A Copula-Based Sample Selection Model of Telecommuting Choice and Frequency

The objective of this study is to contribute to the telecommuting literature by jointly examining the propensity and frequency of workers to telecommute, using a rich set of individual demographics, work-related and industry characteristics, household demographics, and commute trip/work location characteristics. The data are drawn from the Chicago Regional Household Travel Inventory, collected between 2007 and 2008. From a methodological standpoint, the current study adopts a copula approach that allows the testing of several types of dependency structures between the telecommuting choice and frequency behavioural processes. To our knowledge, this is the first formulation and application in the econometric literature of a copula approach for the case of a binary self-selection mechanism with an ordered-response outcome. The results clearly indicate that telecommuting choice and the frequency of telecommuting are governed by quite different underlying behavioral processes. For instance, women are less likely to telecommute relative to men, though there is no statistically significant difference in telecommuting frequency propensity between men and women telecommuters. Similarly, full-time employed individuals (≥ 30 hours per week) are more likely to have a telecommuting arrangement than those working part-time (< 30 hours per week). However, among those who telecommute, fulltime employed individuals telecommute less frequently than part-time employed individuals. Further, the results suggest that the analyst risks the danger of incorrect conclusions regarding dependency in the telecommuting choice and frequency behavioral processes, as well as inconsistent and inefficient parameter estimates, by imposing incorrect dependency structures or assuming independence between the two behavioral processes. Overall, the empirical results indicate the important effects of several demographic and work-related variables on telecommuting choice and frequency, with implications for transportation planning and transportation policy analysis.