Efficiency Analysis of the Nigerian Airports: an Application of Dea-Bcc Model

EFFICIENCY ANALYSIS OF THE NIGERIAN AIRPORTS: AN APPLICATION OF DEA-BCC MODEL

Obioma R. Nwaogbe Innocent C. Ogwude Callistust C. Ibe Dept. of Transport Management Dept. of Transport Management Dept. of Transport Management Federal University of Technology Federal University of Technology Federal University of Technology Minna, Nigeria. Owerri, Nigeria. Owerri, Nigeria. [email protected] [email protected] [email protected]

Abstract The increase in international and domestic movement of passengers and cargos have made the air transport business more especially airports to be more busy in terms of paseenger and aircraft traffic. The aim of this study is to evaluate the airport efficiency performance in Nigeria using DEA-BCC model approch. Non parametric method of production frontier approach were used to analyse 30 airports panel data of the study period from 2003- 2013 in relation with the airport multiple inputs (terminal capacity, runway dimension, number of employees, total assets and total cost) and outputs (passenger throughputs and aircraft movement). The result shows that there is high significant relationship between some inputs (total assets, runway dimension and employees) and the outputs. The efficiency scores of the various airports are determined from the output-orientation analysis. The result also shows that hub airport, commercial/large city airport and private airport are the ones that are productive and efficient. Finally, policy implication on how the inputs are to be improved to make the airports that are not efficient to be efficient.

Keywords airport, efficiency, productivity, DEA

productive efficient in Nigeria. The airports are going to be 1. Introduction ranked based on the efficiency scores, recommendations on possible improvements on the operations will be made to Aviation industry is one of the major important sectors in the enhance more productivity and efficiency. Furthermore, the transportation industry in the world. Its continuous model will enable us observe the airports that are operating on development and technological service improvement makes the scale of vairble return to scale. After the analysis, it shows the sector to be a major contributor to the modern and the the airports under increasing return to scale, decreasing return global standard of advanced development in the to scalle and constant return to scale. transportation sector. The growth of this sector cannot be compared to any other major transport sector due to its sophisticated technological equipment and continuous 1.2. Problem Statement innovation in the sector because of its haul of vehicle (Aircraft) The growth in air travel is outstripping the capacity of airport that it uses as a carrying system. With the help of this sector and air traffic control system. That has resulted in increasing much has being achieved throughout the world in terms of congestion and delay in air transport operations in Nigeria. The economic development, tourism, connectivity, logistics and consistent growth of air traffic and passenger demand is supply chain activities. The demand for transport services has a causing the operational movements at hub airports to greater influence on the air transport sector in so many approach their maximum capacities. With this growth, delays countries and their economy at large. It acts as catalyst to to aircrafts and passengers are increasing and safety is passenger movement, cargo, domestic and international in the becoming a major problem. In Nigeria, statistics have shown air transport market (Nwaogbe et al., 2013). The high growth that in the beginning of the millennium year, there has been an rate in Nigeria population and high traffic growth of domestic increase in air transportation traffic; more especially in the and international demand due to high economic development areas of passengers’ departure and arrivals, cargo and aircraft of the country has a big effect on the air transport sector. This movement. Much attention has to be focused on the Nigeria study will be of great help to the aviation sector in the Nigeria. airport system by the government, management of the airports Furthermore various airport infrastructural challenges and (FAAN) and agencies on how to assess the performance of sophisticated equipment for safety and security, traffic various airports to meet international standards. The interest congestions, delays of flight has cause a heavy deterioration at in assessment of productivity efficiency is somewhat recent. In the airports. the late 20th century, monitoring and comparison of airports productive efficiency was not a common practice in the air 1.1. Objective of the Study transport industry. Due to deregulation, the aviation industry has moved from being entirely public utilities to private and The objective of theis study is to evaluate the performance of commercial utilities. Therefore, with airport privatization and the airports in Nigeria using Banker Charnes Cooper model commercialization, a lot of competition has risen among (BCC-model) to examine amongst the airports that are

