Estimating Elasticity of Demand for Tourism in Dubai
Cedwyn Fernandes*
Associate Professor in Economics
Middlesex University
P.O Box 50067, Dubai, U.A.E.
Tel: +9714 3693972
Fax: +9714 3672956
Email: [email protected]
&
Ajit V Karnik
Professor in Economics
University of Wollongong in Dubai
P.O Box 20183, Dubai, U.A.E.
Tel: +9714 3672479
Fax: +9714 3672754
Email: [email protected]
* Corresponding author
Estimating Elasticity of Demand for Tourism in Dubai
Abstract
This study estimates the elasticity of demand for inbound tourism from 24 countries to
Dubai with a view to understand the factors that influence this demand. The variables
tourist arrivals, real per capita income , relative prices, accommodation costs were
tested for panel unit roots and panel cointegration was employed to determine the
specification of the models to be used. These models were estimated employing Fixed
Effects and Random Effects approaches. The choice between Fixed and Random Effects
models was made using the Hausman Test.
Determinants of the elasticity of demand for the entire panel are consistent with theory.
Within the subgroups there are differences. Developed Countries and Arab Countries
have an income-elasticity of demand > 1.Tourists from the Developed Countries seem
to be the most sensitive to relative prices and the cost of accommodation is significant
only for tourists from the Arab and Indian Sub-continent Countries.
Income elasticity of tourism especially from Arab countries is high indicating that
marketers should tailor their strategies accordingly. Accommodation costs have
negative impact on demand highlighting the need for more budget hotels. Relative
increase in prices has a negative impact on tourism demand, highlighting the need to
control domestic inflation.
Keywords:
Tourism Elasticity of Demand, Dubai Tourism Demand, Dubai Tourism
Estimating Elasticity of Demand for Tourism in Dubai
Introduction
The United Arab Emirates (UAE) is a federation of seven Emirates with Dubai,
perhaps, the most well known thanks to its iconic tourism projects. Burj Dubai (the
world’s tallest tower), The Palm (palm shaped man made islands which can be seen
even from outer space), Burj Al Arab (the sail boat shaped hotel in the sea which is also
the world’s tallest hotel), SkiDubai (an indoor ski resort) and the Dubai Shopping
Festival when the country opens it doors to over 3 million tourists over a 45 day period,
are some of Dubai’s well known tourist attractions. Dubai attracted over 6.44 million
tourists in 2007 (DTCMa 2007) despite being located in Middle East which is often
viewed as “unstable”.
The UAE is the third most attractive destination in Middle East Asia with 7.9
million international visitors in 2006 (WTO, 2007). Dubai accounts for more than 80%
of these visitors (DTCM, 2007a). The number of visitors to Dubai is remarkable given
that it is a new city with limited historic sites. However, Dubai is blessed with 365 days
of sunshine, excellent beaches, liberal living and a most modern city where the
infrastructure supporting tourism – airlines, airports, hotels, transport, shopping malls
and entertainment activities - are world class.
Tourism plays a critical role in the economy of Dubai. Unlike the oil-rich Emirate
of Abu Dhabi, oil accounted for less than 5% of Dubai’s GDP and the need to diversify
into non-oil areas is not out of choice, but necessity. One estimate is that Tourism
directly contributed 18% to Dubai’s GDP and 29%, indirectly (UAE Interact, 2007).
Under the Dubai Strategic Plan 2007-2015 tourism has been identified as one of the six
building blocks for future growth thereby enabling Dubai’s GDP to grow from US $
46.24b in 2006 to US $ 107 b in 2015 (Dubai Strategic Plan, 2007).
The number of tourists visiting Dubai has grown exponentially over the years.
Table 1 shows the number of visitors registered with Hotels and Hotel Apartments in
Dubai. In fact, the actual number of international visitors to Dubai is in excess of those
registered with Hotels and Hotel Apartments as many of them would be staying with
friends and relatives. However, due to the lack of consistent data on this segment of
international visitors, this study takes into consideration only those international tourists
staying in Hotels and Hotel Apartments.
Insert Table 1 about here
Given the importance of Tourism in Dubai’s economy it is critical that the growth
in tourists arrivals is sustained and that its position as the region’s premier destination is
maintained. In view of this, it is important to understand the factors that influence the
demand for inbound tourism to Dubai. This will enable (a) planners to ensure that
resources are allocated adequately, (b) policy makers to initiate tourist-friendly
measures in time and (c) marketers to use this information to streamline their marketing
plans.
