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Driver's licensing delay: A retrospective case study of the impact of attitudes, parental and social influences, and intergenerational differences

Calvin Thigpen and Susan Handy

Transportation Research Part A: Policy and Practice, 2018, vol. 111, issue C, 24-40

Abstract: Young adults currently obtain driver’s licenses at a substantially lower rate than previous generations. In a handful of recent studies, scholars have evaluated this trend by investigating the association of various factors, primarily personal characteristics and the built environment, with driver’s licensing. However, these studies have examined a limited set of possible explanatory factors and in some cases used only descriptive statistical analyses. To explore the causes of the licensing trend in more depth, this study uses retrospective questions asked of respondents to the 2014–15 UC Davis Campus Travel Survey, an annual online survey of students and employees at the University of California, Davis. We test the influence of an array of explanatory factors on driver’s license possession, using a binomial logistic model, and on license timing, using multilevel survival analysis and censored regression models. The results show that delay in licensing is associated with travel attributes and attitudes, parental influences, and graduated driver’s licensing policies. After controlling for these factors, the variables accounting for unexplained cohort influences had a small and uncertain effect on delay. Since we observe generational differences in eagerness to get a driver’s license and find that driver’s licensing attitudes substantially increase delay, this result suggests that cultural changes may be driving the decreased licensing trend. This generational shift in attitudes may have synergistic effects with policies designed to encourage smart growth and with the proliferation of innovative travel options that provide alternatives to car ownership and use.

Keywords: Driver’s licensing; Delay; Retrospective survey; Survival analysis; Censored regression (search for similar items in EconPapers)
Date: 2018
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