Current Issues in Tourism
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The asymmetric impact of air transport on
economic growth in Spain: fresh evidence from the
tourism-led growth hypothesis
Daniel Balsalobre-Lorente, Oana Madalina Driha, Festus Victor Bekun &
Festus Fatai Adedoyin
To cite this article: Daniel Balsalobre-Lorente, Oana Madalina Driha, Festus Victor Bekun &
Festus Fatai Adedoyin (2021) The asymmetric impact of air transport on economic growth in
Spain: fresh evidence from the tourism-led growth hypothesis, Current Issues in Tourism, 24:4,
503-519, DOI: 10.1080/13683500.2020.1720624
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CURRENT ISSUES IN TOURISM
2021, VOL. 24, NO. 4, 503–519
https://doi.org/10.1080/13683500.2020.1720624
The asymmetric impact of air transport on economic growth in
Spain: fresh evidence from the tourism-led growth hypothesis
Daniel Balsalobre-Lorentea, Oana Madalina Drihab, Festus Victor Bekunc,d and
Festus Fatai Adedoyin e
aDepartment of Political Economy and Public Finance, Economics and Business Statistics and Economic Policy,
University of Castilla-La Mancha, Ciudad Real, Spain; bDepartment of Applied Economics, International Economy
Institute, University of Alicante, Alicante, Spain; cFaculty of Economics Administrative and Social sciences, Istanbul
Gelisim University, Istanbul, Turkey; dDepartment of Accounting, Analysis and Audit, School of Economics and
Management, South Ural State University, Chelyabinsk, Russia; eDepartment of Accounting, Economics and Finance,
Bournemouth University, Poole, UK
ABSTRACT ARTICLE HISTORY
The tourism sector has emerged as an essential driver for economic Received 29 October 2019
growth strategies during the last decades. An asymmetric long-run Accepted 19 January 2020
effect of air transport on economic growth is validated assuming a
process of social globalization in Spain between 1970 and 2015. To KEYWORDSAir transport; renewable
achieve the study’s objective, the recent asymmetric autoregressive energy use; social
distributed lag methodology framework advanced by Shin, Yu, and globalization; tourism
Greenwood-Nimmo (2014) is applied. For determining the causality development; economic
direction, this methodology is applied in conjunction with the non- growth; cointegration
parametric causality test proposed by Diks and Panchenko (2006). The analysis
current study also accounts for the effects of renewable energy use and
urbanization process over economic growth. Empirical results showed
that air transport, urbanization process and social globalization exert
positive and significant implications over economic growth, while
renewable energy use reduces economic growth, as consequence of an
energy mix sustained by fossil sources. Based on these outcomes several
policy recommendations were offered in the concluding section.
1. Introduction
During the last decades, tourism has been a leading sector and engine of economic growth and
development in both developing and developed economies (Akadiri, Akadiri, & Alola, 2019). This is
because the increases in movement of tourists give a signal of development across the globe and
this era of development has witnessed many ways through, which tourism can influence the econ-
omic activity of a country. This paper offers fresh evidence on the impact of air transport as a proxy for
tourism on economic growth in Spain. Air transport is one of the major drivers of tourist movement
and Spain is one of the top 10 destination earning most from activities in the tourism industry (WTTC,
2017). Tourism contributes about 15% to gross domestic product (GDP) through several direct and
indirect tourism activities (WTTC, 2017). Over the past few decades, Spain has been one of the
most popular destinations for international tourists across the globe. Therefore, tourism is regarded
as the third major contributor to the growth of the national economy of Spain after the industrial and
the banking sectors contributing about 10% to 11% of GDP and also generating a substantial rate of
employment. In 2018, Spain was regarded as the second most visited nation globally with a
CONTACT Daniel Balsalobre-Lorente Daniel.Balsalobre@uclm.es
© 2020 Informa UK Limited, trading as Taylor & Francis Group
504 D. BALSALOBRE-LORENTE ET AL.
patronage of over 80 million tourists from different parts of the world (INE, 2019). This antecedence
has drawn the attention of scholars recently to investigate the demand/determinant of tourism-led
growth hypothesis (TLGH).
Nevertheless, whether and how the expansion of the tourism industry affects the growth of the
Spanish economy has been a subject of debate. In fact, according to World Bank’s development indi-
cator database, a comparison of economic growth in Spain with the UK, Italy, Germany and France
reveals that Spain grows the least in terms of its GDP per capita, with over 10 years (between
2005 and 2015) of no real change in economic growth (World Bank Group, 2019).
Thus, as an engine of economic growth in this globalized era, tourism has evolved around not only
the movement of capital and labour for pleasure and business alone but also as stimulation for more
investment in infrastructural development, human capital, urbanization of the destinations and
employment generation. Unquestionably, there is hardly an argument on whether or not, tourism
is an important driver of the global economy, and is the fastest and largest contributor to inter-
national trade. However, despite the importance of the industry, there is no consensus as to the
strength, direction and other potential variables that mediate the causal link between contributions
of the tourism industry and the growth of the economy. In fact, while most empirical evidence
suggests that the tourism-led growth assumptions holds (Jalil, Mahmood, & Idrees, 2013; Seghir,
Mostéfa, Abbes, & Zakarya, 2015; Katircioglu, 2009; Katircioglu, 2014; Tugcu, 2014; Shahzad,
Shahbaz, Ferrer, & Kumar, 2017; Perles-Ribes, Ramón-Rodríguez, Rubia, & Moreno-Izquierdo, 2017;
Etokakpan, Bekun, & Abubakar, 2019; Aratuo & Etienne, 2019), with different impacts in the short
run as well as in the long run, only few studies exists on the strength of the relationship (Antonakakis,
Dragouni, & Filis, 2015; Santamaria & Filis, 2019), and the intervening role of other variables, such as
social globalization and urbanization.
