Analyzing the Nexus Between Geopolitical Risk,Policy Uncertainty,and Tourist Arrivals: Evidence From the United States

Abstract

This study attempts to explore the causal linkage of the COVID-19 pandemic, economic policy uncertainty, geopolitical risk, and tourism arrivals in the United States taking data from January to November 2020. In order to analyze the above relationship, this study uses a novel time-varying granger causality test developed by Shi et al. (2018), which incorporates its three causality algorithms such as forward recursive causality, rolling causality, and recursive evolving causality. The findings from forward recursive causality could not confirm any significant causal relationship between COVID-19 and tourism, geopolitical risk (GPR) and tourism, economic policy uncertainty and tourism, and geopolitical risk and COVID-19 but found causality between economic policy uncertainty and COVID-19. The rolling window causality reported bidirectional causality between COVID-19 and tourism and unidirectional causality running from tourism to geopolitical risk. However, the recursive evolving causality identified a significant bidirectional causal relationship between all the variables. Based on the findings, policy implications for the tourism sector are provided.

Keywords

Time-varying causality Geopolitical risk economic policy uncertainty tourist arrivals COVID-19 USA

Research Highlights

• Causality among geopolitical risk, policy uncertainty, COVID-19, and tourism is investigated for the USA.

• Time-varying causality is used for empirical analysis.

• COVID-19 daily new cases granger cause tourism growth.

• Policy Uncertainty and geopolitical risk significantly influence tourism arrivals.

Introduction and Contribution

In this modern era, the tourism sector is a crucial component for the economic advancement of both developed and developing nations due to its unprecedented contribution in increasing foreign reserves by tourist income and providing employment opportunities for domestic citizens (Demir, Gozgor, & Paramati, 2020). Meanwhile, the tourism industry contributes to socioeconomic development by generating tax revenue and reducing poverty and contributing to socio-cultural development by promoting peace and human prosperity (Alam & Paramati, 2016; Manzoor, Wei, & Asif, 2019). Therefore, tourism-related revenue inflows are a significant part of each economy. The tourist’s arrivals to the host country stimulate foreign investment through various channels, including currency earnings, captivating foreign investments, jobs creation, and tax-related revenues. Thus, each country must promote and sustain a favorable tourism landscape to attract tourists from all over the world. In terms of statistics, the tourism industry ranks third after the IT and financial sectors, with an annual growth rate of 3.5% and 10.3% to global GDP in 2019. Over the last 5 years, the sector has generated 1/10th of total employment and 1/4th of net jobs, with 330 million jobs created only in 2019. Furthermore, the industry accounts for 4.4% of total national investments, with an amount of US$ 941 billion, and this ratio is projected to rise to 5.0% by 2029 (World Travel and Tourism Council, 2020).

Having said this, it is also true that tourism is a highly sensitive industry, affecting the decision-making process and the perspectives of key stakeholders. Tourism stakeholders, like most investors, are likely to invest more when industry forecasts indicate a low level of risk or uncertainty (Akadiri, Eluwole, Akadiri, and Avci, 2020).Whereas the degree of uncertainty about potential development cannot be constant, practitioners frequently strive to keep it to a minimum. It is logical to capitulate that event such as terrorism and political instability, which results in policy uncertainty, influences a country’s tourism earnings, as tourists, instinctively seek out sites with an established safety and security track record (Tiwari, Das, & Dutta, 2019). Consequently, tourism reacts to geopolitical events, and as it changes and evolves, it adapts to a wider political environment. In essence, the economy, tourism, and other business activities are all affected significantly by the various local and foreign political environments (Antonakakis et al., 2017). In this sense, geopolitical tensions and associated events such as elections trigger political uncertainty and frictions, leading to unexpected consequences or events resulting in economic policy uncertainty (Khalfaoui et al., 2022). Thus, political and policy uncertainty can significantly affect tourism imports, overnight stays numbers, tourist arrivals, and other tourism-related indicators (Demir, Gozgor, & Paramati, 2019; Hailemariam & Ivanovski, 2021a).

On the other side, the COVID-19 pandemic has created global health, social, and economic emergency that has never been seen before. In the meantime, the most affected sector by the COVID-19 pandemic is the tourism industry due to border shutdown, travel bans, and decline in market demand, which has ravaged the entire world and destroyed political and economic structures (Sikarwar, 2021; Sharma et al., 2022). In 2020, an estimated US$ 4.5 trillion massive loss had been suffered by the world tourism sector and caused its contribution to global GDP to plummet by 49.1%, which is about $4.7 trillion. The pandemic also has snatched the 62 million jobs in just 1 year (The World Travel and Tourism Council, 2021).

In comparison to the rest of the world, the spread of the novel COVID-19 outbreak is causing significant economic shocks in the United States, and these challenges are probable to provoke a long-term economic slowdown. In 2019, the United States led in terms of the highest GDP contribution from the travel and tourism sector, with 1839 billion dollars (World Travel and Tourism Council, 2020). However, the COVID-19 pandemic continues to spread rapidly in the United States, having drastic effects on the country’s economic policy instability and hurting the tourism industry, with jobs falling by 22.4 million in March and April 2020 only, while unemployment increased by a smaller threshold of 15.9 million (ILO-OECD, 2020). The United States' vulnerability to macroeconomic turmoil events could stifle tourist inflows, slowing the pace of national economic growth.

In recent years, the USA as a global destination for tourism has been losing appeal due to several problems, including the pandemic. According to Majcher (2021), in this country, in 2018, the overseas travelers' share reduced from 13.7% to 11.7%. According to the US travel association, this would further lead to a decline of 10.4% within 2023. From 2015, international visitors’ number declined by 2% in 2016. Several reasons include rising hate crime, mass shootings, racism, ban on several countries during the Trump era, and hostility toward foreigners. These all lead to uncertainty and risk in the economy, hurting the overall economy and its service sector, of which tourism is a major product. Therefore, given this above scenario, it is pertinent to delve into the USA case to examine the nexus between geopolitical risk, uncertainty, and the tourism sector of the country. Policymakers and other stakeholders emphasize examining the risk factors associated with the tourism sector since it has such a crucial role in the economy (Hailemariam and Ivanovski, 2021b). This is one of the major reasons, that authors select the USA as a case study.

