Joint impact of competition, ownership form and economic regulation on airport performance and pricing

Transportation Research Part A 64 (2014) 92–109

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Transportation Research Part A journal homepage: www.elsevier.com/locate/tra

Joint impact of competition, ownership form and economic regulation on airport performance and pricing Nicole Adler a,b,⇑, Vanessa Liebert c a

Hebrew University of Jerusalem, Israel University of British Columbia, Canada c School of Humanities and Social Sciences, Jacobs University Bremen, Germany b

a r t i c l e

i n f o

Article history: Received 3 April 2012 Received in revised form 10 January 2014 Accepted 18 March 2014 Available online 19 April 2014 Keywords: Airport efficiency Data envelopment analysis Ownership form Regulation Competition Pricing

a b s t r a c t The combined impact of ownership form, economic regulation and competition on airport performance is analyzed using data envelopment analysis to measure cost efficiency in the first stage and regression analysis to measure the impact of the environment in the second stage. The empirical results of an analysis of European and Australian airports over a 10 year timeframe reveal that under relatively non-competitive conditions, public airports operate less cost efficiently than fully private airports. Irrespective of ownership form, regulation is necessary to emulate competitive forces thus pushing airport management towards cost efficiency and reasonable pricing policies. Under potential regional or hub competition, economic regulation inhibits airports of any ownership form from operating and pricing efficiently. Although public and fully private airports operate equally efficiently in a competitive setting, private airports still set higher aeronautical charges. Furthermore, mixed ownership forms with a majority public holding are neither cost efficient nor low price, irrespective of the level of competition. Ó 2014 Published by Elsevier Ltd.

1. Introduction Historically, airports were mostly deemed state-owned entities with the objective to provide and operate infrastructure for airlines. Frequently viewed as natural monopolies with large economies of scale, airports were subject to economic regulation in order to prevent abuse of market power. However, the nature of the airport industry has changed over the last two decades. Moving away from viewing the airport as a public utility, airports have begun to operate as modern enterprises pursuing commercial objectives. A number of privatization processes have been actively promoted by governments with the proclaimed intention of reducing government involvement and increasing airport productivity and innovation. However, given the assumed profit-maximizing behavior of private companies working in a natural monopolistic environment, the majority of privatized airports in Europe remain subject to economic regulation (Gillen, 2011). Whilst some studies have analyzed the separate impact of ownership form, regulatory regime and level of competition from nearby airports on efficiency and the level of airport charges, none have examined their joint effects. In other words, the literature has yet to discuss whether the deregulation of the airline industry and changes in airport ownership and management has affected the competitive situation and cost efficiency to the extent that the benefits of economic regulation ⇑ Corresponding author. Address: Jerusalem School of Business Administration, Hebrew University, Mount Scopus, Jerusalem 91905, Israel. Tel.: +972 2 5883449. E-mail addresses: [email protected] (N. Adler), [email protected] (V. Liebert). http://dx.doi.org/10.1016/j.tra.2014.03.008 0965-8564/Ó 2014 Published by Elsevier Ltd.

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are potentially unnecessary. For example, deregulation has led to increased competition between gateway hubs (e.g. Frankfurt and Amsterdam) and former military airports have opened to serve low cost carriers within the catchment area of existing airports (e.g. Hahn in Germany), substantially changing the downstream airline market and potentially impacting the airport market too. Furthermore, as a result of increasing commercialization, many airports have augmented their revenues from non-aeronautical sources in order to reduce aviation charges and attract additional airlines and passengers to their airport (Zhang and Zhang, 2010). The aim of this research is therefore to analyze the impact of the structural changes in the aviation markets on airport efficiency and aeronautical charges in order to further our understanding of the most appropriate ownership form and regulatory regime given the level of potential regional and hub competition. Three well-documented quantitative methods have been applied to analyze the productivity and efficiency of government and private enterprises. A non-parametric, index number approach has been used to measure total factor productivity (Caves et al., 1982), however this approach requires input and output prices and quantities which are not always available. Parametric stochastic frontier analysis (SFA) assesses efficiency utilizing regression analysis and disentangles unobservable random error from technical inefficiency (Aigner et al., 1977; Meeusen and van den Broeck, 1977) based on assumptions as to the distributional forms of the production and efficiency functions and error term. Non-parametric data envelopment analysis (DEA), based on linear programming, categorizes data into efficient and inefficient groups hence produces weaker results than those of SFA, but does not require assumptions with respect to the functional form therefore has been chosen for the purposes of this study. Airport studies of efficiency utilizing all three approaches are reviewed in Liebert and Niemeier (2010). Various environmental variables that are beyond managerial control at least in the short-term may affect the DEA efficiency estimates. Previous research argues that airport characteristics such as hub status or traffic structure, outsourcing policies, regulatory procedures and ownership structure all may contribute to airport efficiency (Gillen and Lall, 1997; Oum et al., 2006). Banker and Natarajan (2008) demonstrate that two-stage procedures in which DEA is applied in the first stage and regression analysis in the second stage provide consistent estimators and outperform parametric one- or two-stage applications. Published airport studies have applied simple ordinary least squares (Yuen and Zhang, 2009), Tobit regression (e.g. Gillen and Lall, 1997) and truncated regression (Barros, 2008) for this purpose. A recent debate in the literature discusses the most appropriate second stage regression model to be applied when investigating DEA efficiency estimates. Simar and Wilson (2007) argue that truncated regression, combined with bootstrapping as a re-sampling technique, best overcomes the unknown serial correlation complicating the two-stage analysis. Banker and Natarajan (2008) conclude that simple ordinary least squares, maximum likelihood estimation or Tobit regression dominate other alternatives. Combining the arguments of Simar and Wilson (2007) and Banker and Natarajan (2008), we apply robust cluster regression in order to account for the correlation across observations. Furthermore, in order to consider the panel structure of our sample, the random effects model is applied which allows us to consider time-invariant environmental variables. The second stage analysis of this research considers the impact of efficiency on ownership form, economic regulation and levels of local and hub competition amongst other factors. Such an analysis contributes to the search for the more desirable combinations and may indicate whether potential competition from nearby airports or gateway competition replaces the need for economic regulation. In addition, we examine the combined impact on aeronautical revenues generated from passengers, cargo handling and movements in order to further our understanding regarding the joint impacts on efficiency and pricing. The dataset in this research consists of European and Australian airports in order to include a sufficiently heterogeneous sample with respect to ownership structure, regulatory mechanism and competitive environment. The empirical results reveal that under relatively non-competitive conditions, airports should be regulated to encourage cost efficiency and dual-till price-cap regulation appears to be preferable to other forms of regulation. Confirming theoretical arguments by Armstrong and Sappington (2006), privately owned and regulated airports operating under monopolistic conditions tend to be more efficient than publicly owned and regulated airports. Furthermore, unregulated airports operating under monopolistic conditions irrespective of ownership form are likely to set higher aeronautical charges than those that are regulated. On the other hand, the existence of potential gateway or regional competition replaces the need for economic regulation, thereby supporting the argument of Vickers and Yarrow (1991) that competition rather than privatization is the key driver of efficiency. Nevertheless, the level of competition in the airport market has not proven to be sufficient to transfer the efficiency gains to consumers. Although unregulated, majority privately-owned and fully private airports tend to set lower aeronautical fees than those facing no competition, they charge higher landing based and passenger related fees than public airports that operate equally efficiently under competitive conditions. The paper is organized as follows: the theoretical and empirical literature discussing ownership form, economic regulation and competition is presented in Section 2, the methodology and model specifications are introduced in Section 3, the dataset for the two stage analysis is discussed in Section 4 and the results are presented in Section 5. Conclusions and directions for future research are suggested in Section 6.

