Modelling Tourism Demand in Tunisia Using Cointegration and Error Correction Models
5.4 Error Correction Models .1 Estimation of ECM
The little value of price elasticities is compliant with the idea that international tourism is more and more popular. This fact is due to the development of the orga- nized trip and packaged tours, which makes the price of holiday less and less high since tourism operators could minimize their costs through a massive selling.
Tourists from France (–0.93) and UK (–1.17) seem to be more responsive to price changes than tourists from the other countries, which confirms that French and British tourists make arbitration between tourism in their respective countries and tourism in Tunisia before holidaying. This result seems to be realistic in the French case, because it is recognized that France is the first destination in the world and it is common that French holidaymakers choose between a domestic tourism and an international one.
Our results are in line with those of the majority of tourism demand studies.
For example, Crouch (1996) found that the average of price elasticity of 77 stud- ies reviewed is –0.63 which is close to our price elasticity-average which is equal to – 0.53.
5.3.3.3 Cross Price Elasticities
The great value of cross price elasticity of British tourists (2.89) is consistent with their relative price elasticity (–1.17) and emphasizes that Spain, Morocco, Cyprus and Egypt are perceived by the British tourists as an important substitute to Tunisia. In other words, they seem to be very sensitive to a change in the rapport of tourism prices between Tunisia and these destinations. Also, there is an important substitution effect in the case of Italian market (1.25).
The little value of cross price in the case of France (0.20) illustrates the loyalty of French tourists to Tunisia. This fact can be interpreted as a natural consequence of a colonialist past and the important links between the two countries, moreover, it is known that Tunisia is one of the first “non-European” destinations of French tourists.
The value of cross price elasticity of tourism demand of Germany (0.35) may indicate that residents of this country are not very sensitive to the prices practised by Tunisia’s competing destinations since they decide to visit it. This could signify that an enhancing-price policy might encourage more French and German tourists to travel to Tunisia, ceteris paribus.
5.4 Error Correction Models
Table 5.6 Estimates of ECM (dependant variable LNTj,T,t)
Variable France Germany UK Italy
Intercept 0.108127 0.067669 0.100146 0.040102
(1.978218) (0.952493) (1.551520) (1.150373)
LNTj,T, t–1 0.351211
(2.627539)
LYj, t 2.024834 0.433345
(1.065961) (0.175635)
LYj, t–1 –2.692343 2.830348
(–1.671229) (2.308005)
LYj, t–2 3.075014
(1.941549)
LRPT/j, t –0.886663 –0.295278 –0.886793
(–2.238933) (–0.659020) (–2.971321)
LRPT/j, t–1 0.755670 0.094744
(2.012499) (0.206529)
LSPT/j, t 0.770335 0.152299 2.066152 1.204668
(2.024012) (0.395649) (2.671250) (2.921999)
D86 –0.431153
(–2.495696)
D91 –0.929216
(–5.876217)
D02 –0.612688
(–4.483379)
Uj,T,t(–1) –0.447369 –0.705395 –0.426735 –0.655204
(–3.683510) (–5.066791) (–4.051897) (–5.439289)
R2 0.622283 0.610498 0.429815 0.713801
LM (1) 0.555672 0.947091 0.921015 0.465926
RESET (1) 0.336830 0.563250 0.504185 0.317614
N◦of observations 40 39 40 40
LM (1) is the Breusch-Godfrey test for autocorrelation
RESET (1) is Ramsey’s test for the functional form of the model.
We use the “general-to-specific” approach of Hendry (1995) which consists on testing significance of variables and then deleting one by one to have a final good model with statistically significant estimators. In other words, “the least significant variable is dropped from the model and the simplified model is re-estimated. This process is repeated until the coefficients are both statistically significant and cor- rectly signed (Song et al. 2003b)”. The results of ECM estimates to tourism demand in Tunisia are shown in Table 5.6.
The Error Correction Terms (ECT) Uj,T,t(–1) are significant in all models and have expected negative signs. The ECT results show that adjustment capacity is faster in the case of Germany followed by Italy, which means that the loyalty of these tourists to Tunisia as a tourist destination is better than the fidelity of the tourists from France and the UK. The occurrence of a shock in one determined period will have a larger effect on tourism demand from French or British tourists as they present the lowest coefficients (Daniel and Ramos 2002).
The significance and the positive sign of the lagged dependent variable in the German case mean that residents from this country may exemplify a psychocentric profile to a great degree according to the Plog’s assumption. In fact, Plog (1974) argued that habits may explain the presence of a psychocentric type of tourist based on the positive relationship between current and past demand.
The values of income elasticity are still elevated in the dynamic model especially in the German and Italian models. In the case of Germany, an increase in income per capita will trigger a 3.07 increase in the number of nights spent in Tunisian hotels at the date t–2, and 2.83 at the date t–1 in the Italian case. These results differ from the long-run estimates where French tourists exhibit the greatest income elasticity. However, the highest elasticities are in accordance with assumption of Crouch (1996) who stated that “estimated income elasticity is significantly higher when income is lagged. This result implies a delay between improvements in income and an increase in international tourism”. Also, Ouerfelli (1998) called this fact as a
“catalogue effect” which means that tourists prefer to be informed on the prices of tourism services early before travelling.
Contrary to the cointegration results, relative prices have showed a negative and significant sign in the cases of France and Italy; whereas, the model of Germany (positive inconsistent elasticity) and the UK exhibit non-significant coefficients of prices.
The signs of substitute price elasticities are similar to the long-run results in the sense that French and German tourists still not sensitive to the substitute prices oppositely to the British and Italian tourists.
As far as the dummy variables are concerned, D91 and D02 are highly significant and have an important negative effect on tourism demand, respectively for French tourists (the Gulf war of 1991) and for German ones (the terrorist attack on German tourists in 2002). The dummy variable D86 (the economic recession) has also a significant coefficient in the Italian case.
To evaluate model performance, various diagnostic tests are carried out on each model including test for autocorrelation and test for functional form. All of them have passed these tests. Besides, the results of Table 5.6 show that the dynamic models fit the data well according to the R-squared values except the British model.
The LM test shows that there are no serial correlations.
5.4.2 Graphical Representation of Long-Run Residuals and Dependent Variables in First Difference Level
Figures 5.1, 5.2, 5.3 and 5.4 allow us to observe the conformity of our long-run model with the real observations. In fact, we can observe two different curves; the dotted curve of long-run residuals RESIDj and the curve of dependent variable in first difference level DLNTj. From our four figures, we notice that the two curves are relatively parallel which means that the model allows a good estimation of tourism demand in Tunisia.
–1.5 –1.0 –0.5 0.0 0.5 1.0
65 70 75 80 85 90 95 00 05
DLNTFR RESIDFR
Fig. 5.1 France model
–0.8 –0.4 0.0 0.4 0.8
65 70 75 80 85 90 95 00 05
DLNTGER RESIDGER Fig. 5.2 Germany model
Nevertheless, the periods of divergence between the two curves could be inter- preted as follow: when the dotted curve is below the other one, tourism industry in Tunisia is within an empowering phase for the considered market. On the other hand, if the dotted curve is over the dark curve, the tourism industry is in a phase of underperformance.