impact of concession regime on the performance of nigerian

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International Journal of Scientific Research in ___________________________ Research Paper. Multidisciplinary Studies E-ISSN: 2454-9312

Vol.7, Issue.10, pp.08-15, October (2021) P-ISSN: 2454-6143

Impact of Concession Regime on the Performance of Nigerian Seaports

I. Njoku1*

, C.C. Ibe2

1Department of Logistics and Transport Technology, Federal University of Technology Akure, Ondo State, Nigeria

2Department of Logistics and Transport Technology, Federal University of Technology Owerri, Imo State, Nigeria

*Corresponding Author: [email protected], Tel.: +2348036645117

Available online at: www.isroset.org

Received: 13/Oct/2021, Accepted: 19/Oct/2021, Online: 31/Oct/2021

Abstract— This study examines the impact of concession regime on the performance of Nigerian seaports. The essence is

to find out if the landlord port model adopted in 2006 is better than the service port model used since 1956. Data were

mainly derived from the Nigerian Port Authority abstract of ports operation statistics. The study used the t-test statistic to

analyse the general port performance using cargo throughput, turnaround time and berth occupancy ratio as variables. A

sample of six (6) major ports in Nigeria were analysed to rank and compare their pre- and post-concession efficiencies. The

study relied on input-oriented Data Envelopment Analysis (DEA) model, using Ship traffic and Cargo throughput as

inputs; and Total Number of Equipment, Berth length, Total Number of Berth and Total Number of Staff as outputs. The

study revealed a fluctuation in cargo movement during pre-concession era while the throughput continues to increase

unabated during the post concession era. The analysis also revealed that there is a significant difference between

turnaround time during pre and post concession periods and that there is no significant difference between pre and post

concession berth occupancy. The Onne port with an average of 86.80% was ranked the most efficient while Calabar was

the least with 42.27% efficiency level. This study recommends that the government should encourage public ownership

and private sector operations of the port infrastructure in Nigeria.

Keywords— Regime, concession, performance, model, throughput, infrastructure, efficiency

I. INTRODUCTION

A concession contract is, by definition, more complex than

a license, since it involves not only questions about service

provision and delivery, but also about adequate

maintenance of assets, investments to be made and risk

apportionment between the concessionaire and regulator.

Concession contracts can be regarded as an intermediate

solution between public ownership and full privatization of

a port. Private participation was introduced to achieve

efficiency gains in the industry and at the same time

political concerns are safeguarded by not making society

lose ownership of essential assets [1].

According to [2] port size and infrastructure, private sector

participation and quality of both cargo-handling and

logistics services are important determinants of efficiency.

Transport terminals’ efficiency intensely increases the

speed of transshipments and consequently, of supply

chains.

Also, time savings occasioned by requisite efficient port

infrastructure development has led to competitive

dynamism and has also offered itself as a catalyst for an

increased productivity in a given port system [3].

The competition among ports is more severe at the present

time than it used to be. Reference [4] considered that port

markets used to be described as monopolistic due to the

exclusive and immovable geographical location of the

ports and the unavoidable concentration of port traffic. The

development of international container and intermodal

transportation has changed the market structure from one

of monopoly to one where hardline competition takes place

worldwide. Many ports, no longer enjoy the monopoly of

handling the cargoes that is from their hinterland rather

they compete for cargoes from distant neighbouring ports.

Due to severe competition, many studies focus their

attention to measuring efficiency in ports. Reference [5]

are of the view that the analysis of individual ports and

terminals are essential for the health of the industry and the

survival and competitiveness of its players. In addition to

provision of valuable tool for the use of port managers and

operators, these studies also serve as input for regional port

planning and operations.

As a result of this, performance measurement has become a

fundamental issue to stakeholders in the port industry

globally. Ports in the West African sub region are not left

out of the new world order of severe competition and

technological changes in the port market.

The major problem in the Nigerian port industry is the

persistent performance gaps in sea ports and the need to

adapt the capacity of all ports to a new world order in the

maritime sector at a time when there is scarce public

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Int. J. Sci. Res. in Multidisciplinary Studies Vol.7, Issue.10, Oct 2021


funding. Competitiveness and technological changes are

the new world order in the maritime industry. Due to the

inability of the ports to meet the growing gap between

demand and capacity, Nigerian ports have growingly been

under pressure to improve on their productivity and

efficiency by making sure that port services are provided

on an internationally competitive basis.