2. Competition, regulation and ownership form Neoclassical theory states that under monopolistic conditions firms generally seek to maximize their profits by limiting output. The introduction of competition may lead to increased productive and allocative efficiency as a result of lower prices

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and higher outputs. Consequently, social welfare may increase when market conditions exist (Leibenstein, 1966). On the other hand, network utilities providing substantial infrastructure may be natural monopolies in that one large firm might produce at lower costs in which case the introduction of competition is not desired. According to Posner (1974), in order to encourage efficiency and avoid abuse of market power, the natural monopolist ought to be subject to economic regulation. In Europe1, airport charges have traditionally been regulated according to a rate of return or cost-plus principle as is true for the majority of German airports (Reinhold et al., 2010). Littlechild (1983) proposes an incentive based, price-cap regulation in order to solve the likelihood of over-investment, as described by Averch and Johnson (1962). Both cost-based and incentive based pricing mechanisms may be applied according to a single-till or dual-till approach. Under the single-till approach, both aeronautical (landing, passenger and aircraft parking charges, etc.) and non-aeronautical (food, retail and car parking services, etc.) revenues are constrained simultaneously. For example, Brussels airport is limited by single-till, rate-of-return regulation whereas in the UK, the price-cap single-till approach is applied. This is similar to the North American system in which airports are constrained to not-for-profit business models. In contrast, the dual-till approach regulates aeronautical revenues alone in order to constrain only those activities with a monopolistic server. Whereas Vienna and Hamburg airports were moved from a cost-based to an incentive based regulation via a price-cap approach, unlike the UK model they follow a dual-till mechanism (Gillen and Niemeier, 2008). Shleifer (1985) proposes yardstick competition as an alternative approach to stimulate efficiency based on a benchmarking process. Yardstick competition has since been applied to the British water and railway industries but has yet to be applied to airports. To the best of our knowledge, the Dublin Airport Authority is the only European example that attempted to implement yardstick competition. However, it was highly criticized by airport management for identifying inappropriate peer airports and the attempt was eventually abandoned (Reinhold et al., 2010). Over the years, the traditional perspective of airports being a natural monopoly has gradually changed as a result of the deregulation of the downstream aviation industry (Tretheway and Kincaid, 2010). Today, competition for airport services covers a multiplicity of markets including (1) a shared local catchment area, (2) connecting traffic through regional hubs and international gateways and (3) alternative modes of transport such as high speed rail in the medium distance markets. Although some degree of competition between airports may occur, we assume that the airport market structure can be better described as imperfect. Airport management often choose to serve different markets to their potential competitors in the nearby catchment area in order to limit the degree of competition. For example, London-Heathrow is Europe’s largest gateway hub and offers different services to the airport in London-Luton which tends to serve short haul destinations within Europe via low cost carriers. Amongst the empirical literature, only Yuen and Zhang (2009) examine the effects of regional competition utilizing a Chinese airport dataset and conclude through an OLS regression that airports operating in a locally competitive environment tend towards efficiency. However, the outcome of a Tobit regression found competition intensity to be insignificant. In the theoretical literature, the debate as to the necessity for and type of airport regulation has proven rather controversial. Oum et al. (2004) analytically assess the impact of economic regulation on the total factor productivity (TFP) of airports. They reveal that price-cap dual-till regulation provides the strongest incentives towards improving TFP due to full exploitation of concessionary profits. In order to optimize social welfare, Czerny (2006) reveals in his analytical model that for uncongested airports, single-till regulation controls profits more effectively than dual-till regulation. Yang and Zhang (2011) recently extended the research by Czerny (2006) to congested airports. They argue that dual-till regulation yields higher welfare at significantly congested airports, provided aeronautical charges cover costs. Starkie (2002) obviates the need for economic regulation arguing that demand complementarities across aeronautical and terminal activities will prevent airports from abusing market power. In particular, airports generating additional revenues from non-aeronautical activities are more likely to lower their charges in order to attract airlines and greater passenger throughput, thus maximizing their commercial revenues. If airports need to be subject to regulation, he suggests that dual-till regulation is preferable irrespective of the level of congestion (Starkie, 2001). The impact of regulation on efficiency and airport pricing has been empirically assessed in Barros and Marques (2008), Oum et al. (2004), Bel and Fageda (2010) and Van Dender (2007). Barros and Marques (2008) conclude that incentive based price-caps generally encourages cost efficiency compared to rate of return approaches. Oum et al. (2004) empirically confirm their analytical findings and conclude that dual-till regulation generally tends to improve economic efficiency. However, the application of dual-till approaches in combination with rate of return regulation leads to a complex cost allocation problem. With respect to pricing, Bel and Fageda (2010) analyze a European sample of airports and conclude that competition with nearby airports and other transport modes is likely to decrease the potential to abuse market power. Furthermore, private, unregulated airports generally charge higher prices than public, regulated airports. They also conclude that whether or not an airport is regulated is more important than the form or scope with respect to the level of aeronautical charges. Van Dender (2007) assesses the US market and finds that airports under regional competition charge lower aeronautical fees. He also argues that slot-constrained airports are likely to charge higher aeronautical fees, which are explained by the airport management’s ability to capture scarcity rents. Based on the theory with regard to the optimal form of economic regulation, we formulate the following hypothesis:

1

Gillen and Niemeier (2008) provide a comprehensive overview of the current economic regulation at European airports.

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Hypothesis 1 (Form of regulation). (a) When airports are subject to economic regulation, a dual-till incentive approach best aligns the management towards productive efficiency. (b) The optimal scope of regulation with respect to aeronautical charges depends on the level of congestion, thus unconstrained airports should be regulated according to the incentive, single-till approach and congested airports according to the incentive, dual-till regulation. A wave of airport privatizations began in the late eighties in the UK with the stated aim of reducing government involvement, minimizing costs and maximizing productivity. As a result of successful initial public offerings and increasing share prices, many European countries began to partially privatize their airports in the mid-nineties (Gillen and Niemeier, 2008). The theoretical literature discussing the impact of privatization on efficiency and social welfare has led to an interesting debate. Sappington and Stiglitz (1987) argue that the transaction costs of government intervention are lower under public ownership. The government is better informed and more capable of regulating state-owned firms. With respect to cost efficiency, Armstrong and Sappington (2006) argue that regulated public enterprises facing monopolistic conditions tend to enjoy weaker budget constraints because the government may cover any losses. This was tested by Martín and Socorro (2009) who conclude that public airports not subject to budget restrictions tend to charge prices below their marginal costs. Additionally, Boyko et al. (1996) argue that public ownership may lead to excessive employment. Hence, there appears to be a trade-off between efficiency and welfare optimization: whereas public companies reduce the problem of information asymmetry thereby lowering costs, they also face weaker budget constraints resulting in less cost efficient operations. Under competitive conditions, Caves and Christensen (1980) reveal empirically that public and private companies could operate equally efficiently. They argue that ownership per se does not cause cost inefficiency rather the lack of effective competition. On the other hand, Shapiro and Willig (1990) argue that managers of public firms pursue personal targets rather than maximizing social welfare. Consequently, we test the following hypothesis with regard to the combined effects of ownership, regulation and competition: Hypothesis 2 (Interaction of ownership, regulation and competition). (a) Under monopolistic conditions: (i) fully private airports operate more cost efficiently than their public counterparts and (ii) regulation does not encourage greater cost efficiency or lower airport charges at private airports due to information asymmetries. (b) Under imperfect competitive conditions: (i) public and fully private airports operate equally efficiently when unregulated and (ii) irrespective of ownership form, economic regulation restricting aeronautical charges is unnecessary to engender cost efficiency. The emergence of partially privatized business models further complicates the debate as to the effects of ownership on productivity. Boardman and Vining (1989) review the effects of mixed ownership structures based on theoretical arguments and empirical studies. They conclude that large, industrial, partly privatized firms perform in a less productive and profitable manner than their fully private counterparts, which may be caused by the public and private shareholders’ differing objectives. Empirical studies that attempt to assess the effects of ownership on the efficiency of airports are so far rather inconclusive. Parker (1999) estimates the technical efficiency of the BAA airports pre- and post-privatization. No evidence is found that complete privatization leads to improved technical efficiency and he concludes that the UK government’s golden share limits the impact of capital market pressures. Furthermore, he argues that BAA remained subject to economic regulation hence incentives to operate more efficiently are distorted as a result of government intervention. In contrast, Yokomi (2005) finds that the BAA airports exhibited positive changes in efficiency and technology as a result of the privatization. It should be noted that commercial growth after privatization was substantial however this activity was not considered in Parker’s analysis. The effects of different ownership forms on efficiency were also analyzed but again the results have not reached clear conclusions. Barros and Dieke (2007) analyze Italian airports and reveal that private airports operate more efficiently than their partially private counterparts. Lin and Hong (2006) find no connection between ownership form and efficiency. Oum et al. (2006, 2008) distinguish between public airports owned by public corporations and those owned by more than one public shareholder (multilevel). Oum et al. (2006) reach the conclusion that the productivity of a public corporation is not statistically different from that of a major private airport. However, airports owned by a minority private shareholding or multiple government involvement operate significantly less efficiently than other ownership forms. Oum et al. (2008) conclude that airports with major private shareholders are more efficient than public airports, particularly those with a major public ownership structure. Hypothesis 3 (Public–private partnerships). (a) Under monopolistic conditions, airports owned by a public–private partnership operate less efficiently than fully private airports, where minority private holdings are less efficient than majority private holdings.

