Kyoto University,
Graduate School of Economics
Research Project Center Discussion Paper Series

Geographic Concentration of Foreign Visitors to Japan

Ayumu Tanaka

Discussion Paper No. E-12-013

Research Project Center
Graduate School of Economics
Kyoto University
Yoshida-Hommachi, Sakyo-ku
Kyoto City, 606-8501, Japan

March 2013

Geographic Concentration of Foreign Visitors in Japan
Ayumu Tanaka†
March 19, 2013

Abstract
This paper provides the ﬁrst evidence of geographic concentrationof foreign visitors in Japan, using a new data on nights spent by foreign
visitors in each region. Using locational Gini coeﬃcients, I show that
foreign visitors are more geographically concentrated than Japanesevisitors and the level of geographical concentration vary across source
countries. In addition, I employ gravity equations to examine the determinants of nights spent by foreign visitors in each prefecture. The
results suggest that visa policy, transport infrastructure, and naturaland cultural factors, as well as traditional gravity variables such as distance and economic size, play a role in international travel to Japanese
prefectures.
Keywords: foreign visitors; geographic concentration; locational Ginicoeﬃcient; gravity equation
JEL Classiﬁcation: F14, L83, R12†

Research Fellow, Research Project Center, Graduate School of Economics, Kyoto
University; Fellow, Research Institute of Economy, Trade and Industry (RIETI).
E-mail: tanaka-ayumu<at>rieti.go.jp

11

Introduction

Recently, Japanese government has begun to promote travel to Japan fromabroad. Since 2003, it has conducted a campaign named, “Visit Japan
Campaign (VJC)” to increase foreign visitors. Japan ranks 28th worldwide(6th in Asia) in terms of the numbers of inbound travelers in 2008, while
15th worldwide (2nd in Asia) in 2007 in terms of the numbers of outboundtravelers. Table 1 shows that the number of foreign visitors has grown
rapidly for the period 2003-2009. The average growth rate is over 30 percent
during this period.
Despite the recent growth of the number of inbound travelers in Japan,
this study reveals that huge disparity exists in the number of foreign visitors
among regions in terms of the number of nights spent by foreigners. I showa small number of prefectures have an overwhelmingly high share of nights
spent by foreign visitors in Japan.
Table 1: Foreign visitores in Japan since 2003
level
（10 thousand）
521.2
613.8
672.8
733.4
834.7
835.1
679.0
698.6

growth rate
(%)

2003
2004
17.8
2005
9.6
2006
9.0
2007
13.8
2008
0.0
2009
-18.7
2003-2009
30.3
Source: Data are from Japan Tourism Agency.

It is important to reveal what causes this geographic concentration of
the number of nights by foreigners among prefectures. The purpose of thispaper is to provide the ﬁrst evidence of the remarkable geographic concentration and analyze the reason for it. Recently, several studies in the trade
literature have examined the international ﬂow of travel since international
ﬂow of travel can be regard as a services trade. Neiman and Swagel (2009)
investigate the impacts of the visa policy on the travel to the US. Theyﬁnd that post-9/11 changes in visa policy were not important contributors
to the sharp decline in travel to the US following the attacks. Yasar et al.
(2012) also investigate the impacts of the visa policy and ﬁnd that the impact of the US Visa Waiver Program (VWP) tends to increase the bilateral
trade levels between the US and the selected VWP countries, especially for
US exports. Cristea (2011), Hovhannisyan and Keller (2011), and Poole2

(2010) analyze the role of travel as an input that facilitates trade or innovation by face-to-face communication. Earlier studies such as Kulendranand Wilson (2000) and Shan and Wilson (2001) employ time-series econometric techniques such as cointegration and Granger-causality approaches
to explore the relationship between trade and international travel. Kulendran and Wilson (2000) ﬁnd the two-way Granger-causality between total
travel and real total trade, using data for Australia and four importan travel
and trading partners. Shan and Wilson (2001) also ﬁnd two-way Grangercausality between international travel and international trade in the case of
China. In addition, Katircioglu (2009) also employs the Granger-causality
test and reveals that growth in international trade (both exports and imports) stimulates an increase in international tourist arrivals to Cyprus.
Fischer and Gil-Alana (2009) uses a methodology based on long memory
regression models and reveals that German tourism to Spain has an eﬀect
on German imports of Spanish wine that lasts between 2 and 9 months. Unlike previous studies, this study focuses on the geographic concentration offoreign visitors and analyzes the relationship between the number of nights
spent by foreign visitors and the host regions’ factor endowments, economic
size, and distance from source countries.
The remainder of this paper is divided into four sections. In Section
2, I explain and brieﬂy describe the data used in this paper. In Section
3, using locational Gini coeﬃcient, I reveal the geographic concentration offoreign visitors in terms of nights spent by them. In Section 4, I examine
what determines the number of nights spent by foreign visitors, using gravity
equations. The summary and conclusion are presented in the ﬁnal section.

