Discussion Paper No.306
An Investigation into the Impact of Climatic andEconomic Variables on Tourism Demand in Japan
Kazuhiko NAKAHIRA
Associate Professor
Faculty of Economics
Meikai University

Masahiro YABUTA
Professor
Faculty of Economics
Chuo University

December 2018

INSTITUTE OF ECONOMIC RESEARCH
Chuo University
Tokyo, Japan

An Investigation into the Impact of Climatic and Economic Variables
on Tourism Demand in Japan(Preliminary Working Draft: Please Do Not Quote or Cite Without Authors' Permission.)
Kazuhiko NAKAHIRA
Associate Professor, Faculty of Economics, Meikai University
E-mail: nakahira@meikai.ac.jp
Masahiro YABUTA
Professor, Faculty of Economics, Chuo University,
E-mail: yabuta@tamacc.chuo-u.ac.jp
Abstract
The aim of this study is to analyze the influence of climatic and economic variables on Japan’s seasonal
tourism demand variation. Seasonal tourism fluctuations can present a serious problem, since the profit
and daily administration of stakeholders, as well as tourism policy design, are affected.
Considering these aspects, this study investigated the influence of climatic and economic variables on
tourism demand fluctuations in typical tourist destinations in western Japan. GMM (Generalized Methodof Moments) estimations, based on two models, are implemented in an empirical study. Measurement
error and the endogeneity of the variables, as well as the correlation between the explanatory variables
and the error term, are considered.
The first model’s estimated parameters imply that rainfall is a negative factor in Kyoto’s tourism,
while temperature and price levels are positive elements. In spite of the presence of these positive factors
in Hiroshima, we could not determine the effects of rainfall and vitalization of the regional economy ontourism in this area. In Naha, temperature, price level, and vitalization of the regional economy may
increase the number of visitors.
The second model’s estimates for Kyoto imply that temperature, sunshine duration, and price level are
positive influences. For Osaka, Fukuoka, and Hiroshima, we find that sunshine duration, and price levelare positive factors. In Hiroshima, temperature is an additional positive factor, while the effects ofvitalization of the regional economy cannot be determined. For Naha, temperature, price level, andvitalization of regional economy may increase the number of visitors, but no conclusion can be drawn
about the effect of sunshine duration.
Key words: tourism demand, climatic variable, seasonal fluctuation
JEL Classification: Z32, R58, O18

1

1. Introduction
This study examines the influence of climatic and economic variables on tourism demand fluctuations
in western Japan’s typical tourism destinations, namely, Kyoto, Osaka, Hiroshima, Fukuoka, and Naha.
Seasonal tourism fluctuation can be a serious problem since it has a certain influence on the profit anddaily administration of stakeholders and tourism policy design. GMM (Generalized Method ofMoments) estimations are implemented to investigate this problem, considering measurement error andthe endogeneity of the variables as well as the correlation between the explanatory variables and the error
term.
This paper is organized as follows. Section 2 provides a review of previous studies on the seasonalvariation of tourism, while Section 3 describes the seasonality in tourism in Japan. The estimation
method is described in Section 4, and Section 5 outlines an empirical analysis to investigate the influenceof climatic and economic variables on tourism demand fluctuation in the typical tourist destinations inwestern Japan by applying GMM (Generalized Method of Moments) estimation. Finally, Section 6summarizes and concludes the study.
2. Seasonality in Tourism: A Review
Previous studies on seasonal tourism variations mainly describe the technical aspects that are required
to accurately comprehend seasonality for tourism demand prediction and to provide policy perspectives.
Lundtorp (2001) provides a comprehensive explanation of seasonal tourism variation, and mentions therole of the supply chain and the continuity of transportation in seasonal variations of tourists’ destination
selections. Butler (2001) reviews the causes and effects of seasonal variations in tourism by pointing out
the practical difficulties with regard to measures to mitigate variations. Lundtorp (2001) discusses someapproaches to understanding seasonal tourism demand variations, including the use of the seasonalvariation index and the Gini coefficient to reflect the size of the annual seasonal variation. To minimize
the effects of seasonal variations on tourism, some researchers - including Kulendran and King (1997),
Lim and McAleer (2001), and Goh and Law (2002) - estimate tourism demand precisely. By contrast,
Koc and Altinay (2007) analyzes the Turkish seasonal tourism variation patterns by taking advantage ofthe X-12-ARIMA model, and outlines different patterns of visitors, tourism expenditures, and tourism
expenditure seasonality.
Nadal et al. (2004) consider the relationship between the Gini index and economic variables such as
GDP in assessing UK and German tourists who visit the Baleares Islands. They show that an increase in
the UK or Germany’s GDP, and a rise in the relative price between these two countries and the Baleares
Islands, would decrease the Gini index. They also find that economic conditions such as income andrelative price, are decisive factors in determining the number of visitors based on gravity theory.
The seasonal tourism fluctuation further depends on the characteristics of the destination. Visitors tonature-oriented destinations such as national parks are more influenced by climatic conditions than those2

