Equivalence and Clustering in Worker Flows: Stochastic Blockmodels for the Analysis of Mobility Tables

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 the flow is sparse. Yet clustering is not the only way worker flows can be structured. This article shows how a degree-corrected stochastic blockmodel can detect patterns of mobility that are more general than clustering and consistent with the homogeneity criterion laid out by Goodman as well as the internal homogeneity thesis proposed by Breiger. Because of the intractable marginal likelihood of the model, parameters are estimated using a variational expectation maximization algorithm. Simulation results suggest the estimation algorithm successfully recovers (conditionally) stochastically equivalent mobility classes. Analysis of two real-world examples shows the model is able to detect meaningful mobility patterns, even in situations in which commonly used community detection algorithms fail.

• Publication year

  2025

• Venue

  Sociological methodology

• Publication date

  2025-09-02

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1177/00811750251357950
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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