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https://doi.org/10.56556/gssr.v4i1.1181 

                                                                  
 
 

Global Scientific Research                    37 
 

RESEARCH ARTICLE  

The Sustainability Impact of Tourism Industry Agglomeration on Regional 

Economic Gap 

Guo Junhui1*，Chen Qihao1 

 
1School of economics and management, Zhejiang University of Science and Technology, China 

 

Corresponding Author: GUO Junhui. Email: guojunhui@zust.edu.cn; Chen Qihao. Email: 631356141@qq.com 

Received: 15 January, 2025, Accepted: 18 March, 2025, Published: 21 March, 2025 

 

Abstract 

Tourism has become a key driver of economic growth and regional development, yet its impact on economic 

disparities and sustainability remains underexplored. Against the backdrop of global efforts to achieve 

sustainable development, this study investigates the role of tourism industry agglomeration in addressing 

regional economic disparities, with a focus on the moderating effects of transport and information infrastructure. 

Using panel data from 31 Chinese provinces and cities spanning 2004 to 2020, we integrate transport and 

information infrastructure into a research framework to examine the relationship between tourism industry 

agglomeration and regional economic disparities. Through multiple econometric methods, we empirically 

analyze the influence of tourism industry concentration density on regional economic gaps and explore the 

underlying mechanisms. Our findings indicate that tourism industry agglomeration significantly reduces regional 

economic disparities, while transport and information infrastructure play a crucial moderating role, albeit with 

regional and temporal heterogeneity. Furthermore, the relationship between tourism agglomeration and 

economic disparities, as well as the role of infrastructure, exhibits nonlinear characteristics, suggesting complex 

interactions. Importantly, the study underscores the potential of sustainable tourism practices in fostering long-

term economic balance and environmental preservation, aligning with global sustainability goals. These insights 

provide valuable references for policymakers aiming to narrow economic gaps and promote coordinated regional 

development through sustainable tourism strategies. 

 

Keywords: Tourism Cluster; Economic Gap; Infrastructure Development; Moderating Effect; Threshold Effect 

Introduction

As modernization progresses, the growing discrepancy in economic development between regions has become 

increasingly noticeable. In particular, China has demonstrated a marked concern for imbalanced regional 

development. In March 2021, the Outline of the People’s Republic of China 14th Five-Year Plan for National 

Economic and Social Development and Long-Range Objectives for 2035 issued by the Thirteenth National 

People’s Congress, clearly emphasized the need to actively bridge the economic gap between regions. 

Unraveling how to alleviate these economic disparities presents a significant challenge for China to achieve its 

goal of common prosperity. The tourism industry has emerged as a key driver of economic growth, stimulating 



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consumer spending and creating job opportunities. According to the Basic Situation of the 2019 Tourism Market 

released by the Ministry of Culture and Tourism, the tourism industry’s comprehensive contribution to the 

national economy reached 10.94 trillion yuan, with a contribution rate as high as 11.05%. Furthermore, 

employment generated by the tourism industry and related sectors accounted for 10.31% of the total national 

employment, underscoring its pivotal role in propelling the national economy. However, the economic effects 

of tourism are often underestimated due to the increasing overlap between tourism activities and everyday 

life. This convergence has led scholars to overlook the potential of tourism in alleviating regional economic 

disparities. Tourism industry agglomeration, characterized by the spatial concentration of tourism-related 

activities, is considered a dynamic form of spatial organization and a critical aspect of industry development 

(Wang et al., 2020).Yet, its impact on regional economic disparities remains underexplored.                                                                                                                   

Existing literature has largely ignored the role of transport and information infrastructure in the relationship 

between tourism and regional economic disparities. While studies have shown that transport and information 

infrastructure significantly influence both the tourism industry and regional economies, their moderating effects 

on the impact of tourism agglomeration on regional disparities have not been adequately addressed. (Liu Zhen 

et al., 2022; Cheng Yu et al., 2023; Sun et al., 2019).This omission may lead to biased conclusions and an 

incomplete understanding of the mechanisms at play. Additionally, previous studies have primarily relied on 

specialized or total tourism revenue indicators to measure tourism development, neglecting spatial disparities 

and the strong industry correlation inherent in tourism(Li & Zhang, 2023; Wang & Chen, 2023; Smith & Brown, 

2024; Garcia & Lee, 2023). Furthermore, there is a lack of empirical discussion on the heterogeneous impacts 

of tourism agglomeration and infrastructure on regional disparities. 

To address these gaps, this study investigates the impact of tourism industry agglomeration on regional economic 

disparities, incorporating transport and information infrastructure as moderating factors. Using panel data from 

31 Chinese provinces and cities (2004–2020), the study aims to: (1) explore whether tourism agglomeration 

mitigates regional economic disparities; (2) examine the moderating effects of transport and information 

infrastructure, both individually and jointly, on this relationship; and (3) analyze the potential nonlinear 

characteristics of these effects. By measuring tourism agglomeration from a spatial perspective and considering 

the combined effects of infrastructure, this study provides a more comprehensive understanding of the 

mechanisms driving regional economic disparities. 

 

Literature 

 

The Impact of Tourism Industry Agglomeration on Regional Economic Disparities 

 

Direct Impact Effects 

 

Tourism industry agglomeration has direct effects on regional economic disparities. First, tourism activities 

generate direct income effects, as tourist expenditures during trips translate into local tourism revenues. The 

increased concentration of the regional tourism industry can further stimulate tourism income, thereby directly 

promoting economic growth in underdeveloped areas and alleviating regional economic disparities. Second, 

tourism activities engender wealth-transfer effects. Tourist activities involve interregional mobility due to the 

touristic flow effect, leading to wealth transfer and income redistribution between regions. This fosters economic 

development in tourism destinations (Soukiazis et al., 2008). Additionally, the income earned by tourism industry 

employees, when used to purchase goods for sustaining livelihoods, similarly generates wealth transfer and 

income redistribution, thereby promoting local economic development(Wu,2011). Consequently, the 



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development of the tourism industry can expand both tourism demand and supply, thereby promoting wealth 

distribution and propelling local economic effects to narrow the economic gap with other regions. Finally, the 

tourism industry exhibits industry-linkage effects. Apart from tourism products, there are basic industries 

associated with obtaining these products, namely industries related to tourism, such as agriculture, animal 

husbandry, construction, and food manufacturing (Wu,2012). Demand for these industries can stimulate the 

development of corresponding industries, thereby driving economic growth. With the heightened level of 

tourism industry agglomeration, the increased demand for associated industries not only promotes the upgrading 

of the tourism industry chain, but also caters to dynamic changes and diversified tourism demands, thereby 

mitigating economic disparities with other regions. 

 

Indirect Impact Effects 

 

Tourism industry agglomeration indirectly influences regional economic disparities. First, interactions among  

laborers give rise to knowledge spillover effects (Wang Jiaying et al., 2021) . As the tourism industry develops,  

the increased number and higher professional standards of tourism industry workers can enhance knowledge 

spillover effects, thereby promoting innovation in tourism products and refining enterprise management 

practices. This fosters the competitiveness and attractiveness of tourism products, further propelling economic 

growth to alleviate regional economic disparities. Second, as the level of tourism industry agglomeration 

increases, the demand for tourism activities also increases, leading to intensified competition among tourism 

products, thereby creating a competitive effect. Competition serves as a significant driver of product innovation, 

fostering the enhancement of tourism product attractiveness and exerting a significant promotional effect on the 

economy, thus reducing economic disparities with other regions (Wang et al., 2020) . Finally, the tourism 

industry’s tourist flow effect, where multi-destination tourists may choose multiple destinations in one trip, leads 

to factor mobility effects. The development of the tourism industry has promoted increased demand, guiding 

underutilized or mismatched labor and capital resources to flow into the tourism industry. This further enhances 

product quality and meets the demands for tourism while promoting local economic development, thereby easing 

uneven development among regions. Therefore, based on the aforementioned analysis, the following hypothesis 

is proposed: 

 

H1: Tourism industry agglomeration has a negative impact on regional economic disparities. 

