<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Counteracting poverty is one of the objectives of the European Commission clearly emphasized in the Europe 2020 strategy. Conducting appropriate social policy requires knowledge of the extent of this phenomenon. Such information is provided through surveys on living conditions conducted by, among others, the Central Statistical Office (CSO). Nevertheless, the sample size in these surveys allows for a precise estimation of poverty rate only at a very general level - the whole country and regions. Small sample size at the lower level of spatial aggregation results in a large variance of obtained estimates and hence lower reliability. To obtain information in sparsely represented territorial sections, methods of small area estimation are used. Through using the information from other sources, such as censuses and administrative registers, it is possible to estimate distribution parameters with smaller variance than in the case of direct estimation.</p><p>This paper attempts to estimate the poverty rate at LAU 1 level of Poland. This estimation will be possible through the use of data from different sources describing the living conditions of households and the use of the Fay-Herriot model with spatial correlation. As a result, estimates for previously unpublished levels of aggregation will be obtained.</p></abstract>

Counteracting poverty is one of the objectives of the European Commission clearly emphasized in the Europe 2020 strategy. Conducting appropriate social policy requires knowledge of the extent of this phenomenon. Such information is provided through surveys on living conditions conducted by, among others, the Central Statistical Office (CSO). Nevertheless, the sample size in these surveys allows for a precise estimation of poverty rate only at a very general level - the whole country and regions. Small sample size at the lower level of spatial aggregation results in a large variance of obtained estimates and hence lower reliability. To obtain information in sparsely represented territorial sections, methods of small area estimation are used. Through using the information from other sources, such as censuses and administrative registers, it is possible to estimate distribution parameters with smaller variance than in the case of direct estimation.This paper attempts to estimate the poverty rate at LAU 1 level of Poland. This estimation will be possible through the use of data from different sources describing the living conditions of households and the use of the Fay-Herriot model with spatial correlation. As a result, estimates for previously unpublished levels of aggregation will be obtained.

The Spatial Fay-Herriot Model in Poverty Estimation

Poznań University of Economics and Business, Faculty of Informatics and Electronic Economy, Department of Statistics, Al. Niepodległości 10, 61-875 Poznań,

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"The Spatial Fay-Herriot Model in Poverty Estimation"}

Counteracting poverty is one of the objectives of the European Commission clearly emphasized in the Europe 2020 strategy. Conducting appropriate social policy...

The Spatial Fay-Herriot Model in Poverty Estimation

Published Online: Apr 04, 2017

Page range: 191 - 202

Received: Feb 01, 2016

Accepted: Oct 06, 2016

DOI: https://doi.org/10.1515/foli-2016-0034

Keywords
small area estimation, poverty rate, the spatial Fay-Herriot model

© 2016 University of Szczecin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The Spatial Fay-Herriot Model in Poverty Estimation

Published Online: Apr 04, 2017

Page range: 191 - 202

Received: Feb 01, 2016

Accepted: Oct 06, 2016

DOI: https://doi.org/10.1515/foli-2016-0034

Keywordssmall area estimation, poverty rate, the spatial Fay-Herriot model

© 2016 University of Szczecin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
small area estimation, poverty rate, the spatial Fay-Herriot model