This paper evaluates the influence of multidimensional phenomena on a guaranteed minimum income policy aimed at supporting the incomes of Italian families in difficulty, namely the Italian Citizenship Income, from 2018 to 2022. We implement a variety of spatial econometric models that relate the number of households benefiting from income support interventions with wealth and poverty indicators, including the average per capita income, share of poverty, and the Gini index. Spatial models handle the strong spatial heterogeneity exhibited by the recipient households by grouping municipal units into homogeneous and spatially-contiguous groups and estimating local relationships. In this way, we are enabled to evaluate how geographical and local factors influence the effectiveness of income support policies. Results show that the presence of multidimensional phenomena significantly influences the requests for income support. However, the sign and the magnitude of the estimated correlation strongly depend on the type of indicator used and by the local structural characteristics. Also, a remarkable augment in term of complexity of the social phenomenon and spatial heterogeneity throughout the period of interest. We estimate positive and statistically significant correlations regarding per capita income and the share of municipal poverty, in particular where both higher socio-economic vulnerability and low-income levels persist. Also, we observe that where both average per capita income and income inequality are high, the policy was unable to reach potential household targets, while in areas characterized by low income but lower income inequality, the income support reached a high number of households.
JEL Classification: H53 , I38 , R12 , C21