Nexus Between Geopolitical Risk and Tourism Demand in India: Evidence from the ARDL Approach

Abstract

The present study aims to examine the nexus between geopolitical risk and foreign tourist arrivals in India. In addition, other variables which impact tourist arrival in other parts of the world have also been included. To achieve the objectives of the study, the auto-regressive distributed lag (ARDL) model propounded by Pesaran et al. has been applied using quarterly data from 2001: Q1 to 2019: Q4. The findings of the study reveal that geopolitical risk negatively affects foreign tourist arrival in India. Besides, the study also captures the income and price determinants as propounded by classical economists.

JEL Codes: C500; P0; Z30

Keywords

Auto-regressive distributed lag model geopolitics India tourism

I. Introduction

The tourism sector plays an essential role in the process of development both in developing and developed countries. According to Alam and Paramati (2016), tourism activities act as drivers of development, particularly in developing countries, attracting foreign direct investment, tax-related revenue and generating employment opportunities. Thus, given the importance and economic benefits associated with the tourism sector, it is essential for developing countries to pay attention to this sector (Tiwari et al., 2019). According to the World Travel and Tourism Council Report (2019), the tourism sector, only marginally behind the manufacturing sector, contributes over 10% of the world’s gross domestic product, creates more than 319 million jobs and accounts for a share of 6.5% in the global exports. However, compared to developed countries, given the weak institutional arrangements, developing countries are more vulnerable to economic and geopolitical risks (Gray, 1997). These risks adversely impact the economic performance of different sectors, including tourism, which impedes development in these countries. Given the rising importance of India in developing countries as an economic power, it is interesting to examine the impact of geopolitical risk on tourism activities in the country.

India has been the source of attention to foreign tourists due to its historical and cultural heritage, medical, pilgrimage, beaches and scenic beauty. In 2019, the tourism industry contributed over 5% to the GDP of the country and generated 12.75% of total employment (Ministry of Tourism, India, 2019). Some of the important tourist destinations in the country are Jammu and Kashmir, Shimla, Darjeeling, Goa and other such places. Despite great potential, the country has not grabbed its due share from the global tourism market and ranks at the 26th position (Ministry of Tourism, 2018). This poor performance of the tourism industry is attributed to various incidents like the Mumbai attack and the insecure situation in the North-eastern states (Tiwari et al., 2019). According to Santosh and Sanjeev (2020), all these incidents have put India on the list of unsafe countries. According to the Country Risk Report (2020), India ranks fifth on the impact of geopolitical risk.

Against this background, it is essential to respond to challenges posed by geopolitical risk. Following Caldara and Iacoviello (2018), the present study investigates the impact of geopolitical risk on tourist arrival in India using the advanced econometric technique. It is essential to mention here that India shares a border with many South Asian countries and is prone to geopolitical risk given the volatile situation in the region. In addition, given the presence of different religions and internal security issues, examining geopolitical risk has gained more importance in the Indian context. The present study covers an extended period (2001 Q1–2019 Q4) compared to available literature and uses an advanced econometric technique.

Furthermore, this study has given due consideration to the seasonality present in the tourism data, which was not considered in available studies. The rest of the paper is structured as follows. After introducing the topic in Section I, Section II deliberates on the literature review, Section III elucidates the data description and econometric methodology. Empirical findings are discussed in Section IV. Section V discusses the main findings.

II. Literature Review

Regarding the determinants of tourism, a large number of studies have investigated economic factors such as income, inflation and exchange rate (Dogru et al., 2019a; Martins et al., 2017; Wang, 2009) and non-economic factors like culture (Gholipour & Tajaddini, 2014; Hu et al., 2018), climate change (Dogru et al., 2019b), air pollution (Zhou et al., 2019) and transport infrastructure (Khadaroo & Seetanah, 2008). The available literature has also examined the impact of potential sources of uncertainty, such as terrorism (Fourie et al., 2019; Liu & Pratt, 2017), economic crises (Fourie et al., 2019) and political risk (Aloui et al., 2020; Benítez-Aurioles, 2019; Ghalia et al., 2019) on tourism. However, the above-mentioned studies did not measure uncertainty in a unified way. They investigated the impacts of financial events or geopolitical news, including economic crises or terrorism, using dummy variables or event studies to measure these events (Athanasopoulos & Hyndman, 2008; Demiralay & Kilincarslan, 2019; Khalid et al., 2019; Kubickova et al., 2019; Perles-Ribes et al., 2017; Qu & Or, 2006; Sio-Chong & So, 2020; Wang, 2009). These proxy variables are not continuous and cannot cover the entire range of uncertainties. Caldara and Iacoviello (2018) have developed a geopolitical risk index to overcome this limitation. Using this index, some scholars have examined the impact of geopolitical risk on tourism. For instance, Webster and Ivanov (2014) view that change in power and global command may affect the tourism and hospitality industries. They further argue that a new order of global power may impact the supply of low-cost fuel, thus increasing transportation costs and directly affecting the tourism industry. Besides, in recent years, the world has faced many challenges like financial crises, which have impeded the progress of the tourism industry. According to Flint (2016), geopolitical risk can be defined as the risk of terrorist attacks, war between countries and domestic conflicts that cannot be solved peacefully.