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(b) Under competitive conditions, unregulated mixed ownership is the least efficient ownership form. Whereas empirical research on airport efficiency to date has been limited to analyses of the individual effects of ownership, regulation and competition on efficiency, the joint impacts may be of great importance. Button and Weyman-Jones (1992, p. 440) clearly state the ‘‘degree of competitiveness in a firm’s market, the extent to which it is incorporated as part of a public-sector bureaucracy, and the nature of the regulatory regime under which a firm operates are all primary sources of possible X-inefficiency’’. In addition to the cost efficiency perspective, the analysis also considers the aeronautical charging policy. Consequently, we assess the combined impact of ownership structure and economic regulation (or lack thereof) given relevant levels of potential, local and hub competition. We argue that the choice of ownership form and the regulatory procedures instituted are clearly within the bounds of public policy initiatives over the medium term, whereas the competitive environment remains more costly to change. 3. Methodology and model specification The following section presents the data envelopment analysis model which we apply in the first-stage analysis in order to account for both the desired equi-proportional reductions in all inputs and any remaining slacks. We then discuss the second-stage regression specifications in which the DEA efficiency estimates and the level of aeronautical charges are regressed against environmental factors. 3.1. Data envelopment analysis DEA is a non-parametric method of frontier estimation that measures the relative efficiency of decision-making units (DMUs) utilizing multiple inputs and outputs. DEA accounts for multiple objectives simultaneously without attaching exante weights to each indicator and compares each DMU to the efficient set of observations, with similar input and output ratios, and assumes neither a specific functional form for the production function nor the inefficiency distribution. DEA was first published in Charnes et al. (1978) under the assumption of constant returns-to-scale and was extended by Banker et al. (1984) to include variable returns-to-scale. This non-parametric approach solves a linear programming formulation per DMU and the weights assigned to each linear aggregation are the results of the corresponding linear program. The weights are chosen in order to present the specific DMU in as positive a light as possible, under the restriction that no other DMU, analyzed under the same weights, is more than 100% efficient. Consequently, a Pareto frontier is attained, marked by specific DMUs on the boundary envelope of input–output variable space. The weighted additive model (Charnes et al., 1985; Lovell and Pastor, 1995), chosen for its units and translation invariance properties, reflects all inefficiencies identified in the inputs. The input oriented model is chosen because we assume that airport managers control operational costs and to a lesser extent airport capacities, but have relatively minor control over traffic volume. By comparing n units with q outputs denoted by Y and r inputs denoted by X, the efficiency measure for airport a is expressed as in model (3.1).

Max wt s s;r

s:t: Yk  s ¼ Y a  Xk  r ¼ X a

ð3:1Þ

ek ¼ 1 k; s;

rP0

where k represents a vector of DMU weights chosen by the linear program, wt a transposed vector of the reciprocals of the sample standard deviations, e a vector of ones, r and s vectors of input and output slacks respectively and Xa and Ya the input and output column vectors for DMUa respectively. Hence DMUa, the airport under investigation, is therefore efficient if and only if all input slacks equal zero. Variable returns-to-scale is assumed (ek = 1) because the sample dataset consists of airports of substantially different sizes, ranging from 0.5 million passengers to more than 50 million per annum. It should be noted that the objective function value of Eq. (3.1) lies between zero and infinity with a DMU deemed efficient when the sum of slacks equal zero. In order to interpret the coefficients obtained from the second-stage regression as percentages, the efficiency scores were normalized to a range from zero to one according to a slack-based measure (Tone, 2001). 3.2. Second-stage regression The raw inefficiency scores estimated in the first stage may be explained by factors beyond managerial control in addition to managerial inefficiency. In order to conduct hypothesis testing, regression analyses is often applied in a second stage in which the DEA efficiency estimate is regressed against a set of potential environmental variables. As a result, the net inefficiency estimators (ui) are computed after taking the exogenous variables into account. Banker and Natarajan (2008) and Simar and Wilson (2007) independently review appropriate forms to conduct second-stage regressions of DEA estimates which lead them to different conclusions. Based on Monte Carlo simulations, Banker and Natarajan (2008) argue that

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ordinary least squares, Tobit (censored) regression and maximum likelihood estimation in the second-stage outperform onestage and two-stage parametric methods. Simar and Wilson (2007) argue that the majority of empirical two-stage studies do not properly define the data generating process. The efficiency estimates are likely to be serially correlated via the efficiency frontier hence the error term, eit, will also be serially correlated thereby violating the common assumption that the errors are identically and independently distributed. They also state that any bias in the efficiency estimate is ignored and will be automatically included in the error term. Consequently, Simar and Wilson advocate truncated regression in the second stage, which removes the efficient units from the sample. The problem with this approach is that we would then ignore all airports deemed to be lying on the efficient frontier, yet we are searching for the most appropriate form of ownership and regulation given the competitive environment. Drawing from both approaches, we apply a linear regression model to explain the DEA efficiency estimates thereby following the Banker and Natarajan (2008) results. In order to account for the issues identified in Simar and Wilson (2007), we apply a robust between-cluster approach which is clearly described in Williams (2000). The sample will be clustered in order to overcome the limitation that the error terms are not independently distributed at the airport level. The airport observation is repeated for multiple periods hence the correlation within the cluster may be different to that across clusters, which the approach estimates ensuring robust variance estimators. Accounting for heteroscedastic robust standard errors as proposed by White (1980), we construct unbiased t-tests and confidence intervals hence attempt to solve the limitation that the error term is not necessarily identically distributed. Consequently, in order to account for the panel structure2 of our sample, a random effects model has been applied as shown in Eq. (3.2).

ln yit ¼ a þ bk xitk þ ui þ eit

ð3:2Þ

where ln yit represents the efficiency estimate for airport i at time t in the first set of regressions and the aeronautical revenues respectively for the second set. In order to apply linear regression according to Banker and Natarajan (2008), the dependent variable is transformed to logarithmic form. The k regressors, xitk, represent the exogenous environmental variables in our analysis, ui indicates the random effect at the airport level and eit represents the error term. This model accounts for time-invariant covariates because the form of ownership, regulation and degree of competition remained constant over time for the majority of airports. The robust cluster approach accounts for correlation within a cluster thus we consider the impact of analyzing the same airport over time. An alternative approach would be to include an instrumental variable which may ameliorate any limitations related to endogeneity, however the collection of appropriate data is problematic and may cause additional bias in the case of weak instruments, namely were the instrumental variable to be only loosely correlated with the independent variables. 4. Dataset In this section we discuss the variables collected for the first-stage relative efficiency analysis and then the environmental variables included in the second-stage regression. Appendix A lists the complete set of airports in the sample, which includes 48 European airports of which half are located in Germany and the United Kingdom. To ensure a sufficiently heterogeneous dataset with respect to the form of ownership, economic regulation and level of local and hub competition, we also include three fully privatized Australian airports, Melbourne, Perth and Sydney, which are neither ex-ante regulated3 nor located in a competitive environment due to the great distances across the continent. The pooled data consists of an unbalanced set of 398 observations covering the time period between 1998 and 2007.4 Given the rather short time period of on average 7 years per airport and limited technological changes for such an industry, we construct a single intertemporal frontier from the entire data set according to Tulkens and van den Eeckaut (1995). 4.1. Variables in the first-stage efficiency analysis For the first-stage efficiency analysis, three inputs and four outputs were collected as summarized in Table 1. Similar data has been collected for multiple airport analyses as documented in Liebert and Niemeier (2010).5 The operating inputs consist of staff costs and other operating costs, including materials and outsourcing. It is also necessary to consider capital however this variable is extremely problematic since it is often unreported. Furthermore, when the dataset covers more than one country, the monetary measurement of physical capital creates difficulties due to different national accounting standards and depreciation methods or periods across countries. For example, the airports of the British Airports Authority depreciate their runways over 2 For purposes of sensitivity analysis, we also conducted pooled truncated regressions, ordinary least squares and (censored) Tobit regressions. Under ordinary least squares and Tobit regression we obtain very similar results. Under truncated regression, slightly lower statistical significance was found compared to the other functions due to the removal of all relatively efficient observations from the sample however the qualitative conclusions remain the same. All the results are presented in Appendix C. 3 According to the Trade Practices Act 1974, the Australian Competition & Consumer Commission (ACCC) is responsible for the monitoring of prices, costs and profits related to aeronautical and airport car parking services and facilities in Adelaide, Brisbane, Melbourne, Perth and Sydney. Hence the airports experience some form of ex-post regulation (Forsyth, 2004). 4 It should be noted that we have attempted to maximize the number of observations available but could not include airports belonging to airport groups such as AdP and Aena, due to the lack of disaggregated information. 5 The data is available to any interested reader by contacting Prof. Hans-Martin Niemeier, the leader of the German Airport Performance Project.