This paper is organized as follows: Section 2 provides the literature review related
to modelling tourism demand. In Section 3, we explain the methodology for data
collection and results of the estimated model; Section 4 contains a discussion of the
results while Section 5 discusses implications of our findings and concludes.
Section 2
Determinants of Tourism Demand
A vast body of empirical work relating to tourism demand modelling exists, with
many of these studies relate to developed countries mainly in North America and
Europe. Crouch (1994), Witt and Witt (1995) and Lim (1997) provide an extensive
survey of tourism demand models. Song, et al (2003) evaluates the accuracy of six
alternative econometric models based on data on inbound international tourism for
Denmark. Algieri (2006) estimates a tourism demand model for Russia, Han and
Sinclair (2006) modelled US tourism demand for European destinations, Saayman and
Saayman (2008) identify the determinants of inbound tourism to South Africa. Akis
(1998) has developed a compact econometric model of tourism demand in Turkey while
Tan (2000) has done a detailed study on the determinants of inbound tourism to
Malaysia and Indonesia and has also provided an extensive survey of tourism demand
modelling for East and South East Indian Sub-Continent countries.
Not much work has been done in modelling tourist demand in the UAE. Anwar
and Sohail (2004) analyze the perception of first-time versus repeat visitors among
tourists visiting the UAE during festivals. Henderson (2006) provides insights as to how
Dubai has overcome barriers such as being located in the Middle East, lack of
conventional attractions, limited promotion, but has successfully overcome these
impediments in developing itself as a tourism destination. Balakrishnan (2008) uses a
branding framework model to study how Dubai has successfully branded itself as a
destination. No study thus far has attempted to empirically estimate the elasticity of
demand for tourism in Dubai; we believe this study will help rectify this gap.
Traditionally the factors that influence tourism demand relate to income, relative
prices, transport costs, exchange rates, supply constraints, marketing factors and dummy
variables to track major events. A variety of alternative specifications of the tourism
demand model exist. These include, dynamic elasticises (Morley 1998), habit
persistence (Lyssiotou, 2000), technology diffusion (Rosselló et al, 2005) and sunshine
days (Saayman and Saayman , 2008).
The specification of the tourism demand model for this study builds from the
concept that tourism is an invisible good and its demand is analogous to international
trade (Gunadhi and Boey, 1986). The quantity demanded of an invisible good will
depend on income, exchange rate adjusted relative prices, and, if relevant and required,
some dummy variables to account for any major events impacting demand.
This study employs panel data modelling in itd empirical exercises. Saayman and
Saayman (2008) highlight some of the advantages of using the panel data techniques
which are: larger number of observations, more informative data, less multicollinearity,
more degrees of freedom and efficient estimates. In our exercises, we employ data from
24 countries over a 10 year period giving us 240 data points to work with. The pooling
of cross-section and time-series data does imply that standard estimation techniques
cease to apply and special panel data estimation techniques, such as Fixed Effects or
Random Effects models – have to be used. Given that we have a uniform number of
observations for each country indicates that we employ a balanced panel. One final
consideration while employing panel data is whether one should use a static panel or a
dynamic panel. It is well known that once a lagged dependent variable is included as a
regressor, standard estimation of fixed effects model introduces biases. This is
especially true when the time dimension of the data is small. Judson and Owen (1996)
point out that even with a time dimension as large as 30, the bias may be as high as 20%
of the true value of the coefficient. The solution to this problem is using Generalised
Method of Moments (GMM) estimation techniques developed by Arellano and Bond
(1991) and Blundell and Bond (1998). The only problem with these techniques is that
they are appropriate for large number of cross-section units and as these increase the
bias reduces substantially (Judson and Owen, 1996). In view of the fact that for our
data, neither the time dimension is large nor is the number of cross-section very large,
we have opted for static panel data models. The estimation techniques that we shall
employ will be Fixed Effects model and Random Effects model and be guided by the
Hausman Test regarding the correct choice of the model.
