analysis of international tourist arrivals worldwide

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Analysis of international tourist arrivals worldwide: The role of worldheritage sites

Yu-Wen Su, Hui-Lin Lin *

Department of Economics, National Taiwan University, 21 Hsu-Chow Road, 10055 Taipei, Taiwan

h i g h l i g h t s

Investigate in the positive in uence of world heritage sites on international tourist arrivals.Divide the sample into several groups according to the number of WHSs to study effects of WHSs across these groups.Explore the pooled, xed and random effect models with panel data (66 countries, 2000 e 2009).Eliminate the problem of time-invariant variables in panel data model by increasing the number of countries.

a r t i c l e i n f o

Article history:Received 19 August 2012Accepted 24 April 2013

JELS:L83C23

Keywords:WHSsTourism demandCultural sitesNatural sitesPanel data

a b s t r a c t

This study examines the impact on inbound tourism caused by the presence of world heritage sites. Thestatistics are derived from panel data for 66 countries for the period 2006 e 2009. The results indicate thatthere exists a positive relationship between having such heritage sites and tourist numbers, and therelationship is stronger for natural rather than for cultural heritage sites. The evidence also indicates thepresence of a U-shaped relationship between numbers of world heritage sites in a country and touristnumbers. These relationships are found to be robust even though differences in patterns are found indifferent regions.

2013 Elsevier Ltd. All rights reserved.

1. Introduction

Tourism is one of the leading economic sectors in the world, andrepresents a major source of income, employment, exports andtaxes. According to the World Travel and Tourism Council (WTTC),in 2011 the tourism sector (domestic and international) contributedalmost 5992 billion USD to the global economy. With con rmedstrong linkage effects, the tourism industry also provides almost260 million job opportunities, accounting for nearly 9% of globalemployment. In addition, according to the World Bank CarbonFinance Unit (CFU) the tourism sector is relatively eco-friendlycompared to the manufacturing sector, and has led to more

sustainable development. Therefore, many countries are empha-sizing the development of tourism to drive their green economicgrowth.

As disposable incomes and the awareness of the importance of leisure have increased, so too have the numbers of tourists ( Lim,2006 ). World Tourism Organization (WTO) statistics reveal thegrowth of international tourist arrivals (the x-axis on the right-hand side) between 1995 and 2011, as shown in Fig. 1. The num-ber of international tourist arrivals increased from 538 million in1995 to 940 million in 2010, representing growth of 4.7% on averagein each year. Meanwhile, according to the World Heritage Centre of the United Nations Educational, Scienti c and Cultural Organiza-tion (UNESCO), the total number of World Heritage Sites (WHSs)has risen steadily. Fig.1 also shows that the number of WHSs (the x-axis on the left-hand side) increased from 468 in 1995 to 936 in2011, or by 6% per year on average. Thus, these growing trendsappear to suggest that, if the positive effect of WHSs on interna-tional tourism is proved, having such sites will lead to increases in

* Corresponding author. Tel.: 886 2 2321 7730; fax: 886 2 2322 5657.E-mail addresses: [email protected] (Y.-W. Su), [email protected]

(H.-L. Lin).

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Tourism Management

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http://dx.doi.org/10.1016/j.tourman.2013.04.005

Tourism Management 40 (2014) 46 e 58
mailto:[email protected]:[email protected]://www.sciencedirect.com/science/journal/02615177http://www.elsevier.com/locate/tourmanhttp://dx.doi.org/10.1016/j.tourman.2013.04.005http://dx.doi.org/10.1016/j.tourman.2013.04.005http://dx.doi.org/10.1016/j.tourman.2013.04.005http://dx.doi.org/10.1016/j.tourman.2013.04.005http://dx.doi.org/10.1016/j.tourman.2013.04.005http://dx.doi.org/10.1016/j.tourman.2013.04.005http://www.elsevier.com/locate/tourmanhttp://www.sciencedirect.com/science/journal/02615177http://crossmark.crossref.org/dialog/?doi=10.1016/j.tourman.2013.04.005&domain=pdfmailto:[email protected]:[email protected]


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international tourist arrivals and consequently tourism expendi-ture, thereby bene ting the economies of the destination countries.However, there have so far been few studies on this subject.

In this paper, we investigate the relationship between the WHSsand tourism demand not only for speci c countries but also on a

worldwide level. First, by using data on the number of WHSs in 66countries between 2000 and 2009, we explore the positive in u-ence of these sites on international tourist arrivals (internationaltourism demand). Second, we divide our sample into severalgroups according to the number of WHSs and study the differenteffects of WHSs on international tourism demand across thesegroups. Third, we apply various xed and random effects models tothe panel data. In addition, we increase the number of countries inthe panel data model to eliminate the problem of time-invariantvariables, or rarely changing variables.

The remainder of this paper is organized as follows. In Section 2,we provide a literature review together with an analytical frame-work of WHSs and international tourism worldwide. In Section 3,we introduce the model s setting and the methodology of the paneldata. In Section 4, we present the results of the analysis and discussthe economic implications. In Section 5, we conclude.

2. World heritage sites and international tourists

In this section, we provide a literature review and brie ydescribe the analytical framework of WHSs and internationaltourism worldwide based on the current situation.

2.1. Literature review

As the number of tourists increase, governments and privateenterprises around the world have been eager to expand theirtourism. Many studies have examined the key elements affecting

tourism demand ( e.g., Dhariwala, 2005 ; Dougan, 2007 ; Dritsakis,2004 ; Naude and Saayman, 2005 ; Patsouratis, Frangouli, &Anastasopoulos, 2005 ; Payne and Mervar, 2002 ; Tan, McCahon, &Miller, 2002 ). It has also been found that tourism destinationswith typical cultural or natural elements constitute one of the chief attractions for international tourists ( e.g., Bille and Schulze, 2008 ;Bonet, 2003 ; Cooke and Lazzaretti, 2008 ; Deng, King, & Bauer,2002 ; Dritsakis, 2004 ).

Since cultural or natural attractions lead to increased tourismdemand, it could be argued that those attractions that are of ciallyauthenticated, i.e., inscribed on the list of WHSs by UNESCO, shouldbe relatively appealing to international tourists. WHSs have beenfound to have signi cantly positive effects on the promotion of domestic or foreign tourism in some speci c countries, such as

England ( e.g., Herbert, 2001 ; McIntosh and Prentice, 1999 ), China

(e.g., Li, Wu, & Cai, 2008 ; Yang, Lin, & Han, 2010 ) and Germany,Hungary and Romania ( Light, 2000 ). Nevertheless, studies on thepositive effect of WHSs on tourism have been limited to a singlecountry, and little research has been done to expand this effect to aworldwide level.

In addition, in terms of the methodology adopted, some studiesemploy the panel data model ( e.g., Garin-Munoz and Amaral, 2000 ;Ledesma-Rodriguez, Navarro-Ibanez, & Perez-Rodriguez, 2001 ;Maloney and Montes-Rojas, 2005 ; Naude and Saayman, 2005 ; Yanget al., 2010 ), because of the availability of the data. However, in thepanel data model, the problem of time-invariant variables, or rarelychanging variables, is widely discussed ( Cellini, 2011 ; Yang and Lin,2011 ). In this paper, we also use the panel data model, and thenumber of countries is increased to eliminate the problem of time-invariant variables.

