Discussion Paper No. 532

MODELLING THE DETERMINANTS
OF INTERNATIONAL TOURISM
DEMAND TO AUSTRALIA
Christine Lim
andMichael McAleer

April 2001

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Modelling the Determinants of International Tourism Demand to Australia

Christine Lim
School of Tourism and Hotel Management
Griffith University
PMB 50 Gold Coast Mail Centre
Queensland 9726
Australia
Tel:Fax:
E-mail:+617 5552 8824
+617 5552 8507
Christine.Lim@mailbox.gu.edu.auandMichael McAleer
Department of Economics
University of Western Australia
Australia

Modelling the Determinants of International Tourism Demand to Australia

Abstract
Prior to the recent Asian currency and economic crises, tourism from Asia had rapidly become
Australia’s major tourism export industry. Tourists from Singapore, which is Australia’s fifth
major market, represented 6% of international tourist arrivals to Australia in 1996. The averageannual growth rate of tourist arrivals from Singapore of around 20% over 1990-96 far exceeded
the 10.5% average annual percentage growth rate of all tourist arrivals to Australia over the same
period (Australian Bureau of Statistics, 1997). Despite the Asian currency and economic crises in
1997-98, tourist arrivals to Australia from Singapore has continued to grow slowly. It isimperative from the tourism marketing SWOT (strengths, weaknesses, opportunities and threats)
analysis to consider the economic factors influencing international tourism demand for Australia
by Singapore, and to undertake a primary competitor analysis of New Zealand. The purpose of
the paper is to estimate the income, price and transportation cost elasticities of inbound tourism
from Singapore to Australia using seasonally unadjusted quarterly data, to determine if Australia
and New Zealand are substitute or complementary destinations for Singaporean tourists, and to
examine issues such as nonstationarity, cointegration and spurious regressions.

Keywords:

International tourism, elasticities, income, tourism prices, transportation costs,
substitute and complementary destinations.

Introduction
Australia was ranked tenth in 1996 in world travel service exports, measured in terms of travel
credits, and ranked ninth in travel surplus, in millions of U.S. dollars (International Monetary
Fund, Balance of Payments Statistics Yearbook, 1997). In terms of world market share, Australia
attracted 0.7% of international tourist arrivals and was ranked at number 30 in the world’s top
tourism destinations in 1996. Between 1990 and 1996, Australia experienced an average annual
growth rate of 23% in tourist arrivals from Asia (Australian Bureau of Statistics, 1997).

Tourists from Singapore, which is Australia’s fifth major market, represented 6% of international
tourist arrivals to Australia in 1996. The average annual growth rate of tourist arrivals fromSingapore in 1990-96 was around 20%, which had increased from an average growth rate of
12.6% in 1985-89. Singapore has the highest Gross Domestic Product per capita in South-East
Asia. Like Hong Kong, South Korea and Taiwan, it has emerged as one of the four Newly
Industrialised Economies (NIEs) of East Asia. Its impressive economic growth has taken off andhas been sustained mainly through its outward-oriented export-led and import-substitution
industrialization policies. Singapore is a small densely populated city state which ranked 6th as a
tourism spender in East Asia and the Pacific in 1997 (World Tourism Organization, 1999). In
terms of the share of Asia-Pacific destinations, Singapore resident departures to Australia wasover 11% in 1996 (Pacific Asia Travel Association, 1996). Although tourist arrivals from the restof Asia fell by about 30% due to the currency and economic crises, Singapore has remained thelargest non-Japanese Asian tourist market for Australia (Stretton and Thomas, 1998). Despite the
Asian crisis in 1997-98, tourist arrivals from Singapore continued to rise during this period, but
the growth rate fell substantially from 7.4% in 1996/97 to 3.3% in 1997/98.

1

The main reason stated on their disembarkation cards by inbound tourists for visiting Australia is
for holiday and recreation.

