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Comparing the Determinants of Tourism Demand in Singapore and French Polynesia: Applying the Tourism Demand Model to Panel Data Analysis

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Comparing the Determinants of Tourism Demand in Singapore and French Polynesia: Applying the Tourism Demand Model to Panel Data Analysis Tourism Analysis, Vol. 25, pp. 175–181 1083-5423/20 $60.00 + .00 Printed in the USA. All rights reserved. DOI: https://doi.org/10.3727/108354220X15758301241585 Copyright Ó 2020 Cognizant, LLC. E-ISSN 1943-3999 www.cognizantcommunication.com RESEARCH NOTE COMPARING THE DETERMINANTS OF TOURISM DEMAND IN SINGAPORE AND FRENCH POLYNESIA: APPLYING THE TOURISM DEMAND MODEL TO PANEL DATA ANALYSIS KANTARO TAKAHASHI Faculty of Tourism and Business Management, Shumei University, Chiba, Japan This study explores the differences in tourism demand between French Polynesia and Singapore by applying the panel data technique. Although the tourism industry in these small states tends to be the main economic activity, they have a different economic structure: French Polynesia is highly depen- dent on the tourism industry, whereas Singapore has several service industries. This article applies the tourism demand model to panel data from 2008 to 2013. Different elasticities are observed in the model estimation between the two islands, such as income elasticity and transportation accessibil- ity. Additionally, this article compares time dummies to estimate the impact of global bankruptcy in 2008. The results show that French Polynesia has slightly declined, while Singapore has gradually increased since 2008. An implication of this study is that the demand in a destination highly depen- dent on the tourism industry tends to result in a relatively high-income market, but the economy is affected by global phenomena. A destination that owns diversified industries is likely to have good accessibility, and the global economic impact is lower in the tourism market. Key words: Tourism demand model; Panel data analysis; French Polynesia; Singapore Introduction in some works (Etzo, Massidda, & Piras, 2014; Fourie & Santana-Gallego, 2011, 2013; Khadaroo Tourism demand determinants are a significant & Seetanah, 2008; Vietze, 2008). issue in tourism research. Although several scales Gravity theory is convenient for describing the are considered to measure tourism demand, inter- interaction between two sites. However, most tour- national trade theory, such as “Gravity Theory,” is ism studies mainly focus on the demand side effects applied to discuss the international tourism demand on one destination. Therefore, some works call it Address correspondence to Kantaro Takahashi, Faculty of Tourism and Business Management, Shumei University, 1-1 Daigaku-Cho, Yachiyo, Chiba, 276-0003, Japan. Tel: 070-1640-8039; E-mail: [email protected] 175 Delivered by Ingenta IP: 192.168.39.211 On: Thu, 30 Sep 2021 01:13:32 Article(s) and/or figure(s) cannot be used for resale. Please use proper citation format when citing this article including the DOI, publisher reference, volume number and page location. 176 TAKAHASHI the “Tourism Demand Model” (Garín-Mun, 2006; much bigger states. Following this relation, the Khadaroo & Seetanah, 2007; Santana-Jimenez & determinants of tourism demand are also likely to Hernandez, 2011). In the demand model, the tour- be different even in small regions where tourism ism flow level from the place of origin is regarded and other service industries play strong roles in the as the demand level, and most papers discuss the economy. The implications of this study might refer determinants of tourism demand using the econo- to regional development policy in small regions, as metric approach (Witt & Witt, 1995). well as their tourism impact. To estimate tourism demand, an econometric model is applied to some regions, and small island regions are often focused on (Garín-Mun, 2006; Theoretical Background and Methodology Khadaroo & Seetanah, 2007; Santana-Jimenez & Tourism Demand Model Hernandez, 2011). In small states, such as island regions, the service sector, especially the tourism Although the model is diversified and applied to industry, is generally important for the economy explain determinants or predict tourism demand, (Armstrong & Read, 1995). For example, the the basic formula is simple: tourism industry in Fiji is related to the economic growth (Narayan, 2004). Meanwhile, some stud- Yijt = f (INCOMEit, PRICEijt, TRANSPORTATION ies show different results in each region. Chou COSTij) (1) (2013) mentioned that the effects of tourism development on economic growth differ between where i and j denote the origin and destination, re- 10 countries in Eastern Europe and the Mediter- spectively; t signifies time; and Y represents tour- ranean area. Lee and Chang (2008) also showed ism demand. In previous papers, tourism demand the differences among OECD and non-OECD basically uses