Forecasting the International Tourism Demand from East Asia to Japan

Naohisa OKAMOTO, Takeshi KURIHARA, Haruo ISHIDA
 University of Tsukuba Graduate School of Systems and Information Engineering
305-8537 Tennodai1-1-1 Tsukuba City, Ibaraki Prefecture, Japan
E-mail: kuriha20@sk.tsukuba.ac.jp

Abstract: This paper suggests a method in forecasting tourism. The forecasting model has two stages of estimation. First stage is the “International Trip Generation”. We explained this by using a logistic curve. Second, we proceed to find out the travel destination by using the aggregate logit model. Results for the forecast of tourists coming to Japan led to two main conclusions. 1) The number of tourists from every Asian country shows a growing tendency until 2030. 2) Moreover, the rate of tourist visiting Japan will decrease gradually compared to other regions such as North America where the rate is steadily increasing.

Key Words: international tourism, demand forecasting, international trip generation, travel destination

1 Introduction

 The 2004 is the first year for Japan to accommodate more the 6 million foreign tourists. (Tourism Report [2005]). Then in year 2005, 6.72 million foreign tourists visited Japan, while Japanese tourists traveling overseas accounted for 17.40 million. This situation continues to be a serious problem because the international balance of payments of tourism in Japan is at a deficit stage. However, Tourism Report [2005] showed that the problem has improved. The main reasons are 1) various kinds of campaigns to promote and increase the number of foreign tourists coming to Japan like the Visit Japan Campaign since 2003 and 2) increasing number of international tourist from Asian countries according to their economic growth.
 On the other hand, the rate of tourist visiting Japan from Asian countries decreases, especially Korea (69.41% [1987] to 28.91% [1995]) (Watanabe [1997]). Generally, although the number of foreign tourist is increasing, it is not clear how many tourists will totally visit Japan in the future.
The main aim of this paper is to suggest a method in forecasting tourism. In this paper, the forecasting model takes two stages of estimation. First stage is the “International Trip Generation” (International trip generation and international tourism are different terms. International trip generation is considered as a volume of international trip. On the other hand, international tourism indicates travelers’ travel behavior). As Morichi [2001] tackled the international trip generation in Japan. We applied his method for estimating tourism flow from Asian countries. It is explained by using a logistic curve, which is often used as a growth function in economics. The second stage is to find out the travel destination by using the aggregate logit model.

2 Existing research

A series of study established the way for forecasting the international trip generation and travel destination (e.g. Morichi [2001], Nakamura [2005], Lemire [1989]). However, it is known that there are few studies for international tourism. The reason for the limited study on international tourism is the lack of statistical data. It is easy to estimate the international freight because it only takes economical variables in the model. On the other hand, however, it needs data regarding on human activity for estimating the international tourism. This is why tourism statistical data has not been completed yet.
Like the World Tourism Organization’s (WTO) study which forecasted the number of tourists for each country by using the Delphi method. It just took into account former trend of international trip generation. The WTO estimated the international tourism demand for 2020, where in it accounted 140 million in Japan, 21.7 million in Korea and 100 million in China. However, this figure seems to be overestimated. One possible could be population decrease brought by the aging society in every country. Forecasting from former trend is also concentrated only the generation of the country, however they have missed to take into account tourism competition among countries.
In this study, we tried to forecast the international tourism with two points of view; 1) International trip generation has some saturation point because of the cost of travel, 2) Travel destination will expand as the income of traveler grows; each country has to face tourism competition.
Although the data of international tourism has not yet fully completed, we tried to get them from various organizations and private sectors. Moreover, we tried to collect various kinds of data in order to explain tourist’s behavior.

3 Data

The data on international tourism is not yet fully collected and also has not been updated for a long period. The analysis of Japanese international tourist uses The Ministry of Justice ‘Annual report on Immigration’. The data from Korea National Tourism Organization is used for the analysis of Korean tourism. Hong Kong Tourism Board is used for the analysis of Hong Kong tourism while Thailand National Statistical Office is applied for the analysis of Thailand. Asia-Pacific Tourism Exchange Center ‘World Tourism Statistics’ is used for the analysis of the other Asian countries not mentioned above.
 Determination of GDP and other variables uses United Nations ‘Statistical Yearbook’ and other materials from official government and private sector. Table 1 shows the data source for the number of tourists in Asian countries. It also presents the data period available for analysis and whether or not it includes the travel destination. More detailed data information for analysis is shown in Table 2.

