Evaluating the operational efficiency of major ports in the Asia-Pacific region using data envelopment analysis

In recent years, as competition among international ports has intensified, the impartial and objective evaluation of port operational efficiency has become increasingly important in enabling each individual port to understand its peculiar strengths and weaknesses, as well as any immediate threats or opportunities that may affect its competitive environment. This study applies CCR model, BCC model and 3-stage DEA model to evaluate the changes in efficiency that have taken place between 1998 and 2001 in 10 ports in the Asia-Pacific region using cross-period data. The empirical results show that different model will lead to different result. On average, the efficiency estimated by 3-stage DEA procedure is the highest, while CCR efficiency is the lowest. It should be noted that the efficiencies based on CCR and BCC model are somewhat lower than the 3-stage DEA approaches, because they do not take the environmental factors, managerial inefficiency and statistical noises into account.

[1]  Zinan Liu,et al.  THE COMPARATIVE PERFORMANCE OF PUBLIC AND PRIVATE ENTERPRISES: THE CASE OF BRITISH PORTS. , 1995 .

[2]  Antonio Estache,et al.  Efficiency Gains from Port Reform and the Potential for Yardstick Competition: Lessons from Mexico , 2002 .

[3]  Harold O. Fried,et al.  The measurement of productive efficiency : techniques and applications , 1993 .

[4]  C. Lovell,et al.  On the estimation of technical inefficiency in the stochastic frontier production function model , 1982 .

[5]  Jose L. Tongzon,et al.  DETERMINANTS OF PORT PERFORMANCE AND EFFICIENCY , 1995 .

[6]  Jose L. Tongzon,et al.  Efficiency measurement of selected Australian and other international ports using data envelopment analysis , 2001 .

[7]  Pablo Coto-Millán,et al.  ALLOCATIVE EFFICIENCY AND OVER-CAPITALIZATION: AN APPLICATION , 1999 .

[8]  M. Farrell.  The Measurement of Productive Efficiency , 1957 .

[9]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

[10]  Jose L. Tongzon,et al.  Systematizing international benchmarking for ports , 1995 .

[11]  C. Lovell,et al.  Stochastic Frontier Analysis: Frontmatter , 2000 .

[12]  G. Battese,et al.  Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data , 1988 .

[13]  Y Roll,et al.  Port performance comparison applying data envelopment analysis (DEA) , 1993 .

[14]  Michael Norman,et al.  Data Envelopment Analysis: The Assessment of Performance , 1991 .

[15]  Pablo Coto-Millán,et al.  Economic efficiency in Spanish ports: some empirical evidence , 2000 .

[16]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[17]  Rajiv D. Banker,et al.  Efficiency Analysis for Exogenously Fixed Inputs and Outputs , 1986, Oper. Res..

[18]  Kevin Cullinane,et al.  A stochastic frontier model of the productive efficiency of Korean container terminals , 2003 .

[19]  Eduardo Martínez-Budría,et al.  A STUDY OF THE EFFICIENCY OF SPANISH PORT AUTHORITIES USING DATA ENVELOPMENT ANALYSIS , 1999 .

[20]  Rajiv D. Banker,et al.  The Use of Categorical Variables in Data Envelopment Analysis , 1986 .

[21]  Harold O. Fried,et al.  Incorporating the Operating Environment Into a Nonparametric Measure of Technical Efficiency , 1999 .

[22]  Subal C. Kumbhakar,et al.  Stochastic frontier analysis , 2000 .

[23]  Yaakov Roll,et al.  An application procedure for DEA , 1989 .

[24]  H. O. Fried,et al.  Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis , 2002 .

[25]  A. Lewin,et al.  Determining organizational effectiveness: another look, and an agenda for research , 1986 .