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What Drives Telecommuting? The Relative Impact of Worker Demographics, Employer Characteristics, and Job Types

Author

Listed:
  • Margaret Walls

    (Resources for the Future)

  • Safirova, Elena

    (Resources for the Future)

  • Jiang, Yi

Abstract

We analyze a 2002 survey of Southern California residents to evaluate the relative importance of factors that affect workers’ propensity to telecommute and telecommuting frequency. The survey collected a wealth of individual demographic information as well as job type, industry, and employer characteristics from about 5,000 residents. In agreement with previous studies, we find that the propensity to telecommute is increasing with worker age and educational attainment. At the same time, we conclude that the propensity to telecommute depends to a large extent on a worker’s job characteristics and that the quantitative effects of job characteristics are at least as important as demographic factors. We also study what factors affect telecommuting frequency based on a one-week commuting diary of the telecommuters in the survey. The industry and occupation categories that play a significant role in affecting propensity to telecommute do not have similar effects on telecommuting frequency. On the contrary, some other job-related factors show substantial influences.

Suggested Citation

  • Margaret Walls & Safirova, Elena & Jiang, Yi, 2006. "What Drives Telecommuting? The Relative Impact of Worker Demographics, Employer Characteristics, and Job Types," RFF Working Paper Series dp-06-41, Resources for the Future.
    • Handle: RePEc:rff:dpaper:dp-06-41
    as

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    File URL: http://www.rff.org/RFF/documents/RFF-DP-06-41.pdf
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    References listed on IDEAS

    as
    1. Joanne Pratt, 2000. "Asking the right questions about telecommuting: Avoiding pitfalls in surveying homebased work," Transportation, Springer, vol. 27(1), pages 99-116, February.
    2. Walls, Margaret & Safirova, Elena, 2004. "A Review of the Literature on Telecommuting and Its Implications for Vehicle Travel and Emissions," Discussion Papers 10492, Resources for the Future.
    3. Mokhtarian, Patricia L. & Salomon, Ilan, 1997. "Modeling the desire to telecommute: The importance of attitudinal factors in behavioral models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 31(1), pages 35-50, January.
    4. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
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    Citations

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    Cited by:

    1. Tang, Wei & Mokhtarian, Patricia L & Handy, Susan L, 2008. "The Role of Neighborhood Characteristics in the Adoption and Frequency of Working at Home: Empirical Evidence from Northern California," Institute of Transportation Studies, Working Paper Series qt13x2q3rb, Institute of Transportation Studies, UC Davis.
    2. Angel Belzunegui-Eraso & Amaya Erro-Garcés, 2020. "Teleworking in the Context of the Covid-19 Crisis," Sustainability, MDPI, vol. 12(9), pages 1-18, May.
    3. Haddad, Hebba & Lyons, Glenn & Chatterjee, Kiron, 2009. "An examination of determinants influencing the desire for and frequency of part-day and whole-day homeworking," Journal of Transport Geography, Elsevier, vol. 17(2), pages 124-133.
    4. Palvinder Singh & Rajesh Paleti & Syndney Jenkins & Chandra Bhat, 2013. "On modeling telecommuting behavior: option, choice, and frequency," Transportation, Springer, vol. 40(2), pages 373-396, February.
    5. Simon J. Berrebi & Kari E. Watkins, 2020. "Whos Ditching the Bus?," Papers 2001.02200, arXiv.org, revised Mar 2020.
    6. Khandker Nurul Habib & Ph. D. & PEng, 2020. "On the Factors Influencing the Choices of Weekly Telecommuting Frequencies of Post-secondary Students in Toronto," Papers 2004.04683, arXiv.org.

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    More about this item

    Keywords

    telecommuting; telework; transportation planning; econometric estimation; telecommuting frequency; telecommuting propensity;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • 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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