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Modeling the relation between income and commuting distance

Author

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  • Giulia Carra

    (IPHT - Institut de Physique Théorique - UMR CNRS 3681 - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - DRF (CEA) - Direction de Recherche Fondamentale (CEA) - CEA - Commissariat à l'énergie atomique et aux énergies alternatives)

  • Ismir Mulalic

    (DTU - Danmarks Tekniske Universitet = Technical University of Denmark)

  • Mogens Fosgerau

    (DTU - Danmarks Tekniske Universitet = Technical University of Denmark)

  • Marc Barthelemy

    (CAMS - Centre d'Analyse et de Mathématique sociales - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique, IPHT - Institut de Physique Théorique - UMR CNRS 3681 - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - DRF (CEA) - Direction de Recherche Fondamentale (CEA) - CEA - Commissariat à l'énergie atomique et aux énergies alternatives)

Abstract

We discuss the distribution of commuting distances and its relation to income. Using data from Denmark, the UK, and the US, we show that the commuting distance is (i) broadly distributed with a slow decaying tail that can be fitted by a power law with exponent $\gamma \approx 3$ and (ii) an average growing slowly as a power law with an exponent less than one that depends on the country considered. The classical theory for job search is based on the idea that workers evaluate the wage of potential jobs as they arrive sequentially through time, and extending this model with space, we obtain predictions that are strongly contradicted by our empirical findings. We propose an alternative model that is based on the idea that workers evaluate potential jobs based on a quality aspect and that workers search for jobs sequentially across space. We also assume that the density of potential jobs depends on the skills of the worker and decreases with the wage. The predicted distribution of commuting distances decays as $1/r^{3}$ and is independent of the distribution of the quality of jobs. We find our alternative model to be in agreement with our data. This type of approach opens new perspectives for the modeling of mobility.

Suggested Citation

  • Giulia Carra & Ismir Mulalic & Mogens Fosgerau & Marc Barthelemy, 2017. "Modeling the relation between income and commuting distance," Post-Print cea-01568435, HAL.
  • Handle: RePEc:hal:journl:cea-01568435
    DOI: 10.1098/rsif.2016.0306
    Note: View the original document on HAL open archive server: https://cea.hal.science/cea-01568435
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    Cited by:

    1. Marc Barthelemy, 2016. "A global take on congestion in urban areas," Environment and Planning B, , vol. 43(5), pages 800-804, September.
    2. Anzhelika Antipova, 2020. "Analysis of Commuting Distances of Low-Income Workers in Memphis Metropolitan Area, TN," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
    3. Eliasson, Jonas & Fosgerau, Mogens, 2019. "Cost-benefit analysis of transport improvements in the presence of spillovers, matching and an income tax," Economics of Transportation, Elsevier, vol. 18(C), pages 1-9.
    4. Yunke Zhang & Fengli Xu & Lin Chen & Yuan Yuan & James Evans & Luis Bettencourt & Yong Li, 2024. "Counterfactual mobility network embedding reveals prevalent accessibility gaps in U.S. cities," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    5. Volckart, Oliver, 2023. "How well-integrated was the sixteenth-century Holy Roman Empire?," Economic History Working Papers 118264, London School of Economics and Political Science, Department of Economic History.

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