Bassil et al. (2019) believe that the perception of insecurity among tourists can negatively impact the tourism industry of the destination country. According to Maslow’s (1943) five-level hierarchical motivational pyramid, the second most important psychological factor tourists consider while planning their destination is safety-related issues. Utilising the wavelength method, Balli et al. (2019) examined the impact of geopolitical risk on tourism demand in emerging countries, including South Africa, Mexico, Turkey, South Korea, Philippines, Thailand, Malaysia and Indonesia. The study concludes that some countries take more time to adjust to the impact of geopolitical risk than other countries that can adjust in a short period. Demir et al. (2019) found that geopolitical risk significantly affects tourism arrival as tourists quickly respond to changing political situation in any country. Demiralay and Kilincarslan (2019) analysed the effects of geopolitical risks on four regional travel and leisure industry stock indexes and found a negative effect of geopolitical risk on tourism stock except for Asia and Pacific index. Demir et al. (2020) found asymmetric relationship between geopolitical risk and tourism demand in the case of Turkey. The study concludes that an increase in geopolitical risk reduces foreign tourist arrivals, while a decrease in geopolitical risk has no significant effect on tourist arrivals in the short run. The study further argues that there is no long-run relationship between geopolitical risk and tourism demand.

Using panel data between 2005 and 2017, Lee et al. (2021) examined how geopolitical risk impacts international tourism demand. The finding indicates that geopolitical risk negatively impacts tourism demand and is an essential determinant.

Considering both geopolitical risk and economic policy uncertainty, Jiang et al. (2020) conclude that Chinese tourism stock returns are highly and asymmetrically influenced by geopolitical risk compared to economic policy uncertainty. Similarly, Polat et al. (2021) examined the effect of geopolitical risk on the Borsa Istanbul tourism index (BIST) and tourist arrivals using time-series data from January 1998 to October 2020. The study concludes an asymmetrical relationship between the BIST tourism index and geopolitical risk, which indicates that geopolitical risk negatively impacts tourism arrival and vice versa.

In addition, the available literature on the relationship between geopolitical risk and tourism is diverse, supporting the negative relationship between the two. However, many studies (Smith, 1998; Weaver, 2011) suggest that the two variables under certain conditions are complementary, which indicates that geopolitical risk supports tourism growth.

In case of India, the available literature (Nair & Ramachandran, 2016; Ranga & Pradhan, 2014) has mainly highlighted the role of tourism industry in generating employment opportunities and enhancing economic development. Examining the role of geopolitical risk and economic uncertainty, Tiwari et al. (2019) concluded that geopolitical risk has significant and long-run implications compared to economic uncertainty, which has short-run consequences on tourist arrival. Similarly, Ghosh (2021) examined the role of geopolitical risk in tourist arrival in India by applying the cointegration technique. The findings confirm the long-run repercussions of geopolitical risk on the tourism sector and argue for developing a pre-crisis management system to protect the tourism sector from such repercussions. However, only few years were covered in the available literature, and some critical variables were ignored. The present study adds to the available literature by considering the extended period (2001Q4–2019Q4) and taking important variables into account. The present study would help policymakers to formulate appropriate short-term and long-term policies which can enhance the tourism sector’s performance in India.

