This article focuses on the effect of Covid-19 on tourism in four Eastern African countries and two Southern African countries. Fractional integration is used to analyze the effects of shocks. Results for East Africa indicate that if we use data before Covid19, the series are mean-reverting and the degree of persistence moves from low values in Mauritius and Seychelles to the highest in Eswatini. However, including data for the Covid-19 an increase in the degree of persistence is observed, and the unit root hypothesis cannot be rejected for Eswatini, Mauritius Burundi or Seychelles. For South Africa, evidence of unit roots was found in all cases. This implies that these economies need to increase their economic diversification to reduce excessive reliance on tourism where shocks tend to persist. Only Kenya displays mean reversion and the development of innovative tourism products in Kenya can make tourism an even more important pillar of the economy.
AbbasJMubeenRIoremberPT, et al. (2021) Exploring the impact of Covid-19 on tourism: transformational potential and implications for a sustainable recovery of the travel and leisure industry. Current Research in Behavioral Sciences2: 100033.
2.
AdenstedRK (1974) On large-sample estimation for the mean of a stationary random sequence. Annals of Statistics2: 1095–1107.
3.
BakarANRosbiS (2020) Effect of coronavirus disease to tourism industry. International Journal of Advanced Engineering Research and Science7(4): 189–193.
4.
BaumTHaiNTT (2020) Hospitality, tourism, human rights and the impact of Covid-19. International Journal of Contemporary Hospitality Management32(7): 2397–2407.
5.
BimbaH (2019) Community project implementation issues in the Kingdom of Eswatini: a business management perspective. Journal of Management and Administration1: 45–68.
6.
ChihwaiP (2024) Covid-19 Impact on Tourism Performance in Africa. Singapore: Springer.
7.
DickeyDAFullerWA (1979) Distributions of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association74(366): 427–481.
8.
DieboldFXRudebuschG (1991) On the power of Dickey-Fuller tests against fractional alternatives. Economics Letters35: 155–160.
9.
DurbarryR (2004) Tourism and economic growth: the case of Mauritius. Tourism Economics10(4): 389–401.
10.
ElliotGRothenbergTJStockJH (1996) Efficient tests for an autoregressive unit root. Econometrica64: 813–836.
11.
FooLPChinMYTanKL, et al. (2021) The impact of Covid-19 on tourism industry in Malaysia. Current Issues in Tourism24(19): 2735–2739.
12.
GewekeJPorter-HudakS (1983) The estimation and application of long memory time series models. Journal of Time Series Analysis4(4): 221–238.
13.
Gil-AlanaLA (2011) Tourism in South Africa- time series persistence and the nature of the shocks: are they transitory or permanent?African Journal of Business Management5(12): 4686–4698.
14.
Gil-AlanaLAPozaC (2022) The impact of Covid-19 on the Spanish tourism sector. Tourism Economics28(3): 646–653.
15.
Gil-AlanaLARobinsonPM (1997) Testing of unit root and other nonstationary hypotheses in macroeconomic time series. Journal of Econometrics80: 241–268.
16.
Gil-AlanaLAMudidaRPerez de GraciaF (2014) Persistence, long memory and seasonality in Kenyan tourism series. Annals of Tourism Research46: 89–101.
17.
GrangerCWJ (1980) Long memory relationships and the aggregation of dynamic models. Journal of Econometrics14(2): 227–238.
18.
GrangerCWJJoyeuxR (1980) An introduction to long memory time series models and fractional differencing. Journal of Time Series Analysis1(1): 15–29.
19.
HasslerUWoltersJ (1994) On the power of unit root test against fractional alternatives. Economics Letters45: 1–5.
HyllebergSEngleRGrangerCWJ, et al. (1990) Seasonal integration and cointegration. Journal of Econometrics44(1): 215-238.
22.
International Monetary Fund (2021) Regional economic outlook: sub-Saharan Africa: Navigating a long pandemic.
23.
KutoBGrovesJ (2004) The effects of terrorism: evaluating Kenya’s tourism crisis. E-review of Tourism Research2(4): 88–95.
24.
KwiatkowskiDPhillipsPCBSchmidtP, et al. (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root?Journal of Econometrics54: 159–178.
25.
LeeC-CChenM-P (2022) The Impact of Covid-19 on the travel and leisure industry returns: some international evidence. Tourism Economics28(2): 451–472.
26.
MabundzaS (2020) Assessment of the impact of the coronavirus on the economy and labour market in Eswatini, Report for Business Eswatini.
27.
MakochekanwaA (2013) An analysis of tourism contribution to economic growth in SADC countries. Botswana Journal of Economics11(15): 42–56.
28.
MearnsK (2015) Addressing the operational challenges of community-based tourism in Swaziland. African Journal of Hospitality, Tourism and Leisure4(1): 1–13.
29.
MuchapondwaEPimhidzaiO (2011) Modelling international tourism demand for Zimbabwe. International Journal of Business and Social Science2(2): 71–81.
30.
MudidaR (2021) An Emerging Africa in the Age of Globalisation. London: Routledge.
31.
MudidaRGil-AlanaLALopezG (2023) The impact of the Covid-19 pandemic on exports in Africa: an approach based on fractional integration. Journal of African Trade10(1): 4.
32.
MuokiD (2021) Impact Assessment of Covid-19 Pandemic on the Tourism and Hospitality Industry in the EAC and Post-Recovery Strategy for the Sector. AERC Working Paper Covid-19_018. Nairobi: African Economic Research Consortium.
33.
NaudéWASaaymanA (2005) Determinants of tourist arrivals in Africa: a panel data regression analysis. Tourism Economics11(3): 365–391.
34.
NelsonCRPlosserCI (1982) Trends and random walks in macroeconomic time series. Journal of Monetary Economics10: 139–162.
35.
NgSPerronP (2001) Lag length selection and the construction of unit root tests with good size and power. Econometrica69: 1519–1554.
36.
PayneJEGil-AlanaLAMervarA (2022) Persistence in Croatian tourism: the impact of Covid-19. Tourism Economics28(6): 1676–1682.
37.
PayneJEGil-AlanaLAMervarA, et al. (2023) Tourist arrivals and overnight stays along the Croatian Adriatic Coast: changes in persistence and seasonality from the Covid-19 disruption. Tourism Economics29(6): 1679–1693.
38.
PhillipsPCBPerronP (1988) Testing for a unit root in time series regression. Biometrika75(2): 335–346.
39.
RobinsonPM (1978) Statistical inference for a random coefficient autoregressive model. Scandinavian Journal of Statistics5: 163–168.
40.
RobinsonPM (1994) Efficient tests of nonstationary hypotheses. Journal of the American Statistical Association89: 1420–1437.
SowellF (1992) Maximum likelihood estimation of stationary univariate fractionally integrated time series models. Journal of Econometrics53(1-3): 165–188.
43.
UgurNGAkbiyikA (2020) Impacts of Covid-19 on global tourism industry: a cross-regional comparison. Tourism Management Perspectives36: 100744.
44.
Vermeulen-MiltzEAClifford-HolmesJKSnowB, et al. (2022) Exploring the impacts of Covid-19 on coastal tourism to inform recovery strategies in Nelson Mandela Bay, South Africa’ system dynamics models for public health and health care. Systems10(4): 120.
45.
World Bank (2013a) Tourism in Africa: Harnessing Tourism for Growth and Improved Livelihoods. Washington, DC: World Bank.
46.
World Bank (2013b) Seychelles Tourism Sector Review: Sustaining Growth in a Successful Tourism Destination. Washington, DC: World Bank.