2

Data and overview

In this section, I brieﬂy describe data used in this paper and provide an
overview of the data. In this paper, I use recently available data from theSurvey of Hotels by the Japan Tourism Agency (JTA). JTA has conducted
the survey since 2007. The target of the survey is all hotels with more than
10 employees in Japan. Hotels are required to report the total number ofnights by Japanese and foreign visitors. Total number of nights is deﬁned
by the sum of nights spent by visitor. From this survey, I construct data
which covers the period 2007–2009.
Table 2 shows the number of nights spent by foreign visitors and its
ranking and the share in all of 47 prefectures, as well as the fraction ofnights spent by foreign visitors. Table 2 reveals that a few prefectures has

3very large share of total number of nights, while most of prefectures has
only tiny share. In 2009, Tokyo has 34.86% of nights, while Mie, the median
prefecture, has only 0.47%.

4

5

Share
(%)
100.00
34.86
10.75
9.87
8.87
4.47
3.85
3.33
2.23
2.05
2.03
1.70
1.60
1.18
1.04
1.02
0.94
0.94
0.84
0.67
0.63
0.49
0.48
0.48
0.47

Nights(thousand)
18297.8
6377.7
1966.51806.71622.7
818.0
704.2
609.2
408.8
374.8
372.2
311.8
293.0
216.7
190.8
186.8
172.5
171.9
153.3
122.0
115.1
88.8
88.3
87.4
86.6

Prefecture

Total
Tokyo
Osaka
Hokkaido
Chiba
Kyoto
Aichi
Kanagawa
Yamanashi
Fukuoka
Shizuoka
Hyogo
Okinawa
Nagasaki
Kumamoto
NaganoOita
Hiroshima
Gifu
Ishikawa
Miyagi
Wakayama
Shiga
Niigata
Mie

Rank

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

Fraction of
Foreiners
(%)
6.07
18.47
12.52
7.36
10.45
9.58
7.49
5.35
10.18
4.23
2.78
3.76
2.55
5.18
3.83
1.89
4.19
3.49
4.26
2.18
1.74
2.63
3.53
1.20
1.63
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

Rank

Tochigi
Kagoshima
Saitama
Iwate
AomoriToyamaFukushima
IbarakiOkayama
Akita
Miyazaki
Yamagata
Gunma
Nara
Ehime
Saga
KagawaYamaguchi
Kochi
Fukui
Tottori
Tokushima
ShimanePrefecture

85.6
67.5
66.5
64.6
59.2
57.3
56.9
56.2
51.6
45.1
42.9
41.7
40.9
34.7
34.0
27.1
25.5
25.2
16.3
16.0
14.0
12.6
10.3

(thousand)NightsTable 2: Nights spent by foreigners in Japan (2009)

0.47
0.37
0.36
0.35
0.32
0.31
0.31
0.31
0.28
0.25
0.23
0.23
0.22
0.19
0.19
0.15
0.14
0.14
0.09
0.09
0.08
0.07
0.06

(%)

Share

1.30
1.46
2.33
1.55
1.71
2.22
0.80
1.97
1.42
1.56
1.77
1.04
0.69
3.13
1.42
1.40
1.18
0.90
0.89
0.73
0.59
1.05
0.47

Fraction of
Foreiners(%)

3

Geographic concentration

3.1

Locational Gini coeﬃcients

In this section, I employ a Lorenz curve and locational Gini coeﬃcients toassess the degree of geographical concentration of foreign visitors in Japan.
The prefectures are adopted as region since prefecture is the most disaggregated level of region in my data, although city or town levels might bedesirable for analysis.
The use of Gini coeﬃcients as a measure of geographic concentration iscommon since Krugman (1991).*1 Gini coeﬃcients are calculated for the
total number of the nights spent by each foreign country’s visitors and by
all visitors and Japanese and foreign visitors.
Gini coeﬃcients are constructed as follows. First, I calculate each prefecture’s share of GDP and share of nights:
yp,t =

Yp,t
Yt

(1)

np,t =

Np,t
Nt

(2)

where Y and N indicate real GDP (value added) and number of nights spentby visitors, respectively. Subscription p and t index destination prefecture
and year, respectively. Data on real GDP are taken from the JapaneseCabinet Oﬃce’s Annual Report on Prefectural Accounts.
Second, I calculate the Balassa index:
Bp,t =

np,t
.
yp,t

(3)

