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INTERNATIONAL TOURISM SPECIALISATION
OF SMALL COUNTRIES WITH HIGH GROWTH RATES
Bernardina Algieri
Abstract
International tourism has expanded enormously over the last decades, fuelled by
changing consumer tastes, advances in transport, and new holiday destinations. The
present study aims at analysing the linkages between economic growth and tourism
based economies. An econometric model for a selected number of small countries has
been implemented to investigate the nature, magnitude and overall significance of the
demand for tourism. Countries were selected to capture regional diversity, varied
market orientations and a range of experiences, from emerging to long-standing
industries. The results show that tourism can be a significant engine of economic
growth, when the elasticity substitution between manufacturing goods and tourist
services is less then one. Finally two stylised facts were found out, namely: 1) countries
specialised in tourism register good economic performances; 2) these same countries
have small dimensions as defined by international trade theory.
ESADE III 25-26 May 2005
International tourism specialisation of small countries with high growth rates
Introduction.
The aim of this work is to analyse the relationship between economic growth
and international specialisation in tourism. The interest for the topic has risen because
of the economic literature ascribes the good economic performance of a country mainly
to its manufacturing sector. The study highlights the importance of tourism as possible
source of income and economic growth.
The work is organised as follows: part one deals with the international
specialisation patterns of a group of selected countries. Part two discusses the
correlation between economic growth and specialisation in tourism and explicates the
conditions to generate economic growth. Part three displays the econometric findings of
the aggregate tourist demand.
1. International specialisation indices.
Tourism is one of the most important tradable sector. It accounts for about 9% of
world GDP and employs about 200 million people worldwide. Between 1970 and 2001,
international tourism receipts experienced a 26-fold increase, rising from US $ 17.9
billion to US $ 463 billion annually (WTO, 2003). Moreover, since there are about 700
million international travellers per year, tourism has become a dynamic source of
income and a strategic sector for development in many countries.
To understand the linkage between economic growth and tourism we have firstly
considered a set of economies that the World Bank analysts classify as high income and
upper-middle income countries (tab.1), their annual percentage GDP growth rates then
we have computed their specific international specialisation. Among the top high
income economies in per capita terms, the highest values are recorded in the Bermuda
and in the Channel islands, the lowest figure is registered in the Bahamas. Among the
top upper middle income economies there are the Seychelles, Barbados and Mauritius
(tab.1).
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International tourism specialisation of small countries with high growth rates
Tab.1 GNI per capita, 2003
Upper middle income economies
GNI per capita (Purchasing Power
Parity, international $)
15960
15060
11260
11040
9810
9590
9450
8940
7450
6590
Seychelles
Barbados
Mauritius
St. Kitts and Nevis
Chile
Antigua and Barbuda
Trinidad and Tobago
Malaysia
Thailand
St.Vincent and the Grenadines
High income economies
GNI per capita (Purchasing Power
Parity, international $)
42340
Bermuda
35870
Channel Islands
30560
Andorra
29890
Aruba
29230
Cayman Islands
28810
Hong Kong, China
28700
Antilles
24180
Singapore
19540
French Polynesia
19530
Cyprus
17930
Korea Rep.
17870
Malta
17560
Faeroe Islands
16140
Bahamas
World Development Indicators database, World Bank, September 2004
Gross domestic output grew consistently between 1990 and 2003 in many
economies. China is the world’s fastest-growing country. It grew by an average of 9.5%
between 1990 and 2003 (tab.2). China’s national income per head in 2003, measured in
current dollar adjusted for purchasing power was $4900, compared with $6590 in St.
Vincent and the Grenadines. Table 2 shows that many Caribbean islands record fast
pace in GDP growth, specifically Trinidad and Tobago, Antigua and Barbuda,
Bermuda, Cayman islands, St. Kitts, St. Vincent and Aruba.
To detect the factors that drive these positive performances, the Balassa index
has been calculated. The latter allows to understand the structure and determinants of
international trade and to identify the origins of comparative advantages. Empirically
quantifying comparative advantages is a non-trivial task: the rigor of economic theory
imposes severe restrictions and country and commodity aggregations necessarily entail
conceptual compromise.
