From: Biniam Tekle (biniamt@dehai.org)
Date: Thu Jan 29 2009 - 12:40:00 EST
Over the past few decades, seaport industry in many countries of the world
has witnessed remarkable
development. This is obvious, particularly in the East African countries,
such as Sudan, Eritrea,
Djibouti, Kenya, and Tanzania, and the Middle Eastern countries,
particularly Saudi Arabia, Yemen,
Oman, the United Arab Emirates, and Iran. These countries possess seaports
which are strategically
located in the international maritime trade route between the East and the
West (Figure 1) and are
considered as middle distance seaports. Goods carried from Europe and Far
East/Australia and vice
versa can be exchanged and transhipped to all countries in the Middle East,
Red *Sea*, and East Africa.
Since the olden days, these seaports have provided services for the regional
coasters and as time went
by, they have developed to be among the important maritime international
trade centres in the region.
The geographically strategic location of some of these seaports, have also
encouraged modern
container vessels to make short duration calls upon them (e.g. shipping
lines operating along
Asia/Europe route, Asia/Mediterranean route and Asia/US East Coast route).
These seaports and their
characteristics are displayed in Table 1.
------------------------------
*Page 3*
600
Ahmed Salem Al-Eraqi, Adli Mustafa, Ahamad Tajudin Khader and Carlos Pestana
Barros
*Table 1:*
Characteristics of seaports in Middle Eastern and East African regions
*No. Port *
*Berth Length *
*Equipment *
*Area M sq *
*Ship Call Total Tons *
1
Dubai Emirates (B)
5519
24
2209000
3916
12971235
2
Jeddah Saudi (B)
1330
26
50000
2049
12292704
3
Salalah Oman (B)
4296
14
341292
1506
1367404
4
Dammam Saudi (B)
1780
54
1032692
1653
19874564
5
Kuwait (B)
1750
23
538898
1636
3836840
6
Aden Yemen (B)
4875
176
1948610
6352
66541268
7
Mombasa Kenya (B)
4055
12
1586458
3148
16106155
8
Khor Fakkan Sharjah (B)
320
2
250567
398
1239645
9
Yanbu Saudi (M)
2004
34
843015
2463
14762086
10
Hodeidah (M)
1165
18
1321000
2042
8338290
11
Jubail Saudi (M)
8454
39
1843720
2782
16210109
12
Djibouti (M)
4800
9
1438800
1462
8556476
13
Dar es Salaam Tanzania (M)
1930
9
727000
1466
10720699
14
Sudan (M)
11200
114
2500000
4365
39245363
15
Mascut Oman (M)
2254
44
540253
2431
5102331
16
Asmara Eritrea (M)
1650
68
114117
1670
13916858
17
Khalid Sharjah (M)
2444
63
46864
1615
6232654
18
Bander Abbas Iran (M)
381
13
20000
195
334189
19
Mukalla Yemen (S)
385
6
400000
174
276681
20
Assab Eritrea (S)
1140
35
275319
819
535736
21
Tanga Tanzania (S)
1120
18
204057
1602
1509422
22
Mtwara Tanzania (S)
1795
20
151200
2165
6290892
B: Big port, M: Medium port and S: small port
*Figure 1: *Map of the region
------------------------------
*Page 4*
Efficiency of Middle Eastern and East African Seaports: Application of
DEA *Using* Window Analysis
601
*3. Literature Survey *
There is extensive literature on *DEA*, applied to a wide diversity of
economic fields and in particular to
seaports transportation. Cullinane et al. (2005) used *DEA *to highlight the
major objective of port
privatisation to improve the efficiency of this sector, with data of the
container throughput as output
and area and length terminal, quay crane, yard crane, straddle as inputs.
These authors concluded that
public and private/public *ports* perform better than public/private and
private *ports*.
Barros (2006) evaluated the performance of Italian seaports for the 2002
-2003 period *using*
*DEA *with Charnes, Cooper and Rhodes (*CCR*) model and Banker, Charnes and
Cooper (*BCC*) model,
to analyze 24 seaports. Barros (2006) used multiple efficiency models, such
as *DEA CCR*, *BCC*, Cross
efficiency *DEA *and *DEA *Super efficiency for Italian seaports, whereas
previously published articles
were limited to one or two analysis models. Because of this, the general
conclusion emerged that the
Italian companies display relatively high management skills, with most of
them being Variable Return
to Scale (*VRS*) efficient. Barros (2006) provides benchmarks to improve the
functioning of the port in
terms of efficiency.
Cullinane et al. (2004) applied window analysis in order to evaluate the
efficiency score of the
world's major container *ports* over time by *using* panel data and
cross-section data for 2003. They
concluded that the cross-section method is poor because it does not provide
details of port
performance, whereas the panel data with window analysis reflect a variation
of the absolute
performance of a port over time, and the relative performance of that port
in comparison to the others
at the same time.
Barros & Manolis (2004) compared the efficiency of *ports* of two European
countries, Greece
and Portugal. They took data from several *ports* of each of these countries
during the 1998-2000
periods. Their paper is intended to evaluate the efficiency of major
seaports in two small European
countries *using* the *CCR *and *BCC *models.
