The Impact of Distance on International Tourist Movements

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DOI: 10.1177/0047287508321191

2008 47: 208 originally published online 8 July 2008Journal of Travel Research Bob McKercher, Andrew Chan and Celia Lam

The Impact of Distance on International Tourist Movements

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208

The Impact of Distance on International Tourist

Movements

Bob McKercherAndrew Chan

Celia LamThe Hong Kong Polytechnic University

This article examines the impact of distance on global tourist flows through an analysis of departing visitor share from 41

major source markets to 146 destinations. The study concludes that 80% of all international travel occurs to countries within

1,000 kilometers of the source market and that, with few exceptions, distant destinations have great difficulty attracting

more than a 1% or 2% share of departures. However, high volatility in share within each distance cohort was also noted.

Regression analysis of variation in share by distance suggests that market access and the level of tourism development

within a destination distort movement patterns regardless of distance. Relationship variables played an important role in

short-haul travel; a mix of source, destination, and relationship characteristics influence travel to medium haul destinations;and destination attributes influence share at long-haul destinations.

Keywords: distance decay; tourist flows

Introduction

This paper examines the impact of distance on inter-

national tourism movements. It has two broad goals.

First, the validity of the distance decay concept is tested

at a generalizable level. In doing so, source markets thatshow divergent movement patterns are identified, and the

reasons for this divergence are proposed. Second, individ-

ual origin-destination relationships are examined within

discrete distance parameters to test the universality of dis-

tance decay. Arrival data for 1,915 origin-destination pairs

are analyzed, representing 41 major outbound markets vis-

iting 146 destinations. The data account for 77.3% of

global tourism in 2002.

Distance decay theory suggests that demand for any

good or service should decline exponentially as distance

increases. It is thought to play such an important role in all

spatial relationships that it has been identified as the first

law geography (Eldridge and Jones 1991). It can, therefore,

be considered a universal construct. The idea has been

tested empirically in various tourism settings and found to

be broadly applicable (Baxter 1979, Greer and Wall 1979,

Hanink and White 1999, Kerkvliet and Nowell 1999,

McKercher and Lew 2003, Paul and Rimmawi 1992,

Zhang et al. 1999). However, these studies usually involved

small samples in single locales. No systematic analysis has

been undertaken using global tourism movement data to

test its validity; hence, the first objective.

In addition, if distance decay is indeed a “law,” then

one could argue that it is a deterministic factor that

affects destination choice. All destinations located in

close proximity to source markets should have an inher-ent advantage over more distant destinations. When mea-

sured by share, therefore, all proximate destinations

should record higher shares than any more distant one.

Such an assertion seems intuitively illogical, for it ignores

the effect of differences in market appeal, tourism infra-

structure, level of development, ease of entry, and a host of

other factors affecting tourism flows. It further contra-

dicts contemporary thinking about destination choice

that sees it as a complex and often messy process involv-

ing the consideration of a bundle of tangible and intangi-

ble attributes (Sirakaya and Woodside 2005), with many

contextual influences (Decrop and Snelders 2005),where the ultimate choice represents the means by which

consumers can gain the multiple benefits they seek

(Klenosky 2002). Yet, if true, then it should apply in all

situations. Intervening factors may moderate its impact,

but the underlying pattern should still be present. Hence,

the second objective tests whether distance decay can be

considered as a deterministic variable at an individual

origin-destination relationship level.

Journal of Travel Research

Volume 47 Number 2

November 2008 208-224

© 2008 Sage Publications

10.1177/0047287508321191

http://jtr.sagepub.com

hosted at

http://online.sagepub.com








McKercher et al. / The Impact of Distance on International Tourist Movements 209

Context

Distance decay is widely used in many areas. Tobler

(1970, p. 236) called it the first law of geography,

observing that “everything is related to everything else,

but near things are more related than distant things.”

Thus, the “gravitational” pull of a proximate place is

higher than that of a more distant location resulting in

observed differences in the level of demand. And, as with

other gravitational effects, the impact is geometric. It has

been used in such diverse fields as biology, ecology,

location planning, and attractiveness measurement.

Poulin (2003) examined the rate at which similarity of

parasites decayed as distance increased. Nekola and

White (1999) looked into the distance effects on similar-

ity of plants, Farhan and Murray (2006) used it in plan-

ning the location of a facility-like stations and shops, and

Drezner (2006) examined the relationship between the

attractiveness of a shopping mall and the distance.McKercher and Lew (2003), summarizing the tourism

literature, note that distance decay was popular in tourism

research in the 1960s and 1970s, when it was used as a

proxy for forecasting. However, it fell into disuse as more

sophisticated forecasting techniques were developed.

Today, relatively little attention is devoted to this issue as

a discrete topic in the tourism literature, yet, distance prin-

ciples are implicit in the modeling and forecasting of

tourist flows through such proxy variables as the cost of

travel. The studies cited above found it to be broadly

applicable, but the shape of the curve and the rate of decay

seemed to be modified by a range of source and destina-tion considerations as well as the relationship between

markets and destinations.

Greer and Wall (1979) observed that the type of trip

undertaken influences the decay rate—short duration trips

experienced the steepest curve, while longer duration trips

tended to display more of a plateauing curve. Paul and

Rimmawi (1992) add further that a large source population

located some distance from the destination may provide a

disproportionately large volume of visitors, appearing to

distort the impact of distance on demand when raw

numbers are considered. The authors address this concern

by calculating share of total departures rather than absolutevolume. Hanink and White (1999) note that the appeal of

the destination or the quality of the asset will also affect

demand, with higher quality places having greater per-

ceived value and generating a proportionately higher share

of visitors per unit of distance traveled.

The relationship between the origin and possible des-

tinations seems to exert the greatest impact on changing

rates of demand over distance. Both Fotheringham

(1981) and Eldridge and Jones (1991) suggest that the

rate of decay is a function of the overall spatial structure

of the receiving area. “Space warping,” in which equiva-

lent distances have spatially uneven effects on interac-

tion, can occur because of differences in the number of

intervening destination opportunities. The decaying

effect can be accelerated when few intermediate oppor-

tunities exist, whereas an extended decay relationship

can exist when a large number of relatively nearby desti-

nation opportunities are present. In addition, cultural dis-

tance, the degree of cultural similarity between the origin

and destination, will also influence movements (Hanink

and White 1999, Smith and Xie 2003), with culturally

similar destinations attracting more visitors than cultur-

ally distant ones. Indeed, cultural dissimilarity can

be a major inhibiting factor for travel to proximate

destinations.

Three types of tourism-oriented distance decay relation-

ships have been identified (figure 1). The classic curve sug-

gests that demand peaks close to the origin and then

declines exponentially as the perceived costs of travel dis-tance and time increase (Bull 1991). McKercher (1998a)

identified a second plateauing pattern. The dampening

effect was caused by a limited number of destination

choices located along a linear touring route that resulted in

the dispersal of demand over a longer distance. Demand

fell only after a certain distance threshold was reached.

McKercher and Lew (2003) identified a third pattern with

a secondary peak or tail located at some large distance

from the source market. This model recognizes that some

very attractive, but distant, destinations may have such

great market appeal that their pulling power supersedes the

normal frictional effect of distance, thus distorting thedecay curve.

