Does World Uncertainty and Economic Policy Uncertainty Affect Tourism in G7 Countries? Evidence From Quantile-on-Quantile Regression

Abstract

This study empirically examines the correlations between economic policy uncertainty (EPU) and world uncertainty (WU) and tourism in G7 countries. We use the quantile-on-quantile approach to explore some more subtle features between the variables and reveal asymmetric and heterogeneous relationships. The results show that the effects of EPU and WU are stronger at higher quantiles, and considerable variation is revealed across countries due to differing characteristics and economic circumstances. We determine that EPU has a negative impact on tourism and more significant positive effects are evident at different quantile combinations in all G7 countries, whereas the impact of WU on tourism is negative. We also found more significant positive effects of WU on tourism at different quantile combinations in all G7 countries.

Keywords

economic policy uncertainty world uncertainty tourism quantile-on-quantile approach

Introduction

As the engine of a service-based economies, tourism has a considerable pulling effect on related industries (Alam & Paramati, 2016; Dogan et al., 2017; Dogru et al., 2019; Dogru & Bulut, 2018; Katircioğlu, 2010; Shahzad et al., 2017); however, it is also vulnerable to the effects of other factors. Tourism is a flexible consumer good based on a subjective choice; thus, the random and sudden nature of a vast majority of uncertain events mean that domestic and global uncertainty can have an impact on countries’ tourism industry. For instance, in pre-pandemic times, international tourist arrivals reached 1.5 billion, reaching the tenth consecutive year of sustained growth in 2019, successfully becoming an essential sector for many developed and emerging economies, growing faster than the global economy. However, the COVID-19 pandemic inflicted considerable damage to the international tourism industry, returning international tourism to levels from 30 years ago, with international tourist arrivals plummeting 74% and international tourism receipts down by US$1.3 trillion (United Nation World Tourism Organization [UNWTO], 2020). Therefore, comprehensively analyzing the impact of uncertainty on tourism is an essential undertaking.

Lacking a uniform approach for measuring uncertainty in the initial literature, most studies have analyzed the impact of a single specific event on tourism (Fourie et al., 2020; Liu & Pratt, 2017; Perles-Ribes et al., 2017; Sio-Chong & Yuk-Chow, 2020). Research regarding single events tends to emphasize the impact of the event itself, making it challenging to capture the whole spectrum of uncertainty. To address this issue, Baker et al. (2016) and Ahir et al. (2022) respectively proposed economic policy uncertainty (EPU) and world uncertainty (WU) indices. The EPU index¹ is based on countries’ national newspapers, from which text mining is conducted for terms linked to the economy, policy, and uncertainty to form a composite quantification of uncertainty (Baker et al., 2016). However, since the EPU index covers only 28 countries and regions, it is generally only applicable to individual country studies (Gozgor et al., 2021; Manrique-de-Lara-Peñate et al., 2022). The WU Index² is computed by referencing the proportion of the term “uncertainty” (and/or its variants) reported in the Economist Intelligence Unit’s (EIU) report of 143 countries (Ahir et al., 2022). The WU Index is often used as a more specific measure of global uncertainty due to its broad country coverage. Nevertheless, the WU Index was developed later and has rarely been used in tourism-related articles (Zhang et al., 2022). Therefore, this study employs these two indices to separately analyze the impact of domestic and global uncertainty on tourism in G7 countries.

From a methodological perspective, previous research has analyzed the relationship between uncertainty and tourism activity using several different methods, including the common correlated effects mean group, the continuous wavelet transform, causality approaches, and other techniques (Akadiri et al., 2020; Demir, Gozgor, & Paramati, 2020; Wu & Wu, 2021). Notably, a variety of complexities and heterogeneities generally exist in the correlations between variables, which makes it challenging to capture and analyze these relationships comprehensively. Some contemporary studies have demonstrated that asymmetry is among the effects of uncertainty on tourism (Gozgor & Ongan, 2017; Sharma, 2021; Uzuner & Ghosh, 2021). To fully analyze the effect of uncertainty on tourism in G7 countries and investigate possible heterogeneity or asymmetry, we employ the quantile-on-quantile (QQ) approach to analyze quarterly data from 1997 Q1 to 2021 Q4. This approach combines quantile regression (QR) and nonparametric estimation techniques to analyze how the quantile of the explanatory variable affects the quantile of the dependent variable, thereby avoiding the possibility of ignoring the state of the explanatory variable. Benefiting from the merits of the QQ approach, this study can more comprehensively observe the complex links between variables to examine the potential heterogeneity and asymmetry between variables.

This study aims to fill the gaps in previous research. To our knowledge, this is the first study to analyze the impact of domestic and global uncertainty on tourism in G7 countries. Our main rationale for selecting the G7 economies is that the tourism industries of these countries are relatively well-developed and are pillar industries of each country. According to the Report on World Tourism Economic Trends (World Tourism Cities Federation [WTCF], 2019), G7 countries’ total tourism revenue and total tourism arrivals accounted for more than 70% of all developed economies. Overall, this study makes the following three contributions to existing literature. (i) Previous studies have primarily emphasized the impact of a single form of uncertainty on tourism without considering either domestic or global uncertainties. We consider both domestic EPU and global WU indicators separately to investigate how uncertainty from different sources affects tourism and provide a more comprehensive analysis. (ii) To reflect tourism in a multi-dimensional manner, we use a composite tourism indicator as a proxy. This comprehensive index includes multiple facets of tourism to produce more accurate results. (iii) To our knowledge, this is the first attempt to explore the asymmetric and heterogeneous effects of EPU and WU on tourism development. We employ the QQ approach, which can identify the full distribution characteristics between variables at each quantile, making the results more policy informative and providing valuable insights for policymakers to strategically tailor policies to different circumstances.

The remainder of the paper is structured as follows. Section 2 reviews recent studies and theories regarding uncertainty and tourism. Section 3 describes the QQ approach, and Section 4 presents the data used. Section 5 presents the estimated results and explanations. Section 6 summarizes our main conclusions and proposes policy implications.

Literature Review

Keynes introduced the concept of uncertainty in the General Theory of Employment, Interest, and Money published in 1936. In Keynes’ view, uncertainty tends to determine the economic behavior of the subject and stock prices. Since then, uncertainty has become an increasingly important research topic. For instance, some studies have revealed the impact of uncertainty on investment (Ghosal & Loungani, 2000; Nickell, 1977), trade (Handley, 2014; Handley & Limão, 2017), and real estate (Choudhry, 2020). In recent years, the demand for travel has been increasing as a result of technological progress and rising living standards, and tourism is also a very important role in various countries’ economies (Dwyer et al., 2004). Tourism is vulnerable to shocks from natural disasters, public health issues, geopolitical crises, and security emergencies, all of which are characterized by uncertainty.

Theoretically, the impact of uncertainty on tourism can be positive or negative. From the negative perspective, uncertainty can be expected to hinder tourism development. First, uncertainty can affect consumer behavior, which is an aspect of the precautionary saving motive inherited from Keynes’ theory of money demand. When confronting economic uncertainty, people will reduce consumption and increase savings to navigate the impact of the shock, choosing to reduce or postpone nonessential consumption needs (Gozgor & Ongan, 2017). Second, based on the theory of reasoned action (Fishbein & Ajzen, 1975), people will reduce outbound tourism based on concerns about safety, security, and social stability in the face of increased economic uncertainty, which may also affect travel intentions (Nguyen et al., 2022). In addition, based on the hierarchy of needs theory (Maslow, 1987), consumers facing uncertainty will tend to focus on basic needs, asking is travel a necessity or a luxury? In response to this question, based on Harrod (1958), Theuns (2014) determined that travel is a superfluous luxury; therefore, people focus more on basic needs in times of high uncertainty such as recessions and economic crises and luxuries such as travel tend to be temporarily postponed or canceled (Işık et al., 2020; Singh et al., 2019). Finally, uncertainty affects consumers’ consumption behavior as well as firms’ investment behavior. In general, investors prefer a relatively stable investment environment, and increased uncertainty can cause investors to temporarily postpone or abandon investment projects, which may hinder tourism development (Demir, Gozgor, & Paramati, 2020). From the positive perspective, under certain circumstances, uncertainty can also promote tourism. First, when uncertainty increases, the relevant industry could lower prices to attract more visitors and promote tourism (Demir, Gozgor, & Paramati, 2020; Zhang et al., 2022). Second, people living in areas of high uncertainty sometimes choose to travel internationally to find jobs or emigrate abroad to escape uncertain circumstances temporarily or permanently (Nguyen et al., 2020). Third, due to financial challenges, increased uncertainty can lead to currency depreciation. When a country’s currency weakens, its goods are cheaper and travel costs become lower for foreign currency holders, attracting considerable numbers of foreign tourists (Chisadza et al., 2022; Khan et al., 2021).