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International tourism specialisation of small countries with high growth rates
Tab.2 Gross domestic output. Average annual % growth (1990 to 2003)
Average annual % growth 19902003
9.5
China
9.1
Trinidad and Tobago
9.1
Antigua and Barbuda
7.7
Bermuda
7.6
Channel Islands
7.4
St.Vincent and the Grenadines
7.2
Barbados
6.6
St. Kitts and Nevis
6.4
Bahamas
6.3
Aruba
6.3
Singapore
6.1
Cayman Islands
5.9
Malaysia
5.9
Antilles
5.7
French Polynesia
5.6
Chile
5.5
Korea Rep.
5.4
Faeroe Islands
5.2
Mauritius
4.8
Andorra
4.8
Cyprus
4.8
Malta
4.5
Seychelles
3.7
Thailand
3.7
Hong Kong, China
Source: World Development Indicators 2005.
Formally the Balassa index is given by:
B yi = 100 ⋅
x yi
N
∑ x
y =1 yi
M
∑x
i =1 yi
N M
∑ ∑ x yi
y =1 i =1
where xyi stands for country i’s exports of commodity y. The Balassa index has a lower
bound of zero and no upper bound. A country that is more specialised in some industry
than the average of all countries taken together has an index value greater than 1 for that
industry, and, conversely, a value below 1 reveals a lack of specialisation compared to
the average for all countries. Thus, values above 1 indicate the presence of comparative
advantages. The standard deviation of this index across products can be used as measure
of the comparative importance of inter-industry specialisation and intra-industry trade.
The greater the degree of inter-industry specialisation, the greater the standard deviation
of the Balassa index. The calculated values are reported in table 3.
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International tourism specialisation of small countries with high growth rates
Tab.3 Balassa index
International specialisation
Tourism
1985
1990
2003
0.76
0.47
0.74
China
8.41
8.97
9.04
Trinidad and Tobago
11.40
13.00
11.94
Antigua and Barbuda
7.01
7.45
7.78
Bermuda
7.12
6.98
7.11
Channel Islands
7.89
8.12
8.43
St.Vincent and the Grenadines
3.22
3.45
3.67
Barbados
…
9.57
9.26
St. Kitts and Nevis
4.55
5.14
5.11
Bahamas
2.44
2.57
2.61
Aruba
4.01
2.31
5.75
Singapore
2.11
2.22
2.45
Cayman Islands
0.48
0.50
1.38
Malaysia
11.10
8.73
10.81
Antilles
3.12
3.33
3.66
French Polynesia
0.54
0.84
0.71
Chile
0.44
0.72
0.57
Korea Rep.
1.34
1.39
1.42
Faeroe Islands
1.94
2.35
4.92
Mauritius
8.78
9.12
9.45
Andorra
6.69
7.00
7.93
Cyprus
4.71
4.22
3.23
Malta
11.18
9.52
9.69
Seychelles
2.45
1.11
1.61
Thailand
0.97
0.80
0.70
Hong Kong, China
Source: Own calculations on Tourism Economic Report and Comtrade dataset, 2004
Manufacturing
1985
4.01
0.23
0.18
0.55
0.34
0.34
0.54
…
0.67
0.33
0.99
0.32
3.03
0.38
0.13
1.03
1.03
0.32
0.57
0.15
0.59
0.77
0.38
0.97
3.00
1990
6.04
0.19
0.06
0.45
0.44
0.31
0.58
0.33
0.72
0.45
0.99
0.24
3.01
0.40
0.13
1.13
1.02
0.34
0.65
0.11
0.46
0.75
0.34
0.99
4.02
2003
7.02
0.21
0.12
0.55
0.42
0.27
0.52
0.33
0.77
0.44
0.10
0.21
3.65
0.21
0.16
1.02
1.03
0.31
0.58
0.12
0.44
0.82
0.38
1.06
5.02
Most of the countries reported in tab.3 are specialised in tourism. In 2003, Antigua &
Barbuda, the Antilles, the Seychelles, and Andorra show the highest Balassa index
values. China, Hong Kong and Malaysia register soaring values in the production and
export of manufactured goods. Chile and Korea reveal a slight tendency toward
manufacturing specialisation. Two striking aspects emerge from the figures. Firstly, the
set of countries specialised in tourism has small dimensions. Secondly, the tourism-led
economies are all “sea and beach countries” with the exception of Andorra. Generally,
the dynamic of specialisation patterns is stable over the considered time frame.