The majority of visitors are independent tourists who are nottravelling with any tour groups. In 1998, 46% of Singaporean visitors were between the ages of
20 and 39, and the gender balance was almost equal for this age group. Many Singaporean
tourists to Australia are repeat tourists, which is supported by surveys undertaken by the Bureauof Tourism Research (BTR): 74% of about 1,300 interviewed Singaporean tourists (aged 15 years
and above) responded that they had previously been to Australia (Bureau of Tourism Research,
1999). Invariably, Perth, the capital of Western Australia, attracts most, or roughly one-third, of
tourists from Singapore, followed closely by Sydney. This attraction to Perth can be attributed toproximity, same time zone and frequent daily flights. Until recently, there were as many as 2
flights per day between Singapore and Perth, with 3 flights on Sundays. The number of flights
between Perth and Singapore has recently increased to 3 flights per day throughout the week. Not
surprising, BTR’s survey showed that the dominant activities undertaken in Australia by tourists
are shopping and visiting friends and relatives.

Most economic variables are available at least on a quarterly basis. With this in mind, thepurpose of the paper is to use time series econometric modelling of inbound tourism based on
seasonally unadjusted quarterly data to obtain estimates of income, price and transportation costelasticities of travel to Australia by Singaporean residents. Very few published empirical tourism
studies have used quarterly data. The number of observations used in recent studies (see, forexample Bakalis et al (1994); Di Matteo and Di Matteo (1993); Kulendran (1996); Morris et al(1995); Seddighi and Shearing (1997)) ranges from 44 to 90.

2The primary purpose of this paper is to use ordinary least squares to estimate the important
influences of several economic factors on tourism demand by Singapore for Australia. The
results will be compared with the estimated coefficients obtained using the cointegration model,
which accommodates the presence of unit roots (nonstationarity) in the variables. Finally, theempirical section will evaluate the sensitivity of tourist arrivals from Singapore to changes in theeconomic variables.

Data and Methodology
Growth in international tourism is closely aligned to economic variables, which at a
microeconomic level influence the consumer’s decision to undertake overseas travel. Empiricalresearch on international tourism demand has overwhelmingly been based on aggregate timeseries data, which permits estimation of income and price elasticities on inbound tourism (seeLim, 1997). A simple origin-destination demand model for international tourism can be writtenas:
D t = f(Yt , TC t , Pt )

(1)

where
D t is a measure of travel demand at time t (t = 1, … , T);
Yt is a measure of income of the tourist-generating or origin country at time t;
TC t is a measure of transportation costs from the origin to destination country at time t;
Pt is a measure of tourism price of goods and services at time t.

3

Equation (1) is often expressed in log-linear (or logarithmic) form to capture the multiplicative
effects in the levels of the variables. Furthermore, the estimated elasticities are obtained as thecoefficients of the following equation:ln D t = α + β ln Yt + γ ln TC t + δ ln Pt + u t(2)

where β, γ and δ are elasticities, and u t is a zero mean independently and identically distributed
error term. The estimation of equation (2) requires data on tourist arrivals, income, transportation
costs and prices. In this study, aggregate tourist arrivals from Singapore to Australia are used torepresent international tourism demand by Singaporean residents. Numerous variables have beenused in previous tourism studies to represent income (see Lim, 1997). In this paper, real Gross
Domestic Product (GDP) per capita of Singapore at 1990 prices (S$ millions) is used as a proxy
for the income of tourists.

The amount of international travel demanded is also likely to depend to a significant extent on
prices. Tourism expenditures compete with other goods and services for the consumer’s budget.
Thus, any divergence between the price of goods and services in the destination country(Australia) and the domestic price of goods and services in the origin country (Singapore) islikely to have implications for the tourist industry in Australia. Transportation cost is typicallythe single most important item in the overall travel costs for a tourist.

