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.