Evaluating the Regional Impact of a New Road on Tourism

Javier Ferri

University of Valencia

Javier Ferri. Departamento de Análisis Económico. Universitat de València. Campus dels Tarongers s.n. 46022. Valencia. Spain. Telephone: +34 963828695. Fax: +34 963828249. Email: [email protected]. Abstract

The aim of this work is to establish whether the opening of a motorway that extends along the east coast of Spain has significantly contributed to expanding hotel tourism in the Valencia Region. Some of the most important tourist destinations in Spain, both domestic and international, are located in this region, such as Benidorm, Peñíscola and Gandía. The A-7 motorway, also part of the E-15 road network, is not only a faster and safer means of road communication for tourists but also provides a new gate to Europe, connecting with other motorways. Using monthly data on nights spent in hotels by residents in Spain and abroad, an intervention analysis has been performed for the three different provinces in the Valencia Region: Valencia, Castellón and Alicante. Robustness of the results has been checked by means of three different approaches. The conclusion is that the impact on tourism of the motorway has been varied and depends on three factors: 1) the origin of the tourism; 2) the province of destination and 3) the section of the motorway in question.

Keywords: Motorway, regional impact, tourism, Spain, intervention analysis. JEL: R42, C53

Acknowledgements: I wish to thank Vicente Monfort for the data he provided for this research. Oscar Alvarez, two anonymous referees, and the editor offered helpful comments. Financial support from CICYT grant SEC2002-00266 and AUMAR is gratefully acknowledged.

2 1. INTRODUCTION

In June 1974 the first section of the new A-7 toll motorway extending along the East Coast of Spain was opened. The road was finished in March 1985. It was thought that, among other positive effects, this new road would contribute to increasing the demand for tourist facilities in all the regions involved. This paper aims to establish, using as a partial indicator the number of nights spent in hotels, to what extent the observed developments in hotel tourism in the Valencia Region have been due to the construction of this motorway. Some of the most important tourist destinations in Spain, both domestic and international, are located in the Valencia Region such as Benidorm, Peñíscola and Gandía. It is the second most visited region by domestic tourists and it enjoys a 10 per cent share of total international visitors to Spain. During the year 2000, visitors spent almost 18 million nights in hotels located in the Valencia Region.

The links between transport infrastructure investment, such as the construction of a motorway or an airport, and regional development are quite complex and depend on market factors as well as on governance factors (see figure 1 in Evans and Hutchins, 2002). Both of them will influence the direct and the indirect economic impact of the motorway. The direct impact includes the increase in output and employment generated by the construction and maintenance of the motorway. The indirect economic effects on the region are originated mainly by changes in accessibility, inward investments and tourism. The problem with measuring these indirect effects in a reliable way is the difficulty in finding empirical evidence of the way the transport infrastructure affects them. The empirical link between transport and tourism destination becomes, therefore, an important piece in any research attempting to measure how infrastructures affect the development of a region. However as Prideaux (2000) recognises, although the economic significance of transport as a factor in tourism demand has been acknowledged by a number of researchers, the literature fails to identify any specific causal relationships. Also Hakfoort et al. (2001) comment in their study to evaluate the regional impact of an airport that “there is no doubt that expansion of airport activity has an impact on the number of firms located in the area, the number of visitors to conferences, the number of tourists and so on, but in many

3 cases is hard to find a causal relationship”. In fact, one reason why different regional studies come to different conclusions is because they make different assumptions about how tourism reacts to a transport improvement.

W hether a new and better means of road communication is actually a decisive factor in boosting tourism in a region, is a question that depends on different variables. In particular, the location of the region, the size of the outbound and inbound tourism of the cities that it connects and the length of the average stay are three points which are likely to influence its impact on tourism.

