Measuring Uncertainty for Poverty Indicators at Regional Level: The Case of Mediterranean Countries

Abstract

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.

Income Inequality in Mountain Areas: The Case of Agroforestry Farming Systems in Uluguru Mountains, Tanzania

“…Using the example of income distributions and the Luxembourg Income Study datasets, Park et al (2021) show that, the normalized norm indexes evaluated income inequality appropriately and solved the negative income problem. Elsewhere in the Mediterranean countries, Benedetti et al (2020) provide point and variance estimates of two widely used income-poverty indicators, belonging to the class of the Foster-Greer-Thorbecke (FGT), and two widely used incomeinequality indicators. They analyse the spatial distribution of poverty by constructing maps at territorial level.…”

Redesigning consolidated data for handling extreme poverty in rural areas based on SDGS DESA

“…The number of poverty data on a regional, national, and international scale depends on the categorization of the accuracy. It depends on the indicators used in predicting community poverty (Benedetti et al, 2020;Herrero, 2017;Pienkhuntod et al, 2020;Siksnelyte-Butkiene et al, 2021;Susilowati, 2010). The Ministry of National Development Planning/National Development Planning Agency has set a standard based on purchasing power parity (PPP) set by the World Bank at USD 1.91 per capita per day.…”

“…Data that is not integrated within the government impacts the unfair distribution of social and economic rights (Agnia Dwi Khasanah & R.A.E Virgana Targa Sapanji, 2021;Blumenstock, 2016;Ibrahim et al, 2021;Leidig & Teeuw, 2015). The data depends on the indicators used to predict community poverty (Benedetti et al, 2020;Herrero, 2017;Pienkhuntod et al, 2020;Siksnelyte-Butkiene et al, 2021;Susilowati, 2010). On a global scale, poverty also depends on state investment volatility (Gnangnon, 2021).…”