tourism industry is closely related to many other economic sectors, including agriculture and retail; therefore, the development of rural tourism may have positive external effects on other economic sectors [2]. Sharpley [7] noted that rural tourism can complement existing related industries in rural areas by providing more innovative entrepreneurial opportunities and revitalizing traditional industries. The development of rural tourism stimulates entrepreneurship in rural areas, creating more job opportunities and reducing seasonal fluctuations, thereby increasing resident income.

Rural tourism can facilitate the adjustment of industrial structures and promote industrial integration. Tourism development can change the regional industrial structure, release resources from underdeveloped industries [37], and drive the development of other industries. Rural tourism development facilitates industrial structural adjustments and promotes the overall development of primary, secondary, and tertiary industries, particularly by increasing the share of the service sector in labor income [38]. The development of rural tourism-related services also generates more revenue and jobs, ultimately increasing rural resident income. Based on this analysis, we propose the following hypothesis:

Hypothesis 3. 

The advancement of rural tourism catalyzes the growth of interconnected industries, thereby fostering an increase in rural resident income.

2.2.4. Resource Allocation Optimization Effect

The development of rural tourism faces various issues including income inequality and susceptibility to market failures and distortions. Effective government intervention possesses the capacity to effectively ameliorate resource allocation challenges and enhance production efficiency [39]. Promoting sustainable rural tourism development in underdeveloped rural areas is crucial for supporting economic and social development [40] and improving resource utilization efficiency. Rural tourism development integrates agriculture, the natural environment, and local culture, concentrating more resources in rural areas and optimizing spatial resource allocation [41]. Labor and capital productivity vary significantly across different labor sectors [42]. Rural tourism development can generate higher profits and labor efficiency relative to the agricultural sector. Providing rural tourism participants with the ability to utilize resources more efficiently improves production efficiency and leads to overall regional benefits [43].

Rural tourism development can attract talent accumulation [30], which can further promote the optimization of human resource allocation. Rural areas have a labor force with different skills as well as existing capital, land, and other assets, which can enter areas with higher production efficiency through the development of the tourism industry [7]. This, in turn, leads to an increase in total factor productivity in rural areas. An increase in total factor productivity implies optimized rural labor force allocation, enabling workers to receive income commensurate with their abilities and ultimately increasing rural resident income. Based on this analysis, we propose the following hypothesis:

Hypothesis 4. 

Rural tourism development enhances resource allocation, bolsters total factor productivity, and ultimately augments rural resident income.

The research framework is presented as follows (See Figure 1).

3. Policy Background, Empirical Design, and Data Sources

3.1. Policy Background

According to documents from Chinese government departments [44], rural areas possess unique agricultural resources and natural landscapes that form the foundation for leisure agriculture and rural tourism development; leisure agriculture and rural tourism development can transform agriculture from a singular food security function to a multifunctional sector encompassing raw material supply, income generation, ecological conservation, sightseeing and leisure, and cultural heritage preservation; this meets the needs of both urban and rural residents to connect with nature, understand agriculture, experience rural life, cultivate sentiments, and enjoy leisure and entertainment. By integrating agriculture with leisure and entertainment, encompassing “eating, lodging, traveling, shopping, and entertainment” in one package, it can break down the boundaries between primary, secondary, and tertiary industries; this, in turn, drives the development of related industries such as agricultural processing, services, transportation, construction, and culture, and promotes the adjustment and optimization of the rural industrial structure.

In this context, China initiated the “National Demonstration Counties for Leisure Agriculture and Rural Tourism” program for Leisure Agriculture Activities, with the fundamental goal of promoting rural employment and income growth. From the inception of these activities in 2010 up to 2017, eight batches, totaling 389 national-level demonstration counties, were officially announced. The demonstration counties for each batch are presented in Figure 2.

According to documents from Chinese government departments [44], China’s National Demonstration Counties for Leisure Agriculture and Rural Tourism follow the principle of voluntary application for autonomous creation. County-level agricultural administrative departments, in conjunction with tourism administrative authorities, apply for certification after a self-assessment. After a preliminary evaluation by provincial-level leisure agriculture and tourism administrative authorities, followed by competitive selection, the list is formally approved and published by the State Council’s agricultural and tourism administrative departments. The essential criteria for the selection of demonstration counties include scientific tourism planning, improved support policies, a well-established working system, standardized industry management, adequate infrastructure, prominent industrial advantages, and significant development results. The government continually adjusts the list of demonstration counties based on a dynamic assessment of rural tourism development. Consequently, these counties are motivated to develop rural tourism sustainably. This provides a quasi-natural experiment for this study.