Some methodological issues and data problems arise concerning the measurement of the tourism
price and transportation cost variables. Many past tourism studies have used the ratio of theconsumer price indices (CPI) of the destination country and the CPI of the origin country as aproxy for the tourism price variable. The choice of such a measure is debatable. It is argued in
4Lim (1997, p. 842) that: “In measuring relative price movements in the origin and destination, it
is desirable to have indices constructed using a basket of goods purchased by tourists.” However,the use of the CPI ratio as a proxy for the tourism price variable is appropriate when data on the
tourist price indices are not available.

The exchange rate has also been used to represent tourism prices in the empirical literature, sincesuch information is readily available to tourists and is generally known in advance. Alternatively,
the CPI ratio could be adjusted for differences in exchange rates between the origin anddestination currencies. The exchange rate-adjusted CPI ratio, also known as the real exchange
rate, is included here as a proxy for tourism prices. Transportation costs are represented by twovariables. Real round-trip normal coach economy class airfares from Singapore to Sydney,
published in Neutral Units of Construction (formerly known as Fare Construction Units) and in
Singaporean currency, are used as a proxy for transportation costs.
economy low apex fares, excursion fares, or discount fares are

Real return-trip coachpreferable as proxies fortransportation costs, but they are published only occasionally. A lagged dependent variable,
namely previous values of tourist arrivals, is also included to capture the simple dynamics of
tourism. The presence of a significant lagged dependent variable implies the existence of lagged
values of all the explanatory variables in the model.

Given New Zealand’s proximity to Australia, it would be useful to examine the effects of therelative price changes in New Zealand and Australia on international travel demand for Australia.
If a fall in the relative tourism prices in New Zealand reduces the demand for international
tourism demand for Australia, New Zealand could be considered as Australia’s competing (or5substitute) destination for the Singaporean tourist market. Specifically, Singapore may consider
New Zealand as a substitute overseas destination for Australia. When a fall in New Zealandrelative tourism prices increases international tourism demand for Australia, the two countries arecomplementary destinations for Singaporean tourists. It is imperative from the tourism marketing
SWOT (strengths, weaknesses, opportunities and threats) analysis to examine whether New
Zealand is Australia’s substitute or complementary destination. The inclusion of the CPI ratio
and the real exchange rate of Australia and New Zealand as proxy variables for competitive prices
is to accommodate such a possibility.

The regression analysis undertaken in this paper is based on seasonally unadjusted quarterly data.
Since the exchange rate data are available only from 1980, inbound tourism from Singapore toAustralia is examined for the period 1980(4) to 1996(4). Data for all the variables are obtained
from various statistical publications by the Australian Bureau of Statistics, Singapore Departmentof Statistics, Statistics New Zealand Infos database, IMF Balance of Payments Statistics
Yearbook, ABC World Airways Guide and OAG World Airways Guide.

Empirical Results and Analysis
The graphs of the logarithms of the variables for the period 1980(4) to 1996(4) are given in
Figures 1 and 2. Several of the series display upward trends. If income, tourism prices andtransportation costs are important factors that affect international tourism demand, economic
theory postulates that the coefficient of the income variable will be positive, and the coefficientsof tourism (or relative) prices, exchange rate, real exchange rate and transportation costs will be
negative.