The previous studies have focused on Economic Policy Uncertainty (EPU) and/or Geopolitical Risks (GEO) for investigating their role in tourism inflows. In contrast, the effect of COVID-19 has been rarely studied in the tourism literature. Thus, this novel study investigates the causal effect between EPU, GEO, COVID-19, and tourist arrivals in the United States. Furthermore, the study also explores the time-varying causality between GEO, EPU, and COVID-19. To achieve this, the time-varying causality technique initiated by Shi et al. (2018), a novel version of the Granger causality approach, was employed. To the best of author’s knowledge, this is the first work in the tourism sector to investigate the time-varying causality between these variables using daily frequency data from January 22, 2020, to November 22, 2020, as well as to study the pandemic data for geopolitical risk and uncertainty. Thus, the current work adds to the current literature in several ways. First, the studies on the role of the EPU in tourist arrivals are generally available, while evidence of the role of the GEO in tourism is scarce in previous studies. The EPU index is constructed based on uncertainty regarding trade, fiscal, monetary, healthcare, national security, and other related policies. On the other hand, GEO index comprises information on several critical events such as diplomatic discrepancies, transnational political disputes, and war-like events that seem to directly impact tourism and travel (Tiwari, Das, & Dutta, 2019).

The underlying factor may be that economic actors encourage typical risk-aversion behaviors. Thus, by illustrating a comparative evaluation between EPU and GEO about tourist arrivals in the United States, it is significant to scrutinize the source of uncertainty to which tourists are more susceptible. Considering this fact, the study provides practitioners and policymakers with real-time connectivity among these factors, allowing them to formulate flexible policies to thrive in the tourism sector. Second, to the best of our knowledge, based on the available literature, no research has been conducted on the implications of COVID-19 for tourism for the USA, accounting for these factors. As previously mentioned, this epidemic halted cross-border travel due to travel bans, which obliterated policies and stranded tourist travel worldwide. Hence, this research is unique in the tourism literature. Last, it has been noted that previous studies utilized classical linear or nonlinear Granger causality to explore the impact of EPU and GEO on tourist arrivals, which only depict the causality direction. However, real-time causalities among factors do not remain constant over time; they change. Therefore, this is the first attempt to use a novel time-varying Granger causality method to assess the time-varying causal linkage from EPU, GEO, and the COVID-19 to tourist arrivals in the United States, as well as time-varying causality between COVID and TA, TA and EPU, TA and GPR, GPR and COVID, and EPU and COVID.

The remainder of this paper’s contents is arranged as follows. The review of relevant literature and their respective inferences are discussed in the section Literature Review. In the section Data and Methodology, we go through the details of the materials and methods used in this research. The details about the findings and their discussion are given in the section Empirical Results and Discussion. The concluding remarks are presented in the subsequent section.

Literature Review

It is generally accepted that people, by nature, are risk-averse in both cases, whether it is related to investment or personal pleasure through travel and tour (Demir et al., 2020). The EPU contains information on the level of uncertainty underlying all a country’s major macroeconomic policies, such as national security, medical care, monetary, fiscal, trade, and other linked policies. There has been a surge in interest in the literature since Baker et al. (2016) introduced the EPU. It was commonly used as a proxy for risk or uncertainty. Although many studies measure the effect of uncertainty on economic development, some also examine its effects on tourist demand. For instance, Dragouni et al. (2016) was the first to present a study on the effect of the economic policy uncertainty index on tourism demand, which used data from 1996 to 2013. The EPU index was used as a proxy for mood and sentiment in this study, and it was hypothesized that sentiment and mood have a time- and event-dependent effect on tourism demand. The study discovered a significant spillover effect of EPU on tourism when there is high uncertainty, whereas there is no spillover effect when there is low uncertainty.

More recently, Gozgor and Ongan (2016) investigated the dynamic impact of EPU on tourism demand in the United States using quarterly data from 1998 to 2015. The study found that when policy uncertainty was high, tourism demand dropped significantly in the long run, utilizing Maki Cointegration, Dynamic ordinary least square (DOLS), error correction model (ECM), and Granger causality test. By utilizing the same methodology, Ongan and Gozgor (2018) reported that a 4.7% drop in Japanese tourist travel to the United States occurs in the long run due to a 1% rise in the degree of economic uncertainty. Balli et al. (2018) inspected the impact of EPU on tourism demand in selected sex countries for the period of January 1997 to August 2017. By using partial and multiple wavelets approaches, it is found that the impact of EPU on tourism demand varies by country, and this effect is at its peak and has a severe effect on tourism during times of high uncertainty. Tiwari et al. (2019) also employed a wavelet approach in India and found that EPU led to significant negativity for tourism arrivals. Sharma (2021) established that EPU has a negative asymmetric effect on tourism in India by employing the NARDL approach. Furthermore, Chen et al. (2020) used the Markov regime-switching test to investigate the impact of EPU on hotel room demand in China, Japan, and Taiwan in a recent study. This study’s findings revealed a significant decrease in demand for rooms in China and Japan due to a lack of certainty.

The Geopolitical risks consist of information about the uncertainty associated with tensions between states, terrorism, and wars that disrupt foreign relations' healthy and peaceful path (Jiang et al., 2020). Balcilar et al. (2018) used a nonlinear causality approach to investigate the first-time GEO index and assess its effect on the stock markets of BRICS countries. The research revealed that the GEO index is the most significant determinant of investment in all countries studied. However, a rare number of researchers have used the GEO index as a proxy for political uncertainty to determine its effect on tourism demand, with the majority of them finding an inverse relationship. However, the intensity of the linkage between GEO and tourist arrivals varies by country.

