Discussion Paper No.280

Panel data analysis of the factors
for determining inbound tourism in Japan
Tomoya Mori
Yamaguchi University

Masahiro Yabuta
Chuo University

April 2017

INSTITUTE OF ECONOMIC RESEARCH
Chuo University
Tokyo, Japan

Panel data analysis of the factors
for determining inbound tourism in Japan∗
Tomoya Mori §,Masahiro Yabuta §§Abstract
The demand for inbound tourism in Japan has been increasing since 2013. In 2015, thenumber of inbound tourists reached approximately 20 million. The Japanese government
is making various efforts to increase the number of foreign visitors and has set a target of
approximately 40 million inbound tourists to be reached by 2020. The aim of this paper
is to demonstrate empirically the demand for inbound tourism in Japan by using thegravity model with panel data. According to demand theory, the income factor, as well as
the relative prices in origin countries, influence the demand for inbound tourism.
Focusing on a visa-permission tourism policy, the paper clarifies that not only income
and relative price effects but also travel policy will have a significant effect on increasing
inbound tourism in Japan.

Keywords: inbound tourism, demand theory, visa-permission policy, panel data,
gravity model.
JEL Classification Code: z32, z38∗ The authors highly appreciate the financial supports from the MEXT-support

Program for the Strategic Foundation at Private Universities 2013-2017, Joint
Research Grant of Chuo University, and the Grants-in-Aids for Scientific Research(C)(No.17K02127, 2017-2019).
§ Lecturer, Faculty of Education, Yamaguchi University,1677-1,Yoshida,Yamaguchi,
753-8513, moritomo@yamaguchi-u.ac.jp§§Professor,Faculty of Economics, Chuo University, 742-1,Higashinakano, Hachioji,
Tokyo,192-0393, yabuta@tamacc.chuo-u.ac.jp1

1.
Introduction
1-1.
Research BackgroundThe number of inbound tourists in Japan has been increasing since 2013, despite the“Great East Japan Earthquake” of 2011, while the domestic tourism market in Japan has
not noticeably expanded. According to the Japan National Tourism Organization(JNTO), the number of inbound tourists reached approximately 20 million in 2015 andexceeded the number of outbound tourists for the first time in 45 years. In this regard,
the Japanese government has introduced several policies to promote inbound tourism.
For example, the Japanese government’s Cabinet indicated that the demand for inboundtourism was one of the challenges in the “Basic Policy about Economic and Fiscal
Management and Structural Reform 2002” and established the headquarters of the“Visit Japan Campaign” in 2003. The Japanese government has set a target of 40 million
inbound tourists to be reached by 2020.
The aim of this paper is to understand the demand trends for inbound tourism anddemonstrate empirically the factors that influence demand. It is expected that the
number of inbound tourists will continuously increase as part of the tourism strategy set
by the government in the “Tourism-based Country Promotion Basic Act” enforced in2007. In this context, it is important to clarify the factors that influence inbound tourism
in Japan in order to consider the future of Japan as a tourism-based country.
According to the Japan Tourism Agency (2016), “an inbound international visitor
refers to foreign travelers entering Japan. This group consists of foreign nationals
legally entering Japan minus foreign nationals who are residents of Japan (both [ofwhich are] according to the nationality-based statistics [issued] by the Ministry ofJustice), plus those in transit.” 1 The visitors based on this definition include those who
visit for purposes other than simply travel, such as business, cultural and artistic
activities, and visits to relatives. In order to focus only on the trend of traveling toJapan, this paper considers just international visitors in accordance with the nationalitybased statistics of the Ministry of Justice in terms of the definition of an “inbound”
tourist.
Figure 1 shows that the number of inbound tourists decreased in 2011, the year of theGreat East Japanese Earthquake; however, the number has been increasing since 2013.
Prior studies indicate that this growth is related to the quantitative and qualitative
monetary easing by Japan’s central bank. As can be seen in Figure 2, the exchange rateof the Japanese currency, the yen, against the U.S. dollar has continued to be weak since1

Refers to HP in the Japan Travel Agency
http://www.mlit.go.jp/kankocho/en/siryou/toukei/in_out.html (accessed June 16, 2016).
2

2013. Such currency depreciation makes the cost of travel cheaper in the destination
country and thus encourages an increase in inbound tourism.
Figure 1：The numbers of inbound tourists
2,500
1,973

Number(ten million)

2,000

1,500

1,341
1,036

1,000
673

733

835

861
679

836
622

500

0

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Source: Author’s own construction based on the statistical data of the Ministry of Justice

