Does the COVID-19 pandemic affect the tourism industry in China? Evidence from extreme quantiles approach

Figures & data

Figure 1. China’s tourism revenue from 2008 to 2019.

Source: Statista 2021

Table 1. Summary of COVID-19 and tourism literature.

Author references	Country	Time	Method	Key findings
(Hoque et al., 2020)	China	Post Pandemic	Secondary data methodology (Boolean search technique)	↑COVID-19 ↓ Tourism
(McCartney, 2020)	Macao	Post Pandemic	Three Wave Analogy	↑COVID-19 ↓ Tourism ↑ Economic Consequences
(Bakar & Rosbi, 2020)	COVID-19 Top affected countries	Post Pandemic	Demand and supply curve analysis	↑COVID-19 ↓demand of traveling & Tourism
(Foo et al., 2020)	Malaysia	Post Pandemic	Statistic review	↑COVID-19 ↓ Tourism
(C. Huang et al., 2020)	China	23-01-2020 to 28-02-2020	Automated content analysis (499 Newspapers Articles)	↑COVID-19 ↓ Tourism
(Rogerson & Rogerson, 2020)	South Africa	Pre & Post Pandemic	Spatial Analysis	↑COVID-19 ↓ Tourism
(Jones & Comfort, 2020)	World	Post Pandemic	Statistic review	↑COVID-19 - ↓Tourism contribution to Sustainable development
(Pham et al., 2021)	Australia	Post Pandemic	general equilibrium modeling method	↑COVID-19 – No effect of Tourism Contribution to economic development
(Sikiru & Salisu, 2021)	USA	Pre & Post Pandemic 2016-2020	Engle–Ng test	↑COVID-19 –tourism stocks fluctuations ↑ gold prices
(Škare et al., 2021)	185 countries	Pre & Post Pandemic 1995-2019	Panel structural vector auto-regressive method (PSVAR)	↑Previous Pandemics ↓Tourism

Source: Authors’s own creation.

Table 2. Results of descriptive statistics.

Variables	Mean	Min.	Max.	Std. Dev.	J-B Stats
TOR	0.0025	0.0011	0.0038	0.1014	18.4861***
COVID	0.0041	0.0033	0.0057	1.0312	17.1015***
ER	0.0137	0.0110	0.0281	1.1138	45.2289***
OIL	0.0248	0.0115	0.0426	0.1126	22.5590***

Note: The asterisk ***, ** and * represent level of significance at 1%, 5% and 10% respectively. .

Source: Authors Estimations.

Table 3. Results of unit root test.

Variables	ADF (Level)	ADF (Δ)	ZA (Level)	Break Year	ZA (Δ)	Break Year
TOR	−1.305	−7.448***	−1.004	12\02\2020	−9.446***	06\11\2020
COVID	−0.557	−3.800***	−0.993	05\03\2020	−7.426***	19\01\2021
ER	−1.279	−4.558***	−0.477	16\06\2020	−11.331***	02\05\2020
OIL	−0.667	−6.073***	−0.599	15\03\2020	−8.220***	19\09\2020

Note: The values in the table specify statistical values of the ADF and ZA tests. The asterisk ***, **, and * represent the level of significance at 1%, 5%, and 10%, respectively.

Source: Author Estimation.

Table 4. Results of quantile autoregressive distributed lag (QARDL).