who visit culture-oriented destinations. Morales (2003) analyzes the seasonality of three tourism
destinations in Spain (Malaga, Granada, and Armenia), and emphasized the effect of culture-oriented
policies such as the construction of an art museum in decreasing the Gini index. Morales (2003) also
finds that attractions such as cultural exhibitions and theatre performances are unrelated to tourism
seasonality. Cuccia and Rizzo (2011) classify six Sicilian destinations (in Italy) into four clusters
according to their cultural attractiveness, showing that seasonality depends on the variety of cultural
attractions.
Tourism demand seasonality is rather a supply-side issue; its impact affects tourism management.
Decision-making by the managers of accommodation facilities, catering services, attractions, and tour
operators is based on investment and fund management, and aims to realize flexible employment,
tourism, and a differentiation strategy. In this respect, a policy to mitigate seasonal variation is necessary
for stable and profitable management (Lee et al. (2008)). However, because some causes of tourism
seasonality, which are related to natural- and socio-economic factors, do not correspond with each other,various strategies and individual evaluations should be determined. On the other hand, Kulendran and
Dwyer (2010; 2012) focus on economic factors, in addition to climatic elements, when considering theinfluence of seasonal fluctuations on tourism demand.
3. Trends in Tourism Seasonality in Japan
In this section, we examine tourism seasonality in the Japanese context. Figure 1(a) shows thequarterly seasonal variation in tourism demand for ten prefectures - Tokyo, Miyagi, Fukushima, Chiba,
Kanagawa, Kyoto, Osaka, Hiroshima, Fukuoka, and Okinawa. Figure 1(b) displays estimated Gini
coefficient on tourism variation. The areas for analysis include prefectures located in eastern Japan, in
addition to the ones in western Japan, so as to consider the effect of the Great East Japan Earthquake ofMarch 2011. From Figure 1(a), we derive important information with respect to the seasonal variation intourism demand. First, seasonal tourism demand fluctuates with a regular form. Second, the Great East
Japan Earthquake might not have had a large influence on western Japan. Figure 1(b) indicates that theGini coefficient for 2011 is exceptionally large compared with the other years. Especially, Miyagi,
Fukushima, and Chiba suffered great losses, due to their proximity to the seismic sources of theearthquake.
The Gini coefficients for overseas tourists as well as for all tourists (including both domestic andinbound tourists) can be calculated by using the accommodation data of tourists visiting the ten
prefectures listed above. The comparison of Table 1(a) and 1(b) shows that the Gini coefficients for
inbound tourists tend to be larger than those for all tourists. This might imply that the seasonal variationof inbound tourists is relatively larger than that of the others.
Figure 2(a) shows seasonal variations in guests at accommodation facilities and Figure 2(b) displays
the estimated Gini coefficients for the prefectures of Kyoto, Osaka, Hiroshima, Fukuoka, and Okinawa.

3

Figure 1(a): Seasonal Variation in Overnight Guests (Quarterly Basis)

2008/I
2008/II
2008/III2008/IV
2009/I
2009/II
2009/III2009/IV
2010/I
2010/II
2010/III2010/IV
2011/I
2011/II
2011/III
2011/IV
2012/I
2012/II
2012/III2012/IV
2013/I
2013/II
2013/III2013/IV
2014/I
2014/II
2014/III
2014/IV
2015/I
2015/II
2015/III2015/IV
2016/I
2016/II
2016/III2016/IV
2017/I
2017/II
2017/III2017/IV

