An Assessment of Productivity and Efficiency in Nigerian Airports Using Data Envelopment Analysis Obioma R. Nwaogbea, Innocent C. Ogwudeb, Victor Omokea Carlos P. Barrosc Department of Transport Management Technology, Federal University of Technology Minna, Niger State, Nigeriaa. [email protected], +2348034350201. Department of Transport Management Technology, Federal University of Technology , , . [email protected]. Instituto Superior de Economia e Gestão, Technical University of Lisbon; Rua Miguel Lupi, 20, 1249- 078 Lisbon, Portugal. [email protected].

Abstract: This paper analyses the efficiency of the Nigerian airport with a DEA-Data Envelopment analysis model. The aviation industry in Nigeria has been deregulated since mid-1990s following the commercialisation of state owned airports and the licensing of private airlines to participate in the aviation industry. The airports have operated largely in a deregulated environment, although largely state owned with some aspects of their operations concessioned to the private sector. Since 2005 efforts have been made to rebuild the infrastructure base of the airports and to improve airport management following best global practice. It has been the aspiration of Nigeria to develop into a viable market that will become the hub of the aviation industry in Africa. This is possible only to the extent that the airports are able to handle effectively the growing demands of the aviation industry in terms of passenger and cargo traffic. It is therefore timely to begin the study of the productivity and efficiency that have been achieved in the airports so far and to benchmark them against both African and world standards in terms of scale effects, technical efficiency and sources of growth. The DEA model is adopted and the airports ranks presented permitting to conclude which are the best airport and the weakest airport in the sample. Policy Implications are derived. Keywords: Nigeria, Airports, DEA model, Efficiency.

1. INTRODUCTION The global expansion in investment in the African countries has lead Nigeria which is the largest in population, gaint of Africa and large economic activiies due to its natural resources to work towards the development of it transport infrastructure more especially aviation industry to accommodate its traffic at the airports from all over the world. The development includes both the international and domestic airport all over the country. Nigeria is aiming to be a hub in terms of economic activity and air transport business for the West African region as well as Africa entirely. Due to this fact, the Nigerian governmnet and their transport professional has recently become very interested in assessing and as well evaluating the overall performance, productivity and efficiency of aviation industry in the country. The role of air transportation in Nigeria is highly significant due to the fact that it does not have any other competitor in terms of speed and safety in the aspect of direct passenger and cargo transportation round the countries entire region, thereby making passenger and freight transportation very difficult. With this, it make the airport/aviation industry to be an important aspect of transport infrastructure that is highly needed in the social, tourism and economic development in the country.

Many studies have been conducted in Nigeria on air transportation, airport capacity utilization performance and efficiency in other to make suggestions on how to increase airport and aviation performance. But much work need to be done in the country on the aspect of airport productivity, airport competition, airport quality of service and other operational efficiency to enable the airport operators/management, airlines and government to benchmark their aairport operational activities. Through this process, the government will go along way in developing its

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airport infrastructure and the human resources in other to achieve their goal of being the hub airport of Africa which is the objective of the present government before the year 2020

Aviation industry is one of the major important sectors in the transportation industry in the world. Its continuous development and technological service improvement makes the sector to be a major contributor to the modern and the global standard of advanced development in the transportation sector. The growth of this sector cannot be compared to any other major transport sector due to its sophisticated technological equipment and continuous innovation in the sector because of its haul of vehicle (Aircraft) that it uses as a carrying system. With the help of this sector much has being achieved throughout the world in terms of economic development, tourism, connectivity, logistics and supply chain activities. The demand of transport services has greater increased the influence of the aviation sector in most countries as well as the global economy. It has enhanced rapid movement of passengers, goods and services to the domestic and international markets of the air transport sector. The industry plays a major role in the country and in the world in the aspect of work and leisure, improve quality of life, living standard of people, generate economy growth, create or generate employment opportunities, increase revenue through tax, all this are generated through supply chain transformation from the airport which act as the landlord to the airline that operate the transport services within the globe (Nwaogbe et al., 2013).

With high growth rate of Nigeria population and high traffic growth of domestic and international demand due to high economic development of the country and its position as the giant of Africa, the study will be of hope to the aviation sector. Furthermore various airport infrastructural challenges and sophisticated equipment for safety and security, traffic congestions, delays of flight has cause a heavy deterioration in the airport in term of drawback on passenger throughput, revenue and other airport outputs. This study airport productivity will help to examine the relationship between inputs (runway dimension, terminal capacity, number of employees, total assets, and total cost) and outputs (passenger throughput and aircraft movement) of airport operation in Nigeria.

The paper will help the airport managers of the various airports or the federal airport authority of Nigeria to easily determine the probability of the general traffic level (outputs) that exists at all the airports in the country and how to accommodate them at a given level of input of the airports. It will be very useful in accessing, monitoring, managing and as well planning all other operational activities at various airports in the country. Furthermore, the assessment allows or helps the airports directors of operations, managers of operation to benchmark their operational performance or activities and how to set up appropriate standard on their targeted outputs and their improvement in the airport businesses and activities to generate more output or maximize the management and the Federal government objectives of setting the airport.