Wang & Cullinane (2006) focused on measuring the efficiency of container
terminals in
Europe. They proposed *DEA *with *CCR *and *BCC *models to evaluate
efficiency. They concluded that
management skills are crucial and emerge as a core in terms of business
competence.
Cullinane et al. (2006) contribute richly to this research by supporting
existing research which
leads to an estimates approach of relative efficiency in this active private
sector of *ports*. Their study
focuses on a sample comprising 69 of Europe's container terminals with
annual throughput of over
10,000 TEUs for the year 2002.
*4. Data Envelopment Analysis (DEA) *
*4.1. Standard DEA Models *
The basic concept of efficiency measurement is the ratio of total outputs to
total inputs. Charnes et al.
(1978) were the first to introduce the *DEA *as a multi-factor productivity
analysis module for measuring
the relative efficiencies on making units (*DMU*s). This model can not
support imperfectly competitive
markets. To overcome this limitation, Banker et al. (1984) described
*BCC *model,
this model estimates
its productivity level at the given scale of operation and identifies return
to scale. The goal is to select a
set of inputs and outputs that are relevant to the evaluation of performance
and for which a moderate
statistical relationship exists.
In *DEA*-*CCR *model all observed production combinations can be scaled up
or down
proportionally, and in *DEA*-*BCC *model the variables allow return to scale
and is graphically
represented by a piecewise linear convex frontier (Cullinane et al. 2006).
The *DEA *is normally applied
to analyse the cross section data, where time is ignored and *DMU *are
compared with the others at the
same period. In this paper, we propose the output-oriented *DEA *model to
maximize the output while
the given current inputs remain the same. The mathematical expression of the
*DEA *models as follow:
1) *CCR *Model (Charnes, Cooper and Rhodes) (1978).
------------------------------
*Page 5*
602
Ahmed Salem Al-Eraqi, Adli Mustafa, Ahamad Tajudin Khader and Carlos Pestana
Barros
*Max *
*k*
φ
*ik*
*ij*
*n*
*j*
*j*
*x*
*x*
*ts*
≥
∑
=1
..
λ
*i=1, 2… m;*
(1)
*rk*
*k*
*rj*
*n*
*j*
*j*
*y*
*y*
φ
λ
≤
∑
=1
*r=1,2,…,s;*
0
≥
*j*
λ
*j*
∀
*.*
And 2) *BCC *Model, (Banker, Charnes and Cooper 1984) is defined by adding
equations (2) to
expression (1) above.
1
1
=
∑
=
*n*
*j*
*j*
λ
(2)
Where *n *is number of *DMU*,
*k*
φ
is the efficiency of the kth *DMU*, *x*
*ij*
are *i-th *inputs of the *j-th*
*DMU*, *y*
*rj*
are the outputs of *j-th DMU *and *λ*
*j*
is weight of *j-th DMU*. The *DEA*-technique requires a
large number of medium-sized linear programming problems to be solved. The
two models, described
previously, the first is called *CCR *model (constant return to scale) which
is a scale efficiency and
technical efficiency, and the second is called *BCC *model (variable return
to scale) which is a pure
technical and scale efficiency (Fare et al. 1994). That output-oriented
efficiency problem can be written
in the form of N linear programming system (Cullinane et al. 2004). The
technical efficiencies derived
from the *DEA*-*CCR *and *DEA*-*BCC *models are frequently used to obtain a
measure of scale for *DMU*,
given by SE
k
=U
*CCR*k
/ U
*BCC*k
(William et al.2000), where U
*CCR*_k
and U
*BCC*_k
are the technical efficiency
measures for *DMU k *derived from applying the *DEA*-*CCR *and
*DEA*-*BCC *models
respectively. *CCR*
score is called technical efficiency (TE), *BCC *called pure technical
efficiency (PTE), and scale
efficiency noted by (SE) with TE = PTE * *SE*, if *SE*
*k*
*=*1 then the score is efficiency (constant return to
scale) otherwise the score is inefficiency if SE
k
<1(Increasing or decreasing return to scale). The
constant return to scale means that the firm able to operate the inputs and
outputs linearly without
increasing or decreasing. The increasing return to scale means that the firm
operating at lower scale
sizes, while decreasing return to scale means that the firm operating at
higher scale sizes.
*4.2. Window Analysis *
A *DEA *window analysis calculates the average efficiency of *CCR *and *BCC
*models, and is useful for
detecting efficiency trends of unit over time (Charnes et al., 1994b). In
such a circumstance, *DEA*
window analysis can be adopted to detect a trend of *DMU *over time (Asmild
et al. 2004; Charnes et al.
1994b; Yue, 1992). The procedure is to consider each *DMU *is represented as
if it were different *DMU*
in each period under analysis. There is no theory underpins the
justification for the choice of window
size. The common notation is describing as follow:
*n*=Number of *DMU*
*T*=Number of periods
*p*= Altitude of window
(*p*
≤
*T*) (Number of columns in window)
*w*= Number of windows
(Number of rows for each firm)
where *w=T-p+1 *are number of analysis for each *DMU *and *n x p *will be
the total number analysis for
all *DMUs *as mention above. The identification of performance trends in row
window and the stability
is defined in column. The variation in row reflects both the absolute
performance of a port over time
and the relative performance of that port in comparison to the others *ports
*.
