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
Does measurement error bias employment pathways? The case of Italy / Pavlopoulos, Dimitris; Varriale, Roberta; Loriga, Silvia. - In: RIVISTA ITALIANA DI ECONOMIA, DEMOGRAFIA E STATISTICA. - ISSN 0035-6832. - LXXIX n.1:(2025), pp. 211-222.
Does measurement error bias employment pathways? The case of Italy
Roberta Varriale;Silvia Loriga
2025
Abstract
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| File | Dimensione | Formato | |
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