Their study also identified the existence of an

Effective Tourism Exclusion Zone (ETEZ), an area

where effectively no tourism activity occurs. It can be

comprised of spatial (i.e., oceans, unpopulated areas) or

product voids in which prospective destinations offer

little of interest to the source market. They speculated

further, but did not test empirically, that the existence,

proximity, and width of the ETEZ could distort tourist

movements. A large ETEZ in close proximity to a source

market could dampen international travel propensity

overall due to the high costs and time required to cross it.It would also effectively displace or shift the starting

point of the decay curve to the outer boundary of the

zone, resulting in higher market shares for distant desti-

nations than would normally be expected if no ETEZ

was present. A large ETEZ located a moderate distance

from the source market, on the other hand, would tend to

concentrate tourism activity prior to its inception, pro-

ducing an abnormally high demand spike close to home.

There is also a high probability of a secondary peak

located after the outer boundary is crossed. Little impact








210 Journal of Travel Research

may be felt if the ETEZ is narrow or if it is located a

great distance from source markets.

The tourist literature suggests a relationship exists

between distance traveled and tourism behavior.

Nyaupane, Graefe, and Burns (2003) found a positive

relationship between distance and age of the tourists, des-

tination expenditure, and place attachment, but noted a

lower propensity to revisit. Yeoman and Lederer (2005)

further discuss the aspirational nature of much long-haul

travel and how it is viewed as a rare, often once-in-a-life-

time occurrence. Short-haul travel, by extension, is more

common, and the motives are more escapist or recreation-

oriented. Perhaps because of these factors, people choos-

ing nearby destinations focus on price and value for

money considerations when they buy, while those choos-

ing more distant places consider quality and product fea-tures (Lo and Lam 2005) and are less concerned about

price (Song and Wong 2003). Hwang and Fesenmaier

(2003) and Tideswell and Faulkner (1999) observed that

long-haul tourists engaged in multi-destination trips and

sought to have multiple trip purposes satisfied, while

short-haul tourists tended to take single destination, single

purpose trips. McKercher and Lew (2003) further note

that short-haul trips tend to be of a short break nature and

are often booked as part of a packaged tour, while long-

haul travel involves longer trip durations, a substantial

touring element, and are far more likely to be organized

independently.

Method

This study investigates international tourism move-

ments for the year 2002 between 41 source markets and

146 destinations. Market shares for 1,915 discrete origin-

destination pairs are analyzed. The census year was

selected because it is the last non-SARS (Severe Acute

Respiratory Syndrome) year where complete departure

and arrival statistics were available when the project

commenced. Outbound, or departure data were derived

from the United Nations (UN) World Tourism

Organization (WTO) Tourism Market Trend reports

(WTO 2004), while inbound or arrival data were sourcedfrom the UNWTO Annual Statistical Reports (WTO

2005). As such, countries are the unit of analysis.

The initial goal was to analyze movements from all

source markets that generated at least one million depar-

tures in 2002. Fifty-six markets met this criterion. But

for reasons discussed below, 15 markets were subse-

quently excluded, leaving the final sample of 41 out-

bound markets. Nonetheless, 444,884,000 international

overnight departures were registered by these markets,

while the destinations recorded a total of 543,715,000

Classic Decay Curve

DISTANCE FROM ORIGIN

V O L U M E

Plateauing Decay Curve


V O L U M E

Decay Curve with a Secondary Peak


V O L U M E

Figure 1

Distance Decay Curves








arrivals. These latter figures represent some 77.3% of the

703 million arrivals reported by UNWTO members in

2002 (WTO 2005). No formal record of departures is

maintained.

Shares were calculated by dividing the number of visi-

tors entering the destination from a source market (numer-

ator) by the total number of tourists departing from that

source market (denominator). The production of valid

share figures, therefore, was dependent on both the origin

and destination using the same method to count departures

and arrivals, respectively. However, departures and arrivals

can be measured in different ways. The majority of source

markets count overnight departures, while a small number

count all overnight and day trip departures in one figure.

Likewise, the majority of destinations record overnight

arrivals, while a small number do not differentiate between

same day and overnight visits. Unrealistically high share

figures will result if “all arrivals” forms the numerator and

“overnight departures” acts as the denominator.Unrealistically low figures will result when the opposite

case applies. Because most destinations count overnight

trips, reliable figures could not be generated for source

markets that did not distinguish between day and overnight

departures. Austria, Spain, the Czech Republic, Hungary,

Malaysia, Poland, Saudi Arabia, and Turkey count all

departures, and therefore, were excluded.

Seven countries—Iran, Chile, Morocco, Oman,

Sweden, Syria, and Tunisia—were also subsequently

excluded due to insufficient arrival data reported by des-

tinations. The study team set a minimum criterion that

70% of total outbound share had to be explained by theset of origin-destination pairs examined to gain an accu-

rate image of changes in demand over distance. This

proved impossible in the case of these seven countries, as

most destinations did not record discrete arrivals for

each. Instead, their arrivals were grouped with other

source markets. Aggregation may occur for one of two

reasons. In some cases, multiple sources from the same

area are thought to behave in a homogenous manner and

are, therefore, treated as a single entity. This situation

was especially notable in Scandinavia. In other cases,

aggregate arrivals can be reported because the source

provides so few visitors or is of so little strategic impor-tance that no benefit is gained from keeping separate

records. This situation was common when arrivals from

South America, Central Asia, and the Middle East were

reported.

The final sample of 41 source markets is shown in table

1. The table also identifies the proximity of the nearest des-

tination, the total number of origin-destination pairs ana-

lyzed, and the total share of departures represented by these

pairs. The sample includes countries from all inhabited

continents, with the largest number coming from Europe

(17) and Asia (13). The set includes both source markets in

the developed and developing worlds and a number of mar-

kets that Westerners would not normally associate with

large volumes of international tourists. The number of ori-

gin/destination pairs ranges from a low of 20 for Azerbaijan

and Kazakhstan, and 23 or 24 for Baltic states, to more than

80 for the United Kingdom, Canada, and the United States.


Table 1

Source Markets, Proximity of Nearest Neighbor and

Number of Origin-Destination Pairs Analyzed

Nearest Number of

Destination Origin-Destination Total Share

Source Market (km = kilometers) Pairs Analyzed Represented

Algeria Land neighbor 25 75.30Argentina Land neighbor 53 95.80

Australia 2,001-3,000 km 59 179.10

Azerbaijan Land neighbor 20 131.20

Belgium Land neighbor 54 248.30

Brazil Land neighbor 53 81.00

Bulgaria Land neighbor 30 84.00

Canada Land neighbor 83 107.40

China Land neighbor 44 90.70

Colombia Land neighbor 35 73.40

Denmark Land neighbor 57 164.40

Egypt Land neighbor 37 72.50

Estonia Land neighbor 23 95.40

Finland Land neighbor 55 103.90

France Land neighbor 76 183.80Germany Land neighbor 73 142.40

Hong Kong <1,000 km 29 149.90

India Land neighbor 46 94.60

Ireland Land neighbor 46 108.00

Israel Land neighbor 39 75.90

Italy Land neighbor 75 89.30

Japan <1,000 km 65 128.60

Jordan Land neighbor 28 76.80

Kazakhstan Land neighbor 20 143.60

Korea <1,000 km 42 104.50

Latvia Land neighbor 23 102.70

Lithuania Land neighbor 24 92.50

Mexico Land neighbor 55 114.90

Netherlands Land neighbor 63 180.60New Zealand 2,001 – 3000 km 48 126.10

Philippines <1,000 km 35 133.10

Romania Land neighbor 33 122.00

Russia Land neighbor 57 79.40

Singapore Land neighbor 35 256.00

Slovenia Land neighbor 29 86.80

South Africa Land neighbor 45 85.70

Switzerland Land neighbor 59 110.60

Taiwan <1000 km 31 79.40

Thailand Land neighbor 36 139.10

United Kingdom Land neighbor 80 110.50

United States Land neighbor 95 123.20









A median of 45 pairs was calculated for each source mar-

ket. The following section describes the selection process

for candidate destinations. The total outbound share ranges

from 75% to more than 250% of departures. The reasons

why shares may exceed 100% are explained below.