Employing panel data notation, we express the demand for tourism as:
TAPCjDt = α0 + α1RGDPjt + α2RELPRICEjDt + α3ACCOMjDt + α4DUM + u
(1)
Where,
TAPCjDt = Ratio of tourist arrivals from country j to Dubai in period t to population of
country j in period t.
RGDPjt = Real per capita GDP of country j in period t
RELPRICEDjt = Ratio of GDP deflator of UAE in period t to GDP deflator of country j
in period t multiplied by the reciprocal of the nominal exchange rate.
Nominal exchange rate is understood as number of units of the Dirham that can be
purchased with one unit of country j's currency (Note: We had to use the GDP deflator
of UAE since a deflator for Dubai is not available).
ACCOMjDt = Cost of hotel stay plus cost of hotel apartment stay for tourists from
country j in Dubai in period t as a ratio to per capita real GDP of country j in period t.
Cost computed using average length of stay in each kind of accommodation. It may be
noted that even though this variable and the previous one (RELPRICEDjt) appear to
measure inflation (broadly defined) and inflation in accommodation prices, the
correlation between the two is quite low. It is as low as 0.3005 for the entire dataset,
0.2570 for the group of developed countries, -0.1050 for Arab countries and 0.1415 for
Indian sub-continent countries.
DUM = A dummy variable that takes on a value of one from 2002 onwards and zero for
earlier years. The dummy is meant to capture any post-9/11 effects on tourism.
u, e = Disturbance terms
α0…α4= Parameters to be estimated
Our expectations for the signs of the parameters are:
α1 > 0: Increase in real income will boost tourist demand
α2 < 0: Increase in relative prices will push down tourist demand
α3 < 0: An increase in the cost of accommodation stay as a proportion of per capita real
GDP will lower the number of tourists coming into Dubai
α5 > 0 or < 0: We are unable to anticipate the effect of 9/11 on tourism in Dubai. We
have reasons to believe that it would positive but we maintain an agnostic position.
As in conventional methodology of modelling tourist demand, a double-log
specification of (Equation 1) is adopted. The advantage of such a specification is that
the resulting coefficients can be interpreted directly as demand elasticises. The
dependent variable for this study is the ratio of tourist arrivals to the population of the
country of nationality of the tourists. Using this ratio instead of tourist arrivals effects a
normalisation that allows a comparison of tourist arrivals from countries with widely
different population levels. Data on tourist arrivals was obtained from DTCM (2007a).
To reiterate a point made earlier: we measure tourist arrivals into Dubai in terms of
guests staying in Dubai hotels and hotel apartments which would underestimate total
tourist arrivals since it excludes tourists staying with friends and relatives. Additionally
using data according to nationalities does pose a minor problem for tourists who may
normally be resident in countries other than their country of nationality. Such tourists
will be assumed to have originated in the country of their nationality and will also be
assumed to be affected by the same factors as their compatriots who are resident in their
country of nationality.
Table 2 shows the Number of Tourists by Nationality for 2006 from our sample
countries and show the percentage to the total number of International Hotel Guests. For
the year 2006, our sample covers 67% of the Total Hotel Guests in Dubai, Developed
countries account for 31.2%, AGCC and Arab Countries 26.93% and Indian SubContinent Countries 9.43 %. Interestingly India accounts for only 6.06%, Pakistan
2.74% and Bangladesh 0.64% of Hotel Guests in Dubai while theses nationalities
comprise of over 80% of the total resident population of Dubai (DTCM, 2007b). One
explanation for this discrepancy is that a large number of international visitors from
these Indian Sub-Continent countries stay with friends and relatives rather than as Hotel
Guests.
Insert Table 2 about here
Before we present our estimated models, some comments about the exact nature
of the data used are in order:
(i)
Real per capita GDP of the originating country converted to US $ was used
to represent the income variable.
(ii)
Relative prices in the origin countries and UAE were measured using the
GDP deflator in each of the countries. Ideally, the Consumer Price Index
(CPI) would have been a better measure of relative prices, however, data on
CPI was not available for many of the countries over the entire time period
of our analysis.
(iii)
A Dummy variable for the Year 2002 onwards has been introduced to
capture any changes in tourism demand post-9/11. Increase in demand for
tourism to Dubai post-2001 could be due to the stringent visa restrictions and
travel measures for tourist going to developed countries. The number of
tourists coming into Dubai between the years 2001 and 2002 increased by
25% from AGCC countries, by 45% from India, by 23% from Arab
countries and, surprisingly, by 30% even from the Developed Countries. Part
of this increase could also be attributed to the promotions done by Dubai
Department of Tourism and Commerce Marketing (DTCM) using the iconic
Burj Al Arab Hotel, which opened in 2000, put Dubai on the tourist map.
