ANALYSIS OF ECONOMIES OF SCOPE IN EUROPEAN RAILWAY BY THE USE OF DATA ENVELOPMENT ANALYSIS



ANALYSIS OF ECONOMIES OF SCOPE IN EUROPEAN RAILWAY BY THE USE OF DATA ENVELOPMENT ANALYSIS

Authors

EMMANUEL BOUGNA, Laboratory of Transport Economic, YVES CROZET, Laboratory of Transport Economic

Description

This study use DEA method and a pan-European dataset from 27 European countries for the period 2000 to 2010 to test the hypothesis that integrate railway realizes economies of scope and thus produce railways service with higher level of efficiency

Abstract

In the late 1980s and early 1990s, European national governments and the European Union Commission have developed several reforms into the European railway industry. These reforms aim to completely liberalize the European railways but also to separate infrastructure manager and railway operator. The rationale for separation was that it would provide free access to the infrastructure for transport operators and enhance competition within the railway industry. However in many European countries, vertically integrated firms still own the railway infrastructure and participate in the transport segment . Thus a decision for or against institutional separation necessitates an analysis of potential economies of scope within the railway sector. Economies of scope exist when joint production is more efficient than separate production for several kinds of activities. In the case of railways organization, economies of scope occur when it is more efficient for a single firm to produce certain output vector than for two or more firms to produce the same output vector separately. In this paper, we analyze the performance of European railways companies. In particular we focus on economies of vertical integration. Like Growitsch and Wetzel (2009), we test the hypothesis that integrate railway realizes economies of scope and thus produce railways service with higher level of efficiency. Our analysis adopts two-step approach. In the first step, we estimate the technical efficiency of integrated and non-integrated railways using the non-parametric data envelopment analysis (DEA), which allows us to avoid any specific assumption about the underlying technology’s functional from. In the second step, we determine whether joint or separate production is more efficient by applying a DEA super-efficiency model, which relates the efficiency for the integrated production to a reference set consisting of the separate production technology. Our pan-European dataset incorporates railway firms from twenty-seven European countries for the period 2000 to 2010. In our dataset firms are divided into four different groups: Integrated firms, infrastructure managers, passenger operators, and freight operators. The data was taken from the railways statistics published by Union Internationale des Chemins de Fer (UIC) and combine with information from the companies annuals reports.

Publisher

Association for European Transport