Methodology and Data

3.3.1 Input-oriented, Variable Returns to Scale, Russell Measure of Data Envelopment Analysis (DEA)

Additive models aim at maximizing the total input or output slacks, or both, according to the selected orientation (input, output or non-oriented) to calculate the technical efficiency. A basic input-oriented additive model is specified as in Equation (3.1).

(3.1)

The major problem with the basic additive models is that scale differences are not taken into consideration as depicted in the objective function S in the equation. In the input-oriented additive models, for instance, solely non-weighted sum of input slacks are maximized irrespective of the magnitude of differences in input variables across the decision making units (DMUs)²⁰. For this reason, it is not straightforward how to interpret the DEA results when comparing the efficiency levels of various DMUs. In order to overcome this problem, a scale-invariant additive measure, called as Russell measure, was introduced by Färe and Lovell (1978). In input (output) oriented Russell models, the slacks of inputs are weighted by the corresponding number of inputs (outputs) as well as the values of observation in the objective function, hence delivering the maximum of averaged sum of possible improvements.

20 Each DMU refers to a single airport in a single year in this research.

s

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A Russell measure of Data Envelopment Analysis (DEA) is used in this chapter in order to measure the relative technical efficiency levels of 41 Spanish and 32 Turkish airports. Due to the differences in scale of the airports in the sample, variable returns to scale specification is implemented. Furthermore an input oriented model is chosen, where the airports are required to minimize their inputs by keeping the output levels constant. Last but not least, the variables which cannot be controlled by the managers in the short-run are considered as non-discretionary.

Based on Färe and Lovell (1978), Ray (2004) and Cooper et al. (2007), “the input-oriented variable returns to scale Russell measure” utilized in this chapter can be described as follows:

In Equation (3.2), 𝑥 represents the inputs, 𝑦 stands for the outputs, 𝑚 is the number of discretionary inputs, 𝑙 is the number of non-discretionary inputs, 𝑠 is the number of discretionary outputs, 𝑞 is the number of non-discretionary outputs, 𝜃 is the weighted input slacks and is the intensity variable. The results were obtained by the EMS Software.

λj

30 3.3.2 Scale Efficiency

Previous literature on airport benchmarking has given a great attention on the scale of airport operations and generally assumed that the airports operate under variable returns to scale (VRS) rather than under constant returns to scale (CRS), due to the fact that the airports are not flexible in the short-run considering the choice of input levels. Thus, very small or very large airports are treated in an unbiased way when calculating the DEA efficiency scores. Two questions emerge with respect to the scale.

First one deals with the level of inefficiency, which results from not operating on the optimal size. Unless the efficiency scores from CRS-DEA and VRS-DEA are equal to each other, inefficiencies due to scale will exist and the level of scale efficiency for input-oriented models can be calculated by the ratio of distances attained from CRS-DEA and VRS-DEA, respectively. Due to the fact that the distances are the technical efficiency scores from CRS-DEA and VRS-DEA models, scale efficiency can be easily attained by the ratio of technical efficiency scores of two specifications. (Coelli, 2005; Färe et al., 1998)

𝑆𝐸 =

𝑣𝑐𝑐 (3.3)

Second question, on the other hand, investigates whether the airports operate under decreasing, constant or increasing returns to scale (DRS, CRS and IRS, respectively). Literature on production of airport services shows that a vast majority of airports operate under IRS, mainly due to the large, indivisible fixed investments, which cannot be matched with an adequate traffic demand. For instance, Martin and Voltes-Dorta (2011) argues that even for large hubs, there is a potential advantage of expanding the size of operations. A Cobb-Douglas type long-run cost function applied to 41 airports from Australia, Asia, North America and Europe delivers these conclusions. Furthermore, Assaf (2010) estimates a Cobb-Douglas specification of cost function and the analysis delivers results that support increasing returns to scale production.

31 3.3.3 Data

Initially, financial data from AENA and DHMI were collected for the years between 2009 and 2011. Detailed analyses of the financial data together with traffic figures and additional information have led to restricting the dataset. For example, 2 heliports Algeciras and Ceuta as well as the airports Madrid-Cuatro Vientos, Huesca-Pirineos, Sabadell and Son-Bonet in Spain have been removed from the sample due to their very low and volatile traffic and inconsistent financial situation.

