Disaggregation of poverty indicators by small area methods for assessing the targeting of the Reddito di Cittadinanza national policy in Italy

In Italy, a crucial anti-poverty policy “Reddito di Cittadinanza” (RdC), a measure of guaranteed minimum income, was introduced in April 2019. We aim to evaluate the targeting of the RdC policy at the local level, as aggregated analyses could mask important misalignments between the share of beneficiaries of the RdC and the share of poor households. To measure the poverty share in the local areas of interest, two main indicators to capture and monitor poverty are used in Europe: the At-Risk-of-Poverty Rate based on the EU Statistics on Income and Living Conditions survey and the Absolute Poverty Index based on consumption data collected through the Household and Budget Survey. To obtain reliable estimates of these indicators at the local level, it is necessary to introduce small area estimation models that allow the use of data from different sources. We apply a bivariate Fay and Herriot model to provide reliable estimates of absolute and relative poverty for the assessment of RdC policy targeting in the 59 areas represented by the region by degree of urbanisation level in Italy. The degree of urbanisation is indeed a key geographical variable in the study of the poverty phenomenon. Our results suggest that the RdC policy implemented at the national level shows heterogeneous targeting performance at the local level, excluding large shares of poor households from the program. These findings yield a set of policy implications for improving the targeting of the measure.