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This study presents an evaluation of the relative efficiency of sixteen container ports in Sub-Sahara Africa using three DEA models namely CCR, BCC and Super-Efficiency over the year 2012. The CCR and BCC models are used to estimate the technical and scale efficiency while the super-efficiency technique provides a ranking of efficient ports. The efficiency results indicate that on average the inefficiency observed in the container ports under evaluation is due to scale rather than technical inefficiency. Further, by investigating the nature of the returns to scale, the study concludes that the majority of the container ports exhibit variable returns to scale while fewer experience constant returns to scale in their operations. In order to improve their overall efficiency, the ports showing increasing and decreasing returns to scale need to increase and decrease their size, respectively. Consequently, for container ports to survive in the competitive environment, port authorities should examine their operational scale to identify whether the production size is appropriate or not before making investment decision in terms of inputs resources enhancement or capacity expansion.

Standing at the interface of sea and inland transportation, seaport represents major gateway for international trade in Sub-Saharan African (SSA) international trade. 90% of SSA total imports and exports volume is conducted by way of maritime transportation [

Port efficiency evaluation is essential to both the economic theorist and the economic policy maker [

Efficiency measurement has been addressed in port sector by many researchers using two popular methodologies, that is, parametric approaches using econometric tools [

For instance, a research conducted in China, using cross-sectional data, examined the technical efficiency of 42 container terminals [

Since the introduction of DEA model in port efficiency evaluation by researchers [

Based on the literature, 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. In addition, it is identified that various combinations of inputs and outputs have provided different efficiency results. However, a research investigating the operational efficiency of Sub-Saharan Africa container ports has not been found in the literature. Therefore, this study attempt to examine the efficiency of sixteen ports in Sub-Saharan Africa using three types of DEA models. The remainder of this study is organized in following ways. The data source and DEA models are introduced in Section 2. Section 3 presents and discusses the results of empirical study conducted on the sixteen containers ports and we conclude our study in Section 4.

This paper selected major container ports in Sub-Saharan Africa. In order to be consistent with the production function framework, proxies have been used to estimate labor and capital inputs in previous studies applying DEA technique to evaluate the efficiency of ports. For instance, handling equipment has been measured as a proxy for labor input; terminal area and quay length have been selected as a proxy for capital [

Data envelopment analysis is a non-parametric method for estimating the relative performance of decision making units (DMUs). The DEA technique requires no assumption related to the nature of the relation between the inputs and the outputs and can handle multiple inputs and multiple outputs in the computation of efficiency values [

where

To solve the fractional mathematical programming problem, Equation (1) has been transformed into a linear programming model (primal formal) written below:

To obtain the solution of Equation (2), the dual form has been considered and presented as follow:

The CCR model given above follows an input-oriented approach that is the minimization of resources for a desired amount of outputs. The present study adopted an output-oriented approach in order to determine how a port could efficiently increase its throughput from a particular quantity of resources. Similar to the input- oriented CCR model formulation, the output-oriented CCR dual form is shown as follows:

where

By assuming that not all the decision making units are operating at an optimal scale, the constraint presented below is added to the CCR also called constant return to scale (CRS) model, to obtain the BCC known as variable returns to scale (VRS) model introduced by [

The inverse of the estimated score of

Analyzing the efficiency of the DMUs under VRS assumptions, the scale efficiency (SE) of each DMU has been estimated using the efficiency scores obtained under CCR and BCC models. In fact, the efficiency observed under the CRS model is the overall measure of technical and scale efficiency (TE) while the one deriving from the VRS model is pure technical efficiency (PTE). Hence, scale efficiency is calculated as follows:

where SE equal to 1 indicates scale efficiency and less than one demonstrates scale inefficiency.

After estimation of scale efficiency, the nature of returns to scale has been investigated to determine whether the scale inefficiency is related to either increasing (IRS) or decreasing returns to scale (DRS). In order to find the nature of returns to scale, a comparison is made between the efficiency value given by BCC model and the one calculated under non increasing returns to scale (NIRS) model. According to researchers [

As mentioned above, the standard DEA models CCR and BCC give the same efficiency value of one to all the efficient DMU. Consequently, it is not possible to identify among the efficient DMUs, the best performer. In order to provide ranking among only the efficient ports, the super-efficiency model developed by [

The characteristics of the variables used to estimate the relative efficiency of the sample ports are presented in

The study used CCR and BCC DEA models under the assumption of output maximization to estimate the overall technical efficiency, pure technical efficiency, scale efficiency and the nature of returns to scale for sixteen container ports. On the other hand, Super-efficiency model which extend the standard DEA models by providing a ranking of the efficient decision making unit based on its super-efficiency scores has been employed as well. The results are summarized in

The efficiency estimates of the CCR model of Mombasa, Tema, Lagos, Douala and Luanda are equal to one, revealing that these ports define the best practice frontier. The efficiency scores of the remaining ports are less than one, demonstrating that they are relatively inefficient compared with the efficient ports on the best practice frontier. Considering the efficiency scores derived from BCC model, it is found that, in addition to, the efficient ports identified under the CCR model, Lome, Cotonou, Cape Town, Libreville and Maputo have their efficiency

Statistics | Inputs | Output Container Throughput (TEU) | |||
---|---|---|---|---|---|

Terminal Area (Ha) | Total Quayside Crane | Total Yard Equipment | Berth Length (m) | ||

Max | 55 | 35 | 40 | 1132 | 1,230,398 |

Min | 7 | 2 | 10 | 220 | 101,231 |

Mean | 21.859 | 7.375 | 25.125 | 641.437 | 555,409.3 |

St. Dev. | 10.935 | 7.364 | 8.630 | 252.047 | 304,307.5 |

No. | Container Ports | DEA-CCR | DEA-Super Efficiency | DEA-BCC | SE | Return to Scale | |
---|---|---|---|---|---|---|---|

