Peering Through the Fog of Uncertainty: Out-of-Sample Forecasts of Post-Pandemic Tourism

Serhan Cevik

doi:10.1515/roe-2023-0110

Article

Peering Through the Fog of Uncertainty: Out-of-Sample Forecasts of Post-Pandemic Tourism

Published/Copyright: April 4, 2024

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From the journal Review of Economics Volume 75 Issue 1

Abstract

This paper uses an augmented gravity model framework to investigate the historical impact of infectious diseases on international tourism and develops an out-of-sample prediction model. Using bilateral tourism flows among 38,184 pairs of countries during the period 1995–2017, I compare the forecasting performance of alternative specifications and estimation methods. These computations confirm the statistical and economic significance of infectious-disease episodes in forecasting international tourism flows. Including infectious diseases in the model brings a significant improvement in forecast accuracy relative to the standard gravity model. The magnitude of these effects, however, is likely to be much greater in the case of COVID-19, which is a highly contagious virus that has spread fast throughout populations across the world.

Keywords: infectious diseases; tourism flows; gravity model; predictability; out-of-sample forecasting

JEL Classification: C21; C23; F10; F11; F14; F47; R12

Corresponding author: Serhan Cevik, International Monetary Fund, 700 19th Street, NW, 20431-0001, Washington, DC, USA, E-mail: scevik@imf.org

Acknowledgements

The author would like to thank the editor and an anonymous referee for helpful comments and suggestions that led to marked improvements in the paper. An earlier version of this article benefited from comments by Alice Fan, Gyowon Gwon, Axel Schimmelpfennig, and Glebs Starovoits. The views expressed in this paper are those of the author and do not necessarily represent the views of the International Monetary Fund (IMF), its Executive Board, or IMF management.

Competing interests: The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability: The data that support the findings of this study are available at the World Trade Organization and the World Health Organization and in the IMF’s International Financial Statistics and World Economic Outlook databases, and the World Bank’s World Development Indicators database.

Appendix A

Table A1:

List of countries and territories.

Afghanistan	Denmark	Liberia	Rwanda
Albania	Djibouti	Libya	Saba
Algeria	Dominica	Liechtenstein	Saint Eustatius
American Samoa	Dominican Republic	Lithuania	Saint Maarten
Andorra	Ecuador	Luxembourg	Samoa
Angola	Egypt	Macao SAR	San Marino
Anguilla	El Salvador	Madagascar	Sao Tome And Principe
Antigua And Barbuda	Equatorial Guinea	Malawi	Saudi Arabia
Argentina	Eritrea	Malaysia	Senegal
Armenia	Estonia	Maldives	Serbia
Aruba	Eswatini	Mali	Seychelles
Australia	Ethiopia	Malta	Sierra Leone
Austria	Fiji	Marshall Islands	Singapore
Azerbaijan	Finland	Martinique	Slovak Republic
Bahamas, The	France	Mauritania	Slovenia
Bahrain	French Guiana	Mauritius	Solomon Islands
Bangladesh	French Polynesia	Mexico	Somalia
Barbados	Gabon	Micronesia	South Africa
Belarus	Gambia, the	Moldova	South Sudan
Belgium	Georgia	Monaco	Spain
Belize	Germany	Mongolia	Sri Lanka
Benin	Ghana	Montenegro	St. Kitts and Nevis
Bermuda	Greece	Montserrat	St. Lucia
Bhutan	Grenada	Morocco	St. Vincent and the Grenadines
Bolivia	Guadeloupe	Mozambique	Sudan
Bonaire	Guam	Myanmar	Suriname
Bosnia And Herzegovina	Guatemala	Namibia	Sweden
Botswana	Guinea	Nauru	Switzerland
Brazil	Guinea-Bissau	Nepal	Syria
British Virgin Islands	Guyana	Netherlands	Taiwan Province of China
Brunei Darussalam	Haiti	New Caledonia	Tajikistan
Bulgaria	Honduras	New Zealand	Tanzania
Burkina Faso	Hong Kong SAR	Nicaragua	Thailand
Burundi	Hungary	Niger	Timor-Leste
Cabo Verde	Iceland	Nigeria	Togo
Cambodia	India	Niue	Tonga
Cameroon	Indonesia	North Korea	Trinidad And Tobago
Canada	Iran	North Macedonia	Tunisia
Cayman Islands	Iraq	Northern Mariana Islands	Turkey
Central African Republic	Ireland	Norway	Turkmenistan
Chad	Israel	Oman	Turks And Caicos Islands
Chile	Italy	Pakistan	Tuvalu
China	Jamaica	Palau	Uganda
Colombia	Japan	Palestine	Ukraine
Comoros	Jordan	Panama	United Arab Emirates
Congo, Republic of	Kazakhstan	Papua New Guinea	United Kingdom
Cook Islands	Kenya	Paraguay	United States
Costa Rica	Kiribati	Peru	United States Virgin Islands
Côte d’Ivoire	Korea	Philippines	Uruguay
Croatia	Kuwait	Poland	Uzbekistan
Cuba	Kyrgyz Republic	Portugal	Vanuatu
Curacao	Lao P.D.R.	Puerto Rico	Venezuela
Cyprus	Latvia	Qatar	Vietnam
Czech Republic	Lebanon	Reunion	Yemen
Democratic Republic Of The Congo	Lesotho	Romania	Zambia
		Russia	Zimbabwe