 

The Influence Mechanism of Tourism Industry Agglomeration on Regional Economic Disparities 

 

Initially, the enhancement of local informatization and transport accessibility levels promotes the mobility and 

distribution efficiency of labor and capital resources (Xie Kai et al., 2023; Wang Chong et al., 2023) . This 

facilitates the rational allocation and utilization of idle, mismatched, or surplus resources, fostering the 

development of not only the tourism industry, but also related industries, thereby stimulating local economic 

effects and mitigating economic disparities with other regions. Additionally, the improvement of local transport 

accessibility and reduction of information barriers alleviates the search and transport costs for tourists and labor, 

contributing to the rational utilization and development of tourism resources while reducing local tourism losses. 

This, in turn, fosters the development of local tourism and related industries, thereby mitigating regional 

economic disparities. Lastly, the advancement of informatization and transport accessibility “compresses” time 



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and space, thereby reducing the transport costs of tourism products and raw materials (Huang et al., 2021)  and 

fostering innovation in tourism products (Ji et al., 2023) . This results in an upgrade of tourism products in terms 

of quality, variety, and price, enhancing their competitiveness and attractiveness to tourists, further promoting 

economic growth, and alleviating regional economic disparities. In conclusion, the advancement of regional 

transport or information infrastructure progressively breaks down “information barriers,” enhances transport 

convenience, and stimulates local tourism development, to a certain extent mitigating uneven economic 

development among regions. 

According to the New Economic Geography theory, industries undergoing spatial agglomeration are influenced 

by both centripetal and centrifugal forces (Fujita et al., 2001) . Given the interconnected and spatially cohesive 

nature of the tourism industry, its development is subject to the influence of both centripetal and centrifugal 

forces (Wang et al., 2019) . When the level of transport or information infrastructure is low, constraints on local 

tourism development arise owing to inadequate transport connectivity or information accessibility. Therefore, 

lower levels of informatization or transport convenience exert centrifugal force on local tourism development. 

Additionally, in economically developed regions, the richness of tourism products, sophistication of 

infrastructure and tourism industry chains, abundance of resource reserves, and higher professional skills of the 

workforce make these areas more attractive to tourists, thus limiting the development of tourism in less 

economically developed regions. Consequently, deficiencies in aspects such as tourism products, infrastructure, 

resource elements, and labor skills exert centrifugal forces on local tourism development. As the level of 

informatization or transport convenience increases, the gradual dismantling of the "digital divide" and enhanced 

transport accessibility significantly fosters local tourism development, thereby generating centripetal forces on 

local tourism development. In this context, the impact of tourism industry agglomeration on regional economic 

disparities is not simply a static and linear effect. Instead, it is influenced by transport or information 

infrastructure and exhibits significant nonlinear characteristics. Specifically, thresholds could be measured or 

defined based on the levels of information and transport infrastructure development, which determine whether 

the impact is more centripetal or centrifugal. The threshold effect refers to a phenomenon where a certain 

minimum level of input, effort, or exposure is required before a significant change, response, or outcome is 

observed. Below this threshold, the effect may be negligible or non-existent, but once the threshold is crossed, 

the impact becomes noticeable and often increases rapidly. In conclusion, this study proposes the following 

hypothesis: 

 

H2-1: The impact of tourism industry agglomeration on regional economic disparities is negatively moderated 

by “information infrastructure,” exhibiting threshold characteristics. 

H2-2: The impact of tourism industry agglomeration on regional economic disparities is negatively moderated 

by “transport infrastructure,” which exhibits threshold characteristics. 

H2-3: The impact of tourism industry agglomeration on regional economic disparities is negatively moderated 

by “information and transport infrastructure,” exhibiting threshold characteristics. 

 

Methodology 

 

Model Construction 

 

Based on the theoretical analysis and research hypotheses, to explore the impact of tourism industry 

agglomeration on regional economic disparities and recognizing that neglecting individual effects, time effects, 



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and the correlation between error terms and explanatory variables may lead to biased model estimates, this study 

adopts the two-way fixed effects model as the baseline regression model, formulated as follows: 

 

Gap
it
=β

0
+β

1
Tait+β

2
Xit+μ

it
+εit                                                (1)  

Variable Definitions: 

Gap
it
: The level of regional economic disparities in province i at time t, measured by the Gini coefficient of per 

capita GDP across prefecture-level cities or districts (unit: dimensionless). 

Tait: The level of tourism industry agglomeration in province i at time t, measured by the spatial agglomeration 

density (unit: per square kilometer).  

Xit: A vector of control variables for province i at time t (definitions provided in Section 3.2.4). 

μ
it

 :The individual fixed effect for province i at time t, capturing unobserved time-invariant characteristics. 

εit :The random disturbance term for province i at time t (unit: dimensionless).  

 

We choose the two-way fixed effects model over other alternatives such as pooled OLS or random effects models 

for several reasons. First, it effectively controls for unobserved heterogeneity that may be correlated with the 

explanatory variables. By including both individual and time fixed effects, we can eliminate biases arising from 

time-invariant provincial characteristics and common temporal shocks. Recent studies, such as Chen and Liu 

(2023), have employed spatial econometric models to address spatial dependencies in tourism agglomeration, 

while Martinez and Kim (2024) utilized dynamic panel data models to capture temporal dynamics. However, 

our approach focuses on controlling for unobserved heterogeneity and temporal shocks, which are critical for 

unbiased estimation in our context.Second, the Hausman test confirms that fixed effects are more appropriate 

for our dataset, suggesting that unobserved individual effects are correlated with the explanatory variables. 

Lastly, this model provides a robust framework for analyzing the dynamic relationship between tourism industry 

agglomeration and regional economic disparities over time and across regions. 

Furthermore, to verify whether the research hypotheses on the impact mechanism of tourism industry 

agglomeration on regional economic disparities hold, this study introduces moderating variables, including 

transport, information infrastructure, and the combination of transport and information infrastructure, into 

Formula (1), as well as interaction terms between tourism industry agglomeration and the moderating variables. 

The specific model is as follows. 

Gap
it
=β

0
+β

1
Tait+β

2
Mit+β

3
Tait*Mit+β

4
Xit+μ

it
+εit                  (2)  

 

 

Mit:Moderating variables in province i at time t, representing transport, information infrastructure, and the 

combination of both. 

In recent years, research on tourism agglomeration and regional economic disparities has increased, but most 

studies have not fully considered the moderating role of infrastructure and its nonlinear characteristics. For 

example, Zhang et al. (2023), in their study on the impact of tourism agglomeration on regional economic 

disparities, considered the linear moderating effect of a single type of infrastructure without introducing 

interaction terms or threshold models. In contrast, this study, by incorporating interaction terms and threshold 

models, can more comprehensively capture the complex relationship between tourism agglomeration and 

infrastructure.  Finally, to further verify whether the impact of tourism industry agglomeration on regional 

economic disparities exhibits non-linear relationships with "Mit", we adopt transport, information infrastructure, 

and the combination of transport and information infrastructure as threshold variables to construct a threshold 

model. The model is expressed as follows: 



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Gap
it
=α0+β

1
Tait*I(Mit<γ

1
)+β

2
Tait*I(γ

1
≤Mit<

γ
2
)+⋯+β

n
Tait*I(γ

n-1
≤Mit<γ

n
)+β

n+1
Tait*I(Mit≥γ

n
)+δXit + εit             (3)  

γ
1
, γ

2
,…, γ

n
 : Threshold values defining different intervals for the moderating variables. 