For instance, Demir, Gozgor, and Paramati (2019) investigated the impact of political risk on inbound tourism in selected 18 countries throughout 1995–2016. Using the LSDV model, it is found that GEO risk causes a significant decrease in inbound tourism. In Turkey, Akadiri, Eluwole, Akadiri, and Avci (2020) used Toda-Yamamoto causality to examine the causal relationship between geopolitical risk, economic development, and tourism for quarterly data from 1985Q1 to 2018Q4. This study established that political risk has a unidirectional causal relationship with tourism and significantly reduces tourism in both the long and short term. Likewise, Tiwari, Das, and Dutta (2019) employed wavelet approaches to infer that the GOE has long-term implications for Indian tourism. Demir et al. (2020) discovered an asymmetric relationship between political uncertainty and tourist arrivals in Turkey from January 1990 to December 2018. In the short run, the results of the NARDL approach revealed an asymmetric negative relationship between these two factors.

Moreover, Hailemariam and Ivanovski (2021a) also revealed that political instability negatively impacts tourism in the United States. Furthermore, Lee et al. (2021) identified a bidirectional causality between tourist arrivals and geopolitical risk in the United States between April and November 2020. For the case of five European countries, Balsalobre-Lorente et al. (2021) mentioned that tourism and economic growth have direct and moderating impacts on environmental pollution. Sharma, Thomas, and Paul (2021) reported detailed review on revival of tourism industry after the pandemic. The authors mention the need for novel implications to revive the tourism sector and social well-being of people. In another study, Sharma, Kumar, Jain, Yadav, and Srivastava (2021) reported the environmental, social, and economic repercussions of COVID-19, which include the tourism sector.

Xuefeng et al. (2021) recently examined the dependence between several variables in the USA. The authors examined several variables in their research, such as tourism, the COVID pandemic, oil price, and carbon emission. They used daily data for COVID-19 and applied the Wavelet approach. From their results, it was revealed that tourism and COVID-19 had an anti-cyclic relationship, and that tourism was led by COVID-19 with negative c-movements. Utilizing a similar Wavelet framework, Yan et al. (2022) examined the linkage between tourism, COVID, and air quality in Hawaii. In phase coherence was discovered between COVID-19 case and tourists visit from the Wavelet Coherence analysis. The wavelet causality further informed that there is bidirectional causality between tourism and COVID-19 in this state. More recently, Pata and Balsalobre-Lorente (2022) argued that tourism and economic growth significantly influence load factor capacity in Turkey.

Zhang et al. (2022) have examined the relationship between inbound tourism and uncertainty, taking China as a case study. The uncertainty was measured by domestic and global uncertainty, and they took monthly data from 2000 to 2018. A novel method such as the time-varying parameter vector autoregression (TVP-VAR) model was applied in their study. The result from this model demonstrated that uncertainty affects tourism differently, and it varies with time. With a rise in the lag period, this effect also gets weakened. The authors also found that the effect of tourism on economic policy uncertainty was lower than that of geopolitical risk in the 2008 crisis while the 9/11 attack had a substantially higher impact on tourism.

In another study, Lu et al. (2022) examined the impact of COVID-19 on the US’s airline industry. In contrast to previous literature studies, the authors investigated the risk perception on the stock market of the airline industry. The result revealed this regardless of the type of airline; this pandemic caused a disastrous impact on the airline industry. However, as risk perception decreased gradually, the impact of COVID-19 also decreased owing to the measures taken by this industry.

Lee and Chen (2021) utilized over 100 countries to see the role of economic, financial, and political stability on tourism sector returns, revenue, and arrivals. They utilized method of moment quantile regression approach and took annual data from 2006 to 2017. Different risks were found to impact tourism development differently. But tourism develops with a greater level of stability in the country. In another study of Lee and Chen (2022), the authors examined the impact of COVID-19 on travel and leisure industry returns. In this study, they took 65 countries and daily data from the beginning of the pandemic to May 2020. Comparing how deaths and cases due to COVID affect the returns, they found that death has a more adverse impact. Also, the authors discovered a correlation which is V-shaped between returns and recovery case of COVID-19.

Hailemariam and Ivanovski (2021b) applied the SVAR framework to study the linkage between economic policy uncertainty and tourism net export in the USA. They considered monthly data from the beginning of 1999 to October 2020. From the SVAR result, it was detected that policy uncertainty adversely affects the net export of the USA. In the long term, global economic policy uncertainty induces more than 26 percent variation of tourism net export.

Research Gap

Tourists and tourism-related activities require interactions between organizations and individuals from various countries and frequent travel from one place to another; however, the COVID-19 pandemic halted all of these activities due to global border closures and travel bans. The tourism sector has been the hardest hit by the outbreak in this regard (Polyzos, Samitas, & Spyridou, 2020). To the best of our knowledge, only a few studies in the tourism literature in which scholars simply qualitatively demonstrate the COVID-19 implications for the tourism industry. For example, Gallego and Font (2021) exposed that from May to September 2020, international flight travel in America and Europe fell by 30%, and travel in Asia plunged by 50%. To combat crises like COVID-19, Wen et al. (2020) proposed that interdisciplinary research collaboration is necessary. Fotiadis and Huan (2021) recently predicted that international tourist arrivals are expected to decrease by 30.8%–76.3% until June 2021. Albulescu (2020) explored that the rising number of COVID cases would cause unpredictable delays in the institutions' activities, resulting in increased policy uncertainty.