4 Analysis on international trip generation

 Morichi [2001] explained about international trip generation in Japan as follows, ‘International trip generation is growing little by little because there is little information about travel abroad or so for first stage. Second stage will face radical increasing number of tourists as the international travel spreading for Japanese residents. However, international trip generation does not increase permanently because international tourism takes a lot of costs including flight time, flight fee, hotel charge and so on. Eventually international trip generation will approach some saturation point in the future.’
 This paper analyzed the international trip generation from Asian countries. It could be possible to apply the above mentioned theory of the process of Japanese international trip generation. Hence, this analysis is explained by using a logistic function. The logistic function is calculated based on the following formula:

                                       : number of travel per year per person

                                       : saturated number of travel

                                       : variables

                                       : parameter

 K presents a saturated number of international travel. Figure 1 shows that international trip generation is growing and approaching to the saturated point K.

4.1 The analysis targets and explanatory variables
 The selected countries for this analysis are Japan, Korea, Hong Kong, Thailand and China. Although it does not have enough data for analysis, China is also included the analysis because China is now watched with keen interest among international tourism market.
 Explanatory variables are GDP / capita, exchange rate and Gini coefficients. GDP / capita is included since it exhibits its own economic power as it takes a lot of money to travel abroad. Exchange rate is also significant explanatory variable. If exchange rate falls (e.g. 1$ = 150 yen changes to 1$ = 100 yen), then tourists can take advantage for international tourism. Gini coefficient is a zero to one value showing income differential. Income differential may disturb international tourism especially in developing countries.

4.2 Parameter estimation
Table 3 shows the results of parameter estimation for each country. R-squared is high enough to say that this model was well estimated.
 GDP / capita variable is included in the model for every country. Exchange rate variable is used for Korea, China, Hong Kong and Thailand. It did not include in the Japanese model because there are strong correlations between GDP variable and exchange rate variable. Gini coefficient variable is applied only for Thailand. Parameter sign of exchange rate variable is different from each country. Usually it seems that the number of international trip increases as the exchange rate decreases. So the expected parameter sign is negative. China and Hong Kong shows the negative parameter as we expected. On the other hand, the parameter estimation resulted positive for Korea and Thailand. It thought to be caused by the Asian financial crisis occurred in 1997. Now we try to confirm the effect of Asian financial crisis by taking a dummy variable of the year 1997 and 1998 which mostly affected the exchange rate in Korea and Thailand. The result is shown in table 4. Dummy variable is significant in the case of Korea, which suggests the strong effect of Asian financial crisis. On the other hand, it is not significant in Thailand. Figure 2 and Figure 3 show that the number of travel per one year per population in Thailand between 1997 and 1998 decreased slightly comparing with the data of Korea.

4.3 Forecasting the number of international tourist
 We forecasted the number of international trip generation in the future by using the parameter estimated in the last section (4.2) and the future value of each variable estimated by some organizations. Future population data uses the middle estimation of National institute of Population and Social Security Research in Japan, and the middle estimation of United Nations in other Asian countries (see Table 5). Future GDP data uses Ministry of Land, Infrastructure and Transport, Socio-Economic Research Center and Japan Center for Economic Research in Japan, and International Energy Agency in other Asian countries (see Table 6). It is difficult to forecast the future exchange rate and Gini coefficient, so we substituted the forecasting value to average value of latest 10 years (see Table 7 for future exchange rate). This paper uses 0.507 for future Gini coefficient in Thailand.
 Forecasting the number of international tourist in Japan is shown in figure 4. The graph draws the real value and the estimated value until 2005. The real value and the estimated value present a similar tendency. The rest is estimated value after 2005. The biggest forecast of this paper is 29.4 million Japanese international tourists from Ministry of Land, Infrastructure and Transport in 2030. On the other hand, the smallest forecast accounts for 14.5 million from Japan Center for Economic Research. The various forecasts used various GDP forecasting by each organization. The number of international tourist from Japan will range between 20 million to 30 million.
 Morichi [2001] forecasted the number of Japanese international tourist by airports in Japan. They estimated that the number of international tourist will be double at the Narita international airport in 2020 compared to 2000. While in this paper, we estimated that the number of future international tourist would be mostly doubled. Hence, the forecasting value in this analysis estimated with a high accuracy to some extent.
 Figure 5 shows the number of future international tourist from Asian countries. The number of international tourist from every Asian country shows the growing tendency until 2030. We estimated each amount like 16.1 million in China, 10.9 million in Korea, 7.7 million in Hong Kong and 3.5 million in Thailand.
 The number of Chinese international tourist does not grow constantly after 2010. It is possible to undervalue for Chinese tourists because there are not enough data for estimation.