Airports performance is usually analyses in terms of efficiency or productivity. DEA models (Gillen and Lall, 1997; Gillen and Lall, 2001; Adler and Berechman, 2001; Barros and Dieke, 2007; Barros, Managi and Yoshida, 2011) and SFA- stochastic frontier models are usually adopted (Barros and Sampaio, 2004; Barros, 2008a; Barros, 2009; Diana, 2010). However European and USA airports are usually analyzed, while African airports are rarely analyzed (Barros and Marques, 2010; Barros, 2014). Therefore this paper contributes for airport efficiency analysing the Nigerian airports with a DEA model. The motivation for the present research is first to analyse for the first time the Nigerian airports with a DEA model. The model of DEA Solver pro. Version 10.0 will be used. The analysis of airport efficiency can yield significant insights into the competitiveness of airports and their potential for increasing productivity and improving resource use, Biesebroeck (2007). The research on airports has adopted DEA models (Sarkis, 2000; Sarkis & Tallury, 2004) or homogenous production frontier models (Pels et al., 2001, 2003).

2. CONTEXTUAL SETTING

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This section describes the Nigerian airports and the authorities that are taking care of the operational activities at the airports. Nigeria, a former British colony, attained self government in 1960, maintaining its colonial structure of three regions and one central government. Administrative convenience and political expediency led to further subdivision into twelve-state structure in 1967. However, as a result of the persistent agitations for more states, the number later increased to nineteen states in 1976, twenty-one in 1987, thirty in 1991 and to the current figure of thirty-six states in 1996. Civil aviation began in the country in 1935 when aerodromes were built in Lagos, Kano, and Maiduguri (Bureau of Public Enterprises, 2003). With the oil boom era of the 1970s the need for more airports became apparent. The major criterion for locating the new airports seems to be the desire of the government to open up corners of the country to access and development. Towards the realization of this goal, government appeared to be pursuing a policy of one airport for each state capital. Thus, as more states were created more airports were built, until cost consideration curtailed the construction of any more airports. Currently Nigeria has a total of twenty five airports five of which are international while the rest operate domestic routes and one of the airports is for aviation training school Zaria. Two of the original three aerodromes had in the course of time developed into international airports. One of these was the Murtala Muhammed Airport in Lagos which was, until 1991, the seat of government. The other was Aminu Kano Airport in Kano, which selection for upgrading must have been because Kano is a commercial hub. Similar reasons appear to inform subsequent development of the three additional international airports at Kaduna, Port Harcourt and Abuja. The previous two cities have high volume of commercial activities while Abuja is the new federal capital (Barros & Ibiwoye, 2012).

The industry grew with the oil boom period of the 1970s but witnessed decline as the economic downturn set in. Facilities at most of the airports are old and poorly maintained. The industry is also faced with the problem of an aging workforce that has not benefited from a consistent replacement policy. Operational efficiency and safety are also low and the various policies introduced by successive governments to turn around the airports yielded no fruitful results (Ayodele, 2009). To address some of these difficulties government intervened decisively by promulgating the Civil Aviation Act (CAC) in 2006.

The airports are managed by the Federal Airports Authority of Nigeria (FAAN) on behalf of the Federal Government of Nigeria who owns the facilities. FAAN was established in 1995 to carry out the functions of two erstwhile organisations, the Nigerian Airports Authority (NAA) and the Federal Civil Aviation Authority (FCAA). However the need to conform to International Civil Aviation Organisation’s (ICAO) requirement led to another restructuring in 1999. Since ICAO stipulates the separation of regulatory bodies from service providers it became imperative for all affiliates to establish a state organisation that will ensure compliance with air navigations. This led to the creation of a fully autonomous Nigerian Civil Aviation Authority (NCAA) in 1999 (Balogun, 2008).

The aviation sector in Nigeria comprises of 25 airports, under the authority of Federal Airport Authority of Nigeria (FAAN) that acts as landlord to the various airlines and other airport tenants. Among these airports, 5 of them are classified by FAAN as international airports (they are always scheduled for international flights), while the remaining 19 airports are taken as domestic airports (which means that they are always scheduled for domestic flights). The airports are operating with a commercial objective are 24 while 1 airport are managed by the private industry (Shell Petroleum Development Company Limited). Among the 24 commercial airports, 1 terminal of the Murtala Muhammed Airport Lagos is controlled by a private sector under the government agreement of the public private partnership policy. The terminal is concessioned while the other airports and their terminal are controlled by the federal government under the federal ministry of aviation. The infrastructures that are used at the airports are provided by the federal government.

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Murtala Muhammed International Airport is Nigeria’s principal airport with the highest aviation operations and largest productivity output. As a hub airport for intercontinental passenger traffic, Murtala Muhammed International airport can offer Nigerian residents and businesses a better offer in terms of access to their various destinations, at a higher frequency and at lower price in terms of fare charges. With such hub and spoke network, its benefits are to enhance the country’s various connectivity in terms of air transport system, which in the other way round contributes to high global, and nations‟ overall international trade and economic levels of productivity and Gross Domestic Product of Nigeria (Nwaogbe, 2013). With all this comment on aviation industry, airport must be cognizance of general concern of the operations more especially on the aspect safety and security of their passengers and their businesss in other to have a successful and effective operational productivity and efficiency.

As all the airports are owned by the state and federal government, there is very little room for competition. However, the industry is now going through a process of transformation that would increase the levels of competition as government is planning to concession four of the airports (Shadare, 2009). These are the Murtala Muhammed Airport, Lagos, Port Harcourt International Airport, Margaret Ekpo International Airport, Calabar and Aminu Kano International Airport, Kano. Generally, there is a shift in government policy as airports are now regarded more like commercial entities than public utilities and government is now encouraging and promoting public-private partnership in the provision of airport services (Arogunjo, 2008). This used to be the exclusive preserve of FAAN.

