the determinant of tourist arrivals in malaysia a panel data regression analysis

THE DETERMINANT OF TOURIST ARRIVALS IN MALAYSIA: A PANEL DATA REGRESSION ANALYSIS. TABLE OF CONTENT CONTENTPAGE Chapter 1- Introduction Background of the Study 1 Problem Statement 2 Scope and Rational of the Study 2 Significance of Study 2 Research Objectives 3 Chapter 2- Literature Review History of Tourism in Malaysia 4 Chapter 3- Methodology Methodology 6 Model Specification 10 References CHAPTER 1 INTRODUCTION The tourism industry continued to contribute towards generating foreign exchange earnings, employment and income.

Although in Malaysia, the industry was affected by the economic crisis in 1997 and 1998, its quick rebound contributed to the strong economic recovery of the nation. This was attributed mainly to the concerted efforts by the public and private sectors as well as the successful implementation of measures outlined in the National Economic Recovery Plan (NERP) to revitalize the tourism industry. Tourist arrivals increased at an average rate of 6. 5 per cent per annum during the Seventh Plan period.

Tourist arrivals however declined in 1997 and 1998 mainly due to occurrences of haze, localized outbreaks of Nipah and Coxsackie viruses as well as the Asian financial crisis. The number of tourist arrivals to Malaysia declined by about 13 per cent in 1997 and 10. 6 per cent in 1998. However, the tourism industry responded well to the measures taken to revitalize the industry, which included increased promotional efforts targeted at markets not affected by the economic crisis such as China, India, Middle East, Australia and Europe.

As a result, the tourism industry recovered quickly as reflected by the rapid increase in the number of tourists to 7. 9 million in 1999, which represented an increase of 43. 6 per cent over the 1998 figure. In 2000, a record of 10. 2 million tourist arrivals was achieved, which surpassed the target by 3. 7 million tourists. This paper uses a computable general equilibrium (CGE) model to analyze tourism in Malaysia. The development of the tourism industry in Malaysia has a long history.

Before its dependence in 1957 and a few decades after, the Malaysian economy was heavily dependent on primary commodities mainly tin, rubber, palm oil and petroleum products. In the 1970’s, the government had seriously started to stimulate the development of the manufacturing industry in an effort to diversify the country’s economy. These two sectors, however, were highly export-oriented and their performance was directly influenced by changes of the world economic climate.

The severe economic recession that hit most of the Asian region in the mid 1980’s had badly hurt the Malaysian economy and the government started to search for a more robust industry to broaden the country’s economic base. Tourism was identified as a potential industry that could encourage and stimulate the socio-economic development of the country especially as a supplier of foreign exchange earnings, and employment opportunities. Tourism sector also contribute to regional development, encourage the development of supporting sectors and reduction in rural-urban migration.

After the severe recession in the mid 1980’s the government has given a very high priority to the development of the tourism industry. The seriousness of the government in promoting the tourism industry was manifested by the establishment of the Ministry of Culture, Arts and Tourism in 1987. In 2004, this ministry was restructured into three ministries and one of them is the Ministry of Tourism which was assigned to take care of, coordinating and implementing government policies and strategies pertaining to tourism development.

Various tourism-related agencies at the state level were also set up, besides having some promotional activities such as the declaration of Visit Malaysia Year’ (VMY) in the 1990’s, 2000, and 2007, and active participation of the private agencies. Most of the tourists in Malaysia till today come from the Asian countries. Asian tourists dominated more than 80 percent of tourist arrivals to Malaysia.

Since Asian tourists comprise the prevalent proportion of visitors to Malaysia, this paper attempts to study the long-run and short-run relationship between the demands for tourism to Malaysia from several selected countries namely Singapore, Indonesia, Thailand, Hong Kong, China, Japan, USA, and United Kingdom and several macroeconomic variables. These variables include arrival of tourists from the selected countries to Malaysia, tourism price, traveling costs, tourism price at the alternative tourism destinations, their income and exchange rates, which will be utilized as determinants to explain the demand for tourism in the long-run.

In the short-run, dummy variables are also included. Annual data will be used, covering the period from 1995-2005. This paper will examine the factor that determinant of tourist arrivals in Malaysia from the most famous tourist country arrivals. However, only the economic factors will be considered. For this purpose, in section 2, we provide our theoretical arguments and present a model that is used to assess the determinant of tourist arrivals. While, Section 3 provides our empirical findings supporting mostly that more smuggling leads to the higher determinant. CHAPTER 2 LITERATURE REVIEW

In the traditional tourism demand analysis, the most popular method of estimation is the Ordinary Least Square (OLS). Based on the studies by Crouch (1994) and Witt and Witt (1995), 73 out of 97 studies on demand for tourism are based on the OLS regression. OLS is a static analysis, thus it relies heavily on the basic assumptions in the Classical Linear Regression Model (CLRM), especially the assumptions related to the error term. Any violation of the assumptions would result in invalid regression estimation. In order to overcome this problem, the data used in regression analysis should be stationary.

