School of Civil and Environmental Engineering
Ph.D. Thesis Defense Announcement
The Teleworking Treatment Effect on Travel Behavior Changes: An In-Depth Exploration of Endogenous Switching Regression Models
By: Xinyi Wang
Advisor:
Dr. Patricia Mokhtarian (CEE)
Committee Members: Yajun Mei (ISyE), Dr. Kari Watkins (CEE), Dr. Ram Pendyala (Arizona State University), Dr. Subhrajit Guhathakurta (City Planning)
Date and Time: July 7th, 2023 at 1:00 PM
Location: (Hybrid) SEB 122 and Zoom: https://gatech.zoom.us/j/94894673881?pwd=R203NWxXZUhacERscEo0S3VyK1B3Zz09
In the past three years, the COVID-19 pandemic has boosted teleworking to unprecedented levels. As the pandemic appears to have been brought under control, many “pandemic teleworkers” have chosen to keep teleworking even though this work arrangement is no longer required by their employers and/or the risk of serious illness from the coronavirus has been dramatically reduced. The reshaped teleworker composition will unavoidably lead to travel demand changes. The objectives of this dissertation fall into two categories: policy-oriented and methodology-oriented. The policy-oriented objectives aim to understand teleworking-related motives, factors influencing teleworking frequency, and the behavioral outcome of teleworking. The methodology-oriented objectives aim to improve and compare endogenous switching regression models, which we adopt to quantify the impact of teleworking on vehicle-miles driven (VMD).
Specifically, to understand “why do people telework?”, we apply a latent class choice model (LCCM) and identify five heterogeneous teleworker segments based on teleworking-related motives, namely travel-motivated, flexibility-motivated, career-oriented, workplace-discouraged, and family-motivated. With the same model, we also identify factors that influence the teleworking frequency for each motive segment, which explains “what influences teleworking frequency?”. In particular, we find that factors such as gender, education, and job characteristics have heterogeneous impacts on teleworkers with different motives.
Next, to deepen the understanding of teleworking-induced behavioral changes on VMD (“what are the teleworking outcomes?”), we adopt three endogenous switching regression models, namely, binary probit switching regression (BPSR), ordered probit switching regression (OPSR), and multinomial logit switching regression (MNLSR). Compared to non-teleworkers, we consider the behavioral difference between “non-usual” and “usual” teleworkers. Also, we separate teleworkers based on teleworking-related motives (specifically, travel stressed or not) to compare results when the outcome variable (VMD) likely conforms to the teleworking motivation versus when it does not. Moreover, we expand the application of endogenous switching regression models, such as allowing more flexible model specifications; developing analytical representations of, or simulation procedures for estimating, the back-log-transformed treatment effect; and improving estimation efficiency by adopting maximum likelihood estimates.
The results of the dissertation provide a unique perspective on understanding the post-pandemic teleworker segmentation, factors that have heterogeneous impacts on teleworking frequency, and teleworking treatment effects on travel behavior changes accounting for the influence of teleworking-related motivations and frequency. The application, improvement, and comparison of endogenous switching regression models will also be relevant to numerous instances of self-selection beyond the present context.