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Does the level of a country's resilience moderate the link between the tourism
industry and the economic policy response to the COVID-19 pandemic?
Luke Emeka Okafor 1, Usman Khalid 2*, Katarzyna Burzynska3
1 School of Economics, University of Nottingham Malaysia, Semenyih, Selangor, Malaysia
2 Department of Innovation in Government and Society, College of Business and Economics,
United Arab Emirates University
3Institute for Management Research, Radboud University, Nijmegen, the Netherlands
*Corresponding author
P.O. Box No. 15551, Al Ain, UAE. E-mail: usman.khalid@uaeu.ac.ae, Telephone:
+97137136513.
Acknowledgements: This study is supported by the United Arab Emirates University under the
Start-Up grant (# 31B126). The authors would like to thank Uzair Ahmed for his assistance in
this research.
Note: This is the accepted version of the aritcle before copy editing. Published version of this
article is available at: https://doi.org/10.1080/13683500.2021.1956441
Please cite this article as: Luke Emeka Okafor, Usman Khalid & Katarzyna Burzynska (2021)
Does the level of a country's resilience moderate the link between the tourism industry and the
economic policy response to the COVID-19 pandemic?, Current Issues in Tourism, DOI:
10.1080/13683500.2021.1956441
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Does the level of a country's resilience moderate the link between the tourism
industry and the economic policy response to the COVID-19 pandemic?
This study examines whether the level of a country's resilience to shocks moderates the link
between the size of the tourism industry and the economic policy response to the COVID-19
pandemic using data from 113 countries. The findings suggest that countries with large tourism
sectors responded more aggressively by using economic stimulus packages to mitigate the
impact of the COVID-19 pandemic; however, the impact of the tourism sector is moderated by
the country's resilience to shocks. The study also finds that both high level of economic
resilience and high level of risk quality of a country moderate the link between the tourism sector
and the economic policy response to the COVID-19 pandemic. The findings of the study suggest
that tourism businesses in high resilient countries are better prepared to cope with the disruptive
challenges posed by the COVID-19 pandemic and thus needed less assistance from governments.
Improving a country's resilience to shocks is an important strategy to minimise the impact of
future negative shocks in the tourism sector.
Keywords: COVID-19; pandemic; tourism; resilience index; economic resilience; risk quality
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Introduction
Economic forecasts suggest that the COVID-19 pandemic will trigger a global economic
recession, which impact may be larger and more pervasive than that of the 2008–2009 financial
crisis (OECD, 2020b; World Bank, 2020). To overcome the negative effects of this downturn
and to support the vulnerable and worst affected groups and industries, governments are
introducing various economic policy measures (Elgin et al., 2020). While these economic policy
responses typically include fiscal, monetary and financial initiatives, they vary in type and scope
across countries. Recent studies have linked the strength of the government economic response
across countries to factors such as income level, population age, hospital capacity, the number of
COVID-19 cases (Elgin et al., 2020, Khalid, Okafor, & Burzynska, 2021), the country sovereign
credit ratings (Balajee et al., 2020) and stock market fluctuations (Shafiullah et al., 2020).
However, the extent of economies' vulnerability to the crisis and the resulting policy response
likely also depends on countries' economic structure and the ability of various agents to
overcome economic shocks and adapt to the new conditions imposed by the containment
measures.
In particular, countries depending on international tourism are likely to bear higher
economic costs of the pandemic and introduce larger economic stimulus packages than those that
do not depend on tourism to a large degree. There is growing evidence that economies dominated
by sectors requiring physical interaction and mobility, such as tourism, are most affected by the
physical distancing measures and mobility restrictions imposed to contain the spread of the virus
(Djankov & Panizza, 2020; Fernandes, 2020; OECD, 2020a). International tourist arrivals in
2020 are estimated to decline to a phenomenal 60%–80%, which translates into a loss of US$910
billion to US$1.2 trillion in export revenues from tourism and risking 100–120 million direct
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tourism jobs (UNWTO, 2020). Owing to the global scale of the crisis in international tourism
and the resulting general decrease in tourism demand, tourism-dependent countries face high
economic risks even if they themselves record fewer cases of COVID-19 infections than other
countries (Noy et al., 2020).
Furthermore, the degree to which the dependence on tourism is associated with the
economic policy response is likely to be influenced by a country's overall resilience. Resilience
can be defined as a system's ability to anticipate and recover from the shocks and crises and the
capacity to transform and reach a new 'normal' (Folke et al., 2010; Walker et al., 2004). In the
context of tourism-based economies, Kim and Marcouiller (2015) found that stronger economies
recover faster than weaker economies when struck by exogenous shocks. Country case studies
emphasise the importance of reducing the vulnerability of the tourism sector to shocks and
ensuring that the industry is robust and flexible enough to overcome crises (De Sausmarez, 2007;
Gurtner, 2016).
However, little is known about the moderating role of the level of a country's resilience to
shocks on the link between the size of the tourism industry and economic policy response to
crises. Studies that relate to crisis and disaster management in tourism focus on recovery rather
than on preparedness or readiness for economic crises (Aliperti et al., 2019; Ritchie & Jiang,
2019). Moreover, despite the fact that the countries most reliant on tourism tend to be at a low-
or middle-income level, studies on health-related crises in tourism focus primarily on developed
countries (Mooney & Zegarra, 2020; Novelli et al., 2018).
