This study investigates how human mobility has changed in the long-term in response to the COVID-19-related information in Japan. We use publicly available data from Google on human mobility in retail & recreation and residential spent time. These variables can be explained using daily data on the number of infected cases, whether the state of the emergency is declared or not, and the cumulated number of the vaccinated person. In the regression analysis, we use the 'interactive effects model' to control complicated unobservable factors that vary across time and cross-sectional dimensions. Our regression results find that people feared an unknown virus in the 1st wave, but the habituation trend for human mobility is noticed for the repeated similar infection information. However, from a different kind of information about the spread of new variants, people's habituation comes to a halt to some extent. Further, the spatial interaction of infection information is observed. We also show that people reacted appropriately to infection information even without a state of emergency declaration. Also, vaccination promotion encourages people to go out with security. When implementing policies to control human mobility, it is essential to consider the timing, and the degree of information penetration, carefully.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.