Abstract:
ﺻﻨﻌﺖ ﮔﺮدﺷﮕﺮی ﻧﻘﺶ ﻣﻬﻤﻲ دراﻗﺘﺼﺎد ﻛﺸﻮرﻫﺎ دارد؛ ﺑﻪﻃﻮری ﻛﻪ اﻗﺘﺼﺎد ﺑﺮﺧﻲ از ﻛﺸﻮرﻫﺎ ﺷﺪﻳﺪا واﺑﺴﺘﻪ ﺑﻪ ﺗﺤﻮﻻت اﻳﻦ ﺻﻨﻌﺖ اﺳﺖ. ﺑﺮاﻳﻦاﺳﺎس، ﻣﻘﺎﻟﻪ ی ﺣﺎﺿﺮ ﺑﻪﺑﺮرﺳﻲ ﻋﻮاﻣﻞ ﻣﺆﺛﺮ ﺑﺮ ﺗﺮاﻛﻢ ﮔﺮدﺷﮕﺮی در ﻛﺸﻮرﻫﺎی درﺣﺎل ﺗﻮﺳﻌﻪ ی ﻣﻨﺘﺨﺐ ﻣﻲ ﭘﺮدازد. ﺑﺮای اﻳﻦﻣﻨﻈﻮر، از ﻣﺪل داده ﻫﺎی ﺗﺎﺑﻠﻮﻳﻲ ﻛﺸﻮرﻫﺎی ﻣﻨﺘﺨﺐ ﻃﻲ دوره ی زﻣﺎﻧﻲ2010- 1995 اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. ﻧﺘﺎﻳﺞ ﺑﻪ دﺳﺖ آﻣﺪه از اﻳﻦ ﻣﻄﺎﻟﻌﻪ ﺣﺎﻛﻲ از آن اﺳﺖ ﻛﻪ ﻣﺪل ﺟﻐﺮاﻓﻴﺎی ﺟﺪﻳﺪ اﻗﺘﺼﺎدی ﺗﻮﺿﻴﺢ ﻣﻨﺎﺳﺒﻲ ﺑﺮای ﺗﺮاﻛﻢ ﮔﺮدﺷﮕﺮ در ﻛﺸﻮرﻫﺎی درﺣﺎل ﺗﻮﺳﻌﻪ ی ﻣﻨﺘﺨﺐ ﻓﺮاﻫﻢ ﻣﻲ ﻛﻨﺪ. ﻣﺸﺨﺼﺎ ﺻﺮﻓﻪ ﻫﺎی ﻧﺎﺷﻲ از ﻣﻘﻴﺎس اﻧﺪازه ی ﻧﺴﺒﻲ ﺑﺎزار( و ﺳﻄﺢ ﻧﺴﺒﻲ ﺗﻮﺳﻌﻪ ﻳﺎﻓﺘﮕﻲ دارای اﺛﺮ ﻣﺜﺒﺖ ﺑﺮ ﺗﺮاﻛﻢ ﮔﺮدﺷﮕﺮ ﻫﺴﺘﻨﺪ. ﻫﻤﭽﻨﻴﻦ، ﻫﺰﻳﻨﻪ ی ﮔﺮدﺷﮕﺮی اﺛﺮ ﻣﻨﻔﻲ و ﻣﻌﻨﺎدار ﺑﺮ ﺗﺮاﻛﻢ ﮔﺮدﺷﮕﺮ دارد. اﻓﺰون ﺑﺮ اﻳﻦ، اﻧﺘﻈﺎرات و ﻋﺎدات رﻓﺘﺎری ﮔﺮدﺷﮕﺮان ﻧﻴﺰ ﻣﻮﺟﺐ اﻓﺰاﻳﺶ ﺗﺮاﻛﻢ ﮔﺮدﺷﮕﺮ ﻣﻲ ﺷﻮد. ﺑﺮ اﺳﺎس ﻧﺘﺎﻳﺞ ﺗﺤﻘﻴﻖ ﺣﺎﺿﺮ، ﺗﻮﺻﻴﻪ ﻣﻲﺷﻮد در ﺑﺮﻧﺎﻣﻪ ﻫﺎی ﺗﻮﺳﻌﻪ ی اﻗﺘﺼﺎدی، ﻮﺟﻪ وﻳﮋه ای ﺑﻪ ﺻﻨﻌﺖ ﮔﺮدﺷﮕﺮی و ﻣﻨﺎﻓﻊ ﺣﺎﺻﻞ از ﺗﺮاﻛﻢ ﮔﺮدﺷﮕﺮی ﻣﺒﺬول ﮔﺮدد.
