A latent class approach for estimating labour market mobility in the presence of multiple indicators and retrospective interrogation

Measurement errors can induce bias in the estimation of transitions, leading to erroneous conclusions about labour market dynamics. A large body of literature on gross flows estimation is based on the assumption that errors are uncorrelated over time. This assumption is not realistic in many contexts, because of survey design and data collection strategies. We use a model-based approach to adjusting observed gross flows for classification errors, eventually correlated. A convenient framework is provided by latent class Markov models. We refer to data collected with the Italian Continuous Labour Force Survey, which is cross-sectional, quarterly, with a 2-2-2 rotating design. The questionnaire allows to dispose of multiple indicators of labour force condition for each quarter: two collected in the same interview and a third one collected after one year. Our approach provides a means to estimate labour market mobility taking into account correlated errors and the rotating design of the survey.