Discussion Papers In Economics
And Business

Impacts of Tourism and Fiscal Expenditure on Remote Islands in Japan:
A Panel Data Analysis

Noriko ISHIKAWA
Mototsugu FUKUSHIGE

Discussion Paper 06-21

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANImpacts of Tourism and Fiscal Expenditure on Remote Islands in Japan:
A Panel Data Analysis

Noriko ISHIKAWA
Mototsugu FUKUSHIGE

Discussion Paper 06-21
July 2006

この研究は「大学院経済学研究科・経済学部記念事業」
基金より援助を受けた、記して感謝する。

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANImpacts of Tourism and Fiscal Expenditure on Remote Islands in Japan:
A Panel Data Analysis

Noriko ISHIKAWA
Graduate School of Science and Technology, Kobe University
1-1, Rokkodai-cho, Nada-ku, Kobe, 657-8501, JAPAN

Mototsugu FUKUSHIGE∗

Graduate School of Economics, Osaka University
1-7, Machikaneyama-cho, Toyonaka, Osaka, 560-0043, JAPAN

Abstract

Japan consists of many small inhabited islands in addition to four main islands. We examine
the impact of fiscal expenditure and the number of tourists on per capita taxable income in remoteislands using panel data analysis. The results show that both fiscal expenditure and population size
have significant positive impacts on per capita taxable income, whereas the number of tourists
does not have statistically significant impact. They indicate that tourism development would notwork as a substitute for financial support from the government. In other words, continuous
financial support may be needed to maintain the islands’ economies.

Running Title: Impacts of Tourism and Fiscal Expenditure in Japan
Keywords: Tourism Multiplier, Fiscal Multiplier, Remote Islands, Panel Data Analysis
JEL classification: O23, R58, Q56, L83.
∗

Correspond to: Mototsugu FUKUSHIGE
Graduate School of Economics, Osaka University
1-7, Machikaneyama-cho, Toyonaka, Osaka, 560-0043, JAPAN
Phone. & fax: +81-6-6850-5248;
E-mail: mfuku@econ.osaka-u.ac.jp (M. Fukushige)1INTRODUCTION AND BACKGROUND
There is a large body of empirical analyses of the impact of international economic support
or tourism on internal economic growth in some island states; for example, GANI, 1998,
examined the macroeconomic determinants of economic growth in the South Pacific island statesusing cross-country data and GOUNDER, 2001, demonstrated the relationship between foreign
aid and economic growth in the Fiji islands using time series data. We propose several reasons
why the economies of most island states seem unable to grow very rapidly. First, the economies ofmost island states depend largely on agriculture, fisheries, and forestry. Second, due to theconstraints of small populations, it is hard to foster the manufacturing industry, which has
economies of scale and agglomeration externalities. Some developed countries, however, also facethese issues and try to deal with them to develop the local economy.
Japan is an island state. It consists of about 6,800 small inhabited islands in addition to thefour main islands (Hokkaido, Honshu, Shikoku, and Kyushu) and Okinawa Main Island located in
the southwest of the Japanese Archipelago1. As many of these islands are located far from themain islands or lack affordable transportation, their economies are isolated from the mainland;that is, there is a difference in affluence between the people living in small islands and those livingon the mainland.
The Japanese government has been implementing a wide variety of measures at the national
level to promote the development of remote islands. The Improvement of Sea Routes to RemoteIslands Act enacted on July 4, 1952 (hereafter referred to as the “Remote Islands Act”), was thefirst measure toward assisting the remote islands. It laid down some guidelines for economic
development in remote islands: under this Act, the central government would provide public funds
to maintain and improve access from remote islands to the mainland. The Law for Developmentof Remote Islands was enacted on July 22, 1953, as temporary legislation with a limit of 10 years
following the Remote Islands Act; it has been amended four times and extended five times during
the past 50 years. The Law for Development of Remote Islands aims to improve fundamental
conditions in remote islands, focusing on improving and upgrading social as well as industrial
infrastructure. The role of remote islands in protecting an exclusive economic zone was also
specified through an amendment to the Law for Development of Remote Islands in 2003. There
are also other measures promoting economic development in each region: the Special Measures
Act for the Promotion and Development of the Amami Oshima Islands (enacted June 21, 1954);
the Special Measures Act for the Promotion and Development of the Ogasawara Islands (enacted
December 8, 1969); and the Special Measures Act for the Promotion of Okinawa Prefecture(enacted December 31, 1971) 2 . All these laws lay down the basic principles for promoting
economic development in each region: the municipalities that supervise remote islands underthese Acts had to lay down to develop their own regional plan and have been provided withfinancial and legal support for industrial development from the central government. The funds for2