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Table 1 Variables in analysis (DEA). Variable

Description

Staff costs

Wages and salaries, other staff costs (2000 = 1 and US$ = 1) Costs of materials, outsourcing and other (2000 = 1 and US$ = 1) Number of movements per hour

Other operating costs Declared runway capacity Passengers Cargo Air transport movements Non-aeronautical revenues

Annual passenger volume (only terminal passengers) Metric tons per year (trucking excluded) Annual number of commercial movements Revenues from concessions own retail and restaurants, rents, utilities and ground handling activities (2000 = 1 and US$ = 1)

Average

Standard deviation

Maximum

Minimum

Source

63,654,765

120,554,070

1,080,756,267

3,655,825

Annual Reports

84,811,284

117,603,464

725,987,196

3,631,353

Annual Reports

46

20

110

15

11,091,246

12,761,170

67,673,000

480,011

172,922

366,881

2,190,461

0

Annual Reports

132,482

109,190

492,569

19,397

Annual Reports

124,647,578

186,748,031

1,167,377,411

6,194,408

Annual Reports

IATA (2003), Airport Coordinator Annual Reports

100 years whereas the airports operated by the Aéroports de Paris apply a 10–20 year depreciation rule (Graham, 2005). Consequently, physical data such as the number of runways, gates or check-in-counters and terminal size are often collected for cross-border studies as a proxy for capital (Gillen and Lall, 1997; Pels et al., 2003). In this study, we include declared runway capacity as a proxy for capital, which is defined as the capacity constraint on the number of departure and arrival movements per hour. Declared runway capacity is negotiated twice a year in agreement with airport stakeholders and is primarily used to avoid congestion at schedule facilitated airports and to allocate slots at coordinated airports (IATA, 2010). Compared to pure physical information, the capacity measure considers the runway configuration, weather impacts and/or environmental restrictions thus accounting for bottlenecks in the system that may be amenable to shorter term solutions. Furthermore, this parameter restricts the airport’s aeronautical revenues hence reflects a real measure of capacity. Consistent terminal data proved very difficult to collect hence has been excluded in this study, however runway capacity is highly correlated with terminal capacity therefore this omission should not greatly impact the results. On the output side, the annual traffic volume is represented by the number of passengers, commercial air transport movements and tons of cargo (trucking was excluded). Each of these throughputs generates their own costs and is priced separately, therefore need to be included individually. The fourth output variable captures revenues from the non-aeronautical activities, including concessions, car parking and rent. In addition to the traditional income sources, non-aeronautical revenues also include revenues from labor intensive ground handling activities. Ignoring this operation on the output side would otherwise bias the results since the input data could not be adjusted to exclude this service. At least theoretically we should be able to compare all three airport models, namely airports that produce ground handling services in-house and have relatively higher employee costs and requisite revenues, those who outsource which appear in the other costs category and their respective revenues and the third case in which airports do not provide the service nor earn revenue beyond perhaps a nominal fee from third party contractors. All financial data is deflated to the year 2000 and adjusted by the purchasing power parity according to the United States dollar in order to ensure comparability across countries. We are aware that multiple airport systems, such as BAA, may pay higher salaries than individual airports however, due to lack of data we are not able to capture this effect and note that our efficiency measure will not separate technical inefficiency from labor inefficiencies. In addition, the data has been normalized by the standard deviation which ensures that all inputs are considered equally within the additive model. 4.2. Variables in the second-stage regression Variables describing ownership structure, economic regulation and the level of potential hub and local competition have been collected for this research in addition to specific airport characteristics and information on managerial strategies. All factors are at least in the short-term beyond managerial control yet may impact the inefficiency measurement process. Furthermore, these variables enable us to capture the likelihood that publicly owned airports, as opposed to their privately owned counterparts, may be faced with different objectives to that of profit maximization. All data is expressed in the form of categorical variables. To further assess efficiency changes over the review period and remove time-related effects, categorical variables on the financial years (TIME)6 have been included. We note that revenue source diversification exploits demand complementarities across aeronautical and non-aeronautical services and may impact airport efficiency, therefore we consider the share of non-aeronautical revenues (NA) generated at an airport. Airports are split between those that earn less than 50% of their revenues from non-aeronautical activities and 6

Financial data has been adjusted by the different reporting periods.

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N. Adler, V. Liebert / Transportation Research Part A 64 (2014) 92–109 Table 2 Combination of environmental variables analyzed. Weak local competition

Potential local or hub competition

No. of airports

No. of obs.

No. of airports

No. of obs.

Public

No ex-ante regulation Ex-ante regulation

6 9

37 59

5 7

36 60

Minor private

No ex-ante regulation Ex-ante regulation

0 2

0 13

2 3

8 24

Major private

No ex-ante regulation Ex-ante regulation

1 2

2 9

2 2

19 15

Fully private

No ex-ante regulation Ex-ante regulation

5 2

33 6

6 3

53 24

those that exceed this share.7 The threshold of 50% was chosen such that a rich set of airports exists in the two categories and sensitivity analyses show no change in the results when reducing the threshold to 40% or increasing it to 60%. High levels of delay (D) may cause an efficiency overestimation as suggested in Pathomsiri et al. (2008). In the European market, airport related delays per movement is not publicly available data, hence we include a categorical variable drawn from the ranking of the most delayed airports (departure and arrival) as reported by the European air traffic control provider.8 An airport appearing on the list of the Top 50 most delayed airports in Europe was categorized accordingly.9 Since delays occur for many reasons, we also consider runway utilization (RWY) in order to directly assess the effects of congestion, which are expected to positively impact efficiency.10 The variable was calculated based on annual air transport movements divided by estimated annual declared capacity.11 We define three categories including (1) airports with less than 50% runway utilization indicating under-utilization, (2) airports between 51% and 90% runway utilization and (3) airports achieving more than 90% runway utilization indicating the likelihood of a congested facility. For the aeronautical charge based regression, in which we explain the annual aeronautical revenues of an airport, we include the log forms of air transport movements (ATM)12 and average aircraft size (AC) in order to capture the airport size and substantial differences in fleet mix that exist within the sample. Hence, after accounting for these variables we reduce any size effects that may influence our results. Ownership (O) form is defined according to (1) fully public airports, (2) public–private airports with minority private holdings (less than 50%), (3) public–private airports with majority private holdings (above 50%) and (4) fully private airports. The form of economic regulation (R) has been categorized according to (1) no ex-ante regulation,13 (2) single-till cost-plus regulated, (3) dual-till cost-plus regulated, (4) single-till price-cap regulated and (5) dual-till price-cap regulated airports. Furthermore, in order to test the analytical findings of Czerny (2006) and Yang and Zhang (2011), we include a dummy (I) to represent the combinations of single or dual till price-cap regulation at congested and uncongested airports respectively. Unfortunately, this level of refinement is not possible in the combined model due to an insufficient level of data hence we aggregated the classification to ex-ante unregulated and regulated airports in the combined environmental modeling approach. Regional competition (C) has been defined as the number of commercial airports with at least 150,000 passengers per annum within a catchment area of 100 km around the airport. The radius of the catchment area has been defined in line with Bel and Fageda (2010). In addition, competition between gateway airports is considered. Consequently, weak competition is defined at the regional level as no more than one additional airport within the catchment area and potential competition as a location with at least two airports within the catchment area. However, an airport possessing hub status that serves as a regional or international gateway will also be categorized as working in a competitive market, irrespective of their local catchment area. Due to lack of information for the whole sample, we were not able to capture different product diversification strategies, such as low cost carrier traffic, which may limit the level of competition between closely located airports. Hence, our conservative measure indicates an upper level of likely competition across airports. The random effects model regresses the logged form of airport cost efficiency (ln eff) in Eq. (4.1) and aeronautical revenues (ln arev) in Eq. (4.2) against the following set of exogenous variables:

ln effit ¼ a þ b1 NAit þ b2 Dit þ b3 RWY it þ b4 Oit þ b5 Rit þ b6 C it þ b7 TIMEt þ ui þ eit

7

ð4:1Þ

Revenues generated from ground handling activities are not included in this computation which may reduce any potential endogeneity issues. The measure reported by Eurocontrol does not capture airport-related delays, rather delays caused by many factors including airlines, the air traffic control system and weather, which are beyond the control of the airport manager. 9 Australian airports are included in the group of non-delayed airports for lack of further information. Reallocating these airports to be designated as those suffering from delay does not impact the final results of the analysis. 10 It is also possible for airports not operating near their capacity to cause delays due, for example, to a lack of manpower. 11 The annual capacity has been estimated based on the declared hourly capacity obtained from the airport coordinator. 12 The number of passengers is highly correlated with the number of air transport movements (>94%) thus were not included in the regression due to issues of multi-collinearity. 13 For simplicity we refer to airports subject to ex-post, standard, anti-trust regulation as unregulated airports. 8

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ln arev it ¼ a þ b1 ln AC it þ b2 ln ATMit þ b3 NAit þ b4 Dit þ b5 Iit  RWY it þ b6 Oit þ b7 Rit þ b8 C it þ b9 TIMEt þ ui þ eit ð4:2Þ Table 2 presents the combinations and the number of airports and observations that belong to the different groups in the combined model. Consequently, the models assessing the combined effects of ownership, regulation and competition against cost efficiency (Eq. (4.3)) and aeronautical revenues (Eq. (4.4)) are as follows:

ln effit ¼ a þ b1 NAit þ b2 Dit þ b3 RWY it þ b4 Oit Rit C it þ b5 TIMEt þ ui þ eit

