Key Words: Political stability, Trade openness, Tourism development, Panel data analysis JEL Classification: C33, D70, F41, L83

Transcription

1 POLITICAL STABILITY AND TOURISM DEVELOPMENT IN MEDITERRANEAN COUNTRIES: A PANEL COINTEGRATION AND CAUSALITY ANALYSIS Yilmaz Bayar Usak University/Department of Economics yilmaz.bayar@usak.edu.tr Behçet Yener Ramada Uşak Hotel behcetyener@gmail.com Abstract Tourism has become an important component of national income in many countries together with decreasing and removing the obstacles over the intercountry movement of people and considerable improvements in transportation. Therefore, many countries have taken measures to attract tourists and in turn increase their tourism revenues. This study researches the effect of political stability on tourism development in 18 Mediterranean countries during the period with use of error-correction-based panel cointegration test of Westerlund (2007) and panel causality test of Dumitrescu and Hurlin (2012). The findings revealed that both political stability and trade openness affected the tourism development positively in the long run. Furthermore, there was a two-way causality between political stability and tourism development in the short run. Key Words: Political stability, Trade openness, Tourism development, Panel data analysis JEL Classification: C33, D70, F41, L83 1

2 1. INTRODUCTION Tourism rector has become an important source of revenue and an item of international trade together with the globalization and the significant development in transportation. International tourist arrivals have reached 1,235 million in 2016 from 25 million globally in 1950 and the countries hosting the international tourists earned worldwide have increased US$ 1,220 billion in 2016 from US$ 2 billion in Furthermore, international tourists spent a US$ 216 billion in exports through international passenger transport services in In conclusion, international tourism represents 7% of the global exports of goods and services (UNWTO, 2017). Tourism includes many economic implications for the nations. In this regard, tourism can improve the balance of payments, make a contribution to economic growth through increasing the public and private investments in the sector and demand for local goods and services, decrease unemployment by creating jobs and also raise the tax revenues (Pablo-Romero and Molina, 2013). However, many social, economic and political factors are determinative for tourism attraction. In this study, we will focus on the impact of political stability on tourism development, because political instability theoretically can affect tourism development negatively through increasing security concerns of the tourists with leading social unrest (Saha and Yap, 2014:510). Mediterranean region consists of the countries with different economic development levels, political stability, and openness. This study researches the interaction among political stability, trade openness, and tourism revenues in 18 Mediterranean countries during the period with use of panel cointegration test of Westerlund (2007) and panel causality test of Dumitrescu and Hurlin (2012). In this regard, the paper will be one of the early studies investigating the interaction among tourism development, political stability, and trade openness. The relevant empirical literature was summarized in the next section and Section 3 gives information about dataset and econometric methodology. Then Section 4 presents empirical findings and the paper is concluded with Conclusion section. 2

3 2. LITERATURE REVIEW A limited number of empirical studies have researched the impact of political stability and trade openness on tourism revenues and the studies revealed that political stability affected the tourism development negatively (e.g., see Basu and Marg, 2012; Ingram et al., 2012; Saha and Yap, 2014; Mushtaq and Zaman, 2014)). On the other side, the empirical studies examining the interplay between trade openness and tourism development reached mixed findings for different countries (e.g. see, Shan and Wilson, 2001; Habibi et al., 2009; Surugiu and Surugiu, 2011; Santana-Gallego et al., 2016) Basu and Marg (2012) researched the effect of political instability including terrorism on tourism in Egypt, Jordan and Lebanon from Middle East region during different periods between 1997 and 2011 for each country and discovered that political stability affect tourism receipts negatively. On the other hand, Ingram et al. (2012) investigated the impact of political instability on tourism in Thailand with using questionnaire and semi-structured interviews and discovered that political stability affected the tourism negatively, but the impact was found to be relatively higher in the respondent who have not been in Thailand before. Saha and Yap (2014) also examined the impact of political instability and terrorism on tourism revenue and tourist arrivals in 139 countries during period with panel regression analysis and revealed that political instability affected the tourism development negatively. On the other side, Mushtaq and Zaman (2014) researched the long-run relationship among tourism, political instability, and terrorism in 4 South Asian Association for Regional Cooperation (SAARC) countries including Bangladesh, India, Pakistan, and Srilanka during the period employing panel cointegration analysis and discovered that political stability affected tourism receipts negatively. Shan and Wilson (2001) analyzed the causality between tourism and international trade for China in Toda and Yamamoto (1995) causality test and revealed a twoway causality. On the other side, Habibi et al. (2009) researched the major determinants of tourism in Malaysia over period employing dynamic panel regression and revealed that trade openness had no significant effects on tourism. Surugiu and Surugiu (2011) also analyzed the causal interaction among tourism and trade openness in Romania during period with Granger causality test and revealed a unilateral causality from trade openness to tourism 3