This Balassa index represents each region’s relative importance of visitors
relative to overall economic activity. I also calculate Balassa index using
share of population as denominator and obtain qualitatively similar results.
Third, I draw the Lorenz curve by ranking the Balassa index in descending order and plotting the cumulative of the share of nights on the verticalaxis against the cumulative of the share of GDP on the horizontal axis.
Forth, I obtain Gini coeﬃcients as twice the area between a 45-degreeline and the Lorenz curve. The Gini coeﬃcients can vary from 0 to 1. The
closer the distribution of the nights to that of the overall economic activity
in Japan, the smaller the Gini coeﬃcients.
*1

See Amiti (1998) for the more detail.

6

1
.9
.8
Share of Nights.4 .5 .6 .7
.3
.2
.1
0
0

.1

.2

.3

.4
.5
.6
Share of GDP

.7

.8

.9

1

Figure 1: Lorenz curve for the nights spent by foreigners

3.2

Geographic concentration of visitors

Figure 1 displays the Lorenz curve for total number of the nights spentby foreigners. The Figure reveals that geographic concentration of foreign
visitors is remarkable, compared with overall economic activities. Top seven
prefectures have over 70% of nights, while other prefectures have only tiny
shares. The curve are away from 45-degree line.
Table 3 presents the Gini coeﬃcients for total number of the nights spentby visitors. The Gini coeﬃcients are calculated for visitors from major 12
countries and the rest of world as well as all visitors and foreign and Japanesevisitors.
Higher Gini coeﬃcient indicates that total number of nights spent byvisitors is more geographically concentrated. One of the major ﬁndings
is that the Gini coeﬃcients for foreign travelers are much higher than theGini coeﬃcient for Japanese travelers. This result indicates that destinations of foreign travelers are more geographically concentrated than thoseof Japanese travelers.
The level of geographic concentration substantially varies across 12 major origin countries. The countries with the highest level of geographic con7

Table 3: Locational Gini coeﬃcients for the nights spent by foreigners in
Japan

Total
Japan
Foreign

2007
0.287
0.307
0.419

2008
0.286
0.305
0.425

2009
0.286
0.303
0.452

Korea
China
Hong Kong
TaiwanUSA
CanadaUK
Germany
FranceSingapore
ThailandAustralia
Rest of World

0.489
0.457
0.560
0.477
0.530
0.560
0.602
0.505
0.605
0.621
0.486
0.602
0.482

0.503
0.457
0.577
0.492
0.518
0.574
0.600
0.502
0.609
0.638
0.452
0.598
0.483

0.474
0.501
0.606
0.487
0.527
0.576
0.590
0.499
0.602
0.635
0.488
0.600
0.499

centration in 2009 are Singapore, Hong Kong, France, and Australia. These
countries had the highest level of the Gini coeﬃcients in both 2007 and 2008.
The countries with the lowest level of geographic concentration in 2009 are
Korea, Taiwan, Thailand, Germany, and China. These countries had thelowest level of the Gini coeﬃcients in both 2007 and 2008.
The Gini coeﬃcient for all visitors including both Japanese and foreign
visitors remain almost same for the period 2007–2009. However, the Ginicoeﬃcient for foreign visitors has risen, while the coeﬃcient for Japanese visitors has decreased. These results suggest that the destination of Japanese
travelers has been more geographically dispersed, while the destination offoreign travelers has been more geographically concentrated on popular prefectures.

4

Gravity equation

4.1

Speciﬁcation

I investigate the determinants of the sum of the nights spent by foreign
visitors, using gravity equations. The number of the Gini coeﬃcients is too
8small to estimate, although it is important to examine the determinants ofthe geographic concentration of foreign visitors. I, therefore, estimate thedeterminants of the sum of the nights spent by foreign visitors.
Several studies have employed the gravity equation to examine the determinants of travel since international travel is a kind of international trade.
Neiman and Swagel (2009) derived the gravity equation of international
travel from the theoretical framework of Anderson and van Wincoop (2003).
Following Neiman and Swagel (2009) and other studies, I employ a gravity
model but I use more traditional gravity variables rather than ﬁxed eﬀects
of source countries and destination prefectures*2 since the aim of this study
is to examine the eﬀects of not only time-variant but also time-invariant
variables on the ﬂow of travel.
I estimate the following baseline gravity equations:ln Nf,p = ln α + ln Distancef,p + ln GDPf + V isaf + ln GDPp(4)

