訪問日本、中國、韓國、台灣和香港觀光的要因分析-以Panel Data和重力模型(Gravity Model)分析-

全球觀光產業正蓬勃發展「觀光產業」已成為二十一世紀各國最具社會經濟指標的產業。依據UNWTO 估計觀光業的產值占全球GDP 之貢獻度達 10%，每 10 個就業工作者中，即有 1 位是與觀光產業相關，顯見觀光產業已成為世界各國競相發展之重要產業，對於區域、國家及地方等經濟貢獻更不容忽視，觀光政策的研訂在產業發展的背後扮演極其重要之角色。本研究主要探討旅客訪日本、中國、韓國、台灣和香港觀光的要因分析，並以受訪國家之旅客數為被說明變數，結合來訪國及受訪國家人口數乘積（自然對數）、個人實質 GDP 乘積（自然對數）、兩國之距離（自然對數）、及共同區域等四個變數，並收集自 2001 年至 2016 年間這五個國家觀光訪客人數資料，利用追蹤資分析(Panel Data)和重力模型進行了迴歸分析並驗證這四個變數對觀光影響之關聯性，發現:兩國之間距離對觀光旅遊人數呈現負相關，兩國國民實質GDP的乘積與兩國人口數乘績對觀光旅遊人數的影響呈現正相關，而就共同區域而言對觀光旅遊亦為正向的影響。驗證結果與實際旅遊行為相互呼應，全球觀光產業及觀光活動的發展正處於活潑與蓬勃階段，本研究可作為觀光旅客人數的預測之用，有助於各國在觀光政策制定上訂立具宏觀的觀光政策。

關鍵字

追蹤資料分析；重力模型；日本；中國；韓國；香港；臺灣

並列摘要

The rapid development of the tourism industry has made it become the most important socioeconomic indicator for many countries in the 21th century. According to UNWTO, the estimated value of the tourism industry has reached to 10% of the global GDP. One out of every 10 employed workers has some connection to the tourism industry; this further indicates that the tourism industry has become one of the most valued industries for many countries. The economic contribution of the tourism industry to regions, countries, and locals is not negligible. The enactment of a tourism policy plays a vital role in the development of the tourism industry. This paper discusses the factor analysis for tourism among Japan, China, South Korea, Taiwan, and Hong Kong. We used the number of tourists in the visited country as the dependent variable; combining four other variables which are the product of populations of both visiting and visited countries (Natural logarithm), the product of PPP per capita of both visiting and visited countries (Natural logarithm), the distance between visiting and visited countries (Natural logarithm), the common areas. We collected the data of visitors among these five countries from 2001 to 2016. Then we used Panel Data and the Gravity Model to process the regression analysis and verified the correlation of impact of the four variables on tourism. We found that the distance between visiting and visited countries is negatively correlated with the tourist numbers, the product of PPP per capita of both visiting and visited countries is positively correlated with the tourist numbers, and the product of populations of both visiting and visited countries is positively correlated with the tourist numbers. As for the common area, it also has a positive impact on tourist numbers. The empirical results correspond to the actual travel behavior. Currently, global tourism is booming. Therefore, this research can provide a forecast for tourist numbers and can be beneficial for the enactment of macro tourism policies for many countries.