In early uncertainty research, with no uniform measures of uncertainty, scholars initially considered the impact of specific events such as terrorism (Drakos & Kutan, 2003; Fletcher & Morakabati, 2008; Liu & Pratt, 2017), economic crises (Papatheodorou et al., 2010), epidemics (Baxter & Bowen, 2004), and political risk (Poirier, 1997). Baker et al. (2016) proposed and measured EPU as a unified economic policy uncertainty indicator, which has been widely accepted in the field (Dragouni et al., 2016; Gozgor & Ongan, 2017; Khan et al., 2021; Ongan & Gozgor, 2018). Initially, the impact of uncertainty on single-country tourism was the main research topic. Dragouni et al. (2016) presented the first study to use EPU as a proxy in tourism literature, demonstrating that travelers postpone travel plans when uncertainty is high. Gozgor and Ongan (2017) examined the relationship between uncertainty and US domestic tourist travel expenditure from 1998Q1 to 2015Q4, determining that higher levels of EPU lead to a significant decline in domestic tourism expenditure by US domestic tourists in the long run. However, further investigation was needed to ascertain whether this phenomenon is a general phenomenon that applies to most countries, generating subsequent multi-country studies (Demir & Gozgor, 2018; Ghosh, 2019; Gozgor & Demir, 2018; Wu et al., 2022; Wu & Wu, 2019).

In the first multi-country study, analyzing 15 countries, Demir and Gozgor (2018) found that EPU can be a significant barrier to tourism development. Arguing that previous studies have not focused on the impact of uncertainty on travel expenditure, analyzing 17 countries, Gozgor and Demir (2018) revealed that an increase in uncertainty reduces travel expenditure. Ghosh (2019) used principal component analysis (PCA) to construct a composite indicator of tourism activity and examined the impact of EPU on tourism activity in France, Greece, and the United Kingdom (UK), demonstrating that uncertainty has a long-term negative impact on tourism. While the majority of studies have shown the negative impact of uncertainty on tourism, Nguyen et al. (2020) innovatively analyzed the effect of EPU on tourism in countries with different income levels, finding that higher uncertainty leads to fewer departures in upper-middle-income economies, but increased demand for outbound travel in low- and lower-middle-income economies. Demir, Gozgor, & Paramati (2020) also argued that in times of uncertainty, relevant sectors may reduce prices to attract more tourists. Wu and Wu (2021) use wavelet analysis to investigate the links between EPU and national tourism activity, determining that the two variables are positively correlated in partial periods. Khan et al. (2021) noted that currency devaluation due to uncertainty can make travel costs lower and more attractive to tourists. For example, the devaluation of the pound following the Brexit referendum made traveling to the UK more affordable, which contributed to a boom in tourism. Gholipour et al. (2022) investigated the impact of EPU and changes in consumer confidence on tourism in African countries, also finding a positive and significant relationship between EPU and some countries’ outbound tourism to Africa.

In recent years, some scholars have argued that the impact of uncertainty on tourism should not be confined to the domestic context. From this perspective, domestic EPU refers to respective country-specific uncertainties, whereas global EPU refers to global uncertainty events. As a result, studies began to employ both indicators to analyze the influence of domestic and global uncertainty on tourism (Balli et al., 2018; Demir & Ersan, 2018; Singh et al., 2019). According to Balli et al. (2018), domestic and global EPUs demonstrated a significant negative impact on tourism inflow in OECD countries from January 1997 to August 2017, suggesting that domestic and global uncertainties must be considered by governments and policymakers when analyzing the factors affecting tourism development. Singh et al. (2019) demonstrated that local and global EPU significantly lower the number of foreign visitors to the US. However, Ahir et al. (2022) argued that the measurement of the EPU is limited to only 28 countries, which narrows the scope of application and limits the research; therefore, the WU Index should be used for analysis when investigating the effects of global uncertainty. Following the introduction of the WU Index, some scholars began to use this indicator to investigate the impact of global uncertainty. Gozgor et al. (2021) examined the impact of WU on travelers’ length of stay, determining that uncertainty shocks negatively affect business and holiday travel and visits to friends/relatives. Evidence from Manrique-de-Lara-Peñate et al. (2022) examining the seven aggregated regions of the world from 1995 to 2016 shows that when the global level of uncertainty and insecurity reaches its highest level in each country, a total decrease of 17.5% in travel demand and value added occurs. Similarly, Chisadza et al. (2022) compared the impact of WU on tourism in five regions, finding that increased uncertainty has significantly reduced the number of visitors in North and West Africa, whereas the impact in the European region is positive. Overall, a limited number of studies have comprehensively analyzed the impact of global WU and domestic EPU on tourism development in G7 countries, and to the best of our knowledge, this is the first study to examine the impact of WU on tourism development in G7 countries.

The popularity of the QQ method has inspired some researchers to apply this technique in tourism-related research since it is capable of capturing asymmetry and heterogeneity effectively (Lin & Su, 2020; Shahbaz et al., 2018; Sim & Zhou, 2015). For example, several studies have applied the QQ method to investigate the correlation between tourism and economic growth (Shahzad et al., 2017), FDI (Arain et al., 2020), and carbon emissions (Ozturk et al., 2023), commonly emphasizing that the QQ method provides a clearer view of the close links and asymmetric relationships between variables. However, limited research has examined the asymmetric and heterogeneous relationship between uncertainty and tourism. Some studies have argued that positive and negative fluctuations of uncertainty may have different effects. Meanwhile, the magnitude of the impact varies depending on individual factors (Uzuner & Ghosh, 2021). Therefore, a comprehensive examination of the asymmetric and heterogeneous variations in the impact of uncertainty on a country’s tourism industry is crucial for a thorough analysis. Furthermore, incorporating analyses of asymmetric relationships and heterogeneity provides valuable information for guiding policy development Demir, Simonyan, Chen, & Lau (2020). From both a theoretical and practical perspective, we contend that investigating the relationship between asymmetry and heterogeneity complements previous research and has significant policy implications. From a methodological perspective, the QQ method is better suited for investigating the relationship between non-normal variables due to its nonlinear nature. Based on these aspects and the perspectives of scholars, we argue that it is essential to employ the QQ method to explore the impact of uncertainty on tourism development. Furthermore, we use the QQ method to present three-dimensional images to gain a more comprehensive illustration of the impact of uncertainty on tourism development in G7 countries. This approach also provides clearer evidence regarding the influence of global WU and domestic EPU on tourism development at different quantiles. Accordingly, to fill the gaps in existing research, this study employs the QQ approach to capture the asymmetric and heterogeneous impact of the two forms of uncertainty on tourism development in G7 countries.

Methodology

As previously noted, we employ the recently proposed QQ method to analyze the correlations between EPU and WU and tourism development. The QQ method is an empirical research method that is used to analyze how quantiles of explanatory variables affect the conditional quantiles of the explained variables. This method combines the traditional QR (Koenker & Bassett, 1978) and nonparametric estimation techniques. First, the QR method explores the relationship between the conditional quantiles of the dependent variables and the independent variables and estimates the conditional quantiles of the dependent variable from the independent variable. This reveals the different degrees of variation in explained variables with the continuous change of explanatory variables in different ranges. The locally weighted linear regression technique (Stone, 1977) analyzes the local influence of specific quantiles of independent variables on dependent variables, taking each data point as a “social” center and determining the weight of all the data around it according to the distance, which solves the problem of “dimensional disaster” associated with nonparametric estimation. Third, as noted, the QQ method is a combination of two methods; therefore, the method has inherent advantages compared with the ordinary least squares and traditional QR methods, as it can identify and analyze the deeper internal relationships between explanatory variables and explained variables.

