The business cycle, varying tourist behavior and forecasting performance

The business cycle, varying tourist behavior and

forecasting performanceEgon Smeral

Workshop on tourism forecasting,Potchefstroom, North West University, South Africa

September 17th, 2012

Little research effort has been done to analyze the stability of (tourism) income elasticity across the business cycle.

Mainstream approaches assume that the income effects are symmetric in each phase of the business cycle.

Contrary to the symmetry assumption there are many important reasons why income and (also price) effects may vary.

Introduction (1)

The object of the study is to present reasons and hypotheses for the existence of asymmetric income effects across the business cycles.

The research tool of the study is the application of the modified growth rate model considering varying income elasticities depending on the state of the economy.

Research focuses are the real tourism imports (expenditures for outbound travel) of five source markets ("countries"): Australia, Canada, EU-15, Japan, USA.

Introduction (2)

On the base of the estimated equations forecasts are carried out until 2015.

A comparison of the forecast results will show the potential additional forecast error using the traditional approach.

Varying income elasticities play a role in forecasting situation characterized by a recession or an economic expansion - different income effects (parameters) are relevant.

Introduction (3)

I assume that the income effects differ in magnitude between the several stages of a business cycle.

There are two stages: ◦ the combined periods of expansion, peak and

slowdown (fast growth period [FGP], the actual GDP-growth rate is bigger than the growth rate of the flexible trend),

◦ the combined periods of recession, trough and recovery (slow growth period [SGP], the actual GDP-growth rate is smaller than the growth rate of the flexible trend).

Theoretical background (1)

0 1 2 3 4 5 6 7 8 9 100.0

0.5

1.0

1.5

2.0

2.5

3.0

expansion

peak

slowdown

recession

trough

recovery

flexibletrend

Grow

th ra

te o

f the

real

GDP

time

Theoretical background (2)Stages of the business cycle

Reasons for different income elasticities of tourism imports are the intensity and time structure of substitutions/reallocations between expenditures on◦ tourism imports,◦ domestic tourism,◦ other goods and services and ◦ savings.

It also needs to be considered that substitution/ reallocations may affect more than two components.

Reaction patterns by countries may vary because of structural differences.


Reasons why income elasticities in SGPs are greater than in FGPs ◦ The "loss aversion" concept: In a slowdown period

consumers may expect a future negative income shock, but that may have little or no effect on current consumption and travel behavior.

◦ Losses might weigh heavier than comparable gains, so that consumers do not like to reduce their consumption when they expect their income to decline (Kahneman & Tversky, 1979; Tversky & Kahnemann, 1991).

◦ In a recession the negative income effect is realized and consumption in general will decline significantly – expenditures for international travel may decline stronger (in line with spending on other luxury goods).


Because of high uncertainties and financial precaution the monetary budgets are allocated to savings, travel to domestic destinations and necessary consumer goods and services.

In the recovery period, when the consumers expect an improvement of their economic situation in the near future, they may immediately raise their expenses to their former standards ("keeping up with the Joneses" asap).

In this phase of the business cycle, income elasticity could become very high, supported by a strong reallocation from other spending items and savings.

According to the loss aversion concept it is to summarize that most of the negative and positive adjustments in expenditures are realized during the slow-growth period.


Relative higher income elasticities in the fast-growth period than in the slow-growth period could appear in cases of liquidity constraints.

Even when individuals in the recovery expect an improved economic situation in the expansion period they might not be able to increase consumption and travel because of liquidity constraints.

The existence of precautionary saving because of uncertainties (unemployment) might also be a reason for postponing consumption in the expansion period.


Once economic improvement is realized in the expansion period and liquidity constraints are reduced or eliminated, consumption can be raised and income elasticity will increase supported by a decreasing saving rate.

Also in the case of habit modification through rising household indebtedness, supported by financial innovations an asymmetric adjustment of consumer expenditures in the expansion period is possible.

In the slowdown period negative expectations for the future, appearing liquidity constraints and also precautionary saving decrease their general expenditures as well as the international travel budgets.

Consequences: the negative effects in the coming recession will not affect their consumption standards too strong.


The applied tourism demand model is based on multistage budgeting.

Spending for international travel could be seen as dependent from the real income (– expressed through the real GDP -) and the prices of outbound travel in relation to the prices for domestic stays and other goods in US-$.

Outbound travel is also dependent on transportation cost, marketing expenditures, quality and attractiveness factors and consumer tastes.

But considering the latter variables is difficult as building long and consistent time series is not possible.

For latter reasons we focus here only on the major explaining factors such as incomes and relative prices (Lim, 1997; Song & Li, 2008).

Data and methodology (1)

The influence of prices on tourism import demand is expressed as the weighted sum of the consumer price indices of the source market-specific destinations.