It was operationally impossible to include arrival figures

from all 204 UNWTO member countries and territories.

Many destinations simply do not record this information,

and others record it in a manner that is not compatible with

the denominator. In addition, so few people may visit that

the calculated market share, reported to two significant dig-

its, is effectively 0.00%. Consequently, protocols were

developed to select likely candidate destinations.

Destinations had to satisfy one or more of the following cri-

teria to be considered:

1. immediate land neighbors;

2. one of the 15 most popular destinations for each

source;3. one of the 20 leading global destinations, irre-

spective of the volume generated by a source

market; and

4. regionally significant destinations from each

continent.

These criteria ensured breadth of coverage. They are

not mutually exclusive. France, for example, is an imme-

diate land neighbor of Germany, is one of the Germany’s

15 most popular destinations, and also qualifies as one of

the world’s top 20 destinations.

Origin-destination pairs were grouped by distance fromthe source market for ease of analysis. Land neighbors are

identified as a separate category. After that, cases are

grouped in increments of 1,000 kilometers up to 10,000

kilometers from the source, and in 2,000 kilometers incre-

ments up to 14,000 kilometers away. A final “greater than

14,000 kilometers” category includes all other cases. The

numbers of source markets and origin-destinations pairs

represented within each distance cohort are shown in table

2, along with the volume of arrivals and share figures. The

names of source markets not represented in the cohort are

also reported. Not all sources are represented in every dis-

tance cohort. For example, only 34 source markets haveimmediate land neighbors that permit cross border travel.

Australia, Japan, New Zealand, the Philippines, and Taiwan

are islands. South Korea is deemed to have no immediate

land neighbors for tourism purposes, due to travel restric-

tions to North Korea. China now considers Hong Kong as a

domestic destination, and as such, does not report arrivals to

the UNWTO. Singapore and the United Kingdom, though,

actually do have direct land neighbors. Singapore is con-

nected to Malaysia through a vehicular causeway, while the

United Kingdom shares a land border with Ireland, and has

a direct, albeit underwater, link with France through the

Channel Tunnel. In other cases, sources may be excluded

because large destinations may cover more than one dis-

tance category. For example, Canada has no destinations

within 1,000 kilometers, because of the size of the United

States. And, as mentioned in other instances, separate

arrival data may simply not have been recorded by some

destinations.

Different methods were used to calculate distance

depending on the location of the origin and destination.

Since the UNWTO data do not identify either the originat-

ing city or the destination area within the receiving country,

precise distance measurements cannot be made. A

Canadian living in a major city going to the United States,

for example, could live anywhere from immediately adja-

cent to the United States (Vancouver and Windsor) to more

than 600 kilometers away (Edmonton), and could be over-

nighting in destinations that may sit on the UnitedStates/Canada border (Buffalo, Niagara Falls, Detroit,

Bellingham) or be traveling as far away as Hawaii, which

is a nine-hour flight from Toronto. To overcome this prob-

lem, distances between origin-destination pairs located

within the same continent were calculated on based on the

shortest distance between their borders. For example, the

distance between Belgium and Switzerland presented as

the shortest distance between each country’s borders. This

approach also recognizes that much intra-regional travel is

done by car and that outbound travel could be generated

from anywhere within the source market. The weakness of

this method is that the actual travel distance from populatedregions between countries sharing a land border may be

quite large, especially in the case of some South American

countries, which could distort figures. Brazil shares land

borders with Venezuela, Colombia, and Peru, for example,

but these frontiers abut the sparsely populated Amazon

River and are up to 3,000 kilometers away from the

densely populated southeastern coastal regions. However,

for sake of consistency in measurement, these pairs are

deemed to be immediate land neighbors.

Distances for inter-continental origin-destination pairs

or for pairs between origins and island destinations were

calculated by measuring the physical distance betweenmajor gateways. For example, the distance between the

United Kingdom and China was calculated based on the

distance between London and Beijing. This approach rec-

ognizes that most long-haul tourists usually depart through

their home country’s major hub and arrive in the receiving

country’s gateway hub. Australia, the United States, and

Canada have major gateways on both the east and west

coasts. Distances from these countries were calculated

from the nearest gateway to the destination—Sydney, New








213

T a b l e 2

S u m m a r y o f D a t a S e t b y D i s t a n c e

R a n g e o f

R a n g e o f

N u m b e r o f

N u m b e r o f

S o u r c e M a r k e t s

T o t a l

S h a r e o f

C u m u l a t i v e

I n d i v i d u a l

S o u r c e

O r i g i n -

N o t R e p r e s e n t e d

N u m b e r

T o t a l

O u t b o u n d

O r i g i n -

M e a n

M e d i a n

D i s t a n c e ( k m =

M a r k e t s

D e s t i n a t i o n

i n t h e D i s t a n c e

o f A r r i v a l s

D e p a r t u r e s

S h a r e f o r


I n d

i v i d u a l

I n d i v i d u a l

k i l o m e t e r s )

R e p r e s e n t e d

P a i r s

C o h o r t

( m i l l i o n s )

( s u m =

1 2 2 . 2 % )

