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Construction of Register-based Commuting Measures

Author

Listed:
  • Ina Blind
  • Matz Dahlberg
  • Gustav Engström
  • John Östh

Abstract

Early empirical studies in labour and urban economics addressing the role of commuting (on, e.g., wages and locational choice) have typically been confined to the use of survey data. Researchers are, however, increasingly getting access to large register databases with detailed information on where individuals live and work. A variety of methods have thus emerged to exploit the geocoded characteristic of the data to calculate commuting measures that go beyond simple Euclidean metrics. These methods involve new techniques that make use of geographic information system (GIS) routing software or application programming interfaces provided by third-party developers. This article provides (i) a brief survey of the small but emerging literature that uses geocoded register data to calculate different commuting measures, (ii) an example on how register-based commuting measures can be constructed along with descriptive evidence on how different commuting measures compare for different socio-economic groups using rich Swedish register data, (iii) a discussion of the pros and cons of different methods and measures, and (iv) a discussion of the potential of using mobile phone data to further improve register-based commuting measures.

Suggested Citation

  • Ina Blind & Matz Dahlberg & Gustav Engström & John Östh, 2018. "Construction of Register-based Commuting Measures," CESifo Economic Studies, CESifo Group, vol. 64(2), pages 292-326.
  • Handle: RePEc:oup:cesifo:v:64:y:2018:i:2:p:292-326.
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    File URL: http://hdl.handle.net/10.1093/cesifo/ify014
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    References listed on IDEAS

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    Cited by:

    1. Lina Hedman & Kati Kadarik & Roger Andersson & John Östh, 2021. "Daily Mobility Patterns: Reducing or Reproducing Inequalities and Segregation?," Social Inclusion, Cogitatio Press, vol. 9(2), pages 208-221.

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    More about this item

    Keywords

    commuting measures; geocoded register data; mobile phone data;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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