The principal functions of the Federal Airports Authority of Nigeria (FAAN) are to:  Develop and maintain all the airports within the Nigeria territory provide accommodation and other facilities for effective handling of passengers and freight.  Development and provide facilities for ground transportation Prohibit the installation of structure which by virtue of its high position is considered to endanger the safety of air navigation.  Provide adequate conditions under which passengers and goods maybe carried by air and under which aircraft may be used for other gainful purposes.  Charge for services provided by the authority at airports.  Carryout at airports (either by itself or by agent or in partnership with any other person) such other commercial activities which are not relevant to air transport but which in the opinion of the Authority maybe convenient without prejudice to specified functions.  Provide adequate facilities and personnel for effective security at all airports.  Create conditions for the development in the most economic and efficient manner of air transport and the services Connected to it. There are institutions and organization that need the performance and efficiency of the airports in Nigeria, they are;  Airport management (FAAN), NAMA, and NCAA requires efficiency comparison between airports to improve airport operations which will enhance their operative standards in a competitive environment.  Airlines and their managements are so much interested in identifying the most efficient airport for their operational activities within the country.  Federal government and state government requires efficient airports for attracting more international trade and increase tourism into the country, as well increase domestic travel within the country.  Government policy makers need efficiency airport benchmarking for improvement programmes and optimal decision making process in the country for easy allocation of capital and human resources (FAAN, 2010).

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The Nigerian airports are distributed along the country and comprise international, regional and state airport.

3. LITERATURE SURVEY Papers in airport efficiency and productivity include among others (Humphreys and Francis, 2002; Graham, 2005; Marques and Brochado, 2008). Studies using data envelopment analysis with diverse DEA models include Gillen & Lall (1997), Parker (1999), Murillo-Melchor (1999), Gillen & Lall (2001), Pels et al., (2001), Adler & Berechman (2001), Martín & Román (2001), Fernandes & Pacheco (2002), Sarkis (2000), Sarkis & Talluri (2004), Barros & Sampaio (2004), Yoshida & Fujimoto (2004), Yoshida (2004), Lin & Hong (2006), Barros & Dieke (2007), Fung et al., (2008), Barros & Dieke (2008) and Barros (2008b, 2009). An extensive survey can be found in Barros & Dieke (2008).

Papers using the SF-stochastic frontier model include Pels et al., (2001) and Martín-Cejas (2002), who adopted the standard SF model, and Barros (2008a, 2008b), who adopted the random SF and latent frontier model. A relatively new approach in the context of the stochastic frontier adopts the Bayesian approach (Assaf, 2009, 2010a, 2010b). Stochastic frontier models started with homogenous models that assume that all airports are homogenous (Pels et al., 2003). Recently, a new generation of stochastic models has appeared that take heterogeneity into account, such as the random frontier model (Barros, 2008b), the Bayesian approach of Assaf (2010b). The research in African airports include Barros and Marques (2010) that analyse the efficiency of Mozambican airports with a random and a fixed effects stochastic frontier and then adopt a Bayesian stochastic frontier. Therefore this paper contributes for this research analyzing the Nigerian airports with a DEA model not previously used in airports research.

Parker (1999) analyses the effects of privatization on the efficiency level of British airports using Data Envelopment analysis, the result show that privatization had no noticeable impact on the technical efficiency of the airport business. From the year 2001-2003, in the Asia-pacific, Europe and North America, (Oum, Adler and Yu, 2006) carried out a research on the effect of ownership type to productive efficiency and profitability using panel data for the major airports looking at the airport cost efficiency. Stochastic frontier was used for the analyses. Their results suggest that airports with government majority share ownership structure and those owned by multi-level government are significantly less efficient than the privately majority ownership airports, which means that most of the publicly owned airports need to adjust most of their policy or government should even embrace the public private partnership in other to move most of the airports to device its productivity and efficiency.

Barros & Ibiwoye (2012) study on the performance, heterogeneity and managerial efficiency of African airport the Nigeria case from 2003-2010 using stochastic frontier analysis. Their study of the 30 airports were ranked according to their technical efficiency during the period, homogenous and heterogeneous variable are disentangled in the cost function, which lead to the suggestion of the implementation of common policies as well as policies by segments. In studying airport operational efficiency, some authors (Tseng, Ho & Liu, 2008; Perelman & Serebrisky, 2010; Zhang, Wang, Liu & Zhao, 2012; Jaržemskienė, 2012) use only various technical airport characteristics: number of runways, number of platforms, airport size, number of employees, number of flights, cargo volumes, number of passengers, etc. Research by Tenge (2012), Lopes & Rodrigues (2007) and Sutia, Sudarma & Rofiaty (2013) are based on the social capital and network approach to operations of organizations.

According to Tenge (2012) the quality of airport services and the ability to constantly innovate these qualities are important variables that contribute to the overall attractiveness of an airport. In many cases, airport management underestimates the necessity of insight into the needs of clients. Sutia, Sudarma & Rofiaty (2013) have analysed the relationship among human capital, leadership and strategic orientation with company performance, especially the influence of human capital investment on airport performance. In carrying out their research, airports aim at maximising the

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movement of aircraft, thus increasing the efficiency of operations in the competitive environment in which they function.