The GDP deflator indicates the development of the price index for domestic tourism and other goods.

Dummy variables have been used in cases of data irregularities and statistical problems.

The real incomes and real imports are expressed at constant prices in US-$ and exchange rates.

Data are sourced from the IMF, the OECD and the UNWTO. The estimation period of the different econometric models

reached from the eighties until 2010 (using an annual data base).

Econometric estimates are generated by Eviews 7.2


ΔlnMRt = α1 + α2∆lnYRt*POS + α3∆lnYRt*MIN +

+ α4 Δ(lnMPRt/lnDPRt) + DUMMIES + єt

The variables POS and MIN define if the economy grows faster or slower than a flexible trend generated by an HP-filter (minimizing the variance of a time series around the trend).

The POS-variable has a value of one in every year where the economy grows faster than the flexible trend (and a value of zero in all the other years).

The MIN-variable expresses the opposite state of the economy. Symmetric income effects: α2=α3; α2, α3>0. Loss aversion: α2<α3; α2, α3>0. Liquidity constraints: α2>α3; α2, α3>0. In case of MIN=0 (1) and POS=1 (0) in every year the equation turns into

the traditional constant elasticity approach.


1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Perc

enta

ge c

hang

e fro

m p

revi

ous

year

Data and methodology (4)Development of actual real GDP compared to flexible GDP trend

for Australia.

Source: IMF, OECD and own calculations.

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

Perc

enta

ge c

hang

e fro

m p

revi

ous

year



for Canada.

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0Pe

rcen

tage

cha

nge

from

pre

viou

s ye

ar

Source: IMF, OECD and, own calculations.


for the U.S.A.

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0Pe

rcen

tage

cha

nge

from

pre

viou

s ye

ar



for Japan.

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0Pe

rcen

tage

cha

nge

from

pre

viou

s ye

ar



for the EU-15.

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0Pe

rcen

tage

cha

nge

from

pre

viou

s ye

ar



for all countries.

For Australia and the U.S.A. we found out that the residuals are correlated with their own lagged values.

Corrections: Introduction of an autoregressive error term of the order p=1, [AR (1)].

Critical for the evaluation of the results is to check if the estimated income elasticities are significant different.

The Wald test checks whether we can accept the coefficient restriction α2=α3 for Australia, EU-15 and the U.S.A. and the restrictions α1=α2 for Japan and Canada or whether we must reject the null hypothesis and have to accept that the income elasticities are variable across the business cycles.

The results of the Wald test allowed assuming that there is a high probability for Australia, EU-15, Japan and the U.S.A. that the income elasticities do not remain stable across the business cycles.

Estimation results (1)

For Canada – based on the given data set- we must accept the null hypothesis or the fact that the income elasticity remains stable across the business cycles.

The estimation results for Australia and Japan showed that income elasticities in the slow growth period are bigger than in the fast growth period.

The loss aversion concept delivers here the main arguments for different consumer behavior depending on the state of the economy.


For the EU-15 and the U.S.A the income elasticities in the fast-growth period are bigger than in the slow-growth period.

The consumers bring forward the bulk of their reduction of their consumption level from the recession to the slowdown period.

Reasons are primarily liquidity constraints and precautionary saving in the recovery and slowdown period as well as possible financial innovations in the expansion period.


∆lnMRt Australia Canada EU-15a Japan U.S.A.

Intercept –6.62(–1.55)b

–1.59(–1.99)

–2.92(–2.38)

∆lnYRtPOS 2.95(2.75)

0.92(2.88)

2.43(7.40)

2.17(3.25)

2.37(5.16)

∆lnYRtMIN 4.91(3.53)

1.25(1.86)

1.28(2.89)

3.97(4.30)

1.17(1.85)

∆ln(MPR/DPR)t–0.91

(–6.01)–1.43

(–8.15)–0.99(4.69)

–0.75(–4.56)

–0.87(–6.90)

AR (1) 0.39(1.76)

–0.39(–1,58)

DUM1980 10.70(4.08)

DUM1988 41.35(4.45)

DUM1990 26.34(4.71)

DUM1991 –18.98(–2.30)

DUM1992 12.31(4.69)

DUM2004 13.81(2.31)

DUM2006 –27.75(–3.48)

R2 adj.=0.73 R2 adj.=0.70D.W. = 1.90

R2 adj.=0.78D.W. = 1.71

R2 adj.=0.81D.W. = 2.20 R2 adj.=0.82

Estimation results (4)Econometric explanation of the changes of real tourism imports considering the different states of

country-specific business cycles

Source: IMF, OECD, UNWTO, estimations (generated by EViews 7.2).

a Excluding Austria. b T-statistics are in parentheses.

Estimation results (5)Econometric explanation of the changes of real tourism imports

∆lnMRt Australia Canada EU-15a Japan U.S.A.