To support the Balassa index analysis, the Normalised Balance index has been
computed. The latter is given by the ratio between the value of trade balance and the
value of total trade. This index, which takes into account both imports (m) and exports
(x), indicates the economic performance of a country i. It is defined as
 x ji − m ji 


+
x
m
ji
ji

.
NBji = 
Thus, the ratio ranges between –1 and 1. A Normalised Balance of 1 means the country
or region is completely specialised in the production of commodity j. A Normalised
Balance of –1 implies despecialisation. When the index is zero, imports and exports are
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International tourism specialisation of small countries with high growth rates
even. The NB values for the two considered macro sectors (manufacturing and tourism)
are reported in tab.4.
Tab.4 Normalized Balance.
International specialisation
Tourism
1985
1990
2003
-0.21
-0.07
-0.08
China
0.78
0.84
0.86
Trinidad and Tobago
0.82
0.91
0.98
Antigua and Barbuda
0.68
0.72
0.74
Bermuda
0.78
0.74
0.76
Channel Islands
0.68
0.71
0.72
St.Vincent and the Grenadines
0.48
0.45
0.51
Bardados
0.88
0.88
0.86
St. Kitt and Nevis
0.66
0.71
0.72
Bahamas
0.26
0.27
0.29
Aruba
0.46
0.46
0.51
Singapore
0.48
0.45
0.51
Cayman Islands
-0.05
0.03
-0.01
Malaysia
0.81
0.86
0.91
Antilles
0.78
0.71
0.79
French Polynesia
-0.37
0.01
-0.01
Chile
-0.12
-0.05
-0.02
Korea Rep.
0.68
0.75
0.82
Faeroe Islands
0.81
0.83
0.82
Mauritius
0.68
0.74
0.77
Andorra
0.48
0.44
0.5
Cyprus
0.29
0.27
0.23
Malta
0.7
0.6
0.7
Seychelles
-0.02
-0.01
0.01
Thailand
…
…
…
Hong Kong, China
Source: Own calculations on Tourism Economic Report and Comtrade dataset, 2004
1985
0.59
-0.59
-0.74
-0.54
-0.32
-0.24
-0.09
…
-0.54
-0.01
-0.07
-0.09
0.28
-0.74
0.07
0.27
0.31
-0.54
-0.44
-0.54
-0.09
-0.01
-0.55
0.61
0.08
Manufacturing
1990
0.65
-0.61
-0.84
-0.54
-0.31
-0.23
-0.15
-0.59
-0.56
-0.04
-0.07
-0.19
0.45
-0.81
-0.09
0.32
0.68
-0.56
-0.46
-0.55
-0.15
0.24
-0.53
0.67
0.73
2003
0.9
-0.64
-0.86
-0.43
-0.24
-0.27
-0.12
-0.64
-0.55
-0.03
-0.02
-0.22
0.44
-0.81
-0.22
0.3
0.83
-0.55
-0.5
-0.51
-0.12
0.36
-0.63
0.68
0.85
The results are in line with the Balassa index. 21 out 25 countries with the fastest
growth rate, are specialised in tourism. Antigua & Barbuda and Kitts and Nevis record
the highest rate, Malta the smallest ones. The “small size” characteristic of the tourism
based countries testifies that the so called ‘scale effect’, i.e. the tendency for large
countries to grow faster than small ones, does not always occurs. In particular, it is
plausible that the smaller is a small economy, the easier the pattern of the term of trade
offsets the technology gap disadvantage. This outcome is in accordance with a work by
Candela and Cellini (1997). The two authors prove that “the opportunity cost of
specialisation in tourism is smaller, the smaller is the country”1. In particular, they
consider two countries with different sizes (i and j) and dissimilar labour force (Lj>Li).