Figure 2: The exchange rate of the yen against the U.S. dollar
130.0
120.0
110.0
100.0
90.0

116.3

121.0

117.8
105.9

103.4
93.6

97.6
87.8
79.8

79.8

80.0
70.0
60.0

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Source: Author’s own construction based on the statistical data of the World Bank

Based on demand theory, tourism economics focus on the elements of price andincome as factors that promote demand. Additionally, economic growth in source anddestination countries is considered an income factor that promotes demand.
Further, tourism policy can encourage inbound tourism to increase. Such policy
includes various strategies. This study adopts visa permission as the variable of tourism
policy. Visa permission has various kinds of deregulation (the easing of a primary visa,
a multiple-entry visa, and a visa waiver). This study considers a visa waiver as visa
permission. However, from 2015 we also include the part-easing of a visa restriction
regarding China as visa permission because China has a market share of Japanese3

inbound tourism. 2
1-2. Literature Review
The Mizuho Research Institute Ltd. (2016) empirically analyzes the demand for
inbound tourism based on an econometric methodology. This work attempts to analyze
15 countries (South Korea, China, Taiwan, Hong Kong, Thailand, Singapore, Malaysia,
Indonesia, the Philippines, the United Kingdom, France, Germany, the United States,
Canada, and Australia) using time-series data from the first quarter of 1995 to the fourth
quarter of 2015. The result of this study has shown that the trend of a low yen against
the U.S. dollar increases inbound tourism in newly industrialized countries (NICs).
Moreover, with regard to Southeast Asia, economic growth is a factor of inboundtourism in Japan. Additionally, inbound tourism from China is not a strong response to
the exchange rate; instead, the income factor significantly influences demand. Further,
this study has concluded that visa permission has a positive effect on inbound tourism.
However, the Mizuho Research Institute Ltd. (2016) cannot consider the total of
inbound tourism in Japan because of the nature of the time-series data analysis. In orderto achieve a comprehensive analysis, we need to analyze panel data in which crosssectional data is added to time-series data.
One typical methodology that is used to analyze international tourism with panel datais the gravity model. According to Morley, Rossello, and Santana-Gallego (2014), thegravity model assumes that “bilateral flows between two countries are directly
proportional to the countries’ economic masses and inversely proportional to the
distance between them.” Empirical studies using the gravity model adopt real grossdomestic product (GDP) or real GDP per capita as the countries’ economic masses. The
distance between two countries is regarded as a kind of travel cost. In addition,
expenditure at the destination is an important factor for determining the demand for
inbound tourism. Normally, in the gravity model, expenditure is calculated with theexchange rate and the relative ratio of prices between a destination country and an
origin country. The other variables are population and specific events such as festivals,
disasters, and political disturbances.
Based on the foregoing, this study attempts to estimate demand for inbound tourism
2

The Japanese government has eased the range of multiple-entry visas since 2015 as follows: (1)multiple-entry visas for short-term business purposes and for cultural and intellectual figures; (2)multiple-entry visas for individual tourists visiting Okinawa or one of three prefectures in Tohoku;
and (3) multiple-entry visas for individuals with substantially high incomes (refers to HP in theJapanese Ministry of Foreign Affairs: http://www.mofa.go.jp/press/release/press4e_000576.html(accessed March 30, 2017)).
4

in Japan using the gravity model with panel data. We use visa permission as the policy
variable together with price and income.
2. Methods and Data
This research estimates an expression of a linear logarithmic model (1) based on theprior section.
(1) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1 ln 𝑌𝑌𝑖𝑖𝑖𝑖 + 𝛽𝛽2 ln 𝑌𝑌𝑗𝑗𝑗𝑗 + 𝛽𝛽3 ln 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛽𝛽4 ln 𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 + 𝛽𝛽5 ln 𝑃𝑃𝑃𝑃𝑃𝑃𝑗𝑗𝑗𝑗+ 𝛽𝛽6 (𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉- 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖 × 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 ) + 𝛽𝛽7 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖 + 𝛽𝛽8 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡+ 𝛽𝛽7 𝑦𝑦𝑦𝑦𝑡𝑡 + 𝜇𝜇𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖

In the above model, the subscript 𝑖𝑖 is the origin country and 𝑗𝑗 is the destination(Japan). 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 is the number of tourists and refers to the variable of tourism demand.
𝑌𝑌𝑖𝑖𝑖𝑖 and 𝑌𝑌𝑗𝑗𝑗𝑗 respectively are the income factors in the origin country and in thedestination country. This paper adopts real GDP per capita as the proxy variable.