Section 3
Estimating the Tourism Demand Model
In this section we estimate tourist demand functions for Dubai. Panel data
technique has been used to estimate tourism demand model, Proença, S. & Soukiazis,
E., (2005), Roget and Gonzalez (2006), Van der Merwe et al (2007) and Saayman and
Saayman (2008) are some of the recent studies that have used this technique. The tourist
demand model as outlined in Eq (2) is estimated using a panel of 24 countries listed in
Table 3. The time period for our data is 1997 to 2006, yielding for the full panel 240
observations. We shall be employing the fixed effects approach and random effects
approach to estimating the model and all our variables have been transformed to logs.
As stated above, the Hausman test will help us choose between fixed effects and
random effects models.
Before we estimate the models that we have discussed we shall be testing the
variables for unit roots. Subsequent to unit root testing, we shall carry out panel
cointegration tests to determine which models may be estimated. Even though we have
specified our preferred model as (Equation 1) above, this equation will be valid only it
is a cointegrated equation. Moreover, it is possible that the cointegrating equation may
not be identical for all the subgroups that we have created. Tables 3 reports unit root
results for All Countries and subgroups.
Insert Table 3 about here
Panel Cointegration
We follow the tests proposed by Pedroni (1999) for testing for panel cointegration.
First, a proposed cointegrating regression is estimated:
y it = α i + δ i t + β 1i x1i ,t + β 2 i x 2 i ,t + ... + β M i x M i ,t + e i ,t
for t = 1,..., T ; i = 1,..., N ; m = 1,..., M
(2)
where T is the number of time periods for which data are available, N is the number of
cross-section units and M is the number of regression variables. All the parameters – the
intercept, the slope of the trend variable t and the slope parameters of the x-variables –
are permitted to vary across cross-section units.
In Table 4 we report the panel cointegration statistics for each group of
countries. Even though we have tested the null of no cointegration, we report the
statistics for cointegrating equations with the maximum possible number of regressors.
Insert Table 4 about here
Estimated Tourist Demand Models
The results of Table 4 allow us to identify the following cointegrating equations
the estimated parameters are given in Table 5:
Insert Table 5 about here
1. All Countries: TAPC Æ RGDP, RELPRICE and ACCOM
2. Developed Countries: TAPC Æ RGDP and RELPRICE
3. Arab Countries: TAPC Æ RGDP and ACCOM
4. Indian Sub-continent Countries: TAPC Æ ACCOM
The cointegrating equations reported in the previous sub-section advise us regarding
the tourist demand models that we must estimate for each group of countries. We shall
be estimating the Fixed Effects (FE) and the Random Effects (RE) model for each subgroup. It may be noted that in addition to the regressors indicated by the cointegrating
equations, we shall include in each model a dummy variable to capture the effects of
9/11 on tourism. The choice of model – whether FE or RE – for each sub-group will be
guided by the Hausman test. We shall report only the model that is suggested by the
Hausman test.
The major results to emerge from our estimation are:
1. The Hausman test for all sub-groups suggests that FE models are adequate.
2. All the equations reported in Table 5 yield good but dissimilar results across the
sub-groups of countries. The within transformation R-squared is seen to be
reasonably high for all the equations. F-statistics for group effects is also highly
significant. This tells us that it is appropriate to distinguish among the countries in
each equation and that estimating a pooled OLS without group effects would have
been inappropriate.
3. For the All Countries group (Eq. A, Table 5), we have been able to estimate the
most elaborate model based on the cointegrating equation. The elasticity tourist
demand with respect to RGDP is greater than unity; the elasticity with respect to
RELPRICE is negative and the elasticity with respect to ACCOM though negative
is not significant.
4. For the Developed Countries group (Eq. B, Table 5), the elasticity of tourist
demand with respect to RGDP is relatively elastic at 1.53 and the elasticity with
respect to RELPRICE is expectedly negative. ACCOM was not a relevant variable
as far as this group was concerned.
5.