They assume an exogenous constant positive growth rate of the terms of trade p≡pT/pM
(with T=tourism, M=manufacturing) and provide evidence that each country has a
growth maximizing choice which depends on its size. If p′/p > (λM−λT)*Lj (with λ =
1
Candela G. e Cellini R. (1997), Countries’ size, consumers’ preferences and specialization in tourism;
Rivista internazionale di Scienze Economiche e Commerciali, N44 pag.451-57.
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International tourism specialisation of small countries with high growth rates
learning by doing factor) for the smallest size type country is convenient to specialise in
tourism.
Figure 1 depicts the calculated normalised balances.
Fig. 1 International specialization in tourism (Normalised Balance).
1
0.8
0.6
0.4
0.2
1985
1990
2003
0
-0.2
Thailand
Hong Kong, China
Malta
Seychelles
Cyprus
Andorra
Mauritius
Faeroe Islands
Chile
Korea Rep.
Antilles
French Polynesia
Malaysia
Cayman Islands
Aruba
Singapore
Bahamas
Bardados
St. Kitt and Nevis
St.Vincent and the Grenadines
Bermuda
Channel Islands
Trinidad and Tobago
Antigua and Barbuda
China
-0.4
Source: Elaboration on Tourism Economic Report 2004
3. Tourism and Growth
The second step of the analysis is to investigate the long run benefit associated
with a specialisation in tourism.
It is a common idea that high growth rates of per capita income are triggered by
a strong manufacturing sector, because the latter is able to generate technological
innovation. In addition, since manufacturing is more productive than services and,
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International tourism specialisation of small countries with high growth rates
hence than tourism, the price of manufactured goods are generally lower than those by
tourism goods. The price dynamics are important to compare the growth rate associated
to different specialisation (tourism and manufacturing) patterns.
We extend Lucas’ two sector model (1988) to include tourism (T) and
manufacturing (M). The hypotheses underlying the model are: perfect competition, no
distortions and no externalities.
It is supposed that the engine of growth, the accumulation of human capital (H), takes
the form of learning by doing at sector level. We assume further, that there is only one
productive input, labour.
The production function into the two sectors is the following:
with (i=M,T)
y i ≡ H i ⋅ Li
where
M=manufacturing T=tourism
H= human capital,
L= labour force allocated to each sector.
Since manufacturing is more productive than tourism, called λi the learning by
doing factor, it turns out that λm>λt. Put differently, human capital accumulation is
faster in manufacturing than in tourism when the fixed factor is allocated similarly.
Data taken from OECD confirms this feature. For instance, while output per
employee in the service sector grew by an average of 1,8% per year between 19651996, in manufacturing it increased by 3,9% per year.
The accumulation function of Hi, which results from the learning by doing
process is the following:
Hi′= λi *yi
the growth rate of sector “i” is
Hi′/Hi=λi*Li.
When international trade develops, countries tend to specialise according to their
comparative advantages. The growth rate will strongly depend on the type of
specialisation adopted. With Li normalised to 1, output raises according to:
y′/y= λi.
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International tourism specialisation of small countries with high growth rates
This equation confirms that productivity grows faster for manufacturing specialised
economies than for tourism specialised countries, being λM> λT.
Under
-CES international utility function
U(yM,yT)=[αM yM -ρ +αT yT -ρ ] –1/ρ
-Relative demand function
yT/yM=(αT/αM)σ(pT/pM)-σ
(where σ=1/(1+ρ))
-Rate of change of the terms of trade
p≡pT/pM
-Rate of growth of tourism:
γT=γT + p′/p
and complete specialization
we can express the international relative price as function of quantities. Taking the logs
and differentiating we obtain:
p′/p=[(yM′/yM)- (yT′/yT)]/σ= λΜ−λΤ/σ
γΜ can be bigger or less than γΤ according to the value of σ. Precisely, γΜ≥γΤ if the
elasticity of substitution between tourism and manufacturing is σ≥1, and vice-versa,
γΜ<γΤ if the elasticity of substitution between tourism and manufacturing is σ<1. When
σ<1 tourism is the growth-maximising factor, while if σ>1 manufacturing maximises
growth.
Lucas (1988) in his paper argued that dealing with the problem of uneven
growth: “the interesting case … is when σ>1, since it rules out the possibility of
‘immiserising growth’”, nevertheless as “manufacturing” and “tourism” are distant
substitutes, tourism can be an alternative form to the economic development of an
economy when σ<1 .
