Is the tourism-led growth hypothesis valid after the global economic and financial crisis? The case of Spain 1957–2014

Tourism Management 61 (2017) 96e109

Contents lists available at ScienceDirect

Tourism Management journal homepage: www.elsevier.com/locate/tourman

Is the tourism-led growth hypothesis valid after the global economic and financial crisis? The case of Spain 1957e2014
Francisco Perles-Ribes a, *, 1, Ana Bele
n Ramo
n-Rodríguez a, 1, Antonio Rubia b, 1, Jose Luis Moreno-Izquierdo a, 1 a

Department of Applied Economic Analysis, University of Alicante, Faculty of Economics and Business Sciences, Campus San Vicente del Raspeig, 03080 Alicante, Spain Department of Financial Economics and Accounting, University of Alicante, Faculty of Economics and Business Sciences, Campus San Vicente del Raspeig, 03080 Alicante, Spain b

h i g h l i g h t s
We analyze the impact of the latest economic crisis on the tourism led-growth hypothesis in Spain.
We analyze the effect of tourism growth on economic growth.
We analyze the effect of tourism growth on employment.
Autoregressive Distributed Lag Model and Toda-Yamamoto procedure are used as alternatives to the usual techniques.
Transformation of the variables used affects the results.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 July 2016 Received in revised form 9 January 2017 Accepted 9 January 2017

Since the first paper on the subject was published in 2002, the tourism-led growth hypothesis (TLGH) has constituted one of the most predominant topics in tourism literature. Spain is a leading country in the tourism industry and one where this hypothesis has been tested with several studies confirming the relationship between Spain's tourism development and its economic growth. However, the existing studies for Spain do not take into account recent turbulences such as the Global Financial and Economic Crisis and the Arab Spring uprisings that have shocked tourism markets. This paper re-examines the TLGH for the Spanish case in the light of these events in order to investigate the robustness of the relationship between tourism and economic growth. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Tourism-led growth hypothesis Spain Granger causality Bounds testing Toda-Yamamoto procedure

1. Introduction Theoretically, the tourism-led growth hypothesis (TLGH) was directly derived from the export-led growth hypothesis that

* Corresponding author. Urb. Manzanera 13-R, 03710 Calpe, Alicante, Spain. E-mail addresses: [email protected], [email protected] (J.F. Perles-Ribes),
n-Rodríguez), [email protected] (A. Rubia), luis.moreno@ [email protected] (A.B. Ramo ua.es (L. Moreno-Izquierdo). 1 The authors' research is focused on tourism services, destination competitiveness as well as the innovation and new technologies applied to the tourism sector. In these fields they have published several articles in prestigious international journals such as Tourism Management, Tourism Economics and Current Issues in Tourism as well as several monographs and book chapters. http://dx.doi.org/10.1016/j.tourman.2017.01.003 0261-5177/© 2017 Elsevier Ltd. All rights reserved.

postulates that economic growth can be generated not only by increasing the amount of labour and capital within an economy, but
s-Jime
nez, & Pulina, 2016). also by expanding exports (Brida, Corte Since the publication of the first paper on the subject in 2002, the tourism-led growth hypothesis (TLGH) has constituted one of the most predominant topics in tourism literature with a proliferation of empirical studies testing the relationship between tourism and economic development in many countries. For this reason, testing the TLGH is regarded as a key research line in tourism economics (Song, Dwyer, Li, & Cao, 2012). Pablo-Romero and Molina (2013) provide a thematic and chronological analysis of the empirical research on this subject in accordance with the methodology applied by the authors -time series, panel data and cross-sectional data e. The results reveal that

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

the relationship between tourism and growth is mostly confirmed. From a sample of 87 studies, 55 pointed to a univocal relationship between tourism and economic growth, 16 identified a bi-univocal relationship, nine indicated that the connection flowed from economic growth to tourism and, only four did not identify any relationship at all between them. However, the authors also find that the results depend on various factors, principally the country's degree of specialization in tourism and the high sensitivity of the results to the selection of the model specifications and the econometric techniques used. Another recent review conducted by Brida et al. (2016) also shows that with few exceptions, the TLGH is confirmed for the countries considered. Therefore, it can be inferred that countries can promote their tourism activity as a means to achieving economic growth. However, some authors also identify the need to further expand the validation of the TLGH not only with the use of innovative methodological approaches such as taking into account possible non-linearity between tourism and growth, but also by analyzing different types of tourism and other countries that do not specialize in tourism (Brida et al., 2016, p. 424). Spain is one of the world's most popular tourism destinations. According to the World Tourism Organization (WTO, 2015), Spain's 65 million international tourists placed it in third position in the 2014 world ranking in terms of tourist arrivals and second place in terms of tourism receipts (65.2 billion US dollars). Also, the tourism industry is fundamental for the Spanish economy, representing 10.9 per cent of the Spanish Gross Domestic Product (GDP) in 2014 and 11.9 per cent of total employment (EXCELTUR, 2014). Therefore, it is not surprising that Spain has been used by several researchers as a country for testing the TLGH. However, existing studies for Spain cover the period from 1960 to 2003 and do not take into account recent turbulences such as the Global Financial and Economic Crisis and the Arab Spring uprisings that have shocked tourism markets. In this context, and following a similar line to that proposed by Brida et al. (2016) the objective of this article is to test the TLGH for the Spanish case in the light of these events in order to determine whether the relationship between tourism and economic growth remains robust after the recent turbulent events. 2. Literature review: The tourism growth hypothesis in Spain Three articles (see Table 1) analyze the relationship between tourism and economic growth in Spain. The first is a pioneer article by Balaguer and Cantavella-Jord
a (2002) analyzing the period 1975e1997. It constitutes the first to test the TLGH and it constitutes the starting point and a reference for all the literature on the
ssubject. A second study conducted by Nowak, Sahli, and Corte
nez (2007) for the period 1960e2003 tested the TLGH but Jime also sought to prove the existence of a second channel of transmission between tourism development and economic growth via
s-Jime
nez and investment. Finally, the most recent study by Corte Pulina (2010) for the period 1964e2000 mostly updates the article by Balaguer and Cantavella-Jord
a (2002) and enriches the analysis by considering a production function, introducing variables to represent capital and human resource variables. The three articles share common features in terms of the variables and methodology used. With respect to the variables considered, all of them use the Real Gross Domestic Product (GDP) as a proxy for economic growth. The difference between the articles is that while Balaguer and Cantavella-Jord
a (2002) and Nowak et al.
s-Jime
nez and Pulina (2007) use the growth of this variable, Corte (2010) analyze the growth of real GDP per capita. As for the variables representing tourism development, all of them use interna
s-Jime
nez tional tourism receipts but as in the previous case, Corte

97

and Pulina (2010) use this variable in per capita terms. With regard to the controls, each author introduces the variables that most in
(2002) introduce the terest them. Balaguer and Cantavella-Jorda real effective exchange rate as a control for the price competitiveness of Spain Nowak et al. (2007) control for the import of inputs, industrial goods and machinery that represent capital growth.
s-Jime
nez and Pulina (2010) control for the perMeanwhile, Corte centage of GDP used for investment as a proxy for capital and the percentage of the active population who have completed secondary education as a proxy for human resource endowment. All the authors use the natural log transformation of the variables. Regarding the methodology, another common feature of the three articles is that all of them performed the Augmented Dickey and Fuller (1981) and the Phillips and Perron (1988) unit root tests to determine the order of integration of the series. They also use cointegration analysis which is based on the Johansen methodology presented in the articles by Johansen (1988, 1995) and Johansen and Juselius (1990, 1992). Meanwhile, the causality analysis is based on a bivariate Granger causality test in the Balaguer and Cantavella
(2002) article and multivariate Granger causality tests perJorda formed on a Vector Error Correction Model (VECM) in the other two articles which reveal the difference between the short and long term causality effects. Finally, with respect to the results, while Balaguer and
(2002) find that the TLGH has a unidirectional Cantavella-Jorda causality relationship between tourism development and economic growth, the other articles find a bidirectional relationship between the variables. 3. Methodology In this paper, the Toda and Yamamoto (1995) procedure is used to test for Granger causality. This procedure requires a vector autoregression (VAR) model to be set up in the levels of the data, incorporating a number of extra lags into each of the equations considered in order to conduct an asymptotic analysis and perform a standard Wald test on the first p lags (not the extra lags) of the model to test for inference. The Wald test statistics will be asymptotically chi-square distributed with p degrees of freedom, under the null hypothesis. So, rejection of the null hypothesis will imply a rejection of Granger non-causality. Therefore, a rejection supports the presence of Granger causality. 3.1. Classical unit root testing Before testing for causality, a cointegration analysis to determine the long-term relationship between tourism and growth in Spain is performed. The cointegration analysis is conducted using the Autoregressive Distributed Lag (ARDL) model and the bounds testing of Pesaran and Shin (1999) and Pesaran, Shin, and Smith (2001). This methodology is used instead of the Johansen's cointegration methodology (Johansen, 1988, 1995 and Johansen & Juselius, 1990, 1992) used in previous articles due to the different order of integration I(1) and I(0) of the considered series. The stationarity of the series is investigated using the unit root tests developed by Dickey and Fuller (1979, 1981), Ng and Perron (2001) and the Phillips and Perron (1988) and Breitung (2002) non-parametric tests. The Dickey and Fuller and Phillips and Perron tests are classical unit root tests used in almost all of the existing literature, therefore their use facilitates the comparison of the results obtained in this article with those previously obtained by other authors. To cross-check the results, the Kwiatkowski, Phillips, Schmidt and Shin test (KPSS) (1992), without structural breaks, is also performed.

