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Metropolitan size and the impacts of telecommuting on personal travel

Abstract

Telecommuting has been proposed by policy makers as a strategy to reduce travel and emissions. In studying the metropolitan size impact of telecommuting on personal travel, this paper addresses two questions: (1) whether telecommuting is consistently a substitute or complement to travel across different MSA sizes; and (2) whether the impact of telecommuting is higher in larger MSAs where telecommuting programs and policies have been more widely adopted. Data from the 2001 and 2009 National Household Travel Surveys are used. Through a series of tests that address two possible empirical biases, we find that telecommuting consistently had a complementary effect on one-way commute trips, daily total work trips and daily total non-work trips across different MSA sizes in both 2001 and 2009. The findings suggest that policies that promote telecommuting may indeed increase, rather than decrease, people’s travel demand, regardless of the size of the MSA. This seems to contradict what telecommuting policies are designed for. In addition, model results show that the complementary impact of telecommuting on daily travel is lower in larger MSAs, in terms of both daily total work trips and daily total non-work trips.

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Fig. 1

Notes

  1. 1.

    The numbers calculated from census include both telecommuters (i.e. those have regular workplaces) and home-based workers (i.e. those work exclusively from home). The margin of error is provided in Table 1 of the referenced article.

  2. 2.

    For more details, please refer to http://www.commutesolutionshouston.org/commuters/telework.htm.

  3. 3.

    For these models, geographic variables such as MSA sizes and density were often included as control variables. Their coefficient estimates simply suggest how MSA sizes and density would affect travel behavior (e.g. commute distance, daily VMT). This is different from the purpose of this research--that is, how the impact of telecommuting on travel behavior varies across different MSA sizes.

  4. 4.

    According to NHTS, in 2001, there were 30,460 households sampled from small MSAs, 8371 households from medium MSAs and 17,179 households from large MSAs; in 2009, there were 45,884 households sampled from small MSAs, 32,508 households from medium MSAs and 40,975 households from large MSAs.

  5. 5.

    The NHTS survey questions on telecommuting were only slightly different between 2001 and 2009. And in both surveys, respondents who only work at home (e.g. home-based businesses) are skipped in the questions on telecommuting as well as the questions on one-way commute distance and duration. Therefore, the sample in this paper only includes those workers who have a workplace away from home.

  6. 6.

    Statistical significance are based on two-tailed mean-comparison tests (t test) at the 95% confidence level.

  7. 7.

    Our current grouping is commonly used by many other empirical studies to define small (< 1 million), medium (1-3 million), and large (> 3 million) MSAs in the U.S. During our analyses, we have also tested other groupings, such as {< 0.5 million, 0.5-3 million, > 3 million} and {< 1 million, 1-2 million, > 2 million}. The coefficient estimates are slightly different, but they do not change our conclusions in any significant ways.

  8. 8.

    In 2001, workers commuted by POV, public transit, and other modes were 92.4%, 4.0%, and 3.7%, respectively. In 2009, the shares were 94.0% commuted by POV, 2.5% by public transit, and 3.5% by other modes.

  9. 9.

    Their argument was that the commute PKT in their models measured the actual commute distance traveled on the survey day and was different from those determined by locations of residence and workplace. Thus, they argued that the commute PKT is not suffering from endogeneity bias and hence justified its inclusion as one of the dependent variables.

  10. 10.

    To compare the coefficient estimates from two subsamples (i.e. small MSAs vs. medium MSAs in this case), we applied the procedures as suggested in Wooldridge (2000, pp. 243-246 and pp. 449-450). Specifically, we adopted a fully interacted model that uses interaction terms to “pool” the subsamples and convert the separate models/equations into one giant equation. For the rest of our discussions, we apply the same technique to compare the magnitude of the impact across different MSA categories (i.e. small vs. medium vs. large).

  11. 11.

    For the purpose of discussion, if the impact is statistically insignificant, we treat it as having 0 impact.

  12. 12.

    The impact of telecommuting on commute distance in large MSAs was not statistically different from that in medium MSAs in 2009, based on our test of equality of coefficient estimates.

  13. 13.

    The impact was insignificant in medium MSAs in 2001.

  14. 14.

    We compared coefficient estimates from different subsamples using the technique as explained in previous footnote 10.

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Acknowledgements

The authors gratefully acknowledge funding from the National Natural Science Foundation of China [Project Numbers: 71573232].

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Correspondence to Pengyu Zhu.

Appendix

Appendix

See Table 8.

Table 8 First stage regression results for worker’s one-way commute trips by MSA size 2001 and 2009

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Zhu, P., Wang, L., Jiang, Y. et al. Metropolitan size and the impacts of telecommuting on personal travel. Transportation 45, 385–414 (2018). https://doi.org/10.1007/s11116-017-9846-3

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Keywords

  • Telecommuting
  • Personal travel
  • Complement
  • Substitute
  • Commute
  • Non-work trips