9
International tourism specialisation of small countries with high growth rates
4. An Econometric Analysis: Theoretical bases and previous studies
Tourism’s growing contribution to national economies has been accompanied by
the need to understand the main factors which can determine demand levels. Tourism
demand can be assessed by a host of variables including tourist arrivals, overnight stays,
real revenues, real expenditures and visit per head of the origin’s population. In the
literature, most studies have attempted to model tourism demand either within a time
series framework (both single equation models and system of equations models) or
within gravity type models.
Single equation models have been adopted by Loeb (1982), Uysal and Crompton
(1984), Martin and Witt (1989), Crouch et al. (1992), Chan (1993), Morley (1994),
Morris (1995), Walsh (1996). These authors have used least square regressions to
compute the level of tourist arrivals in a particular country as a linear function of the
factors (such as income, price, and special events) that influence arrivals. Remaining
autocorrelations have been corrected using the Cochrane-Ortcutt procedures.
Shortcomings of these studies are embedded into the stationary paradigm they adopt. In
fact, if variables are not stationary, results will be spurious and estimates will be biased.
The considerable advances in econometric methodology during recent years have
brought many authors such as Gonzales and Moral (1995), Kulendran (1996),
Kulendran and King (1997), Chu (1998), Lathiras and Siriopoulos (1998), Lim (2000),
Payne and Mervan (2002), Hellström (2002), Ferro Luzzi and Flückiger (2003), to
adopt more sophisticated approaches. All of them operate within the framework of
structural models.
Systems of equations models have been used by Brau (1995), Durbarry (2000).
Both authors apply Deaton and Muellbauer’s (1980) Almost Ideal Demand System for
modelling special aspects of tourism. The system incorporates the axioms of consumer
choice and the stage budgeting process. It is above all implemented to explain the
allocation of tourism expenditure among different countries (White, 1985; O’Hahan and
Harrison, 1984; Syriopolos and Sinclair, 1993; Sinclair and Syriopoulos, 2000; De
Mello and Sinclair, 2002). The Almost Ideal Demand System in making use of a set of
equations, is apt especially in clarifying a country’s outbound tourism demand in
particular destinations. Such models sort out own and cross price elasticities, as well as
10
International tourism specialisation of small countries with high growth rates
the income elasticity for a destination country competing for tourists from a particular
origin. These entitle the destination countries to locate their position regarding changes
in their own pricing policies and those of competing destinations. The problems linked
to the system of equations are above all the huge amount of data and long time series
they need.
Gravity models are spatial models which have been adopted in the past to
predict tourism demand. These models rest on Newton’s gravitational law according to
which the degree of interaction between two geographic areas is directly linked to the
level of concentration of people in the two areas and inversely linked to their spatial
distance.
4.1 Model specification
A quantitative approach based on time series analysis has been adopted to assess
the effects of changes in competitiveness and income on tourism demand for the group
of small tourism-based countries2. In this context, a VAR methodology seems to be
suitable to appraise the impact of the considered variables, since it not only resembles
simultaneous equation modelling in that it considers several endogenous variables
together, but also each endogenous variable is explained by its lagged values and the
lagged values of all other endogenous variables in the model. Therefore, the chosen
dynamic cointegration technique permits inter-temporal relationships among variables,
their non-stationary characteristic and endogeneity to be singled out. In our model, real
revenue from visitors has been considered for measuring tourism demand. This allows
for evaluation of the financial impact of tourist activity in real terms. Data have been
collected from World Tourism Organization dataset. The chosen explanatory variables
are income and price variables. Income is expressed in terms of real world GDP.
Figures have been taken from the IMF data stream. The price variable is broken down
into two additional series: the cost of living for tourists in the chosen destination (CL)
and the cost of travel to the destination (CT). The cost of living index is constructed as
the ratio between the country’s CPI and an aggregate of CPI of the client countries
2
Trinidad and Tobago, Antigua and Barbuda, Bermuda, Channel Islands, St.Vincent and the Grenadines,
Barbados, St. Kitt and Nevis, Bahamas, Aruba, Cayman Islands, Antilles, French Polynesia, Malaysia,
Faeroe Islands, Mauritius, Cyprus, Malta, Seychelles.