5 Analysis on travel destination

 We estimated the number of international tourist in Asian countries as shown in the last chapter (4.3). Then, we proceeded to find out the travel destination by using the aggregate logit model.

5.1 The target area
In this model, nine countries (i.e. Japan, Korea, Hong Kong, Thailand, China, Taiwan, Singapore, Malaysia and Indonesia) and two regions (i.e. North America and Oceania) were considered as alternatives of travel destinations. Europe does not include in the model due to unavailability of detail data.

5.2 Explanatory variables and parameter estimation
Explanatory variables are made up of ‘attractiveness’ and ‘resistance’ of the travel and some dummy variables.
For ‘attractiveness’, the variables ‘World heritage’, ‘GDP’ and ‘Hotel’ were used.
World heritage – is one of the most attractive places for tourist to visit. Such world heritages are comprised of natural, cultural and mixed-type places. All types of world heritage are included in this model.
GDP - is a variable perceived to contribute high attractiveness in the model. Moreover the analysis of international trip generation uses international GDP. On the other hand, the analysis of travel destination ratio uses GDP of alternative travel destinations.
Hotel – generally, tourists stay in the hotels during their tour. The quality of the hotels is thought to influence attractiveness to the tourists. Thus, the score of each hotel is computed using the formula below.

                                           Price: stay fee per night of j hotel
                               i : country
Hotel rank is usually determined by the number of stars. The website ‘hotels.com’ was utilized to compare the star rank of the hotels all over the world. Consequently, price is calculated in US dollar. Then, average hotel quality in the country is calculated by the summation of the scores of all the hotels in the target area. Finally it is divided by the number of hotels.
The target cities are Tokyo (Japan), Seoul (Korea), Beijing (China), Taipei (Taiwan), Hong Kong, Bangkok (Thailand), Singapore, Kuala Lumpur (Malaysia), Jakarta (Indonesia), Sydney (Oceania), New York (North America) which are mostly capitals of the country or mega cities. The number of hotels and price were taken from the Japanese travel guidebook published by Diamond Big Co. Ltd ‘Globe-Trotter Travel Guidebook’ and Ota Publications ‘Japan Hotel Almanac’ for Tokyo. The quality of hotel at 2001 is shown in Table 8. Bangkok, Singapore and Jakarta got the highest quality rank. Main reason for this is that 1) star rank is high (Singapore) and 2) price is comparatively cheap (Bangkok and Jakarta).
For ‘resistance’ variables we use ‘Flight time / number of travel’, ‘Big Mac Index’ and ‘Visa’.
Flight time / number of travel - can be treated as a resistant to individual’s travel. Usually it becomes an obstacle for people if the flight time to the destination is longer than normal. In the determination of flight time, we used ABC world airways guide ‘Blue book / Red ABC Travel Guides’ from 1990 to 1995 and OAG world airways guide ‘The complete guide to air travel’ from 1996 to 2001. The target airports are shown in Table 9 while the minimum flight time for the routes is used for the analysis. North America has four airports, so their average flight time is calculated for the analysis. Flight time changes greatly in the case that whether the route is direct or connecting flight. For example, Seoul – Beijing flight had taken 8 hours 45 minutes till 1994. Then it has improved to 1 hour 50 minutes since 1995 when the direct flight started.
Number of travel was estimated in the last chapter. It represents how the international travel is spread for that country. At first, number of travel is small because the international travel has not yet popularized. Eventually, flight time / number of travel will show a higher tendency. However, flight time / number of travel became smaller and smaller as the international travel is spread widely. Figure 6 shows the future flight time / number of travel in Japan. In the year 2005, North America has the biggest flight time / number of travel and Korea has the smallest. The disparity between North America and Korea turns to be small as the number of Japanese tourist grows.
Big Mac Index - is an annual report published by London Economist Newspaper Ltd. We used it as a consumer price index, since McDonald establishments are widely distributed all over the world.
Visa - needed to enter some countries even if the travel is shorter than a week. Visa obligation will also be a resistant of individual’s travel because it takes time and cost to acquire it.

