The role of self-selection in shaping travel patterns, by affecting one's home location choice, is a critical issue. Developers, planners, and policy makers regularly debate to what extent the built environment and land use patterns can alleviate roadway congestion, greenhouse gas emissions, and myriad other urban problems. A study illustrates the use of Heckman's latent index model to ascertain travel impacts of neighborhood type in Austin, Texas. Under this approach, self-selection is formulated as sample selection bias in receiving a treatment. Here, treatment is defined to be one's residence in a suburban or rural zone, rather than Austin's central business district (CBD) and nearby urban zones. This approach of treatment or no-treatment is a meaningful advance in models of self-selection effects and requires estimation of three straightforward models. Depending on model specification used, results suggest that at least half (58% to 90%) of the differences in vehicle miles traveled observed between similar households living in CBD or urban versus rural or suburban neighborhoods of Austin is due to the location or treatment itself, whereas self-selection of such treatment (by households that wish to meet special travel needs or preferences) accounts for the remainder.
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