2.2. Research background

With the increasing trend in terms of the number of WHSs (seeFig. 1), the geographical distribution of heritage sites is relativelyunbalanced. According to data collected by the World HeritageCentre, UNESCO, Fig. 2 shows the pie chart of WHSs by region in2009. WHSs are mainly concentrated in Europe, which accounts for42% of the total amount, followed by the Asia Paci c with 20%, andthe Americas with 17% (the sum of the North and the SouthAmericas). Other areas, that is, Africa and the countries of theMiddle East, each account for around 10%. Overall, Europeancountries, which have highly developed tourism, possess rich cul-tural and historical attractions, including almost half of all WHSs.

400

500

600

700

800

900

1000

T o u r i s t a r r

i v a

l s ( m i l l i o n

)

400

500

600

700

800

900

1000

N u m b e r o

f w o r l

d h e r i

t a g e s i

t e s

1995 1997 1999 2001 2003 2005 2007 2009 2011

Number of world heritage sitesTourist arrivals

Fig. 1. Numbers of world heritage sites and international tourist arrivals. Source: TheWorld Tourism Organization (The International tourist arrivals), The World HeritageCentre, UNESCO (The number of WHSs).

Fig. 2. Number of world heritage sites by location in 2009. Source: The World HeritageCentre, UNESCO.

0 10 20 30 40Number of world heritage sites

CzechIran

PortugalPolandJapan

SwedenCanada

AustraliaBrazil

GreeceU.S.

RussiaIndiaU.K.

MexicoFrance

GermanyChinaSpain

Italy

Cultural siteNatural siteMixed site

Fig. 3. Top 20 countries in terms of world heritage sites in 2009. Source: The World

Heritage Centre, UNESCO.

Y.-W. Su, H.-L. Lin / Tourism Management 40 (2014) 46 e 58 47


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To reveal this phenomenon in better detail, Fig. 3 shows the top20 countries according to the numbers of WHSs in 2009 providedby the World Heritage Centre, UNESCO. The total number of WHSsis the summation of three kinds of sites: cultural, natural and mixedsites. Half of these countries are located in Europe. Italy, the countrywith the most WHSs, possesses 44 WHSs, including 42 culturalsites and 2 natural sites. The country with the second most WHSs,Spain, has 41 WHSs (36 cultural, 3 natural and 2 mixed sites), andChina has 38 WHSs (27 cultural, 7 natural and 4 mixed sites). Inaddition, a large proportion of WHSs are cultural sites, which ac-count for around 80% of all sites. Some countries even possess onlycultural sites, such as Iran and the Czech Republic, each of whichhas 12 cultural sites. However, in a minority of natural resource-abundant countries, such as the United States and Australia, thereare more natural sites. The U.S. has 12 natural sites among 20 WHSs(60%), and Australia has 11 natural sites among 17 WHSs (65%).

As for the demand for tourism, Fig. 4 shows the top 20 countriesranked by international tourist arrivals in 2009, according to datacompiled by the World Tourism Organization. France, the mostpopular country for tourism, received 76.8 million internationaltourists in 2009. Inbound tourist arrivals in the United States, Spainand China were 54.9 million, 52.2 million and 50.9 million,respectively, while Italy received 43.2 million inbound tourists, orabout 60% of the number that France received. International touristarrivals in other countries were all less than 30 million in 2009.

Among the 20 countries in Fig. 4, a total of 11 countriespossess rich world heritage sites, which are also ranked in thetop 20 countries according to the number of WHSs (see Fig. 3). Infact, among 194 countries around the world, the other 9 coun-tries in Fig. 4 are also ranked in the top 40 according to thenumber of WHSs, apart from Thailand, which is ranked ftieth.Meanwhile, the simultaneous growth trends in both WHSs andinternational tourists are also shown in Fig. 1. Therefore, the datareveal that under the growing trend of both WHSs and interna-tional tourism, a large proportion of countries popular withtourists are those which are abundant in cultural or natural

world heritage sites.

2.3. Analytical framework

There are at least two possible reasons why being inscribed onthe WHS list would increase the demand for tourism ( Yang et al.,2010 ). First, the WHSs are widely used to promote or advertisetourism in destination countries, not only by travel agencies, butalso by governments. Because of the of cially strict application and

examination processes, being successfully inscribed on the WHSlist increases the global visibility of the destination countries. SinceWHSs attract the attention of international tourists, the demand forinternational tourism rises. Second, in regard to conservation,UNESCO is prepared to assist those developing countries which lackthe resources or ability to repair and maintain their WHSs. For adestination country, using such aid well will improve tourismconditions and further attract international tourists.

Moreover, the main purpose behind listing WHSs is to raiseawareness and mobilize sustainable resources for long-term con-servation (according to the World Heritage Centre, UNESCO), and isnot the development of tourism. However, if WHSs have positive ef-fects on tourism demand and further on tourism economies, thisadditional bene t could also raise awareness and help fund theconservation efforts. Since the governmentplaysan important role inadministering resources, it is essential to study the economic effectsof WHSs. In spite of this, there has been little research that has done just that. Therefore, under the growing trend of both WHSs and in-ternational tourism, the main purpose of our paper is to con rm thepositive effect of WHSs on the demand for international tourism.Furthermore, we study how this positive effect has changed.

3. Methodology and data

In this section, we introduce the methodology, the panel datamodel that is widely used, and brie y introduce the data weemploy.

3.1. Modeling the international tourism demand

To investigate the determinants of international tourist arrivalsworldwide, especially the effect of world heritage sites, a tourismdemand function is estimated in this study. The demand model isspeci ed as

yit f xit ; z it qi 3it (1)

where y it is the quantity of tourism demand, and the subscripts iand t denote the destination country and time period, respectively.The xit are the main explanatory variables in which we are inter-ested, the z it arecontrol variableswhichalso affect the demand, and 3it is a normally distributed error term. Meanwhile, f (.) is a function,which is set to be linear in this paper. Note that qi is the unobservedcountry-speci c variable that varies across countries but isinvariant within a country over time.

0 10 20 30 40 50 60 70 80Tourist arrivals (million)

SwitzerlandMoroccoHungary

CroatiaNetherlands

PolandEgypt

ThailandGreeceCanada AustriaMexico

GermanyTurkey

U.K.Italy

ChinaSpain

U.S.France

Fig. 4. Top 20 countries in terms of international tourist arrivals in 2009. Source: The World Tourism Organization.