The following variables are used as a dummy variable in this model.
Asia - is a one-zero dummy variable. It takes the value of one if they are Asian countries, and zero other regions.
English - is a one-zero dummy variable. It takes the value of one if their mother tongue is English, and zero otherwise.
We estimated the parameter with these variables. The result of the estimation is shown in Table 10.
R-squared resulted to be over 0.80, these models can present the travel destination with high accuracy. The T value of each variable is mostly significant. Although not all T value of each variable are not significant, we chose model 2 for our estimation because R-squared is resulted to be high.
Figure 7 and 8 show the Japanese travel destination ratio in 2001 with model 2. These two graphs are similar in the point; 1) North America presents the highest rate, and 2) the ratio amounts over 70 % with higher 4 countries (North America, Oceania, Korea and China). There is a differential between real ratio and estimated one in Oceania and Korea. This is a future task that could improve the model.

5.3 Forecasting the future travel destination
 We proceeded to forecasting the future travel destination by using parameters and future estimated value of variables.
Future GDP data uses the same value of international trip generation analysis. Future travel time uses the time in 2001, assumed to be constant after 2001. Future number of travel uses the value estimated in the last chapter. Future World heritage, Hotel and Big Mac Index is used the value of 2001 as constant since it is difficult to forecast. Asia and English dummy were also held constant.
Figure 9 and 10 show the future travel destination in Korea and China. It can be seen that North America became popular destination for both countries. This suggests that future flight time / number of travel becomes small as the number of travel per person grows. That is to say, the time resistance to North America coming far from Asian countries turns to be small according to the spread of international tourism.
These figures also show that many Asian countries diminish its rate including Japan. For example, travel destination rate to Japan by Korean tourist decreases from 11.6 % in 1990 to 10.8% in 2030 and by Chinese tourist decreases from 12.6 % to 11.4 %.

6 Forecasting the future inbound tourists in Japan

Finally we estimate how many foreign tourists will visit Japan. The forecasting was held only with Korea, Hong Kong, Thailand and China because of the data restriction. 
Figure 11 shows the number of tourist and the rate of tourist visiting Japan from Korea. The number of Korean tourists to visit Japan will increase according to the growth of international tourist (from 0.60 million in 2007 to 1.12 million in 2030). The rate of tourist visiting Japan from Korea will decrease gradually from 2020.
Figure 12 shows the number of tourist visiting Japan and the rate of tourist visiting Japan from China. The number of Chinese tourists to visit Japan will decrease after 2020. The main reason for this is the decrease of rate to visit Japan from 2011. Another reason would be the forecast says that the number of Chinese international tourist will not increase so much. The graph presents that the number of tourist to visit Japan will reach 1.73 million in 2007. That will increase to a peak value of 1.92 million in 2020 and then will decrease to 1.85 million in 2030.

7 Conclusion

In this study, the model for forecasting international tourism demand was developed. This model is consisted of two steps 1) demand generation and 2) demand distribution.
From modeling analysis, we got two main conclusions from the two estimations.
1) The number of tourist from every Asian country shows the growing tendency until 2030.
2) The rate of tourist visiting North America showed growing tendency in the future. On the other hand, the rate of tourist visiting Asian countries including Japan will decrease.
Eventually, it is possible that Japanese inbound would decline in the future. 
However, this model dose not includes the tourism policy variables. It can be said that this model can show the trend of international tourism demand.
Japan started inbound campaign since 2001. After that, inbound demand is dramatically increasing now. As a future task on this study, the model improvement included the policy variables should be done.