For ports, measuring performance is an essential tool in

order to sustain and increase competitiveness. In a nutshell,

the growth in ship size, advent of containerisation and

advances in cargo handling methods have imposed

substantial infrastructural requirement on seaports.

Besides, the rising incapability of the government to

funding state owned ports has led to deregulation and

hence removal of ports financing and management

functions from public to private sector. This shift in

responsibility placed much emphasis on productivity and

efficiency in the management of seaports. This is rational

given that privatized ports face competition and must be

productive and efficient to stay viable. This is actually the

condition and challenge facing Nigerian ports under the

concession regime. However, before the advent of port

concession (1956-2005), the Nigerian port system suffered

from numerous ills which inter alia included the

following:

i. High turnaround time for ships which lasted for

weeks, sometimes months, depending on the cargo

being loaded or discharged;

ii. Cargo-handling plants and equipment owned by the

NPA were few and mostly unserviceable leading to

shipping companies hiring these machines from

private sector sources after having paid NPA;

iii. Dwell time for goods in ports was prolonged due to

poor port management and as such overtime cargo

filled the most active seaports leading to port

congestion;

iv. Unproductive labour force; Labour for ship work was

held in the vice-grip of wharf overlords who

controlled dockworker unions and supplied less than

the manpower paid for.

v. Insecurity of cargo in the ports

vi. Multiple government agencies in the port, corrupt

practices and excessive charges, etc.

In order to tackle these problems, Nigeria undertook a

major port reform program which culminated in the

concession of all ports to the private operators. The port

concession programme in Nigeria was completed in 2006

after an international competitive bidding process. This led

to the emergence of 26 terminals which were concessioned

to private terminal operators on the Build, Operate and

Transfer (BOT) model. As a result of the reform, cargo

handling operations at the port was handed over to terminal

concessionaires, leaving NPA as the landlord. The

Nigerian ports witnessed a rapid transformation as a result

of this reform in which Nigerian ports were concessioned

to the port operators called concessionaires. This process

has singularly injected the much-needed private investment

fund into the process of port infrastructure development.

Thus, the central objective of this research is to analyse

and compare ports performance in pre and post concession

regimes. Whereas the specific objectives are to empirically

test the influence of time factor on port efficiency and to

determine port capacity utilisation with respect to berth

occupancy rate.

Hypotheses

H01: There is no significant difference between pre

and post concession ports’ performance


and post concession turnaround time


and post concession berth occupancy

II. RELATED WORK

There are divergent views among authors on the effects of

private sector participation on port efficiency. Reference

[6] investigated the impact of different types of port

ownership on technical efficiency of ports in the UK

employing the Translog production function. He did not

find any significant difference provided the policy

environment is competitive. Similarly, [7] assessed

different types of ownership and administrative systems in

the port sector by applying stochastic parametric frontier

approach and upheld that port performance is not a

function of ownership structure. In the same vein, [8]

studied the financial performance of Greek’s port new

governance model using a stochastic parametric frontier

approach; the result of the study indicated that despite

profitable results in majority of the Greek ports, the

financial statement raised doubt as to the efficiency of the

ports organisational structure.

Conversely, there are many empirical studies supporting

institutional reforms based on private sector participation

as a veritable tool to achieving higher efficiency in port

terminal operations. Particularly, [9] evaluated Mexico’s

port reform of privatisation and decentralisation using

Malmquist productive Index (MPI) the result showed

significant short-term improvements in performance. This

is collaborated by [10] on the study of Spanish port by

applying Translog and Cobb-Douglas production frontier

model which showed improved productivity due to

technological progress after the reform. Reference [11]

study of Portuguese ports using DEA - allocative and

technical efficiency, identified substantial improvements in

efficiency due to the reform. Similarly, [12] examined the

relative efficiency of major Asian container terminals

using Stochastic Frontier Analysis (SFA) using both cross-

sectional and panel data the result obtained supported view

that privatisation improves efficiency.