Y.-W. Su, H.-L. Lin / Tourism Management 40 (2014) 46 e 5848


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More speci cally, the linear international tourism demandmodel is speci ed as

ARRI it a dWHS it b1GDP it b2POP it b3EX it b4RAILit b5 FREEDOM it b6HEALTH it b7 EDU it

X

6

j 1g j AREA jit

X

10

k 1g kYEARkit qi 3it

(2)

where the dependent variable, ARRI , is the international touristarrivals in country i at time t , which is often treated as the tourismdemand in the literature ( e.g., Lim, 2006 ; Song and Li, 2008 ). WHS represents the number of world heritage sites, which is the mainexplanatory variable ( xit ) we are interested in. If the sign of its co-ef cient, d, is positive, we could say that possessing WHSs wouldenhance international tourism after controlling other variables. Inaddition, we have replaced WHS by CULTURAL and NATURAL, thenumbers of cultural and natural WHSs, to differentiate the effects of cultural and natural WHSs on international tourism demand.

The other explanatory variables ( z it ), are regarded as control vari-ables capturing some possible factors which would in uence the de-mand. The gross domestic product ( GDP ) variable represents theincome level, which also captures the degree of economic develop-mentin the destination country. The population variable ( POP ) mainlycontrols the size of the destination country. That is, after consideringPOP , the effect of the GDP and other explanatory variables could bemeasuredaccuratelyunder the samescale of population.Forexample,the positive coef cient of GDP means that among countries with thesame population, international tourists prefer to travel to the richerone with higher income. Moreover, EX denotes the of cial exchangerate between the local currency unit (LCU) and the U.S. dollar, whichrepresents the price factor in the demand function. If EX goes up, thetraveling price (cost) increases, in which case the number of interna-tional tourist arrivals would decrease based on the law of demand.

In addition, the total railway lines ( RAIL) in terms of kilometers indestination countries is employed as a proxy variable for the avail-ability of infrastructure. A country thatpossesses more railway lines isa country inwhich it is more convenient to travel, andthis will attractmore international tourists. The FREEDOM variable is the index of political rights and civil liberties, which is measured on a one-to-seven scale. Theoretically, a smaller value of FREEDOM represents afreer political and civil environment that would make internationaltourists feel more secure without red tape and increase their will-ingness to travel. Moreover, the HEALTH variable is the percentage of health expenditure in GDP, and is used as a proxy variable for theenvironmental sanitationin destinationcountries.If a country spendsmore money caring for its residents health, the sanitary condition inthecountry will be furtherimproved.To measurethe health quality of

residents and the educational environment in destination countries,thepercentage of expenditureon education in GDP( EDU ) isalso usedas a proxy variable. In addition, to control the time and regional fac-tors, YEAR and AREA are dummy variables denoting the time from2000 to 2009 and the geographical position of 6 areas, respectively.

However, there is a potential simultaneous relationship be-tween tourist arrivals and some explanatory variables, such as GDP,and so variables in the form of a lag of one period enter the equa-tion. The results turn out to be quite consistent with those withoutthe lag term. Thus, to keep the sample size as large as possible(using the lag term will reduce the sample size by 66 observations),we choose the original models without lag terms. Other possibleexplanatory variables, such as FDI (measuring the openness level),WHSs in danger, global infectious diseases, or interaction terms,

have also been considered but have turned out to be insigni cant

and cannot improve the model, so they are omitted from the model.More details about our variables are shown in Table 1 , which pro-vides the de nitions and descriptive statistics of the variables.

3.2. Varying marginal effect of world heritage sites

The marginal effect of WHSs is the partial derivative of ARRI it

with respect to WHS it in Eq. (2) .

Marginal effect of WHS v ARRI it v WHS it

d (3)

That is, for a destination country, possessing one more WHSwould increase its inbound tourists by d visits. This d is the averageeffect across all countries with different numbers of WHSs. How-ever, even though the constant marginal effect of WHSs could beeasily concluded by d, this effect may change by the differentnumber of WHSs. Thus, to reveal more details, we divide oursample into several equal parts according to the number of WHSs.The new model is speci ed as

ARRI it

a X

S

s 1dsWHS

it g

sit b1GDP

it b2 POP

it b3EX

it

b4 RAILit b5FREEDOM it b6HEALTH it b7EDU it

X6

j 1g j AREA jit X

10

k 1gkYEARkit qi 3it

(4)

where g sit is the dummy variable for the sth group of the WHS. Thedata are divided into S equal parts, according to the number of WHSs. The WHS it multiplied by g its make us focus on the marginaleffect for a speci c range of the number of WHSs.

However, when we decide to divide oursample, the rst questionis concerned with how many groups we would obtain. On the onehand, if S istoo large,which meansthatthe number of groupsis large,the small size of the subsample would give rise to highly sensitiveestimated results. On the other hand, if S is too small, say, S 2 (as aresult of dividing the sample into two equal parts), the large sub-samplewouldreveallittle of the varyingmarginal effect of the WHSs.Moreover, we should note that the WHS is a discrete right-skewedvariable, which means that the number of WHSs is an integer andthe data are concentrated in small numbers. In this distribution, it isimpossible to divide the data into too many equal parts.

As a result, we start with three equal parts and extend this toseven equalparts( S 3, 4 . 7). It turns out that when S isequalto 4,the decrease pattern is the same as the one when S is equal to 3.When S is bigger than 5, the estimated results of the U-curve effectwould be similar to thecase where S 5. Therefore, in this paper, wepresent two representative results, S 3 and S 5, because they arethe smallest groups to capture the pattern of varying marginal ef-fects and perform well in dividing the data into several equal parts.

3.3. Methodology

To understand the preliminary sign of each determinant, pooledordinary least squares (OLS) regression is employed at rst ( e.g.,Naude and Saayman, 2005 ; Yang et al., 2010 ). Thus, the pooled OLSresidual ( u it ) is the summation of the country-speci c unobservedvariable ( qi) and the error term with a normal distribution ( 3it ):

u it qi 3it (5)

We run the Breusch e Pagan test (Be P test) to test for hetero-

skedasticity ( qis

0) in the pooled OLS models ( Breusch & Pagan,



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1979 ). In the pooled OLS estimation with heteroskedasticity,omitting the unobserved variable, which may be correlated withother explanatory variables ( xit or z it ), will cause severe problems of

bias and inconsistency. Fortunately, this problem could be solved ina panel data model under certain assumptions. Using the xed effects (FE) or the random effects (RE) technique, we could eliminatethe country-speci c effect. The xed effects model assumes thateach country has its own qi and estimates the constant term foreach country, while the random effects model assumes that qifollows a normal distribution, thus estimating one overall constantterm. In this paper, both the xed and random effects models areestimated, and then the Hausman test is employed to determinewhich model is more accurate. Under the null hypothesis (H 0), theRE model performs better, and if the Chi-square statistic is signif-icant (the p-value is small), then H 0 should be rejected and the FEmodel chosen. We also show the estimated results of the pooledOLS for reference. For more details about the panel data model and

the Hausman test, the interested reader should refer toChamberlain (1984) , Hausman (1978) and Wooldridge (2002) .