Intercept-3.74

(-0.83)b-1.05

(-1.27)-2.49

(-1.86)∆lnYRt

2.70(2.22)

0.98(3.45)

2.07(6.57)

2.78(4.95)

2.02(4.53)

∆ln(MPR/DPR)t-0.83(-5.0)

-1.43(-8.25)

-1.07(-3.97)

-0.80(-4.76)

-0.96(-7.39)

AR (1) 0.30(1.85)

-0.28(-1.75)

DUM1980 9.20(3.34)

DUM1988 36.76(4.02)

DUM1990 26.37(4.78)

DUM1991 -21.32(-2.35)

DUM1992 10.83(3.93)

DUM2004 19.20(3.13)

DUM2006 -28.53(-3.46)

R2 adj.=0.67 R2 adj.=0.71D.W. = 1.99

R2 adj.=0.75D.W. = 1.60

R2 adj.=0.79D.W. = 2.13 R2 adj.=0.80

Source: IMF, OECD, UNWTO, estimations (generated by EViews 7.2).

a Excluding Austria. b T-statistics are in parentheses.

For the forecasting period until 2015 we used the GDP-values according to the forecasts of the IMF and the OECD.

Reliable forecasts of the development trends of the relative prices are not available, therefore we assumed that they stay constant in the forecasting period.

Comparing the forecasting results based on the approaches allowing varying elasticities with the constant elasticity approaches provided for almost all source market clear deviations.

In the case of Australia and Japan the constant elasticity approach overestimated the growth of the real tourism imports considerable.

For the EU-15 the overestimation based on the constant elasticity approach was low.

In case of the U.S.A. the constant elasticity approach underestimated the growth of the real tourism imports.

Forecasting results (1)

Forecasting results (2)Development trends for the real GDPs and real tourism imports

2010-2015

Average growth rates per year in percent

GDPFlexible

trend of the GDP

Tourism import

(varying elasticities)

Tourism import

(constant elasticity)

Australia 3.2 2.8 3.9 5.5

Canada 2.2 1.7 2.2 1) 2.2

EU-15 1.3 0.8 1.6 1.7

Japan 1.1 0.7 2.3 3.1

U.S.A 2.3 1.5 2.6 2.2

Total 1.8 1.2 2.0 1) 2.1

Source: IMF, OECD, own calculations. 1) Values based on the results ofthe constant elasticity approach are used.

The forecasting results above demonstrate in a clear way that using the mainstream approach leads to increased forecasting errors (when there is evidence for asymmetric income effects).

This does not mean that the approaches allowing varying elasticities are "free" from forecasting errors.

They contribute to reduce the potential forecasting errors, so that "only" errors accruing from failures to forecast the exogenous variables and to capture unexpected future events are left.

Forecasting results (3)

Based on the available data set there is empirical evidence for Australia, the EU-15, Japan and the U.S.A that the income elasticities of tourism import differ depending on the phase of the business cycle.

In the case of Canada we have to accept that the income elasticities remain stable across the business cycle.

The stability assumption is in general also essential for the price elasticities.

But, we need to consider that even when aggregate elasticities of the different business cycle periods are equal, income as well as price elasticities may be asymmetric in each individual business cycle.

Discussion and conclusions (1)

Main reasons for the possibility of varying income elasticities across the business cycles are:◦ loss aversion,◦ liquidity constraints,◦ precautionary saving,◦ increases in indebtedness (financial innovations),◦ as well as the intensity and time structure of substitution

effects between expenditures on tourism imports, domestic tourism and other goods (expenditures on necessary goods play a role).

For Australia and Japan we showed that income elasticities in the slow growth period are bigger than in the fast growth period (the loss aversion concept is dominant).


In the case of EU-15 and the U.S.A income elasticity in the fast-growth period is bigger than in the slow-growth period.

Reasons for these cases are primarily liquidity constraints and precautionary saving in the recovery and slowdown period as well as possible financial innovations in the expansion period.

To consider different income elasticities depending on the phase of the business cycle is an important factor for calculating the impacts of the business cycles and for forecasting.

It has been demonstrated that using the mainstream approach leads to increased forecasting errors(if there is evidence for asymmetric elasticities).


Further improvements of the results should be based on quarterly data as this reduces the risk of covering any asymmetric behavior of tourism demand.

The use of annual data may have clouded short term effects (Canada!) that results in different elasticities across the business cycles.

The forecast results should then be compared in a further study with the findings of the time-varying parameter model (Smeral & Song, 2012).

Further work will be done based on monthly data using leading business cycle indicators to capture better the expectations according to the prospect theory (Smeral, 2013).

Discussions and conclusions (4)

Thank you for your attention!

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The business cycle, varying tourist behavior and forecasting performance