public works projects under the Remote Islands Development Laws are provided as part of thebudget of the Japanese Ministry of Land, Infrastructure, and Transport and are distributed among
all the government ministries, agencies, and municipalities concerned. The remote islands also
benefit from other programs, such as financial support for economic development in depopulatedareas, which is a grant program providing a higher proportion of grants for projects under the
Remote Islands Development Laws and municipal bond funds. Most projects are implemented by
local municipalities, although some projects, such as shore protection, flood control, conservation
reserves, airport development, and harbor improvement, are carried out by the central government.
According to the Handbook of Remote Islands Development 2004, the Remote Islands
Development Laws cover 315 islands, with more than 737,000 people (0.6% of the total
population) as of April 2001. The total project cost for economic development in remote islands
amounts to around 160 billion yen annually, which is equal to 1.7% of total public works
expenditure. This shows that the government invests quite a large sum of money per capita inremote islands. However, the population in remote islands has been decreasing. The National
Census reports that the population of remote islands declined by 7.2% from 1995 to 2000 whereas
the total population grew by 1.1%.
Since Japan’s economic bubble burst in 1991, the Japanese government’s severe financial
difficulties seem to have worsened, due to the effect of the aging population3. This has resulted ina drastic reduction in financial assistance for promoting economic development in remote islands:
the share of general expenditure for public works in remote islands decreased from 1.70% in 2001
to 1.57% in 2004. We also note that decentralization, which has been driven rapidly by the mergerof municipalities since 19994, is intended to cut financial expenditure by local governments. The
number of municipalities that supervise remote islands decreased from 221 in April 1998 to 210 inApril 2003 in accordance with the promotion of amalgamating municipalities.
On the other hand, the Japanese government has kicked off a nationwide tourism campaign
named “YOKOSO! JAPAN (Welcome to Japan!)” to attract millions of inbound tourists.
According to the Japan National Tourist Organization, there is a large gap between the number ofoutbound and inbound tourists: in 2005, there were about 17.4 million outbound tourists, whereas
the number of inbound tourists was estimated at approximately 6.7 million. The Japanese
government has pushed forward with policies to attract foreign tourists in order to revitalize thelocal and national economies. This indicates that tourism development is expected to be an
important and effective economic measure for local regions including remote islands. However,there has been no empirical analysis of whether tourism development in remote islands can be a
substitute for financial assistance from the central government.
In this paper, we investigate the effectiveness of tourism development in remote islands as an
economic measure. In particular, we examine the impact of fiscal expenditure of municipalities
that supervise remote islands5 and the number of tourists on per capita taxable income in remoteislands using a panel data analysis.

3

The results show that the number of tourists has no significant and positive impact on percapita taxable income in remote islands. In other words, continuous financial support may beneeded to maintain islands’ economies. Furthermore, shrinking populations also have a large
negative impact on the islands’ economies. This indicates that the islands’ economies would
worsen if the residents were forced to relocate to the mainland as a result of a further decrease infinancial aid to remote islands.
The paper consists of three sections. In the next section, we explain the panel data used inthis study and conduct an empirical analysis to estimate the impact of fiscal expenditure onmunicipalities and the number of tourists on per capita taxable income in remote islands. The final
section presents conclusions based on demographic, social, and economic trends, and discusses
implications for further research.

EMPIRICAL ANALYSIS
We conducted an empirical analysis of the islands under the Remote Islands Development
Laws. There are 315 islands and 214 municipalities (54 cities, 115 towns, and 45 villages) thatsupervise the islands as of April 20026. We use a linear regression model, in which per capita
taxable income is expressed as a linear combination of dependent variables such as per capita
fiscal expenditure, the number of tourists per capita, and population size 7 . We also note thattaxable income and fiscal expenditure are deflated by the national consumer price index (based on
2000 figures).
There is a need for a careful examination of the data before an empirical analysis. We had toanalyze two kinds of data: municipality-based data (population size, fiscal expenditure, andtaxable income) and island-based data (the number of tourists). We show further details of eachtype of data in Table 1. To analyze these more easily and clearly, we defined the “island regions”
as outlined below to make the island authorities correspond to the municipal authorities, and use
the data recalculated by each island region.
To assist understanding, we provide an illustration of two islands (Islands 1 and 2) that are
divided into three parts (A, B, and C) and two parts of the mainland (D and E) in Fig. 1. We also
assume that both Islands 1 and 2 are under the Remote Islands Development Laws.
First, we consider the case in which one or more municipalities take charge only of islands. If
one municipality supervises Island 1 (A), we define the island region as Island 1 (A) itself. If two
municipalities share parts of two islands (A + B and C), the island region is defined as bothIslands 1 and 2 (A + B + C).
If a municipality supervises Island 1 as well as a part of the mainland (D), we denote theisland region as Region (A + D). We also note that there are some cases in which two
municipalities share parts of two islands and each of them supervises a part of the mainland (A +B + D and E + C). Then the island region is defined as Region (A + B + C + D + E).
We obtain 165 island regions using all of the available data, 124 after excluding missing

4

values. We show the list of islands in Table A1. Note that we conducted a panel data analysis ofdata from 1975 to 2001 (26 years).
In this paper, we adopt a partial adjustment model and its modification to analyze the data.
First, we set up the following partial adjustment model (note that the subscript i denotes individual
islands and the subscript t denotes time):()

Yit − Yit −1 = θ Yit* − Yit −1 + uit(1)

where Y is the per capita taxable income, θ is the partial adjustment coefficient, and Y * is theideal per capita taxable income given by Yit* = α + β Git + γTit + δN it where G is the per capita
fiscal expenditure, T is the per capita number of tourists, and N is the population size.
Substituting this formula into (1) and adding an error term, we have the following equation for anempirical analysis in this study:

Yit = (1 − θ )Yit −1 + θα + θβGit + θγTit + θδN it + uit .
If we assume Y * is given by a distributed lag model with M lags8, we have

Yit* = α + β 0Git + β1Git −1 + β 2Git − 2 L + β M Git − M + γ 0Tit + γ 1Tit −1 + γ 2Tit − 2 L + γ M Tit − M+ δ 0 N it + δ1 N it −1 + δ 2 Nit − 2 L + δ M N it − M .