Besides, they argued that privatization has become a

strategy to gain competitive edge in the global market



place. Reference [13] investigated the US public port

system and concluded that the US public port system is

dominated by inefficient operation as a result of political

interference and risk aversion. In conclusion, they argued

that the injection of private capital through privatisation

will increase efficiency. Reference [14] is of the view that

private investors and operators’ pursuit of profit

maximization objective may undermine long term

investment in facilities and services in a broader social

perspective. This is mostly true in regions with limited

competition; privatisation will then lead to private

monopolies of port facilities. The study of the efficiency of

27 Spanish 1985 to 1989 by [15] using a translog function

discovered that the most efficient Spanish ports are those

with more centralized management system than those with

autonomous management.

Reference [16] branded long pay-back period, high costs in

the port industry as factors that make outright sale and

complete transfer of operations and regulatory functions

counterproductive. He was of the view that complete

dependence on the private sector may result in delayed

investment in key operational facilities and equipment.

Reference [17] pointed out the need to critically look at the

conceptual and methodological aspects of each reform in

order to gain a global view of the influence of institutional

reforms on national ports efficiency.

The key issue is, whether the efficiency gap in a nation’s

port sector can be explained by the difference of port

ownership and institutional structure and if private sector

participation and port reforms have enabled inefficient

ports to become more efficient. Reference [18] employed

the Stochastic Frontier Analysis in benchmarking container

port technical efficiency in Latin America and the

Caribbean (LAC). The results derived from the model

reveals that, between 1999 and 2009, port technical

efficiencies in LAC ranged between 5% in Rosario to 93%

in San Juan. The overall average is 41.7% and the standard

deviation is 21.4%. This result demonstrates that even the

most technically efficient port in the region still has space

for improvement and, conversely, the least technically

efficient port has a very large gap to close regarding the

frontier.

III. METHODOLOGY

Sources of Data This research entirely relied upon secondary information

for the analysis. The data were obtained from the NPA

Annual Reports and Abstract of Port Statistics, internet,

dissertations and other relevant textbooks.

Methods of Data Analysis

This research made use of t-Test statistic and Data

Envelopment Analysis (DEA). Firstly, the study made a

holistic assessment of the performance of the 26 terminals

in Nigeria. In this case, Cargo throughput, Ship turnaround

time and Berth Occupancy were used as variables and the

two-sample t-Test was used in analyzing these data to test

the difference between the sample means for the pre and

post concession era to substantiate if there is any statistical

significance. The variables used were taken within the

range of seven years of pre concession (1999-2005) and

seven years of post-concession (2006-2012). Secondly, the

study analysed a sample of six (6) major ports in Nigeria to

confirm the difference between the pre and post concession

performance. The analysis was based on ranking and

comparing the pre- and post-concession efficiency gains of

the following ports namely: Lagos Port Complex, Tin Can

Island Port, Delta Port Complex, Rivers Port Complex,

Onne Port Complex and Calabar Port Complex.

The most efficient port was used to benchmark the others.

Parameters such as Ship traffic, Cargo throughput, Total

Number of Equipment, Berth length, Total Number of

Berth and Total Number of Staff were employed as

variables using Data Envelopment Analysis to assess the

efficiency of the ports. The variables used were equally

taken within the range of seven years of pre concession

(1999-2005) and seven years of post-concession (2006-

2012). Other analytical tools include the use of

percentages, charts and graphs, where applicable.

t-Test Statistic

To test the research hypotheses, data in tables 1, 5 and 9

were analysed using t-Test Statistic. The equation of the

t-Test Statistic is presented as follows.

√

Where:

√

Decision Rule:

Reject the H0 if the computed t is greater than the table

value and does not fall within the acceptance region.

Otherwise accept H0.

H0: refers to null hypothesis

H1: refers to alternative hypothesis

Data Envelopment Analysis (DEA)

DEA is a non-parametric efficiency evaluation model

based on mathematical programming theory [19]. It is used

in econometrics and operations research for multi-variant

frontier estimation and ranking which can be employed in

the calculation of efficiency levels within a group of

organisations. This is accomplished by calculating the

relative performance of the units being investigated to the

group’s best practice. Reference [20] assert that efficiency

is a relative term that can be measured by the process of

benchmarking. There are two types of efficiency;

Technical and Scale efficiency. Reference [5] define

technical efficiency as relative productivity over time or



space or both and scale efficiency as possible divergence

between actual and ideal production size.

