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系統識別號 U0026-0812200915143440
論文名稱(中文) 考量異質性與時變性之隨機邊界模式研究—以國道客運業成本效率為例
論文名稱(英文) Considering Heterogeneity and Time-variant in Stochastic Frontier Models : an Application to Cost Efficiency of Taiwanese Intercity Bus Industry
校院名稱 成功大學
系所名稱(中) 交通管理學系碩博士班
系所名稱(英) Department of Transportation & Communication Management Science
學年度 97
學期 2
出版年 98
研究生(中文) 李紹源
研究生(英文) Shao-Yuan Li
學號 R5696108
學位類別 碩士
語文別 中文
論文頁數 101頁
口試委員 口試委員-溫傑華
口試委員-蔡東峻
指導教授-陳勁甫
口試委員-邱裕鈞
中文關鍵字 成本函數  異質性  成本效率  隨機邊界法  國道客運  規模經濟  時變性 
英文關鍵字 Intercity Bus Company  Heterogeneity  Stochastic Frontier  Cost Efficiency  Economies of Scale  Time-variant  Cost Function 
學科別分類
中文摘要 近年來,國道客運產業面臨日益惡化的外部經營環境,在營運成本的逐年增加下,未來各國道客運業者之成本效率將顯得更為重要。自國道客運民營化數年來,國內對於以隨機邊界法衡量國道客運業經營效率之研究仍然相當缺乏。綜觀國內外相關文獻,也鮮少同時對廠商之異質性與效率的時變性進行探討。本研究之目的,旨在探討台灣37家國道客運業2001~2005年的成本效率表現,並藉由不同隨機成本邊界模型之設定,將效率的時變性與異質性納入考量,各模式的實證結果將予以比較與應用,研究結果可供政府、業者作為決策之參考依據。
研究結果顯示,各國道客運業者受到2003年SARS疫情衝擊,導致該年成本顯著攀升。而Greene(2005)所提出之True Random-Effects模式能有效解釋異質性與時變性現象,並對各廠商作出合理且客觀之效率估計。根據Tobit迴歸分析,北部區域的客運公司成本效率表現最優;服務品質越好的客運公司,效率表現越不理想;規模越大的客運公司,成本效率表現亦越佳;經濟特性分析方面,各項要素價格以燃油價格的變動對成本影響最大,勞動要素與燃油要素兩者呈現互補關係,維護管理要素分別與勞動、燃油要素間具有替代關係,整體國道客運產業具有規模經濟之特性。
針對各業者提出的建議為,可重視不同客層的需求,提供更多元化的行銷策略吸引顧客,並與國內相關旅遊景點異業聯盟,發展包車業務,進一步將規模經濟的效益極大化。政府方面,使用True Random-Effects Model或效率區間的方式對補貼申請進行審議,進而將補貼資源作最佳配置,達到整體最佳的社會福利。
英文摘要 Taiwanese intercity bus companies are facing the difficulty of operation due to the increase of operational costs year by year. Unfortunately, the operational efficiency of intercity bus companies have never been carefully investigated. Therefore, the main objective of this study is to examine the cost efficiency of 37 intercity bus companies operating over a five-year period in Taiwan. Several panel data stochastic frontier models were estimated by using both Cobb-Douglas and Translog cost function. The conventional models are extended in order to model the environmentally unobserved heterogeneity and the time-varying effects. The estimated coefficients and inefficiency scores are compared across six models. Finally, tobit regression and economic characteristic analysis are imposed to understand the current situation of intercity bus industry.
The findings indicate that the total cost of intercity bus company arose by the impacts of SARS in 2003. Moreover, “True random-effects model” proposed by Greene (2005) can improve the estimations regarding both heterogeneity and the time-varying nature of efficiency. Besides, the estimate efficiency is higher in the Northern region of Taiwan, the performance of service quality negatively affects the score of efficiency, the scale of bus company positively affects the efficiency scores. Among factors of input, fuel price has the most significant influence on total cost. Base on calculations, the index of economies of scale is greater than one, suggesting the presence of unexploited economies in Taiwanese intercity bus industry.
Final result suggests that all companies should develop various marketing strategy and increase their output to achieve the cost-minimising size. In addition, alternative panel models such as the “True random-effects model” proposed by Greene (2005) can be applid to explore the possible impacts of unobserved firm-specific factors and time-varying effects on efficiency estimates. Further, from government’s perspective, the adopted methodology can be also recommended to subsidiary policy making.