Reference

Ministry of Land, Infrastructure and Transport, (2005), Tourism Report 2005
Watanabe T. and Unuma T, (1997), A Study on Inbound Tourism Policies for Regional Development in Japan, Institute for Transport Policy Studies 2nd research report, pp.57-66
Morichi S. and Todoroki T, (2001), Analysis on Structure and Future Trend of Regional Disparity for Overseas Tourist Demand of Japan, Transport Policy Studies’ Review, Vol.4, No.1, pp.8-18
Nakamura Y. Takebayashi M et al, (2005), An Empirical Analysis of the International Tourism Demand, The 32nd Conference of Infrastructure Planning CD-Rom No.239
Nicole L. and Tae H. O, (1989), Modeling Destination Choice Of International Tourists With An Application To Japanese Tourists, Proceedings of the 5th World Conference on Transport Research, pp.521-535
Yai T. Yamada H and Okamoto N, (1995), Nationwide Recreation Travel Survey in Japan: Outline and Modeling Applicability, Transportation Research Record, No.1493, pp.29-38
Okamoto N. Morichi. S et al, (1995), A study on Regional Difference of Recreational Destination Choice Behavior, Journal of the Eastern Asia Society for Transportation Studies, Vol.1, No.1, pp.351-360
World Tourism Organization, (1997), Tourism 2020 Vision Vol.3 East Asia and the Pacific
Ministry of Justice, Annual Report on Immigration, 1964-2001
Korea National Tourism Organization, Tour 2 Korea website,
 (http://www.etourkorea.com/jsp/cms/eng/about/research/kft/vakd/research01_01.jsp)
Hong Kong Tourism Board, A Statistical Review Of Hong Kong Tourism
Thailand National Statistical Office, (2004), Thailand in Figures 2003,               Alpha Research Co, Ltd
Singapore Department of Statistics, Yearbook of Statistics Singapore
National Institute of Population and Social Security Research website,
     (http://www.ipss.go.jp/)
Ministry of Internal Affairs and Communication, Japan Statistical Yearbook, 1964-2001
China National Economy Publishing House, Almanac of China’s Economy, 1978-2003
Ministry of the Interior, Taiwan Population Statistics, 1990-2001
United Nations Statistics Division, Demographic Yearbook, 1990-2001
Directorate General Budget, Accounting and Statistics, Japan External Trade Organization website (Taiwan GDP) (http://home.att.ne.jp/yellow/motoda/growthrategdp.pdf)
United Nations Statistical Division, Statistical Yearbook, 1980-2001
University of British Colombia, PACIFIC Exchange Rate Service website (exchange rate)
     (http://fx.sauder.ubc.ca/data.html)
Hara Y, (1999), Asia Economic Theory, NTT Publishing Co, Ltd, pp.265-270
UNESCO website (http://whc.unesco.org/en/list/)
Japan Hotel Almanac, Ota Publications, 1990-2001
Chikyunoarukikata, Diamond Big Co. Ltd, 1990-2001
Hotels.com website (http://jp.hotels.com/hotel-htdocs/jp_en/index.htm)
ABC world airways guide, Blue book / Red ABC Travel Guides, 1990-1996
OAG world airways guide, The complete guide to air travel, OAG, 1996-2001
London Economist Newspaper Ltd, Economist, 1990-2001
United Nations Statistics Division, (2005), World Population Prospects
Ministry of Land, Infrastructure and Transport, website (Future GDP estimation)
     (http://www.mlit.go.jp/road/kanren/suikei/3-1.pdf)
Socio-Economic Research Center, website (Future GDP estimation)
     (http://criepi.denken.or.jp/jp/serc/topics/chouki2-01.html)
Japan Center for Economic Research, website (Future GDP estimation)
     (http://www.jcer.or.jp/research/long/long2000.pdf)
International Energy Agency, (2004), World Energy Outlook 2004

Tables

Table 1 Data source of the number of tourist
Country	Source	Year	Destination	
Japan	Annual Report on Immigration	1964-2001	○	
Korea	Tour 2 Korea web site	1961-2003	×	
Hong Kong	A Statistical Review of Hong Kong Tourism	1978-2004	×	
Thailand	Thailand in figures	1982-2003	×	
Singapore	Yearbook of Statistics Singapore	1970-2004	×	
China	World Tourism Statistics	1990-2003	○	
Taiwan		1990-2003	○	
Malaysia		1990-2003	○	
Indonesia		1990-2003	○	

