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Metropolitan size and the impacts of telecommuting on personal travel

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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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Notes

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

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

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

  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. The impact was insignificant in medium MSAs in 2001.

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

References

  • Balepur, P., Varma, K., Mokhtarian, P.: Transportation impacts of center-based telecommuting: interim findings from the Neighborhood Tele-centers Project. Transportation 25(3), 287–306 (1998)

    Article  Google Scholar 

  • Bound, J., Jaeger, D.A., Baker, R.M.: Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. J. Am. Stat. Assoc. 90(430), 443–450 (1995)

    Google Scholar 

  • Bris, M., Pawlak, J., Polak, J.W.: How is ICT use linked to household transport expenditure? A cross-national macro analysis of the influence of home broadband access. J. Transp. Geogr. 60, 231–242 (2017)

    Article  Google Scholar 

  • Cao, X.: E-shopping, spatial attributes, and personal travel: a review of empirical studies. Transp. Res. Rec. J. Transp. Res. Board 2135, 160–169 (2009)

    Article  Google Scholar 

  • Cao, X., Mokhtarian, P.: The intended and actual adoption of online purchasing: a brief review of recent literature. Institute of transportation studies. University of California, Davis, Davis (2005)

    Google Scholar 

  • Cao, X., Douma, F., Cleaveland, F.: The influence of e-shopping on shopping travel: evidence from Twin Cities. Transp. Res. Rec. J. Transp. Res. Board 2157, 147–154 (2010)

    Article  Google Scholar 

  • Cao, X., Xu, Z., Douma, F.: The interactions between e-shopping and traditional in-store shopping: an application of structural equations model. Transportation 39(5), 957–974 (2012)

    Article  Google Scholar 

  • Choo, S., Mokhtarian, P.L.: Telecommunications and travel demand and supply: aggregate structural equation models for the US. Transp. Res. Part A 41, 4–18 (2007)

    Article  Google Scholar 

  • Choo, S., Mokhtarian, P.L., Salomon, I.: Does telecommuting reduce vehicle-miles traveled? An aggregate time series analysis for the U.S. Transportation 32(1), 37–64 (2005)

    Article  Google Scholar 

  • Community Planning Workshop.: Oregon Office of Energy’s telework program evaluation. Extracted on August 08, 2010 from https://scholarsbank.uoregon.edu/xmlui/handle/1794/10687 (2003)

  • Fox, M.: Transport planning and the human activity approach. J. Transp. Geogr. 3(2), 105–116 (1995)

    Article  Google Scholar 

  • Gareis, K.: Home-based versus mobile telework: the interrelationships between different types of telework. In: Jackson, P., Rapp, B. (eds.) Organisation and Work Beyond 2000, pp. 171–185. Springer, Berlin (2003)

    Chapter  Google Scholar 

  • Greene, W.H.: Econometric Analysis, 3rd edn. Prentice Hall, Upper Saddle River (1997)

    Google Scholar 

  • Hall, A.R., Rudebusch, G.D., Wilcox, D.W.: Judging instrument relevance in instrumental variables estimation. Int Econ Rev 37(2), 283–298 (1996)

    Article  Google Scholar 

  • Hamer, R., Kroes, E., Ooststroom, H.: Teleworking in the Netherlands: an evaluation of changes in travel behavior. Transportation 18(4), 365–382 (1991)

    Article  Google Scholar 

  • He, S.Y., Hu, L.: Telecommuting, income, and out-of-home activities. Travel Behav. Soc. 2(2015), 131–147 (2015)

    Article  Google Scholar 

  • Helminen, V., Ristimäki, M.: Relationships between commuting distance, frequency and telework in Finland. J. Transp. Geogr. 15(5), 331–342 (2007)

    Article  Google Scholar 

  • Henderson, D.K., Mokhtarian, P.L.: Impacts of center-based telecommuting on travel and emissions: analysis of the puget sound demonstration project. Transp. Res. D 1(1), 29–45 (1996)

    Article  Google Scholar 

  • Jiang, Y.: Two empirical essays in environmental and urban economics. Ph.D. Dissertation, University of Maryland (2008)

  • Kim, S.-N.: Two traditional questions on the relationships between telecommuting, job and residential location, and household travel: revisited using a path analysis. Ann. Reg. Sci. 56(2), 537–563 (2016)

    Article  Google Scholar 

  • Kim, S.-N.: Is telecommuting sustainable? An alternative approach to estimating the impact of home-based telecommuting on household travel. Int. J. Sustain. Transp. 11(2), 72–85 (2017)

    Article  Google Scholar 

  • Kim, S.-N., Choo, S., Mokhtarian, P.L.: Home-based telecommuting and intra-household interactions in work and non-work travel: a seemingly unrelated censored regression approach. Transp. Res. Part A Policy Pract. 80, 197–214 (2015)

    Article  Google Scholar 

  • Kitamura R., Mokhtarian, P., Pendyala, R., Goulias, K.: An evaluation of telecommuting as a trip reduction measure. In: Proceedings of the 19th Annual Meeting of the Planning and Transport Research and Computation (PTRC), Education and Research Services Limited, University of Sussex. Brighton, England, September 1991, pp. 69–80 (1991)

  • Lee, B.: Urban Spatial Structure and Commuting in US Metropolitan Areas. Conference Paper. Western Regional Science Association 45th Annual Meeting (2006)

  • Mateyka, P.J., Rapino, M.A., Landivar, L.C.: Home-based workers in the United States: 2010. Curr. Popul. Rep. 70–132. downloaded from https://www.census.gov/prod/2012pubs/p70-132.pdf (2012)