airports thus resulting in a high market interest in performance the views of experts. These findings indicate that there are comparison (Graham, 2003). Due to the high rate of increase in some net benefits for airlines and air navigation service demand for air transport services over the last few decades, providers who use AHP, but not for airports. Lai (2013) this has led to a higher level of competitions among Nigerian conducted a study on the relationship between airport airports and also those of the neighboring countries of West privatisation and airport efficiency using AHP and DEA models. African sub-region. In order to handle the increasing level of In his study, he focused on the European and the Asia-pacific competition, benchmarking has become an important source region, on how to improve airport efficiency, such as investing of weakness determination at airports that are faced with in the infrastructure and privatising airport ownership or increasing traffic demand (Lai, 2013). governance. The results for each of the different analysis models/techniques show that there is no statistically significant Other points worth mentioning when studying airport relationship between airport ownership and efficiency. productivity and efficiency are the average delay period of an aircraft in the airport and quality of service provided by the Gitto & Mancuso (2012) conducted a study on bootstrapping airport. Overall performance of an airport is adversely affected the Malmquist indexes’ for Italian airports using data by delays which reduce passengers travel demand over a short envelopment analysis to assess the operational performance of or long run scenario due to the bad reputation of an airport. 28 Italian airports during the period of 2000 to 2006. From Delay in an airport is closely associated with terminal capacity their research, they use recent development in bootstrapping of the airport and reliability of air traffic control system (Adler technique to connect total factor productivity, estimates for & Berchman, 2001). The quality of service in airports consists bias and assess the uncertainty surrounding the estimates. of the service offered to airlines and various facilities offered From their study, they observed that the Italian airport sector for passengers. The quality of ground handling services also experiences a significant technological regression, with few play an important role in the overall efficiency score of an airports achieved an increase or enhance airport productivity airport (Ülkü, 2014). which led to improvements in the efficiency of the airports.

Moreover, the bootstrap approach in the DEA context has 1.3. Motivations of the Study been widely adopted in the past few years (Assaf, 2010; Curi et This study will make a heavy contribution that will enable the al., 2010). In 2011, Assaf also used the Malmquist government and the stakeholders of the aviation sector and bootstrapped combined model to assess the extent of the management of the Federal Airport Authority of Nigeria to productivity, efficiency, scale and technological changes at the identify the airports that are efficient and productive. The major Australian airports. Moreso, in 2012, there was a new study will be very useful in assessing, monitoring, managing model that was applied to evaluate airport efficiency; the and planning all other operational activities at various airports model is known as Bayesian dynamic frontier model. The in the country. The assessment allows or helps the airports model was used by Assaf et al. (2012) to assess UK airport directors of operations, managers of operation to benchmark efficiency. Assaf & Gillen (2012) adopted the model and their operational performance of the airport. Furthermore, the combine it with SFA to compare the efficiency of 73 efficiency performance of airport operations can be achieved international airports. Finally, combinations of models and by various competition amongst the market industry by other approaches have been but in a limited publications (e.g. complying with the quality of service and level of service of the Pels et al. 2001; Martin & Roman, 2006 & Yang, 2010). airports. Therefore, estimation of efficiency performance of Ferreira et al. (2010) contend that DEA model attempts to airports can be enhanced by using various measures of the exploit the relationship between the outputs and inputs, quality service to determine among the airports the best weights of each variable (output/input) are defined. With DEA service quality of airports to enable stakeholders and Federal model, multiple inputs and outputs can be used for the Airport Authority of Nigeria to benchmark the airports both assessment process, different from the non-parametric model internationally and local (Nwaogbe et al., 2013; Pius et al., (Lai et al., 2015). However, DEA limitation is that the efficiency 2017; Nwaogbe et al., 2017 & Nwaogbe et al., 2017). of DMUs should not be more than the unit, if using the weight assigned to the analysed DMU. As noted by Liebert (2011) 2. Literature many studies have quantitatively assessed performance and decision-making units (DMUs), using a range of econometric Many researches on Data Envelopment Analysis have been tools such as Multicriteria Decision Analysis (MCDA), Window conducted severally on the productivity and efficiency of Analysis (WA), Stochastic Frontier Analysis (SFA), and Censored airports with a positive results. The researchers includes: Fung Regression (Lin, Choo, & Oum, 2013; Kao & Liu, 2011). et al. (2008) who analysed the efficiency of Chinese airports. New DEA models include Barros & Dieke (2007), who analyse Serebrisky (2012), conducted a study on airport economics in Italian airports with several DEA models and Barros & Weber Latin America and the Carebian, benchmarking, regulation and (2009), who analyse the productivity change in UK airports pricing. DEA approach, TFPC (Tortal Factor Productivity with a Malmquist index with biased technological change. Computation), Malquist quantity index of TFPC was used to Lozano & Gutierrez (2011) analyze the efficiency of Spanish measure the productivity and efficiency of the airports over the airports. Barros et al. (2010) analyze the productivity change of period of 12 years (1995-2007). The sample covers 22 airports Japanese airports. Gitto & Mancuso (2012b) analyze Italian in America, 23 airports in East Asia and Pacific, 40 airports in airports by using Malmquist indexes and bootstrapping. Europe, and 63 airports in Canada and the United States. In his study, two models are estimated under computing Although other researchers have also look into efficiency using assumptions; Constant Return to Scale (CRS) and Variable other models, Castelli & Pellegrini (2011) used AHP to assess Return to Scale (VRS). This two models estimator allows the the opportunity of implementing this concept by considering