Table 2 Data source
Country	Source	Publisher	
Number of Tourist	
Japan	Annual Report on Immigration	Ministry of Justice	
Korea	Tour 2 Korea website	Korea National Tourism Organization	
Hong Kong	A Statistical Review of Hong Kong Tourism	Hong Kong Tourism Board	
Thailand	Thailand in figures	Alpha Research	
Singapore	Yearbook of Statistics Singapore	Singapore Department of Statistics	
China	World Tourism Statistics	Asia-Pacific Tourism Exchange Center	
Taiwan			
Malaysia			
Indonesia			
Population	
Japan	Japan Statistical Yearbook	Ministry of Internal Affairs and Communication	
China	Almanac of China's Economy	China National Economy Publishing House	
Taiwan	Taiwan Population Statistics	Ministry of the Interior	
Others	Demographic Yearbook	United Nations Statistics Division	
GDP	
Japan	Japan Statistical Yearbook	Ministry of Internal Affairs and Communication	
Taiwan	Japan External Trade Organization website	Directorate General of Budget,	
		Accounting and Statistics	
Others	Statistical Yearbook	United Nations Statistics Division	
Exchange Rate	
All countries	Statistical Yearbook	United Nations Statistics Division	
	PACIFIC Exchange Rate Service	University of British Colombia	
Gini Coefficience	
Thailand	Asia Economic Theory	NTT Publishing Co. Ltd	
World Heritage	
All countries	website	UNESCO	
Hotel	
Japan	Japan Hotel Almanac	Ota Publications	
Others	Globe-Trotter Travel Guidebook	Diamond Big Co. Ltd	
Flight time	
All countries	Blue book / Red ABC Travel Guides	ABC world airways guide	
	The complete guide to air travel	OAG world airways guide	
Visa	
All countries	OAG Flight Guide Supplement	OAG	
Big Mac Index	
All countries	Economist	London Economist Newspaper Ltd	
Future Population	
Japan	website	National Institute of Population	
		and Social Security Research	
Others	World Population Prospects	United Nations Statistics Division	
Future GDP	
Japan	website	Ministry of Land. Infrastructure and Transport	
		Socio-Economic Research Center	
		Japan Center for Economic Research	
Others	World Energy Outlook 2004	International Energy Agency	

Table 3 Parameter estimation (international trip generation)
　	Japan	Korea	Hong Kong	Thailand	China	
Saturated number of tourist	0.310 	0.286	0.959	0.0461 	0.0113 	
(number/population/year)	(1.96)	(2.15)	(2.27)	(2.07)	(4.63)	
GDP/capita	0.93	0.000276 	0.0000130 	0.000838 	0.000618	
(US$/population)	(6.22)	(7.93)	(2.53)	(2.02)	(2.96)	
Exchange rate		0.00146 	-0.0152 	0.0438 	-0.453 	
(each currency/US$)		(6.79)	(-0.10)	(1.87)	(-1.89)	
Gini coefficient				-1.015 		
				(-0.34)		
Constant	4.160 	4.831 	1.788 	2.358 	0.905 	
	(13.67)	(17.14)	(1.44)	(1.72)	(0.077)	
R-squared	0.968	0.977	0.898	0.870 	0.890 	
Number of observation	41	38	25	21	14	
					( ) T value	

Table 4 Parameter estimation (dummy variable of the year)
　	Japan	Korea	Hong Kong	Thailand	China	
Saturated number of tourist	0.310 	0.225	0.959	0.0461 	0.0113 	
(number/population/year)	(1.96)	(3.94)	(2.27)	(2.30)	(4.63)	
GDP/capita	0.930 	0.000300 	0.0000130 	0.000838 	0.000618	
(US$/population)	(6.22)	(10.1)	(2.53)	(2.17)	(2.96)	
Exchange rate		0.00172 	-0.0152 	0.0438 	-0.453 	
(each currency/US$)		(7.29)	(-0.10)	(2.08)	(-1.89)	
Gini coefficient				-1.015 		
				(-0.31)		
Dummy variable of the year		-0.287		-0.442		
		(-2.79)		(-1.10)		
Constant	4.16 	4.84 	1.79 	2.358 	0.905 	
	(13.6)	(30.5)	(1.44)	(1.63)	(0.077)	
R-squared	0.968	0.983	0.898	0.910 	0.890 	
Number of observation	41	38	25	21	14	
					( ) T value	

Table 5 Future Population           (thousand people)
Country	2005	2010	2015	2020	2025	2030	
Japan	127,708 	127,473 	126,266 	124,107 	121,136 	117,580 	
Hong Kong	7,182 	7,537 	7,871 	8,187 	8,490 	8,764 	
Thailand	64,081 	66,951 	69,568 	71,894 	73,857 	75,421 	
Korea	48,182 	49,080 	49,672 	50,020 	50,171 	50,045 	
China	1,322,273 	1,364,875 	1,402,321 	1,429,473 	1,445,100 	1,450,521 	