4 revenues. Finally Santana-Gallego et al. (2016) researched the relationship between tourism and trade in 195 countries in 2012 with employing gravity model and found that tourism increased the trade. 3. DATA AND ECONOMETRIC METHODOLOGY We investigated the short and long run interaction among political stability, trade openness, and tourism development in 18 Mediterranean countries during the period employing panel cointegration test of Westerlund (2007) and panel causality test of Dumitrescu and Hurlin (2012) Data International tourism receipts as % of GDP representing tourism development were used as the dependent variable in the study. On the other side, political stability and absence of violence (PS) variable was employed as a proxy for political stability. The value of the PS index indicates the perceptions of the likelihood of political instability and/or politically motivated violence, including terrorism and varies from -2.5 to 2.5 and higher values mean higher levels of political stability (see details Kaufmann et al. (2010) and World Bank (2017c)). Also sum of export and import as % of GDP representing trade openness was used as a control variable and all the variables were extracted from the database of World Bank (a, b, c, d). The existence of data determined our sample and the sample consisted of 18 Mediterranean countries including Albania, Algeria, Bosnia-Herzegovina, Croatia, Cyprus, Egypt, France, Greece, Israel, Italy, Lebanon, Malta, Morocco, Slovenia, Spain, Tunisia, Turkey, and West Bank and Gaza except Gibraltar, Libya, Monaco, Montenegro, and Syria. Table 1: Data Description Variables Description Data Source TREV International tourism, receipts (% of GDP) World Bank (2017a and 2017b) PS Political stability and absence of World Bank (2017c) violence/terrorism TO Sum of export and import (% of GDP) World Bank (2017d) Gauss 11.0, WinRATS Pro. 8.0, and Stata 14.0, programs were utilized for implementation of econometric analysis. The correlation matrix of the dataset was displayed in Table 2 and the correlation matrix indicated that there was positive 4

5 correlation between both tourism revenues and political stability and trade openness and tourism revenues. Table 2: Correlation Matrix of the Dataset DTREV DPS DTO DTREV DPS DTO Econometric methodology Westerlund (2007) error-correction based panel cointegration test and Dumitrescu and Hurlin (2012) causality test were used to analyze the interaction among tourism revenues, political stability and trade openness. Westerlund (2007) errorcorrection based panel cointegration test considers both cross-sectional dependence and heterogeneity and requires that all the variables are I(1). The test includes four cointegration tests based error correction model and in turn calculates four test statistics (two are called as panel statistics and the other two are called as group average statistics). Westerlund (2007) cointegration examines the null hypothesis by investigating that the error correction term equals to zero or not in a conditional error correction model. The null hypothesis asserting that there is no cointegrating relationship is rejected in case the null hypothesis about non-existence of error correction is denied. The simulation results verified that the results of Westerlund (2007) cointegration test are more robust relative to the tests based on residuals such as Pedroni 1999 and 2004 for small samples. 4. EMPIRICAL ANALYSIS In the empirical analysis, test of Pesaran (2004), test of Pesaran et al. (2008) and delta tilde and adjusted delta tilde tests of Pesaran and Yamagata (2008) are used to see the cross-sectional dependence among the variables. Then, Westerlund (2007) panel cointegration test is used to investigate the long run relationship among the variables. Lastly, Dumitrescu and Hurlin (2012) causality test is used to analyze the causal interaction among tourism revenues, political stability and trade openness. 5