The relative location of a region in the main tourist routes is important in explaining possible diversion effects on tourism. As Linneker and Spence (1992) argue, accessibility to areas which are remote from the actual road improvement may be changed. For instance, assume that using the old road the average duration of a trip of a family travelling from the south of France to Marbella is twelve hours. This family, with a non-zero probability, might think it a good idea to rest (or even spend a night) in the Valencia Region, which is approximately half way. Assume now that the time required for completing the journey is reduced by four hours when they choose the faster new road. It could be the case then that the probability of choosing to spend the night in the Valencia Region decreases. Of course the effect on that probability would also depend on the geographical origin of the tourism, but what should be stressed is that there is no clear a priori effect on the Valencia Region of those tourists in transit to other regions. Another matter is the importance of tourism in the cities the motorway connects. The Valencia Region mainly receives visitors from Madrid, who are not greatly affected by the A-7 road in reaching their destination, and visitors from abroad, who largely enter Spain by air. This aspect lessens the importance of the motorway on tourism. Finally, as Crouch (1994) finds, there exists evidence to suggest a different sensitivity of short-haul tourism and long-haul tourism to different variables, including transport costs. If traditionally the tourism an area receives is for long stays, the opening of a faster route would hardly change the decision to visit it. The average length of stay in a hotel in the Valencia Region is 5 days, the highest figure in Spain of among the non-island destinations.

4 W hat seems quite sure is that the A-7 road has captured a substantial part of the traffic previously moving through the old road towards the Valencia Region. But this in itself is not a sufficient condition to increase the demand for tourism in the region as there exists complex combinations of generation and substitution effects. The following sections focus on isolating, on an empirical basis, that part of the evolution of tourism which is due to the opening of the motorway. For that purpose an intervention analysis approach has been used. Although other econometric models could have been chosen to deal with the problem (see W itt and W itt, 1995), by focusing on particular empirical breakpoints and working with univariate models, I avoid introducing strong assumptions on the treatment of the amount of endogenous variables that influence the decision of travelling to a destination. The rest of the paper is organised as follows: section 2 introduces the methodology used, section 3 deals with the data, while the results are presented in section 4 and the conclusions in section 5.

2. METHODOLOGY

This study employs a time series analysis methodology. That means, firstly, working with observations with only a time dimension, and secondly, considering these observations to be the realisation of some stochastic process. The main objective of time series models in tourism research has traditionally been prediction (see, for instance, González and Moral (1995, 1996) and Kulendran and King, 1997), although some derivations of them can be exploited to contrast hypotheses on relevant parameters, and this latter line is the one that will be developed here. The crucial point in elaborating a time series model consists of identifying the characteristics of the stochastic process that best fit the time observations. This is the first step and it is known as identification. The stochastic process depends in turn on several key parameters that have to be estimated. Once the model has been estimated, it is necessary to check its validity before using it. One important aspect for validating consists of testing if the residuals of the model are white-noise, that is, if there is any correlation pattern among them. Once we are confident that the model estimated is

5 the one that best explains the behaviour of past observations, it can be used to forecast future behaviour or to contrast a set of hypotheses.

This work focuses on the family of linear processes and assumes that the sequence of observations in hand has been generated by an ARIMA process of the form:

d r Φ(L)∆ ∆s yt = Θ (L)ε t + µ (1)

where yt is the underlying stochastic process of the time series being studied (nights spent in hotels); εt a Gaussian white-noise innovation; L the lag operator; ∆ and ∆ s the ordinary difference and seasonal difference operators, d and r being the times we apply these differences and s the number of observations per year; µ a constant; Φ (L) the polynomial with the stationary autoregressive roots, and Θ (L) the invertible moving average polynomial. It is assumed that they can be decomposed into a product of an ordinary times a seasonal polynomials.

p s s×P Φ (L) = (1+φ1L + ...+φpL ) (1+φ1sL + ...+φPsL )

q s s×Q Θ(L) = (1+θ1L + ...+θqL ) (1+θ1sL + ...+θQsL )

Identifying an ARIMA process consists of providing values for d, r, s, q, Q, p, P and µ. In addition, a further transformation consisting of log-transforming the data is sometimes required to obtain a constant-variance process.

