Does measurement error bias employment pathways? The case of Italy
DOI:
https://doi.org/10.71014/sieds.v79i1.325Keywords:
mixture hidden Markov model, employment status, measurement error, multi-source dataAbstract
The exploration of employment trajectories over time may be significantly biased due to measurement errors in the data used for the analysis. This paper addresses this issue by employing a mixture hidden Markov model (MHMM) that detects and corrects for measurement errors. Specifically, we use an MHMM that includes two indicators for employment status, derived from linked data from the Italian Labour Force Survey and Administrative Data for the period 2017-2021.
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