------------------------------
*Page 6*
Efficiency of Middle Eastern and East African Seaports: Application of
DEA *Using* Window Analysis
603
*5. Data and Statistical Analysis *
*5.1. The input and output measures *
The data were obtained from the annual statistics reports of
*ports*authorities, by fax and E-mail and
through internet (*using* Google Earth and *ports* web site as
Maritimechain.com and *Ports* Harbours
Marines Worldwide). The inputs remain unchanged within this time; by
contrast, change occurred in
the cargo throughput and ship calls.
To estimate the efficiency of the *ports* under study, we used data for the
years 2000-2005; the
*ports* considered in analysis are listed in Table 1 and the summary of
their characteristics are described
in Table 2.
The output is measured by two indicators: 1) Ship calls, and 2) Throughput
(movement of
general cargo dry and liquids and containers) load/unload, while the inputs
are measured by the
indicators, such as berth length, *storage* area, and handling equipment.
*Table 2:*
Summary statistics for years 2000-2005
*Inputs *
*Outputs *
*Berth *
*Length(m) *
*Storage *
*Area(m*
*2*
*) *
*Handling*
*Equipment *
*Ship Calls *
*(Units) *
*Throughput (Tons) *
Mean
2938.500
37.318
835584.636
2086.606
12102800.015
Std. Error of Mean
232.772
3.446
66339.004
125.965
1539146.311
Median
1862.500
23.500
539575.500
1818.500
6831638.500
Mode
320.000
9.000
20000.000
1450.000
241950.000
Std. Deviation
2674.343
39.590
762177.126
1447.233
17683444.807
Variance
7152111.641
1567.364
580913971516.997
2094483.080
312704220232032.000
Skewness
1.664
2.233
0.764
1.308
3.929
Kurtosis
0.211
0.211
0.211
0.211
0.211
Range
2.404
4.956
-0.726
2.290
20.790
Minimum
0.419
0.419
0.419
0.419
0.419
Maximum
10880.000
174.000
2480000.000
7450.000
129429309.000
Sum
320.000
2.000
20000.000
124.000
63644.000
Count
22
22
22
22
22
*5.2. Correlation and regression analysis *
The analyses of inputs and outputs variables data show only those that are
highly interrelated (refer to
Table 3).
*Table 3:*
Correlation coefficients with inputs and outputs
*Berth Length Handling Equipment*
*Storage Area*
*Ship Calls *
*Throughput *
Berth Length
1.000*
Handling Equipment
0.469
1.000*
*Storage* Area
0.812*
0.434
1.000*
Ship Calls
0.664*
0.762*
0.679*
1.000*
Throughput
0.561*
0.896*
0.663*
0.879*
1.000*
*Correlation coefficient values are significant at the 0.05 level
(2-tailed).
The multiple regressions are used to determine any relationship between the
inputs and the
outputs. Table 4 shows the "R2" values as the proportion of variation in the
dependent variable ship
calls and throughputs explained by the regression model are 0.801 and 0.907.
The statistics and its
significant values are used to test the null hypothesis that the regression
coefficient is zero that mean
there is a linear relationship between the dependent (ship calls and
throughput) and independent (berth
length, equipment and area) variables.
------------------------------
*Page 7*
604
Ahmed Salem Al-Eraqi, Adli Mustafa, Ahamad Tajudin Khader and Carlos Pestana
Barros
*Table 4:*
Regression results on inputs and output variables
*Outputs *
*Inputs *
*Ship Calls *
*Throughput *
Berth Length
-0.015
-1082.928
Handling Equipment
19.015
290517.165
*Storage* Area
0.001
9.391
Constant
592.118
-3403512.341
R
2
0.801
0.907
The software Efficiency Measurement System version 1.3 from Holger Scheel
was applied to
solve the *DEA *with two models on the return to scale of *ports* production
function, called *CCR *model
(constant return to scale) and *BCC *model (variable return to scale).
*6. Results *
We first applied *DEA *to analyse the efficiency score of the *ports*, we
computed efficiency *using* two
models: *DEA*-*CCR *and *DEA*-*BCC*. *DEA *is carried on 22 *ports* shown in
Table 1. Table 5 represents the
efficiency estimates, the scale efficiency and scale type of each port. The
score report shows that 7 and
9 *ports* out 22 are efficient under *DEA*-*CCR *and *DEA*-*BCC *models,
respectively. The results of two
models show that the number of efficient *ports* in *BCC *is more than *CCR
*with average values of 0.786
and 0.875, respectively.
The output oriented model was applied in this paper to select the *ports* in
terms of berth length,
*storage* area and handling equipment. Theatrically, the output of technical
efficiency is given by
*TE*
k
=1/U
k
for k term of *DMU *(U
k
is an inefficient score under *CCR **using* output-oriented). The *ports*
under study must increase their product on an average of 1.272 times for the
same inputs. The scale
properties of *ports* production show 7 *ports* constant return to scale, 8
increasing return to scale, 7
decreasing return to scale. Note that 7 *ports*, Khor Fakkan, Dubai, Kuwait,
Mukalla, Hodeidah, Yanbu,
and Djibouti are efficient under *CCR *and *BCC*.