If we introduce a parameter that represents the difference between a variable at time t and that attime t-1, that is, ΔGit = Git − Git −1 , ΔTit = Tit − Tit −1 and ΔN it = N it − N it −1 , we then rewrite theabove equation as follows:

Yit* = α + βGit + φ0 ΔGit + φ1ΔGit −1 + φ2 ΔGit − 2 L + φM −1ΔGit − M +1
+ γTit + ψ 0 ΔTit + ψ 1ΔTit −1 + ψ 2 ΔTit − 2 L + ψ M −1ΔTit − M +1

+ δN it + ξ 0 ΔN it + ξ1ΔN it −1 + ξ 2 ΔN it − 2 L + ξ M ΔN it − M +1.

Thus, the fiscal and tourism multipliers and the effect of population size in the long run are
denoted by β , γ , and δ , respectively9. Using this formula, (1) is then modified as:

Yit = (1 − θ )Yit −1 + θα + θβGit + θφ0 ΔGit + θφ1ΔGit −1 L + θφM −1ΔGit − M +1
+ θγTit + θψ 0 ΔTit + θψ 1ΔTit −1 L + θψ M −1ΔTit − M +1

+ θδN it + θξ0 ΔN it + θξ1ΔN it −1 L + θξ M −1ΔN it − M +1 + uit .
Applying this model to panel data allows us to relax the constancy of the intercept α . In
particular, the variable α can be divided into three separate components as follows:

α it = μ + ηi + τ t
where μ is a constant over all time and islands, η i is a variable depending only on the individual
island i, τ t is a variable depending only on the time t. We have two popular models for thisspecification. One is a fixed effects model that treats both η i and τ t as nonstochastic variables,
and the other is a random effects model that assumes both η i and τ t are random variables with5

zero means. Positive variances such as σ η2 and σ τ2 , and u it , η i , and τ t are mutuallyindependent. First, we consider a fixed effects model for the specification of α using individual
and/or time dummy variables.
In this paper, we use two kinds of models in terms of variable transformation: a linear modelusing all of the original variables and a log-linear model with logarithmic transformation of all
variables. The following is the procedure for the choice of lag length and model selection.
First, we assume four kinds of α in terms of their components: α with only the individual
dummy variable η i , with only the time dummy variable τ t , and with both/neither η i and/nor τ t .
We set the maximum lag length to five, and chose one model from both linear and log-linear
models where the SBIC was minimized. Next, we conducted nonnested tests (PE tests), as
proposed by MACKINNON et al., 1983, between these two models, and examined which modelthe data better supported10. We have annual data for 26 years from 124 regions. As data from thefirst five years was used only as explanatory variables to estimate lag length, 2,604 observations
were available for this survey (2,604=124*(26–5)).
In the first procedure, we chose the model that had only a time dummy and 0 lag in each caseof linear and log-linear models as “the selected model” (see Table 2). We also show the results ofnonnested tests between these selected models in Table 3. We then finally select the log-linear
model that has only a time dummy and 0 lag since the null hypothesis is rejected if we set the null
hypothesis that the true model is a linear model and the null hypothesis is not rejected if we set thenull hypothesis that the true model is a log-linear model, simultaneously.
The results of the selected log-linear model in Table 4 indicate that the estimated coefficientof the tourism (per capita tourists) is not statistically significant. We then defined “the minimum
SBIC model” as the log-linear model that has only a time dummy and 0 lag estimated without atourism variable. Table 3 shows that we also selected the minimum SBIC model rather than theselected linear model, as the null hypothesis is not rejected if we set the null hypothesis to be thatthe minimum SBIC model is optimal.
Since we used panel data, we had to examine whether the random effects model could beapplied. In Table 5, we show the estimated results of the random effects model with time-variant
error components ( τ t )11. Although the value of SBIC of the random effects model is smaller thanthat of the fixed effects model (see Tables 4 and 5), the Hausman specification test statistics are
significant, which indicates a correlation between the error term and the explanatory variables.
Then we conclude that the fixed effects model is preferred to the random effects model. We should
also note that there is little difference in any of the estimated coefficients, excluding the constantterm, between the fixed effects model and the random effects model. In the column “transformed”
in Table 5, we show the corresponding coefficients calculated from original estimates of therandom effects model.
Now, we examine the impacts of government expenditure and tourism in accordance with the6results of the selected log-linear model in Table 4. The R-squared value for this model is very high,at almost 0.98, due to dummy variables. There is not much difference between the estimatedcoefficients of government expenditure and tourism: their elasticity values are estimated to be
between 0.23 and 0.24. Tourism, however, has a t-value of 1.14, which is not statisticallysignificant. On the other hand, the t-value for government expenditure is statistically significant.
Taking into account the minimum SBIC model, which is estimated without the tourism variable,
the government expenditure in Japanese island regions has a significant positive impact on the percapita taxable income, whereas the number of tourists does not. Table 4 also shows that the
population size has a positive and statistically significant coefficient. It indicates that population
growth also increases the productivity of the local economy in the island regions. We also notethat the adjustment coefficient is estimated to be 0.026, which indicates a relatively slow
adjustment of taxable income: it takes 88 years to achieve 90% of the “ideal income”, or 27 years
to reach 50%.
We conclude by discussing the following implications of these findings for economic
development in the remote islands in Japan. First, the government expenditure can be consideredan effective method for economic development in remote islands. Second, there is some doubt
about whether tourism development may be regarded as an effective method for economic
development in remote islands. Last, the expected population decrease in remote islands will havea negative impact on economic development.