Furthermore, the fear of catching COVID-19 in the target country is also causing a high level of uncertainty (Uzuner et al., 2020). Hailemariam and Ivanovski (2021b) used the SVAR framework in their study and detected structural breaks in their study during 2012. However, they utilized data up to 2020, yet no structural break for oil price shock, geopolitical risk, and uncertainty associated with COVID-19 was discovered in their study. Therefore, this paper intends to use novel methods which can capture multiple structural breaks, thus identifying the shocks at different periods of the study period.

However, it appears that the effect of COVID-19 on tourism has yet to be fully explored, requiring the use of real-time data. In 2022 still, tackling the impacts of COVID-19 in every economy is a critical challenge to consider (Shahzad et al., 2020). The data used for variables in this study is daily data, which may assist in the discovery of a real-time dynamic causal linkage between EPU, GEO, COVID, and tourism arrivals in the United States. The underlying reason for using daily data is that the literature suggests that the interaction between COVID-19, political and economic uncertainty, and tourist arrivals does not remain constant over time. Thus, the approach used in this research, known as time-varying causality, is novel. It provides real-time causal connectivity between proposed factors to the reader, including policymakers and practitioners.

Data and Methodology

Data Specification

This study aims to see the causal linkage of the COVID-19 pandemic, economic policy uncertainty, geopolitical risk, and tourism arrivals in the USA. The study period is from January 22, 2020, to November 22, 2020. According to Lu et al. (2022), the pandemic caused disastrous impacts on tourism industry, especially the airline industry, which is vital for tourism. But with time as risk perception regarding tourism decreased, the impact of COVID-19 on this industry also gradually weakened. Based on this, we have taken the initial period of COVID-19 data since this had disastrous implications for the tourism industry. Following Drake (2020),¹ the first wave of COVID-19 is defined as from the beginning to May 31, the second wave is from June to August, and the third wave started from September.

Here, we utilize daily data from three different sources. For example, the daily data for COVID-19 new cases come from the European Union database, whereas the data for tourist arrivals come from the International Trade Administration database. The economic policy uncertainty is based on three components and collected from the Policy Uncertainty database. The first component involves policy-related economic uncertainty in the newspaper coverage. The second component involves federal tax code provisions that are to be expired in future years. The third component is the disagreement among the economic forecasters used as a proxy for uncertainty.

On the other hand, the geopolitical risk index was developed by Caldara and Iacoviello (2018) and based on the newspaper coverage of geopolitical tensions such as the 9/11, 2003 Iraq invasion. The authors define the geopolitical risk index as an index of the risks associated with terrorists' attacks, wars, and tension between the states, influencing the normal course of international relations. However, tourist arrivals, economic policy uncertainty, and geopolitical risk data are monthly data. Hence, following the seminal paper by Shahbaz et al. (2017), the quadratic match sum method is used to convert the monthly data daily. One major advantage of this method is that it can take care of the seasonality problem existing in the data and minimize the point to point variation in the data (Zichu and Masih, 2020). The detailed data description and their sources are provided in Table 1.

Table 1.

Description of the Data.

Abbreviation	Variable	Source	Reference
COVID	Daily COVID-19 new cases	European Union Database	Sharif et al. (2020)
TA	Tourists arrivals	International Trade Administration database	Lee et al. (2021)
EPU	Economic policy uncertainty index	Policy Uncertainty database	Akadiri, Alola, and Uzuner. (2020)
GPR	Geopolitical risk index	Policy Uncertainty database	Lee et al. (2021)

Time-Varying Causality Test

This study employs a time-varying causality test to examine the relationship between COVID, Tourism, Economic Policy Uncertainty, and Geopolitical Risk. The reason for choosing the time-varying causality method is because our variables contain daily data, and therefore causality among these variables will not remain constant over time. The causality among the variables may show dynamic character (Hammoudeh et al., 2020). Moreover, time-varying causality can capture multiple structural breaks in the data with having possible shifts in parameters in specific periods (Balcilar et al., 2019). For the granger causality method with variation in time, several techniques exist, such as the forward expanding window test (Thoma, 1994), rolling window Granger causality test (Swanson, 1998), and recursive rolling Granger causality (Shi et al., 2020, 2018). This study uses the most recent time-varying causality developed by (Shi et al. (2020, 2018) with its three causality algorithms as forwarding recursive causality, rolling causality, and recursive evolving causality.

According to Hurn et al. (2016), rolling window Granger causality is superior to the other two tests because it has a high detection rate. However, the rolling window also possesses the highest rate of false detection. Comparatively, recursive rolling Granger causality has a lower false detection rate, but it is at the cost of achieving the highest successful detection rate. It also has a balanced performance relative to the other two tests. On the other hand, the forward expanding window version test has the lowest performance relative to the other two tests.

The corrective bootstrap algorithm is described in several steps as follows:

In the first step, a VAR (1) model is estimated with no granger causality as the null hypothesis. In the second step, a bootstrap sample is calculated as follows

(\begin{matrix} x_{1 t} \\ x_{2 t} \end{matrix}) = (\begin{matrix} ô_{11} & 0_{} \\ ô_{12} & x_{22} \end{matrix}) (\begin{matrix} x_{1 t - 1} \\ x_{2 t - 1} \end{matrix}) + (\begin{matrix} ε_{1 t} \\ ε_{2 t} \end{matrix})

where

x_{1 t}

and

x_{1 t}

are the variables of interest in causality relation,

ε_{1 t}

as well as

ε_{2 t}

denote residuals of VAR(1).

Now, in the third step, the statistic sequence for three tests is developed. For example, Thoma (1994) is based on the procedure below

M_{1, t}^{b} = m a x_{t \in [τ 0 + τ b - 1]} (W_{1, t}^{b})

Swanson (1998) test is based on the following procedure

M_{t - τ 0 + 1, t}^{b} = m a x_{t \in [τ 0 + τ b - 1]} (W_{t - τ 0 + 1, t}^{b})

finally, the test based on the recursive rolling window of Shi et al., (2018) is described as follows

S M_{t}^{b} (τ_{0}) = m a x_{t \in [τ 0 + τ b - 1]} (S M_{t}^{b} (τ_{0}))

In the step 4, for B=1,….,499, steps 2 and 3 are repeated.

