Insularity and the development of a local railway network

Abstract

This paper quantitatively assesses the negative impact of land discontinuity on the development of a railway network on an island. This implicit cost of insularity is because an insular railway network only serves the territory in which it is located while the same network on a mainland also serves other regions. We apply this idea to the case of a simplified Italian railway network and we implement it through a simulation model. The simulation results highlight the strong negative effect of land discontinuity: whereas the railway lines located on the island of Sardinia are the least profitable under the factual scenario, their relative profitability is significantly boosted in every counterfactual scenario where the land discontinuity is artificially removed.

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Fig. 1

Source: CRENoS elaboration on data from Atlante Geografico De Agostini CRENoS (2014)

Fig. 2

Source: CRENoS elaborations on Istat data CRENoS (2015)

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Notes

  1. 1.

    EURISLANDS is part of the ESPON program and its aim is to “deliver an appropriate reference work and a set of policy recommendations and strategic guidance to foster the sustainable development of the European islands within the framework of the Single Market, ensuring equal terms and opportunities with other non-handicapped regions”.

  2. 2.

    The CRENoS Annual Report on Sardinian Economics estimates that its contribution is more than 8% of the total regional value added CRENoS (2014).

  3. 3.

    We emphasize that our paper can be also related to policy-oriented literature. Among these groups of papers we find Armstrong and Read (1998; 2004), Armstrong et al. (2006) and Bertram and Karagedikli (2004) who discuss the evidence on the impact of insularity on economic growth.

  4. 4.

    To manage the computational complexity, we simulated the evolution of a railway network in which a railway station can be located only in an urban center.

  5. 5.

    This equation stems from a “market” approach but, normally, a central planner assesses the expected flow of passengers to evaluate the social profitability of the investment in the construction of a railway line.

  6. 6.

    Of course we admit that different railway lines can be linked to several building costs per km (it is certainly more expensive to build railway lines in mountainous areas), including different expected duration period and maintenance costs. As our main aim is to evaluate the impact of insularity on the profitability of a railway line, we think that, as a first approximation, there are not any a-priori reasons why there should be a significant difference in these elements between islands and the mainland.

  7. 7.

    The actual road distance between Cagliari and Sassari is actually shorter (214 km) than the one between Rome and Florence (274 km).

  8. 8.

    In reality, Rome and Florence are actually much more populated and richer than Cagliari and Sassari.

  9. 9.

    Travelers can also choose the longest path. The choice of the longest path might be motivated by reasons linked to habit or the beauty of the landscape.

  10. 10.

    Any other assumption different from the one induced by a cost-minimizing behavior is difficult to motivate without a fully-specified micro-founded model of passengers’ behavior.

  11. 11.

    We implemented the model using an alternative different assumption. We assumed that passengers choose a given path if and only if it is sufficiently shorter than others. Specifically, the “point 4” of the procedure, illustrated in Sect. 3 to compute \(F_{ab}\), was modified as follows:

    1. 1.

      compute \(f'_{ij}\) and put \(g_{ij}=f'_{ij}\)

    2. 2.

      if \(L'_{ij} < \gamma L_{ij}\) then \(g_{ij}=f'_{ij}\)

    3. 3.

      if \(L_{ij} \le \gamma L'_{ij}\) then \(g_{ij}=0\)

    4. 4.

      if \(\gamma L_{ij} < L'_{ij} \le L_{ij}\) then \(g_{ij}=\frac{1}{2}[1+\frac{L_{ij}-L'_{ij}}{L_{ij}(1-\gamma )}]f'_{ij}\)

    5. 5.

      if \(L_{ij} \le L'_{ij} \le \frac{L_{ij}}{\gamma }\) then \(g_{ij}=\frac{1}{2}[1-\frac{L'_{ij}-L_{ij}}{L'_{ij}(1-\gamma )}]f'_{ij}\)

    6. 6.

      \(F_{ab}=F_{ab}+g_{ij}\)

    The results obtained are very close to those related to the simple version of the procedure presented in Sect. 3.

  12. 12.

    We considered 107 railway stations out of the actual 2212. Source: http://www.rfi.it updated at 01/29/2016

  13. 13.

    We focus on ranking rather than the resulting values of the investment profitability because the model is too simplified to consider these values as a good approximation of reality. Thus, we employ an ordinal approach, rather than a cardinal one. However, this approach is able to generate some interesting quantitative predictions.

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Acknowledgements

This research has benefited from the financial support of the Regione Autonoma Della Sardegna (Legge n. 7) under the project “Analysis of the additional economic costs of the state of insularity”.

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Correspondence to L. Cocco.

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We thank Luca De Benedictis, Italo Meloni, Gianmarco Ottaviano, Anna Maria Pinna, Benedetta Sanjust, Andrés Rodriguez-Pose, Alan Winters for insightful conversations and suggestions. The usual disclaimers apply.

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Cerina, F., Cocco, L., Mannaro, K. et al. Insularity and the development of a local railway network. Econ Polit 37, 683–702 (2020). https://doi.org/10.1007/s40888-019-00158-4

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Keywords

  • Insularity
  • Simulation modeling
  • Railway networks
  • Regional development

JEL Classification

  • R41
  • R12
  • R58
  • C63