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Current Issues in Tourism
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Small sample evidence on the tourismled growth hypothesis in Lebanon
a
Chor Foon Tang & Salah Abosedra
b
a
Department of Economics, Faculty of Economics and
Administration, University of Malaya, Kuala Lumpur, Malaysia
b
Department of Economics, Lebanese American University,
Beirut, Lebanon
Published online: 24 Oct 2012.
To cite this article: Chor Foon Tang & Salah Abosedra (2014) Small sample evidence on the
tourism-led growth hypothesis in Lebanon, Current Issues in Tourism, 17:3, 234-246, DOI:
10.1080/13683500.2012.732044
To link to this article: http://dx.doi.org/10.1080/13683500.2012.732044
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Current Issues in Tourism, 2014
Vol. 17, No. 3, 234 –246, http://dx.doi.org/10.1080/13683500.2012.732044
Small sample evidence on the tourism-led growth hypothesis in
Lebanon
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Chor Foon Tanga∗ and Salah Abosedrab
a
Department of Economics, Faculty of Economics and Administration, University of Malaya,
Kuala Lumpur, Malaysia; bDepartment of Economics, Lebanese American University, Beirut,
Lebanon
(Received 16 March 2012; final version received 26 August 2012)
This paper investigates the contribution of tourism to economic growth in Lebanon for
the time period of 1995–2010. The presence of long-run and causal relationships is
investigated applying the bounds testing approach to cointegration and Granger
causality tests. Because of the small sample (T ¼ 16), econometric approaches and
critical values used for testing receive special attention. Additionally, a number of
diagnostic tests are utilised to ensure that the model is suitable and correct.
Interestingly, our results reveal that tourism and economic growth are cointegrated.
The Granger causality test indicates that the tourism-led growth hypothesis is valid
for Lebanon. Therefore, policy initiatives promoting tourism ought to be further
developed and implemented to stimulate economic growth and development for the
economy of Lebanon.
Keywords: causality; tourism; growth; Lebanon; small sample
1. Introduction
International tourism activities increased substantially during the latter half of the twentieth century and early part of the twenty-first century. Statistics for international tourist arrivals witnessed an estimated annual growth rate of 6.4% between 1950 and 2008. During
the same time period, international tourism receipts increased from USD2.1 billion to
USD942 billion at an average annual growth rate of 11%. This rate of growth is significant
because it is higher than the growth rate of the world economy during the same time
period. In recent years, substantial growth occurred in tourist arrivals and tourism receipts
amongst developing nations. This is apart from, and in addition to, the more traditional
tourist-receiving regions of Europe and the Americas. This growth rate is maintained,
in large part, because of the flexible and responsive nature of tourists’ plans and the
expanding list of worldwide destinations and year-round tourism-related activities
offered to consumers. Tourism sector revenues are therefore, for many developing
nations, an increasingly important source of revenue to maintain and increase over the
short and long runs.
In the past two decades, a number of events and crises negatively impacted the world
tourism industry (Tang, 2011a): for example, the Asian financial crisis of 1997/1998, the
∗
Corresponding author. Email: [email protected]
# 2012 Taylor & Francis
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Current Issues in Tourism
235
terrorist attacks in New York on 11 September 2001, the Bali Bombing in October 2002, the
severe acute respiratory syndrome outbreak and avian flu pandemic of 2003– 2005, the
2004 Indian Ocean earthquake and tsunami, and more recently the global financial crisis
of 2008 and subsequent global recession. Despite obvious short-term negative impacts,
such events viewed individually and collectively rarely, if ever, cause a lasting downturn
for the worldwide tourism industry.
With that said, these events and crises, individually and/or collectively, rarely precipitate any lasting recession within the international tourism industry. A plausible explanation
for the ability of the industry to recover and recoup quickly is that tourism expenditures are
most often discretionary in nature. Tourists facing challenging economic times or affected
by negative events and crises (e.g. terrorism, health scares and weather disasters) will
usually modify the length of their stay or simply visit an alternative destination rather
than abstaining from travel all together. The United Nations World Tourism Organization
identified over 180 supply-side activities connected to the tourism sector. Amongst the
major connected sectors are transportation, communication, accommodations, banking
and finance, cultural and attractions services, and promotion services.