researcher to compute scale efficiencies and identify among aircraft, movements during peak and off peak periods, cargo the airports the one that operates increase return to scale, throughputs and the number of routes operating on. Also, it decrease return to scale, and constant return to scale. So many involves passenger throughputs which include the total researcher conducted several studies on assessment of airport number of passengers served during peak and off-peak efficiency using Data Envelopment Analysis in most of the past periods. and recent research (Seiford & Zhu, 1999, Martin & Roman, 2001, 2006, 2008; Malighetti et al., 2007; Chi-lok & Zhang, The land transport system takes care of the commuter and 2009; Fung et al., 2008; Barros, 2008a,b,c; Barros, 2009). cabs that flow in and out of the airport and as well control them orderly to avoid traffic congestion, while the aviation Furthermore, there are some studies on the airport operational control and fire service covers the traffic men, police and performance in the West African states, such researchers are firefighters that are stationed to take care of the traffic, (Stephens & Ukpere, 2011; Oduwole, 2014; Nwaogbe et al., maintain law and order and as well fire-fighting in case of any 2015; Barros et al., 2015; Wanke et al., 2016; Oyesiku et al., fire outbreak. These overall assessment of operational 2016; Pius et al., 2017). There studies have focused on Fuzzy performance can be conducted using this model by examining DEA, Sarri Productivity model, Stochastic Frontier, OLS, and the productivity using the inputs and outputs variables of the DEA-CCR model. Therefore, there is a strong reason for a cross airports such as employees, total assets, airline service levels, border and comprehensive study that will use DEA-BCC model passenger throughputs, aircraft movements, terminal services to estimate the production function and to determine the and to mention but a few. airports on the frontiers and as well airports that are operating under various Variable Return to Scale (VRS). The estimation will determine the best efficiency scored airport and it will be used to benchmark all the airports in the country to global standard. Finally, Barros,Wanke, Nwaogbe & Azad (2017) study on efficiency in Nigerian airports analyses efficiency levels of Nigeria airports using a stochastic frontier model that capture the the effect of unobserved managerial ability. The study of Alvarez et al. (2004) used the AAG model in the methodology. The result findings shows the variations in efficiency scores that are more sensitive to labour than capital cost, and alsoshows the negative impact of regulation. It further shows the hub operations efficiency level and its posibility in the operational scale of Nigerian airports.

2.1. Airport Productive Efficiency Model Flow

Airport productive model is a model that shows all the indicators/parameters needed for the study of airport productive and efficiency. From the model in Figure 1 below, the productivity and efficiency of airport’s operation in Nigeria are sorrounded by these three major bodies that control the affairs of airports to be productive. These bodies are Federal Airport Authority (FAAN), Nigeria Civil Aviation Authority (NCAA), and Nigeran Airspace Management Agency (NAMA). They control the airports and their operators which comprise of the airline companies, passengers and land transport system of the airport, aviation control, fire service and other airport supply chain/logistics services. These bodies work as a team with all the airports inputs as resources to produce a reasonable output for the airport with high level of efficiency.

Airports are evaluated through their labour force (number of employees), terminal infrastructures which comprise floor area of the terminal building, the number of boarding gates and the number of check-in counters, aviation facilities such as size of the apron, the number of car parking slot spaces and accommodation of traffic volume as well as revenue which comprises of total aeronautical revenues and non-aeronautical revenues to mention but few. Airlines companies take care of the output on transportation which is take-offs and landings of

Figure 1: Airport Productivity Model. Source: Authors.

3. Materials and methods n = number of airports (DMUs), {j=1,2,……..,n} DEA is the measurement of performance with techniques and y = number of outputs {y=1,2,……….,R} it can be used for evaluation of relative efficiency for decision- x = number of inputs {x=1,2,……….,S} making and policy formulation for airports stakeholders. Efficiency and productivity can be derived once the ratio of the yi = quantity of output rthof output of jth DMU inputs and outputs obtained (Mali, 1978, Sumanth, 1984 and xi = quantity of input sthof input of jth DMU Ramanathan, 2003). Eficiency of airport can be denoted as =1, ur = weight of rthoutput Efficiency ≤1 (Mokhtar & Shah, 2013). The measurement of vs = weight of sth input relative efficiency of multiple inputs and outputs were conducted focusing on the construction of hypothetical efficient units (Farrel, 1957; Farrel & Fieldhouse, 1962 and 3.2. BCC-model Charnes, Cooper & Rhodes, 1978, Cooper et al., 2000). They Modifying the Charnes et al. (1978) model constant return to looked at the weighted average of effciency using data scale (CRS), Banker et al. (1984) added the restriction that envelopment analysis to estimate technical efficiency. The BCC generalises the model to variable return to scale (VRS) of the model of DEA were used to analyse the 30 sampled airports in BCC-model as stated. The data envelopment model (VRS Nigeria. Following the Banker et al. (1984) extention of DEA- frontier) is stated below, Variable Returns to Scale (VRS), also CCR model by assuming scale bases as variable return to scale, known as BCC (Banker et al., 1984), where ε is a non- performance is bounded by a piecewise linear frontier. Archimedian element and s_i^- and s_r^+ account, respectively, for the input and output slack variables 3.1. Formulation of Model (Bazarghan & Vasigh , 2003; Zhu, 2003). Variables and parameters are used for the study are determined. With that, the model is based on the following variables and parameters:

Objective function: throughput and aircraft movement, while the inputs (x) are the terminal capacity (PAX), runway dimension, number of 푚 − 푠 + max ∅ − 휀(∑푖=1 푠푖 + ∑푟=1 푠푟 ) (1) employee, total assets and total cost. Data of the inputs and 푠. 푡. outputs are secondary data, sourced from the Federal Airport Authority of Nigeria (FAAN). In running the analysis, DEA 푛 − ∑푗=1 휆푗 푥푖푗 + 푠푖 = 푥푖0 , ∀푖 (2) Software Solver pro.version 10.0 will be used for the analysis. Inputs and outputs variables are adequately sourced based on 푛 + ∑푗=1 휆푗 푦푟푗 − 푠푟 = ∅푦푟0 , ∀푖 the related literature survey to fill the knowledge gap. The input and otuput variables used for the computation are as 휆 푗 ≥ 0, ∀푗 follows; for inputs variable: x_1 is the terminal capacity, x_2 runway dimension, x_3 number of employee, x_4 total assets, ∑푛 휆 = 1 (3) 푗=1 푗 x_5 total cost, while output variables are: y_1 aircraft DEA-BCC-model is able to distingush between technical and movemnet, y_2 passenger throughput. scale inefficiencies by (i) It estimates pure technical efficiency at the given scale of operation (ii) It identify various increasing, 4. Data and results decreasing and constant return to scale present during the This study is focussing on thirty airports in Nigeria, which ten of model estimation.The measure of efficiency provided by BCC them are controlling international air traffic, while twenty are model is known as pure technical efficiency (PTE) (Banker et domestic airports that take care of domestic air traffic in the al., 1984). The nature of returns-to-scale can be determined country. All the airports in Nigeria are controlled by the Federal from the magnitude of optimum efficiency level (Kumar & Airport Authority of Nigeria apart from one the terminal Gulati, 2008). Banker et al (1996a) approach insures that the (Murtala Mohammed Airport Two MMA2) which is under return-to-scale analyses are conducted on the efficiency concession by a private sector and OSUBI (Warri Airport) are frontier (Banker, et al., 2004). private owned by Shell Petroleum Development Company The outputs (y) of the airports which are the variables used by Limited (SPDC). The Table 1 below shows the input and output the DEA of the linear programming model are passenger characteristics of 30 airports in Nigeria.

Table 1: Characteristics of the Nigerian Airports in 2013. Source: Authors.

Inputs Outputs Terminal capacity Runway dimension Total cost Passengers Aircraft Airports Employees Total assets (N) (Pax) (m2) (N) (000s) (times)