CONCLUSIONSIn this paper, we examine the impacts of fiscal expenditure, the number of tourists, and the
population size on the per capita taxable income of 124 island regions in Japan by conducting anempirical analysis using panel data. A log-linear partial adjustment model that has only a time
dummy and 0 lag is selected as the optimal model by the SBIC and a nonnested test. The
estimated results indicate that both fiscal expenditure and population size have a significant
positive impact on the per capita taxable income, whereas the number of tourists has no
statistically significant impact. We discuss the political implications of these results: (1) the
taxable income in the remote islands would decrease if financial support from the central
government to local governments decreases; and (2) the tourism development expected to be aneffective measure for economic development would not work as a substitute for financial support
from the government. There is a possibility that further reduction of taxable income would result
if declines in population size and government expenditure are both taken into account, although
we have not adequately analyzed this effect in this paper.
There is a possibility that the t-value for tourism may be underestimated if the coefficient for
tourism is different in each island region. Taking into account the results for the Amami OshimaIslands, which were shown by ISHIKAWA and FUKUSHIGE, 2006, we cannot deny thepossibility that either a linear or a log-linear model would be appropriate in particular island

7

regions. To resolve this issue, we have to conduct the above model selection procedure in eachisland region. However, we have to interpret the results of this procedure with caution because of
the lack of sufficient annual data.
If the estimated results are different in each island region, then the appropriate measures foreach island region should be implemented. In particular, the economic development issues in the
remote islands should be addressed according to the islands’ individual economic and social
conditions rather than the entire island region being administered by the Law for Development ofRemote Islands.
Acknowledgments: Financial support from the Japan Society for the Promotion of Science(Grant-in-Aid for Scientific Research A(2) 16203020) is gratefully acknowledged. We are also
grateful to Toru Nagahashi for his helpful comments.

NOTES
1

The 6,847 “islands” other than the mainland (Hokkaido, Honshu, Shikoku, Kyushu, andOkinawa) are defined as remote islands in Japan in the Handbook of Remote Islands Development,
which is published by the National Institute for Japanese Islands. This also says that “island” hasto satisfy all the following requirements based on the survey conducted by the Hydrographic andOceanographic Department in 1986: (1) they must have a girth of over 0.1 km; (2) they must havea narrow structure such as bridge or pier if they have a fixed link to the mainland; and (3) theymust not be reclamation lands.
2

We hereafter refer to these three Special Laws and Law for Development of Remote Islands as“Remote Islands Development Laws.”
3

According to the Annual Report on the Japanese Economy and Public Finance 2005 published by

the Cabinet Office, the total amount of outstanding national and local long-term debts wasestimated at 774 trillion yen (the budget deficit in relation to GDP is almost 150%) at the end of
2005.
4

The merger of municipalities has been promoted under the Special Law on the Merger ofMunicipalities, enacted on March 29, 1965, and amended July 26, 1999. The number ofmunicipalities decreased from 3,232 at the end of March 1999 to 1,822 at the end of March 2006.
5

Most financial aid to remote islands from the central government and prefectures is provided toeach municipality that controls remote islands. We therefore equated fiscal expenditure ofmunicipality with financial aid to remote islands. It is difficult to calculate expenditure in eachisland as each has some projects that the central government subsidizes directly.
6This data is from Handbook of Remote Islands Development published by the National Institutefor Japanese Islands.
7

Many studies have been conducted to determine a tourism multiplier, such as ARCHER, 1982,

8

LUNDBERG et al., 1995, VANHOVE, 2005. BALAGUER and CANTAVELLA-JORDA, 2002,
estimated the long-run effect of tourism on economic growth by applying some recent time series
techniques, and BAAIJENS and NIJKAMP, 2000, and BAAIJENS et al., 1998, adopted a metaanalytic approach to empirical research on this topic. We also refer to MURINDE and RARAWA,
1996, who examined the effectiveness of stabilization policy on the Solomon Islands’ economy
using a macroeconomic model, and JOHNSON and THOMAS, 1990, who estimated the impact ofa major tourist attraction on local employment in the north of England.
8

We can definitely set different lag lengths to each variable. In this paper, however, we set all thelag lengths equal (M) for simplicity.
9This model is considered a distribution lag model, which is more flexible than the Koyck

distributed lag model given by a partial adjustment model.
10

All parameter estimates were calculated using TSP 5.0.

