臺灣博碩士論文加值系統

國際旅遊的迅速擴張，使得旅遊出口成為許多國家外匯收入增加的來源（Lim, 1997）。由於已開發國家與新興工業化國家的所得快速成長，以及實質運輸成本的下降，國際旅遊需求在過去二十年快速成長，並因此而產生許多國際旅遊需求的研究（Song and Li, 2008）。然而，雖然估計國際旅遊需求的相關研究很多，但Song and Li （2008） 發現，不論是旅遊目的地國與旅遊來源國，美國、英國與法國皆為主要的研究國家，而亞洲國家僅受到少數研究者的關注。
亞洲國家在出境旅遊與入境旅遊皆呈快速且穩定的成長。首先，在亞洲國家的入境旅遊方面，世界旅遊組織（2007）指出，自2000年起，亞洲地區的入境旅遊人數的年平均成長率為7%，該成長率約為世界平均成長率的2倍。其次，在出境旅遊的部份，由於亞洲地區的經濟成長，以及人口與社會趨勢的改變，亞太地區的出境人數在過去10年的年平均成長率約為6%。由於亞洲地區的出境與入境旅遊穩健的持續成長，且世界旅遊組織也預測該區的旅遊市場極具潛力，因此，估計亞洲國家的國際旅遊需求之相關研究將更顯重要。另一方面，一個在世界上迅速擴展的旅遊市場—賞鯨旅遊，在過去並未有將其旅遊需求數量化的相關研究，再者，由於賞鯨旅遊與捕鯨產業為相互衝突的產業活動，隨著商業捕鯨存在重新開放的可能性，估計全球賞鯨需求將有助於釐清捕鯨活動對賞鯨需求的衝擊與影響。綜上所述，本論文包含三篇估計國際旅遊需求的相關研究，首先，估計傳染性疾病對亞洲地區國際旅遊需求的衝擊與影響，其次為分析亞洲旅遊目的地國的競爭與互補關係，最後，則是估計全球賞鯨旅遊需求。
在過去幾年，亞洲地區的國際旅遊產業受到兩項傳染病的影響—SARS與禽流感。針對事後危機（SARS）與事前危機（禽流感）進行分析與比較，將能據此產生策略性架構以抵抗傳染性疾病對旅遊產業的衝擊，因此，本論文的第一個議題即為估計這兩項傳染病對亞洲國際旅遊需求市場的影響與衝擊。本研究利用ARMAX（an autoregressive moving average model together with an exogenous variables）模型來分析這兩項傳染病對個別國家的影響，另外，利用動態追蹤資料模型（dynamic panel data model）分析對亞洲地區的平均影響。從個別國家的分析結果，與亞洲地區的整體研究結果皆可發現，SARS會對國際旅遊需求造成顯著的負面影響，而禽流感的影響並不顯著。
本論文中的第二個議題乃為分析日本的國際旅遊支出在亞洲五個主要目的地國間的分配關係，這五個主要的旅遊目的地國包括中國、香港、韓國、台灣與泰國。在過去估計國際旅遊需求的研究中，美國與歐洲等主要且傳統的國際旅遊市場一直受到許多研究者的重視；但相對的，僅有少數的研究去分析亞洲地區國際旅遊市場的競爭與互補關係。日本是相當重要的旅遊出口國，其出境旅遊的主要目的地國主要集中在亞洲國家，由於日本是這些亞洲國家主要的國際旅客來源國，因此，針對日本的出境旅遊進行市場份額分析，將可提供這些亞洲主要旅遊目的地國有用的資訊，用以研擬相關策略以維持與增加日本觀光客的旅遊市場。因此，本研究的主要目的為估計日本的出境旅遊需求模型，並分析影響五個主要目的地國市場份額的重要因素，利用靜態與動態的線性近似近乎理想需求體系（linear approximation almost ideal demand system）模型進行分析，研究結果發現，日本國際旅遊需求的改變會顯著受到相對價格改變的影響，而日本旅客旅遊支出的改變之影響則相對不顯著。
賞鯨旅遊是指至少從事一項商業賞鯨活動，這些活動包含觀看、共游與傾聽任何種類的鯨魚與海豚的聲音（Hoyt, 1995, 2001）。自從國際捕鯨委員會（International Whaling Commission, IWC）在1986年頒行商業捕鯨禁令，全球賞鯨產業在90年代迅速發展與成長。由於捕鯨被視為與賞鯨相互衝突的活動，因此，商業捕鯨重新開放的可能性將引起評估捕鯨對賞鯨產業造成可能衝擊的急迫需求。本文的主要目的乃為分析影響賞鯨需求的重要因素，進而評估捕鯨對賞鯨需求的潛在衝擊。估計結果發現，由於商業捕鯨活動將會造成鯨魚數的減少，以及引發賞鯨旅客的負面觀感，因此，捕鯨活動的重新開放將會對賞鯨產業造成極具嚴重性的負面衝擊，尤其是從事捕鯨活動的國家。

Tourism exports, which arisen through the rapid expansion of international tourism, have become an important sector in many countries as a growing source of foreign exchange earnings (Lim, 1997). Due to high growth rates of income in developed and newly industrialized countries, and the decrease in real transportation costs between countries, the growth in demand for international tourism around the world over past two decades results in a growing interest in tourism demand research (Song and Li, 2008). However, although modelling international tourism demand attracts great interest, Song and Li (2008) detected that the USA, UK, and France are the most popular researched countries as both destination and origin countries, while Asian countries gain relatively little attention.
In Asian countries, both outbound and inbound tourism perform well with rapid and stable growth. First, as to Asian inbound tourism, UNWTO (2007) showed that international tourist arrivals to Asian destinations grew by 7% a year since 2000, a rate that doubles that of the world (3.5%). Second, with respect to the outbound tourism, because of the economic growth of the region, shifting demographic and social trends, outbound tourism from Asia and Pacific increased on average by 6% a year during the last 10 years (UNWTO, 