3.2. Empirical Design

Despite growing recognition of the significance of quantitative ex post assessments in the field of tourism, there remains a conspicuous scarcity of robust and credible applications owing to the lack of trustworthy methodologies [20]. Moreover, previous empirical studies have often relied on methods such as Granger causality tests, which are less rigorous in identifying causal relationships. By contrast, quasi-experimental methods, including the Difference-in-Differences approach, provide a robust and credible means for policy evaluation, allowing for a more scientifically grounded identification of causal relationships. To better clarify the empirical strategy, we define the treatment group as counties that were included in the “National Demonstration Counties for Leisure Agriculture and Rural Tourism” program between 2010 and 2017. These counties received government-supported tourism interventions and were selected based on a formal process that involved voluntary application, provincial-level evaluation, and final approval at the national level. Counties that were not selected into the program during the same period serve as the control group. Both groups are observed from 2005 to 2021, enabling a balanced panel structure. To ensure internal validity, we excluded counties with significant missing data, administrative changes, or extreme outliers. This selection process—combined with the multi-year rollout of the program—creates a quasi-natural experimental setting suitable for Difference-in-Differences estimation. Therefore, to investigate the relationship between government intervention in rural tourism development and rural resident income, we constructed the following empirical model:

$${Income}_{it}={\beta }_0+{\beta }_1{{Treat}_{it}\ast Post}_{it}+{\beta }_{2}Control{s}_{it}+{\mu }_i+{\theta }_t+{\epsilon }_{it}$$

(1)

In this study, the dependent variable was represented as Income_it, which signifies the income of rural residents. It was measured using the “per capita disposable income of rural residents” from the county-level statistical yearbooks of China. Treat_it denotes the dummy variable indicating the counties or districts on the list of the “National Demonstration Counties for Leisure Agriculture and Rural Tourism” program. Its value is 1 if the county has implemented the program, 0 otherwise. Similarly, Post_it represents the dummy variable signifying the implementation of the aforementioned policy. Its value is 1 if the county has implemented the program, 0 otherwise. The primary focus of this study lies in the examination of the coefficient β₁ for the interaction term Treat_it ∗ Post_it. A positive β₁ signifies that government intervention in rural tourism development has a promoting effect on rural resident income. Controls_it encompasses the control variables, which capture potential impacts on rural resident income according to previous research [1,45,46]. These impacts may arise from local agricultural development levels, industrial structure, scale of government expenditure, financial development, investment intensity, population density, and local social service levels. Specifically, we employed the following indicators: the ratio of the primary sector to county-level GDP to measure agricultural development levels; the ratio of the secondary sector to county-level GDP to assess the industrial structure; the ratio of general budgetary expenditure to county-level GDP to measure the scale of government expenditure; the ratio of the balance of various loans from financial institutions at year-end to county-level GDP to represent financial development levels; the ratio of total social fixed asset investment to the population of the county to indicate investment intensity; the ratio of the county’s population to its area to account for population density; and the number of hospital beds per ten thousand people to measure medical and healthcare levels. Furthermore, μ_i and θ_t respectively represent individual fixed effects and time fixed effects.

3.3. Data Sources

The county-level data used in this study were sourced from the “China County-Level Statistical Yearbooks” from 2006 to 2022. Their commencement dates were obtained from the official website of the Ministry of Agriculture of China. Regarding the original data, we conducted the following procedures: (1) exclusion of county-level samples with a significant number of missing values; (2) application of a 1% two-sided winsorization to the data to mitigate the impact of outliers; and (3) removal of samples corresponding to county-level administrative region mergers from 2005 to 2022. This resulted in the selection of the final sample for the analysis.

Table 1. Descriptive statistics.

Variable	Symbol	Obs.	Mean	Std. Dev	Min	Max
Residents’ incomes	Income	26,299	0.942	0.553	0.156	2.934
${{Treat}_{it}\ast Post}_{it}$	DID	26,299	0.076	0.266	0	1
Agricultural development level	Agriculture	26,299	0.204	0.115	0.001	7.553
Industrial structure	Industry	26,299	0.433	0.151	0.013	2.554
Scale of government expenditure	Government	26,299	0.209	0.157	0.002	2.469
Financial development	Finance	26,299	0.604	0.387	0.003	76.348
Population density	Population	26,299	0.037	0.031	0.015	477.922
Medical and health level	Medical	26,299	32.867	17.251	0.475	247.391
Investment intensity	Investment	26,299	2.733	3.922	−3.720	6.978

presents the descriptive statistics.

4. Results

4.1. Baseline Regression

We conducted empirical tests on the relationship between government intervention in rural tourism development and rural resident income, as specified in empirical Model (1). The results of these tests are presented in

Table 2. Baseline regression results.

