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Modelling the relation between income and commuting distance.

Giulia Carra1, Ismir Mulalic2, Mogens Fosgerau3

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Commuting distances are broadly distributed and grow slowly with income, challenging classical job search models. An alternative spatial search model accurately predicts these commuting patterns.

Keywords:
job searchmobilitymodellingstatistical physicsurban economics

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Area of Science:

  • Urban Economics
  • Economic Geography
  • Computational Social Science

Background:

  • Classical job search theory assumes sequential wage evaluation over time.
  • Extending this to spatial search contradicts empirical commuting distance data.
  • Understanding commuting patterns is crucial for urban planning and economic analysis.

Purpose of the Study:

  • To analyze the distribution of commuting distances and its relationship with income.
  • To challenge existing job search models with empirical data.
  • To propose and validate a new spatial job search model.

Main Methods:

  • Empirical analysis of commuting data from Denmark, the UK, and the USA.
  • Statistical fitting of commuting distance distributions using power laws.
  • Development and testing of an alternative spatial job search model.

Main Results:

  • Commuting distances exhibit a broad distribution with a power-law tail (exponent γ ≈ 3).
  • Average commuting distance increases slowly with income, following a country-dependent power law.
  • The proposed spatial search model successfully predicts the observed 1/r(3) decay in commuting distance distribution.

Conclusions:

  • Classical job search models are inadequate for explaining spatial commuting patterns.
  • A novel spatial search model, emphasizing job quality and sequential spatial exploration, aligns with empirical findings.
  • This research offers new insights into modeling urban mobility and labor market dynamics.