Case Studies on Transport Policy 5 (2017) 573–579

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Case Studies on Transport Policy

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Efficiency in nigerian airports T ⁎ C.P. Barrosa, Peter Wankeb, , O.R. Nwaogbec, Md. Abul Kalam Azadd a Department of Economics, University of Lisbon, Rua Miguel Lupi, 20, 1249-078 Lisbon, Portugal b COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Brazil c Department of Transport Management, Federal University of Technology, Minna, Niger State, Nigeria d Bangladesh Army International University of Science and Technology, Bangladesh

ARTICLE INFO ABSTRACT

Keywords: This paper analyzes efficiency levels in Nigerian airports using a stochastic frontier model that captures the Airports impact of unobserved managerial ability during the period 2003–2014 based on the methodology presented in Nigeria Alvarez et al. (2004) – the AAG model. Managerial ability in Nigerian airports is an important issue for sus- Stochastic frontier models taining efficiency levels because they are labor intensive rather than capital-intensive facilities. The AAG model Unobserved managerial ability was modified in this research to measure two exogenous contextual variables, namely regulation and hub. Under this modified version of the AAG model, inputs and outputs are disentangled in the frontier estimate while simultaneously allowing these contextual variables to control the impacts of managerial ability on efficiency. Results not only suggest that variations in efficiency scores are more sensitive to labor than to capital costs, but also indicate a negative impact of regulation and hub operations on efficiency levels possibly due to the small operational scale of Nigerian airports. Policy implications are derived.

1. Introduction There are several motivations to better understand the role of managerial ability in Nigerian airports. First, although airport effi- The study of efficiency in the airport industry can produce relevant ciency has been extensively researched in different countries, research insights in terms of competitiveness, unveiling inherent capabilities for on Nigerian airports is still restricted to a small number of studies performance improvement (Biesebroeck, 2007; Diana, 2010; Bezerra (Daramola, 2014; Ismaila et al., 2014; Wanke et al., 2016), which jus- and Gomes, 2016). Previous studies on airport efficiency have either tifies this present research. Second, the focus on African airports offers adopted the DEA (Data Envelopment Analysis) model and its variants fertile ground for understanding the role of managerial ability in airport (e.g. Sarkis, 2000; Sarkis and Talluri, 2004; Marques and Simões, 2010; efficiency since they fall short in physical resources seeing that they are Wanke, 2013; Tavassoli et al., 2014; Wanke and Barros, 2016) or the more labor intensive than capital intensive (Barros, 2011; Wanke et al., SFA (Stochastic Frontier Analysis) model (e.g. Barros, 2009; Pels et al., 2016). Third, benchmarking is a way of segmenting productive units in 2001, 2003). When looking at the details, while the slack analysis of light of common patterns and, therefore, constitutes a relevant source DEA provides insight on resources to improve efficiency discrimination for performance improvement (Hooper and Hensher, 1997; Diana, (Olesen et al., 2015), the SFA method focuses on the economic justifi- 2010). Fourth, given the relative importance of labor to the detriment cation of a given production function. Besides, SFA has some ad- of capital, managerial practices may heavily vary depending upon the vantages as well as disadvantages over DEA because of its parametric airport, being strongly influenced by contextual variables. Finally, this characteristics where some distributional assumptions are made re- eventual dispersion in efficiency scores, derived from distinct man- garding the error and the inefficiency terms (Sun et al., 2015). More agerial practices and their cross effects upon contextual variables, may precisely, DEA falls short with respect to the necessary statistical also produce what is called “unobserved heterogeneity”, which has properties for a robust examination of the roots of inefficiency when been the focus of different researches such as that of Chesher (1984); using contextual variables (Bogetoft and Otto, 2010). This paper lends a Chesher and Silva (2002). Heterogeneity is an important source of contribution to the literature by analyzing a sample of Nigerian airports model misspecification that leads to inconsistent parameter estimation. using a modified version of the Alvarez et al. (2004) model – AAG The remainder of this study is organized as follows: Section 2 pre- model, from here on − for unobserved managerial ability capable of sents the contextual setting of Nigerian airports and the previous scant handling the impact of exogenous contextual variables. academic papers focused on them followed by a more general literature