Dependent Variable	(1)	(2)	(3)	(4)
	Income	Income	Income	Income
DID	0.6263 ***	0.3056 ***	0.0935 ***	0.0855 ***
	(0.0149)	(0.0134)	(0.0211)	(0.0204)
Agriculture		−1.2891 ***		0.2674 **
		(0.0554)		(0.1215)
Industry		−1.0215 ***		−0.0730
		(0.0417)		(0.0795)
Government		−0.5684 ***		−0.4437 ***
		(0.0225)		(0.0705)
Finance		0.2673 ***		0.0611 ***
		(0.0127)		(0.0236)
Population		3.3910 ***		4.5432 **
		(0.1515)		(2.0119)
Medical		0.0106 ***		0.0014 **
		(0.0004)		(0.0005)
Investment		0.0438 ***		0.0184 ***
		(0.0022)		(0.0027)
Constant	0.9117 ***	1.0055 ***	0.3171 ***	0.1110
	(0.0038)	(0.0377)	(0.0071)	(0.0860)
County	NO	NO	YES	YES
Year	NO	NO	YES	YES
Observation	26,299	26,299	26,299	26,299
R2	0.0730	0.5468	0.8655	0.8783

Note: *** indicates 1% and ** indicates 5%.

. Columns (1) and (2) display the results excluding individual and time fixed effects, whereas columns (3) and (4) include these fixed effects. It is noteworthy that irrespective of the inclusion of individual or time fixed effects and the introduction of control variables, the coefficient for DID remained significantly positive at the 1% significance level. This signifies that government intervention in rural tourism development can increase rural residents’ income.

Regarding the control variables, as shown in column (4), the impact of agricultural development on rural residents’ income was significantly positive, whereas the impact of industrial structure on income was not statistically significant. This suggests that industrial restructuring may not translate directly into income gains for rural residents. Conversely, government expenditure exhibited a negative effect on income, which is potentially attributable to government spending competing with social investments and reducing employment opportunities, thereby hindering direct income accruals for rural residents.

The coefficient of the level of regional financial development was positive, suggesting that higher levels of financial development correspond to reduced financial constraints for rural residents and, consequently, greater income potential. Furthermore, the positive coefficient of population density indicates that counties with higher population densities are more conducive to industrial development, facilitating the transition of rural residents from agriculture to other sectors, thus leading to increased income for rural residents. Finally, the positive coefficients of medical and healthcare levels underscore the fact that higher healthcare standards empower rural residents to maintain better health, resulting in greater income opportunities. Consequently, Hypothesis 1 is validated, which is consistent with Lane (1994) [3].

4.2. Robustness Checks

4.2.1. Parallel Trends Test

The parallel trends test constitutes a pivotal step in constructing the Difference-in-Differences model and serves as a prerequisite for its adoption [47]. Given that the policy of the “National Demonstration Counties for Leisure Agriculture and Rural Tourism” program spans multiple periods, we conducted a parallel trends test following the methodology employed by Beck et al. (2010) [47].

The results of the parallel trends test are shown in Figure 3. In the three periods preceding the policy implementation, no significant differences were observed between the control and experimental groups. However, after the policy was implemented, a noticeable discrepancy emerged between the two groups, thereby passing the parallel trend test.

4.2.2. Placebo Test

To validate that the impact of government intervention in rural tourism development on rural resident income is not a result of other random factors, this study employed a placebo test to examine the fortuitous nature of rural tourism development. We randomly selected 500 instances of initiation years and regions for rural tourism development and generated “pseudo-policy dummy variables” for the baseline regression. The estimated coefficients obtained from the pseudo-policy dummy variables are illustrated in Figure 4.

It is evident that nearly all the coefficients of the “pseudo-policy dummy variables” were distributed around zero, concentrated within the interval of −0.05 to 0.05. These values were significantly lower than those obtained from the baseline regression. This observation signifies that the impact of government intervention in rural tourism development on rural resident income is not the result of other random factors.

4.2.3. Replacement of Policy Variables

The longer a policy’s duration, the more enduring its potential impact. Consequently, we replaced the original binary policy variable (0, 1) with a variable representing the duration of policy implementation based on the specific start time of each county’s policy. For instance, if the policy has been implemented for one year, the variable takes the value of 1; if implemented for two years, it takes the value of 2, and so on. This newly constructed variable is Difference-in-Differences terms for alternative policies, denoted DID2, and the regression results are presented in columns (1) and (2) of

Table 3. Robustness test.

