Economic policy research frequently examines population well-being, with a particular focus on the relationships between unequal living conditions, low educational attainment, and social exclusion. Sample surveys, such as EU-SILC, are widely used for this purpose and inform public policy; yet, their sampling designs may fail to adequately represent rare, hard-to-sample, or under-covered subgroups. This limitation can hinder socio-demographic analyses and evidence-based policy design. We propose a generalisable approach based on univariate and multivariate unsupervised learning techniques to detect outliers in survey data that may signal under-represented subgroups. Identified groups can then be characterised to inform targeted resampling strategies that improve survey inclusiveness. An empirical application using the 2019 EU-SILC data for the Italian region of Liguria shows that citizenship, material deprivation, large household size, and economic vulnerability are key indicators of under-representation.

翻译：经济政策研究常关注人口福祉，尤其聚焦于不平等生活条件、低教育水平与社会排斥之间的关联。为此，样本调查（如欧盟收入与生活条件统计）被广泛采用并为公共政策提供依据；然而，其抽样设计可能无法充分代表稀有、难以抽样或覆盖不足的子群体。这一局限可能阻碍社会人口学分析和循证政策设计。我们提出一种基于单变量与多变量无监督学习技术的通用方法，用于检测调查数据中可能指示表征不足子群的异常值。识别出的群体可进一步被特征化，从而为旨在提升调查包容性的定向重抽样策略提供依据。基于2019年欧盟收入与生活条件统计中意大利利古里亚地区数据的实证应用表明，公民身份、物质匮乏、大家庭规模和经济脆弱性是表征不足的关键指标。