2007). Given the Asian international tourism markets with fast and stable growth and promising future as the UNWTO predicts, it is important to model international tourism demand in Asia countries. Furthermore, whale-watching tourism, one of the fastest growing tourism industries worldwide, its demand modelling has not been quantified by any mathematical approach. In addition, because of the urgent need to investigate the conflicting activities between whaling and whale-watching, modelling global whale-watching demand becomes one of important area in tourism research. There are three essays included in this dissertation to implement crisis analysis of infectious diseases to Asian tourism, investigate the substitution and complementary relationships between Asian destinations and model global whale-watching tourism demand.
In past few years, Asian international tourism industries were significantly affected by two infectious diseases, SARS and Avian Flu. Comparing the post-crisis analysis of SARS with the pre-crisis analysis of Avian Flu can form a strategic framework to combat the transmissible diseases, so that, the purpose of the first essay is to assess the impacts of these two infectious diseases on international tourism demand in Asia countries. To estimate the impacts of these two infectious diseases on international tourism demand, an autoregressive moving average model together with an exogenous variables (ARMAX) model are used to estimate the effects of these diseases in each SARS- and Avian Flu-infected country, while a dynamic panel data model is adopted to estimate the average impact on Asian infected countries for these two diseases respectively. The results from both approaches are consistent and indicate that the numbers of affected cases have a significant impact on SARS-affected countries but not on Avian Flu-affected countries.
The second essay is to investigate the tourism expenditure allocation of Japanese international tourism in five Asian destinations, China, Hong Kong, Korea, Taiwan and Thailand. In the field of modelling international tourism demand, the US and Europe, as traditional international tourism markets, attracted most attention in previous studies. On the contrary, little attention has been paid to examine the substitution and complementary relationships between Asian countries. Given the position of Japan as a leading generator of international tourism in the world, and Japan is the major tourist-source market in several Asian countries, the market share analysis of Japanese outbound tourism can provide useful knowledge for these major destination countries. Therefore, the major purpose of this essay is to estimate Japanese tourism demand and investigate the determinants of each destination’s share for the group of Asian destinations by using both static and dynamic forms of linear approximation almost ideal demand system (LA/AIDS) models. The results from both static and dynamic approaches indicate that the changes in market shares of the Japanese tourism demand are significantly influenced by the changes in relative prices rather than in tourists’ expenditure.