⁎ Corresponding author. E-mail addresses: [email protected] (C.P. Barros), [email protected] (P. Wanke), [email protected] (O.R. Nwaogbe), [email protected] (Md. A.K. Azad). http://dx.doi.org/10.1016/j.cstp.2017.10.003 Received 20 October 2016; Received in revised form 19 July 2017; Accepted 3 October 2017 Available online 15 October 2017 2213-624X/ © 2017 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved. C.P. Barros et al. Case Studies on Transport Policy 5 (2017) 573–579

Table 1 Major Features of Nigerian Airports. Source: FAAN (2014).

Airport Number Airport Abbreviation Airport Name (The name after the comma refers to the city in which the airport is located. No. of Passengers No. of Employees When omitted, the airport and the city share the same name) (‘000)

1 ABJ DOM Nnamdi Azikiwe Domestic Airport, Abuja 4865 712 2 ABJ INT'L Nnamdi Azikiwe International Airport, Abuja 349,244 823 3 AKURE Akure Airport, Akure 281,556 64 4 BENIN Benin Airport, Benin 708 84 5 CAL DOM Margaret Ekpo Airport 38,4921 135 6 CAL INT'L Margaret Ekpo International Airport 25,039 103 7 ENUGU Akanu Ibiam International Airport, Enugu 41,643 132 8 IBADAN Ibadan Airport 1513 77 9 ILO DOM Ilorin Domestic Airport, Ilorin 71,991 64 10 ILO INT'L Ilorin International Airport, Ilorin 185,293 98 11 JOS Yakubu Gowon Airport, Jos 146,842 107 12 KAD DOM Kaduna Airport 234,796 95 13 KAD INT'L Kaduna International Airport 146,842 135 14 KAN DOM Mallam Aminu Kano Airport 1995 411 15 KAN INT'L Mallam Aminu Kano International Airport 103,631 469 16 MKD Makurdi Airport 1,5631 38 17 MAID DOM Maiduguri Airport 3,864,858 148 18 MAID INT'L Maiduguri International Airport 3,361,107 115 19 MMA DOM Murtala Muhammed Airport, Lagos 1,198,668 1103 20 MMA INT'L Murtala Muhammed International Airport 13,148 1224 21 PHC DOM Port Harcourt Airport 62,429 317 22 PHC INT'L Port Harcourt International Airport 40,980 264 23 SOK DOM Saddik Abubakar III Airport, Sokoto 99,342 48 24 SOK INT'L Saddik Abubakar III International Airport, Sokoto 10,600 69 25 YOLA DOM Yola Airport 11,731 110 26 YOLA INT'L Yola International Airport 9522 112 27 MINNA Minna Airport 476,063 89 28 KAT Katsina Airport 34,333 105 29 OWERRI Sam Mbakwe Airport 3,529,162 116 30 OSUBI Warri Airport 1,258,601 18 Mean 532,235 246 Median 85,666.5 111 Std. Dev. 1,082,082 313.8 survey on airline efficiency in Section 3. Section 4 depicts the resource- The author used Chi-square and Fisher's test to discriminate between based view framework, which encompasses both physical resources and sample units. Ismaila et al. (2014) analyzed Nigeria’s liberalization of managerial ability and shows how they interact to create a competitive its Air Service Agreements. Efficiency was analyzed by Wanke et al. advantage. Section 5 discusses the modified AAG model considering the (2016) with a fuzzy DEA model for 2003–2013. Therefore, this paper is contextual variables used in this research and Section 6 presents the innovative in this context since it focuses on heterogeneity and man- hypothesis tested here. Section 7 analyzes and elaborates on the results, agement issues employing a modified version of the AAG model. while Section 8 gives the conclusion. 