Second, we would like to express our sincere thanks to our research supervisor, Mr. Lee Chin Yu, for his patient guidance, enthusiastic encouragement, and helpful critique of this research work. They were willing to sacrifice their precious time and vacations to work hard together to complete this research project.
Introduction
The blue line represents developed countries and the red line represents developing countries as shown in the diagram. In our study, we have selected 10 countries, of which 5 are developed countries and 5 are developing countries.
Research Background of Ten Selected Country
Background of Italy
In 2001 to 2008, tourist arrivals having a serious fluctuation and starting from 2009 has increased rapidly until 2015, while tourism income is increasing from 2001 to 2008 and started to fluctuate in the middle of 2009 in 2012 and again slowly increasing annually. 2013.
Background of France
Background of United States
Visitors from Canada, China and Mexico contributed the highest tourism spending at about US$26 billion in 2015. However, domestic tourism is still the largest component of tourism spending in the country.
Contribution of tourism to GDP 2016
- Background of Mexico
- Background of Germany
- Background of Spain
- Background of Turkey
- Background of China
- Background of Russia
- Background of Malaysia
Mexico is named as the second most visited country in the Americas, the first being the United States. Tourism in Malaysia is ranked 12th in the world rankings with a number of around 27.4 million tourists.
Contribution of Tourism to GDP in 2016
Problem statement
Although tourism is the fastest growing industry, not every country is able to adapt economic policies that could stimulate the growth of the tourism sector. This begs the question whether the governments of developing countries have not focused their resources on the right factor to attract more tourists, or whether there are structural differences between developing and developed countries in the tourism sector. According to Balaguer, Cantavella-Jorda (2010), an increase in tourism revenue will lead to an increase in countries' GDP in the long run, but there is no evidence that GDP affects tourism.
Also, many researchers have conducted studies to show the effect of the inflation rate on tourism. Research findings show that inflation has a significant impact on tourism where a high rate of inflation will significantly reduce tourism revenue (Agarwal, 2008). Meanwhile, according to Gareth (2016), the higher the tourism income will only increase the inflation rate, but the inflation rate will hardly affect the tourism income.
However, the top 20 countries in tourist arrivals have more developed countries compared to developing countries. Our study aims to highlight the difference effects of the variables between developing and developed countries. As such, the developing countries could refer to the policies of the developed countries and develop their tourism industry.
Research Objective
- General Objective
- Specific Objective
Research Question
Significance of studies
Chapter Outlay
Conclusion
Literature Review
- Introduction
- Review of Literature
- The relationship between Tourism development and Gross Domestic Product (GDP) (GDP)
- The relationship between Tourism development and Crime rate
- The relationship between Tourism and CO 2 Emission
- The relationship between Tourism and Inflation
- Review of Relevant Theoretical Framework and Models
- Proposed Theoretical Framework and Model
- Conclusion
- Summary of Literatures in Table Form
Samimi, Somave Sadeghi, Soraya, Sadehgi(2011) observed that there is bilateral causality between GDP and tourist arrivals. The Granger causality analysis also shows that there is a long-run relationship between GDP and tourist arrivals. His result shows only one way Granger causality between GDP and tourist arrivals indicating GDP has a positive impact on Tourist arrivals.
Li's (2011) findings contradict Oh's (2005) study, as he found that there is positive bidirectional causality between GDP and tourist arrivals. As a result, the tourist arrivals will sometimes increase even while the crime rate increases. In the journal of Tang (2011), he investigated the dynamic relationship between the tourist arrivals and crime rate in Malaysia using time series analysis for the period 1970 to 2008.
He tested the relationship between crime rates and tourism and the results show that tourism is significant. Moreover, it also shows that crime and tourist arrival are correlated and that in the long run tourist arrival will have a positive relationship with crime. GDP and tourist arrivals are level (1) integrated in certain countries such as Latin America, the Caribbean.
The result shows that different regions have a different relationship between crime rate and tourism. The result of Granger Causality shows unidirectional causality between GDP and tourist arrivals, namely GDP has a positive impact on Tourist arrivals.
Methodology
- Introduction
- Model Specification
- Data Collection Method
- Empirical Methodology
- Pooled OLS Model
- Fixed Effect Model
According to levantis and gani (2000), he stated that the country's tourism development will be higher if the country's crime rate is lower. When crime has increased in a country, it means that the tourist is more likely to fall victim to the criminal cases in that particular country while visiting the country. Therefore, it means that an increase in crime will tend to reduce the tourist's willingness to go to the countryside; this will reduce expenses due to the reduction in tourist visits.