14550000
14050000
13550000
13050000
12550000
12050000
11550000
11050000
10550000
10050000
9550000
9050000
8550000
8050000
7550000
7050000
6550000
6050000
5550000
5050000
4550000
4050000
3550000
3050000
2550000
2050000
1550000
1050000
550000Miyagi

Fukushima

Chiba

Tokyo

Kanagawa

Kyoto

Osaka

Hiroshima

Fukuoka

Okinawa

Notes: The data for making this figure was obtained from “Overnight Travel Statistics” (from 2008 to 2017) by the Japan
Tourism Agency, Ministry of Land, Infrastructure, Transport, and Tourism. (http://www.mlit.go.jp/kankocho/Siryou/
toukei/shukuhakutoukei.html)

Figure 1(b): Gini Index at Prefectural Level
0.120
0.115
0.110
0.105
0.100
0.095
0.090
0.085
0.080
0.075
0.070
0.065
0.060
0.055
0.050
0.045
0.040
0.035
0.030
0.025
0.020
0.015
0.010
0.005

2008
2013

2009
2014

2010
2015

2011
2016

2012
2017

Notes: The Gini index is calculated based on the sequence data obtained from this survey. The data for making this index wereobtained from “Overnight Travel Statistics” (from 2008 to 2017) by the Japan Tourism Agency, Ministry of Land,
Infrastructure, Transport, and Tourism. (http://www.mlit.go.jp/kankocho/siryou/toukei/shukuhakutoukei.html)

4

Table 1(a): Gini Coefficients for Overseas Guests
Gini Index
2008
2009
2010
2011
2012
2013
2014
2015
2016

Miyagi Fukushima Chiba
0.07850.1866
0.10830.13770.41020.05290.14520.14710.14380.24290.44980.09870.1796
0.40240.05590.13700.19030.04250.16770.13180.06870.12500.3201
0.06890.14570.22650.0647

Tokyo Kanagawa
0.09480.06730.10680.0636
0.15950.18220.17380.08740.04290.0487
0.05350.07600.06530.09210.03000.06750.02530.0342

Kyoto
0.09790.10550.14120.15000.12440.13460.12950.08430.0792

Osaka Hiroshima Fukuoka Okinawa
0.06110.20120.10010.14790.05130.14450.08940.12140.14340.25040.14150.17610.13320.07040.11920.13140.07900.14120.0503
0.0961
0.09230.0806
0.06280.12950.08140.12670.08600.09780.05840.11830.0666
0.05780.06390.0991
0.06790.0355

Notes: The data for making this table was obtained from “Overnight Travel Statistics” (from 2008 to 2017) by the Japan
Tourism Agency, Ministry of Land, Infrastructure, Transport, and Tourism. (http://www.mlit.go.jp/kankocho/siryou/
toukei/shukuhakutoukei.html)

Table 1(b): Gini Coefficients for Total Guests
Gini Index
2008
2009
2010
2011
2012
2013
2014
2015
2016

Miyagi Fukushima
0.03440.04200.04780.03830.0616
0.03480.08590.06970.03900.02340.03590.06890.04490.07130.04170.04910.0276
0.0316Chiba
0.02800.04420.03190.11520.03090.04500.02650.01930.0257

Tokyo Kanagawa
0.01000.03460.01610.03560.01290.02230.06280.06940.01670.02280.02950.03950.02560.02960.01430.01440.01420.0248Kyoto
0.05530.0571
0.05460.08100.0656
0.0581
0.04220.05710.0404

Osaka Hiroshima Fukuoka Okinawa
0.01670.04380.02320.06430.04630.05240.04400.05350.02190.03400.02780.06300.0516
0.06720.03580.08450.02320.04150.02200.07270.0326
0.05730.03650.06270.03800.04990.03700.07030.03730.0396
0.04170.04520.0281
0.04440.0087
0.0539Notes: The data for making this table was obtained from “Overnight Travel Statistics” (from 2008 to 2017) by the Japan
Tourism Agency, Ministry of Land, Infrastructure, Transport, and Tourism. (http://www.mlit.go.jp/kankocho/siryou/
toukei/shukuhakutoukei.html)

The former implies instability in the seasonal fluctuation of tourism demand, while the latter indicatesthat Gini coefficients are smaller for Osaka and Fukuoka, but relatively larger for Kyoto, Hiroshima, andOkinawa. In addition, both figures might reflect the impact of the Great East Japan Earthquake of March2011.
4. Method
This section describes the method used in our empirical analysis of the influence of climatic andeconomic variables on tourism in several Japanese areas. The sample period of estimation (monthly
basis) is 2008:M1 (January) to 2017:M12 (December). Our dataset is composed of the following
variables.1