Finally, in the fifth step, provided by 95% percentile, forward, rolling, and recursive processes are given by ${M_{1, t}^{b}, M_{t - τ 0 + 1, t}^{b} a n d S M_{t}^{b} (τ_{0})}$ ^B_b=1 respectively.

Empirical Results and Discussion

Preliminary Analysis

Shi et al. (2018) suggested that the time-varying causality procedure needs to pre-filter the data, but it requires the maximum order of integration for the VAR. Therefore, to check the static properties of our variables, we have employed the Augmented Dickey-Fuller test (ADF) and Phillips–Perron test (PP). Table 2 presents the ADF and PP unit root tests where we find that the maximum order of integration is I (2).

Table 2.

Unit Root Test Results.

	COVID	EPU	GPR	TA
ADF
Level	0.3716	0.4618	0.0194^b	0.3322
First diff.	0.1433	0.0000^a	-	0.4996
Second diff.	0.0000^a	-	-	0.0000^a
PP
Level	0.2511	0.0000^a	0.0000^a	0.8719
First diff.	0.0000^a	-	-	0.4781
Second diff.	-	-	-	0.0000^a
Order of integration	I (1)/I (2)	I (1)/I (0)	I (0)	I (2)

Notes: Figures denote p-values. a and b indicate the rejection of the null hypothesis at the 1% and 5% levels, respectively.

Time-Varying Causality Result

Following Shi et al. (2018), we implement the time-varying Granger causality analysis based upon a lag augmented VAR model with d=2. This methodology helps us figure out the origination and termination dates in the causal association among different variables by modeling the time-varying granger causal relationship (Çağlı, 2019). Here, we implement the causal relationship among the variables using the recursive evolving procedure of Hurn et al. (2016), forward expanding window method of Thoma (1994), and rolling window procedure of Swanson (1998). Figures 1 to 5 show the test statistics sequence and the 5% critical value sequence. The null hypothesis of no Granger causality between the two-time series variable can be rejected if the critical value sequence falls below the test statistics sequence.

Figure 1.

Time-varying causality between COVID and TA.

Figure 2.

Time-varying causality between EPU and TA.

Figure 3.

Time-varying causality between GPR and TA.

Figure 4.

Time-varying causality between GPR and COVID.

Figure 5.

Time-varying causality between EPU and COVID.

Let us first start with Figure 1, where time-varying causality between COVID and tourism arrivals is presented. From both the forward recursive and rolling causality, the null hypothesis of no Granger causality cannot be rejected since the test statistic does not exceed the critical value statistic over the sample period. The recursive evolving causality procedure detects several episodes of Granger causality running from COVID to tourism arrivals. The first is detected on 28 June, which lasts for 1 month up to 28 July 2020 (second wave). The second episode lasts from September 02 to September 27, 2020 (third wave). The final episode between these two variables is detected on October 09, which lasts up to November 01, 2020 (third wave). Thus, only the recursive evolving causality between the three procedures detects three significant episodes of Granger causality running from COVID to tourism arrivals. Such finding indicates that COVID granger causes tourists’ arrivals from the end of June 2020 to July 2020 (second wave), from September to 27 September and finally from October 09 to the first of November 2020 (third wave). USA’s travelling industry, especially the airline industry, was significantly affected by the pandemic, as found in the work of Lu et al. (2022) for the USA. This result is supported by several previous findings such as that of Xuefeng et al. (2021) where the authors found anti-cyclic relationship between tourism and COVID with COVID leading tourism and Yan et al. (2022) where the authors found causality from COVID to tourism.

These dates fit with the USA restrictions put in place for international’s tourists. For example, at the end of June 2020, due to the additional COVID-19 hotspots emergence worldwide, the USA extended travel restrictions with its neighboring countries, Canada and Mexico, which was initially issued in March 2020. As the USA experienced a surge in COVID-19 cases in almost half of the states, the travel ban with Canada and Mexico was further extended by the department of homeland security on July 20, 2020 (Tate et al., 2020). In September, both the Nebraska and Oklahoma states of the USA imposed international and domestic travel restrictions, respectively, which could have affected the tourist arrivals in these states. In addition, some restrictions were extended till November to both international and domestic travel (Georgetown University, 2020). The finding is justified because the USA travel sector experienced a 42% yearly decline in 2020 compared to 2019. Specifically, from March 2020 to the end of 2020, the USA’s travel sector loss due to COVID-19 amounted to $492 billion, equivalent to a $1.6 billion daily loss for the previous 10 months (US Travel Association, 2021).

While examining the time-varying causality from TA to COVID, similar to causality from COVID to TA, only recursive evolving causality reveals a significant episode from 11 to 20 July 2020 which suggests that during this period, tourism arrivals affected COVID incidence in the USA. A survey of American travelers by Destination Analytics (2021), which surveys travelers about their perception, behavior, and feelings about traveling in the pandemic, showed that people’s perception about the safety of traveling increased significantly during June–July 2020. Therefore, people’s perception about the security to travel during this period might have affected the COVID situation in this country at this time of the year. This confirms the finding of Yan et al. (2022). The authors found from their Wavelet causality result that tourism affects COVID-19.