Whale-watching, which can be defined as at least some commercial aspects, to see, swim with, and/or listen to any of the some 83 species of whales, dolphins and porpoises(Hoyt, 1995, 2001). Since the International Whaling Commission (IWC) moratorium on commercial whaling was enacted in 1986, whale-watching becomes one of the fastest growing tourism industries worldwide throughout the 1990s. Because whaling was regarded as an incompatible activity with whale-watching, the possible resumption of commercial whaling caused the urgent need to investigate the potential effect of whaling might have on whale-watching industry. The purpose of final essay is to investigate determinants of the whale-watching demand and examine the potential impacts of whaling on global whale-watching industry using random effect models on unbalanced panel data. The results show that significantly negative influences of whaling on whale-watching tourism demand were induced by the reduction of minke whales available for watching and negative attitudes of whale watchers towards whaling.

謝辭 i
摘要 iii
Abstract v
TABLE OF CONTENTS vii
LIST OF FIGURES AND TABLES ix
1 Assessing Impacts of SARS and Avian Flu on International Tourism Demand to Asia 1
Abstract 1
1.1 Introduction 2
1.2 SARS and Avian Flu 5
1.3 Data Description and Empirical Methodology 6
1.3.1 Data 6
1.3.2 ARMAX Models for Single Data Sets 8
1.3.3 Dynamic Panel Data Models for Panel Data Sets 12
1.4 Empirical Results 14
1.4.1 ARMAX Models for Single Data Sets 14
1.4.1.1 The Impact of SARS on Tourism Demand 14
1.4.1.2 The Impact of Avian Flu on Tourism Demand 17
1.4.2 Dynamic Panel Data Models for Panel Data Sets 19
1.5 Policy Implications and Concluding Remarks 21
Appendix 1-A 30
Appendix 1-B 32
References 33
2 Modelling Japanese Tourism Demand for Asian Destinations: A Dynamic Demand System Approach 36
Abstract 36
2.1 Introduction 37
2.2 Methodology 41
2.3 The AIDS Model of Japan Outbound Tourism Demand 46
2.4 Empirical Results 48
2.4.1 Model Estimation 48
2.4.2 Elasticity Estimation 52
2.5 Conclusion 56
References 64
3 Estimating the Potential Impacts of Whaling on Global Whale-Watching Tourism Demand Using Unbalanced Panel Data 67
Abstract 67
3.1 Introduction 68
3.2 Empirical Model and Data 71
3.2.1 The Model of Whale-Watching Tourism Demand 71
3.2.2 A Bio-economic Model of Whale Population 73
3.2.3 The Data 74
3.3 Empirical Results 76
3.4 Conclusions and Policy Implications 78
Appendix 3-A 86
References 88
Overall Conclusions 90

Akal, M. (2004). Forecasting Turkey’s tourism revenues by ARMAX model. Tourism Management, 25(5), 565-580.
Anderson, G. J., & Blundell, R. W. (1983). Testing restrictions in a flexible dynamic demand system: An application to consumers’ expenditure in Canada. Review of Economic Studies, 40, 397-410.
Arellano, M. & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58, 277-297.
Asche, F., & Wessells, C. R. (1997). On price indices in the almost ideal demand system. American Journal of Agricultural Economics, 79, 1182-1185.
Baldwin, M. A., Hadid, M., & Phillips, G. D. A. (1983). The estimation and testing of a system of demand equations of demand equations for the UK. Applied Economics, 15, 81-90.
Beirman, D. (2003). Restoring tourism destinations in crisis: A strategic marketing approach, Cabi Publishing, Wallingford.