6 4.1. Results of cross-sectional dependence and homogeneity tests First cross-sectional dependence among the cross-section units was analyzed with test of Pesaran (2004) and test of Pesaran et al. (2008), because the cross-section dimension of the dataset (N=18) was found to be higher the time dimension of the dataset (T=14). The test results were displayed in Table 3 and the null hypothesis (there is cross-sectional independence among the cross-section units) was rejected at 5% significance level and we revealed that there was crosssectional among the series. Then we analyzed the homogeneity of the cointegrating coefficients with adjusted delta tilde test of Pesaran and Yamaga (2008) and the results were given in Table 3. The null hypothesis (cointegrating coefficients are homogeneous) was rejected at 5% significance level and we concluded that the cointegrating coefficients differ among the cross-section units. Table 3: Results of Cross-Sectional Dependence and Homogeneity Tests Cross-sectional dependency tests Variables Test Statistic (P value) (P value) TREV (0.009) (0.001) PS (0.015) (0.004) TO (0.026) (0.000) Homogeneity tests Test Test Statistic P value Results of Pesaran (2007) CIPS unit root test The integration levels of the variables are important for selection of cointegration test and implementation of causality test. So the integration levels of the series were analyzed with CIPS (cross-sectionally augmented Im-Pesaran and Shin (IPS) (2003)) unit root test Pesaran (2007) considering the existence of cross-sectional dependence among the series and the test results were displayed in Table 4. The results revealed that all the variables were I(1). 6

7 Table 4: Results of panel CIPS unit Root Test Variables Constant Constant+Trend TREV DTREV * * PS DPS * * TO DTO * * Note: Maximum lag length was taken as 2 and optimal lag length was determined considering Schwarz information criterion * Null hypothesis was rejected at 5% significance level Results of Westerlund (2007) error-correction based panel cointegration test The cointgerating relationship among tourism revenues, political stability and trade openness was analyzed with panel cointegration test of Westerlund (2007) and the test results were displayed in Table 5. The group test statistics should be considered due to the cross-sectional dependence among the variables. Also the cointegration statistics should be compared with bootstrap critical values by Chang (2004) in case of cross-sectional dependency (Westerlund, 2007). So the null hypothesis (there are no cointegrating relationship among the variables) was rejected and we concluded that there was a cointegrating relationship among the variables. Table 5: Results of Westerlund (2007) Panel Cointegration Test Statistic Value Bootstrap P Value Estimation of cointegrating coefficients The cointegrating coefficients were estimated by FMOLS estimator regarding only heterogeneity and DSUR estimator considering both cross-sectional dependency and heterogeneity and the estimations were displayed in Table 6. The findings of both FMOLS and DSUR estimators indicated that both political stability and trade openness affected the tourism development positively. The 7

8 estimations by FMOLS revealed that 1% increase in PS and TO respectively raise the tourism revenues by 12% and 9.3%. On the other side, the estimations by DSUR indicated that 1% increase in PS and TO respectively raise the tourism revenues by 12.6 % and 10.4%. So both political stability and trade openness were found to be important determinants for tourist attraction in the long run. Table 6: Cointegrating Coefficients PS TO Country FMOLS DSUR FMOLS DSUR Albania 0.129* 0.115* 0.094* 0.104* Algeria 0.105* 0.091* 0.042* 0.078* Bosnia-Herzegovina 0.128* 0.125* 0.061* 0.092* Croatia 0.101* 0.112* 0.110* 0.128* Cyprus 0.052* 0.074* 0.074* 0.091* Egypt 0.083* 0.095* 0.036* 0.105* France 0.254* 0.197* 0.142* 0.174* Greece 0.120* 0.138* 0.119* 0.139* Israel 0.103* 0.120* 0.143* 0.125* Italy 0.162* 0.171* 0.179* 0.166* Lebanon 0.094* 0.105* 0.077* 0.062* Malta 0.117* 0.136* 0.112* 0.127* Morocco 0.146* 0.155* 0.074* 0.094* Slovenia 0.103* 0.123* 0.080* 0.042* Spain 0.118* 0.127* 0.162* 0.131* Tunisia 0.132* 0.142* 0.082* 0.090* Turkey 0.135* 0.149* 0.063* 0.073* West Bank and Gaza 0.092* 0.106* 0.041* 0.052* Panel 0.120* 0.126* 0.093* 0.104* *significant at 5% Autocorrelation and heteroscedasticity problems were eliminated by Newey-West method Dumitrescu and Hurlin (2012) panel causality test The causal interaction among tourism development, political stability, and trade openness was analyzed with causality test of Dumitrescu and Hurlin and the test results were displayed in Table 7. The test results revealed a bidirectional causality between tourism development and political stability and a one-way causality from tourism development to trade openness. So there was a mutual interaction between political stability and tourism development. In other words 8