As per the aim of this study, the nonparametric QQ regression model is as follows:

T D_{t} = β^{θ} (X_{t}) + α^{θ} {G D P}_{t} + u_{t}^{θ}

(1)

where $T D_{t}$ represents the tourism development of a country at period t to measure the performance of tourism. $X_{t}$ denotes the two EPU and WU independent variables over time. GDP is used as a control variable, referencing per capita GDP in constant 2015 US dollars of a country at period t . This study adopts the logarithmic form of per capita GDP. $θ$ is the $θ t h$ quantile of the conditional distribution of tourism development, and $u_{t}^{θ}$ is the quantile residual term that has a zero $θ t h$ quantile. Previous information regarding the link between tourism development and the two uncertainties remains unclear; hence, $β^{θ} (\cdot)$ is an uncharted function.

β^{θ} (X_{t}) \approx β^{θ} (X^{τ}) + β^{θ^{'}} (X^{τ}) (X_{t} - X^{τ})

(2)

where $β^{θ^{'}}$ refers to the partial derivative or marginal effect of $β^{θ} (X^{τ})$ with respect to $X^{τ}$ . According to Sim and Zhou (2015), $β^{θ} (X^{τ})$ and $β^{θ^{'}} (X^{τ})$ are equivalent to $β_{0} (θ, τ)$ and $β_{1} (θ, τ)$ ; thus, Equation 2 can be rewritten as follows:

β^{θ} (X_{t}) \approx β_{0} (θ, τ) + β_{1} (θ, τ) (X_{t} - X^{τ})

(3)

We next substitute Equation 3 into Equation 1, yielding the following equation for $T D_{t}$ :

T D_{t} = \underset{(*)}{\underset{︸}{β_{0} (θ, τ) + β_{1} (θ, τ) (X_{t} - X^{τ}) + α (θ) {G D P}_{t}}} + u_{t}^{θ}

(4)

where we use the notation $α (θ) \equiv α^{θ}$ . Part $(*)$ of Equation 4 is the $θ t h$ conditional quantile of tourism development. Since the coefficients depend on $θ$ and $τ$ , the QQ approach can reveal the correlations between the variables at each quantile. Through the coefficients of $β_{0} (θ, τ)$ and $β_{1} (θ, τ)$ , we can identify and analyze the complex relationships between the variables in a more precise manner to reach a more accurate and realistic conclusion.

To estimate Equation 4, we convert the independent variables $X_{t}$ and $X^{τ}$ to the corresponding estimated variable ${\hat{X}}_{t}$ and ${\hat{X}}^{τ}$ , and $β_{0}$ and $β_{1}$ to the estimated parameters $b_{0}$ and $b_{1}$ of the local linear regression. Our estimation is further achieved by applying the following minimization problem:

\begin{matrix} {m i n}_{b_{0} b_{1}} \sum_{i = 1}^{n} ρ_{θ} [T D_{t} - b_{0} - b_{1} ({\hat{X}}_{t} - {\hat{X}}^{τ}) \\ - α (θ) {G D P}_{t}] K (\frac{F_{n} ({\hat{X}}_{t}) - τ}{h}) \end{matrix}

(5)

where $ρ_{θ} (*)$ is the absolute value function of the slope, and the $θ$ conditional quantile of $T D_{t}$ provides its solution. $K (*)$ represents the Gaussian kernel function and $h$ is its bandwidth parameter. By introducing the Gaussian kernel function, we can trade off the observed values around ${\hat{X}}^{τ}$ , which tends to give more weight to more recent observations and exhibit an inversely correlated relationship with the observed separation between the distribution functions, which is denoted as follows:

F_{n} ({\hat{X}}_{t}) = \frac{1}{n} \sum_{k = 1}^{n} I ({\hat{X}}_{k} < {\hat{X}}_{t})

(6)

Finally, the choice of an appropriate bandwidth is extremely important for nonparametric estimation because the size of the target’s neighborhood domain often depends on the size of the bandwidth. A larger bandwidth will lead to a larger deviation in the estimation process, while a smaller bandwidth will result in a larger variance estimation. Therefore, choosing an appropriate bandwidth should achieve a balance between bias and variance. The common method of bandwidth selection has been to apply the empirical rule, which has the advantage of convenient calculation; however, this method is based on the distribution of the density function, and bandwidth selection may not be accurate if the distribution is non-normal (Bashtannyk & Hyndman, 2001; Hall et al., 1991; Jones et al., 1996). This study examines the influence of EPU and WU on tourism development separately, and since its distribution does not have the symmetry of a normal distribution, empirical rules are inappropriate for calculating the bandwidth. Another method of bandwidth selection that has been commonly used in the QQ method is to set a default bandwidth. Sim and Zhou (2015) employed the default bandwidth h = 0.05, and subsequent studies have also adopted this setting (Lin & Su, 2020; Shahbaz et al., 2018); however, this default setting is often unreliable and can sometimes lead to bias and variance issues. To address the issue of bandwidth selection, Duan et al. (2021) employed the cross-validation (CV) technique. This method considers the asymmetric data features and selects the ideal bandwidth by minimizing the comprehensive estimation error. Therefore, we employ the CV approach to determine the ideal bandwidth.

Data Description

To examine the effect of EPU and WU on tourism in the G7 countries (Canada, Japan, Italy, Germany, France, the UK, and the US), we use quarterly time-series data from 1997 Q1 to 2021 Q4. Previous research has primarily used the number of international tourist arrivals, international tourism receipts, or international tourism expenditures as indicators of tourism; however, due to the strong correlation between the three variables, their simultaneous use is likely to lead to multicollinearity problems (Zaman et al., 2016). We use the PCA approach to combine these three indicators and construct a composite indicator, which allows us to combine most of the information from these three indicators into a composite indicator in a weighted manner, which is used as a proxy for tourism activities (Lv, 2020; Shahzad et al., 2017). These data are obtained from the World Tourism Organization’s Tourism Statistics Database (https://www.unwto.org). Table 1 presents the PCA results for the G7 countries. The two first columns show the eigenvalue corresponding to the first principal component and the proportion of total variance explained by the first principal component, respectively, revealing that the eigenvalue corresponding to the first principal component exceeds 1 for each country. Since the remaining eigenvalues of the principal components are less than 1, they are not reported for the sake of brevity. Based on the Kaiser criterion, this study omits the remaining principal component results from the analysis. The factor loadings of the PC1 are presented in the third column.

Table 1.

PCA Results for the Weighted Tourism Activity Index.

Country	Eigenvalue PC1	Proportion explained by PC1	Factor loadings of PC1
Country	Eigenvalue PC1	Proportion explained by PC1	TA	TR	TE
Canada	2.002	0.66725	0.345	0.660	0.668
Japan	2.124	0.70797	0.660	0.669	−0.342
Italy	2.738	0.91272	0.551	0.596	0.584
Germany	2.808	0.93603	0.567	0.583	0.582
France	2.479	0.82644	0.509	0.622	0.595
The UK	2.704	0.90142	0.574	0.567	0.590
The US	2.856	0.95201	0.577	0.579	0.576

Note. This table summarizes the results of the principal component analysis (PCA) conducted to derive the weighted tourism activity index for the G7 countries from 1997 to 2021 (a total of 100 quarterly observations). PC1 denotes the first principal component of the three standard tourism variables (tourist arrivals, tourism receipts and tourism expenditures). The numbers shown include the eigenvalue corresponding to the PC1 for each country, the proportion of total variance accounting for the PC1 and the factor loadings of the PC1. TA, TR, and TE indicate the number of tourist arrivals, tourism receipts and tourism expenditures, respectively.

The EPU is based on key words in national articles about economic policy uncertainty in newspapers of various countries. Where E references “economic” or “economy”; P means “tax,”“government spending,”“regulation,”“central bank,” and certain other terms related to policy; and U means “uncertain” or “uncertainty.” To build the index, articles that use the above terms are given more attention in each publication each month. Each nation or region’s EPU index is produced based on a historical search for events associated with EPU in associated newspaper articles, and the monthly EPU index for each country or region is a relative measure of the proportion of newspaper articles focused on EPU-related issues in a given month. In this study, we turn monthly data into quarterly data, and the data are obtained from the EPU index (http://www.policyuncertainty.com/). WU is captured by calculating the proportion of the word “uncertain” (and its variant) in the EIU reports. The WU Index is then recalculated by multiplying by 1 million, where larger numbers imply greater uncertainty. The data are obtained from the WU Index (https://worlduncertaintyindex.com/).

We also include per capita GDP (in constant 2015 US dollars) as a control variable in logarithm, and the data are collected from the World Bank’s World Development Indicators. We convert the annual time-series data for tourism development and GDP into quarterly frequency using the quadratic match-sum method, which is especially suitable for avoiding the problem of small sample size. The quadratic match-sum method also adjusts the data for seasonal changes while converting the data from low to high frequencies to prevent interference from seasonal complications (Shahzad et al., 2017). The descriptive statistics are presented in Table 2, and Jarque–Bera (JB) statistics reveal that all but one group of data present significant deviations from normality.