When uncertainty arises in an economy, tourists may cancel or delay travel plans due to security and safety concerns (Demir and Gozgor, 2018). Moreover, COVID-19 fear is currently imposing a lot of uncertainty regarding being infected with COVID in the destination country (Uzuner et al., 2020). Therefore, we analyze the relationship between economic policy uncertainty and tourism for the USA during the COVID period. In Figure 2, forward recursive causality from EPA to TA arrival is provided, failing to reject the null hypothesis of no causality. However, both the rolling and recursive evolving causality detect the significant causal relationship between EPU and TA. The causality can be found from the rolling causality on 27 June, 15 August, and 21–22 August. Then from August 23 to 13 November (third wave), there is no causality found between these two variables. Still, from 14 November to 15 November, and between 21 and 22 November (third wave), significant causality between these two variables can be detected. The causality between EPU and TA is detected from the recursive evolving technique from 27 June to 19 July (second wave), for 5 days between 18 and 22 August and 9 days between 14 November and 22 November 2020 (third wave). This part of the result is in line with Dragouni et al. (2016), who found that economic policy uncertainty has a significant spillover effect on the tourism sector in the event of high uncertainty. Since the period of this study involves a highly uncertain period of COVID-19, the finding of the unidirectional causality from EPU to TA is reasonable. For the USA, Gozgor and Ongan (2016) also found that economic policy uncertainty reduces the tourism spending during 1998–2015. While looking at the causality from TA to EPU, rolling and recursive evolving causality shows causality episodes of 5 and 6 days, respectively, during the middle of July 2020 (second wave). This segment of the result extends the finding of Akadiri et al, (2020), who showed that economic policy uncertainty is associated with international tourist arrivals in 7 countries. Furthermore, they confirmed the bidirectional causality between tourists’ arrival and economic policy uncertainty which further supports our results. This is consistent with the finding of Zhang et al. (2022) as well. In their study, the authors measured the association between inbound tourism and uncertainty in China using TVP-VAR model. The authors found that this association differs with time, but it is certain that economic uncertainty due to domestic and global issues definitely affects tourism industry in a significant way.

Overall, the causality between economic policy uncertainty and tourism can be observed mostly in June–July–August and November. These dates coincide with the wildfires which started in the Western part of the USA. The wildfires started in July in Washington state and were further extended to California, which saw a record-breaking wildfire season in its history (Yan et al., 2020). Other US states such as Oregon, Colorado, New Mexico, Arizona, Utah, and Nevada also experienced several wildfires in 2020. These have presented the USA with significant uncertainty, which has further affected the tourists’ arrivals in these states.

Let us now see the time-varying causality from Geopolitical risk to tourism. The finding from forwarding recursive causality does not detect any significant causality for the whole study period between geopolitical risk and tourism. The rolling causality reveals that the geopolitical risk granger causes tourist arrivals for 9 days between 5 April and 13 April 2020 (first wave). Another significant causality is observed at the date of 22 April 2020. On the other hand, recursive evolving causality shows almost the similar impact like rolling causality but with causality running 1 day further to 14 April 2020. Another interesting finding from the recursive evolving causality procedure is that geopolitical risk granger causes tourist arrivals for 5 days from 17 April to 22 April 2020 (first wave). The finding emphasizes the role of geopolitical stability in bringing tourists' revenues into the economy and is validated by several previous studies. The study is congruent with Hailemariam and Ivanovski’s (2021a) findings, who analyzed geopolitical and tourism linkage for the USA and showed that geopolitical risk negatively affects the tourism service exports in this country. The finding also reflects the estimation result of Akadiri et al. (2020), where the authors discovered a unidirectional causality from geopolitical risk to tourism. Moreover, Lee et al. (2021) found that geopolitical risk will have a greater negative impact on tourism in times of pandemic outbreaks than the normal times. Lee and Chen (2021) also found that if a country has a higher level of stability, it might lead to higher tourist visitors in the country. According to Demir, Gozgor, and Paramati (2019), political risk causes a significant decrease in inbound tourism. All these studies, therefore, lend support to our findings.

Following this, the causality from TA to GPR is tested via three causality procedures. While forward recursive and rolling causality do not identify any significant episode, the recursive evolving causality suggests a significant causality episode for 12 days between 11 and 22 November 2020 (third wave). The finding confirms the result of Lee et al. (2021), where the authors demonstrated causality from tourists’ arrivals to geopolitical risk for the five countries of Europe and Asia. The bidirectional causality above can be seen in April and November when tourists’ arrivals and geopolitical risk affected each other. The geopolitical risk of November month coincides with the event of the USA presidential election of 2020.

In addition to the impacts between tourism and COVID, it is also important to analyze the causal relationship between GPR and COVID-19 and EPU and COVID-19. The tourists are considered rational consumers because, before traveling, they always consider the cost-benefit of traveling where cost involves the risk factors they may get exposed to, and the benefits include their satisfaction level (Lee et al., 2021). On the other hand, COVID-19 is also considered the major geopolitical shock for all the world countries, which will certainly affect almost every sector of the economy, including tourism (Sharif et al., 2020).

Now, Figure 4 shows the causality between GPR and COVID. For GPR to COVID or COVID to GPR, the forward recursive causality fails to detect any significant causality for our study period. Rolling window causality detects a significant episode of only 1 day on 10 May, 2020 while the Recursive evolving causality identifies several episodes for GPR to COVID causality. The first episode is detected on May 10, 2020 (first wave), which is similar to what has been found from rolling window causality. But recursive evolving causality also identifies causality on 6 June 2020, from 26 June to July 1 for 6 days, from July 3 to July 9 for 7 days, from 23 August to 22 October 2020 for 61 days (second and third waves). The finding is consistent with Lee et al. (2021), who found that pandemic outbreaks can exacerbate outbreaks of geopolitical risk on tourism.

Additionally, the USA, especially Minneapolis city, experienced significant local unrest during June–October, 2020 as the protests took place due to the murder of George Floyd on May 25, 2020, by the Minneapolis Police Department. (Laurie, 2020; Lauritsen, 2020). These political protests might have affected the COVID incidence in this country.