9 political stability feeds the tourism and in turn tourism feeds the political stability. On the other side, tourism development has significant impact on trade openness by contributing to the transnational flows of goods and services. Tablo 7: Results of Dumitrescu and Hurlin (2012) Panel Causality Test Null Hypothesis Test Test Statistics Prob. TREV PS Whnc Zhnc Ztild PS TREV Whnc Zhnc Ztild TREV TO Whnc Zhnc Ztild TO TREV Whnc Zhnc Ztild CONCLUSION Tourism sector has been expanded significantly in the world together with the globalization process and improvements in the transportation. The significant expansion in tourism sector has potential to affect the economies positively through encouraging the public and private investments, improving the balance of payments, creating jobs. However, many factors will be determinative in tourist attraction. In this study, we researched the interaction among political stability, trade openness, and tourism development in 18 Mediterranean countries during the period employing Westerlund (2007) error correction based panel cointegration test and panel causality test of Dumitrescu and Hurlin (2012). The findings revealed that both political stability and trade openness affected the tourism development positively in the long run. Furthermore, the causality test revealed a bidirectional causality between tourism development and political stability and a one-way causality from tourism revenues to trade openness. So 9

10 there was a mutual interaction between political stability and tourism development. On the other side, tourism development has significant impact on trade openness by contributing to the transnational flows of goods and services. BIBLIOGRAPHY Basu, K., Marg, V.S. (2012), Impact of Political Instability and Terrorism in the Tourism Industry of Three Middle East Countries : An Economic Exploration, Chang, Y. (2004), Bootstrap Unit Root Tests in Panels with Cross-Sectional Dependency, Journal of Econometrics, Vol.120, pp Dumitrescu, E., Hurlin, C. (2012), Testing for Granger Non-causality in Heterogeneous Panels, Economic Modelling, Vol.29, No.4, pp , DOI: /j.econmod Habibi, F., Rahim, K.A., Ramchandran, S., Chin, L. (2009), Dynamic Model for International Tourism Demand for Malaysia: Panel Data Evidence, International Research Journal of Finance and Economics, Issue:33, pp Ingram, H., Tabari, S., Watthanakhomprathip, W. (2013), The impact of Political Instability on Tourism: Case Of Thailand, Worldwide Hospitality and Tourism Themes, Vol. 5, No.1, pp , Im, K. S., Pesaran, M. H., Shin, Y. (2003), Testing for Unit Roots in Heterogeneous Panels, Journal of Econometrics, Vol.115, No.1, pp Kaufmann, D., Kraay, A., Mastruzzi, M. (2010). The Worldwide Governance Indicators : A Summary of Methodology, Data and Analytical Issues. World Bank Policy Research Working Paper No. 5430, [Accessed ] Mushtaq, A., Zaman, K. (2014), The Relationship between Political Instability, Terrorism and Tourism in SAARC Region, Journal of Economic Info, Vol.1, No.1, pp Pablo-Romero, M.P., Molina, J.A. (2013), Tourism and Economic Growth: A Review of Empirical Literature, Tourism Management Perspectives, Vol.8, pp Pesaran, M. H. (2004), General Diagnostic Test for Cross Section Dependence in Panels, Working Paper, University of Cambridge & USC. Pesaran, M. H. (2007), A Simple Panel Unit Root Test in the Presence of Cross- 10

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