SITREP | Usage of DEA Models for Container Port Operational Efficiency Evaluation
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SITREP | Usage of DEA Models for Container Port Operational Efficiency Evaluation

By Anguibi China Flora Carine
School of Economics and Management, Shanghai Maritime University, Shanghai, China

Image Attribute: Port of Mombasa, Kenya / Wikimedia, Creative Commons

Image Attribute: Port of Mombasa, Kenya / Wikimedia, Creative Commons

Port efficiency evaluation is essential to both the economic theorist and the economic policy maker. Previous research on efficiency studied the concept in terms of technical efficiency related to physical quantities as well as technical relationships and allocative efficiency considering profit and cost. In the literature, the most common efficiency concept is technical efficiency. Technical efficiency has been defined as the degree to which a given set of inputs are converted into output relative to the best practice on the efficiency frontier. In other words, a firm is assumed to be technically efficient if that firm can produce maximum outputs from a given inputs without waste of resources when compared with its competitors. Moreover, scholar suggested measuring technical efficiency as the ratio of output to input.

Efficiency measurement has been addressed in the port sector by many researchers using two popular methodologies, that is, parametric approaches using econometric tools  and non-parametric approach based on mathematical programming theory. In the past decades, the non-parametric model namely Data Envelopment Analysis has been given enormous attention in the assessment of efficiency in the port sector. Data Envelopment Analysis (DEA) is an efficiency evaluation model used to estimate the technical efficiency of decision-making units (DMUs). 

According to researchers, the efficiency result obtained depend on the type of DEA model used, which in turn is based on an assumption made about the returns to scale properties of port production function as well as the input and output orientation approach.

For instance, a research conducted in China, using cross-sectional data, examined the technical efficiency of 42 container terminals. The researchers applied CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, Cooper) DEA models developed by scholars and respectively, as estimation methods. The CCR model provided an aggregate measure of pure technical efficiency calculated through BCC model and scale efficiency. Pure technical efficiency is related to the ability of DMUs in utilizing their resources while scale efficiency represents the degree to which the size of a DMU diverges from an optimal scale. In other words, a particular DMU is scale efficient when its operational scale is optimal, indicating that any changes (an increase or decrease) in its size will reduce its efficiency. Researchers found that inefficiency of the coastal container terminals was mainly due to scale inefficiency, that is, on average the production size was not appropriate regarding the activities performed by the ports. The findings also showed an excessive waste in resources utilization in the Chinese container terminals. 

A similar national level study on the basis of one time period data has evaluated port efficiency in Vietnamese context using DEA approach. The outcome revealed useful information on the ability of the Vietnamese ports in managing their inputs resources to get the desired output. Besides, Stochastic Frontier Analysis (SFA) has also been applied to calculate the efficiency. Comparing the results of the models, the scholars found that the estimations derived from both techniques were significant but different due to their underlying assumptions. In another study performed in the Asian region, CCR and BCC techniques have been used to identify the source of inefficiency in the sample ports. The outcome showed that the overall technical inefficiency observed was related to technical rather than scale inefficiency

Further, past study conducted in Northeast Asia, applying three types of DEA especially CCR, BCC, and Super-efficiency, demonstrated that Hong Kong was the most efficient port in the Northeast Asia, relative to the efficient frontier. Investigating the operational efficiency of the world’s major container ports, researchers applied DEA models based on constant and variable returns to scale assumptions to determine the source of inefficiency and areas of improvement for the inefficient ports. Moreover, comparing the efficiency of four Australian and twelve other international container ports using DEA technique, scholar] found that the ports of Melbourne, Rotterdam, Yokohama, and Osaka were the most inefficient in the sample. The researcher indicated that the inefficiency in the sample ports was a result of the waste of inputs such as container berths, terminal area, and labor. Similarly, a study investigating the efficiency of 86 ports across the world highlighted that the most inefficient ports had used excessively their resources, meaning that there was a waste of inputs in the production process.

Since the introduction of DEA model in port efficiency evaluation by researchers, many studies have extended the sample data from cross-sectional to panel data in order to identify the possibility of change in port efficiency over time. The change in efficiency has been studied using the DEA method in Latin American Container seaports. The analysis revealed an improvement of the pure technical efficiency of the Latin American seaports over the period 2000 to 2008. In the same way, a research applying DEA method for the period of 2001 to 2010 in Nigeria, demonstrated a continuous enhancement of the efficiency of Onne and Rivers seaports since the year 2006. In Malaysia, DEA-window analysis based on panel data from 2003 to 2010 has been applied to evaluate the efficiency of six major container ports. Terminal equipment and container throughput have been selected respectively as input and output. The researcher concluded that the cause of inefficiency was due to technical rather than scale efficiency during the period under evaluation. 

Similarly, Middle Eastern and East African Seaports have been compared using standard DEA and DEA-window models. The outcome highlighted a similarity in average efficiency values obtained from the cross-sectional data and the panel data. In addition, among the African ports selected in the study, Mombasa and Djibouti have experienced an improvement during the period. Conversely, analyzing the technical efficiency of African seaports using a bootstrapped DEA, Nigerian seaports have been found as the most efficient, followed by Mozambique and Angola. According to the researchers, ports efficiency could be affected by the political situation experienced by each country as well as national infrastructure plan.

Based on the above situational report, we conclude that DEA models have been widely used to determine among similar ports, the efficient DMUs and identify areas of improvement for the inefficient ones. 

This article is an abridged format of a technical report, titled  
"Analyzing the Operational Efficiency of Container Ports in Sub-Saharan Africa"

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