For the reasons given above, tourism has become an increasingly important economic
factor for a number of developing countries. The tourism industry generates employment
opportunities, income and foreign exchange earnings that are utilised to pay for imports
and maintain the desired level of international reserves for many countries. Furthermore,
tourism plays an important role in stimulating investments in new infrastructure and
helps promote the attraction of new technologies. Because of the inherent benefits, many
governments attempt to, directly and indirectly, promote and support tourism as an important source of economic growth.
Because of the plausible and seemingly logical contribution of tourism to a country’s
overall economic growth, several researchers have empirically examined the role of tourism
in economic growth, also known as the tourism-led growth (TLG) hypothesis. Since international tourism is essentially a source of export earning, it is believed that the TLG hypothesis
is derived from the export-led growth (ELG) hypothesis, which claims that growth can be
increased by expanding the level of exports of an economy (Balassa, 1978).1
According to the neoclassical growth theory, expansion of exports could lead to output
growth through spill-over effects such as economies of scale, incentives for technological
improvement and more efficient skill of management owing to the pressure of foreign competition (Feder, 1982). Moreover, the Heckscher– Ohlin theory indicates that international
trade will enhance the level of productivity and raise the efficiency of resource allocation by
promoting specialisation in the production and export of goods that utilise its abundant
resources intensively. Motivated by these theoretical frameworks, many empirical works
have been completed to verify the validity of the ELG hypothesis. Generally, the empirical
results are varied amongst studies (see Giles & Williams, 2000a, 2000b). Some studies
support the ELG hypothesis (e.g. Bahmani-Oskooee & Alse, 1993; Dash, 2009; Tang,
2006; Xu, 1996), while other studies are less supportive (e.g. Bhagwati, 1988; Greenaway
& Sapsford, 1994; Jung & Marshall, 1985; Kunst & Marin, 1989; Shan & Sun, 1998).
Although the ELG hypothesis has been examined extensively in the literature, there are
surprisingly fewer studies investigating the empirical relationship between tourism and
economic growth – particularly for the Lebanese economy. Empirically examined,
however, the TLG hypothesis was tested in two such frameworks. First, the issue was
examined in a cross-country framework, where socioeconomic features amongst the
countries studied collectively were deemed similar (e.g. Lee & Chang, 2008; Narayan,
Narayan, Prasad, & Prasad, 2010; Po & Huang, 2008). Second, the issue was examined
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236
C.F. Tang and S. Abosedra
in several country-specific studies, rather than in the cross-country analysis in cases where
socioeconomic differences amongst the countries could not be ignored and may therefore
result in heterogeneity bias. Examples of country-specific frameworks on this topic are Belloumi (2010) for Tunisia; Akinboade and Braimoh (2010) for South Africa; Brida and Risso
(2009) for Chile; Brida, Pereyra, Risso, Such, and Zapata (2009) for Colombia; Brida,
Sanchez Carrrera, and Risso (2008) for Mexico; Corte´s and Pulina (2010) for Italy;
Gunduz and Hatemi-J (2005) for Turkey; Dritsakis (2004) for Greece; Balaguer and Cantavella-Jorda` (2002) for Spain; Louca (2006) for Cyprus; Narayan and Prasad (2003) for
Fiji; Tang (2011b, in press) for Malaysia; Shareef and McAleer (2007) for Maldives; and
Vanegas and Croes (2003) for Aruba. Such studies sought to determine whether (a)
tourism activity leads to economic growth, or (b) economic expansion drives tourism
activity, or (c) if a bi-directional relationship exists between the two variables. A majority
of studies found cointegration to exist between tourism and economic growth, but the
Granger causality direction varied amongst studies. Therefore, the causal relationship
between tourism and economic growth remains ambiguous. Owing to differences in estimation techniques, data span and proxy variable selection, the existing empirical studies
reveal mixed and conflicting causality results. Moreover, Ozturk (2010) stipulated that
differences in country characteristics such as differences in political, economic, cultural
and institutional policies are the key reasons for such inconclusive causality results.
