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Determinants of China Inbound
Tourism Flows
Yang Yang
Contents
1 Background of China Inbound Tourism
2 Classical Tourism Flow Model
3 Long-run and Short-run Elascity
4 Modified Gravity Model
5 Conclusion and Suggestion
1 Background
2 Spatial Analysis
3 Basic Theory
4 Model Specification and Methodology
5 Estimation Result
2 Classical Tourism Flow Model6 Conclusion
Background
1From 229,646 in 1978 to 16,932,500 in
2004
2Influenced by political events in 1989,
Asian Financial Crisis in 1998 and SARS
in 2003
3The share of international tourist arrivals
to China represented 27.4% of Asia and
the Pacific in 2004
Background
Evolution of the Number of Inbound Tourist Arrivals
Background
Market Share of Main Origins
1984 1994 2004
Origins Tourist
arrivals
Share
(%)
countries Tourist
arrivals
Share
(%)
countries Tourist
arrivals
Shar
e
(%)
1 Japan 36.82 32.46 Japan 114.1 22.02 Japan 333.43 19.69
2 USA 21.23 18.72 USA 46.98 9.07 Korea 284.49 16.80
3 Australia 7.27 6.41 Russia 39.98 7.72 Russia 179.22 10.58
4 UK 6.22 5.48 Korea 34.03 6.57 USA 130.86 7.73
5 Philippine 4.32 3.81 Mongolia 30.12 5.81 Malaysia 74.19 4.38
6 Singapore 3.74 3.30 Singapore 23.19 4.47 Singapore 63.68 3.76
7 West
Germany
3.43 3.02 Malaysia 20.87 4.03 Mongolia 55.38 3.27
8 Canada 3.03 2.67 Philippine 18.49 3.57 Philippine 54.94 3.24
9 France 2.7 2.38 UK 16.70 3.22 Thailand 46.42 2.74
1
0
Thailand 2.63 2.32 Thailand 16.37 3.16 UK 41.81 2.47
subtotal 91.39 80.57 subtotal 360.85 69.64 subtotal 1264.42 74.67
Background
1Top ten origin countries are almost the
same, the rank changed.
2Japan is the largest origin country of
China, but the share it presented
decreased
3Asian origin countries took the place of
Western ones as main source markets
gradually
Spatial Analysis
距离 距离
距离 距离
图A 图B
图C 图D
Distance Decay Curves
Spatial Analysis
距离 距离
距离 距离
图A 图B
图C 图D
ETEZ
ETEZ ETEZ
ET
EZ
ETEZ Effects on Tourism Flows(McKercher,2003)
Spatial Analysis
• the general
pattern
• the normal model
• the lognormal
model
• Pareto model
• the square-root
exponential model
China Inbound Tourism Flows Distance Decay Curve Fit
Basic Theory
Basic
Theory
CulturalDistance
Gravity
Model
Tourism
Demand
Model
Basic Theory
where Qit is the tourism demand variable from county i to
destination at time t. Pt is the price of tourism at time t,
Pst is the price of tourism in the substitute destination at
time t and Yit is the income level of the origin country i at
time t and eit is the residual term and it is used to capture
the influence of all other factors that are not included in
the demand model.
itstittit ePYAPQ 321
Basic Theory
Qnumber of visitor arrivals, tourist expenditure, number of
visitors lodged or days of visitors stayed
YPDI, NDI, GDP, GNP, GNI in constant price
PRelative CPI
Psindividual relative CPI of competing countries, a weighted
average CPI of them
Basic Theory
The law of gravity can be rephrased to state, ‘Two tourist
areas attract trade from an intermediate (tourist
generating) point in proportion to the size (attractiveness)
of the canters and in inverse proportion to the square of
the distances from these two tourist areas to the
intermediate place.’
Tij is the number of tourists, Pi is the population of each
origin country, Aj is the attractiveness of each destination,
Dij is the distance between origin and destination.
b
ij
ji
ijD
APGT
Basic Theory
Culture, the accumulation of shared meaning, rituals,
norms, and traditions among members of a society, is
the collective programming of the mind that distinguishes
members of one society from another (Soloman, 1996)
Cultural distance (CD) measures cultural difference of
different countries.
