Download The New Tourism: the Growth of a New Middle Class and the

The New Tourism: the Growth of a New Middle Class and
the Expansion of World Tourism
Saud Choudhry† and Byron Lew‡
Trent University
1600 West Bank Drive
Peterborough ON K9J 7B8
Canada
‡
corresponding author: [email protected]
Abstract
There has been an increase in tourism worldwide in the past
decade, and most of that growth is due to growth in tourism
from Asia. The growth of tourism from Asia is a net
addition to world tourist flows; Asian destinations are not
significant competitors for traditional destinations in Europe
and the Americas. We estimate a gravity model of tourist
flows and show that distance is still an important
determinant of tourist flows, much more important than its
role in directing commodity trade. We show that rising
incomes will partly overcome the distance barrier, and will
lead to a more integrated world of tourism linkages.
Acknowledgements: The authors thank the UNWTO for
kindly making available the dataset of bilateral tourist
flows.
The New Tourism: the Growth of a New Middle Class and
the Expansion of World Tourism
After all that has been said of the
levity and inconstancy of human
nature, it appears evidently from
experience that a man is of all sorts
of luggage the most difficult to be
transported.
Adam Smith, Wealth of Nations
Introduction
The growth in tourism around the globe has been substantial over the last
decade. The number of tourist arrivals has increased from 680 million in
2000 to almost 1 billion in 2010, an increase of almost 50% over the
decade. The greatest growth has been among the countries of Asia. While
travel by Europeans and North Americans declined over this period, travel
by Asians has more than doubled.1 Tourist travel for the average person of
an Asian country has become increasingly more important a component of
consumption.
With growth in the incomes among these countries has come a growth in
the size of the middle-class. With an increase in wealth, and importantly, in
leisure time, middle-class Asian consumers are now doing what middleclass consumers elsewhere in the West do with their leisure time ─they
travel.
The increase in the pool of potential tourists has led to an increase in
demand for tourist destinations. While European destinations remain the
most popular among tourists, an increasing share of world tourism reflects
1
Tourist departures did increase for these countries, peaking around 2007 and 2008.
The increases to peak were smaller, approximately 20% from 2000.
visits to tourist destinations in Asia. Tourist arrivals to European
destinations have increased approximately 16% from 2000 to 2010, while
arrivals to Asian destinations have increased almost 80% over the decade.
The increase in tourist activity among the newly middle-class consumers
of Asia has benefited the hospitality industries in both the traditional
tourist destinations and in newly emerging destinations. While the Asian
middle-class has increased dramatically, the average purchasing power of
the new Asian middle-class is not yet as large as that of the western tourist.
So these new tourists will choose locations nearer to home, and thus we
observe an increase in the tourism industry in these countries too.
We will quantity the factors that explain tourism using a modified gravity
model. The gravity model is a useful tool in explaining the movement of
commodities and factors of production by explicitly incorporating the
frictions the inevitably arise in the movement of items. The framework has
been used extensively to explain trade flows and trade costs (), the
movement of migrants, and the movement of financial flows including
foreign aid. Tourist flows are in essence trade in services, and therefore
factors that explain trade flows will also explain tourist flows.
We will show that while growth in income has increased the flow of
tourists, there is a geographical pattern to the choice of tourist destinations,
and it is determined in part by the country of origin. There is a general
preference by tourists of particular countries to spend their leisure time in a
few countries. As expected, distance is very important in capturing this
tourism bias. We also discuss several other factors related to the implicit
cost of tourism. We show that income does help reduce the effect of
distance, but the effect persists.
Background and Literature Review
Tourist flows are assumed to respond to standard economic signals that
influence demand for any good: price and income. Income is a
characteristic of the individual tourist, so income differences across
countries are hypothesized to explain differences in tourist outflows. We
are characterizing not only the source countries, but the flows from source
to destination. Characteristics of destinations will be important in
attracting tourist from any given source country. There may also be
country-pair specific frictions that impede tourist flows. The most obvious
is distance.