Because the TLG study for the Lebanese economy is non-existent, it is implausible to
apply the results derived from other countries to formulate effective tourism and growth
policies for the Lebanese economy. Therefore, a TLG study for Lebanon is imperative.
In light of the non-existence of a TLG study for Lebanon, this study is the first attempt
to investigate the validity of the TLG hypothesis for the case of Lebanon for the time period
of 1995 – 2010. Whereas the Lebanese economy is currently in the process of considering
economic reform programmes, the results of this study will not only contribute to the existing literature on tourism and economics in Lebanon, but also set an initial framework to
guide policy-makers in Lebanon in determining whether or not investments should be channelled to expand and modernise the tourism sector. Thus, assessing the validity of the TLG
hypothesis is critically important for Lebanon as it undertakes potential reforms to current
economic and tourism development policies. Such policy reforms should be undertaken and
accomplished considering that the current tourism infrastructure is, relatively speaking, less
developed in Lebanon.
To achieve this particular objective, we apply the bounds testing approach to cointegration proposed by Pesaran, Shin, and Smith (2001) to analyse the presence of a long-run
relationship between tourism and economic growth. The Granger causality test is employed
to ascertain the direction of causality between tourism and economic growth. Because of the
finite sample of this study (T ¼ 16), the standard critical values are inappropriate since the
tests tend to reject the null hypothesis. Therefore, the critical values for a smaller sample
suggested by Turner (2006) are utilised by this study. The use of such appropriate critical
values ensures that the results of this study will be more accurate and reliable.
The remainder of this research paper is organised as follows. Section 2 briefly reviews
and describes Lebanon’s tourism profiles. Next, Section 3 discusses the econometric
methods and empirical results. Lastly, concluding remarks are provided in Section 4.
2. Lebanon’s tourism profiles
Lebanon is a sectarian-based parliamentary republic located in the Middle East with a population of approximately 4.2 million. The Lebanese economy is largely service oriented, with
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Current Issues in Tourism
237
the tourism and banking sectors as the main drivers, contributing over 70% of gross domestic product (GDP). Additional noteworthy contributors to the economy comprise the
industrial and agricultural sectors as well as the net remittances from the Lebanese Diaspora
living abroad. Although the country has a free-market economy, the investment environment has been negatively impacted for years as a result of the existence of corruption,
high taxes and breakable intellectual property rights.
Lebanon’s economy has faced numerous setbacks related to prolonged political unrest.
The civil war (1975 – 1990) had a significantly negative impact upon the nation, causing a
high budget deficit. Recent events contributing to the country’s political and social unrest
include the assassination of ex-Prime Minister Rafik Hariri in February 2005, the July 2006
war between Lebanon and Israel, the sit-ins, and the protests and clashes between the
opposing government alliances from 2006 to 2008.
Persistent instability and corruption in Lebanon contribute to increases in government
debt levels and growing sectarianism. Although an economic reform programme was previously attempted on several occasions during the 1990s and again in mid-2000s, it has
never been implemented as such. Political instability readily appears to be a significant
country risk and has a negative impact on Lebanon’s overall economy – which is only
exacerbated by the recent 2012 uprising in Syria. The banking sector is one of the key
pillars of the Lebanese economy with a size equivalent to 350% of GDP as of 2009. Lebanese banks benefit from a strong and consistent net inflow from expats and the Gulf States.
Lebanon’s banking sector has recently received worldwide recognition for its comparatively conservative approach, which allowed it to minimise the impact from recent
global financial crises.
In 2010, rising non-resident deposits, an elevated number of tourist arrivals, and a vigorous real-estate market all served to stimulate economic growth in Lebanon.