We next applied *DEA *window to analyse the efficiency score of the *ports*,
with two models
*DEA*-*CCR *and *DEA*-*BCC*. The window analysis is used to examine the
efficiency over time for the
period 2000-2005 (6 years x 22 *ports* = 110 observations), T=6, p=3 and
w=4. *DEA *is carried on 22
*ports* shown in Table 1. As such, the length of the window used here is
defined as three (Charnes et al.
1985). the scale efficiency of each port. Four separate windows are
represented as separate rows in
Tables 6 and 7. Tables 6 and 7 represent the efficiency estimates, the
average of DEA efficiency scores
and its standard deviation in the columns denoted 'Mean' and 'S.D'.
The identification of performance trends in row window and the stability is
defined in column
of each year that allows controlling both of them through the separate
windows. The efficiency score
estimated shows that 16 and 17 *ports* are stable (have low standard
deviation) under *CCR *and *BCC*,
respectively, on the other hand, 6 and 5 *ports* are unstable (have high
standard deviation) under *CCR*
and *BCC*, respectively. The efficiency score mean value shows better under
*BCC *than *CCR*, although
all the *ports* still inefficient and reflect a fluctuation in efficiency
score. There is an improvement in the
efficiency for Khor Fakkan, Kuwait, and Djibouti *ports* with *CCR*, and
Bander Abbas, Khor Fakkan,
Dubai, Kuwait, Mukalla, Mombasa and Djibouti *ports* with *BCC*. The
variation haphazard
(increasing/decreasing or decreasing/increasing) in performance impacted the
main efficiency over the
time period. In general, all the *ports* are stable. Table 8 shows the scale
efficiency of all the *ports* over
the entire time periods of study.
The comparison between cross-section data and panel data in Tables 5 and in
Tables 6, 7 shows
a similarity in average efficiency score for most of these *ports*.
------------------------------
*Page 8*
Efficiency of Middle Eastern and East African Seaports: Application of
DEA *Using* Window Analysis
605
*7. Discussion *
In this paper *DEA *cross-section data and window analysis are used to
determine the relative efficiency
of 22 cargo *ports* in the Middle East and Africans countries. The results
of cross-section provide
information for the overall time period. The panel data provide large
details of performance analysis
over a period of time. The fluctuation of the efficiency score with window
analysis, due to the
comparison between the big *ports* which have high production and small *
ports* which have low
production. This study shows that small *ports* are efficient while big *
ports* are inefficient. The
indicators of production scale in this study as shown in Table 6 and 7 are
the main factors of efficiency
and inefficiency.
The inefficiency of *ports* may also have resulted for reasons, such as 3
rd
Gulf War and to other
reasons related to the security of ship companies particularly in this
region during 2003-2004. We
conclude that for increasing port efficiency, ships arrival should be
encouraged to increase the scale of
production; on the other hand, the inefficient *ports* with declining
efficiency reduce their scale of
operation to be efficient. The comparison of the two methods shows biases in
efficiency over the time
for Dubai, Mukalla, Hodeidah and Yanbu under CCR and Hodeidah, Yanbu under
BBC, respectively.
This result provided with window analysis discusses the recent changes of
performance and
stability of the port over time.
*Table 5:*
The relative efficiency of seaports *using* *DEA*-*CCR *and *DEA*- *BCC *models
in 2001
*Country Port *
*DEA - CCR *
*DEA - BCC *
*Scale Efficiency *
*Return to scale *
Bander Abbas Iran
0.803
1.000
0.803
Decreasing
Khor Fakkan Sharjah
1.000
1.000
1.000
Constant
Khalid Sharjah
0.834
0.848
0.983
Increasing
Salalah Oman
0.918
0.934
0.983
Increasing
Mascut Oman
0.683
0.726
0.941
Decreasing
Dubai Emirates
1.000
1.000
1.000
Constant
Kuwait
1.000
1.000
1.000
Constant
Mukalla Yemen
1.000
1.000
1.000
Constant
Aden Yemen
0.862
0.953
0.904
Decreasing
Hodeidah