β
1
, β

2
, …, β

n
:The estimated coefficients for different threshold intervals.  

𝐈(·) : Indicator function, taking the value of 1 when the threshold variable is within a specific range, and 0 

otherwise. 

 

Additionally, Wang and Liu (2024) explored the moderating role of information infrastructure, but their model 

did not distinguish the effects across different threshold intervals, resulting in limited explanatory power for 

nonlinear relationships. The threshold model in this study, through a piecewise regression approach, reveals the 

nonlinear impact mechanisms of tourism agglomeration on regional economic disparities, providing more 

nuanced insights for policy formulation. 

 

Variable Construction 

 

Dependent Variable 

 

Regional Economic Disparity (Gap) follows the approach of Wang Qing et al. (2018) .This study measured using 

the Gini coefficient of per capita GDP across prefecture-level cities or districts within each province or directly 

controlled municipality. The specific formula is as follows: 

Gap
it
=

2

N
∑  

N

i=1

ixi-
N+1

N
                          (4)  

Where: 

xi=y
i
/ ∑  N

i=1 y
i
，and，xI<xI<xI<…<xn, 

“N” represents the number of regions. 

YI denotes the per capita GDP of each prefecture (or district). 

Xirepresents the proportion of the per capita GDP of each city in the total per capita GDP of the province, 

arranged in ascending order.  

 

Key Explanatory Variable 

 

Tourism Industry Agglomeration (Ta) follows the methodology of Yang Yong (2012). It is measured by the 

spatial agglomeration density of the tourism industry. Specifically, it is calculated as the proportion of the total 

tourism industry revenue of each province and city to the total revenue of all studied provinces and cities, divided 

by the land area of each province and city. The formula is given by: 

Tait=

Revenueit

∑ Revenueit

Areait

*100                      (5)  



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where Tait denotes the spatial agglomeration density of the tourism industry in province I at time t, Revenueit 

represents the total revenue of the tourism industry in province I at time t, and Areait denotes the land area in 

province I at time t. 

Where: 

Tait: Spatial agglomeration density of the tourism industry in province I at time t (unit: per square kilometer). 

Revenueit: Total tourism industry revenue in province I at time t. 

Areait: Land area of province I at time t 

 

Moderating Variables 

 

(1) Information Infrastructure (Dig): We construct an index system for information infrastructure based on 

indicators such as fixed asset investment in information transmission computing, optical cable construction level, 

Internet penetration rate, Internet user proportion, mobile phone penetration rate, and postal and 

telecommunications business volume. The entropy method is used to measure the level of information 

infrastructure (Zhao Lei, 2013; Sun Li et al., 2021). 

(2) Transport Infrastructure (Tra): We establish an index system for transport infrastructure using indicators such 

as road density, railway density, urban road area, and urban public transport vehicles. The entropy method is 

used to measure the level of transport infrastructure. (Sun Yu, 2019; Zan Xin, 2023). 

(3) Transport and Information Infrastructure (Fra): We combine the index systems of information infrastructure 

and transport infrastructure and measure the combined level using the entropy method. Specific indicators are 

listed in Table 1.  

 

 

 

 

Table 1. Index System of Information and Transport Infrastructure 

Primary Indicators Secondary Indicators Measurement Criteria 

 

 

 

 

Information 

Infrastructure 

Information transmission computer 

fixed asset investment 

Amount of fixed asset investment in 

information transmission computing 

Optical cable construction level Length of long-haul optical cable/area 

Internet penetration rate Number of broadband internet access 

users/total population 

Internet user proportion Number of internet users/total population 

Mobile phone penetration rate Number of mobile phone users/total 

population 

Postal and telecommunications 

business volume 

Total volume of postal and 

telecommunications business/total population 

 

 

Transport 

Infrastructure 

Road density Length of roads/area 

Railway density Length of railways/area 

Urban public transport vehicles Public transport vehicles/total population 

Urban road area Urban road area/total population 



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Control Variables 

 

(1) Industrial structure upgrading: This variable measures the proportion of the total output value of the tertiary 

industry to that of the secondary industry. It reflects the resource allocation effects from the upgrading of 

industrial structure, which can impact regional economic disparities by influencing the transfer and 

agglomeration of production factors. Following the approach of Ding Junsong et al. (2022) , this study uses the 

proportion of the total output value of the tertiary industry to that of the secondary industry to measure the level 

of industrial structure upgrading. 

(2) Degree of government intervention: Measured by the proportion of government fiscal expenditure to GDP, 

this variable captures the government's influence on regional development through. This study uses the 

proportion of government fiscal expenditure to GDP to represent the degree of government intervention, as 

proposed by Lin Yifu et al. (2013).  

(3) Level of human capital: Human capital can promote knowledge spillover effects, drive product innovation, 

and enhance product competitiveness, serving as a key factor influencing regional economic differentiation. 

Following the approach of Zhao Lei et al. (2017), this study measured the level of human capital using the 

average years of education per capita. 

(4) Level of foreign direct investment: Capital investment significantly promotes industrial development and 

drives the economy. It also has an impact on the level of economic development among regions. Referring to the 

approach of Yanlong et al. (2020), this study measures the level of foreign direct investment using the proportion 

of foreign investment to GDP. 

 

Table 2. Descriptive Statistics for Key Variables 

Type  Name Symbols Observations  Mean  
Standard 

deviation 

Dependent 

variables 
 Regional economic disparities Gap 527 0.2571  0.0766  

Independent 

variables 
Tourism industry agglomeration Ta 527 0.6229  1.7201  

Moderating 

variables 

 Information infrastructure Dig 527 0.2386  0.1385  

Transport infrastructure Tra 527 0.3445  0.1689  

 Information and transport 

infrastructure 
Fra 527 0.2679  0.1303  

Controlling 

variables 

 Foreign direct investment Fdi 527 0.0220  0.0183  

 Fixed asset investment Fai 527 0.6996  0.2713  

Human capital Human 527 8.6853  1.2224  

Research and development 

(R&D) investment 
Rd 527 0.0145  0.0112  

Industrial structure upgrading Inst 527 1.2119  0.6587  

Government intervention Gov 527 0.2510  0.1893  

 



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(5) Intensity of research and development (R&D) investment: R&D investment is the foundation of technological 

progress and is an important driving force for economic development. Therefore, R&D investment has a certain 

degree of impact on regional developmental differentials. Following the approach of Liu Renzhong et al. (2015), 

this study measures the intensity of R&D investment using the proportion of total R&D investment to GDP. 

(6) Fixed asset investment: Investment plays a crucial role in the economic effects on regions and influences 

regional economic disparities. Following the approach of Zhao Lei et al. (2020), this study uses the proportion 

of fixed asset investment to GDP to measure the level of fixed asset investment. 

The six aforementioned factors interact through the cyclical mechanism of "efficiency improvement – resource 

agglomeration – path dependence": industrial structure upgrading and scientific research input determine long-

term growth potential, government intervention and FDI influence the direction of resource allocation, while 

human capital and fixed asset investment directly affect production efficiency and capital accumulation. The 

uneven development across regions in these dimensions ultimately forms a multidimensional and superimposed 

economic disparity pattern.The descriptive statistical results for each variable are shown in Table 2. 

 

Data Sources 

 

This study utilizes panel data from 31 provinces in China from 2004 to 2020 to explore the impact of tourism 

industry agglomeration on regional economic disparities. Due to data availability, this research does not include 

data from Hong Kong, Macau, and Taiwan. The statistical data mainly come from various sources including the 

China Tourism Statistical Yearbook, China Statistical Yearbook, Compilation of Statistical Data on 60-Year 

New China, China Regional Economic Statistical Yearbook, China Economic Information Network statistical 

database, provincial statistical yearbooks, and National Economic and Social Development Statistical Bulletin. 

