بهره وری شرکت های عوارض جاده ای نروژی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|4395||2011||10 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Utilities Policy, Volume 19, Issue 3, September 2011, Pages 162–171
This paper analyses the level of efficiency at which road toll companies are operated in Norway. Two alternative methods are applied for this purpose: data envelopment analysis (DEA) and stochastic frontier analysis (SFA). The data comprise a total of 20 toll companies that have been in operation in the period 2003–2008. The findings of the paper are as follows: 1) There is a great potential for efficiency improvement in the sector, irrespective of the method used, but the variation in the efficiency scores is dependent on the method used; 2) there is no evidence of economies of scale, as has been found by other authors, such as Odeck 2008, How efficient and productive are road toll companies? Evidence from Norway, Transport Policy. 15, 232–241 and, Amdal, E., Bårdsen, G., Johansen K. and Welde M., 2007. Operating costs in Norwegian toll companies: a panel data analysis. Transportation. 34, 681–695. These results suggest that toll companies could generate significant savings by employing industry best practices. Further, decision makers are warned not to be indifferent to the approach used i.e., DEA and SFA, as these may give very different results.
Tolls are used as an instrument to finance new road infrastructure throughout the world, and the increasing share of toll financing compared to public finance and the increasing number of companies involved illustrate that the practice of toll financing and -collection has become an industry of its own. Norway provides an example of a country that relies heavily on tolls; currently, more than 40 percent of the country’s total annual budget for road construction is paid for by tolls. According to the newly released National Transport Plan for the years 2010 to 2019, this percentage is expected to increase in the future. Toll financing is organised differently between countries. From pure commercial enterprises responsible for construction, maintenance and finance, through public-private partnerships with varying degrees of risk sharing, to not-for-profit companies established solely with the purpose of providing finance to get roads constructed faster, toll roads are organised in many different ways. What all toll roads have in common, though, is a need to collect tolls from the motorists as efficiently as possible, that is, to run the charging points or toll stations at a minimum of cost and to minimise the disturbance to traffic while tolls are collected. From a commercial point of view, the costs of collecting tolls, the operating costs, reduce profit margins and increase the payment period of loans. Operating costs are real costs and are also important from a socio-economic point of view; higher operating costs lead to a lower net present value of a toll-financed road. As stated by Amdal et al. (2007), all toll roads should provide a net benefit in terms of a social cost-benefit analysis, generate substantial net revenues and be acceptable to a major proportion of the public. Minimising the operating costs is critical for meeting all three of these criteria. Data on operating costs in toll companies are rare and often regarded as competition-sensitive information, which is not readily available to researchers. In this context, Norwegian toll financing provides an interesting case. Here, detailed cost data from over 40 toll companies operating in different geographical regions and employing different tolling technologies are available to the authorities annually. This allows us to answer several interesting questions. These are as follows: (1) Do companies operate as efficiently as their peers? (2) Do they progress in their operations? (3) Which factors outside the toll companies’ control determine their inefficiency? And finally, but not least: (4) What could be done to improve the efficiency of toll companies? These questions should be of interest to toll companies, authorities and motorists alike. Because tolls are removed as soon as possible once the costs of constructing the road are covered, this means that if toll companies operate efficiently, then tolls can be removed even earlier. Because tolls are a cost to road users, their removal will incur benefits to roads user and to society. Studies of elasticities in 20 Norwegian toll projects suggests an average elasticity of −0.56, meaning that an increase in generalised costs due to tolls by 10% will reduce traffic by 5.6% (Odeck and Bråthen, 2008). Further, gauging the impact of factors that may influence efficiency such as the technology for toll collection may give additional information relevant for improving performance in the toll road industry. The literature on the efficiency measurement of toll operations is limited, even if tolling is practised widely throughout the world. However, a related issue that has been debated in the transportation literature are the operating costs of tolls; see, for instance, Prud’homme