3.4. Data

In this study, the data on international tourist arrivals ( ARRI )come from the World Tourism Organization (WTO), while the dataon the number of WHSs, including cultural and natural sites, ( WHS ,CULTURAL, and NATURAL) come from the World Heritage Centre,UNESCO. In addition, the other explanatory variables ( GDP , POP , EX ,

RAIL, HEALTH , and EDU ) are collected from the World DevelopmentIndicators (WDI) of the World Bank Online Resources. The data forthe freedom index ( FREEDOM ) come from the annual report of

Freedom House.When combining these four data sets, we try to collect as many

informative observations as we possibly can. However, there aremissing data, more or less, for each variable, and especially forsome developing countries whose statistical surveys are lesscomprehensive. To focus on countries with relatively more infor-mation and avoid too much missing data causing a severe problemof data imbalance in the panel data, countries with too many kindsof data unavailable are deleted without loss. We originally collectedthe data for the WHSs of 148 countries. After combining data sets,the data actually used consist of 66 countries. The data distributionsof WHSs before and after combining the data are shown in Fig. 5.The countries deleted are mostly those containing few or no WHSs.One thing we should mention is that, among these observations,

there is only one, Israel in 2000, that possesses zero WHSs.Therefore, sifting the observations cannot only simplify theanalysis but also will not critically affect the estimated results. Inthis research, the panel data comprise 66 countries over the periodfrom 2000 to 2009 with 359 observations after deducting themissing data for each variable. The names of these 66 countries arelisted in Appendix A .

Two things should be mentioned. First, the literature shows thatthe characteristics of the source country may somehow affect thetourism demand. However, what we only know is the annual

0

. 0 5

. 1

. 1 5

. 2

. 2 5

D e n s i t y

0 10 20 30 40Number of WHSs

(A) Original data

3 6 0

. 0 5

. 1

. 1 5

. 2

. 2 5

D e n s i t y

0 10 20 30 40Number of WHSs

(B) Actual data

3 6

Fig. 5. Distribution of WHSs.

Table 1De nitions of variables and basic statistics.

Variable Description Mean S.D. Min Max

ARRI International tourist arrivals (1000) 5355.10 1100.00 3.00 809.00WHS Number of world heritage sites 5.50 7.29 0.00 44.00CULTURAL Number of cultural world heritage sites 4.23 6.33 0.00 42.00NATURAL Number of natural world heritage sites 1.09 1.89 0.00 12.00

GDP GDP (billion, constant 2000 USD) 246.21 1029.56 0.25 11670.80POP Population (million) 42.68 143.00 0.03 1331.38EX Of cial exchange rate (LCU per US$, period average) 659.77 2078.67 0.00 17065.08RAIL Rail lines (total route-km) 11545.25 25988.60 251.00 228999.00FREEDOM The index of political rights and civil liberties 5.19 2.18 1.00 7.00HEALTH Health expenditure (% of GDP) 6.35 2.22 0.01 16.21EDU Education expenditure (% of government expenditure) 15.48 5.50 6.20 71.09

AREA Dummy variable: Africa, Asia Paci c, Middle East,Europe, N. America, S. America

YEAR Dummy variable: 2000 e 2009



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number of international tourist arrivals for the destination country,according to the UNWTO. We are unable to distinguish from whichcountries these international tourists come. Thus, we cannot pindown the data on GDP or POP for the source countries. Fortunately,the panel data model is employed, so that the effect due to differentsource countries can be captured by a country-speci c factor, qi.Moreover, based on the test results, the random effects modelperforms better, so we consider that the country-speci c factorwould vary in different destination countries.

Second, the effect on income, which usually uses GDP as a proxyvariable, is undoubtedly an important explanatory variable. Whenconstructing the model, we initially considered the GDP per capita,which could eliminate the effect of the country s size because it iscounted based on each person. However, the result turns out to beinsigni cant. Therefore, to control for the income effect as well asthe country size, we separate the GDP per capita into two differentvariables: the GDP and the population ( POP ), and the results isbetter. Note that, regardless of which control variables are chosen,the coef cient of WHSs, which is the point of our paper, is almostthe same.

4. Empirical results

Using the panel data, we investigate the effect of WHSs (bothcultural and natural sites) on international tourist arrivals, whileother possible explanatory variablesare controlled. We also explorehow this effect changes for different numbers of WHSs.

4.1. Main results

The estimated results for Eq. (2) are shown in Table 2 . Models(1), (2) and (3) use the number of WHSs ( WHS ) as their explanatoryvariable, while Models (4), (5) and (6) separate WHSs into culturalandnatural sites ( CULTURALand NATURAL) to betterunderstand thein uence of these two kinds of sites. In these models, we estimatecoef cients by pooled OLS regression, and the xed effects and the

random effects models. The latter two models are ideal for dealingwith the country-speci c unobserved variables, and could also be judged by the Hausman test. According to the results of theBreusch e Pagan test, the pooled OLS regression with hetero-skedasticity is beset by problems resulting in inconsistency andbias. We could observe that the coef cients are quite different

between the pooled OLS regression and the panel data model (boththe xed and random effects models). This result proves that thecountry-speci c effect should be considered. If we were to just grabthe data and run the regression directly, the estimated resultswould be unreliable.

According to the Hausman test, in which the Chi-square statis-tics are insigni cant at the 5% signi cance level, the random effectsmodel, as in Model (3) and Model (6), performs better. This result isquite reasonable. Because the data comprise a cross section of countries, in considering the sampling problem, it makes sense toassume that the omitted variable is distributed randomly. Note thatModel (5), the xed effects model, unexpectedly performs better atthe 10% signi cance level. This may be caused by the imprecisesetting of the WHSs, which are assumed to have constant effects.When the varying effects of WHSs are considered later, all therandom effects models are found to perform better.

In Model (3), the number of WHSs has a signi cantly positiveeffect on international tourist arrivals. That is, adding one WHSwould on average increase the number of international tourist ar-rivals by 382,637 in just one year after controlling other variables.Thus, this positive effect proves that a country possessing moreWHSs would promote international tourism, not only for somespeci c countries but for the whole world. Moreover, possessingmore WHSs increases the international tourism demand, whichalso brings in relatively more tourist expenditures to the tourism-related industries, such as accommodation, transportation oreven retail outlets located around the site. These industrial linkageswill generate several times the revenue earned from the visits tothe WHSs themselves.

In Model (6), both the cultural and natural WHSs have signif-icantly positive effects on the number of international tourist ar-rivals when other variables are controlled. Increasing the numberof cultural sites by one would create an additional 396,659 in-ternational tourist arrivals, while adding one more natural sitewould increase international tourist arrivals by 418,606, which ison average 21,947 more tourist arrivals than for an additional

cultural one. To sum up, both the cultural and natural world her-itage sites could enhance international tourism, and the effect isgreater for the natural world heritage sites than for the culturalones.

Models (3) and (6) assume that the marginal effect of WHSs isconstant, which is quite a simpli cation. However, the marginal

Table 2Estimated results of international tourist arrivals (with constant effects of WHSs).

