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Brazilian railways separability infrastructure/operations: investigation by production indicators

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Abstract

This investigation of the scale, scope and production density of Brazilian railways engaged in freight transportation would be invaluable and the results could support verticalization measures or measures to separate operation and infrastructure as well as providing an understanding of concession performance in recent years. This article proposes a non-parametric approach using Data Envelopment Analysis—DEA—to measure scale and scope and addresses the relation of the railways’ production density, scale scope and efficiency using a system of equations, graphic analysis, simple regressions, and an automatic selection of variables. There is a perceptible, statistically significant relation of production density with scale and positive efficiency, suggesting a relation between increase in net freight per kilometer transported and efficiency gains via scale level, but the relation with economies of scope indicate the possibility of diseconomy occurring, albeit not statistically significant. Furthermore, there is heterogeneity among the production scales. The results show the need for further investigation because scale is influenced by production density insofar as the companies with the greatest production density are those with the greatest efficiency. The results are not sufficiently robust to categorically state which structure is better: verticalization or separation dilemma. However, they do point to heterogeneity of treatment among the companies.

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Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due is a private information worked to the paper but are available from the corresponding author on reasonable request. We have annotated the entire data building process and empirical techniques presented in the paper.

Notes

  1. Morris (2005) describes that relationship in the United States context and particularly the cases of Andrew Carnegie and John D. Rockefeller.

  2. TUthousands = useful ton; TU_MINFER = useful ton ore; TU_OUTROS = useful ton non-ore; TKUmilions = Ton Usable Kilometer; TKU_MINFERR = Ton Usable Kilometer ore; TKU_OUTROS = Ton Usable Kilometer non-ore; VMC = average commercial speed; VMP = Average Travel Speed; Trenskm = kilometer trains; TrensForm = formed trains; PESS_T = total workers; PESS_Opera = operational workers; PESS_Infra = infrastructure workers; LocoMedio = medium locomotives; ManutLoco = Locomotive Maintenance; VagHoras = Average number of wagons per hour; VagMed = Average Number of Wagons; ManutVag = Wagons Maintenance; ConsCombusTKU = TKU Fuel Consumption; ConsCombusTKB = TKB Fuel Consumption; ProdM_Vag_TU = Average Production Wagon TU; ProdM_Vag_TKU = Average Production Wagon TKU; PercMedioLoco = Medium Route Locomotive.

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Funding

This study was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) through grants 422898/2018-0.

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  1. Economy Graduate Program of the Universidade Federal Do Ceará (Caen/UFC), Rua: Marechal Deodoro, 400 Sala 410, Fortaleza, CE, CEP: 60020-060, Brazil

    Francisco Gildemir Ferreira da Silva

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da Silva, F.G.F. Brazilian railways separability infrastructure/operations: investigation by production indicators. SN Bus Econ 2, 78 (2022). https://doi.org/10.1007/s43546-022-00245-y

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