In many countries, airports have turned from state monopolies into competing operators, and flight directions are determined by market changes. In addition, the emergence of low cost carriers in the market forces airports to increase the efficiency of the existing infrastructure in order to preserve competitiveness and to maintain their sales (Pabedinskaite & Akstinaite, 2014). Hooper & Hensher (1997) studied the evolution of total factor productivity of Australian airports for the period 1988-1992 using index number methods for the analyses. Moreso, Pels, NijKamp & Rietveld (2001) also carried out a study on airport productivity and efficiency of European airports, after their analysis with two difference models (Data Envelopment Analysis and stochastic frontier analysis) they compared the two results. From their results it shows that the stochastic frontier model reproduces the DEA results in a quit reasonable way. Furthermore, Barros & Dieke (2008) apply stochastic frontier model in the study of technical efficiency of airports in the United Kingdom.

Many of the researchers that employ such econometric model (DEA) are Pels, Nijkamp & Rietveld (2001); Oum, Yu, & Fu (2003); Pels, Nijkamp & Rietveld (2003); Yoshida (2004); and Yoshida & Fujimoto (2004). On the method of non- parametric, Data Envelopment Analysis model is a typical or popular non parametric method known by so many researchers that was initially studying the airport productivity and efficiency. Such study was employed by Doganis (1992), although since then, many other researchers have applied several model settings, Gillen & Lall (1997, 2001); Parker (1999); Fernades & Pacheco (2002, 2003); Pels, Nijkamp & Rietveld (2001, 2003); Barros & Sampaco (2004); Murillo-Melchor (1999); Abbott & Wu (2002); Sarkis & Talluri (2004); Martin & Roman (2001); Barros & Dieke (2007, 2008) and Tsekeris (2011).

4. METHODOLOGY The research methodology is based on the production theory from the statistics modeling of linear programming modeling. The characteristics of production technology are to represent the relationship that exists between input and output measures. The model will be built on optimization model whereby the directional output distance function will be developed. This is a non-parametric system with multiple inputs and outputs and provides measures of performance and efficiency without appealing the prices.

The production process can be considered as desirable and undesirable output which may produce u a byproduct of the production of y. In the airport operation processes output are the passenger throughput, aircraft movements, mails and cargo throughput, the use of its infrastructure such as runway, terminal area, terminal passenger capacity and to mention but few. Our sample size is going to be based on the 30 airports observations which include the domestic and international airports. DEA solver software will be used to solve the problem after formulating the models with the variables. Data Envelopment Analysis is commonly used to evaluate the efficiency of a number of producers. A typical statistical approach is characterized as a central tendency approach and it evaluates producers relative to an average producer. In contrast, DEA compares each producer with only the “best” producers. Data Envelopment Analysis (DEA) provides a clear answer to the airport manager’s problem. DEA is a linear programming based techniques and basic model only requires information on inputs and outputs. DEA incorporate multiple outputs and inputs, in fact input and outputs can be defined in a very general manner without getting into problems of aggregation. DEA provides scalar measures of relative efficiency by comparing the efficiency achieved by a decision making unit (DMU) with the efficiency obtained by similar DMUs. In the case of a single output this relation corresponds to a production function in which the output is maximal for the indicated inputs. But in the more general case of multiple outputs the relation can be defined as an efficient production possibility surfaced or frontier. DEA, occasionally called frontier analysis, was introduced by Charnes & Rhodes in 1978 (CCR). DEA is a performance measurement technique which can be used for evaluating the relative efficiency of decision-making unit (DMUs), the airports now will serve as the DMUs. 6

Efficiency = (1)

This is often inadequate due to the existence of multiple inputs and outputs related to different resources, activities and other factors of production at the airport. The measurement of relative efficiency where there are multiple possibly incommensurate inputs and outputs was addressed by Farrel (1957) and developed by Farrel & Fieldhouse (1962) focusing on the construction of a hypothetical efficient unit, as a weighted average of efficiency units, to act as a comparator for an inefficient unit. Adopting Charnes – Cooper- Rhodes (1978) Models of linear program.

3.2 FOMULATION OF THE MODEL Efficiency = (2)

This introduce the usual notation which can be written as The CCR model adopted for this research can be expressed by (9)-(12):

u1 y1o  u2 y2o  un yno (FPo )Max   (3) v1x1o  v2 x2o  vm xmo Subject to u y  u y  u y 1 1 j 2 2 j n nj  1 ( j  1,, s) (4) v1x1 j  v2 x2 j  vm xmj v1,v2 ,,vm  0 (5)

u1,u2 ,,un  0 (6)

Given the data x and y , the CCR model measures the maximum efficiency of each DMU (airports) by solving the fractional programming (FP) problem in (3) where x and y are the input and output matrixes, respectively; airport input weights v1, v2, …vm and airport output weights u1, u2, …un are variables to be obtained. o in (3) varies from 1 to s which means s optimisations for all s DMUs (airports). Constraint (4) reveals that the ratio of ‘virtual airport output’

( u1 y1o  u2 y2o  un yno) to ‘virtual airport input’ ( v1 x1o  v2 x2o  vm xmo ) cannot exceed 1 for each DMU (airport), which conforms to the economic assumption that the airport output cannot be more than the airport input in production. The above FP (3)-(6) is equivalent to the following linear programming (LP) formulation given in equations (7)-(11) (see e.g. Cooper, Seiford & Tone, 2006):

(LPo )Max   u1 y1o  u2 y2o  un yno (7) Subject to v1 x1o  v2 x2o  vm xmo 1 (14) u1 y1 j  u2 y2 j  un ynj  v1x1 j  v2 x2 j  vm xmj ( j  1,, s) (9) v1,v2 ,,vm  0 (10) u1,u2 ,,un  0 (11) CCR – Efficiency When DMU (airport), is CCR – efficient if * = 1 and there exist at least one optimal (V*, U*), with V* > 0 and U* > 0 Otherwise, DMU (airport), is CCR – inefficient. Thus, CCR – inefficiency means that either (i) * < 1 or (ii) * = 1 and at least one element of (V*, U*) is zero for every optimal solution of (LPo).