Consequently, this study investigates whether the level of a country's resilience to shocks
moderates the link between the size of the tourism industry and the economic policy response to
the COVID-19 pandemic using a large sample of 113 countries. Specifically, our measure of
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country-level resilience captures three factors: economic resilience, risk quality, and supply
chain. We investigate the moderating influence of the level of resilience, measured using the
overall resilience index, economic resilience index, or risk quality index in the underlying
relationship between the size of the tourism sector and economic stimulus packages introduced
to mitigate the impact of the COVID-19 pandemic. As such, this is the first study to shed light on
whether and to what extent country-level resilience can help alleviate the economic burden of a
health crisis for tourism-dependent countries.
The remainder of the study is structured as follows. The following section presents a
review of related literature. Section three describes the data and methodology used for the
empirical analysis. Section four discusses the results, and the final section concludes.
Literature Review
Tourism literature highlights resilience to crises and disasters such as natural disasters, terrorism
attacks, financial shocks, and political instability. Owing their universality, complexity and
reliance on safety and security, tourism systems are inherently vulnerable to external threats
(Ritchie, 2004). In such context of uncertainty, where the existing and potential risks are
unknown, resilience is a useful strategy to apply (Espiner et al., 2017; Strickland-Munro et al.,
2010). Resilience allows organisations and tourism enterprises to pursue new methods for
planning and operating in times of rapid, unexpected change (Luthe & Wyss, 2014).
Studies include theoretical and practical considerations of resilience concepts to specific
case studies. Studies on resilience focus on building resilience assessment frameworks for the
tourism industry to ensure that businesses and communities are protected from critical shocks
(Brown et al., 2018; Calgaro et al., 2014; Cochrane, 2010; Lew, 2014). These studies outline
various key economic and non-economic factors that influence the resilience of tourism-focused
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sectors and communities and develop theoretical models that help in assessing and monitoring
the resilience of the industry, businesses and communities against various shocks, including
natural disasters, health crises, and climate change.
Empirical studies apply qualitative approaches and use information derived from an
individual or collective stakeholders' experiences to examine the determinants of resilience. Data
are analysed typically within the context of a particular destination and at the level of protected
area (Strickland-Munro et al., 2010), community (Holladay & Powell, 2013; Ruiz-Ballesteros,
2011) or firms (Becken, 2013; Biggs et al., 2012; Williams, You, & Joshua, 2020). Factors that
enhance communities' resilience are social bonds, the capacity of local institutions and
diversification of the tourism product (Holladay & Powell, 2013). Similarly, Becken (2013)
categorised the factors determining the firms' ability to maintain profitability against climatic
events into those related to market (e.g. business diversification), business operations (e.g.
staffing and information) and relationships (e.g. inter-organisational partnerships). Most recently,
in the context of the COVID-19 pandemic Kuščer, Eichelberger, and Peters (2021) identify
stakeholder collaboration as an important factor enabling and enhancing destination resilience.
Quantitative studies on resilience are limited (Chowdhury et al., 2019; Luthe & Wyss,
2014; Orchiston et al., 2016). Using data on tourism organisations in Canterbury, New Zealand,
Orchiston et al. (2016) found 'planning and culture' and 'collaboration and innovation' as
important determinants of organisational resilience in a postdisaster context. Luthe et al. (2012)
applied social network analysis to study the relationship between the network structure of the
tourism supply chain in the Swiss Gotthard region and its resilience to climate change.
Quantitative research directly investigating the relationship between organisational resilience and
business outcomes is particularly scarce, except for the study by Chowdhury et al. (2019), which
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found that adaptive resilience is positively related to business performance in postdisaster Christ
Church, New Zealand.
Some empirical studies have studied regional- or country-level resilience in tourism.
Most of these studies show variations in resilience resulting from underlying economic
conditions, including dependence on tourism. In the context of Italy, Cellini and Cuccia (2015)
found that the tourism industry overall was more resilient to the 2008 financial crisis than other
industries. Moreover, they showed that the differences in regional-level economic resilience of
the tourism sector could explain the degree of success in response to the crisis. However, it is
evident that tourism-dependent regions and countries experience lower resilience levels when
faced with natural disasters such as earthquakes (Cheng & Zhang, 2020) or terrorism (Liu &
Pratt, 2017).
Resilience plays a role in the ability of regional and national economies to recover from
disasters and crises. Economic development, high quality of infrastructure and adequate social
institutions reduce disaster risk by improving a region's adaptability (Kellenberg & Mobarak,
2008). Moreover, more developed regions with better financial systems have well-established
emergency disaster plans and employ additional precautionary measures (Kim & Marcouiller,
2015; Orchiston, 2013). In contrast, poor governance structures, limited human and financial
resources and inadequate tourism planning hinders efficient policy response to tourism crises
(Novelli et al., 2018). Kim and Marcouiller (2015) examined the resilience of businesses in U.S.
national parks and coastal regions to hurricanes and found that tourism-based regions with
stronger economic conditions experience fewer disaster losses than those with weaker economic
conditions. At the country level, Liu and Pratt (2017) showed that high-income countries and
countries with a more open political system are more resilient to the impact of terrorism.
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Overall, studies on resilience in tourism are qualitative or only focus on a specific case to
examine either the determinants of resilience or its consequences for the local economy. The
concept of resilience is mainly examined at the community or enterprise level, and fewer studies
investigate country-level resilience that captures the country's business environment.
Furthermore, literature focusing on health-related crises that goes beyond the context of
developed countries is limited. There is, therefore, a lack of systematic, cross-country analysis
on whether the level of a country's resilience to shocks influences the underlying link between
the size of the tourism industry and economic policy response to a health-related crisis, such as
the COVID-19 pandemic. This study aims to address these gaps in the extant literature.