252 181000 808 29526048386 1121784325 3,015,803 48,561 ABJ DOM

ABJ INT'L 320 216000 934 78345380972 4354835610 854,129 8,592

AKURE 40 126000 72 1970171004 2283221 8,789 1,254

BENIN 250 108000 95 6255839533 370750434 217,254 4,504

CAL DOM 108 110000 153 617565857 74108206 172,810 3,126

CAL INT'L 100 110250 116 3037237655 45875125 234 13

ENUGU 300 108000 149 10112739813 50971957 225,915 3,631

IBADAN 250 108000 87 9920803171 7919526 56,959 2915

ILO DOM 202 128000 72 856571757 6574844 52,938 2,479

ILO INT'L 200 186000 111 10564650736 386639667 0 0

JOS 250 222000 121 158810721 7215496 47,910 1202

KAD DOM 285 152000 107 827330558 655727441 144,583 3,471

KAD INT'L 250 135000 153 448753721 52656553 22,785 138

KAD INT'L 600 275000 466 254784018 31838308 202,934 5,117

KAN INT'L 640 315000 532 5262032933 995298550 127,824 1,803

MKD 63 192000 43 405582707 992027982 1,117 289

MAID DOM 200 168000 168 10146690793 521528223 72,301 1,386

MAID INT'L 50 180000 130 10189660601 591136071 11,935 80

MMA DOM 615 1213435 1252 28331561632 2760775086 3,454,250 65,006

MMA INT'L 3675 234000 1390 27094966746 890652126 3,395,872 31,543

PHC DOM 518 156000 360 9496710128 720248643 1,113,183 20,782

PHC INT'L 700 180000 299 671691131 71068154 129,176 2,845

SOK DOM 194 125000 54 510300399 49062379 78,377 2,161

SOK INT'L 250 108000 78 2912780597 457945559 32,088 103

YOLA DOM 108 120000 124 10223012046 38825983 123,421 3,172

YOLA INT'L 120 108000 127 7703132065 980735237 8,260 43

MINNA 1000 112000 101 448723618 86311953 8,789 520

KAT 120 137025 119 859969649 34209952 2,456 475

OWERRI 800 121500 131 583606940 68809402 279,609 4560

OSUBI 65 81000 20 306796011 33101424 246,560 8,256

Min 40 81000 20 158810721 2283221 0 0

Max 3675 1213435 1390 78345380972 4354835610 3,454,250 65,006 548697247. Mean 417.5 190540.3333 279.066667 8934796863 463,041 7,601 9 921899168. St.Dev 661.8958534 200792.834 356.462885 15555864757 990,423 15,049 5

The main objective of this study is to evaluate the performance between runway dimension and passenger throughputs of 30 airports in Nigerian and estimate the relative efficiency of 0.71907 (72%), there is strong relationship between runway the airports and make recommendations on how to improve dimension and aircraft movement 0.67511 (68%), there is the inefficiency airports to meet the objective of setting such stronger relationship between number of employees and airport. To achieve this objective, a DEA (Data Envelopment passenger throughput 0.84902 (85%), there is a strong Analysis) model was used to run the analyses using BCC relationship beteween number of employees and aircraft (Banker Charnes Cooper of DEA model) output-oriented model. movement 0.67869 (68%), there is a stronger between total assets and passenger throughputs 0.82577 (83%), there is a The combination of input and output variables meets the DEA stronger relationship between total assets and aircraft principle and the minimum number of airport observations movement 0.72113 (72%), there is an average relationship should be greater than two times the number of inputs plus between total cost and passenger throughputs 0.5347 (53%) outputs (Bezić, Šegota, and Vojvodić, 2010). Mantri (2008), while between the total cost and aircraft movement 0.46985 opined that in DEA analysis, it does not consider the time (47%) shows a weak relationship in the model. In general we frame to which the input consumption and output production can say that we have stronger relationship in the correlation refers. However, Charnes et al. (1985) initated the window analysis on runway dimension and passenger throughput, analysis as a time-dependent version of DEA with various number of employee and passenger throughputs, total assets applications. This study analyse the efficiency measurement of and passenger thrpoughputs and total assets and aircraft the airports in Nigeria based on the total cumulative average of movement (Wanke et al., 2016). This means that it is only few 10 years of the study period from 2003-2013. During the inputs and outputs that shows a better correlation in the analysis, the input and output variables are selected to analysis. estimate the efficiency scores of the Nigerian airports for a period of ten years and the relative inefficiency are identified. While the second stage is to proceed the slack and projection analysis, which is applied to provide results that will enhance the productivity and efficiency improvement of the airports that not are relatively efficent to be efficient.

4.1. Discussion

4.1.1. Correlation Matrix Analysis

The correlation analysis for thirty Nigerian airports shows the presence of strong, weak, and very weak relationship between some inputs and output in the correlation matrix. From the Table 2 below, it shows the weak relationship between terminal capacity and passenger throughputs as 0.43519 (44%), there is a very week relationship for terminal capacity and aircraft movement 0.24355 (24%), there a strong relationship

Table 2: Correlation Matrix of the DEA (BCC-Model). Source: Authors.

Terminal Runway capacity (Pax) dimension (m2) Employees Total assets Total cost Passengers Aircraft

Terminal capacity (Pax) 1 0.14585 0.69684 0.39844915 0.21843521 0.43519 0.24355 Runway dimension 0.14585 1 0.63106 0.595649062 0.44422 0.71907 0.67511 (m2) Employees 0.69684 0.63106 1 0.78262754 0.67339575 0.84902 0.67869 Total assets 0.39845 0.59565 0.78263 1 0.66567486 0.82577 0.72113 Total cost 0.21844 0.44422 0.6734 0.665674865 1 0.5347 0.46985 Passengers 0.43519 0.71907 0.84902 0.825769163 0.53469754 1 0.93932 Aircraft 0.24355 0.67511 0.67869 0.721134172 0.46984758 0.93932 1