Nonetheless, due to political instability in the region, tourist arrivals and expatriate
housing demand in the real-estate sector slowed in 2011. In 2012, the continued political
and social unrest in Syria readily appears to threaten Lebanon’s economic growth prospects.
This is especially true where 25% of Lebanon’s exports are to Syria and 11% of its imports
are from Syria.
Development of the tourism industry in Lebanon dates back to the 1960s when Lebanon
first gained worldwide reputation as a tourist destination with its capital, Beirut, often being
referred to as the ‘Paris of the Middle East’ by visitors from around the world. Numerous
factors have historically contributed to tourism being an important component of Lebanon’s
overall economy. Key factors include, but are not limited to, the country’s diverse
cultural and welcoming atmosphere and its historical significance in terms of events and
landmarks, as well as its attractive natural landscape. The country’s historical
significance includes numerous ancient Roman ruin sites, preserved castles and several
historically notable mosques and churches. The natural landscape of the country includes
pristine beaches along the Mediterranean Sea as well as mountainous country with
summits reaching heights over 10,000 feet allowing for ski resort operations during the
ski season.
The percentage of tourism and travel in GDP stood at 31.2% as of 2005 and was
estimated at 37.6% in 2010 (Lanquar, 2011). This represents the highest ratio for these 2
years, 2005 and 2010, amongst the 11 Mediterranean (MED11) countries (Algeria,
Egypt, Israel, Jordan, Lebanon, Libya, Morocco, Palestinian Autonomy, Syria, Tunisia
and Turkey). Furthermore, the tourism sector in Lebanon contributes significantly to
employment opportunities available in the economy. Employment in the tourism sector,
as a share of total employment, was 31.2% in 2005 and estimated at 38% in 2010. This
238
C.F. Tang and S. Abosedra
Table 1. Some tourism indicators of Lebanon, 1995–2010.
Subperiod
International tourist
arrivals (’000s)
Tourism and travel
in GDP (%)
Employment in the tourist sector as a
share of total employment
450
742
1140
2120
9.6
9.3
31.2
37.6
8.8
8.8
31.2
38.2
1995
2000
2005
2010/est.
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Source: Lanquar (2011).
also represents the highest ratio for these 2 years, 2005 and 2010, amongst the MED11
countries (see Table 1).
The Lebanese Civil War, which continued from 1975 to 1990, devastated the country’s
tourism industry. The country’s overall touristic position and income derived from the
tourism industry declined significantly during the 15 years of war, including the years
immediately following the war. During the latter half of the 1990s, considerable efforts
were made by the government to re-establish Lebanon’s tourism industry. Such efforts
experienced a degree of success for some time – especially after the late Prime Minister,
Rafiq Hariri, initiated the rebuilding programme. That said, all such re-establishment
efforts for the tourism industry have since experienced significant setbacks following the
Hariri assassination in 2005 and the war with Israel in 2006.
More recently, additional setbacks were encountered in 2008 as violence pushed
Lebanon towards the brink of yet another civil war. The selection of a new president in
May of that year served to improve the political climate in Lebanon, which has remained
relatively stable since that time. Enhanced government efforts towards ensuring peace
and stability as well as noteworthy private investment currently being made for modernisation and development enhance the ability of the country’s tourism sector to once again
contribute significantly to Lebanon’s economy.
3. Econometric methods and empirical results
3.1 Data, unit root and cointegration tests
Annual data for international tourist arrivals (TOUR) and real GDP from 1995 to 2010 are
utilised to analyse the validity of the TLG hypothesis in Lebanon. Data for this study are
extracted from World Development Indicators, World Bank. All variables are transformed
into natural logarithms to induce stationarity in the variance – covariance matrix.
