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arXiv · 2405.01342

Data-Driven Strategies for Detecting and Sampling Misrepresented Subgroups

G. Lancia·F. Mecatti·E. Riccomagno
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Abstract

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.

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G. Lancia, F. Mecatti, E. Riccomagno. 2024-05-02. Data-Driven Strategies for Detecting and Sampling Misrepresented Subgroups. https://arxiv.org/abs/2405.01342

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