PRIZREN SOCIAL SCIENCE JOURNAL / Volume 2, Issue 2; May - August 2018 / ISSN: 2616-387X 35 THE RELATIONSHIP BETWEEN TOURISM REVENUES AND FINANCIAL RATIOS OF ENTERPRISES IN THE BIST TOURISM: PANEL DATA ANALYSIS Tanju GÜDÜK Gokceada Vocational School Canakkale Onsekiz Mart University tanjuguduk@comu.edu.tr ABSTRACT In today’s world, many countries that want to realize economic development use tourism as a tool. The first definition of tourism that supported this was made in 1910 by the Austrian economist Hermann Von Schullar. He defined tourism as “the whole of the activities that relate to the economic direction of the movement that comes from the arrival of strangers from another country, city or region and their temporary stay” (Kozak vd, 2009: 1). In this study, the relationship between the tourism revenues obtained from TUIK and the financial ratios of the enterprises in the Bist Tourism (XTRZM) Index are examined by panel data analysis. For this purpose, the financial ratios of the tourism revenues and the enterprises in the Bist Tourism Index were used between 2007-2016. In also, hausman test was applied to the data for panel data analysis and the results indicate that there is a random effect. The acceptance of the null hypothesis implies that there is no correlation between random effects and explanatory variables and that constant effects on unit and time dimensions are not taken into consideration. Key Words: Tourism Revenues, Panel Data Analysis, Bist Tourism Jel Code: D1, E2, I13 PRIZREN SOCIAL SCIENCE JOURNAL / Volume 2, Issue 2; May - August 2018 / ISSN: 2616-387X 36 1. INTRODUCTION With the globalization of the world and the economic crises that countries have experienced, tourism comes into prominence day by day. In also, lots of countries all over the world take advantage of tourism for the closure of foreign trade deficits. The international tourism movements, which have grown steadily since the 1950s, have expanded and diversified as much as everyday. The number of international tourists increased from 25 million in 1950 to 1.2 million in 2016. Over the past several years, the number of international tourists and tourism revenues has been steadily increasing, despite the large number of crises that have been influential in some periods and affecting different tourism regions in different ways. It is estimated that international arrivals will reach 1.4 billion by 2020 and 1.8 billion by 2030. According to the figures of the year 2016, international tourism movements increased by 3.9% compared to the previous year and reached 1 billion 235 million people. Also, the expenditures of tourists traveling internationally amounted to 1.22 billion dollars in 2016. According to the statistics on employment in the tourism sector, it is seen that the travel and tourism industry provides employment opportunities to 109 million people in 2016 (UNWTO, 2017; TÜROFED, Turizm Raporu, 2017). Turkey's tourism revenues increased by 37.6 percent compared to the same period of the previous year in the third quarter of this year reached 11 billion 391 million 668 thousand dollars. 77 percent of the tourism income from foreign visitors, 23 percent of citizens who reside abroad were obtained from the camp. 8 billion 855 million 369 thousand dollars in personal spending and 2 billion 536 million 299 thousand dollars in package tour expenses were made in this quarter (TÜİK, 2017). This study, using the data of the companies in the BİST Tourism index, investigates Current Rate (CR), Total Debt / Equity (TDE), Stock Turnover Rate (STR), Profit Per Share (PPS), Net Sales (NS), variables' impact on tourism revenues (TR). 2. LITERATURE REVIEW When we look at the studies about tourism revenues, it is seen that there are many studies in the literature. In a survey conducted by Weber (2001) in Australia, exchange rate changes have affected tourism demand. Dritsakis (2004) argues that there is a relationship between international tourism income and real effective exchange rate and real growth. Sequeira and Campos (2005) found that tourism revenues did not have an impact on economic growth. The research was conducted on Africa, Asia, Latin America and PRIZREN SOCIAL SCIENCE JOURNAL / Volume 2, Issue 2; May - August 2018 / ISSN: 2616-387X 37 European Countries. Khalil et al. (2007) have stated that there is a strong relationship between tourism revenues and growth. Mervar and Payne’s (2007) the impact of the demand for foreign exchange on tourism in Croatia is weak. Fayissa et al. (2007) have concluded that tourism revenues have an effect on GDP and economic