Data and Methodology
Overview of Data and Definition of Variables
A cross-sectional dataset of 113 countries is used for the empirical analysis. The choice of
countries is dictated by data availability. This study uses merged data from various sources such
as the World Development Indicators (WDI) (World Bank, 2019), World Travel and Tourism
Council (WTTC) (WTTC, 2020a), F.M. Global (2020) and Elgin et al. (2020).
Dependent Variable
The COVID-19 economic stimulus index (CESI) developed by Elgin et al. (2020) is used to
capture the countries' economic policy response to the COVID-19 pandemic along three broad
policy measures, namely, fiscal, monetary and balance of payment/exchange rate. Elgin et al.
(2020) used the IMF's COVID-19 Policy Tracker, 2020, as the primary source of data for
developing the index, and they complemented missing or outdated information with data from
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other sources
1
.
Explanatory Variables
To capture the size of the tourism sector in a destination country, five proxies, namely, leisure
tourism spending US$ in bn (real prices), business tourism spending US$ in bn (real prices),
travel and tourism direct contribution to GDP US$ in bn (real prices), travel and tourism direct
contribution to employment (in millions) and the number of tourist arrivals at the national border
(in millions). Except for tourist arrivals, the rest of the variables are for the year 2019 and the
data are sourced from WTTC (2020a). The data for tourist arrivals is for the year 2018 or the
latest available data. The tourist arrival data are collected from the World Bank (2019). These
five variables provide a good approximation of the size of the tourism sector in a destination
country, its revenue-generating capacity, and the level of a country's reliance on the tourism
sector. Several studies have used these variables as a proxy for the size and the extent of tourism
sector development in a destination country (see, e.g., McCartney, 2020; Shafiullah et al., 2019;
Y. Shi, et al., 2020, Okafor, Adeola, & Folarin, 2021a, b; Okafor, Khalid, & Adeola 2021;
Okafor & Khalid, 2020; Okafor, Khalid, & Then, 2018).
The FM Global Resilience Index, which is a composite index measuring the 'countries'
relative enterprise resilience to disruptive events' is used to capture the level of a country's
resilience to shocks (F.M. Global, 2020). The index is developed using 12 factors 'to capture the
level of a country's resilience to shocks, which can be broadly categorised as economic
1
We use the CESI’s fifth update (7 May 2020) by Elgin et al. (2020) to ensure that all economic stimulus
packages introduced since countries adopted strict public health measures, restrictions on social
gatherings and international travel, are captured.
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resilience, risk quality and supply chain. The economic resilience factor captures the impact of
political risk and macroeconomic conditions on resilience. Particularly, economic resilience
contains four drivers, including productivity, political risk (includes terrorism and political
instability), oil intensity and urbanisation rate. The risk quality factor comprises a country's
exposure to natural hazards, natural hazard risk quality, fire risk quality and cyber risk. Finally,
the supply chain comprises of control of corruption, quality of infrastructure, local supplier
quality and supply chain visibility. For more information on the resilience index, please see the
F.M. Global's methodology (F.M. Global, 2020).
Control Variables
Following Elgin et al. (2020b), several control variables were included in the model, as these
variables affect the size of the government's economic policy response. These variables include
the number of hospital beds per 1000 people and a country's health expenditure as a percentage
of the GDP. The data on these variables are for the year 2018 or the latest available year and are
sourced from WDI (2019). The fatality rate (i.e. the ratio of total deaths to total confirmed cases)
is also used as a control variable.
2
Summary Statistics
Table 1 presents the summary statistics for the variables used in the empirical analysis for the
subsample of countries with the overall resilience index above and below the median value. The
summary statistics show that countries with the overall resilience index above the median value
2
Data on this is available from https://ourworldindata.org/grapher/covid-tests-cases-deaths. Based on the
data availability, the data on this variable corresponds to 23rd or 24th of May, 2020.
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tend to have a higher CESI index than countries with an overall resilience index below the
median value when the contribution of the tourism sector is not controlled for. Thus, on average,
without controlling for any additional covariates nor accounting for the interaction effect
between the size of the tourism sector and the level of a country's resilience, highly resilient
countries are more likely to introduce larger economic stimulus packages to mitigate the impact
of the COVID-19 pandemic on their economies compared to those that less resilient. This
preliminary finding is further confirmed by the test of equality of means as reported in Column 4
in Table 1. The test indicates that the difference in means for below and above median countries
is statistically significant. In general, countries with high overall resilient scores, such as
Switzerland and Canada, have well-developed financial markets, efficient labour market, strong
human capital, and financial institutions (Swiss Re Institute, 2020), compared to countries with a
low score on resilience index. Moreover, countries with a high overall resilient score also tend to
be richer compared to countries with a low overall resilient score. As a result, countries with high
overall resilient scores tend to have more fiscal and monetary spaces to support their economies
during a pandemic, such as COVID-19.
[Insert Table 1 here]
Furthermore, compared with below-median countries, above-median countries tend to
have better health infrastructure as captured using hospital beds per 1000 people. They also tend
to spend more on health. Moreover, above-median countries tend to have a higher fatality rate
than below-median countries. In general, above-median countries are more integrated in the
world economy in terms of trade, investment, and travel compared to below-median economies.
The high degree of connectedness of high-resilient countries made it relatively easier for
transmission of COVID-19 across borders. As a result, above-median countries tend to register
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more COVID-19 cases and deaths at least at the beginning of the crisis. This is consistent with
the notion that travel and trade are key drivers of the spread of disease (Shrestha et al., 2020),
such as COVID-19. While the above-median countries are expected to be better at managing
disasters and/or crises, it seems that the resilience infrastructures are not adequately equipped to
cope with the massive disruptions arising from a global health pandemic, such as COVID-19.
