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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," Discussion Papers dp-06-41, Resources For the Future.
  • Handle: RePEc:rff:dpaper:dp-06-41
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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. 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.
    2. Walls, Margaret & Safirova, Elena, 2004. "A Review of the Literature on Telecommuting and Its Implications for Vehicle Travel and Emissions," Discussion Papers dp-04-44, Resources For the Future.
    3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
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    Cited by:

    1. 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.
    2. Tang, Wei & Mokhtarian, Patricia & Handy, Susan, 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 qt9rg8w9c4, Institute of Transportation Studies, UC Davis.

    More about this item

    Keywords

    telecommuting; telework; transportation planning; econometric estimation; telecommuting frequency; telecommuting propensity;

    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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