  • Mokhtarian, P.L.: Telecommuting and travel: state of the practice, state of the art. Transportation 18, 319–342 (1991)

    Google Scholar 

  • Mokhtarian, P.L.: A synthetic approach to estimating the impacts of telecommuting on travel. Urban Studies 35(2), 215–241 (1998)

    Article  Google Scholar 

  • Mokhtarian, P.L., Handy, S.L., Salomon, I.: Methodological issues in the estimation of the travel, energy and air quality impacts of telecommuting. Transp. Res. A 29(4), 283–302 (1995)

    Google Scholar 

  • Mokhtarian, P.L., Ho, C.I., Hung, S.W., Lam, T.B., Raney, E.A., Redmond, L.S. et al.: Residential Area-Based Offices Project: Final Report on the Evaluation of Impacts. No. Research Report UCD-ITS-RR-97-17). Institute of Transportation Studies, University of California, Davis (1997)

  • Mokhtarian, P.L., Collantes, G.O., Gertz, C.: Telecommuting, residential location, and commute-distance traveled: evidence from State of California employees. Environ. Plan. A 36(10), 1877–1897 (2004)

    Article  Google Scholar 

  • Muhammad, S., Ottens, H.F.L., Ettema, D., Jong, T.: Telecommuting and residential locational preferences: a case study of the Netherlands. J. Hous. Built Environ. 22(4), 339–358 (2007)

    Article  Google Scholar 

  • NAE: Telecommunications for enhanced metropolitan function and form: report to the director of Telecommunications Management. National Academy of Engineering, Washington, D.C. (1969)

    Google Scholar 

  • Nelson D, Niles J (2000) Observations on the causes of nonwork travel growth. In: Transportation Research Board Paper. No. 00-1232, 79th Annual Meeting, January 9–13

  • Nelson, P., Safirova, E., Walls, M.: Telecommuting and environmental policy: lessons from the ecommute program. Transp. Res. Part D 12(3), 195–207 (2007)

    Article  Google Scholar 

  • Nilles, J.M., et al.: Telecommuting–alternative to urban transportation congestion. IEEE Trans. Syst. Man Cybern. 6(2), 77–84 (1976)

    Article  Google Scholar 

  • Olszewski, P., Mokhtarian, P.: Telecommuting frequency and impacts for State of California employees. Technol. Forecast. Soc. Change 45(3), 275–286 (1994)

    Article  Google Scholar 

  • Ory, D.T., Mokhtarian, P.L.: The impact of telecommuting on the commute time, distance, and speed of state of California workers. Extracted from http://repositories.cdlib.org/itsdavis/UCD-ITS-RR-05-23 on April 02, 2009 (2005)

  • Ory, D.T., Mokhtarian, P.L.: Which came first, the telecommuting or the residential relocation? An empirical analysis of causality. Urban Geogr. 27(7), 590–609 (2006)

    Article  Google Scholar 

  • Pendyala, R., Goulias, K., Kitamura, R.: Impact of telecommuting on spatial and temporal patterns of household travel. Transportation 18(4), 383–409 (1991)

    Article  Google Scholar 

  • Salomon, I.: Technological change and social forecasting: the case of telecommuting as a travel substitute. Transp. Res. Part C-Emerg. Technol. 6(1–2), 17–45 (1998)

    Article  Google Scholar 

  • Shea, J.: Instrument relevance in multivariate linear models: a simple measure. Rev. Econ. Stat. 79, 348–352 (1997)

    Article  Google Scholar 

  • Srinivasan, S., Ferreira, J.: Travel behavior at the household level: understanding linkages with residential choice. Transp. Res. Part D 7(3), 225–242 (2002)

    Article  Google Scholar 

  • Staiger, D., Stock, J.H.: Instrumental variables regression with weak instruments. Econometrica 65(3), 557–586 (1997)

    Article  Google Scholar 

  • Stock, J.H.: The other transformation in econometric practice: robust tools for inference. J. Econ. Perspect. 24(2), 83–94 (2010)

    Article  Google Scholar 

  • Strathman, J.G., Dueker, K.J., Davis, J.S.: Effects of household structure and selected travel characteristics on trip chaining. Transportation 21, 23–45 (1994)

    Article  Google Scholar 

  • Tal, G.: Overestimation reduction in forecasting telecommuting as a TDM Policy. Transp. Res. Rec. 2082, 8–16 (2008)

    Article  Google Scholar 

  • Wooldridge, J.M.: Introductory Econometrics: A Modern Approach. South-Western, Cincinnati (2000)

    Google Scholar 

  • Wooldridge, J.M.: Econometric Analysis of Cross Section and Panel Data, pp. 623–625. MIT Press, Cambridge (2002)

    Google Scholar 

  • Zhu, P.: Are telecommuting and personal travel complements or substitutes? Ann. Reg. Sci. 48(2), 619–639 (2012)

    Article  Google Scholar 

  • Zhu, P.: Telecommuting, household commute and location choice. Urban Stud. 50(12), 2441–2459 (2013)

    Article  Google Scholar 

  • Zhu, P., Dong, H., Wu, C.: Do residents of smart growth neighborhoods in Los Angeles travel “smarter”? Transp. Res. Rec. J. Transp. Res. Board 2397, 61–71 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

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

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Authors and Affiliations

  1. The University of Hong Kong, Hong Kong, China

    Pengyu Zhu, Yanpeng Jiang & Jiangping Zhou

  2. Zhejiang University, Hangzhou, China

    Liping Wang

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  1. Pengyu Zhu
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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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