Airport efficiency in the dawn of privatization: The case of Greece

Until recently, all airports in Greece were owned and managed by the state; however, the recent economic crisis led the Greek government to privatize 14 regional airports. This paper evaluates the performance of the 38 Greek airports open to civil aviation just before the privatization was concluded. It also aims to highlight that the majority of airports with high efficiency were selected to be privatized. The analysis was performed applying Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI) using 2011 and 2015 operational and financial data. Results showed that, despite the economic crisis, overall efficiency improved between 2011 and 2015, mainly due to the spectacular increase in incoming international passenger traffic. Changes in efficiency can be attributed by and large to exogenous factors such as operations of low-cost carriers and charter operators along with the seasonal operations of established carriers at individual airports. Findings confirm that the majority of high efficiency airports were offered to private investors.     

Evaluating financial performance of commercial service airports in the United States

Studies on airport performance have focused primarily on productivity and operational efficiency. There are very few studies on airports’ financial viability and strength, especially those in the United States. Most of the U.S. commercial service airports are government-owned entities and operated for the service of the community rather than for profit, but expected to be financially self-sufficient and raise capital funds mainly through the bond markets. However, past studies of the financial performance of U.S. airports have relied on traditional profitability measures and financial efficiency measures that are more appropriate for airports owned and operated by for-profit entities. Based on the literature for non-profit organizations and the practices of credit rating agencies and government oversight bodies, this paper adopts six pertinent financial performance metrics to measure and compare the operational financial performance, leverage, and liquidity of 60 large and medium commercial service airports in the United States during the 2010–2017 period. The paper further examines factors that may affect airport financial performance. The results reveal that large hub airports have better liquidity while medium airports have better leverage during the study period. These results suggest that the effect of airport size on financial performance is inconclusive. Regression analysis shows that airports with high productive efficiency and those without a dominant carrier tend to have more surplus revenues for meeting their operational financial needs and capital spending and have better liquidity. The paper highlights the importance of using appropriate metrics to evaluate the financial performance of public sector entities and provides relevant information to bond investors. The devastating impacts of the COVID-19 pandemic on airports accentuate the significance of leverage and liquidity. Therefore, the financial metrics discussed in this paper would help support public policy debates and allocate public funds to the airports.     

Comparing efficiency of holding business model and individual management model of airports

This research compares the efficiency of holding business model to individual management model of airports, employing some robust non-parametric partial frontier-based methods to compare the statistical distributions of efficiency, under different scenarios, to find out which group of airports yields better global performance. The comparison between groups will follow a Malmquist index decomposition, which seems to be the most appropriate tool for within- and inter-group performance comparison. For this purpose, a sample of 145 airports from three continents is utilized. The results provide evidence that European airports are the most productive ones, and within this cluster, the individual management model presented a significant frontier shift with respect the holding cluster frontier, meaning that the former is much more productive than the latter.     

Efficiency measurement of Chinese airports with flight delays by directional distance function

This paper employs directional distance function to evaluate the technical efficiency of twenty major Chinese airports from 2006 to 2009 within a joint production framework of desirable and undesirable output (i.e. flight delays). The results indicate that the overall average efficiencies of Chinese airports increased over the period of time. The international hub airports are found to operate at higher efficiency level, which indicates that these airports run well in producing more desirable outputs and controlling flight delays. Although the average efficiency score of listed airports is higher than non-listed ones, the difference between the two group is statistically insignificant. A comparison between the results without and with flight delays show that several airports experienced significant changes in their efficiency scores after considering undesirable output.     

The geographical efficiency of Spain's regional airports: A quantitative analysis

Previous studies of the role of regional airports in the Spanish economy have focused on calculating efficiency using air traffic as the output and airport infrastructure data as the input. We present an alternative quantitative analysis based upon the geographical structure within the airport's market basin. We formulate a ‘geographical efficiency’ model that considers territorial variables within the airport hinterland. Input data for our model include the socio-economic structure of the population, intermodal transport links, industrial and tourism potential and existing leisure-related services. The output is the annual number of passengers associated with such constraints. The result is a relative efficiency estimate that shows uneven patterns of geographical efficiency amongst Spain's regional airports and also provides insights into opportunities for expanding some of these critical items of infrastructure. They suggest that some of Spain's regional airports may be better placed than others to compete in a liberalized market that exhibits a clear tendency in favor of coastal, tourism-based airports.     

