Correlation within SNCF administrative regions among track segment maintenance cost equation residuals of a country-wide model

Marc Gaudry and Emile Quinet
Additional contact information
Marc Gaudry: AJD - Agora Jules Dupuit - UdeM - Université de Montréal
Emile Quinet: PSE - Paris-Jourdan Sciences Economiques - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

PSE Working Papers from HAL

Abstract: Using as reference a recent France-wide model of rail infrastructure maintenance cost where regression model residuals associated to track segments are assumed to be similarly correlated among themselves within the 23 administrative regions of the national firm (SNCF), we attempt to explain the presence of the strong positive and stationary correlation coefficient estimated in this manner and to probe the extent to which the assumption of a common correlation coefficient across administrative regions might be refined and interpreted. We first find that country-wide within-region correlation among residuals is not weakened if regional dummy variables are added to the model in the hope of finding interpretative clues, notably of the presence of climate effects unaccounted for in the model specification. The implicit region-specific weather effects indirectly so represented turn out to be extremely weak, if present at all, and do not affect the strength of extant within-region correlation or the need to make sense of it. We then explore differences in correlation coefficient estimates among regions and show that, within the reference maintenance cost model, two large geographic groupings of regions, each comprising in the East or in the North about 15% of total available track segments, in fact have own residuals that are uncorrelated among themselves, in contrast to the more numerous 70% of segment residuals remaining in the rest of regions, which as a group remain robustly and positively correlated, and in a stationary manner. As the two groupings with uncorrelated residuals closely match the networks of regional firms merged into the SNCF conglomerate in 1938, we hypothesize, faute de mieux and in the absence for the moment of refined local climate variables to pursue unpromising missing variable tests, that within-firm accounting traditions might have survived centralized management control of the assignment of track surveillance, maintenance and repair invoices to track sections centrally defined for accounting management purposes.

Keywords: rail infrastructure; current maintenance cost; rail track degradation; Box-Cox transformation; directed autocorrelation; spatial autocorrelation; SNCF; French rail network; network data by segment; SNCF administrative regions; regional differences (search for similar items in EconPapers)
Date: 2015-02
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01112249v1
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://shs.hal.science/halshs-01112249v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:psewpa:halshs-01112249

Access Statistics for this paper

More papers in PSE Working Papers from HAL
Bibliographic data for series maintained by CCSD ().