The concept of DEA is developed from the basic idea that

efficiency of a DMU is determined by its ability to convert

inputs into desired outputs. This idea of efficiency was

adopted from engineering which defines efficiency of a

machine/process as output/input ≤ 1. In this approach,

efficiency is always less than or equal to unity as some

energy loss will always occur during transformation

process. There are various models of the DEA

methodology; however, DEA Window Analysis model was

used in this research to calculate the average efficiency

using CCR and BCC model. This version of DEA was first

used by Charnes et al (1985); window analysis is a time-

dependent version of DEA which is applied with panel

data. The basic idea is to treat each port as if it was a

different port in each of the years under review. Each

port’s performance is not only compared with that of other

ports but also against its own performance at different

times. This approach is necessary since we are comparing

the performance of Nigerian ports at different times and

with different ownership structures.

From the study, four inputs and two outputs were utilized

in the derivation of the efficiency level:

a) Input 1- Berth length

b) Input 2- Total No. of Berth

c) Input 3- Total No. of Equipment

d) Input 4- Total No. of Staff

e) Output 1- Cargo Throughput

f) Output 2 - Total Ships Traffic

The Basic Mathematical Formulation of DEA is presented

as follows:

Maximize

{∑

}

{∑ }

Subject to

{∑ }

{∑ }

Where:

Eb is the efficiency of any unit b

yrj is observed quantity of output r produced by unit j = 1,

2…, N

xij is observed quantity of input I used by unit j = 1, 2…, N

urb is the weight (to be determined) given to output r by

base unit b

vib is the weight (to be determined) given to input i by base

unit b

c is a very small number

IV. RESULTS AND DISCUSSION

Test of Hypothesis 1

H01: There is no significant difference between pre and

post concession ports’ performance

Table 1. Variables for Test of Hypothesis 1

CARGO THROUGHPUT

Year X1 (pre-concession) Year X2 (post-

concession)

1999 22232936 2006 46150518

2000 28932880 2007 57473350

2001 35940692 2008 63982749

2002 36987241 2009 65775509

2003 39765945 2010 76744727

2004 40816947 2011 83450032

2005 44952078 2012 77104738

Source: Nigerian Ports Authority (1999 - 2012)

To test Hypothesis 1, the data in Table 1. were analysed

using t-Test statistic and the result is as shown in Table 2,

Table 3 and Table 4. The table value t = 0.025(12) = ±2.19

at 0.05 significance with 12.0 degrees of freedom. Since

the computed value tc = -13.21 is more than the critical

value -2.19 (i.e., in absolute terms) and falls outside the

acceptance region, thus, we reject H0 and accept H1 and

conclude that there is a significant difference between pre

and post concession ports performance. In Table 2, the

mean of X1 is 35661245.57 which is the average cargo

throughput of the pre concession period. This is lower than

67240231.85 which is the mean of X2 which is the cargo

throughput of the post concession period. This implies that

the average cargo throughput of post concession period

doubled that of pre concession period.

Table 2. Paired Samples Statistics 1

Mean N

Std.

Deviation

Std. Error

Mean

Pai

r 1

x1 35661245.5

714 7

7715526.955

73

2916195.079

81

x2 67240231.8

571 7

12931396.86

599

4887608.601

73

Table 3 shows a positive correlation of 0.9 between X1 and

X2 and Table 4 shows that the calculated t = -13.21 is more

than -2.19 table value in absolute terms.

Table 3. Paired Samples Correlations 1

N Correlation Sig.

Pair 1 x1 & x2 7 0.936 0.002

Table 4. Paired Samples Test 1 Paired Differences t d

f

Sig.

(2tail

ed) Mea

n

Std.

Deviatio

n

Std.

Erro

r

Mean

95%

confidence

interval of

the

Difference

Low

er

Upp

er

Pa

ir

x1-

x2

-

3E+0

07

6323338

.620

2389

997

-

4E+0

07

-

3E+0

07

-

13.2

13

6 .000



Fig1. shows that trend of cargo throughput during post

concession increased sharply as against the pre concession.

It rose from 44952078mt in 2005 to 77104738mt in 2012

which translates to 72% increase.