論文目次 第一章 緒論...............................................1
1.1 研究動機與背景........................................1
1.2 研究目的..............................................3
1.3 研究範圍與對象........................................3
1.4 研究流程..............................................4
1.5 論文架構..............................................5
第二章 文獻回顧...........................................6
2.1 國道客運業介紹........................................6
2.1.1 臺灣城際旅客運輸概況...............................11
2.2 效率的定義與衡量.....................................15
2.3 公路客運業效率相關文獻...............................17
2.3.1 資料包絡法相關文獻.................................17
2.3.2 隨機邊界法相關文獻.................................21
2.3.3 運輸業成本結構相關文獻.............................27
2.4 資料包絡法與隨機邊界法比較...........................29
2.5 綜合評析.............................................31
第三章 研究方法..........................................33
3.1 隨機邊界法介紹.......................................33
3.1.1 隨機成本邊界.......................................34
3.1.2 成本效率...........................................35
3.2 各種隨機邊界模型.....................................35
3.2.1 橫斷面資料(Cross-Section)之隨機邊界模型............37
3.2.2 跨期追蹤資料(Panel Data)之隨機邊界模型.............39
3.2.3 考量異質性(Heterogeneity)之隨機邊界模型............42
3.3 變數定義與選擇.......................................48
3.3.1 變數選擇...........................................49
3.3.2 變數定義...........................................50
3.3.3 樣本敘述性統計.....................................51
3.4 實證模型之建構.......................................52
3.4.1 成本函數...........................................52
3.4.2 分配選擇...........................................53
第四章 實證分析..........................................54
4.1成本函數估計結果......................................54
4.1.1 成本函數參數估計...................................54
4.1.2 無效率估計值表現狀況...............................59
4.1.3 相關性分析.........................................63
4.1.4 成本效率估計結果...................................66
4.2 模式配適度表現.......................................69
4.3 Tobit迴歸分析........................................70
4.4 國道客運業經濟特性分析...............................73
第五章 結論與建議........................................77
5.1 結論.................................................77
5.2 建議.................................................80
5.3 後續研究方向.........................................82
參考文獻.................................................83
一、中文部分.............................................83
二、英文部分.............................................85
參考文獻 一、中文部分
1.王小娥、許凱翔,汽車貨物運輸業成本結構與相關彈性之分析,運輸計畫季刊,30卷3期,民國90年9月,603~634。
2.呂文哲,開放天空後台灣航空業成本效益分析-動態隨機邊界模型,成功大學交通管理科學系碩士論文,民國88年。
3.李文彬,以資料包絡分析法探討台灣客運公司之營運與環境績效研究,成功大學環境工程學系碩士論文,民國97年。
4.李翠捥,知識經濟對國道客運業經營績效影響之研究,成功大學交通管理科學系碩士論文,民國96年。
5.邱冠熒,非意欲產出對市區公車營運效率之影響-隨機邊界分析法,交通大學交通運輸研究所碩士論文,民國96年。
6.范植谷,汽車客運業績效評估之研究-資料包絡分析法,交通大學運輸科技與管理學系博士論文,民國93年。
7.張雪君,「促進大眾運輸發展方案」影響公路客運績效之實證研究:1992-2001,交通大學交通運輸研究所碩士論文,民國93年。
8.梅興邦、孫遜,資料包絡分析法應用於台北市聯營公車經營績效之評估,2000年科技與管理學術研討會論文集。
9.許凱翔,汽車貨物運輸業成本函數之研究,成功大學交通管理科學系碩士論文,民國89年。
10.郭文凱,台灣國際觀光旅館經營效率與生產力之研究—DEA、SFA、Malmquist之應用,成功大學交通管理科學系碩士論文,民國97年。
11.陳俊杰、孫遜,台北市聯營公車營運績效評估之研究,2001年科技與管理學術研討會論文集。
12.陳柏茹,國道客運公司行銷策略之探討,成功大學管理學院EMBA高階企管在職專班碩士論文,民國92年。
13.陳敦基、李明彥,台北市公車受補貼路線1996-1999年度生產力變動之研究,運輸計畫季刊,32卷1期,民國92年3月,1~30。
14.陳敦基、蕭智文,公路客運業總體績效DEA評估模式建立之研究,運輸計畫季刊,23卷1期,民國83年3月,11~40。
15.陳勁甫、王亭瑜,國際觀光旅館經營效率衡量之研究-隨機邊界法之應用,旅遊管理研究,第三卷,第一期,民國92年6月,63~77。
16.蕭聿宏,國道客運業者就聯合票價及班距規劃之最佳化模式研究,中原大學土木工程系碩士論文,民國94年。
17.藍武王、許書耕,台灣地區民營公路客運業成本函數與經濟特性分析,運輸計畫季刊,18卷3期,民國78年9月,603~634。
18.顧志遠、張國平,數據包絡分析(DEA)效率評估方法之應用-以台北市公車為例,運輸計畫季刊,19卷1期,民國79年3月,27~38。
19.交通部公路總局網站:http://www.thb.gov.tw/
20.交通部運輸研究所網站:http://www.iot.gov.tw/

二、英文部分
1.Abbes S., Bulteau J., (2006) Analysis of the Productive Efficiency of the Urban Transport Networks in France. Transportation conference, Alberta, Canada.