(1)Pooled OLS

(2)Fixed effects

(3)Random effects

(4)Pooled OLS

(5)Fixed effects

(6)Random effects

WHS 533384.15***[11.77]

89416.14[0.64]

382637.04***[4.69]

CULTURAL 563357.95***[10.79]

15548.61[0.09]

396658.60***[4.16]

NATURAL 637057.13***[4.57]

292458.01[1.07]

418605.71**[2.10]

Control var. Yes Yes Yes Yes Yes YesConstant 6461538.15***

[ 3.54]3396688.79[1.43]

3148908.15[ 1.41]

6531921.78***[ 3.60]

3631736.20[1.53]

3099144.91[ 1.38]

Be P test 116.26***( p-value 0.000)

111.78***( p-value 0.000)

Hausman test 22.00( p-value 0.143)

25.27*( p-value 0.089)

R-square 0.815 0.539 0.737 0.818 0.547 0.739Chi-square 461.239*** 460.099***Observations 359 359 359 359 359 359

1. t statistics are in parentheses.

2. *, ** and *** denote signi cance at the 10%, 5% and 1% statistical levels.



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effect may vary according to the number of WHSs. That is, the effectof WHSs on international tourist arrivals may differ betweencountries with an abundant supply of WHSs and countries with fewWHSs. Thus, to better understand the marginal effect of WHSs for aspeci c range of numbers, we have divided our sample into threeand ve equal parts. Still, the pooled OLS, xed effects and randomeffects models of Eq. (4) are estimated. The pooled OLS model istested for heteroskedasticity by B e P test, and the latter two modelsare judged using the Hausman test. According to the test results, allthe random effects models, i.e., Model (9), (12), (15) and (18), aremore accurate ( Table 3 ).

In Model (9), just like in the previous analysis, the marginaleffectof the WHSs is positive. Moreover, this positive effect declinesas the number of WHSs rises. For countries possessing 0 e 5, 6e 11and more than 12 WHSs, the marginal effects of WHSs are around692, 495, and 408 thousand, respectively. The positive effect of WHSs on international tourist arrivals is larger in countries withfewer WHSs. This result is quite reasonable. For WHS-poor coun-tries, once unknown sites become famous after being included onthe WHS list, this will attract more visits from international tour-ists. On the contrary, for WHS-abundant countries, which alreadypossess many attractions and are famous in the global tourismmarket, adding one more WHS will result in a smaller increase ininbound tourists than for WHS-poor countries.

Correspondingly, in Model (12), both cultural and natural siteshave positive effects on international tourist arrivals. These effectsalso decrease as the number of cultural and natural WHSs in-creases. The marginal effects are 562, 490 and 402 thousand forcountries with 0 e 5, 6e 11 and more than 12 cultural WHSs,respectively. Meanwhile, for countries with 0 e 3 and more than 4natural WHSs, the marginal effects are 514 and 419 thousand,respectively. Note that because the sample size of natural WHSs isrelatively small, it is divided into two equal parts only.

To understand the marginal effects of WHSs in more detail, thesample is also divided into ve equal parts. In Models (15) and (18)of Table 4 , the effects of WHSs, cultural WHSs and natural WHSs arestill positive. Moreover, the pattern of the decreasing marginal ef-fects as the number of WHSs increases is almost the same. How-ever, after controlling for more WHSs, the marginal effects of WHSsfor WHS-abundant countries (possessing more than 21 WHSs) in-crease instead. This increase means that when a country possessessuf cient WHSs, the gearing effect of WHSs will emerge. Forcountries possessing 0 e 3, 4e 6, 7e 10, 10 e 20 and more than 21WHSs, the marginal effects of WHSs decrease from 975, to 580, 498and 375 thousand, and rise slightly rise to 475 thousand, respec-tively. Similarly, the marginal effects of cultural WHSs are 776, 361,373, 285 and 509 thousand, respectively. Meanwhile, the marginaleffects of natural WHSs for countries with 0 e 3 and more than 4natural WHSs are 514 and 419 thousand, respectively. In addition,compared with Models (9), (12), (15) and (18), Model (15) with thehighest R-square of 0.749, is a relatively accurate model. Based onModels (15) and (18), Fig. 6 shows how these marginal effects of WHSs vary based on the number of WHSs, and the U-curve of theeffect is quite obvious.

We also considered the quadratic form of the WHSs when a U-curve resulting in S 5 was observed, but the estimated results of the quadratic form are not good enough. A possible reason for thebadly-performing quadratic form is that, in Table 4 , the coef cientsare not very smooth so that the quadratic form cannot capture thepattern well. Therefore, dividing the data into 5 groups createsmore exibility to the varying coef cients and ts the model better,even though the number of coef cients needed to be estimatedincreases.

In addition, the other explanatory variables merit discussion.Among Models (15) and (18), the coef cients of the variables arequite similar, regardlessof the number of WHSs or the cultural and

Table 3Estimated results of international tourist arrivals (with 3 variant effects of WHSs).

(7)Pooled OLS

(8)Fixed effects

(9)Random effects

(10)Pooled OLS

(11)Fixed effects

(12)Random effects

WHS (0e 5) 649675.36***[3.42]

471627.62***[2.34]

691823.08***[4.30]

WHS (6 e 11) 524222.32***[6.37]

261853.32*[1.74]

494946.73***[5.13]

WHS (12 up) 552693.31***[11.03]

148088.03[1.07]

408442.34***[4.91]

CULTURAL (0e 5) 343297.35[1.60]

328116.38[1.44]

561704.25***[2.96]

CULTURAL (6 e 11) 415442.66***[4.64]

149397.07[0.78]

489724.36***[4.35]

CULTURAL (12 up) 574952.78***[10.33]

45192.87[0.26]

402313.64***[4.14]

NATURAL (0e 3) 1245048.05***[4.24]

431978.19[1.39]

514498.16*[1.88]

NATURAL (4 up) 525079.02***[3.59]

319811.62[0.85]

419242.31*[1.82]

Control var. Yes Yes Yes Yes Yes YesConstant 6738359.51***

[ 3.50]1631441.45[0.67]

4313752.97*[ 1.87]

6414358.53***[ 3.40]

2276009.67[0.93]

3789343.37[ 1.63]

Be P test 132.97***( p-value 0.000)

165.27***( p-value 0.000)


23.03( p-value 0.288)






8/13

natural sites that are included in the model. In Model (15), for

example, the marginal effect of GDP is positive, which means thattourism demand bene ts from economic development. When theGDP of the destination country ( GDP ) increases by 1 billion USD,international tourist arrivals will increase by 1192. Meanwhile, thesigni cantly negative effect of the population ( POP ) indicates that,when other things are equal, international tourists would like totravel to destinations with fewer people or smaller country size.For instance, among countries with the same numbers of WHSs,economic achievements, and infrastructure, etc., internationaltourists would be more likely to choose those smaller in size orthat are less crowded. This kind of destination may be easier totravel around or more suitable for short vacation arrangements. Inaddition, railway lines ( RAIL) have a positive effect on interna-tional tourist arrivals, with the number of international tourist

arrivals increasing by 102 for each extra kilometer that the

Table 4Estimated results of international tourist arrivals (with 5 variant effects of WHSs).