However, sometimes, series are subject to exogenous breaks which deserve independent consideration from the endogenous strengths that explain their performance. The opening of a motorway is a clear example of an exogenous intervention that could have effects on the dynamics of the time series of nights spent in hotels. In order to determine the nature and magnitude of these effects I will rely on an intervention analysis which Box and Tiao (1975) first applied to economic and environmental problems. In tourism research, this method has been very seldom

6 exploited, with some exceptions, such as Hultkrantz and Olsson (1997), Bonham and Gangnes (1996) and in some sense Enders et al. (1992).

In tracking for the effects of the motorway on tourism by means of an intervention analysis three different approaches have been exploited. All of them are closely related and they can be used jointly as a means of checking the robustness of the results. As has already been pointed out, the satisfactory identification of the model governing the dynamics of the time series is the most important issue in ARIMA models. However, the presence of outliers can greatly affect the process of identification leading to miss-specified models. Although time series models traditionally seem to perform better than econometric models in forecasting tourism flows (see Kulendran and W itt, 2001), scant attention has been repeatedly paid to interactions between identification and outlier detection in this field of research. In all of the approaches that follow special care has been taken both in the identification of the model and in the detection and correction of outliers. For detecting outliers and removing their effect a procedure similar to that of Chen and Liu (1993) has been used.

Approach I

The first approximation consists of identifying the model using information from the complete sample with no restriction based on a priori knowledge. This model theoretically represents the true dynamics of the time series. I then look for observations that do not match with the model, in the sense of being very far from the predicted ones. These discordant observations are called outliers and may be the consequence of an error, such as a misplaced point, but they may also represent any kind of exogenous non-repetitive event. In particular, if an observation is three times the standard error above or below a prediction, it is initially computed as an outlier.

Therefore, any impact on the series as a result of the motorway opening should translate into a type of outlier. This approach is similar, in a broad sense, to the event study method proposed by Mazzochi and Montini (2001). The problem with this approach is that the detected outliers depend very much on the model identified, which, in turn, is very sensitive to the presence of outliers. W hile the methods

7 employed in this study for outlier detection take into account the feedback between identification and outliers, this merely reduces the problem described, it does not eliminate it.

Approach II

The idea behind the standard intervention analysis is quite simple: identify the model with no intervention (i.e. from January 1966 until May 1974). Add, in an appropriate way, the exogenous change (the intervention variables), and finally contrast for possible effects using the complete sample. Only if these effects are significant can it be said that the intervention (the motorway) has changed the dynamics that would otherwise have been observed with no intervention.

Now let β’ be a row vector of unknown parameters and wt a column vector of intervention variables related to the motorway openings. The models estimated to test for the effects on tourism caused by the motorway have the following form:

zt = "'wt + yt (2)

where yt is the “noise” described in (1) specified according to the pre-intervention sample. The coefficients β’ give the direct impact on nights spent in hotels for different periods.

The danger of this approach is that it extrapolates the noise fitted with only part of the observations in the full period, an assumption easier to maintain if the intervention occurs near the end of the series.

Approach III

If the intervention does not take place near the end of the series, the specification of the model and the intervention must be carried out jointly (see Harvey, 1989). In this step, a set of intervention variables is introduced as in equation (2), but choosing the

8 model for the noise that best matches the whole sample of observations (i.e. from January 1966 until December 2000). The main problem here, however, is that the intervention variables can contaminate the process of identification.

3. DATA AND VARIABLES

The empirical analysis is based on monthly data of lodgings, measured as visitor nights spent in hotels. Hotels are defined in a broad sense to include hotels, motels and resort hotels. The data comes from the Statistics of Travellers Lodged in Hotels and Camp Sites (Estadística del Movimento de Viajeros en Establecimientos Hoteleros y Acampamentos), conducted by the National Institute for Statistics (INE) in Spain from January 1966 until December 2000. From this information six time series have been built according to the origin of the tourist (domestic or foreign) and the destination (Alicante, Castellón and Valencia). These destinations are the three provinces that make up the Valencia Region in Spain. As such I am able to study the possible impact of the motorway on six different groups of tourists. The plots representing the time behaviour of the series can be found in the Figures 1 to 6. A quick initial scan of the data reveals different trends and a marked seasonality in all the series that should be captured in the models.