Above-median countries tend to be more attractive to international tourists and/or earn
more revenue from tourism than below-median countries. In contrast, the travel and tourism
sector tends to contribute more to employment in below-median than above-median countries.
This suggests that countries that are most reliant on tourism for jobs, such as Antigua and
Barbuda, Maldives, Philippines, and Cambodia, (Neufeld, 2020), also tend to have low resilience
scores. Below-median countries tend to have a less diversified economy compared to above-
median countries. This explains why tourism tends to contribute a larger share in total
employment in below-median countries compared to above-median countries.
Methodology
We augment the econometric model used by Elgin et al. (2020) by including the size of the
tourism sector and the measure of the level of a country's resilience to shocks as explanatory
variables. Further, we also include an interaction term between a country's resilience and the size
of the tourism sector to capture the moderating effect of resilience on the underlying link
between the tourism sector and CESI. Thus, our econometric model is specified as follows:
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(1)
The coefficient of interest include and is the error term for country i.
is a variable that captures the contribution of the tourism sector to a country's
economy, is a dummy variable that takes the value 1 if the value of the
resilience index for country i is above the mean minus one standard deviation.
3
is the number of hospital beds (per 1,000 people) in country i and accounts for the capacity of the
health care system to cope with the increasing number of patients during the pandemic. The
better the capacity to deal with many patients, the lower is the likelihood of a large stimulus
package (Elgin et al., 2020). is the ratio of total number of deaths a country has
experienced to total positive cases till 24 May 2020. Fatality rate serves as a proxy for the spread
of the COVID-19 pandemic in a country and the severity of the pandemic. Following Elgin et al.
(2020), this study includes health care expenditure as a percentage of GDP
() to account for the capability, sophistication and robustness of the
domestic health care system.
3
This approach to distinguish high and low values of a variable is widely used in the economics and
management literature (see, e.g. Alkærsig, et al., 2013; Eichengreen & Gupta, 2016; Grjebine &
Tripier, 2015; W. Shi et al., 2019), including de Groot et al. (2005); Hameed et al. (2010); and Kim et
al. (2011).
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Equation (2) presents the calculation of the marginal effect of tourism on the CESI index
as follows:
(2)
The equation suggests that the effect of tourism on the CESI index depends on the
country's resilience to unforeseen events, as captured by the dummy. We
expect, and indicating that a larger tourism sector leads to a more aggressive
economic policy response; however, the response is moderated by the level of a country's
resilience to disruptive events. We expect that countries that are more resilient have a less
aggressive economic policy response than those that are less resilient because of their
preparedness to tackle unforeseen events.
In addition to the overall resilience index, we also use the two sub-index, namely
economic resilience and risk quality, to capture the role of resilience to shocks in the underlying
relationship between the size of the tourism sector and the economic policy response to the
COVID-19 pandemic. To deepen the understanding of how various types of resilience can affect
the economic policy response, economic resilience and risk quality are used in lieu of the overall
resilience index to understand how the economic policy response is influenced by the size of the
tourism sector augmented by economic resilience or risk quality. Thus, we replace
in Equation (1) with and as
alternative measures of the level of a country's resilience to shocks.
is a dummy variable that takes the value 1 if the value of the
economic resilience for country i is above the mean minus one standard deviation, whereas
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is a dummy variable that takes the value 1 if the value of the risk quality for country
i is above the mean minus one standard deviation.
Discussion of Results
The Relationship between Different Measures of the Tourism Sector, Overall Resilience
Index and CESI Index
Table 2 presents the parameter estimates of the association between measures of the tourism
sector, overall resilience index, and the CESI index. As shown in columns 1 to 5, a country's
dependence on tourism is positively related to the CESI index for countries not categorised as
having a high overall resilience index regardless of the measure of tourism, such as tourist
arrivals, business tourism spending, and leisure tourism spending. In contrast, the effect of
tourism dependence on CESI is close to zero for countries with a high overall resilience index.
The moderating effect of a high overall resilience index on the underlying association between
tourism dependence and CESI is captured by the interaction effect, which is negative for all
measures of tourism. Thus, for a country classified as having a high overall resilience index, a
change in tourist arrivals by one million results in a change in the CESI index by 0.01, whereas
for countries that are not categorised as having a high overall resilience index, a change in tourist
arrivals by one million results in a change in the CESI index by 0.122 (column 5 of Table 2).
[Insert Table 2 here]
Overall, resilient countries have political stability, good corporate governance, better
management of risk, good supply chain management and transparency (F.M. Global, 2020).
These factors potentially help tourism businesses in high-resilience countries to respond quickly
to the COVID-19 pandemic and maintain resilience during the crisis. Therefore, tourism
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businesses in these countries do not need as much support from the government compared with
their counterparts. Furthermore, this finding implies that tourism businesses in high-resilient
countries are better prepared to recover faster from the COVID-19 pandemic than those that do
not. Thus, the resilience of a country's operating business milieu is a reliable indicator for
companies trying to recover from the impact of the COVID-19 pandemic (F.M. Global, 2020)
and that high-resilient countries are better equipped to recover post-COVID-19 pandemic
(Indvstrvs, 2020). On the other hand, less resilient countries have less diversified economies and
thus more likely to rely heavily on the tourism sector compared to high-resilient countries. This
suggests that the tourism sector would be a significant driver of economic stimulus packages in
low-resilient economies relative to high-resilient economies.