If the data are stationary, then the error term should meet all the basic requirements under the CLRM assumptions. However, most tourism demand data are non-stationary, and the issue of stationary has been ignored by many researchers in the field of tourism. Estimation based on non-stationary data is flawed (Philips, 1986). This can lead to a serious problem of spurious regression (Morley, 1998; Song and Witt, 2006). The consequence for ignoring data stationary is that the estimated parameters are unreliable and the t-tests and F-tests produce misleading results.

In some cases, in order to make the data stationary, differenced variables are used in regression analysis. In other words, the Cochrane-orcutt (CO) procedures are applied, especially when there is a presence of autocorrelation (Uysal and Crompton, 1984; Hollender, 1982; Loeb, 1982; Martin and Witt, 1987 and 1988). This will lead to another serious problem with the traditional tourism model which is related to the forecasting performance. Differenced variables generate only the short-run estimation. How could the long-run relationship among the variables be taken into account in the traditional tourism demand method?

To overcome this problem, the modern econometric methodologies are employed in recent studies on the demand for tourism. After the mid-1990’s, most researchers apply the dynamic analysis since the static analysis suffers from the problem of spurious regression. Furthermore, the static analysis is associated with structural and forecasting problems (Song and Witt, 2000). One of the most popular dynamic methodologies in the field of tourism at present is the co-integration method. Co-integration shows the long-run equilibrium relationship while accommodating the dynamic short-run relationship.

Co-integration analysis requires the use of stationary data. Therefore, the regression is free from spurious results. To avoid the same problems, the co-integration method will be used in this study. There are a few approaches of co-integration analysis, namely the Engle-Granger co-integration (1987) framework, Johansen and Juselius (1990) multivariate co-integration framework and Pesaran and Shin (1995, 1999); Pesaran et al. (1996); and Pesaran et al. (2001) framework, which is referred to the Autoregressive Distributed Lag (ARDL). CHAPTER 3 METHODOLOGY

The number of tourist arrivals has been used as a proxy of the demand for tourism by a majority of researchers (Witt and Martin, 1987; Crouch, 1994; and Li, 2004). This study uses the same variable. Data on tourist arrivals from Singapore, Indonesia, Thailand, Hong Kong, China, Japan, USA, and United Kingdom for the periods of 1995-2005 have been collected from the Tourism Malaysia (Annual Statistical Report). The independent variables include the tourism price, traveling cost, prices of alternative tourism destinations, income, exchange rates, and the dummy variables.

In this study, tourism price refers to the price of all goods and services consumed by tourists at the destination. The calculation of tourism price is based on the consumer price index (CPI) of the visited country divided by the CPI of the country of Contemporary Management Research 355 origin (Salman, 2003; Lim, 2004; Dritsakis, 2004; and Toh, Habibullah and Goh, 2006). Please refer to equation (1). CPI visited destination CPI origin country(1) In this research, tourism price proxies by the ratio of the cost of living in Malaysia relative to the selected country of the research.

It is expected that tourism price and arrivals will have a negative relationship. Traveling cost can be measured by some variable such as air fares between the visited destination and the country of origin (Bechdolt, 1973; Gray, 1966; Kliman, 1981; Kulendran and Witt, 2001; Lim and McAleer, 2002; and Dritsakis, 2004); ferry fares and or petrol costs for surface travel (Quayson and Turgut, 1982; and Witt and Martin, 1987); and price of crude oil (Munoz, 2006). In this study, the price of crude oil will be used.

Similar to relative price, ceteris paribus, if the traveling cost rises, the cost of traveling becomes more expensive, and this will reduce the number of visitors to travel. It is hypothesized that traveling cost is inversely related to the demand for tourism. Another important variable is tourism price at alternative tourism destinations. Tourism prices at alternative tourism destinations are a substitute price. Substitute price has also been proven to be an important determinant in some studies (Gray, 1966; Kliman, 1981; Witt and Martin, 1987; Witt, 1980a,b; and Song et. l. , 2003). The calculation is similar to the estimating of tourism price, where the visiting destination now refers to the alternative tourism destination. In this study, the alternative tourism destinations are Singapore, Thailand and Indonesia. Thus, tourism price at an alternative tourism destination will be the cost of living for tourist in Singapore, Thailand or Indonesia relative to the each selected country (please refer to Equation 2). The relationship between the substitute price and the demand for tourism can be positive or negative.