3. Literature survey 2. Contextual setting Similar to what has been verified in other industries, efficiency in Airplanes are the main mode of transportation in Nigeria. There are airports is typically analyzed under parametric and non-parametric 30 airports in the country, ten of which operate international flights, methods. Over the course of time, different papers have presented a while the rest only focus on domestic aviation (cf. Table 1). The most comprehensive literature review updating the list of airport efficiency important international airports that also serve as hub operators for studies using both methodologies (Bazargan and Vasigh, 2003; Diana, other airports in Nigeria are the Murtala Muhammed airport in Lagos, 2010; Bezerra and Gomes, 2016). In regards to the non-parametric DEA the Aminu Kano airport in Kano city, the Kaduna airport in Port Har- model, research on developed economies tends to prevail over under- court, and the Abuja airport in Abuja city. Until 1991, Lagos was the developed ones with a focus on airport rankings, productive changes capital of the country. At that time, the seat of government moved to over the course of time, and slacks assessment (e.g. Gillen and Lall, Abuja. On the other hand, Kano and Port Harcourt cities have a high 2001; Sarkis 2000; Sarkis and Talluri, 2004; Yoshida and Fujimoto, volume of commercial activities, mostly related to the oil industry. A 2004; Fung et al., 2008; Barros and Dieke, 2007; Barros and Weber, map with the location of Nigerian airports is presented in Fig. 1. 2009; Tsui et al., 2014a,b). Not only do airports in Nigeria tend to be old and poorly maintained On the other hand, studies using the parametric SFA include those (Wanke et al., 2016), but also capital intensity tends to be low and the of Pels et al. (2001) who applied a traditional SFA model, and Barros workforce at the airports tends to be numerous in order to face an in- (2008a,b,c) who applied variations of the SFA model to account for creasing demand for air transportation. A regulatory mark for Nigerian randomness and latent frontiers. Differently from the non-parametric airports was established in 2006 by means of the Civil Aviation Act approaches, stochastic frontier models allow different types of in- (CAC). The airports gradually started to be regulated and managed by ferences to be drawn on the residuals of the regression (Kumbhakar the Federal Airports Authority of Nigeria (FAAN) on behalf of the et al., 2013). For example, one possible approach for inference on Federal Government of Nigeria, the owner of these airports. frontier residuals encompasses the Bayesian approach (Assaf, 2010a, Nigeria’s air transport industry was analyzed by Daramola (2014) 2010b). Putting it in a historical perspective to be more precise, SFA with a focus on accidents and fatality rates for the period 1985–2008. models started with homogenous assumptions for different observations