E a c h S o u r c e

S h a r e s

S h a r e

S h a r e

L a n d N e i g h b o r

3 4

1 3 6

A u s t r a l i a , H o n g K o

n g ,

2 4 9 . 8

5 6 . 1

9 . 6 - 1 7 1 . 6

< 0 . 1 - 1 7 1 . 6

1

6 . 7

6 . 1

J a p a n , K o r e a ,

N e w Z e a l a n d ,

P h i l i p p i n e s ,

T a i w a n

≤

1 , 0 0 0 k m ,

3 4

1 8 5

A r g e n t i n a , A u s t r a l i a ,

1 0 6 . 9

2 4 . 0

0 . 3 - 1 0 7 . 7

0 . 3 - 1 0 7 . 7

4 . 8

1 . 0

b u t n o t a

B r a z i l , C a n a d a ,

l a n d

K a z a k h s t a n ,

n e i g h b o r


S o u t h A f r i c a

1 , 0 0 1 - 2 , 0 0 0 k m

3 7

2 5 1

A r g e n t i n a , A u s t r a l i a ,

5 7 . 8

1 3 . 0

0 . 2 - 3 6 . 4

0 . 1 - 2 8 . 2

1 . 5

0 . 4

I n d i a , N e w Z e a l a

n d ,

U n i t e d S t a t e s

2 , 0 0 1 - 3 , 0 0 0 k m

4 0

1 6 3

B r a z i l

2 4 . 0

5 . 4

0 . 3 - 6 4 . 7

< 0 . 1 - 5 7 . 5

2 . 1

0 . 6

3 , 0 0 1 - 4 , 0 0 0 k m

3 6

1 0 0

B r a z i l , C o l o m b i a ,

1 1 . 8

2 . 7

< 0 . 1 - 2 5 . 1

< 0 . 1 - 1 3 . 3

1 . 3

0 . 2

L a t v i a , N e w Z e a l a n d ,

S l o v e n i a ,

S w i t z e r l a n d

4 , 0 0 1 - 5 , 0 0 0 k m

2 8

7 0

A u s t r a l i a , B e l g i u m ,

6 . 9

1 . 6

< . 0 1 - 2 8 . 1

< 0 . 1 - 2 7 . 3

1 . 3

0 . 1

B u l g a r i a , E g y p t ,

H o n g K o n g , J o r d

a n ,

K a z a k h s t a n , M e x

i c o ,


P h i l i p p i n e s , T a i w

a n ,

U n i t e d K i n g d o m ,

U n i t e d S t a t e s

5 , 0 0 1 - 6 , 0 0 0 k m

2 3

6 5

A l g e r i a , A u s t r a l i a ,

1 9 . 8

4 . 4

< . 0 1 - 1 7 . 5

< 0 . 1 - 6 . 4

1 . 1

0 . 4

A z e r b a i j a n , B u l g a r i a ,

C o l o m b i a , E s t o n i a ,

H o n g K o n g , I s r a e l ,

J o r d a n , L a t v i a ,

L i t h u a n i a , M e x i c

o ,

N e t h e r l a n d s ,



R o m a n i a , S l o v e n

i a ,

S w i t z e r l a n d , T a i w

a n

( c o n t i n u e d )








214

T a b l e 2


R a n g e o f

R a n g e o f

N u m b e r o f

N u m b e r o f

S o u r c e M a r k e t s

T o t a l

S h a r e o f

C u m u l a t i v e

I n d i v i d u a l

S o u r c e

O r i g i n -

N o t R e p r e s e n t e d

N u m b e r

T o t a l

O u t b o u n d

O r i g i n -

M e a n

M e d i a n

D i s t a n c e ( k m =

M a r k e t s


i n t h e D i s t a n c e

o f A r r i v a l s

D e p a r t u r e s

S h a r e f o r


I n d

i v i d u a l

I n d i v i d u a l

k i l o m e t e r s )


P a i r s

C o h o r t

( m i l l i o n s )

( s u m =

1 2 2 . 2 % )

E a c h S o u r c e

S h a r e s

S h a r e

S h a r e

6 , 0 0 1 - 7 , 0 0 0 k m

3 5

1 1 7

C o l o m b i a , J a p a n ,

1 3 . 1

2 . 9

< . 0 1 - 2 3 . 7

< 0 . 1 - 1 4 . 6

0 . 7

0 . 2



S l o v e n i a , T a i w a n

7 , 0 0 1 - 8 , 0 0 0 k m

3 6

1 4 3

A l g e r i a , I r e l a n d ,

1 1 . 0

2 . 5

< . 0 1 - 2 7 . 6

< 0 . 1 - 1 7 . 7

0 . 8

0 . 2


L i t h u a n i a ,

S i n g a p o r e ,

S w i t z e r l a n d

8 , 0 0 1 - 9 , 0 0 0 k m

3 7

1 4 7

A z e r b a i j a n , E s t o n i a

,

1 1 . 9

2 . 7

< . 0 1 - 2 5 . 8

< 0 . 1 - 2 2 . 0

0 . 8

0 . 2

L a t v i a , S w i t z e r l a n d

9 , 0 0 1 - 1 0 , 0 0 0 k m

3 6

1 3 9

A z e r b a i j a n , B u l g a r i a ,

9 . 6

2 . 2

< . 0 1 - 1 1 . 6

< 0 . 1 - 8 . 1

0 . 6

0 . 2

I n d i a , R o m a n i a ,

S l o v e n i a

1 0 , 0 0 1 - 1 2 , 0 0 0 k m

3 3

1 7 1


1 2 . 4

2 . 8

< . 0 1 - 1 4 . 1

< 0 . 1 - 9 . 0

0 . 6

0 . 2

E s t o n i a , K a z a k h s

t a n ,

L a t v i a , L i t h u a n i a

,

R o m a n i a , S l o v e n

i a

1 2 , 0 0 1 - 1 4 , 0 0 0 k m

2 4

8 0

A l g e r i a , B e l g i u m ,

4 . 1

0 . 9

< . 0 1 - 1 8 . 4

< 0 . 1 - 1 1 . 8

0 . 6

0 . 2


E s t o n i a , F r a n c e ,

G e r m a n y , I r e l a n d

,

I t a l y , K a z a k h s t a n

,

L a t v i a , L i t h u a n i a

,

N e t h e r l a n d s , R o m

a n i a ,

R u s s i a , S l o v e n i a ,

S w i t z e r l a n d ,

U n i t e d K i n g d o m

>

1 4 , 0 0 0

3 8

1 4 8


4 . 6

1 . 0

< . 0 1 - 6 2 . 6

< 0 . 1 - 2 0 . 3

0 . 6

0 . 1

k i l o m e t e r s

S o u t h A f r i c a ,

S w i t z e r l a n d ,

T h a i l a n d









York, and Toronto in the east and Perth, Los Angeles, and

Vancouver in the west, respectively.

As noted in tables 1 and 2, total share figures can

exceed 100%. Up to 250% of departures are noted in

table 1. The sum of arrival shares reported in table 2 from

the 1,915 pairs equals 122.2% of total departures.

Individual share figures were calculated for each origin-

destination pair and then summed to produce a cumula-

tive figure. A cumulative share in excess of 100% can

arise from one of two causes. On the one hand, the same

person engaging in multi-destination travel would be

counted separately on entry into each country. For

example, a tourist visiting France, Belgium, the

Netherlands, and Germany during the same trip would

be counted at least four times. This phenomenon is most

prevalent among long-haul travel, especially to Europe,

which has a large number of small countries. It is espe-

cially noticeable in figures reported for Australia. On the

other hand, it could be due to double counting the sameindividual entering one country on different legs of the

journey. For example, a Malaysian may make an

overnight transit stop in Singapore on the outward leg of

a journey and overnight again on the return leg back to

their home country. This person would be counted as two

separate arrivals. This phenomenon occurs most notice-

ably in land neighbors in which the destination serves as

an overnight transit stop on the way to a third destina-

tion. These destinations have been labeled as “transit

destinations” for the sake of this study.

Two-Stage Analysis

Aggregate tourism movement patterns are examined

first to test the generalizability of distance decay. Source

markets and destinations that display anomalous charac-

teristics are identified, with reasons for the anomalies

posited. The second stage examines variability of share

within specific distance cohorts to test the universality of

the phenomenon. Ordinary least squares regression using

the stepwise function is used to explain observed fluctu-

ations in market share. Stepwise regression was used

because it does not require the causal ordering of vari-ables for entry into the model.

Aggregate Movement Patterns

The number and share of arrivals as a percentage of

total departures are reported by distance in table 2 and

shown graphically in figure 2. The impact of distance on

demand is striking. Land neighbors account for 57% of

all departures recorded. This figure is especially com-

pelling, considering that 7 of the 41 markets did not have

any direct land neighbors. Destinations located within a

1,000-kilometer radius captured one-fourth of depar-

tures. Together, these two most proximate regions

accounted for 80% of departures, yet represented only

17% of total origin-destination pairs—a classic Pareto

relationship. Destinations located within a 2,000 kilome-ter radius of source markets remained somewhat attrac-

tive. Global tourism demand declines sharply thereafter,

though with cumulative shares stabilizing at between 2%

and 3% of departures, with the exception of a small blip

between 5,000 and 6,000 kilometers away. Absolute

aggregate demand, therefore, falls by about 50% with

each 1,000 kilometers of added distance from the source

market.