The coefficient on the high overall resilience index, together with the interaction term,
indicates that for an average value of the tourism indicator, countries categorised as having a
high overall resilience index will have, on average, a lower CESI index than those that are not
categorised as having a high overall resilience index. For instance, for a country with an average
tourist arrival of 11.517 million, the change in the overall resilience index from 0 to 1 yields a
−0.411 unit change in the CESI index (see, column 5 of Table 2). This indicates that countries
with high overall resilience index introduced lower economic stimulus packages to prop up the
economy compared with those that did not. This implies that tourism businesses in countries
with a high overall resilience index were better prepared to mitigate the impact of the COVID-19
pandemic than those that did not.
As shown in columns 1 to 5 of Table 2, there is no strong evidence that hospital beds per
1000 people is associated with the economic policy response to the COVID-19 pandemic as
captured using CESI index. Similarly, the fatality rate is positively related to the CESI index, but
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its impact is statistically insignificant. This suggests that hospital beds per 1000 people and
fatality rate are not strong predictors of how countries responded to the COVID-19 pandemic
using the economic stimulus package. Health expenditure as a percentage of GDP is positively
associated with the CESI index and statistically significant at the conventional level, regardless
of the measure of the tourism sector. This suggests that countries that traditionally spend more on
health are also those allocating more resources in terms of fiscal and/or monetary packages to
address the impact of the COVID-19 pandemic on the economy.
The Relationship between Different Measures of the Tourism Sector, Economic
Resilience and CESI Index
Table 3 reports the parameter estimates of the relationship between different measures of the
tourism sector, economic resilience, and the CESI index. Consistent with the initial finding,
tourism is positively associated with the CESI index for countries not categorised as having a
high economic resilience index (columns 1 to 5, Table 3). In contrast, the effect of tourism on the
CESI index is negligible for countries that are categorised as economically resilient.
[Insert Table 3 here]
Given that the interaction term is negative, it also suggests that, on average, the CESI
score is lower for countries categorised as having a high economic resilience index when the
tourism sector becomes large. In fact, for the average value of three out of five tourism
indicators, the CESI index is lower on average for countries categorised as having a high
economic resilience index (see Table 3, columns 1 to 5). For instance, based on the coefficient
reported in Table 3, for the average values of Leisure Tourism Spending, Travel and Tourism
Direct Contribution to GDP and Business Tourism Spending, the CESI index is lower for
countries categorised as having a high economic resilience index than those that are not. For the
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other two indicators, the tourism indicator should be above the mean values for the CESI index
to be lower for countries classified as having a high economic resilience index than those that are
not. For example, based on the coefficient reported in column 5 of Table 3, for a country with
tourist arrivals of 20 million, the CESI index is on average −0.042 units lower for countries
categorised as having a high economic resilience index than those that are not.
These results are consistent with the notion that countries with high economic resilience
augmented by the contribution of their tourism sector introduced a lower economic stimulus to
combat the effect of the COVID-19 pandemic on tourism businesses than those that do not. In
general, countries with a high economic resilience index are characterised by macroeconomic
stability, lower political risk, higher productivity, less dependence on oil and urban economic
resilience. Therefore, tourism businesses in countries with a high economic resilience index are
better equipped to respond and recover from the COVID-19 pandemic than those that do not.
The remaining parameter estimates are consistent with those reported in Table 2, in terms of
signs.
The Relationship between Different Measures of the Tourism Sector, Risk Quality Index
and CESI Index
Table 4 shows the parameter estimates of the relationship between different measures of the
tourism sector, risk quality and CESI index. Generally, the parameter estimates are consistent
with those reported in Tables 2 and 3. These results reinforce the notion that resilient countries,
measured using the overall resilience index, economic resilience index or risk quality, introduced
smaller economic stimulus to combat the effect of the COVID-19 pandemic than less resilient
countries (for an average value of the tourism indicator).
[Insert Table 4 here]
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High-risk quality augmented by tourism is negatively associated with the CESI index,
regardless of the model specifications. Generally, countries with high-risk quality have better
natural hazard risk management, fire risk management, infrastructure to mitigate cyber-attacks
and crisis management plans than countries that do not. Countries with high-risk quality can
afford to spend less on an economic stimulus package for tourism businesses than those that do
not possess a high-risk quality. The remaining parameter estimates are consistent with those
reported in Tables 2 and 3, in terms of signs. These findings show that tourism businesses in
resilient countries are better prepared to respond and potentially recover faster from the impact of
the COVID-19 pandemic than less resilient countries.
As a robustness check, we also test the sensitivity of our results to the inclusion of
additional control variables. To this end, we re-estimate our main model by adding additional
control variables such as the percentage of the population over the age of 65, the log of
population density, and the log of urban population as a percentage of the total population. In
addition, we also included total confirmed deaths and total confirmed cases as explanatory
variables instead of fatality rate. The results of this exercise are reported in Table 5. Overall, the
results are similar to the ones reported in Table 2, with no major changes in the signs and
significance of the key explanatory variables. In line with the extant literature, the percentage of
the population over 65 and total confirmed deaths have a positive and significant association
with CESI (see, e.g., Khalid, Okafor, and Burzynska 2021, Shafiullah, Khalid, and Chaudhry
2021).
Further, it could be argued that a relative measure of the tourism sector is more
appropriate than absolute measure. We checked the robustness of our estimates by rerunning the
regressions in which we use the ratio of tourist arrivals in country to total tourist arrivals across
20
all the countries in our sample or ratio of tourist arrivals in country to total tourist arrivals by
region for all the countries covered in the sample. By construction, the higher the value of these
ratios, the larger the size of the tourism sector in a particular country. The results of the exercise
are presented in Table 5 (Columns 6 and 7). In general, the parameter estimates of the variables
of interest are relatively similar to the initial estimates in terms of signs and magnitudes.