growth. Lee and Chang (2008) have come to the conclusion that per capita tourism spending is influential on the number of tourists and real exchange rate growth. Bahar and Bozkurt (2010) found that a positive and meaningful relationship between tourism and economic growth in terms of developing countries. Ünlüönen and Şahin (2011) claimed that all income entering the tourism sector directly affects employment in the tourism sector and indirectly affects employment in other sectors. Samimi et al. (2011) and Lashkarizadeh et al. (2012) argue that there is a long-term bilateral relationship between tourism revenues and growth, and that both variables influence each other. Srinivasan et al. (2012) in Sri Lanka have observed that tourism revenues have a positive impact both on short and long term on economic growth. Chatziantoniou et al. (2013) indicate that ndicate that oil specific demand shocks contemporaneously affect inflation and the tourism sector equity index, whereas these shocks do not seem to have any lagged effects. By contrast, aggregate demand oil price shocks exercise a lagged effect, either directly or indirectly, to tourism generated income and economic growth. Krelling et al. (2017) found that the trade-off local authority's make between investments to prevent/remove beach litter and the potential reduction in income from a tourist destination change. 3. METHODOLOGY The data used in this study were obtained from the website of the Kamuyu Aydınlatma Platformu (2017), the related companies' own sites, the Financial Information News Network (2017) website and TÜİK official site. The data set consisted of 10 years observation values covering the years 2007-2016 and analyzes were made using Eviews 9 package program. In this study, located in Bist Tourism Index (AVTUR, AYCES, ETILR, KSTUR, MAALT, MARTI, MERIT, METUR, PKENT, TEKTU, ULAS, UTPYA), with tourism revenues between the years of 2007- 2016 in Turkey it was examined using data generated by the company's twelve variables. Using the financial data of the companies included in the Bist Tourism Index in Annex 1; The model created to investigate the relationship between variable of Tourism Revenue (TG) and variables of Current Rate (CO), Total Debt / Equity (TBO), Stock Turnover Rate (STH), Profit Per Share (HBK), Net Sales (NS): TGit = β0 + β1COit + β2TBOit + β3STHit + β4FKit + β5HBKit + β6NSit + εit PRIZREN SOCIAL SCIENCE JOURNAL / Volume 2, Issue 2; May - August 2018 / ISSN: 2616-387X 38 Table 1: Pooled Estimate Results Dependent Variable: LOGTG? Method: Pooled Least Squares Date: 01/06/18 Time: 22:33 Sample (adjusted): 2008 2016 Included observations: 9 after adjustments Cross-sections included: 10 Total pool (unbalanced) observations: 34 Cross sections without valid observations dropped Variable Coefficient Std. Error t-Statistic Prob. LOGCO? -2.63E-17 1.15E-16 -0.228664 0.8208 LOGTBO? 2.97E-17 5.00E-17 0.594671 0.5568 LOGSTH? -1.92E-17 7.59E-17 -0.253595 0.8017 LOGFK? 1.01E-16 9.28E-17 1.084109 0.2876 LOGHBK? -3.79E-17 7.87E-17 -0.481940 0.6336 LOGNS? 0.374883 0.283415 1.322735 0.1966 R-squared 0.115947 Mean dependent var 41.83524 Adjusted R-squared -0.041919 S.D. dependent var 73.42058 S.E. of regression 74.94364 Akaike info criterion 11.63014 Sum squared resid 157263.4 Schwarz criterion 11.89949 Log likelihood -191.7123 Hannan-Quinn criter. 11.72199 Durbin-Watson stat 0.870345 (LOGTG)it = 2.63E-17(LOGCO)it + 2.97E-17(LOGTBO)it + (-1.92E-17)(LOGSTH)it + 1.01E-16(LOGFK)it + (-3.79E-17)(LOGHBK)it + 0.374883(LOGNS) + εit Table 2: Random Impact Test Results Dependent Variable: LOGTG? Method: Pooled EGLS (Cross-section random effects) Date: 01/06/18 Time: 22:35 Sample (adjusted): 2008 2016 Included observations: 9 after adjustments Cross-sections included: 10 Total pool (unbalanced) observations: 34 Swamy and Arora estimator of component variances Cross sections without valid observations dropped Variable Coefficient Std. Error t-Statistic Prob. C -92.25602 108.1784 -0.852814 0.4013 LOGCO? 1.39E-17 1.76E-16 0.078739 0.9378 PRIZREN SOCIAL SCIENCE JOURNAL / Volume 2, Issue 2; May - August 2018 / ISSN: 2616-387X 39 LOGTBO? 1.76E-16 1.77E-16 0.995130 0.3285 LOGSTH? 6.08E-17 1.42E-16 0.428743 0.6715 LOGFK? 1.59E-16 1.12E-16 1.423693 0.1660 LOGHBK? -9.04E-17 1.35E-16 -0.669952 0.5086 LOGNS? 