Introduction
Rapid growth of tourism industry after 1950s is one of the main and most important characteristics of tourism industry.But according to the statistics of World Tourism Organization, tourism activities and its incomes are not distributed equally across the world. It can certainly be said that the share of developed countries in tourism income is higher than the developing countries (WTO,2012).Interestingly, the distribution of tourism among developing countries is non-uniform. What are the determinants of tourism agglomeration?To answer this question, it can be said that geographical variables such as economics of scale are some of the factors among these. Thus, the distribution of tourism activities in the framework of new economic geography (NEG) is considered.In a new economic geography framework (NEG), firms tend to agglomerate where the large regional marketsare (Chen et. al, 2008). In terms of distribution of tourism activities, it seems that in addition to factors such as accessing sea, appropriate climate, historical sites in countries with a high tourism agglomeration, accessing larger markets, economies of scale, tourism costs and development level are important. Regarding the lack of equal distribution of tourism activities in selected developing countries,assessing determinants of the tourism agglomeration is essential.
Materials And Methods
In this research, in order to examine the hypothesis and to estimate the model, the Econometric Method for panel data is used for 67 selected developing countries (based on data availability) from 1995 to 2010. The model we use adopts the following form: Where, is tourism agglomeration (the number of tourists in the studied country to the total global tourists); is the relative economies of scale (relative market size); is the tourism cost (relative consumer price index); is the relative development level (relative human development index) and is the behavioral expectations and habits previous time tourism agglomeration), in s country. Discussion and
Results
The results of the unit root test indicated that none of the studied variables were on stationary level and all of them would be stationary through making one time difference. But, based on Kao-Cointegration Test, the hypothesis based on lack of agglomeration is rejected and non-spurious regression is approved. Then in order to evaluate the Panel model, first according to the statistics of F-Limer, a selection is done between Panel data and Pooled data methods. Regarding the F-Limer test, Panel data can be used in the evaluating process. After making sure that model evaluating is carried out as Panel data, the most important question that remains is to finding out whether sectional effects are fixed or random. According to the results of Hausman test, the fixed effects model is accepted as the evaluating model. Regarding the results obtained from F-Limer and Hausman tests, the pattern is estimated by using the fixed effects model. According to results, regression adjusted determination coefficient is equal to %99; thus, independent variables describe %99 of dependent variables changes. Also, common F test reflects that all regression is meaningful. According to the obtained results from this study, coefficients of Variables are meaningful and work in accordance with theory.
Conclusion
According to the results of this research, a relative amount of economics of scale has a direct and meaningful relationship with tourism agglomeration. The relative cost of tourism has a meaningful and negative effect on tourism agglomeration. Also, development has positive and meaningful effect on tourism agglomeration in selected countries. So the economic and social development of countries will increase tourism agglomeration. In addition, expectations and behavioral habits have positive and meaningful effects on tourism agglomeration in selected countries.
Machine summary:
جدول (٣): بررسی پایایی متغیرهای مورد استفاده در برآورد تراکم گردشگری در کشورهای درحال توسعه ی منتخب آزمون ها متغیرها LLC IPS ADF- FISHER PP- FISHER تراکم گردشگری (تفاضل اول ) -27/6304 (0/0000) -20/6526 (0/0000) 587/205 (0/0000) 726/533 (0/0000) صرفه های ناشی از مقیاس (تفاضل اول ) -20/6993 (0/0000) -15/5554 (0/0000) 470/762 (0/0000) 505/460 (0/0000) هزینه ی گردشگری ( تفاضل اول ) -19/8299 (0/0000) -14/7436 (0/0000) 446/323 (0/0000) 530/417 (0/0000) توسعه یافتگی (تفاضل اول ) -13/1771 (0/0000) -12/0807 (0/0000) 393/722 (0/0000) 461/694 (0/0000) منبع : محاسبات تحقیق (اعداد داخل پرانتز نشان دهنده ی مقادیر احتمال است ) باتوجه به نتایج حاصل از آزمون ها در جدول (٣)، هیچ کدام از متغیرها در سطح مانا نبوده و همه ی آن ها با یک مرتبه تفاضل گیری مانا می شوند.
جدول (٥): برآورد عوامل مؤثر بر تراکم گردشگری در کشورهای درحال توسعه ی منتخب با روش اثرات ثابت طی دوره ی زمانی ٢٠١٠-١٩٩٥ نام متغیرها ضرایب آماره ی t مقدار احتمال (P-Value) Constant -0/298831 -2/511063 0/0122 lnESs 0/104709 3/092305 0/0020 lnTCs -0/041132 -2/434436 0/0151 lnDVPs 1/165776 2/297734 0/0218 lnHBs 0/790814 49/44051 0/0000 ضریب تعیین تعدیل شده 0/98 آماره ی معناداری کل رگرسیون 1003/552 F- لیمر 3/814623 ----- 0/0000 هاسمن 128/969300 ----- 0/0000 منبع : محاسبات تحقیق حاضر 1 Hausman test بر اساس جدول (٥) ضریب تعیین تعدیل شده ی رگرسیون در مدل برابر با ٩٨درصد است که بر این اساس متغیرهای مستقل درصد بالایی از تغییرات متغیر وابسته را توضیح می دهند.