Measuring economic insecurity using a fuzzy sets approach

Economic insecurity has gained increasing attention over the last decade, particularly in terms of its measurement and how it affects everyday life. This paper contributes to the literature on measurement by proposing a new individual-level, multidimensional index based on a fuzzy sets approach. The fuzzy logic moves beyond the classic binary framework of set theory, which classifies elements strictly as 0 or 1. In the fuzzy sets approach, each set is defined by a membership function that indicates the degree to which each element belongs to the set. This flexibility makes it particularly well suited for capturing complex socio-economic conditions such as economic insecurity. The proposed measure incorporates a range of economic insecurity indicators and offers some advantages. First, it produces an individual score that can be easily aggregated for geographical and socio-demographic comparisons. Second, the methodology allows for precise estimation of the variance, which is useful for assessing the reliability of aggregate estimates. The new index is applied to the Italian context using the most recent EU-SILC data. Aggregate estimates by region and socio-demographic group are derived and compared. Results indicate that the well-known North-South gradient persists and that economic insecurity is higher among the most disadvantaged sub-populations. In particular, individuals with low educational attainment and those who are unemployed or inactive experience the highest levels of economic insecurity.