Kurzbeschreibung

Network analyses in labor market research traditionally focus on individual job mobility across industry or occupational boundaries. However, this overlooks the structure of overlapping task inputs between pairs of occupations and neglects temporal or contextual properties of their interactions. To address this limitation and capture the time-varying and multilayered nature of labor markets, we create an occupational similarity measure from task data to analyze the interplay between job mobility and underlying task input structures. In this study, we employ a network framework to analyze labor market dynamics comprehensively. Our approach integrates two network layers: we capture job mobility across occupational boundaries and analyze wage dynamics in response to job mobility based on the underlying task structure of occupations. By structurally and dynamically analyzing the relation between job mobility and occupational task information in relation to labor market outcomes such as occupational wage differentials, we aim to contribute to the literature on labor mobility and job-to-job transitions. We apply statistical analyses to network measures derived from a similarity measure that captures the overlap of task inputs across occupations to assess whether its structure and dynamics offer relevant insights for labor market research. This novel approach allows us to uncover hidden patterns and interactions that contribute to a more nuanced understanding of labor market dynamics and the factors influencing individual career pathways.