Fig1. Pre and Post concession’s Trend of Cargo Throughput



post concession turnaround time


TURNAROUND TIME (DAYS)

Year X1 (pre-

concession)

Year X2 (post-concession)

1999 6.31 2006 5.31

2000 7.01 2007 3.75

2001 7.91 2008 4.59

2002 11.34 2009 6.55

2003 7.89 2010 5.38

2004 6.44 2011 5.48

2005 7.40 2012 5.75


To test Hypothesis 2, the data in Table 5 were analysed


Table 7 and Table 8. The table value t = 0.025(12) = ±2.19

at 0.05 significance with 12.0 degrees of freedom. Since

the computed value tc = 4.71 is more than the critical value

2.19 and falls outside the acceptance region, we therefore,

reject

H0 and accept H1 and draw a conclusion that there is a

significant difference between turnaround time during pre

and post concession periods. In Table 6, the mean of X1 is

7.75 which is the average turnaround days of the pre

concession period. This is higher than 5.25 which is the

mean of X2 which is the average turnaround days of the

post concession period. This implies that turnaround time

in pre concession period than that post concession period.


Mean N

Std.

Deviation

Std. Error

Mean

Pair 1 x1 7.7571 7 1.70258 0.64352

x2 5.2586 7 0.88492 0.33447

Table 7 shows a positive correlation of 0.56 between X1

and X2 and Table 8 shows that the calculated t is equal to

4.7.


N Correlation Sig.

Pair 1 x1 & x2 7 0.567 0.184

Table 8. Paired Samples Test

Paired Differences T d

f

Sig

.

2tail

ed

Me

an

Std.

Devi

ation

Std.

Erro

r

Mea

n

95%

confidence

interval of

the

Difference

Low

er

Uppe

r

Pair

x1-x2

2.49

857

1.40

465

.5309

1

3.79

766

4.7

06

6 .00

3

Fig 2 indicates that the trend of vessel turnaround time

during post concession period is decreasing as against the

trend in pre concession period. There was an average of

5.5days turnaround time at the post concession against

7.7days turnaround time at the pre concession. This means

a 32% reduction in number of days a vessel would have

waited to have its cargo discharged.

Fig. 2 Pre & Post Concession’s Trend of Vessel Turnaround

Time



post concession berth occupancy


BERTH OCCUPANCY (%)

Year X1 (pre-concession) Year X2 (post-concession)

1999 47.09 2006 48.49

2000 44.76 2007 44.95

2001 51.78 2008 36.72

2002 56.58 2009 47.46

2003 52.75 2010 51.21

2004 50.93 2011 51.45

2005 49.70 2012 45.92


To test Hypothesis 3, the data in Table 9 were analysed


Table 11 and Table 12. The table value t = 0.025(12) =

0

10,000,000

20,000,000

30,000,000

40,000,000

50,000,000

60,000,000

70,000,000

80,000,000

90,000,000

0

2

4

6

8

10

12

Series1, 1999, 6.31 Series1, 2000, 7.01

Series1, 2001, 7.91

Series1, 2002, 11.3

Series1, 2003, 7.89

Series1, 2004, 6.44

Series1, 2005, 7.4

Series1, 2006, 5.31

Series1, 2007, 3.75 Series1, 2008, 4.59

Series1, 2009, 6.55

Series1, 2010, 5.38 Series1, 2011, 5.48 Series1, 2012, 5.75



±2.19 at 0.05 significance and 12.0 degrees of freedom.

Since the calculated value tc = 1. 703 is less than table

value t = 2.19 and falls within the acceptance region, thus,

we accept H0 and conclude that there is no significant

difference between pre and post concession berth

occupancy. The reason for the acceptance of H0 here is

informed by the Decision Rule of the t-Test statistic (as in

3.3.1 in Chapter Three) which states that H0 should be

accepted if tc is less than the table value. In Table 10, the

mean of X1 is 50.51 which is the average berth occupancy

rate of the pre concession period and is higher than 46.6

which is the mean of X2 which is the average berth

occupancy rate of the post concession period. This means a

7.7% reduction in pre concession berth occupancy rate.


Mean N

Std.

Deviation

Std.

Error

Mean

Pair 1 x1 50.5129 7 3.85285 1.45624

x2 46.6000 7 4.99809 1.88910

Table 11 shows a positive correlation of 0.075 between X1

and X2 while Table 12 shows that the calculated t is equal

to 1.703.