Hofstede (1980, 2001) identified five value dimensions
that distinguish peoples from various nations
Basic Theory
Cultural Distance
(CD)
B
E
C
D
ALong-Term
Orientation
Text (LTO)
Power Distance
Index (PDI)
Individualism
(IDV)
Uncertainty
Avoidance
Index (UAI)
Masculinity
(MAS)
Basic Theory
Result of Cultural Distance
Countries PDI IDV MAS UAI LTO CD score
Australia 36 90 61 51 31 4.080
Canada 39 80 52 48 23 3.956
France 68 71 43 86 0 5.158
Germany 35 67 66 65 31 3.550
Italy 50 76 70 75 0 4.992
Japan 54 46 95 92 80 2.513
Korea 60 18 39 85 75 1.955
Malaysia 104 26 50 36 0 3.420
Netherland 38 80 14 53 44 4.700
New
Zealand
22 79 58 49 30 4.294
Philippine 94 32 64 44 19 2.310
Singapore 74 20 48 8 48 1.412
Thailand 64 20 34 64 56 1.888
UK 35 89 66 35 25 4.162
USA 40 91 62 46 29 3.992
(Data source: Personal website of Prof. Hofstede
http://www.geert-hofstede.com/hofstede_dimensions.php 2006-4-11)
Model Specification and Methodology
where Disti is the physic distance between origin country
i and China, Culi is the cultural distance between origin
country i with China, Chni is the Chinese immigrant
population of origin country i.
itmi
iiitititit
eDDChn
CulDistPSYPATF
ln...ln
lnlnlnlnlnlnln
16
54321
Model Specification and Methodology
Tourist arrival data
in China Tourism
Statistic Yearbook
Measurement of Variables
ii
chnchn
itEXCPI
EXCPIP
6
1
)(j
jjjst wEXCPIP
Model Specification and Methodology
Mainly 1980 to
2004, some from
1981 to 2004, and
others from 1992
to 2004
Australia,
Canada, France,
Germany, Italy,
Indonesia, Japan,
Korea, Philippine,
Malaysia,
Netherland, New
Zealand,
Singapore,
Thailand, UK and
USA
WDI data base.
CPI data of
China is
obtained from
euromonitor
database
China Tourism
Statistic
Yearbook
Selected
Origins PeriodData
Source
Model Specification and Methodology
better representation of
adjustment dynamics
1
2
3
4Merits of Panel Data Estimation
reducing the problem
of collinearity
providing more degrees
of freedom
the control of individual
heterogeneity
Estimation Result
Primary Estimation Result
OLS estimator Fixed Effects Random
Effects
Constant1.736 -3.446** -3.310**
lnYit
0.231 1.404** 1.382**
lnPit
0.855** -0.639** -0.618**
lnPst
4.306** 1.721** 1.761**
D1989 -0.119 -0.368** -0.364**
D1998 0.715** 0.668** 0.670**
D2003 0.292 -0.004 0.0002
D.W test 0.128 0.823 0.689
F test 51.870** 501.839** 626.695**
Adjusted R2
0.454 0.966 0.911
(** indicates significant in 0.05 level, * indicates significant at 0.1 level.)
Estimation Result
OLS estimator
Only three coefficients are significant
The sign of the own price elasticity opposite to the expected.
Adjusted R2 shows the model does not fit well
Fixed Effect and Random Effect Model
Coefficients for variables are significant with right sign.
The estimated parameters are nearly the same
Adjusted R2 shows the models are well fitted
The Durbin-Watson tests of the three models show a positive correlation in the residuals.
Estimation Result
Estimation of Panel Data Model With AR(1) Disturbance
Model 1
All Origins
Model 2
Western
Origins
Model 3
Asian
Origins
lnYit
2.564** 3.709** 2.506*
lnPit
-0.309** -1.890** -0.410**
lnPst
1.350** 1.320** 0.739**
D1989 -0.374** -0.418** -0.366**
D1998 0.294** 0.198** 0.266**
D2003 -0.311** -0.321** -0.312**
Constant -8.343** -13.886** -7.420**
AR(1) 0.861** 0.815** 0.893**
Wald tset 736.83** 519.46** 331.40**
Adjusted
R2
0.335 0.303 0.596
(** indicates significant in 0.05 level, * indicates significant at 0.1 level.)
Estimation Result
All parameters in three models are significant with
expected sign.