[Insert Figures 1 to 6]

Let’s turn now to the way the exogenous changes have been modelled. The intervention variables can be introduced by means of two basic dummy variables: pulse and step. A pulse is suitable to deal with certain kinds of exogenous changes that eventually disappear and is defined as:

Pt=1 if t = t*

Pt=0 if t ≠ t* where t* is the moment in which the change occurs. A pulse intervention represents a transitory shock that affects the level of the time series, and can be modelled as an

9 abrupt change (the event causes an effect only during period t*) or a delayed change (the event causes a decreasing response during periods t*, t*+1, t*+2...). The former type of change is also called an additive oulier (AO) and the latter, a temporary change (TC).

A step is defined as:

Et=1 if t ≥ t*

Et=0 if t < t*

This fits to include structural or permanent changes in the model, and is also called a level shift. One major problem in the specification of an intervention model is the difficulty of determining whether a pulse or a step guides the dynamics of the intervention. As for the problem in hand, the opening of a new motorway section can be clearly considered to be a structural exogenous change because as of its opening the motorway lasts forever. Therefore, below step variables are used to model the impact on nights spent in hotels. However, the motorway was sequentially opened over a period of almost eleven years in nine sections. Defining correspondent standard step variables, as above, for such sections, creates problems related with multicollinearity, because the openings of some sections are very close in time, which worsens the quality of estimation results. To solve this problem, I create a number of step variables that are suitable to capture the cumulative effects of each different motorway section. Table 1 displays the calendar of the opening dates in the period stretching from 1974 until 1985 and the variables used.

[Insert Table 1]

The cumulative step variables are defined as dummy variables taking the value 1 during the period extending from the opening of one section and one month before the opening of the following section. Thus, for instance, the coefficient for the variable TC will capture the effects on lodgings during June 1978 and May 1979, due to the seven sections opened prior to April 1979. And the coefficient for XO

10 represents the influence of the whole motorway on nights spent, for tourists visiting between April 1986 and December 2000.

4. RESULTS

First, six ARIMA models have been fitted to the six different time series for monthly nights spent in hotels. At this stage only observations corresponding to the period prior to the opening of the first section of the motorway have been considered. The software package TRAMO-SEATS (Gómez and Maravall, 1996) has been used to fit ARIMA and intervention models to the time series. This programme takes as its starting point the “airline model” which has often been found to be appropriate for many series (see Bell and W ilcox, 1993, for a theoretical justification of using the “airline model” when modelling spending on tourism). The “airline model” takes the form:

12 ∆∆12 yt = (1 + θ1L )(1 +θ12 L )εt (3)

Taking as the starting point the “airline model”, an automatic procedure for identification based on the comparison of Bayes information criterion (BIC) for alternative models has been employed. More parsimonious models are also favoured. In the presence of outliers, the algorithm iterates between outlier detection and correction, and automatic model identification. Table 2 shows the resulting models. In all the cases there exists a seasonal unit root. Also, for all the foreigners’ series, but not for the domestic series, there is an ordinary unit root. Castellón/domestic seems to be determined by a completely seasonal process with a constant term, while the only AR(1) parameters appear for Alicante/domestic and Valencia/domestic. The ‘airline model’ only holds for the Valencia/foreigners case.

[Insert Table 2]

Table 3 displays the results from Approach I. The complete sample up to December 2000 has been used to estimate the models and detect discordant observations. No

11 intervention variable has been introduced in the model. The working assumption is that if the motorway has any effect at all, it must cause a step variation in the dynamics of the tourism variables. Therefore I look for outliers that induce a level shift affecting the period between June 1974 and December 2000. According to Table 3, the initial candidates to be influenced by the motorway are Valencia/foreigners and Alicante/domestic both in a positive way and Castellón/domestic in an ambiguous way. For the Valencia/foreigners time series, October 1976 appears to be the beginning of a level shift which coincides with the opening of the section from Silla to Xeresa. For Alicante/domestic and Castellón/domestic the origin of the level shift does not match any of the opening dates, but, perhaps, comprises some of their effects.