Conclusion
To combat the spread of the COVID-19 pandemic, many countries have introduced physical
distancing measures and mobility restrictions, including restrictions on international travel.
These restrictions have resulted in a slowdown of economic activity and affected millions across
the globe. To counter the economic fallout from these restrictions, both developing and
developed countries introduced economic stimulus packages to combat the impact of the
pandemic on their economies. However, economies dominated by sectors requiring physical
interaction and mobility are most affected. More specifically, the tourism sector is one of the
worst-hit sectors by the COVID-19 pandemic. The pandemic would have huge negative impacts
on economies globally, especially in countries that rely heavily on the tourism sector in terms of
job creation, income generation, foreign exchange earnings and stimulating economic growth
(Khalid, Okafor, & Aziz, 2020; Okafor & Khalid, 2021; Khalid, Okafor, & Sanusi, 2021; Khalid,
Okafor, & Shafiullah, 2020; Okafor & Teo, 2019).
In this study, we posit that it is likely that tourism-dependent countries introduced larger
economic stimulus packages than those that do not because the tourism sector is one of the worst
affected by the COVID-19 pandemic. While tourism-dependent countries introduce a larger
economic stimulus, the extent of a country's resilience is likely to moderate the size of the
21
economic stimulus response because high-resilient countries are better equipped to respond to
shocks, such as the COVID-19 outbreak and recover faster than those that do not. This implies
that tourism businesses in high-resilient countries are better prepared to respond to the COVID-
19 outbreak and thus need smaller economic assistance from governments.
Thus, this study investigates ''whether the level of a country's resilience to shocks
moderates the link between the size of the tourism industry and the economic policy response to
the COVID-19 pandemic using data from 113 countries. Given the sizeable adverse effect of the
COVID-19 pandemic on global economies, whether the level of a country's resilience has a
moderating role in the relationship between the size of the tourism sector and the economic
policy response to combat the impact of the COVID-19 pandemic is worth investigating.
The results show that countries with high resilience introduced lower economic stimulus
packages to combat the impact of the COVID-19 pandemic than those that do not. Furthermore,
the results indicate that countries with a high overall resilience index augmented by the
contribution of the tourism sector introduced lower economic stimulus packages than those that
do not possess a high overall resilience index while being highly dependent on the tourism
sector. Similarly, the results show that countries with high economic resilience augmented by the
contribution of the tourism sector introduced lower economic stimulus packages than those that
do not possess high economic resilience, while being highly dependent on the tourism sectors.
Moreover, countries with high-risk quality augmented by the contribution of the tourism sector
introduced lower economic stimulus packages than those that do not possess high-risk quality
while depending on the tourism sector.
Generally, high-resilient countries are better prepared to combat shocks, such as the
COVID-19 pandemic, than those that do not. The findings of the study are consistent with the
22
notion that tourism businesses in high-resilient countries are better equipped to respond to
challenges posed by the COVID-19 pandemic and, thus, needed little help from governments
compared to those that do not. Furthermore, tourism businesses in high-resilient countries are
less dependent on economic stimulus packages as they have access to well-developed financial
markets, banks, strong human capital, and an efficient labour market (Swiss Re Institute, 2020),
compared to tourism businesses in low-resilient countries.
The study findings suggest that strengthening a country's resilience is crucial for tourism
businesses to better respond to the impact of negative shocks, such as the COVID-19 pandemic
and, by extension, recover faster from the impact of the negative shocks. Countries can improve
the resilience of their tourism business environments by designing targeted and effective health
crisis management while creating an enabling environment for political stability, good corporate
governance, low corruption, improvement in economic productivity and effective natural hazard
risk quality.
Finally, the findings of this study contribute to the current discussion in the tourism
literature on the need for a radical change towards sustainability when addressing the COVID-19
pandemic (e.g., Galvani et al., 2020; Gössling et al., 2020). Espiner et al. (2017) argued that
resilience is a prerequisite for sustainability. In line with this notion, our results suggest that
country-level resilience is potentially important as it allows governments to minimise the
economic costs of mitigating the impact of the COVID-19 pandemic on tourism businesses. The
introduction of smaller economic stimulus packages in high resilient countries suggests that
tourism businesses in these countries are better positioned to survive amidst a disruptive event,
like the COVID-19 pandemic, compared to their counterparts. This implies that tourism
businesses in high resilient countries may be more sustainable compared to those in low-resilient
23
countries. Investigating the role of country-level resilience in enabling policy responses geared
toward supporting the sustainable transformation of tourism is, therefore, a fruitful avenue for
further research.