ð4:3Þ

ln arev it ¼ a þ b1 ln AC it þ b2 ln ATMit þ b3 NAit þ b4 Dit þ b5 RWY it þ b6 Oit Rit C it þ b7 TIMEt þ ui þ eit

ð4:4Þ

5. Empirical results In the following section we first discuss the DEA efficiency results. The complete set of normalized DEA efficiency scores is listed in Appendix B. The second part of this section discusses the results of the regression analyses explaining the DEA efficiency estimates and aeronautical revenues, initially discussing the individual impacts and followed by the combined environmental regression approach. A complete set of regression results can be found in Table 3 and Appendix C. 5.1. Estimates from the first-stage efficiency analysis The average efficiency score of the dataset obtained from the input-oriented, slack-based measure is 0.62 with 14% of all airports categorized as relatively efficient. The majority of airports exhibit an efficiency decrease over time, which proved significant according to the paired sample Wilcoxon sign rank test (p-value = 0.000). As a result of the general economic downturn and the attacks on the World Trade Center in New York in 2001, the majority of airports experienced stable or declining traffic rates with disproportionate increases in staff and other operating costs. Increased security measures for baggage screening require the recruitment and training of specialized workers, expenses which have been covered at least partially by the airports (Vienna International Airport, 2004). Hence one could argue that the additional costs provide an increased quality with respect to security. Consistently efficient airports include Ljubljana and Malta that represent the smaller airports in the dataset, many of the Australian airports, as well as the largest operators, Frankfurt and London-Heathrow. In Australia, domestic terminals are often operated by incumbent airlines under long-term leases, thereby lowering maintenance and staff costs (Hooper et al., 2000). Frankfurt and London-Heathrow obtain reasonably high cost efficiency estimates over time. Both airports place great emphasis on cost efficiency with Heathrow attempting to minimize staff costs and Frankfurt tending to reduce other operating costs. The diverse strategies are not surprising given the different levels of outsourcing, including ground handling provision. Whereas Frankfurt provides ground handling services in-house, this operation has long been operated by airlines and third-party providers at Heathrow. It should be noted that both airports are severely congested and require airside capacity expansions. Except for Sydney, no airport consistently improved their relative efficiency scores over time. Between 2003 and 2007, Sydney increased its score from 0.56 to 1.00 which is mainly attributable to a large increase in non-aeronautical revenues with fairly constant cost inputs. In contrast to Melbourne and Perth, a cost increase from the sale of the Ansett terminal back to Sydney’s airport management is not reported as the review period begins in 2003. However, the ACCC responsible for the price monitoring of aeronautical charges and car parking fees at the top five Australian airports accused Sydney airport of abusing market power. In March 2010, the ACCC reported that the airport had almost doubled car parking fees from 2008 to 2009 (ACCC, 2010). The airports of Athens, Budapest, Cologne-Bonn, Hanover, Leipzig, Lyon, Manchester and Munich appear to be the least relatively cost efficient airports in the sample. Athens underwent substantial capacity expansions and a new green-field location hence capacity utilization is low in comparison to reference airports such as Gatwick and Nice. Furthermore, the German airports of Cologne-Bonn and Leipzig suffer from excess airside capacities despite the extensive cargo operations resulting from their positions as the European hubs for UPS and DHL respectively. Hanover also suffers from excessively low capacity utilization and exhibits relatively high operating costs compared to its benchmarks. However, service quality indicators such as congestion and delay are not included in the first stage analysis, hence we refer to these estimators as gross relative measures to be adapted after considering these additional variables in the second-stage of the analysis. 5.2. Second-stage regression results In this section we analyze the impact of additional variables on the DEA cost efficiency scores and aeronautical revenues and also include time indicators as identified in Section 5.1. Table 3 presents the results obtained from the regression models introduced in Section 3 with respect to the individual effect models (Eqs. (4.1) and (4.2)) and the combined models (Eqs. (4.3) and (4.4)). With respect to model (4.3), the monopolistic, minority private shareholding, regulated airport is defined as the base case because it represents the least efficient group of airports in the dataset. The monopolistic, fully private, regulated

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N. Adler, V. Liebert / Transportation Research Part A 64 (2014) 92–109 Table 3 Second-stage regression results. Dependent variable

Individual effects

Combined effects

DEA efficiency scores (log) (Model 4.1) (a) Airport characteristics and management strategies Average aircraft size (log) – Number of air transport movements (log) – Share of non-aviation revenues > 50% 0.101 (0.052)** Heavy delays 0.115 (0.046)*** Runway capacity utilization between 50% 0.127 (0.034)*** and 90% Runway capacity utilization > 90% 0.538 (0.124)*** (b) Ownership, regulation and competition – individual effects Minor private airport 0.180 (0.090)*** Major private airport 0.150 (0.129) Fully private airport 0.095 (0.084) Heavy competition 0.003 (0.070) Cost-plus regulation, single-till 0.253 (0.082)*** Cost-plus regulation, dual-till 0.192 (0.078)*** Price-cap regulation, single-till 0.183 (0.092)*** Price-cap regulation, dual-till 0.030 (0.097) Price-cap regulation, single-till – (uncongested) Price-cap regulation, single-till (congested) – Price-cap regulation, dual-till (uncongested) – Price-cap regulation, dual-till (congested) –

Public Minor private Major private Fully private

(d) Time trends 1999 2000 2001 2002 2003 2004 2005 2006 2007

0.012 0.046 0.126 0.179 0.234 0.210 0.261 0.285 0.294

Intercept R2 Sigma u Sigma e Rho Observations (n)

0.307 (0.086)*** 0.5096 0.1852 0.1495 0.6055 398

(0.024) (0.035) (0.045)*** (0.056)*** (0.062)*** (0.061)*** (0.061)*** (0.065)*** (0.071)***

DEA efficiency scores (log) (Model 4.3)

Aeronautical revenues (log) (Model 4.4)

0.418 (0.094)*** 0.841 (0.077)*** 0.146 (0.034)*** 0.019 (0.020) –

– – 0.101 (0.050)*** 0.139 (0.048)*** 0.123 (0.037)***

0.438 (0.103)*** 0.849 (0.059)*** 0.140 (0.023)*** 0.009 (0.015) 0.009 (0.015)

–

0.615 (0.164)***

0.133 (0.074)*

0.067 (0.059) 0.085 (0.046)* 0.046 (0.065) 0.052 (0.047) 0.045 (0.061) 0.117 (0.062) – – 0.080 (0.073)

– – – – – – – – –

– – – – – – – – –

– – –

– – –

*

0.224 (0.095)*** 0.220 (0.050)*** 0.584 (0.089)***

(c) Ownership, regulation and competition – combined effects Low competition Public No regulation Regulation Minor private No regulation Regulation Major private No regulation Regulation Fully private No regulation Regulation Potential competition

Aeronautical revenues (log) (Model 4.2)

No regulation Regulation No regulation Regulation No regulation Regulation No regulation Regulation

0.025 0.043 0.083 0.089 0.114 0.113 0.174 0.170 0.205

– – – – – – – –

– – – – – – – –

0.349 (0.154)*** 0.316 (0.143)*** n/a Base case 0.250 (0.143)* 0.767 (0.194)*** 0.465 (0.166)*** 0.480 (0.181)***

– – – – – – – –

– – – – – – – –

0.589 0.081 0.322 0.128 0.646 0.433 0.564 0.358

(0.011)*** (0.013)*** (0.016)*** (0.016)*** (0.017)*** (0.021)*** (0.021)*** (0.029)*** (0.031)***

2.846 (0.380)*** 0.8767 0.1328 0.0632 0.8152 398

0.157 0.054 0.133 0.182 0.239 0.213 0.258 0.279 0.279

(0.131)*** (0.146) (0.181)* (0.154) (0.215)*** (0.130)*** (0.143)*** (0.156)***

(0.025) (0.037) (0.048)*** (0.058)*** (0.064)*** (0.063)*** (0.061)*** (0.065)*** (0.069)***

0.730 (0.149)*** 0.6399 0.1371 0.1498 0.4561 398

0.408 (0.103)*** 0.335 (0.081)*** n/a 0.498 (0.108)*** 0.466 (0.099)*** 0.416 (0.091)*** 0.279 (0.028)*** Base case 0.218 0.389 0.281 0.387 0.351 0.373 0.329 0.484

(0.094)*** (0.092)*** (0.104)*** (0.087)*** (0.125)*** (0.096)*** (0.094)*** (0.090)***

0.034 0.047 0.093 0.086 0.112 0.132 0.173 0.170 0.204

(0.009)*** (0.011)*** (0.014)*** (0.018)*** (0.018)*** (0.023)*** (0.023)*** (0.031)*** (0.031)***

2.370 (0.321)*** 0.9001 0.1257 0.6336 0.7961 398

Note: Robust standard errors in parentheses; efficiency model clustered at airport level. Uncongested airports with a runway utilization < 90%, congested airports otherwise. * Significance at 10% level. ** Significance at 5% level. *** Significance at 1% level.