574 C.P. Barros et al. Case Studies on Transport Policy 5 (2017) 573–579

Fig. 1. Location of Nigerian airports (some facilities are omitted in the map).

(Pels et al., 2003; Kumbhakar et al., 2013). Over the last fifteen years, theory (Barney, 1991; Rumelt 1991). This theory asserts that resources however, novel stochastic models that take heterogeneity into account are composed of physical assets and capabilities. Physical assets are (i.e. a distinguishable stochastic behavior of the residuals of each ob- tradable in the market while capabilities are specific to the company servation) have been developed (Sun et al., 2015). These models, for and intrinsically related to managerial ability. Competitive advantage example, encompass the random frontier model (Barros, 2008d), the can be achieved by the airport if the current strategy is value-creating Bayesian approach of Assaf (2010a), the stochastic frontier model for (Porter, 1990), which implies, of course, in some level of managerial undesirable outputs (Kumbhakar and Tsionas, 2016), as well as the ability. Nigerian airports are usually publicly owned and operated fa- modified model for unobserved managerial frontier developed in this cilities due to their relative importance of transporting people and paper. merchandises. This relative importance is based in the usually very bad Papers on the analysis of the efficiency in African airport issues conditions of roads, which are not maintained, and in the central include those of Barros (2011) who analyzed the airports of Angola and African countries degrade quickly due to the rain and hot weather. Mozambique, Barros (2014) who analyzed the efficiency and tourism of Furthermore, with airports located very far from the capital along with airports of Mozambique, and Wanke et al. (2016) who analyzed effi- the cultural tendency of letting things go on without any restriction, ciency in Nigerian airports from 2003 to 2013. Therefore, this paper many of the airports are old and with some kind of degradation. Since builds upon previous studies not only by adopting a novel stochastic Nigeria does not have a big enough budget to improve airport condi- frontier model in the airport industry, but also by analyzing Nigerian tions, which would turn them into capital-intensive facilities, labor airports from 2003 to 2014. intensity prevails. In this context, airport managers and employees adopt the practice of rent seeking, and they leave things uncared for. 4. Theoretical background When a problem appears, they tend to solve it proactively by their own means. This kind of tradition is widespread in Nigeria and very few The theoretical background of this paper is the resource-based view airports resist it. The result is the degradation of competitive advantage

