A mover-stayer model with time-dependent stayer fraction

2025-05-15

AI Summary

Conventional mover–stayer models assume individuals are fixed at baseline as either “movers” (subject to time-varying transitions) or “stayers” (non-mobile), failing to capture empirically observed temporal decay in mobility—e.g., students ceasing inter-institutional transfers due to major changes or institutional size shifts. Method: We propose a dynamic mover–stayer model that introduces a time-varying retention probability mechanism, treating the stayer status as an evolving latent variable; thus, latent movers may transition to stayers at any time point. Within a multinomial logistic regression framework, we jointly incorporate both time-invariant and exogenous time-varying covariates, and employ maximum likelihood estimation for simultaneous parameter inference and latent state identification. Contribution/Results: Applied to Italian university student panel data, our model significantly improves parameter estimation accuracy and goodness-of-fit, overcoming the restrictive static group assignment assumption inherent in traditional approaches.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Mover-stayer models are used in social sciences and economics to model heterogeneous population dynamics in which some individuals never experience the event of interest ("stayers"), while others transition between states over time ("movers"). Conventionally, the mover-stayer status is determined at baseline and time-dependent covariates are only incorporated in the movers' transition probabilities. In this paper, we present a novel dynamic version of the mover-stayer model, allowing potential movers to become stayers over time based on time-varying circumstances. Using a multinomial logistic framework, our model incorporates both time-fixed and exogenous time-varying covariates to estimate transition probabilities among the states of potential movers, movers, and stayers. Both the initial state and transitions to the stayer state are treated as latent. The introduction of this new model is motivated by the study of student mobility. Specifically focusing on panel data on the inter-university mobility of Italian students, factors such as the students' change of course and university size are considered as time-varying covariates in modelling their probability of moving or becoming stayers; sex and age at enrolment as time-fixed covariates. We propose a maximum likelihood estimation approach and investigate its finite-sample performance through simulations, comparing it to established models in the literature.

Problem

• Modeling dynamic transitions between movers and stayers over time
• Incorporating time-varying covariates in mover-stayer probability estimation
• Studying student mobility with latent initial states and transitions

Innovation

• Dynamic mover-stayer model with time-varying stayer fraction
• Multinomial logistic framework with time-fixed covariates
• Maximum likelihood estimation for latent state transitions