Regarding the Turkish airports some airports have not been included in the sample, because Agri, Balikesir, Siirt, Tokat and Balikesir-Körfez airports lack traffic in some years; Batman, Gökceada and Kocaeli airports were opened within the sample period and some variables needed for the second stage regression were not available for Canakkale and Sinop airports.

Furthermore the two main hub airports in both countries, Madrid-Barajas and Istanbul-Atatürk have been excluded from the sample because of two reasons. First reason is their relative larger size in comparison to other airports and the second is their hub status with very high concentration of flag carriers Iberia and Turkish Airlines. It seems more reasonable to compare the efficiency levels of these airports with other international hub airports, because their characteristics are more similar and they compete for a high amount of transfer traffic.

Consequently, the analyses in this chapter are based on 41 Spanish and 32 Turkish airports covering a three-year period from 2009 to 2011. For the Spanish airports, balanced data is available for the entire time period, whereas data for some years are missing for eight airports in Turkey. The reason behind the exclusion of these Turkish airports for some years is the closure of the airports for several months within the time period of study due to runway extensions and maintenance. By excluding those from the dataset, any distortion due to sudden changes in traffic levels can be avoided.

Staff costs (StaffC), other operating costs (OtherC) and total runway area (RWY) are selected as the inputs. Depreciation is not included in the other operating costs,

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because the capital base of the airports is measured by using the physical indicator RWY, due to possible differences in the accounting methods between the two countries²¹. Furthermore, taxes or financial expenditures are removed from the costs.

Runway area is calculated as the length of a runway multiplied by the width over all available runways at an airport. In the sample, Barcelona and Antalya airports have 3 runways, 7 airports from Spain and 8 airports from Turkey have 2 runways and the rest of the airports operate with a single runway.

Outputs include the three traffic statistics number of passengers (PAX), air traffic movements (ATM) and the level of cargo (Cargo) as well as the total operating revenues (TotRev). Total operating revenues are calculated as the sum of aeronautical and non-aeronautical revenues. The high correlation between the aeronautical revenues and the three traffic outputs PAX, ATM and Cargo can be considered to be problematic and in the optimal case use of non-aeronautical revenues alone might be preferable. However a detailed disaggregation of data on revenues is not obtainable from both countries, which would allow for ensuring whole comparability of two revenue types with their corresponding sub-accounts. In order to avoid this possible distortion due to incomparability, “total operating revenues” is preferred to “non-aeronautical revenues” as one of the outputs used in the DEA.

AENA reportedly clarified that the costs from the head-quarter are effectively allocated to the available data for each airport under the management of AENA according to a sophisticated methodology, which accounts for various cost centers within the organizational structure as well as the use of resources. On the other hand, DHMI reports the head-office costs separately without distributing them to the airports. For this reason, these costs are distributed by weighting according to the total costs of the individual airports, which delivers a more comparable cost data among airports from the two countries. Financial, traffic and technical data as well

21 A specification of the model, where “depreciation” is used as an input instead of “RWY”, has been applied to check the robustness of the model and it delivers very similar results. The detailed results are not presented in this dissertation, but they are available upon request.

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as the entire data on second stage variables except population density have been gathered directly from AENA and DHMI. Population density (NUTS) data used in the second stage regression have been collected from the Eurostat webpage. All the financial variables are converted to euro by using the purchasing power parity and inflation indicators obtained from the OECD database, in order to account for the differences across two countries and across various years, respectively.

It should be noted that the efficiency scores calculated are intended to be evaluated from the point of view of the two airport authorities AENA and DHMI. As there is no private involvement at Spanish airports it is not necessary to have any concerns about the results on AENA’s airports. On the other hand, the situation regarding 5 Turkish airports²² in the sample is rather different, because these airports are jointly operated by DHMI and private firms. Private firms pay fees to DHMI for the operational rights and there are different agreements at each airport concerning how the revenues are shared between the two parties. Furthermore, the accounts of the private firms in Turkey, which jointly operate the airports, are not publicly available.

Hence, the revenues of DHMI from these airports include the fees paid by the private firms for the operating rights of terminals either as a part of either BOT or concession agreement. Besides, the costs accrued to DHMI at these airports are lower than airports with similar size, mainly because DHMI employs much less employees at these airports. As a result, the outcomes of the analysis can be seen as the ability of the airport authority to generate profits while maintaining the airport services, either operating them by itself or delivering these rights or responsibilities to the private firms.