For the Arab group of countries (Eq. C, Table 5), the elasticity with respect to
RGDP is as high as 2.54. This is not only numerically larger than unity but is in
fact statistically significantly greater than unity. The elasticity with respect of
ACCOM is significantly negative.
6.
The model that we are able to estimate for the Indian Sub-continent countries (Eq.
D, Table 5) is the simplest of all. It has only one regressor, namely ACCOM, and
the elasticity is expectedly negative.
7.
The dummy variable to capture the effect of 9/11 on tourist demand was
significantly positive for all groups of countries.
Section 4
Discussion and Implications
The estimated equations reported in Table 5 show clearly that our findings are
consistent with theory with regards to income, relative prices, exchange rates and cost
factors. For the entire panel the estimate of income-elasticity of demand is greater
than 1. For Arab Countries the income-elasticity of demand is 2.54. Marketers should
focus their attention to the Arab region as Per Capita incomes, especially in the Gulf
Cooperation Countries (Saudi Arabia, Kuwait, Oman, Qatar and Oman)
are
increasing at a fast pace and the demand for tourism from these areas will see an
exponential growth.
The income-elasticity for the Indian sub-continent group is not significant. The
reason for this could be that the GDP per capita variable may not be a good proxy for
tracking income levels in the Indian sub-continent. Tourists from these countries
mainly come from the urban middle class representing just 4.5 % of the population. In
any case, a rise in urban incomes in the Indian sub-continent will increase the inflow
of tourists into Dubai. To meet the increased demand Dubai must ensure that the
tourism infrastructure is ready to cope with it if it is to meet its target of attracting 15
million visitors a year by 2015. There are a large number of tourism related projects
underway for example, the Bawadi Hotel Development is a Las Vegas style strip of
51 hotels and 6000 hotel rooms, in addition to 40,000 sq ft of retail space which
should be ready by 2011(Bawadi, 2008). Dubailand, which will add 3 billion square
feet of world class Theme Parks as well as projects related to Culture, Well-being,
Sports, Shopping, Hospitality and Entertainment, is set to launch its first phase in
December 2010 (Dubailand, 2008). In addition to these large scale projects, a number
of hotel chains will begin operations which will increase accommodation capacity
tremendously.
It does seem that Dubai is ready to double its tourist numbers by 2015 and has
invested in the infrastructure to make it happen. However, the success of these
projects hinges on quick and relatively cheap transport options that should be
available to the tourists.
One of the issues of concern in Dubai are the rapidly rising prices. UAE inflation
rate of 11% in 2007 (Khaleej Times, 2008) will diminish the attractiveness of Dubai
as a tourist destination. Tourists from the Developed Countries seem to be the most
sensitive to relative prices as seen from Eq. A (Table 5). Given that tourists from
developed countries are almost one-third of total tourist arrivals, this sensitivity to
rising prices in the UAE needs careful attention in the form of policy initiatives to
control domestic inflation. The impact of relative inflation will be felt all the more if
the Dirham strengthens against most Developed Countries currencies. The impact of
relative prices on the Arab Countries group is not significant. One reason for this
could be that other factors such as proximity or difficulty in travelling to alternative
Western destinations dominate. For the Indian Sub-Continent Countries group as well
relative prices are not significant. Perhaps this could be due to the majority of the
visitors in the Indian Sub-Continent Group in our sample were could be on business
where shopping is not their prime motivation to take the trip. This, of course, should
not lull the UAE into thinking that inflation will not deter tourists from the Indian
Sub-continent.
The cost of accommodation is significant for tourists from the Arab and Indian
Sub-continent countries and not for those from Developed countries. For the time
period under consideration most of the developed countries currencies appreciated
over 30% when compared to the UAE Dirham and hence perhaps the rise in
accommodation costs were mitigated. . Dubai had the highest average room rate of
US $ 280 according to SR Global Consultancy (Gulf news, 2008). From a policy point
of view what this calls is development of hotel accommodation not only at the higher
end but also cater to those who are looking for more economic options, especially if
Dubai has to attract price sensitive tourists. The consultancy firm Lodging
Econometrics estimates just 15% of hotels in the Middle East region can be classified
as Budget hotels (The National, 2008).
