Export to your Reference Manger

Please Copy & Paste



Bookmark and Share

Robust small area estimation and oversampling in the estimation of poverty indicators

Stabile Schätzung von Kleinflächen und Oversampling bei der Schätzung von Armutsindikatoren
[journal article]

Giusti, Caterina; Marchetti, Stefano; Pratesi, Monica; Salvati, Nicola

fulltextDownloadDownload full text

(external source)

Citation Suggestion

Please use the following Persistent Identifier (PID) to cite this document:http://dx.doi.org/10.18148/srm/2012.v6i3.5131

Further Details
Abstract "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)
Keywords method; measurement; poverty; indicator; indicator research; construction of indicators; data; data organization; data quality
Classification Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods; Social Problems
Document language English
Publication Year 2012
Page/Pages p. 155-163
Journal Survey Research Methods, 6 (2012) 3
Issue topic Papers from ITACOSM11
ISSN 1864-3361
Status Published Version; peer reviewed
Licence Deposit Licence - No Redistribution, No Modifications