The Tourism Service Trade Network: Statistics from China and ASEAN Countries
Abstract
:1. Introduction
2. Data and Methodology
2.1. Data Sources
2.2. Research Methods
2.3. Key Indicators for Social Network Analysis
- (1)
- Network density indicator
- (2)
- Centrality indicators
2.4. QAP Analysis
3. Results
3.1. Overall Network Structure and Centrality Measurements
3.1.1. Measurement Methodology
3.1.2. Visualization of the Network Structure
3.1.3. Measurement Results
3.2. Network Centrality Analysis
3.2.1. Overall Centrality
3.2.2. Analysis of the Centrality Measures for Different Countries
- (1)
- Most centrally located countries in the network
- (2)
- Sub-central countries in the network
- (3)
- Countries at the periphery of the network
3.3. Analysis of Factors Affecting the China–ASEAN Tourism Services Trade Network
3.3.1. Indicator Selection and Model Construction
Indicator Selection
- (1)
- Geographic distance (bod): The distance between countries is the main factor affecting the scale of trade in services. The closer the distance between two countries, the lower the transportation cost of trade and the greater the possibility of trade. We constructed bod as follows: for two countries bordering each other, the corresponding matrix element takes the value of 1; otherwise, it takes the value of 0, forming a two-valued symmetric matrix.
- (2)
- Economic distance (gdp): The impact of the economic development level on trade relations differed, with countries that tended to have similar levels of economic development trading more often. We obtained data from the GDP per capita of each country in the World Development Indicators (WDI) database. We divided our data into pairs to form a variable matrix.
- (3)
- Institutional distance (ins): Institutional differences at the national level generated institutional distance. The greater the institutional distance between two countries, the less institutional similarity between them and the more obstacles and frictions there were in trade exchanges. We used the Worldwide Governance Indicators (WGI) to measure institutional distance [28]. It contains six indicators: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption. We measured institutional distance as the absolute value of the difference between the composite scores of two countries. The greater the institutional distance, the smaller the institutional similarity between the two countries, and the greater the number of obstacles and frictions in the mutual trade of tourism services. These data were purchased from the Home of Economics and Management.
- (4)
- Cultural differences (cul): Cultural awareness is also a major factor influencing inter-country trade. Usually, countries with similar cultural backgrounds belong to the same sector and influence the formation of inter-country trade networks. We constructed cul as follows: for countries with the same official language, the corresponding matrix element took the value of 1; otherwise, it took the value of 0, forming a binary symmetric matrix.
Model Construction
3.3.2. Measuring Influencing Factors
QPA Correlation Analysis
QAP Regression Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Year | Web Density | Inter-Nodal Number of Connections | Out-Centralization | In-Centralization | Web Diameter | Shortest Average Rails |
---|---|---|---|---|---|---|
2015 | 0.264 | 29 | 0.370 | 0.260 | 4 | 1.758 |
2018 | 0.564 | 62 | 0.370 | 0.370 | 3 | 1.400 |
Out Degree Centrality | In Degree Centrality | Betweenness | Out Closeness Centrality | In Closeness Centrality | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ranking | Country | Numerical Value | Ranking | Country | Numerical Value | Ranking | Country | Numerical Value | Ranking | Country | Numerical Value | Ranking | Country | Numerical Value |