24 SOK INT'L 0.2812 21 4.2. Estimation of efficiency scores 25 YOLA DOM 0.373 19 26 YOLA INT'L 0.1378 26 4.2.1. Analysis of Efficiency Scores 27 MINNA 0.0411 27

28 KAT 0.0135 30 Table 3: BCC- Model (Banker, Charnes, Cooper DEA Model Efficiency scores) 30 Airports in Nigeria. Source: Authors. 29 OWERRI 0.196 22 30 OSUBI 1 1 No. DMU BCC-MODEL Score (VRS) Rank Average 0.599

1 ABJ DOM 1 1 Max 1

2 ABJ INT'L 0.1568 25 Min 0.0135

3 AKURE 1 1 St Dev 0.4025

4 BENIN 0.3183 20

5 CAL DOM 0.6262 16 From the analysis, Table 3 above shows the results of the BCC- model shows efficiency scores of the thirty airports in Nigeria. 6 CAL INT'L 0.0161 29 The efficiency indices inclined from 0.0135 to 1 in the BCC- 7 ENUGU 0.997 12 model result. The result shows that at least 8 different airports 8 IBADAN 0.9979 10 are considered to be technically efficient. Furthermore, during the analyses, it was observed that the BCC-model focusses on 9 ILO DOM 0.9998 9 variable return to scale and its efficiency score as (1). The 10 ILO INT'L 0.188 24 result shows that eight airports are efficient, the airports are as follows: ABJ DOM (Nnamdi Azikiwe Airport, Abuja), AKURE 11 JOS 1 1 (Akure Airport, Ondo), JOS ( Yakubu Gowon Airport, Jos), MAID 12 KAD DOM 0.7689 15 INTL( Maiduguri International Airport, Maiduguri), MMA DOM 13 KAD INT'L 0.8946 14 (Murtala Muhammed Domestic Airport, Lagos), MMA INTL (Murtala Muhammed International Airport, Lagos), PHC DOM 14 KAN DOM 0.939 13 (PortHarcourt Airport) and OSUBI (Warri Airport) during the 15 KAN INT'L 0.3955 18 study. Among the 8 efficient airports, 5 of them are hub airports, 2 are commercial city airports (MMA DOM and MMA 16 MKD 0.0163 28 INTL) while 1 is a private airport (OSUBI), and the rest ordinary 17 MAID DOM 0.1938 23 airports. These efficient airports are optimally efficient. While 18 MAID INT'L 1 1 the least inefficiency airports is the KAT. Sengupta (1995) opined that during industrial competition of airports, DEA can 19 MMA DOM 1 1 be used to evaluate average efficiencies levels of airports. The 20 MMA INT'L 1 1 average efficiency for the 30 airports is 0.599 which is approximately (60%). The differences in average BCC efficiency 21 PHC DOM 1 1 scores point to some varying return to scale in the data set. 22 PHC INT’L 0.421 17 This means that a relationship exists between the input and 23 SOK DOM 0.9977 11

output values depend on the importance or size of the data (Kastina Airpot), PHC INT’L (PortHarcourt Inernational Airport), set. CAL DOM (Magrate Ekpo Airport, Calabar), and CAL INT’L (Magrate Ekpo International Airport, Calabar) and to mention Although some of the airports efficiency are above the model but few. This might be part of the factors that act as a average efficiency scores. In the BCC-model, the least drawback for those airports not meeting their technical technically inefficient DMU (airport) is KAT (Kastina airport) efficiency. From the efficiency analysis in table 3 above, shows with 0.0135 efficiency score. See Table 1 for the abbreviation the average efficiency score as 0.599, maximium efficiency of airport names. The issue of the inefficiency in the study may score of 1 and maximum rank as 30, minimium efficiency score be that some airports were targeted for less air carrier, market as 0.0135, while the standard deviation of the eficiency score is fluctuations, safety problems, low infrastructure in the airports 0.4025. This means that on the average during the study, the and as well may be due the low standard of aircraft 30 airports observed shows that there is no airport that maintenance by the airline which leads to many aircraft operates at a maximium efficiency level (as in operating at an accidents at the airports, and to mention but few (Ülkü, 2009). optimal scale of relative efficiency) since the overall average There are number of points that are emerged by means of the efficiency score is 0.599< 1 (Nwaogbe et al., 2015). basic DEA models and it shows that: Furthermore, going through the overall efficiency performance 1. Few airports are on the efficient frontier; hence there is for all the 30 airports in Nigeria, it was observed that the need to help Federal Airport Authorities of Nigeria through largest percentage of respondents performed most efficiently suggestions and recommendation need to improve the were 27%, while 73% of the observations were inefficient in operations and the management techniques of the airports the BCC-model result. A slight increase in both percentage of that are not efficient base on the policy implications. The efficient airports and average efficiency scores means that techniques and operational facilities that need to be airport operations are becoming more competitive. Sarkis improved are the inputs that are of low standard so as to (2000) postulated in his study that with less than half of the enhance a good number of passenger throughputs and airports in their sample size, still not all the airports obtain an aircraft moevment so that the airports can reach an efficiency scores equal to 1, rather, there is space to increase optimal technical efficiency. efficiency when compared to other airports. 2. During the analysis, it was observed that almost all the 30 airports in Nigeria are operating at a high level of relative 4.2.2. DEA Model Efficiency and Ranking inefficiency, with the exception of 8 airports from the BCC-