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* So where DMU0 (airport) has  < 1 (CCR – inefficient). Then there must be at least one constraint (or DMU) in equation (14) for which the weight (V*, U*) produces equality between the left and right hand sides since, otherwise, * could be enlarged. Let the set of such j  {1, …, n} be (12)

The subset of E0 of , composed of CCR – efficient DMUs (airports), is called the reference set or the peer group to the DMU0 (airports). It is the existence of this collection of efficient DMUs (airports) that forces the DMU0 (airport) to be inefficient. The set spanned by E0 is known as the efficient frontier of DMU0 (airport). Meaning of Optimal Weights * * The (V , U ) obtained as an optimal solution for (LP0) results in a set of optimal weights for the DMU0 (airports). The ratio scale is evaluated by: (13) from the equation (14) the denominator is 1 and have (14) * * Since we earlier stated that, (V , U ) are the set of most favorable weights for the DMU0 (airport) in the sense of maximizing the ratio scale. is the optimal weight for the input variable from airport and its magnitude expresses how highly the item is evaluated. Similarly, does the same for the output variable r of various airports. Furthermore, if we examine each variable in the virtual input, then we can see the

(15) relative importance if each variable by reference to the value of each . The same situation holds for where the provides a measure of the relative contribution of to the overall * value of  (Cooper, Seiford, & Tone, 2000). The input variables are rpresented as follows: x1 is the terminal capacity x2 runway dimension x3 number of employee x4 total assets x5 total cost while ouput variables are: y1 aircraft movemnet y2 passenger throughput The model compares the production unit o with all the convex linear combinations of production units. The linear programming problem is solved for every airport in the sample in order to obtain its relative performance. It is necessary to note that the computation of the above DEA CCR model by transforming the FP model into the LP model has been of great significance for the rapid development and wide application of DEA. As a long-established mathematical method with various sophisticated computation methods and commercially available solution software, LP possesses inherent advantages that make the complicated computation both easier and more feasible (Cooper, Seiford & Tone, 2000).

5 DATA PRESENTATION There are thirty airports observation in this study, which ten of them controls international air traffic in Nigeria, while twenty airports are domestic airports that takes care of domestic air traffic in the country. All the airports in Nigeria are controlled by the Federal Airport Authority of Nigeria apart from one the terminal (Murtala Mohammed Airport Two MMA2) which is under concession by a private sector. Furthermore, OSUBI airport is controlled by the private sector (Shell Petroleum Development Company Limited). Secondary data were source from publish and 8

unpublished papers, such as seminar papers, FAAN annual report, NAMA, NCAA, internets, journals, magazines, and conducted research study from Federal Airport Authority of Nigeria Library etc. The table 2 below shows the input and output of the panel data for the sample of Nigerian airports 2013.

<< Place table 2 here>>

The major objective of this research is to make full airport performance comparison of the Nigerian airports. To acheive this objective, a DEA- (Data Envelopment Analysis) model is used to run an anlysis with using CCR (Charnes Cooper and Rhodes DEA model) output-oriented model. Inputs and outputs variables are adequately selected based on the literature survey. The input variables are: Terminal capacity, runway dimension, the number of employees, total assets and total cost. While the outputs are measured by two variables, they are: total passenger throughputs, and number of aircraft movement. Data of all the inputs and outputs were sourced from the Federal Airport Authority of Nigeria (FAAN).

The combination of input and output variables meets the DEA convention that the minimum number of DMU observations should be greater than two times the number of inputs plus outputs (Bezić, Šegota, and Vojvodić, 2010). According to Mantri (2008), conventional DEA is static, i.e. the analysis does not consider the time frame to which the input consumption and output production refers. However, multiperiod efficiency measurement is possible through window analysis. Initiated by Charnes et al. (1985), window analysis is a time-dependent version of DEA with various applications. The input/output data of the DMUs (airports) for this study is used the total number of periods (years) to assess the efficiency of each DMU for the total periods.

In running the analysis, the first thing is to select input and output variables, and the efficiency scores of the Nigerian airports for the ten years are analysed. Followed by identifying the sources and relative inefficiency that will come out. While the second stage is to proceed with window analysis, which is applied to provide trend information on the relative efficiency scores of Nigeria airports over the period of ten years (2003-2013). The results of the analysis is discussed in the next section.

6 RESULTS AND DISCUSSION 6.1 Correlation Matrix Analysis The correlation analysis for thirty Nigerian airports shows that there is a presence of weak, and very weak relationship between some inputs and output in the correlation matrix of the DEA (CCR- model) analysis. From the table 3 below, it shows that between terminal capacity and passenger throughputs 0.43519 (weak), terminal and aircraft movement 0.24355 (very weak), runway dimension and passenger throughputs 0.71907 (stronger), runway dimension and aircraft movement 0.67511 (strong), number of employees and passenger throughput 0.84902 (stronger), number of employees and aircraft movement 0.67869 (strong), total assests and passenger throughputs 0.82577 (stronger), total assests and aircraft movement 0.72113 (stronger), total cost a and passenger throughputs 0.5347 (slightly strong) and between the total cost and aircraft movement 0.46985 shows a weak relationship in the model.