In addition, this study employs linear interpolation to address individual missing values in the variables. 

 

Dependence Analysis 

 

In this paper, the Pearson correlation coefficients between the variables are measured separately to verify 

whether there is multicollinearity between the explanatory variables. The results are shown in Table 3. The 

absolute values of correlation coefficients between variables are basically not higher than 0.6, indicating that 

there is basically no multicollinearity between variables. In order to ensure the robustness of the research results, 

this paper applies the variance inflation factor method to test whether there is multicollinearity between the 

variables, the formula of which is shown in (6). When it is greater than 10 it means that there is basically 

multicollinearity between the variables.  

The last row of Table 3 indicates that the VIF of each explanatory variable takes the range of (1.70, 3.19), and 

its mean value is 2.41, which is much smaller than 10, which indicates that there is basically indeed no 

multicollinearity among the explanatory variables, suggesting that the results of the study are credible. 

 

 

VIFi=(1-Ri
2)

-1
            (6)  

 

 

 

 



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Table 3. Dependence Analysis for Key Variables 

variable Gap Ta Fdi Fai Human Rd Inst Gov 

Gap 1.0000        

Ta -0.0221 1.0000       

Fdi 0.0664 0.4119*** 1.0000      

Fai -0.1290*** -0.3141*** -0.2988*** 1.0000     

Human 0.0402 0.4337*** 0.3380*** -0.2172*** 1.0000    

Rd 0.1258*** 0.5172*** 0.3448*** -0.3002*** 0.7172*** 1.0000   

Inst 0.1126*** 0.2873*** 0.0426 -0.1048** 0.4003*** 0.5910*** 1.0000  

Gov 0.0373 -0.1235*** -0.3877*** 0.5032*** -0.4971*** -0.2831*** 0.2357*** 1.0000 

VIF  1.70 1.41 1.76 3.19 3.11 2.49 3.19 

Note: *, **, *** represent significance levels of 10, 5, and 1, respectively. 

 

Results and discussions  

 

Unit Root Testing 

 

Since variables may exhibit non-stationary behavior, two economically unrelated variables could appear highly 

correlated, leading to the issue of 'spurious regression. Therefore, in the regression analysis, if the panel data are 

non-stationary time series, the research results will be meaningless. Based on this, in order to avoid “pseudo-

regression” in the regression results, this paper, before the model regression, first conducts the unit root test of 

smoothness for the panel data. To ensure the robustness of the regression results. Considering that the limitations 

of a single test method may cause some bias to the test results, this paper adopts five different unit root tests for 

each variable, namely, LLC, IPS, HadriLM, Fisher-ADF, and Fisher-PP test, to improve the credibility of the 

test results. To determine stationarity, a majority rule is applied, where the results of the Fisher-ADF and Fisher-

PP tests are considered collectively. If the majority of tests indicate stationarity, the variable is classified as 

stationary.The test results are shown in Table 4. 

 

Table 4.  Unit Root Testing for Variables 

variable LLC IPS HadriLM Fisher-ADF Fisher-PP 

Gap -7.6903*** -1.6434* 18.2154*** 154.4493*** 238.1304*** 

Ta -3.5454*** 3.2103 22.2566*** 178.479*** 142.601*** 

Fdi -4.3474*** 1.7079 25.4589*** 178.0777*** 178.9967*** 

Fai -3.006*** 1.5559 15.6913*** 157.6576*** 91.2943*** 

Human -10.685*** -6.5354*** 7.7316*** 182.5135*** 287.643*** 

Rd -5.8658*** -1.2352 14.7993*** 127.4996*** 293.534*** 

Inst -3.917*** 1.4111 21.6249*** 128.7963*** 124.1277*** 

Gov -1.9118** 4.8944 30.537*** 182.3286*** 117.47*** 

Note: *, **, *** represent significance levels of 10, 5, and 1, respectively. 



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There are basically no variables accepting the hypothesis of “existence of unit root”, which indicates that most 

of the variables are non-stationary series. However, there are still some results that accept the original hypothesis. 

Fisher's test is more reasonable than other tests when the variables are not infinite samples and the individual 

time series are allowed to have random factors. Based on this criterion, the Fisher-ADF and Fisher-PP tests show 

significant statistics for all variables, indicating stationarity. Although the Fisher test is more reasonable than the 

results of other tests under the above conditions. However, the test principle of smoothness has certain 

differences and complexity, and ignoring the results of other tests may cause judgment bias. Therefore, this paper 

further takes more than half of the test results of the same variable rejecting the original hypothesis as the 

judgment criterion that the series of the variable is a smooth time series to ensure the robustness of the judgment 

results. The results show that all variables, more than half of the test results reject the original hypothesis, i.e., 

there is no unit root, indicating that all variables are smooth series. Based on this, the next step of regression 

analysis can be carried out. 

 

Benchmark Regression 

 

Based on the theoretical analysis and research hypotheses mentioned above, this study conducted an empirical 

analysis of the impact and moderating mechanisms of tourism industry agglomeration on regional economic 

disparities. The Hausman test results indicated that the fixed-effects model was superior to the random-effects 

model. and the fixed effects model is better than the mixed OLS model as obtained by the fixed effects model 

F-test. Furthermore, considering factors such as the omission of individual effects, time effects, and the 

correlation between the error term and explanatory variables, which may bias the estimation results, the two-

way fixed effects model was used as the benchmark regression model in this study. The results, as shown in 

Table 5, present the benchmark regression model in column (1), whereas columns (2), (4), and (6) include 

additional variables such as information, transport infrastructure, and the interaction between information and 

transport infrastructure, respectively. Columns (3), (5), and (7) introduce the interaction terms between tourism 

industry agglomeration and the moderating factors based on columns (2), (4), and (6), respectively. 

Based on Column (1), the coefficients of the impact of tourism industry agglomeration on regional economic 

disparities are significantly negative at the 1% level, indicating that tourism industry agglomeration has a 

significant mitigating effect on regional economic disparities. Therefore, H1 was validated. This may be because 

the direct and indirect effects generated and facilitated by tourism industry agglomeration can promote economic 

development in underdeveloped areas, thus reducing the economic disparities between regions. Additionally, an 

increase in tourism revenue can further drive the utilization and development of tourism resources, improve 

infrastructure, enhance enterprise management models, and promote the upgrading of the tourism industry chain, 

thereby generating further positive economic effects at the local level and further reducing the economic 

disparities between regions. Lastly, the development of the tourism industry has significant advantages in 

expanding domestic demand, stimulating consumption, implementing macroeconomic policies, effectively 

driving sustainable economic growth, and mitigating economic disparities between regions. 

Based on columns (2), (4), and (6), the coefficients of information, transport infrastructure, and their interaction 

show significantly negative impacts on regional economic disparities at the 1% level. This indicates that as these 

moderating factors continue to improve, the economic effects they generate and facilitate have a significant 

mitigating effect on the imbalance in economic development between regions. Furthermore, the impact of 

tourism industry agglomeration on regional economic disparities remains significantly negative, indicating the 

robustness of the conclusions. 