Now, while examining causality from COVID to GPR, literature has identified several mechanisms. For instance, COVID-19 has led to huge unemployment across the globe, especially in the USA economy. Such facts argue that unemployment further leads to a decline in people’s income, and as a result, the protests and violence may increase, implying a surge of geopolitical risk. But on the other hand, travel restrictions may also indicate a lower probability of terrorism-related activities, suggesting lower geopolitical risk during COVID-19 (Wang et al., 2020). Therefore, there might be two effects occurring simultaneously, which can be found from our result. Looking at the causal impact from COVID to GPR, the result reveals several significant episodes from the rolling and recursive evolving causality methods. The rolling causality detects causality on September 6, November 2 and 7 and from November 11 to 14 for 4 days in 2020.

On the other hand, the recursive evolving causality suggests causality from June 4 to 6 and June 9 to 11 for 3 days, from September 6 to October 1 for 26 days, on November 2, from November 11 to 15 for 5 days, November 18 to 19 and November 21 to 22 for 2 days, during the year 2020 (second and third waves). The significant causality is detected during the October–November months of 2020. This again can be attributed to the US presidential election of 2020, a major political event in the world for several decades. The outcome is consistent with Sharif et al. (2020), who found that an increase in COVID-19 cases, which has been exponential so far, is associated with a substantial rise in the geopolitical risk of the USA. The authors note that this substantial increase in geopolitical risk has been mainly due to the USA’s COVID-19 and oil price shock.

Rajendran (2021) noted that the pandemic outbreak escalated tensions around social injustice, health care access, and economic inequality. Therefore, it is very much a geopolitical issue. Moreover, geopolitical risk has been multiyear high due to the COVID-19 outbreak. But our result can also be explained by the fact that the higher fatality rate of COVID-19 brings more lockdown and restrictions within the boundary of an economy, reducing geopolitical risk (Wang et al., 2020). Therefore, from the causality result, it might be said that COVID might have increased geopolitical risks in the USA. Still, at the same time, restrictions and lockdown imposed due to this virus might have reduced some geopolitical events which could have taken place had there been no lockdown.

Finally, the causal association between COVID and EPU is presented in Figure 5. While the forward recursive causality detects a significant episode only on April 6, 2020, from EPU to COVID, the other two causality techniques identify several more causality episodes in the April, July, August–September, and November (first, second, and third waves). For instance, causality during July 24, from July 27 to August 6 for 11 days are detected by rolling causality and during July 24, July 27 to August 3 and August 5 to 12 for 8 days, September 7 to 26 for 20 days and November 13 to 14 for 2 days by the recursive evolving causality. During these turbulent times of pandemic outbreak, the economic policies have been highly uncertain, which further exacerbated the pandemic situation as the uncertain economic policies affected all the market participants and distorted the vision of the country’s economy. Historically, economic policy uncertainty is associated with adverse effects such as lower economic performance and higher unemployment. This can be seen from the USA where the pandemic has led to the stoppage of production, and as a result of uncertainty, many workers were laid off, and unemployment even rose 14.7% in April 2020 (Al-Thaqeb et al., 2020). Moreover, higher COVID cases and the fatality rate can lead to delay reactions of the authorities involved, suggesting an increase in policy uncertainty (Albulescu, 2020). This demonstrates that a lack of capacity to coordinate and cooperate during the outbreak can further exacerbate the already existing situation.

Now, for causality from COVID to EPU, unlike other causal associations detected before, all the three causality techniques identify significant causality episodes during our study period. From all three tests, 3 days causality from April 2 to 4 is confirmed (first wave). The causality from April 7 to 24 for 18 days is confirmed by forwarding recursive and rolling causality, while recursive evolving causality detects the causality for 17 days during almost the same period. The causality from April 28 to May 1, June 28 to July 9 for 12 days, July 19 to 20 for 2 days, and August 28 to November 22 for 87 days is detected by Forward recursive causality (first, second, and third waves). Moreover, rolling causality confirms the COVID to EPU causality during April 26 and 29, August 30, September 6, September 8 to 23 for 16 days, September 26 to 27 for 2 days, September 29 to October 7 for 9 days, October 10 to 19 for 9 days, and during 26 October 2020 (first, second, and third waves). However, from recursive evolving causality procedure, apart from April 2 to 4, 8 to 24, 26 and 29–30, causality is detected from June 28 to 29 for 2 days, June 3 to 5 for 3 days, and finally from September 6 to November 16, 2020, for 72 days (first, second, and third waves). This segment of the outcome can be justified because announcements of COVID cases and deaths each day which increased in the USA substantially during the above periods might have also increased the economic policy uncertainty of the country (Albulescu, 2020). For example, in July 2020, as the previous round of COVID aid was running out in the USA and the next round was about to be declared, lots of uncertainty and disagreements related to several issues such as aid for the unemployment and school funding existed which created policy uncertainty in this country (ABC, 2020). The outcome is also in line with Sharif et al. (2020), who found a profound impact of COVID-19 on economic policy uncertainty.

Concluding Remarks and Implications

In the 21^st century, tourism is one of the fastest and largest growing sectors worldwide, which substantially contributes to a nation’s GDP through the increase in employment generation and governmental revenues. However, this sector witnessed a major blow with the onset of deadly COVID-19 in late 2019. This pandemic adversely affects all the economic sectors, supply chain, education, social life, and even the governance system of countries and destroys many lives and livelihoods. Hit hard by the pandemic, the United States of America is also going through different geopolitical risks and policy uncertainty risks simultaneously, and there lies the contribution of this study.

This paper aims to investigate whether the geopolitical risk, policy uncertainty, and COVID-19 influence the daily tourist arrivals in the USA. Further, the study examines the time-varying forward recursive, rolling, and recursive evolving causality between the key variables of interest. According to the economic reports, tourism contributes to the $1.6 trillion in GDP, supporting 7.8 million US jobs. Such facts demonstrate that tourism is the largest and single source of revenue of capital inflows for several states of the United States. For the empirical examination, the study employs novel econometric techniques such as time-varying causality with forward and recursive and recursive evolving causality on the daily data of variables covering the period from January 22, 2020, to November 22, 2020.

