Data Methods for the Tourism Industry

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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 worlds youngest countries after having got its independence in 2002 from Indonesia. The countrys 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 countrys 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

Variable code	Variable name	Description
Arrivals	Number of arrivals of international inbound tourists.	International inbound tourists are people who travel from their countries of residence to other countries for a period not less than one year. Moreover, the tourists must not earn income from the countries they visit.
Travel	Receipt of items from international tourism in US Dollars.	The items include expenditure by international inbound travelers or visitors in the reporting countries.
Receipt	International tourism receipt in US Dollars.	International tourism receipts include the expenditure of international inbound visitors on items on the reporting countries including expenses on national carriers and international transport.
GDP growth	Annual percentage GDP growth.	Annual percentage growth in gross domestic product at market price based on the currency of the reporting country.

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

Variable	No. of Observations	Mean	Standard Deviation	Medium	Skewness
Arrivals	178	7,127,118	8,296,090	4,241,500	1.55
Receipts (Mn $)	178	7,690.00	10,900.00	3,480.00	2.4433
Travels (Mn $)	178	6,950.00	9,930.00	3,170.00	2.49
GDP Growth (Annual %)	178	5.56	5.38	5.83	0.38

Correlation between variables

Pearsons 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: Pearsons correlation between variables

Correlation	Receipts	GDP growth	Arrivals	Travels
Receipts	1.000	-0.116	-0.116	-0.116
GDP growth	-0.116	1.000	0.927	0.927
Arrivals	0.9269	0	1.000	0.998
Travels	0.998	-0.0962	0.921	1.000

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.

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