Dependent Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Income	Income	Income	Income	Income	Income	Income	Income
DID					0.0931 ***	0.0850 ***	0.0853 **	0.0752 ***
					(0.0211)	(0.0203)	(0.0186)	(0.0176)
DID2	0.0199 ***	0.0175 ***
	(0.0041)	(0.0040)
LDID			0.0904 ***	0.0848 ***
			(0.0203)	(0.0197)
ECDID					0.1831 ***	0.1770 ***
					(0.0208)	(0.0210)
Agriculture		0.2602 **		0.2141 *		0.2847 **		0.2927 **
		(0.1216)		(0.1223)		(0.1215)		(0.1234)
Industry		−0.0725		−0.0476		−0.0662		−0.1204
		(0.0796)		(0.0731)		(0.0791)		(0.0882)
Government		−0.4411 ***		−0.3371 ***		−0.4458 ***		−0.4567 ***
		(0.0703)		(0.0642)		(0.0708)		(0.0711)
Finance		0.0602 **		0.0457 **		0.0615 ***		0.0267
		(0.0236)		(0.0221)		(0.0236)		(0.0228)
Population		4.3779 **		4.4123 **		4.5301 **		3.6083 **
		(2.0181)		(1.9128)		(2.0090)		(1.8338)
Medical		0.0014 **		0.0013 **		0.0013 **		0.0016 ***
		(0.0005)		(0.0005)		(0.0005)		(0.0005)
Investment		0.0183 ***		0.0176 ***		0.0184 ***		0.0219 ***
		(0.0027)		(0.0026)		(0.0027)		(0.0031)
Constant	0.3171 ***	0.1185	0.3543 ***	0.1573 *	0.3017 ***	0.0902	0.3171 ***	0.1673 *
	(0.0071)	(0.0863)	(0.0068)	(0.0866)	(0.0077)	(0.0859)	(0.0063)	(0.0858)
County	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observation	26,299	26,299	24,752.0000	24,752.0000	26,299	26,299	23,205.0000	23,205.0000
R2	0.8661	0.8787	0.8689	0.8797	0.8661	0.8789	0.8561	0.8729

Note: *** indicates 1%, ** indicates 5% and * indicates 10%.

. Remarkably, even after replacing the policy variable with the duration variable, the coefficient remained significantly positive at the 1% significance level. Table 3 presents the regression results after replacing the policy variables.

4.2.4. Use of Lagged Policy Variables

Recognizing that policies may require some time to take effect, we introduced a one-period lag in the original interaction term, named the lagged term of Difference-in-Differences, denoted as LDID. Subsequently, we conducted a regression analysis, the results of which are presented in columns (3) and (4) of Table 3. Even with the inclusion of the lagged policy variables, the coefficient remains significantly positive at the 1% significance level.

4.2.5. Removal of Interfering Policies

Between 2005 and 2021, in addition to the policies mentioned in this study, several nationwide policies could potentially have affected rural resident income. Among these, the e-commerce pilot policy emerged as a notable influence. Similar to the construction of interaction terms of previous DID, we constructed interaction terms using the e-commerce pilot policy, named the Difference-in-Differences term of e-commerce pilot policy, denoted as ECDID. We then conducted another empirical test, the results of which are displayed in columns (5) and (6) of Table 3. The coefficient of DID remains significantly positive at the 1% level even after accounting for the e-commerce pilot policy.

4.2.6. Sample Adjustment

Major public crises can disrupt industrial development. In 2020, the global economy was significantly affected by the COVID-19 pandemic, affecting various sectors. Therefore, we excluded samples from 2020 and 2021 and re-conducted the regression analysis. The results in columns (7) and (8) of Table 3 still reveal a significantly positive coefficient for DID at the 1% significance level. This further validates our hypotheses.

5. Mechanism Testing

5.1. The Test of Entrepreneurial Spirit Stimulus Effect

The tourism industry can stimulate entrepreneurial development in related regions, and regional entrepreneurial activities are crucial for increasing rural resident income. To measure the promotion of entrepreneurial activity in regions through rural tourism development, we used data from Tianyancha’s official website to compile statistics on newly registered firms from 2005 to 2021 to measure entrepreneurship, drawing on Youssef et al. (2018) [48], which were then matched to various counties and districts. We classified these enterprises according to the National Economic Industry Classification and Code of China. The industries most directly associated with rural tourism development include accommodation and catering services; cultural, sports, and entertainment services; and healthcare and social work services. We denoted these as Industry ACS, CSE, and HSW, respectively. Rural tourism development relies on local public facilities, and the agricultural development technologies required for rural tourism development fall under scientific research and technical services. Therefore, we also tallied firms in the “water conservation, environmental, and public facility management industry” and the “scientific research and technical services industry”, denoted as Industry WCE and PFM, respectively.

We measured the entrepreneurial spirit stimulus effect facilitated by rural tourism development by tracking the annual increase in firms in these five industries. The empirical results, shown in columns (1) to (5) of

Table 4. Results of entrepreneurial spirit stimulus effect.