1

The data on “approximate total number of overnight guests” can be retrieved from the website of the Japan
Tourism Agency, Ministry of Land, Infrastructure, Transport, and Tourism (http://www.mlit.zgo.jp/kankocho/
siryou/toukei/shukuhakutoukei.html). The “amounts of rainfall,” “average air temperature,” and “total sunshine
duration” were obtained from the Japan Meteorological Agency’s website (https://www.data.jma.go.jp/gmd/risk/

5

Figure 2 (a): Seasonal Variation in Tourism Demand for Western Japan (Quarterly Basis)

2008II
2008III
2008IV
2009I
2009II
2009III
2009IV
2010I
2010II
2010III
2010IV
2011I
2011II
2011III
2011IV
2012I
2012II
2012III
2012IV
2013I
2013II
2013III
2013IV
2014I
2014II
2014III
2014IV
2015I
2015II
2015III
2015IV
2016I
2016II
2016III
2016IV
2017I
2017II
2017III

8000000
7500000
7000000
6500000
6000000
5500000
5000000
4500000
4000000
3500000
3000000
2500000
2000000
1500000
1000000
500000

Kyoto

Osaka

Hiroshima

Fukuoka

Okinawa

Notes: The data for making this figure was obtained from “Overnight Travel Statistics” (from 2008 to 2017) by the Japan Tourism
Agency, Ministry of Land, Infrastructure, Transport, and Tourism. (http://www.mlit.go.jp/kankocho/Siryou/
toukei/shukuhakutoukei.html)

Figure 2 (b): Gini Index for the Prefectures in Western Japan (Quarterly Basis)
0.085
0.080
0.075
0.070
0.065
0.060
0.055
0.050
0.045
0.040
0.035
0.030
0.025
0.020
0.015
0.010
0.005

2009

2010
Kyoto

2011
Osaka

2012
Hiroshima

2013

2014
Fukuoka

2015

2016

Okinawa

Notes: The data for making this figure was obtained from “Overnight Travel Statistics” (from 2008 to 2017) by the Japan Tourism
Agency, Ministry of Land, Infrastructure, Transport, and Tourism. (http://www.mlit.go.jp/kankocho/Siryou/
toukei/shukuhakutoukei.html)

obsdl/index.php#). The “consumer price index” is available from the “e-stat” website (https://www.estat.go.jp/dbview?sid=0003143513). The data on “Indices of Industrial Production (prefectural data)” can be
retrieved from the website of the Ministry of Economy, Trade, and Industry (http://www.meti.go.jp/statistics/tyo/iip/chiiki/index.html).