24
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33
Table 1. Summary Statistics
1
2
3
4
VARIABLES
Below
Median1
Above
Median2
Whole
Sample
Test of
equality of
means3
CESI Index
−0.536
1.092
0.314
-1.63
(0.664)
(1.368)
(1.358)
(0.2)***
Hospital beds (per 1,000 people)
1.957
4.329
3.196
-2.37
(1.759)
(2.474)
(2.460)
(0.40)***
Fatality Rate (%)
2.993
4.576
3.819
-1.58
(2.571)
(4.429)
(3.731)
(0.67)**
Health expenditure (% of GDP)
5.835
7.810
6.866
-1.97
(1.842)
(2.733)
(2.541)
(0.435)***
Overall Resilience Index
34.746
74.300
55.398
-39.55
(10.431)
(15.638)
(23.916)
(2.48)***
Economic Resilience
27.759
60.671
44.943
-32.91
(13.944)
(15.56)
(22.139)
(2.78)***
Risk Quality
29.12
64.657
47.675
-35.54
(13.522)
(23.363)
(26.213)
(3.55)***
Leisure Tourism Spending US$ in bn (Real prices)
25.085
51.217
38.729
-26.12
(121.384)
(111.473)
(116.530)
(21.98)
Travel and Tourism direct contribution to GDP US$ in bn (Real prices)
14.506
31.967
23.623
-17.46
(62.837)
(75.885)
(70.195)
(13.06)*
Travel and Tourism total contribution to employment (in millions)
1.132
0.977
1.051
0.156
(4.015)
(3.633)
(3.804)
(0.722)
Business Tourism Spending US$ in bn (Real prices)
5.856
16.189
11.251
-10.33
(28.335)
(43.471)
(37.221)
(6.84)*
Tourist Arrivals (in millions)
5.996
16.476
11.517
-10.48
(11.321)
(19.943)
(17.190)
(3.03)***
Number of Observations
54
59
113
Note: In column 1-3, values without parenthesis represent the mean and values in the parenthesis are standard deviation.
1 Below median represents group of countries having Overall Resilience Index below the median value.
2 Above median represents group of countries having Overall Resilience Index above the median value.
3 The null hypothesis for the test of equality of means is that the means of variables for below median and above median countries are equal. The alternative
hypothesis is that the means of variables are different. Test of equality of means is performed under the assumption of unequ al variances. The values without
parenthesis represent the difference in mean of variables for below median and above median countries and values in the parenthesis are standard deviation. *** p
< 0.01, ** p < 0.05, * p < 0.1
34
Table 2. The relationship between different measures of tourism sector, overall resilience index and CESI index
Dependent variable: CESI index
(1)
(2)
(3)
(4)
(5)
Measures of Tourism Sector (Tourism)
VARIABLES
Leisure
Tourism
Spending
Travel and
Tourism
Direct
Contribution
to GDP
Travel and
Tourism Total
Contribution to
Employment
Business
Tourism
Spending
Tourist
Arrivals
High Overall Resilience Index
0.841***
0.911***
1.017***
0.865***
0.878***
(0.224)
(0.227)
(0.232)
(0.229)
(0.240)
Tourism
0.0497**
0.0927***
0.788***
0.209*
0.122***
(0.0220)
(0.0310)
(0.195)
(0.112)
(0.0228)
High Overall Resilience Index × Tourism
−0.0492**
−0.0919***
−0.793***
−0.206*
−0.112***
(0.0220)
(0.0310)
(0.195)
(0.112)
(0.0242)
Hospital beds (per 1,000 people)
0.0366
0.0376
0.0363
0.0382
0.0315
(0.0470)
(0.0471)
(0.0469)
(0.0472)
(0.0476)
Fatality Rate (%)
0.0614
0.0607
0.0653
0.0610
0.0434
(0.0419)
(0.0418)
(0.0418)
(0.0412)
(0.0449)
Health expenditure (% of GDP)
0.146***
0.148***
0.160***
0.140***
0.151***
(0.0470)
(0.0484)
(0.0456)
(0.0501)
(0.0476)
Constant
−1.795***
−1.878***
−2.056***
−1.801***
−1.884***
(0.275)
(0.295)
(0.316)
(0.316)
(0.305)
Observations
113
113
113
113
112
R-squared
0.255
0.258
0.260
0.261
0.271
Robust standard errors in parentheses
*** p < 0.01, ** p < 0.05, * p < 0.1
Note: High Overall Resilience Index is a dummy variable taking the value one if the overall resilience index score is above mean – 1*sd.
35
Table 3. The relationship between different measures of tourism sector, economic resilience and CESI index
Dependent variable: CESI index
(1)
(2)
(3)
(4)
(5)
VARIABLES
Leisure
Tourism
Spending
Travel and
Tourism
Direct
Contribution
to GDP
Travel and
Tourism Total
Contribution to
Employment
Business
Tourism
Spending
Tourist Arrivals
High Economic Resilience
0.814***
0.842***
1.096***
0.916***
0.820***
(0.228)
(0.225)
(0.233)
(0.226)
(0.223)
Tourism
0.0263***
0.0437***
0.830***
0.192***
0.0526***
(0.00326)
(0.00672)
(0.127)
(0.0346)
(0.00572)
High Economic Resilience × Tourism
−0.0257***
−0.0428***
−0.835***
−0.189***
−0.0431***
(0.00334)
(0.00689)
(0.127)
(0.0349)
(0.00951)
Hospital beds (per 1,000 people)
0.0448
0.0457
0.0462
0.0464
0.0379
(0.0450)
(0.0450)
(0.0450)
(0.0453)
(0.0456)
Fatality Rate (%)
0.0606
0.0600
0.0604
0.0585
0.0415
(0.0424)
(0.0423)
(0.0426)
(0.0419)
(0.0453)
Health expenditure (% of GDP)
0.142***
0.142***
0.158***
0.135***
0.148***
(0.0474)
(0.0490)
(0.0463)
(0.0506)
(0.0478)
Constant
−1.781***
−1.810***
−2.157***
−1.850***
−1.829***
(0.290)
(0.295)
(0.309)
(0.308)
(0.302)
Observations
113
113
113
113
112
R-squared
0.260
0.262
0.264
0.267
0.275
Robust standard errors in parentheses
*** p < 0.01, ** p < 0.05, * p < 0.1
Note: High Economic Resilience is a dummy variable taking the value one if the economic resilience score is above mean – 1*sd.