Table 6 Future GDP                       (%)
Country	Data	2005～	2010～	2015～	2020～	2025～	
		2010	2015	2020	2025	2030	
Japan	Socio-Economic 	high	1.0 	1.0 	1.0 	1.0 	1.0 	
	Research Center	mid	0.7 	0.7 	0.7 	0.7 	0.7 	
	　	low	0.3 	0.3 	0.3 	0.3 	0.3 	
	Japan Center of 	high	2.2 	1.0 	0.6 	0.5 	0.5 	
	Economic Research	mid	1.0 	0.5 	0.3 	0.3 	0.3 	
	　	low	0.1 	-0.1 	-0.1 	0.2 	0.2 	
	Ministry of land, 	1.9 	1.5 	1.3 	1.0 	0.8 	
	Infrastructure and Transport						
Hong Kong	International Energy Agency	2.4	1.9	1.9	1.6	1.6	
Thailand		5.2	4.7	4.7	4.1	4.1	
Korea		2.4	1.9	1.9	1.6	1.6	
China		4.3	4.3	4.3	4.3	4.3	

                          Table 7 Future exchange rate
　	Hong Kong	Thailand	Korea	China	
Exchange rate	7.77 	0.51 	1112.25 	8.32 	
Currency	HK$/US$	Baht/US$	Won/US$	RMB/US$	

Table 8 Quality index of hotel at 2001
City	Quality	City	Quality	
Tokyo	0.740	Hong Kong	0.956	
New York	0.786	Bangkok	1.02	
Sydney	0.731	Singapore	1.06	
Seoul	0.789	Kuala Lumpur	0.892	
Beijing	0.711	Jakarta	1.06	
Taipei	0.849	　	　	

Table 9 Target airport
North America	Asia	
New York ( J. F. K airport )	Tokyo ( Narita airport )	
San Francisco airport	Seoul ( Kimpo airport )	
Los Angels airport	Beijing airport	
Chicago ( Ohea airport )	Taipei ( C. K. S airport )	
Oceania	Hong Kong ( Chek Lap Kok airport )	
Sydney ( Kingsford Smith airport )	Bangkok ( Don Muang airport )	
	Singapore ( Changi airport )	
	Kuala Lumpur international airport	
　	Jakarta ( Soekarno-Hatta airport )	

Table 10 Parameter estimation (travel destination)
　	model 1	model 2	
	coefficience	t value	coefficience	t value	
World heritage	0.0725	(5.42)	0.0604	(3.17)	
GDP(US $/population)	0.0000296	(2.16)	0.0000460	(2.69)	
Hotel	2.17	(2.32)	2.37	(2.25)	
Flight time / number of travel ( LOG )	-1.27	(-6.94)	-1.48 	(-6.80)	
(hour/number of travel/year/population)					
Big Mac Index	-0.364	(-1.85)	-0.792 	(-2.71)	
Asia			-1.12	(-1.76)	
English			-0.838	(-2.05)	
Visa			-0.516	(-1.47)	
R-squared	0.741	　	0.820	　	
Number of observation	108	　	108	　	

Figures

　　　　　　　　　　  number of travel per person

                                                     year
Figure 1 S shaped logistic curve and saturation point K

Figure 2 Number of travel and exchange rate in Korea

Figure 3 Number of travel and exchange rate in Thailand

　　　　　million people

WTO estimation

Estimated value

Real value

                                                Ministry of Land, Infrastructure
                                                and Trnasport

                                                Japan Center for Economic
                                                Research; Lower estimate

　　　　　　　　　　　　　　　　　　　　　　　　year

      Figure 4 Forecasting future international tourists in Japan

　　     million people

                                                         China

                                                         Korea

                                                         Hong Kong

                                                         Thailand

                                                          year

Figure 5 Forecasting future international tourists in Asian countries

flight time / number of travel in Japan

North America

Figure 6 Japanese travel destination ratio in 2001 (estimated value)

Korea

               Figure 6 Future flight time / number of travel in Japan

Figure 7 Japanese travel destination ratio in 2001 ( real value )

Figure 8 Japanese travel destination ratio in 2001 ( estimated value )

Indonesia

Malaysia

Singapore

Thailand

Hong Kong

Taiwan

China

Oceania

North America

Japan

Figure 9 Future travel destination in Korea

Indonesia

Malaysia

Singapore

Thailand

Japan

North America

Hong Kong

Taiwan

Korea

Oceania

Figure 10 Future travel destination in China

Figure 11 The number of tourist and the rate of tourist visiting Japan from Korea

Figure 12 The number of tourist and the rate of tourist visiting Japan from China