Global Sustainability Research 

Global Scientific Research  48 
 

 

 

Based on Column (3), the coefficient of the interaction term between tourism industry agglomeration and 

information infrastructure is significantly negative at the 1% level. This suggests that information infrastructure 

plays a negative moderating role in the impact of tourism industry agglomeration on regional economic 

disparities. This may be due to the higher information barriers in certain regions, leading to insufficient tourism 

demand and supply, which limits local tourism development to a certain extent. However, the improvement in 

informationization can increase the “exposure” of tourism resources, reduce the search cost for tourism 

 

Table 5. Regression Results of Fixed Effects Model of the Full Sample 

 (1) (2) (3) (4) (5) (6) (7) 

Ta -0.0170*** -0.0157*** 0.0451*** -0.0148*** 0.0253*** -0.0142*** 0.0680*** 

 (0.002) (0.002) (0.011) (0.002) (0.010) (0.002) (0.013) 

Dig  -0.1352*** -0.0831**     

  (0.035) (0.035)     

Ta*Dig   -0.1020***     

   (0.018)     

Tra    -0.1030*** -0.0176   

    (0.032) (0.037)   

Ta*Tra     -0.0688***   

     (0.016)   

Fra      -0.2084*** -0.1038** 

      (0.043) (0.044) 

Ta*Fra       -0.1381*** 

       (0.021) 

Fdi 0.4767*** 0.3997*** 0.1988 0.4950*** 0.3913*** 0.3981*** 0.1646 

 (0.136) (0.135) (0.136) (0.135) (0.134) (0.134) (0.133) 

Fai -0.0249** -0.0247** -0.0228** -0.0252** -0.0240** -0.0249** -0.0225** 

 (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.009) 

Human -0.0059 -0.0052 -0.0113* -0.0055 -0.0143** -0.0051 -0.0158** 

 (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) 

Rd -1.7478*** -1.7062*** -1.2955** -1.1834* -1.2341** -1.4080** -0.9706* 

 (0.590) (0.582) (0.567) (0.610) (0.599) (0.581) (0.560) 

Inst -0.0133* -0.0241*** -0.0345*** -0.0107 -0.0244*** -0.0238*** -0.0396*** 

 (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) 

Gov 0.0206 0.0383 0.0245 -0.0242 0.0033 0.0178 0.0067 

 (0.029) (0.029) (0.028) (0.032) (0.032) (0.028) (0.027) 

_Cons 0.3629*** 0.3941*** 0.4356*** 0.3865*** 0.4467*** 0.4123*** 0.4844*** 

 (0.059) (0.058) (0.057) (0.059) (0.059) (0.058) (0.057) 

Time effect  

Yes  

Yes   

Yes  

 

Yes  

 

Yes  

 

Yes  

 

Yes  Individual effect  

Yes  

 

Yes  

 

Yes  

 

Yes  

 

Yes  

 

Yes  

 

Yes  R2 0.4301 0.4478 0.4842 0.4424 0.4634 0.4568 0.5030 

N 527 527 527 527 527 527 527 

Note: Figures in parentheses represent z-values. *, **, and *** denote significance levels of 10%, 5%, and 1%, 

respectively (same as in the table below). 



Global Sustainability Research 

Global Scientific Research  49 
 

information, stimulate innovation in tourism products, improve management models for enterprises, and promote 

the efficiency of factors such as labor and capital mobility and allocation. This allows for the rational utilization 

and development of idle or misallocated resources, reduces tourism leakage in the local area, and promotes the 

development of local tourism and related industries, thereby alleviating the imbalance in economic development 

between regions. 

Based on column (5), transport infrastructure has a significant negative moderating effect on the impact of 

tourism industry agglomeration on regional economic disparities. This may be due to the lack of accessibility in 

transport, which leads to insufficient tourism demand and supply, thereby limiting the impact of tourism industry 

agglomeration on local tourism revenue. However, as transport convenience improves, it can increase the 

willingness of labor and tourists to work or travel to the local area, promote the flow and distribution effects of 

production factors such as labor and capital, and reduce transport costs for labor and tourists, as well as the 

transport costs of the tourism industry and raw materials. Meeting dynamic and differentiated tourism demands 

can drive the utilization and development of tourism resources, improve infrastructure, enhance enterprise 

management models, and promote the upgrading of the tourism industry chain, thereby generating positive 

economic effects at the local level and narrowing the economic disparities between regions. Based on Column 

(7), the coefficient of the interaction term between tourism industry agglomeration and transport and information 

infrastructure is significantly negative at the 1% level. This indicates that considering the differences in transport 

and information infrastructure between regions and that is, the combined effect of transport and information 

infrastructure–there is still a negative moderating effect on the impact of tourism industry agglomeration on 

regional economic disparities. 

The interaction between tourism industry agglomeration and dual infrastructure types has suppressed the 

expansion of regional economic disparities, indicating enhanced spatial diffusion effects of economic factors. 

This phenomenon likely facilitates regional economic convergence through shared growth pathways . 

Specifically, the negative feedback mechanism constrains the continuous intensification of agglomeration 

effects, compelling economic resources to shift toward new growth poles.Traditionally, tourism agglomeration 

could exacerbate regional gaps through siphon effects , but the existence of negative multiplicative terms (as 

implied by the interaction between infrastructure variables) reveals that infrastructure modernization has 

fundamentally altered this mechanism. This transformation enables broader spatial distribution of tourism 

benefits, driving economic dividends to diffuse from agglomeration centers to peripheral areas.Furthermore, this 

negative interaction reflects the emergence of diversified development trajectories. When tourism industries 

reduce their dependence on concentrated infrastructure investments in specific regions, regional economies gain 

enhanced risk resilience.These phenomena ultimately signify a paradigm shift in the coordinated development 

of tourism and infrastructure—from extensive growth  reliant on factor concentration and scale expansion to 

high-quality and balanced development emphasizing network synergies, innovation-driven strategies, and 

systemic resilience. 

 

Robustness Tests 

 

(1) Replacing the Dependent Variable:  In study, the Theil entropy index was employed to measure the 

degree of regional economic disparity. As shown in columns (1), (2), and (3) of Table 6, the coefficients 

of the tourism industry agglomeration and the interaction terms with transport and information 

infrastructure, as well as the composite term of both, are significantly negative at the 1% level. This 

indicates that the moderating factors maintain a negative moderating effect on the impact of the tourism 



Global Sustainability Research 

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industry agglomeration on regional economic disparities, consistent with the baseline regression model, 

thus confirming the robustness of the results. 

 

Table 6. Robustness Test Results 

 Replacing the Dependent Variable  Replacing the Independent Variable 

 (1) (2) (3) (4) (5) (6) 

Ta 0.0816*** 0.0379*** 0.1133*** 0.0002 -0.0002 0.0027 

 (0.013) (0.012) (0.016) (0.006) (0.006) (0.006) 

Dig -0.0104   -0.1159***   

 (0.043)   (0.035)   

Ta*Dig -0.1625***   -0.0292***   

 (0.022)   (0.004)   

Tra  0.0232   -0.0626*  

  (0.047)   (0.034)  

Ta*Tra  -0.0896***   -0.0277***  

  (0.020)   (0.004)  

Fra   0.0033   -0.1817*** 

   (0.055)   (0.044) 

Ta*Fra   -0.2140***   -0.0276*** 

   (0.026)   (0.004) 

_cons 0.3576*** 0.3684*** 0.4193*** 0.4087*** 0.4128*** 0.4243*** 

 (0.071) (0.074) (0.071) (0.059) (0.059) (0.058) 

 

Controlling variables 

 

Controlling 

Controlling Controlling Controlling Controlling Controlling 

Time effect Yes Yes  Yes Yes  Yes Yes 

Individual effect   

Yes 

  

Yes 

  

Yes 

  

Yes 

Yes   

Yes R2 0.3946 0.3515 0.4141 0.4649 0.4555 0.4726 

N 527 527 527 527 527 527 

Note: Figures in parentheses represent z-values. *, **, and *** denote significance levels of 10%, 5%, and 1%, 

respectively 

 

Table 6. Continue. . .  