11
International tourism specialisation of small countries with high growth rates
adjusted by the nominal effective exchange rate. Tourists are usually more aware of
exchange rates than they are of relative prices because they are better informed about
the former. Nevertheless, albeit the exchange rate in a given country may become more
favourable to tourists, its apparent cheapness could still be offset by a high inflation
rate. The cost of living index is formalised as:

 CPI tourismcou ntries
CL ≡ 
 CPI i ⋅ E$
i






where E$/i (source: IMF) is the nominal effective exchange rate, CPItourismcountries and
CPIi (source: IMF) are the price indices of the sample and of a group of countries,
namely the Euro area and USA, which represent more than 78% of tourist visits to the
tourism destination. The weighting system reflects the relative importance of each
country as far as the number of tourists is concerned. This index gives a clear picture of
the effective price impact for tourists as it also takes inflation differentials into
consideration. An increase in the cost of living index implies a reduced competitiveness
in their tourist destinations.
Costs of travel are a significant component of the price of a tourism product, but
they are particularly difficult to measure. In our analysis, we have adopted the
international air fares index, provided by the US Bureau of Transportation Statistics,
since the small-size country group is mainly reachable by flights.
In detail, the following specification has been estimated:
Rev = f (CL, WGDP, CT)
where
Rev are the tourism receipts expressed in US$ ;
CL is the cost of living index expressed in terms of CPI and nominal effective exchange rate;
WGDP is the real world GDP;
CT is the cost of transport.
Quarterly data ranging from 1990:12 to 2003:12 have been considered.
12
International tourism specialisation of small countries with high growth rates
4.2 Variables Analysis
First, the quantitative variables have been transformed into natural logarithms,
namely, the natural logarithms of (1) real revenues (LREV), (2) cost of living (LCL),
(3) real world GDP (LWGDP) and (4) real cost of travel (LCT). The logarithmic form
has an added advantage in that the resultant coefficients are parameters which express
the elasticities of the variable included. The seasonal nature of world GDP and real cost
of travel have been overcome by seasonally adjusting the data using the Census X-11
procedure.
Second, the order of integration in each series is tested. The augmented DickeyFuller (ADF) test for the individual time series and their first differences are shown in
tab. 5.
Table 5. Augmented Dickey-Fuller Test
ADF-Test on level
lrev
lcl
lwgdpsa
lctsa
1
ADF-Test on first differences
-2.178293
-2.321425
-2.144035
-2.413009
2
-4.723591
-4.195321
-6.853506
-3.366204
1) 1% Critical Value -4.0503
5% Critical Value -3.4539
10% Critical Value -3.1523
2) 1% Critical Value -4.0512
5% Critical Value -3.4543
10% Critical Value -3.1526
Based on the results, the null hypothesis of non-stationarity of the variables
cannot be rejected. The calculated statistics exceed in fact, the Mac Kinnon’ critical
values. The ADF results show that all the variables are integrated of order one (I(1)) i.e.
the series become stationary after their first differentiation.
4.3 VAR-cointegration analysis
To determine whether the series are cointegrated and to identify the long-run
equilibrium, a VAR-based cointegration analysis using the methodology developed by
Johansen (1991) has been carried out. In our case, after normalising the cointegrating
vector, the demand equation can be written as:
13
International tourism specialisation of small countries with high growth rates
log REVt= η0+ηcllog CLt+ηwgdplogWGDPsat+ηct log CTsat + εt
where η0=log α is the intercept term and εt is the disturbance term which is
supposed to be normally distributed with zero mean and constant variance. To identify
the model, the five possibilities considered by Johansen (1995) have been tested,
specifically: 1) series have no deterministic trends and the cointegrating equations do
not have intercepts, 2) series have no deterministic trends and the cointegrating
equations have intercepts, 3) series have linear trends but the cointegrating equations
only have intercepts, 4) both series and the cointegrating equations have linear trends
and 5) series have quadratic trends and the cointegrating equations have linear trends.