The significance of this parameter shows the need to
introduce an autoregressive structure for the residuals.
Income is the key determinant of China inbound tourist
flows
China suffered little shock than other competing
destination countries. The substitute price declined
acutely, but the own price did not. So the coefficient for
dummy variable of 1998 has a positive sign. !!!!!!!!!!
Estimation Result
When lagged dependent variables are included as
regressors, both the within groups (WG) and random
effects estimators are biased and inconsistent
The OLS estimator which omits the country-specific
effects is also biased if these effects are relevant.
One solution to this problem is to first difference the
model and use lags of the dependent variable as
instruments for the lagged dependent variable.
Estimation Result
GMM procedure of Arellano and Bond (1991)
This estimator (GMM-DIFF) makes use of the fact that
values of the dependent variable lagged two periods or
more are valid instruments for the lagged dependent
variable.
only the one-step results for inferences regarding the
coefficients. The two-step results were mainly used to
assess the validity of the specification
Estimation ResultArellano-Bond Dynamic Panel Estimation
All Origins Western Origins Asian Origins
First-
step
Second-
step
First-
step
Second-
step
First-
step
Second-
step
lnQit(lagged) 0.127** 0.070* 0.230** 0.145 0.030 0.283
lnYit 0.512** 0.434 -1.100** -0.532 1.118** ——
lnPit -0.223** -0.236** -0.256** -0.197* -0.218** 0.0971
lnPst -0.330** 0.361** 0.315** -0.290 0.091 2.781**
D1989 -0.410** -0.422** -0.360** -0.576 -0.440** -0.875
D1998 -0.038 -0.047** 0.022 —— -0.102 -1.305
D2003 -0.205** -0.222** -0.237** —— -0.205 -0.449**
Constant -0.101** 0.114** 0.118** 0.118** 0.110** 0.284
m1 -5.42** -2.85** -4.72** -2.36** -2.34** -0.04
m2 -0.95 -1.12 -1.88* 0.15 -0.47 -0.30
Sargan 273.54** 14.05 195.46** 7.63 140.32** 0
Wald test 294.76** 5165.97
**
194.69** 10.86* 225.91** 166.87**
(** indicates significant in 0.05 level, * indicates significant at 0.1 level.)
Estimation Result
Why shall we use Panel Data???
Basic gravity model, which is estimated with cross
section time-specified, can not capture the influence of
time-varied indicators.
The panel data gravity model could identify the influence
of both the time-varied variables and time-constant
variables with panel data GLS estimation.
Estimation Result
Panel Data Gravity Model EstimationModel 4 Model 5 Model 6 Model 7 Model 8
lnYit 2.593** 2.584** 2.600** 2.586** 2.592**
lnPit -0.304** -0.303** -0.305** -0.307** -0.307**
lnPst 1.336** 1.342** 1.333** 1.335** 1.333**
lnDisti -0.515 -0.768** -0.399
lnCuli -0.642 -1.460** -1.191 -1.793**
lnChni 0.188* 0.217** 0.168
D1989 -0.376** -0.376** -0.375** -0.375** -0.375**
D1998 0.290** 0.291** 0.290** 0.291** 0.291**
D2003 -0.312** -0.312** -0.313** -0.312** -0.312**
Constant -5.766* -4.704 -8.974** -3.613 -6.339**
AR(1) 0.861** 0.861** 0.861** 0.861** 0.861**
Chi-square
test
747.98** 749.07** 747.29** 746.52** 747.34**
Adjusted R2
0.618 0.610 0.602 0.562 0.552
(** indicates significant in 0.05 level, * indicates significant at 0.1 level.)
Conclusion
Income is the key determinant of China inbound tourism
flows. The income elasticity is about 2.6.
Own price and substitute price are also very important
determinants. The own price elasticity is about -0.3 and
the substitute price elasticity is 1.34.
Political event in 1989 and SARS in 2003 had negative
influence while Asian Financial Crisis in 1998 had a
positive one.
Conclusion
Western market is more sensitive to the change of
economic indicators for larger absolute value of income
elasticity, own price elasticity and substitute price
elasticity.
It indicates that there is habit persistence features in
western origins through dynamic model analysis.
Both panel data gravity models based on physic distance
and cultural distance are carried out with perfect data fit.