98

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

Table 1 Literature review. Authors

Variables

Period and frequency

Balaguer and CantavellaJord
a (2002)

Quarterly data Natural logarithm of: from 1975 to Real Gross Domestic Product (GDP) 1997 Real Effective Exchange Rate International Tourism Earnings in real terms

Nowak et al. (2007)

Natural logarithm of: Real Gross Domestic Product (GDP) Real Tourism Exports Real Imports of inputs and manufactured items (Imports of industrial goods and machinery)


s-Jime
nez Corte and Pulina (2010)

Annual from Natural logarithm of: Real Gross Domestic Product per capita (GDP) 1960 to 2004 Investment (Capital)- percentage of GDP Percentage of active population having completed secondary education (human resources) International tourism receipts per capita

Annual from 1960 to 2003

Unit root testing

Cointegration method and result

Causality analysis and results

Augmented Dickey-Fuller Phillips-Perron testing all cases (non constant, constant and constant and trend) All series are I(1) Augmented Dickey-Fuller, PhillipsPerron (constant and trend) All series are I(1)

Johansen with trend The series are cointegrated

Bivariate Granger Tourism Economic growth (one-way)

Johansen The series are cointegrated

Multivariate Granger (VECM) Tourism Economic growth Long-term: Tourism export þ Economic growth Import of capital goods Short term: No relationship Long term: Import of capital goods þ Tourism export Economic growth Short-term: Tourism export Economic growth Capital import Economic growth Multivariate Granger (VECM) Long term and short term Tourism Economic growth Long-term: Capital þ human resources Economic growth Capital þ human resources Tourism

Augmented Dickey-Fuller, PhillipsPerron (constant and trend) All series are I(1)

Johansen The series are cointegrated

Authors' own elaboration.

The explanation of these tests can be found elsewhere. Without
n, Rubia, and Moreno going into technicalities, Perles, Ramo (2016:22) explain that the unit root test for one series using the Augmented Dickey-Fuller (ADF) tests can be expressed as follows:

Dyt ¼ u þ bt þ ayt1 þ

k X

ci Dyt1 þ εt

i¼1

where D denotes the first difference, yt is the time series being tested, t is the time trend variable, and k is the number of lags which are added to the model to ensure that residuals, εt are white noise. The model is estimated by Ordinary Least Squares (OLS) and the null hypothesis of a unit root is a ¼ 0 against the alternative a < 0. A time trend is included to allow for the possibility of a deterministic trend in the alternative hypothesis.2 The t-statistic does not have the common t-distribution to test zero null hypotheses for regression coefficients and critical values must be specifically generated. Model selection criteria (mainly BIC or AIC) are used to determine the optimal lag length or k. A non-rejection of the null hypothesis would suggest that the time series under consideration is non-stationary (Perles et al., 2016, p. 23). The Phillips-Perron (PP test) test is a non-parametric modification of the standard Dickey-Fuller test to account for the autocorrelation and heterogeneous variance in the residuals. Several experimental studies report size distortions of the Dickey and Fuller and Phillips and Perron tests. Also, it is generally perceived that these tests have low power when applied to finite

2 When testing for I(2) a trend term is not a plausible alternative. Therefore, the tests are performed with and without a constant term.

samples, especially when the alternative hypothesis is trend stationarity. This finding has motivated the undertaking of subsequent studies on new tests that outperform the Dickey and Fuller and Phillips and Perron tests in terms of finite-sample power (Choi, 2015). Ng-Perron unit root tests are also modified versions of the ADF and PP tests, resolving the size distortions in the presence of moving average errors and improving the effectiveness of these basic unit root tests (Perles et al., 2016, p. 23). On the other hand, the KPSS is a test in which the null hypothesis of stationarity is tested against a non-stationarity alternative. In particular the KPSS specification is:

yt ¼ xt þ rt þ εt where εt is the stationary process and rt is the random walk given by rt ¼ rt-1þmt with ut e iid(0,s2u ). The null hypothesis of trend stationarity is tested by estimating an equation on an intercept and trend (Perles et al., 2016, p. 23). The KPSS test statistic is given by

b ¼ T 2 LM

T X S2t s2 ðlÞ t¼1

where St is the partial sum of the deviations of the residuals from the sample mean, s2 ðlÞ is a consistent estimator of the long run variance (s2) of the regression error, l is a lag truncation parameter and w(s,l) ¼ 1-[s/(lþ1)] is an optional weighting function used to smooth the sample autocovariance function, which ensures that s2(l) is non-negative. The null hypothesis of stationarity is accepted if the value of the KPSS test is less than its critical value computed by Kwiatkowski et al (1992). It is often suggested that the KPSS test

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

can be used to confirm the results of the ADF and PP tests (Perles et al., 2016, p. 23). The KPSS test also generates size distortions and test results should be taken with caution, especially when the null hypothesis is rejected. The variance ratio of Breitung (2002) is similar to the test statistic suggested by the KPSS. It has a reasonably good finitesample size and power properties and works well under integrated GARCH errors (Choi, 2015). 3.2. Unit root testing in the presence of structural breaks This article seeks to establish the effects of the latest economic crisis and the Arab Spring on the relationship between tourism and development in the case of Spain. It is expected that these events will represent a structural break in the series analyzed. Therefore, the use of the unit root tests designed for this context is most appropriate. Perron (1989) and Hendry and Neale (1991) found that in the presence of a structural break, the standard ADF tests are biased towards the non-rejection of the null hypothesis. These findings gave rise to the development of new unit root tests that allow for structural breaks in the time series (Perles et al., 2016, p. 23). Likewise, if breaks are large enough, I(1) processes or trendstationary variables with a break in the slope may appear as I(2) processes (Campos, Ericsson, & Hendry, 1996). The proposed tests are normally modified versions of the Dickey-Fuller unit root test that include dummy variables to account for the structural breaks (Perles et al., 2016, p. 23). Lee and Strazicich (2003) propose a minimum Lagrange Multiplier (LM) unit root test that allows for one or two endogenous breaks under both the null and the alternative hypotheses (Perles et al., 2016, p. 23). Meanwhile, Carrion-i-Silvestre, Kim, and Perron (2009) offer a methodology that allows for an arbitrary number of changes (up to five) in both the level and slope of the trend function using the quasi-generalized least square detrending method advocated by Elliot, Rothenberg, and Stock (1996). To check the robustness of our results, in this paper we have tested our series using the Lee and Strazicich (2003, 2004) and Carrion-i-Silvestre et al. (2009) unit root tests. But, due to the limited sample size, we have allowed for only one or two endogenously determined structural breaks. 3.3. Autoregressive Distributed Lag (ARDL) methodology The use of the Autoregressive Distributed Lag model (ARDL) was introduced in the cointegration analysis by Pesaran and Shin (1999) and Pesaran et al. (2001) as a method to be applied when regressor variables are I(0), I(1) or mutually cointegrated. Traditional methods of estimating cointegrating relationships, such as Engle and Granger (1987) or Johansen's (1988, 1995) method, or single equation methods such as Fully Modified OLS, or Dynamic OLS either require all variables to be I(1), or require prior knowledge and specification where variables are I(0) and I(1). In non-tecnhical terms, an ARDL is a least squares regression containing lags of the dependent and explanatory variables. In an ARDL (p,q1, …, qk), p is the number of lags of the dependent variable, q1 is the number of lags of the first explanatory variable, and qk is the number of lags of the k-th explanatory variable. An ARDL model may be written as:

yt ¼ a þ

Xp

gy I¼1 i ti

þ

Xk Xqj j¼1

X i¼0 j;ti

0

bj;i þ εt

(1)

This model allows for dynamic regressors for both static and fixed ones when explanatory variables, Xj appear with no lagged

99

terms (qj ¼ 0). To determine lag lengths, common selection procedures are available and since an ARDL model can be estimated through least squares regression, standard information criteria (AIC, BIC, etc.) may be used for model selection. Pesaran and Shin also note that unlike other methods for estimating cointegrating relationships, the ARDL representation does not require the symmetry of lag lengths; each variable can have a different number of lag terms. The cointegrating regression form of an ARDL model is obtained by transforming equation (1) into differences and substituting the long-run coefficients, obtaining equation (2).

Dyt ¼ 

Xp1 i¼1

g*i Dyt1 þ

Xk Xqj 1 j¼1

i¼0

b t1 þ εt DXj;ti 0 bj;i*  ∅EC (2)

where the error-correction term (EC) is the OLS residuals series from the long-run cointegration regression.

ECt ¼ yt  a 

k X

Xj;t 0 b qj

j¼1

Using the cointegrating relationship form in equation (2), Pesaran et al. (2001) describe a methodology for testing whether the ARDL model contains a level (or long-run) relationship between the independent variable and the regressors. The Bounds tests procedure transforms equation (2) into the following representation:

Dyt ¼ 

Xp1



i¼1

Xk

g*i Dyt1 þ 0

X d j¼1 j;t1 j

Xk Xqj 1 j¼1

i¼0

DXj;t1 0 b*j;i  ryt1  a

þ εt (3)

Testing for the existence of level relationships is then simply a test of

r¼0 d1 ¼ d2 ¼ / ¼ dk ¼ 0 The test statistic based on equation (3) has a different distribution under the null hypothesis (of no level relationships), depending on whether the regressors are all I(0) or all I(1). Furthermore, in both cases the distribution is non-standard. Pesaran, Shin and Smith provide critical values for the cases where all regressors are I(0) and the cases where all regressors are I(1), and suggest using these critical values as bounds for the more typical cases where the regressors are a mixture of I(0) and I(1). 3.4. Toda and Yamamoto procedure for Granger (non-) causality testing Generally speaking, a variable X is said to Granger-cause Y if Y can be better predicted by using the histories of both X and Y than by using the history of Y alone. Vector Autoregression modeling (VAR) has been used to test for Granger causality. Testing for H0 ¼ b1 ¼ b2 ¼ … bp ¼ 0 against not H0 in the following VAR model is a test in which x does not Granger-cause y.

yt ¼ a0 þ a1 yt1 þ / þ ap ytp þ b1 xt1 þ / þ bp xt1 þ mt xt ¼ c0 þ c1 xt1 þ / þ cp xtp þ d1 yt1 þ / þ dp yt1 þ vt