Table 2.

Descriptive Statistics of the Variables.

	Mean	Min	Max	Std. dev.	Skewness	Kurtosis	Jarque–Bera
Data A: Tourism development
Canada	0.089	−4.270	6.386	0.840	2.871	40.34	5,887.693***
Japan	0.075	−1.856	3.585	0.504	2.896	28.11	2,739.064***
Italy	0.077	−2.427	2.270	0.444	0.319	20.42	1,252.871***
Germany	0.110	−2.189	5.650	0.650	5.950	56.17	12,244.07***
France	0.130	−2.329	4.658	0.662	3.541	27.48	2,578.180***
The UK	0.110	−2.269	3.454	0.579	2.736	21.86	1,590.331***
The US	0.023	−6.280	2.492	0.759	−5.643	51.02	10,038.55***
Data B: GDP growth rate
Canada	0.004	−0.043	0.029	0.009	−1.551	12.20	388.4435***
Japan	0.001	−0.036	0.032	0.007	−1.154	12.81	418.8079***
Italy	0.001	−0.062	0.046	0.011	−0.944	14.59	569.0172***
Germany	0.003	−0.039	0.026	0.008	−1.664	11.86	369.2043***
France	0.003	−0.058	0.043	0.010	−1.456	19.57	1,167.162***
The UK	0.003	−0.067	0.047	0.011	−1.838	20.19	1,274.077***
The US	0.004	−0.036	0.031	0.007	−1.197	13.41	470.5662***
Data C: EPU and WU Index
Canada	0.505	0.130	1.632	0.327	1.090	3.725	22.00187***
Japan	0.331	0.173	0.625	0.097	0.822	3.332	11.71402***
Italy	0.336	0.154	0.611	0.103	0.600	2.808	6.153459**
Germany	0.437	0.142	1.298	0.223	1.406	5.122	51.72812***
France	0.519	0.060	1.364	0.293	0.370	2.345	4.068649
The UK	0.373	0.118	1.079	0.184	1.101	4.638	31.37023***
The US	0.394	0.156	1.205	0.173	1.805	8.073	161.5524***
WU Index	19.37	5.570	55.69	9.273	1.323	5.511	55.42100***

***

and **indicate that the value is significant at the 1% and 5%, levels of significance, respectively

Empirical Results

Prior to constructing the QQ model for analysis, a unit root test must be conducted to determine whether the data are stationary. Table 3 presents the results of unit root tests for the variables using ADF (Dickey & Fuller, 1981) and PP (Phillips & Perron, 1988) tests. The results indicate that all variables are stationary at level with intercept and trend; therefore, since our series are stationary and non-normal, the QQ method is suitable for our research objectives and we use the level data for empirical research. The QQ method can also capture the complex relationships between variables and uncover potential asymmetries. In this section, we systematically illustrate and analyze the links between EPU and WU and tourism development at different quantiles in the G7 countries.

Table 3.

Unit Root Tests.

	ADF	PP
	At level	At level
Data A: Tourism development
Canada	−7.198***	−7.258***
Japan	−4.387***	−4.387***
Italy	−4.450***	−4.522***
Germany	−6.445***	−6.443***
France	−5.116***	−5.168***
The UK	−6.638***	−6.617***
The US	−7.605***	−7.864***
Data B: GDP growth rate
Canada	−7.198***	−7.258***
Japan	−4.387***	−4.387***
Italy	−4.450***	−4.522***
Germany	−6.445***	−6.443***
France	−5.116***	−5.168***
The UK	−6.638***	−6.617***
The US	−7.605***	−7.864***
Data C: EPU and WU Index
Canada	−4.300***	−4.296***
Japan	−4.235***	−4.354***
Italy	−4.980***	−4.980***
Germany	−4.471***	−4.302***
France	−5.310***	−5.310***
The UK	−3.794**	−3.977**
The US	−4.335***	−4.312***
WU Index	−5.347***	−5.432***

***

and **indicate that the value is significant at the 1% and 5% levels of significance, respectively

Quantile-on-Quantile Estimates

Figure 1 illustrates the estimated value of the slope coefficient $β_{1} (θ, τ)$ , which reflects the influence of the $τ t h$ quantile of EPU (WU) on the $θ t h$ quantile of tourism development at different values of $θ$ and $τ$ for the G7 countries. The figure demonstrates that the change in $β_{1} (θ, τ)$ differs for each country, indicating considerable heterogeneity among the sample countries. In addition, the estimated $β_{1} (θ, τ)$ presents different changes at different variable quantiles, indicating that the relationship between EPU (WU) and tourism development is indeed asymmetric. To fully capture the links and heterogeneity between each variable more comprehensively, we introduce the specific circumstances of each country, presenting a certain analysis and summary.

Several interesting results are revealed in Figures 1 and 2. First, the negative impact of uncertainty on tourism development is observed for all countries for some quantile combinations, regardless of whether we are considering EPU or WU. This finding aligns with the majority of previous research outcomes, confirming that uncertain events harm tourism development in each country to some extent. The negative effect is also enhanced for each quantile of tourism development with the continuous increase in uncertainty level. The results suggest that rising uncertainty tends to depress the travel industry and add to losses. Second, while negative associations are found across all countries between EPU and WU and tourism development, considerable heterogeneity in the relationship between the variables is evident. This result may be attributable to significant differences between countries in terms of the significance of tourism in the country’s economy, the economic level, national circumstances, and externalities of tourism development. Ignoring this heterogeneity across countries may lead to inaccurate inferences. Third, and most importantly, we find certain positive effects between variables at different quantiles, which contradicts most previous studies. Furthermore, we also show a more pronounced asymmetrical relationship between EPU and WU and tourism development across the G7 countries.

The Impact of EPU on Tourism Development

More specifically, Canada, Germany, and Italy share some similarities concerning the link between tourism development and EPU. The negative link between tourism development and EPU is evident for the vast majority of quantiles in these countries, which is consistent with Balli et al. (2018), Wu and Wu (2019), Akadiri et al. (2020), Wu et al. (2021), and Wu et al. (2022). Notably, the intensity of the negative relationship is relatively high at the high quantile of tourism development for these countries. One possible rationale for this result is that increased uncertainty can have a more significant detrimental effect on tourism during a period of rapid tourism development. The unique result for Germany shows that the intensity of this negative impact remains relatively high at the lower quantile of tourism development, indicating that uncertainty also significantly damages the German tourism industry in times of slow tourism development. Notably, an obvious positive effect is evident in the partial quantile combinations. For example, for Canada, part of the positive effect is concentrated in the area combining the median quantiles of tourism development (0.4–0.6) with the lower to moderately high quantiles of EPU (0.05–0.75). In Germany, a positive effect occurs in the region that intersects the lower to moderately high quantiles of EPU (0.05–0.85) and the moderately low quantiles of tourism development (0.3–0.4). Similarly, the positive effect is also captured for Italy at the moderately low to middle quantiles of tourism development (0.3–0.55) and the moderately low to high quantiles of EPU (0.35–0.8). The rationale for these results may be that tourism has an indispensable role as a pillar industry in the national economies of these countries. During periods of relatively stable tourism development, tourism thrives when EPU is relatively low; however, when EPU is high and affects the domestic tourism market, the government will often implement relevant policies to promote the development of local tourism to attract interior tourists and drive the economy.

For Japan, the correlation between tourism development and EPU is negative for the vast majority of quantile combinations, and this impact is particularly strong when tourism development levels are low. This result suggests that uncertainty hinders Japan’s tourism industry to a certain extent, which aligns with the findings of Gozgor and Demir (2018). Additionally, the effect of EPU on tourism development is positive at low to high quantiles of EPU (0.05–0.9) and middle to mid-high quantiles of tourism development (0.45–0.7). We conjecture that this is due to tourism policies adopted by the Japanese government to support stable development. For example, Japan implemented a national tourism strategy in 2003 and has been refining it since then. One of the goals of this policy is to boost the tourism industry, revitalize local economies, and boost small and medium-sized enterprises. Notably, the positive effect is also observed at high quantiles of EPU (0.75–0.95) and low quantiles of tourism development (0.1–0.4). Over the last 20 years or so in Japan, a majority of the significant EPU events are related to financial issues; therefore, we contend that one possible reason for this positive impact is the devaluation of Japan’s currency, which increases the unexpected volatility of the currency market when EPU is high and rising. Congruently, a sluggish tourism market may cause relevant departments to lower prices to attract more tourists. In this case, an increasing number of tourists realize that they can obtain more yen in exchange for their national currency and visit Japan for a lower cost than ever before; therefore, EPU promotes tourism development (Chisadza et al., 2022; Khan et al., 2021).