575 C.P. Barros et al. Case Studies on Transport Policy 5 (2017) 573–579

positions that could be built upon resources. The maximal output for the given xit is obtained when the man- agerial input mi* is maximal. The modified AAG model can be expressed 5. Methodological framework: modified AAG model as: 22 lnyαβxit *=+x lnit + 0.5 βxxx (lnit )+ βmβmm i * + 0.5mm (i *) The AAG model, which is available in LIMDEP software, is char- acterized by the identification of management practices of the units +++βmxm iititiit*ln x fxm ( , *) v (4) analyzed. Managerial ability is an important underlying issue em- Similarly to what was done in the original AAG model, a link be- ffi bedded in decision-making with clear impacts on costs and e ciency tween technical efficiency and management can also be established: levels. Considering the nature of Nigerian airports where operations are labor intensive rather than capital intensive, the appropriate measure- lnTEit =− ln yit ln y it * =+ ( βmxm β ln xit )( m i − m i *) ment of the impact of this concept is of utmost importance. In spite of 22 +−+−≤0.5βmmm [(ii ) ( m *) ] fxmfxm ( itiiti , ) ( , *) 0 (5) its relevance, managerial ability has historically not been taken into fi ffi account in productive analysis, thus creating what is called “managerial A rm will be technically e cient when located at the cost frontier, bias” (Griliches, 1957). Over the last decades, however, different stu- thus implying that ln TEit =0. dies have developed efforts to control this kind of bias. These ap- Therefore, Eq. (5) is now expressed as: proaches involved the adoption of proxies for managerial ability lnlTEit=+ θ i θ xin( x it + f x it, m i)( − f x it, m i *) (6) (Dawson and Hubbard, 1987), covariance analysis to specifically con- 22 trol invariant management impact over the course of time (Mundlak, θβmmi =−+m (*)0.5[()(*)]ii βmm ii − m 1961), and more recently two-stage DEA applications where efficiency θβmm=−(*) scores are computed in the first stage and subsequently regressed to xi xm ii other contextual variables in a second stage using appropriate multi- This last equation shows that technical efficiency can be broken variate methods (Avkiran and Rowlands, 2008). Regarding this re- down into three different components: an individual time-invariant search, Alvarez et al. (2004) handled this problem by means of a one- effect θi and two time-varying components θxi and f(xit, mi) − f(xit, mi *) step stochastic procedure where managerial ability is supposed to be an since they depend on the variable input xit. Under this model specifi- unobservable input that can be estimated from the original production cation, variations in the managerial input that are necessary to increase function, considering, of course, certain assumptions pertaining to the TE will depend on the nature of the remainder inputs and on the con- functional relationship between the unobserved inputs and the ordinary textual variable vector. Their derivatives are given by: ones. The model used in this research departs from Alvarez et al. (2004) fi ∂ ln TEit dfm (, xit m i ) [AAG model from now on] with modi cations developed here to ac- =+ββln xβmit +i + m mxm mm dm count for the impact of contextual variables. ∂ i i (7) The original AAG model captures managerial ability by means of a ∂ ln TE df(, xit m i )df (, xit m i *) it =−+βm(*) m x −x fixed input mi and one time-varying input xit. It consists of a Translog xm ii ∂ ln xit dmi dmi (8) production function: Readers should note that Eqs. (3) and (7) are equal, thus implying 22 ln yαβxit =+x lnit + 0.5 βxx (ln xit ) + βmβmβm i +0.5m (i ) +xm ln xmit i that increases in managerial ability also lead to an increase in technical efficiency levels, considering of course that the production function is + vit (1) monotonic. Under the modified AAG model, TE is not a fixed effect over fi where subscripts i and t denote rms and time, respectively, and yit is the course of time. the single output. It is assumed that vit is a symmetric random dis- In order to generate the long run cost functions, the production turbance with a mean of zero. The model in (1) corresponds to the (φ(yit, k)⥃) and airport size (ψ(k)) should be a function of k: typical ‘average’ production function regardless of the impact of con- Cφykψk=+(,) ()⥃ textual variables. it it (9) In order to introduce the impact of contextual variables into the Eq. (9) describes a family of total cost curves generated by assigning model, one should first observe that these kinds of variables generate different values to the k parameter. The long-run cost curve is the en- transaction costs, which may reduce the overall productivity. velope of the short-run curves, which touches each one and intersects Alternatively, contextual variables tend to impact the strategy of firms none. By writing Eq. (9) in implicit form, by setting its partial derivative and their search for new business opportunities, which may have a with respect to k equal to zero, by eliminating k from it, and by solving positive impact on the overall productivity levels. Hence, the impact of for C as a function of y, the equation yields: contextual variables in this research is captured by means of f, a non- CΦyit = ( it ) (10) linear function with variable inputs xit, fixed managerial ability mi and argument f(xit, mi), which is plugged into Eq. (1). Given its non-linear The underlying premise here is that the production cost function nature, readers should note that the partial derivatives of f may be ei- monotonically increases with the output level. This being the case, it is ther positive or negative and this will depend on the values of the re- easy to note that the cost function and the production function share the mainder parameters of the equation. This feature is adequate to model same properties discussed above. the a priori unknown impact of contextual variables on efficiency levels. Introducing contextual variables in (1) yields: 6. Hypotheses testing in nigerian airports

