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Abstract
The Markov chain model (MCM) has become a popular tool in the agricultural economics
literature to describe how farms experience structural change and to study the impact of
various drivers of this process, including public support. Even though some studies have
accounted for heterogeneity across farms by letting transition probabilities depend on co-
variates depicting farms and/or farmers' characteristics, only observed heterogeneity has
been considered so far. Assuming that structural change may also relate to unobserved
farms and/or farmers' characteristics, we present how to implement the mover-stayer
model (MSM) which considers a mixture of two types of farms, the `stayers' who always
remain in their initial size category and the `movers' who follow a first-order Markovian
process, and how to estimate it thanks to the expectation-maximization (EM) algorithm.
This modeling framework relaxes the assumption of homogeneity in the transition process which grounds the usual MCM. An empirical application to a panel of French farms
over 2000-2013 shows that the MSM outperforms the MCM in recovering the underlying
year-to-year transition process as well as in deriving the long-run transition matrix and
forecasting future farm size distributions.