There has been rising interest in research on poverty mapping over the last decade, with the European Union proposing a core of statistical indicators on poverty commonly known as Laeken Indicators. They include the incidence and the intensity of poverty for a set of domains (e.g. young people, unemployed peo- ple). The EU-SILC (European Union - Statistics on Income and Living Condi- tions) survey represents the most important source of information to estimate these poverty indicators at national or regional level (NUTS 1-2 level). However, local policy makers also require statistics on poverty and living conditions at a lower ge- ographical/domain levels, but estimating poverty indicators directly from EU-SILC for these domain often leads to inaccurate estimates. To overcome this problem there are two main strategies: i. increasing the sample size of EU-SILC so that di- rect estimates become reliable and ii. resort to small area estimation techniques. In this paper we compare these two alternatives: with the availability of an over- sampling of the EU-SILC survey for the province of Pisa, obtained as a side result of the SAMPLE project (Small Area Methods for Poverty and Living Conditions, http://www.sample-project.eu/), we can compute reliable direct estimates that can be compared to small area estimates computed under the M-quantile approach. Re- sults show that the M-quantile small area estimates are comparable in terms of efficiency and precision to direct estimates using oversample data. Moreover, consider- ing the oversample estimates as a benchmark, we show how direct estimates com- puted without the oversample have larger errors as well as larger estimated mean squared errors than corresponding M-quantile estimates.
|Autori:||Giusti C; Marchetti S; Pratesi M; Salvati N|
|Titolo:||Robust Small Area Estimation and Oversampling in the Estimation of Poverty Indicators|
|Anno del prodotto:||2012|
|Appare nelle tipologie:||1.1 Articolo in rivista|