100

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

Similarly, testing H0 ¼ d1 ¼ d2 ¼ … dp ¼ 0 against H0 is a test where y does not Granger-cause x. Once the order of integration of the considered series has been determined, the Toda and Yamamoto (1995) procedure is applied as follows. The maximum order of integration of the series is m. A vector autoregression (VAR) model in the levels of the data is performed determining the maximum lag-length for the variables (p) using the usual information criteria. The preferred VAR is used and m additional lags are incorporated into each of the equations. The hypothesis of non-Granger causality is tested using standard Wald tests on the first p lags (not the extra lags). The Wald test statistics will be asymptotically chi-square distributed with p degrees of freedom, under the null hypothesis. So, the rejection of the null hypothesis will imply a rejection of Granger non-causality. That is, a rejection supports the presence of Granger causality. This procedure has several advantages. First, it is useful when a mix of I(0) or I(1) or other possible combinations of series are found. Second, the procedure can be applied when all variables are I(1) regardless of whether they are cointegrated or not. Third, normality is not required because the whole procedure relies on asymptotic properties. Finally, structural breaks can be accommodated by using dummy variables such as exogenous regressors. These reasons justify the use of this procedure in the following section. 4. Data and results Table 2 displays the variables and data used for the analysis. These data come from several sources. The data for tourism demand have been obtained from Tena (2005) which reproduces data from Spanish official sources for the period 1957e2000. Meanwhile, the data for the period 2001e2015 have been obtained from ~ a. The the Instituto de Estudios Turísticos and the Banco de Espan gross domestic product (GDP) series has been drawn from Maluquer de Motes (2009:35e40) which provides a homogenous series of the Spanish GDP for the period 1850e2000. GDP data for the period 2001e2014 have been obtained from the Spanish National Accounts compiled by the INE (Spanish Statistics Office) and are backward linked with the previous data using values of year 2000. Data for gross value added (GVA) and employment have been drawn from De la Fuente (2015), who constructs homogeneous series of several value added and employment aggregates at current and constant prices for Spain covering the period 1955e2014. The series are constructed by splicing together different bases of the (yearly and quarterly) National Accounts with those of the Labor Force Survey. Finally, data for the exchange rate between the Spanish peseta and the US dollar and sterling pound have been obtained from Martín and Pons (2005:703e704) who provide data for the period 1821e1988. The values up to 2014 are obtained from the OECD database using bilateral exchange rates between the Euro, the US dollar and the sterling pound. De la Fuente (2015) not only provides the GVA series in both nominal and real (constant prices) terms but also a GVA deflator. To ensure consistency in the treatment of the real variables, in this

paper we have also used the De la Fuente (2015) GVA deflator to construct real series for GDP and tourism demand. The use of this GVA deflator to construct real variables differentiates this paper from previous ones. It should be noted that none of the articles reviewed clearly explain the treatment given to the real variables, obliging readers to consult the source provided by
(2002) use the real GDP the authors. Balaguer and Cantavella-Jorda provided by the International Financial Statistics issued by the International Monetary Fund which seems to be constructed by using a GDP deflator. Nowak et al. (2007) use the real GDP series provided by the World Development Indicators of World Banks which also seems to be constructed by using a GDP deflator. Meanwhile,
s-Jime
nez and Pulina (2010) use a real GDP series provided in Corte the Penn World Tables which seems to use the Consumer Price Index as a deflator. Fig. 1 shows that for the case of GDP, minimum differences exist in the evolution of the real series if the Consumer Price Index or De la Fuente's GVA deflator (2015) are used. In this sense, we would expect the treatment of the variables to have a minimum influence on the results obtained. Fig. 2 shows that most of the series are upward trending and, with the exception of international visitors, the rest of the series appear to present structural breaks. The growth of the GDP and GVA series is highly stable, with only one break during the last Global Economic Crisis. The employment series has several breaks along the plot, the first of which is related to the heavy job losses during the Oil Crisis of the 1970s and the industrial restructuring of the 1980s and the last break is related to the latest Global Economic Crisis. Likewise, a deterministic trend does not seem plausible for the employment series. Meanwhile, tourism receipts appear to reproduce the international visitor's series with a higher volatility. 4.1. Unit root testing Table 3 shows the classical unit root tests performed for the variables in levels and log levels. As the table reflects, most of the series in the levels have an integration of order one I(1) when an intercept and deterministic trend is included in the model. Only doubts arise for the employment (JOB and JOBP) series where ADF and PP tests with intercept and trend open the possibility of a higher order of integration I(2) ethe series without trend indicate that these series are I(1)- but as highlighted above, a deterministic trend for these series seems redundant and the KPSS and Breitung (2002) non parametric tests confirm that both series are I(1). With the more efficient DF-GLS and Ng-Perron test (2001) esee Table 4tourism receipts (TR) are revealed to be trend-stationary I(0) and employment series are confirmed as I(1) when tested without the deterministic trend. The results for the variables in the log levels are less clear. First, contradictory results are found for tourism demand variables (LVISIT and LTR). The logs of international visitors now appear to be stationary I(0) according to the ADF and PP tests and I(2) according to the stationarity KPSS and Breitung (2002) test and similar cloudy

Table 2 Data and sources. Variable

Data

Source

Tourism demand

Arrivals of international visitors (in thousands) (VISIT) Real International Tourism Receipts (millions of euros) (TR) Real Gross Domestic Product (millions of euros) (GDP) Real Gross Value Added (millions of euros) (GVA) Number of employees (in thousands) (JOB) Number of jobs (in thousands) (JOBP) Real effective exchange rate Peseta (Euro) versus Dollar Real effective exchange rate Peseta (Euro) versus Dollar

Tena (2005) and IET (2016) ~ a (2016) Tena (2005) and Banco de Espan Maluquer de Motes (2009) and INE (2016) De la Fuente (2015) De la Fuente (2015) De la Fuente (2015) Martín and Pons (2005) and OECD (2016) Martín and Pons (2005) and OECD (2016)

Economic growth Job creation Price competitiveness

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

101

Fig. 1. Comparison of Spanish Real GDP using CPI or GVA deflator. Source: Authors' own elaboration. Notes: GDPDCPI Real GDP using Consumer Price Index as deflator. GDPDD Real GDP using GVA deflator provided by De la Fuente (2015). GDPFed Real GDP provided by De la Fuente (2016).

results are found for the log levels of tourism receipts that are I(0) according to the ADF and PP tests, I(1) according to the KPSS test and I(2) according to the Breitung (2002) test. Also, the job series reproduces the results of variables in levels with a result of I(1) when the deterministic trend is not considered in the alternative hypothesis. According to the efficient DF-GLS and Ng-Perron test (2001), both the logs of the visits and tourism receipts generate a result of I(1), and the employment series are confirmed as I(1) when tested without the deterministic trend. Table 5 presents the results of the unit root tests performed allowing for one or two structural breaks under the null hypothesis for the variables in levels and log levels. The breaks are endogenously determined from the data. As the left-side of the table reflects, when only one potential shift in the intercept is considered, (first column in the table), only the TR series rejects the unit root null hypothesis and appears to be stationary, with most of the variables remaining as I(1).3 When only one potential shift in the intercept and trend (second column) is considered, the TR, GDP and GVA series eonly in levels ereject the null hypothesis and appear to be stationary I(0). The suggested breakpoints are 1973 and 1978 for TRecoinciding with the Oil Crises of the 1770s-, 1996 for GDP and 2011 for the GVA series. Allowing for two breaks in the intercept (Column 3), the results are the same as in the case of one break (Column 1), with only TR rejecting the null hypothesis with breakpoints suggested in 1973 and 1996. However, allowing for a more flexible specification for the unit root testing by including two breaks in the intercept and trend we found that all of the variables -except international

3 With this specification is not possible to reject the null hypothesis of the unit root for the 5 per cent significance level, or in levels and the first differences appear as I(2). However when a 10 per cent significance level is considered (critical value 3.21) the series are I(1).

visitors in levels- rejected the null unit root hypothesis and turned out to be stationary. The breakpoints suggested for each variable are different, particularly in the tests to detect the Oil Crises (1972, 1973 or 1978, 1979) as the first break. However, the Lee & Strazicich tests fail to detect the latest global economic and financial crisis as the second breakpoint, which is surprisingly unexpected for the case of the output (GDP and GVA) and employment (JOB and JOBP) series, as a simple visual inspection of these series (see Fig. 1) would confirm the shift experienced in 2007. Conversely, the Carrion-i-Silvestre et al. (2009) test allowing for two breaks esee Table 6- clearly detects the 2007 economic crisis as a breakpoint for most of the series, but fails to detect the Oil Crises as the first breakpoint. Also, the results of this unit root test contradicts the results of the previous ones, because most of the series are now classified as I(2), so these results should be treated with caution. In summary, the series considered are found to be a mix of I(1) and I(0). Under these circumstances, applying the Autoregressive Distributed Lag (ARDL) model is an efficient way of determining the long-term relationship between the variables under investigation. The results are explained in the next subsection. 4.2. The ARDL cointegration approach Table 7 shows the results of the pairwise ARDL-Bound Testing procedure for the series without structural breaks. Table 8 performs the same analysis allowing for an exogenous structural break representing the latest Global Economic Crises using a dummy variable (crisis 2008) taking the values 1 for the period 2008e2013 and 0 otherwise. The specification that is preferred for all tests is the ARDL with restricted term (RT). However, when the null hypothesis of no cointegration is not rejected, a model with restricted constant is also performed.

102

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

Fig. 2. Analyzed series 1957e2014. Source: Authors' own elaboration.

With respect to the variables in levels, the tables reflect that tourism receipts (TR) are clearly cointegrated with the national output (GDP), added value (GVA) and employment (JOB and JOBP) variables regardless of whether a structural break is allowed or not. Conversely, it is difficult to observe cointegration between the number of visitors (VISIT) and the rest of the variables of interest. Only a 10 per cent significant cointegration is observed between visitors and output (GDP) when a deterministic trend and no structural break is allowed, and a 5 per cent cointegration relationship with a structural break and a restricted constant (no trend) is allowed. Contemplating the variables in log levels affects the results considerably, which could be attributable to the fact that tourism receipts and visitors generate a result of stationarity when natural log transformation is applied and no structural break is considered. So, in order to observe the cointegration between the log of tourism receipts (LTR) and the logs of the rest of the variables, the introduction of the dummy reflecting the structural break is now needed. As Table 6 reflects, this is the case where LTR appears to be I(1). This result differs from that obtained in previous articles by Balaguer and
(2002), Nowak et al. (2007) and Corte
s-Jime
nez Cantavella-Jorda and Pulina (2010) where the logs of tourism receipts and the logs

of GFP appear to be easily cointegrated using the Johansen procedure without the need to model a structural break for the series. Specifically, it is difficult to find cointegration between the employment variables (LJOB and LJOBP) e a significance of only 10 per cent is observed in Table 8- and tourism receipts. Conversely, cointegration is easily observed between the natural log of visitors and the rest of the variables. As for the case of tourism receipts, the cointegration relationship appears clearer when the structural break is considered. In view of the results, it seems clear that the latest economic crisis complicates the analysis by obscuring the clear cointegration relationship that most pre-crisis articles established for these variables. The cointegration relationships after the crisis are, in fact, cloudier.