11

If we assume that the error term in the partial adjustment model follows an error component

structure with individual effect, there exists a correlation between the error term and the
explanatory variables. In this paper, a model can be estimated using the Maximum Likelihood
Estimation method because the error term is supposed to be composed of only a time effect. The
dynamic panel data model, which is seen in BALTAGI, 2005, also has an individual effect on theerror term. In terms of estimation of tourism demand, SONG and WITT, 2000, comment on themodel and provide some applications.

9REFERENCES
ARCHER, B. (1982) The value of multipliers and their policy implications, Tourism Management
3, 236–241.
BAAIJENS, S. and NIJKAMP, P. (2000) Meta-analytic methods for comparative and exploratory
policy research: an application to the assessment of regional tourist multipliers, Journal of
Policy Modeling 22, 821–858.
BAAIJENS, S.R., NIJKAMP, P., and MONFORT, K.V. (1998) Explanatory meta-analysis for thecomparison and transfer of regional tourist income multipliers, Regional Studies 32, 839–849.
BALAGUER, J. and CANTAVELLA-JORDA, M. (2002) Tourism as a long-run economic growth
factor: the Spanish case, Applied Economics 34, 877–884.
BALTAGI, B.H. (2005) Econometric Analysis of Panel Data, 3rd edition, John Wiley & Sons,
New York.
GANI, A. (1998) Macroeconomic determinants of growth in the South Pacific island economies,
Applied Economics Letters 5, 747–749.
GOUNDER, R. (2001) Aid-growth nexus: empirical evidence from Fiji, Applied Economics 33,
1009–1019.
ISHIKAWA, N. and FUKUSHIGE, M. (2006) Impacts of tourism and fiscal expenditure to remoteislands: the case of the Amami islands in Japan, Applied Economics Letters, forthcoming.
JOHNSON, P. and THOMAS, B. (1990) Measuring the local employment impact of a tourist
attraction: an empirical study, Regional Studies 24, 395–403.
LUNDBERG, D.E., KRISHNAMOORTHY M., and STAVENGA, M.H. (1995) Tourism
Economics, John Wiley & Sons, New York.
MACKINNON, J.G., WHITE, H., and DAVIDSON, R. (1983) Tests for model specification in thepresence of alternative hypotheses: some further results, Journal of Econometrics 21, 53–70.
MURINDE, V. and RARAWA, D.H. (1996) Modelling the potency of stabilization policy for theSolomon Islands 1978:1Q–1992:4Q, Applied Economics 28, 309–319.
SONG, H. and WITT, S.F. (2000) Tourism Demand Modelling and Forecasting: Modern
Econometric Approaches, Pergamon, Amsterdam.
VANHOVE, N. (2005) Economics of Tourism Destinations, Elsevier, Amsterdam.

10

Table 1. Island regions

Variables

Definition

SourcesTaxable income

Total taxable income of cities, towns, andvillages

Survey of taxation of cities, towns, and villages

Population size

Total population of cities, towns, and villages

Account book based on inhabitants

Local government expenditure

Total fiscal expenditure of cities, towns, andvillages

Survey of statements of accounts of cities, towns, andvillages

Number of tourists

Total numbers of tourists in cities, towns, andvillages

National Institute for Japanese Islands

Price level

Consumer Price Index

Statistics Bureau, Ministry of Internal Affairs andCommunications

11

Table 2 SBIC for Model Selection
Linear Model

Log-linear Model

Without Dummies
Lag = 019904.5
Lag = 119900.7
Lag = 2
19911.0
Lag = 3
19922.3
Lag = 4
19933.2
Lag = 5
19943.8
With Individual Dummy
Lag = 020133.3
Lag = 1
20134.3
Lag = 2
20137.2
Lag = 3
20142.5
Lag = 420151.2
Lag = 520153.7
With Time Dummy
Lag = 019581.9
Lag = 1
19587.0
Lag = 2
19592.6
Lag = 3
19602.6
Lag = 4
19613.9
Lag = 5
19624.6
With Individual & Time Dummies
Lag = 019801.7
Lag = 1
19812.7
Lag = 2
19808.0
Lag = 3
19811.0
Lag = 4
19819.4
Lag = 519820.3
Note: Minimum SBIC cases are indicated in bold face type in each model.

12–3702.85–3709.04–3701.22–3690.84–3683.38–3672.27–3544.78–3557.78–3548.42–3539.28–3538.62–3525.31–4004.08–3998.87–3991.41–3980.79–3969.87–3959.11–3830.26–3819.33–3824.41–3824.02–3824.31–3832.20

Table 3 Results of Nonnested Test
Nonnested test between selected models
Null Hypothesis
Alternative Hypothesis

Linear Model

Linear Model–
Log-Linear Model
7.7387**
Note: **= the null hypothesis is rejected at a significance level of 1%.