6𝑉 : approximate total number of overnight guests (accommodation facilities with 10 or more
employees); prefectural data (Kyoto, Osaka, Hiroshima, Fukuoka, Okinawa), monthly, final figures(Transition Table in Table 4-2, result of the survey “Overnight Travel Statistics,” January to
December 2017, final report, the Japan Tourism Agency, Ministry of Land, Infrastructure,
Transport, and Tourism).
𝑅: amounts of rainfall (mm) at observation sites (in the cities of Kyoto, Osaka, Hiroshima, Fukuoka,
and Naha (in the Okinawa prefecture)), monthly, issued by the Japan Meteorological Agency.
𝑇: average air temperature (°C) at observation sites (in the cities of Kyoto, Osaka, Hiroshima,
Fukuoka, and Naha (in the Okinawa prefecture)), monthly, issued by the Japan Meteorological
Agency.
𝑆: total sunshine duration (hours) at observation sites (in the cities of Kyoto, Osaka, Hiroshima,
Fukuoka, and Naha (in the Okinawa prefecture)), monthly, issued by the Japan Meteorological
Agency.
𝑃 : consumer price index, prefectural data (Kyoto, Osaka, Hiroshima, Fukuoka, and Okinawa),
monthly, original index, all items, base year = 2015, issued by the Ministry of Internal Affairs andCommunications.
𝐼 : indices of industrial production, prefectural data (Kyoto, Osaka, Hiroshima, Fukuoka, andOkinawa), monthly, original index, manufacturing (Item Number: 2A00000000) (Kyoto andOsaka), mining and manufacturing (Item Number: 2000000000) (Hiroshima, Fukuoka, andOkinawa), base year = 2010, issued by each prefectural government office and the Ministry of
Economy, Trade, and Industry.
“𝑉” is the proxy variable for tourism demand and “𝐼” is the proxy variable for the level of vitalization of
the regional economy. In addition, our empirical analysis focuses on western Japan, namely, on the cities
of Kyoto, Osaka, Hiroshima, Fukuoka, and Naha (in the Okinawa prefecture). The climatic variables, 𝑅,
𝑇, and 𝑆 were observed at the observation sites of the Japan Meteorological Agency in each area or city.
In short, they represent a city-level or town-level data set. By contrast, the economic variables, 𝑉, 𝑃, and𝐼 reflect the prefectural data that were observed by several governmental offices. In this study, theprefectural data are regarded as proxy variables for city- or town-level data in conducting the empiricalresearch based on the regional tourism.
For our empirical study, we implemented a GMM (Generalized Method of Moments) estimation byconsidering the measurement error and endogeneity of the variables, in addition to the correlation
between the explanatory variables and the error term. Our GMM estimation was conducted with theassumption that the consumer price index for each area is endogenous, and with the Newey-West HAC(heteroskedasticity and autocorrelation consistent) weighting matrix to accommodate the possibility ofserial correlation and heteroscedasticity. Concretely, our estimations were conducted by the following
two types of specification ("𝑙𝑛" means the natural logarithm):

7

<Model (1)>𝑙𝑛𝑉𝑡 = 𝛼0 + 𝛼1 𝑙𝑛𝑅𝑡 + 𝛼2 𝑙𝑛𝑇𝑡 + 𝛼3 𝑙𝑛𝑃𝑡 + 𝛼4 𝑙𝑛𝐼𝑡 + 𝑒𝑡 ,<Model (2)>𝑙𝑛𝑉𝑡 = 𝛼0 + 𝛼1 𝑙𝑛𝑇𝑡 + 𝛼2 𝑙𝑛𝑆𝑡 + 𝛼3 𝑙𝑛𝑃𝑡 + 𝛼4 𝑙𝑛𝐼𝑡 + 𝑒𝑡 .
Instrument specifications regarding these two estimations are provided in the notes of Tables 6 and 7.
5. Empirical Results
We implemented a GMM (Generalized Method of Moments) estimation for Model (1) and Model (2),under the conditions and assumptions described in the former section. Further details regarding thisestimation are provided in the notes of Tables 6 and 7. Since we should take a critical stance toward thistype of estimation, Hansen’s test for over-identification, the endogeneity test for variables, and the test
for weak instrument variables utilizing Cragg-Donald statistic and Stock-Yogo critical values, wereconducted.
The results of the endogeneity tests (Wu - Hausman test) for the variables in Models (1) and (2) of ourestimations - ln(P), price level - for our areas of research interest are shown in Tables 2 and 3,
respectively. The null hypotheses of exogeneity can be rejected at the conventional level by the test
statistics for the case of Naha (in Okinawa prefecture) alone. This result means that our estimation
Models (1) and (2) do not always fit well for the areas of interest, except for Naha, in terms of setting theendogenous variable. However, the main purpose of this research is to analyze the influence of climaticand economic variables on seasonal tourism variation as a type of spatial movement in several Japaneseareas. Thus, we were obliged to proceed with the investigation, with careful attention to the possibility of
an incomplete estimation model.
Further, to investigate the weak identification problem pointed out by some studies (including
Mavroeidis (2004)), we used the Cragg and Donald (1993) statistic and the Stock and Yogo (2005)
critical values2. In Table 4, the Cragg-Donald F-statistics of the five areas under consideration in Model(1) are apparently larger than the Stock-Yogo critical values for both relative bias and size. Thus, the null
hypothesis of weak identification is rejected for each area. In addition, we can obtain the same result for
Model (2) by considering Table 5. Therefore, the test results imply that the sets of instrumental variables
for our two estimation models are valid.
Table 6 reports the GMM estimation results for the five areas under consideration, namely, Kyoto,
Osaka, Hiroshima, Fukuoka, and Naha (in Okinawa), with respect to Model (1). Considering Hansen’s
diagnostic test, the null hypotheses of over-identification for the GMM estimation cannot be rejected,
thus supporting the validity of the moment conditions, as shown by the J-statistic and the p-value in thetable.
2

See Cragg and Donald (1993), Stock, Wright, and Yogo (2002), and Stock and Yogo (2002) for details.

