TY -
GEN
AU - Giusti, Caterina
AU - Marchetti, Stefano
AU - Pratesi, Monica
AU - Salvati, Nicola
TI -
Robust small area estimation and oversampling in the estimation of poverty indicators
KW - Methode
KW - Messung
KW - Armut
KW - Indikator
KW - Indikatorenforschung
KW - Indikatorenbildung
KW - Daten
KW - Datenorganisation
KW - Datenqualität
PY - 2012
N2 - Veröffentlichungsversion
N2 - begutachtet (peer reviewed)
N2 - In: Survey Research Methods ; 6 (2012) 3 ; 155-163
N2 - "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 people). The EU-SILC (European Union - Statistics on Income and Living Conditions) 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 lower geographical/domain levels, but estimating poverty indicators directly from EU-SILC for these domains often leads to inaccurate estimates. To overcome this problem there are two main strategies: i. increasing the sample size of EU-SILC so that direct estimates become reliable and ii. resort to small area estimation techniques. In this paper the authors compare these two alternatives: with the availability of an oversampling 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/ ), they can compute reliable direct estimates that can be compared to small area estimates computed under the M-quantile approach. Results show that the M-quantile small area estimates are comparable in terms of efficiency and precision to direct estimates using oversample data. Moreover, considering the oversample estimates as a benchmark, they show how direct estimates computed without the oversample have larger errors as well as larger estimated mean squared errors than corresponding M-quantile estimates." (author's abstract)
UR - http://slubdd.de/katalog?TN_libero_mab2
ER -