Additionally, the reason for accounting for these ‘contemporary’ variables is not far-fetched. On
one hand, the share of urban population (urbanization) is important for tourism policy in Spain,
since the number of people who live in areas regarded as ‘urban’ per 100 of the total people has con-
sistently increased in the last five decades. In fact, between 1970 (66%) and 2015 (79.6%), the urban
population has grown notably due to the rapid development of urban areas across the globe. This
growth represents a significant shift from rural to urban-composition effect, which is not uncon-
nected to shifts from a farming-based economy to mass industry, innovation and service (World
Bank Group, 2019). In fact, urban areas have a higher and better set up to achieve the positive
goals of social and environmental issues than rural regions. Urban areas generate more employment
opportunities as well as training and medical services, among others.
The increasing patronage of Spain as one of the major destinations for international tourists has
been directed towards the movement to continuous globalization. Thus, for social globalization, on
the other hand, the free flow of information through the internet, social media, popular books, TV
series and films are drivers and can serve as a key motivation for travel and tourism (Dwyer, 2015).
The growing demand for tourism in Spain has impacted the changing direction of development of
the global economy in terms of entrepreneurship, investments, innovations, civilization, cultural and
political development. As such, access to different forms of transport, development in renewable
energy, amongst others appear as important factors in the TLGH. In fact, an examination of data
from World Bank’s development indicator database (2019), shows that increase in international
tourist arrivals can be traced to the growth in air transport, which has witnessed an upward trend
since 2010 after the global financial crisis (for example, an 18% increase between 2015 and 2017).
Similarly, there’s been a sharp increase in renewable energy use in Spain up to double (100%
increase) in 2010 from its 2005 levels. This has implications on tourism activities as well as the
environment (Balsalobre-Lorente, Shahbaz, Roubaud, & Farhani, 2018) and growth prospects, and
is worth a revisit in order to strengthen existing knowledge on tourism-growth nexus in Spain.
Furthermore, past studies on the tourism-growth nexus in Spain have not established the relation-
ship in line with the effects of urbanization, air transport demand and social globalization. A close
analysis for Spain, near to the current study is that of Perles-Ribes et al. (2017). Their study presented
CURRENT ISSUES IN TOURISM 505
empirical evidence for the TLGH (TLGH) considering some evolving events, and to check for the
strength of the tourism-growth nexus. However, the current study differs substantially from existing
studies. Thus, the main aim of this study is to investigate the dynamic effect of air transport on econ-
omic growth assuming a process of social globalization in Spain. The role of urbanization and social
globalization are considered in the model to avoid potential omitted variable bias and to further
account for tourism impacts on economic growth and consequently offer fresh evidence on the
impact of air transport as a proxy of tourism on economic growth in Spain. The present study also
sidesteps for omitted variables bias by the addition of other variables, which the previous study
failed to address. The empirical results validate TLGH through a nonlinear autoregressive distributed
lag (N-ARDL) estimation methodology.
The paper is organized in the following manner. The next section is a stylized review of the related
study. Section 3 presents the data and methodological route applied in the study. Empirical results
interpretation is offered in Section 4. The conclusion is rendered in Section 5.
2. Literature review
A systematic sequential analysis of past empirical studies on what we know about the tourism-growth
nexus showed that most studies confirmed the tourism-growth linkage (Pablo-Romero & Molina,
2013). In their review, nearly 64% of past studies found a one-to-one tourism-growth linkage, 19%
discovered a one-to-one relationship, 10% revealed a unidirectional causality from growth to
tourism. While less than 5% found no causality. Also, it is noteworthy that the authors gathered
their results based on various push and pull factors like the nation’s level of concentration in
tourism and much attention is given to the choice of model stipulations and econometric models
as sources of deriving the results. This is to provide evidence for the variance in the submissions
of different scholars and policymakers about the tourism-led hypothesis.
In the same vein, Brida, Cortes-Jimenez, and Pulina (2016) conducted a review on the tourism-
growth nexus, based on the main empirical implications that have been suggested so far in the lit-
erature. The authors reviewed 100 related research papers and found that most of the studies
suggest that overall, economic growth is driven by international tourism, though there are some
exceptions. In modelling tourism-growth nexus, the bulk of studies in the literature adopt a bi-
variate framework (Brida et al., 2016; Brida, Lanzilotta, & Pizzolon, 2019) in a linear setting.
However, from an economic view, it is known that most macroeconomic indicators exhibit nonlinear
traits given the wave of economic uncertainty, shocks and business cycles. The already established
tourism-induced growth hypothesis underlines there could be nonlinear relationships between vari-
ables. Thus, considering only linear relations the study would be flawed with model bias. This would
lead to spurious policy recommendations. The present study extends empirical literature on tourism
modelling applying a N-ARDL technique that admits the analysis of the potential asymmetric impact
of air transport over economic growth, both in the long and short-run. The N-ARDL methodology is
appropriate for the understanding of nonlinear dynamics between tourism and economic growth, in
order to sketch policy recommendations (Meo et al., 2018). Thus, this study aims to fill this gap by
modelling the connexion between air transportation and economic growth based on nonlinearity
and account for the impact of other covariates on economic growth.
Additionally, due to increased earnings from tourism development, many governments have
sought to invest more in tourism infrastructures with the motive of growing their economies. This
increasing attention has led to a recurrent examination of the association among the travel industry,
economic growth and other factors in the literature. However, there are mixed estimations by
researchers on the TLGH. A review of past empirical studies has justified the existence of inconsisten-
cies as regards the tourism-growth nexus owing to the variation in methodology, data used, country
or economic and geographical region of study, as well as econometrics model employed.
For instance, based on country-specific analysis for Malaysia, Tang and Tan (2013) tested the
rationality of TLGH in alignment to 12 selected tourism market and reaffirmed the validity of
506 D. BALSALOBRE-LORENTE ET AL.
the hypothesis in 8 markets out of the 12 examined in the study. Nepal, Indra al Irsyad, and Nepal
(2019) also employed an econometric model based on autoregressive distributed lag model
(ARDL) and Granger Causality test to assess both short and long-run linkages amongst tourist arrivals,
per capita output, CO2 emissions, energy consumption and gross capital formation using Nepal as the
focus of study. It was discovered that there exists a bidirectional causal link between tourists’ arrival
and gross capital formation though tourism is negatively affected by the increasing CO2 emission.
