Does the size of the tourism sector influence the economic policy response to the COVID-19 pandemic?

ABSTRACT

The COVID-19 pandemic has resulted in an unprecedented slowdown of economic activity worldwide, with an especially negative impact on the tourism sector. The adoption of international travel restrictions to contain the spread of the COVID-19 outbreak has brought much of the global tourism industry to a virtual standstill. Governments have introduced a range of economic stimulus packages designed to mitigate the negative effects of the pandemic, including its impact on travel and tourism. This article investigates whether the size of the tourism sector influences the economic policy response to COVID-19 pandemic using data from 136 countries. The findings show that the larger the tourism sector, the larger the economic stimulus package introduced by governments globally. Furthermore, we find that the size of the tourism sector is positively associated with both fiscal and monetary policy responses to the pandemic. The findings suggest that countries with larger tourism sectors adopted more aggressive economic stimulus packages to mitigate the impact of COVID-19 pandemic and reinvigorate floundering economies.

KEYWORDS:

• COVID-19
• coronavirus pandemic
• tourism
• government stimulus package
• fiscal policy
• monetary policy
• tourism sector recovery

Acknowledgments

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.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Scenario planning is a strategic management tool often applied by businesses to cope with changing competitive environments. In the tourism sector, it has been used for example to understand how the war in Iraq may affect tourism in Scotland (for overview, see Yeoman et al., 2005).

2 List of countries in the sample are provided in Table B1.

3 We utilized the 5th update (May 07, 2020) of the CESI by Elgin et al. (2020) to ensure that we are capturing all economic stimulus packages that have been offered since countries adopted strict public health measures–such as restrictions on domestic movements, social gatherings, operation of businesses and factories, and international travel.

4 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.

5 Since the euro zone countries lacked the necessary margin to apply interest-rate cuts, we exclude those countries when using interest-rate cuts as a dependent variable.

6 The monetary policy index is constructed using principal component analysis (PCA), following the methodology of Elgin et al. (2020). The PCA results are presented in Table B3.

7 Table B2 in the supplementary materials provides detailed summary statistic, including the minimum, median, and maximum values of all the variables used in the analysis.

8 AIC provides a way to compare and test the goodness of fit of the different models using the same data, thus providing a means for model selection. AIC rewards model with high goodness of fit and penalizes them if they are overly complex. Using this criterion, the model with the smallest AIC is preferred.

9 We also perform a likelihood-ratio test to establish whether our tourism variables significantly improve Model (1). The null hypothesis of this test is that the addition of tourism variables in Model (1) does not significantly improve the model relative to the alternative hypothesis that the addition of tourism variables significantly improves the model. Table A4, Panel A reports the results of the test comparing Model (1) to Models (2), (3), (4), and (5) in Table 3. Based on these results, we reject the null hypothesis for all four variables and conclude that adding tourism variables to Model (1) significantly improves the model. Therefore, the tourism contribution variables are found to be a significant determinant in explaining the economic stimulus package introduced by the governments as captured by CESI.

10 We also perform a likelihood ratio test to determine whether including population over the age of 65 significantly improves our main model or not. The results of this exercise, which are presented in Table A4, Panel B, indicate that for all four tourism variables, adding population over the age of 65 does not significantly improve our main model.

Additional information

Funding

This study is supported by the United Arab Emirates University under the Start-Up grant (# 31B126).