Dependent Variable	(1)	(2)	(3)	(4)	(5)
	ACS	CSE	HSW	WCE	PFM
DID	38.1514 **	14.0284 ***	3.8541 ***	3.4337 ***	59.7239 ***
	(19.3574)	(4.4189)	(1.2083)	(1.2364)	(21.2980)
Agriculture	−57.0496	13.6771	15.9514 ***	1.5568	112.6548
	(113.9560)	(12.8582)	(4.1215)	(2.2044)	(68.7061)
Industry	72.9943	0.3138	2.2479	2.1862	−8.3021
	(66.4953)	(8.3391)	(2.1161)	(1.4207)	(48.8640)
Government	−544.9981 ***	−23.2914 ***	−4.5547 ***	−4.6707 ***	−134.5241 ***
	(57.1531)	(5.7757)	(1.4260)	(1.0631)	(23.9784)
Finance	161.2521 ***	11.7707 ***	3.3156 ***	1.5203 ***	44.3509 ***
	(17.5450)	(2.9056)	(0.7848)	(0.4210)	(14.1997)
Population	26,693.4407 ***	2006.7086 ***	418.7083 ***	209.9881 ***	8672.3668 ***
	(1103.5352)	(373.4167)	(114.3069)	(46.3950)	(2601.2601)
Medical	3.6777 ***	0.2588 ***	0.0260	0.0253 ***	0.7436 *
	(0.5231)	(0.0774)	(0.0194)	(0.0093)	(0.3888)
Investment	11.0545 ***	1.0458 ***	0.1108	0.1149 ***	4.9924 ***
	(1.4771)	(0.2649)	(0.0844)	(0.0409)	(1.6351)
Constant	−930.7052 ***	−70.0548 ***	−17.3724 ***	−7.6340 ***	−339.4222 ***
	(70.6561)	(15.0617)	(4.6616)	(1.9960)	(107.7589)
County	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes
Observation	26,146.0000	26,146.0000	26,146.0000	26,146.0000	26,146.0000
R2	0.2016	0.1944	0.2101	0.1697	0.2012

Note: *** indicates 1%, ** indicates 5% and * indicates 10%.

, reveal that government intervention in rural tourism development fosters entrepreneurial activity in these industries. This is evident from the increase in the number of newly registered firms in these sectors. Consequently, this expansion in entrepreneurship offers rural residents the potential for employment creation and income growth. Thus, rural tourism development can stimulate an entrepreneurial spirit in rural areas and ultimately boost rural resident income. Consequently, Hypothesis 2 is validated, which is consistent with McAreavey and McDonagh (2011) [4]. Table 4 presents the results of the empirical analysis.

5.2. The Test of Driving Effect of Related Industrial Development

To examine the driving effect of related industrial development on rural tourism development, we measured the development of primary, secondary, and tertiary industries in terms of total and per capita output. We aimed to reveal the role of rural tourism by studying its impact on the outputs of these three industries. The empirical results are presented in

Table 5. Results of the driving effect of related industrial development.

Dependent Variable	(1)	(2)	(3)	(4)	(5)	(6)
	TOF	TOS	TOT	PTOF	PTOS	PTOT
DID	1.2117 *	13.6892 **	14.3182 ***	0.0216	0.2085 ***	0.2119 ***
	(0.7048)	(5.3901)	(5.0350)	(0.0147)	(0.0732)	(0.0527)
Agriculture	25.6100 ***	82.0432 ***	−118.1579 ***	1.2010 ***	2.7941 ***	−1.6436 ***
	(3.7789)	(30.9050)	(28.6389)	(0.1291)	(0.6724)	(0.4227)
Industry	10.7805 ***	152.1838 ***	−141.2654 ***	0.3492 ***	4.3631 ***	−2.4094 ***
	(2.0295)	(19.7892)	(20.9968)	(0.0596)	(0.5102)	(0.3172)
Government	−16.7543 ***	−149.9002 ***	−147.4572 ***	−0.2254 ***	−2.6096 ***	−2.0281 ***
	(2.5187)	(21.1267)	(21.9614)	(0.0385)	(0.3723)	(0.2732)
Finance	−2.7162 ***	−13.6321 ***	5.9028	−0.0916 ***	−0.3464 ***	−0.0344
	(0.4712)	(3.7595)	(7.8942)	(0.0195)	(0.0790)	(0.0880)
Population	−18.9496	2059.2480 ***	3128.9757 ***	−8.0013 ***	−2.6497	8.6752
	(36.9989)	(673.0623)	(1008.7215)	(1.2304)	(5.7988)	(9.1496)
Medical	0.0519 ***	0.6627 ***	0.8716 ***	0.0007	0.0086 ***	0.0114 ***
	(0.0158)	(0.1379)	(0.1729)	(0.0006)	(0.0024)	(0.0022)
Investment	−0.2185 ***	4.0501 ***	3.3675 ***	0.0018	0.1536 ***	0.1011 ***
	(0.0515)	(0.6796)	(0.6343)	(0.0017)	(0.0192)	(0.0105)
Constant	2.8062	−117.5736 ***	−4.5946	0.0788	−1.6564 ***	1.4656 ***
	(2.2421)	(30.7048)	(39.1516)	(0.0814)	(0.4736)	(0.4095)
County	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
Observation	26,299	26,299	26,299	26,299	26,299	26,299
R2	0.6172	0.3768	0.4351	0.5843	0.4858	0.6247

Note: *** indicates 1%, ** indicates 5% and * indicates 10%.