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airport has been chosen as the base case in model (4.4) as an example of the least expensive airport in the sample set. The time trend dummies prove to be statistically significant across all regressions from 2001. Hence, after accounting for the efficiency decreases and revenue increases over time, we conclude that ownership form and regulation play an important role in explaining efficiency and pricing differences across airports. The results with respect to airport characteristics and management strategies reveal that airports earning more than 50% of their revenues from non-aeronautical sources achieve higher levels of efficiency and set lower aeronautical charges, supporting the results of Oum et al. (2006) and indicating a marginal contribution to cost efficiency of approximately 10%. The model of airport pricing reveals that the same airports charge on average 15% less for aeronautical activities than otherwise. This confirms the analytical findings of Zhang and Zhang (2010) in which it was argued that airports may reduce their aeronautical costs in order to attract additional airlines and passengers. Delay and congestion have a statistically significant impact on airport cost efficiency, as discussed in Pathomsiri et al. (2008). Delay impacts cost efficiency negatively in the region of 12%. In contrast, efficiency significantly increases with runway capacity utilization. Congestion, proxied by capacity utilization above 90%, has a large positive impact of approximately 50% on airport efficiency compared to underutilized airports. Given that the positive impact of congestion is higher than the negative effect of delays, this would indicate the need for service quality indicators written into contracts between airlines and airports or internalization through compensation to airlines and passengers for airport related delays. Furthermore, whereas delay was found to have no impact on aeronautical revenues, charges are significantly higher at congested airports thereby supporting the empirical outcome of van Dender (2007) that congested airports are in a position to exploit scarcity rents. Reviewing the individual effects of ownership, regulation and competition in model (4.1), we see that mixed ownership with less than 50% of shares traded privately is significantly less efficient than other ownership forms. Furthermore, major and fully private airports appear not to be statistically significantly different in terms of cost efficiency than their fully public counterparts, in line with Oum et al. (2006). Consequently, ignoring interactions between potential competition and regulation, the results indicate that fully public or private ownership operate equally efficient and behave similarly in setting their prices. The importance of local and gateway competition is not statistically significant in either model. With respect to regulation, it would appear that unregulated airports generally operate more efficiently than their regulated counterparts, with the single exception being price-cap dual-till regulation. Cost-plus regulation appears to be the least appropriate form of economic regulation whether single-till or dual-till, reducing efficiency by 25% and 19% respectively. In addition, single-till price-caps also appear to be dominated by dual-till price-caps and standard ex-post anti-trust monitoring. Hence we accept hypothesis (1a) in which it is argued that a dual-till, incentive approach such as a price-cap would be the most preferable form of economic regulation, were it to prove necessary to implement. From the airport pricing model (4.2) we find that congested, single-till, incentive regulated airports charge lower aeronautical prices than its dual-till regulated counterparts. Hence we confirm the argument of Beesley (1999) that single-till regulation of large, congested airports such as London Heathrow leads to lower aeronautical charges due to large concession revenues, possibly further increasing congestion. On the other hand, we note that at uncongested airports the incentive dual-till mechanism leads to lower charges than the single-till incentive option. These results support the argument by Starkie (2001) that regulated, uncongested airports set lower aeronautical charges in order to earn additional revenues from non-aeronautical sources. Hence we do not accept hypothesis (1b) for the uncongested case and do not find support for the analytical findings of Czerny (2006) and Yang and Zhang (2011). Furthermore, in contrast to the empirical outcome of Bel and Fageda (2010), we find that the form and scope of regulation may substantially affect the level of aeronautical charges of an airport. In summation, ignoring the interaction between ownership, regulation and competition, the results reveal that airports should either be unregulated or, if subject to economic regulation, dual-till price-capped in order to encourage cost efficiency. Airports should either be fully privatized or remain entirely in public hands because the mixed models result in greater inefficiency and higher aeronautical prices. Finally, the level of local or hub competition does not affect cost efficiency or the pricing behavior of an airport in the individual effects model. Under the combined models (4.3) and (4.4), the impact of potential competition becomes clearer. Under weak competitive conditions, defined as at most one airport within the catchment area, we find that privatized airports with at least 50% of the shares in private hands are the most cost efficient ownership form. In comparison to airports with minority private ownership (the base case), the major or fully privatized airports are on average 60% more efficient when regulated and 15% more efficient when unregulated also suggesting that economic regulation is desirable. Referring back to the individual regression model results, dual-till price-cap regulation would appear to be the most preferable instrument. Confirming Armstrong and Sappington (2006), fully private airports operate more cost efficiently than their public counterparts in a monopolistic setting as argued in hypothesis (2ai). Consequently, monopolistic public airports may exhibit excessive employment and face lower budget constraints than major and fully private airports. Furthermore, unregulated airports irrespective of the ownership form charge higher aeronautical prices than their regulated counterparts, suggesting that regulation is also essential in order to prevent exploitation of market power. Hence economic regulation may improve both welfare and cost efficiency of public and private airports, therefore we reject hypothesis (2aii). Publicly owned and regulated airports perform slightly less efficiently than their unregulated counterparts. Furthermore, charges at regulated public airports exceed those of regulated fully private airports. This may be explained by the fact that regulated, monopolistic, public airports in this sample are all subject to cost-plus regulation whereas regulated, monopolistic, fully private airports follow a price-cap regulation approach. Interestingly, the price difference between regulated and unregulated fully private airports is fairly high. Considering that the efficiency level between unregulated and regulated fully private airports is fairly similar, this result indicates the exploitation of market power under this ownership form.

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In a potentially competitive environment, unregulated, purely public airports and major or fully privatized airports operate in an equally cost efficient manner. It is clear that such airports do not require economic regulation to maintain cost efficiency as compared to airports operating in a weakly competitive environment. Major and fully private unregulated airports perform to the order of 20% more cost efficiently than their regulated counterparts. Apart from private airports with a minority of shares in private hands, airports located in a potentially competitive environment generally operate more efficiently than those operating in weakly competitive markets. Similarly we find that regulation generally increases aeronautical prices. Consequently, we accept hypothesis (2b). Government intervention would appear to incur high transaction costs and is required to emulate the competitive environment when missing but is very expensive when such conditions already exist in the market. Although unregulated public and major or fully private airports may operate equally efficient, the level of charges between these airports varies considerably. Private airports tend to charge higher prices compared to their public counterparts. Finally, we find minor private airports to be the least efficient ownership form, irrespective of the level of competition, hence we reject hypotheses (3a) and (3b). In summary, the results obtained from the combined approach in models (4.3) and (4.4) reveal that the level of competition has a substantial impact on the appropriate form of ownership and the need for economic regulation. As shown in Table 3, airports should be fully privatized in order to encourage cost efficiency and dual-till incentive regulation should be applied in order to avoid excessive pricing under weakly competitive conditions. Under conditions of potentially (imperfect) competitive markets, we conclude that public and major or fully private airports operate equally efficiently. Nevertheless, airport charges are higher at unregulated, private airports as compared to their public counterparts.

6. Conclusions and future research The inefficiency of airports may be explained not only by input excess and output shortfalls but also by exogenous factors over which management have little to no control. A number of empirical studies have assessed the impact of ownership structure, economic regulation and levels of competition on efficiency and aeronautical charges however the effects were always considered separately hence significance was sometimes an issue. The aim of this research has been to extend the current literature on airport ownership, regulation and competition by assessing the combined impact of the environmental variables in order to gain insight into the most efficient ownership form and regulatory framework whilst accounting for levels of regional and hub competition as well as other managerial strategies. The two-stage analysis combined data envelopment analysis in the first stage and regression analysis in the second stage. The non-radial, weighted additive, input-oriented DEA model has been chosen to identify all relative cost inefficiencies. Following the recent debate on the most appropriate second-stage regression model, Banker and Natarajan (2008) and Simar and Wilson (2007) propose ordinary least squares and truncated regression respectively. Due to the fact that we have a panel dataset, we apply robust clustering to each of these forms as well as the random effects model in order to consider unobserved heterogeneity and allow for time-invariant covariates. The regression results were consistent across the pooled OLS, truncated and tobit, robust cluster, random effects models. Data availability remains a serious issue and the attempt to include all combinations of institutional settings proved difficult. However, the results of the joint analysis provide additional information beyond that of the individual regression models. The results suggest that private, regulated airports are more efficient under monopolistic conditions whereas pure public and private airports operate equally efficiently given potential local, regional and gateway competition. Furthermore, exante regulation at all airports located in a competitive environment is unnecessary and generates X-inefficiency of the order of 15%, which rises substantially at purely public airports. However, unregulated, fully private and majority privately owned airports located in a competitive setting, charge higher aeronautical fees than unregulated public airports. Combining the results from the efficiency and revenue models reveal that imperfect competition is sufficient to encourage airport cost efficiency and reduce the likelihood of abuse of market power, although private airports still transfer fewer of these gains to airlines. On the other hand, non-hub airports with weak local competition generally require economic regulation in order to prevent an exploitation of market power and to encourage cost efficiency. Dual-till price-cap regulation is shown to be the most efficient form. Having defined competition at the regional and hub level in a relatively simple manner, we may have assumed excessive levels of competition having ignored product diversification strategies such as market destination separation. In other words, we categorized airports as competitive that may be avoiding direct competition, however despite this shortcoming, the impact of competition remains clear. The policy implications from this analysis suggest that if an airport is to be privatized, for example to apply to the capital markets to fund further investment, it is better to completely privatize the airport. Partial privatization appears to lead to conflicting objectives. Irrespective of ownership form, under weakly competitive markets, regulation is worthwhile and dual-till price caps appear to be the most appropriate form. Under relatively competitive markets, regulation appears to be unnecessary to encourage cost efficiency. However, unregulated, privatized airports are likely to set higher charges than their public counterparts. Consequently it is up to the government to determine which is less problematic; the additional cost of regulation or the higher prices that the airlines are likely to be charged. Future research would require substantially more data to permit an improved analysis of all the categories described here. Finer distinctions with respect to ownership form and regulation might better highlight the most efficient institutional setting given alternative levels of competition. The consideration of product differentiation may render the actual degree of competition more precisely. Additional environmental variables, including airport-related delays, noise and air pollution,