Moreover, if New Zealand is a substitute foreign destination for Australia, the6coefficient of the relative price variable will be positive. The opposite is true for the coefficientof the relative price variable if New Zealand and Australia are complementary destinations fortourists from Singapore. As a model is a set of assumptions, and the variables included in themodel are well known and firmly grounded in demand theory, no further discussion of thesevariables would seem to be warranted (see Lim, 1997).
Using the EViews 3 (1997) software package to estimate a single-equation model by ordinary
least squares, the results shown in Tables 1 to 4 are obtained for twelve dynamic non-nested
variations of equation (2), as follows:ln D t = α + β ln Yt + γ ln{F1t or F2 t } + δ ln{RPt , ER t or RER t } + φ ln D t −1 + θ ln CPt + u t
where
ln D t = logarithm of short-term quarterly tourist arrivals (or demand) from Singapore to Australia
at time t;ln Yt = logarithm of Singaporean real GDP per capita at time t;ln F1t = logarithm of real round-trip coach economy airfares in Neutral Units of Construction(NUC) between Singapore and Sydney at time t;ln F2 t = logarithm of real round-trip coach economy airfares in Singapore currency between
Singapore and Sydney at time t;ln RPt = logarithm of relative prices [or CPI (Australia)/CPI (Singapore)] at time t;ln ER t = logarithm of exchange rate (Singaporean dollar per Australian dollar) at time t;
ln RER t = logarithm of real exchange rate [or CPI (Australia)/CPI (Singapore) * 1/ER] at time t;ln CPt = logarithm of competitive prices [using CPI (Australia)/CPI (New Zealand) or
CPI (Australia)/CPI (New Zealand) * 1/ER] at time t;
7

u t = independently distributed random error term, with zero mean and constant variance σ 2u attime t;α, β, γ, δ, φ, θ = parameters to be estimated;
β > 0, γ < 0 , δ < 0, 0 < φ < 1, θ > 0 (substitutes)andθ < 0 (complements)

are

the

prior

restrictions on the parameters.

In Tables 1 and 2, the CPI ratio of Australia and New Zealand is used as a proxy for competitive
prices, whereas the real exchange rate of Australia and New Zealand is included as a proxy forcompetitive prices in Tables 3 and 4. Real GDP per capita (measure of income) has a positiveand highly significant effect in all the models. Tables 1 and 2 show that all the estimatedcoefficients are significant at the 5% level for model 1. Between one and as many as four
insignificant coefficients are obtained for the other models at the 5% level. The results of model
1 and 4 in Table 1 and 2, respectively, support the view that inbound tourism is positively related
to the income of the origin country and negatively related to tourism prices. Furthermore, thesignificant negative estimate of the competitive price variable suggests that New Zealand is acomplementary destination for Australia. However, the estimated coefficients of transportation
costs and the lagged dependent variable in model 1 do not have the correct signs, even though
they are significant. In fact, none of the estimated coefficients of the lagged dependent variable
has the correct sign in any models.

The adjusted R-squared (R 2 ) values, as measures of goodness of fit, are quite high and all
exceed 0.89. Since none of the six models in Tables 1 and 2 has serial correlation in the residuals,8as indicated by the Lagrange multiplier test for serial correlation (LM(SC)), the OLS estimates are
consistent. Serial correlation is present (marginally) in models 8 and 11 of Tables 3 and 4. Thus,
models 1 and 4 have the largest number of significant estimated coefficients (with the correctsign) and no serial correlation.

As a guide to model selection, the Akaike Information Criterion (AIC) and Schwarz Bayesian
Criterion (SBC) are often used, in which case the model with the smallest AIC and SBC values ispreferred. Since model 1 has the smaller information criterion, it is considered to be the ‘best’estimated regression model to represent international tourism demand by Singapore for Australia.
If the model is re-specified by deleting the transportation cost and the lagged dependent variables,
there is a problem with serial correlation in the residuals. The serial correlation problem is also
encountered when only the lagged dependent variable is deleted from model 1. However, byomitting the transportation cost variable only, the estimated model has no serial correlation andthe estimates are quite similar to the initial results obtained for model 1, namely (with absolute tstatistics in parentheses):ln D t = −24.6 + 4.76 ln Yt - 2.12 ln RPt − 0.30 ln D t −1 − 4.11 ln CPt(7.17) (9.69)
R 2 = 0.905,(2.44)AIC = 0.233,(3.16)(3.01)

SBC = 0.400.