. Columns (1) to (6) represent the dependent variables, which are the total output of the first industry (TOF), the total output of the second industry (TOS), the total output of the third industry (TOT), per capita total output of the first industry (PTOF), per capita total output of the second industry (PTOS), and per capita total output of the third industry (PTOT). The findings indicate that, except for the per capita total output of the first industry, rural tourism development significantly promotes the other variables. Notably, the effects on the development of the second and third industries were substantially greater than those on the first industry. This suggests that rural tourism development facilitates the extension of the industrial chain, drives the growth of related industries, and ultimately leads to an increase in rural resident income. Thus, Hypothesis 3 is confirmed. This result contradicts the findings of Sokhanvar et al. (2018) and Shahbaz et al. (2018), indicating that rural tourism development can stimulate regional economic growth [49,50].

5.3. The Test of Resource Allocation Optimization Effect

Total factor productivity is a crucial indicator of resource allocation efficiency. The commonly used methods for measuring regional total factor productivity include the Levinsohn-Petrin and Olley-Pakes methods [51]. Based on labor, human capital, and final output data for each county, we calculated the total factor productivity values for various regions using both the Levinsohn–Petrin and Olley–Pakes methods, denoted as LPTFP and OPTFP, respectively. Higher values signify greater total factor productivity. Columns (1) and (2) of

Table 6. Results of resource allocation optimization effect.

Dependent Variable	(1)	(2)
	LPTFP	OPTFP
DID	0.0259 **	0.0258 **
	(0.0123)	(0.0123)
Agriculture	−1.3929 ***	−1.3969 ***
	(0.1442)	(0.1442)
Industry	0.5019 ***	0.5025 ***
	(0.1037)	(0.1038)
Government	−0.7930 ***	−0.7918 ***
	(0.0807)	(0.0806)
Finance	−0.2366 ***	−0.2361 ***
	(0.0199)	(0.0199)
Population	−0.4865	−0.4576
	(0.9280)	(0.9253)
Medical	0.0005	0.0006
	(0.0003)	(0.0003)
Investment	0.0115 ***	0.0115 ***
	(0.0020)	(0.0020)
Constant	2.7535 ***	2.8205 ***
	(0.0929)	(0.0929)
County	Yes	Yes
Year	Yes	Yes
Observation	26,299	26,299
R2	0.4848	0.4843

Note: *** indicates 1% and ** indicates 5%.

indicate that rural tourism development significantly enhances county-level total factor productivity, ultimately increasing rural resident income. This result confirms Hypothesis 4, which is consistent with Fleischer and Tchetchik (2005) [43]. Compared to developed countries, China’s total factor productivity remains relatively low, with significant regional disparities. This suggests that rural tourism plays a crucial role in improving resource allocation efficiency in rural areas. Furthermore, rural tourism development contributes to reducing the total factor productivity disparity between urban and rural areas.

6. Heterogeneity Analysis

6.1. Economic Development Disparities

Substantial differences exist in the economic development bases among counties. We measured the economic development levels of various regions using per capita GDP. Counties with a per capita GDP higher than the median were classified as high-economic-development counties, whereas those below the median were classified as low-economic-development counties. The regression results are presented in columns (1) and (2) of

Table 7. Results of the heterogeneity analysis.

Dependent Variable	Economic Development Disparities		Agricultural Development Disparities		Topographical Disparities
	High	Low	High	Low	High	Low
	(1)	(2)	(3)	(4)	(5)	(6)
	Income	Income	Income	Income	Income	Income
DID	0.0658 **	0.0153	0.0799 ***	0.0548 **	0.0815 ***	0.0992 ***
	(0.0279)	(0.0167)	(0.0270)	(0.0239)	(0.0192)	(0.0362)
Agriculture	0.1989 ***	0.0545 ***	0.0927 ***	0.1968 ***	0.1508 ***	0.1203 ***
	(0.0365)	(0.0165)	(0.0251)	(0.0257)	(0.0268)	(0.0295)
Industry	0.1695	−0.0725	0.1607	0.5927 ***	0.3188 ***	0.5753 ***
	(0.2877)	(0.0695)	(0.1886)	(0.1770)	(0.1137)	(0.2217)
Government	0.0469	0.0158	−0.0467	0.0080	−0.0342	0.0301
	(0.1595)	(0.0411)	(0.1330)	(0.0890)	(0.0575)	(0.1699)
Finance	−0.4611 ***	−0.0847 **	−0.5973 ***	−0.3154 ***	−0.2117 ***	−1.2385 ***
	(0.1427)	(0.0352)	(0.0981)	(0.0670)	(0.0503)	(0.1579)
Population	0.1982 ***	−0.0033	0.0479	0.0306 *	0.0208	0.1082 **
	(0.0533)	(0.0108)	(0.0299)	(0.0178)	(0.0173)	(0.0423)
Medical	2.0042	8.8713 ***	−0.8625	5.5049 ***	7.5570 ***	0.8946
	(2.1625)	(1.2737)	(3.0458)	(1.7150)	(2.8632)	(2.3723)
Investment	0.0024 ***	0.0000	0.0007	0.0014 **	0.0004	0.0029 ***
	(0.0008)	(0.0004)	(0.0007)	(0.0007)	(0.0005)	(0.0010)
Constant	0.0122 ***	−0.0019	0.0149 ***	0.0206 ***	0.0144 ***	0.0265 ***
	(0.0028)	(0.0029)	(0.0029)	(0.0061)	(0.0030)	(0.0054)
County	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
Observation	13,158.0000	13,141.0000	13,158.0000	13,141.0000	14,059.0000	12,240.0000
R2	0.8800	0.9378	0.8819	0.8874	0.9013	0.8846

