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On modeling telecommuting behavior: option, choice, and frequency

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Abstract

The current study contributes to the already substantial scholarly literature on telecommuting by estimating a joint model of three dimensions—option, choice and frequency of telecommuting. In doing so, we focus on workers who are not self-employed workers and who have a primary work place that is outside their homes. The unique methodological features of this study include the use of a general and flexible generalized hurdle count model to analyze the precise count of telecommuting days per month, and the formulation and estimation of a model system that embeds the count model within a larger multivariate choice framework. The unique substantive aspects of this study include the consideration of the “option to telecommute” dimension and the consideration of a host of residential neighborhood built environment variables. The 2009 NHTS data is used for the analysis, and allows us to develop a current perspective of the process driving telecommuting decisions. This data set is supplemented with a built environment data base to capture the effects of demographic, work-related, and built environment measures on the telecommuting-related dimensions. In addition to providing important insights for policy analysis, the results in this paper indicate that ignoring the “option” dimension of telecommuting can, and generally will, lead to incorrect conclusions regarding the behavioral processes governing telecommuting decisions. The empirical results have implications for transportation planning analysis as well as for the worker recruitment/retention and productivity literature.

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Notes

  1. See Bhat and Guo (2007) for a description of the data sources and methodologies for computing the BE measures.

  2. The descriptive sample statistics for the exogenous variables in our analysis are suppressed here due to space considerations. Suffice it to say that the sample statistics are close to the corresponding population statistics for the San Francisco Bay region. Interested readers may refer to the supplementary document by Singh et al. (2012) available at: http://www.caee.utexas.edu/prof/bhat/ABSTRACTS/Telecommuting/SuppNote.pdf.

  3. The model in Eq. (4) is a generalization of the usual count data model with a Poisson discrete distribution with mean λ q . To see this, assume that α k  = 0∀k and γ = 0. Then, the probability expression in the GORP model of Eq. (4) collapses to a Poisson model.

  4. The scale of each of the error terms must be set to 1 for identification resulting in a correlation matrix.

  5. As we will discuss later, the option effect mentioned here is only valid for women who have a one-way commute of less than or equal to 20 miles, though the choice effect is for all women.

  6. We also tested the interaction of female and the presence of children in different age categories (0–5 years, 5–10 years, and 11–15 years). However, none of these came out to be even marginally statistically significant in all three telecommuting components.

  7. Admittedly, the occupation type categorization in the NHTS data is very coarse. It also co-mingles industry type and job type. The effects of the occupation type variables should, therefore, be viewed with some caution, and only as broad characterizations of job and industry mix.

  8. More broadly speaking, there could be some validity to the argument that all work-related decisions (including telecommuting, work schedule flexibility, full time versus part-time) and residential location choice decisions should be modeled in one single joint model system that implicitly determines the choice of a work location and commute trip attributes. But such a framework would become unwieldy. Also, there is some suggestion in the literature (see Ellen and Hempstead 2002; Ory and Mokhtarian 2006) that individuals tend to make their work/home location choices prior to decisions on telecommuting. Further exploration of this endogeneity issue is needed to inform modeling.

  9. A “Second City” as used in the NHTS data, refers to secondary cities surrounding a major metropolitan area. However, they are not equivalent to suburbs of the metropolitan area, which are within the major metropolitan area boundary. “Second cities” are generally satellite cities outside the major city metropolitan area. They may be viewed as somewhere between a suburb and a rural area in built-up land-use density.

  10. The adjusted rho-bar squared value \( \bar{\rho }_{c}^{2} \) is computed as \( \bar{\rho }_{c}^{2} = 1 - [(L(\hat{\beta }) - H)/L(C)] \), where \( L(\hat{\beta }) \) is the predictive log-likelihood for the trivariate model and the convergent log-likelihood for the bivariate model, H is the number of model parameters excluding the constants in the binary models and the constant in the ς vector for the count model, and L(C) is the log-likelihood from the bivariate model with only the constant in the binary choice model and the constant in the ς vector.

  11. The hurdle set-up for the telecommuting frequency in the bivariate case is derived in the same way as that for the trivariate case.

  12. The elasticity effects here computed in this section provides the cumulative effect of variables on the expected number of days of telecommuting per month. While helpful in terms of the bottom line, the effects of variables on each individual dimension still provides insights for policy analysis, as already discussed in the previous section. The elasticity effects presented here are simply to illustrate the overall differences in magnitude effects between the bivariate model (that ignores the option dimension) and the trivariate model. One can further break down the elasticity effects shown here into separate components specific to each dimension of effect to gain more insights, but this is straightforward to do and we do not pursue this here to conserve on space.

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Acknowledgments

This research was partially funded by a Southwest Region University Transportation Center grant. The authors are grateful to Lisa Macias for her help in formatting this document. Three anonymous reviewers provided useful comments on an earlier version of the paper.

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Authors and Affiliations

  1. Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, 301 E. Dean Keeton St. Stop C1761, Austin, TX, 78712-0278, USA

    Rajesh Paleti & Chandra R. Bhat

  2. Department of Civil and Environmental Engineering, Louisiana State University, 3418 Patrick F. Taylor – LSU, Baton Rouge, LA, 70803, USA

    Syndney Jenkins

  3. Parsons Brinckerhoff, 400 SW Sixth Avenue, Suite 802, Portland, OR, 97204, USA

    Palvinder Singh

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  1. Palvinder Singh
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  2. Rajesh Paleti
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  4. Chandra R. Bhat
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Correspondence to Chandra R. Bhat.

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Singh, P., Paleti, R., Jenkins, S. et al. On modeling telecommuting behavior: option, choice, and frequency. Transportation 40, 373–396 (2013). https://doi.org/10.1007/s11116-012-9429-2

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