未來五年中，中國金融改革和對外開放的速度都將進一步加快，利率市場化將取得重大進展，混業經營會展露頭角，銀行業結構會多元化，金融體系更加開放，當局對銀行業的監管日益強化，導致大陸銀行已進入高度競爭的時代，銀行經營者必須因應經營環境的嚴峻考驗，增進銀行經濟效率與經營績效，以反映銀行對其資源的有效配置、銀行市場競爭能力、投入產出能力和可持續發展的能力。但是由於中國大陸銀行體系隨著中國整體經濟的發展與體制變革而改變，其運行狀況、發展趨勢及其對經濟運行的效應都受到很多外部因素的影響。因此，準確測量中國銀行經營者的管理效率必須將外部環境因素剔除才能了解之。
本文利用1996-2003年間存續的18家中國大陸銀行，藉由Fried et al. (1999)提出之調整DEA方法的四階段模式來進行分析，以中介法決定投入與產出變數，投入變數為固定資產淨值、存款和員工人數，產出變數為投資、貸款和其他收入。第一階段使用DEA模式來衡量此七年期各家銀行生產效率值，利用線性規劃求出Farrell技術效率值及各投入項之投入差額。第二階段以投入的差額變數為被解釋變數，及所有權結構、性質別、上市與否、全國或地區性等外在環境為解釋變數，採取Tobit模型之應用，判斷外在環境對各投入差額的關係及影響，第三階段預期個別銀行各項投入差額值，依據此預期值調整投入資料使所有銀行皆處於相同之作業環境。第四階段利用調整後的數據資料重新估計銀行效率值。
實證結果發現，政策性銀行業務來源、資金籌措有政府強力支援，相對於專業性銀行與股份制商業銀行處於較有利於經營的環境；官股比重高需配合政府政策目標為導向，經營方式不自主和保守，相對於部分官股與純民營的銀行處於較差的經營環境。考慮外在因素之後，無效率來源主要源自於不同規模報酬之營運。控制環境前後，技術效率、純粹技術效率和規模效率平均排序變動分別為6.111名、5.556名和7.556名，技術效率差異十名以上的銀行有中國銀行、中國工商銀行、中國建設銀行、中國農業銀行、中國農業發展銀行、中國進出口銀行，可知樣本銀行管理效率受到外在環境影響顯著。全國性銀行管理監督不易、要素投入閒置，經營環境相對上較地區性銀行不利，而兩類銀行差異主源自於規模效率。上市銀行集資便利、財務會計和監理制度透明化，相對於未上市銀行擁有優勢的經營環境，而兩類銀行差異同樣多來自於規模效率。

In the coming five years, Chinese finance reform and opening up to foreign countries will all further speed up; interest rate market will make a significant progress; assorted industry management will show talents, construction of banking industry will be more multiplicated; financial system will be more opened; banking authorities will strengthen in supervising and governing the banking industry day by day, leading mainland Chinese banks into a fiercely competing era. Bank operators will have to act in accordance to the stern tests of the managing environment in order to stimulate bank economic efficiency and management achievement. It will reflect that the banks are dispositioning effectively to its resources. This will also reflect the bank’s ability in competence, production, and sustainable development. On the other hand because the bank system of mainland China transforms along with the Chinese overall economy development, its operating condition and development tendency will all receive various influences from many external factors as to the economical movement. Therefore, to accurately calculate the Bank of China operator’s management efficiency, is to dismiss the external environmental elements to fully understand it.
This article uses audits of 18 mainland Chinese banks during 1996-2003 and applies Fried’s four-step model for adjusting the DEA for analysis (Fried et al 1999). By using the intermediary method to determine the input and output variables, the input variable as the fixed asset net worth, financial deposit and the staff population, the output variable as investment, loan and other forms of income. The first stage uses the DEA pattern to weigh various bank production efficiency value in these seven years and extracts the Farrell technology efficiency value and each investment input differences using the linear programming. The second stage uses the input differential variable as the passive explained variable. Property holding rights structure, characteristic nature differences, marketing opportunity, and the environment of national or regional areas is the active explained variable by applying the Tobit model in judging the external environment to each input difference relations and influences. The third stage anticipates the investment input difference of each individual bank. According to the above estimation to adjust the required investment material values so that all will be in the same working surroundings. The fourth stage uses adjusted data material to re-estimate the bank efficiency value.
The proven end result revealed that clientele acquisition policy and government support in funding are more favorable towards governed banks in operational environment compare to the specialized banks and the joint stock system commercial banks; the high rate of official stock proportion coordinates the government policy goal is the guidance. The operating way is constrained, conservative and is opposite with the worse management environment to the partial official stock with total privately operated bank. After considering the external factor, the inefficiency mainly originates from different approaches towards profit gained. Prior to and after having controlled the environment, the technical efficiency, dedicating technical efficiency and the scale efficiency, their average arrangements change respectively in 6.111, 5.556 and 7.556. The banks whose efficiency differences that is ranked above ten are: the Bank of China, the Chinese Industry and Commerce Bank, the Chinese Construction Bank, the Agriculture Bank of China, the China Agricultural Development Bank, and the China Import Export Bank. It can be recognized that the managing efficiency within the sample banks is significantly related to the external environment. Nationwide banks are not effortless to manage and supervise. Other factors aside, therefore they are at a more disadvantage operating condition. But these two kinds of bank can be differentiated from their scaling efficiency. This is quite advantageous for established banks to gather capital, and also to make the process of financial inventory accounting and supervising policy transparent; in contrast of un-established banks with superior managing environment. And these two kinds of bank are once again can be distinguished through their managing policy and efficiency.