(13)Pooled OLS

(14)Fixed effects

(15)Random effects

(16)Pooled OLS

(17)Fixed effects

(18)Random effects

WHS (0e 3) 717354.26***[2.38]

759195.41***[2.86]

975394.75***[4.15]

WHS (4e 6) 585643.22***[3.95]

346695.08*[1.91]

580418.01***[4.06]

WHS (7 e 10) 531494.95***

[5.83]

254907.28*

[1.64]

498169.26***

[4.61]WHS (11 e 20) 299724.16***

[4.54]107881.51[0.76]

356668.67***[4.01]

WHS (21 up) 716652.88***[13.62]

184221.66[1.27]

474846.97***[5.42]

CULTURAL (0e 3) 214136.49[0.86]

446149.55[1.59]

776329.93***[3.29]

CULTURAL (4e 6) 389665.43***[2.75]

33589.64[0.16]

360513.45***[2.34]

CULTURAL (7 e 10) 289594.82***[3.21]

45539.87[ 0.23]

373257.65***[3.08]

CULTURAL (11e 20) 354390.56***[5.20]

129725.75[ 0.73]

284574.55***[2.75]

CULTURAL (21 up) 913255.68***[12.23]

131702.65[0.79]

508761.04***[5.16]

NATURAL (0e 3) 1259993.21***

[4.52]

599675.81**

[2.13]

750817.04***

[2.93]NATURAL (4 up) 439219.79***[3.26]

158265.28[ 0.44]

298612.05[1.34]

GDP 1011.06***[4.09]

3912.38***[2.73]

1191.96***[2.36]

351.46[1.29]

3096.00**[2.17]

1026.69**[1.99]

POP 22989.06***[ 7.83]

25653.01[ 0.76]

14762.50***[ 3.31]

18369.94***[ 6.30]

22490.11[ 0.69]

11419.79***[ 2.46]

EX 392.67***[ 2.96]

158.20[ 1.02]

102.65[ 0.81]

514.67***[ 3.85]

149.48[ 1.00]

115.16[ 0.92]

RAIL 168.28***[10.78]

47.84*[1.83]

102.34***[6.07]

206.62***[11.49]

61.58***[2.39]

109.33***[6.56]

FREEDOM 167820.66[1.18]

460859.81***[ 2.37]

217766.63*[ 1.69]

258111.45*[1.80]

518235.42***[ 2.69]

273159.23*[ 1.64]

HEALTH 200791.39[ 1.48]

516818.85***[3.00]

308048.78**[2.01]

157775.79[ 1.16]

359633.58**[2.14]

231209.95[1.52]

EDU 354455.52***[7.00]

51271.76[ 1.17]

1071.33[ 0.03]

332053.13***[6.65]

48415.31[ 1.14]

7087.38[ 0.17]

AREA Yes Yes Yes Yes Yes YesYEAR Yes Yes Yes Yes Yes YesConstant 6039499.56***

[ 3.12]1659178.51[0.68]

4300985.68*[ 1.85]

6256850.52***[ 3.46]

4221109.45*[1.75]

2887056.58[ 1.23]

Be P test 181.48***( p-value 0.000)

223.73***( p-value 0.000)


11.37( p-value 0.955)


1. t statistics are in parentheses.2. *, ** and *** denote signi cance at the 10%, 5% and 1% statistical levels.

0

400

800

1200

1600

0-3 4-6 7-10 11-20 20 upNumber of WHSs

WHSs Cultural WHSs Natural WHSs

T o u r

i s t a r r

i v a

l s ( 1 0 0 0 )

Fig. 6. Marginal effects of WHSs, cultural WHSs and natural WHSs.



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railroad route is extended. It thus makes sense that the destina-tion will be more attractive to international tourists when thebasic transportation is more convenient. The political and civilfreedom ( FREEDOM ) variable negatively affects the tourism de-mand at the 10% signi cance level. When the index of freedom,measured on a one-to-seven scale, increases by one (becomingless free), the number of international tourist arrivals declines by217,767. That is, freer countries attract more international tourists.In addition, the health expenditure share of GDP ( HEALTH ) has apositive in uence. When a country spends more money on health,say, 1% of GDP, it will improve the sanitary conditions and increaseits inbound tourist visits by 308,048.

The effects of other controlled variables, namely, the exchangerate ( EX ) and the expenditure proportion of education ( EDU ), areinsigni cant in Model (15). However, it should be noted that thesign of the exchange rate is negative, which means thatincreasing the relative price will make the number of interna-tional tourist arrivals drop. Thus, the price effect of tourism ex-ists, even though the coef cient is insigni cant at the 10%signi cance level.

4.2. Comparison of regions and time periods

The behavior of tourists may vary in different destinations, andsome effects may also change over time. Therefore, based on Model(15), we separate our observations according to the region and thetime period to reveal more details. This further research may alsobe seen as a robustness check of our model, especially for WHSvariables. The estimated results of eight models are classi ed

according to four regions in Table 5 : Africa, Asia, Europe andAmerica, and four time periods in Table 6 : 2000 e 2001, 2003 e 2003,2004 e 2006 and 2007 e 2009.

Three things should be noted. First, to avoid the small sampleproblem, we combine the Asia Paci c with the Middle East as the Asia group, and combine North America with South America as the America group. Second, because the numbers of WHSs of Africancountries are all below ten, the coef cients of WHS (11 e 20) andWHS (21 up) are eliminated in Model (19). Third, in mid-2003, theSARS epidemic occurred, especially in China, Singapore and Can-ada. Later, in 2009, the H1N1 epidemic also occurred in severalcountries. These two dummy variables of SARS (for countrieswhose con rmed cases were over 200 in 2003) and H1N1 (forcountries whose con rmed cases were over 5000 in 2009) enterour models to control for the effect of these diseases in the rela-tively small sample.

Even though the coef cients of the WHSs are not quite constantbetween Models (19) and (26), their marginal effects are all posi-tive. After controlling other explanatory variables, the estimatedresults show that the positive effect of WHSs is quite robust so thatthe sign would not change for the different subgroups. In Section4.1, we know that as the number of WHSs increases, the marginaleffect declines, and then rises after a country possesses suf cientWHSs. However, in Models (19) e (22), this U-curve is notobvious ineach region. For each region, the marginal effects of the WHSsexhibit different patterns. In Africa, where the numbers of WHSsare all below 10, the marginal effect is less for WHS-poor countriesthan for WHS-rich countries. In Asia, international tourists aremainly attracted by countries possessing fewer than 10 WHSs. On

Table 5Estimated results of international tourist arrivals (by region).

