The ISEE Income Indicator: A New Proposal

The Equivalent Economic Situation Indicator (ISEE) is a key measure used in Italy to assess household economic conditions, considering income, assets, and family characteristics. It serves as a benchmark for determining eligibility for social benefits by enabling fair comparisons between households of different sizes and compositions. A crucial component of the ISEE calculation is the equivalence scale, which adjusts income and wealth to account for household structure. However, despite its significance in measuring inequality and poverty, there is no consensus on the most appropriate equivalence scale to use. Existing methodologies tend to be normative rather than derived from a rigorous analytical framework. To address these challenges, we propose a novel approach that leverages Aggregation Operators, with parameters defined by expert evaluation, to create an interpretable and transparent equivalence scale. Additionally, we design a Fuzzy Inference System (FIS) to compute the ISEE, incorporating income, assets, and household composition as input variables. The system’s membership functions, rules, and operators are designed to capture the inherent nonlinearities of the model, ensuring a more robust and heuristically justified approach. We demonstrate the feasibility of our method through a case study using simulated data and discuss potential enhancements, such as integrating machine learning techniques.