1.000
1.000
1.000
Constant
Dammam Saudi
0.515
0.725
0.711
Decreasing
Jubail Saudi
0.708
0.735
0.964
Increasing
Yanbu Saudi
1.000
1.000
1.000
Constant
Jeddah Saudi
0.526
0.840
0.626
Decreasing
Sudan
0.683
0.862
0.792
Decreasing
Mombassa Kenya
0.876
0.985
0.889
Decreasing
Dar es Salaam Tanzania
0.841
0.870
0.966
Increasing
Tanga Tanzania
0.331
1.000
0.331
Increasing
Mtwara Tanzania
0.261
0.331
0.789
Increasing
Assab Eritrea
0.454
0.460
0.987
Increasing
Asmara Eritrea
0.989
0.989
1.000
Increasing
Djibouti
1.000
1.000
1.000
Constant
Average
0.786
0.875
0.894
------------------------------
*Page 9*
606
Ahmed Salem Al-Eraqi, Adli Mustafa, Ahamad Tajudin Khader and Carlos Pestana
Barros
*Table 6:*
DEA-CCR window analysis for cargo port efficiency (100='efficient')
*Efficiency Scores *
*Summary Measures *
*Port*
*2000*
*2001*
*2002*
*2003*
*2004*
*2005*
*Mean *
*S. D.*
Bander
67.101
73.811
68.539
Abbas
71.300
66.207
64.423
66.882
65.080
66.345
62.831
64.052
68.768
67.111
3.129
Khor
90.640
97.756
100.000
Fakkan
97.756
100.000
97.516
Sharjah
100.000
97.516
100.000
93.498
92.995
100.000
97.306
3.224
Khalid
69.749
88.855
95.933
Sharjah
76.323
82.403
59.862
80.665
58.599
61.503
53.922
56.594
94.932
73.278
15.289
Salalah
26.373
29.326
44.516
Oman
29.326
44.516
96.202
51.151
90.736
60.580
84.196
63.741
99.573
60.020
26.954
Mascut
44.747
51.780
65.175
Oman
47.543
59.841
60.494
61.166
62.056
65.836
58.799
62.381
62.285
58.508
6.794
Dubai
53.026
63.466
67.141
63.466
67.141
73.917
74.111
82.503
100.000
84.964
100.000
93.775
76.959
15.334
Kuwait
83.692
88.705
100.000
72.271
83.293
100.000
83.293
100.000
93.758
100.000
96.596
100.000
91.801
9.364
Mukalla
100.000
67.376
63.905
Yemen
71.588
71.588
74.913
76.587
80.566
82.385
76.780
77.920
100.000
78.634
11.265
Aden
60.713
82.786
74.605
Yemen
81.824
73.739
51.281
84.716
65.301
66.361
63.829
65.036
73.488
70.306
10.003
Hodeidah
53.286
100.000
89.660
Yemen
100.000
89.660
45.157
100.000
52.105
52.449
66.915
66.945
71.653
73.986
20.952
Dammam
31.622
46.264
48.696
42.238
44.459
33.832
43.981
33.746
47.459
48.492
47.789
60.458
44.086
8.031
------------------------------
*Page 10*
Efficiency of Middle Eastern and East African Seaports: Application of
DEA *Using* Window Analysis
607
*Table 6:*
Continued
*Efficiency Scores *
*Summary measures *
*Port*
*2000*
*2001*
*2002*
*2003*
*2004*
*2005*
*Mean *
*S. D.*
Jubail
46.310
95.000
100.000
Saudi
95.000
100.000
39.770
100.000
39.770
55.600
39.770
55.600
76.246
70.255
26.467
Yanbu
80.026
94.000
100.000
94.000
100.000
47.190
100.000
47.448
56.096
45.630
85.462
62.511
76.030
22.618
Jeddah
29.590
29.714
32.182
29.136
31.075
39.391
31.075
39.121
38.889
45.708
45.489
66.746
38.176
10.843
Sudan
69.583
46.165
71.068
46.165
71.068
52.799
73.213
54.394
74.386
51.850
70.232
48.949
60.823
11.558
Mombasa
65.910
62.270
52.749
62.270
52.749
88.780
52.749
88.780
58.148
85.122
55.751
47.767
64.420
14.855
Dar es
67.154
89.369
73.981
Salaam
89.369
73.981
87.177
73.981
87.177
83.560
83.585
80.117
88.519
81.497
7.529
Tanga
30.364
30.529
28.714
30.529
28.714
35.150
28.784
35.235
25.310
33.553
24.101
40.641
30.969
4.581
Mtwara
20.946
18.390
12.194
18.359
12.174
13.941
14.449
16.527
27.565
20.200
33.116
20.585
19.037
6.227
Assab
33.147
35.249
38.134
35.249
38.134
44.538
39.294
45.894
40.806
42.600
37.877
48.570
39.958
4.673
Asmara
64.047
100.000
82.910
96.442
79.910
88.231
80.856
89.275
88.606
76.499
76.054
100.000
85.236
10.706
Djibouti
90.000
98.995
100.000
79.212
80.017
100.000
79.092
100.000
100.000
91.688
89.609
100.000
92.384
8.794
------------------------------
*Page 11*
608
Ahmed Salem Al-Eraqi, Adli Mustafa, Ahamad Tajudin Khader and Carlos Pestana
Barros
*Table 7:*
*DEA-BCC *window analysis for cargo port efficiency (100='efficient')
*Efficiency Scores *
*Summary Measures *
*Port*
*2000*
*2001*
*2002*
*2003*
*2004*
*2005*
*Mean *
*S. D.*
Bander
90.963
100.000
94.027
Abbas
100.000
93.489
91.288
Iran
95.482
92.910
94.715
91.367
93.205
100.000
94.787
3.423
Khor
90.640
97.756
100.000
Fakkan
97.756
100.000
97.516
Sharjah
100.000
97.516