The time-series plots for international tourist arrivals and real GDP are presented in
Figure 1. Granger and Newbold (1974) noted that estimation results may be spurious if
the series are non-stationary and/or non-cointegrated. Motivated by this
understanding, we perform the generalised least square (GLS) version of Dickey –
Fuller (DF-GLS) and Kwiatkowski – Phillips – Schmidt – Shin (KPSS) unit root tests
to determine the order of integration of each series. In light of the small sample utilised by this study (T ¼ 16), we adjust the critical values of the DF-GLS and KPSS
tests with the surface response procedure suggested by Cheung and Lai (1995) and
Sephton (1995), respectively. The results of the unit root tests and the adjusted critical
values are reported in Table 2.
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Current Issues in Tourism
Figure 1.
239
Time-series plots for all variables.
Table 2. The results of the unit root analysis.
Variables
DF-GLS
KPSS
ln GDPt
D ln GDPt
ln TOURt
D ln TOURt
–1.4729 (1)
–4.6683 (0)∗∗∗
–3.1235 (1)∗
–3.2834 (0)∗
0.2345 (1)∗∗∗
0.0945 (0)
0.1171 (0)
0.0851 (1)
Significance level
Adjusted critical values for a small sample (T ¼ 16)
1%
5%
10%
Cheung and Lai (1995)
Sephton (1995)
–3.770
–3.518
–3.047
0.203
0.150
0.126
Notes: The number in parentheses indicates the optimal lag order and bandwidth for the DF-GLS and KPSS tests,
respectively. The Akaike information criterion (AIC) is used to determine the optimal lag order for the DF-GLS
test, while the bandwidth for the KPSS test is selected by the Newey–West procedure. The adjusted critical
values for the DF-GLS and KPSS tests are derived from the response surface method advocated by Cheung
and Lai (1995) and Sephton (1995).
∗
Significant at the 10% level.
∗∗∗
Significant at the 1% level.
At the 10% significance level, both unit root tests consistently recommend that real
GDP of order one, I(1), be integrated, whereas tourism of order zero, I(0), be integrated.
These results indicate that the bounds testing approach to cointegration proposed by
Pesaran et al. (2001) is suitable in the present case because the approach remains valid irrespective of whether the variables are purely I(0), purely I(1) or mutually cointegrated. Apart
240
C.F. Tang and S. Abosedra
from this, the Monte Carlo evidence provided by Pesaran and Shin (1999) revealed the
bounds testing approach to have superior performance in small samples. To conduct the
bounds testing approach to cointegration, we estimate the following autoregression distributed lag (ARDL) equations:
D ln GDPt = b0 + d1 ln TOURt−1 + d2 ln GDPt−1 +
k
hi D ln GDPt−i
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i=1
+
k
qj D ln TOURt−j + 11t ,
(1)
j=0
D ln TOURt = a0 + d1 ln TOURt−1 + d2 ln GDPt−1 +
k
fi D ln TOURt−i
i=1
+
k
uj D ln GDPt−j + 12t .
(2)
j=0
Here, D is the first difference operator, ln TOURt is the natural logarithm of tourism and
ln GDPt is the natural logarithm of real GDP. The residuals (11t , 12t ) are assumed to be
white noise and normally distributed. With this approach, the presence of a long-run
relationship between tourism and economic growth is tested by performing the standard
F-statistic on the one-period-lagged level variables [ln GDPt−1 , ln TOURt−1 ] from the
ARDL equations. Nevertheless, it is important to point out that the asymptotic critical
values provided by Pesaran et al. (2001) are based upon 1000 observations. Therefore,
these asymptotic critical values are subjected to small sample bias, and it is therefore inappropriate for our study utilising only 16 observations. To circumvent this bias, we apply the
surface response procedure suggested by Turner (2006) to compute the critical values for
T ¼ 16. The calculated F-statistics for cointegration together with the critical values are
reported in Table 3.