N Correlation Sig.

Pair 1 x1 & x2 7 .075 .874

Table 12. Paired Samples Test 3

Paired Differences t d

f

Sig.

2tai

led Me

an

Std.

Devia

tion

Std.

Erro

r

Mea

n

95%

confidence

interval of

the

Difference

Low

er

Uppe

r

Pair

x1-x2

3.91

268

6.079

08

2.297

68

-

1.709

35

9.535

07

1.7

03

6 .139

Fig 3 signifies that the trend of berth occupancy during

post concession period slightly declined as against the

trend in pre concession period. There was an average of

51% occupancy rate at the pre concession against 47%

occupancy rate at the post concession. This implies a 7.7%

decrease in occupancy rate at the ports.

Fig 3. Pre & Post Concession’s Trend of Berth Occupancy

4.4 DEA Based Efficiency Measurement of Nigerian

Ports

The study analysed the efficiency of six Nigerian ports

namely Lagos Port Complex, Tin Can Island Port, Delta

Port Complex, Rivers Port Complex, Onne Port Complex

and Calabar Port Complex; seven years before concession

and seven years after i.e., 1999-2012 using the DEA

Window technique to capture the trend of efficiency

changes over the period under review. The data for this

analysis are contained in Table 13 and the result is as

shown in Table 14.

Table 13. Input and Output Variables for DEA Analysis

Ports

Year

Inputs Outputs

1 2 3 4 1 2

Berth

Length

No. of

Berths

Total

No. of

Equipt.

Total

No.

of

Staff

Throughput

(Tons)

No.

of

Ship

Calls

LPC 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500

15 15 15 15 15 15 15 15 15 15 15 15 15 15

98 99 98 92 85 80 81 81 81 80 80 80 89 97

3110 3027 2894 2943 2817 2731 2179 760 959 717 726 712 664 661

7890170 9164477 11461451 11754539 11875265 12294640 13432106 15112819 18547253 20809224 21566202 21239855 22808353 19957706

837 858 1022 929 882 891 955 1376 1359 1452 1545 1588 1594 1444

TCIP

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045

5 5 5 5 5 5 5 5 5 5 5 5 5 5

79 81 85 89 96 101 101 137 145 150 168 188 188 190

1279 1276 1264 1372 1376 1362 1131 850 799 668 637 604 537 530

2921032 3138007 4133077 4105028 4583505 4075386 4743741 7399531 10003300 12807920 13541016 14461638 16230591 15281010

357 435 474 405 549 504 495 903 1185 1367 1389 1504 1628 1507

Delta Ports

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1790 1790 1790 1790 1790 1790 1790 1790 1790 1790 1790 1790 1790 1790

11 11 11 11 11 11 11 11 11 11 11 11 11 11

123 79 55 54 56 56 56 80 88 82 60 60 62 64

1271 1216 1214 1206 1169 388 942 836 480 426 424 422 411 410

1146946 1612034 1480163 1668663 1635847 1296050 1922254 1162404 1249763 1913238 6590471 9807661 8538831 6987533

331 266 307 292 261 229 295 193 205 246 321 341 362 109

Rivers Port

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1117 1117 1117 1117 1117 1117 1117 1117 1117 1117 1117 1117 1117 1117

8 8 8 8 8 8 8 8 8 8 8 8 8 8

77 77 64 53 40 49 56 59 58 65 60 68 71 74

1322 1252 1243 1286 1249 1096 929 868 506 424 415 407 391 390

1933830 2255178 3518229 3666413 3702553 3210907 3516188 3001019 3112518 3488791 5141451 5808533 7463620 5574653

271 265 313 329 303 261 239 270 317 378 459 482 584 461

Onne Port

Complex

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

2063 2063 2063 2063 2063 2063 2063 2063 2063 2063 2063 2063 2063 2063

8 8 8 8 8 8 8 8 8 8 8 8 8 8

1 1 2 2

13 13 13 40 45 48 48 55 58 60

383 368 375 595 392 1185 362 336 206 572 499 459 207 205

2527970 7166436 9056487 10182079 11994981 2158548 2554328 2535759 2482177 3144943 23832763 17215120 2470112 27580642

