  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.