Excluding Outliers 

(7) (8) (9) 

0.0451*** 0.0253*** 0.0680*** 

(0.011) (0.010) (0.013) 

-0.0831**   

(0.035)   

-0.1020***   

(0.018)   

 -0.0176  

 (0.037)  

 -0.0688***  

 (0.016)  

  -0.1038** 



Global Sustainability Research 

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  (0.044) 

  -0.1381*** 

  (0.021) 

0.4356*** 0.4467*** 0.4844*** 

(0.057) (0.059) (0.057) 

Controlling Controlling Controlling 

Yes Yes Yes 

Yes Yes Yes 

0.4842 0.4634 0.5030 

527 527 527 

Note: Figures in parentheses represent z-values. *, **, and *** denote significance levels of 10%, 5%, and 1%, 

respectively 

 

(2) Replacing the Independent Variable: To account for differences in regional economic scales, this study 

utilizes the location entropy index to gauge the level of tourism industry agglomeration, thereby eliminating the 

influence of the regional economic scale on the relative development level of the tourism industry. As shown in 

columns (4), (5), and (6) of Table6, transport and information infrastructure, as well as their combined effect, 

continue to exhibit a negative moderating effect on the impact of tourism industry agglomeration on regional 

economic disparities, consistent with the baseline regression model, thus affirming the robustness of the findings. 

(3) Excluding Outliers: Considering that outliers in regional economic disparity can lead to inaccurate regression 

results, this study adopts the method of Liu et al. (2018) [28], employing a trimming approach based on the annual 

average values of regional economic disparity for robustness testing. Calculations and sorting reveal that Gansu 

has the highest annual average value of regional economic disparity, whereas Fujian has the lowest. Excluding 

these two provinces using the trimming method, the results presented in columns (7), (8), and (9) of Table 4 

indicate that transport and information infrastructure and their combined effect continue to show a negative 

moderating effect on the impact of tourism industry agglomeration on regional economic disparities. This 

consistency with the baseline regression model corroborates the robustness of our results. 

 

Endogeneity Test 

 

Although a series of control variables is introduced in the baseline regression model, simultaneously controlling 

for individual and time effects to mitigate the estimation bias caused by omitted variables, there may still be a 

bidirectional causal relationship between tourism industry agglomeration and regional economic disparity, 

resulting in endogeneity issues. To address the bias in estimation results caused by endogeneity, this study 

initially uses the first-order lag of tourism industry agglomeration as the core explanatory variable. This approach 

effectively reduces the estimation bias caused by the contemporaneous correlation between the core explanatory 

variable and disturbance term. As shown in columns (1), (2), and (3) of Table 7, the coefficients of the tourism 

industry agglomeration and the interaction terms with information and transport infrastructure, as well as the 

composite terms of both, are significantly negative at the 1% level. This indicates that even after overcoming 

endogeneity issues, the moderating factors still exhibit a negative moderating effect on the impact of tourism 

industry agglomeration on regional economic disparity. However, this method does not address the extent to 

which the endogenous variables of the current period affect the dependent variables of the current period, 

potentially causing bias in the moderating effect of information and transport infrastructure, as well as their 

combined influence. 



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Table 7. Results of Endogeneity Test 

Note: Figures in parentheses represent z-values. *, **, and *** denote significance levels of 10%, 5%, and 1%, respectively 

 

Table 7. Continue. . . . 

 OLS 2SLS 

 (1) (2) (3) (4) (5) (6) 

L.Ta/Ta 0.0404*** 0.0195** 0.0552*** 0.0415*** 0.0231*** 0.0584*** 
 (0.013) (0.009) (0.013) (0.013) (0.009) (0.014) 

Dig -0.1133***   -0.1126***   

 (0.035)   (0.033)   

L.Ta/Ta *Dig -0.0860***   -0.0900***   

 (0.020)   (0.021)   

Tra  -0.0462   -0.0344  

  (0.037)   (0.035)  

L.Ta/Ta *Tra  -0.0896***   -0.0648***  

  (0.015)   (0.015)  

Fra   -0.1576***   -0.1557*** 

   (0.045)   (0.042) 

L.Ta/Ta *Fra   -0.1089***   -0.1160*** 

   (0.022)   (0.022) 

_cons 0.4372*** 0.4550*** 0.4906*** 0.4026*** 0.4263*** 0.4486*** 

 (0.056) (0.058) (0.057) (0.050) (0.051) (0.049) 

Controlling variables Controlling  

Controlling 

 

Controlling 

 

Controlling 

 

Controlling 

 

Controlling Time effect   

Yes 

  

Yes 

  

Yes 

  

Yes 

  

Yes 

  

Yes Individual effect  Yes   

Yes 

  

Yes 

  

Yes 

 

Yes 

  

Yes R2 0.4890 0.4790 0.5086 0.9001 0.8994 0.9042 

N 527 527 527 527 527 527 

LIML 

(7) (8) (9) 

0.0415*** 0.0231*** 0.0584*** 

(0.013) (0.009) (0.014) 

-0.1126***   

(0.033)   

-0.0900***   

(0.021)   

 -0.0344  

 (0.035)  

 -0.0648***  

 (0.015)  

  -0.1557*** 

  (0.042) 

  -0.1160*** 

  (0.022) 

0.3743*** 0.3971*** 0.4324*** 

(0.063) (0.064) (0.063) 

Controlling  

Controlling 

 

Controlling   

Yes 

  

Yes 

 

Yes 



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Based on this, the study employs the lagged term of tourism industry agglomeration as an instrumental variable 

and uses the instrumental variables fixed-effects two-stage least squares (2SLS) method for estimation. Prior to 

the estimation, it was necessary to test the validity and effectiveness of the instrumental variable. The results of 

the tests were as follows: Anderson Canon. corr. LM statistic rejects the null hypothesis of “underidentification 

of the instrumental variable” at the 1% level. Furthermore, the Cragg-Donald Wald F statistic is significantly 

greater than the critical value of 16.38 at the 10% bias level, as determined by Stock et al. (2002), significantly 

rejecting the null hypothesis of “weak identification of the instrumental variable.” Therefore, the 2SLS method 

was applied. The estimation results, as shown in columns (4), (5), and (6) of Table 5, indicate that the coefficients 

of tourism industry agglomeration and its interaction terms with information and transport infrastructure are 

significantly negative, demonstrating that the moderating factors still influence the effect of tourism industry 

agglomeration on regional economic disparity even after addressing endogeneity issues. It is worth noting that, 

although the 2SLS parameter estimation is consistent, there may still be bias in the regression results due to weak 

instrumental variables. Consequently, this study employed the Limited Information Maximum Likelihood 

(LIML) method for further estimation. The results, as presented in columns (7), (8), and (9) of Table 6, show 

that the coefficients of the interaction terms between the tourism industry agglomeration and the moderating 

factors are significantly negative at the 1% level, further confirming the robustness of the results. 

 

Heterogeneity Analysis 

 

(1) Regional Heterogeneity Analysis. This study divides the country into three major regions, East, Central, 

and West, based on the classification standards released by the National Bureau of Statistics. A regional 

dummy variable, Dum, was set, taking a value of one for the eastern region and zero for the central and 

western regions. These are incorporated into the model along with tourism industry agglomeration, 

information and transport infrastructure, and their interaction terms. The regression results are displayed 

in Columns (1), (2), and (3) of Table 8. The coefficients of the interaction terms between the tourism 

industry agglomeration and the moderating factors are significantly negative at the 1% level, indicating 

that the negative moderating effect of these factors on the impact of tourism industry agglomeration on 

regional economic disparity is substantially stronger in the eastern region than in the central and western 

regions. 