+ ln Airportsp + ln P arkp + ln T reasurep ++N aturep · ln P arkp + Culturep · ln T reasurep + ln ϵf,pwhere Nf,p is the sum of the nights spent by foreign visitors from country
f in prefecture p. The sample size is 564 since the number of prefectures
is 47 and the number of source countries is 12. Distancef,p is the distance
between capital city of country f and capital city of prefecture p. GDPfand GDPp are value added of source country and destination prefecture,
respectively. The data on GDPf are the purchasing-power-parity (PPP)
adjusted GDP from the Penn World Table. The data on GDPp are real
GDP from the Cabinet Oﬃce’s Annual Report on Prefectural Accounts.
V isaf is a dummy variable which takes value of one for countries participating in the visa waiver program (VWP) and zero otherwise. Following
Neiman and Swagel (2009) and Poole (2010), I use this variable as one of
the independent variables. Airportsp is the sum of airports in prefecture p.
I use this variable as a measure of physical capital or transport infrastructure. Since Japan is consisted of islands, I consider that the airports play
an important role. P arkp and T reasurep are the sum of the national parks
and the sum of the national treasures in prefecture p, respectively. They aremeasures of natural and cultural capitals as deﬁned in Throsby (1999; 2001),
respectively. N aturep and Culturep are dummy variables which equal one
for prefectures that have the UNESCO world natural heritage and the UNESCO world cultural heritage, respectively and zero otherwise. They are*2

The ﬁxed-eﬀect method has been used by many studies such as Redding and Venables(2004) and Helpman et al. (2008) to take account of the unobserved price indexes.

9also measures of natural and cultural capitals, respectively. I include theirinteraction terms with Natural Park and national treasures since the registration as a world heritage may enhance the foreign visitors’ knowledge andwillingness to experience the national parks and national treasures.
Descriptive statistics for the all variables are shown in Table 6 of the
Appendix.

4.2

Regression results

I estimate equation (4) by ordinary least square (OLS) method. The regression results for the year 2009 are presented in the left panel of Table 4. The
speciﬁcations of natural and cultural variables vary across Column (1)–(3).
Traditional gravity variables show the expected signs in all speciﬁcations.
Distance between source country and destination prefecture are negatively
and signiﬁcantly related to the number of the sum of the nights spent indestination prefecture. Both destination prefectures’ and source countries’
GDP are signiﬁcantly positively related to the number of nights. The estimated coeﬃcients of destination prefectures’ GDP is much larger than thoseof source countries’ GDP.
VWP has positive and signiﬁcant coeﬃcients. This result is in line withprevious studies such as Poole (2010) and suggests that an increase in thenumber of countries that participating in VWP results in an increase in thenumber of foreign visitors. The number of airports is positively associatedwith the number of nights, in line with the tourism management literaturesuch as Khadaroo and Seetanah (2008) and Massidda and Etzo (2012).
The positive coeﬃcients on the number of national parks and nationaltreasures are statistically signiﬁcant across all speciﬁcations. The positive
coeﬃcients on the dummy variables for the world heritage are statisticallysigniﬁcant for cultural heritage only. The world cultural heritage dummy
and its interaction terms with the number of national treasures are both
statistically signiﬁcantly associated with increased nights in destination prefectures.
I also estimate equation (4) by Poisson pseudo maximum likelihood(PPML) method, using the dependent variable in levels. Silva and Tenreyro(2006) propose this method because this method will produce consistent estimates as long as the conditional expectation function is not misspeciﬁced.
The method has been employed by Head et al. (2009) for the services tradeand Neiman and Swagel (2009) for the international travel. In the case of
PPML, I do not report the results using interaction terms since interactioneﬀect in nonlinear models is diﬃcult to interpret as discussed by Ai and10Table 4: Gravity equation (2009)
Dep. var.
(1)-1.215***[0.064]ln Nf,pOLS(2)-1.213***[0.064](3)-1.216***[0.064]

Nf,p
PPML
(4)
(5)
-0.699*** -0.711***[0.069][0.066]ln GDPf

0.499***[0.046]0.499***[0.046]0.499***[0.046]

0.353***[0.095]0.354***[0.097]

V isaf0.807***[0.152]0.805***[0.152]0.808***[0.152]0.496*[0.222]0.504*[0.221]ln GDPp

1.470***[0.069]1.651***[0.058]1.534***[0.080]1.212***[0.129]1.310***[0.078]ln Airportsp

0.259**[0.086]0.221*[0.092]0.249*[0.106]

