Pasenger Transport companies CZE-AUT
Description
Data for Austrian companies are sourced from the ORBIS (Bureau van Dijk) database. Data for Czech companies is drawn from the MagnusWeb (Dun & Bradstreet Czech Republic, a.s.) database. Data were cross-referenced and validated using annual reports from the Register of Deeds. We collected data for companies that published their reports and were active for more than three years during the period from 2015 to 2019. These five years were selected as the most distant period in time for the Czech companies, as the first one started to operate in 2011, and others joined in the years to come, with Arriva in 2013. We wanted the companies to be somewhat established, as most of the Austrian non-incumbent companies already existed longer in the market. Table 1: Overview of Passenger Railway Companies (2015-2019) COMPANY OBSERVATIONS SERVICES Czech companies České dráhy, a.s. 5 (2015-2019) Passenger ARRIVA vlaky s.r.o. 5 (2015-2019) Passenger CityRail, a.s. 5 (2015-2019) Mixed GW Train Regio a.s. 4 (2016-2019) Passenger Jindřichohradecké místní dráhy, a.s. 5 (2015-2019) Passenger Leo Express Global a.s. 5 (2015-2019) Passenger RegioJet a.s. 5 (2015-2019) Passenger Austrian companies Innsbrucker Verkehrsbetriebe und Stubaitalbahn GmbH 5 (2015-2019) Passenger Lokalbahn Mixnitz-St. Erhard AG 5 (2015-2019) Mixed Montafonerbahn AG 5 (2015-2019) Passenger ÖBB-Personenverkehr Aktiengesellschaft 5 (2015-2019) Passenger Salzburg AG fuer Energie, Verkehr und Telekommunikation 5 (2015-2019) Passenger Stern & Hafferl Verkehrsgesellschaft M.B.H. 5 (2015-2019) Passenger WESTbahn Management GmbH 5 (2015-2019) Passenger Wiener Lokalbahnen GmbH 5 (2015-2019) Passenger Zillertaler Verkehrsbetriebe AG 5 (2015-2019) Passenger
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The core of our quantitative analysis relies on Data Envelopment Analysis (DEA), a non-parametric, linear programming-based method for evaluating the relative efficiency of a set of comparable decision-making units (DMUs). DEA constructs an efficiency frontier from the observed input-output combinations of all DMUs and evaluates each unit’s performance relative to this frontier. To capture inefficiencies more precisely, we adopt the Slack-Based Measure (SBM) of efficiency (Tone, 2001; Strange et al., 2021). Unlike radial models such as CCR or BCC, the SBM model is non-radial and non-oriented, directly accounting for input excesses and output shortfalls (slacks). This makes it particularly suitable for analyzing performance in contexts—like rail transport—where input and output inefficiencies may not be proportional. We implement an input-oriented SBM model under Variable Returns to Scale (VRS) to accommodate the heterogeneity in company size across our dataset. The input orientation aligns with managerial control, focusing on reducing input consumption without sacrificing output levels. VRS is particularly appropriate in this context, as it does not assume proportional scaling of inputs and outputs and avoids penalizing small or large firms due to scale differences. To further refine our analysis, we decompose each firm’s overall efficiency into pure technical efficiency (PTE) and scale efficiency (SE). This is achieved by running the same SBM model under both VRS and Constant Returns to Scale (CRS). We compute annual input-oriented SBM efficiency scores for each railway operator over two separate periods: 2015–2019 and 2022–2024. To contextualize performance, we also calculate each firm’s efficiency relative to the national incumbent: České dráhy, a.s. (Czech Republic) and ÖBB-Personenverkehr AG (Austria). These results are aggregated to produce both period averages and firm-level multi-year averages. In addition, we calculate Scale Efficiency (SE) for each firm by comparing SBM scores under CRS and VRS assumptions. This allows us to differentiate whether a firm’s inefficiency stems from operational issues or suboptimal scale. Such decomposition is crucial for informing both internal benchmarking and potential policy recommendations regarding market structure or consolidation. Limitations of the Study While this study provides a valuable application of Data Envelopment Analysis (DEA) to the Czech and Austrian passenger rail markets, several limitations must be acknowledged. First, DEA’s non-parametric nature makes it sensitive to outliers and measurement error, as it does not account for statistical noise. Efficiency scores are relative to the best-performing firm in the sample and can be distorted when the number of variables exceeds the number of decision-making units (DMUs).
Institutions
- Univerzita Karlova