Log-Linear Model
1.86178–

Nonnested test between the linear selected model and minimum SBIC model (see Table 4)
Null Hypothesis
Alternative Hypothesis

Linear Model

Linear Model–
Minimum SBIC Model
7.63997**
Note: **= the null hypothesis is rejected at a significance level of 1%.

13

Minimum SBIC Model
1.77553–

Table 4 Estimated Results of Selected Model
Tourism
Government Expenditure
PopulationθConstant
Dummies

Selected Log-Linear Model

Minimum SBIC Model

0.023400(1.1405)
0.239760*(2.0454)
0.109877**(3.2424)0.026027**(5.9840)5.48255**(4.4867)

0.256613*(2.2069)
0.091558**(2.8534)
0.025977**(5.9788)5.51734**(4.5018)

Time

Time

2

R
0.98049
0.98040
Standard Error
0.050311
0.050311
Log Likelihood
4102.394101.89
SBIC–4004.08–4007.51
Note: t-values are given in brackets.
**= the null hypothesis that this coefficient is zero is rejected at a significance level of 1%.
*= the null hypothesis that this coefficient is zero is rejected at a significance level of 5%.

14

Table 5 Estimated Results
Random Effects Model
Tourism
Government Expenditure
Population
Lagged Income / θConstant

Estimated

Transformed

0.000635186(1.0350)
0.00601940*(2.2165)
0.00288659**(2.4650)
0.973210**(281.103)0.176233**(4.6254)0.023710
0.224688
0.107749

0.026790
6.578313

Error Components

Time

Hausman’s Specification Test
σ2ρi
Log Likelihood

14.519**0.00306801
0.176233
4057.0–4033.4

SBIC
Note: See Note in Table 4.

15

Table A1 List of Islands
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58

Islands
Rebun-toRishiri-to
Yagishiri-to, Teuri-to
Okushiri-to
O-shimaIzu-shima, E-no-shima
Aji-shima
Tashiro-jima
Sabusawa-jima, Nono-shima, Katsura-jima, Ho-jimaTobi-shima
O-shimaTo-shima
Nii-jima, Shikine-jima
Kozu-shima
Hachijo-shima
Chichi-jima, Haha-jima, Io-jima, Minamitori-shima
Awa-shima
Hegura-shima
Hatsu-shima
Saku-shima
Himaka-jima, Shino-jimaKami-shima, Toshi-jima, Suga-shima, Sakate-jima
Watakano-jima
Mazaki-jima
Nu-shima, Awaji-shima
Tanga-shima, Ie-shima, Boze-jima, Nishi-jima
Dogo

Cities, Towns, and Villages
Rebun-cho
Rishiri-cho, Rishirifuji-cho
Haboro-cho
Okushiri-cho
Kesennuma-shi
Onagawa-cho
Oshika-choIshinomaki-shi
Shiogama-shi
Sakata-shi
Oshima-cho
Toshima-mura
Niijima-mura
Kozushima-muraHachijo-cho
Ogasawara-mura
Awashimaura-mura
Wajima-shi
Atami-shi
Isshiki-cho
Minamichita-cho
Toba-shi
Isobe-cho
Shima-choNandan-cho, Sumoto-shi
Ieshima-cho
Saigo-cho, Fuse-mura, Gokamura, Tsuma-muraNaka-no-shima
Ama-choNishi-no-shima
Nishi-no-shima-cho
Chiburi-jima
Chibu-muraKakui-jima, Otabu-jima, Kashira-jima, Ko-jimaHinase-cho
Inu-jima
Okayama-shi
Matsu-shima, Muguchi-jima
Kurashiki-shi
Taka-shima, Shiraishi-jima, Kitagi-shima, Manabe- Kasaoka-shi
shima, Kobi-shima, Obi-shima, Mu-shima
Hashiri-jima
Fukuyama-shi
Hoso-jima
Innoshima-shi
Sagi-jima, Kosagi-jima
Mihara-shi
Osaka-shimo-shima, Mikado-jima, Toyo-shima, Itsuki- Yutaka-machi, Toyohama-cho
shima
Atada-shima
Otake-shi
Ha-shima, Hashira-jima, Kuro-shima
Iwakuni-shi
Kasasa-shima
Oshima-cho
Iwai-shima, Ya-shima
Kaminoseki-cho
Futaoi-jima, Mutsure-jima
Shimonoseki-shi
Uma-shima
Tabuse-cho
Nasake-jima
Towa-choMi-shima, O-shima, Hitsu-shima, Ai-shima
Hagi-shi
Sago-jima
Hirao-choNo-shima
Hofu-shiHeigun-to
Yanai-shi
U-shima
Hikari-shi
I-shimaAnan-shiTeba-jima
Mugi-cho
Ode-shima, Te-shima
Tonosho-cho
Nao-shima, Ushigakubi-jima, Byobu-jima, Muku-shima Naoshima-cho
Ogi-jima, Megi-jimaTakamatsu-shi
Hitsuishi-jima, Iguro-shima, Yo-shima, Oyo-shima
Sakaide-shi
Hon-jima, Ushi-jima, Hiro-shima, Te-shima, Ote-shima Marugame-shi
Sanagi-jima, Takami-shima
Tadotsu-cho