0.348663 0.291160 1.197497 0.2415 Random Effects (Cross) _AVTUR--C -37.44688 _AYCES--C 31.44854 _ETILR--C -34.96717 _KSTUR--C -8.371120 _MAALT--C 15.79555 _MARTI--C -14.56646 _METUR--C 20.66410 _PKENT--C -16.40221 _TEKTU--C -0.943638 _UTPYA--C 44.78927 Effects Specification S.D. Rho Cross-section random 51.72806 0.3331 Idiosyncratic random 73.19280 0.6669 Weighted Statistics R-squared 0.174677 Mean dependent var 24.33830 Adjusted R-squared -0.008728 S.D. dependent var 69.47116 S.E. of regression 69.13964 Sum squared resid 129067.8 F-statistic 0.952411 Durbin-Watson stat 1.010426 Prob(F-statistic) 0.475139 Unweighted Statistics R-squared 0.112436 Mean dependent var 41.83524 Sum squared resid 157888.0 Durbin-Watson stat 0.825987 According to Hausman test results in Table 3 Probe = 0.9072> 0.050, the H0 hypothesis was accepted at both the unit and time dimensions at the level of 5% significance. So there is a random effect. The acceptance of the null hypothesis implies that there is no correlation between random effects and explanatory variables and that constant effects on unit and time dimensions are not taken into consideration. PRIZREN SOCIAL SCIENCE JOURNAL / Volume 2, Issue 2; May - August 2018 / ISSN: 2616-387X 40 Table 3: Hausman Test Results and Random Impact Forecast Results Correlated Random Effects - Hausman Test Pool: Untitled Test cross-section random effects Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob. Cross-section random 2.132098 6 0.9072 Cross-section random effects test comparisons: Variable Fixed Random Var(Diff.) Prob. LOGCO? -0.000000 0.000000 0.000000 0.4865 LOGTBO? 0.000000 0.000000 0.000000 0.9812 LOGSTH? 0.000000 0.000000 0.000000 0.8922 LOGFK? 0.000000 0.000000 0.000000 0.6186 LOGHBK? -0.000000 -0.000000 0.000000 0.9154 LOGNS? 0.375365 0.348663 0.002874 0.6184 Cross-section random effects test equation: Dependent Variable: LOGTG? Method: Panel Least Squares Date: 01/06/18 Time: 22:36 Sample (adjusted): 2008 2016 Included observations: 9 after adjustments Cross-sections included: 10 Total pool (unbalanced) observations: 34 Variable Coefficient Std. Error t-Statistic Prob. C -112.1366 125.8953 -0.890713 0.3848 LOGCO? -3.70E-17 1.91E-16 -0.193855 0.8485 LOGTBO? 1.78E-16 1.91E-16 0.930776 0.3643 LOGSTH? 8.36E-17 2.20E-16 0.379930 0.7084 LOGFK? 2.02E-16 1.41E-16 1.431415 0.1694 LOGHBK? -1.08E-16 2.12E-16 -0.508674 0.6172 LOGNS? 0.375365 0.296054 1.267893 0.2210 Effects Specification Cross-section fixed (dummy variables) R-squared 0.457925 Mean dependent var 41.83524 Adjusted R-squared 0.006195 S.D. dependent var 73.42058 PRIZREN SOCIAL SCIENCE JOURNAL / Volume 2, Issue 2; May - August 2018 / ISSN: 2616-387X 41 In accordance with the data set, the natural logarithms of the series are taken first. The results of the Hausman Test are given in Table 3. According to the test results obtained, Probe= 0.9072> 0.050, the H0 hypothesis was accepted at both the unit and time dimensions at the level of 5% significance. So, there is a random effect. The acceptance of the null hypothesis implies that there is no correlation between random effects and explanatory variables and that constant effects on unit and time dimensions are not taken into consideration. 4. RESULT This paper empirically investigated both the short-run and long- run effects of inbound tourism on financial ration in Turkey, directly to Bist Tuourizm index over the period of 2007–2016. We collect yearly data betwen these period in BIST tourizm index. The model created to investigate the relationship between variable of Tourism Revenue and variables of Current Rate, Total Debt / Equity , Stock Turnover Rate, Profit Per Share, Net Sales. The analysis in Table 2 we analyzed Random effect betwen values, as the result shows that there is no correlation between random effects and explanatory variables and that continuous effects on unit and time dimensions are not taken into consideration. The Hausman test was also conducted to prove this data, the test results were significant at the 5% level (Probe= 0.9072> 0.050), there is a random effect. The acceptance of the H0 hypothesis indicates that there is no correlation between random effects and explanatory variables and that continuous effects on unit and time dimensions are not taken into consideration. The result is that although there is a harmony between the data sets, the Bist tourism index and Finacial ratios does not seem to be a direct contribution to tourism. REFERENCES Bahar O. ve Bozkurt K. (2010). “Tourism and Economic Growth Relationship in Developing Countries: Dynamic Panel Data Analysis,” Anatolia: Turizm Araştırmaları Dergisi, 21(2), Güz, 255-265. Chatziantoniou, I., Filis, G., Eeckles, B. & Apostolakis, A., “Prices, Tourism Income and Economic Growth: A Structural VAR Approach for European Mediterranean Countries”, Tourism Management 36, 331-341. 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