Public transit performance: What does one learn from frontier studies?

Borger, Bruno De; Kerstens, Kristiaan; Costa, Álvaro

doi:10.1080/01441640010020313

Cited by 189 publications

(114 citation statements)

References 40 publications

(50 reference statements)

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“…The results obtained are consistent with those from previous research. Great variations in the technical efficiency of urban transit systems were reported in the review study by De Borger et al (2002), where diverse authors' results vary from 24% to 100%. That study highlights the results obtained by Gathon (1989), who performed a parametric frontier model (translog production function) and found technical efficiencies between 58% and 100%.…”

Section: Fig 2 Efficiency and Effectiveness Scoresmentioning

confidence: 99%

“…The operational performance of transport networks has been studied by numerous authors; comprehensive reviews of these studies can be found in Dodgson (1985), Oum et al (1992), De Borger et al (2002), Brons et al (2005), and Karlaftis (2008). In spite of many previous studies on the operational performance of bus firms (Pina and Torres 2001;Boame 2004;Odeck 2008;Sampaio et al 2008;von Hirschhausen and Cullmann 2010;Karlaftis and Tsamboulas 2012) and long-distance railway firms (Caves et al 1980;Tretheway et al 1997;Cantos et al 1999;Casson 2009;Merkert et al 2010;Couto 2011;Wheat and Smith 2014), there has been less research on urban rail networks, and particularly on metro systems and the extent to which their productivity is affected by the internal management and socioeconomic environment, which is the scope of the current study.…”

Section: Literature Reviewmentioning

confidence: 99%

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Technical Efficiency of European Metro Systems: The Effects of Operational Management and Socioeconomic Environment

Lobo

Couto

2015

Netw Spat Econ

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This study focuses on the relationship between the operational performance of metro systems and their socioeconomic contexts. We use a two-stage methodology applied to a sample of 17 European metro systems. First, we apply a stochastic frontier approach to establish the optimal production function and to evaluate the efficiency and effectiveness levels of each firm through offer and demand-characterizing indicators, respectively. Only internal production factors are included in the first stage of this analysis. In a second stage, we use a similar modeling approach, but considering an additional set of variables characterizing the socioeconomic environment of the urban areas in which metro systems operate. This method allows observing the effects on operational performance measurements due to the inclusion of external factors, and consequently, drawing some conclusions on the technical efficiency of metro systems and their operations in beneficial or adverse surrounding environments. Different scores resulting from both perspectives evidence the contributions of the socioeconomic factors to improve the reliability of performance measurements and to reduce false inefficiencies. The results show that 12 of the analyzed systems are being affected by an unfavourable socioeconomic environment and/or their network suffers from some adequacy problems with regard to demand. The remaining 5 systems should improve their management strategies, since their results are being supported by a favourable surrounding environment.

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Section: Fig 2 Efficiency and Effectiveness Scoresmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Technical Efficiency of European Metro Systems: The Effects of Operational Management and Socioeconomic Environment

Lobo

Couto

2015

Netw Spat Econ

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“…During the early 1980s, performance measurement using frontier analysis entered the discussion (for a review, see e.g., De Borger et al 2002 andKerstens 2008).…”

Section: Introductionmentioning

confidence: 99%

Cost Efficiency and Subsidization in German Local Public Bus Transit

Nieswand

Walter²

2010

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract Subsidies are considered important means to facilitate the provision of public transit, yet the empirical evidence implies that they can have harming effects on costs and possibly also on operators' performance. This paper examines the impacts of deficit-balancing subsidies on the cost inefficiency of local public bus companies in Germany, where a complex system allocates ample financial support. Our empirical analysis relies on a unique dataset of 33 companies observed over a period of up to twelve years for a total of 231 observations. We employ a stochastic frontier cost function for panel data that account for unobserved heterogeneity and provide firm-specific, time-varying inefficiency estimates. Further, we allow variations in the optimal technology by randomizing some cost functions' coefficients in one of our model specifications. Subsidies directly enter the inefficiency function as a heteroscedastic variable. We find a positive effect of subsidies on the standard deviation of inefficiency, which implies that the range of companies' inefficiency increases with the level of subsidies relative to total costs. However, we also find that non-subsidized firms perform better in terms of cost efficiency. Terms of use: Documents in JEL Codes: C13, D24, L92

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“…Since the first papers applying DEA to public transportation were published in 992, the procedure has become increasingly popular for comparing transit organizations with each other (Brons et al 2005;De Borger, Kerstens, and Costa 2002). However, DEA has not been used to compare subunits within a given transit organization.…”

Section: Data Envelopment Analysismentioning

confidence: 99%