The ARDL models pass a number of diagnostic tests.2 The results given in Table 3 indicate that the calculated F-statistic for the real GDP equation FGDP (GDP|TOUR) is greater
than the upper-bound critical value at the 10% significance level. Thus, it rejects the null
hypothesis of no cointegration relationship. Nonetheless, when tourism is the dependent
variable, FTOUR (TOUR|GDP), it cannot reject the null hypothesis of no cointegration
relationship at the 10% significance level. As a result, we conclude that there is one
cointegrating vector between tourism and economic growth in Lebanon.3 This cointegration result is corroborated with the findings of Narayan and Prasad (2003), Kim, Chen,
and Jang (2006), Brida et al. (2009), Katircioglu (2009) and Corte´s-Jime´nez and Pulina
(2010).
3.2 The Granger causality test
In the previous section, we have found that the variables are cointegrated. This finding
implies that there must be at least one way of causation, but the direction of causality
remains uncertain. In light of this, it is necessary for us to conduct the Granger causality
test to ascertain the direction of causality between tourism and economic growth.
Current Issues in Tourism
241
Table 3. The results of the ARDL cointegration analysis.
FGDP (GDP|TOUR)
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Bounds test
FTOUR (TOUR|GDP)
Optimal lag order
F-statistics
(2, 2)
6.6681∗
(2, 2)
4.7224
Significant level
Adjusted critical values for a small sample (T ¼ 16)a
Lower bound I(0)
Upper bound I(1)
1%
5%
10%
9.839
6.012
4.608
11.485
7.202
5.572
Diagnostic tests
Statistics
Statistics
0.9104
0.7851
7.2609∗∗
0.4365
{1}: 0.1555
{1}: 0.5064
{1}: 0.7874
Stable at the 5% level
Stable at the 5% level
0.8306
0.5933
3.5013∗
0.9164
{1}: 1.7731
{1}: 0.0424
{1}: 0.6296
Stable at the 5% level
Stable at the 5% level
2
R
Adjusted R2
F-statistics
x2NORMAL
x2SERIAL
x2ARCH
x2RESET
CUSUM
CUSUMSQ
Notes: The optimal lag order is selected by the Akaike information criterion (AIC). { } is the order of the diagnostic
tests.
a
Adjusted critical value bounds for the small sample are derived from the response surface method suggested by
Turner (2006).
∗
Significant at the 10% level.
∗∗
Significant at the 5% level.
Because the variables are cointegrated, we estimate the following error-correction models
(ECMs) for the Granger causality tests:4
D ln GDPt = a0 +
p
i=1
D ln TOURt = a1 +
p
i=1
qi D ln GDPt−i +
p
lj D ln TOURt−j + c1 1t−1 + m1t ,
(3)
j=1
4i D ln TOURt−i +
p
wj D ln GDPt−j + c2 1t−1 + m2t .
(4)
j=1
The residuals (m1t , m2t ) are assumed to be normally distributed and white noise. 1t−1 is
the one-period-lagged error-correction term derived from the cointegration equation.
Through the ECMs, we test the short-run, long-run and strong Granger causality. The significance of 1t−1 indicates long-run Granger causality. Furthermore, to ascertain short-run
Granger causality, we use the likelihood ratio (LR) test on the first difference lagged explanatory variables. If, for example, lj = 0 holds, it is implied that tourism Granger-causes
economic growth in the short run, while wj = 0 holds means that economic growth
Granger-causes tourism. Ultimately, the strong Granger causality (i.e. short and long
runs) can be implemented via the LR test on the first difference lagged explanatory and
the one-period-lagged error-correction term. For example, lj = c1 = 0 hold implies
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C.F. Tang and S. Abosedra
Table 4. The results of the Granger causality analysis.
Granger causality test
Source of Granger causality
Dependent variable
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D ln GDPt
Short run
D ln GDPt−k
D ln TOURt−k
–
24.5103∗∗∗
Long run
Coefficients [t-statistics]
1t−1
–0.1017 [–5.9492]∗∗∗
Joint (short and long runs)
D ln GDPt−k , 1t−1
D ln TOURt−k , 1t−1
LR statistics
D ln TOURt
LR statistics
–
49.0111∗∗∗
Diagnostic tests
Test statistics
x2NORMAL
x2SERIAL
x2ARCH
x2RESET
0.6719
{1}: 1.0674
{1}: 0.2889
{1}: 0.6724
20.2269∗∗∗
–
– 0.4970 [ –2.7740]
23.1759∗∗∗
–
0.4737
{1}: 2.4293
{1}: 0.7414
{1}: 0.8839
Notes: The AIC is used to determine the optimal lag order. { } indicate the order of the diagnostic tests. Following
Tang and Lean (2007), the autoregressive distributed lag (ARDL) framework is used to estimate the ECMs to
enhance the robustness of the Granger causality test.
