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Over the last few years, there has been increased interest in compiling poverty indicators for children, as well as in providing uncertainty measures that are associated with point estimates. In this paper, we provide point, variance, and interval confidence estimates of the at-risk-of-poverty rate indicator for 33 European countries. Using the 2018 EU-SILC survey, we analysed the spatial distribution of poverty by providing graphical representations at the national level. Our results reveal rates of child poverty that are higher than in the national estimates for most of the countries. By considering the computation of standard errors, we used the bootstrap method thanks to its convenient properties. It is worth noting that, for some countries, such as Finland, Belgium, and Ireland, the confidence intervals do not overlap. These results suggest differences among countries not only in terms of child poverty, but also in terms of social protection and the welfare state. [ABSTRACT FROM AUTHOR]

Over the last years, there has been an increased interest in compiling poverty indicators as well as in providing uncertainty measures both at national and regional level. In this paper, we provide point and variance estimates of two widely used income-poverty indicators, which belong to the class of the Foster-Greer-Thorbecke (FGT), and two widely used income-inequality indicators. We focused on Mediterranean countries since they have been severely hit by the Great Recession which increased poverty intensity and socio-economic inequalities. By using the 2018 EU-SILC data we analysed the spatial distribution of poverty by constructing maps at NUTS2 territorial level. Our estimation results reveal that national poverty indicators hide a high heterogeneity of poverty across regions within each country, especially for Italy and Spain. This study also provides computations of standard errors at regional level which have been explored only in a limited number of papers. To this aim we adopted the Jackknife replication method thanks to its convenient properties. As expected, the uncertainty measure is influenced by the reduced number of sampling units in each NUTS2 region especially in some regions of Spain and Italy. The Jackknife method proved to perform well in the case of income-inequality indicators especially for Greece, Italy, Croatia and Portugal. [ABSTRACT FROM AUTHOR]