Bierens, H. J. (1987). ARMAX model specification testing with an application to unemployment in the Netherlands. Journal of Econometrics, 35, 161-190.
Björgvinsson, A. (1999). Iceland whale watching report 1999. Húsavík: Húsavík Whale Center.
Bjørndal, T., & Conrad, J. M. (1998). A report on the Norwegian minke whale hunt. Marine Policy, 22(2), 161–174.
Brahmbhatt, M. (2005). Asian influenza: Economic and social impact. Accessed September 23, 2005, accessed at http:www.worldbank.org
Buhalis, D. (2000). Marketing the competitive destination of the future—Growth strategies for accommodation establishments in alpine regions. Tourism Management, 21(1), 97-116.
Buse, A. (1998). Testing homogeneity in the linearized almost ideal demand system. American Journal of Agricultural Economics, 80, 208-220.
Cambers, M. J. (1990). Forecasting with demand systems: A comparative study. Journal of Econometrics, 44, 363-376.
Cheng, M. H. (2006). Cash boost for avian influenza exceeds expectations. The Lancet, 367 (9507), 289.
Clark, C. W. (1976). A delay-recruitment model of population dynamics, with an application to baleen whale populations. Journal of Mathematical Biology, 3(3), 381–391.
Conrad, J. M. (1989). Bioeconomics and the bowhead whale. Journal of Political Economy, 97(4), 974–987.
Conrad, J. M., & Bjørndal, T. (1993). On the resumption of commercial whaling: the case of the minke whale in the Northeast Atlantic. Arctic, 46(2), 164–171.
De Mello, M., & Fortuna, N. (2005). Testing alternative dynamic systems for modelling tourism demand. Tourism Economics, 11, 517-537.
De Mello, M., Pack, A., & Sinclair, M. T. (2002). A system of equations of UK tourism demand in neighbouring countries. Applied Economics, 34, 509-521.
Deaton, A. S., & Muellbauer, J. (1980). An almost ideal demand system. American Economic Review, 70(3), 312-326.
Divisekera, S. (2003). A model of demand for international tourism. Annuals of Tourism Research, 30(1), 31-49.
Duffy, M. (2003). Advertising and food, drink and tobacco consumption in the United Kingdom: a dynamic demand system. Agricultural Economics, 28, 51-70.
Durbarry, R., & Sinclair, M. T. (2003). Market shares analysis: French tourism demand. Annals of Tourism Research, 30(4), 927-941.
Edgerton, D. L., Assarsson, B., Hummelmose, A., Laurila, I. P., Rickertsen, K., & Vale, P. H. (1996). The econometrics of demand systems: With applications to food demand in the Nordic countries. US: Kluwer Academic Publishers.
Enders, W., Sandler, T. & Parise, G. F. (1992). An econometric analysis of the impact of terrorism on tourism. KYKLOS, 1992, 45(4), 531-554.
Engle, R. F. & Granger, C. W. J. (1987). Cointegration and the error correction: Representation, estimation and testing. Econometrica, 55, 251-276.
Faulkner, B. (2001). Towards a framework for tourism disaster management. Tourism Management, 22(2), 135–147.
Franses, P. H. (1991). Primary demand for beer in the Netherlands: an application of ARMAX model specification. Journal of Marketing Research, 28, 240–245.
Garín-Muñoz, T. (2006). Inbound international tourism to Canary Islands: a dynamic panel data model. Tourism Management, 27(2), 281-291.
Garín-Muñoz, T. (2007). German demand for tourism in Spain. Tourism Management, 28(1), 12-22.
Garín-Muñoz, T., & Pérez-Amaral, T. (2000). An econometric model for international tourism ows to Spain. Applied Economics Letters, 7, 525–529.
Glaeßer, D. (2005). Crisis management in the tourism industry (2nd ed.), Butterworth Heinemann, Oxford.
Greene, W. H. (2000). Econometric analysis. Fourth edition. New Jersey: Prentice-Hall, Inc.