4.3. Granger causality: the Toda-Yamamoto procedure Finally, Tables 9 and 10 reflect the result of the Granger causality test using the Toda and Yamamoto (1995) procedure. Table 9 shows the test results for the series without a deterministic trend or structural breaks. Table 10 performs the same analysis considering the structural break by introducing a constant, a deterministic

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

103

Table 3 Linear unit root testing. Tests

Case 5%cv

VISIT

DVISIT

LVISIT

ADF

C 2.91 Ct 3.49 Diagnosis C 1.94 Ct 3.16 Diagnosis C 2.91 Ct 3.49 Diagnosis C 0.46 Ct 0.14 Diagnosis C 0.01004 Ct 0.00342

1.02(0) 1.70(0) I(1) 1.86(1) 1.52(0) I(1) 1.26(5) 1.79(3) I(1) 0.91(6) 0.21(5) I(1) 0.09709 0.01645 I(1)

¡6.18(0) ¡6.28(0)

¡7.30(0) ¡4.89(0) I(0) 0.37(1) 0.99(1) I(1) ¡7.20(2) ¡6.58(10) I(0) 0.85(6) 0.21(5) I(2) 0.08390 0.01633 I(2)

GVA

DGVA

LGVA

DLGVA

JOB

DJOB

LJOB

C 2.91 Ct 3.49 Diagnosis C 1.94 Ct 3.16 Diagnosis C 2.91 Ct 3.49 Diagnosis C 0.46 Ct 0.14 Diagnosis C 0.01004 Ct 0.00342

0.39(1) 2.72(1) I(1) 0.63(1) 2.45(1) I(1) 0.19(4) 2.15(4) I(1) 0.91(6) 0.16(5) I(1) 0.09896 0.01232 I(1)

¡3.80(0) ¡3.77(0)

¡3.10(1) 1.03(1) I(1) 0.45(1) 0.68(1) I(1) ¡4.58(2) 0.84(2) I(1) 0.90(6) 0.23(5) I(1) 0.09577 0.01911 I(1)

¡5.11(0)

1.09(1) 2.62(1) I(1) 0.84(1) 2.45(1) I(1) 0.74(5) 1.77(5) I(1) 0.67(6) 0.17(6) I(1) 0.07253 0.01737 I(1)

¡2.93(0) 2.93(0)

1.02(1) 2.67(1) I(1) 0.75(1) 2.50(1) I(1) 0.67(5) 1.74(5) I(1) 0.69(6) 0.17(6) I(1) 0.07455 0.01743 I(1)

DF-GLS

PP

KPSS

BR

ADF

DF-GLS

PP

KPSS

BR

¡5.96(0) ¡6.33(0) ¡6.06(5) ¡6.22(7) 0.24(4) 0.05(6) 0.00727 0.00101

¡3.72(0) ¡3.80(0) ¡3.80(0) ¡3.77(0) 0.09(4) 0.09(4) 0.00495 0.00407

DLVISIT

¡3.65(0) ¡5.84(0)

0.65(5) 0.22(4) 0.03248 0.00625

¡3.35(0) ¡5.20(0) ¡5.04(3)

0.77(4) 0.07(1) 0.00570 0.00228

TR

DTR

LTR

0.75(1) 3.28(1) I(1) 0.42(1) ¡3.34(1) I(0) 0.48(2) 2.35(2) I(1) 0.91(6) 0.04(2) I(1) 0.09170 0.00619 I(1)

¡4.32(0) ¡4.28(0)

¡5.85(1) 9.86(1) I(0) 0.40(1) 2.16(1) I(1) ¡5.13(2) ¡5.20(3) I(0) 0.96(5) 0.15(5) I(1) 0.07648 0.00937 I(2)

¡4.23(0)

¡4.12(5) ¡4.07(5) 0.04(2)

0.00333 0.00140

¡2.91(0) 2.95(0) ¡3.03(2) 3.05(2) 0.10(5) 0.08(5) 0.00711 0.00234

DLTR

GDP

DGDP

LGDP

DLGDP

0.49(1) 2.60(1) I(1) 0.70(1) 2.47(1) I(1) 0.45(4) 2.05(4) I(1) 0.90(6) 0.13(5) I(1) 0.09838 0.00982 I(2)

¡4.04(0) ¡4.01(0)

2.54(1) 0.93(1) I(1) 0.65(1) 0.68(1) I(1) ¡3.85(3) 1.12(3) I(1) 0.89(6) 0.21(5) I(1) 0.09451 0.01692 I(1)

¡4.32(0) ¡5.07(0)

DLJOB

JOBP

DJOBP

LJOBP

DLJOBP

¡2.87(0) 2.88(0)

1.18(1) 2.66(1) I(1) 0.95(1) 2.50(1) I(1) 0.77(5) 1.74(5) I(1) 0.65(6) 0.17(6) I(1) 0.07031 0.01747 I(1)

¡2.81(0) 2.82(0)

1.08(1) 2.64(1) I(1) 0.83(1) 2.48(1) I(1) 0.70(5) 1.70(5) I(1) 0.66(6) 0.17(6) I(1) 0.01004 0.01761 I(1)

¡2.82(0) 3.33(3)

¡3.96(0) ¡4.39(0)

0.44(4) 0.14(3) 0.01745 0.00452

¡2.78(0) 2.86(0) ¡3.06(1) 3.07(1) 0.10(5) 0.08(5) 0.00570 0.00228

¡4.08(0) ¡4.08(0) ¡4.09(1) ¡4.06(1) 0.08(4) 0.08(4) 0.00362 0.00372

¡2.78(0) 2.83(0) ¡2.94(2) 2.96(2) 0.11(5) 0.08(5) 0.01004 0.00236

¡2.74(0) ¡4.74(0) ¡5.07(0)

0.56(5) 0.07(3) 0.02880 0.00223

¡2.72(0) 2.80(0) ¡2.96(2) 2.99(2) 0.11(5) 0.08(5) 0.00570 0.00228

Authors' own elaboration. Notes: In parentheses ADF lag-length; PP Bandwidth. Lag-length selection: ADF Schwarz info criterion max lag ¼ 10; PP Bandwidth based on Newey-West. Boldface values express the rejection of null hypothesis at 5% significance level.

Table 4 Ng Perron (2001) unit root testing. Tests Case 5% critical value VISIT Mza

Mzt

MSB

MPT

Mza

Mzt

MSB

MPT

C 8.10 Ct 17.30 Diagnosis C 1.98 Ct 2.91 Diagnosis C 0.23 Ct 0.16 Diagnosis C 3.17 Ct 5.48 Diagnosis

C 8.10 Ct 17.30 Diagnosis C 1.98 Ct 2.91 Diagnosis C 0.23 Ct 0.16 Diagnosis C 3.17 Ct 5.48 Diagnosis

DVISIT

LVISIT

DLVISIT

TR

2.07 (1) 4.67(0) I(1) 2.31 (1) 1.39(0) I(1) 1.11 (1) 0.29 (0) I(1) 103.5(1) 18.61(0) I(1)

¡26.77(0) 0.56 (1) ¡27.32(0) 1.98(1) I(1) ¡3.61(0) 0.51(1) ¡3.68(0) 0.84(1) I(1) 0.13(0) 0.92(1) 0.13(0) 0.42(1) I(1) 1e06(0) 54.99(1) 3.38(0) 37.21(1) I(1)

¡17.51(0) ¡26.46(0) ¡22.95(1) I(0) ¡2.93(0) ¡3.62(0) ¡3.38(1) I(0) 0.16(0) 0.13(0) 0.14(1) I(0) 1.49(0) 3.98(1) 3.54(0) I(0)

GVA

DGVA

DLGVA

0.88(1) 12.81(1) I(1) 0.69(1) 2.50(1) I(1) 0.77(1) 0.19(1) I(1) 43.94(1) 7.23(1) I(1)

¡17.97(0) 0.58(1) ¡18.46(0) 2.66(1) I(1) ¡2.99(0) 0.50(1) ¡3.03(0) 0.85(1) I(1) 0.16(0) 0.86(1) 0.16(0) 0.32(1) I(1) 1.36(0) 49.22(1) 4.94(0) 25.31(1) I(1)

LGVA

JOB

¡15.92(0) 3.24(1) ¡24.78(0) 12.00(1) I(1) ¡2.76(0) 1.08(1) ¡3.51(0) 2.44(1) I(1) 0.17(0) 0.33(1) 0.14(0) 0.20(1) I(1) 1.73(0) 7.36(1) 3.69(0) 7.59(1) I(1)

DTR

DJOB

LTR

DLTR

0.22(1) 5.94(1) I(1) 0.14(1) 1.64(1) I(0) 0.65(1) 0.27(1) I(1) 24.80(1) 15.23(1) I(1)

¡19.28(0) 0.91(1) ¡21.19(0) 13.30(1) I(1) ¡3.10(0) 0.76(1) ¡3.25(0) 2.53(1) I(1) 0.16(0) 0.83(1) 0.15(0) 0.19(1) I(1) 1.27(0) 50.06(1) 4.30(0) 7.07(1) I(1)

¡20.00(0) 0.71(1) ¡20.00(0) 2.54(1) I(1) ¡3.16(0) 0.70(1) ¡3.16(0) 0.85(1) I(1) 0.15(0) 0.98(1) 0.15(0) 0.33(1) I(1) 1.22(0) 64.09(1) 4.55(0) 26.57(1) I(1)

LJOB

DLJOB

DJOBP

¡12.94(0) 2.88(1) 13.18(0) 12.23(1) I(2) ¡2.54(0) 0.99(1) 2.56(0) 2.47(1) I(1) 0.19(0) 0.34(1) 0.19(0) 0.20(1) I(1) 1.89(0) 7.98(1) 6.92(0) 7.44(1) I(1)

GDP

JOBP

¡12.10(0) 3.85(1) 12.55(0) 12.41(1) I(1) ¡2.46(0) 1.21(1) 2.50(0) 2.49(1) I(1) 0.20(0) 0.31(1) 0.19(0) 0.20(1) I(1) 2.02(0) 6.48(1) 7.26(0) 7.33(1) I(1)

Authors' own elaboration. Notes: In parentheses: lag-length. Spectral GLS-detrended AR based on SIC, maxlag ¼ 10. Boldface values express the rejection of null hypothesis at 5% significance level.