A positive sign of substitute price means that the country is a substitute destination for Malaysia, while a negative sign means that the country is a complementary destination to Malaysia. CPI substitute destination CPI origin country(2) The income variable refers to the real per capita income (RPI), please refer to Equation (3). Income is the most popular variable included in the tourism demand function (Lim and McAleer, 2002; Dritsakis, 2004; and Munoz, 2006). Normally, a higher income will increase total arrivals. RPI origin country = GDPorigin country POP origin country * CPI origin country (3)

Another important variable is the exchange rate. The exchange rate is the ratio of currency between the receiving country and the country of origin. The change in exchange rate will affect the currency value of the origin country, please refer to Equation (4). Any change in exchange rate will lead to the appreciation or depreciation of tourist currency (Salman, 2003; Lim, 2004; Dritsakis, 2004; and Toh, Habibullah and Goh, 2006). Any appreciation in tourist currency may encourage more people to travel. Exchange Rate = Cost of Malaysia ringgit The origin country dollar(4)

In some studies, dummy variables are also included. The purpose of including dummy variables is to measure the impact of “one shot” events. Dummies are specially constructed variables which take the value “1” when the event occurs and “0” otherwise. In this study two dummy variables are incorporated in the model specification, namely the 1997 East Asian economic crisis (D97) and the outbreak of SARS (D03). Model Specification The proposed model is given as below: LnTAijt = ? 0 + ? 1LTPijt + ? 2LTCijt + ? 3LSPSingit + ? 4LSPThaiit + ? 5LSPIndoit + ? 6LRPIit + ? 7LERijt + ? 8D97 + ? 9D03 + ? (1) where i and j refer to the each selected country and Malaysia respectively. LnTAijt refers to the log of tourist arrivals from the every selected country to Malaysia in year t; LTPijt is the log of tourism price from the each selected country to Malaysia in year t; LTCijt is the log of traveling cost from the selected country to Malaysia in year t; LSPSingit, LSPThaiit, and LSPIndoit is the log of tourism price of the each selected country to an alternative tourism destinations referred as Singapore, Thailand and Indonesia respectively; LRPIit is the log of real per capita income of the very selected country in year t; LERijt is the log of exchange rate between the every selected country and Malaysia in year t; D97 is the economic crisis in 1997-98; and D03 is the SARS outbreak in 2003. The dummy variables are used to capture the effect of economic crisis and the outbreak of SARS. The variables take the value of 1 in the year of the economic crisis and SARS, and 0 otherwise; ? t is the error term; and the ? 0, ? 1, ? 2, ? 3, ? 4, ? 5, ? 6, ? 7, ? 8 and ? 9 are the elasticity to be estimated. Annual data are used and they cover the period from 1995-2005.

Data on tourist arrivals are collected from Annual Statistical Report, Tourism Malaysia while other data are collected from the World Bank database and IMF International Financial Statistics database. REFERENCES Martin, C. A. , and Witt, S. F. (1988). Substitute prices in models of tourism demand. Annals of Tourism Research, 15(2), 255-268. Morley, C. L. (1998). A Dynamic international tourism demand model. Annual of Tourism Research, 25, 70-84. Munoz, T. G. (2006). Inbound international tourism to Canary Islands: a dynamic panel data model. Tourism Management, 27, 281-291. Narayan, P. K. (2004).

Fiji tourism demand: the ARDL approach to cointegration. Tourism Economics, 10(2), 193-206. Pesaran, M. H. , and Shin, Y. (1995). Autoregressive distributed lag modeling approach to cointegration analysis. DAE Working Paper Series No 9514. Department of Economics, University of Cambridge. Pesaran, M. H. , and Shin, Y. (1999). An autoregressive distributed lag modeling approach to cointegration analysis, in storm, S. , ed, Econometrics and Economic Theory in the 20th Century: the Ragnar Frish Centennial Symposium, Cambridge University Press, Cambridge, chapter 11. Pesaran, M. H. , Shin, Y. , and Smith, R. J. (1996).

Testing the existence of long-run relationship. DAE Working Paper Series No 9622. Department of Applied Economics, University of Cambridge. Pesaran, M. H. , Shin, Y. , and Smith, R. J. (2001). Bound testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289-326. Philips, P. C. B. (1986). Understanding spurious regression in econometrics. Econometrica, 311-340. Mohd Salleh N. H, Hook L. S, Ramachandran S. Shuib A. Mohd Noor Z. (2008). Asian Tourism Demand for Malaysia: A Bound Test Approach. Contemporary Management Research, 351-368, Vol 4, No4. Eight Malaysian Plan, Chapter 8.

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