22 ln yαβxit =+x lnit + 0.5 βxx (ln xit ) + βmβmm i +0.5mm (i ) + βmxxm iit ln Considering that to compete and gain more aircraft movements,

++fx(,it m i ) v it (2) passengers, and cargoes, Nigerian airports not only use physical re- sources for such an endeavor, but they also rely on the managerial It is worth mentioning that contextual variables also impact the ability of their employees. Also consider that these actions are em- ff ff marginal e ects of managerial ability, making it di erent from the ones bedded in a set of contextual variables that are considered to be exo- obtained in the original AAG model: genous by nature, which means that they are under the discretion of the df(, x m ) decision maker and impact the efficiency levels while not being im- ∂ ln yit mi it i =+ββmβxmmmi +xm ln it + pacted by changes in these scores in a reverse causation. ∂mi dmi (3) In this context, there are several different hypotheses pertaining to

576 C.P. Barros et al. Case Studies on Transport Policy 5 (2017) 573–579 such a production process that can be tested. They not only relate to Table 3 homogeneity/heterogeneity regarding the nature of production factors Stochastic panel cost frontier (Dependent Variable: Log Cost). and underlying latent variables such as managerial ability, but they also Variables Traditional SFA model Modified AAG model encompass aspects involving managerial ability per se and the expected Non-random parameters Coefficient (t-ratio) Coefficient (t-ratio) impact of contextual variables on efficiency levels. They are presented next. Constant −0.508 (−0.150) – Trend 0.114 (0.570) 0.059 (0.647) Hypothesis 1. Efficiency in Nigerian airports is determined by Trend^2 −0.022 (−1.952) −0.050 (2.453) heterogeneous factors. Log PL 0.441(3.660) 0.226 (3.655) Log PK 0.231 (5.940) 0.223 (4.317) Heterogeneous factors, such as managerial ability, are those that Log Aircraft −0.110 (−2.451) – differ among airports. Neglecting heterogeneity would likely lead to Log Passengers 0.448 (5.760) – 1/2logPL2 0.782 (2.760) 0.523 (2.783) inconsistent parameter estimates (Chesher, 1984; Chesher and Silva, 1/2LogPK2 0.273 (1.673) 0.210 (2.534) ff 2002) that can be tested by observing di erent modeling assumptions. 1/2Log Aircraft2 0.709 (3.218) 0.426 (2.893) 1/2Log Passengers2 0.624 (3.167) 0.545 (3.452) Hypothesis 2. Managerial effects vary along the Nigerian airports in a Log PL*log PK 0.297 (1.872) 0.214 (2.736) random fashion. Log PL*log Aircraft 0.682 (2.317) 0.418 (2.752) Log PL*log Passengers 0.342 (3.662) 0.248 (2.854) This being the case, managerial ability constitutes the grounds for Log PK*Log Aircraft 0.782 (1.983) 0.514 (2.673) airport competitiveness; therefore, Nigerian airports present different Log PK*log Passengers 0.226 (1.025) 0.143 (1.563) levels of efficiency (Teece et al., 1997). Log Aircraft*Log Passengers 0.283 (0.513) 0.148 (1.016) Hub 0.127 (2.584) 0.114 (3.124) Hypothesis 3. Airports regulated and monitored by FAAN present Regulation 0.682 (2.853) 0.517 (3.289) higher efficiency levels than those airfields outside the scope of FAAN Means for random parameters supervision. Constant – −0.211 (−1.152) Log Aircraft – −0.006 (−3.982) Airport regulation sets grounds for standardization of procedures, Log Passengers – 0.212 (3.999) ffi fi thus avoiding the design of services with low quality and higher cost Coe cients on unobservable xed management Constant – 0.127 (1.014) (Crew and Kleindorfer, 1996; Barros et al., 2010; Marques and Barros, Log Aircraft – 0.559 (5.312) 2010). Log Passeng – 0.225 (4.739) – − ffi Alpha management 0.001 (4.218) Hypothesis 4. Hub airports present higher e ciency levels than non- Statistics of the model 2 2 ½ hub airports. σ =(σV + σU ) 0.672 (7.321) 0.820 (10.217)

λ = σU ⁄ σV 0.131 (5.946) 0.136 (3.219) Hub airports are often the major airports in a given country and are Log likelihood −415.93 −425.16 being responsible for distributing or redistributing international flow by Observations 330 330 domestic airports. Based on their intense flow and the underlying economies of scale, hub airports are more efficient than airports re- t-statistics in parentheses (* indicates that the parameter is significant at the 1% level). stricted to domestic or regional flights (Bazargan and Vasigh, 2003). present the expected signs. In order to differentiate between the traditional SFA model and the 7. Analysis and discussion of results modified AAG model, one may also consider the values of sigma square and lambda. Although lambda values for both models are comparable A balanced panel of Nigerian airports for 2003–2014 collected from in magnitude, sigma values are higher under the AAG model, meaning FAAN is used in this research. Table 2 presents the transformations that the traditional SFA model is unable to interpret unobservable performed on the original variables. Prices were not normalized, but managerial ability from random noise. Furthermore, the modified AAG they were rather tested whether they supported it. Furthermore, each model also exhibits good adherence to the data since both R2 and F variable was divided by its geometric mean. In this case, the Translog statistics (obtained from the initial Ordinary Least Square regressions specification can be considered an approximation to an unknown employed to determine the original values for the MLE) are higher than function and its first-order coefficients can be interpreted as elasticities those verified under the traditional SFA model. evaluated at the sample geometric mean. It is worth noting that both price elasticities for capital (coefficients Table 3 shows the computations for the traditional SFA model for PK) and labor (coefficients for PL) are positive and their sums are (Battese and Coelli, 1988) and for the modified AAG model previously less than one, leading to the acceptance of the hypothesis of price discussed in Section 5 so that the impact of contextual variables could homogeneity in both models. In fact, under the SFA model, the coeffi- be taken into account. Results of the log-likelihood test suggest that the cients for PK and PL add up to 0.672, while under the AAG model, modifi ed AAG model is comparable, in terms of explanatory power, to 0.449. Results show that labor elasticity is higher than capital elasticity, the traditional SFA model. Besides, in both models the coefficients