2015 | ||||||||||||||
1 | CHN | 6.000 | 1 | CHN | 5.000 | 1 | CHN | 19.670 | 1 | CHN | 0.435 | 1 | CHN | 0.476 |
1 | MYS | 6.000 | 1 | THA | 5.000 | 2 | MYS | 11.167 | 2 | MYS | 0.435 | 2 | THA | 0.455 |
2 | SGP | 5.000 | 2 | VNM | 4.000 | 3 | THA | 8.500 | 3 | SGP | 0.417 | 2 | PHL | 0.455 |
3 | THA | 4.000 | 2 | SGP | 4.000 | 4 | VNM | 8.000 | 4 | THA | 0.400 | 3 | VNM | 0.435 |
4 | LAO | 2.000 | 2 | MYS | 4.000 | 5 | SGP | 3.167 | 4 | MMR | 0.400 | 3 | SGP | 0.435 |
4 | MMR | 2.000 | 3 | PHL | 3.000 | 6 | PHL | 0.000 | 5 | LAO | 0.370 | 3 | MYS | 0.435 |
4 | IDN | 2.000 | 3 | IDN | 3.000 | 6 | LAO | 0.000 | 6 | IDN | 0.357 | 4 | IDN | 0.400 |
5 | VNM | 1.000 | 4 | LAO | 2.000 | 6 | MMR | 0.000 | 7 | VNM | 0.345 | 5 | LAO | 0.370 |
5 | KHM | 1.000 | 5 | BRN | 1.000 | 6 | IDN | 0.000 | 8 | KHM | 0.313 | 5 | BRN | 0.370 |
6 | PHL | 0.000 | 6 | MMR | 0.000 | 6 | KHM | 0.000 | 9 | PHL | 0.200 | 6 | MMR | 0.200 |
6 | BRN | 0.000 | 6 | KHM | 0.000 | 6 | BRN | 0.000 | 9 | BRN | 0.200 | 6 | KHM | 0.200 |
2018 | ||||||||||||||
ranking | country | numerical value | ranking | country | numerical value | ranking | country | numerical value | ranking | country | numerical value | ranking | country | numerical value |
1 | THA | 9.000 | 1 | CHN | 9.000 | 1 | THA | 12.567 | 1 | THA | 0.909 | 1 | CHN | 0.769 |
1 | SGP | 9.000 | 1 | THA | 9.000 | 2 | CHN | 10.233 | 1 | SGP | 0.909 | 1 | THA | 0.769 |
2 | CHN | 8.000 | 2 | VNM | 7.000 | 3 | MYS | 6.567 | 2 | CHN | 0.833 | 2 | VNM | 0.667 |
2 | MYS | 8.000 | 2 | MYS | 7.000 | 4 | SGP | 6.067 | 2 | MYS | 0.833 | 2 | MYS | 0.667 |
3 | VNM | 7.000 | 3 | SGP | 6.000 | 5 | VNM | 3.667 | 3 | VNM | 0.769 | 3 | SGP | 0.625 |
4 | IDN | 6.000 | 3 | PHL | 6.000 | 6 | KHM | 0.500 | 4 | IDN | 0.714 | 3 | PHL | 0.625 |
5 | PHL | 5.000 | 4 | IDN | 5.000 | 7 | IDN | 0.200 | 5 | PHL | 0.667 | 4 | IDN | 0.588 |
5 | KHM | 5.000 | 5 | KHM | 4.000 | 7 | PHL | 0.200 | 5 | KHM | 0.667 | 5 | LAO | 0.556 |
6 | LAO | 3.000 | 6 | LAO | 3.000 | 8 | LAO | 0.000 | 5 | LAO | 0.556 | 5 | KHM | 0.556 |
7 | MMR | 2.000 | 6 | MMR | 3.000 | 8 | MMR | 0.000 | 7 | MMR | 0.526 | 6 | MMR | 0.526 |
8 | BRN | 0.000 | 7 | BRN | 2.000 | 8 | BRN | 0.000 | 8 | BRN | 0.250 | 7 | BRN | 0.500 |
Variable Name | Actual Correlation Coefficient | Significance Level | Mean Value of Correlation Coefficient | (Statistics) Standard Deviation | Minimum Value | Maximum Value | p ≥ 0 | p ≤ 0 |
---|---|---|---|---|---|---|---|---|
bod | 0.346 | 0.012 | 0.004 | 0.142 | −0.454 | 0.472 | 0.012 | 0.996 |
gdp | 0.496 | 0.009 | 0.007 | 0.245 | −0.731 | 0.660 | 0.009 | 0.991 |
ins | 0.173 | 0.221 | −0.001 | 0.291 | −0.571 | 0.483 | 0.221 | 0.779 |
cul | 0.190 | 0.150 | 0.003 | 0.143 | −0.398 | 0.308 | 0.150 | 0.948 |
Variable | Unstandardized Regression Coefficients | Standardized Regression Coefficients | Probability of Significance | p ≥ 0 | p ≤ 0 |
---|---|---|---|---|---|
Intercept | 0.580 | 0.000 | 0.000 | 0.000 | 0.000 |
Bod | 0.261 | 0.256 | 0.075 | 0.925 | 0.152 |
gdp | 0.000 | 0.427 | 0.054 | 0.054 | 0.947 |
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Liu, Q.; Liu, Y.; Zhang, J. The Tourism Service Trade Network: Statistics from China and ASEAN Countries. Sustainability 2022, 14, 9950. https://doi.org/10.3390/su14169950
Liu Q, Liu Y, Zhang J. The Tourism Service Trade Network: Statistics from China and ASEAN Countries. Sustainability. 2022; 14(16):9950. https://doi.org/10.3390/su14169950
Chicago/Turabian StyleLiu, Qing, Yaping Liu, and Jun Zhang. 2022. "The Tourism Service Trade Network: Statistics from China and ASEAN Countries" Sustainability 14, no. 16: 9950. https://doi.org/10.3390/su14169950