目 錄
第一章 緒論............................................................. 1
第一節 研究背景......................................................... 1
第二節 研究目的與限制................................................... 2
第一項 研究目的......................................................... 2
第二項 研究限制......................................................... 3
第三節 本文架構與研究流程............................................... 4

第二章 文獻回顧......................................................... 6
第一節 中國銀行業改革之歷史與背景....................................... 6
第二節 國內外銀行業相關文獻探討......................................... 10
第一項 國外以DEA法衡量銀行經營績效之相關文獻............................ 12
第二項 國內以DEA法衡量銀行經營績效之相關文獻............................ 14
第三項 中國大陸以DEA法衡量銀行經營績效之相關文獻........................ 15

第三章 實證模型......................................................... 25
第一節 DEA效率評估模式.................................................. 25
第二節 DEA四階段分析法.................................................. 31

第四章 實證結果與分析................................................... 37
第一節 資料蒐集與變數定義............................................... 37
第一項 樣本銀行......................................................... 37
第二項 變數定義與資料處理............................................... 37
第二節 實證分析......................................................... 42
第三節 效率相關性分析與差異性檢定....................................... 61

第五章 結論與建議....................................................... 65

參考文獻.................................................................69
附錄.................................................................... 73

一、中文部分
王美惠(民91)，台灣銀行業經濟效率與規模經濟分析－參數法與無參數法之比較，淡江大 學管理科學學系博士論文。
李軍(1999)，國有商業銀行產權制度效率的探討，財經問題研究，第12期，38-40頁。
李琪、李光泉和韓澤縣(2005)，我國商業銀行效率評价的DEA模型，天津大學學報，第7卷，第1期，6-10頁。
吳世勛與古永嘉(民84)，以DEA模式評估我國商業銀行之經營效率，管理與系統，第2卷，第2期，145-165頁，。
林珍如(民90)，台灣地區銀行業經營效率之評估－資料包絡分析法之應用，實踐大學企業管理研究所碩士論文。
林莊傑(民92)，國內銀行業經效率分析之研究-運用資料包絡分析法與主成分分析法，真理大學管理科學研究所碩士論文。
林鐵鋼(2005)，中國銀行業改革:歷史回顧與展望—訪中國銀行業監督管理委員會副主席唐雙寧，中國金融，第3期，16-18頁。
徐萬盧(民90)，台灣地區商業銀行效率之研究－應用資料包絡分析法，暨南國際大學國際企業研究所碩士論文。
殷乃平編著(1999)，中國大陸金融制度演變與發展，喜馬拉雅基金會。
高強、黃旭南和T. Sueyoshi (2003)，管理績效評估–資料包絡分析法，台北：華泰出版社。
郭妍(2005)，我國商業銀行效率決定因素的理論探討與實証檢驗，金融研究，第 2期，115-123頁。
張健華(2003)，我國商業銀行效率研究的DEA方法及1997-2001年效率的實証分析，金融研究，第3期，11-25頁。
張江湖(2005)，銀行業；注資、監管與信貸緊縮，中國科技財富，第29期，84-88頁。
單欣佳(2003)，開放環境下中外商業銀行現況之比較，廣西審計，第S1期，37-40頁。
萬靜芳(2004)，商業銀行環境變化與內部控制管見，財會月刊，第17期，66-67頁。
樓繼偉、許美征、謝平主編(1998)，中國國有專業銀行商業化改革，北京:中國金融出版社。
趙旭、凌亢(2001)，影響我國銀行業效率因素的實證研究，決策借鑒，第14卷，第2期，24-29頁。
劉漢濤(2004)，對我國商業銀行效率的測度:DEA方法的應用，經濟科學，第6期，48-58頁。
霍穎勵(2005)，2004年銀行間市場發展回顧，貨幣政策，第四期，39頁。
潘鑫與楊寶臣(2005)，中國商業銀行效率及影響因素分析，遼寧工程技術大學學報，第7卷，第1期，44-46頁。
鞏浩成、戴國強主編(2000)，2000中國金融發展報告，上海財經大學出版社。
鍾怡如(民88)，開放新銀行設立對舊銀行經營效率的影響，國立政治大學會計系研究所碩士論文。
蘇義淵(民91)，中國大陸銀行產業所有權型態與效率之研究，淡江大學中國大陸研究所碩士論文。
The Economist Intelligence Unit (2004)，中國商業銀行新面貌，遠見雜誌。
二、英文部分