Han, Z., Durbarry, R., & Sinclair, M. T. (2006). Modelling US tourism demand for European destination. Tourism Management, 27, 1-10.
Harvey, A. C. (1990). The econometric analysis of time series. Cambridge, MA: MIT Press.
Henderson, J. C. (2007). Corporate social responsibility and tourism: hotel companies in Phuket, Thailand, after the Indian Ocean tsunami. Hospitality Management, 26, 228–239.
Herrera, G. E., & Hoagland, P. (2006). Commercial whaling, tourism, and boycotts: An economic perspective. Marine Policy, 30(3), 261–269.
Horan, R. D., & Shortle, J. S. (1999). Optimal management of multiple renewable resource stocks: an application to minke whales. Environmental and Resource Economics, 13, 435–458.
Hoyt, E. (1995). The worldwide value and extent of whale watching: 1995. Bath, UK: Whale and Dolphin Conservation Society.
Hoyt, E. (2001). Whale watching 2001: worldwide tourism numbers, expenditures, and expanding socioeconomic benefits. Yarmouth Port, MA: International Fund for Animal Welfare.
Hoyt, E., & Hvenegaard, G. T. (2002). A review of whale-watching and whaling with applications for the Caribbean. Coastal Management, 30 (4), 381–399.
Hsiao, C. (2003). Analysis of panel data. Second edition. Cambridge: Cambridge University Press.
Hsiao, C. (2003). Analysis of panel data. Second edition. Cambridge: Cambridge University Press.
Humane Society of the United States. (2007). The 2007 International Whaling Commission meeting highlights, accessed at http://www.hsus.org/
marine_mammals/save_whales_not_whaling/learn_more/2007_IWC_highlights.html (Accessed May 2008).
Humane Society of the United States. (2008). Iceland and whaling: consequences and background, accessed at http://www.hsus.org/marine_mammals/save_whales
_not_whaling/iceland_whaling/iceland_and_whaling.html (Accessed May 2008).
International Monetary Fund. (2007). International Financial Statistics.
International Whaling Commission (2008), accessed at http://iwcoffice.org/
conservation/estimate.htm (Accessed May 2008).
Japan Tourism Marketing. (2008). http://www.tourism.jp/.
Johnson, J. A., Oksanen, E. H., Veall, M. R., & Fretz, D. (1992). Short-run and Long-run elasticities for Canadian consumption of alcoholic beverages: An error-correction mechanism/cointegration approach. Review of Economics and Statistics, 74(1), 64-74.
Karagiannis, G., Katranidis, S., & Velentzas, K. (2000). An error correction almost ideal demand system for meat in Greece. Agricultural Economics, 22, 29-35.
Ledesma-Rodríguez F. J., Navarro-Ibáñez M., & Pérez-Rodríguez J. V. (2001). Panel data and tourism: a case study of Tenerife. Tourism Economics, 7(1), 75-88.
Li, G., Song, H., & Witt, S. F. (2004). Modeling tourism demand: A dynamic linear AIDS approach. Journal of Travel Research, 43, 141-150.
Li, G., Song, H., & Witt, S. F. (2006). Time varying parameter and fixed parameter linear AIDS: An application to tourism demand forecasting. International Journal of Forecasting, 22, 57-71.
Lim, C. & McAleer, M. (2001). Monthly seasonal variations in Asian tourism to Australia. Annals of Tourism Research, 28(1), 68-82.
Liu, J.-T., Hammitt, J. K., Wang, J.-D., & Tsou, M.-W. (2005). Valuation of the risk of SARS in Taiwan. Health Economics, 14, 83-91.
Mackinnon, J. G. (1991). Critical values for cointegration test. In R. F. Engle and C. W. J. Granger (eds.), Long-Run Economic Relationships. Oxford University Press, 267-276.
Meinser, J. F. (1987). This sad fate of the asymptotic Slutsky test for large systems. Economics Letters, 2, 231-233.
