A mixed latent class Markov approach for estimating labour market mobility with multiple indicators and retrospective interrogation
Bassi,Francesca Croon,M.A. Vidotto,D.
Bassi,Francesca
Croon,M.A.
Vidotto,D.
Abstract
Measurement errors can induce bias in the estimation of transitions, leading to erroneous conclusions about labour market dynamics. Traditional literature on gross flows estimation is based on the assumption that measurement errors are uncorrelated over time. This assumption is not realistic in many contexts, because of survey design and data collection strategies. In this work, we use a model-based approach to correct observed gross flows from classification errors with 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 us to use multiple indicators of labour force conditions for each quarter: two collected in the first interview, and a third one collected one year later. Our approach provides a method to estimate labour market mobility, taking into account correlated errors and the rotating design of the survey. The best-fitting model is a mixed latent class Markov model with covariates affecting latent transitions and correlated errors among indicators; the mixture components are of mover-stayer type. The better fit of the mixture specification is due to more accurately estimated latent transitions.
Description
Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
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Journal Issue
Keywords
Gross flows, Labour market, Mixture models, Latent class models, UNEMPLOYMENT, MODELS, ERRORS, SDG 8 - Decent Work and Economic Growth, SDG 9 - Industry, Innovation, and Infrastructure
Citation
Bassi, F, Croon, M A & Vidotto, D 2017, 'A mixed latent class Markov approach for estimating labour market mobility with multiple indicators and retrospective interrogation', Survey Methodology, vol. 43, no. 1, pp. 107-124. < http://www.statcan.gc.ca/pub/12-001-x/2017001/article/14820-eng.htm >