2017•Statistics in Transition New SeriesOpen access

Mapping Poverty at the Level of Subregions in Poland Using Indirect Estimation

Marcin Szymkowiak, Andrzej Młodak, Łukasz Wawrowski

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Abstract

Abstract The European Survey on Income and Living Conditions (EU-SILC) is the basic source of information published by CSO (the Central Statistical Office of Poland) about the relative poverty indicator, both for the country as a whole and at the regional level (NUTS 1). Estimates at lower levels of the territorial division than regions (NUTS 1) or provinces (NUTS 2, also called ’voivodships’) have not been published so far. These estimates can be calculated by means of indirect estimation methods, which rely on information from outside the subpopulation of interest, which usually increases estimation precision. The main aim of this paper is to show results of estimation of the poverty indicator at a lower level of spatial aggregation than the one used so far, that is at the level of subregions in Poland (NUTS 3) using the small area estimation methodology (SAE), i.e. a model–based technique – the EBLUP estimator based on the Fay–Herriot model. By optimally choosing covariates derived from sources unaffected by random errors we can obtain results with adequate precision. A territorial analysis of the scope of poverty in Poland at NUTS 3 level will be also presented in detail4. The article extends the approach presented by Wawrowski (2014).

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What this paper is about

Abstract The European Survey on Income and Living Conditions (EU-SILC) is the basic source of information published by CSO (the Central Statistical Office of Poland) about the relative poverty indicator, both for the country as a whole and at the regional level (NUTS 1). Estimates at lower levels of the territorial division than regions (NUTS 1) or provinces (NUTS 2, also called ’voivodships’) have not been published so far. These estimates can be calculated by means of indirect estimation methods, which rely on information from outside the subpopulation of interest, which usually increases estimation precision. The main aim of this paper is to show results of estimation of the poverty indicator at a lower level of spatial aggregation than the one used so far, that is at the level of subregions in Poland (NUTS 3) using the small area estimation methodology (SAE), i.e. a model–based technique – the EBLUP estimator based on the Fay–Herriot model. By optimally choosing covariates derived from sources unaffected by random errors we can obtain results with adequate precision. A territorial analysis of the scope of poverty in Poland at NUTS 3 level will be also presented in detail4. The article extends the approach presented by Wawrowski (2014).

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Available abstract

Abstract The European Survey on Income and Living Conditions (EU-SILC) is the basic source of information published by CSO (the Central Statistical Office of Poland) about the relative poverty indicator, both for the country as a whole and at the regional level (NUTS 1). Estimates at lower levels of the territorial division than regions (NUTS 1) or provinces (NUTS 2, also called ’voivodships’) have not been published so far. These estimates can be calculated by means of indirect estimation methods, which rely on information from outside the subpopulation of interest, which usually increases estimation precision. The main aim of this paper is to show results of estimation of the poverty indicator at a lower level of spatial aggregation than the one used so far, that is at the level of subregions in Poland (NUTS 3) using the small area estimation methodology (SAE), i.e. a model–based technique – the EBLUP estimator based on the Fay–Herriot model. By optimally choosing covariates derived from sources unaffected by random errors we can obtain results with adequate precision. A territorial analysis of the scope of poverty in Poland at NUTS 3 level will be also presented in detail4. The article extends the approach presented by Wawrowski (2014).

Key concepts: Small area estimation, Estimation, Estimator, Poverty, Econometrics, Statistics, Covariate, Geography

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