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would enable the development of a social welfare analysis of airports and the trade-off across the different stakeholders. We were in a position to compare delay and congestion, but only in a limited manner. We find that the positive impact of congestion is higher than the negative effect of delays, which indicates the need for service quality indicators written into contracts between airlines and airports or internalization through compensation of airport related delays to airlines and passengers. Malmquist measure of changes in technical and managerial efficiency over time would also strengthen such an analysis, however the dataset must be balanced and substantial for this to be a possibility. Finally, in order to improve the benchmarking process, a more accurate measure of the capital required to build, maintain and expand or reduce airports would only be possible if the ACI, ICAO or equivalent organization were to develop standardized data collection procedures that airports globally reported annually. This is extremely important if we are to be able to analyze this industry and provide sensible recommendations for future development. Acknowledgments We would like to thank the German Airport Performance research project funded by the German Ministry of Education and Research for providing the data. Further, we thank Prof. David Gillen, Prof. Hans-Martin Niemeier, and the participants of the World Bank roundtable on Transport Productivity in February 2010, the German Aviation Research Society Conference in November 2010 and the Aviation seminar at the University of British Columbia in February 2011 for fruitful discussions. Nicole Adler thanks the Recanati Fund for partial funding of this research. Appendix A. List of airports Code

Airport

Country

Time period

Ownership

Regulation

Competition

ABZ AMS

Aberdeen Amsterdam

UK NL

Fully private Public

Athens Belfast Birmingham Bologna Bremen Brussels Bratislava

GR UK UK IT DE BE SK

No ex-ante regulation Cost-plus, dual till Incentive, dual till Cost-plus, dual till No ex-ante regulation No ex-ante regulation No ex-ante regulation Cost-plus, dual till Cost-plus, single till No ex-ante regulation

Weak Heavy

ATH BFS BHX BLQ BRE BRU BTS BUD CGN CPH DRS DTM DUB DUS EDI EMA FRA GLA GVA HAJ HAM

Budapest Cologne Bonn Copenhagen Dresden Dortmund Dublin Dusseldorf Edinburgh East Midlands Frankfurt Glasgow Geneva Hanover Hamburg

HU DE DK DE DE IE DE UK UK DE UK CH DE DE

Leeds Bradford London-City Leipzig London-Gatwick London-Heathrow Ljubljana London-Luton Lyon Manchester Melbourne

UK UK DE UK UK SI UK FR UK AU

No ex-ante regulation Cost-plus, dual till Incentive, dual till Cost-plus, dual till Cost-plus, dual till Incentive, single till Cost-plus, dual till No ex-ante regulation No ex-ante regulation Incentive, dual till No ex-ante regulation No ex-ante regulation Cost-plus, dual till Cost-plus, dual till Incentive, dual till No ex-ante regulation No ex-ante regulation Cost-plus, dual till Incentive, single till Incentive, single till No ex-ante regulation No ex-ante regulation No ex-ante regulation Incentive, single till Incentive, dual till

Weak Heavy Weak Weak Heavy Weak Heavy Heavy Heavy Heavy Heavy Weak Weak Heavy

LBA LCY LEJ LGW LHR LJU LTN LYS MAN MEL

99-05 98-06 2007 05-07 99-07 98-07 00-05 98-07 99-04 03-05 06-07 00-01 98-07 01-04 98-06 98-07 06-07 99-07 98-07 98-06 02-07 98-06 98-07 98-07 98-99 00-07 98-02, 06-07 99-07 98-06 98-05 98-05 98-06 00-07 98-06 98-07 99-01

Minor private Fully private Major private Public Public Major private Public Major private Public Public Major private Public Public Public Major private Fully private Fully private Minor private Fully private Public Minor private Public Minor private Public Fully private Public Fully private Fully private Major private Fully private Public Public Fully private

Weak Weak Heavy Heavy Heavy Heavy Weak

Heavy Heavy Weak Heavy Heavy Heavy Heavy Weak Heavy Weak

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Appendix A (continued) Code

Airport

Country

MLA MLH MRS MUC NCE NUE OSL

Malta Basel Mulhouse Marseille Munich Nice Nuremberg Oslo

MT FR FR DE FR DE NO

PER

Perth

AU

RIX SOU STN STR SYD SZG

Riga Southampton London-Stansted Stuttgart Sydney Salzburg

LV UK UK DE AU AT

TLL VCE

Tallinn Venice

EE IT

VIE ZRH

Vienna Zurich

AT CH

Time period 02-07 02-06 98-07 98-06 98-05 98-06 98-07 99-03 04-07 99-01 02-07 04-06 99-05 98-06 98-07 03-07 2004 05-07 02-07 00-04 2005 98-07 01-07

Ownership Major private Public Public Public Public Public Public Fully private Public Fully private Fully private Public Fully private Public Public Public Minor private Minor private Minor private

Regulation

Competition

No ex-ante regulation Incentive, dual till No ex-ante regulation No ex-ante regulation Cost-plus, dual till No ex-ante regulation Cost-plus, dual till Cost-plus, single till Incentive, single till Incentive, dual till No ex-ante regulation No ex-ante regulation No ex-ante regulation Incentive, single till Cost-plus, dual till No ex-ante regulation Cost-plus, single till Incentive, single till Cost-plus, dual till No ex-ante regulation Incentive, single till No ex-ante regulation

Weak Weak Heavy Heavy Heavy Weak Weak Weak Weak Heavy Heavy Weak Weak Weak Weak Heavy Heavy Heavy

Appendix B. DEA efficiency scores14

ABZ AMS ATH BFS BHX BLQ BRE BRU BTS BUD CGN CPH DRS DTM DUB DUS EDI EMA FRA GLA GVA HAJ HAM LBA LCY

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

– 1.000 – – 0.549 – 0.587 – – – 0.390 – 0.639 1.000 – – 0.664 0.637 – 0.702 0.635 0.313 0.535 1.000 –

0.579 1.000 – 0.512 0.577 – 0.648 1.000 – – 0.410 – 0.588 0.843 – 1.000 0.712 0.628 – 0.712 0.660 0.319 0.524 1.000 1.000

0.599 1.000 – 0.489 0.598 0.755 0.602 1.000 – 0.357 0.390 – 0.575 0.732 – 1.000 0.714 0.629 – 0.714 0.791 0.331 0.426 0.901 1.000

0.546 0.496 – 0.487 0.582 0.715 0.589 0.653 – 0.336 0.417 1.000 0.547 0.472 – 0.605 0.702 0.606 – 0.705 0.720 0.316 0.394 0.830 0.933

0.522 0.309 – 0.480 0.568 0.710 0.585 0.455 – – 0.409 0.617 0.559 0.433 – 0.682 0.713 0.629 1.000 0.693 0.709 0.314 0.399 0.842 0.887

0.505 0.249 – 0.485 0.583 0.733 0.568 0.417 0.854 – 0.388 0.562 0.574 0.418 – 0.472 0.718 0.640 1.000 0.685 0.700 0.310 0.381 0.881 0.867

0.515 0.253 – 0.575 0.535 0.698 0.575 0.431 0.732 – 0.361 0.635 0.548 0.408 – 0.588 0.738 0.637 1.000 0.697 0.676 0.308 0.420 0.888 0.799

0.536 0.297 0.421 0.502 0.530 0.740 0.568 – 0.575 – 0.350 – 0.538 0.410 – 0.531 0.711 0.584 1.000 0.703 0.638 0.301 0.404 – 0.744

– 0.259 0.432 0.500 0.514 – 0.562 – 0.579 – 0.337 – 0.532 0.410 0.504 0.508 0.620 0.558 0.732 0.692 0.645 0.302 0.406 – 0.888

– 0.280 0.450 0.502 0.481 – 0.562 – 0.563 – 0.323 – – 0.408 0.475 0.574 0.661 – 1.000 – 0.687 0.297 0.414 – 1.000

(continued on next page)

14

A score of 1.000 represents a relatively cost efficient airport and a value below that indicates cost inefficiency.