The Dummy variable for post-2001 is significant for all groups. This is indicative
of two forces in operation: one, post-9/11 tourism to the region increased from Arab
and Indian Sub-Continent countries and, two, the success of Dubai in aggressively
marketing itself, especially in Western countries, has led to an increase in demand for
tourism.
Section 5
Conclusions
The main purpose for this paper was to estimate the demand elasticises for
tourism to Dubai. We are also interested in segmenting tourists according to their point
of origin and investigating if there are important behavioural differences. Importance of
these differences would mean that appropriate tourism promotion would have to be
targeted at specific segments of tourists.
Our results indicate that the demand for tourism in Dubai by and large follows
the conventional theoretical expectations though there may be differences across subgroups. Demand is seen to be highly income elastic right across the spectrum though
possibly not as much for tourists coming from Developed countries. Tourists from the
Developed countries are price sensitive which suggests that policies that moderate
inflation in the UAE will see some benefits. Accommodation costs are significant in
determining demand for tourists from the Arab world and from the Indian SubContinent Group of countries. Hotels which cater to the budget traveller might have to
be offered for tourists from this region.
This study is one of the first attempts in estimating the elasticity of demand for
tourism in Dubai and can be used as a starting point for more in depth studies.
International visitors that stay with friends and relatives consist an important segment
and should be included if data are available. To measure relative prices, the construction
of a Shopping Price index would be more accurate to use when measuring relative
prices. Another area to focus on is the tourists spend from each of the countries and
based on these data marketing strategies can be put into place.
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TABLE 1
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Hotel & Hotel Apartment Guests in Dubai
Hotels
Hotel Apartments
Total
% Increase
1,791,994
322,896
2,114,890
(84.73)
(15.27)
2,184,292
359,797
2,544,089
(85.86)
(14.14)
20.29%
2,480,821
545,913
3,026,734
(81.96)
(18.04)
18.97%
2,835,638
584,571
3,420,209
(82.91)
(17.04)
13.00%
3,064,701
561,924
3,626,625
(84.51)
(15.49)
6.04%
4,107,236
649,044
4,756,280
(86.53)
(13.65)
31.15%
4,342,341
637,887
4,980,228
(87.19)
(12.81)
4.71%
4,724,543
696,181
5,420,724
(87.16)
(12.84)
8.84%
5,294,485
865,518
6,160,003
(85.95)
(14.05)
13.64%
5,473,509
968,161
6,441,670
(84.97)
(15.03)
4.57%
Note: Figures in brackets are percentages to total
Source: DTCM 2007a
Table 2
Hotel Guests by Nationality - 2006
2006
Canada
Denmark
Finland
France
Germany
Italy
Japan
Netherlands
Norway
Russia
Spain
Sweden
UK
USA
Total Developed Countries
Bangladesh
India
Pakistan
Total Indian Sub-Continent Countries
AGCC
Egypt
Iran
Jordan
Lebanon
Sudan
Syria
Total Arab Countries
Source: DTCM Reports
% Total
Tourists
72,098
13,210
14,699
103,821
252,977
79,016
81,346
64,558
18,430
276,921
17,112
23,261
687,138
313,004
1.12
0.21
0.23
1.61
3.93
1.23
1.26
1.00
0.29
4.30
0.27
0.36
10.67
4.86