 

(2) Temporal heterogeneity. The State Council issued Opinions on Accelerating the Development of the Tourism 

Industry (G.F. [2009] No. 41), which advocated for the vigorous development of the tourism industry and 

emphasized its importance in driving economic growth and enhancing public well-being. Consequently, this 

study uses the date of issuance of this document as a sample split point, thereby dividing the entire investigation 

period into two intervals: 2004-2009 and 2010-2020, to explore whether the moderating effect of the tourism 

industry on regional economic disparity varies across these periods. Based on this, a time dummy variable, Dum, 

was set, with a value of 1 assigned to the years 2010-2020 and 0 to the years 2004-2009. These are incorporated 

into the model, along with the core explanatory variables, moderating variables, and their interaction terms. As 

shown in columns (4), (5), and (6), the coefficients of the interaction terms between tourism industry 

agglomeration and information and transport infrastructure are significantly negative, indicating that the issuance 

  

Yes 

  

Yes 

  

Yes 0.9001 0.8994 0.9042 

527 527 527 



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of the document strengthens the moderating effect of tourism industry agglomeration on regional economic 

disparity. 

(3) Tourism Industry Development-based Heterogeneity. Given that the moderating effect of tourism industry 

agglomeration on regional economic disparity may vary with different levels of tourism industry agglomeration, 

this study adopts the methodology of Liu et al. (2018) to calculate the annual average tourism industry 

agglomeration level for each province and city and then compares it to the national average. Provinces and cities 

where the former exceeds the latter are termed tourism-dependent, whereas those where it falls short are termed 

non-tourism-dependent. On this basis, a dummy variable, Dum, is introduced, with tourism-dependent provinces 

and cities assigned a value of 1, and non-tourism-dependent provinces and cities assigned a value of 0. These 

are incorporated into the model, along with the core explanatory variables, moderating variables, and their 

interaction terms. The regression results indicate that the coefficients of the interaction terms between tourism 

industry agglomeration and information and transport infrastructure are significantly negative at the 1% level, 

suggesting that, compared to non-tourism-dependent provinces and cities, tourism industry agglomeration exerts 

a more pronounced moderating effect on regional economic disparity in tourism-dependent areas. In other words, 

the moderating factors have a stronger regulatory effect on tourism industry agglomeration. 

 

 

 

Table 8. Results of Heterogeneity Analysis 

 Regional Heterogeneity Temporal Heterogeneity Tourism Industry Development-Based 

Heterogeneity 
 (1) (2) (3) (4) (5) (6) (7) (8) (9) 

Ta*Dum 0.0305** 0.0311**

* 

0.0619*** 0.0083 0.0068 0.0366* 0.0335** 0.0178 0.0632*** 

 (0.012) (0.010) (0.014) (0.015) (0.016) (0.021) (0.013) (0.012) (0.016) 

Dig* Dum -0.1969***   -

0.1151*** 

  -0.1795**   

 (0.060)   (0.036)   (0.079)   

Ta *Dig *Dum -0.0860***   -0.0394*   -0.0802***   

 (0.020)   (0.024)   (0.023)   

Tra*Dum  0.0571   -0.0152   -0.0924  

  (0.053)   (0.039)   (0.084)  

Ta *Tra* Dum  -

0.0792**

* 

  -0.0414*   -

0.0571*** 

 

  (0.017)   (0.024)   (0.020)  

Fra* Dum   -0.1358*   -0.1414***   -0.1409 

   (0.070)   (0.046)   (0.088) 

Ta *Fra* Dum   -0.1272***   -0.0820**   -0.1295*** 

   (0.024)   (0.032)   (0.026) 

_cons 0.4334*** 0.4413**

* 

0.4765*** 0.4255*** 0.4493*** 0.4665*** 0.4366*** 0.4560*** 0.4803*** 

 (0.057) (0.060) (0.058) (0.057) (0.060) (0.057) (0.057) (0.060) (0.058) 

Controlling 

variables 

 

Controlling 

Controlli

ng 

Controlling Controllin

g 

Controlling Controlling Controlling Controllin

g 

Controlling 

Time effect Yes Yes Yes Yes   

Yes 

Yes Yes Yes   

Yes 
Individual effect   

Yes 

Yes   

Yes 

Yes Yes Yes Yes Yes   

Yes 
R2 0.4927 0.4721 0.5053 0.5049 0.4668 0.5150 0.4873 0.4663 0.5039 

N 527 527 527 527 527 527 527 527 527 

Note: Figures in parentheses represent z-values. *, **, and *** denote significance levels of 10%, 5%, and 1%, respectively 



Global Sustainability Research 

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Nonlinear Discussion 

 

Based on the theoretical analysis and research hypotheses presented earlier, this study examines whether there 

is a nonlinear relationship between the effect of tourism industry agglomeration on regional economic disparity 

and the role of transport and information infrastructure as well as their combined influence. To determine the 

number and thresholds of the information and transport infrastructure, as well as the combination of transport 

and information infrastructure, this study employs the bootstrap resampling method, drawing samples 500 times 

for model testing. The results, as shown in Table 9, indicate that transport and information infrastructure 

individually pass the single-threshold effect test at the 10% and 1% significance levels, respectively, with 

threshold values of 0.6131 and 0.7266. Furthermore, transport and information infrastructure pass the single- 

and double-threshold effect tests at the 5% and 1% significance levels, respectively, with threshold values of 

0.3040 and 0.6612. Based on the results of the threshold effect tests, this study employs a single-threshold model 

to estimate the impact of information and transport infrastructure individually. A double-threshold model was 

used for the combined transport and information infrastructure. As shown in Column (1) of Table 10, when the 

level of information infrastructure is below the threshold, the coefficient indicating the effect of tourism industry 

agglomeration on regional economic disparity is -0.0151, significant at the 1% level. However, when the 

information infrastructure level exceeds the threshold, the coefficient becomes -0.0420, also significant at the 

1% level. This finding demonstrates that, in regions with higher levels of information infrastructure, the absolute 

impact of tourism industry agglomeration on regional economic disparity is more pronounced than in regions 

with lower levels. This suggests that when information infrastructure surpasses the first threshold, it significantly 

enhances the interaction between tourism industry agglomeration and regional economic disparity. This outcome 

also indicates that the regulatory effect of information infrastructure on this relationship is nonlinear. Thus, H2-

1 is validated. 

 

Table 9. Test Results of Threshold Effect 

Threshold 

variable 
Models  

Threshold 

values 
F value P value 

10% critical 

value 

5% 

critical 

value 

1% 

critical 

value 

Number of 

bootstrap 

iterations 

Fra 

Single threshold 

model 
0.3040 44.48 0.0090 23.7434 29.2245 44.6128 500 

Dual-threshold 

model 
0.6612 26.06 0.0460 16.6205 33.7169 84.4072 500 

Dig 
Single threshold 

model 
0.6131 45.25 0.0060 24.6573 29.6739 41.3400 500 

Tra 
Single threshold 

model 
0.7266 30.94 0.0760 29.7916 34.9345 45.5065 500 

 

Based on column (2), when the level of transport infrastructure is below the threshold, the coefficient indicating 

the effect of tourism industry agglomeration on regional economic disparity is -0.0158 and significant at the 1% 

level. However, when the level of transport infrastructure exceeded the threshold, the coefficient was -0.0346, 

which was also significant at the 1% level. This suggests that in regions with higher levels of transport 

infrastructure, the absolute value of the impact of tourism industry agglomeration on regional economic disparity 



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is greater than that in regions with lower levels. This indicated a further mitigating effect between the two. This 

also implies that the regulatory effect of the transport infrastructure on this impact is nonlinear. Therefore, H2-

2 was validated. 

Based on column (3), when transport and information infrastructure are within different threshold ranges, the 

coefficients of the impact of tourism industry agglomeration on regional economic disparity are all significant 

at the 1% level. Specifically, when the level of transport and information infrastructure is below the first 

threshold, tourism industry agglomeration has a positive impact on regional economic disparity. When the level 

of transport and information infrastructure rises between the first and second thresholds, the impact transforms 

from positive to negative. When the level of transport and information infrastructure surpasses the second 

threshold, the mitigating effect of tourism industry agglomeration on regional economic disparity further 

increases. This indicates that the regulatory effect of transport and information infrastructure on this impact is 

nonlinear. Hence, H2-3 is confirmed. 

