Studying employment pathways of graduates by a latent Markov model
Fulvia Pennoni · BOA (University of Milano-Bicocca) · 2013
Motivated by an application to a longitudinal dataset deriving from administrative data which concern labour market and academic performances in Lombardy, we propose a multivariate latent Markov model with covariates for panel data. Our aim is to investigate how covariates influence labour market performance of the graduates which is measured through three type of response variables. The model is based on a Markov process to represent the latent characteristics of the subjects. Maximum likelihood estimation of the model parameters is based on the Expectation-Maximisation algorithm and it is performed by using a two-step approach first estimating a latent class model and then the latent Markov model.