[Insert Table 3]

Table 4 displays the results for the estimated coefficients of intervention variables assuming that the models identified with pre-intervention observations (Table 2) prevail during the whole period. According to the results, the effects of the different openings of the motorway on nights spent in hotels for domestic tourism going to the province of Alicante have been highly significant. For instance, the impact of the motorway on nights spent in hotels during the period between June 1974 and July 1974 (PC) amounted to an increase of 26.8 per cent1. The most important influence takes place during November 1976 and May 1977 (SX) with a 102.7 per cent increase, and between June 1977 and January 1978 (AP) with a 113.3 per cent rise. It is important to notice that the ARIMA parameters are also significant, a necessary condition to validate the pre-identified model. Conversely, there is no evidence that foreign tourism travelling to the province of Alicante has been influenced by the motorway. For domestic tourism going to Castellón, there are also positive effects coinciding with the openings of sections AS, SX, AP, PT and TC, this influence varying from 14.6 per cent, for the period between August 1974 and May 1976, to 66.1 per cent for the November 1976-May 1977 period. There are also important significant effects for foreigners spending nights in Castellón hotels matching in time the periods corresponding to AP, PT and XO. However, in this case the estimated mean in the process (µ) is not significant. This outcome indicates that the underlying

12 process has changed overtime and therefore results for this time series should be treated with great care. Perhaps the most striking consequences obtained using this approach are those for the province of Valencia. For domestic tourists spending nights in Valencia, the variable SX is positive and significant, meaning an increase in tourism for the period in hand due to the motorway, but also significant and negative are the impacts due to the Ondara-Altea (OA) section and the Xeresa-Ondara (XO) section. This rather moderate negative effect can be understood to be a substitution effect as a result of the new motorway sections opened in the Alicante province. As for foreign tourists to Valencia, the results display an increasing impact beginning in November 1976 (SX) up to the present. Since April 1979, the effects of the motorway on foreign tourism for the Valencia province, have more than duplicated the number of nights spent in hotels.

[Insert Table 4]

However, as intervention variables are not close to the end of the sample, Table 5 goes over the estimated coefficients when the identification stage is worked simultaneously using the complete number of observations. As a consequence of the identified processes, time series for Alicante/domestic, Alicante/foreigners and Castellón/domestic are taken in logarithms. The rest of the time series are in levels. The results for this approach confirm the positive impact on domestic tourism going to Alicante and Castellón. It also corroborates the absence of any effect on foreign tourism travelling to Alicante. The main changes in the results are produced for the foreigners journeying to Castellón, nationals spending nights in Valencia and foreigners travelling to Valencia. For all these cases, the strong influence of the motorway found in Table 4 disappears. For Castellón/foreigners there is even a negative effect counting for the period between June 1976 and October 1976 of about 13,600 nights less a month. For the Valencia examples, although there does exist some intuition of positive effects, the related coefficients are not significant at standard levels.

[Insert Table 5]

13 Table 6 summarises the results according to the three approaches followed: a positive sign meaning a positive impact, a negative sign standing for a negative effect and a zero for no evidence of effects. Two robust results exist: that of a positive impact of the motorway on domestic tourism going to Alicante, and that of no effects on foreigners travelling to Alicante. An almost robust result has also been found: a positive influence on domestic tourism to Castellón. For the Castellón/foreigners case the three approaches give completely different outcomes. Moreover, it also seems quite possible, in the light of some approaches, that there is a positive effect on foreign tourism to Valencia and an absence of influence on domestic tourism that decide to spend the night in a hotel in the province of Valencia.

[Insert Table 6]

The results in this paper in terms of nights spent in hotels by tourists can be translated into monetary terms. To do so, it is first of all necessary to change the estimated coefficients in rates of growth using the procedure indicated in endnote 1. Then the increase in the number of tourists corresponding to the opening of each section of the motorway can be derived. Finally, the increase in total tourism expenditure can be estimated multiplying the resulting figures by the average expenditure per day. According to the survey Familitur conducted by the Institute for Tourism Studies (IET), the average expenditure for domestic tourists travelling to Valencia Region in 1999 was 5,307 pesetas (31.9 euros) per day. Let us now focus on what seems to be the two most robust results, that is, the positive impact on domestic tourism visiting the Alicante and Castellón provinces. Assuming that expenditure per day has been constant during the period considered, calculation on the basis of the results in Table 5 yields a total cumulative expenditure on tourism as a consequence of the motorway equal to 1,312 million euros which means approximately 49 million euros per year (valued in terms of 1999 currency) since construction began.

