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Does measurement error bias employment pathways? The case of Italy

Year: 2025

Type: article

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.

Source: Rivista italiana di economia, demografia e statistica

DOI: 10.71014/sieds.v79i1.325

Authors: Dimitris Pavlopoulos, Roberta Varriale, Silvia Loriga

Institutions: Vrije Universiteit Amsterdam, Sapienza University of Rome, Istituto Nazionale di Statistica

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Decent work and economic growth, Reduced inequalities

Open Access status: diamond

Funder: European Commission