(19)Africa

(20)Asia

(21)Europe

(22)America

WHS (0e 3) 311945.43[1.48]

569240.22[1.21]

1556751.62***[3.48]

1491710.19[0.59]

WHS (4e 6) 700653.37***[5.26]

306748.19[1.18]

895027.86***[4.09]

368894.77[0.31]

WHS (7 e 10) 668600.19***[8.44]

578652.66***[2.35]

633983.70***[4.54]

601731.02[1.50]

WHS (11 e 20) e 27387.03[0.18]

428506.56***[3.38]

1309071.96***[3.57]

WHS (21 up) e 52326.23[0.31]

465289.26[1.41]

1133299.43***[5.19]

GDP 6097.11[ 0.30]

350.46[0.63]

4338.24[ 0.86]

4629.76***[2.73]

POP 21793.65[ 0.57]

15756.36***[ 4.51]

455643.96***[6.01]

108256.42***[ 2.46]

EX 102.11[0.52]

134.88[ 1.06]

1130.06[1.00]

1709.17[0.43]

RAIL 226.39***[2.61]

245.68***[4.93]

278.66*[1.72]

43.47[0.58]

FREEDOM 88542.01[ 0.80]

11224.13[ 0.05]

1270677.58***[ 3.07]

1696149.2[ 0.54]

HEALTH 119010.42[0.60]

271410.15[ 1.10]

450324.73**[2.04]

689745.98[ 0.95]

EDU 71233.96[1.53]

78745.46[1.02]

107376.01[0.79]

522070.2[ 1.34]

AREA No No No NoYEAR Yes Yes Yes YesConstant 2510878.64

[ 1.56]92106.25[0.03]

2528566.8[ 1.11]

19603323.34[1.14]

R-square 0.956 0.542 0.891 0.997Chi-square 719.79*** 109.96*** 1003.18*** 5060.39***Observations 53 126 145 35





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the contrary, in Europe and America, the marginal effects of WHSsare found to be U-shaped, but the turning points are different. InEurope, the turning point occurs when the number of WHSs rangesbetween 11 and 20, while in America, the turning point is located inthe 4 e 6 WHSs group. Even though the individual pattern in eachregion is different, the overall U-shaped pattern can be observed inFig. 7(A).

Moreover, in Models (23) e (26), which areclassi edby fourtimeperiods, the U-shaped features of the marginal effects of the WHSsare obvious. The marginal effects are particularly large in WHS-poor and WHS-abundant countries, and they are small betweenthese two groups. This pattern is also displayed in Fig. 7(B). Basedon the time periods, these sub-samples reveal that our model isquite robust.

Table 6Estimated results of international tourist arrivals (by time period).

(23)2000 e 2001

(24)2002 e 2003

(25)2004 e 2006

(26)2007 e 2009

WHS (0e 3) 479370.55[0.08]

403720.74[1.05]

613022.27*[1.65]

1227144.45***[4.47]

WHS (4e 6) 411201.49*[1.82]

394058.86[1.32]

442751.21*[1.92]

309651.41[1.50]

WHS (7 e 10) 343859.20**[2.09]

307086.84**[2.08]

510984.50***[2.64]

284435.48*[1.70]

WHS (10 e 20) 360265.29***[2.54]

245966.27*[1.88]

271689.13*[1.70]

224635.71[1.48]

WHS (21 up) 376666.37***[2.77]

542954.80**[4.32]

834250.42***[5.95]

909319.29***[4.93]

GDP 685.57[1.04]

828.67[1.47]

853.48[0.91]

711.92[ 0.64]

POP 18545.22***[ 3.36]

21720.38***[ 4.94]

4842.39[ 0.27]

175.16[ 0.01]

EX 1047.28[ 0.90]

475.25[ 1.38]

344.05[ 0.81]

193.14[ 0.75]

RAIL 179.00***[3.73]

162.64***[3.94]

52.80***[3.69]

318.41***[4.22]

FREEDOM 256215.71[ 0.63]

527021.99[ 1.43]

13912.68[ 0.04]

123137.59[ 0.65]

HEALTH 311163.79[ 0.78]

340883.73[ 1.36]

2511.2[0.01]

82209.48[ 0.62]

EDU 24163.51[0.32]

106243.45*[ 1.91]

64066.16[1.11]

50353.33[ 1.04]

SARS 2306389.69***[ 3.72]

H1N1 359981.67[ 0.57]

AREA Yes Yes Yes YesYEAR No No No NoConstant 3827725.47

[0.58]2939585.09[0.81]

2011503.17[ 0.57]

2696260.86[ 0.86]

R-square 0.825 0.889 0.839 0.782Chi-square 170.96 262.29 207.1 188.65Observations 72 70 120 97



0

400

800

1200

1600

0-3 4-6 7-10 11-20 20 upNumber of WHSs

Africa Asia Europe America

(A)

0

400

800

1200

1600

0-3 4-6 7-10 11-20 20 upNumber of WHSs

2000-2001 2002-2003 2004-2006 2007-2009

(B)

T o u r

i s t a r r

i v a

l s ( 1 0 0 0 )

T o u r

i s t a r r

i v a

l s ( 1 0 0 0 )

Fig. 7. Marginal effects of WHSs (by region and time period).



11/13

In addition, as for the control variables, the signi cant effectindicates that regional differences exist in Tables 5 and 6. Theeffect of population ( POP ) in Table 5 , for example, changes fromnegative to positive for Europe. The source of this negative effectof POP may be the Asian countries. The larger countries in Asia,such as China or Russia, are relatively poor in terms of security andsanitation. Thus, international tourists may become hesitant toselect them as their rst choice. On the contrary, Europe is themost frequently visited region in the world, according to theUNWTO. The wealth of European cultures, the variety of itslandscapes and the exceptional quality of its tourist infrastructureare likely to be among the reasons why tourists choose to taketheir holidays in Europe. In 2009, ve of the top ten countries fortourists in the world were in Europe, namely, France, Spain, Italy,

the United Kingdom and Germany, which are the relatively largeand more populated countries in Europe. Thus, the common factorof these countries, which is not captured by any other controlvariables, is captured by the variable POP , which causes the coef-

cient to be positive.Moreover, the two global epidemics, SARS and H1N1, have

indeed had negative impacts on international tourism worldwidein the last ten years. The SARS epidemic resulted in a signi cantreduction of around 2.3 million international tourists in 2003,while the H1N1 outbreak was insigni cant, with the number of international tourists being reduced by about 359,982 in 2009. Inaddition, it should be noted that the size of the subsampleused to perform the robustness check is relatively small so thatthe coef cients would become more sensitive than those in

Model (15).

4.3. Evaluation of the economic contribution of WHSs

In this section, we employ our model to calculate the contribu-tion of newly-inscribed WHSs to destination countries. According tothe World Heritage Center, the latest list of newly-inscribed WHSsre ects the 2011 vision. Table 7 lists these newly-inscribed WHSsand their related economic contributions. In Table 7 , the marginaleffect comes from our model, while the average receipt is calculatedby dividing the total tourism receipts in 2009 by the internationaltourist arrivals in 2009, based on data provided by the WorldDevelopment Indicators. Note that according to the number of WHSs in each country, our model proves that the marginal effect of WHS on international tourist arrivals differs from country to coun-try. In addition, these 25 countries are divided into two groups. The

rst is the in-sample country, which is included in the 66 countriesof our panel data, while the other is the out-of-sample country.The contributionof WHSs is obtained by multiplying themarginal

effect of WHSs by the average receipts (expenditure) of inboundtourists in destinationcountries.Because the buyingpowerand travelcosts are different, the average receipts vary across countries, and sodo the contributions of WHSs. Australia, for example, possesses 19WHSs, and the marginal effect of WHSs on international tourist ar-rivals is around 375,000. However, the average receipt from inter-national tourists is quite high, amounting to 4990 USD per person.The forecasted contribution of this newly-inscribed WHS is about1781 million USD.Comparativelyspeaking, eventhoughthe marginaleffect of WHSs is higher in China thanin Australia, thecontribution of WHSs is lower, or around 398 million USD, because the average

receipt is much lower than for other countries. In addition, the

Table 7Contribution of newly-inscribed WHSs in 2011.