model used during the study. Table 4: BCC- Models (Banker, Charnes, Cooper DEA Model Ranking) for 3. In the efficiency model of BCC, Variable Return to Scale 30 Airports in Nigeria. Source: Authors. (VRS) of the Data Envelopment Analysis was put into consideration, all efficient airports determined by the model (Golany & Roll, 1989). No. DMU (NAMEOFAIRPORTS) Score Rank 1 ABJ DOM 1 1 4. The objective of BCC model as a management technique is based on the difference between the CCR and BCC models. 3 PHC DOM 1 1 Therefore, according to the BCC scores, twenty-two 11 OSUBI 1 1 airports are found to be inefficient (Lai, 2013). 18 JOS 1 1 5. Among these inefficient airports BCC model, only KAT 19 MMA INT'L 1 1 (Kastina Airport) is relatively smaller than other airports and it is far away from the other results (0.0135 in VRS 20 MMA DOM 1 1 efficiency). 21 KAN DOM 1 1

Furthermore, economic and financial crises that are facing 30 ENUGU 1 1 most aviation industries (more especially the airports some air 9 KAD INT'L 0.9998 9 carriers) in Nigeria made them to face various high airport 8 MAID INT'L 0.9979 10 charges problem, aviation fuel scarcity, all those problems have led the airport users to run away from most of such 23 CAL DOM 0.9977 11 airports, thereby using the neighbouring countries airports. 7 KAD DOM 0.997 12 Another problem that affects the aviation sector (airport) is the issue of insecurity such as Boko-Haram (terrorism) in the 14 SOK DOM 0.939 13 North-East, North-West, and the issue of insurgency and 13 YOLA DOM 0.8946 14 kidnapping in some part of the Niger Delta region of the 12 BENIN 0.7689 15 Nigeria. The airports that terrorism and kidnapping are likely to affect mostly are KAD DOM (Kaduna Airport), KAD 5 ILO DOM 0.6262 16 INT’L(Kaduna International Airport), KAN DOM (Mallam Amino 22 PHC INT’L 0.421 17 Kano Airport), KAN INT’L (Mallam Amino Kano International Airport), MAID DOM (Maiduguri Airport), SOK DOM (Sadiq 15 SOK INT'L 0.3955 18 Abubarka III Airport, Sokoto), SOK INT’L (Sadiq Abubarka III 25 KAN INT'L 0.373 19 International Airport, Sokoto), YOLA DOM (Yola Airport), YOLA INT’L (Yola International Airport), MINNA (Minna Airport), KAT 4 AKURE 0.3183 20

24 MAID DOM 0.2812 21 of efficiency of the airports) order from the analysis of the BCC- model result. 29 OWERRI 0.196 22 17 ABJ INT'L 0.1938 23 Table 5: Projected Return to Scale of Airports. Source: Authors.