When the environment of the country is having a serious problem of pollution, it will make the tourist to become uncomfortable which will eventually cause the decrease of the tourist visit. Air Pollution World Bank CO2 Emissions CO2 emissions are defined as the release of carbon dioxide into the atmosphere which can cause climate change. World Bank Definition of CPI is a measure of the weighted average price of goods and services in a country.
First, the pooling method allows to examine the cross-sectional units along with the individual time units. The pooled OLS model is one of the most rigid models because it has a specific constant interception coefficient. If the sample size eventually increases to infinity, the sampling distribution will implode more into the actual value of the parameter.
- Random Effect Model
- Hausman Test
- Redundant Fixed Effect Test
- Conclusion
The random effect model assumes the effect of the independent variables, while the individual specific effect on the dependent variable is the same over time. In addition, Random effect models also assume that the error variances are constant over time (σ2µt=σ2µ). As we can see in the random effect model, the individual special effect (𝐶𝑖) is a random variable, which means that it does not consist of a relationship with the independent variable.
If the autocorrelation problem is observed in the random effects model (REM), the OLS regression model cannot be used because it is not the best linear regression estimator. Additionally, the random effect model can also be used to estimate generalized least squares and feasible generalized least square. Since the random effects model satisfies the assumption requirement, we can say that generalized least square or feasible generalized least square will provide an unbiased and consistent estimator for the model.
In random effects model (REM), error terms variance and coefficient estimators and also individual specific effect usually use the feasible generalized least squares estimator. To decide between using fixed effect model or random effect model, we proceed to the Hausman test. If the result of this test shows no correlation between independent variable and the unit's effect, then random effect is more appropriate to be used.
Data Analysis
- Introduction
- Comparison between Pooled OLS Model, Fixed Model and Random Effect Model for developing country Random Effect Model for developing country
- Interpreting the Result
- Comparison between Pooled OLS Model, Fixed Model and Random Effect Model for developed country
- Interpreting the result
- Conclusion
If a developing country's GDP per capita increases by 1%, its tourism revenue is expected to increase by an average of 1.1586%, holding other variables constant. If a developing country's crime rate increases by 1%, its tourism revenue is expected to increase by 0.01701% on average, holding other variables constant. The result is contrary to our expectations as we expect the crime rate to be negatively related to tourism income.
If the CPI of a developing country increases by 1%, its tourism receipts are expected to increase by 0.6592% on average, holding other variables constant. If a developing country's carbon dioxide (CO2) emissions increase by 1%, its tourism revenue is expected to decrease by 0.02031% on average, holding other variables constant. If GDP per population of a developed country increases by 1%, its tourism revenue is expected to increase by 1.8825% on average, holding other variables constant.
If the crime rate of a developed country increases by 1%, its tourism revenue is expected to decrease by 0.04252% on average, holding other variables constant. If the CPI of a developed country increases by 1%, its tourism revenue is expected to increase by 2.2634% on average, holding other variables constant. If a developed country's carbon dioxide emissions increase by 1%, its tourism revenue is expected to increase by 0.3444% on average, holding other variables constant.
Implication, limitation and conclusion
- Introduction
- Summary
- Policy Implications
- Limitations
- Recommendation
Apart from that, we did not perform unit root test due to the short period of data. This means that when the overall development of the country is good, more tourists will be attracted to the country. However, the increase in CPI will increase the cost of living of the citizen in the country.
This suggests that the rise in inflation will lead to lower tourism earnings in real income terms due to the loss of monetary value. Therefore, we suggest that the policy makers implement policies that encourage economic growth and in the meantime provide incentives to the businesses in the transport, catering and accommodation sectors. The positive relationship of CPI may also be due to the fact of weaker currency according to the tourism demand model.
For example, Gul, Asik, and Gurbuz (2014) found that tourism demand in Turkey increases even after inflation due to the depreciation of the Turkish lira. This is one of the reasons that we were only able to use static panel data for this study. Future researchers could also focus on a region specifically to examine the different effects of variables on tourism revenue.
Developing country
Developed Country
Retrieved from https://www.researchgate.net/profile/Nazan_SAK/publication/263651586_Rel ationship_between_Tourism_and_Economic_Growth_A_Granger_Causality_. Retrieved from http://www.diplomatie.gouv.fr/en/french-foreign-policy/economic-diplomacy- Foreign-Trade/facts-about-france/one-figure-one-fact/article/france-the- leading tourist in the world. The Impact of the Global Economic Crisis on Turkish Tourism Demand and Review for the Period 2003-2013.
Retrieved from http://s3.amazonaws.com/academia.edu.documents/39426555/KGKriz.pdf?A WSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires amp;Si gnature=EhzbPbPbGzRkVex8%3%2ZAhxrespons%3FZRkVx8%3%2ZAhx8 An exploration of dynamic relationships between tourist arrivals, inflation, unemployment and crime in Malaysia.