DGDP

LGDP

LJOBP

¡12.12(0) 3.28(1) 12.38(0) 11.94(1) I(1) ¡2.46(0) 1.08(1) 2.48(0) 2.44(1) I(1) 0.20(0) 0.33(1) 0.20(0) 0.20(1) I(1) 2.02(0) 7.28(1) 7.36(0) 7.63(1) I(1)

DLGDP ¡11.77(0) ¡22.86(0) ¡2.36(0) ¡3.36(0) 0.20(0) 0.14(0) 2.30(0) 4.06(0)

DLJOBP ¡11.69(0) 12.17(0) ¡2.41(0) 2.46(0) 0.20(0) 0.20(0) 2.09(0) 7.49(0)

104

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

Table 5 Unit root tests with endogenous determination of trend-breaks. Variable

VISIT

LVISIT

TR LTR

GDP

LGDP

GVA

LGVA

JOB

LJOB

JOBP

LJOBP

Lee and Strazicich (2003) with one break

Lee and Strazicich (2003) with two breaks

C

Ct

C

Ct

T ¼ 2.07 (1) [-3.56] 1978 T ¼ -5.87 (0) [-3.56] 2006 T ¼ 0.65 (6) [-3.56] 1979 T ¼ -5.99 (0) [-3.56] 1985 T ¼ -3.91 (6) [-3.56] 1973 T ¼ 0.94 (1) [-3.56] 1976 T ¼ -5.02 (0) [-3.56] 2001 T ¼ 2.90 (6) [-3.56] 2009 T ¼ -3.53 (2) [-3.56] 2010 I(2) T ¼ 1.34 (1) [-3.56] 1981 T ¼ -4.05 (4) [-3.56] 1993 T ¼ 3.15 (1) [-3.56] 2009 T ¼ -4.61 (0) [-3.56] 2007 T ¼ 1.20 (1) [-3.56] 1977 T ¼ -4.49 (0) [-3.56] 2007 T ¼ 2.51 (7) [-3.56] 2001 T ¼ -3.50 (0) [-3.56] 2007 I(2) T ¼ 3.21 (1) [-3.56] 2009 T ¼ -3.32 (3) [-3.56] 1993 I(2) T ¼ 2.57 (7) [-3.56] 2001 T ¼ -3.48 (3) [-3.56] 1993 I(2) T ¼ 3.05 (1) [-3.56] 2009 T ¼ -3.57 (3) [-3.56] 1993

T ¼ 3.95 (1) [-4.50] 1996 T ¼ -6.64 (0) [-4.47] 2007 T ¼ 3.38 (6) [-4.50] 1973 T ¼ -6.38 (0) [-4.50] 1992 T ¼ -4.51 (6) [-4.47] 1978 T ¼ 3.79 (6) [-4.50] 1972 T ¼ -5.09 (0) [-4.45] 2001 T ¼ -4.67 (4) [-4.50] 1996

T ¼ 2.43 (1) [-3.84] 1978, 1997 T ¼ -6.39 (0) [-3.84] 2005, 2010 T ¼ 0.94 (6) [-3.84] 1979, 2008 T ¼ -7.12 (0) [-3.84] 1972, 1977 T ¼ -4.53 (6) [-3.84] 1973, 1996 T ¼ 1.30 (1) [-3.84] 1969, 1976 T ¼ -5.46 (1) [-3.84] 1975, 2001 T ¼ 3.69 (1) [-3.84] 1974, 2009 T ¼ -4.70 (1) [-3.84] 1996, 2007 T ¼ 1.95 (8) [-3.84] 1970, 1982 T ¼ -4.79 (4) [-3.84] 1970, 2007 T ¼ 3.33 (1) [-3.84] 1976, 2009 T ¼ -4.80 (0) [-3.84] 1979, 2007 T ¼ 1.48 (3) [-3.84] 1977, 1983 T ¼ -5.33 (1) [-3.84] 1976, 2008 T ¼ 3.42 (1) [-3.84] 1980, 2009 T ¼ -4.21 (4) [-3.84] 1993, 2007 T ¼ 3.49 (1) [-3.84] 1987, 2009 T ¼ -4.19 (5) [-3.84] 1973, 2008 T ¼ 3.31 (1) [-3.84] 1983, 2009 T ¼ -4.16 (5) [-3.84] 1973, 2007 T ¼ 3.27 (1) [-3.84] 1987, 2009 T ¼ -4.00 (3) [-3.84] 1979, 2008

T ¼ 5.59 (1) [-5.73] 1994, 2007 T ¼ -6.85 (3) [-5.73] 1994, 2006 T ¼ -5.76 (6) [-5.74] 1972, 1982

T ¼ 4.45 (8) [-4.50] 1989 T ¼ -4.02 (4) [-4.50] 1970 T ¼ -4.56 (3) [-4.45] 2001

T ¼ 3.51 (6) [-4.50] 1989 T ¼ -4.60 (1) [-4.50] 1996 T ¼ 4.30 (3) [-4.50] 1997 T ¼ -4.20 (4) [-4.47] 2004 I(2) T ¼ 4.35 (3) [-4.50] 1994 T ¼ -3.63 (3) [-4.47] 2006 I(2) T ¼ 4.42 (3) [-4.50] 1997 T ¼ -4.31 (3) [-4.47] 2006 I(2) T ¼ 4.45 (3) [-4.50] 1994 T ¼ -3.85 (3) [-4.47] 2006 I(2)

T ¼ -6.18 (7) [-5.74] 1973, 1997 T ¼ -6.35 (7) [-5.74] 1972, 1993

T ¼ -5.82 (4) [-5.73] 1993, 2001

T ¼ -5.90 (7) [-5.71] 1978, 2001

T ¼ -7.23 (4) [-5.65] 1991, 2004

T ¼ -6.53 (6) [-5.71] 1979, 2002

T ¼ -6.08 (8) [-5.71] 1978, 2000

T ¼ -6.46 (3) [-5.71] 1979, 2001

T ¼ -6.35 (3) [-5.71] 1982, 2001

T ¼ -6.25 (3) [-5.71] 1979, 2001

*In (): lags, based on General to Specific Procedure (max lags ¼ 8). In []: Lee and Strazicich (2003, 2004) based on 49 observations. Cursive: test results on first differenced variables. Boldface values express the rejection of null hypothesis at 5% significance level.

trend, the dummy variable (crisis 2008) and an interaction term between the deterministic trend and the dummy crisis variable in the regressions as exogenous variables. As in the previous case, the results appear to be robust or not depending on whether the variables are considered in levels or log levels or whether a structural break is allowed. The upper half of Table 9 shows that when no structural break is allowed and the variables are considered in levels, the causal relationship tends to appear from economic cycle to tourism demand. This is clear for the employment variables (JOB and JOBP) when only a one-sided relationship is observed from these variables to tourism demand, regardless of whether the latter is measured in terms of the number of visitors or tourism receipts. With respect to the output variables, the relationship changes depending on which variable, GVA or GDP, is used. For the case of value added (GVA), a one-sided relationship is observed from tourism demand (VISIT or TR) to this variable

confirming the led growth hypothesis established by the previous literature. Conversely, when GDP is used, a one-sided relationship is observed from GDP to tourism demand when this variable is measured as the number of visitors (VISIT), and a two-sided relationship is observed between tourism receipts (TR) and economic output GDP. The results for the variables in log levels with no structural breaks, in line with the previous articles, favor the led growth hypothesis. First, a one-sided relationship is mostly observed from tourism demand to employment reversing the previous results when the variables are analyzed in levels ethe absence of a causal relationship between LJOBP and LVISIT is consistent with the inconclusive result shown in Table 7 for this pair of variables-. With respect to the output variables, a two-sided relationship is observed between the natural logarithm of GDP and tourism demand when the latter variable is measured in terms of tourism

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

105

Table 6 Unit root tests with endogenous determination of trend-breaks. Variable Carrion-i-Silvestre (2009) with one break PT VISIT

LVISIT

TR

LTR

GDP

LGDP

GVA

LGVA

JOB

LJOB

JOBP

LJOBP

MZA

T ¼ 9.34 (6.27) T ¼ 14.94 (22.48) T ¼ 4.01 (5.49) T ¼ -27.64 (-20.52) T ¼ 19.13 T ¼ 10.23 (7.00) (23.96) T ¼ 6.69 (6.48) T ¼ -23.62 (-22.29) T ¼ 16.43 T ¼ 8.78 (22.48) (6.27) T ¼ 5.44 (5.89) T ¼ -23.11 (-21.82) T ¼ 8.66 (6.01) T ¼ 14.97 (21.18) T ¼ 5.62(5.91) T ¼ -22.97 (-21.57) T ¼ 28.46 T ¼ 4.99 (21.97) (6.13) T ¼ 4.80(5.59) T ¼ -22.48 (-19.76) T ¼ 11.16 T ¼ 17.25 (6.76) (23.47) T ¼ 5.34(5.65) T ¼ -22.81 (-22.36) T ¼ 29.07 T ¼ 4.30 (22.16) (5.55) T ¼ 4.45 (5.72) T ¼ -25.51 (-19.72) T ¼ 28.09 T ¼ 4.22 (23.91) (6.81) T ¼ 5.39 (6.08) T ¼ -25.31(-22.88) T ¼ 40.57 T ¼ 2.95 (23.49) (5.65) T ¼ 7.24 (5.72) T ¼ -16.36(-19.72) T ¼ 39.65 T ¼ 2.83 (22.47) (6.07) T ¼ 7.44(5.72) T ¼ -15.86(-19.72) T ¼ 39.43 T ¼ 2.70 (22.47) (5.98) T ¼ 7.10(5.72) T ¼ -16.76(-19.72) T ¼ 40.82 T ¼ 2.75 (22.47) (6.07) T ¼ 7.70(5.72) T ¼ -15.37 (-19.72)

Carrion-i-Silvestre (2009) with two breaks

MSB

MZT

B.P

PT

MZA

T ¼ 0.18 (0.14) T ¼ 0.13 (0.15) T ¼ 0.22 (0.14) T ¼ 0.14(0.14) T ¼ 0.23 (0.14) T ¼ 0.14(0.15) T ¼ 0.18 (0.15) T ¼ 0.14 (0.15) T ¼ 0.29 (0.15) T ¼ 0.14(0.15) T ¼ 0.16 (0.14) T ¼ 0.14(0.15) T ¼ 0.29 (0.15) T ¼ 0.13 (0.15) T ¼ 0.24 (0.14) T ¼ 0.14(0.14) T ¼ 0.36 (0.14) T ¼ 0.17 (0.15) T ¼ 0.34 (0.14) T ¼ 0.17(0.15) T ¼ 0.34 (0.14) T ¼ 0.17(0.15) T ¼ 0.35 (0.14) T ¼ 0.17 (0.15)