Analysis of mean share per market within each dis-

tance category reveals an even faster rate of decay, and

poses an even greater challenge for distant destinations

trying to attract significant volumes of international vis-itors. Mean demand per destination declines by two-

thirds with every additional 1,000 kilometers traveled,

falling from 17% for immediate land neighbors, to 5%

for destinations within 1,000 kilometers, to 1.5 percent

for destinations located more than 1,000 kilometers

away. Average shares remain stable at slightly over 1%

for travel between 3,000 and 6,000 kilometers from

home, before declining again to well under 1% for travel

over 6,000 kilometers from home. Just under half (42%)

0

50

100

150

200

250

300

350

400

450

500

550

L a n d

n e i g h b

o r

< = 1 0 0

0 k m

1 0 0 1

- 2 0

0 0 k m

2 0 0 1

- 3 0

0 0 k m

3 0 0 1

- 4 0

0 0 k m

4 0 0 1

- 5 0

0 0 k m

5 0 0 1

- 6 0

0 0 k m

6 0 0 1

- 7 0

0 0 k m

7 0 0 1

- 8 0

0 0 k m

8 0 0 1

- 9 0

0 0 k m

9 0 0 1

- 1 0

0 0 0 k m

1 0 0 0

1 - 1 2 0

0 0 k m

1 2 0 0

1 - 1 4 0

0 0 k m

> 1 4 0

0 0 k m

Distance

V o l u m e ( m i l l i o n

s )

0

10

20

30

40

50

60

70

80

90

100

O u t b o u n d S h a r e

( % )

Cumulative volume Share

Figure 2

Cumulative Volume of Arrivals








of all land neighbors generate a minimum 10% share of

outbound travel. By contrast, 90% or more of all desti-

nations located more than 1,000 kilometers from the

source market attract 5% of departures or less.

Movement patterns from 39 of the 41 source markets

adhered to the one of the three distance decay models. But

as shown in figure 3, the presence of the ETEZ does seem

to exert a significant moderating effect on seven markets.

New Zealand, the Philippines, India, and Israel have sub-

stantial proximate ETEZ’s: the first two are a function of

their island status; the latter two are politically induced.

Regardless of the cause, though, the impact of a proximate

ETEZ is similar, effectively shifting the curve to the outer

edge of the zone. A decay curve with a substantial sec-

ondary peak results. The ETEZ for Brazil, Colombia, and

the United States is located between 1,000 and 3,000 kilo-

meters from the borders of these nations. The result is a

modified decay pattern. Strong demand occurs among

direct land neighbors, but the ETEZ here shifts demand,producing an almost equally strong secondary spike. In the

case of travel from Brazil and Colombia, the spike corre-

sponds with travel to the United States. It is the most pop-

ular destination for these two countries, generating 18%

and 27% share, respectively.

Only Japan and Australia defied the overall pattern.

Their movements are shown in figure 4. Japanese out-

bound travel displays distinct short-haul and long-haul dis-

tance decay patterns. The short haul pattern coincides with

travel within Asian destinations, with the peak found at a

distance equivalent to travel to China and Taiwan. The

long-haul pattern peaks at a distance equivalent to travel tothe United States. Australia has three distinct peaks coin-

ciding with travel to the South Pacific (peaking in New

Zealand), South East Asia (peaking in Singapore), and

Europe. The European peak requires further explanation.

Europe is a popular destination, with the United Kingdom

alone attracting 20% of Australian outbound travel. The

cumulative share of 62% of all Australian departures

beyond 14,000 kilometers must be read with caution, for it

is caused by multiple counting of the same individual on a

long duration, multi-destination trip.

Arrival shares were also examined at a destination level

to determine if any destination had broad enough appeal todistort demand across a number of markets. Some destina-

tions may have special relationships with individual source

markets that may produce anomalous share figures, as

noted in the origin-destination relationship between

Australia and the United Kingdom. However, the ability of

a single destination to distort share figures from multiple

origins is rare. Only two destinations were found to have

such appeal, the United States and Spain. Table 3 shows the

disproportionately strong drawing power of each destina-

tion. The United States is such a strong attraction that it

will distort movements on a global scale, while Spain’s

appeal is more localized, affecting movements only from

within Europe.

Share Variation Within Each Distance Cohort

Table 2 documents substantial fluctuations in individual

share within specific distance categories. For example,

departure shares from land neighbors range from a low of

Proximate ETEZ

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

L a n d

n e i g h b

o r

< = 1 0 0

0 k m

1 0 0 1

t o 2 0 0

0 k m

2 0 0 1

t o 3 0 0

0 k m

3 0 0 1

t o 4 0 0

0 k m

4 0 0 1

t o 5 0 0

0 k m

5 0 0 1

t o 6 0 0

0 k m

6 0 0 1

t o 7 0 0

0 k m

7 0 0 1

t o 8 0 0

0 k m

8 0 0 1

t o 9 0 0

0 k m

9 0 0 1

t o 1 0 , 0 0

0 k m

1 0 , 0 0 1

t o 1 2 , 0 0

0 k m

1 2 , 0 0 1

t o 1 4 , 0 0

0 k m

> 1 4 , 0 0

0 k m

Distance

C u m u l a t i v e S h a r e ( %

)

India Israel New Zealand Philippines

Markets With Mid Range ETEZ

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

L a n d

n e i g h b

o r

< = 1 0 0

0 k m

1 0 0 1

t o 2 0 0

0 k m

2 0 0 1

t o 3 0 0

0 k m

3 0 0 1

t o 4 0 0

0 k m

4 0 0 1

t o 5 0 0

0 k m

5 0 0 1

t o 6 0 0

0 k m

6 0 0 1

t o 7 0 0

0 k m

7 0 0 1

t o 8 0 0

0 k m

8 0 0 1

t o 9 0 0

0 k m

9 0 0 1

t o 1 0 , 0 0

0 k m

1 0 , 0 0 1

t o 1 2 , 0 0

0 k m

1 2 , 0 0 1

t o 1 4 , 0 0

0 k m

> 1 4 , 0 0

0 k m

Distance

C u m u l a t i v e S h a r e ( % )

Brazil Colombia USA

Figure 3

Impact of Effective Tourism Exclusion Zone on

Movements









less than 0.1% to a high of 171.6%, with shares from desti-

nations located more than 14,000 kilometers from the source

market also varying from less than 0.1% to more than 20%.

This observation suggests that although distance decay may

be a generalizable concept at a global level, it may not be a

universal “law” at an individual origin-destination case level.

The second goal of the study, therefore, is to explain this

variability. Ordinary least squares regression analysis is used

to examine variation in share within each of the specified

distance cohorts.

The purpose of the following discussion is to deter-

mine the influence of origin, destination, and/or relation-

ship variables on share. Using outbound data taken from

the UNWTO Tourism Market Trend reports (WTO

2004), separate regression functions were estimated for

each distance cohort. Origin variables relate to, for

example, the size of the country, its population, gross

domestic product (GDP), and GDP per capita and travel

expenditure. Destination variables include such factors

as size of the country, population, GDP, and GDP per

capita, the number of hotel rooms, plus data relating to

inbound tourism arrivals and receipts and tourism bal-

ance of payments. Relationship variables record, for

example, the number of land neighbors, the ratio

between origin and destination GDP, and a comparison

of tourism balance of payments and tourist movements.