Given the characteristics of the bilateral flow data, we adopt a model used
extensively in trade to explain movement of goods between countries. The
analogy to trade is obvious; countries export their unique attributes by
opening to tourism. We estimate a gravity model of tourist flows from the
origin to destination countries. Gravity models are used extensively in
trade, and have been defended theoretically as being consistent with
demand theory.
The basis for a gravity model is the maintained hypothesis that in a
frictionless world where goods and information flowed without cost, each
country's trade flows will be proportional to its share of world GDP.
Observed deviations of trade flows from a proportionate share could
reflect costs of trade (Deardorff, 1995).
As tourism flows are only one component of the current account, we do
not expect balance to be evident. Because countries export in order to
import, trade will tend to balance in theory if we assume borrowing and
lending reflect intertemporal trade. In contrast, countries are not equal as
potential destinations. Consumers prefer diversity to uniformity and so will
value variety. But tourists will have preferences and some countries will be
natural tourist destinations. So a country's deviation from its share of
world GDP will reflect two factors: the country's tourist characteristics,
and tourism frictions.
The examples of the use of gravity models in explaining tourist flows are
few. Santana-Gallego (2010) assess the impact of common currencies on
tourism. Their analysis is restricted to OECD origin countries. Keum
(2010) examines South Korea and the bilateral tourism flows with 28
partner countries. While a useful example of the gravity model usage, it is
a narrowly-focused topic and it is unclear if results are generalizable.
There is an active debate over whether tourism enhances or is detrimental
to growth. Examples of luxury tourist resorts in poor countries suggest few
possible linkages. Empirical evidence is mixed, and often focuses on an
individual country of a region, but is generally somewhat supportive of the
hypothesis that tourism may enhance growth when specific country cases
are examined (Ekanayake and Long, 2012; Arslanturk et al., 2011;
Balaguer and Cantavella-Jorda, 2002; Kamas and Salehi-Esfahani, 1992).
On the other hand, Sequeira and Campos (2007) do not find support for a
link between tourism and economic growth using a long-run panel, even in
subsamples divided by population, per capita incomes, and degree of
tourism-specialization.
Santana-Gallego et al. (2010) find that the growth effect of tourism
increases with the income of the destination country though their sample is
restricted to high income origin countries. Lee and Chang (2008) using
time series techniques, in contrast, find that growth enhancing effects of
tourism are stronger in non-OECD countries, though weak for Asia. That
latter result may reflect the time period examined. Support is provided by
Sequeira and Nunes (2008) who find a link between tourism and growth
only for poor countries. Adamou and Clerides (2010) estimate the
relationship between tourism receipts and growth allowing for a non-linear
relationship, and find that tourism affects growth most for smaller,
tourism-specialized countries while the effect diminishes as a country's
tourism sector grows beyond a critical barrier of 20% of GDP.
Effects of tourism on growth will be difficult to detect in a cross-section or
panel because the data will invariably include the tourist destinations that
cater to both the middle class and the wealthy. The linkage effects for
tourist services provided to the wealthy may be limited as most of what is
provided is likely to be imported, and local labour working at luxury
resorts may well compete down the wages in exchange for the opportunity
to received the better gratuities from well-heeled patrons. In contrast,
linkages from the provision of basic tourist services are likely to be greater
as more of the services provided will be sourced locally in order to offer
services at modest prices. Therefore the growth of middle class tourism
may well provide a bigger boost to incomes. That is an hypothesis to be
explored elsewhere. Here we explore the growth of the middle-class tourist
from the newly developing nations.
Models and Data
Tourist Flows
We use data on bilateral tourist flows among all countries for the years
2005 through 2009 (UNWTO, 2012). There are several categories of
tourist flows recorded by the World Tourism Organization (UNWTO). Our
preference is for number of personal visits distinct from business or
professional. The bilateral data do not always report this. Therefore we use
total overnight visitor arrivals and adjust that value by the destinationcountry specific value for personal share of total arrivals for each year
(Eilat and Einav, 2004, p. 1320).
