Citation

Paul J, Blumenberg E. Transp. Res. Interdiscip. Persp. 2023; 21: e100892.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trip.2023.100892

PMID

unavailable

Abstract

Researchers and policymakers often attempt to forecast trends in automobile ownership. But to understand recent changes in demand for cars, researchers must account for behaviors specific to different generations, while simultaneously controlling for the influence of lifecycle and historical effects. To overcome the analytical challenges of cross-sectional data in Age-Period-Cohort (APC) analysis, we apply three different approaches largely used by biostatisticians to isolate how cohort effects influence the likelihood that a U.S. adult lives in a zero-vehicle household. Our analyses draw on data from the U.S. Census Public Use Microdata Samples (PUMS) from 1970 to 2019. To test for cohort effects, we use constraint-based binary logistic regression, a nonlinear parametric approach to log-linear models, and median polish analysis. We find that people born from 1935 to 1944 experienced the strongest negative cohort effect of all groups, and thus were least likely to live in zero-vehicle households (after accounting for age and period effects). Compared to this cohort, persons born before 1924 and after 1955 saw higher likelihoods of living in zero-vehicle households, all else equal. The peak cohort effect of people born in the 1930s to 1940s may please those interested in reducing automobile use. But because automobiles offer access benefits, more recent cohorts may experience transportation challenges. Negative effects may be especially salient for Millennials, a group faring worse economically than previous generations. Further, recent changes in the transportation landscape - including the growth of services like carshare and ride-hail and behavioral changes emerging from the COVID-19 pandemic - complicate efforts to forecast demand for automobiles.

Language: en

Keywords

Age-Period-Cohort effects; Cohort effects; Demographic change; Millennial travel; Transportation access; Vehicle ownership