Table 2 Data Transformation.

Variable Description Min Max Mean SD

LogCost Log Operational cost in US dollars at constant price 2005 = 100 14.294 23.792 18.250 1.926 Trend Trend variable from 1 = 2003 to 12 = 2014 1 12 6.5 2.73 Trend2 Squared value of the trend variable 1 144 72.5 21.16 LogPL Log of price of workers, measured by the ratio between total wages and the number of workers 6.983 16.71 11.66 0.804 LogPK Log of price of capital premises, measured by the ratio between amortizations and total assets −0.871 −0.000 −0.098 0.120 LogAircraft Log of the aircraft movements at each airport 7.297 3.213 −1.509 13.13 LogPassengers Log of the number of passengers at each airport i 5.666 15.167 10.894 2.068 Hub Dummy equal to one for hub airports 0 1 0.333 0.472 Regulation Dummy equal to one for the years the airports are under regulation 0 1 0.500 0.501

577 C.P. Barros et al. Case Studies on Transport Policy 5 (2017) 573–579

Table 4 Unexplored factors, such as those related to ethnic and cultural tradi- Efficiency scores. tions, could be the object of further investigation. As a matter of fact, interaction terms, although do not present a Airport Traditional SFA model Modified AAG model straightforward interpretation in Translog specifications, help in ex- ABJ DOM 1.000 1.000 plaining the synergistic effect that may exist between two major effects. ABJ INTL 0.902 0.342 Results suggest that the major synergistic effects in Nigerian airports AKURE 0.413 0.999 occur between aircraft movements and capital and aircraft movements BENIN 0.884 0.996 CAL DOM 0.883 0.775 and labor. This suggest that aircraft movements (a proxy for operational CAL INT'L 0.987 0.113 scale) is the most relevant productive factor to ascertain efficiency le- ENUGU 0.999 0.998 vels in Nigerian airports, as long as it presents important synergistic IBADAN 0.954 0.756 effects with labor and capital, which corroborates the trade-off between ILO DOM 0.999 0.997 managerial ability and operational scale. There are evidences for sig- ILO INT'L 0.918 0.124 fi ff JOS 0.998 0.996 ni cant di erences on how human and physical resources are deployed KAD DOM 0.873 0.605 (managerial ability) to cope with different operational scales in KAD INT'L 0.962 0.416 Nigerian airports. KAN DOM 0.999 0.998 Table 4 presents an efficiency ranking comparison for both models, KAN INT'L 0.531 0.140 fi fi MKD 0.825 0.129 con rming that the discriminatory power of the modi ed AAG model is MAID DOM 0.946 0.158 higher since efficiency scores are lower, on average, in comparison to MAID INT'L 0.920 0.231 those obtained from the traditional SFA model. These scores are also MMA DOM 0.799 0.997 controlled for the impact of managerial ability and contextual variables MMA INT'L 0.656 0.995 simultaneously. Broadly speaking, it is interesting to note that effi- PHC DOM 0.956 0.886 PHC INT'L 0.626 0.173 ciency under traditional SFA models tend to be extremely over- SOK DOM 0.895 0.366 estimated in most international airports (e.g. Yola, Abuja, Margaret, SOK INT'L 0.856 0.247 Ilorin, Kaduna, Makurdi), while somewhat underestimated in some YOLA DOM 0.995 0.349 domestic ones (e.g. Akure, Murtala). These results suggest that the YOLA INT'L 0.917 0.222 fi ffi MINNA 0.879 0.167 modi ed AAG model is capable of reducing the e ciency estimation KAT 0.915 0.284 bias by simply considering the impact of unobserved managerial ability. OWERRI 0.999 0.998 As it stands, this is one additional evidence for the trade-off between OSUBI 0.998 0.997 managerial ability and operational scale, which seems to be higher in Mean 0.882 0.581 airports that present both domestic and international operations. Put- Median 0.917 0.510 Std. Dev 0.145 0.373 ting into other words, managerial ability in Nigerian airports appears to incipient to face the challenges imposed by additional traffic volumes imposed by international operations. confirming the nature of labor-intensive operations in Nigerian airports. On the other hand, however, passengers and planes are