Newly-inscribed WHSs in 2011 Type Country # of WHSs

Marginal effect of WHSs (1000)(A)

Averagereceipt (USD)(B)

Contribution of WHSs (million USD)(A) (B)

In-sample countries Petroglyphic Complexes of the Mongolian Altai C Mongolia 3 975 616 600 Coffee Cultural Landscape of Colombia C Colombia 7 498 1244 620

Selimiye Mosque and its Social Complex C Turkey 10 498 965 480 The Persian Garden C Iran 13 357 1136 405 Hiraizumi (Temples, Gardens and ArchaeologicalSites Representing the Buddhist Pure Land)

C Japan 16 357 1846 659

Ogasawara Islands N Japan 16 357 1846 659 Ningaloo Coast N Australia 19 357 4990 1781 Fagus Factory in Alfeld C Germany 36 475 1959 931 The Causses and the Cvennes, Mediterraneanagro-pastoral Cultural Landscape

C France 37 475 763 362

West Lake Cultural Landscape of Hangzhou C China 41 475 838 398 Cultural Landscape of the Serra de Tramuntana C Spain 43 475 1141 542 Longobards in Italy. Places of the Power (568 e 774 A.D.) C Italy 47 475 970 461

Out-of-sample countries Cultural Sites of Al Ain (Ha t, Hili, BidaaBint Saud and Oases Areas)

C United ArabEmirates

1 975 1032 1006

Historic Bridgetown and its Garrison C Barbados 1 975 2162 2108 Archaeological Sites of the Island of Meroe C Sudan 2 975 712 694 Len Cathedral C Nicaragua 2 975 358 349 Wadi Rum Protected Area M Jordan 4 580 916 531 Residence of Bukovinian and Dalmatian Metropolitans C Ukraine 5 580 209 121 Ancient Villages of Northern Syria C Syrian Arab

Republic6 580 621 360

Fort Jesus, Mombasa C Kenya 6 580 807 468 Kenya Lake System in the Great Rift Valley N Kenya 6 580 807 468 Saloum Delta C Senegal 6 580 542 314 Citadel of the Ho Dynasty C Vietnam 7 498 814 405 Konso Cultural Landscape C Ethiopia 9 498 3391 1689

1. C: cultural site, N: natural site, M: mixed site.2. Missing data of tourist arrivals in 2009: Iran, Ethiopia (replaced by 2008 data), Senegal (replaced by 2007 data), United Arab Emirates (replaced by 2005 data).



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economic contributionof WHSs canbe seenas thelowerboundof theincrease in tourism income while other control variables are un-changed. That is, after considering the changes in other variables,such as economic growth, the improvement of transportation orincreased political liability, the number of inbound tourists will in-crease further, and bring in more income from tourism. Thus, theauthorities of the destination countries could refer not only to theresult, but also to the method in order to evaluate the economiccontribution of WHSs and to budget for their conservation.

5. Conclusion

In this paper, we investigate the positive relationship betweenthe WHSs and international tourist arrivals at the worldwide levelusing panel data for 66 countries between 2000 and 2009. We alsostudy the effect of new inscriptions on the World Heritage List, andobserve how this in uence changes over time.

According to theestimated results, a country possessing onemoreWHS would increase its annual international tourist arrivals by382,637. While both cultural and natural WHSs could enhance theinboundtourism, the effect of naturalWHSs is slightly larger than theeffect of cultural ones. The marginal effects of WHSs on internationaltourist arrivals are 396,659 million and 418,606 million for culturaland natural WHSs, respectively. Moreover, in considering that themarginal effect may vary with the number of WHSs, we divided oursample into three and ve equal parts to better understand themarginaleffectofWHSs fora speci c rangeof numbers.Afterdividingourdata into ve equalparts,the positive effectof WHSs wasfoundtodecline while the number of WHSs actually rose. However, when acountry possesses suf cient WHSs, this effect increases slightly. Theeffect of WHSs exhibits a U-shaped pattern as the number of WHSsincreases. In addition, for each region, the marginal effects of WHSsdemonstrate differentpatterns, butour results remainquite robustindifferent time periods.

As for WHS-poor countries, once unknown sites become famousafter being inscribed on the WHS list, there will be more visits from

international tourists. On the contrary, for WHS-rich countries,which already possess many attractions and are famous in theglobal tourism market, adding one more WHS would lead tosmaller increases in inbound tourists than in the case of WHS-poorcountries. However, for the WHS-abundant countries (possessingmore than 21 WHSs), the marginal effects of WHSs increaseinstead. This increase means that when a country possesses suf -cient WHSs, the gearing effect of WHSs will emerge.

Tosumup,increasing thenumber ofWHSs will have a signi cantlyandrobustlypositive effect on international touristarrivals. Therefore,a country possessing a WHS is in a win-win situation not only for thesustainable conservation of cultural achievements and natural re-sources, but also for the development of the tourism economy.Moreover,we could saythat thesetwo purposesare notcontradictory,

but rather complementary. It is because conservation is the only wayto maintain sustainable tourism income from WHSs, andthistourismincome is indispensable for the further preservation of WHSs.

More informationwillbe gainedafterextending thetime span orthe cross section of available data associated with WHSs. Further-more, extending the data will make the regional analysis moremeaningful and reliable. Even though the positive impact of WHSson international tourist arrivals does not change across regions ortime periods in our paper, the coef cient itself is different and de-serves further study.In addition, we use internationaltourist arrivalsas the dependent variable, which captures the international tourismdemand. However, a gap between tourist arrivals and tourist in-comes may exist, because the consumption behaviorof touristsmaydiffer across countries. Thus, the exchanges between tourist arrivals

andincomes,compared with thecostsof maintaining WHSs, should

be explored using cost-bene t analysis. These topics are thus bothimportant and interesting for further research.

Appendix A. 66 countries whose data were used (inalphabetical order)

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Argentina Armenia Australia Austria Azerbaijan BangladeshBelgium Benin Brazil Bulgaria Cameroon CanadaChile China Colombia Croatia Czech

RepublicDenmark

Egypt Estonia Finland France Georgia GermanyGreece Hungary Indonesia Iran Ireland IsraelItaly Japan Korea Kyrgyzstan Latvia LithuaniaMadagascar Malaysia Mali Mexico Moldova MongoliaMorocco Netherlands Norway Pakistan Peru PhilippinesPoland Portugal Romania Russia Saudi Arabia SlovakiaSlovenia South Africa Spain Sweden Switzerland ThailandTunisia Turkey Ukraine U.K. U.S. Uruguay

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Yu-Wen Su received Ph.D. from National Taiwan Univer-sity (NTU). Her research focuses on econometrics, timeseries analysis and tourism economics.

Hui-Lin Lin is Professor of Economics, NTU. She receivedPh.D. from Brown University. Her research focuses oneconometrics, industrialeconomics and tourismeconomics.


analysis of international tourist arrivals worldwide

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