Income factors in both countries have positive effects on tourism demand. 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 is therelative price, which means expenditure in the destination country relative to
expenditure in the origin country. Relative price is represented as follows (2).
𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖���(2) 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 = 𝐶𝐶𝐶𝐶𝐶𝐶𝑗𝑗 𝐸𝐸𝐸𝐸𝑖𝑖 = �𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖�𝐶𝐶𝐶𝐶𝐶𝐶 � / �𝐸𝐸𝐸𝐸𝑖𝑖�𝐸𝐸𝐸𝐸 ���𝐸𝐸𝐸𝐸 �𝑗𝑗

𝑗𝑗

𝑗𝑗

𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖 and 𝐶𝐶𝐶𝐶𝐶𝐶𝑗𝑗 respectively are the consumer price indexes in the origin country, 𝑖𝑖,
and the destination country (Japan). 𝐸𝐸𝐸𝐸𝑖𝑖 and 𝐸𝐸𝐸𝐸𝑗𝑗 respectively are the exchange ratesof the domestic currencies in 𝑖𝑖 and 𝑗𝑗 in terms of the U.S. dollar. The relative price has
a negative influence on inbound tourism demand. 𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 and 𝑃𝑃𝑃𝑃𝑃𝑃𝑗𝑗𝑗𝑗 each represent the

populations in i and j. Culiuc (2014) concludes that the populations in the origin and thedestination countries have a negative effect on inbound tourism demand. However,some research finds that population encourages an increase in the number ofinternational tourists (e.g., Hanafiash and Harun 2010; Massidda and Etzo 2012; and soon).
𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉- 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖 is the dummy variable, which is 1 if visa restrictions are
eased and 0 otherwise. 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 is the time dummy variable, which is 1 after visa
restrictions are eased and 0 before they are eased. The cross term
𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉- 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖 × 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 is the difference between a country that eases
5visa restrictions and countries that do not ease visa restrictions after a year of visa
permission. Thus, the coefficient 𝛽𝛽4 refers to the effect of visa permission. 𝜇𝜇𝑖𝑖 refers toa specific fixed country and the time-invariant effect, such as the distance between two

countries. This investigation attempts to remove 𝜇𝜇𝑖𝑖 using a fixed-effect model. 𝑦𝑦𝑦𝑦𝑡𝑡 is
the time dummy that indicates political disturbance or disaster. This estimation uses thetime dummy to represent the Great East Japan Earthquake. 𝜀𝜀𝑖𝑖𝑖𝑖 is the error term.
This paper sets a target for 190 countries as the cross-sectional data (see Table 1) andthe 10 years from 2006 to 2015 as the time-series data. Table 2 shows the source of thedata, and Table 3 presents the basic statistics. We try to estimate the inbound demandmodel using unbalance panel data because parts of the data are missing. Table 4 shows
the visa-permission schedule in Japan since 2006. The Japanese government has eased
visa restrictions for Indonesia since December 2014; thus, this paper assumes that visa
restrictions for Indonesia eased in 2015.
Table 1: A list of countries’ cross-sectional data
Asia (39 countries)

Afghanistan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Hong Kong, India,
Indonesia, Iran, Iraq, Islamic Rep., Israel, Jordan, Korea, Rep. Kuwait, Lao PDR, Lebanon, Malaysia,
Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka,
Syrian Arab Republic, Taiwan, Thailand, Timor-Leste, Turkey, United Arab Emirates, Vietnam, Yemen Rep.
Europe (50 countries)
Albania, Andorra, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia,
Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland,
Italy, Kazakhstan, Kosovo, Kyrgyz Republic, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia FYR,
Malta, Moldova, Monaco, Netherlands, Norway, Poland, Portugal, Romania, Russian Federation, San Marino,
Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Tajikistan, Turkmenistan, United Kingdom, Ukraine,
Uzbekistan
Africa (53 countries)
Algeria, Angola, Arab Rep., Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African
Republic, Chad, Comoros, Congo Dem. Rep., Congo Rep., Côte d'Ivoire, Djibouti, Egypt, Ethiopia, Equatorial
Guinea, Eritrea, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar,
Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome andPrincipe, Senegal, Seychelles, Sierra Leone, South Africa, South Sudan, Sudan, Swaziland, Tanzania, Togo,
Tunisia, Uganda, Zambia, Zimbabwe
North America (22 countries)
Antigua and Barbuda, Bahamas, Barbados, Belize, Canada, Costa Rica, Dominica, Dominican Republic, El