Table A2:

Panel unit root tests.

	Test-statistic
Variables
ln (tourist)	−4.477^c
ln (GDP)	−3.746^c
ln (pop)	−11.89^c
REER	−2.740^c
ln (life)	−18.162^c
ln (Ebola)	−3.294^c
ln (malaria)	−4.603^c
ln (SARS)	−3.398^c
ln (yellow fever)	−4.174^c

All unit root tests include intercept and trend. ^a, ^b, and ^c denote significance at the 10 %, 5 %, and 1 % levels, respectively.

References

Anderson, J. (1979). A theoretical foundation for the gravity equation. Am. Econ. Rev. 69: 106–116.Search in Google Scholar

Anderson, J. and van Wincoop, E. (2003). Gravity with gravitas: a solution to the border puzzle. Am. Econ. Rev. 93: 170–192, https://doi.org/10.1257/000282803321455214.Search in Google Scholar

Bergstrand, J. and Egger, P. (2007). A knowledge-and-physical-capital model of international trade, foreign direct investment, and foreign affiliates sales: developed countries. J. Int. Econ. 73: 278–308, https://doi.org/10.1016/j.jinteco.2007.03.004.Search in Google Scholar

Cevik, S. (2022). Going viral: a gravity model of infectious diseases and tourism flows. Open Econ. Rev. 33: 141–156, https://doi.org/10.1007/s11079-021-09619-5.Search in Google Scholar

Cevik, S. and Miryugin, F. (2021). Pandemics and firms: drawing lessons from history. Int. Financ. 24: 276–297, https://doi.org/10.1111/infi.12392.Search in Google Scholar

Coibon, O., Gorodnichenko, Y., and Weber, M. (2020). The cost of the COVID-19 crisis: lockdowns, macroeconomic expectations, and consumer spending, NBER Working Paper No. 27141. National Bureau of Economic Research, Cambridge, MA.10.3386/w27141Search in Google Scholar

Conte, M., Cotteriaz, P., and Mayer, T. (2022). The CEPII gravity database, CEPII Working Paper No. 2022-05. Centre d’Etudes Prospectives et d’Informations Internationales, Paris.Search in Google Scholar

Cooper, M. (2006). Japanese tourism and the SARS epidemic of 2003. J. Travel Tour. Mark. 19: 117–131, https://doi.org/10.1300/j073v19n02_10.Search in Google Scholar

Correia, S., Guimaraes, P., and Zylkin, T. (2019). “PPMLHDFE: fast possion estimation with high-dimensional fixed effects,” arXiv: 1903.01690.Search in Google Scholar

Della Corte, V., Doria, C., and Oddo, G. (2023). The impact of COVID‐19 on international tourism flows to Italy: evidence from mobile phone data. World Econ. 46: 1378–1407, https://doi.org/10.1111/twec.13380.Search in Google Scholar

Egger, P. (2000). A note on the proper econometric specification of the gravity equation. Econ. Lett. 66: 25–31, https://doi.org/10.1016/s0165-1765(99)00183-4.Search in Google Scholar

Eichenbaum, M., Rebelo, S., and Trabandt, M. (2020). The macroeconomics of epidemics, NBER Working Paper No. 26882. National Bureau of Economic Research, Cambridge, MA.10.3386/w26882Search in Google Scholar

Fornaro, L. and Wolf, M. (2020). Covid-19 coronavirus and macroeconomic policy, CEPR Discussion Paper No. 14529. Centre for Economic Policy Research, London.Search in Google Scholar