France presents a negative effect of EPU on tourism development at the intersection of low quantiles of tourism development (0.05–0.15) and low to moderately high quantiles of EPU (0.05–0.75). This effect is also evident at low to high quantiles of tourism development (0.2–0.95) and middle to high quantiles of EPU (0.45–0.95). These results align with those of Demir and Gozgor (2018), confirming that uncertainty tends to inhibit the growth of the domestic tourism sector. A positive effect of EPU on tourism development is evident at the intersection of low quantiles of EPU (0.05–0.4) with low to high quantiles of tourism development (0.2–0.95). We posit that this phenomenon is likely due to France being the world’s leading tourist country, with a more developed tourism industry, which enables France to withstand and offset the adverse shocks of a certain degree of uncertainty when the EPU is low. In addition, at the lower quantiles of tourism development (0.05–0.1), the impact of EPU on tourism development is strongly negative at low quantiles of EPU and gradually becomes significantly positive at high quantiles of EPU, revealing that the impact of EPU on tourism development fluctuates at different quantiles.

The UK and the US present similarities within some of the quantile combinations of tourism development and EPU, indicating that the effect of EPU on tourism development is predominantly negative for both countries. This finding is consistent with Gozgor and Ongan (2017), Ongan and Gozgor (2018), Singh et al. (2019), Işık et al. (2020), Khan et al. (2021), and Dutta et al. (2021). In addition, an obvious fluctuation can be observed at low quantiles of tourism development, implying that increased uncertainty can have a more significant detrimental effect on tourism during lower periods of tourism development. A unique finding is the positive effect between the two variables at the partial high quantile of tourism development in the US, which suggests that tourism development in the US will not suffer a large adverse shock when the tourism industry is booming and EPU is low. This is perhaps attributable to the well-developed tourism industry in the US.

We summarize our main findings in Table 4.

Table 4.

Summary of the QQ Results for EPU on Tourism Development.

Country	Negative correlation	Plausible explanation	Positive correlation	Plausible explanation
Canada	( $θ$ =.05–.3, $τ$ =.05–.95); ( $θ$ =.4–.6, $τ$ =.85–.95); ( $θ$ =.7–.95, $τ$ =.05–.95)	The EPU hinders the development of the tourism industry and the detrimental effect is more severe during a period of rapid tourism development.	( $θ$ =.4–.6, $τ$ =.05–.75)	As a pillar industry of the country, tourism plays an indispensable role in the national economy.
Japan	( $θ$ =.05, $τ$ =.05–.95); ( $θ$ =.1–.4, $τ$ =.05–.7); ( $θ$ =.75–.95, $τ$ =.4–.95)	The expansion of EPU has a detrimental impact on tourism, with a particularly pronounced effect during a period of sluggish tourism development.	( $θ$ =.1–.4, $τ$ =.75–.95); ( $θ$ =.45–.7, $τ$ =.05–.9)	The positive impacts at low to high quantiles of EPU and the middle quantiles of tourism development perhaps due to the tourism policy. Furthermore, the positive effect at high quantiles of EPU and low quantiles of tourism development perhaps due to currency devaluation.
Italy	( $θ$ =.1–.2, $τ$ =.05–.95); ( $θ$ =.6–.95, $τ$ =.05–.95)	The EPU hinders the development of the tourism industry and the detrimental effect is more severe during a period of rapid tourism development.	( $θ$ =.3–.55, $τ$ =.35–.8)	As a pillar industry of the country, tourism plays an indispensable role in the national economy.
Germany	( $θ$ =.05–.25, $τ$ =.05–.95); ( $θ$ =.3–.4, $τ$ =.9–.95); ( $θ$ =.45–.95, $τ$ =.05–.95)	The EPU hinders the development of the tourism industry and the detrimental effect is more severe during periods of lull and rapid tourism development.	( $θ$ =.3–.4, $τ$ =.05–.85)	As a pillar industry of the country, tourism plays an indispensable role in the national economy.
France	( $θ$ =.05–.15, $τ$ =.05–.75); ( $θ$ =.2–.95, $τ$ =.45–.95)	The EPU will tend to inhibit the growth of the domestic tourism sector. Meanwhile, there are fluctuates at low quantiles of tourism development.	( $θ$ =.2–.95, $τ$ =.05–.4)	The well-established tourism markets can offset a certain level of uncertainty.
The UK	Almost the entire quantiles region.	The increased uncertainty has a detrimental impact on tourism, with a particularly pronounced effect during lull periods in tourism development.
The US	( $θ$ =.05–.75, $τ$ =.05–.95);( $θ$ =.8–.85, $τ$ =.45–.95);( $θ$ =.9–.95, $τ$ =.05–.95)	The increased uncertainty has a detrimental impact on tourism, with a particularly pronounced effect during lull periods in tourism development.	( $θ$ =.8–.85, $τ$ =.05–.4)	The well-established tourism markets can offset a certain level of uncertainty.

The Impact of WU on Tourism Development

Canada and Japan present similar characteristics in the results, revealing a negative impact of WU on tourism development, with a positive effect in some quantile combinations, including the region that intersects the low to middle quantiles of WU (0.05–0.55 for Canada and 0.05–0.65 for Japan) and the low to high quantiles of tourism development (0.2–0.95 for Canada and 0.15–0.95 for Japan). The positive impact is extremely significant at the combination of a low quantile of WU and a high quantile of tourism development. These positive effects suggest that tourism development is subject to shocks at low to moderate levels of uncertainty for Japan and Canada, but does not tend toward negative levels. This may be due to these countries’ well-established tourism markets, which are capable of coping with or offsetting a certain level of uncertainty.

Italy and Germany also have some similarities in results. The correlation between tourism development and WU is predominantly negative, and this negative impact is particularly strong at high quantiles of tourism development. This indicates that the tourism markets in Italy and Germany are relatively fragile during a period of rapid tourism development, making it more vulnerable to uncertainty and increased fluctuations. Furthermore, the effect of WU on tourism development is positive at low to middle quantiles of tourism development (0.35–0.7 for Italy and 0.3–0.5 for Germany) and low to high quantiles of WU (0.05–0.8 for Italy and 0.05–0.95 for Germany). This result shows that WU does not hinder tourism development in Italy and Germany during a period of steady growth. The positive correlation may be because some uncertainty events can start in a different country or continent and Italy and Germany may not be affected as much due to distance from the event center; thus, tourism continues to rise. In addition, an increasing number of people who are at the center of such events may choose to travel as a means to escape, which is likely to increase tourist arrivals and tourism revenue, which boosts tourism development.

The impact of WU on tourism development in France is predominantly negative, except at the intersection of the low to the high quantile of WU (0.05–0.9) and the low quantile of tourism development (0.25–0.35). A potential rationale for the positive correlation between WU and tourism development may be that global events did not yet spread to France, and the French government taking precautionary measures in advance to navigate the impact of uncertainty on tourism. Another notable phenomenon emerges at the lower quantiles of tourism development, indicating that the impact of the WU on tourism development gradually changes from positive to strongly negative as the uncertainty level moves from low to high. This implies relatively fragile resilience of French tourism during slow development, which makes it more vulnerable to uncertainty and a greater degree of volatility.

The nexus is also generally negative for most quantile combinations of tourism development and WU in the UK and US. A relatively strong negative correlation is also evident in the lower quantile of tourism development (.05), indicating that WU intensity has a detrimental impact on tourism, with a particularly pronounced effect during sluggish periods of tourism development. Furthermore, the effect of WU on tourism development is positive at the low to middle quantiles of tourism development (0.3–0.5) and the lower to upper quantiles of WU (0.05–0.95) for the UK, and a positive effect occurs at the intersection of the lower to mid-high quantiles of WU (0.05–0.7) and the low quantiles of tourism development (0.15–0.35) for the US. These results show that the UK and US travel markets largely remain stable and orderly during this period, which can offset the negative shocks of uncertainty. A positive impact is also noted for the UK at the high quantile of uncertainty and the low to medium quantile of tourism development. A possible rationale for this is that the UK was not yet subject to the shock of uncertainty, or it could also possibly be related to the implementation of relevant policies in the UK. For example, when the UK first announced the lifting of its pandemic lockdown and control measures, a wave of tourism occurred, resulting in a 600% surge in summer holiday travel bookings. An obvious fluctuation is evident at the high quantile of tourism development in the US, suggesting that the US should consider the impact of WU in periods of rapid tourism development.