6

Salvador, Grenada, Guatemala, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, St. Kitts and Nevis, St.
Lucia, St. Vincent and the Grenadines, Trinidad and Tobago, United States
South America (12 countries)
Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela
Oceania (14 countries)
Australia, Fiji, Kiribati, Marshall Islands, Micronesia Fed. Sts., Nauru, New Zealand, Palau, Papua New
Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu

Table 2: Information about each variable
Variable
The

Inbound demanddependence
variable

SourceThe number of inbound tourists

The

statistics of the Ministry(𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 )of Justice

The

Real GDP per capita

explanatory
variable

Income(𝑌𝑌𝑖𝑖𝑖𝑖 )Price(𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 )Others(𝑋𝑋𝑖𝑖𝑖𝑖 )nationality-based

Database of the World
Bankiii

Relative price
The population (𝑃𝑃𝑖𝑖𝑖𝑖 )Time dummy 2011 (𝑌𝑌2011)

Table 3: Basic statistics
Frequency

Min.

Max.

Average

S.E.

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖

1,862

1

3,367,616

34,724.637

214,608.8272

𝑌𝑌𝑖𝑖𝑖𝑖

1,807

208.650

145,221.192

12746.806

433.608

𝑌𝑌𝑗𝑗𝑗𝑗

1,899

41,010.788

44,656.832

43,407

1,006.847

𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖

1,886

9,732

1,371,220,000

35810453.661

136,601,189.534

𝑃𝑃𝑃𝑃𝑃𝑃𝑗𝑗𝑗𝑗

1,900

219,956

128,070,000

127,617,090.087

2,950,407.149

𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖𝑖𝑖

1,709

0.479

7.452

1.033

0.271

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2013

1,900

0

1

0.300

0.458

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2015

1,900

0

1

0.100

0,300

𝑦𝑦𝑦𝑦2011

1,900

0

1

0.100

0.300

Table 4: The list of visa permissions
Year

Content

7

Sep. 2013

Visa permission for Thailand and Malaysia

Dec. 2014

Visa permission for Indonesia under a preregistration system

Jan. 2015

Part easing of the visa restriction in China

3. Results and Analysis
Table 5 shows the results of the estimation. The results of the Wu–Hausman test
statistically support the use of the fixed-effect model and not a random-effect model.
Thus, we can remove the effect from the fixed-effect term. According to the estimation
results, it is statistically significant in all models that the income in the origin countries
has a positive effect on inbound tourism demand. However, the income in Japan does
not significantly influence such demand. Although real GDP per capita in Japan has notincreased significantly in recent times, the demand for inbound tourism has tended toincrease. Thus, income in Japan is not statistically correlated with inbound tourism
demand.
Table 5: The panel analysis results
Model 1Model 2Model 3Model 4𝐶𝐶

-28.294*** (9.372)-22.933** (9.485)-28.147*** (9.368)-22.956** (9.478)

𝑌𝑌𝑖𝑖𝑖𝑖0.747*** (0.268)

0.656*** (0.234)

0.749*** (0.268)

0.655*** (0.234)

𝑌𝑌𝑗𝑗𝑗𝑗0.792 (0.743)

0.528 (0.753)

0.796 (0.743)

0.527 (0.756)

𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖

1.371*** (0.391)

1.241*** (0.381)

1.359*** (0.396)

1.243*** (0.385)-0.153*** (0.006)-0.149*** (0.006)-0.061*** (0.006)-0.149*** (0.006)-0.053*** (0.020)-0.040** (0.016)-0.061*** (0.023)-0.038*** (0.016)s

𝑃𝑃𝑃𝑃𝑃𝑃𝑗𝑗𝑗𝑗
𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖𝑖𝑖

𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖𝑖𝑖 × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸-1.119*** (0.248)-1.121*** (0.249)
0.037 (0.047)-0.008 (0.047)

𝑇𝑇ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎

0.916*** (0.045)

0.708*** (0.067)

0.913*** (0.046)

0.708*** (0.067)

𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀

0.491*** (0.046)

0.354*** (0.055)

0.490*** (0.046)

0.354*** (0.055)

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼

0.424*** (0.057)

0.328*** (0.058)

0.423*** (0.057)

0.328*** (0.058)

𝐶𝐶ℎ𝑖𝑖𝑖𝑖𝑖𝑖

0.744*** (0.058)

0.417*** (0.098)

0.741*** (0.059)

0.417*** (0.098)

0.270*** (0.037)

0.246*** (0.038)

0.271*** (0.038)

0.246*** (0.038)

× 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2013
× 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2013
× 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2015
× 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2015
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2013

8

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2015

0.344*** (0.039)

0.318*** (0.039)

0.343*** (0.039)

0.318*** (0.039)

𝑦𝑦𝑦𝑦2011-0.480*** (0.039)-0.463*** (0.038)-0.480***(0.039)-0.463***(0.038)

Obs.