8Test instruments
ln(P) for KYOTO
ln(P) for OSAKA
ln(P) for HIROSHIMA
ln(PO) for FUKUOKA
ln(PO) for NAHA

Test instruments
ln(P) for KYOTO
ln(P) for OSAKA
ln(P) for HIROSHIMA
ln(P) for FUKUOKA
ln(P) for NAHA

Cragg-Donald F-stat
5%
10%
20%
30%
10%
15%
20%
25%

Cragg-Donald F-stat
5%
10%
20%
30%
10%
15%
20%
25%

Table 2: Endogeneity Test for Model (1)Difference in J-statistics
Value
d.f.
p-value0.001137
1
0.97312.280432
10.13100.069769
1
0.79170.688497
10.40675.850323
10.0156

Restricted
J-statistic
3.622982
7.627244
5.814584
4.253648
9.141542

Unrestricted
J-statistic
3.621845
5.346813
5.744814
3.565151
3.291219

Table 3: Endogeneity Test for Model (2)Difference in J-statistics
Value
d.f.
p-value
0.002659
10.95890.368527
1
0.54380.436444
10.50880.996301
1
0.31823.100485
10.0783

Restricted
J-statistic
3.599661
1.931043
3.280394
4.797472
5.488646

Unrestricted
J-statistic
3.597002
1.562516
2.843951
3.8011722.388161

Table 4: Weak Instrument Diagnostics for Model (1)
Kyoto
Osaka
Hiroshima
Fukuoka
387.4917
621.9983
550.7365644.6691
Stock-Yogo TSLS critical values (relative bias)
18.37
10.83
6.77
5.25
Stock-Yogo critical values (size)
26.87
15.09
10.98
8.84
Table 5: Weak Instrument Diagnostics for Model (2)
Kyoto
Osaka
Hiroshima
Fukuoka
522.1223
798.7839
676.8538
782.9972
Stock-Yogo TSLS critical values (relative bias)
16.85
10.27
6.71
5.34
Stock-Yogo critical values (size)
24.58
13.96
10.26
8.31

Okinawa
561.6538

Okinawa
745.3656

9

VariableC
std. error
t-statistic
prob.
lnI
std. error
t-statistic
prob.
ln(T)
std. error
t-statistic
prob.
ln(P)
std. error
t-statistic
prob.
ln(I)
std. error
t-statistic
prob.
weight iterations
s.e. of regression
instrument rank
J-statistic
prob(J-statistic)

Table 6: GMM Estimation for Model (1)Coefficient
Coefficient
Coefficientfor Kyoto
for Osakafor Hiroshima-4.614378-8.444575-7.845590
8.169264
9.051577
4.081459-0.564846-0.932940-1.922252
0.57330.35280.0571-0.053257-0.012658-0.007516
0.027098
0.017901
0.022304-1.965352-0.707080-0.336977
0.05180.48100.73680.152832
0.034756
0.176992
0.043960
0.053835
0.032241
3.476623
0.645611
5.489596
0.00070.51980.00003.746781
4.601562
4.685646
2.032413
2.020348
1.094210
1.843513
2.277608
4.282218
0.06790.02460.00000.263452
0.371284-0.165525
0.318585
0.427801
0.307667
0.826943
0.867890-0.538000
0.41000.38730.5916910
130.227336
0.222925
0.1934769993.619882
4.640390
5.850846
0.459886
0.326224
0.210568

Coefficientfor Fukuoka-5.772492
4.357503-1.324725
0.18790.002632
0.022119
0.118984
0.90550.018060
0.039140
0.461434
0.64544.024557
1.024956
3.926567
0.00010.232424
0.230923
1.006501
0.3163190.170578
93.752282
0.440568

Coefficientfor Naha-21.24247
5.173116-4.106320
0.0001
0.021971
0.014845
1.480017
0.14170.475974
0.113259
4.202523
0.0001
6.929490
1.165738
5.944296
0.00000.4031800.136861
2.945905
0.003950.167781
92.921277
0.571085