We summarize our main findings in Table 5.

Table 5.

Summary of the QQ Results for WU on Tourism Development.

Country	Negative correlation	Plausible explanation	Positive correlation	Plausible explanation
Canada	( $θ$ =.05–.15, $τ$ =.05–.85);( $θ$ =.2–.4, $τ$ =.6–.9);( $θ$ =.65–.95, $τ$ =.7–.95)	The world uncertainty hinders the development of the tourism industry.	( $θ$ =.2–.95, $τ$ =.05–.55)	The well-established tourism markets can offset a certain level of uncertainty.
Japan	( $θ$ =.05–.1, $τ$ =.05–.95);( $θ$ =.25–.6, $τ$ =.7–.95)	The world uncertainty hinders the development of the tourism industry.	( $θ$ =.15–.95, $τ$ =.05–.65)	The well-established tourism markets can offset a certain level of uncertainty.
Italy	( $θ$ =.05–.3, $τ$ =.05–.95);( $θ$ =.75–.95, $τ$ =.05–.95)	The world uncertainty hinders the development of the tourism industry. Furthermore, the tourism market is relatively fragile during a period of rapid tourism development.	( $θ$ =.35–.7, $τ$ =.05–.8)	Distance from the center of uncertainty events.
Germany	( $θ$ =.05–.15, $τ$ =.05–.95);( $θ$ =.55–.95, $τ$ =.05–.95)	The world uncertainty hinders the development of the tourism industry. Furthermore, the tourism market is relatively fragile during a period of rapid tourism development.	( $θ$ =.3–.5, $τ$ =.05–.95)	Distance from the center of uncertainty events.
France	( $θ$ =.05, $τ$ =.2–.95);( $θ$ =.1–.2, $τ$ =.05–.95);( $θ$ =.5–.95, $τ$ =.05–.95)	The world uncertainty hinders the development of the tourism industry. Meanwhile, there are fluctuates at low quantiles of tourism development.	( $θ$ =.05, $τ$ =.05–.15);( $θ$ =.25–.35, $τ$ =.05–.9)	Distance from the center of uncertainty events and take precautionary measures in advance.
The UK	( $θ$ =.05–.25, $τ$ =.05–.95);( $θ$ =.6–.95, $τ$ =.05–.95)	The expansion of WU has a detrimental impact on tourism, with a particularly pronounced effect during periods of sluggish tourism development.	( $θ$ =.3–.5, $τ$ =.05–.95)	The stable and orderly tourism markets can offset a certain level of uncertainty. Furthermore, part of the positive impact at the high quantile of WUI may be related to the actual situation and the implementation of policies.
The US	( $θ$ =.05–.1, $τ$ =.05–.95);( $θ$ =.55–.65, $τ$ =.15–.95);( $θ$ =.75–.95, $τ$ =.1–.95)	The expansion of WU has a detrimental impact on tourism, with a particularly pronounced effect during periods of sluggish tourism development.	( $θ$ =.15–.35, $τ$ =.05–.7);( $θ$ =.8–.9, $τ$ =.05)	The stable and orderly tourism markets can offset a certain level of uncertainty.

Robustness Tests

The QQ method is an enhanced quantile technique that presents a decomposition of traditional QR. Taking the variables in this study as an example, the conventional QR approach regresses the $θ$ quantile of tourism development for each nation on the EPU (WU). The sole indicator used to measure the coefficient is $θ$ ; however, the QQ method regresses the $θ$ quantile on the $τ$ quantile of the change in EPU (WU), and the coefficients are indexed by $θ$ and $τ$ . Therefore, the QQ method provides more precise details regarding the explanatory variables and explained variables than the conventional QR technique.

Given the QQ method’s inherent decomposition capabilities, a robustness test can be conducted by restoring the QQ estimate to the QR estimate. Specifically, we sum and average the slope coefficient $β_{1} (θ, τ)$ , which is estimated using the QQ approach along $τ$ to obtain the $τ$ –averaged QQ parameters, and the $τ$ –averaged QQ parameters are then only indexed by $θ$ . That is, the original slope coefficient $β_{1} (θ, τ)$ is transformed into $\bar{{\hat{β}}_{1}} (θ)$ , which is denoted by $γ_{1} (θ)$ in the following equation:

γ_{1} (θ) \equiv \bar{{\hat{β}}_{1}} (θ) = \frac{1}{s} \sum_{τ} {\hat{β}}_{1} (θ, τ)

(7)

where S = 19 denotes the number of quantiles $τ$ = [0.05,0.1,…,0.95] considered.

We can then compare the outcomes of the QR method and average QQ estimation for the robustness test. Figure 3 presents the EPU robustness check on tourism development, revealing that the slope coefficient of the averaged QQ results is notably comparable to the QR results for Canada and Japan. While the values are slightly different for Italy, Germany, France, the UK, and the US, the average QQ estimation line generally presents the same trend as the QR line. The results of QR analysis indicate that EPU has a positive effect on tourism development at partial quantiles of EPU for all countries, which also confirms the validity of the QQ method. This shows that the major information of the conventional QR model can be recovered by summarizing the more disaggregated information of the QQ estimations, demonstrating that the baseline findings of QQ estimation are consistent with the traditional quantile estimation.

Figure 3 affirms the outcomes of the benchmark QQ analysis. The graphs presented in Figure 4 show the WU robustness check, revealing that the slope coefficient of the averaged QQ results is notably comparable to the QR results for most countries, and the values for Canada, Japan, and France are somewhat different, but the two curves generally follow the same trend. These results further confirm the reliability of the baseline QQ results. Overall, these results support our previous finding that the WU has a positive impact on tourism development at partial quantiles of WU for all countries.

Conclusion and Policy Implications

Using the QQ approach, this research examines how EPU and WU affect tourism in the G7 nations, focusing on asymmetric relationships and heterogeneity. Previous studies have primarily emphasized the impact of single uncertainty events on tourism, while neglecting either domestic or global uncertainty (Demir & Gozgor, 2018; Nguyen et al., 2020; Wu & Wu, 2021). We consider domestic EPU and global WU indicators separately to determine how different sources of uncertainty affect tourism, and provide a more comprehensive analysis of the impact in G7 countries. This study applies the relatively novel QQ method to explore deeper intrinsic associations between variables. To the best of our knowledge, no previous study has used the QQ method to examine the association between tourism development and uncertainty. We also use the JB test in our preliminary tests to assess whether our variables are normally distributed, and the results indicate non-normal distribution, confirming that it is appropriate to employ the QQ approach for analysis. Our research reveals a primarily negative relationship between tourism development and uncertainty (whether EPU or WU) for all G7 countries; however, significant heterogeneity in tourism development and uncertainty is evident in different quantile combinations for each country. This heterogeneity may be attributed to the significance of tourism in each country’s economy, the implementation of tourism-related policies, the degree of tourism development, and the size and openness of each country’s economy. In addition, the magnitude of the effect of uncertainty is relatively strong at the upper quantiles, which indicates the asymmetric association between variables.

Our empirical findings indicate that EPU has a detrimental impact on tourism, with stronger negative effects or fluctuations for some quantile combinations. For example, strong negative impacts or fluctuations are found at the high quantile of tourism development in Canada and Italy; at the low quantile of tourism development in Japan, France, the UK, and the US; and at both the high and low quantiles of tourism development in Germany. This implies that these countries should identify and consider such stages, as tourism is relatively fragile and susceptible to uncertainty during these periods. EPU is also found to have a positive impact on tourism development, except for the negative correlation, which is likely because tourism is a pillar industry in affected countries such as Canada, Germany, and Italy. It is also possible that a well-developed tourism industry such as that in France and the US can withstand a certain degree of uncertainty shocks on its own. Notably, a positive impact of EPU on tourism development is found at the intersection of the high EPU quantile and the low tourism development quantile for Japan. One possible rationale for this phenomenon is currency devaluation, which makes it less expensive to travel to these regions as uncertainty increases, attracting an influx of visitors. In these cases, the tourism market can mitigate the shock of uncertainty through self-regulation. At such times, excessive government intervention could potentially lead to other challenges due to policy time lag.