∗∗∗
Significant at the 1% levels.
that there is a strong Granger causality running from tourism to economic growth, whereas
wj = c2 = 0 hold indicates that economic growth does not Granger-cause tourism.
Based upon the testing procedure described above, the Granger causality results
together with the results of the diagnostic tests are presented in Table 4. In line with the
results of the diagnostic tests of the ARDL models for cointegration (Table 3), the residuals
of the ECM equations are free from serial correlation, autoregressive conditional heteroskedasticity (ARCH) and specification errors. In addition, the residuals are normally distributed. Therefore, the results of this study avoid bias despite a simple bi-variate framework.
Turning to the Granger causality results, we find uni-directional Granger causality
running from tourism to economic growth in Lebanon in the long run, while in the short
run, tourism and economic growth are bi-directional Granger causality. The strong
Granger causality results suggest that there is bi-directional Granger causality
between tourism and economic growth.5 Overall, our results support the findings of Kim
et al. (2006), Brida and Risso (2009), Katircioglu (2009) and Lean and Tang (2010).
With these findings, we surmise that the TLG hypothesis is valid and tourism is
therefore an appropriate policy instrument for generating long-term economic growth in
Lebanon.6
4. Concluding remarks
The main objective of this study has been to investigate whether tourism has contributed to
economic growth in Lebanon over the time period of 1995 –2010. Incorporating annual
data figures from 1995 to 2010, this study sought to investigate the validity of the TLG
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Current Issues in Tourism
243
hypothesis in Lebanon. In light of the small sample utilised, the study was undertaken with
an increased level of care so as to avoid any bias in the estimation results. Therefore, we
utilised a simple bi-variate model and selected a more appropriate econometric method
and critical values for a small sample. The bounds testing approach to cointegration and
Granger causality tests were utilised in determining the long-run and causal relationship
between tourism and economic growth. Our empirical results indicate the presence of a
robust long-run equilibrium relationship between tourism and economic growth for
Lebanon. In the long run, our findings support uni-directional Granger causality running
from tourism to economic growth. That said, in the short run, our findings demonstrate
that tourism and economic growth Granger-cause each other indicative of bi-directional
causality.
Our Granger causality results indicate that tourism does Granger-cause economic
growth in both the short and long runs. These results support the finding that the TLG
hypothesis is valid for Lebanon. Based upon these results, the government of Lebanon
should support policies and practices advantageous to the tourism industry in Lebanon
because an increase in tourism would serve to stimulate overall economic growth.
First, because Lebanon’s liberal economy is based on free-market competition and
private ownership, the government has a key role in managing tourism development
through its legal and regulatory power. This includes all commercial activities that either
intersect with and/or enhance the development of Lebanon’s tourism sector. The government’s legal and regulatory power should also be used to significantly encourage domestic
and foreign investment in the tourism sector.
Second, resource allocation and support for the adequate training of a skilled and
specialised labour pool are of vital importance for supporting growth in the tourism
sector. This will serve to attract and increase domestic and foreign investments as well as
international tourist arrivals to Lebanon and simultaneously serve to increase the overall
level of competitiveness of the tourism sector.
Third, government policy-makers should support local academic institutions by organising academic conferences promoting and supporting additional research on the impact of
tourism on the development of Lebanon’s economy. Additional research studies, and the
data acquired by means of such studies, will be useful for government policy-makers in
effectively determining the types and scale of tourism activities best suited to support
short- and long-run economic growth for Lebanon.