Jalil et al. (2013) also examined the tourism-growth nexus in Pakistan and affirmed a positive and
one-way causal relationship from international tourism to economic growth. In the case of Turkey,
Katircioglu (2009) also tested for the validity of the TLGH, and found a distinctive result from the pre-
vious studies on Turkish economy as it was found that no cointegration exists in the tourism-growth
nexus, which disenabled the author to further the process of finding the causality effect.
Additionally, Shahzad et al. (2017) investigated the validity of TLGH in 10 most visited tourists’ des-
tinations in the world and found a direct link in the tourism-growth nexus, though there are variations
in the level of the relationships across the countries. Chou (2013) also used causal analysis to assess
whether a link exists in the tourism-growth nexus for 10 transitional nations. The study showed
several relationships across countries; an independent relationship in the case of Bulgaria,
Romania and Slovenia; for Slovakia, Cyprus and Latvia, the TLGH holds; for Poland and Czech Repub-
lic, reverse relationships were found; and in the case of Estonia and Hungary, a feedback TLGH also
holds. Seghir et al. (2015) also examined whether a causal link holds for the tourism-growth nexus in
49 countries, utilizing both cointegration and Granger causality analysis. The study found a two-way
direction of causality for the nexus.
A survey of prior studies reveals that several studies that have adopted air transport (AT) as a proxy
for tourism and investigate its linkage to economic growth, although most studies reveal differing
forms and direction of causality. Marazzo, Scherre, and Fernandes (2010) analysed the case of
Brazil and found that the growth in the number of air transport (AT)1 passengers cointegrates with
the growth of the economy. Their study showed that a strong positive AT demand-growth nexus
owing to the positive changes in GDP. Similarly, Hu, Xiao, Deng, Xiao, and Wang (2015) showed
that a long-run and robust two-way causal link exists in AT-growth nexus, but only a short-run
one-way causality exists and runs from AT to economic growth. For the OECD, Küçükönal and Sede-
foğlu (2017) also found a one-way causal linkage in the short run, which runs from GDP, employment
and tourism to air transport. Baker, Merkert, and Kamruzzaman (2015) also found a significant bidir-
ectional relationship implying that airports have an impact on the growth of the Australian economy,
and that the economy also directly impacts air transport. Saidi and Hammami (2017) showed that
bidirectional causality occurs between environmental degradation and growth of the economy
and a one-way causal link from transport to environmental degradation.
Further evidence also exists to support the presence of a long-run link in the AT-growth nexus. For
instance, the study by Hakim and Merkert (2016) showed that in South Asia, there exists a long-run
one-way causality from GDP to air passenger traffic. In the same vein, Van De Vijver, Derudder, and
Witlox (2014) examined the causal relationship between trade and air passenger travel through the
use of diverse Time-Series Cross-Section (TSCS) Granger causality evaluation in some areas in Asia-
Pacific. Their study revealed four major findings on the causal relationship, first, independent patterns
of relationship; second, bidirectional link between air traffic and trade and lastly, the existence of
bidirectional causal link across the different pairs of countries employed in the study.
In a bid to further explore the growth led to the impact of air transport, Abate (2016) investigated
the impact of liberalizing air transport in Africa. The study found that regions that liberalize experi-
ence increase in departure frequency. Furthermore, Smyth, Christodoulou, Dennis, Marwan, and
Campbell (2012) investigated the necessity of air transport funding in promoting social inclusion
and economic development in Scotland. They discovered that funding the air transport system
has significantly increased passenger flows and travel conditions for all passengers, and favoured
the increased economic prowess Scottish economy. Rashid Khan et al. (2018) also found different
directions of causality as well as no causal relationships amongst the studied variables through
CURRENT ISSUES IN TOURISM 507
diverse transport means. Saidi, Shahbaz, and Akhtar (2018) also examined the effect of transport
energy utilization and transport structure on economic development by using information on
MENA nations from 2000 to 2016. The causality analysis in the study established a two-way causal
linkage between energy consumption and transport as well as between transport infrastructure
and economic growth. Furthermore, recently Brida, Lanzilotta, Rodríguez-Collazo, and Zapata-
Aguirre (2018) analysed the dynamic relationship between air transportation and economic
growth in four South American countries, (Argentina, Brazil, Chile and Uruguay), concluding that
relationship between air transport and growth contain an asymmetry behaviour. In line with these
results, we have applied an empirical model that explores the asymmetric behaviour of air transport
over economic growth in Spain. Brida et al. (2016) found a nonlinear relationship between air trans-
port development and economic growth in the cases of Chile and Uruguay. Husein and Kara (2020)
confirmed the existence of an asymmetric or nonlinear cointegration relationship between Puerto
Rico’s tourism demand and its determinants.
In terms of other variables that affect the TGLH, a number of studies have shown the link between
renewable energy use (RNW) and economic growth. The majority of these studies examined the link
between the two variables in an attachment to a multivariate framework, which includes variables
such as CO2 emission, financial development, import and export and globalization (Le & Nguyen,
2019). For instance, Apergis, Payne, Menyah, and Wolde-Rufael (2010) found a long-run connection
between variables while, Tugcu, Ozturk, and Aslan (2012) found a bidirectional causality. Similarly,
Bhattacharya, Paramati, Ozturk, and Bhattacharya (2016) showed that renewable energy has a sub-
stantial positive effect on growth. Maji and Sulaiman (2019) also investigated the RNW-growth
nexus of 15 West African countries using dynamic ordinary least squares (DOLS). They found that
RNW decelerates economic growth in these countries based on the poor utilization of wood bio-
masses in the estimated countries.