16

RegionRebun-toRishiri-to
Teuri/Yagishiri
Okushiri-to
O-shima
Oshika-shoto
Oshika-shoto
Oshika-shoto
Urato-shoto
Tobi-shima
Izu-shotoIzu-shotoIzu-shotoIzu-shotoIzu-shoto
Ogasawara-shoto
Awa-shima
Hegura-shima
Hatsu-shima
Aichi-mishima
Aichi-mishima
Shima-shoto
Shima-shoto
Shima-shoto
Nu-shima/Nada
Ieshima-gunto
Oki-shima

Prefecture
Hokkaido
Hokkaido
Hokkaido
Hokkaido
Miyagi
Miyagi
Miyagi
Miyagi
Miyagi
Yamagata
Tokyo
Tokyo
Tokyo
Tokyo
Tokyo
Tokyo
Niigata
Ishikawa
Shizuoka
Aichi
Aichi
Mie
Mie
Mie
Hyogo
Hyogo
ShimaneOki-shimaOki-shimaOki-shima
Hinase-shoto
Inu-jima
Kojima-shoto
Kasaoka-shoto

ShimaneShimaneShimane
OkayamaOkayamaOkayamaOkayamaHashirijima-gunto
Geibi-gunto
Geibi-gunto
Shimo-osaki-gunto

Hiroshima
Hiroshima
Hiroshima
Hiroshima

Aki-gunto
Hashirajima-guntoSuo-oshima-gunto
Kumage-gunto
Hibikinada-shoto
Kumage-guntoSuo-oshima-gunto
Hagi-shoto
Kumage-gunto
Shunan-shoto
Heigun-to
Shunan-shoto
I-shimaTeba-jima
Naoshima-shoto
Naoshima-shoto
Naoshima-shoto
Shiwaku-shoto
Shiwaku-shoto
Shiwaku-shoto

Hiroshima
Yamaguchi
Yamaguchi
Yamaguchi
Yamaguchi
Yamaguchi
Yamaguchi
Yamaguchi
Yamaguchi
Yamaguchi
Yamaguchi
Yamaguchi
Tokushima
Tokushima
KagawaKagawaKagawaKagawaKagawaKagawaTable A1 List of Islands (continued)
No
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85

Islands
Awa-shima, Shishi-jima
Ibuki-jimaTakaikami-shima, Uo-shima
Yuge-shima, Sa-shima, Toyo-shima
Ikina-jimaIwagi-jima
U-shima, Tsu-shima
Oge-shima, Koge-shima, Okamura-jima
O-shima, Kuru-shima, Uma-shima, Hiki-jima
O-shimaAi-jima
Nogutsuna-jima, Muzuki-jima, Naka-jima, Nuwa-shima,
Tsuwaji-shima, Futagami-jima
Ao-shima
O-shima
Ku-shima, Ka-shima, To-jima, Hiburi-jimaTake-ga-shima
Oki-no-shima, Uguru-shima
Uma-shima, Ai-no-shima
O-shimaAi-no-shima
Genkai-jima, Oro-no-shima
Hime-shima
Taka-shima, Kashiwa-jima
Ogawa-shima
Karara-shima, Matsu-shima, Madara-shima
Muku-shima
Taka-shimaKuro-shima

86 Ao-shimaTobi-shima
87 O-shima
88 Uku-shima, Tera-shima
Mu-shima, Nozaki-jima, No-shima, Ojika-jima, Kuro89
shima, O-shima
90 Taka-shima, Kuro-shima
Nakadori-jima, Kashira-ga-shima, Kiri-no-ko-jima,
91 Wakamatsu-jima, Hino-shima, Arifuku-jima,
Ryozegaura-shima
Naru-shima, Mae-shima
92

Cities, Towns, and Villages
Takuma-cho
Kan-onji-shi
Uoshima-muraYuge-cho
Ikina-mura
Iwagi-mura
Miyakubo-cho, Yoshiumi-cho
Sekizen-muraImabari-shi
Niihama-shi
Hojo-shi
Nakajima-cho

RegionShiwaku-shoto
Ibuki-jima
Uoshima-gunto
Kamijima-shoto
Kamijima-shoto
Kamijima-shoto
Ochi-shoto
Sekizen-shoto
Kurushima-gunto
Nii-oshima
Ai-jima
Kotsuna-shoto

Prefecture
KagawaKagawa
Ehime
Ehime
Ehime
Ehime
Ehime
Ehime
Ehime
Ehime
Ehime
Ehime

