ABSTRACT This paper reviews how large‐scale mobility data can enhance economic analyses, highlighting its contributions to understanding travel behavior, labor markets, social interactions, and health outcomes. We discuss its advantages over traditional mobility data sources, which include real‐time location information and fine spatial resolution, while addressing key empirical challenges such as measurement errors, sampling biases, and privacy concerns. Additionally, we examine how machine learning and interdisciplinary approaches can enhance the usefulness of mobility data for applied economic research. By synthesizing previous studies and identifying future directions, our review provides a roadmap for leveraging human mobility data at scale to refine economic models and inform policy decisions, underscoring the potential of human mobility data to enhance empirical research across various economic research fields.
Connolly et al. (2026) studied this question.