III. Data Description

This study employs data on five variables—foreign tourist arrivals (FTA), geopolitical risk, real effective exchange rate (REER), oil prices and world gross domestic product (WGDP) per capita—with quarterly frequency for the period 2001:Q1 to 2019:Q4 in case of India. Foreign tourist arrival is the dependent variable for which data was retrieved from India Tourism Statistics (Various issues) published by the Ministry of Tourism, Government of India. Geopolitical risk data, pioneered by Caldara and Iacoviello (2018), are extracted from the website http://www.policyuncertainty.com,¹ whereas data on the oil price is extracted from IMF International Financial Statistics. Data on the remaining two variables are retrieved from the Federal Reserve Bank of St. Louis. Geopolitical risk is the variable of interest, while the rest are control variables. The justification for using the above period for this study is based on the availability of the data from reliable sources. All the variables are transformed into logarithmic form to reduce the variability and present the estimated slope quotients in respective elasticities. Furthermore, considering the seasonality present in the tourism and World Gross Domestic Product per capita data, the series are seasonally adjusted using X-13-ARIMA filter in E-Views.

Definition of Variables

Foreign tourist arrival is defined as the number of foreign tourists who have arrived in the reporting country during a specific period. There are many proxies available in the literature for tourism demand notable among them are revenue receipts from tourism, the length of stay of the tourists and foreign tourist arrival. Following Liu and Pratt (2017), the present study applies foreign tourist arrivals as a proxy for tourism demand.

Geopolitical risk is defined as the risk associated with the threat of war, terrorism and state of conflict that affects the ordinary and peaceful course of action (Caldara & Iacoviello, 2018).

REER is the value of a country’s currency against its major trading partners’ weighted average of currencies. Foreign tourist arrival and the length of stay to any foreign destination are affected by the living cost. Following Gordon (1981), REER is taken as a proxy for the price competitiveness of the destination. Incorporating REER as a proxy for the relative cost of living is based on purchasing power parity theory which states that exchange rate across countries better reflects the cost of living. As foreign tourists are not well aware of the prices prevailing in the destination to be visited, they consider the exchange rate a relevant factor in getting information about the cost of living. Therefore, an increase in REER, that is, appreciation in the destination country’s currency, is expected to decline the international tourist arrival in the country.

World gross domestic product (WGDP): According to the classical economic theory, income is the most important determinant of demand for tourism (Stronge & Redman, 1982). The theory backed by the empirical literature also suggests that the elasticity of demand for tourism goods and services is usually very high, which captures its luxurious nature. Following the available literature Sharma (2021), the present study uses world gross domestic product to proxy income.

Oil price: Transportation cost is considered an essential determinant of tourism demand. Various proxies like distance of travel, oil price and air travel prices have been used in the available literature. Because of the energy-intensive nature of the tourism industry, many researchers in the recent literature use oil prices as a proxy for travel costs (Al-Mulali et al., 2020). This study uses Brent crude oil price as a proxy for transportation costs.

IV. Methodology

In the present study, the auto-regressive distributed lag (ARDL) model is utilised to examine the effect of geopolitical risk, REER, oil prices (OIL) and world gross domestic product (WGDPS) on foreign tourist arrivals in case of India. The selection of ARDL model is based on its advantages over the earlier cointegration techniques of Engle and Granger (1987) and Johansen and Juselius (1990). These models require all the incorporated variables to be integrated in the same order and with large sample size. However, ARDL model can produce reliable results in a small sample size. This model is flexible and yields equally reliable results regardless of whether the variables are integrated of order I (0), I (1) or a mixture of both. Another advantage of ARDL model is that it adequately addresses the problem of endogeneity and autocorrelation (Jalil & Ma, 2008).The drawback of an ARDL model is that it cannot be applied to I(2) series. Keeping in view the above advantages and following Wani and Mir (2021), the ARDL model is specified as:

{LFTAS}_{t} = β_{0} + β_{1} {LGPR}_{t} + β_{2} {LREER}_{t} + β_{3} {LWGDPS}_{t} + β_{4} LOIL + ε_{t},

(1)

where LFTAS, LGPR, LREER, LWGDPS and LOIL denote natural logarithm of foreign tourist arrivals, geopolitical risk, Real exchange rate, oil price and WGDPS, respectively. Similarly, Parameters $β_{1,} β_{2}, β_{3}, β_{4}$ represent long-run coefficients of the regressors, whereas, $β_{0}$ and $ε_{t}$ are the constant and the error term, respectively. Subscript t designates the time index. The apriori expected sign of the parameters is as: $β_{1,} β_{2}, β_{4} < 0$ , $β_{3} > 0$ .