Mohsin, A. (2005). Tourist attitudes and destination marketing—The case of Australia’s Northern Territory and Malaysia. Tourism Management, 26(5), 723-732.
Moschini, G. (1995). Units of measurement and the Stone index in demand system estimation. American Journal of Agricultural Economics, 77, 63-68.
O’Hangan, J. W., & Harrison, M. J. (1984). Market shares of US tourist expenditure in Europe: an econometric analysis. Applied Economics, 16, 919-931.
Orams, M. B. (2001). From whale hunting to whale watching in Tonga: A sustainable future? Journal of Sustainable Tourism, 9, 128-146.
Page, S., Yeoman, I., Munro, C., Connell, J., & Walker, L. (2006). A case study of bestpractice—Visit Scotland’s prepared response to an influenza pandemic. Tourism Management, 27(3), 361-393.
Papatheodorou, A. (1999). The demand for international tourism in the Mediterranean region. Applied Economics, 31, 619-630.
Parsons, E. C. M., & Rawles, C. (2003). The resumption of whaling by Iceland and the potential negative impact in the Icelandic whale-watching market. Current Issues in Tourism, 6(5), 444–448.
Richter, L. K. (2003). International tourism and its global public health consequences. Journal of Travel Research, 41(4), 340-347.
Ritchie, B. W. (2004). Chaos, crises and disasters: A strategic approach to crisis management in the tourism industry. Tourism Management, 25(6), 669-683.
Sloboda, B. W. (2003). Assessing the effects of terrorism on tourism by use of time series methods. Tourism Economics, 2003, 9(2), 179-190.
Song, H. & Witt, S. F. (2000). Tourism demand modeling and forecasting-model econometric approaches. Amsterdam, New York Elsevier.
Song, H., & Li, G. (2008). Tourism demand modelling and forecasting— A review of recent research. Tourism Management, 29, 203-220.
Syriopoulos, T. & Sinclair, M. T. (1993). An econometric study of tourism demand: the AIDS model of US and European tourism. Applied Economics, 25(12), 1541-1552.
The World Bank. (2004). The world development indicators (WDI) database.
Tuma, N. B., & Hannan, M. T. (1984). Social dynamics: models and methods. New York: Academic.
UNWTO. (2001). Tourism 2020 Vision – Global Forecast and Profiles of Market Segments. Madrid: World Tourism Organization Press.
WHO. (2004). Summary of probable SARS cases with onset of illness from 1November 2002 to 31 July 2003. World Health Organization, accessed at http://www.who.int/csr/sars/country/table2004_04_21/en/, 8 June 2006.
WHO. (2005). Responding to the avian influenza pandemic threat: Recommended strategic actions. World Health Organization, accessed at http://www.who.int/csr/ resources/publications/influenza/WHO_CDS_CSR_GIP_05_8-EN.pdf, 8 June 2006.
WHO. (2006). Cumulative Number of Confirmed Human Cases of Avian Influenza A/(H5N1) Reported to WHO, accessed at http://www.who.int/csr/disease/avian_ influenza/country/cases_table_2006_06_06/en/index.html, 8 June 2006.
Wilder-Smith, A. (2006). The severe acute respiratory syndrome: impact on travel and tourism. Travel Medicine and Infectious Diseases, 4, 53-60.
Wooldridge, J. M. (2001). Applications of generalized method of moments estimation. Journal of Economic Perspectives, 15(4), 87-100.
World Tourism Organization. (1982-2000). Yearbook of Tourism Statistics. Madrid: World Tourism Organization Press.
World Tourism Organization. (2007). International trends on outbound tourism- A focus on Asia. JATA World Tourism Congress, WTO.
World Wide Fund. (2003). Whale watching: a future for whales? accessed at www.panda.org (Accessed May 2008).
WTTC. (2003). Special SARS analysis: Impact of travel and tourism (Hong Kong, China, Singapore and Vietnam reports). London: World Travel and Tourism Council.
Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and test for aggregation bias. Journal of the American Statistical Association, 57, 348-368.