T ¼ 2.72 (3.33) 1996 T ¼ 8.31 (6.64)

T ¼ 20.66 (25.30) T ¼ -3.70 (-3.18) T ¼ 6.88 (6.93) T ¼ -27.97(-28.03) T ¼ 2.25 (3.44) 1972 T ¼ 9.16 (6.96) T ¼ 21.31 (28.23) T ¼ -3.42(-3.31) T ¼ 7.34 (6.96) T ¼ -26.92 (-27.75) T ¼ 2.08 (3.33) 1996 T ¼ 21.51 (7.26) T ¼ 9.45 (27.90) T ¼ -3.39(-3.28) T ¼ 6.99 (5.29) T ¼ -22.91 (-26.64) T ¼ 2.70 (3.22) 1962 T ¼ 11.92 (7.57) T ¼ 18.00 (28.24) T ¼ -3.38(-3.24) T ¼ 8.88 (7.17) T ¼ -23.86 (-28.45) T ¼ 1.45 (3.29) 1999 T ¼ 18.28 (6.73) T ¼ 9.59 (26.17) T ¼ -3.33(-3.13) T ¼ 6.44(5.02) T ¼ -21.64(-25.31) T ¼ 2.77 (3.41) 1987 T ¼ 54.23 (7.07) T ¼ 4.44 (27.22) T ¼ -3.37(-3.29) T ¼ 7.67 (7.14) T ¼ -26.69(-27.76) T ¼ 1.28 (3.30) 1998 T ¼ 18.19(6.73) T ¼ 10.65 (26.17) T ¼ -3.53(-3.13) T ¼ 5.85(5.02) T ¼ -25.15(-25.31) T ¼ 1.03 (3.43) 1973 T ¼ 17.11 (6.96) T ¼ 12.29 (27.25) T ¼ -3.55 (-3.36) T ¼ 12.31(7.24) T ¼ -18.69(-29.03) T ¼ 1.09 (3.38) 1978 T ¼ 26.42 (6.83) T ¼ 6.65 (26.21) T ¼ -2.82(-3.13) T ¼ 7.62 (5.02) T ¼  0.98 1996 T ¼ 7.40 (7.03) (3.33) T ¼ -2.79 (-3.13) T ¼ 7.98(5.02) T ¼ 0.93 (3.33) 1996 T ¼ 26.50 (6.94)

T ¼ -18.73(-25.31) T ¼ 24.11 (25.94) T ¼ -17.63 (-25.31) T ¼ 6.59 (25.90)

T ¼ -2.86(-3.13) T ¼ 7.13(5.02) T ¼ 0.97 (3.33) 1996 T ¼ 7.65 (6.84)

T ¼ -20.03 (-25.31) T ¼ 22.75 (25.85) T ¼ -17.85(-26.07)

T ¼ -2.75(-3.13)

T ¼ 10.47(6.70)

MSB

MZT

B.P

T ¼ 0.15 (0.13) T ¼ 0.13 (0.13) T ¼ 0.15 (0.13) T ¼ 0.13 (0.13) T ¼ 0.22 (0.13) T ¼ 0.14(0.14) T ¼ 0.16 (0.13) T ¼ 0.14 (0.13) T ¼ 0.22 (0.13) T¼0.15 (0.14) T ¼ 0.33 (0.13) T ¼ 0.13(0.13) T ¼ 0.21 (0.13) T ¼ 0.13 (0.14) T ¼ 0.20 (0.13) T ¼ 0.16 (0.12) T ¼ 0.27 (0.13) T ¼ 0.16(0.14) T ¼ 0.14 (0.13) T ¼ 0.16 (0.14) T ¼ 0.27 (0.13) T ¼ 0.15(0.14) T ¼ 0.14 (0.13) T ¼ 0.16 (0.13)

T ¼ 3.18 (3.55) T ¼ -3.73(-3.73) T ¼ 3.21 (3.73) T ¼ -3.66 (-3.70) T ¼ 2.12 (3.72) T ¼ -3.36(-3.61) T ¼ 2.99 (3.75) T ¼ -3.45 (-3.74) T ¼ 2.17 (3.61) T ¼ -3.27 (-3.52) T ¼ 1.49 (3.68) T ¼ -3.64(-3.72) T ¼ 2.28 (3.61) T ¼ -3.50 (-3,52) T ¼ 2.47 (3.68) T ¼ -3.05 (-3.78) T ¼ 1.79 (3.61) T ¼ -3.02(-3.52) T ¼ 3.46 (3.60) T ¼ -2.94 (-3.52) T ¼ 1.78 (3.59) T ¼ -3.12 (-3.52) T ¼ 3.36 (3.59) T ¼ -2.95 (-3.60)

1996, 2007 1964, 1973 1988, 2001 1962, 2001 1992, 2007 1984, 2007 1992, 2007 1970, 2008 1992, 2007 1994, 2007 1994, 2007 1994, 2007

*In parenthesis critical values of Carrion-i-Silvestre et al. (2009). Max k ¼ 6 in levels or log-levels. Max k ¼ 1 in first differences. Boldface values express the rejection of null hypothesis at 5% significance level.

Table 8 ARDL Bound testing procedure allowing for one exogenous structural break.

Table 7 ARDL Bound Testing procedure without structural break. Variables

Trend

Model

F Test Statistic 5% vc

Comments

GDP VISIT

RT

ARDL(4,1)

5.00* (4.68) [5.15]

GVA VISIT JOB VISIT JOBP VISIT GDP TR GVA TR JOB TR JOBP TR

RT RT RT RT* RT* RT* RT

ARDL ARDL ARDL ARDL ARDL ARDL ARDL

4.05 2.95 2.81 7.94 6.44 9.30 8.61

5% Inconclusive 10% Cointegrated No reject No reject No reject Cointegrated Cointegrated Cointegrated Cointegrated

LGDP LVISIT LGVA LVISIT

RT* RT**

ARDL (2,2) ARDL (3,4)

9.16 (4.68) [5.15] 9.16 (4.68) [5.14]

LGVA LVISIT LJOB LVISIT LJOBP LVISIT

RC RT* RT*

ARDL (3,4) ARDL (2,2) ARDL (4,3)

6.76 (3.62) [4.16] 6.29 (4.68) [5.15] 5.09 (4.68) [5.15]

LJOBP LVISIT

RC

ARDL (4,3)

4.89 (3.62) [4.16]

Cointegrated 5% Inconclusive 10% Cointegrated Cointegrated Cointegrated 5% Inconclusive 10% Cointegrated Cointegrated

LGDP LTR LGVA LTR LJOB LTR LJOB LTR LJOBP LTR LJOBP LTR

RT* RT* RT** RC RT* RC

ARDL ARDL ARDL ARDL ARDL ARDL

7.67 (4.68) [5.15] 14.45 (4.68) [5.15] 2.01 (4.68) [5.15] 4.35 (3.62) [4.16] 2.21 (4.68) [5.15] 2.52 (3.62) [4.16]

Cointegrated Cointegrated No reject Cointegrated No rejection No rejection

(4,4) (4,0) (4,0) (2,3) (4,0) (2,3) (2,3)

(4,0) (3,2) (4,0) (2,1) (4,0) (4,0)

(4.68) (4.68) (4.68) (4.68) (4.68) (4.68) (4.68)

[5.15] [5.15] [5.15] [5.15] [5.15] [5.15] [5.15]

Notes: RT. Restricted Trend. RC. Restricted constant.*Trend coefficient not significant (model specification does not affect conclusion) **Trend coefficient not significant (model specification does affect conclusion).

Variables

Trend

Model

F Test Statistic 5% vc

Comments

GDP VISIT GDP VISIT GVA VISIT JOB VISIT JOBP VISIT GDP TR GVA TR JOB TR JOBP TR

RT** RC RT* RT RT RT RT* RT RTa

ARDL(4,1) ARDL(4,1) ARDL (4,4) ARDL (4,0) ARDL (4,0) ARDL (1,2) ARDL (4,0) ARDL (2,3) ARDL (3,2)

2.69* (4.68) [5.15] 4.89* (3.62) [4.16] 0.18 (4.68) [5.15] 3.84 (4.68) [5.15] 3.50 (4.68) [5.15] 14.61 (4.68) [5.15] 5.17 (4.68) [5.15] 11.10 (4.68) [5.15] 10.82 (4.68) [5.15]

No rejection Cointegrated No rejection No rejection No rejection Cointegrated Cointegrated Cointegrated Cointegrated

LGDP LVISIT LGVA LVISIT LGVA LVISIT LJOB LVISIT LJOBP LVISIT LJOBP LVISIT

RT RT** RC RT* RT* RC

ARDL ARDL ARDL ARDL ARDL ARDL

9.10 4.01 9.50 6.32 5.87 5.72

Cointegrated No rejection Cointegrated Cointegrated Cointegrated Cointegrated

LGDP LTR LGVA LTR LJOB LTR

RT RT* RT**

ARDL (4,0) ARDL (3,2) ARDL (4,3)

9.30 (4.68) [5.15] 12.84 (4.68) [5.15] 4.99 (4.68) [5.15]

LJOB LTR LJOBP LTR

RC RT*

ARDL (2,3) ARDL (4,3)

5.42 (3.62) [4.16] 5.00 (4.68) [5.15]

LJOBP LTR

RC

ARDL (4,3)

5.01 (3.62) [4.16]

(4,0) (3,4) (3,4) (2,3) (4,3) (4,3)

(4.68) (4.68) (3.62) (4.68) (4.68) (3.62)

[5.15] [5.14] [4.16] [5.15] [5.15] [4.16]

Cointegrated Cointegrated 5% Inconclusive 10% Cointegrated Cointegrated 5% Inconclusive 10% Cointegrated Cointegrated

Notes: RT. Restricted Trend. RC. Restricted constant.*Trend coefficient not significant (model specification does not affect conclusion) **Trend coefficient not significant (model specification does affect conclusion). a Best model based on AIC criteria does not meet errors adequately.