To further show causality between renewable energy use and economic growth the variables,
Kahia, Aïssa, and Charfeddine (2016) and Huang and Huang (2019) found that there is a long-run
relationship, which is a one-way causal link from economic growth to RNW in the short-run and a
two-way causality in the long run. Boontome, Therdyothin, and Chontanawat (2017) in the same
vein showed that a one-way causality from non-renewable energy consumption to carbon dioxide
(CO2) emissions in the case of Thailand. Troster, Shahbaz, and Uddin (2018) also found a two-way
causal connection between changes in RNW and economic growth as well as a one-way causality,
which runs from fluctuations in oil prices to economic growth.2
However, despite the rich literature on tourism-energy-growth nexus, there is insufficient evi-
dence on whether and/or how this link holds when social globalization and urbanization are con-
sidered. The Globalization Index provided by the KOF Swiss Economic Institute gives measures of
different aspects of globalization vis-a-viz social, political, economic and financial globalization.
According to Salifou and Haq (2017), economic globalization positively drives growth, thereby estab-
lishing the TLGH for countries in West Africa. This result also holds for financial globalization in devel-
oping countries (Combes, Kinda, Ouedraogo, & Plane, 2019). Additionally, in the case of social
globalization, (Marques, Fuinhas, & Marques, 2017) found no short-run impact, while in general glo-
balization drives economic growth in the long run as well as tourism (Javid & Katircioglu, 2017). In
summary, although globalization drives both tourism and growth independently, yet, examining
this causal link within the framework adopted in this study could provide useful information to
both stakeholders and policymakers.
3. Data and empirical strategy
This study uses a Nonlinear AutoRegressive Distributed Lag (N-ARDL) framework (Shin, Yu, & Green-
wood-Nimmo, 2014) to explore the long-run effects that air transport (as proxy of tourism), and
potential additional determinants, exert over economic growth. Our main hypothesis tries to validate
the TLGH for Spain, between 1971 and 2015.
508 D. BALSALOBRE-LORENTE ET AL.
This relationship can be specified as follows.
GDPt = bGDPPCGDPt−i + bATATt+i + bRNW (RNW)t−i + bURBURBt−i + bSGSGt−i + 1t (1)
where the per capita gross domestic product (GDPt) is determined by its persistence element,
(GDPt−i), air transport (passengers) (AT ), renewable energy use (RNW), urbanization process, (share
urban population) (URB) and social globalization. All variables are expressed in logarithm.
All these explanatory factors are theoretically perceived and often empirically proven to be the
determinants of economic growth are also influenced by these factors and their dynamics (Saidi &
Hammami, 2017; Saidi et al., 2018; Balsalobre, Driha, Shahbaz, & Sinha, 2019).
Assumed that economic growth is influenced by its past values (GDPt−i), we apply the N-ARDL
methodology, which it considers the asymmetries and nonlinearities (Pesaran & Shin, 1999;
Pesaran, Shin, & Smith, 2001; Shin et al., 2014).
Therefore, the N-ARDL is a suitable framework for investigating the asymmetries and nonlineari-
ties; trying to validate the TLGH in Spain, between 1971 and 2015.
Previously, we can specify the Equation (1) in the following long-run model of economic growth:
GDPt = a0 + a AT+1 t + a AT−2 t + a3RNWt + a4URBt + a5SGt + 1t (2)
where GDPt is the gross domestic product being their determinants specified in Equation (1), where
a = (a0 − a5) is a co-integrating vector of long-run parameters.
In Equation (2) the AT+t and AT
−
t are partial sums of positive and negative changes in the air trans-
portation, it can be specified as:
+ ∑
t ∑t
ATt = DAT+i = max(DATi , 0) (3)
i=1 i=1
and
∑t− − ∑
t
ATt = DATi = min(DATi , 0) (4)
i=1 i=1
In the formulation presented above (Equation (2)), the relationship between Air Transport (AT) and
economic growth (GDPt) is expected to be positive (a1), confirming the TLGH, while a2 captures
the association between air transportation and economic growth, while there are reductions in
them. As AT is expected to generate co-movement, estimates of a2 are expected to have positive
signs. Furthermore, we also check if the increase in the air transport will result in a higher increase
in the economic growth than the decrease in the air transport, which may lead to a decrease in
the economic growth. In other words, the positive AT shocks will have a greater impact than the
negative AT shocks (i.e. a1 . a2). Concomitantly, the long-run relationship presented in the Equation
(2) is expected to reflect an asymmetric pass through.
At this point, we frame the Equation (2) into a N-ARDL setting (see, Shin et al., 2014) as follows;
∑j
DGDPt = a+ b1GDP + −t−1 + b2ATt−1 + b3ATt−1 + b4RNWt−1 + b5URBt−1 + b6SGt−1 + ∅iDGDPt−i
i=1
∑k ∑l ∑m ∑n
+ (u+DAT+i t−i + u−i DATP−t−i )+ giDRNWt−i + diDURBt−i + ViDSGt−i + et
i=0 i=0 i=0 i=0
(5)
Being defined all the variables previously, j, k, l, m, n are lag orders, and a1 = −b2/b1;
a2 = −b3/b1 are the earlier mentioned long-run impacts of increase/decrease in the air transpor-
tation on inflation (Equation (5)).
CURRENT ISSUES IN TOURISM 509
∑k
In Equation (5), the u+i measures the short-run impacts of an increase in air transportation on
i=0 ∑k
economic growth whereas u−i measures the short-run impacts of a decrease in air transportation
i=0
on economic growth.
The N-ARDL framework will be entailed on the following steps. First of all, we investigate the lin-
earity properties of the variables. This study applies the BDS test (Broock, Scheinkman, Dechert,
& LeBaron, 1996) to detect the nonlinearity characteristics of the selected variables. This is to
avoid the error of linearity assumption. The results present that all variables are nonlinear. Thus,
the use of asymmetric setting like the N-ARDL is suitable to explore nonlinearity as well as structural
shift between the outlined variables under consideration.3 Secondly, we would perform the ADF unit
root test with structural break to find the order of integration (Table 1). It is necessary to perform to
unit root test to confirm that there is no I (2) variable.4 I (2) invalidates the computation of F-statistics
to test the cointegration (Ibrahim, 2015). Given that all variables are I(1), we then proceed to apply the
bounds testing approach proposed by Pesaran et al. (2001) and Shin et al. (2014) to test for the pres-
ence of cointegration among selected data series (Table 2). We will perform the Wald F-test with the
null hypothesis, b1 = b2 = b3 = b4 = b5 = 0. After that we would examine the long and short run
asymmetries in the relationship between air transportation and economic growth, we would also
discuss the impact of additional explanatory variables included in the model. With specific to the
TLGH expectations, we would derive the asymmetric cumulative dynamic multiplier effects of a
1% change in the air transport i.e. AT+ −t−1 and ATt−1 as:
∑h y h∂
m+ = t+j , m−
∑
h + h =
∂yt+j
− , h = 0, 1, 2 . . . (6)
= ATt−1 = ATj 0 j 0 t−1
A point to note here is that as h  1, m+h  a1 and m−h  a2.
