Survey Methodology
A mixed latent class Markov approach for estimating labour market mobility with multiple indicators and retrospective interrogation

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by Francesca Bassi, Marcel Croon and Davide VidottoNote 1

  • Release date: June 22, 2017
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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.

Key Words: Gross flows; Labour market; Mixture models; Latent class models.

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How to cite

Bassi, F., Croon, M. and Vidotto, D. (2017). A mixed latent class Markov approach for estimating labour market mobility with multiple indicators and retrospective interrogation. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 43, No. 1. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2017001/article/14820-eng.htm.

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ISSN : 1492-0921

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Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Copyright

Published by authority of the Minister responsible for Statistics Canada.

© Minister of Industry, 2017

All rights reserved. Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: semi-annual

Ottawa

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. The latent class Markov model
Section 3. The data
Section 4. Results: Comparisons of mixed and standard LCM models
Section 5. Results: Mixed LCM model with covariates and correlated measurement errors
Section 6. Concluding remarks
References

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