Performance assessment of UK airports: Evidence from a Bayesian dynamic frontier model

This paper analyzes the cost efficiency of UK airports over the period 1998-2008, using a Bayesian dynamic frontier model. This model provides a more structural explanation for the variation in airports inefficiency than has been presented by previous models, and also allows for cost inefficiency effects. On average, the dynamic frontier results, estimated via the Markov Chain Monte-Carlo simulation, indicate that UK airports improved their efficiency over time. Factors found to be important determinants of cost efficiency include airport size, price regulation, price cap variations and airport competition. Policy implications of the results are derived.     

Spatial and operational factors behind passenger yield of U.S. nonhub primary airports

Nonhub airports are an essential component in the National Plan of Integrated Airport Systems (NPIAS) of the United States in that they connect regional towns and small communities to the air transportation network. Understanding the interplay of operational and spatial factors in determining average passenger yield of nonhub airports provides airlines with valuable information for network planning and revenue management. This study examines factors contributing to the yield variation among nonhub airports in the U.S. Using ordinary least squares (OLS) based econometric models, this study captures the spatial dependence of passenger yield of nonhub airports, which tends to increase with a corresponding increase in distance to the nearest large hub airport. Nonhub airports surrounding large hub airports with higher passenger enplanements and higher average yields also have higher yields than other nonhub airports. In addition, this study finds the effect of Allegiant Airlines in lowering the average passenger yield of the nonhub airports served directly by the airline, which can be termed as ‘Allegiant Effect’. Findings of this study could provide valuable guidance for airlines to analyze network planning strategies and to identify future markets for growth and for policymakers when allocating resources to communities relying on these nonhub airports.     

Sustainable efficiency drivers in Eurasian airports: Fuzzy NDEA approach based on Shannon's entropy

This research explores the physical infrastructure and flight consolidation efficiency drivers of Eurasian airports regarding their infrastructure and movement productivity levels. A novel Fuzzy Double-Frontier Network DEA (FDFNDEA) model is proposed to investigate the relationship between desirable (freight and passenger turnovers) and undesirable (pollutant emission levels due to aircraft movements) outputs against the respective infrastructure usage, fuel consumed, and movements performed at each of the 23 Eurasian airports from 2000 to 2018. This balance between desirable and undesirable outputs emerges spatially and temporally due to the evolution of the airport system's productive resources at each one of the Eurasian countries over the period observed. Shannon's entropy is used as the cornerstone to quantify the input and output vagueness of this evolution in Triangular Fuzzy Numbers (TFN), thus allowing the accurate building of alternative optimistic and pessimistic double-frontier efficiency. Differently from previous research, Shannon's entropy is the key for measuring input and output vagueness levels in light of the maximal entropy principle. This principle states that the distribution that best represents the current state of knowledge is the one with largest entropy. Maximal entropy yields bias-free decision-making in the sense that the input/output distributional profiles for Eurasian airports contain the maximal possible heterogeneity, working as a robust or best/worst-case scenario against eventual unconsidered assumptions. Hence, optimistic and pessimistic Malmquist Productivity Indexes (MPI) for overall and each stage productivity results are subsequently regressed against contextual variables related to airport characteristics and regional socio-demographic and economic indicators of each Eurasian country using bootstrapped Cauchy regressions. The findings revealed the spatial heterogeneity of productivity factors and airport performance across Eurasia. Results also demonstrated the negative impact of income inequality and the positive impact of private participation on technological progression in the Eurasian airport industry.     

Efficiency and productivity changes in Greek airports during the crisis years 2010–2014

The aim of this study is to evaluate the operating efficiency and productivity changes of the Greek airports, during the first years of the severe economic crisis in Greece (2010–2014), by using two methods: Data Envelopment Analysis (DEA) and Malmquist Productivity index (MPI). Findings have shown that, despite the dramatic effects of the economic crisis on the socio-economic life of the country, overall airport efficiency and productivity improved, mainly due to exogenous factors such as international tourism growth. The MPI reveals that over the period of the study, airports have experienced an annual average increase in total factor productivity (TFP) of 0.9% (an increase of 3.6% over the examined period). On examining the components of this productivity change, it becomes evident that this is due to the combination of both positive (a slight progress) annual average technology change (0.5%) and technical efficiency change (0.4%). The results also indicate that 65.8% of airports have an increase in average TFP during the period 2010–2014, ranging between 0.4% and 20%. However, as Greek airports operate at poor levels of efficiency, there is still considerable space for improvements in most of the airports.     