Finally, on the empirical analysis steps, the direction of causality among the outlined variables is
detected by the non-parametric causality test advanced by Diks and Panchenko (2006). The Diks and
Panchenko (2006) causality test is adopted for its strength over the Hiemstra and Jones (1994) test,
which is plagued with over-rejection issues in favour of the alternative hypothesis when it is not true.
Furthermore, owing to the fact that conventional Granger causality fails to account to nonlinearity
and asymmetry informed the choice of the Diks and Panchenko (2006), that ameliorate the
Table 1. ADF test with structural break: Additive & innovative outliers.
Level
Variables ADF test statistic (IO) P-values Breaking point ADF test statistic (AO) P-values Structural break
LGDP −4.592323 0.0337 1985 −3.069243 0.6457 1984
LAT −4.112719 0.1221 1994 −3.380530 0.4539 1995
LURB −8.535964* < 0.01 1973 −4.392008 0.0578 2014
LRNW −4.015319 0.1518 1993 −2.916545 0.7309 1991
LSG −2.799458 0.7877 1994 −2.039170 0.9804 1994
1st Difference
ΔLGDP −6.005537* < 0.01 1995 −4.592095* 0.0337 1979
ΔLAT −5.747132* < 0.01 2007 −5.809720* < 0.01 2007
ΔLURB −6.455630* < 0.01 1980 −6.118721* < 0.01 1980
ΔLRNW −5.257174* < 0.01 1979 −5.374439* < 0.01 1979
ΔLSG −9.168830* < 0.01 1994 −9.383930* < 0.01 1994
*1% level of significance ** 5% level of significance ***10% level of significance.
Table 2. Bounds test for the nonlinear cointegration.
Dependent variable F-statistics Lower-Bound (95%) Upper-Bound (95%) Conclusion
LGDP 5 2.39 3.38 Cointegration
510 D. BALSALOBRE-LORENTE ET AL.
shortcomings of both traditional Granger causality and Hiemstra and Jones (1994) test. The choice of
the Diks–Panchenko’s test is premise on the superiority of the test to render more robust and con-
sistent causality tests results in a non-Granger causality terrane. Also, the test help in the avoidance of
the over-rejection drawback of Hiemstra and Jones (1994) test that is observed in the test statistics,
which fails to account for possible variation in the conditional distribution that may occur under the
null hypothesis when the sample size tends to unit. Thus, to circumvent for the above-mentioned
issues the Diks and Panchenko non-parametric non-Granger causality test presents a solution that
amends aforementioned setbacks and offers robust and consistent results.
4. Empirical analysis, findings and discussion
To start with, we performed the Unit root test to determine the order of integration of the series.
Although in the conduction of N-ARDL some scholars have argued that there is no need for
protest. Though there should be no I(2) variables to avoid spurious analysis Ibrahim (2015). The
chosen approach is ADF Unit root test with the structural break in the data series. Accounting for
a structural break is important as in the existence of a structural break, the unit root test, which con-
dones it is prone to be biased towards null of random walk (Ranganathan and Ananthakumar, 2010;
Nasir, Rizvi, & Rossi, 2018). We let the date of the break to be determined endogenously, rather than
choosing it exogenously, in simple words we let the data speak. In so doing, we choose the alternate
minimize and maximize options to permit for assessment of one-sided alternatives, this produces dis-
similar critical values for the final Dickey–Fuller test statistic and tests with greater power than the
non-directional alternatives.5 The ADF is applied to test for the unit root in the presence of break
with both Innovative Outliers (IO) and Additive Outliers (AO).6 In order to choose the optimal
number of lags for the ADF test, we used the Schwarz Information Criteria (SIC), which is particularly
appropriate in the presence of structural break (Asghar and Abid 2007). These results are presented in
Table 1; -
The results stated that at the level the null of no unit root could not be rejected at (5%) level of
statistical significance. However, at the first difference, all the series were found to be stationary
i.e. I (1).
Table 2 presents the results of Bounds testing for the nonlinear Cointegration for Spain:
The bound testing showed that the critical values of the F-statistics were greater than upper-
bound at 95% level of confidence, indicating strong evidence of Cointegration expectation models
(Equation (5)). This implied that there is a long-run relationship between the under-analysis variables
and hence, we can proceed with the estimation and further analysis.
After unit root testing, we come to the estimation of N-ARDL model (Equation (5)).
Table 3 results shows the estimation results showed that in the short run, the lagged values of
economic growth GDPt−1 had a positive and statistically significant impact on the economic growth.
The positive air transportation AT+t has a positive impact on the economic growth while the nega-
tive AT+t also had a positive impact on the economic growth in the short run. The positive DAT
+
t were
lower in the magnitude than the negative DAT−t .
The fitted model is free from all diagnostic errors and suitable for policy direction. The negative
and significant values of Error Correction Term (ECT) also indicated the stability of the model and
adjustment pace in terms of disequilibrium on an annual basis. Lastly, the Ramsey RESET test
showed that the null of no misspecification could not be rejected at the statistical level of signifi-
cance. Concomitantly, the model is correctly specified.
The diagnostic test performed for both sub-periods showed a significant value of ECT. The JB test
suggested that the null of normality of errors, no auto-correlation and no misspecifications were not
rejected at the statistical level of significance.
To further test the stability of the estimates, we also performed the CUSUM and CUSUMSQ and the
results are presented for the full and sub-samples in Figure 1.