We illustrate some highly aggregated regional patterns of the bilateral
tourist flows in Table T1. We have amalgamated the flows by World Bank
region categories to illustrate the substantial home bias evident in the
tourist flows. Destination regions are identified by row and source regions
by column. The most important feature is the degree to which the size of
intra-regional flows exceed inter-regional flows. Intra-regional flows are
shown along the diagonal of each year's table. Inter-regional flows are all
the off-diagonal values.
[insert Table 1 about here]
Europeans make up half of world tourists. The large majority of European
tourist flows are within Europe. More than 80% of European tourists travel
to European destinations. Tourist flows from East Asia are increasing, yet
these tourists show an even greater home bias, with better than 85%
choosing destinations within East Asia. Tourists from the Americas have
modestly less home bias, but only a little less. Tourists from South Asia are
the exception, but they are relatively few as a proportion of world tourist
flows.
Aggregation may be hiding regional differences, so we also examine the
regional trade flows with a matrix of 26 regions defined by the UNWTO
rather than just six.2 With this finer division, some different patterns
emerge, but importantly, the patterns differ less for the regions with large
tourist outflows: Northeast Asia and Western Europe.
For tourists from the Northeast Asian countries of China, Japan and Korea,
2
Results not shown, but available from authors on request.
more than 82% are visiting each other. For countries of Southeast Asia,
that proportion is 68%. Europeans are a little more varied in their choice of
destinations, but generally only within Europe. Over 80% of Southern and
Western European tourists choose destinations in Europe, while over 70%
of Northern European tourists also choose European destinations. North
American tourists of Canada, the U.S. and Mexico, show a relatively
diverse set of destination choices, but more than 50% still choose North
America.
The regions that show the least home bias other than South Asia tend to be
small, and the flows are quite specific. For example, tourists from East and
Central Africa visit Southern Africa. Tourists from the Caribbean visit
North America. Tourists from South Asia also have a fairly specific set of
destinations. About one-third visit the Middle East, another 20% visit
Southeast Asia and 10% visit Northeast Asia, while 12% visit other South
Asian countries. Tourists from Australia and New Zealand choose most
widely and visit many different regions. But they do not constitute a large
share of world tourist flows at just over 1%.
To better capture the degree to which tourist flows are integrated by
regions, we report tourism intensity indexes by aggregated regions as in
Table 1 above. We calculate the index in the same way the trade intensity
index is calculated.
T ij =
where
x ij /x iw
x wj / x
x ij is tourist flows from country i to country j,
tourist flows from country i,
x iw is total
x wj is total tourist flows to country j, and
x are total world tourist flows. The index has a range from 0 to ∞, with
numbers greater than 1 indicating an intense relation.
[insert Table 2 about here]
Table 2 reports tourism intensity indexes aggregated by region and
averaged over all five years. The diagonal in the table represents intraregional tourist flows, and all values are very high with the exception of
European intra-regional flows. While the index for European intra-regional
tourist flows is greater than 1 and is therefore of high intensity, it is much
smaller than all other intra-regional values reported in the table. So while
Europeans also display a home bias, the bias is not as large relative to the
size of European tourist flows. Of note are the very low values for the cells
from Europe to East Asia and to the Americas.
There are a few off-diagonal cells with tourism intensity index values
greater than 1: flows from South Asia to the Middle East, bidirectional
flows between the Middle East to Africa, from South Asia to East Asia,
and from the Americas to South Asia. All other values are smaller than 1,
and many are much smaller than 1. That the tourism intensity indexes for
South Asia as a destination are relatively large is due more to the small
denominator of the index rather than a large numerator. South Asian tourist
flows are relatively small compared to others.
Gravity Model
The flows and tourism intensity index results discussed above illustrate the
effect of distance and income given the apparent substantial home bias to
the flows. In addition to distance and income, the flows will be determined
by other travels costs, as well as price differences and other economic
determinants. We estimate a gravity model of the flows to isolate the
effects independent of income and other economic determinants.
The basic gravity-model estimating equation is
T ij =aY αi Y αj N βi N βj Aγi Aγj F δij
i
j
i
j
i
j
(1)
where Y is income as real per capita GDP, N is population, A is a vector of
any other country-specific shift variables, and F are the trade frictions
specific to each source country i and destination country j pair. We will
estimate this in log-linear form so all variables are natural log-transformed
unless specifically noted. The components of A and F are potentially
significant (Prideaux, 2005). We discuss our choices as follows.