In addition, dummy relationship variables were added to

reflect findings in the previous section, including the

identification of “transit destinations” in which doublecounting of arrivals may occur, the United States and

Spain demand anomaly, and the distance from the source

to the first destination encountered or between the first

and subsequent destination clusters to account for the

ETEZ. The “Distance between destinations” dummy

variable represents the number of distance categories

between the first destination category encountered and

the next destination category encountered. It is coded as

“1” for no gap, “2” for one-distance threshold gap, “3”

for a two-distance gap, and “4” for a three-distance

threshold gap. Other dummy variables were coded as “1”

if a respective characteristic was present and “0” if notpresent. Stepwise regression was used to select the vari-

ables that were to be included in the models, taking mul-

ticollinearity into consideration. The general model can

be summarized as:

SH = (S, D, R)

where: SH = share of visitors from a source country, S =

source country factors, D = destination factors, and R =

source-destination factors.

The findings are presented in table 4. The models explain

between 35% and 83% of share variance depending on thedistance category examined. Generally, it is more reliable

for proximate and distant destinations and less reliable in

explaining variations observed in travel between 6,000 and

10,000 kilometers from the source. Four variables loaded

most frequently, suggesting they exert broad influence on

share, regardless of distance. “Distance to the first destina-

tion” and “Island status” relate to the relationship between

the origin and destination while the “USA/Spain pull anom-

aly” and “Inbound receipts” reflect destination attributes.

Otherwise, relationship variables were most influential in

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

L a n d

n e i g h b

o r

< = 1 0 0

0 k m

1 0 0 1

t o 2 0 0

0 k m

2 0 0 1

t o 3 0 0

0 k m

3 0 0 1

t o 4 0 0

0 k m

4 0 0 1

t o 5 0 0

0 k m

5 0 0 1

t o 6 0 0

0 k m

6 0 0 1

t o 7 0 0

0 k m

7 0 0 1

t o 8 0 0

0 k m

8 0 0 1

t o 9 0 0

0 k m

9 0 0 1

t o 1 0 , 0 0

0 k m

1 0 , 0 0 1

t o 1 2 , 0 0

0 k m

1 2 , 0 0 1

t o 1 4 , 0 0

0 k m

> 1 4 , 0 0

0 k m

Distance

C u m u l a t i v e S h a

r e ( % )

Japan Australia

Figure 4

Anomalous Movement Patters from

Australia and Japan

Table 3

Impact of USA and Spain on Outbound Share

Mean Share

Mean Share Mean Share for Spain From

Distance (km = for All for United European

kilometers) Destinations States Source Markets

Land neighbor 16.8 88.8 46.8

≤ 1,000 km 4.8 — 20.5

1,001-2,000 km 1.5 — 15.52,001-3,000 km 2.1 — 7.4

3,001-4,000 km 1.3 — 6

4,001-5,000 km 1.3 — —

5,001-6,000 km 1.1 4.1 —

6,001-7,000 km 0.7 1.1 —

7,001-8,000 km 0.8 4.7 —

8,001-9,000 km 0.8 9.3 —

9,001-10,000 km 0.6 2.4 —

10,001-12,000 km 0.6 4.5 —

12,001-14,000 km 0.6 5.8 —

>14,000 km 0.7 4.1 —

(text continues on p. 14)at Nat'l Hogeschool Toerisme en Verkeer on October 4, 2011 jtr.sagepub.comDownloaded from






218

T a b l e 4

R e g r e s s i o n A n a l y s i s b y D i s t a n c e ( k m =

k i l o m e

t e r s )

L a n d

1 , 0 0 1 -

2 , 0 0 1 -

3 , 0 0 1 -

4 , 0 0 1 -

5 , 0 0 1 -

6 , 0 0 1 -

7 , 0 0 1 -

8 , 0 0 1 -

9 , 0 0 1 -

1 0 , 0 0 1 -

1 2 , 0 0 1

-

N e i g h b o r ≤ 1 , 0

0 0 k m 2 , 0 0 0 k m 3 , 0 0 0 k m

4 , 0 0 0 k m 5 , 0 0 0 k m

6 , 0 0 0 k m

7 , 0 0 0 k m

8 , 0 0 0 k m 9

, 0 0 0 k m

1 0 , 0 0 0 k m

1 2 , 0 0 0 k m

1 4 , 0 0 0 k m > 1 4 , 0 0 0 k m

T o t a l s

N u m b e r o f

3 3

3 3

3 7

4 1

3 6

2 8

2 3

3 5

3 5

3 7

3 6

3 3

2 4

3 8

—

S o u r c e

M a r k e t s


N u m b e r o r

1 3 6

1 8 5

2 5 1

1 6 3

1 0 0

7 0

6 5

1 1 7

1 4 3

1

4 7

1 3 9

1 7 1

8 0

1 4 8

1 , 9 1 5

O r i g i n -


P a i r s

O u t b o u n d

< 0 . 1

0 . 3

< 0 . 1

< 0 . 1

< 0 . 1

< 0 . 1

< 0 . 1

< 0 . 1

< 0 . 1

< 0 . 1

< 0 . 1

< 0 . 1

< 0 . 1

< 0 . 1

—

S h a r e

R a n g e —

L o w

O u t b o u n d

1 7 1 . 5 8

1 0 7 . 7

2 8 . 2

5 7 . 5

1 3 . 3

2 7 . 3

6 . 4

1 4 . 7

1 7 . 7

2 1 . 2

8 . 1

9

1 1 . 8

2 0 . 3

—

S h a r e

R a n g e —

H i g h

R e g r e s s i o n

r 2

0 . 7 5 4

0 . 6 2 1

0 . 8 1 6

0 . 5 1 0

0 . 5 5 3

0 . 8 9 7

0 . 7 8 1

0 . 3 6 8

0 . 4 1 3

0 . 5 1 4

0 . 3 2 7

0 . 7 7 8

0 . 7 9 5

0 . 6 1 8

—

a d j r 2

0 . 7 3 9

0 . 6 0 9

0 . 8 1 1

0 . 4 9 2

0 . 5 3 6

0 . 8 8 9

0 . 7 5 3

0 . 3 4 8

0 . 3 9 1

0 . 4 9 8

0 . 3 1 4

0 . 7 7 0

0 . 7 8 2

0 . 6 0 6

—

V a r i a b l e s

6

5

6

5

3

4

6

4

3

4

2

5

4

4

—

( t o t a l

n u m b e r )