CURRENT ISSUES IN TOURISM 511
Table 3. Results of nonlinear (N-ARDL) estimation for Spain (1971–2015).
Variables Coefficient Prob.
GDPt−1 0.436055* (0.0001)
AT+t−1 0.740489* (0.0003)
AT−t−1 0.811227* (0.0001)
LRNWt−1 −0.201826* (0.0017)
LURB t−1 70.55963* (0.0000)
LSGt−1 0.274075 (0.8179)
DLRNWt−1 −0.144995** (0.0598)
DLURB t−1 −59.26481* (0.0000)
DLSGt−1 4.013367* (0.0007)
C −24.06296* (0.0000)
Long-run estimation
AT+t−1 1.313052* (0.0000)
AT−t−1 1.438486* (0.0012)
LRNWt−1 −0.614990* (0.0000)
LURB t−1 20.02823* (0.0000)
LSGt−1 7.602588* (0.0009)
C −42.66898 (0.0000)
R2 0.995206
DW 1.568248
ECT −0.563945* (0.0000)
JB test 0.783562 (0.67582)
BG LM test 2.282559 (0.1184)
BPG test 1.14735 (0.3576)
Harvey test 1.713562 (0.1240)
Ramsey REST test 0.8937 (0.3513)
Wald test 44.93583 (0.0000)
*1% level of significance ** 5% level of significance ***10% level of significance, ˟ interpreted as
zt = zt−1 + Dz whereas the JB is Jarque–Bera test for the error normality. BG is Breusch-Godfrey
LM test with two lags for auto-correlation, BPG is Breusch–Pagan–Godfrey Test and White-test
was used for heteroskedastic. Note: White heteroskedasticity-consistent standard errors and
covariance. Optimal lag selection based on AIC.
The CUSUM and CUSUMSQ test for structural change plots of cumulative sum and cumulative sum
of squares of recursive residuals. The straight lines describe critical bounds at the 5% level of signifi-
cance. CUSUM and CUSUMSQ parameter stability tests indicate the stability of estimates. After the
stability test, we estimate the multiplier impact of air transport on the economic growth. The
results of N-ARDL multiplier analysis are presented in Figure 2.
Figure 2 represents the dynamic results of the multiplier test of air transportation on the economic
growth for Spain reflects that in response to a 1% increase in the air transportation the economic
growth shows a positive response. Similarly, in response to the negative air transportation the econ-
omic growth presents a negative response across the annual time horizon.
Finally, our study proposes the non-parametric Diks and Panchenko (2006). This non-parametric
causality reduces the bias and lessens the risk of over-rejection of the null hypothesis. The results
of the Diks and Panchenko (2006) nonlinear causality test are reported in Table 4. The non-parametric
Diks and Panchenko Causality test ameliorate for the pitfalls of conventional Granger causality. Thus,
the need for the direction of causality direct flow is pertinent to adequately arm policymaker and sta-
keholder of direction of the relationship between the outlined variables contemporaneous term and
past realization. That is, the predictability power of each variable to another. Table 4 presents insight-
ful results with feedback causality observed from tourism proxy by air transport and economic
growth (GDP). This implied that both tourism and GDP are a key predictor of each other. This
outcome validates the TLGH and vice versa. This is consistent with the study of Katircioglu (2014).
Furthermore, two-way causality test is seen running from renewable energy consumption and econ-
omic expansion. This denoted that renewable energy consumption is a key determinant for econ-
omic growth. This is desirable as most nation including the Spanish economy is on the trajectory
to decrease fossil fuel energy-based energy consumption. Also, interesting for an industrialized
512 D. BALSALOBRE-LORENTE ET AL.
Figure 1. CUSUM AND CUSUMS Q Parameters stability test.
economy like Spain economy globalization is seen as a driver for economic growth as unidirectional
causality seen running from globalization to economic growth. This outcome is also validated as the
social dimension of globalization triggers urbanization. In addition to the already stated causality
analysis, globalization also engenders urbanization as both two-ways and one-way causality from
economic growth to urbanization. This is insightful that more urban population with a global
effect trigger economic growth in Spain and by extension affect tourism expansion. Further insights
into causality can be seen from the schematic in Figures 3–4.
This is insightful for Spanish government administrators that there is a need to strengthen its
transportation sector given that the sector in both period trigger economic growth. This is a desirable
result given the strategic position of Spain in the community of countries in the European Union (EU)
region. This finding aligns with the study of (Hu et al., 2015; Marazzo et al., 2010). The use of renew-
able sources has a negative impact on economic growth, as a consequence of distribution of energy
mix Spain, dominated by fossil sources. This is disturbing, as policymakers need to intensify efforts on
her energy mix changes from non-renewable energy sourced-derived economic growth to renew-
able energy. (Bekun, Alola, & Sarkodie, 2019; Bekun, Emir, & Sarkodie, 2019). This is because of the
trade-off of the environmental effect of fossil fuel energy sources, which is characterized by pollutant
CURRENT ISSUES IN TOURISM 513
Figure 2. N-ARDL Multiplier of air transportation and response of economic growth in Spain.
Table 4. Diks and Panchenko (2006) Granger causality test.
Sample: 1971–2015
Null hypothesis: Obs T-statistic Prob.
LAIRP does not Granger Cause LGDP 45 1.450*** (0.07358)
LGDP does not Granger Cause LAIRP 1.309*** (0.09534)
LRNW does not Granger Cause LGDP 45 1.516** (0.06472)
LGDP does not Granger Cause LRNW 1.957* (0.02516)
LURB does not Granger Cause LGDP 45 0.200, (0.42060)
LGDP does not Granger Cause LURB 1.256*** (0.10448)
LSG does not Granger Cause LGDP 45 1.427*** (0.07679)
LGDP does not Granger Cause LSG 0.879 (0.18975)
LRNW does not Granger Cause LAIRP 45 1.267*** (0.10266)
LAIRP does not Granger Cause LRNW 1.804** (0.03559)
LURB does not Granger Cause LAIRP 45 0.732, (0.23198)
LAIRP does not Granger Cause LURB 1.303*** (0.09625)
LSG does not Granger Cause LAIRP 45 1.358*** (0.08726)
LAIRP does not Granger Cause LSG 1.110 (0.13357)
LURB does not Granger Cause LRNW 45 1.115 (0.13233)
LRNW does not Granger Cause LURB 1.345*** (0.08924)
LSG does not Granger Cause LRNW 45 1.118 (0.13171)
LRNW does not Granger Cause LSG 1.598** (0.05499)
LSG does not Granger Cause LURB 45 1.256*** (0.10453)
LURB does not Granger Cause LSG 1.576** (0.05748)
Figure 3. Diks and Panchenko (2006) causality relationship.
