1. Introduction
All the service activity related to international tourism is essentially a form of international trade, i.e. tourism services export or services import, and better to be considered as a single industry study. According to the World Tourism Organization (henceforth UNWTO in this paper), in 1980 countries’ receipts from international tourism were more than 280 billion dollars, and they already surpassed the one-trillion-dollar mark in a single year after year 2000, accounted for more 10% of the value of international trade annually.
Revealing the importance of international tourism, these numbers suggest that better understanding of this industry can promote our empirical understanding in various fields. Firstly, tourism is an important earner of foreign exchange, contributing substantially in financing the imports of foreign capital goods, the current account deficit of the balance of payments, also allows “non-traded goods”
to be consumed by inbound tourists. Secondly, tourism spurs investments in new infrastructure, and stimulates services and quality improving competition between firms in tourism industries across countries, and other economic industries by direct and induced effects. Thirdly, tourism directly contributes to generate employment especially for relatively unskilled labors, and indirectly increases tenured employees and the use of temporary employment by firms in the non-traded goods sectors.
Finally, but not least important, international tourism provides more opportunity to meet new people, enhances community image and preserves local culture/heritage, and also helps to “promote world peace by providing an incentive for peacekeeping and by building bridges between cultures” (Eilate and Einav, 2004).
Some common reasons of tourism program attracting foreign visitors include the economic and social image of the destination country, cultural heritage and cross-border cultural differences, geographical resources, etc. This thesis attempts to
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analyze tourist flows from those biggest spenders, namely China, Germany, and the United States, and their impacts of the international tourists on a small open economy like Taiwan.1 By further taking a glimpse on the tourist arrival data of Taiwan for the past few years, in year 2009, China leaped to first place, overtaking both long-time top spender Japan and second largest spender United States. It may due to rising disposable incomes, relaxing restrictions on foreign travel and an appreciating currency in China.2 Furthermore, the way by which new elected President of Taiwan attempted to have a closer tie with mainland China, responded to the challenge of sluggish growth and also to meet the need of transportation of cross-strait tourists with raising daily entry quota for Chinese tourists, could also reinforce the effects on tourist flows from China to Taiwan.3
Although tourism can bring some economic social benefits, but mass tourism is usually associated with negative effects. For example, in Taiwan, jobs created by tourism are often seasonal and relatively low-paid, while money generated by
1 Based on the UNWTO report, there were 1,035 million international tourist arrivals worldwide, while international tourism receipts grew to 1,075 billion dollars in year 2012, corresponding to a 4.0% increase in both tourist arrivals and real terms of tourism receipts from year 2011. China, Germany, and the United States are the top three biggest spenders on international tourism for the year 2012, with the market shares 9.5%, 7.8% and 7.7%. When ranking the world’s top international tourist arrivals and international tourism receipts, it is interesting to note that 7 of the top 10 destinations appear on both lists. Countries appearing on both lists include: France, the United States, China, Spain, Italy, Germany, and the United Kingdom (Source: UNWTO Tourism Highlights, 2013 Edition).
2 Chinese travelers have increased their expenditures each year in recent years. In year 2012, they spent more 102 billion dollars on international tourism and have increased almost eightfold in 12 years, up from 13 billion dollars in year 2000, and bounded to the first place of biggest spenders on international tourism.
3 A new agreement between both governments has reached on the daily quota arrangements of Mainland Chinese tourists and scheduled direct flights to Taiwan in June 2008.
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Chinese tourism does not always benefit the local community, as most of it leaks out to foreign tourist agency and huge international companies, such as hotel chains.
Mass tourism can have a detrimental effect on the quality of life of the local community, e.g. it can push up local property prices and the cost of goods and services, and also cause increased crowding, congestion and pollution through traffic emissions, littering, increased sewage production and noise. Thus, it is interest to examine if Taiwan stands to benefit from being more open to Chinese tourists without substituting the potential tourists from the other major partner countries.4
There are many popular approaches to study the determinants of tourism flows.
Vanhove (2005) provides three quantitative methods for forecasting tourism demand, namely univariate time-series methods, regression analysis, and gravity and trip-generation models. Univariate time-series methods such as moving average, exponential smoothing, trend curve analysis, and the Box-Jenkins approach are widely used for forecasting tourism demand (Frechtling, 1996). However, as Vanhove (2005) argues, time-series analysis requires a stable environment for using univariate time-series methods. Regression analysis introduces more than one causal factor, by making turning points and trends being detected. The basic gravity models applied to tourism can pay more attention to the attractiveness between two countries.