<> <> The table 4 shows the decriptive statistics of the Nigeria airport for 2013.

6.2 Analysis of Efficiency Scores

<> From the analysis, the results of the CCR- model shows efficiency scores for the thirty airports in Nigeria are shown in the Table 5 above. The efficiency indices inclined from 0.0123 to 1 for CCR-model result. The result shows that at least 3 different airports are considered to be 9

technically or scale-efficient from the CCR-model analysis result. Furthermore, when analyzing the CCR-model, it was observed that the model is interested in constant return to scale and its efficiency score (1) result shows that three (DMUs) airports are efficient. The DMUs are : ABJ DOM, PHC DOM and OSUBI are the efficient airports in Nigeria from the CCR-model result. Among these efficient airports two (ABJ DOM and PHC DOM) of them are operated by Federal Airport Authority of Nigeria (public), while one (OSUBI) are operated by a private sectror (Shell Petroleum Development Company). These efficient airports have a max-efficiency value of 1.0. While the least technical inefficiency airports from the CCR- model is the KAT. Sengupta (1995) states that industrial competiveness can be evaluated through analysis of average efficiencies. The average efficiency for the 30 DMUs of the CCR-model is 0.4223 (42%). This means that a relationship exists between the input and output values depend on the importance or size of the data set.

Although some of the airports efficiency are above the model average efficiency scores. The model result shows that the least technically inefficient DMU is KAT airport. See table 5 for the abbreviation of airport names. The issue of this inefficiency may be that some airports were targeted for less air carrier and also due low infrastructure in the airports and as well may be due the low standard of aircraft maintenance by the airline which leads to many aircraft accidents at the airports. For example DANA airline crash in year 2013 and so many other aircraft crash that have occured in the country (Nigeria) earlier than year 2013. From 1969 -2012, Nigeria have had a total of 2,021 passengers/crew members on board aircraft have lost their precious life and properties to hell mishaps due to lack of skilled workers and safety equipment at the airport (www.thenigerianvoice.com). Also number of points emerge by means of the basic DEA models and these shows that: * Few airports are on the efficient frontier; hence there is need to help Federal Airport Authorities of Nigeria through suggestions and recommendation to improve the operations and the management techniques of the airports that are not efficient. * Using the best practice of efficiency evaluation in the study, it was observed that almost all Nigerian airports are operated at a high level of relative inefficiency, with the exception of 3 airports from the CCR-model that are efficient. * Going through the efficiency model CCR-model (CRS) of the Data Envelopment Analysis, all efficient airports are determined by the CCR model. * Also according to the CCR scores, there are only 27 airports that are operated relatively inefficiently. * Among these inefficient airports in CCR model, only KAT (Kastina Airport) is relatively smaller than other airports results (0.0123 in CRS efficiency).

Furthermore, economic and financial crises that are facing most air carriers in Nigeria made them to face various high airport charges problem thereby leading them to run away from most of such airports. Another case is the issue of insecurity such as Boko Haram (Terrorism) also affected some of the airports in the North-East, North-West and some part of the Niger Delta region of the country such as (KAD DOM, KAD INT’L, KAN DOM, KAN INT’L, MAID DOM, SOK DOM, SOK INT’L, YOLA DOM, YOLA INT’L, MINNA, KAT, PHC INT’L, CAL DOM, and CAL INT’L,), this might be part of the factors that act as a drawback for those airports not meeting their technical efficiency.

Generally, going through the overall efficiency performance for all the airports, it was observed that the largest percentage of respondents performed most efficiently is 10% for CCR- model analysis. A slight increase in both percentage of efficient airports and average efficiency scores means that airport operations are becoming more competitive. Sarkis (2000) postulated in his study that with less than half of the airports in their sample size, still not all the airports obtain an efficiency scores equal to 1, rather, there is an ample room to increase efficiency to compare to that of the other airports

6.3 DEA Model Efficiency Ranking

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The ranking position is based on the efficiency scores from the DEA (CCR-Model) analysis, the ABJ DOM, PHC DOM and OSUBI are ranked the first or best airport from the CCR- model. Amongst the three efficiency airports, one (OSUBI) is privately managed by the Shell Petroleum Development Company Limited at Warri., while the other two are public airports under the agency of Federal Airport Authority of Nigeria. The remaining DMUs (airports) are also displayed in the fourth column of both model in the table 6 above.

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The figure 1 graph shows the efficiency scores result scientifically of three airports (ABJ DOM, PHC DOM, and OSUBI) from the CCR-model which considered constant return to scale of input and output variable. Furthermore, the other DMUs (airports) as shown in the figure 1 above expresses accordingly the efficiency ranking order from the analysis of the CCR-model result.

7 CONCLUSION AND POLICY IMPLICATION This paper assess Nigeria airport productivity using Data Envelopment Analysis (DEA) model to determine the relative efficiency and productivity among the airports that exist in Nigeria. The model used helps to estimate the efficiency improvemnet that can be of help in enhancing the airports (DMUs) that are inefficient to produce more outputs for the airports which will also enhance economic development of the country through the aviation sector and foreign investments.