514 D. BALSALOBRE-LORENTE ET AL.
Figure 4. Empirical scheme, based in N-ARDL econometric results.
emission (CO2). Furthermore, urbanization and social globalization reflect a positive impact, which
varied in magnitude and significance over different lags. The long-run estimates for the full period
presented in the bottom of Table 3 that the positive air transportation has a positive impact on econ-
omic growth while the air transportation also presents a positive impact indicating an asymmetric
relationship between the air transportation and economic growth in Spain. Among the other vari-
ables, renewable energy shows a negative impact though results significant in the long run. On
the other hand, urbanization process and social globalization exert a positive impact on the economic
growth.
5. Conclusion
Due to increased earnings from tourism development, many governments have sought to invest
more in tourism with the motive of growing their economies. This increasing attention has led to
a recurrent investigation of the connection between tourism and economic growth in the tourism
literature. It is on this premise that the present study re-investigates the dynamic interaction
between tourism and economic growth with a new perspective from the Spanish context. The
current study is different from the previous in terms of scope by accommodating for other key
growth driver like air transportation, social globalization and urbanization. Furthermore, the
present study contributes and complement to the existing literature in terms of methodological
front by the application of recently developed N-ARDL methodology proposed by Shin et al.
(2014) that account for asymmetry and nonlinearity over the outlined variables.
Empirical investigation traces long-run asymmetry relationship between the variables under
review. This implies that there is a strong connection between economic growth and tourism
sector in conjunction with the transportation sector of the Spanish economic growth over the inves-
tigated period. This study lends support to the finding of Katircioglu (2014) as tourism is seen as a key
growth determinant.
It is well documented in the literature that tourism is pivotal to economic growth as air transpor-
tation engenders economic growth in a term of dynamic globalized changing world and the need for
tourism arrival. However, there is need to apply the brakes on fossil energy sources as the current
study observed an inverse relationship between renewable energy and economic growth. Thus,
the need for a paradigm shift in energy consumption from tourism sector should be revisited by
appropriate strategies from the government officials in Spain. There is an environmental impli-
cation(s) for non-renewable induced tourism-growth economy. These consequences are enormous.
CURRENT ISSUES IN TOURISM 515
Consequences range from poor environmental air and health hazard in the long run. Given the high-
lighted outcomes, pragmatic action step is required that the tourism-induced growth should be
green and from cleaner energy basis. Furthermore, the need for policymakers to reinforce tourism
infrastructures like more recreational centres, amusement parks and regulations in air transport to
warranty the tourism sector attracts more tourist arrival as well increase the promotion of renewable
sources, which are cleaner and more eco-system friendly.
In summary, the adoption of linear symmetry modelling can lead to spurious results and mislead-
ing policy implications for the Spanish economy. Thus, the use of appropriate N-ARDL methodology
offers better and robust conclusions, forecast and policy direction for stakeholders in the related field.
Further studies can reinvestigate the theme under review for other top tourism destination and query
other co-variates not accommodated for in the present study like role of demographic indicators like
democracy or political regime in the TLGH literature.
Notes
1. Air transportation (AT) in this study context refers to the movement of persons, customer’s cargo by planes, air-
crafts. AT in recent times has evolved to be main/preferred sources of movement given in unique traits of comfort
and speed in the transportation sector. This study uses AT as to proxy tourism after studies of (Brida et al., 2018;
Husein and Kara, 2020).
2. For brevity, details on other related literature are presented in in appendix section.
3. For brevity, the results of the BDS test can be made available upon request.
4. Even some academics recommend that there is no need for stationary checking for the ARDL method (Ibrahim,
2015), ARDL contains one limitation; when any series in model is stationary at second difference I(2), ARDL cannot
be employed, becoming F-statistics value invalid (see Ibrahim, 2015). Therefore, to leave second difference it is
recommended to use Dickey–Fuller (ADF) test. Our study applies ADF with structural brake, reported in Table 1.