The larger the countries, the greater the potential tourist flows between the
two countries. We capture country size with GDP, population and
geographical area.3 Note that GDP is included in the per capita GDP ratio.
GDP per capita will also capture the effect of income on demand for the
source country.
Distance is used as a proxy for friction as the greater the distance the lesser
the flow between two countries.4 In addition to distance, we consider
several variables that would tend to mitigate the effect of distance and
difference. We include indicators for countries that are contiguous and that
share a common language---both official languages and most commonlyused languages. We also include indicators for a shared colonial
experience.
Following Eilat and Einav (2004, p. 1320), for price of tourism we use the
ratio of the reciprocals of the PPP conversion factor representing the cost
of a basket of goods in the destination country in terms of a basket of
goods in the origin country. We hypothesize that the cheaper the basket of
3
4
These data are from World Bank's World Development Indicators database. Many
other independent variables used in this study are from this source, and those that are
not are otherwise indicated.
Distance and other friction variables are taken from Mayer and Zignago (2011).
goods in the destination country relative to the origin country, the greater
will be the flow of tourists.
Geography will be a factor in determining the relative cost of tourist
inflows and outflows. We include a measure of the remoteness of each
country, defined as the distance from a country to a population-weighted
sum of the distances of all other countries. The more remote a country, the
more costly it is for tourists to get to, so the lower the inflow of tourists.
Symmetry would suggest this should work for tourist outflows as well.
Inflation rates in source and destination countries could influence tourist
flows to the extent that tourists book travel in advance. A landlocked
country may also be more expensive for tourism. At a minimum,
landlocked countries are not eligible to be cruise-ship destinations.
In characterizing destinations we intend to capture only major differences.
Variety will mean that many countries are potential tourist destinations.
For variation in attractiveness we include the absolute value of a country's
latitude, on the assumption that better climate is preferred. We include the
absolute value of the latitude of the source country as we expect that
countries at the extremes of latitude which experience greater seasonality
may have greater tourist outflows and be otherwise less attractive
destinations.5 We also include the length of coastline for a destination
country as an indicator of potential as a tourist destination.6
Tourism will also suffer where tourists feel unsafe. To capture factors that
keep tourists away, we use some indicators of potential violence and
lawlessness. To capture institutional conditions, we include measures of
political stability and rule of law. To capture direct risk to tourists, we
5
6
The latitude is not log-transformed.
CIA World Factbook.
include the homicide rate. Tourism will also be highly sensitive to conflict
within a country so we include an indicator of conflict intensity (Themnér
and Wallensteen, 2012).7 None of these variables are log-transformed.
Tourism flows between any two countries may be influenced by the degree
to which two countries are already economically engaged with each other.
To capture this economic engagement, we include the value of trade
between the two countries, separately including exports and imports.8
Many country-pairs have no reported trade, so we also include indicators
differentiating between countries that trade with each other and those that
do not.9
Our log-transformed base gravity-model is as follows:
GDP it
+ ∑ c ln popit + ∑ d i ln areai + ∑ e i ln remote it
pop it i∈ {o , d } i
i ∈{o , d }
i ∈{o , d }
i∈{o ,d }
+ f PPP odt + ∑ g i ln inflationit + ∑ hi ruleofLaw it + ∑ j i politicStab it
lnT odt =a +
∑
bi ln
i ∈{o , d }
i ∈{o , d }
i∈{o ,d }
+ k conflict dt +l homRatedt + mod imports odt + m do imports dot +
+
∑ (2)ni lat i
i∈ {o ,d }
∑ p i landLock i+ q lenCoast d + r ln distance od + s contig od
+ ∑ v i comLang i ,od + ∑ w i colonial i ,od + ∑ τ t year t + u odt
i∈{o ,d }
i
i
t
We estimate this model twice, with and without source country and
destination country dummies. In order to fully account for any bias that
7
8
9
The UCDP/PRIO conflict intensity indicator ranges from 0 (no armed conflict) to 2
(intense armed conflict). Downloaded February 2012.