2.Abdulai, A., Tietje, H., (2007) Estimating technical efficiency under unobserved heterogeneity with stochastic frontier models: application to northern German dairy farms. European Review of Agricultural Economics, Vol 34, (3), 393-416.
3.Aigner, D. J., C. A. K. Lovell, and P. Schmidt, (1977) Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics 6, 21–37.
4.Alvarez A., Arias C. and Greene W. H., (2004) Accounting for unobservables in production models: management and inefficiency. Working Paper No. E2004/72, Centro de Estudios Andaluces, Spain.
5.Barros C.P., Guironnet F., Peypoch N., Roy W., (2008) Heterogeneity in Technical Efficiency of the French Urban Transport: 1995 to 2002. Working Paper, Department of Economics, School of Economics and Management, Technical University of Lisbon.
6.Battese, G. E. and T. J. Coelli (1988) Prediction of Firm-Level Technical Efficiencies with a Generalized Frontier Production Function and Panel Data. Journal of Econometrics 38, 387–399.
7.Battese, G. E. and T. J. Coelli (1992) Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India. Journal of Productivity Analysis 3 (1-2), 153–169.
8.Battese, G. E. and T. J. Coelli (1995) A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data. Empirical Economics 20,325–332.
9.Battese, G.E., D.S.P. Rao, and C.J. O’Donnell, (2004) A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies. Journal of Productivity Analysis, 21, 91-103.
10.Berger, Allen N., Diana Hancock, and David B. Humphrey, (1993) Bank Efficiency Derived from the Profit Function. Journal of Banking and Finance 17, 317-347.
11.Bhattacharyya, A., S. C. Kumbhakar, and A. Bhattacharyya, (1995) Ownership Structure and Cost Efficiency: A Study of Publicly Owned Passenger-Bus Transportation Companies in India. Journal of Productivity Analysis 6, 47–61.
12.Caudill, S. B., J. M. Ford, and D. M. Gropper (1995) Frontier Estimation and Firm-Specific InefficiencyMeasures in the Presence of Heteroscedasticity. Journal of Business and Economic Statistics, 13 (1), 105–111.
13.Chang,K.,Kao,P. (1992) The relative efficiency of public versus private municipal bus firms: An application of data envelopment analysis. The Journal of Productivity Analysis,3,67-84.
14.Charnes, A., Cooper, W.W. and Rhodes, E, (1978) Measure the efficiency of Decision Making units. European Journal of Operational Research. Vol.2, 429-444.
15.Coelli, Rao, Battese (1995) An introduction to efficiency and productivity analysis. Kluwer Academic Publishers.
16.Coelli, Rao, O'Donnell, Battese, (2005) AN INTRODUCTION TO EFFICIENCY AND PRODUCTIVITY ANALYSIS, Second Edition.
17.Cornwell, C., P. Schmidt, and R. Sickles (1990) Production Frontiers with Cross-Sectional and Time-Series Variation in Efficiency Levels. Journal of Econometrics 46, 185–200.
18.Cornwell, C., P.Schmidt, and R.C.Sickles, (1990) Production Frontiers with Cross-Sectional and Time-Series Variation in Efficiency Levels. Journal of Econometrics, 46, 185-200.
19.Costa A.,Markellos R.N., (1997) Evaluating public transport efficiency with neural network models. Transportation Research Part C,Vol.5,No.5, 301-312
20.Cowie,J.,Asenova,D., (1999) Organisation form, scale effects and efficiency in the British bus industry, Transportation, 26, 231–248,
21.Cullinane, K., Song, D.W., Ji, P., Wang, T.F., (2004) An application of DEA windows analysis to container port production efficiency. Review of Network Economics 3 (2), 186–208.
22.Dalen, D. M. and A. G`omez-Lobo. (2003) Yardsticks on the Road: Regulatory Contracts and Cost Efficiency in the Norwegian Bus Industry. Transportation 30: 371–386.
23.De Borger, B. ,Kerstens, K. and Costa,A. (2002) Public transit performance : what does one learn from frontier studies? Transport Reviews, 22(1), 1-38.
