A multiple-group hidden Markov model for multi-source data. Cross-country differences in employment mobility in the presence of measurement error

In this paper, we develop a multigroup hidden Markov model to tackle the issue of measurement error in multi-source data from different countries. We focus, in particular, on the measurement of employment mobility in the Netherlands and Italy using linked data from the Labour Force Survey and administrative sources. The measurement-error correction we apply reconciles differences between data sources and shows that cross-country differences in employment mobility are smaller than originally thought. Error-corrected estimates indicate that mobility from temporary to permanent employment has become, over time, larger in Italy than in the Netherlands, while mobility from non-employment to temporary employment has steadily been higher in the Netherlands than in Italy.