S o u r c e M a r k e t

I s l a n d

—

0 . 1 3 7

—

—

0 . 5 7 3

—

—

0 . 5 6 5

—

—

—

- 0 . 1 8 9

0 . 2 0 9

—

5

S o u r c e

M a r k e t

P e r C a p i t a

0 . 1 9 3

—

—

—

—

—

—

0 . 1 7 0

—

—

—

—

—

—

2

E x p e n d i t u r e

O n T r i p s

P o p u l a t i o n

—

—

—

0 . 1 8 7

0 . 4 4 0

—

—

—

—

—

—

—

—

—

2

S i z e o f

—

—

—

- 0 . 1 4 6

—

—

—

—

—

- 0 . 1 4 0

—

—

—

—

2

C o u n t r y

G D P P e r

—

—

—

—

—

—

—

—

—

—

—

0 . 0 9 3

—

—

1

C a p i t a

D e s t i n a t i o n F a c t o r s

S p a i n o r

0 . 1 8 5

0 . 1 6 9

0 . 5 5 6

—

—

0 . 8 7 3

0 . 3 2 1

—

—

0 . 3 2 0

—

0 . 1 2 3

1 . 2 7 8

—

8

U n i t e d

S t a t e s

a n o m a l y









219

T a b l e 4


L a n d

1 , 0 0 1 -

2 , 0 0 1 -

3 , 0 0 1 -

4 , 0 0 1 -

5 , 0 0 1 -

6 , 0 0 1 -

7 , 0 0 1 -

8 , 0 0 1 -

9 , 0 0 1 -

1 0 , 0 0 1 -

1 2 , 0 0 1 -

N e i g h b o r ≤ 1 , 0

0 0 k m 2 , 0 0 0 k m

3 , 0 0 0 k m

4 , 0 0 0 k m 5 , 0 0 0 k m 6 , 0 0 0 k m

7 , 0 0 0 k m 8 , 0 0 0 k m

9 , 0 0 0 k m

1 0 , 0 0 0 k m

1 2 , 0 0 0 k m 1 4 , 0 0 0 k m > 1 4 , 0 0 0 k m

T o t a l s

T o u r i s m

0 . 1 1 2

—

—

—

—

—

– 0 . 5 9 1

—

—

—

—

—

—

– 0 . 5 1 5

3

B a l a n c e

o f P a y m e n t s

I n b o u n d

– 0 . 1 8 4

—

—

—

—

—

—

—

—

—

—

—

—

—

1

R e c e i p t s

P e r A r r i v a l

I n b o u n d

—

0 . 1 9 8

—

0 . 2 4 2

—

0 . 2 0 7

1 . 4 4 6

—

—

—

0 . 5 1 4

0 . 7 5 0

—

1 . 3 7 9

7

R e c e i p t s

( T o t a l )

P o p u l a t i o n

—

0 . 2 6 3

0 . 5 4 3

—

—

0 . 2 3 0

—

—

—

—

—

—

—

—

3

G D P P e r C a p i t a —

—

—

—

—

—

– 0 . 8 4 0

0 . 2 1 9

0 . 2 9 7

—

—

—

—

—

3

G D P ( T o t a l )

—

—

—

—

—

—

—

—

—

—

—

—

—

– 0 . 8 0 5

1

N u m b e r o f

—

—

0 . 1 3 5

—

0 . 2 4 9

—

—

—

—

0 . 4 2 0

—

—

– 0 . 7 1 6

—

4

R o o m s

A r r i v a l s P e r

—

—

—

—

—

—

—

—

0 . 1 9 0

—

—

—

—

—

1

1 0 0 H e a d o f

P o p u l a t i o n

S i z e o f C o u n t r y

—

—

– 0 . 1 2 0

—

—

—

—

—

—

—

—

—

0 . 2 2 0

– 0 . 2 5 9

3

R e l a t i o n s h i p B e t w e e n O r i g i n a n d D e s t i n a t i o n

N u m b e r o f L a n d – 0 . 1 2 8

—

—

—

—

—

—

—

—

—

—

—

—

—

1

N e i g h b o r s

T r a n s i t

0 . 6 9 8

0 . 5 8 4

—

—

—

—

—

—

—

—

—

—

—

—

2


D i s t a n c e t o

—

—

0 . 1 8 1

0 . 4 9 3

—

—

0 . 5 1 7

—

0 . 4 9 6

0 . 2 7 9

0 . 2 4 5

0 . 2 9 3

—

—

7

F i r s t


D i s t a n c e

—

—

—

0 . 3 0 5

—

—

—

—

—

—

—

—

—

—

1

B e t w e e n

D e s t i n a t i o n s

R a t i o o f O r i g i n

—

—

– 0 . 0 8 1

—

—

—

– 0 . 2 1 8

—

—

—

—

—

—

—

2

G D P P e r

C a p i t a t o


G D P P e r

C a p i t a









220

T a b l e 4


L a n d

1 , 0 0 1 -

2 , 0 0 1 -

3 , 0 0 1 -

4 , 0 0 1 -

5 , 0 0 1 -

6 , 0 0 1 -

7 , 0 0 1 -

8 , 0 0 1 -

9 , 0 0 1 -

1 0 , 0 0 1 -

1 2 , 0 0 1 -

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short-haul travel; a mix of source, destination, and relation-

ship characteristics influence medium-haul travel; and des-

tination variables become increasingly important in

long-haul travel.

The emergence of distance to the first destination and

island status as significant variables explaining share dif-

ference across a large number of distance cohorts con-

firm the observed impact of the ETEZ on tourist

movements. Destinations that serve markets with no

nearby neighbors tend to generate a higher share of

arrivals compared to their other markets that have direct

land neighbors, regardless of distance.

This finding also raises some interesting observations

of the impact of market access on tourism flows. Market

access is a little discussed concept in the tourism litera-

ture. It is a term that assesses destination attractiveness

or competitiveness based on relative proximity to source

markets, rather than by absolute distance (Pearce 1989).

Relative proximity can be measured by the number of intervening opportunities offering similar experiences

before the ultimate destination is reached. As with dis-

tance decay, it argues that more proximate destinations

have an inherent advantage over less proximate ones. But

unlike distance decay, absolute distance becomes less

important than the number of intervening opportunities.

McKercher (1998b) cited the example of residents of the

sub-tropical city of Brisbane, Australia, that have the

choice of literally dozens of beaches within a 150-kilo-

meter radius of the city, but must travel more than 2,000

kilometers to access Australia’s nearest downhill ski

resort, Falls Creek, to explain how market access works.A beach located 100 kilometers from the city may be

deemed to have poor market access (or been seen to be

uncompetitive) if the consumer must pass many other

beaches, while a ski resort located 2,000 kilometers

away might enjoy strong market access because no other

closer opportunities exist. This study suggests that mar-

ket access is an influential factor in international tourism

movements as well.

The impact of the United States/Spain pull anomaly

and the level of inbound receipts is largely self explana-

tory. Developed destinations are relatively more attrac-

tive than less-developed ones. The model demonstratesthat relative attractiveness increases with distance. Thus,

developed destinations that are proximate to source mar-

kets may enjoy some competitive advantage over their

less-developed neighbors. But, the advantage increases

dramatically with distance—developed destinations have a

larger opportunity to attract long-haul visitors. Additionally,

as mentioned earlier, the United States and, to a lesser

degree, Spain seem to be unique in their ability to over-

come distance barriers.

Most of the observed variation in share among short-

haul destinations, though, was attributable to the infla-

tionary effect of double counting that occurs in

neighboring transit destinations. Proximate destinations

play one of two roles in international tourism. They can

act as attractive main destinations in their own right, or

can serve as necessary secondary, transit destinations for

tourists on the way to third countries. In fact, the dummy

transit destination variable added robustness to the

model, increasing the adjusted r2 figure from .413 to .739

for land neighbors and from .373 to .609 for destinations

located within a 1,000 kilometers radius. Not unexpect-

edly, the number of land neighbors also emerged as a sig-

nificant variable, as one would expect dilution of share

with more opportunities.

The inflationary effect caused by double counting also

explains the apparent inconsistency between the positive

association of source market expenditure with share,

with the contradictory observation of a negative relation-ship between inbound expenditure from land neighbors

and share. One indicates that the more the source market

spends on trips, the higher the share, while the other indi-

cates that the less spent by the arriving visitor, the higher the

share. Outbound tourists who engage in multi-destination,

longer-haul travel generate the highest expenditures, but

often stay for very short periods of time in transit desti-

nations. Thus, while their total “origin” expenditure may

be high, their transit destination expenditure per visit

may be low.

