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Exponential or power distance-decay for commuting? An alternative specification


  • Jacob J. De Vries


  • Peter Nijkamp


  • Piet Rietveld



In this paper we investigate the form of the distance-decay function for commuting, on the basis of an analysis of home-to-work relationships between municipalities in Denmark. The equation for the number of commuters is taken from Alonso’s Theory of Movements, in which the Spatial Interaction Models of Wilson’s Family are nested. Our estimation method separates the decay function F from the balancing factors, and includes a weighting procedure that takes specification error and heteroscedasticity into account. It appears that neither an exponential nor a power distance-decay function fits the data well. The specification of log F as a (downwards) logistic function of log cost results in a better fit. We find that the cost elasticity reaches a value of –4 for distances around 24 km, while it is close to for both very short and very long distances. Finally, we demonstrate that the choice of functional form for distance-decay can make an important difference for predictions concerning the effect of infrastructure improvements on commuting flows.

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  • Jacob J. De Vries & Peter Nijkamp & Piet Rietveld, 2005. "Exponential or power distance-decay for commuting? An alternative specification," ERSA conference papers ersa05p261, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa05p261

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    Cited by:

    1. Tobias Scholl & Thomas Brenner, 2013. "Detecting Spatial Clustering Using a Firm-Level Index," Working Papers on Innovation and Space 2012-02, Philipps University Marburg, Department of Geography.
    2. Aura Reggiani & Pietro Bucci & Giovanni Russo, 2011. "Accessibility and Network Structures in the German Commuting," Networks and Spatial Economics, Springer, vol. 11(4), pages 621-641, December.
    3. Matthias Duschl & Tobias Scholl & Thomas Brenner & Dennis Luxen & Falk Raschke, 2015. "Industry-Specific Firm Growth and Agglomeration," Regional Studies, Taylor & Francis Journals, vol. 49(11), pages 1822-1839, November.
    4. Katarzyna Kopczewska, 2011. "Roads as Channel of Centrifugal Policy Transfer. Spatial Interactions Model Revised," ERSA conference papers ersa11p720, European Regional Science Association.
    5. M. Alonso & M. Beamonte & P. Gargallo & M. Salvador, 2014. "Labour and residential accessibility: a Bayesian analysis based on Poisson gravity models with spatial effects," Journal of Geographical Systems, Springer, vol. 16(4), pages 409-439, October.
    6. Duschl, Matthias & Schimke, Antje & Brenner, Thomas & Luxen, Dennis, 2011. "Firm growth and the spatial impact of geolocated external factors: Empirical evidence for German manufacturing firms," Working Paper Series in Economics 36, Karlsruhe Institute of Technology (KIT), Department of Economics and Business Engineering.
    7. Moises Lenyn Obaco Alvarez & Vicente Royuela & Xavier Vítores, 2016. "Computing functional urban areas using a hierarchical travel time approach," ERSA conference papers ersa16p238, European Regional Science Association.
    8. Floriana Gargiulo & Maxime Lenormand & Sylvie Huet & Omar Baqueiro Espinosa, 2012. "Commuting Network Models: Getting the Essentials," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(2), pages 1-6.
    9. John Östh & Aura Reggiani & Giacomo Galiazzo, 2014. "Novel methods for the estimation of cost–distance decay in potential accessibility models," Chapters,in: Accessibility and Spatial Interaction, chapter 2, pages 15-37 Edward Elgar Publishing.
    10. Karst T. Geurs & Kevin J. Krizek & Aura Reggiani, 2012. "Accessibility analysis and transport planning: an introduction," Chapters,in: Accessibility Analysis and Transport Planning, chapter 1, pages 1-12 Edward Elgar Publishing.
    11. Chakraborty, A. & Beamonte, M.A. & Gelfand, A.E. & Alonso, M.P. & Gargallo, P. & Salvador, M., 2013. "Spatial interaction models with individual-level data for explaining labor flows and developing local labor markets," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 292-307.
    12. Matthias Duschl & Antje Schimke & Thomas Brenner & Dennis Luxen, 2011. "Firm Growth and the Spatial Impact of Geolocated External Factors – Empirical Evidence for German Manufacturing Firms," Working Papers on Innovation and Space 2011-03, Philipps University Marburg, Department of Geography.

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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