Note: *** indicates 1%; ** indicates 5%; * indicates 10%.

. Evidently, the coefficients of DID were positive in both cases. However, the impact of rural tourism development on rural residents’ income was significantly greater in counties with higher levels of economic development. While rural tourism development positively contributes to rural resident income in both cases, it is more pronounced in counties with higher levels of economic development. This finding suggests that the income gap between the two groups could widen in the future.

6.2. Agricultural Development Disparities

The development of leisure agriculture and rural tourism is directly related to regional agricultural development levels. Differences in agricultural development levels may influence the impact of rural tourism development on rural residents. We measured the regional agricultural development levels using the per capita value added in the primary industry. Counties with per capita value-added higher than the median were categorized as high-agricultural-development counties, whereas those below the median were categorized as low-agricultural-development counties. The group regression results are shown in columns (3) and (4) of Table 7. The coefficients of DID were significantly positive in both cases. However, rural tourism development had a more pronounced effect on rural residents’ income in counties with higher levels of agricultural development.

6.3. Topographical Disparities

Geographical variation can affect the development of leisure agriculture and rural tourism, thereby influencing their impact on rural residents. We used a topographical relief index to measure the effects of geographical disparities. Counties were divided into high- and low-topographical relief groups based on the median relief index. The regression results are presented in columns (5) and (6) of Table 7. The coefficients of DID were significantly positive in both cases. However, compared with counties with high topographical relief, rural tourism development had a more noticeable impact on increasing rural residents’ income in counties with lower topographical relief.

7. Further Research

7.1. Impact on Urban–Rural Disparities

Folarin and Adeniyi (2020) argued that tourism contributes to poverty reduction [52]. While the tourism industry has succeeded in some countries, it has not uniformly reduced income inequality globally [46]. Zhang et al. (2021) found that higher levels of tourism industry development can narrow urban–rural income disparities [45]. However, the relationship between government intervention in rural tourism development and urban–rural income inequality remains unclear. We used the ratio of rural per capita income to the income of urban workers in the district under the jurisdiction of the city-level administrative unit as a measure of urban–rural income development, denoted as “gap”. A larger gap value signifies a smaller urban–rural income disparity. The results are presented in columns (1) and (2) of

Table 8. Results of the additional analyses.

Dependent Variable	(1)	(2)	(3)	(4)
	Gap	Gap	Share	Share
DID	−0.0070 *	−0.0111	−0.0099 *	−0.0111 **
	(0.0042)	(0.0074)	(0.0057)	(0.0045)
Agriculture		0.0386		0.5135 ***
		(0.1000)		(0.0467)
Industry		0.0915		−0.2084 ***
		(0.0613)		(0.0313)
Government		−0.0676		0.2917 ***
		(0.0780)		(0.0335)
Finance		0.1451		0.0498 ***
		(0.1285)		(0.0072)
Population		−0.3552		2.5207 ***
		(0.6236)		(0.4265)
Medical		0.0009 **		−0.0006 ***
		(0.0004)		(0.0001)
Investment		0.0012		−0.0025 ***
		(0.0008)		(0.0005)
Constant	0.2034 ***	0.0924	0.4029 ***	0.2124 ***
	(0.0028)	(0.0568)	(0.0025)	(0.0308)
County	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Observation	26,299	26,299	26,299	26,299
R2	0.0054	0.0262	0.1659	0.4885

Note: *** indicates 1%, ** indicates 5% and * indicates 10%.

. While the coefficient for DID was no longer statistically significant after controlling for variables, the negative coefficient suggests that government intervention in rural tourism development may reduce urban–rural income disparities, albeit not significantly.

7.2. Impact on Labor Income Share

Extensive empirical research has documented a continuous decline in labor income share relative to total output [53,54]. Can government intervention in rural tourism development alter this trend? We used the ratio of per capita income to per capita GDP as a measure of labor income share, denoted as “Share”. A higher share value indicates a larger share of labor income. We used this as the dependent variable in our regression analysis. The results are presented in columns (3) and (4) of Table 8. The DID coefficient was significantly negative, indicating that rural tourism development exacerbates the decline in the income share of rural residents. Their income growth has remained lower than the GDP growth rates. This underscores the need for future development efforts to not only focus on increasing rural residents’ income, but also to further optimize income distribution structures and moderately expand the income share of rural residents.