5. See, Zivot and Andrews (1992), Banerjee, Lumsdaine, and Stock (1992) and Vogelsang and Perron (1998).
6. See, Fox (1972) and Tsay (1988).
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Festus Fatai Adedoyin http://orcid.org/0000-0002-3586-2570
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Appendix
Schematic representation of empirical literature of selected studies
Empirical strategy [Unit root
Variables; Data period and testing; and Cointegration
S/N Author(s) Frequency method] Causality analysis and results
1. Gurgul and Lach Y; GFCF; Total Labour force, OLS Globalization → Y
(2014) Average years of schooling over
age 25, Government
Consumption, Inflation (CPI), FDI
(Net inflow), Money and Quasi
money; Annual data from 1990–
2009
2. Coulibaly, Erbao, Y; Opportunity entrepreneurship Arellano–Bond and dynamic Globalization → Y
and Mekongcho contribution (ENT); Economic system estimation
(2018) globalization (EGI), Physical
investment (GCF), National
Saving (SAV); Annual data from
2002 to 2013
3 Küçükönal and Air Transport; Y; Employment; Granger causality analysis Short term: Air transport → Y
Sedefoğlu Tourism; Annual data from
(2017) 2000–2013
4 (Meersman & Y; Imports and Exports; total Granger Causality Test (VAR) Transport infrastructure → Y
Nazemzadeh, length of the road and rail
2017) network; The private capital
stock; Employment; Annual data
from 1980–2012
5 (Marazzo et al., Gross Domestic Product (GDP) ADF (Constant and trend); Granger Causality Test (VECM)
2010) Passenger-kilometre (PAX); Johansen; Series are Air Transport demand ↔ Y
Annual data from 1966 to 2006 cointegrated
6 (Hu et al., 2015) Y; Air transport passenger ADF, Phillips–Perron Bivariate Granger (PVECM); Short
throughput; Quarterly data from (First difference); Pedroni and term: Domestic air passenger
2006 to 2012 Kao cointegration tests: The traffic → Y; Long Term:
series are cointegrated Domestic air passenger traffic
↔ Y
7 (Saidi & Y; EC; Freight transport; Carbon Levin–Lin–Chu (LLC), Im-Pesaran– SGMM
Hammami, dioxide emissions; Financial Shin (IPS) Freight transportation ↔ Y;
2017) development; Capital stock; (constant and trend) Freight transportation + Y →
Trade openness; Population; All series are stationary at first economic degradation
Foreign direct investment difference
Urbanization; Annual data from
2000 to 2014
8 (Hakim & Merkert, Number of air passenger; Volume Im-Pesaran–Shin (IPS) – (Constant Granger long-run and Wald
2016) of air freight; Y; Annual data and Trend); Pedroni/Johansen short-run causality tests.
from 1973 to 2014 cointegration test. The series are Short term: No relationship
cointegrated Long term: Air transport → Y
9 Saidi et al. (2018) Y; Road transport related energy Levin–Lin–Chu (LLC), Im-Pesaran– Dumitrescu–Hurlin causality
use, Road transport Shin (IPS) – (constant and trend) analysis (GMM); Transport
infrastructures, Capital stock; energy consumption +
Annual data from 2000 to 2016 Transport infrastructure ↔ Y
(Continued )
CURRENT ISSUES IN TOURISM 519
Continued.
Empirical strategy [Unit root
Variables; Data period and testing; and Cointegration
S/N Author(s) Frequency method] Causality analysis and results
10 Rashid Khan et al. Energy demand; Air transport; CIPS panel unit root;
(2018) Railways transport; Customs and Johansen Fisher panel
other import duties as % of tax cointegration
revenue; Y Analysis; The series are
cointegrated
11 Carfora, Pansini, Y; energy consumption, energy ADF, Phillips–Perron (PP) – Granger Causality Test (ECM)
and Scandurra prices (CPI); 1971–2015 (Constant); Johansen’s Y ↔ Energy prices
(2019) multivariate maximum
likelihood tests; All series are
cointegrated
12 Bhattacharya Y; GFCF; RE; Total labour force (LF); CIPS panel unit root; Panel Pedroni Dumitrescu–Hurlin causality
et al. (2016) Annual from 1991 to 2012 cointegration, (panel FMOLS, DOLS)
All series are cointegrated RE ↔ Y
13 Kahia et al. (2016) Y; Total renewable and non- Panel unit root tests; Panel Panel FMOLS estimates and
renewable electricity cointegration tests analysis Granger causality test
consumption, GFCF; Labour Short run: Y → RE; RE ↔ NRE
force (LF); Annual data from Long run: Y ↔ RE
1980 to 2012
14 Troster Oil prices (OP); Industrial ADF; Zivot and Andrews test (ZA), Granger causality test
et al.(2018) Production Index (IPI); EC; ADF Least Squares (ADF-GLS); RE ↔ Y
Monthly data from July 1989 to Johansen linear cointegration Oil Price →Y
July 2016 test.
15 Huang and Y; FDI; Per capita Import and ADF test; Phillips–Perron test (PP Individual Energy consumption
Huang (2019) Export trade volume; Annual test) – All the variables are I (1); → Y
data from 2004 to 2017 Bound test cointegration results;
ARDL
16 Boontome et al. Y; CO2 emissions per capita (C); ADF; Phillips–Perron test; All the Granger causality test (VECM)
(2017) renewable energy consumption variables are I (1); Multivariate C → NREC → REC + Y
(REC); Non-renewable energy Johansen cointegration test
consumption (NREC); Annual
data from 1971–2013
17 (Tang & Tan, Industrial production index; ADF; Johansen cointegration test; Recursive Granger Causality test
2013) International visitor arrivals; All series are cointegrated Tourism → Economic growth
Monthly data from January 1995
to February 2009
18 Nepal et al. (2019) Y; Tourist arrival GFCF; Energy use, ADF; ARDL Bound testing Granger Causality test (ARDL)
and carbon dioxide emissions
(CO2)
19 (Jalil et al., 2013) Y; International tourism receipts, ADF; All variables are either I (0) or Granger Causality test (ARDL)
Capital stock, Inflation and Trade I (1); ARDL Bound testing Tourism → Economic growth
openness; Annual data 1972–
2011
20 (Tugcu, 2014) Y; tourism receipts (RCPT) Levin–Lin–Chu (LLC), Im-Pesaran– Panel Granger causality test;
tourism expenditures (EXP); Shin (IPS) Tourism receipts ↔ Y in
Annual data from 1998 to 2011 Europe; Tourism expenditures
↔ Y in Asia; No causality found
in Africa
21 (Schubert, Brida, Y of host country; Y of USA; Real ADF and KPSS; Johansen Granger Causality test (VECM)
& Risso, 2011) Exchange rate; Annual data from Cointegration test tourism demand → Y
1970–2008
22 (Seghir et al., Y; Tourism Spending; Annual data Levin, Lin and Chu (LLC); Breitung Panel Granger Causality test
2015) from 1988 to 2012 t-stat; Im, Pesaran and Shin (IPS) Tourism spending ↔ Y
W-stat; MW–ADF Fisher Chi-
square; MW–PP Fisher Chi-
square; Hadri Z-stat;
Heteroscedastic consistent Z-
stat; and Panel cointegration test
Notes: EG→ Economic Growth; ADF→ Augmented Dickey–Fuller; GFCF→ Gross fixed capital formation; Y→ Gross dom-
estic product; EC → Energy consumption.