241 342 479 532 554 390 423 444 394 379 689 769 885 873

0

10

20

30

40

50

60

199

9

200

0

200

1

200

2

200

3

200

4

200

5

200

6

200

7

200

8

200

9

201

0

201

1

201

2

Berth Occupancy



Calabar 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1037 1037 1037 1037 1037 1037 1037 1037 1037 1037 1037 1037 1037 1037

8 8 8 8 8 8 8 8 8 8 8 8 8 8

70 69 70 46 38 34 38 32 38 44 51 56 58 61

598 583 566 393 597 264 590 459 274 254 262 265 261 261

226635 300124 324866 399595 481062 752910 857726 776787 949523

1245599 1721249 1594277 1878753 1723195

140 207 154 109 163 213 276 321 897 351 321 197 179 303

Source: NPA Annual Reports and Abstract of Port Statistics LPC ≡ Lagos Port Complex and TCIP ≡Tin Can Island Port

Result of the DEA

Table 13 gives is an overview of the input and output data

used for the analysis. The result of this analysis as

presented in table 14 shows that the efficiency of virtually

all the ports, except Onne port, fluctuated in the period

before the concession but experienced remarkable increase

in the concession regime. The Onne port recorded a

significant and stable efficiency before and during the

concession regime. The good performance of this port in

pre concession era may be attributed to a kind of partial

concession to INTELS which it enjoyed as at that time and

the outstanding performance during concession regime as a

result of full concession. The port scored 86.1% and

ranked Number One port while LPC recorded 73.6 to

emerge Number Two port. TCIP, Rivers and Warri ports

ranked Number Three, Four and Five ports respectively.

Table 14. Average Relative Efficiencies (%) of Six Sample Ports

Ports Year LPC TCIP Warri Rivers Onne Calabar

1999 54.0 58.9 48.0 55.0 90.0 26.8

2000 64.4 61.2 58.2 56.5 95.0 24.7

2001 58.5 54.0 55.1 51.1 91.0 26.8

2002 55.8 54.0 52.0 57.0 93.0 40.2

2003 57.2 54.0 43.0 53.3 67.1 68.5

2004 65.3 54.0 52.7 51.2 82.2 47.2

2005 69.3 71.4 56.6 59.8 79.0 50.0

2006 86.7 89.7 60.1 71.4 89.7 58.7

2007 97.4 85.9 64.9 59.8 79.5 53.8

2008 92.1 86.2 69.3 71.4 87.6 59.7

2009 90.1 86.2 69.4 79.9 85.3 55.4

2010 75.4 86.2 72.6 90.1 70.5 19.3

2011 91.0 83.5 72.6 95.1 95.0 24.7

2012 64.1 73.0 73.3 78.1 100 24.3

Average 73.6 73.0 56.9 66.4 86.1 41.4

Source: Result of DEA analysis 2019

Table 15 deals with result of the pre-concession

efficiencies of the ports. It is remarkable to note that Onne

port continues to lead with 85.33% this time while Calabar

maintains the last position with 40.60%.

Table 15. Pre-Concession Ports Efficiencies Ports Year LPC TCIP Warri Rivers Onne Calabar

1999 54.0 58.9 48.0 55.0 90.0 26.8

2000 64.4 61.2 58.2 56.5 95.0 24.7

2001 58.5 54.0 55.1 51.1 91.0 26.8

2002 55.8 62.8 52.0 57.0 93.0 40.2

2003 57.2 54.8 43.0 53.3 67.1 68.5

2004 65.3 60.4 52.7 51.2 82.2 47.2

2005 69.3 71.4 56.6 59.8 79.0 50.0

Average 60.64 60.50 52.23 54.84 85.33 40.60

Rank 2 5 4 1 6


Table 16 shows the result of the post-concession

efficiencies of the ports. It is also interesting to note that

Onne port maintains the lead with 86.80% where Calabar

retained the last position with 42.27%.

Table 16. Post-Concession Ports Efficiencies Ports Year LPC TCIP Warri Rivers Onne Calabar

1999 86.7 89.7 60.1 71.4 89.7 58.7

2000 97.4 85.9 64.9 59.8 79.5 53.8

2001 92.1 86.2 69.3 71.4 87.6 59.7

2002 90.1 90.6 69.4 79.9 85.3 55.4

2003 75.4 67.2 72.6 90.1 70.5 19.3

2004 91.0 83.5 91.6 95.1 95.0 24.7

2005 64.1 95.4 73.3 78.1 100.0 24.3

Average 85.26 85.50 71.60 77.97 86.80 42.27

Rank 3 2 5 4 1 6


V. CONCLUSION AND FUTURE SCOPE

The result of the analysis shows that the pre concession era

witnessed a fluctuation in cargo throughput while the post

concession era recorded spectacular increase in throughput.