In closing, this study may shed some light as to the direction of future research studies.
Our study utilises a small sample size with results based upon a simple bi-variate framework
between tourism and economic growth. Therefore, many important additional variables
such as the currency exchange rate, the characteristics of the existing labour pool, capital
availability, domestic and foreign investment statistics and so forth are not taken under consideration. With the availability of a sufficient sample size, future research could take into
account the aforementioned variables using a standard growth model. Such future research
would lead to the availability of data that could be utilised by government policy-makers to
enact more comprehensive and effective policies pertaining to tourism and economic
growth for Lebanon.
Acknowledgements
The authors thank the three anonymous reviewers for their valuable comments and suggestions to the
earlier draft of this research. Any shortcomings that remain in the paper are solely our responsibility.
244
C.F. Tang and S. Abosedra
Notes
1.
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2.
3.
4.
5.
6.
It is interesting to point out here that international tourism is a source of export earning, but
tourism is different from commodity exports in the sense that the consumer (or the visitor)
must consume the product in the exporting (or the visiting) countries. Therefore, the tourism
sector has important implications for other sectors in the economy as illustrated by the computable general equilibrium analysis stressed in Blake, Gillham, and Sinclair (2006).
The diagnostic tests indicate that the residuals of the ARDL models are normally distributed, free
from serial correlation and without the presence of ARCH. In addition, the Ramsey RESET
(regression equation specification error test) tests indicate that both ARDL models are free
from specification errors. The cumulative sum (CUSUM) and cumulative sum of squares
(CUSUMSQ) tests for parameter stability demonstrate that the estimated parameters of the
ARDL models are stable at the 5% significance level.
It could be argued that there are statistical techniques to increase the number of observations by
increasing the frequency of the data from annual to quarterly. However, Hakkio and Rush (1991)
articulated cointegration as a concept of a long-run relationship; thus the cointegration results
may not change by merely changing the frequency of the data. Moreover, Tang (2008) affirms
the idea that interpolated data do not enhance the statistical power of a test. Therefore, the original
data will speak better than interpolated data.
We appreciate an anonymous reviewer’s comment noting that the meaning of Granger causality
is different from the meaning of causality in the theoretical sense. Granger causality only shows
the predictability behaviour of one variable based on the past values of another group of variables. Hence, Masih and Masih (1998) claimed that Granger causality is actually a predictability
test. On the other hand, causality in a theoretical sense is difficult to examine through statistical
approaches because the statistical relationship itself cannot logically imply the meaning of causation. Furthermore, Kendall and Stuart (1979) revealed that the statistical relationship is strong and
suggestive, but it can never show the direction of causality because a causal relationship must be
supported by theories, logics and/or universal laws (see also Hoover, 2001).
Because of the finite sample utilised by this study, we additionally performed the Toda-YamamotoDolado-Lu¨tkepohl (TYDL) Granger causality tests proposed by Toda and Yamamoto (1995) and
Dolado and Lu¨tkepohl (1996) in association with the leveraged bootstrap critical values to
confirm the causal relationship between tourism and economic growth. On the basis of the
Monte Carlo experiment, Mantalos (2000) and Hacker and Hatemi-J (2006) found that the bootstrap tests improved the robustness of the causality test, particularly for a small sample. Similar
to the conclusion provided in Table 4, the leveraged bootstrap TYDL causality tests indicate that
tourism and economic growth in Lebanon are bi-directional Granger causality in nature. Hence,
the Granger causality results given in Table 4 are valid. To conserve space, the entire results of
the leveraged bootstrap TYDL causality tests are not reported here, but are available upon
request from the authors.
To confirm the robustness of the estimation results, we re-tested the cointegration and Granger
causality results by dropping the first observation (using only 1996–2010) as suggested by an
anonymous reviewer. Dropping the first observation (1995), we find that the conclusions for
cointegration and Granger causality remain unchanged. Hence, we affirm the robustness of the
results. To conserve space, the full results are not reported here, but are available upon request
from the authors.
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