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Measuring commute patterns over time: Using administrative data to identify where employees live and work

Author

Listed:
  • Richard Fabling

    (Independent Researcher)

  • David C. Maré

    (Motu Economic and Public Policy Research)

Abstract
We use administrative and survey data in the Integrated Data Infrastructure (IDI) to allocate workers to job locations (plants), which enables the production of over a decade of commute distance population statistics for New Zealand employees. We find that average commute distance (meshblock centroid-to-centroid) fell from 2005 to 2009 before rising again through to 2018 (the final analysis year), with most regions displaying this general temporal pattern. Census 2013 place of residence and work is used to test our methodology against the alternative of using pre-existing plant allocations from the Linked Employer-Employee Data (LEED) production system. For a consistent set of individuals, our estimate of the commute distance distribution closely matches the corresponding distribution in Census. In contrast, LEED-based estimates tend to significantly overestimate commute distances, including radically overestimating the likelihood of inter-island commuting. Our more plausible results are primarily due to re-engineering the job allocation process, as opposed to exploiting better administrative data, though we make marginal improvements to residential address identification through a new prioritisation method, allowing us to use a broader set of residential address sources than available in LEED.

Suggested Citation

  • Richard Fabling & David C. Maré, 2020. "Measuring commute patterns over time: Using administrative data to identify where employees live and work," Working Papers 20_05, Motu Economic and Public Policy Research.
    • Handle: RePEc:mtu:wpaper:20_05
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    File URL: https://motu-www.motu.org.nz/wpapers/20_05.pdf
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    References listed on IDEAS

    as
    1. Lídia Farré & Jordi Jofre-Monseny & Juan Torrecillas, 2023. "Commuting time and the gender gap in labor market participation," Journal of Economic Geography, Oxford University Press, vol. 23(4), pages 847-870.
    2. Richard Fabling & David C Maré, 2015. "Production function estimation using New Zealand’s Longitudinal Business Database," Working Papers 15_15, Motu Economic and Public Policy Research.
    3. David Christopher Maré & Richard Fabling, 2013. "Productivity and Local Workforce Composition," Advances in Spatial Science, in: Riccardo Crescenzi & Marco Percoco (ed.), Geography, Institutions and Regional Economic Performance, edition 127, pages 59-76, Springer.
    4. Richard Fabling & Lynda Sanderson, 2016. "A Rough Guide to New Zealand's Longitudinal Business Database (2nd edition)," Working Papers 16_03, Motu Economic and Public Policy Research.
    5. Papps, Kerry L. & Newell, James O., 2002. "Identifying Functional Labour Market Areas in New Zealand: A Reconnaissance Study Using Travel-to-Work Data," IZA Discussion Papers 443, Institute of Labor Economics (IZA).
    6. Richard Fabling, 2009. "A Rough Guide to New Zealand's Longitudinal Business Database," Global COE Hi-Stat Discussion Paper Series gd09-103, Institute of Economic Research, Hitotsubashi University.
    7. Richard Fabling, 2011. "Keeping it Together: Tracking Firms on New Zealand’s Longitudinal Business Database," Working Papers 11_01, Motu Economic and Public Policy Research.
    8. Petrongolo, Barbara & Ronchi, Maddalena, 2020. "Gender gaps and the structure of local labor markets," Labour Economics, Elsevier, vol. 64(C).
    9. David C. Maré & Richard Fabling & Steven Stillman, 2014. "Innovation and the local workforce," Papers in Regional Science, Wiley Blackwell, vol. 93(1), pages 183-201, March.
    10. Giménez-Nadal, José Ignacio & Molina, José Alberto & Velilla, Jorge, 2022. "Trends in commuting time of European workers: A cross-country analysis," Transport Policy, Elsevier, vol. 116(C), pages 327-342.
    11. Fredrik Andersson & John C. Haltiwanger & Mark J. Kutzbach & Henry O. Pollakowski & Daniel H. Weinberg, 2018. "Job Displacement and the Duration of Joblessness: The Role of Spatial Mismatch," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 203-218, May.
    12. Richard Fabling & David C. Maré, 2019. "Improved productivity measurement in New Zealand's Longitudinal Business Database," Working Papers 19_03, Motu Economic and Public Policy Research.
    13. Richard Fabling, 2018. "Entrepreneurial beginnings: Transitions to self-employment and the creation of jobs," Working Papers 18_12, Motu Economic and Public Policy Research.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    commuting patterns; linked employer-employee data (LEED); Integrated Data Infrastructure (IDI); administrative data;
    All these keywords.

    JEL classification:

    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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