and Bocajero, 2005 and Mackie, 2005 and Raux (2005) regarding the operating costs in the London congestion-charging scheme. The Stockholm congestion-charging system has attracted great attention from researchers since its implementation in 2006. Eliasson (2009) has conducted a cost-benefit analysis of the scheme and have found the operating costs to represent the highest loss to society, but also to be the variable with the largest potential for efficiency improvements. The Italian motorway network is mainly financed by tolls. There, Benfratello et al. (2008) have studied the costs of the motorway concessionaires over the years 1992–2004 and found significant technical progress and sizable economies of density and scale with an L-shaped average cost curve over the range of output. In the Norwegian context, which we relate to in this paper, Welde and Amdal (2006) and Amdal et al. (2007) have investigated the levels of operating cost per vehicle in the Norwegian toll road industry. They applied regression analysis using panel data and found that operating costs varied tremendously from 5% to 40% of gross revenues with the larger toll companies serving larger traffic levels having lower operating costs per vehicle served. With an L-shaped average cost curve and most companies operating at traffic levels below their minimum efficient scale, the authors concluded that there were very important unexploited economies of scale in the industry and that inefficiencies most likely were present. Odeck, 2008a and Odeck, 2008b extended these studies, but in the context of efficiency and productivity measurement using data envelopment analysis (DEA). He verified the claims by Welde and Amdal (2006) and Amdal et al. (2007) to the extent that there are scale economies in the sector and that there are potentials for efficiency improvements and added that toll companies have in fact improved their productivity over the years studied, possibly as a result of using new technologies for toll collection. The objective of this paper is to contribute further to the debate surrounding the performance of toll companies in Norway. Specifically, this study extends those of Odeck, 2008a and Odeck, 2008b in two particular directions; it uses newer data and compares two methods to efficiency measurements (DEA and SFA) to determine how the magnitudes of inefficiencies are impacted by the method being used. The rest of this paper is organised as follows. Section 2 gives a short overview of the tolling industry in Norway. Section 3 assesses the potential for efficiency improvements in light of principal agent theories. Section 4 gives a brief account of the theoretical model to be applied. Section 5 describes the data to be used in the analysis, and Section 6 presents the results. Concluding remarks are given in section 7.
نتیجه گیری انگلیسی
The objective of this paper has been to analyse the efficiency of Norwegian non-profit toll companies established for the purpose of collecting funds for road investments. We used two approaches, DEA and SFA, to infer the level of inefficiency in the sector. Our results reveal the following: 1) A formidable potential for efficiency improvement exists in the Norwegian toll road industry, and this potential varies to a great extent among toll the companies; this is irrespective of the method applied. 2) The technology used for collecting tolls matters to the efficiency of companies to the degree that companies who use electronic tolling systems are more efficient as compared with those that only use manual collection systems. Further, charging passengers rather than only vehicles leads to more inefficiency. 3) There is no evidence that that efficiency of tolls vary with the size of companies. This result is contrary to what has been observed by other authors, such as Odeck, 2008a and Odeck, 2008b and Amdal et al. (2007). 4) Finally, the methods applied give different results and hence one should not be indifferent as to the method being applied when measuring efficiency in sectors such as the road toll industry. The findings of this paper have implications for decision makers in the Norwegian toll industry. A first recommendation is that the use of modern technology has been a success both in providing a better service to motorists and as a tool for making toll companies more efficient. However, as there are indications that service improvements have caused costs to increase, there is reason to consider a separation of the role of the issuer of the payment means and the operation of payment systems. Secondly, the NPRA should provide guidelines on how toll companies can be run efficiently. Such guidelines should provide examples of best-practice companies that less inefficient companies can learn from. The DEA framework used in this study readily reveals such best performances for individual inefficient companies to compare themselves with. Finally, researchers and authorities alike should not be indifferent as to the methodology being used to assess efficiency, as the two different methods used in this study produced different results.