A multiple-group hidden Markov model for multi-source data. Cross-country differences in employment mobility in the presence of measurement error

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Research Pub Date : 2025-05-19 DOI:10.1016/j.bdr.2025.100527

Roberta Varriale , Mauricio Garnier-Villarreal , Dimitris Pavlopoulos , Danila Filipponi

引用次数: 0

Abstract

In this paper, we develop a multigroup hidden Markov model to tackle the issue of measurement error in multi-source data from different countries. We focus, in particular, on the measurement of employment mobility in the Netherlands and Italy using linked data from the Labour Force Survey and administrative sources. The measurement-error correction we apply reconciles differences between data sources and shows that cross-country differences in employment mobility are smaller than originally thought. Error-corrected estimates indicate that mobility from temporary to permanent employment has become, over time, larger in Italy than in the Netherlands, while mobility from non-employment to temporary employment has steadily been higher in the Netherlands than in Italy.

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多源数据的多组隐马尔可夫模型。存在测量误差的就业流动性的跨国差异

在本文中，我们建立了一个多组隐马尔可夫模型来解决来自不同国家的多源数据的测量误差问题。我们特别关注荷兰和意大利的就业流动性，使用来自劳动力调查和行政来源的相关数据。我们采用的测量误差修正调和了数据源之间的差异，并表明就业流动性的跨国差异比最初想象的要小。修正错误的估计表明，随着时间的推移，意大利从临时就业到永久就业的流动性比荷兰大，而荷兰从非就业到临时就业的流动性一直高于意大利。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data Research Computer Science-Computer Science Applications

CiteScore

8.40

自引率

3.00%

发文量

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