A different set of dynamics affects share for travel

between 1,000 kilometers and 4,000 kilometers, for herethe population of the source market plays a significant

role. Destination attributes are most influential in

explaining share between 1,000 kilometers and 2,000

kilometers, while source market attributes are more

influential at greater distances. These findings would

suggest the presence of a demand shadow from the most

populous source markets. Greer and Wall (1979) sug-

gested that the unique shape of the tourism distance

decay curve with a peak located some distance from the

person’s home is caused by the human desire to exact a

sense of escape when traveling. McKercher (1998a) fur-

ther noted that some segments seemed more willing totravel further to exact that sense of escape as a possible

explanatory factor for the plateauing decay curve in

domestic tourism. The same situation may apply in inter-

national tourism as well, where some residents of popu-

lous source markets feel the urge to travel further to

engender that sense of escape.

Relatively few destinations are located between 4,000

and 6,000 kilometers from major source markets. This

area coincides with much of the Pacific and Atlantic










Oceans and South America for travel from North

America, the Atlantic, much of Middle East, and Western

and Sub-Saharan Africa from Europe and the Pacific and

Indian Oceans, as well as the Middle East from Asia. It

is not surprising that destination factors influence share,

more than anything else. The impact of the United

States/Spain pull anomaly is especially strong here, as is

the level of tourism development as reflected in the

inbound receipts. The model proved least effective in

explaining travel between 6,000 and 10,000 kilometers

from source markets, with few variables other than the

aforementioned distance to first destination and the

United States pull anomaly influencing variation.

The model becomes more robust again after 10,000

kilometers from source markets, where destination

attributes appear to exert the greatest influence on long

haul share. Some far off destinations that have developed

tourism infrastructure may be regarded as being more

exotic and effect a stronger “pull” power that overcomesthe inherent resistance to long distance travel.

Discussion and Conclusions

This study examined the effect of distance on interna-

tional tourism movements using the receiving country as

the unit of analysis. Outbound travel from 41 major source

markets was analyzed that accounted for more than three-

fourths of all arrivals in 2002. Generally, the principle of

distance decay was supported. Tourist flows from 39 of the

41 source markets corresponded closely to one of the dis-

tance decay patterns identified in previous research. Thirty-two source markets displayed the classic decay curve with

demand peaking at immediate land neighbors and then

declining rapidly. Seven others reflected patterns that were

moderated by the presence of a substantial ETEZ. Only

Japan and Australia demonstrated aberrant movement pat-

terns, with two distinct distance decay relationships evident

from Japan and three from Australia.

Aggregate arrival figures and mean cumulative share

figures reveal that international travel is tightly concen-

trated in proximate destinations. More than half of all

international travel is generated in immediate land neigh-

bors, and 80% is concentrated in countries located within1,000 kilometers of the source market’s border.

Aggregate global tourism demand declines by about

50% with every 1,000 kilometers traveled, while mean

demand for any destination declines at an even faster

rate, falling to 2% or less beyond 1,000 kilometers.

Although widespread, the decaying impact of distance

on demand is not equal, as wide share fluctuations were

noted in each distance class. These variations could be

explained largely by the existence of an ETEZ, which

tends to push demand to its outer boundary, the level of

destination development in general, and of the United

States and Spain in particular. The latter two variables

confirm the assertion that appealing assets have dispro-

portionately strong pulling powers. The necessity to use

proximate destinations as transit points has the greatest

influence on share among land neighbors, while the

apparent desire to escape compatriots plays some role in

attracting medium-haul tourists. Destination attributes

alone influence long-haul travel.

Three broad implications arise from the study. First, the

results demonstrate unequivocally that the vast majority of

international tourist movements can be defined as short

haul in nature. Relatively few people are willing to travel

more than 2,000 kilometers from their home country, and

those that do are often forced to do so due to the presence

of a substantial destination opportunity void that pushes

them farther. The vast majority of destinations locatedmore than 2,000 kilometers from any market attract less

than 1% share of visitors—those that do are developed des-

tinations. The ability of most destinations to attract long-

haul markets, therefore, is limited. Only rarely will distant

destinations manage to attract a significant share of visi-

tors, and even here, that share will most likely be capped at

2% to 3% or less unless the destination exerts some excep-

tional appeal to the source market.

This observation suggests that few long-haul destina-

tions will gain significant share from emerging source mar-

kets such as China, the former Soviet Union, and India.

The ubiquity of distance decay suggests, instead, that prox-imate destinations will benefit most as these markets

emerge, while distant ones will fight for small shares.

However, the absolute size of these markets may make

them attractive for long-haul destinations, as a 2% share of

the China market could equate to 2 million arrivals by

2020. Proximate destinations face just the opposite prob-

lem. They are at risk of being overwhelmed by large

numbers of new tourists unless they plan for the expected

demand.

Instead, most destinations will receive greater returns

on their marketing efforts and generate larger visitor

numbers by focusing on nearby source markets and espe-cially on immediate land neighbors. The importance of

these markets may be taken for granted, for they may not

been seen as having a particularly up market or high

spending visitors. But, source markets located within

1,000 kilometers of the destination represent the back-

bone of inbound tourism for almost every destination.

Second, the study focused exclusively on outbound

tourism and not arrivals. Outbound share is an important









consideration to understand the aggregate willingness of

markets to travel certain distances, but it may not be partic-

ularly relevant for small island destinations with limited

capacities to cater for visitors, for their size may limit their

ability to increase share. A country like Jamaica, for

example, attracted 1.7% of U.S. outbound travel, but

Americans represented 73.1% of all arrivals in 2002. The

United States is, therefore, a critical source market, while

Jamaica is a largely unimportant destination for most

Americans. The level of dissonance between market depen-

dency, on the one hand, and destination irrelevancy, on the

other hand, creates a number of challenges for destinations

that are reliant on a single market.

Third, these findings challenge some of the assump-

tions about destination choice. The prevailing literature

suggests that distance does not play an explicit role in

destination selection. Yet, the findings of this study are

unequivocal—distance is closely related to share. Thus

while distance may not be a deterministic variable, perse, distance is a valid proxy variable that reflects the cul-

mination of a number of factors, including time avail-

ability, cost considerations, preferred transport mode,

travel budget, the likely willingness or ability to engage

with different cultures, and a variety of other factors that

influence how far people are willing to travel. As such,

distance can be considered as a proxy variable that

accounts for many other factors that do affect the attrac-

tiveness or unattractiveness of travel between two points.

The impact of distance on tourism flows has not

received the attention it deserves in the tourism literature

in recent years. Yet, this study demonstrates clearly thatinternational tourist movements are affected by distance

on a global scale. The outbound travel patterns of 39 of

the world’s leading 41 major source markets adhere

closely to distance decay principles, with the other two

showing multiple regional distance decay curves. The

profound and ubiquitous decaying effect of distance on

global tourism demand cannot be ignored. Only a small

number of tourists are willing or able to travel long dis-

tances each year, and an even smaller number of destina-

tions have the ability to overcome that level of resistance.

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Bob McKercher is a professor in the School of Hotel and

Tourism Management at the Hong Kong Polytechnic

University, Hung Hom, Kowloon, Hong Kong SAR.

Andrew Chan, PhD, is an assistant professor in the School of

Hotel and Tourism Management at the Hong Kong Polytechnic


Celia Lam is a research assistant in the School of Hotel and

Tourism Management at the Hong Kong Polytechnic


Documents

The Impact of Distance on International Tourist Movements