24.De Rus G (1988) El Transporte Público Urbano en España: Comportamiento de los Costes y Regulación de la Industria. IV Jornadas de Economía Industrial Fundación Empresa Pública. Madrid, September.
25.DeYoung, R. (1997) A Diagnostic Test for the Distribution-Free Efficiency Estimator: An Example Using U.S. Commercial Bank Data, European Journal of Operational Research, 243-249.
26.Farrell, M. J., (1957) The Measurement of Productive Efficiency. Journal of the Royal Statistical Society Series A CXX(Part 3), 253–281.
27.Farsi, M., M. Filippini and M. Kuenzle. (2006) Cost efficiency in regional bus companies: An application of new stochastic Frontier Models. Journal of Transport Economics and Policy, 40,1, 95–118.
28.Farsi, M., M. Filippini and W. Greene. (2005) Efficiency Measurement in Neteork Industries: Application to the Swiss Railway Companies. Journal of Regulatory Economics, 28,1, 69–90.
29.Farsi, M., M. Filippini, (2004) Regulation and Measuring Cost-Efficiency with Panel Data Models: Application to Electricity Distribution Utilities. Review of Industrial Organization, 25, 1-19.
30.Filippini, M. and P. Prioni, (1994) Is Scale and Cost Inefficiency in the Swiss Bus Industry a Regulatory Problem? Evidence from a Frontier Cost Approach. Journal of the Economics of Business 2, 219–231.
31.Filippini, M., Maggi, R. and Prioni, P., (1992) Inefficiency in a regulated industry: the case of Swiss regional bus companies. Annals of Public and Cooperative Economics, 63, 437-455.
32.Filippini, M., N., Hrovatin and F., Zoric (2008) Cost efficiency of Slovenian water disdribution utilities: an application of stochastic frontier frontier methods. Journal of Productivity Analysis, 29, 169-182.
33.Greene, W. (2005) Fixed and Random Effects in Stochastic Frontier Models. Journal of Productivity Analysis, 23, 7-32.
34.Greene, W. H. (1980) Maximum Likelihood Estimation of Econometric Frontier Functions. Journal of Econometrics, 13:1(May), 27-56.
35.Greene, W. H. (2001) Fixed and Random Effects in Nonlinear Models. Working Paper, Department of Economics, Stern School of Business, New York University.
36.Greene, W. H. (2002) Alternative Panel Data Estimators for Stochastic Frontier Models. Working Paper, Department of Economics, Stern School of Business, New York University.
37.Greene, W. H. (2002) Fixed and Random Effects in Stochastic Frontier Models. Working Paper, Department of Economics, Stern School of Business, New York University.
38.Greene, W. H. (2003) Distinguishing Between Heterogeneity and Inefficiency: Stochastic Frontier Analysis of the World Health Organizations Panel Data on National Health Care Systems. Working Paper, Department of Economics, Stern School of Business,New York University.
39.Greene, W.H. (2005) Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics 126: 269–303.
40.Hadri, K., C. Guermat and J. Whittaker, (2003) Estimating farm efficiency in the presence of double heteroscedasticity using panel data. Journal of Applied Economics, Vol.VI, No.2, 255-268.
41.Hensher, D. and Greene, W., (2001) The Mixed Logit Model:The State of Practice and Warnings for the Unwary.10 Oct. 2002.
42.Heshmati, A. (1998) Efficiency measurement in ritating panel data, Applted Economics, 30, 919-30.
43.Huang, H.C., (2004) Estimation of technical inefficiencies with heterogeneous technologies. Journal of Productivity Analysis, 21(3), 277–296.
44.Jha, R. and S. K. Singh (2001) Small is Efficient: A Frontier Approach to Cost Inefficiencies in Indian State Road Transport Undertakings. International Journal of Transport Economics 18 (1), 95–114.
45.Jondrow, J., C. A. K. Lovell, I. S. Materov, and P. Schmidt, (1982) On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model. Journal of Econometrics 19 (2-3), 233–238.
46.Jorgensen, F., P. A. Pedersen, and R. Volden, (1997) Estimating the Inefficiency in the Norwegian Bus Industry from Stochastic Cost Frontier Models. Transportation 24,421–433.
47.Karlaftis, M G., McCarthy, P., Cost structures of public transit systems: a panel data analysis. Transportation Research Part E, 38, 1-38.
48.Karlaftis, M.G. and McCarthy P., (2002) Cost structures of public transit systems : a panel data analysis” Transportation Research. Part E: Logistics and Transportation Review. 38, 1-18.
49.Koopmans, T. C., (1951) An Analysis of Production as an Efficient Combination of Activities. In T. C. Koopmans (Ed.), Activity Analysis of Production and Allocation, Number 13, New York. Wiley.