14 5. CONCLUSIONS

The results reported in this paper demonstrate how intervention analysis can facilitate the evaluation of the effects of a new motorway on tourism in a region rising a variety of conclusions. On methodological grounds, this paper stresses the need to take special care to detect and correct outliers. In particular three different approaches have been considered: a standard intervention analysis, an unrestricted outlier detection procedure and a model in which identification happens simultaneously with the intervention estimation. The outcome in terms of coefficients can be rather sensitive to the approach used, as the results show.

On empirical grounds, the main qualitative conclusion is that the impact on tourism of the motorway studied has been very varied and depends on three factors: 1) the origin of the tourism; 2) the province of destination and 3) the section of the motorway in question. In particular, the A-7 motorway has influenced from its very beginnings the domestic tourism travelling to the province of Alicante, although the varying degrees to which this has occurred depends greatly on the period considered. Also for the province of Castellón, the A-7 motorway is very probably responsible for the contribution to the increase in tourism coming from other Spanish regions. In this case, the motorway began to show a positive and significant impact as soon as the section from Silla to Xeresa was opened in October 1976. This positive outcome, however, finished once the section from Ondara to Altea was opened in April 1979. In contrast, there is no evidence that the number of nights spent in hotels in the Valencia province has significantly increased due to the motorway. Neither, would it seem, has the international tourism which the province of Valencia receives been influenced by the motorway. All these results illustrate the complex links between generation and diversion effects caused on a subset of tourist destinations by a new transport asset connecting a wider geographic area.

The indirect monetary impact of the motorway is undoubtedly of interest for the tourism sector but can also be applied in a general framework to show how additional income to a region in terms of tourist expenditures induces economic effects on other sectors. There is a variety of empirical methods which deal with this question. For

15 instance, an input-output regional model in line with the ideas introduced in Bishop et al. (2000) and with the tourism sector conveniently disaggregated could initially fit this objective. A slightly more sophisticated procedure would consist of the use of a social accounting matrix (SAM) as in Hakfoort et al. (2001) to extend the input-output model in order to incorporate the link between the value added generated by the initial injection and private consumption. The above two methods are limited in that they neglect the substitution effects in expenditures produced as a consequence of variations in prices, so a richer computable general equilibrium model deserves further attention. For this case, a SAM could be used to calibrate some of the parameters implied in the model. One application of a general equilibrium model to the effects of tourism on a small economy can be found in Adams and Parmenter (1995).

Different methods could also have been exploited to deal with the issue pursued in this paper. From a more geographic perspective one possibility is a gravity model relating the number of tourists with the population and with a measure of impedance or transportation costs from origin to destination before and after the end of the construction of the motorway (see Gordon and Edwards, 1973, or a more advanced study, not as yet applied to tourism, as in Linneker and Spence, 1991). A more traditional multivariate regression model to take account of other variables such as income, own price and substitute prices could also be suitable once a set of dummy variables for qualitative effects such as the opening the motorway has been included (see W itt and W itt, 1995). One of the more obvious extensions to the method used here is the generalization of the univariate time series approach to a multivariate context, to incorporate the influences of other factors by means of a transfer function (see Bonham and Gangnes, 1996).

From the point of view of how tourism has been measured, one last comment should be made given that the same rate of growth is unlikely to occur in each form of accommodation. So in addition to the consideration of hotels, motels, resort hotels and guest houses in this paper, the impact of the motorway on other types of accommodation such as permanent homes, summer flats and camping or caravanning sites would also deserves attention in any future analysis.