Trip-generation models are derived from basic gravity models and refine forms of Taiwan is the United States’ ninth-largest and Japan’s fourth-largest trading partner, and China’s fifth-largest trading partner. In year 2012, Taiwan is the United States’ eleventh-largest and Japan’s sixth-largest trading partner, and China’s fifth-largest trading partner.
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Crouch et al. (1992) applied conventional economic and marketing theory to measure the effect of marketing efforts on few tourist-origin countries of Australia.
Multivariable regression analysis is employed to estimate the elasticities of demand from tourist-origin countries. Their finding suggests that the marketing activities (measured as total expenditure and advertising expenditure) are significant in all countries, and that marketing activities have an important role in affecting tourism inbound is Australia. Borjas (1989) suggests that a model of immigration should include wage earning function for both origin countries and destination countries and moving cost function.
Dritsakis and Athanasiadis (2000) build up several explanatory factors to study the variable influencing the tourist demand for Greece. The OLS model is employed in their study to estimate the separate demand functions for each tourist-origin countries. They conclude that Greece continued to attract tourists even under an economic recession, but the tourist host countries would have to face a more demanding, more competitive, and intensely differentiated tourist market in the future. Karemera et al. (2000) examine the influence of political, economic and demographic on the size of migration flows to North America. They argue that population of origin countries, income of destination countries and domestic policy are important factors affecting the migration flows. And less civil freedom has negative effect on migration flows.
To capture the possibility of endogeneity and dynamism in tourism, Khadaroo and Seetanah (2008) use a dynamic panel data model of gravity type to evaluate the importance of transport infrastructure, namely the length of pave road, number of airports and ports, and number of hotel rooms, for tourism attractiveness. The data in their research is disaggregated into four subpanels representing different continent of destination and origin. Their result suggests that the level of transport infrastructure
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has an importance role in tourist arrivals except for Africa. Saray and Karagöz (2010) use a panel data of 1992-2007 to investigate the effective factors for tourist inflows to Turkey. Using a model of gravity-type, the paper adopts weighted distance as measure of distance between two countries, estimates on per capita GDP and population. Santeramo et al. (2008) examine the tourism demand for non-urbanized rural in Italy. Their study explains the agritourist flows to Italy by applying gravity model and controlling for the effect of rurality and European integration. According to their result, the supply of Italian agritourism should be enhanced due to its advantage in international markets. Su et al. (2010) employ seasonal ARIMA models, focusing on evaluating the impact of Chinese tourists on Taiwan’s international tourism. By examining the monthly tourist arrivals data, they find out that whether the crowding-out effect of Chinese tourists exists in Taiwan’s tourist market and whether the government should keep running the openness policy. According to their finding, the effect may exist for tourists from Japan and United States but not for tourists from Hong Kong.
Edwards (1988) introduces two possible of ceilings for tourism demand. First is the annual leave and public holidays out of which the time for tourism is taken.
Second is the share of spending on holidays in total expenditure after deal with essential needs. In many countries governors try to handle the ceiling effect by shifting from domestic and international tourism. Kusni et al (2013) investigate the determinants of tourism demand for Malaysia by using a panel data of selected OECD countries during 1995-2009. They argue that tourists from OECD countries are sensitive to the relative price of tourism. Also, they argue that Singapore is a substitute destination of Malaysia, any increase of price in Malaysia would lead to increase the tourist arrivals for Singapore. The number of tourist arrivals to Malaysia has increased during global financial crisis. They suggest that the level of price of
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tourism in Malaysia is lower than other neighboring destination, which makes international tourists be able to travel and spend even during recession.
Han (2008, in Chinese) uses a panel data of 2000-2005 to analyze how the World Heritage List influences the tourism demand in China. The result suggests that tourism infrastructure, political stability, and numbers of tourist attractions are the key determinants of tourist arrivals to China. Wu (2014, in Chinese) evaluates the effect of international events and disasters on tourism demand for travel to East Asia, using a panel data of 2002-2011. The result shows that relative consumption price is an important factor to affect tourism destination choices. Natural disasters and man-made attacks could also have negative impact on tourism demand.
The structure of this thesis is organized as below. Section 2 gives a review of Taiwan’s inbound tourism, which includes the situation of the main tourist source markets surveys literatures of international trade and tourism. Section 3 presents the empirical model of this thesis and the data employed to the estimations. The estimation results and further analysis are presented in Section 4. The conclusion is provided in Section 5.
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