The results indicate that the proper model is the fourth, i.e. both series and the
cointegrating equations have linear trends. The trend variable can be interpreted as a
proxy for tastes, i.e. it mirrors a steady change in the popularity of the holiday over the
estimation period as a result of changing tastes and preferences. However, the trend
variable also picks up the time effects of all other variables not explicitly included in the
equation.
Based on the trace statistic analysis, it results that the null hypothesis of no
cointegrating vector can be rejected at the 5% level. Consequently, real tourism revenue
(LREV), cost of living (LCL), real world GDP (LWGDP) and cost of travel (LCT) are
cointegrated and the normalised cointegration relationship is given by:
log REVt= -1.984370 *log (CL)t + 5.7856* log (WGDPSA)t –3.22345* log (CTSA)t +0.22trend
(-4.43621)
(2.27689)
(-3.14348)
(-2.00115)
R2=0.6547
where the numbers in brackets are t-statistics and R2 indicates the goodness of fit of the
model.
The properties of the residuals of the estimated model have been warily
evaluated. The Lagrange multiplier test for serial correlation (LM), indicates that the
null hypothesis of no serial correlation is not rejected, and hence serial correlation is
deemed to be absent in the residuals [Obs*R_square 0.092526<9.8 crit.].
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International tourism specialisation of small countries with high growth rates
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared
0.048476
0.092526
Probability
Probability
0.964114
0.962115
In line with literature, cost of living, world GDP, and cost of travel enter as highly
significant in the final equation and have the expected signs. A spur in a country’s cost
of living and an upsurge in air fares bring about drops in tourism revenues, whereas a
rise in world GDP leads to higher tourism receipts.
The coefficient on WGDP is positive, showing that tourism revenues are highly
sensitive to changes in world output. The estimated income elasticity (5.78), since it
exceeds unity, shows that foreign tourism is regarded as a “luxury”. Economic theory
considers, in fact, foreign holidays to be “superior” goods, and thus an increase in
income is expected to increase demand.
The cost of living index variable is negatively related to tourism revenues. The
coefficient of the price variable should be interpreted as an indicator of competitiveness
such that if the strong competition from alternative destinations is overcome, the payoff
could be significant. A real appreciation makes the destinations less competitive and
attractive for tourists. In particular, a 1% increase in the index induces a fall in revenues
of 1.98%.
The international air fares index enters the final equation with a negative sign,
meaning that a 1% increase in its value induces a revenue downturn of 3.22%. Also as
tourism is a luxury good, travel cost elasticities, reveal themselves to be
highly
sensitive to changes in prices.
The overall predictive power of the regression is satisfactory, as the R2 value is
about 65%. There is furthermore no autocorrelation in the residuals and there is a long
run relationship among variables.
5. Conclusions.
The present paper has shown that specialisation in tourism can be associated with
a fast growth rate. Tourism turns out to be a substantial world-wide source of income
for many economy.
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International tourism specialisation of small countries with high growth rates
21 out 25 countries with high growth rates in terms of per-capita income, are
specialised in tourism activities. These economies share some common characteristics: they are well endowed with high-quality natural attractions, - and they are of “small”
dimensions as defined by international trade theory.
Albeit the manufacturing sector is characterised by high productivity due to
ongoing product and process innovations, also tourism based economy can grow at a
non-decreasing rate and can promote sustainable economic development. Extending
Lucas’ (1988) “two sector model”, we have shown that the growth rates of the
manufacturing (γm) can be greater, smaller or equal to those generated by tourism
(γt). Specifically, growth depends on the values of the elasticity of substitution σ.
Specialisation in tourism is desirable only if σ<1.
In the last part of the work, an econometric analysis of the demand for tourism has
been carried out over the period 1990 to 2003. This approach allows us to identify the
underlying determinants of tourism receipts and to measure the impact of such factors
on international demand for the considered tourism-based countries. The results suggest
that cost of living and international air fares indices are highly significant factors of
tourism revenues for the tourism-based economies and all variables have the expected
signs. Findings obtained with regard to the income effect are very remarkable. An
increase in world GDP of 1% leads, in fact, to a rise in tourism revenues of about 5.8%.
This means that income tends to be the most important determinant of international
tourism demand.
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