Gil-Pareja, S., Llorca-Vivero, R., and Martínez-Serrano, J. (2007). The effect of EMU on tourism. Rev. Int. Econ. 15: 302–312, https://doi.org/10.1111/j.1467-9396.2006.00620.x.Search in Google Scholar

Goretti, M., Leigh, L., Cevik, S., Kaendera, S., Muir, D., Nadeem, S., and Salinas, G. (2021). Tourism in the post-pandemic world: economic challenges and opportunities for Asia-Pacific and the western hemisphere, IMF Departmental Paper No. 21/02. International Monetary Fund, Washington, DC.10.5089/9781513561905.087Search in Google Scholar

Hassan, T., Hollander, S., van Lent, L., and Tahoun, A. (2020). Firm-level exposure to epidemic diseases: covid-19, SARS, and H1N1, NBER Working Paper No. 26971. National Bureau of Economic Research, Cambridge, MA.10.3386/w26971Search in Google Scholar

Head, K. and Ries, J. (2008). FDI as an outcome of the market for corporate control: theory and evidence. J. Int. Econ. 74: 2–20, https://doi.org/10.1016/j.jinteco.2007.04.004.Search in Google Scholar

Im, K., Pesaran, M., and Shin, Y. (2003). Testing for unit roots in heterogeneous panels. J. Econom. 115: 53–74, https://doi.org/10.1016/s0304-4076(03)00092-7.Search in Google Scholar

Kuo, H.-I., Chen, C.-C., Tseng, W.-C., Ju, L.-F., and Huang, B.-W. (2008). Assessing impacts of SARS and avian flu on international tourism demand to Asia. Tour. Manag. 29: 917–928, https://doi.org/10.1016/j.tourman.2007.10.006.Search in Google Scholar

Ludvigson, S., Ma, S., and Ng, S. (2020). COVID-19 and the macroeconomic effects of costly disasters, NBER Working Paper No. 26987. National Bureau of Economic Research, Cambridge, MA.10.3386/w26987Search in Google Scholar

Mayer, T. and Zignago, S. (2011). Notes on CEPII’s distance measures: the GeoDist database, CEPII Working Paper No. 2011-25. Centre d’Etudes Prospectives et d’Informations Internationales, Paris.Search in Google Scholar

Roselló Nadal, J. and Santana Gallego, M. (2022). Gravity models for tourism demand modeling: empirical review and outlook. J. Econ. Surv. 36: 1358–1409, https://doi.org/10.1111/joes.12502.Search in Google Scholar

Roselló, J., Santana-Gallego, M., and Awan, W. (2017). Infectious disease risk and international tourism demand. Health Policy Plan. 32: 538–548, https://doi.org/10.1093/heapol/czw177.Search in Google Scholar

Santana-Gallego, M., Ledesma-Rodríguez, F., and Pérez-Rodríguez, J. (2010). Exchange rate regimes and tourism. Tour. Econ. 16: 25–43, https://doi.org/10.5367/000000010790872015.Search in Google Scholar

Santos Silva, J. and Tenreyro, S. (2006). The log of gravity. Rev. Econ. Stat. 88: 641–658, https://doi.org/10.1162/rest.88.4.641.Search in Google Scholar

Wilder-Smith, A. (2006). The severe acute respiratory syndrome: impact on travel and tourism. Travel Med. Infect. Dis. 4: 53–60, https://doi.org/10.1016/j.tmaid.2005.04.004.Search in Google Scholar

Yang, Y., Zhang, L., Wu, L., and Li, Z. (2023). Does distance still matter? Moderating effects of distance measures on the relationship between pandemic severity and bilateral tourism demand. J. Travel Res. 62: 610–625, https://doi.org/10.1177/00472875221077978.Search in Google Scholar

Zeng, B., Carter, R.-W., and De Lacy, T. (2005). Short-term perturbations and tourism effects: the case of SARS in China. Curr. Issues Tour. 8: 306–322, https://doi.org/10.1080/13683500508668220.Search in Google Scholar

Received: 2023-11-17

Accepted: 2024-01-26

Published Online: 2024-04-04

Published in Print: 2024-04-25

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Articles in the same Issue

https://doi.org/10.1515/roe-2023-0110

Keywords for this article

infectious diseases; tourism flows; gravity model; predictability; out-of-sample forecasting