36
Table 4. The relationship between different indicators of tourism sector, risk quality and CESI index
Dependent variable: CESI index
(1)
(2)
(3)
(4)
(5)
VARIABLES
Leisure
Tourism
Spending
Travel and
Tourism
Direct
Contribution
to GDP
Travel and
Tourism Total
Contribution to
Employment
Business
Tourism
Spending
Tourist Arrivals
High-Risk Quality
0.929***
0.985***
1.009***
1.260***
1.034***
(0.249)
(0.250)
(0.257)
(0.283)
(0.276)
Tourism
0.0509*
0.104**
1.094***
0.647***
0.123***
(0.0263)
(0.0439)
(0.235)
(0.140)
(0.0399)
High-Risk Quality × Tourism
−0.0504*
−0.104**
−1.100***
−0.645***
−0.114***
(0.0263)
(0.0439)
(0.234)
(0.140)
(0.0406)
Hospital beds (per 1,000 people)
0.0521
0.0543
0.0545
0.0548
0.0440
(0.0449)
(0.0451)
(0.0451)
(0.0452)
(0.0454)
Fatality Rate (%)
0.0529
0.0518
0.0544
0.0518
0.0348
(0.0419)
(0.0418)
(0.0419)
(0.0411)
(0.0446)
Health expenditure (% of GDP)
0.157***
0.158***
0.165***
0.156***
0.167***
(0.0459)
(0.0469)
(0.0440)
(0.0480)
(0.0471)
Constant
−1.952***
−2.017***
−2.081***
−2.295***
−2.125***
(0.309)
(0.322)
(0.332)
(0.382)
(0.360)
Observations
113
113
113
113
112
R-squared
0.268
0.273
0.274
0.287
0.287
Robust standard errors in parentheses
*** p < 0.01, ** p < 0.05, * p < 0.1
Note: High-Risk Quality is a dummy variable taking the value one if the risk quality score is above mean – 1*sd.
Table 5: Robustness check. The relationship between different measures of tourism sector, overall resilience index and CESI index
with additional control variables.
37
Dependent variable: CESI index
(1)
(2)
(3)
(4)
(5)
(6)
(7)
VARIABLES
Leisure
Tourism
Spending
Travel and
Tourism
Direct
Contributio
n to GDP
Travel and
Tourism Total
Contribution to
Employment
Business
Tourism
Spending
Tourist
Arrivals
Tourist
Arrivals as a
ratio of total
tourist arrivals
in the sample
Tourist
Arrivals as a
ratio of total
tourist arrivals
in the region
High-Risk Quality
0.574**
0.644**
0.597**
0.573**
0.646**
0.646**
0.534*
(0.261)
(0.270)
(0.277)
(0.273)
(0.264)
(0.264)
(0.272)
Tourism
0.0636***
0.112***
0.591**
0.231*
0.143***
184.9***
4.383*
(0.0184)
(0.0277)
(0.260)
(0.119)
(0.0461)
(59.46)
(2.520)
High-Risk Quality × Tourism
-0.0624***
-0.109***
-0.580**
-0.223*
-0.137***
-176.1***
-4.154*
(0.0181)
(0.0270)
(0.252)
(0.118)
(0.0414)
(53.42)
(2.284)
Hospital beds (per 1,000 people)
-0.0666
-0.0642
-0.0566
-0.0733
-0.0648
-0.0648
-0.0585
(0.0544)
(0.0544)
(0.0551)
(0.0542)
(0.0532)
(0.0532)
(0.0555)
Health expenditure (% of GDP)
0.00244
0.00788
0.0171
0.00544
0.0166
0.0166
0.0138
(0.0590)
(0.0591)
(0.0626)
(0.0588)
(0.0657)
(0.0657)
(0.0608)
Total confirmed cases
-3.81e-06
-3.95e-06*
-3.48e-06
-4.40e-06*
-3.05e-06
-3.05e-06
-3.51e-06
(2.31e-06)
(2.27e-06)
(2.32e-06)
(2.28e-06)
(2.71e-06)
(2.71e-06)
(2.26e-06)
Total confirmed deaths
7.36e-05*
7.26e-05*
7.45e-05*
6.98e-05*
6.30e-05
6.30e-05
7.40e-05*
(4.31e-05)
(4.33e-05)
(4.22e-05)
(4.13e-05)
(5.38e-05)
(5.38e-05)
(4.20e-05)
Log (population density)
-0.0398
-0.0410
-0.0365
-0.0436
-0.0316
-0.0316
-0.0312
(0.103)
(0.103)
(0.105)
(0.103)
(0.100)
(0.100)
(0.102)
Percentage of the population above 65
0.0953***
0.0937***
0.0925***
0.0957***
0.0906**
0.0906**
0.0942***
(0.0334)
(0.0334)
(0.0339)
(0.0329)
(0.0376)
(0.0376)
(0.0334)
Log (Urban population as a % of total
population)
-0.108
-0.113
-0.0897
-0.111
-0.106
-0.106
-0.0837
(0.0838)
(0.0840)
(0.0867)
(0.0781)
(0.104)
(0.104)
(0.0871)
Constant
0.747
0.747
0.338
0.820
0.535
0.535
0.292
(1.291)
(1.280)
(1.301)
(1.205)
(1.499)
(1.499)
(1.291)
Observations
113
113
113
113
112
112
112
R-squared
0.373
0.376
0.367
0.382
0.375
0.375
0.369
Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1
Note: High-Risk Quality is a dummy variable taking the value one if the risk quality score is above mean – 1*sd.