The research findings for WU reveal four notable results. First, WU has a generally negative impact on tourism development in all countries. Second, some positive correlations between WU and tourism development are evident for Canada and Japan. This may be because these countries’ tourism markets can withstand a certain level of uncertainty shock. Third, for Germany, Italy, France, and the UK, we note the positive correlation between WU and tourism development at the high quantile of WU. Possible reasons for this are that these countries were not yet hit by uncertainty or the implementation of tourism policy offset the potential effects. Finally, we observe a noticeable fluctuation at the high quantile of tourism development for the US.

Our findings have significant implications for policymakers, governments, and professionals seeking to navigate the effects of uncertainty or the tourism industry. First, the findings confirming heterogeneity and asymmetry suggest that the influence of uncertainty on tourism development is not uniformly negative, but varies depending on different levels of uncertainty and tourism development. This implies that, rather than a single uniform policy, each country should implement strategic policy measures based on the level of uncertainty encountered. In addition, when confronting uncertain events, the government must maintain the tourism industry’s development and mitigate the adverse effects. Policymakers should implement supportive measures such as fostering tourism industry diversification to mitigate the impact of uncertainty based on the prevailing environmental circumstances. For instance, Canada, Germany, and Italy initiated measures toward digital transformation of the tourism market, fostering innovation in tourism practices, and promoting sustainable development during the pandemic. Second, when analyzing the impact of uncertainty on tourism, policymakers should expand their focus from the impact of domestic uncertainty. According to our findings, fluctuations in the WU also affect tourism development; therefore, each country should comprehensively consider both domestic and global uncertainties when exploring the links between uncertainty and tourism. Third, in the long term, it is imperative for tourism practitioners to enhance capacities to effectively mitigate uncertainties and risks by enhancing innovation and adaptability to resiliently confront emerging challenges. In an uncertain environment, professionals must identify new opportunities and solutions to adapt to changes in the environment and fluctuations in market demand. Furthermore, tourism practitioners must also proactively focus on both domestic and global uncertainties and make adequate preparations in advance.

Despite this study providing some new evidence for investigating the effects of uncertainty on tourism, some limitations remain that could inspire future related studies. First, this research primarily considers the impact of uncertainty on tourism in G7 countries. Future studies could examine different impacts in developing and developed countries to analyze the impact of uncertainty on countries with different levels of development. Furthermore, the level of uncertainty tends to vary over time; however, we did not segment the data based on different periods; therefore, the time-varying impact of uncertainty on tourism could be examined by dividing the period of uncertainty based on intensity. Finally, we focus on the impact of uncertainty on the tourism industry but do not examine how it affects tourism-related industries; therefore, future research can further investigate the relationship between uncertainty and tourism-related industries, including (but not limited to) hospitality, catering, and transportation to facilitate the development of more precisely targeted policy recommendations.

Footnotes

Acknowledgements

We thank especially the Editors Prof. Nancy McGehee and Prof. James Petrick, and the anonymous referees for very constructive remarks and suggestions. They make some pertinent comments on the previous version of this article. Nevertheless, any shortcomings that remain in this research paper are solely our responsibility.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is partially supported by Foundation of Hunan Educational Committee (No. 21A0097) and Hunan Province Degree and Graduate Teaching Reform Research Project (No. 2021JGYB075), and also supported by Hunan Natural Science Foundation of China (No. 2023JJ30603). Any shortcomings that remain in this research paper are solely our responsibility.

ORCID iDs

Zhike Lv

Ting Xu

Notes

Author Biographies

Zhike Lv, Ph.D., is a professor in the School of Business at Xiangtan University. His research interests include: Tourism economics, regional economics and human capital. His recent work has been published in journals such as Tourism Management, Annals of Tourism Research, Technological Forecasting & Social Change, Current Issues in Tourism, Economics Letters, Journal of Behavioral and Experimental Economics, and The Social Science Journal.

Yixin Sun is a postgraduate student in the School of Business at Xiangtan University. His research interests include: Tourism economics and energy economics.

Ting Xu, Ph.D., is an assistant professor in the School of Business at Xiangtan University. Her research interests include: Tourism economics and human capital. Her recent work has been published in journals such as Technological Forecasting & Social Change, Current Issues in Tourism, Journal of Managerial Psychology, and The International Journal of Human Resource Management.

References

Ahir

Bloom

Furceri

(2022). The world uncertainty index (No. w29763). National bureau of economic research.

Akadiri

S. S.

Alola

A. A.

Uzuner

(2020). Economic policy uncertainty and tourism: Evidence from the heterogeneous panel. Current Issues in Tourism, 23(20), 2507–2514.

Alam

M. S.

Paramati

S. R.

(2016). The impact of tourism on income inequality in developing economies: Does Kuznets curve hypothesis exist? Annals of Tourism Research, 61, 111–126.

Arain

Han

Sharif

Meo

M. S.

(2020). Investigating the effect of inbound tourism on FDI: The importance of quantile estimations. Tourism Economics, 26(4), 682–703.

Baker

S. R.

Bloom

Davis

S. J.

(2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636.

Balli

Shahzad

S. J. H.

Salah Uddin

(2018). A tale of two shocks: What do we learn from the impacts of economic policy uncertainties on tourism? Tourism Management, 68, 470–475.

Bashtannyk

D. M.

Hyndman

R. J.

(2001). Bandwidth selection for kernel conditional density estimation. Computational Statistics & Data Analysis, 36(3), 279–298.

Baxter

Bowen

(2004). Anatomy of tourism crisis: Explaining the effects on tourism of the UK foot and mouth disease epidemics of 1967–68 and 2001 with special reference to media portrayal. International Journal of Tourism Research, 6(4), 263–273.

Chisadza

Clance

Gupta

Wanke

(2022). Uncertainty and tourism in Africa. Tourism Economics, 28(4), 964–978.

10.

Choudhry

(2020). Economic policy uncertainty and house prices: Evidence from geographical regions of England and Wales. Real Estate Economics, 48(2), 504–529.

11.

Demir

Ersan

(2018). The impact of economic policy uncertainty on stock returns of Turkish tourism companies. Current Issues in Tourism, 21(8), 847–855.

12.

Demir

Gozgor

(2018). Does economic policy uncertainty affect Tourism? Annals of Tourism Research, 69(C), 15–17.

13.

Demir

Gozgor

Paramati

S. R.

(2020). To what extend economic uncertainty effects tourism investments? Evidence from OECD and non-OECD economies. Tourism Management Perspectives, 36, 100758.

14.

Demir

Simonyan

Chen

M. H.

Marco Lau

C. K.

(2020). Asymmetric effects of geopolitical risks on Turkey’s tourist arrivals. Journal of Hospitality and Tourism Management, 45, 23–26.

15.

Dickey

D. A.

Fuller

W. A.

(1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49, 1057–1072.

16.

Dogan

Seker

Bulbul

(2017). Investigating the impacts of energy consumption, real GDP, tourism and trade on CO₂emissions by accounting for cross-sectional dependence: A panel study of OECD countries. Current Issues in Tourism, 20(16), 1701–1719.

17.

Dogru

Bulut

(2018). Is tourism an engine for economic recovery? Theory and empirical evidence. Tourism Management, 67, 425–434.

18.

Dogru

Isik

Sirakaya-Turk

(2019). The balance of trade and exchange rates: Theory and contemporary evidence from tourism. Tourism Management, 74, 12–23.

19.

Dragouni

Filis

Gavriilidis

Santamaria

(2016). Sentiment, mood and outbound tourism demand. Annals of Tourism Research, 60, 80–96.

20.

Drakos

Kutan

A. M.

(2003). Regional effects of terrorism on tourism in three Mediterranean countries. Journal of Conflict Resolution, 47(5), 621–641.

21.

Duan

Ren

Shi

Mishra

Yan

(2021). The marginal impacts of energy prices on carbon price variations: evidence from a quantile-on-quantile approach. Energy Economics, 95, 105131.

22.

Dutta

Mishra

Uddin

G. S.

Yang

(2021). Brexit uncertainty and volatility persistence in tourism demand. Current Issues in Tourism, 24(16), 2225–2232.

23.

Dwyer

Forsyth

Spurr

(2004). Evaluating tourism’s economic effects: New and old approaches. Tourism Management, 25(3), 307–317.

24.