16 ENDNOTES

1. If b is the estimated coefficient of a dummy variable and V(b) is the estimated variance of b then, Kennedy (1981) shows that when the dependent variable is log-transformed the estimate of the percentage impact of the dummy variable on the variable being explained is given by: g = 100 (exp(b-V(b)/2)-1)

17

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19

Table 1. A-7 motorway opening dates

Cumulative variable Cities Km Opening PC Puzol-Castellón 56 8th June 1974 AS Amposta-Salou 66 13th August 1974 SA San Juan-Altea 45 8th June 1976 SX Silla-Xerasa 43 25th October 1976 AP Amposta-Peñíscola 46 17th June 1977 PT Peñíscola-Torreblanca 26 17th February 1978 TC Torreblanca-Castellón 35 8th June 1978 OA Ondara-Altea 35 6th April 1979 XO Xeresa-Ondara 35 28th March 1985

20 Table 2. Identification for the pre-intervention sample

Time series Identification Alicante/domestic 12 (1+φ1L )∆12 yt = (1+θ12L )εt + µ Alicante/foreigners 12 ∆∆12 yt = (1+θ12L )εt Castellón/domestic 12 ∆12 yt =(1+θ12L )εt + µ Castellón/foreigners 12 ∆∆12 yt = (1+θ1L )(1+θ12L )εt + µ Valencia/domestic 12 (1+φ1L)∆12yt = (1+θ12L )εt + µ Valencia/foreigners 12 ∆∆12 yt = (1+θ1L )(1+θ12L )εt In all cases logarithms on the original series have been taken. Sample for identification: January 1966-May 1974 Automatic model identification in the presence of outliers has been used.

21 Table 3. Unrestricted outlier detection

Time series AO1 TC1 LS1 Alicante/domestic 1967:04 (-) 1974:01 (+) 1968:06 (-)

Alicante/foreigners 1967:03 (+) 1971:11 (+) 1968:06 (-) 1975:12 (-) 1976:01 (+) 1980:01 (-) 1981:12 (+) Castellón/domestic 1968:06 (-) 1980:01 (-) 1977:02 (+) 1983:11 (-) 1978:02 (+) 1991:12 (-) 1978:11 (+) 1998:12 (+) 1989:04 (-) Castellón/foreigners 1968:06 (-) 1983:02 (+) 1980:11 (+) 1981:03 (+) 1981:11 (+) 1993:11 (+) Valencia/domestic 1969:04 (-) 1993:12 (-) Valencia/foreigners 1968:06 (-) 1978:07 (+) 1972:08 (+) 1969:04 (-) 1976:10 (+) 1979:04 (+) 1980:05 (+) 1985:06 (+) 1989:10 (+) 1993:12 (-) (1) AO: Additive outlier; TC: Temporary change; LS: Level shift

Sample for identification: January 1966-December 2000 Automatic model identification in the presence of outliers has been used

22 Table 4. Estimation of intervention coefficients with identified pre-intervention model Coefficient Alicante/ Alicante/ Castellón/ Castellón/ Valencia/ Valencia/ Domestic Foreigners Domestic Foreigners Domestic Foreigners PC 0.243* -0.076 -0.057 -0.084 0.008 0.002 (2.29) (-0.61) (-0.40) (-0.43) (0.11) (0.02) AS 0.460* -0.156 0.138* 0.086 0.063 0.003 (8.29) (-0.90) (2.28) (0.55) (1.71) (0.02) SA 0.432* -0.222 0.166 0.095 -0.053 0.029 (4.89) (-0.96) (1.60) (0.39) (-0.87) (0.16) SX 0.710* -0.381 0.512* 0.391 0.195* 0.482* (8.83) (-1.46) (5.48) (1.53) (3.46) (2.36) AP 0.761* -0.452 0.402* 0.619* 0.059 0.662* (9.53) (-1.41) (4.19) (2.00) (0.98) (2.65) PT 0.476* -0.206 0.349* 0.763* 0.068 0.699* (4.92) (-0.60) (2.93) (2.24) (0.96) (2.55) TC 0.458* -0.258 0.372* 0.625 -0.033 1.080* (5.84) (-0.65) (3.81) (1.66) (-0.53) (3.49) OA 0.525* -0.313 0.084 0.516 -0.182* 1.416* (7.57) (-0.75) (0.86) (1.22) (-2.67) (4.14) XO 0.593* -0.342 -0.006 0.875 -0.321* 1.395* (6.41) (-0.79) (-0.05) (1.95)* (-3.88) (3.87) µ 0.035* 0.080* 0.031 0.054* (7.81) (10.64) (0.30) (10.99) φ1 -0.374* -0.836* (-8.11) (-28.57) θ1 0.224* -0.681* (4.62) (-18.13) θ12 -0.752* -0.445* -0.574* -0.560* -0.393* -0.593* (-20.59) (-10.01) (-14.17) (-13.07) (-8.27) (-14.31) In all cases logarithms on the original series has been taken. Sample for identification: January 1966-May 1974 Estimations have been corrected for the presence of outliers.