It is generally recognised that seasonality in tourist arrivals gives rise to distinct patterns in theseries. For instance, using seasonal dummy variables (see Lim and McAleer, 2000), the 4th
quarter tourist arrivals from Singapore tends to be high whereas the growth rates of arrivals
decline to the lowest level in the 1st quarter. In this case, the seasonal pattern is assumed to be9

constant, that is, seasonality is deterministic. However, the test of the presence of seasonal unit
roots has also been examined in Lim and McAleer (2000) using the Hylleberg, Engle, Granger
and Yoo (1990) (HEGY) test, indicating a varying and changing seasonal pattern for tourist
arrivals from Singapore to Australia. More specifically, the results suggest that quarterly tourist
arrivals from Singapore have unit roots at the zero and semi-annual frequencies but not at theannual unit frequency.

The inclusion of two seasonal dummy variables, D2t and D4t in model 1 yields the following
results (with absolute t-ratios in parentheses):ln D t = −10.1 + 1.54 ln Yt + 0.31lnF1 - 0.87 ln RPt + 0.53 ln D t −1 − 1.85 ln CPt + 0.39D 2 t + 0.90D 4 t(5.08)(5.56)

R 2 = 0.985,(2.16)AIC = -1.503,(2.28)(7.92)(3.04)(9.75)(16.5)

SBC = -1.236.

Respecification of model 1 by accommodating seasonal factors has led to a better estimatedmodel. The lagged dependent variable is significant with the correct sign, and the model has noevident serial correlation.

Tests of unit roots and cointegration for tourist arrivals from Singapore to Australia are reported
in Lim and McAleer (2001). Tests of nonstationarity using the augmented Dickey-Fuller test
provides useful information to explain the long-run relationships of these variables. Unit roots
are present in all variables and they are integrated of order one, I(1). Johansen’s maximum
likelihood procedure, which is used to estimate and test the cointegrating relations, shows that along-run relationship exists among international tourism demand, real income, real airfares (inSingapore currency) and the exchange rate for tourist arrivals from Singapore to Australia. In

10contrast to model 1 using univariate OLS estimation, the cointegrating regression of the long-run
demand for international travel to Australia by tourists from Singapore is given as follows (withabsolute t-ratios in parentheses):a = 16.5 + 1.59 y1 - 2.29rf2 - 1.27er(6.44) (3.60)(5.99)

wherea = logarithm of tourist arrivals from Singapore to Australia;
y1 = logarithm of real GDP per capita in Singaporean dollar;
rf2 = logarithm of real round trip coach economy airfares in Singaporean dollar;
er = logarithm of exchange rate.
In the cointegration model, the transportation cost variable is significant, while using theexchange rate as the proxy renders the tourism price variable significant. The competitive pricevariable was not included in the earlier study. According to the error-correction model (ECM),
changes in tourist arrivals from Singapore react in the short-run to the seasonal dummy variables
and the one-period lagged change in tourist arrivals, with all variables significant at the 5% level.

Table 5 shows that all of the diagnostic tests for the selected model 1 and the cointegration model
are insignificant, except for Chow 1 (for breakpoint) in the single equation model. Thus, Chow’s
first test rejects the null hypothesis of parameter constancy for the tourism demand single
equation model before and after the first quarter of 1990.

It is evident from model 1 that real GDP per capita of Singapore and tourism prices havesignificant positive and negative effects, respectively, on inbound tourism to Australia from11

Singapore. In order to test whether tourist arrivals from Singapore are sensitive to changes in theorigin’s income and tourism prices (namely, the income elasticity exceeds 1.0 and the tourism
price elasticity is less than –1.0), respectively, the null and alternative hypotheses to be tested are
specified as follows:
H 0 : β = 1,

H1 : β > 1 ;H 0 : δ = −1,

H 1 : δ < −1 .