The study also reveals that the average turnaround time of

7.34days at the pre concession period went down to 5.25 at

post concession period. This shows a 32.25% decrease.

The Berth Occupancy Rate declined from an average of

51% to an average of 47%. This implies a 7.7% decrease in

occupancy rate at the ports. The berth occupancy at

average of 47% in the ports is low. This implies under

capacity utilization of the ports. This is because

international standard recommends berth occupancy values

within the range of 60% and 80% as the safest to aim at.

Personnel strength of the NPA naturally, reduced from an

average of 1233 at pre-concession period to 503 at post-

concession. The analysis as well reveals that out of the six

sample ports assessed, Onne port complex with an average

of 86.80% maintained a lead in the post-concession regime

and thus ranked the most technically efficient port while

Calabar was the least with 42.27% efficiency level in same

period. These results agree with the views of authors who

believed that port reforms and private sector participation

have positive effects on port efficiency. The participation

of the private sector has proven to be a key ingredient in

improving the fortunes of Nigerian Ports. The Nigerian

environment and its port sector in particular equally proved

to be conducive for private sector participation.

The study made the following findings based on the

strength of these objectives;

a) That the concession regime has a tremendous

positive impact on the performance of Nigerian

seaports with an upsurge in cargo throughput.

b) That there is a reduction in turnaround time and

vessel/cargo dwell time in ports.

c) That there is no significant difference between pre

and post concession berth occupancy

d) That there is a significant difference in the nature

of models on the trend of cargo movement in pre

and post concession



A lot of infrastructures have been put up in the ports during

the concession regime and thus there is need for properly

maintenance of these facilities. Beside the fact that

adequate maintenance of the infrastructure will lead to

greater productivity, Nigeria would have acquired adequate

infrastructural foundations upon which her future port

growth and development will be sustained. Given the

upsurge in cargo throughput in the post-concession regime,

government should be proactive, knowing that this

portends future port congestion problems and should use

the present data to plan for the future development of the

ports. Since the participation of the private sector has

proven to be a key ingredient in improving the fortunes of

Nigerian ports, government should support more public

ownership and private sector operations of the port system.

The average turnaround time at post concession period is

5.25. This is not yet down to 48hours international

standard for ship turnaround time. The port operators

should strive hard to improve on this. In effect,

government should also strive to reduce customs clearance

in the port to 24 hours. It will help reduce the average

turnaround time at the ports. Finally, the berth occupancy

rate at an average of 47% in the post concession regime is

poor. This means the concessionaires should beef up their

efforts to attract more vessels to the ports so as to meet up

with the international standard of 60-80% range of

occupancy rate.

ACKNOWLEDGMENT

We wish to express our profound gratitude to these two

institutions — Federal University of Technology, Akure

and Federal University of Technology Owerri, for

providing the insight, expertise and conducive environment

that are very helpful in research.

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AUTHORS’ PROFILE

Dr. I. Njoku bagged Bachelor of Technology, Master of

Science and Doctor of Philosophy degrees in Maritime

Transport Management from the Federal University of

Technology, Owerri Nigeria (FUTO) in 2000, 2009 and

2015 respectively. He also obtained another Master’s

degree in Financial Management from FUTO in 2007. He

is a Lecturer at the Department of Logistics and Transport

Technology, Federal University of Technology, Akure

Nigeria. His area of specialization includes maritime

transport and shipping finance. He has published widely in

both national and international journals. He is a member of

CILT, Nigeria.

Prof. C. C. Ibe pursed B.Sc. from University of Benin,

M.Sc. and Ph.D. from FUTO. He is currently working as

Professor in Department of Logistics and Transport

Technology, FUTO. He is a fellow of CILT, Nigeria. He

has published more than 50 research papers in reputed

international journals. He has over 20 years of teaching

experience and 5 years of industry experience.

impact of concession regime on the performance of nigerian

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