Analyzing changes in work-from-home frequency before and after COVID-19: Longitudinal revealed-preference panel data

This study examines the long-term impacts of the COVID-19 pandemic on work-from-home (WFH) behavior using a unique longitudinal panel dataset from the COVID Future survey. While many studies predicted WFH behavior in the post-pandemic period, they mostly relied on cross-sectional or stated-preference data collected during the pandemic to predict post-pandemic WFH behavior. In contrast, this study uses revealed-preference data from the post-pandemic period, enabling a more realistic assessment of behavioral change over time. The analysis focuses on individuals who participated in the four survey waves, comparing pre-pandemic WFH behavior captured in the first wave with post-pandemic behavior observed in the final wave representing the “new normal.” To assess the effects of socio-demographic characteristics, commute patterns, occupational attributes, and attitudinal factors on WFH frequency, ordered logit models are estimated for both periods. Results show that demographic variables such as gender and age were significant predictors of WFH frequency before the pandemic but are no longer important in the post-pandemic period, as WFH has become widespread across population groups. In contrast, household characteristics, job categories, and personal attitudes remain significant determinants in both periods. These findings highlight a lasting shift in teleworking patterns and provide valuable insights for transportation demand forecasting, land use planning, and job market policy aimed at improving predictions of future behavioral responses to large-scale disruptions.