The empirical results indicate some interesting findings. Firstly, the causality estimates demonstrate that COVID-19 daily cases granger cause the tourist arrivals. Such finding might be due to the traveling restrictions for international tourists in the United States from June 2020, to November 2020. During this period, the USA witnessed a sudden surge in COVID-19 cases in half of the states, the department of homeland security imposed a ban on tourism from Canada, Mexico, and other countries. Concerning the causality from tourism toward COVID-19, the causality results indicated recursive evolving causality in the period of July 2020. Secondly, the empirical results of recursive causality indicate significant variations between the tourist arrivals, geopolitical risk, COVID-19 daily cases, and policy uncertainty. The empirical findings conclude that there is unidirectional causality from economic policy uncertainty toward the tourist arrivals during the high uncertain period. It is justified that during the high uncertain times (in a pandemic) people might think to delay or cancel the travelling plans. Further, different states in the United States have different traveling regulations. The causality between policy uncertainty and tourism was found in June to August and November, which might be due to the wildfire in the western part of the USA.

Further, the empirical findings for geopolitical risk mention that geopolitical stability is an important factor for tourist revenues in the economy. Such a finding is very interesting and eye-catching, it allows us to conclude that geopolitical risk and stability is critical factor for tourism growth. The findings further highlight bidirectional causality between tourism and geopolitical risk. In addition, the empirical results highlight bidirectional causality among geopolitical risk with tourist arrivals and geopolitical risk to COVID-19 daily new cases. Lastly, the study concludes that COVID-19 might exaggerate the uncertainty and adversely affect tourism growth.

The findings of this paper allow us to draw some fruitful implications and innovative conclusions for the tourist destinations in the United States. The governments and policymakers should strive to forecast the adverse impacts of the tourism industry on the magnitude of COVID-19. The closure of international borders for a certain time might be an effective policy to control the pandemic. Further, the countries might introduce some contact tracing and artificial intelligence-related software to reduce and trace the cases of COVID-19. Such policies would adversely affect the revenues from tourism in the short term; however, it would contain the infection in limited areas and contain from spreading exponentially.

Several challenges severely hurt US tourism industry during Trump’s administration as we have already mentioned before. But President Biden has taken different steps to revive the tourism industry. For example, in January 2021, the ban on Muslim majority countries was lifted up. But many challenges lie ahead of the Biden administration in recovering the tourism sector completely out of the risk uncertainty associated with COVID-19. Also, problems like hate crime and racism need to be tackled all across the USA, which induces economic uncertainty (Majcher, 2021). To revive the tourism industry, the airline and transport sector should adopt new preventive strict measures in line with public health safety regulations of the USA. Also, a flexible booking system and travel warning may reduce the uncertainty associated with COVID-19 in the tourism industry. Avoiding misinfodemics is also necessary during the pandemic, which can negatively influence tourists' perspectives (Pandey et al., 2022).

Further, the innovative policies regarding geopolitical risk, pandemic, and tourism might be useful to revive the tourism industry and other economic sectors with the improvements on pandemic situation. The future research can be conducted on the major tourist destinations in the world, role of vaccination for tourism, and production sectors. Such interesting research might be fruitful for gaining innovative policies regarding COVID-19, industry, and tourism sector.

Footnotes

Acknowledgments

We would like to extend our gratitude toward our employers for providing us the infrastructure and resources to work in this research.

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

Data Availability

Data are available upon request from the corresponding author.

Correction (May 2024):

Article updated online to change the affiliation of the third author. Please see <> for more details.

ORCID iDs

Umer Shahzad

Muhammad Ramzan

Note

Author Biographies

Umer Shahzad currently works as Assistant professor in Anhui University of Finance and Economics. Dr. Umer’s current research is focused on green innovation, energy finance, trade aspects, economic complexity, and ecological issues. Dr. Umer’s research has been published in several top-notch Journals such as; Current Issues in Tourism, Technological Forecasting & Social Change, IEEE Transactions on Engineering Management, Renewable & Sustainable Energy Reviews, Energy & Buildings among others; focused on environmental aspects of energy, climate change, tourism and international trade. He is also serving as guest editor and review editor in several Journals.

Muhammad Ibrahim Shah is currently pursuing his MSc in Agricultural and Resource Economics at the University of Alberta, Canada. He has completed both his bachelor’s and master’s degree in Economics from University of Dhaka. His research interests include Applied Macroeconomics, Environmental economics, Energy Economics, sustainable development etc.

Muhammad Ramzan is pursuing his PhD in international trade at Shandong University of Finance and Economics, Shandong, Jinan, China. Since 2013, he has worked at several universities in Pakistan. His research interests include stock markets, governance, sustainable development, financial economics, energy economics, environmental economics, and agricultural economics.

Buhari Doğan is working as PhD in the Faculty of Economics and Administrative Sciences in Suleyman Demirel University. Then, he holds BA from Kocaeli University, Department of Economics of the Faculty of Economics and Administrative Sciences. He completed his MA in Department of Econometrics at Suleyman Demirel University, and then he completed his Ph.D. education in Department of Economics at the same university. He writes on issues of energy, environmental economic growth, and globalization.

Ahdi Noomen Ajmi holds a PhD in Mathematical Economics and Econometrics from the University of the Mediterranean (France) and obtains his HDR (Habilitation for Supervising Doctoral Research) in Economics from University of Manouba (Tunisia). He joined Prince Sattam bin Abdulaziz University (KSA) as Professor of Economics. Previously, he served as Assistant Professor in Quantitative Methods at ESC of Tunis (Tunisia) and Teaching Assistant at High Institute of Management of Tunis. His principal research areas concern: time series analysis, financial econometrics, long Memory processes, causality analysis, energy economics and volatility modeling.

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