Conceptual Theoretical Model
In most countries, particularly in developing nations, tourism is one of the significant sources of revenue and the main contributor to economic growth. Tourism also plays a vital role in promoting economic growth by contributing to the gross domestic product (GDP) (Shih, and Do 2016, 371-372). East Timor (also known as Timor-Leste) is one of the world’s youngest countries after having got its independence in 2002 from Indonesia. The country’s tourism sector is still at the infancy stage, owing to continuous conflict and clashed between various security agencies and the government. However, despite its dormancy, the tourism sector in Timor-Leste, just like in any other Southeastern Asian countries has an immense potential to contribute to the country’s economic growth and significantly boost its GDP growth for sustainable development.
Research Data
Data Description
The time series data on tourism growth in Southeast Asia used in this study was obtained from the World Bank website. The GDP growth data that covered 11 countries on the Asian continent (Timor-Leste, Vietnam, Thailand, Singapore, Philippines, Myanmar, Malaysia, Lao PDR, Indonesia, Cambodia, and Brunei Darussalam) included annual net tourism income as well as an annual number of travelers entering the respective countries from the year 2000 to 2018. Moreover, the data set consists of four variables, as described in Table 1 below.
Table 1: Variable description table
Summary Statistics
The mean international receipts are 7.6 billion dollars, with a standard deviation of 10.9 billion dollars. The significant standard deviation indicates that the data entries are widely spread around the mean. Furthermore, the difference between the mean and 50th percentile (medium) is significantly large (4.2 billion dollars), indicating that the data is skewed to the right. This result is also supported by the positive skewness score of 2.44.
The South Asian countries recorded an average percentage annual growth of 5.56 percent between the years 2000 and 2018. With a skewness score of just 0.38, the distribution of GDP growth across the countries is reasonably symmetrical. This result is supported by the small (1.8 percent) difference between the mean and 50th percentile.
The variables “Arrivals” and “GDP growth” have relatively small variations from the mean with standard deviations of 8,296,090 and 5.53 against means of 7,127,118 and 5.56, respectively. The small variations show that the variables data are relatively symmetrical, and, therefore, increase the accuracy of the regression model.
Table 2: Descriptive statistics
Correlation between variables
Pearson’s correlation presented in Table 3 below shows that there is no correlation between the number of arrivals of international inbound tourists and the annual percentage GDP growth. Interestingly, there is a negative correlation of 0.0962 between the receipt of items from international inbound tourists and GDP growth. This result is against the research study that established that income from tourism income is directly proportional to annual percentage GDP growth in Southeast Asian countries (Nguyen 2015, 4).
However, a high positive correlation (of 0.998) between the number of arrivals and the total receipts indicates that the variables are not independent. In regression analysis, independent variables must have a low correlation of less than 0.5 to obtain an accurate model (Flatt and Jacobs 2019, 484-500). Therefore, the high positive correlation between the two independent variables reduces the accuracy of the model below.
Table 3: Pearson’s correlation between variables
Regression Model
The regression result presented in Table 4 below shows that the regression model has R², (R-squared) of 0.0144, which indicated that only 1.4 percent of the variability in dependent variable (annual percentage GDP growth) can be explained by the independent variables. Furthermore, the regression model is not statistically significant, F(3,174)=0.85, p=0.469, this result shows that the model cannot statistically significantly predict the dependent variable, annual percentage GDP growth. According to the regression result below, the regression model will be as shown in the equation below:
The regression model above shows that there is a negative association between internal tourism receipts and GDP growth, which is against the finding of Nguyen (2015) who established that the GDP growth in developing countries is directly proportional to the number of receipts received from international tourists.
Table 4: Linear regression model
Limitations of the data discovered
Although the data file was obtained from the World Bank database, which is a reputable institution, it had two major limitations. First, there were missing observations in variables for some countries. For instance, the data entries for the variables “GDP growth,” “Arrivals” and “Travels” or East Timor were mission from the year 2000 to 2006. Secondly, the number of observations for some countries was too small to depict the correct economic growth trend in those countries.
Bibliography
Flatt, Candace, and Ronald Jacobs. 2019. “Principle Assumptions of Regression Analysis: Testing, Techniques, and Statistical Reporting of Imperfect Data Sets.” Advances in Developing Human Resources 21 (4): 484-502.
Nguyen, Anh Tru. 2015. “Examining the Relationship between Tourism and Economic Growth in Southeast Asia: A Vector Autoregressive Model Approach.” International Tourism and Hospitality Journal 1 (2): 1-17.
Shih, Wurong, and Ninh TH Do. 2016. “Impact of Tourism on Long-Run Economic Growth of Vietnam.” Modern Economy 7 (3): 371-376.