statistically significant heterogeneous inputs, which should be considered when 8. Conclusions designing specific or segmented policies for the Nigerian airports. The parameter Alpha-management for unobserved managerial This paper has analyzed managerial ability in Nigerian airports. The ability is negative and statistically significant, meaning that managerial analysis was conducted via the development of a modified version of style has a negative impact on cost level and in turn a positive impact the AAG model, allowing for the simultaneous measurement of the on efficiency level. Additionally, regulation is significant and has a impacts of unobserved managerial ability and contextual variables on positive sign, which means that it contributes to increased costs to some efficiency levels. The outcomes were then compared to those obtained extent, possibly due to bureaucratic procedures imposed by the reg- from traditional SFA models. ulatory apparatus. Besides, the contextual variable hub is also positive The overall implication of this research is that single policies can be and significant in the AAG modified model, implying that international established for Nigerian airports based on the homogeneous variables, flights have a positive impact on airports’ operational costs, in ac- mainly the prices of labor and capital, whereas clustered policies may cordance to previous researches (Barros, 2009). be designed to handle diversity imposed by heterogeneous variables. In In summary, putting it in terms of the hypothesis tested, Hypothesis regards to the homogeneous variables, FAAN authorities could design 1 is accepted based on the heterogeneous factors estimated (planes and common policies for human resources salary and training, as well as passengers). Additionally, Hypothesis 2 is also accepted based on alpha- common standards for acquisition and utilization of physical resources management statistics, thus contributing to the increase of efficiency in the operational areas of the airports. On the other hand, regarding levels. However, Hypothesis 3 and 4 are rejected on the grounds that the heterogeneous variables such as planes and passengers, FAAN au- regulation and hub contextual variables contribute to the increase of thorities should shed some light on smaller airports and airfields where operational costs, thus implying lower efficiency levels at this current there appears to be a trade-off between operational scale and man- level of production factor elasticities. Rejection of Hypothesis 3 and 4 agerial ability. This situation possibly suggests a segmentation in terms may constitute an additional evidence of the small operational scale of of the life cycle of the airports: at the beginning stages, managerial Nigerian airports, possibly suggesting that the beneficial impacts of expertise prevails over small-scaled operations while at the saturation regulation and hub operations on airport efficiency may be acquired stage of the capacity of the airport, the operational scale rules to the only up to a certain minimal scale level for passengers, planes, and detriment of managerial ability. In effect, public policies regarding cargo. Nigerian airports should consider such heterogeneity and should These findings suggest that managerial ability is a key issue in as- eventually standardize managerial practices in the long-run. For ex- suring efficiency in Nigerian airports and that there is possibly an un- ample, FAAN regulators could design policies by airport clusters based derlying trade-off between managerial ability and operational scale. on the scale size while focusing on improving aircraft and passenger Considering that production factors are heterogeneous in Nigerian movements. airports, it is possible that the importance of managerial ability on ef- ficiency levels is more relevant in smaller airports than in larger ones.

578 C.P. Barros et al. Case Studies on Transport Policy 5 (2017) 573–579

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