2,017,591
31.32
41,245
389,262
177,094
607,601
1040939
109,247
0.64
6.04
2.75
9.43
16.16
1.70
341,876
5.31
70,743
1.10
84,080
41,035
47,095
1,735,015
1.31
0.64
0.73
26.93
Table 3
Panel Unit Roots
All Countries
TAPC*
RGDP*
RELPRICE*
ACCOM*
LEVELS
W-tbar
t-bar
2.136
(0.98) -0.80 (> 0.05)
1.454
(0.93) -0.97 (> 0.05)
0.041
(0.52) -1.31 (> 0.05)
-1.477
(0.07)
-1.68 (> 0.05)
FIRST DIFFERENCE
W-tbar
t-bar
-1.973
(0.02)
-1.80
(> 0.05)
-4.395
(0.00)
-2.30
(< 0.05)
-8.693
(0.00)
-3.43
(< 0.05)
-2.025
(0.02)
-1.81
(> 0.05)
Developed Countries
TAPC*
RGDP*
RELPRICE*
ACCOM
LEVELS
W-tbar
t-bar
1.596
(0.95) -1.37 (> 0.05)
0.459
(0.68) -1.76 (> 0.05)
2.47
(0.99)
-1.07 (> 0.05)
-2.36
(0.00)
-2.72 (< 0.05)
FIRST DIFFERENCE
W-tbar
t-bar
-7.75
(0.00)
-4.56
(< 0.05)
-2.51
(0.01)
-2.77
(< 0.05)
-8.48
(0.00)
-4.80
(<0.05)
-
LEVELS
W-tbar
t-bar
-0.355
(0.36)
-1.48 (> 0.05)
0.929
(0.82) -1.47 (> 0.05)
-7.624
(0.00)
-5.59 (< 0.05)
-0.824
(0.21)
-1.69 (< 0.05)
FIRST DIFFERENCE
W-tbar
t-bar
-1.150
(0.13)
-1.84
(> 0.05)
-1.243
(0.11)
-2.51
(< 0.05)
-2.158
(0.02)
-2.29
(< 0.05)
Arab Countries
TAPC*
RGDP*
RELPRICE
ACCOM*
Indian Sub-continent Countries
TAPC*
RGDP
RELPRICE
ACCOM*
LEVELS
W-tbar
t-bar
-0.187
(0.43)
-2.05 (> 0.05)
0.677
(0.75)
-1.42 (> 0.05)
1.955
(0.98)
-0.48 (> 0.05)
-0.996
(0.16)
-2.65 (< 0.05)
FIRST DIFFERENCE
W-tbar
t-bar
-12.973 (0.00)
-11.47 (< 0.05)
-0.810
(0.21)
-2.51
(< 0.05)
0.751
(0.77)
-1.36
(> 0.05)
-4.952
(0.00)
-5.56
(< 0.05)
Note: Figures in brackets are p-values. For the t-bar statistic, the critical value for T = 25 and N = 100
(the maximum that has been tabulated) at the 5% level has been used from Im et al (2003, pp. 61-62).
* Denotes variables that are stationary in first differences and will be included in the estimated models.
Table 4
Panel Cointegration Results: Dependent Variable: TAPC
REGRESSORS Æ
ALL
COUNTRIES
DEVELOPED
COUNTRIES
ARAB
COUNTRIES
RGDP,
RELPRICE
ACCOM
RGDP
RELPRICE
RGDP
ACCOM
INDIAN SUBCONTINENT
COUNTRIES
ACCOM
panel v-stat
panel rho-stat
panel pp-stat
panel adf-stat
1.5291
3.5528
-6.2654***
-3.9097***
0.7483
1.5346
-5.0200***
-4.0581***
1.8293
1.0386
-3.0646***
-2.6009***
0.3198
-0.0918
-6.0029***
-4.6829***
group rho-stat
group pp-stat
group adf-stat
5.4883
-6.3378***
-7.4491***
2.9184
-5.7288***
-5.3935***
2.2311
-2.3366***
-2.8837***
0.8776
-4.5370***
-3.5937***
*** denotes significant value of the relevant statistic indicating that the regressors listed in each column
along with TAPC form a cointegrating equation.
TABLE 5
Estimated Tourist Demand Models
Regressors ↓
RGDP
RELPRICE
ACCOM
DUM
Indian SubAll
Developed Arab
Continent
Countries Countries Countries
Countries
(Eq. A)
(Eq. B)
(Eq. C)
(Eq. D)
1.3956
1.5280
2.5419
(0.00)
(0.01)
(0.00)
-0.0705
-0.0541
(0.00)
(0.07)
-0.0401
-0.3456
-0.2229
(0.61)
(0.00)
(0.09)
0.4702
0.5649
0.3181
0.4178
(0.00)
(0.00)
(0.00)
(0.01)
0.5979
0.5957
0.8184
0.6118
Within
Transformation
R-Square
Significance of
196.12
69.33
1395.00
195.37
Group Effects: F=
(<0.01)
(<0.01)
(<0.01)
(<0.01)
Hausman Test
0.1555
0.5674
0.2829
-0.0001
Model Chosen
FE
FE
FE
FE
No. of Obs.
240
140
70
30
Note:
(1) Figures in brackets are p-values
(2) All variables (except DUM) are logged
(3) For a fixed effects model with dummies for each country, the intercept term is
suppressed to avoid perfect multicollinearity.