Don't Work, Work at Home, or Commute? Discrete Choice Models of the Decision for San Francisco Bay Area Residents

Using socio-demographic, personality, and attitudinal data from 1,680 residents of the San Francisco Bay Area, we develop and estimate binary, multinomial, and nested logit models of the choice to work or not, whether or not to work at home, and whether to commute all of the time or some of the time (either by only working part time, or by working a compressed work week, or by telecommuting some of the time). To our knowledge, these are the first models of all these choices simultaneously. This work is relevant both to travel demand modeling, which usually bases trip or activity generation models on a given set of employment status inputs, and to labor force engagement modeling, which typically ignores the impact of travel-related variables. The model results indicate that the typical predictors of labor force engagement (gender, household income, and education) play an important role here, with family variables having an especially complex effect. Other interesting findings are that telecommuters tend to be adventure-seekers and home-based workers tend to be workaholics; those who like travel tend to commute five or more times per week; and mobility constraints are significant in the decisions to work part-time and to commute full-time.