شواهد جدید درمورد بهره وری فرودگاه های ایتالیا : تجزیه و تحلیل تحلیل پوششی داده راه اندازی شده
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|4371||2011||10 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Socio-Economic Planning Sciences, Volume 45, Issue 2, June 2011, Pages 84–93
A bootstrapped DEA procedure is used to estimate technical efficiency of 18 Italian airports during the period 2000–2004. Departing from previous studies, we separate the efficiency related to ability to manage airside activities (operational) from that related to the management of all business activities (financial). In general, Italian airports operate at poor levels of efficiency, with slightly better performance in terms of their financial activities. In the current study, selected intrinsic and environmental characteristics are considered as possible drivers of Italian airport performance. In particular, we found that: (i) the airport dimension does not allows for operational efficiency advantages, (ii) on the other hand, the airport dimension allows for financial efficiency advantages for the case of hubs and disadvantages for the case of the smallest airports (iii) the type(s) of concession agreement(s) might be considered as important source of technical efficiency differentials for those airports running marginal commercial activities; (iv) the introduction of a dual-till price cap regulation might create incentives which lead to the increase of financial efficiency at the detriment of the operational performance. Lastly, the development of a second hub (Milano Malpensa), has negatively affected the performance of the country’s national hub (Roma Fiumicino).
In recent years, airports have been under growing pressure to be more financially self-sufficient and less reliant on government support. Many airports around the world have been commercialized and/or privatized so that airports are operated more like a business  and . Moreover, the increased airline competition brought about by deregulation and liberalization has heightened this recognition and placed airports in a much more competitive environment . To keep pace with such developments, selected recent research efforts have devoted themselves to analyzing the operational performance, in terms of efficiency, of airports, and key issues related to changes in the industry. Despite this interest, the measurement of airport efficiency is not an easy task given the complexity involved at both the firm and industry levels. Indeed, the airport could be considered as a multi-product firm, where disparate elements and activities are brought together to facilitate, for passengers/customers and freight, an interchange between air and surface transport . In addition, government interventions and industry structure could make performance assessment that much more involved. Given these conditions, benchmarking might represent an effective approach for moving airports in the direction of ‘best practices’ . Various approaches have been used in the literature to deal with benchmarking. One of the most widely known and accepted is Data Envelopment Analysis (DEA) where the frontier, constructed using linear programming (LP), is the benchmark against which the relative performance of the decision making units (DMU) such as airports is measured. Most empirical applications of DEA to airports have investigated their efficiency at the individual country level. For example, beginning with the seminal work of Gillen and Lall , recent papers have focused on the U.S. (e.g.,  and ), Australian (e.g., ), Japanese (e.g., ), and Brazilian (e.g., ) markets. Within the European context, Parker  examined the UK market while Martìn and Romàn  considered that of Spain. Importantly, there appear to be relatively few cross-country studies: Adler and Berechman , Oum et al. , ,  and  and Ling and Hong  analyzed the performance of airports around the world, while Pels et al.  and  focused on Europe. As far as the Italian case is concerned, a number of DEA-based studies have appeared in recent years, but with mixed or apparently contradictory results. Malighetti et al.  investigated the efficiency and productivity changes of 34 Italian airports for the period 2005–2006. Low average efficiencies were found with evidence of improved performance among airports larger than 5 millions of passengers. Further, hub premiums and the privatization process were found to be positive drivers of performance, while military activities and seasonality effects appear to operate as obstacles . The authors also investigated scale inefficiency at firm level, finding that Milano Malpensa and Roma Fiumicino work under decreasing returns to scale, while other airports with less than 5 millions of passengers operate with increasing returns to scale . Two papers by Barros and Dieke  and  analyzed 31 Italian airports during the period 2001–2003. They introduced the Simar and Wilson methodology  shown high values of efficiency,2 positively affected by drivers such as size, private management, as well as high levels of workload units (WLU). In results different from those of Malighetti et al. , Barros and Dieke  and  found that most airports in their sample operated under a constant returns to scale. Recent work by Curi et al. , extend the findings by Barros and Dieke  and found low levels of efficiency amongst Italian airports, in line with results offered by Malighetti et al. . The present work aims to measure the efficiency of Italian airports from two managerial points of view: the first is strictly related to operational activities, while the second considers the ability to generate financial returns from all airport business activities. This breakdown may hopefully provide support for decisions regarding the effective utilization of airport infrastructure, as well as the generation of meaningful financial returns. Results from the two perspectives are then jointly discussed. Moreover, we formally test for global returns to scale, distinguishing technology (namely, the frontier), in the context of a non-parametric model . We then explore recently developed statistical inference tools for DEA , which have realized success in earlier studies  and . These tool sets (bias correction, and confidence intervals associated with DEA scores) are particularly useful when the sample size is small, and the number of dimensions used in the production model is high, as is the case of the Italian airport industry. In what follows, we first describe the Italian airport industry (Section 2), then briefly discuss the methodology adopted (Section 3), as well as the data (Section 4). In Section 5, the study’s results and selected comments are presented, with concluding remarks offered in Section 6.
نتیجه گیری انگلیسی
This paper makes two potentially crucial contributions to the literature in identifying new evidence of efficiencies in Italian airports. The first, involves the value in exploiting advanced bootstrapping tools when applied to DEA. This helps disentangle some of the main efficiency drivers in the analysis of the Italian scenario. The high level of heterogeneity across airports and the small sample size, both of which negatively affect any DEA’s measurement in terms of bias and variability, are well captured by this mechanism. Second, modelling the production process from operational (airside activities) and financial (airside and landside activities) points of view can be helpful in defining the direction of future policy interventions. Comparison of the two models reveals that, while operational efficiency steadily declined by 2001, financial efficiency rose. These opposite trends may reflect both traffic contraction, due to the 11th September attacks, and the introduction of a dual-till price cap on aeronautic services, which might have caused capacity under-investment with a subsequent reduction in services’ quality and a connected decline in operational efficiency . Our statistical analyses provide evidence that airport dimension is not necessarily critical in creating differences in operational efficiency across Italian airports. However, it does assume greater weight when the entire business is considered. In this regard, the SRA airports are the only exceptions: They show, doubtless, the lowest level of technical efficiency under both perspectives. As a consequence, efficiency gain could be reached by an authority intervention through traffic reallocation from airports which are close to each other, such as Ancona and Pisa. The bi-matrix analysis indicates that airports holding a total concession tend to obtain the best technical efficiency under both perspectives. Thus, our results support the Government’s decision to grant NAA and LRA airports a Total concession from a technical perspective. Regarding the two Italian hubs, Roma Ciampino and Milano Malpensa, the Government’s intervention had, at least in the short run, a negative impact on the operational efficiency of Ciampino. As part of our future research program, we will further investigate, in a more robust statistical format, the impacts of concession agreements and dual-till price caps on technical efficiency.