Fishbein

Ajzen

(1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley.

25.

Fletcher

Morakabati

(2008). Tourism activity, terrorism and political instability within the Commonwealth: The cases of Fiji and Kenya. International Journal of Tourism Research, 10(6), 537–556.

26.

Fourie

Rosselló-Nadal

Santana-Gallego

(2020). Fatal attraction: How security threats hurt tourism. Journal of Travel Research, 59(2), 209–219.

27.

Gholipour

H. F.

Nunkoo

Foroughi

Daronkola

H. K.

(2022). Economic policy uncertainty, consumer confidence in major economies and outbound tourism to African countries. Tourism Economics, 28(4), 979–994.

28.

Ghosal

Loungani

(2000). The differential impact of uncertainty on investment in small and large businesses. The Review of Economics and Statistics, 82(2), 338–343.

29.

Ghosh

(2019). Uncertainty, economic growth its impact on tourism, some country experiences. Asia Pacific Journal of Tourism Research, 24(1), 83–107.

30.

Gozgor

Demir

; Istanbul Medeniyet University, & Istanbul Medeniyet University. (2018). The effects of economic policy uncertainty on outbound travel expenditures. Journal of Competitiveness, 10(3), 5–15.

31.

Gozgor

Ongan

(2017). Economic policy uncertainty and tourism demand: empirical evidence from the USA. International Journal of Tourism Research, 19(1), 99–106.

32.

Gozgor

Seetaram

Lau

C. K. M.

(2021). Effect of global uncertainty on international arrivals by purpose of visits and length of stay. International Journal of Tourism Research, 23(6), 1086–1098.

33.

Hall

Sheather

S. J.

Jones

M. C.

Marron

J. S.

(1991). On optimal data-based bandwidth selection in kernel density estimation. Biometrika, 78(2), 263–269.

34.

Handley

(2014). Exporting under trade policy uncertainty: Theory and evidence. Journal of International Economics, 94(1), 50–66.

35.

Handley

Limão

(2017). Policy uncertainty, trade, and welfare: Theory and evidence for China and the United States. The American Economic Review, 107(9), 2731–2783.

36.

Harrod

(1958). The Possibility of Economic Satiety—Use of Economic Growth for Improving the Quality of Education and Leisure. Problems of United States economic development (Vol. 1, pp. 207–213).

37.

Işık

Sirakaya-Turk

Ongan

(2020). Testing the efficacy of the economic policy uncertainty index on tourism demand in USMCA: Theory and evidence. Tourism Economics, 26(8), 1344–1357.

38.

Jones

M. C.

Marron

J. S.

Sheather

S. J.

(1996). A brief survey of bandwidth selection for density estimation. Journal of the American Statistical Association, 91(433), 401–407.

39.

Katircioğlu

S. T.

(2010). International tourism, higher education and economic growth: The case of North Cyprus. World Economy, 33(12), 1955–1972.

40.

Khan

C. W.

Xiao

Y. D.

Zhu

Zhang

(2021). Trends in tourism under economic uncertainty. Tourism Economics, 27(4), 841–858.

41.

Koenker

Bassett

(1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 46, 33–50.

42.

Lin

(2020). The linkages between oil market uncertainty and Islamic stock markets: Evidence from quantile-on-quantile approach. Energy Economics, 88, 104759.

43.

Liu

Pratt

(2017). Tourism’s vulnerability and resilience to terrorism. Tourism Management, 60, 404–417.

44.

(2020). Does tourism affect the informal sector? Annals of Tourism Research, 80, 102816.

45.

Manrique-de-Lara-Peñate

Gallego

M. S.

Valle

E. V.

(2022). The economic impact of global uncertainty and security threats on international tourism. Economic Modelling, 113, 105892.

46.

Maslow

A. H.

(Ed.). (1987). Motivation and personality (3rd ed.). Harper Collins.

47.

Nguyen

C. P.

Schinckus

T. D.

(2020). Economic policy uncertainty and demand for international tourism: An empirical study. Tourism Economics, 26(8), 1415–1430.

48.

Nguyen

C. P.

Thanh

S. D.

Nguyen

(2022). Economic uncertainty and tourism consumption. Tourism Economics, 28(4), 920–941.

49.

Nickell

S. J.

(1977). The influence of uncertainty on investment. The Economic Journal, 87(345), 47–70.

50.

Ongan

Gozgor

(2018). Tourism demand analysis: The impact of the economic policy uncertainty on the arrival of Japanese tourists to the USA. International Journal of Tourism Research, 20(3), 308–316.

51.

Ozturk

Sharif

Godil

D. I.

Yousuf

Tahir

(2023). The dynamic nexus between international tourism and environmental degradation in top twenty tourist destinations: New insights from quantile-on-quantile approach. Evaluation Review, 47(3), 532–562.

52.

Papatheodorou

Rosselló

Xiao

(2010). Global economic crisis and tourism: Consequences and perspectives. Journal of Travel Research, 49(1), 39–45.

53.

Perles-Ribes

J. F.

Ramón-Rodríguez

A. B.

Rubia

Moreno-Izquierdo

(2017). Is the tourism-led growth hypothesis valid after the global economic and financial crisis? The case of Spain 1957–2014. Tourism Management, 61, 96–109.

54.

Phillips

P. C.

Perron

(1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.

55.

Poirier

R. A.

(1997). Political risk analysis and tourism. Annals of Tourism Research, 24(3), 675–686.

56.

Shahbaz

Zakaria

Shahzad

S. J. H.

Mahalik

M. K.

(2018). The energy consumption and economic growth nexus in top ten energy-consuming countries: Fresh evidence from using the quantile-on-quantile approach. Energy Economics, 71, 282–301.

57.

Shahzad

S. J. H.

Shahbaz

Ferrer

Kumar

R. R.

(2017). Tourism-led growth hypothesis in the top ten tourist destinations: New evidence using the quantile-on-quantile approach. Tourism Management, 60, 223–232.

58.

Sharma

(2021). Testing the asymmetric effects of the economic policy uncertainty on the tourism demand in India. Tourism Economics, 27(4), 867–873.

59.

Sim

Zhou

(2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8.

60.

Singh

Das

Jana

R. K.

Tiwari

A. K.

(2019). A wavelet analysis for exploring the relationship between economic policy uncertainty and tourist footfalls in the USA. Current Issues in Tourism, 22(15), 1789–1796.

61.

Sio-Chong

Y. C.

(2020). The impacts of financial and non-financial crises on tourism: Evidence from Macao and Hong Kong. Tourism Management Perspectives, 33, 100628.

62.

Stone

C. J.

(1977). Consistent nonparametric regression. The Annals of Statistics, 5, 595–620.

63.

Theuns

H. L.

(2014). Is tourism a luxury: Can we do without? Tourism Recreation Research, 39(2), 221–233.

64.

United Nation World Tourism Organization (UNWTO). (2020). COVID–19 and tourism.

65.

Uzuner

Ghosh

(2021). Do pandemics have an asymmetric effect on tourism in Italy? Quality & Quantity, 55(5), 1561–1579.

66.

World Tourism Cities Federation (WTCF). (2019). Report on World Tourism Economy Trends 2019.

67.

T. P.

H. C.

(2019). Causality between European economic policy uncertainty and tourism using wavelet-based approaches. Journal of Travel Research, 58(8), 1347–1356.

68.

T. P.

H. C.

(2021). Global economic policy uncertainty and tourism of fragile five countries: Evidence from time and frequency approaches. Journal of Travel Research, 60(5), 1061–1073.

69.

T. P.

H. C.

C. F.

Liu

C. Y.

(2022). The influence of economic policy uncertainty on PIIGS tourism: Evidence from three-dimensional wavelet analyses. Tourism Analysis, 27(1), 19–27.

70.

T. P.

H. C.

S. T.

Y. Y.

(2021). Economic policy uncertainty and tourism nexus dynamics in the G7 countries: Further evidence from the wavelet analysis. Tourism Planning & Development, 18(5), 529–546.

71.

Zaman

Shahbaz

Loganathan

Raza

S. A.

(2016). Tourism development, energy consumption and Environmental Kuznets curve: Trivariate analysis in the panel of developed and developing countries. Tourism Management, 54, 275–283.

72.

Zhang

Jiang

Gao

Yang

(2022). Time-varying impact of economic policy uncertainty and geopolitical risk on tourist arrivals: Evidence from a developing country. Tourism Management Perspectives, 41, 100928.