1,594

1,594

1,594

1,594

Modified 𝑅𝑅2

0.985

0.985

0.985

0.985

Notes: ** denotes significance at 5%. *** denotes significance at 1%.
The numbers in parentheses are robust standard errors.

The population in an origin country positively influences inbound tourism demand.
The interpretation of this result is that there is a positive coefficient because population
growth increases the potential number of tourists coming to Japan. However, thepopulation in a destination country has a negative effect on inbound tourism. In the caseof Japan, this is because the Japanese government and travel companies have placed
resources in the inbound tourism market at the expense of international tourism demandbecause demand in the domestic tourism market has been sluggish.
The results show that the coefficient of relative price is negative but low. Thus, relative
price does not greatly affect inbound tourism demand. However, the cross term with the
Asian countries’ dummy and relative price demonstrates that the influence of this crossterm is greater compared with other countries. However, the cross term with theEuropean countries’ dummy and relative price is not statistically significant. This meansthat no difference exists between European countries and other countries regarding
relative price.
The coefficients of 𝑇𝑇ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 × 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2013 , 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 × 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2013 , 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 ×
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2014 and 𝐶𝐶ℎ𝑖𝑖𝑖𝑖𝑖𝑖 × 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴2015 are statistically significant and positive. Thisfinding suggests that the number of inbound tourists is increasing to a greater extent inthose countries for which visa permission has been eased compared with those countries
for which visa permission has not been eased. Further, the result of the Great East Japan
Earthquake demonstrates that inbound tourism demand decreased by 46.3%–48.0%
compared with other years.
4. Conclusion
This study analyzes inbound tourism demand in Japan using the gravity model withpanel data (annual data from 2006 to 2015 for 190 countries). This study’s estimation
clarifies that inbound tourism demand is statistically related to the factors of income,price, and tourism policy.
The results indicate that income growth in origin countries increases inbound tourism
demand in Japan. Moreover, the effect of the relative price variable on such demand is9

statistically significant, although the coefficient is low. However, a difference exists
between Asian countries and others.
The trend of a low yen and high U.S. dollar following the quantitative and qualitative
monetary policy in Japan positively influences the value of the relative price. Further,
the relative price may experience unstable changes because of trends in the global
economy. If the exchange rate of the yen against the U.S. dollar is high, inbounddemand may decrease. In order to achieve the goal of 40 million inbound tourists by
2020, the Japanese government must keep the exchange rate stable.
Further, this research demonstrates that tourists from Thailand, Malaysia, Indonesia,
and China are increasing to a greater extent than those from other countries after theeasing of visa restrictions. It is difficult to ease visa restrictions from the national
security perspective; however, in order to increase the number of inbound tourists, it isdesirable that the Japanese government should relax the visa restrictions for countries
where such restrictions have not currently been eased.
In the future, we should address a further three points. First, we should use the numberof international tourists as the proxy variable of inbound tourism demand. We should
also pay attention to the amount of expenditure by foreign tourists as well as their
numbers. Consequently, we need to make an estimation by employing the amount ofexpenditure as a proxy variable. Because the pattern of expenditure differs between eachcountry, such an analysis may introduce another perspective to the study of inboundtourism demand in Japan.
Second, this research analyzes all inbound tourists in Japan regardless of the airport
entrance point. In reality, foreign tourists visit Japan by using many different routes andtravel to various locations within Japan. An estimation of inbound tourism demand thatconsiders each international airport entry point would enable us to understand theconcrete structure of inbound tourism more comprehensively. Further, we could employ
tourism policy or strategy as the variable.
Finally, this study uses a static model with panel data. In a further step, we could
develop a dynamic model such as the generalized method of moments (GMM) model of
Arellano and Bond (1991). Because a dynamic panel model employs lag variables ofthe dependent variable, an estimation of a dynamic model can enable us to capture the“repeater” effect and the “word-of-mouth” effect.
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