The t-ratio for the null hypothesis for the income variable in model 1 is 1.95. Since the null
hypothesis of a unitary income elasticity is not rejected (marginally) at the 5% significance level,
inbound tourism from Singapore is income inelastic. In addition, the t-ratio for the tourism pricevariable in model 1 does not reject the null hypothesis at the 5% significance level (with a t-ratioof 0.34), implying that international tourism demand is price inelastic

Conclusion
Singapore has the second highest per capita GDP in Asia of US$29,610 in 1999, after Japan,
which ranked sixth in the world (World Bank, 2000 ). The affluence of Singaporean residents,
the country’s proximity to Australia, and Australia’s reputation as a safe and clean country, havemeant that Australia is one of the top overseas holiday destinations for Singaporeans. Using a
dynamic log-linear single equation model, the empirical results show that real income per capita
of Singapore and the relative (or tourism) prices of the two countries have significant influenceson inbound tourism from Singapore to Australia.

12

If international travel demand for Australia by different tourist source markets enters the cycle ofintroduction, growth, maturity and decline (also known as the product life cycle), the Singapore
market is at the growth stage of the life cycle. The empirical results suggest that Australia and
New Zealand are complementary destinations for Singaporean tourists. An important marketing
implication is the possible extension of the growth stage through joint marketing efforts by the
Tourist Commissions of both Australia and New Zealand. The two Commissions could promote
more frequent repeat tourists to Oceania and expand the current market by encouraging new
tourists to participate in the tourism product, service and experience they can offer jointly.

The findings of the paper also show that international tourism demand by Singapore for Australia
is income and price inelastic. But according to the cointegration model, the long-run real income,real airfare and exchange rate (proxy for price) effects are elastic.

This paper highlights anempirical issue involving the estimation of demand models using non-stationary data. A clear
message here is a need to distinguish between spurious and cointegrating regressions. The single
equation regression model modelled in this paper may be deceptive in suggesting that such a
relationship exists between tourist arrivals, real income and tourism prices.

Cointegration

provides a way of avoiding a deceptive inference associated with a spurious regression (see, forexample, the discussion in Kulendran, 1996 and Morley, 2000). However, it is also worthwhilebearing in mind that the number of observations available is reduced in a differenced series andtherefore a large sample size is usually required to be able to use a cointegration model sensibly.

13

Acknowledgements
The authors wish to acknowledge the financial support of the Australian Research Council. This
paper was written while the second author was visiting the Institute of Social and Economic
Research at Osaka University.

14

References

ABC World Airways Guide, Various issues. T. Skinner, London.
Australian Bureau of Statistics, 1980-1999, Various Issues of Overseas Arrivals and Departures,
Canberra.
Bakalis, S., A. Morris and K. Wilson (1994), Tourism Trends to 2000 from the Antipodes to the
Old Continent, in A.V. Seaton (ed.), Tourism, The State of the Art, New York: Wiley, 415427.
Bureau of Tourism Research (1999), 1998 International Visitor Survey, Canberra.
EViews 3 (1997), Quantitative Micro Software, California : Irvine.
Di Matteo, L. and R. Di Matteo (1993), The Determinants of Expenditures by Canadian Visitors
to the United States, Journal of Travel Research, 31, 34-42.
Hylleberg, S., R.F. Engle, C.W.J. Granger and B.S. Yoo (1990), Seasonal Integration and
Cointegration, Journal of Econometrics, 99, 215-238.
International Monetary Fund (1997), Balance of Payments Statistics Yearbook, Part 2,
Washington D.C.: IMF.
Kulendran, N. (1996), Modelling Quarterly Tourist Flows to Australia using Cointegration
Analysis, Tourism Economics, 2(3), 203-222.
Lim, C. (1997), Review of International Tourism Demand Models, Annals of Tourism Research,
24, 835-849.
Lim C. and M. McAleer (2000), A Seasonal Analysis of Asian Tourist Arrivals to Australia,
Applied Economics, 32, 499-509.