Operational performance evaluation of international major airports: An application of data envelopment analysis

This paper uses data envelopment analysis to assess the operational performance of 20 major airports around the world. It is found that the form of ownership and the size of an airport are not apparently correlated with operational performance of airports. In contrast, the existence of a hub airport, the location of the airport, and the economic growth rate of the country in which the airport is located are all related to the operational performance of airports. The 20 airports are put into four groups according to their efficiency values.     

Operational efficiency of Asia–Pacific airports

Airports are important drivers of economic development and thus under tremendous pressure from emerging competitors. However, few studies have analysed the operational efficiency of Asia–Pacific airports. This study therefore evaluated the operational efficiency of 21 Asia–Pacific airports between 2002 and 2011. A two-stage method was used: Data Envelopment Analysis (DEA) to assess airport efficiency, followed by the second-stage regression analysis to identify the key determinants of airport efficiency. The first-stage DEA results indicated that Adelaide, Beijing, Brisbane, Hong Kong, Melbourne, and Shenzhen are the efficient airports. The second-stage regression analysis suggested that percentage of international passengers handled by an airport, airport hinterland population size, dominant airline(s) of an airport when entering global airline strategic alliance, and an increase in GDP per capita are significant in explaining variations in airport efficiency.     

Spanish airports performance and efficiency benchmark. A PESA-AGB study

This study uses a MCDA tool to analyse and improve Spanish airports performance and efficiency. Thus, a holistic study using MACBETH (with PESA-AGB) is used. This study has never been applied before in Spanish airports. Firstly, a literature review related to this study keywords is conducted, as well as about benchmarking concept applied specifically to airports. Secondly, several methodologies in used to benchmark airports are reviewed and compared. Thirdly, airport performance and efficiency issues are addressed and described. Finally, the MCDA-MACBETH (with PESA-AGB) tool is applied to 4 Spanish airports.Spanish airports belonging to AENA transported 263, 753, 406 passengers in 2018 with an increase compared to 2017 of 5.8%. General data enables to conclude that.Spanish air transportation system is growing annually and hence there is the need to improve airports performance and efficiency, also to maintain the high levels of quality to address the growing demand. Spanish air transportation system is growing annually and is it upmost important to maintain high levels of quality to address such demand. Through this study, performance and efficiency improvements are seek within several airport key areas such as Safety and Security, Quality Service, Productivity and Effectiveness, Financial and Environment.     

The impact of airline lease agreements on the financial performance of US hub airports

This paper employs benchmarking analysis to examine the financial implications of the different types of airline lease agreements used by US airports. Five key financial performance areas relating to cost effectiveness, revenue generation, commercial performance, financial profitability and capital investment are analysed using financial data from 2011/12 for 23 of the 29 large-hub airports. The results show that compensatory airports are the most financially efficient, particularly in terms of debt efficiency, revenue generation and profitability while the vertical airport airline relationship that is common at residual airports delivers higher levels of commercial performance and cost efficiency.     

Competition and efficiency in the Italian airport system: new insights from a conditional nonparametric frontier analysis

We analyse the effect of competition on technical efficiency of Italian airports by applying a novel conditional nonparametric frontier analysis for the first time to the airport industry. We find that competition affects mostly the frontier of best performers, whilst airports that are lagging behind are less influenced. A novel two stage approach shows that, on average, competition has a negative impact on technical efficiency. We estimate a measure of pure efficiency, whitened from the main effect of the competition, whose distribution has a bi-modal shape, indicating the existence of two differently managed groups of airports.     

Hub airport slot Re-allocation and subsidy policy to speed up air traffic recovery amid COVID-19 pandemic --- case on the Chinese airline market

With international air travels largely banned around the world amid the global COVID-19 pandemic, many gateway and hub airports have more ideal slots available for reallocation. Airport traffic recovery replaces airport congestion to become the primary challenge of major airports around the world. With the pandemic well controlled domestically in China, the government liberalizes the hub airport slots for those previously forbidden services to the small/regional airports. This paper thus analytically examines the effect of this slot liberalization. The government subsidy to the small airports has also been considered. It is found that the slot liberalization can speed up airport traffic recovery for both hub and small airports. The hub airport slot liberalization leads to a lower level of minimum subsidy to sustain the survival of the small airports. Given any fixed level of subsidy to the small airport, both the total airport traffic and social welfare would improve with the slot liberalization at the hub airport. When the government can adjust the level of subsidy after liberalizing the hub airport slots, the subsidy could be excessive, if the government emphasizes too much on airport traffic recovery. This would, however, harm the overall social welfare.     