In running the analysis, the input data used includes terminal capacity, runway dimension, number of employees, total assets and total cost, while the output data used includes passenger throughput and aircraft movement. In analysing this data, a DEA model software solver pro. version 10.0 was used to analyse an output-oriented CCR-model (Constant Return to Scale) to examine the overal efficiency of the thirty airports in Nigeria in the 2003-2013.

From the analysis, the results of the CCR- model shows efficiency scores for the thirty airports in Nigeria are shown in the Table 5 above. The efficiency indices inclined from 0.0123 to 1 for CCR-model result. The result shows that at least 3 different airports are considered to be technically or scale-efficient from the CCR-model analysis result. Furthermore, when analyzing the CCR-model, it was observed that the model is interested in constant return to scale and its efficiency score (1) result shows that three (DMUs) airports are efficient. The DMUs are : ABJ DOM, PHC DOM and OSUBI are the efficient airports in Nigeria from the CCR-model result. Among these efficient airports two (ABJ DOM and PHC DOM) of them are operated by Federal Airport Authority of Nigeria (public), while one (OSUBI) are operated by a private sectror (Shell Petroleum Development Company). These efficient airports have a max-efficiency value of 1.0. While the least technical inefficiency airports from the CCR- model is the KAT. The average efficiency for the 30 DMUs of the CCR-model is 0.4223 (42%).

The policy implication of the present research is that the Nigeria airports operated by the Federal Airport Authority of Nigeria have to adopt a policy of improving airports efficiency based on observed correlation metrics and adopting a procedure (models) such as the DEA in evaluating their technical efficiency, so as to improve the airports. The Federal Airport Authority of Nigeria is the Nigeria airports managerial organization and therefore this organization should adopt a managerial efficient improvement. The procedure should identify the best practice airports which should be peers for the less efficient to follow. This procedure will also improve the efficiency of Nigeria airports. More research is needed to confirm the present conclusions.

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Table 1: List of Airports Airports Name and State ABJ DOM Nnamdi Azikiwe Domestic Airport, Abuja ABJ INT'L Nnamdi Azikiwe International Airport, Abuja AKURE , Akure BENIN , Benin CAL DOM Magrate Ekpo Airport CAL INT'L Magrate Ekpo International Airport ENUGU Akanu Ibiam International Airport, Enugu ILO DOM Ilorin Domestic Airport, Ilorin ILO INT'L Ilorin International Airport, Ilorin JOS Yakubu Gowon Airport, Jos KAD DOM Kaduna Airport KAD INT'L Kaduna International Airport KAN DOM Mallam Amino Kano Airport KAN INT'L Mallam Amino Kano International Airport MKD Markurdi Airport

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MAID DOM Maiduguri Airport MAID INT'L Maiduguri International Airport MMA DOM Murtala Muhammed Airport, Lagos MMA INT'L Murtala Muhammed International Airport PHC DOM Portharcourt Airport PHC INT'L Portharcourt International Airport SOK DOM Sadiq Abubarka III Airport, Sokoto SOK INT'L Sadiq Abubarka III International Airport, Sokoto YOLA DOM YOLA INT'L Yola International Airport MINNA KAT Kastina Airport OWERRI OSUBI

Table 2. The input and output of the panel data for the sample of Nigerian airports 2013. Inputs Outputs Terminal Runway Airport capacity dimension Employe Total Total cost Passenger Aircraft s (Pax) (m2) es assets (N) (N) s (000s) (times)

ABJ 295260483 11217843 DOM 252 181000 808 86 25 3817142 60515 ABJ 783453809 43548356 INT'L 320 216000 934 72 10 1361303 8011 AKUR 197017100 E 40 126000 72 4 2283221 5525 481 625583953 37075043 BENIN 250 108000 95 3 4 396629 1997 CAL DOM 108 110000 153 617565857 74108206 319757 6950 CAL 303723765 INT'L 100 110250 116 5 45875125 0 0 ENUG 101127398 U 300 108000 149 13 50971957 0 0 IBADA 992080317 N 250 108000 87 1 7919526 28436 2915 ILO DOM 202 128000 72 856571757 6574844 47293 3355 ILO 105646507 38663966 INT'L 200 186000 111 36 7 0 0 JOS 250 222000 121 158810721 7215496 81758 1870 KAD 65572744 DOM 285 152000 107 827330558 1 210433 6359 KAD INT'L 250 135000 153 448753721 52656553 166765 41

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KAD INT'L 600 275000 466 254784018 31838308 266653 5722 KAN 526203293 99529855 INT'L 640 315000 532 3 0 166765 2068 99202798 MKD 63 192000 43 405582707 2 2265 382 MAID 101466907 52152822 DOM 200 168000 168 93 3 117691 1998 MAID 101896606 59113607 INT'L 50 180000 130 01 1 17751 266 MMA 283315616 27607750 DOM 615 1213435 1252 32 86 4389241 81680 MMA 270949667 89065212 INT'L 3675 234000 1390 46 6 3817142 28309 PHC 949671012 72024864 DOM 518 156000 360 8 3 1361303 23069 PHC INT'L 700 180000 299 671691131 71068154 14931 909 SOK DOM 194 125000 54 510300399 49062379 70899 2002 SOK 291278059 45794555 INT'L 250 108000 78 7 9 46540 139 YOLA 102230120 DOM 108 120000 124 46 38825983 112820 2243 YOLA 770313206 98073523 INT'L 120 108000 127 5 7 12038 70 MINNA 1000 112000 101 448723618 86311953 13322 761 KAT 120 137025 119 859969649 34209952 10813 895 OWER RI 800 121500 131 583606940 68809402 540655 6846 OSUBI 65 81000 20 306796011 33101424 38991 13190 Min 40 81000 20 158810721 2283221 0 0 783453809 43548356 Max 3675 1213435 1390 72 10 4389241 81680 190540.33 279.0666 893479686 54869724 581162.03 Mean 417.5 33 67 3 7.9 33 8768.1 661.89585 200792.83 356.4628 155558647 92189916 1214130.9 18396.59 St.Dev 34 4 85 57 8.5 38 39

