Data-Driven Strategies for Detecting and Sampling Misrepresented Subgroups
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914246058573824 |
|---|---|
| author | Lancia, G. Mecatti, F. Riccomagno, E. |
| author_facet | Lancia, G. Mecatti, F. Riccomagno, E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01342 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Strategies for Detecting and Sampling Misrepresented Subgroups Lancia, G. Mecatti, F. Riccomagno, E. Applications Computation Other Statistics 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. |
| title | Data-Driven Strategies for Detecting and Sampling Misrepresented Subgroups |
| topic | Applications Computation Other Statistics |
| url | https://arxiv.org/abs/2405.01342 |