Abstract
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.
Highlights
Introduction and MotivationOver the past few decades, there has been increased interest in comparative analysis of poverty and social exclusion in European countries
Of the Laeken indicators, which are key in the Europe 2020 strategy, we focused on the at-risk-of-poverty rate indicator (AROP)
Child poverty is significantly higher with respect to the national estimates in the UK, France, Albania, and Italy, while it is significantly lower in Poland and Greece
Summary
Over the past few decades, there has been increased interest in comparative analysis of poverty and social exclusion in European countries. In the case of complex statistics, such as the at-risk-of-poverty rate, where the poverty threshold is estimated on the basis of the survey data, the computation of standard errors is not straightforward [5] In this case there are two main sources of variability: one is related to the process of estimating the threshold, and the other arises from the estimate of the proportion of people living in poverty given the estimated threshold [6]. This study contributes to advancing the existing literature on the computation of standard errors and confidence intervals for children To this aim, we provide an empirical application while using the bootstrap replication method, which gave us reliable information regarding the uncertainty of child poverty estimates information that is of crucial importance for policy makers [3].
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