Nagahama-cho
Yahatahama-shi
Uwajima-shi
Tsushima-cho
Sukumo-shi
Kitakyushu-shi
Oshima-mura
Shingu-machi
Fukuoka-shi
Shima-machi
Karatsu-shi
Yobuko-cho
Chinzei-machi
Hizen-machi
Takashima-cho, Ashibe-cho,
Katsumoto-cho, Gonoura-cho
Matsushima-shi
Oshima-mura
Uku-machi
Odika-choAo-shima
Uwakai-shoto
Uwakai-shoto
Uwakai-shoto
Oki-no-shima
Chikuzen-shoto
Chikuzen-shoto
Chikuzen-shoto
Chikuzen-shoto
Chikuzen-shoto
Genkai-shoto
Genkai-shoto
Genkai-shoto
Genkai-shoto
Hirado-shoto

Ehime
Ehime
Ehime
Ehime
Kochi
Fukuoka
Fukuoka
Fukuoka
Fukuoka
Fukuoka
Saga
Saga
Saga
Saga
Nagasaki

Hirado-shoto
Hirado-shoto
Hirado-shoto
Hirado-shoto

NagasakiNagasakiNagasakiNagasaki

Sasebo-shi
Shinuonome-cho

Hirado-shoto
Goto-retto

NagasakiNagasakiNaru-machi, Kamigoto-cho,
Arikawa-cho, Wakamatsucho, Narao-cho
Fukue-shi

Goto-retto

Nagasaki

Goto-retto

NagasakiKakiura-oshima
Matsu-shima
Io-jimaMatsu-shima
Amakusa-shoto
Hime-shima
Bungo-shoto
Bungo-shoto
Bungo-shoto
Bungo-shoto
Shimanoura-shima
Minaminaka-gunto
Minaminaka-gunto
Naga-shima
Katsura-jima
Koshiki-jima
Koshiki-jimaNagasakiNagasakiNagasakiNagasaki
Kumamoto
Oita
Oita
Oita
Oita
Oita
MiyazakiMiyazakiMiyazakiKagoshima
Kagoshima
Kagoshima
Kagoshima

Hisaka-jima, Warabi-ko-jima, Kaba-shima, Fukue-jima,
93 Aka-shima, O-shima, Kuro-shima, Shimayama-jima,
Saga-no-shima
94 Kakinoura-shima, Sakito-jima, E-no-shima, Hira-shima Sakito-cho
95 Matsu-shima
Oseto-cho
96 Io-jima, Oki-no-shima
Iojima-machi
97 Ike-shima
Sotome-cho
98 Yokoura-jima, Maki-shima, Goshonoura-shima
Goshoura-machi
99 Hime-shima
Himeshima-mura
100 Jimuku-shima, Hodo-jima
Tsukumi-shi
101 Onyu-jima
Saeki-shi
102 O-shima
Tsurumi-machi
103 Yakata-jima, Fuka-shima
Kamae-cho
104 Shimanoura-shima
Nobeoka-shi
105 O-shima
Nango-cho
106 Tsuki-shima
Kushima-shi
107 Shishi-jima
Higashi-cho
108 Katsura-jima
Izumi-shi
109 Kamikoshiki-jimaNakakoshiki-jimaSato-mura, Kamikoshiki-muraShimokoshiki-jima
Kashima-mura, Shimokoshiki110
mura

17

Table A1 List of Islands (continued)
No
111
112
113
114
115
116
117
118
119
120
121
122

Islands
Tane-ga-shima, Mage-shima

Yaku-shima, Kuchinoerabu-jimaTake-shimaIo-jima, Kuro-shima
Iheya-jima, Noho-jimaIzena-jimaMinna-shima
Tsuken-jima
Kudaka-jima
Zamami-jima, Aka-shima, Geruma-jima
Tokashiki-jima
Miyako-jima, Ikema-jima, Ogami-jima, Kurima-jimaIshigaki-jima
Taketomi-jima, Iriomote-jima, Hatoma-jima, Yubushima, Kohama-jima, Kuro-shima, Aragusuku-jima123
kamichi, Aragusuku-jima-shimochi, Hateruma-jima,
Sotopanari-jima, Kayama-jima
124 Yonaguni-jima

Cities, Towns, and Villages
Nishinoomote-shi, Nakatanecho, Minamitane-cho
Kamiyaku-cho, Yaku-cho
Mishima-mura
Iheya-son
Izena-sonMotobu-cho
Katsuren-cho
Chinen-son
Zamami-son
Tokashiki-son
Hirara-shi
Ishigaki-shi
Taketomi-cho

RegionTanega-shima

Prefecture
Kagoshima

Yaku-shima
Nansei-shoto
Hokubu-ken-iki
Hokubu-ken-iki
Hokubu-ken-iki
Chunanbu-ken-iki
Chunanbu-ken-iki
Chunanbu-ken-iki
Chunanbu-ken-iki
Miyako-ken-iki
Yaeyama-ken-iki
Yaeyama-ken-iki

Kagoshima
Kagoshima
Okinawa
Okinawa
Okinawa
Okinawa
Okinawa
Okinawa
Okinawa
Okinawa
Okinawa
Okinawa

Yonaguni-cho

Yaeyama-ken-iki

Okinawa18

Fig. 1. Illustration of Islands

19