Data from World Bank's World Integrated Trade Solution, downloaded December
2012.
We do not log-transform the export and import values because of the presence of
many zero-value observations. In empirical work it is common to substitute 0 for
ln(0) on the assumption that 0 and 1 are generally not very different. In this case, the
estimated coefficients are very sensitive to the value used for ln(0) even with
inclusion of the dummy variables for country-pairs with no trade.
may arise, we also estimate the model using source-destination pair
dummies. The disadvantage to this specification is that all pair fixed
effects, like distance, contiguity, etc. are embedded in the pair dummy
variable and cannot be separately identified. We consider this specification
our test version against which we can compare the other two.
We are principally interested in the regional bias to tourist flows. We first
posit the tourist behaviour of those at the income extremes. Those who are
very wealthy will choose their tourist destinations for a variety of reasons
reflecting personal taste, but distance is probably not of much
consequence. The cost of transport is likely only a small proportion of their
vacation expense. At the other end of the income distribution, those just
barely middle class will likely be highly sensitive to all price differences
and for this group distance may be very important. As with individuals, so
too with countries.
To quantify the potential decline in the effect of distance with income, we
add an additional term into the regression in equation (2), the interaction of
source per capita GDP times distance
γ ln
GDP ot
×ln distance od
popot
(3)
and estimate the coefficient γ.10 The marginal effect of income on tourist
flows is
∂ lnT od
=b + γ ln distance od
∂(ln GDP o / pop o) o
(4)
where γ is the coefficient on the interaction term
10 We also estimated the model adding in the square of distance and its interaction with
source per capita GDP. The results derived therefrom are unchanged regardless of the
inclusion of these additional covariates.
ln
GDP ot
×ln distance od . The marginal effect of source GDP in our
pop ot
specification is then a function of distance, as shown in equation (4).
We include the interaction term from equation (2) into two of the
specifications, and estimate five different regression models.11 Our results
are reported in Table T3. Regressions 1 and 2 do not include country
dummies, regressions 3 and 4 include country dummies, and regression 5
is a fixed effects regression using country-pairs as the fixed effect.
Standard errors in all regressions are estimated to be robust to clustering
by country-pair.
[insert Table 3 about here]
Coefficient estimates on income of the destination country are quite stable
across specifications. Coefficients on the origin-country income are stable
for regression 1, 3 and 5, but in regression 2 and 4 they differ as origincountry income is interacted with distance. The interaction terms source
income times distance are statistically significant. Other coefficients are
less stable across different specifications. The country price lp3cons is
negative and significant in regressions 3-5 as hypothesized. It is negative
but insignificant in regressions 1 and 2, but these regressions are least
preferred.
The regressions use the income variables GDP per capita, so the effect of
population is determined by the difference between the coefficients on
lPop−lIncome , both for source and destination. In regression 5 we find
that the effect of origin country population is negative and significant
11 The fixed effects regression embeds all bilateral pair effects into the fixed effect so no
interaction term with distance is possible.
while destination population is not statistically significant. Smaller
countries tend to send more tourists abroad. We also include geographic
area, so we can calculate the effects of population density as the sum
lPop−lIncome−lArea . Population density for destination countries is
positive and statistically significant while for source countries it is not
statistically significant.
Our preferred specification is the fixed effects regression 5 as it fully
controls for unique attributes by country-pairs, but at the cost of
embedding any time-independent bilateral-pair effects into the fixed effect.
Some interesting results are the positive and significant coefficients on
destination RuleOfLaw and the negative and modestly significant
coefficients on destination HomRate and the noExports dummy variable.
The coefficients on RuleOfLaw and HomRate capture the destination risk.
Interestingly, political stability is positive and significant only in
regressions 1 and 2. So we know that after controlling for fixed effects,
political stability is less important than direct risk measures of rule of law
and homicide rates.
