A mover-stayer model with time-dependent stayer fraction

TL;DR

This paper introduces a dynamic mover-stayer model that accounts for individuals changing their status over time based on time-varying factors, enhancing the modeling of heterogeneous population transitions.

Contribution

It presents a novel multinomial logistic framework allowing potential movers to become stayers over time, incorporating both fixed and time-varying covariates, with application to student mobility data.

Findings

Model effectively captures dynamic transition probabilities.

Simulation studies show good finite-sample performance.

Application to Italian student data demonstrates practical utility.

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.