tourism flow from origin i to destina- countries. Although many factors of these results tion j (Witt & Witt, 1995). INCOME indicates the are considered, size of market, policy differences, income effect on the tourism demand from origin and social-economic situations are regarded as the countries. This variable basically shows elasticity main reasons. for tourism demand because tourism is regarded as This article selects two islands from the cat- luxury goods in international trade (Lim, 1997). To egory of Small Island Developing States (SIDS). measure the income effect, most papers use GDP SIDS are designated by the United Nations (UN- or GNI per capita as a variable (Lim, 1997; Witt & OHRLLS 2016) as vulnerable countries faced with Witt, 1995). PRICE represents the price difference similar challenges, such as global warming or eco- between origin and destination (Dogru, Sirakaya- nomic issues. Although SIDS face common eco- Turk, & Crouch, 2017). This variable indicates the nomic challenges, the economic development level tourist preference for price difference and gener- is different for each island. Singapore, for example, ally shows negative elasticity for tourism demand is well known as an economic giant despite being a (Dogru et al., 2017; Lim, 1997). To measure the member of SIDS. The economy is highly dependent price difference, some works construct this variable on international trade and a well-known global hub with the “consumer price index” and “exchange (Central Intelligence Agency [CIA], 2016). French rate” (Dogru et al., 2017; Lim, 1997). TRANSPOR- Polynesia is a well-known resort island. Most of TATION COST represents the transportation cost its economic resources are from the military sector between the origin and destination. Transport cost and tourism industry (CIA, 2016). generally shows negative elasticity for tourism de- The purpose of this article is to discuss the deter- mand (Witt & Witt, 1995). To capture the transporta- minants of tourism demand with the panel data tion effect, the “air fare” or “geographical distance” technique focusing on the two islands, which pos- is used as a variable (Khadaroo & Seetanah, 2007; sess different regional characteristics. Regional Nelson, Dicke, & Smith, 2011; Seetaram, 2012). differences cause different impacts regarding tour- Previous works also applied other variables and ism development and economic growth accord- factors to tourism demand (Lim, 1997). These ing to the previous works, which mainly focus on mainly compose the qualitative variables in the Delivered by Ingenta IP: 192.168.39.211 On: Thu, 30 Sep 2021 01:13:32 Article(s) and/or figure(s) cannot be used for resale. Please use proper citation format when citing this article including the DOI, publisher reference, volume number and page location. TOURISM DEMAND IN SINGAPORE AND FRENCH POLYNESIA 177 model. For example, the relationships between rate of change expenditure per capita based on the origin and destination, such as colony, language, year 2010 to show the living cost in a destination, and religion, are often used to explain tourism and the defined index is as: demand (Fourie & Santana-Gallego, 2011, 2013; Vietze, 2008). In the time series data and panel PRICE = (TPIj/CPIi)/(EXj/EXi) (3) data approach, the time dummy also indicates the event impact (Fourie & Santana-Gallego, 2011; where “TPI” represents the living cost in a destina- Seetaram, 2012). tion. “CPI” indicates the consumer index price based on the year 2010. “EX” indicates the exchange rate (LCU per US dollar). The assumption of this vari- Estimation Model and Study Data able shows that resort islands have more significant Tourism demand is generally assumed to be a variables because they attract much richer tourists low-power functional relationship. The estimation than other destinations. model modifies the style to both sides of the loga- “DIST” represents the distance between the ori- rithmic model to determine the coefficients inter- gin and destination. The distance variable is often preted as elasticity for tourism demand. The model interpreted as the transportation cost and tends to for this article for the difference in tourism demand represent negative elasticity for tourism demand between the two islands is: (Witt & Witt, 1995). Singapore is a well-known international trade hub with a developed transpor- log(TFijt) = a+b1log(GDPCAPit) + b2log(POPit) tation system. Accessibility is much higher, and the + b3log(PRICEijt) + b4log(DISTij) transportation cost is lower than in other destina- + b5COLONYij + b6LANGAGEij tions. Therefore, this variable in Singapore is lower + eijt (2) than in French Polynesia. Furthermore, the article adds “COLONY” and where “b” represents the coefficients, which inter- “LANGUAGE” to explain the colonial and lan- pret the elasticity for the dependent variable “TF”. guage relations. Basically, island regions have “TF” shows tourism flow from
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