An ARDL depiction of (1) is written as

\begin{array}{l} {Δ LFTAS}_{t} = β_{0} + \sum_{i = 1}^{q} β_{1 i} Δ L F T A S_{t - i} + \sum_{i = 0}^{q} β_{2 i} {ΔLGPR}_{t - i} + \\ \sum_{i = 0}^{q} β_{3 i} Δ L R E E R_{t - i} + \sum_{i = 0}^{q} β_{4 i} {Δ LWGDPS}_{t - i} + \sum_{i = 0}^{q} β_{5 i} {Δ LOIL}_{t - i} + \\ β_{6} {LGPR}_{t - 1} + β_{7} {LREER}_{t - 1} + β_{8} {LWGDPS}_{t - 1} + β_{9} {LOIL}_{t - 1} + ε_{t}, \end{array}

(2)

where $Δ$ is the first difference, $β_{1}$ $β_{2,} β_{3}$ , $β_{4}, β_{5}$ denote the short-run dynamics of the model and $β_{6}$ , $β_{7}$ , $β_{8}$ , $β_{9}$ indicate the long-run elasticities. A long-run relationship called cointegration among the variables is confirmed by performing a bounds test. If the F-statistics value is greater than the upper bound value provided by Pesaran et al. (2001), the null hypothesis of no cointegration among variables ( $H_{0} : β_{6} = β_{7} = β_{8} = β_{9} = 0$ ) is rejected, and the alternate hypothesis ( $H_{1} : β_{6} \neq β_{7} \neq β_{8} \neq β_{9} \neq 0$ ) of the existence of a long-run relationship is accepted. If the F-statistic value falls below the lower bound critical value, the null hypothesis of no cointegration is accepted. However, if it falls between the upper and the lower bounds, the conclusion remains indecisive.

Once the long-run relationship among the specified variables is established, the error correction version of equation (2) can be expressed as

\begin{matrix} {ΔLFTAS}_{t} = β_{0} + \sum_{i = 1}^{q} β_{1 i} {ΔLFTAS}_{t - i} + \sum_{i = 0}^{q} β_{2 i} {ΔLGPR}_{t - I} + \sum_{i = 0}^{q} β_{3 i} {ΔLREER}_{t - i} \\ + \sum_{i = 0}^{q} β_{4 i} {Δ LWGDPS}_{t - i} + \sum_{i = 0}^{q} β_{5 i} {Δ LOIL}_{t - i} + γ {ECM}_{t - 1} + μ_{t} \end{matrix}

(3)

where $γ$ is the speed of adjustment coefficient, it shows an average speed of convergence towards long-run equilibrium with a lag of one quarter. It is expected to be negative and statistically significant.

V. Empirical Analysis

Unit Root Results

Before applying the ARDL model, the stationarity of all the variables is checked using the augmented Dickey–Fuller (ADF) test to ensure that no variable incorporated in the model is integrated of the order two, that is I(2), as the inclusion of I(2) variable(s) in ARDL model render the results invalid (Ibrahim, 2015; Meo et al., 2018; Ouattara, 2004). The results of ADF are reported in Table 1, and it is clear that all the variables except LGPR are non-stationary at level but are stationary at the first difference at a 1% level of significance. The significant improvement of ARDL model over the earlier standard cointegration methods (Engle & Granger, 1987; Johansen & Juselius, 1990) is that it can be applied in the situation when variables are I (0), I (1) or a combination of I (0) and I (1) (Pesaran et al., 1999, 2001) which was the significant limitations in the earlier cointegration methods.

Table 1.

Unit Root Test Results.

Variables	ADF (Inlevel)	ADF (In 1st Difference)	Decision
LFTAS	−0.5406 (0.876)	−9.3128 (0.00)	I (1)
LGPR	−3.4339 (0.012)	−11.5507 (0.00)	I (0)
LREER	-2 −2.749 (0.70)	−6.584 (0.00)	I (1)
LOIL	−2.1938 (0.210)	−6.560 (0.00)	I (1)
LWGDPS	−0.984 (0.754)	−3.205 (0.02)	I (1)

Source: Authors’ calculation.

Note: ADF refers to the augmented Dickey–Fuller unit root test. () indicates P value.