100.000
93.498
92.995
100.000
97.306
3.224
Khalid
70.151
89.367
96.486
Sharjah
78.884
85.168
61.871
83.436
60.612
63.615
54.526
57.228
95.995
74.778
15.272
Salalah
31.556
35.089
50.860
Oman
32.295
49.408
100.000
51.281
94.244
62.024
84.566
64.217
100.000
62.962
25.870
Mascut
47.347
54.790
68.962
Oman
49.878
62.780
63.465
64.277
65.041
68.962
64.501
68.384
68.311
62.225
7.471
Dubai
78.977
94.527
100.000
Emirates
85.861
90.833
100.000
84.909
93.478
100.000
93.478
100.000
93.775
92.987
6.857
Kuwait
85.051
89.360
100.000
72.271
84.080
100.000
84.614
100.000
100.000
100.000
96.596
100.000
92.664
9.350
Mukalla
100.000
100.000
80.610
Yemen
100.000
95.562
100.000
93.352
99.591
100.000
98.563
98.969
100.000
97.221
5.644
Aden
73.885
100.000
90.982
Yemen
95.302
85.885
59.728
91.801
67.378
68.312
68.946
69.940
80.496
79.388
13.115
Hodeidah
53.286
100.000
89.660
Yemen
100.000
89.660
45.157
100.000
52.258
52.632
67.923
67.923
72.701
74.267
20.853
Dammam
50.800
72.533
76.347
Saudi
68.040
71.618
55.430
71.105
55.326
76.228
50.416
75.837
73.053
66.394
10.270
------------------------------
*Page 12*
Efficiency of Middle Eastern and East African Seaports: Application of
DEA *Using* Window Analysis
609
*Table 7: *Continued
*Efficiency Scores *
*Summary Measures *
*Port*
*2000*
*2001*
*2002*
*2003*
*2004*
*2005*
*Mean *
*S. D.*
Jubail
48.728
95.000
100.000
Saudi
95.000
100.000
41.032
100.000
40.996
57.314
40.229
56.242
77.127
70.972
25.865
Yanbu
80.026
94.000
100.000
Saudi
94.000
100.000
47.986
100.000
47.986
56.191
46.029
93.637
63.057
76.909
22.806
Jeddah
75.111
79.286
83.276
Saudi
70.984
76.238
97.236
70.329
72.351
75.872
72.024
75.529
80.760
77.416
7.390
Sudan
93.729
62.186
95.729
57.247
88.127
65.473
87.313
64.869
88.711
61.612
84.258
57.424
75.556
15.182
Mombasa
100.000
100.000
94.916
Kenya
85.214
86.237
100.000
80.427
100.000
95.947
100.000
88.092
79.873
92.559
8.067
Dar es
69.572
92.587
76.645
Salaam
92.444
76.527
90.177
Tanzania
76.527
90.177
86.436
86.298
82.717
91.392
84.291
7.756
Tanga
99.460
100.000
100.000
Tanzania
86.855
86.978
100.000
86.978
100.000
71.831
82.558
59.302
100.000
89.497
13.289
Mtwara
28.715
25.210
16.717
Tanzania
33.453
22.182
25.403
22.927
26.255
42.711
21.898
35.623
22.361
26.955
7.168
Assab
33.753
35.894
38.831
Eritrea
36.430
39.411
46.030
40.470
47.267
42.027
42.767
38.025
48.784
40.807
4.717
Asmara
64.054
100.000
82.918
Eritrea
100.000
82.893
91.512
84.008
92.757
92.062
76.540
76.112
100.000
86.905
11.213
Djibouti
90.000
98.995
100.000
79.212
80.017
100.000
79.092
100.000
100.000
92.045
89.640
100.000
92.417
8.791
------------------------------
*Page 13*
610
Ahmed Salem Al-Eraqi, Adli Mustafa, Ahamad Tajudin Khader and Carlos Pestana
Barros
*Table 8:*
Score Efficiency (1='efficient')
*Scores Efficiency*
*(return to scale)*
*Port *
*2000 *
*2001 *
*2002 *
*2003 *
*2004 *
*2005 *
Bander Abbas 0.74 (Decreasing) 0.74 (Decreasing)
0.73 (Decreasing)
0.71 (Decreasing) 0.71 (Decreasing) 0.71 (Decreasing)
0.70 (Decreasing) 0.70 (Decreasing) 0.70 (Decreasing)
0.69 (Decreasing) 0.69 (Decreasing) 0.69 (Decreasing)
Khor
1.00(Constant)
1.00(Constant)
1.00(Constant)
Fakkan
1.00(Constant)
1.00(Constant)
1.00(Constant)
Sharjah
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
Khalid
0.99(Inc)
0.99(Increasing)
0.99(Increasing)
Sharjah
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.99(Increasing)
0.99(Increasing)
0.99(Increasing)
Salalah
0.84(Increasing)
0.84(Increasing)
0.88(Increasing)
Oman
0.91(Increasing)
0.90(Increasing)
0.96(Increasing)
1.00(Constant)
0.96(Increasing)
0.98(Increasing)
1.00(Constant)
0.99(Increasing)
1.00(Constant)
Mascut
0.95(Increasing)
0.95(Increasing)
0.95(Increasing)
Oman
0.95(Increasing)
0.95(Increasing)
0.95(Increasing)
0.95(Increasing)
0.95(Increasing)
0.95(Increasing)
0.91(Increasing)
0.91(Increasing)
0.91(Increasing)
Dubai
0.67(Decreasing)
0.67(Decreasing)
0.67(Decreasing)
0.74(Decreasing)
0.74(Decreasing)
0.74(Decreasing)
0.87(Increasing)
0.88(Decreasing)
1.00(Constant)
0.91(Decreasing)
1.00(Constant)
1.00(Constant)
Kuwait
0.98(Increasing)
0.99(Increasing)