Bootstrapped scale efficiency measures of UK airports

This paper introduces the bootstrapping methodology to analyse and test the scale efficiency of airports. The results from a sample of 27 small and large UK airports indicate that most large airports are scale efficient or operating in a region of decreasing returns to scale, while most small airports are operating in a region of increasing returns to scale. We relate the results to the current industry trends and provide directions for future research.     

What are the sources of regional airport economic effectiveness? The dynamic capabilities perspective with evidence from Poland

The aim of this paper is to examine the sources of regional airport economic effectiveness from the perspective of the Dynamic Capabilities View, a much debated theoretical construct of organization and management discourse in recent years. The category of dynamic capabilities embraces those organizational competencies which enable organizations to modify their routines, operational capabilities and resources with the aiming of adapting to business environmental changes. The research results, conducted on a full sample of Polish regional airports, suggest that there is a difference among regional airports in perceiving the role of particular dynamic capabilities in achieving economic effectiveness. Some dynamic capabilities are more important for small regional airports in comparison with their larger rivals. Research conducted on similar groups suggests that heterogeneity in shaping dynamic capabilities exists within organizations operating in the same sector.     

Comparative analysis of efficiency for major Southeast Asia airports: A two-stage approach

Within the past few years, the economy of Southeast Asia has been expanding rapidly with the rise in demand for air transport services. International airports are important driving forces for Southeast Asia countries' economies. Therefore, the competitive ability of these airports is very essential in terms of development and competitiveness within the region. This paper investigates the capabilities of these airports by a two-stage approach. The strengths and weaknesses of each Southeast Asia airport are evaluated by Data Envelopment Analysis – Slack-Based Measurement (DEA-SBM) in the first stage. This is a non-parametric approach to measure the efficiency of 9 major airports in the area including Changi Airport (Singapore), Suvarnabhumi Airport (Thailand), Kuala Lumpur Airport (Malaysia), Soekarno Hatta Airport (Indonesia), Ninoy Aquino Airport (the Philippines), Noi Bai Airport, Tan Son Nhat Airport (Vietnam) and Guangzhou Baiyun Airport (China), Hong Kong Airport (Hong Kong). In the second stage, Tobit regression is used to estimate the influence of multiple factors toward the efficiency of each airport. By generalizing and comparing two stages, the analysis allows us to evaluate each airport's efficiency in seven years, as well as the impact of multiple factors in one airport's performance. The implications for improvement are also obtained and discussed.     

The difference between hub and non-hub airports – An airside capacity perspective

Airport airside capacity is limited by the runway system capacity or apron capacity, whichever is more constraining. Sometimes, taxiway system can also impose constraint to airside capacity, but in the case of fully developed taxiway systems (involving parallel taxiway, high speed exits, etc.) that is usually not an issue. To determine which airside element is more constraining it is not always as simple as comparing runway system and apron capacities directly one to another. It is important to understand and take into consideration their relationship. Runway-apron relationship depends on demand characteristics e.g. dominant market segments (e.g. scheduled, charter, low-cost, general aviation, cargo), and/or specific traffic patterns (hubbing or point-to-point services, seasonality in demand, etc.).The paper brings up the issue of available airside capacity under different traffic characteristics, faced by hubs vs. non-hub airports, and the necessity to understand runway-apron interdependency in order to properly identify the bottleneck on the airside. Referring to earlier findings related to apron capacity analysis, the paper summarizes various factors that affect apron capacity at non-hub and hub airports and uses them to define the runway-apron relationship, as well as its role in the process of analyzing airside capacity under various demand characteristics.The main finding is that functional relationship between the runway system and aprons is much stronger in the case of hub airports, and should be carefully considered when analyzing airside capacity. Besides runway capacity, few other variables that affect apron capacity at hub airports are discussed. Generic examples are used to support the discussion.     

Airport performance in a tourism receiving country: Evidence from Greece

The aim of this study is to estimate the performance of 38 Greek airports. The analysis is performed in two stages. Firstly, efficiency scores for each airport are estimated using Data Envelopment Analysis (DEA) original and bootstrap techniques. Secondly, statistical assessments (Mann–Whitney U and Kruskal–Wallis tests) and a censored Tobit regression model are employed to identify which factors significantly explain variations in the airport efficiency. The results indicated the scope for substantial efficiency improvements. In addition, island location, connectivity, and hotel infrastructure in the area were found to be significant factors affecting airport efficiency.