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A (continued) (continued) Appendix B

LEJ LGW LHR LJU LTN LYS MAN MEL MLA MLH MRS MUC NCE NUE OSL PER RIX SOU STN STR SYD SZG TLL VCE VIE ZRH

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

0.571 1.000 1.000 1.000 – 0.487 0.477 – – 0.887 0.832 0.335 1.000 0.628 – – – 0.847 0.670 0.561 – – – – 0.428 –

0.570 0.861 1.000 1.000 0.584 0.504 0.425 0.862 – 0.821 0.844 0.338 1.000 0.613 0.555 1.000 – 0.919 0.770 0.564 – – – – 0.456 –

0.392 1.000 1.000 1.000 0.518 0.525 0.407 1.000 – 0.670 0.932 0.355 1.000 0.636 0.517 1.000 – 0.943 0.651 0.559 – – – 0.625 0.471 –

0.382 0.744 0.907 1.000 0.525 0.509 0.334 1.000 – 0.542 0.808 0.353 0.857 0.619 0.515 0.972 – 0.720 0.595 0.534 – – – 0.599 0.396 0.656

0.374 0.682 1.000 1.000 0.513 0.459 0.352 0.815 1.000 0.502 0.621 0.332 0.684 0.596 0.440 0.790 – 0.617 0.723 0.513 – – 0.904 0.627 0.365 0.532

0.369 0.619 0.852 1.000 0.520 0.443 0.434 0.760 1.000 0.439 0.586 0.263 0.612 0.574 0.479 0.689 – 1.000 0.707 0.505 0.563 – 0.690 0.671 0.338 0.516

0.367 0.658 1.000 1.000 0.564 0.448 0.504 0.903 1.000 0.432 0.597 0.264 0.543 0.578 0.441 0.678 0.547 0.751 0.661 0.462 0.748 0.733 0.628 0.682 0.338 0.481

0.337 0.537 1.000 1.000 0.511 0.414 0.442 1.000 1.000 0.438 0.600 0.234 0.526 0.570 0.458 0.673 0.558 – 0.635 0.516 1.000 0.716 0.522 0.545 0.325 0.342

0.359 – – 1.000 0.529 0.417 0.434 1.000 1.000 0.414 0.588 – 0.538 0.568 0.494 0.703 0.563 – – 0.513 1.000 0.722 0.516 – 0.320 0.315

– – – – 0.473 – 0.397 1.000 – 0.413 – – – 0.564 0.543 1.000 – – – 0.519 1.000 0.723 0.513 – 0.310 0.306

Appendix C. Results from second-stage OLS, truncated and censored regressions over DEA efficiency scores Individual effects Robust Cluster OLS

Robust Cluster Tobit

Combined effects Robust Cluster Truncated

Robust Cluster OLS

Robust Cluster Tobit

Robust Cluster Truncated

(a) Airport characteristics and management strategies Share of non-aviation 0.109 0.137 revenues > 50% (0.060)** (0.038)*** Heavy delays 0.253 0.292 (0.062)*** (0.034)*** Runway capacity utilization 0.136 0.154 between 50% and 90% (0.055)*** (0.033)*** Runway capacity utilization > 90% 0.708 0.972 (0.106)*** (0.105)***

0.074 (0.067) 0.196 (0.071)*** 0.081 (0.065) 0.774 (0.064)***

0.095 (0.055)** 0.278 (0.066)*** 0.141 (0.054)*** 0.733 (0.164)***

0.112 (0.038)*** 0.311 (0.034)*** 0.154 (0.031)*** 1.019 (0.100)***

0.049 (0.055) 0.220 (0.069)*** 0.084 (0.053) 0.718 (0.109)***

(b) Ownership, regulation and competition – individual effects Minor private airport 0.252 0.256 (0.070)*** (0.050)*** Major private airport 0.210 0.279 (0.089)*** (0.049)*** Fully private airport 0.028 0.028 (0.072) (0.039) Heavy competition 0.027 0.028 (0.060) (0.030) Cost-plus regulation, single-till 0.272 0.303 (0.076)*** (0.083)*** Cost-plus regulation, dual-till 0.275 0.286

0.255 (0.076)*** 0.086 (0.097) 0.057 (0.086) 0.004 (0.068) 0.267 (0.076)*** 0.274

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

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A (continued) (continued) Appendix C Individual effects

Price-cap regulation, single-till Price-cap regulation, dual-till

Robust Cluster OLS

Robust Cluster Tobit

Robust Cluster Truncated

(0.071)*** 0.206 (0.084)*** 0.026 (0.089)

(0.036)*** 0.211 (0.048)*** 0.046 (0.063)

(0.081)*** 0.148 (0.096) 0.091 (0.080)

(c) Ownership, regulation and competition – combined Low competition Public No regulation Regulation Minor No regulation Private Regulation Major No regulation Private Regulation Fully No regulation Private Regulation Potential competition

Public Minor Private Major Private Fully Private

No regulation Regulation No regulation Regulation No regulation Regulation No regulation Regulation

(d) Time trends 1999 0.022 (0.028) 0.033 (0.068) 2000

0.057 (0.042) 0.063 (0.067)

2001

0.134 (0.049)*** 0.160 (0.057)*** 0.221 (0.061)*** 0.192 (0.061)*** 0.234 (0.060)*** 0.264 (0.065)*** 0.280 (0.080)*** 0.227 (0.084)*** 0.5626 398

2002 2003 2004 2005 2006 2007 Intercept R2 Observations (n)

Combined effects

0.176 (0.066)*** 0.198 (0.065)*** 0.270 (0.065)*** 0.230 (0.065)*** 0.271 (0.065)*** 0.312 (0.067)*** 0.324 (0.072)*** 0.169 (0.063)*** 0.5622 398 (56 rightcensored)

effects – – – – – – – – – – – – – – – – – – – – – – – –

– – – – – – – –

Robust Cluster OLS

Robust Cluster Tobit

Robust Cluster Truncated

–

–

–

–

–

–

– – – – – – – –

0.436 (0.112)*** 0.296 (0.120)*** n/a Base case 0.379 (0.103)*** 0.785 (0.134)*** 0.378 (0.147)*** 0.580 (0.151)***

– – – – – – – –

0.662 0.112 0.268 0.154 0.661 0.518 0.581 0.406

0.013 (0.029)

(0.087)*** (0.104) (0.107)*** (0.119) (0.151)*** (0.097)*** (0.106)*** (0.131)***

0.028 (0.028) 0.029 (0.041) 0.068 (0.041) 0.038 (0.047) 0.146 (0.049)*** 0.067 (0.047) 0.165 (0.058)*** 0.132 0.220 (0.047)*** (0.062)*** 0.113 0.192 (0.047)*** (0.061)*** 0.172 0.226 (0.044)*** (0.059)*** 0.190 0.251 (0.050)*** (0.064)*** 0.221 0.236 (0.066)*** (0.115)*** 0.313 0.695 (0.088)*** (0.115)*** 0.4995 0.6333 342 (56 398 truncated)

0.427 (0.077)*** 0.277 (0.074)*** n/a Base case 0.357 (0.180)** 0.900 (0.113)*** 0.364 (0.087)*** 0.685 (0.148)*** 0.672 0.100 0.272 0.117 0.724 0.541 0.574 0.425

(0.078)*** (0.073) (0.106)*** (0.085) (0.087)*** (0.093)*** (0.075)*** (0.099)***

0.453 (0.123)*** 0.333 (0.123)*** n/a Base case 0.419 (0.114)*** 0.538 (0.104)*** 0.345 (0.137)*** 0.958 (0.331)*** 0.687 0.095 0.282 0.096 0.495 0.418 0.603 0.573

(0.099)*** (0.119) (0.113)*** (0.105) (0.090)*** (0.122)*** (0.114)*** (0.114)***

0.041 (0.062)

0.007 (0.031)

0.074 (0.062)

0.042 (0.041)

0.193 (0.060)***

0.070 (0.043)

0.204 (0.060)***

0.087 (0.042)*** 0.143 (0.044)*** 0.123 (0.043)*** 0.178 (0.042)*** 0.191 (0.047)*** 0.195 (0.057)*** 0.786 (0.122)*** 0.5444 342 (56 truncated)

0.271 (0.059)*** 0.230 (0.059)*** 0.262 (0.059)*** 0.294 (0.061)*** 0.272 (0.066)*** 0.635 (0.085)*** 0.6187 398 (56 rightcensored)

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