TE | TE | Rank | PTE | ||||

1 | MOMBASA | 1 | 1.434 | 1 | 1 | 1 | CRS |

2 | TEMA | 1 | 1.266 | 2 | 1 | 1 | CRS |

3 | LAGOS | 1 | 1.221 | 3 | 1 | 1 | CRS |

4 | LUANDA | 1 | 1.170 | 4 | 1 | 1 | CRS |

5 | DOUALA | 1 | 1.111 | 5 | 1 | 1 | CRS |

6 | DJIBOUTI | 0.972 | 0.972 | 6 | 0.984 | 0.988 | DRS |

7 | CAPE TOWN | 0.945 | 0.945 | 7 | 1 | 0.945 | DRS |

8 | DAR ES SALAAM | 0.813 | 0.813 | 8 | 0.868 | 0.937 | IRS |

9 | LOME | 0.748 | 0.748 | 9 | 1 | 0.748 | IRS |

10 | POINTE NOIRE | 0.726 | 0.726 | 10 | 0.746 | 0.973 | IRS |

11 | PORT LOUIS | 0.682 | 0.682 | 11 | 0.707 | 0.965 | IRS |

12 | ABIDJAN | 0.660 | 0.660 | 12 | 0.749 | 0.881 | DRS |

13 | DAKAR | 0.613 | 0.613 | 13 | 0.696 | 0.881 | IRS |

14 | COTONOU | 0.595 | 0.595 | 14 | 1 | 0.595 | IRS |

15 | LIBREVILLE | 0.468 | 0.468 | 15 | 1 | 0.468 | IRS |

16 | MAPUTO | 0.304 | 0.304 | 16 | 1 | 0.304 | IRS |

AVERAGE | 0.783 | 0.858 | 0.922 | 0.855 |

score equal to one, indicating that they are efficient in terms of resource utilization. Therefore, among the sixteen ports under evaluation, five are efficient in constant returns to scale model whereas ten are operating efficiently under variable returns to scale model.

Analyzing the source of inefficiency, the overall technical efficiency has been decomposed into pure technical efficiency and scale efficiency in order to determine whether the inefficiency is a consequence of an inefficient utilization of input or an inappropriate production scale or size. Under the assumption of VRS, the average technical efficiency score is 92.2%, suggesting that on average using the same amount of inputs, the ports outputs can be increase by 7.8%. The average efficiency value obtained from the CRS model is 78.3%, which is less than the average efficiency score estimated in VRS model. The average scale efficiency score (value = 85.5%) indicates that on average the ports actual scale of production has deviated from the most productive scale size (MPSS) by 14.5%. On the whole, the results reveal that the source of the overall inefficiency is due to scale rather than pure technical inefficiency. The ports of Mombasa, Tema, Lagos, Douala and Luanda are scale and technically efficient with a score of one. Hence, the ports were operating at the optimal scale. Conversely, Lome, Cotonou, Cape Town, Libreville and Maputo of which the efficiency scores is less than 1 in CCR model, are efficient under BCC model, demonstrating that they are technically efficient but scale inefficient. In other words, the ports were efficient in the utilization of input resources but they are either too small or too large regarding the activities they perform. The remaining ports are both scale and technically inefficient. Therefore, the inefficient ports have to adjust their scale of operations in order to move towards the efficient frontier.

The results derived from the analysis of the nature of returns to scale are summarized in the right-most column of

As emphasized above, the standard CCR and BCC models classify the decision making unit into best performers and inefficient units but cannot determine the relative ranking of the efficient DMUs. Hence, this study adopted the super-efficiency model to rank the efficient performers in the sample ports. The results indicate that Mombasa has the best performance with a score of 1.434, followed by Tema (score = 1.266), Lagos (score = 1.221), Luanda (score = 1.170) and Douala (score = 1.111). The difference observed in the efficiency scores illustrates that with the same scale, Mombasa port has better management practices compared with the other ports. Conversely, as the efficiency values of the inefficient ports are the same in both the super-efficiency and CCR model, Maputo is considered as the most inefficient with a value of 0.304.

This paper evaluates the relative efficiency of sixteen container ports in Sub-Saharan Africa using three DEA models namely CCR, BCC and Super-Efficiency over the year 2012. The DEA technique was performed based on output-oriented approach.

The efficiency scores obtained in CCR and BCC models reveal that 31.25% of the ports are relatively efficient and experience constant returns to scale while the other ports (68.75%) are operating under variable returns to scale. Further, the analysis of the nature of returns to scale indicates that about 18.75% of the ports exhibit decreasing returns to scale while 50% shows increasing returns to scale. It is suggested that the ports observed in the decreasing returns to scale zone should reduce their scale of operation whereas the others ports showing increasing returns to scale should increase their production scale. On the whole, the inefficient ports being assessed suffer from the effect of inappropriate operational scale. Consequently, for container ports to survive in the competitive environment, port authorities should examine their operational scale to identify whether the production size is appropriate or not before making investment decision in terms of inputs resources enhancement or capacity expansion. Finally, the results given by super-efficiency indicate that among the sample ports Mombasa has the best management practices while Maputo is the most inefficient one suffering from scale inefficiency.

In the case of this research, the inefficiency of Abidjan, one of the major ports in West Africa, could be related to the political crisis in Ivory Coast (2010-2011). In fact, the crisis has caused a decline in the numbers of ship call which resulted in the decrease of the throughput. Thus, as pointed out by researchers [

Anguibi China FloraCarine, (2015) Analyzing the Operational Efficiency of Container Ports in Sub-Saharan Africa. Open Journal of Social Sciences,03,10-17. doi: 10.4236/jss.2015.310002