15

Lim C. and M. McAleer (2001), Cointegration Analysis of Quarterly Tourism Demand by Hong
Kong and Singapore for Australia, to appear in Applied Economics.
Morley, C. (2000), Demand Modelling Methodologies: Integration and Other Issues, Tourism
Economics, 6(1), 5-19.
Morris, A., K. Wilson and S. Bakalis (1995), Modelling Tourism Flows from Europe to Australia,
Tourism Economics, 1(2), 147-167.
OAG World Airways Guide, Various issues, London: T. Skinner.
Pacific Asia Travel Association, Various issues of Annual Statistical Report, San Francisco.
Seddighi, H.R. and D.F. Shearing (1997), The Demand for Tourism in North East England withspecial reference to Northumbria: An Empirical Analysis, Tourism Management, 18(8),499-511.
Singapore Department of Statistics, Various issues of Monthly Digest of Statistics, Singapore:
Department of Statistics.
Stretton, R. and I. Thomas (1998), Asian turmoil blows away tourist numbers faster than
predicted, Australian Financial Review, June 12, p. 12.
World Bank (2000), World Development Indicators, International Bank for Reconstruction andDevelopment, Washington D.C.: World Bank.
World Tourism Organization (1998), Yearbook of Tourism Statistics, Volume 1 (49th ed.),Madrid: World Tourism Organization.
World Tourism Organization (1999), Tourism Market Trends: East Asia and the Pacific 19891998, Madrid: World Tourism Organization.

16

Table 1
Demand Elasticities for Inbound Tourism from Singapore to Australia
ExplanatoryaVariables

Model 1Model 2Model 3

Constant-28.68(7.38)-9.00(2.11)-13.40(2.20)

Y

4.66(9.71)

3.30(9.19)

3.31(6.80)

F1

0.70(2.07)-0.38(0.85)

0.10(0.19)

RP-2.53(2.92)

_

_

ER

_

-1.39
(2.82)

_

RER

_

_

0.53
(1.05)

D t −1-0.34(3.62)-0.40(4.02)-0.35(3.40)

CP*-4.84(3.52)

0.21(0.27)

0.09(0.07)

R2
F
LM(SC)
AIC
SBC

0.912
121.76
1.407
0.194
0.394

0.911
120.62
1.504
0.202
0.403

0.901
107.06
0.670
0.310
0.511

a

All variables are in logarithms, with absolute t-statistics in parentheses.
* Using the CPI ratio of Australia and New Zealand as a proxy variable.

17

Table 2
Demand Elasticities for Inbound Tourism from Singapore to Australia
ExplanatoryaVariables

Model 4Model 5Model 6

Constant-31.27(4.99)-5.80(0.95)-7.96(1.00)

Y

4.96(9.66)

3.14(8.23)

3.11(5.82)

F2

0.63(1.28)-0.58(1.10)-0.37(0.63)

RP-2.47(2.73)

_

_

ER

_

-1.32
(3.21)

_

RER

_

_

0.75
(1.76)

D t −1-0.30(3.20)-0.41(4.15)-0.36(3.47)

CP*-4.73(3.28)

0.18(0.25)

0.69(0.56)

R2
F
LM(SC)
AIC
SBC

0.908
116.16
0.161
0.237
0.437

0.912
121.70
1.964
0.194
0.395

0.901
107.77
0.475
0.304
0.505

a

All variables are in logarithms, with absolute t-statistics in parentheses.
* Using the CPI ratio of Australia and New Zealand as a proxy variable.
18

Table 3
Demand Elasticities for Inbound Tourism from Singapore to Australia
ExplanatoryaVariables

Model 7

Model 8Model 9Constant-18.11(5.62)-9.19(2.45)-11.84(3.10)

Y

3.46(7.68)

3.25(9.21)

3.12(7.63)

F1

0.49(1.37)-0.28(0.74)

0.14(0.39)

RP-0.55(1.16)

_

_

ER

_

-1.39
(3.71)