8. Conclusions and Discussion

The primary objective of this study was to causally evaluate the effect of government intervention in rural tourism development on rural residents’ income. Our findings show that such intervention significantly increases rural income, and this effect remains robust across multiple empirical tests. Mechanism analysis revealed three key pathways through which the intervention operates: stimulating entrepreneurship, fostering related industrial development, and improving total factor productivity. Heterogeneity analysis further indicated that the income-enhancing effect was stronger in counties with better economic and agricultural foundations and more favorable topography.

These results support the view that government intervention plays a critical role in promoting rural income, aligning with He et al. (2021) [55], but contrasting with Kalvelage et al. (2021), who argue that policy-driven tourism mainly benefits a limited group [56]. They also directly respond to the concerns of Hwang and Lee (2015) and Mei et al. (2015) regarding the limited effectiveness of such interventions [20,24].

However, the intervention did not significantly reduce the urban–rural income gap or reverse the decline in labor income share. This contrasts with findings from Wang and Tziamalis (2023) [57] and Wattanakuljarus and Coxhead (2008) [12], who highlight tourism’s potential to reduce or exacerbate income inequality.

Overall, while the policy improved income levels, it fell short in addressing structural disparities. Relative to similar efforts in other developing countries, the program appears more effective. To enhance equity outcomes, governments should adopt more targeted approaches, such as those proposed by Blake et al. (2008), which advocate channeling more tourism income toward the poorest segments of the population [58].

8.1. Theoretical Implications

Our study sheds light on the causal impact of government intervention in rural tourism development on rural residents’ income—an area rarely explored with empirical rigor, particularly in developing countries.

First, we provide robust evidence that such intervention significantly increases rural income, using a quasi-natural experiment based on China’s “National Demonstration Counties for Leisure Agriculture and Rural Tourism” program. Prior studies have been limited by descriptive methods and data constraints, rarely assessing the effect of state-led tourism programs [20,24]. Our findings directly address doubts about the effectiveness of government intervention and support its potential to reduce rural income disparities, as highlighted by Zhang et al. (2021) [45], thereby contributing to the achievement of the UN’s SDGs [59].

Second, we contribute to mechanism-based theory by identifying how government intervention operates through three channels: stimulating entrepreneurship [2,31,32], promoting related industries [6,7], and optimizing resource allocation [39,43]. These pathways explain how tourism policies can foster not only income growth but also rural economic transformation—dimensions often overlooked in previous research [15].

Third, by employing a multi-period Difference-in-Differences approach, we enhance methodological rigor in this domain. Unlike earlier work based on qualitative or micro-level analyses [60,61], our macro-level study offers generalizable insights on tourism’s economic impact. This approach also addresses endogeneity concerns and yields more credible estimates [62].

In sum, the study fills key research gaps by establishing causal links, revealing transmission mechanisms, and examining broader distributive outcomes—particularly the urban–rural income gap and labor income share.

8.2. Practical Implications

Our findings offer important implications for rural tourism policy in China and other developing countries.

First, this study supports further expansion of the “National Demonstration Counties for Leisure Agriculture and Rural Tourism” program nationwide. The evidence shows that sustained government intervention has effectively raised rural incomes by addressing market failures and mitigating rural marginalization. Developing countries should adopt similar policies promptly to improve rural livelihoods. Efforts should focus on integrated tourism development through improved planning, infrastructure, industrial integration, service quality, and innovative marketing. However, the continued decline in rural labor income share remains a concern, requiring policy attention to optimize income distribution and expand the labor share in rural economies.

Second, our analysis highlights that government intervention promotes entrepreneurship, drives related industries, and enhances resource allocation. Local governments should cultivate an enabling environment for innovation by supporting the coordinated development of tourism-linked sectors and improving the spatial efficiency of resource allocation to maximize policy impact.

Finally, this study provides broader lessons for countries facing acute rural challenges such as income inequality, population aging, and community decline. Proactive government involvement in rural tourism can serve as a key strategy to address these issues. Central governments should develop evidence-based tourism policies by conducting rigorous research, encouraging entrepreneurship, strengthening regional cooperation, and advancing sustainable tourism development.

8.3. Limitations and Future Research

Nevertheless, this study has certain limitations, and further research is warranted. Kumail et al. (2023) found an inverse U-shaped relationship between the tourism industry and income inequality [63]. Because we employed the Difference-in-Differences method, we were unable to conduct further examinations in this regard. Blake et al. (2008) and Incera and Fernández (2015) discovered that, although increased tourism development benefits impoverished households, middle- and high-income households reap the greatest rewards, potentially exacerbating income disparities [58,64]. However, owing to the limitations of county-level data, we were unable to further investigate the existence of these phenomena. In future research, we intend to use microdata to examine the impact of government interventions on rural tourism development in various income groups.