0.482***[0.113]0.364**[0.118]ln P arkp

0.369**[0.133]0.344**[0.128]0.113[0.222]ln T reasurep

0.215***[0.041]0.130*[0.057]0.007[0.102]ln Distancef,pN aturep

0.031[0.195]0.59[0.365]

Culturep

0.408***[0.111]-0.088[0.189]N aturep * ln P arkp

0.186
[0.199]Culturep * ln T reasurep

0.114*[0.052]

Observations
R-squared

564
0.682

564
0.670

564
0.687

564
0.724

564
0.730

Notes: Robust standard errors are shown in brackets. Constants are suppressed. ***, **, *indicate signiﬁcance at the 1%, 5%, and 10% levels, respectively.

11Norton (2003) and Green (2010).
The estimation results using PPLM method are presented in the rightpanel of Table 4. The results are qualitatively almost same with the OLS
results but the sign and statistical signiﬁcance of coeﬃcients on natural andcultural variables has changed. These variables are positive in column (4)but insigniﬁcant in columns (4) and (5).
To summarize, the ﬂow of international travel is associated with the visa
policy, transport infrastructure, and natural and cultural factors in manycases as well as traditional gravity variables. As a robustness check, I obtain
results using the data for the year 2007 and 2008. They are qualitatively
similar with the results of Table 4.

5

Conclusions

This paper investigates the geographic concentration of foreign visitors inJapan, using the data on the nights spent by visitors in 47 prefectures. I ﬁnd
that foreign visitors are more geographically concentrated than Japanese visitors and the level of geographical concentration vary across source countries.
Estimation results using gravity equations suggest that not only traditional
gravity variables but also visa policy, transport infrastructure, and naturaland cultural factors play a role in international travel to Japanese prefectures.

Acknowledgment
I thank Alan Collins (University of Portsmouth), Naoto Jinji (Kyoto University), Tatsuya Kikutani (Kyoto University), Ilde Rizzo (University of
Catania), Yasuyoki Todo (The University of Tokyo), Ryuhei Wakasugi (Kyoto University), and Munehisa Yamashiro (RIETI), and other participantsat the ACEI 2012 Conference in Kyoto and seminars at Kyoto University
and RIETI for their helpful comments. I also thank the Shikoku District
Transport Bureau and the Kagawa Prefectural Government for their kind
support and explanation of their policy. I gratefully acknowledge the ﬁnancial support from the Japan Society for the Promotion of Science in the formof a Grant-in-Aid for Scientiﬁc Research (No. 22830134; No. 24730234).

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14

Appendix
Table 5: Number and share of nights spent by Japanese and foreigners(2007-2009)2007(10 thousand)30938.2
28672.7

2007(share)2008(10 thousand)
30969.928745.0

2008(share)2009(10 thousand)30130.428300.6

2009(share)foreigners(share of foreigners)

2265.4(0.07)

1.00

2224.8(0.07)

1.00

1829.8(0.06)

1.00

Korea
China
Hong Kong
TaiwanUSA
CanadaUK
Germany
FranceSingapore
ThailandAustralia
Rest of World

435.2
221.0
175.5
388.4
299.5
25.4
55.2
45.4
43.5
53.2
44.1
52.5
343.6

0.19
0.10
0.08
0.17
0.13
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.15

380.3
247.8
184.9
372.7
273.7
25.5
53.7
43.7
47.9
58.9
46.1
62.8
354.7

0.17
0.11
0.08
0.17
0.12
0.01
0.02
0.02
0.02
0.03
0.02
0.03
0.16

218.8
258.1
157.1
263.7
231.3
23.2
44.6
37.4
43.9
54.1
44.4
53.9
323.4

0.12
0.14
0.09
0.14
0.13
0.01
0.02
0.02
0.02
0.03
0.02
0.03
0.18

Japanese and foreigners
Japanese15

Table 6: Descriptive statistics for the gravity equations (2009)
Nf,pln Distancef,pln GDPf
V isafln GDPp
Airportsp
P arkpT reasurep
N aturep
Culturep

N
564
564
12
12
47
47
47
47
47
47

Min
10.0006.275
12.3830.000
14.620
0.000
0.000
0.000
0.000
0.000

Mean
25364.540
8.455
14.2040.833
15.8372.106
1.617
23.000
0.085
0.277

16

Max
989160.000
9.413
16.4551.000
18.34014.0006.000
267.000
1.000
1.000

SD
82670.570
0.836
1.265
0.389
0.824
2.994
1.153
56.6280.282
0.452