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 dependent 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 accessibility. 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 dependent 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

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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 origin “i” to desti- a colonial history. In French Polynesia, the colo- nation “j.” Tourism flow is interpreted as tourism nial dummy indicates positive elasticity for tour- demand for the destinations. ism demand because of a strong relationship with GDP per capita, or “GDPCAP,” indicates income a suzerain state. On the other hand, speaking the variables. This variable is interpreted as the income same language is an advantage. Singapore is a elasticity for tourism demand. In this article, the multilingual country, and the language advantage is assumption of this variable shows that French Poly- significant for tourism demand.

nesia has higher elasticity than Singapore. Tourism “eijt” represents the error term. This composes is regarded as luxury goods in the international both time and the cross-section vector; thus, the trade field, although tourism demand in a resort panel data technique is applied to estimate the island is closely related to the income of origins. model. Although a few ideas for the error term “POP” represents the population number in ori- exist, such as fixed or random effect, this article gins to show the size of the origin effect on tourism conducts the Hausman test to choose the appro- demand. The size of origins is interpreted as the scale priate method. If the null hypothesis in the Haus- effect. This variable is also significant to explain and man test is rejected, a fixed-effect method will be show the positive elasticity of tourism demand. adopted. In contrast, if an alternative hypothesis is “PRICE” represents the price difference between adopted, a random-effect model will be employed the origin and destination. Previous works use the to estimate the coefficients. consumer price index ratio of the destination and Table 1 shows the data resources for the esti- origin adjusted by the relative dollar exchange rate mation model. The tourism flow is from a dataset (Park, 2016; Seetaram, 2012). Another uses tourist published by World Tourism Organization (WTO, consumption to express the living cost in the desti- 2015a, 2015b). The explanatory variables, such nation (Garín-Mun, 2006). This article utilizes the as GDP per capita, population number, CPI, and

Table 1 Data Resources

Variable Explanatory Note Source

TFijt The number of tourists from origin i to destination j WTO (2015a, 2015b) GDPCAPit GDP per capita in origin country (constant 2010 US$) World Bank (2016) POPit Population in origin country World Bank (2016) PRICEijt PRICEijt = (TPIjt/CIPit)/(EXjt/EXit) World Bank (2016) TPIj: the rate of change expenditure per capita in destination j (2010 = 100)a CIIPi: consumer price index in origin i (2010 = 100) EX: exchange rate (LCU per US$, period average) in each origin i and destination j, respectively

DISTij Geo distance between origin and destination Mayer and Zignago (2011) COLONYij Dummy variable showing the colony relationship between origin i and Mayer and Zignago (2011) destination j (yes = 1, no = 0)

LANGUAGEij Dummy variable showing the relationship whether same language between Mayer and Zignago (2011) origin i and destination j (yes = 1, no = 0) Note. aOwn calculation based on WTO (2015a, 2015b).

exchange rate, are from the World Development Results Indicators from the World Bank (2016). Distant variables, colony, and language relationship are For accurate estimation, a Hausman test was taken from CEPII. Appendix 1 and 2 show the done. Test results showed that the null hypothesis origin list in each destination. The origin numbers was not rejected in either region. The statistical are 65 in Singapore and 36 in French Polynesia. value in Singapore was χ2 = 6.02 (p = 0.65) and the To estimate the event effect, this article uses the statistical value in French Polynesia was χ2 = 4.46 time dummy. The period is from 2008 to 2013 in (p = 0.81), so the random effect model was adopted both islands. The year base for the time dummy is for this article. Table 2 shows the model estimation 2008, when global bankruptcy occurred. The coef- results. ficient is able to approximate the change in tourism GDPCAP, the income variable, was greater than demand compared with 2008. 1 in the model of French Polynesia. When income

Table 2 Estimation Result

Coefficients Singapore (N = 388) French Polynesia (N = 215)

(Intercept) 3.53* (1.84) 1.60 (2.79) log(GDPCAP) 0.91*** (0.08) 1.33*** (0.16) log(POP) 0.83*** (0.07) 0.89*** (0.09) log(PRICE) −0.02 (0.03) −0.16* (0.07) log(dist) −1.85*** (0.16) −2.52*** (0.40) colony 1.03 (0.97) 2.26* (0.93) comlang_off 0.92*** (0.28) 0.54 (0.48) time2009 −0.02 (0.03) −0.10 (0.11) time2010 0.07** (0.03) −0.37*** (0.11) time2011 0.12*** (0.03) −0.23* (0.11) time2012 0.18*** (0.03) −0.31** (0.11) time2013 0.21*** (0.03) −0.31** (0.11) Adj. R2 0.58 0.45 Note. Standard errors shown in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01.

increased by 1%, tourism demand in French Poly- Qualitative variable analysis results show other nesia was increased by 1.33%. Singapore’s result, differences between the two destinations. The col- however, was lower than 1. When income increased ony variable in French Polynesia is highly elastic by 1%, tourism demand increased by 0.91%. In and significant, and the language variable in Sin- regards to income elasticity for tourism demand, gapore is significant. The other dummy variable is French Polynesia is elastic while Singapore is a time variable based on 2008. Figure 1 shows the inelastic. different changes on both islands. Tourism demand “POP,” the variable of size effect in origins, was in Singapore slightly increased after the period of significant in both regions. Although the results global bankruptcy while French Polynesia began show both are inelastic, the market size of origin is stagnating. a relatively important factor for tourism demand in both destinations. Price elasticity in French Polynesia was sig- Discussion and Conclusion nificant and negative, with a coefficient of −0.16 demonstrating inelasticity. Price elasticity in Singa- This article used the panel data technique to pore was negative, but the result was insignificant, explore the determinants of tourism demand on meaning that price difference is likely to be a more two islands with different regional characteristics. important explanatory variable in resort destina- The implication of this study is that the demand tions than others. in a small resort destination highly dependent on The distance variable in French Polynesia was the tourism industry such as French Polynesia is significant and elastic, showing that when the dis- likely to acquire a high income market. However, tance increased by 1%, tourism demand decreased their economy may be affected by global phe- by 2.52%. Singapore was also significant and elas- nomena. Destinations with diversified industries tic; when the distance increased by 1%, tourism such as Singapore are likely to have several mar- demand decreased by 1.85%. However, the acces- kets because of their accessibility, and the global sibility of transportation was better in Singapore economic impact is comparatively less in the than French Polynesia. tourism market.

Figure 1. Comparing time dummy variables.

Appendix 1: List of Origins in Singapore

Afghanistan Hungary Netherlands Australia India New Zealand Austria Indonesia Norway Pakistan Bahrain Iran Papua New Guinea Bangladesh Ireland Philippines Belgium Israel Poland Brazil Italy Portugal Saudi Aribia Brunei Japan South Korea Cambodia Jordan Spain Canada Kuwait Sri Lanka Chile Laos Sweden China Lebanon Switzerland Columbia Libya Thailand Czech Republic Luxembourg Turkey Uruguay Denmark Macao United Arab Emirates Egypt Malaysia United Kingdom Finland Fiji Mauritius United States France Mexico Venezuela Germany Mongolia Viet Nam Greece Myanmar Hong Kong Nepal

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