1.00(Constant)
1.00(Constant)
0.99(Increasing)
1.00(Constant)
0.98(Increasing)
1.00(Constant)
0.94(Decreasing)
1.00(Constant)
1.00(Constant)
1.00(Constant)
Mukalla
1.00(Constant)
0.67(Increasing)
0.79(Increasing)
Yemen
0.72(Increasing)
0.75(Increasing)
0.75(Increasing)
0.82(Increasing)
0.81(Increasing)
0.82(Increasing)
0.78(Increasing)
0.79(Increasing)
1.00(Constant)
Aden
0.82(Increasing)
0.83(Decreasing)
0.82(Decreasing)
0.86(Decreasing)
0.86(Decreasing)
0.86(Increasing)
0.92(Decreasing)
0.97(Increasing)
0.97(Increasing)
0.93(Increasing)
0.93(Increasing)
0.91(Increasing)
Hodeidah
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
0.99(Increasing)
0.99(Increasing)
0.99(Increasing)
Dammam
0.62(Increasing)
0.64(Decreasing)
0.64(Decreasing)
0.62(Decreasing)
0.62(Decreasing)
0.61(Increasing)
0.62(Decreasing)
0.61(Increasing) 0.62(Decreasing)
0.96(Increasing) 0.63(Decreasing)
0.83(Increasing)
------------------------------
*Page 14*
Efficiency of Middle Eastern and East African Seaports: Application of
DEA *Using* Window Analysis
611
*Table 8: *Continued
*Port *
*2000 *
*2001 *
*2002 *
*2003 *
*2004 *
*2005 *
Jubail
0.95
1.00(Constant)
1.00(Constant)
Saudi
1.00(Constant)
1.00(Constant)
0.97(Increasing)
1.00(Constant)
0.97(Decreasing)
0.97(Increasing)
0.99(Increasing)
0.99(Increasing)
0.99(Increasing)
Yanbu
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
0.98(Increasing)
1.00(Constant)
0.99(Increasing)
1.00(Constant)
0.99(Increasing)
0.91(Decreasing)
0.99(Increasing)
Jeddah
0.39(Dec)
0.37(Dec)
0.39(Dec)
0.41(Decreasing)
0.41(Decreasing)
0.41(Decreasing)
0.44(Decreasing)
0.54(Decreasing)
0.51(Decreasing)
0.63(Decreasing)
0.60(Decreasing)
0.83(Increasing)
Sudan
0.74(Decreasing)
0.74(Increasing)
0.74(Decreasing)
0.81(Increasing)
0.81(Decreasing)
0.81(Increasing)
0.84(Decreasing)
0.84(Increasing)
0.84(Decreasing)
0.84(Increasing)
0.83(Decreasing)
0.85(Increasing)
Mombasa 0.66(Decreasing)
0.62(Decreasing)
0.56(Decreasing)
0.73(Decreasing)
0.61(Decreasing)
0.89(Decreasing)
0.66(Decreasing)
0.89(Decreasing)
0.61(Decreasing)
0.85(Decreasing)
0.63(Decreasing)
0.60(Decreasing)
Dar es
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
Salaam
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
Tanga
0.31(Decreasing)
0.31(Decreasing)
0.29(Decreasing)
0.35(Decreasing)
0.33(Decreasing)
0.35(Decreasing)
0.33(Decreasing)
0.35(Decreasing)
0.35(Decreasing)
0.41(Decreasing)
0.41(Decreasing)
0.41(Decreasing)
Mtwara
0.73(Increasing)
0.73(Increasing)
0.73(Increasing)
0.55(Increasing)
0.55(Increasing)
0.55(Increasing)
0.63(Increasing)
0.63(Increasing)
0.65(Increasing)
0.92(Increasing)
0.93(Increasing)
0.92(Increasing)
Assab
0.98(Increasing)
0.98(Increasing)
0.98(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
0.97(Increasing)
1.00(Constant)
1.00(Constant)
1.00(Constant)
Asmara
1.00(Constant)
1.00(Constant)
1.00(Constant)
0.96(Increasing)
0.96(Increasing)
0.96(Increasing)
0.96(Increasing)
0.96(Increasing)
0.96(Increasing)
1.00(Constant)
1.00(Constant)
1.00(Constant)
Djibouti
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
1.00(Constant)
*Acknowledgment *
The authors are grateful to the *ports* authorities for providing data and
information.
------------------------------
*Page 15*
612
Ahmed Salem Al-Eraqi, Adli Mustafa, Ahamad Tajudin Khader and Carlos Pestana
Barros
*References *
[1]
Asmild, M., J.C. Paradi, V. Aggarwall and C. Schaffnit, 2004. "Combining DEA
Window
Analysis with the Malmquist Index Approach in a Study of the Canadian
Banking Industry",
*Journal of Productivity Analysis *21, pp. 67–89.
[2]
Banker R. D., Charnes A. and Cooper W. W. (1984). Model for Estimating
Technical and Scale
Efficiencies in Data Envelopment Analysis. Management Science 30(9):pp.
1078-1092.
[3]
Barros, C.P., 2006. "A Benchmark Analysis of Italian Seaports *using* Data
Envelopment
Analysis", Maritime *Economics & Logistics *8, pp. 347–365.
[4]
Barros, C.P. and A. Manolis, 2004. "Efficiency in European Seaports with
DEA: Evidence
from Greece and Portugal", *Maritime Economics & Logistics *6, pp. 122–140.