Lag Selection Criterion

The long-run results are highly dependent on identifying the appropriate lag length used to specify the model (Bahmani-Oskooee, & Bohl, 2000). Utilising too many or too few lags overfits the model or does not capture the valuable information (Stock and Watson, 2012). Hence, based on the Shwartz information criteria (SIC), the ARDL (1,1,1,1,1) model is selected. The reason for using SIC is its ability to use only a few lagged differences compared to other model selection criteria. In addition to this, it is also agreed upon that SIC is a consistent model selector (Gereziher & Nuru, 2021).

Bounds Test

After the unit root test, the bounds test is undertaken following (Pesaran et al., 2001), and the results of the F-statistic value are reported in Table 2. Since the computed F-statistic value (7.7) is higher than the upper bound critical value (5.06) at a 1% level of significance, the null hypothesis of no long-run relationship among the variables can be rejected in this case. Therefore, we conclude a long-run relationship between foreign tourist arrivals, geopolitical risk, real exchange rate, oil price and WGDPS per capita.

Table 2.

ARDL Bounds Test Results.

Model	F-statistic	Lower Bounds I (0)	Upper Bounds I (1)	Decision
LFTAS/(LGPR, LREER, LOIL, LWGDPS)	7.7			Cointegration
Critical values
10%		2.45	3.52
5%		2.86	4.01
2.5%		3.25	4.49
1%		3.74	5.06

Source: Authors’ calculation.

Long-run and Short-run Relationship

The long-run equilibrium relationship between the estimated variables from ARDL (1,1,1,1,1) model is reported in Table 3. The results in the table reveal that all the explanatory variables carry the expected theoretical sign and are significant at a 1% level of significance except LOIL, which is significant at a 10% level. The findings show an inverse relation between geopolitical risk and foreign tourist arrival in India. This signifies that foreign tourists are risk averse, sensitive to geopolitical risk and do not prefer to visit a destination susceptible to insecurity. The coefficient (0.30) of LGPR shows that a 1% increase in the geopolitical risk leads to a 0.30% decrease in tourist inflow in the long run. The present study emphasises that to promote tourism development in the country, all the necessary steps must be taken to meet the international security benchmark. The results conform to the findings of Demir et al. (2019), Haddad et al. (2015) and Lee et al. (2021).

Table 3.

Long-run Estimates.

Variables	Coefficients	Standard Error	t-statistic
LGPR	−0.306880	0.040379	−7.599981*
LREER	−3.135433	0.901236	−3.479036*
LOIL	−0.038661	0.019632	−1.969281**
LWGDPS	5.100214	0.084112	60.63585*

Source: Authors’ calculation.

Note: *, ** significant at the 1% and 10% levels, respectively.

Due to relatively easy comparable measures, foreign tourists use REER as a proxy to capture the price competitiveness across destinations, which strongly influences tourists’ decisions. The empirical results reveal that tourism demand is negatively associated with the price competitiveness of the destination. An increase in the REER, that is, appreciation of the domestic currency, will make the destination relatively less attractive to the foreign tourists and consequently reduce the international tourist arrival to the concerned destination. The coefficient (3.1) shows that a 1% increase in the LREER leads to 3.1% decrease in foreign tourist inflow in the long run, ceteris paribus. These findings are aligned with those of Gholipour et al. (2016) and Demir and Gozgor (2018).

The coefficient of LWGDPS used to measure the income responsiveness of the tourism demand was found positive and statistically significant at a 5% level both in the long and the short run. The results indicate that a 1% increase in WGDPS would lead to a 5% increase in tourism demand in the long run. The results align with the theoretical underpinning that all else equal, an increase in purchasing power would lead to an increase in the demand for tourism. Furthermore, the estimated value of the income elasticity coefficient of the variable pinpoints the luxurious nature of tourists.

The impact of oil prices on tourism demand is found to have a negative effect as expected since an increase in oil prices is likely to increase the cost of transportation and other tourist-related activities. However, the coefficient is significant only at the 10% level. The less impact of oil prices on tourism demand may be attributed to the lower pass-through of the hike in international oil prices to various stakeholders in the tourism industry.

Table 4 presents the error correction representation of the selected ARDL model. Coefficients with the D operator show the short-run elasticities. The estimated coefficient of error correction term is negative and significant at the 1% level, which reinforces that the series is non-explosive and converges back to the equilibrium level with a speed of 54% per quarter, that is, 100% disequilibrium is eliminated in two quarters. Furthermore, the negative relationship between geopolitical risk and foreign tourist arrivals is also evident in the short run. In addition, the lagged term of the dependent variable, which captures the word-of-mouth, plays a significant role in the tourism demand in the case of India.