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Table 3: Correlation Matrix of the DEA (CCR-Model) from 2003-2013

Termin al Runway capacit dimensio Employe Total Passenge Aircraf y (Pax) n (m2) es assets Total cost rs t Terminal capacity 0.3984491 0.218435 0.2435 (Pax) 1 0.14585 0.69684 5 21 0.43519 5 Runway dimensio 0.1458 0.5956490 0.6751 n (m2) 5 1 0.63106 62 0.44422 0.71907 1 Employe 0.6968 0.7826275 0.673395 0.6786 es 4 0.63106 1 4 75 0.84902 9 Total 0.3984 0.665674 0.7211 assets 5 0.59565 0.78263 1 86 0.82577 3 0.2184 0.6656748 0.4698 Total cost 4 0.44422 0.6734 65 1 0.5347 5 Passenge 0.4351 0.8257691 0.534697 0.9393 rs 9 0.71907 0.84902 63 54 1 2 0.2435 0.7211341 0.469847 Aircraft 5 0.67511 0.67869 72 58 0.93932 1

Table 4: Descriptive Statistics of the Data 2013

Standard Variables Description Minimum Maximum Mean Deviation Passenger Number of passenger traffic landing 0 4389241 581162 1214131 Throughputs or departing and passengers on (PAX) transit. Aircraft Number of aircraft that lands and 0 81680 8768 18397 Movement takes off from the airport. (ATM) Terminal Terminal capacity at which the 40 3675 418 662 Capacity(TC) terminal can accormodate passengers.

Runway The runway dimension in size at 81000 1213435 190540 200793 Dimension(RD which the runway can accomodate ) the aircraft during take off and landing.

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Number of Number of employees working 20 1390 279 356 Employee(NE) under the FAAN to oversee the successful runing of the airport.

Total Assets Total Assets is the total value of the 783453809 893479686 155558647 158810721 (TA) FAAN assets at each airports. 72 3 57

Total Cost of running the airport 435483560 Total Cost(TC) 2283221 548697248 921899169 (operational cost). 10

Source: Author Table 5.CCR- Models (CRS) (Charnes, Cooper, Rhodes DEA Model Efficiency scores) for Major Nigeria Airports from 2003-2013. No. DMU(AIRPORTS) Score Rank ABJ DOM 1 1 1 ABJ INT'L 2 0.1462 23 AKURE 3 0.1879 20 BENIN 4 0.2505 15 CAL DOM 5 0.5157 11 CAL INT'L 6 0.015 28 ENUGU 7 0.8321 8 IBADAN 8 0.1295 25 ILO DOM 9 0.2496 16 ILO INT'L 10 0.1153 26 JOS 11 0.9635 4 KAD DOM 12 0.4696 12 KAD INT'L 13 0.7621 9 KAN DOM 14 0.8497 7 KAN INT'L 15 0.1953 19 MKD 16 0.0148 29 MAID DOM 17 0.1596 21 MAID INT'L 18 0.5735 10

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MMA DOM 19 0.8973 6 MMA INT'L 20 0.9463 5 PHC DOM 21 1 1 PHC INT’L 22 0.2294 17 SOK DOM 23 0.3361 13 SOK INT'L 24 0.2256 18 YOLA DOM 25 0.2769 14 YOLA INT'L 26 0.1304 24 MINNA 27 0.0373 27 KAT 28 0.0123 30 OWERRI 29 0.1475 22 OSUBI 30 1 1 Average 0.4223 Max 1 Min 0.0123 St Dev 0.3571

Table 6.CCR-Model (CRS) (Charnes, Cooper, Rhodes DEA Model Ranking) for Major Nigeria Airports from 2003-2013.

No. DMU Score Rank ABJ 1 DOM 1 1 PHC DOM 21 1 1 OSUBI 30 1 1 JOS 11 0.9635 4 MMA INT'L 20 0.9463 5 MMA DOM 19 0.8973 6 KAN DOM 14 0.8497 7

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ENUGU 7 0.8321 8 KAD INT'L 13 0.7621 9 MAID INT'L 18 0.5735 10 CAL DOM 5 0.5157 11 KAD DOM 12 0.4696 12 SOK DOM 23 0.3361 13 YOLA DOM 25 0.2769 14 BENIN 4 0.2505 15 ILO DOM 9 0.2496 16 PHC INT’L 22 0.2294 17 SOK INT'L 24 0.2256 18 KAN INT'L 15 0.1953 19 AKURE 3 0.1879 20 MAID DOM 17 0.1596 21 OWERRI 29 0.1475 22 ABJ INT'L 2 0.1462 23 YOLA INT'L 26 0.1304 24 IBADAN 8 0.1295 25 ILO INT'L 10 0.1153 26 MINNA 27 0.0373 27

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CAL INT'L 6 0.015 28 MKD 16 0.0148 29 KAT 28 0.0123 30

Figure 1. The graph of efficiency scores from CCR-model for 2003-2013

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