Immigrant groups in Luxembourg's labour market: A symbolic data analysis approach.

This article focuses on analyzing immigrant groups in Luxembourg's labor market from 2014 to 2022 using Luxembourgish Labour Force Survey (LFS) data, Symbolic Data Analysis (SDA), and the Monitoring the Evolution of Clusters (MEC) framework. Immigrants were categorized by birthplace into four groups—Neighbouring Countries (NC: Belgium, France, Germany), Portugal (PT), other European Union countries excluding the previous, and non-EU countries (OUTEU)—and by length of residence, forming 21 symbolic objects annually described by six key modal variables related to occupation, education, wages, telework ability, economic sector, and urbanization. Cluster analysis revealed distinct labor market profiles, with Portuguese immigrants typically engaged in blue-collar jobs with lower education and wages, EU and NC immigrants in high-skilled white-collar professions with higher education and telework capacity, and OUTEU groups showing heterogeneous patterns. The MEC framework traced cluster transitions over time, highlighting stability in Portuguese and EU/NC clusters and variability among OUTEU groups. The study demonstrates the novel integration of SDA and MEC for official statistics, enabling detailed, dynamic monitoring of immigrant labor market profiles and offering a replicable methodology for other countries with high immigration.