Economic policy sciences are constantly investigating the quality of well-being of broad sections of the population in order to describe the current interdependence between unequal living conditions, low levels of education and a lack of integration into society. Such studies are often carried out in the form of surveys, e.g. as part of the EU-SILC program. If the survey is designed at national or international level, the results of the study are often used as a reference by a broad range of public institutions. However, the sampling strategy per se may not capture enough information to provide an accurate representation of all population strata. Problems might arise from rare, or hard-to-sample, populations and the conclusion of the study may be compromised or unrealistic. We propose here a two-phase methodology to identify rare, poorly sampled populations and then resample the hard-to-sample strata. We focused our attention on the 2019 EU-SILC section concerning the Italian region of Liguria. Methods based on dispersion indices or deep learning were used to detect rare populations. A multi-frame survey was proposed as the sampling design. The results showed that factors such as citizenship, material deprivation and large families are still fundamental characteristics that are difficult to capture.

翻译：经济政策科学持续研究广大民众的福祉质量，旨在描述不平等生活条件、低教育水平与社会融入不足之间的相互依存关系。此类研究通常以调查形式开展，例如作为EU-SILC（欧盟收入与生活条件统计）项目的组成部分。当调查在国家或国际层面设计时，研究结果常被众多公共机构作为参考基准。然而，抽样策略本身可能无法捕获足够信息以准确反映所有人口阶层特征。稀有群体或难以抽样群体可能引发问题，导致研究结论存在偏差或脱离实际。本文提出一种两阶段方法：首先识别稀有且抽样不足的群体，随后对难以抽样的阶层进行再抽样。我们聚焦于2019年EU-SILC中意大利利古里亚地区的调查数据，采用基于离散指数或深度学习的方法检测稀有群体，并提出多框架调查作为抽样设计方案。结果表明，公民身份、物质匮乏及大家庭等特征仍是难以捕获的关键要素。