50.Kopsakangas-Savolainen, M., Svento, R., (2008) Estimation of cost -efficieness of the Finnish electricity distribution utilities. Energy Economics, 30, 212-229.
51.Kuenzle, M. (2005) Cost Efficiency in Network Industries: Application of Stochastic Frontier Models. Dissertation ETH no. 16117, ETH Zu¨rich.
52.Kumbhakar S. C., (1991) Estimation of technical inefficiency in panel data models with firm– and time-specific effects. Economics Letters, 36, 1991, 43–48.
53.Kumbhakar, S. C. (1990) Production Frontiers, Panel Data, and Time-Varying Technical Inefficiency. Journal of Econometrics 46, 201–211.
54.Kumbhaker, S.C. and Knox Lovell, C.A. (2000). Stochastic Frontier Analysis, Cambridge University Press.
55.Lawal, B.(2003)Categorical Data Analysis with SAS and SPSS Applications, London: Lawrence Erlbaum Associates.
56.Lee, L.-F. (1983) A Test for Distributional Assumptions for the stochastic Frontier Functions. Journal of Econometrics 22:3(August), 245-67.
57.Lee, Y.H. and P. Schmidt (1993) A Production Frontier Model with Flexible Temporal Variation in Technical Efficiency, in H.O. Fried, C.A.K. Lovell, and S.S. Schmidt(eds), The Measurement of Productive Efficiency: Techniques and Applications, 237-255, New York: Oxford University Press.
58.Loizides, I. and B. Giahalis (1995) The performance of public enterprises: a case of the Greek railway organization, International Journal of Transport Economics, vol. 22, 283-306.
59.Loizides, I. and Giahalis, B., (1995) The performance of public enterprises: a case of the Greek railway organization. International Journal of Transport Economics, 22, 283 - 306.
60.Matas, A. and J.-L. Raymond, (1998) Technical Characteristics and Efficiency of Urban Bus Companies: The Case of Spain. Transportation 25, 243–263.
61.Meeusen, W. and J. van den Broeck (1977) Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error. International Economic Review 18 (2), 435–444.
62.Mitzutani F. and Urakami T., (2003) A Private-Public Comparison of Bus Service Operators, International Journal of Transport Economics, 30(2), June.
63.Odeck,J.,Alkadi,A., (2001) Evaluating efficiency in the Norwegian bus industry using data envelopment analysis. Transportation,28,211-232.
64.Piacenza M., (2006) Regulatory contracts and cost efficiency: Stochastic frontier evidence from the Italian local public transport. Journal of Productivity Analysis, 25,257–277.
65.Pitt, M. and L.-F. Lee, (1981) The Measurement and Sources of Technical Inefficiency in the Indonesian Weaving Industry. Journal of Development Economics 9, 43–64.
66.Raftery, A.E. and Akman, V.E. (1986) Bayesian analysis of a Poisson process with a change-point. Biometrika, 73, 85-89.
67.Ritter, C. and L. Simar, (1997) Pitfalls of Nornmal-Gamma Stochastoc Frontier Models," Journal of Productivity Analysis, 8, 167-182.
68.Roy W. ,Billion A. (2007) Ownership, Contractual Practices and Technical Efficiency: The Case of Urban Public Transport in France. Forthcoming in the Journal of Transport Economics and Policy
69.Sakano, R., Obeng, K. and Azam, G., (1997) Subsidies and inefficiency: stochastic frontier approach. Contemporary Economic Policy, 15, 113- 127.
70.Schmidt, P. and R. Sickles, (1984) Production Frontiers with Panel Data. Journal of Business and Economic Statistics 2 (4), 367–374.
71.Stevenson, R. E. (1980) Likelihood Functions for Generalized Stochastic Frontier Estimation. Journal of Econometrics 13:1(May), 57-66.
72.Train, K.E., (2003) Discrete Choice Methods with Simulation, Cambridge: University Press.
73.Tsionas, E. G. (2002). Stochastic Frontier Models with Random Coefficients. Journal of Applied Econometrics 17, 127–147.
74.Viton, P., (1986) The question of efficiency in urban bus transportation. Journal of Regional Science, 26, 499 - 513.
75.Yan, J., Sun, X., Liu J.J. (2009) Assessing container operator efficiency with heterogeneous and time-varying production frontiers. Transportation Research Part B: Methodological, 43(1), 172-185.
76.Yu, M. (2008) Measuring the efficiency and return to scale status of multi-mode bus transit–Evidence from Taiwan’s bus system. Applied Economics Letters, 1-7.
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