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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.
| Original language | English |
|---|---|
| Pages (from-to) | 211-222 |
| Number of pages | 12 |
| Journal | Rivista Italiana di Economia Demografia e Statistica |
| Volume | LXXIX |
| Issue number | 1 |
| Early online date | 13 Feb 2025 |
| DOIs | |
| Publication status | Published - Mar 2025 |
Funding
This paper is part of the project DYNANSE that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 864471
| Funders | Funder number |
|---|---|
| ERC | 864471 |
Keywords
- measurement error
- Italy
- employment trajectories
- labour market
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DYNANSE: Righting the Wrongs, A Life Course Dynamics Approach for Non-Standard Employment (ERC Consolidator Grant project)
Pavlopoulos, D. (Principal Investigator), Garnier Villarreal, M. (Project Researcher), Bouwhuis, S. (Project Researcher), Csorba, I. (Project Researcher), Chowdhury, T. M. (PhD candidate), Eberlein, L. (PhD candidate), Pankowska, P. K. (Project Researcher), Pankowska, P. K. (Project Researcher), Eberlein, L. (Project Researcher), Chowdhury, T. M. (Project Researcher), Steenks, K. (Project Researcher) & Jiménez Henríquez, M. J. (Project Researcher)
1/09/20 → 28/02/26
Project: Research