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研究生:黃彥昇
研究生(外文):Yen-Sheng Huang
論文名稱:應用隨機前緣分析法探討效率與效率改變來源-以亞太國際機場為例
論文名稱(外文):Applying stochastic frontier analysis to investigate efficiency and efficiency change source - International Airport as an example the Asia-Pacific
指導教授:楊旭豪楊旭豪引用關係
指導教授(外文):Hsu-Hao Yang
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:工業工程與管理系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:70
中文關鍵詞:隨機前緣分析法(SFA)Malmquist生產力指標(MPI)邊際產出規模彈性
外文關鍵詞:Stochastic Frontier AnalysisMalmquist Productivity IndexMarginal ProductElasticity
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航空業是經濟發展最重要產業之一,無論是運輸、旅遊或是休閒活動,都與航空業息息相關。中國近年來的經濟成長突飛猛進,各行各業都有亮眼的成績,其航空業之進步更是不在話下。影響所及,亞太地區的各國都全力的建設機場,增加其機場的競爭力。
有鑑於此,本文蒐集1998到2006年亞太地區十二座國際機場的資料,使用隨機前緣分析法(Stochastic frontier analysis; SFA)來探討亞太地區各國機場之效率,並採用假設檢定,確定無效率因子之分配、無效率效果存在與否、與時間的相關性。評估的機場包括臺灣(TPE)、上海(PVG)、廣東(SZX)、馬來西亞(KUL)、香港(HKG)、新加坡(SIN),南韓(ICN),泰國(DMK),菲律賓(MNL),越南(HAN),廣州(CAN)和北京(PEK)等十二座國際機場。選擇之投入項為「員工人數」、「跑道長度」及「營運成本」;產出項則為「營運收入」。 本文除了運用SFA分析各機場效率值之外,更計算Malmquist生產力指標(Malmquist productivity index; MPI)來探討十二座機場9年間生產力改變情形,並討論效率變動(Efficiency change; EC)、技術變動(Technical change; TC)、規模變動(Scale change)與MPI逐年變動之比率,來了解各年間各項不同指標之差異。除此之外,本研究也計算各項投入項之邊際產出與各機場之規模彈性,以了解不同投入項對產出項之影響並分析各機場9年之規模報酬。
本研究在分析SFA前,利用假設檢定,確定隨機前緣之模型、技術無效率是否存在、技術無效率和時間之關係與生產函數之型態。隨機前緣模型檢定之結果適合半常態分配,技術無效率之影響存在並會隨著時間變動,且生產函數之型態為Translog。經SFA分析結果顯示,新加坡樟宜機場獲得最高之效率值,排名第二的是北京首都機場,排名最後的則是馬來西亞機場。
MPI分析的結果顯示,在十二座機場中,有十座機場9年之MPI值為大於1,表示就平均而言,其生產力呈現正成長。而菲律賓與越南河內機場MPI值小於1,顯示這兩座機場之平均生產力有衰退之趨勢。由邊際產出分析中得知,部份機場與越南河內機場對於員工人數之投入項結果為負值,新加坡樟宜機場與部份機場對於跑道長度投入項之表現也為負,唯獨營運成本之邊際產出皆為正。在規模彈性方面,十二座國際機場有六座機場為規模報酬遞增(Increase return to scale; IRS),其餘六座機場為規模報酬遞減(Decrease return to scale; DRS)。
本研究的結果有助於機場決策者了解其效率不彰的真正原因,使其可針對問題的癥結修正與改善,進而提升機場之效率。
Airports promote a country’s economic development due to its expedient advantage that rapidly transports passengers and cargo. The air freight market of Asia-Pacific region has persistently grown in part due to China’s continuous economic rise. To meet the growing demands of this market, Asian-Pacific governments have launched numerous projects in attempts to increase competitiveness of their international airports.
This study uses stochastic frontier analysis (SFA) to measure the efficiency of 12 international airports in Asia-Pacific area over 1998-2006. The airports selected include Taiwan (TPE), Shanghai (PVG), Guangdong (SZX), Malaysia (KUL), Hong Kong (HKG), Singapore (SIN), South Korea (ICN), Thailand (DMK), Philippines (MNL), Vietnam (HAN), Guangzhou (CAN), and Beijing (PEK). The input variables include “the number of employees,” “length of runway,” and “operating costs;” the output variable is “operating revenue.” In addition, this study uses Malmquist productivity index (MPI) to investigate the efficiency change over the period. The efficiency change is decomposed into efficiency change (EC), technological change (TC), and scale change. Finally, this study computes marginal product of each input variable and scale elasticity to understand how input levels affect the efficiency.
Using the SFA model requires testing the distribution of the inefficiency, whether the inefficiency effects exist, and whether the inefficiency effects are time-invariant. After testing three hypotheses, the SFA model uses a Translog production, is constructed with a half-normally distributed inefficiency term, with inefficiency effects existing over time, and with time-varying inefficient effects. According to the SFA results, Singapore performs the best, while Malaysia is the worst.
In terms of MPI, 10 out of 12 airports have the MPI value greater than one, indicating the productivity growth. Philippines and Vietnam are the two with values less than one, suggesting that their productivities were in decline. From the analysis of marginal products, some airports such as Vietnam turn out to have negative value of the number of employees, and Singapore has negative value of the length of runway. In terms of scale elasticity, six out of 12 have increasing returns to scale, while the other six have decreasing returns to scale.
中文摘要 i
英文摘要 iii
誌謝 v
目錄 vi
表目錄 viii
圖目錄 ix
一、緒論 1
1.1 研究動機 1
1.2 研究目的 3
1.3 研究對象 3
1.4 研究架構 3
1.5 研究流程 5
二、文獻探討 6
2.1 績效衡量的意義 6
2.2 生產前緣模型 7
2.3 SFA相關文獻 9
2.4 MPI相關文獻 13
三、研究方法 15
3.1 SFA 15
3.1.1 常見之生產函數型態 17
3.1.2 參數估計 18
3.1.3 各種分配模式 19
3.2 MPI 21
3.2.1 產出導向之MPI 22
3.2.2 投入導向之MPI 22
3.2.3使用DEA前緣計算MPI 23
3.2.4使用SFA前緣計算MPI 24
3.3 邊際產出與規模彈性 26
四、實證研究與分析 28
4.1 資料來源及投入與產出變數說明 28
4.2 假設檢定 29
4.3 SFA分析 31
4.4 MPI分析 35
4.5 邊際產出與規模彈性分析 38
4.6 小節 40
五、結論及未來研究建議 41
5.1 結論 41
5.2 未來研究建議 43
參考文獻 44
附錄 50
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