We hypothesized that exposure to trade would increase tourist flows, but
we find that after controlling for the country-pair fixed effects in
regression 5, all but the noExports are insignificant. The noExports
coefficient is only significant at the 10% level, but it does indicate that
while the level of trade does not matter to tourism—it seems to be
determined by idiosyncratic country-pair effects—the presence of exports
from origin to destination does increase tourist flows, while the presence
of imports to origin from destination does not.
The bilateral-pair variables present in regressions 1-4 are generally
properly signed and statistically significant. Distance is negative and
significant in all four specification. Note that regressions 1 and 3 can be
compared and regressions 2 and 4 where distance is interacted with source
income. Contiguous countries have greater tourist flows, as do countries
with common languages. Countries with common colonial origins have
greater tourist flows, as do countries that had a colonial relationship after
1945.
Using the estimates from the model with country dummies and the
interaction terms, we then plot the marginal effect of a source country's
income on tourist flows as a function of distance, in Figure F1. The
marginal effect of source country income on tourism is negative for short
distances, distances less than about 60km. Tourist trips of short distances
are less likely as incomes rise, so short distances are affordable for those
from lower income countries. Those from wealthier countries are able to
afford variety, and so will prefer to stray farther from home and make
longer trips to see new places.
[insert Figure 1 about here]
For trips of distances ranging from about 60km through about 500km, the
marginal effect of source country income is not statistically significant; the
95% confidence interval includes a value of zero. So we conclude income
has no effect on tourism at these distances. For these medium-distance
trips, tourists from rich and from poor countries are equally likely to make,
given all other characteristics. For trips of greater distance, more than
500km, source country income has a positive marginal effect on tourist
flows. The marginal effect is also increasing with distance. For longer
distances, there are more tourist flows from wealthier countries, and the
effect of wealth is increasing with distance. So tourists from wealthier
countries are more likely to make long distance tourist trips.12
We are observing that distance affects who travels. For short distances,
income correlates negatively with tourism flows, though it should be noted
that there are very few observations for trips of this length. We see more
clearly that income matters for tourist trips greater than 500km. These
constitute the large majority of the observations. The strong intra-regional
flows we observe are consistent with this result.
While we draw this conclusion using model 4, we can also comment on
the results from the fixed effects regression. While much of the
explanation of tourist flows in the fixed effects model is in the countrypairs, the R2 is very low, we conclude that the country-pair effects are not
simply random, but rather depend on distance between the origin and
destination countries.
Conclusions
The increasing wealth of Asia and the Middle East has given rise to new
tourists. The resultant tourist flows illustrate that tourists are highly
sensitive to price, and distance matters greatly. This has created new intraregional tourist flows, and the growth of a new tourist industry in those
parts of the world where the new middle class is beginning to consume
more. This new tourist flow is complementary to the already existing
patterns of tourist flows from Europe and North America.
As incomes grow, we predict that the intra-regional bias to tourist flows
will be partly mitigated. How far this will go remains to be seen. Transport
12 Inclusion of the square of ln distance with and without its interaction with source per
capita GDP has no material effect on the marginal effect of income on tourist flows.
That means there is no tendency for the effect of income to level off at a certain
distance. The linear trend is robust.
costs are highest in the services sector, so it is not surprising that we find
distance and other tourists costs very important. That means in the near
future, tourism operators will not face competition for their market. But for
the regions of the new middle class, opportunities for tourism abound. As
incomes in Asia continue to rise, demand from these new tourists for
destinations farther away will increase. But even with rising incomes,
tourist flows will likely continue to display the home bias that we observe.
We see a possible benefit to income growth from the regional expansion of
tourism demand. This demand is particularly important because it is
demand from tourists of more moderate means; the services they demand
will tend not to be luxurious. Instead we see opportunity for the small
hotelier providing adequate accommodation and for food service of the
practical variety. The linkage effects to growth are likely to be of more
importance compared to the services provided in luxury resorts in poorer
countries in tropical destinations.
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Table 1: Tourism Flows, 2005-2009 (thousands)
Table 2: Tourism Intensity Index, by Region, Average 2005-2009
Table 3: Regression Results
Figure 1: Average Marginal Effect of Income on Tourist Flows