Notes: Instrument specification: Constant, 𝑙𝑛𝑅𝑡 , 𝑙𝑛𝑅𝑡−1 , 𝑙𝑛𝑇𝑡 , 𝑙𝑛𝑇𝑡−1 , 𝑙𝑛𝑃𝑡−1 , 𝑙𝑛𝑃𝑡−2 , 𝑙𝑛𝐼𝑡 , 𝑙𝑛𝐼𝑡−1 , Sample(adjusted): 2008:M03 – 2017:M12. Included observations = 118 (after adjustments). Estimation weighting matrix: HAC(Bartlett kernel, Newey-West fixed bandwidth = 5). Standard errors and covariance computed using HAC weighting matrix(Bartlett kernel, Newey-West fixed bandwidth = 5).

VariableC
std. error
t-statistic
prob.
ln(T)
std. error
t-statistic
prob.
ln(S)
std. error
t-statistic
prob.
ln(P)
std. error
t-statistic
prob.
ln(I)
std. error
t-statistic
prob.
weight iterations
s.e. of regression
instrument rank
J-statistic
prob(J-statistic)

Table 7: GMM Estimation for Model (2)Coefficient
Coefficient
Coefficientfor Kyoto
for Osakafor Hiroshima-8.624910-18.10068-4.741969
8.583699
7.618994
5.117800-1.004801-2.375731-0.926564
0.31710.01920.35610.105964
0.007213
0.136608
0.044736
0.049997
0.033653
2.368637
0.144263
4.059334
0.01950.88550.00010.162462
0.230834
0.138073
0.063716
0.070414
0.057348
2.549775
3.278259
2.407618
0.01210.00140.01774.515104
6.525695
3.699646
2.178762
1.786953
1.328033
2.072326
3.651856
2.785808
0.04050.00040.00630.165876
0.301224-0.004973
0.342871
0.438822
0.350609
0.483787
0.686439-0.014183
0.62950.49380.9887108
80.229657
0.221383
0.179335
8883.574340
1.680483
3.057462
0.311248
0.641281
0.382850

Coefficientfor Fukuoka-8.051280
3.762183-2.140055
0.0345-0.010439
0.045783-0.228022
0.82000.126638
0.045021
2.812844
0.00584.500800
0.923435
4.873977
0.00000.137256
0.235586
0.582616
0.5613150.173273
84.087280
0.252192

Coefficientfor Naha-23.17704
5.876944-3.943723
0.0001
0.422147
0.145409
2.903177
0.00440.018337
0.046898
0.391007
0.69657.425631
1.301462
5.705608
0.00000.366916
0.143904
2.549733
0.012180.168073
82.333840
0.506069

Notes: Instrument specification: Constant, 𝑙𝑛𝑇𝑡 , 𝑙𝑛𝑇𝑡−1 , 𝑙𝑛𝑆𝑡 , 𝑙𝑛𝑆𝑡−1 , 𝑙𝑛𝑃𝑡−1 , 𝑙𝑛𝐼𝑡 , 𝑙𝑛𝐼𝑡−1 , Sample (adjusted):
2008:M02 – 2017:M12. Included observations = 119 (after adjustments). Estimation weighting matrix: HAC (Bartlett kernel,
Newey-West fixed bandwidth = 5). Standard errors and covariance computed using HAC weighting matrix (Bartlett kernel,
Newey-West fixed bandwidth = 5).