[5]
Charnes, A., W.W. Cooper and E. Rhodes, 1978. "Measuring the Efficiency of
Decision
Making Units" *European Journal of Operational Research *2(6), pp. 429–444.
[6]
Charnes, A., C.T. Clark, W.W. Cooper, and B. Golany, 1985. "A Developmental
Study of Data
Envelopment Analysis in Measuring the Efficiency of Maintenance Units in the
US Air Force"
*Annals of Operations Research *2, pp. 95–112.
[7]
Charnes, A., W.W. Cooper, A.Y. Lewin and L.M. Seiford, 1994a. *Data
Envelopment Analysis: *
*Theory, Methodology and Application. *Norwell: Kluwer Academic Publishers.
[8]
Charnes, A., W.W. Cooper and L.M., 1994b. "Extension to DEA Models" In A.
Charnes, W.
W. Cooper, A. Y. Lewin, & L. M. Seiford (Eds.). *Data Envelopment Analysis:
Theory,*
*Methodology and Applications. *Norwell: Kluwer Academic Publishers.
[9]
Cullinane, K., D.W. Song and R. Gray, 2002. "A Stochastic Frontier Model of
the Efficiency of
Major Container Terminals in Asia: Assessing the Influence of Administrative
and Ownership
Structures" *Transportation Research Part A *36, pp. 734–762.
[10]
Cullinane, K., P. Ji and T. Wang, 2004. "An Application of DEA Windows
Analysis to
Container Port Production" *Review of Network Economics *3(2), pp. 184–206.
[11]
Cullinane, K., D. Song and T. Wang, 2005. "The Application of Mathematical
Programming
Approaches to Estimating Container Port Production Efficiency" *Journal of
Productivity *
*Analysis *24, pp. 73–92.
[12]
Cullinane K., P. Ji and T. Wang, 2006. "The Efficiency of European Container
*Ports*: A Cross-
sectional Data Envelopment Analysis" *International Journal of Logistics:
Research and *
*Applications *9(1), pp. 19–31.
[13]
Fare R.S., Grosskopf S., Lovel C.A.K.. (1994). Production Frontiers.
Cambridge University
Press, Cambridge.
[14]
Haralambides, H.E., A. Verbeke, E. Musso and M. Benacchio, 2001. "Port
Financing and
Pricing in the European Union: Theory, Politics and Reality. *International
Journal of Maritime *
*Economics *3, pp. 368–386.
[15]
Lee Chee Xui, 2005. "Malaysian Container Port Performance Measurement" *M.Sc
Thesis*,
Malaysia University of Science and Technology.
[16]
Mahadevan, R., 2002. "A DEA Approach to Understanding the Produc¬tivity
Growth of
Malaysia's Manufacturing Industries" *Asia Pacific Journal of Management *19,
pp. 587–600.
[17]
Park, R.K. and P. De, 2004. "An Alternative Approach to Efficiency
Measurement of Seaports"
*Maritime Economics & Logistics *6, pp. 53–69.
[18]
Rios, L.R. and A.C.G Maçada, 2006. "Analysing the Relative Efficiency of
Container
Terminals of Mercosur *using* DEA" *Maritime Economics & Logistics *8(4),
pp. 331–346.
[19]
Tongzon, J, 2005. "Port Privatization, Efficiency and Competitiveness: Some
Empirical
Evidence from Container *Ports* (Terminals)" *Transportation Research Part A
– Policy and *
*Practice *39, pp. 405–424.
[20]
Trujillo, L. and B. Tovar, 2007. "The European Port Industry: An Analysis of
its Efficiency"
*Maritime Economics and Logistics *9(2), pp. 148–171.
[21]
Wang T., K. Cullinane, 2006. "The Efficiency of European Container Terminals
and
Implications for Supply Chain Management" *Maritime Economics & Logistics *8,
pp. 82–99.
------------------------------
*Page 16*
Efficiency of Middle Eastern and East African Seaports: Application of
DEA *Using* Window Analysis
613
[22]
William W. Cooper, Seiford L. M, Kaoru Tone (2002). DATA ENVELOPMENT
ANALYSIS.
Kluwer Academic Publishers. New York, Boston, Dordrecht, London, Moscow.
Over the past few decades, seaport industry in many countries of the world
has witnessed remarkable development. This is obvious, particularly in the
East African countries, such as Sudan, Eritrea, Djibouti, Kenya, and
Tanzania, and the Middle Eastern countries, particularly Saudi Arabia,
Yemen, Oman, the United Arab Emirates, and Iran. These countries possess
seaports which are strategically located in the international maritime trade
route between the East and the West (Figure 1) and are considered as middle
distance seaports. Goods carried from Europe and Far East/Australia and vice
versa can be exchanged and transhipped to all countries in the Middle East,
Red Sea, and East Africa. Since the olden days, these seaports have provided
services for the regional coasters and as time wentby, they have developed
to be among the important maritime international trade centres in the
region. The geographically strategic location of some of these seaports,
have also encouraged modern container vessels to make short duration calls
upon them (e.g. shipping lines operating along Asia/Europe route,
Asia/Mediterranean route and Asia/US East Coast route). These seaports and
their characteristics are displayed in Table 1.
http://www.eurojournals.com/ejsr_23_4_10.pdf
----[This List to be used for Eritrea Related News Only]----