106

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

Table 9 Granger causality. No deterministic trend and no breaks allowed. Variables

Lag order

Chi-Square p-value

Causality (10 percent significance level)

GDP VISIT

Lag 5 m ¼ 1

Economic growth to tourism demand

GVA VISIT

Lag 5 m ¼ 1*

JOB VISIT

Lag 6 m ¼ 1*

JOBP VISIT

Lag 5 m ¼ 1*

GDP TR

Lag 5 m ¼ 1

GVA TR

Lag 2 m ¼ 1

JOB TR

Lag 2 m ¼ 1

JOBP TR

Lag 3 m ¼ 1

GDP VISIT 19.14 (0.00) VISIT GDP 4.43 (0.48) GVA VISIT 6.38 (0.27) VISIT GVA 13.97 (0.01) VISIT 10.86 (0.09) JOB VISIT JOB 5.29 (0.50) JOBP VISIT 12.83 (0.02) VISIT JOBP 5.23 (0.38) GDP TR 10.21 (0.06) TR GDP 13.84 (0.01) GVA TR 2.95 (0.22) GVA 4.49 (0.10) TR JOB TR 7.36 (0.02) TR JOB 0.54 (0.76) JOBP TR 9.83 (0.02) TR JOBP 3.78 (0.28)

LGDP LVISIT

Lag 7 m ¼ 1

LGDP LVISIT

Lag 5 m ¼ 1

LGVA LVISIT

Lag 4 m ¼ 1

LJOB LVISIT

Lag 3 m ¼ 1

LJOBP LVISIT

Lag 5 m ¼ 1

LGDP LTR

Lag 6 m ¼ 1

LGVA LTR

Lag 2 m ¼ 1

LJOB LTR

Lag 3 m ¼ 1

LJOBP LTR

Lag 3 m ¼ 1

LGDP LVISIT 10.40 (0.16) LVISIT LGDP 6.46 (0.48) LVISIT 13.11 (0.02) LGDP LVISIT LGDP 0.10 (0.99) LGVA LVISIT 16.03 (0.00) LVISIT LGVA 6.99 (0.13) LJOB LVISIT 3.13 (0.37) LVISIT LJOB 6.24 (0.10) LJOBP LVISIT 6.28 (0.27) LVISIT LJOBP 7.44 (0.18) LGDP LTR 14.44 (0.02) LTR LGDP 18.55 (0.00) LGVA LTR 1.02 (0.60) LTR LGVA 9.73 (0.00) LJOB LTR 2.70 (0.43) LTR LJOB 8.84 (0.03) LJOBP LTR 4.14 (0.24) LTR LJOBP 8.14 (0.04)

Tourism demand to Gross Value Added Job creation to tourism demand. Job creation to tourism demand. Bilateral Tourism demand to Gross Value Added Job creation to tourism demand. Job creation to tourism demand. None Economic growth to tourism demand Gross Value Added to tourism demand Tourism demand to job creation. None Bilateral Tourism demand to Gross Value Added Tourism demand to job creation Tourism demand to job creation

Notes: * Dynamically unstable, at least 1 root outside the unit circle.

receipts (LTR). This result coincides with the findings of Nowak
s-Jime
nez and Pulina (2010). Conversely, et al. (2007) and Corte no relationship is observed between the logs of GDP and the number of visitors (LVISIT). This result is inconsistent with what is reflected in Table 7 where these pair of variables appear cointegrated; however, in a pair of cointegrated variables, it is expected that at least one causal relationship should be found between them. Although with annual data it is difficult to expect events occurring seven years ago to affect the current circumstances, as suggested by the model based on AIC, the latest economic crisis has shown for the case of Spain that this is plausible. Therefore, a reduction in the number of lags in the VAR to five gives rise to a stable model and resolves the problem, indicating a one-sided causal relationship from economic growth to tourism demand. Finally, the relationship between gross value added (LGVA) and tourism demand is very strange, because first, a one-sided relationship from LGVA to LVISIT is observed, but a contrary one-sided relationship is observed from LTR to LGVA. Our intuition for this result is that it also points to a bidirectional relationship between tourism demand and economic output. The introduction of the structural break variables further complicates the analysis by reducing the power of the Toda and Yamamoto (1995) procedure in the presence of many exogenous variables. As a result, a contradictory relationship appears between the variables and more inconsistent results are obtained with respect to the cointegration analysis (Table 8). For example, most of the tests on the variables in log levels reveal no causal relationships between the variables. This indicates the existence of overparametrized models. Of course, another plausible explanation for

this result would be that the cointegration found in the previous section is spurious. In general, the results reflect that no test clearly supports the tourism-led growth hypothesis and only a two-sided relationship is observed for the pairs LGVA - LVISIT and LGDP e LTR, with the latter result coinciding with those obtained by Nowak
s-Jime
nez and Pulina (2010). et al. (2007) and Corte The results for the pairs LJOB-LVISIT and LJOBP-LVISIT are inconsistent with those shown in Table 8 where these pairs of variables appear cointegrated. In both cases, the variation of the number of lags in VAR does not change the absence of a causal relationship, and only the exclusion of the deterministic trend “t” and the interaction term “d*t” results in a one-sided causal relationship from tourism demand to job creation. For the pairs LJOBLTR and LJOPB-LTR, the increase by one lag in the VAR solves the problem of finding a one-sided relationship from tourism demand to job creation favoring the led growth hypothesis. When the variables are considered in levels, the tourism-led growth hypothesis is upheld for employment variables only when tourism demand is measured using the tourism receipts variables. Conversely, using the number of visitors, a one-sided relationship is observed from employment levels to tourism demand. As in the previous case, a plausible explanation for this result would be the existence of a bilateral relationship between these variables. With regard to the output variables, the tourism-led growth hypothesis is confirmed only for the case of gross value added (GVA) when tourism demand is proxied by the number of visitors. By contrast, when tourism receipts are used, no relationship is observed between these variables. Moreover, a relationship from GDP to tourism demand is observed when the latter is measured in

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

107

Table 10 Granger causality. Deterministic trend and breaks allowed. Variables

Lag order

Chi-Square p-value

Causality (10 percent significance level)

GDP VISIT

Lag 6 m ¼ 1*

Economic growth to tourism demand

GVA VISIT

Lag 3 m ¼ 1*

JOB VISIT

Lag 7 m ¼ 1*

JOBP VISIT

Lag 7 m ¼ 1*

GDP TR

Lag 3 m ¼ 1

GVA TR

Lag 2 m ¼ 1

GVA TR

Lag 3 m ¼ 1

JOB TR

Lag 2 m ¼ 1*

JOBP TR

Lag 3 m ¼ 1

GDP VISIT 13.59 (0.03) VISIT GDP 6.67 (0.35) GVA VISIT 3.59 (0.30) VISIT GVA 6.53 (0.08) VISIT 19.55 (0.00) JOB VISIT JOB 8.28 (0.30) JOBP VISIT 23.12 (0.00) VISIT JOBP 9.62 (0.21) GDP TR 8.99 (0.02) TR GDP 7.06 (0.06) GVA TR 3.31 (0.19) GVA 3.26 (0.19) TR GVA TR 3.81 (0.28) TR GVA 7.07 (0.06) JOB TR 1.14 (0.56) TR JOB 6.45 (0.03) JOBP TR 4.34 (0.22) TR JOBP 10.83 (0.01)

LGDP LVISIT

Lag 6 m ¼ 1

Economic growth to tourism demand

LGVA LVISIT

Lag 7 m ¼ 1

LJOB LVISIT

Lag 6 m ¼ 1

LJOB LVISIT No det. trend

Lag 5 m ¼ 1*

LJOBP LVISIT

Lag 6 m ¼ 1

LJOBP LVISIT No det. trend LGDP LTR

Lag 5 m ¼ 1* Lag 7 m ¼ 1

LGVA LTR

Lag 2 m ¼ 1

LGVA LTR

Lag 3 m ¼ 1

LJOB LTR

Lag 2 m ¼ 1

LJOB LTR

Lag 3 m ¼ 1

LJOBP LTR

Lag 2 m ¼ 1

LJOBP LTR

Lag 3 m ¼ 1

LVISIT 16.40 (0.01) LGDP LVISIT LGDP 7.24 (0.29) LGVA LVISIT 14.15 (0.04) LVISIT LGVA 15.83 (0.02) LJOB LVISIT 5.03 (0.53) LVISIT LJOB 5.61 (0.46) LJOBP LVISIT 4.70 (0.45) LVISIT LJOBP 9.94 (0.41) LJOBP LVISIT 5.46 (0.45) LVISIT LJOBP 6.03 (0.41) LJOBP LVISIT 5.49 (0.35) LVISIT LJOBP 11.35 (0.04) LGDP LTR 18.55 (0.00) LTR LGDP 15.58 (0.02) LGVA LTR 2.80 (0.24) LTR LGVA 4.03 (0.13) LGVA LTR 4.05 (0.25) LTR LGVA 7.66 (0.05) LJOB LTR 1.37 (0.50) LTR LJOB 3.61 (0.16) LJOB LTR 1.63 (0.65) LTR LJOB 6.57 (0.08) LJOBP LTR 2.43 (0.29) LTR LJOBP 3.05 (0.21) LJOBP LTR 2.63 (0.45) LTR LJOBP 5.99 (0.11)

Tourism demand to Gross Value Added Job creation to tourism demand. Job creation to tourism demand. Bilateral None Tourism demand to Gross Value Added Tourism demand to job creation. Tourism demand to job creation.