Table 4.

ECM of ARDL Model.

Variables	Coefficients	Standard Error	T-statistic	Probability
Constant	−14.27729	2.223114	−6.422201	0.0000
D(LFTAS(−1))	−0.542745	0.094619	−5.736142	0.0000
D(LGPR)	−0.115739	0.025894	−4.469768	0.0000
D(LREER)	−0.690405	1.135671	−0.607926	0.5454
D(LOIL)	−0.073918	0.026921	−2.745722	0.0078
D(LWGDPS)	1.796320	0.419437	4.282691	0.0000
CointEq(−1)*	−0.542745	0.084423	−6.428850	0.0000

Source: Authors’ calculation.

Notes: R² = 0.52, Adj. R² = 0.48, F-statistic = 15.04, rob. (F-statistic) = 0.000, Durbin Watson statistic= 2.2.

Diagnostic and Stability Tests

To check the precision of the estimated model, various diagnostic tests, such as serial correlation, heteroskedasticity, normality of residuals and model specifications, have been performed, and the results are reported in Table 5. It is evident from the table that the model is free from all the issues as mentioned above.

Table 5.

Diagnostic Test Results.

Test Type	Problem	F- Statistic	P Value	Decision
LM	Serial correlation	2.376022	0.1281	No serial correlation exists
Breusch–Pagan–Godfrey	Heteroskedasticity	1.321317	0.2434	No heteroskedasticity exists
Jarque–Bera	Normality	-	0.15988	Residuals are normally distributed
Ramsey RESET test	Model specification	1.788671	0.1858	Model is correctly specified

Source: Authors’ calculation.

Stability Estimates

In order to ensure the stability of the estimated ARDL model, CUSUM and CUSUMSQ tests are performed as shown in Figures 1 and 2, respectively. Since the CUSUM and CUSUMSQ line lies with a 95% confidence band, this confirms that the model is free from any structural instability.

Figure 1.

Source: Authors’ calculation.

Figure 2.

Source: Authors’ calculation.

VI. Conclusion

The present study empirically examined the short-run and long-run effects of geopolitical risk, real exchange rate, oil price and WGDPS on foreign tourist arrivals in India using quarterly data for the period 2001 to 2019. Apart from different unit root tests, the study applies ARDL and ECM techniques to examine the relationship between tourism arrivals and other variables. The study concludes that geopolitical risk negatively impacts foreign tourist arrival in India.

The empirical results indicate that geopolitical risk, real exchange rate, oil prices, WGDPS and tourism arrival are co-integrated. The results indicate that a 10% increase in geopolitical risk leads to a 3% decrease in tourist arrival. Further increase in real exchange rate by 10% leads to a 31% decline in tourist arrival, which shows the intensity of inflation’s impact on tourists’ planning. Similarly, an increase in WGDPS leads to enhancement in foreign tourist arrivals in India, indicating that income is directly related to tourism activities. Thus, the findings in the present study indicate a negative relationship between foreign tourist arrivals and geopolitical risk in the case of India. Since tourists are more concerned about the security of the destination to be visited, any increase in the geopolitical risk results in destination substitution and substantial economic losses for the tourism sector of the tourism receiving country. The present study’s conclusion indicates that the tourism sector in India is impacted by geopolitical tensions of the country with its neighbouring nuclear powers, past horrific incidents and other such activities. These risks create a deep negative impression among tourists and consequently choked the tourism sector development in India.

The above discussion indicates that India needs to focus more on improving the security situation in the country. Therefore, a proper policy must be put in place to meet the international standard of security and boost confidence among foreign tourists to mitigate the adverse effects of geopolitical risk on the development of the tourism industry in the country. Moreover, the large corporations and individuals dealing with this sector need to innovate new techniques to benefit from the prospects of the tourism industry. In addition, security-related confidence needs to be built among travellers who would help enhance tourism flow and economic growth in the long run.

Limitations of the Study

The main limitation of the study is that it presents the impact of the included variables in a linear framework; however, most of the time-series data exhibit hidden asymmetric characteristics. Thus there is scope for conducting the same study in a nonlinear framework.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Note

ORCID iDs

Mohammed Ayub Soudager

Suadat Hussain Wani

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