10 YOLA INT'L 0.188 24 No DMUs RTS of Projected DMU Score 2 IBADAN 0.1568 25 . (Airports) (Airports) 26 ILO INT'L 0.1378 26 1 ABJ DOM 1 Constant Return to Scale 27 MINNA 0.0411 27 2 ABJ INT'L 0.1568 Constant Return to Scale 16 CAL INT'L 0.0163 28 3 AKURE 1 Increasing Return to Scale 6 MKD 0.0161 29 4 BENIN 0.3183 Constant Return to Scale 28 KAT 0.0135 30 5 CAL DOM 0.6262 Constant Return to Scale 6 CAL INT'L 0.0161 Constant Return to Scale The ranking position is based on the efficiency scores from the DEA (BCC-Model) analysis. The ABJ DOM (Nnamdi Azikiwe 7 ENUGU 0.997 Constant Return to Scale Airport, Abuja), AKURE (Akure Airport), JOS (Yakubu Gowoni0 8 IBADAN 0.9979 Increasing Return to Scale Airport, Jos), MAID INT’L (Maiduguri International Airport), MMA DOM (Murtala Muhammed Domestic Airport, Lagos), 9 ILO DOM 0.9998 Increasing Return to Scale MMA INT’L ( Murtal Muhammed International Airport, Lagos), 10 ILO INT'L 0.188 Constant Return to Scale PHC DOM (PortHarcourt Airport), and OSUBI (Warri Airport) are ranked as the first or best airport from the model, while 11 JOS 1 Increasing Return to Scale the remaining (airports) are also displayed in the next column of the Table 4 above as the low productivity and efficient 12 KAD DOM 0.7689 Constant Return to Scale airports. The airports that are ranked low need much 13 KAD INT'L 0.8946 Constant Return to Scale improvement so as to enhance productivity and efficiency for the government and as well meet the optimal scale of setting 14 KAN DOM 0.939 Constant Return to Scale such airport. The ranking is base according to the level of the 15 KAN INT'L 0.3955 Constant Return to Scale technical efficiency of the airports in the study sample within the study period. 16 MKD 0.0163 Increasing Return to Scale 17 MAID DOM 0.1938 Constant Return to Scale 18 MAID INT'L 1 Increasing Return to Scale 19 MMA DOM 1 Decreasing Return to scale 20 MMA INT'L 1 Decreasing Return to scale 21 PHC DOM 1 Constant Return to Scale 22 PHC INT'L 0.421 Constant Return to Scale 23 SOK DOM 0.9977 Increasing Return to Scale 24 SOK INT'L 0.2812 Constant Return to Scale 25 YOLA DOM 0.373 Constant Return to Scale 26 YOLA INT'L 0.1378 Constant Return to Scale 27 MINNA 0.0411 Constant Return to Scale 28 KAT 0.0135 Constant Return to Scale

29 OWERRI 0.196 Constant Return to Scale Figure 2. The graph of efficiency scores from BCC-model for the 30 Airports Analyzed. Source: Authors. 30 OSUBI 1 Constant Return to Scale

The figure 2 graph shows the efficiency score results of the eight airports scientifically, they are as follows; ABJ DOM (Nnamdi Azikiwe Airport, Abuja), AKURE (Akure Airport), JOS (Yakubu Gowon Airport, Jos), MAID INT’L ( Maiduguri International Airport), MMA DOM (Murtala Muhammed Domestic Airport, Lagos), MMA INT’L (Murtala Muhammed International Airport, Lagos), PHC DOM (PortHarcourt Airport), and OSUBI( Warri Airport). While the other airports (Figure 2) above are explained according to the efficiency ranking (level

Table 6:Summary of Projected Return to Scale of Airports implication of this study is that the Nigeria airports operated by the Federal Airport Authority of Nigeria has to adopt a policy of No. of Increasing RTS 7 improving airports efficiency based on observed correlation metrics and adopting a procedure (models) such as the DEA in No. of Constant RTS 21 evaluating their technical efficiency so as to improve the No. of Decreasing RTS 2 airports. The Federal Airport Authority of Nigeria is the Nigeria airports managerial organization and therefore this Total 30 organization should adopt a managerial efficient improvement. Moreso, the result shows that hub airport, commercial/large Table 5 shows the various return to scale with the efficiency city airport and private airport are productive, the airports are scores of all the airports in Nigeria during the study period. ABJ DOM, MMA DOM, MMA INT’L and OSUBI. Amongst the Table 6 shows the summary of the return to scale. From the airports that are productive and efficient, only one analysis, it shows that seven (7) airports are operating under international of the airport is productive and efficient, while increasing returns to scale (IRS). In this case it means that the the rest are domestic airports. The procedure should identify airports are said to be operating at increasing return to scale the best practice airports which should be peers for the less (IRS). The IRS occurs if there is a proportionate in all the efficient to follow. That procedure will also improve the airports inputs results is greater than proportionate increase in efficiency of Nigeria airports. The airports can enhance TE by its outputs. If for a given unit the sum of its dual weights in the increasing their size. Thus, downsizing seems to be an dual model turns out to be less than 1, then that unit can be appropriate strategic option for these airports in their pursuit said to be operating at IRS, assuming it is technically efficient to reduce unit costs and as well some other inputs so as to (Vanem and Trainfis, 2001). Furthermore, 21 airports are increase efficiency. operating in constant return to scale (CRS). In the constant return to scale, units operates if there is an increase in inputs 6. Area of further study resultant in a proportionate increase in the output levels. If the Further studies need to be carried out by comparing the two inputs values for a unit are all doubled, then the unit must major DEA models (CCR and BCC) results to determinne the produce twice as much as outputs. 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