Bilateral None Tourism demand to job creation. None Tourism demand to job creation. Bilateral None Tourism demand to Gross Value Added None Tourism demand to job creation None Tourism demand to job creation

Notes: * Dynamically unstable, at least 1 root outside the unit circle. Exogenous variables: constant, deterministic trend, crisis dummy and interaction between trend and crisis dummy.

terms of the number of visitors. This relationship becomes bilateral when tourism receipts are used as a proxy for tourism demand. To sum up, although the comparable results tend to agree with the findings of previous literature that establishes a bidirectional relationship between the growth of tourism demand and the economic growth of Spain, the analysis performed reveals that the results obtained are not robust. This is because they are highly sensitive to the specific variables used to proxy tourism demand (number of visitors or tourism receipts), output (gross domestic product, value added or employment), the transformation (logarithmic or not) of the variables and the specification (inclusion or not of trend terms, consideration of structural breaks and the exact number of lags considered for the variables) of the particular VAR models. 5. Conclusions This article examines the tourism-led growth hypothesis for Spain after the economic crises of 2008 and the uprisings in Arab countries of the South Mediterranean. Previous literature analyzing

the relationship existing between tourism and economic growth in Spain focuses on tourism receipts and gross domestic product from 1960 to 2004, and finds that the natural logs of both variables are I(1) and cointegrated according to the Johansen procedure and that, in general, there is a bilateral Granger-causality between these variables by using bivariate or multivariate VECM. This article is innovative with respect to the previous analyses in several ways. First, it expands the time window from 1957 to 2014, enabling the authors to include the effects of the latest global economic crisis. Second, it considers a wider range of variables to proxy tourism demand (not only tourism receipts, but also the number of visitors) and output (not only gross domestic product, but gross value added and employment). €In this way, the analysis is expected to better capture the impact of tourism development not only on economic growth but on the well-being of residents. Third, the analyses are performed not only on the natural logarithms of the variables, but also on the levels of the variables themselves, which allows the authors to determine the robustness of the results of the transformations carried out on the variables. Fourth, it considers more modern and efficient tools to determine the order of

108

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109

integration of the variables than previous studies. Likewise, it uses alternative cointegration and Granger causality approaches for obtaining results. The analysis carried out shows that the latest economic crisis and the uprisings in the competing Mediterranean destinations have notably complicated the tasks in several aspects. First, uncertainty has arisen regarding the order of integration of the variables, specifically when they are considered in natural logarithms. For example, the logarithm of tourism receipts which the previous literature clearly determines as I(1), now appears mostly as I(0) when a deterministic trend and one structural break is considered, and the same occurs for the logarithm of the number of visitors. Therefore, appropriate cointegration procedures for this mix of results are considered, such as the ARDL and Bounds testing. Second, although with the recent crisis and events the clear cointegration relationship established in pre-crisis articles between the logs of tourism receipts and the logs of gross domestic product prevails, cointegration relationships between other variables representing the same concepts (tourism demand or output) have become cloudier. Third, the causality results obtained in this paper tend to agree with the findings of previous studies that establish a bidirectional relationship between the growth of tourism demand and the economic growth of Spain. However, the results are highly sensitive to the selected variables and their transformation and the specification of the models which indicates a lack of robustness of the causality procedures when applied to real series. If in Spain, where it seems very clear that tourism development is a main source of the country's economic development and employment generation, such a sensitive empirical relationship using these techniques has been obtained, a much more unstable relationship would be expected in countries where tourism is less important. Therefore, in view of the results obtained, we would recommend caution when using these techniques. We suggest that the robustness of the results with respect to transformations and the use of different proxy variables are tested. Furthermore, the possibility of conducting a cross-check of the results obtained should be assessed using alternative econometric techniques for those countries or destinations in which the weight of the tourism industry is not so important or where a relationship of this kind is not sufficiently justified from a theoretical point of view. The literature on non-linear unit root testing, non-linear cointegration and non-linear Granger causality is developing rapidly. Further research on this topic should apply these efficient techniques designed to deal with structural breaks and nonlinearities to confirm the results obtained through classic techniques. Otherwise, spurious results may be obtained which could contaminate the recommendations for economic and tourism policies aimed at enhancing the development of tourism destinations. Acknowledgement Financial support from ECO2014-58434-P project is gratefully acknowledged. References
, M. (2002). Tourism as a long-run economic growth Balaguer, J., & Cantavella-Jorda factor: The Spanish case. Applied Economics, 37(7), 877e884. ~ a. (2016). Balanza de Pagos de Espan ~ a frente a otros residents de la zona Banco de Espan Euro y al resto del mundo. Resumen y detalle de la cuenta corriente www.bde.es. Breitung, J. (2002). Non-parametric tests for unit root and cointegration. Journal of Econometrics, 108, 343e364.


s-Jime
nez, I., & Pulina, M. (2016). Has the tourism-led growth hypothesis Brida, J., Corte been validated? A literature review. Current Issues in Tourism, 19(5), 394e430. Campos, J., Ericsson, N., & Hendry, D. (1996). Cointegration tests in the presence of structural breaks. Journal of Econometrics, 70, 187e220. Carrion-i-Silvestre, J., Kim, D., & Perron, P. (2009). GLS based unit root tests with multiple structural breaks under both the null and the alternative hypotheses. Econometric Theory, 25, 1754e1792. Choi, I. (2015). Almost all about unit roots. Foundations, developments and applications. New York: Cambridge University Press.
s-Jime
nez, I., & Pulina, M. (2010). Inbound tourism and long-run economic Corte growth. Current Issues in Tourism, 13(1), 61e74. ~ a, 1995-2014. De la Fuente, A. (2015). Series enlazadas de empleo y VAB para Espan ~ ola e 2015/11. FEDEA e Instituto de An
Estudios sobre la Economía Espan alisis
mico (CSIC). Econo De la Fuente, A. (2016). Series enlazadas del PIB y otros agregados de contabilidad ~ a, 1955-2014. Estudios sobre la Economía Espan ~ ola e Nacionalidad para Espan
lisis Econo
mico (CSIC). 2016/02. FEDEA e Instituto de Ana Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427e431. Dickey, D. A., & Fuller, W. A. (1981). Distribution of the estimators for autoregressive time series with a unit root. Econometrica, 49, 1057e1072. Elliot, G., Rothenberg, T., & Stock, J. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64, 813e836. Engle, R. F., & Granger, C. W. (1987). Co-Integration and error correction: representation, estimation, and testing. Econometrica, 55(2), 251e276. EXCELTUR. (2014). La importancia del turismo y sus efectos multiplicadores a nivel
mico. Comparacio
n del impacto del turismo en Comunidades Auto
nomas y autono ~ a. http://www.exceltur.org/pib-y-empleo-turistico-por-c-c-a-a/. Espan Hendry, D., & Neale, A. (1991). A Monte Carlo study of the effects of structural breaks on test for unit roots. In P. Hackl, & A. Westlund (Eds.), Economic structural change: Analysis and forecasting (pp. 95e119). Berlin: Springer-Verlag. ~ a. (2016). Contabilidad Anual Nacional INE Instituto Nacional de Estadística de Espan ~ a base 2010. http://www.ine.es/jaxi/menu.do?type¼pcaxis&path¼% de Espan 2Ft35%2Fp008&file¼inebase&L¼0. Instituto de Estudios Turísticos IET. (2016). Movimientos Turísticos en Fronteras. Madrid. http://estadisticas.tourspain.es/. Johansen, S. (1988). Statistical analysis of cointegrating vectors. Journal of Economic Dynamics and Control, 12, 231e254. Johansen, S. (1995). Likelihood-based inference in cointegrated vector autorregressive models. New York: Oxford University Press Incorporated. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration ewith applications to the demand for money. Oxford Bulletin of Economcis and Statistics, 52, 169e210. Johansen, S., & Juselius, K. (1992). Structural tests in a multivariate cointegration analysis of the PPP and the UIP for UK. Journal of Econometrics, 53, 211e244. Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54, 159e178. Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. The Review of Economics and Statistics, 85(4), 1082e1089. Lee, J., & Strazicich, M. C. (2004). Minimum LM unit root test with one structural break. Manuscript. Department of Economics, Appalachian State University. Maluquer de Motes, J. (2009). Del caos al cosmos: una nueva serie enlazada del ~ a entre 1850 y 2000. Barcelona: Primer Producto Interior Bruto de Espan
n Espan ~ ola de Historia Econo
mica (AEHE), 9 de Encuentro Anual de la Asociacio septiembre de 2009. Martín, P., & Pons, M. (2005). Sistema monetario and Financiero. In A. Carreras, &
ricas de Espan ~ a, siglos XIX-XX (pp. 644e706). X. Tafunell (Eds.), Estadísticas histo
n BBVA. Fundacio Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69(6), 1519e1554.
s-Jime
nez, I. (2007). Tourism, capital good imports and Nowak, J., Sahli, M., & Corte economic growth: Theory and evidence for Spain. Tourism Economics, 13(4), 515e536. OECD Organisation for Economic Cooperation and Development. (2016). Statistical Database. http://stats.oecd.org/. Pablo-Romero, M. P., & Molina, J. A. (2013). Tourism and economic growth: A review of empirical literature. Tourism Management Perspectives, 8, 28e41.
n, A., Rubia, A., & Moreno, L. (2016). Economic crisis and tourism Perles, J., Ramo competitiveness in Spain: Permanent effects or transitory shocks? Current Issues in Tourism, 19(12), 1210e1234. Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57, 1361e1401. Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis. In S. Strom (Ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium. Cambridge University Press. Pesaran, M. H., Shin, Y., & Smith, R. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289e326. Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression.

J.F. Perles-Ribes et al. / Tourism Management 61 (2017) 96e109 Biometrika, 75, 335e346. Song, H., Dwyer, L., Li, G., & Cao, Z. (2012). Tourism economic research: A review and assessment. Annals of Tourism Research, 39(3), 1653e1682. Tena, A. (2005). Sector exterior. In A. Carreras, & X. Tafunell (Eds.), Estadísticas
ricas de Espan ~ a, siglos XIX-XX (pp. 573e644). Fundacio
n BBVA. histo Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66, 225e250. WTO World Tourism Organization. (2015). Tourism highlights, 2015 Edition. Madrid: OMT.

109 Antonio Rubia. The author research is focused in financial econometrics and empirical finances as well as tourism economics. He has published several articles related to these fields in prestigious international journals such as Oxford Bulletin of Economics and Statistics, Econometric Theory and Tourism Economics.


F. Perles. The author research is focused on tourism Jose services, destination competitiveness as well as residential tourism. He has published several articles related to these fields in prestigious international journals such as Tourism Management, Tourism Economics and Current Issues in Tourism and participated in several conferences on these papers.


n. The author research is focused on tourism Ana Ramo services, firm competitiveness as well as the innovation and new technologies applied to tourism sector. She has published several articles related to these fields in prestigious international journals such as Tourism Management, Tourism Economics and Current Issues in Tourism and several monographs and book chapters.

Luis Moreno. The author research is focused on tourism services, airline industry as well as the innovation and new technologies applied to tourism sector. He has published several articles related to these fields in prestigious international journals such as European Journal of Operational Research, Tourism Economics and Journal of Air Transport Management.