Banker, R. D., A. Charnes and W. W. Cooper (1984), “Some Models for Estimating Technical and Scale Inefficiencies in Date Envelopment Analysis,” Management Science, 30, pp.1078-1092.
Banker, R. D., and R. C. Morey (1986b), “The Use of Categorical Variablea in Data Envelopment Analysis, ”Management Science, 32, pp.1613-1627.
Banker, R. D. and Morey, R. C. (1986a), “Efficiency analysis for exogenously fixed inputs and outputs,” Operations Research 34, pp.513-521.
Bhattacharyya, A., C. A. K. Lovell and P. Sahay (1997), “The Impact of Liberalization on Productive Efficiency of Indian Commercial Banks,” European Journal of Operational Research, 98, pp.332-347.
Chen, T. Y. and T. L. Yeh (2000), “A Measurement of Bank Efficiency, Ownership and Productivity Changes in Taiwan,” Service Industries Journal, 20, pp.95-109.
Charnes, A., W.W. Cooper and E. Rhodes (1978), “Measuring the efficiency of decision making units,” European Journal of Operational Research, pp.429-444.
Coelli, T. (1996), “A Guide to DEAP Version 2.1: A Data Envelopment Analysis Program,” CEPA Working Paper, 08/96, Department of Econometrics, University of New England, Armidale NSW Australia.
Farrell, M.J. (1957), “The Measurement of Productive Efficiency,” Journal of Royal Statistical Society, 120, Part3, pp.253-281.
Favero, C. A. and L. Papi (1995), “Technical Efficiency and Scale Efficiency in the Italian Banking Sector: a Non-parametric Approach,” Applied Economics, 27, pp.385-395.
Fried, H. O., S. S. Schmidt and S. Yaisawarng (1999), “Incorporating the Operating Environment into a Nonparametric Measure of Technical Efficiency,” Journal of Productivity Analysis, 12, pp.249-267.
Fried, H. O., C. A. K. Lovell, S. S. Schmidt and S. Yaisawarng (2002), “Accounting for Environmental Effect and Statistical Noise in Data Envelopment Analysis,” Journal of Productivity Analysis, 17, pp.157-174.
Lovell, C.A.K (1993), “Productiong Frontiers and Productive Efficiency. In H. Fried, C.A.K. Lovell, and S.S. Schmidt (eds), “The Measurement of Productive Efficiency” Efficiency Techniques and Applications, New York: Oxford University Press.
McCarty, T. and S. Yaisawarng. (1993), “Technical Efficiency in New Jersey School Districts,” in H.O. Fried, C. A. K. Lovell and S. S. Schmidt (eds.), The Measurement of Productive Efficiency: Techniques and Applications, New York: Oxford Univesity Press.
Miller, S.M. and A.G. Noulas (1996), “The Technical Efficiency of Large Bank Production,” Journal of Banking and Finance, 20, pp.495-509.
Sathye, M (2001),“X-efficiency in Australian Banking: An Empirical Investigation,” Journal of Banking and Finance, 25, pp.613-630.
University of New England (2004), “A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program”, http://www.une.edu.au/ econometrics/ cepa.htm.