10With regard to the estimated parameters of Model (1) for Kyoto, the coefficient of 𝑅 is barely
significant at the 10% level, with our expected sign. The coefficient of 𝑇 is significant at the 1% level,
and the coefficient of 𝑃 is also significant, but the level of significance is 10%. The signs of 𝑇 and 𝑃 arecontroversial. By contrast, 𝐼 is apparently insignificant. This result implies that rainfall is a negative
factor for tourism in Kyoto, although temperature and price level are positive elements. As for Osaka andFukuoka, only the coefficient of 𝑃 is significant. In this respect, the estimation results for Osaka andFukuoka do not have any positive implications. The estimated parameters of 𝑇 and 𝑃 for Hiroshima aresignificant at the 1% level. Thus, temperature and price level are positive factors. However, we cannot
determine the effects of rainfall and vitalization of the regional economy on tourism of Hiroshima. By
contrast, Naha’s coefficients on 𝑇, 𝑃, and 𝐼 are sufficiently significant at the 1% level, but 𝑅 is not
significant at the conventional level. Therefore, temperature, price level, and vitalization of the regional
economy may enlarge the number of visitors to Naha (in the Okinawa Prefecture), although nothing canbe concluded with respect to the effect of rainfall.
Overall, we cannot find any areas in our investigation that correspond entirely to estimation Model (1).
However, Model (1) relatively fits with Kyoto and Naha. In addition, it still needs further work to
consider the signs of the estimated coefficients on temperature and price level, as well as the causality
between these two factors and the number of visitors.
Table 7 reports the estimation result by the GMM method for the five focus areas of Model (2). By
considering the J-statistic and the p-value in the table, we can determine that the null hypotheses of overidentification for GMM estimation cannot be rejected by Hansen’s test, and that the validity of the
moment conditions is supported.
With respect to the parameter estimation based on Model (2) for Kyoto, the coefficients on 𝑇, 𝑆, and 𝑃
are significant at the 5% level, while the one on 𝐼 is apparently insignificant. This result implies thattemperature, sunshine duration, and price level are positive elements for tourism in Kyoto. As for Osakaand Fukuoka, 𝑆, and 𝑃 are significant at the 1% level, while the others are not. In this respect, we findthat sunshine duration and price level are positive factors for tourism in Osaka and Fukuoka. The
coefficients on 𝑇 and 𝑃 are significant at the 1% level, and that of 𝑆 is significant at the 5% level in thecase of Hiroshima. 𝐼 is insignificant. Thus, temperature, sunshine duration, and price level can be
regarded as positive factors for Hiroshima’s tourism, although we cannot determine the effects ofvitalization of the regional economy. By contrast, the coefficients on 𝑇 and 𝑃 for Naha are noticeably
significant at the 1% level, and the one on 𝐼 is significant at the 5% level. However, 𝑆 is not significant at
the conventional level, and this result might contradict our intuition in general. Thus, temperature, pricelevel, and vitalization of the regional economy may increase the number of visitors to Naha (in the
Okinawa Prefecture), although nothing can be concluded about the effect of sunshine duration.

11

On the whole, Model (2) fits Kyoto, Hiroshima, and Naha relatively well. As for Model (1), we needto consider the signs of the estimated coefficients on temperature and price level, as well as the causality
between these two factors and the number of visitors.
6. Concluding Remarks
This study investigated the influence of climatic and economic variables on tourism in several areas inwestern Japan. For our empirical study, the GMM (Generalized Method of Moments) estimations basedon two kinds of specifications (Model (1) and Model (2)) were implemented, considering themeasurement error and endogeneity of the variables, in addition to the correlation between the
explanatory variables and the error term. Our GMM estimations were made under the assumption thatthe consumer price index is endogenous, and with the Newey-West HAC (heteroskedasticity andautocorrelation consistent) weighting matrix to accommodate the possibility of serial correlation andheteroscedasticity.
The estimated parameters of Model (1) imply that rainfall is a negative factor for Kyoto’s tourism,
while temperature and price level are positive elements. Positive implications cannot be derived for
Osaka and Fukuoka. While temperature and price level are positive factors, we cannot determine theeffects of rainfall and vitalization of the regional economy on Hiroshima’s tourism. For Naha (in the
Okinawa Prefecture), temperature, price level, and vitalization of the regional economy may increase thenumber of visitors; however, no conclusions can be drawn about the effect of rainfall.
The Model (2) estimations for Kyoto imply that temperature, sunshine duration, and price level are
positive tourism elements. By contrast, we find that sunshine duration and price level are positive factors
for Osaka and Fukuoka’s tourism. With respect to Hiroshima, temperature, sunshine duration, and pricelevel can be regarded as positive factors, although the effects of vitalization of the regional economy
cannot be determined. For Naha, temperature, price level, and vitalization of the regional economy may
increase the number of visitors; however, nothing can be concluded in terms of the effect of sunshine
duration.
Note that we cannot find any areas of investigation that fits the estimation Models (1) and (2) entirely.
Further work is required to consider the signs of the estimated coefficients on temperature and price level,
as well as the causality between these two factors and the number of visitors. In this respect, it is clearthat many issues remain unanswered.
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