Equivalence and Clustering in Worker Flow
Stochastic Blockmodels for the Analysis of Worker Flows

Mobility scholars are increasingly turning to computational methods to analyze mobility tables. Most of these approaches start with the detection of mobility clusters: namely, sets of occupations within which the flow of workers is dense and across which it is sparse. Yet, clustering is not the only way in which worker flows can be structured. This paper shows how a degree-corrected stochastic blockmodel is able to detect patterns of mobility that are more general than clustering and consistent with the homogeneity criterion laid out by Goodman (1981) as well as the internal homogeneity thesis proposed by Breiger (1981). Due to the intractable marginal likelihood of the model, parameters are estimated via a variational Expectation Maximization algorithm. Simulation results suggest that the estimation algorithm successfully recovers (conditionally) stochastically equivalent mobility classes Further, the analysis of two real-world examples shows that the model is able to detect meaningful mobility patterns, even in situations where commonly used community detection algorithms fail.