_

RER

_

_

0.68
(2.83)

D t −1-0.31(3.05)-0.41(4.21)-0.38(3.77)

CP*

0.61(1.30)

0.54(1.46)

0.78(1.90)

R2
F
LM(SC)
AIC
SBC

0.896
101.79
0.594
0.356
0.556

0.914
125.21
3.782
0.168
0.369

0.906
114.34
1.403
0.251
0.452

a

All variables are in logarithms, with absolute t-statistics in parentheses.
* Using the real exchange rate of Australia and New Zealand as a proxy variable.
19

Table 4
Demand Elasticities for Inbound Tourism from Singapore to Australia
ExplanatoryaVariables

Model 10Model 11

Model 12Constant-17.95(3.38)-6.34(1.14)-9.61(1.69)

Y

3.69(8.61)

3.13(8.53)

3.12(7.47)

F2

0.18(0.36)-0.47(0.99)-0.13(0.28)

RP

0.50(1.04)

_

_

ER

_

-1.35
(4.02)

_

RER

_

_

0.72
(3.09)

D t −1-0.28(2.78)-0.42(4.31)-0.38(3.76)

CP*

0.51(1.08)

0.53(1.43)

0.77(1.87)

R2
F
LM(SC)
AIC
SBC

0.893
98.551
1.124
0.385
0.585

0.914
126.20
4.449
0.161
0.362

0.906
114.17
1.071
0.252
0.453

a

All variables are in logarithms, with absolute t-statistics in parentheses.
* Using the real exchange rate of Australia and New Zealand as a proxy variable.
20

Table 5
Diagnostic Test Results for Inbound Tourism from Singapore
using Single Equation and Cointegration Models

Diagnostics

Single Equation Model

Cointegration ModelFLM(SC)

1.047 (0.25)

2.02 (0.14)

F(H)1.017 (0.44)

0.79 (0.72)

LM(N)1.325 (0.52)

0.78 (0.68)

F(CHOW 1)

2.713 (0.02)

0.71 (0.74)

F(CHOW 2)

0.955 (0.55)

0.52 (0.95)

Notes:FLM(SC) and F(H) are Lagrange multiplier tests for serial correlation and heteroskedasticity,respectively; LM(N) refers to the Jarque-Bera Lagrange multiplier test for normality; F(CHOW 1) andF(CHOW 2) are Chow’s first and second tests for structural change, respectively; figures inparentheses denote probability values.

21

Figure 1
L oga rithm s of T ourist Arriva ls, Re al GDP Per Ca pita, T ransportations Costsa nd Com petitive Pric e s, Singa pore 1980-1996
12

8.4
8.2

11

8.0
10
7.8
9

7.6

8

7.4
82

84

86

88

90

92

94

96

82

84

Touris t Arrivals from Singapore

86

88

90

92

94

96

94

96

Real GDP per capita

8.0

8.5
8.4

7.8

8.3
7.6
8.2
7.4

8.1

7.2

8.0
82

84

86

88

90

92

94

96

82

Real Round-Trip Airfare in NUC

84

86

88

90

92

Real Round-Trip Airfares in Singapore Currency

0.30

0.2

0.25

0.1

0.20

0.0

0.15

-0.1

0.10

-0.2

0.05

-0.3

0.00

-0.4
82

84

86

88

90

92

94

96

82

CPI ratio of Aus tralia & New Zealand

84

86

88

90

92

94

96

Real Exchange Rate of Aus tralia & New Zealand22

Figure 2
Logarithms of Tourism Prices, Singapore, 1980-1996
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
82

84

86

88

90

92

94

96

92

94

96

92

94

96

Relative Prices

1.0
0.8
0.6
0.4
0.2
0.0
-0.2
82

84

86

88

90

Exchange Rate
0.4
0.0
-0.4
-0.8
-1.2
-1.6
82

84

86

88

90

Real Exchange Rate23