Quantiles (τ)	Constant	ECM	Long-Run Estimates			Short-Run Estimates
	α∗(τ)	ρ∗(τ)	βCOVID(τ)	βER(τ)	βOIL(τ)	φ1(τ)	ω0(τ)	λ0(τ)	θ0(τ)
0.05	0.005 (0.009)	−0.120* (-1.806)	−0.123** (-2.201)	0.331*** (2.991)	−0.206*** (-3.164)	0.570** (2.979)	−0.041** (-2.351)	0.011* (1.727)	−0.042** (-2.350)
0.10	0.012 (0.004)	−0.111* (-1.707)	−0.157** (-2.208)	0.340*** (3.000)	−0.214*** (-3.253)	0.559** (2.960)	−0.053** (-2.463)	0.016* (1.922)	−0.049* (-1.841)
0.20	0.014 (0.011)	−0.126* (-1.955)	−0.147** (-2.212)	0.311*** (2.991)	−0.211*** (-3.145)	−0.568** (2.984)	−0. 048** (-2.152)	0.025** (2.228)	−0.014 (-1.552)
0.30	0.009 (0.006)	−0.115** (-1.960)	−0.124*** (-1.607)	0.300** (2.975)	−0.219** (-2.260)	0.549** (2.969)	−0.040** (-2.049)	0.015** (2.220)	−0.008 (-0.939)
0.40	0.015 (0.010)	−0.118** (-1.974)	−0.135 (-1.200)	0.308** (2.952)	−0.122 (1.551)	0.461** (2.958)	−0.051* (-1.858)	0.021** (2.230)	−0.013 (-0.727)
0.50	0.021 (0.012)	−0.121** (-1.980)	−0.140 (-1.604)	0.318** (2.933)	−0.131 (-1.149)	0.479** (2.988)	−0.046 (-1.442)	0.029 (1.526)	−0.019 (-0.438)
0.60	0.025 (0.008)	−0.110** (-2.001)	−0.121 (-1.213)	0.310** (2.942)	−0.121 (-0.965)	0.458*** (2.992)	−0.050 (-1.340)	0.024 (1.416)	−0.029 (-0.846)
0.70	0.020 (0.013)	−0.128** (-1.990)	−0.119 (-1.621)	0.306* (1.958)	−0.113 (-0.879)	0.467*** (3.001)	−0.056 (-1.047)	0.017 (1.218)	−0.037 (-1.165)
0.80	0.025 (0.007)	−0.117** (-2.000)	−0.126*** (-2.916)	0.308* (1.957)	−0.109 (-0.658)	0.476*** (3.002)	−0.044 (-0.654)	0.023 (0.610)	−0.028 (-1.552)
0.90	0.022 (0.018)	−0.119** (-1.991)	−0.111*** (-3.222)	0.309* (1.941)	−0.117 (-0.347)	0.457*** (3.000)	−0.059 (-0.241)	0.014 (0.314)	−0.020* (-1.772)
0.95	0.027 (0.005)	−0.112** (-2.002)	−0.118*** (-3.415)	0.304* (1.954)	−0.110 (-0.162)	0.448*** (3.001)	−0.042 (-0.161)	0.010 (0.221)	−0.022* (-1.900)

Note: The table reports the quantile estimation results. The t-statistics are between brackets. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Source: Author Estimations.

Table 5. Results of the Wald test for the constancy of parameters.

Variables	Wald-statistics
ρ	9.948*** [0.000]
βCOVID	6.337*** [0.000]
βER	4.573*** [0.000]
βOIL	16.582*** [0.000]
φ1	4.918** [0.090]
ω0	3.746*** [0.000]
λ0	1.372 [0.447]
θ0	0.347 [0.999]

The p-values are between square brackets. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Source: Author Estimations.

Table 6. Granger causality in quantile test results.

Quantiles	ΔTORt ↓ ΔCOVIDt	ΔCOVIDt ↓ ΔTORt	ΔTORt ↓ ΔERt	ΔERt ↓ ΔTORt	ΔTORt ↓ ΔOILt	ΔOILt ↓ ΔTORt
[0.05-0.95]	0.000	0.000	0.000	0.000	0.000	0.000
0.05	0.000	0.000	0.000	0.000	0.000	0.000
0.10	0.000	0.000	0.000	0.000	0.000	0.000
0.20	0.000	0.000	0.000	0.000	0.000	0.000
0.30	0.000	0.000	0.000	0.000	0.000	0.000
0.40	0.000	0.000	0.000	0.000	0.000	0.000
0.50	0.000	0.000	0.000	0.000	0.000	0.000
0.60	0.000	0.000	0.000	0.000	0.000	0.000
0.70	0.000	0.000	0.000	0.000	0.000	0.000
0.80	0.000	0.000	0.000	0.000	0.000	0.000
0.90	0.000	0.000	0.000	0.000	0.000	0.000
0.95	0.000	0.000	0.000	0.000	0.000	0.000

Source: Authors Estimation.

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