23 Table 5. Simultaneous identification and estimation of intervention coefficients

Coefficient Alicante/ Alicante/ Castellón/ Castellón/ Valencia/ Valencia/ Domestic1 Foreigners1 Domestic1 Foreigners2 Domestic2 Foreigner2 PC 0.222* -0.071 -0.007 -5152 -396 765 (2.33) (-0.63) (-0.05) (-1.30) (-0.05) (0.20) AS 0.471* -0.142 0.136 -2410 5596 -3242 (6.15) (-1.07) (1.11) (-1.00) (0.83) (-0.66) SA 0.392* -0.133 0.194 -13615* -2246 -5577 (3.67) (-0.71) (1.05) (-3.37) (-0.22) (-0.83) SX 0.621* -0.098 0.482* -96 20312 -3222 (5.81) (-0.48) (2.48) (-0.03) (1.84) (-0.41) AP 0.701* -0.058 0.520* -3639 16345 842 (6.20) (-0.24) (2.33) (-0.83) (1.20) (0.09) PT 0.482* 0.163 0.932* 1632 15306 1968 (3.88) (0.62) (3.68) (0.37) (1.02) (0.19) TC 0.443* 0.098 0.636* -2521 20219 4184 (3.69) (0.33) (2.40) (-0.55) (1.19) (0.35) OA 0.422* -0.079 0.425 -1784 23036 16745 (3.49) (-0.25) (1.45) (-0.39) (1.21) (1.30) XO 0.446* -0.160 0.561 -359 22162 14415 (2.93) (-0.47) (1.77) (-0.06) (1.11) (1.07) (1) Series in logarithms (2) Series in levels

Sample for identification: January 1966-Decembre 2000 Estimations have been corrected for the presence of outliers.

24

Table 6. Robustness of the results

Coefficient Alicante/ Alicante/ Castellón/ Castellón/ Valencia/ Valencia/ Domestic Foreigners Domestic Foreigners Domestic Foreigners Approach I + 0 +, - 0 0 + Approach II + 0 + + +, - + Approach III + 0 + - 0 0

25 Figures

1200000

1000000

800000

600000

400000

200000

0 1970:01 1980:01 1990:01 2000:01

Figure 1. Number of nights a month spent at hotels by domestic tourists in the province of Alicante

1000000

800000

600000

400000

200000

0 1970:01 1980:01 1990:01 2000:01

Figure 2. Number of nights a month spent at hotels by foreign tourists in the province of Alicante

26

500000

400000

300000

200000

100000

0 1970:01 1980:01 1990:01 2000:01

Figure 3. Number of nights a month spent at hotels by domestic tourists in the province of Castellon

140000

120000

100000

80000

60000

40000

20000

0 1970:01 1980:01 1990:01 2000:01

Figure 4. Number of nights a month spent at hotels by foreign tourists in the province of Castellon

27

300000

250000

200000

150000

100000

50000

0 1970:01 1980:01 1990:01 2000:01

Figure 5. Number of nights a month spent at hoteles by domestic tourists in the province of Valencia

120000

100000

80000

60000

40000

20000

0 1970:01 1980:01 1990:01 2000:01

Figure 6. Number of nights a month spent at hotels by foreign tourists in the province of Valencia

28