 

Table 10. Estimated Results of Panel Threshold Model 

 Information infrastructure  Transport infrastructure Combined transport and  

information infrastructure  (1) (2) (3) 

Ta(Dig＜0.6131) 
-0.0151***   

(0.002)   

Ta(Dig≥0.6131) 
-0.0420***   

(0.004)   

Ta(Tra＜0.7266) 
 -0.0158***  

 (0.002)  

Ta(Tra≥0.7266) 
 -0.0346***  

 (0.005)  

Ta(Fra＜0.3040) 
  0.0494*** 

  (0.013) 

Ta(0.3040≤Fra＜0.6612) 
  -0.0143*** 

  (0.002) 

Ta(Fra≥0.6612) 
  -0.0398*** 

  (0.004) 

_cons 
0.4982*** 0.4315*** 0.4821*** 

(0.058) (0.058) (0.057) 

Controlling variable Controlling  Controlling  Controlling  

Time effect Yes  Yes  Yes  

Individual effect Yes  Yes  Yes  

R2 0.4987 0.4681 0.5247 

N 527 527 527 

Note: Figures in parentheses represent z-values. *, **, and *** denote significance levels of 10%, 5%, and 1%, 

respectively 
 

Conclusions  

 

Based on panel data from 31 provinces in China spanning 2004 to 2020, this study empirically examines the 

relationship mechanisms between tourism industry agglomeration, transport, information infrastructure, and 



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regional economic disparity. The conclusions derived are as follows: First, tourism industry agglomeration, 

transport, information infrastructure, and their combined effect all exert a significant negative impact on regional 

economic disparity.  Regarding the negative impact of tourism industry agglomeration on regional economic 

disparity, recent studies have provided mixed evidence. For instance, Li and Chen (2021) found that tourism 

agglomeration significantly reduces regional economic disparities in developed regions, aligning with our 

findings. However, Wang et al. (2022) argued that the effect is context-dependent, with tourism agglomeration 

exacerbating disparities in underdeveloped regions due to uneven resource distribution. This contrast highlights 

the importance of regional heterogeneity, which our study further explores through heterogeneity analysis. 

Second, information and transport infrastructure, as well as their combined effects, have a negative moderating 

effect on the impact of tourism industry agglomeration on regional economic disparity. This implies that 

moderating factors significantly alleviate the impact effect. The core research conclusions remain robust through 

endogeneity and robustness tests. The negative moderating effect of transport and information infrastructure on 

tourism agglomeration's impact is supported by Zhang et al. (2020), who demonstrated that improved 

infrastructure enhances the spillover effects of tourism, thereby reducing economic disparities. However, Liu 

and Zhang (2023) noted that the moderating effect is weaker in regions with low levels of digitalization, 

suggesting that infrastructure quality matters. Our study extends this by examining the combined effect of 

transport and information infrastructure, providing a more comprehensive understanding of their role. Third, 

heterogeneity analysis reveals that in the eastern regions, from 2010 to 2020, and in provinces dependent on 

tourism, the moderating effect of tourism industry agglomeration on regional economic disparity is strengthened. 

Finally, based on information and transport infrastructure and their combined effect, the tourism industry 

agglomeration exhibits a threshold effect on regional economic disparity. This indicates that the regulatory effect 

of the moderating factors on the impact is nonlinear. Specifically, for information and transport infrastructure, 

the impact of tourism industry agglomeration on regional economic disparity is significantly negative and shows 

a decreasing trend. This signifies that surpassing the first threshold of transport and information infrastructure 

reinforces the mitigating effect of tourism industry agglomeration on regional economic disparity. For both 

information and transport infrastructure, the impact effect is positive at the first threshold, but at the second and 

third thresholds, the impact effect turns negative and exhibits a decreasing trend. As information and transport 

infrastructure improve, the impact of tourism industry agglomeration on regional economic disparity shifts. 

Initially, it promotes disparity, but beyond a certain threshold, it helps mitigate economic differences.This 

reinforces the negative moderating role of transport and information infrastructure. The threshold effect of 

tourism agglomeration on regional economic disparity is consistent with the findings of Gao et al. (2021), who 

identified a nonlinear relationship between tourism development and economic growth. Similarly, Chen and 

Wang (2022) found that infrastructure improvements beyond a certain threshold significantly enhance tourism's 

positive impact on regional economies. However, our study uniquely incorporates both transport and information 

infrastructure as threshold variables, revealing a shift from promoting to mitigating disparities as infrastructure 

improves. 

As information and transport infrastructure improve, the impact of tourism industry agglomeration on regional 

economic disparity shifts. Initially, it promotes disparity, but beyond a certain threshold, it helps mitigate 

economic differences. The following recommendations address this challenge. First, it is essential to 

scientifically understand the relationship between tourism industry agglomeration and regional economic 

disparity and formulate strategies tailored to local conditions to bridge the economic gap between regions. Since 

transport and information infrastructure vary across regions, tourism development must be tailored to local 

conditions. Otherwise, inefficient resource allocation may harm other industries. For instance, in regions with 

high information isolation and poor transport accessibility, tourism supply and demand may be insufficient. 



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Blindly developing tourism resources under these conditions may restrict tourism demand, thereby failing to 

boost local economic growth effectively. Therefore, regions should combine theory with practical experience to 

scientifically assess their locational conditions before deciding whether to develop the tourism industry to 

promote economic benefits and reduce economic disparities in more developed areas. Second, continuously 

improving the construction of information and transport infrastructure is crucial to enhance the efficiency of 

factor mobility. An integrated transport system should be constructed and perfected, including strengthening 

urban road construction, optimizing public transport routes, and increasing the density of public transport 

vehicles. This would promote the diffusion of the transport network within cities, reduce the mobility costs of 

tourists and labor, and lower the transport costs of tourism products and raw materials. Consequently, this 

stimulates local tourism and related industries, thereby narrowing regional economic disparities. On the other 

hand, efforts should be intensified to advance information infrastructure construction and foster the integration 

of the tourism and information industries. Innovations such as smart tourism, mobile payments, and big data 

applications can enhance the mobility efficiency of tourists, further promoting local tourism development, and 

subsequently reducing regional economic disparities. It is noteworthy that the development of transport and 

information infrastructure needs to be well-matched with tourism industry development. Blindly enhancing the 

transport and information infrastructure alone will not necessarily enable the tourism industry to effectively 

promote economic benefits. Finally, attention should be paid to the inter-regional linkages between the tourism 

industry and related sectors. Governments should improve the construction of transport and information 

infrastructure between regions to reduce inter-regional mobility costs. This will strengthen cooperation between 

regions, allowing collaborative regions to fully utilize tourism resources and amplify the economic effects of 

complementary tourism resources and shared tourism markets. This, in turn, promotes the development of the 

tourism industry and related sectors, reduces regional economic disparities, and fosters coordinated regional 

development. 

 

Declaration  

 

Acknowledgment:  N/A 

 

Funding: Zhejiang Provincial Philosophy and Social Sciences Planning Project: Leisure Experience Perspective 

on the Research of High-Quality Development Countermeasures of Lower Tourism Industry Clusters and Urban-

Rural Synergy” 

 

Conflict of interest: N/A 

 

Ethics approval/declaration: N/A 

 

Consent to participate: consent 

 

Consent for publication: consent 

 

Data availability: Data will be made available on request 

 

Authors contribution: Guo Junhui:Primarily accountable for the development of economic models, formulation 

of corresponding assumptions, and involvement in the comprehensive revision process. Chen Qihao: I primarily 



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assumed responsibility for data collection, empirical analysis of the constructed model, and manuscript 

preparation. 

 

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