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The Spatial Fay-Herriot Model in Poverty Estimation

Author

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  • Wawrowski Łukasz

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

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 As a result, estimates for previously unpublished levels of aggregation will be obtained.

Suggested Citation

  • Wawrowski Łukasz, 2016. "The Spatial Fay-Herriot Model in Poverty Estimation," Folia Oeconomica Stetinensia, Sciendo, vol. 16(2), pages 191-202, December.
    • Handle: RePEc:vrs:foeste:v:16:y:2016:i:2:p:191-202:n:14
    DOI: 10.1515/foli-2016-0034
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    References listed on IDEAS

    as
    1. Jan Drewnowski, 1977. "Poverty: Its Meaning and Measurement," Development and Change, International Institute of Social Studies, vol. 8(2), pages 183-208, April.
    2. Monica Pratesi & Nicola Salvati, 2008. "Small area estimation: the EBLUP estimator based on spatially correlated random area effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 113-141, February.
    3. María Guadarrama & Isabel Molina & J. N. K. Rao, 2016. "A Comparison Of Small Area Estimation Methods For Poverty Mapping," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 41-66, March.
    4. repec:csb:stintr:v:17:y:2016:i:1:p:41-66 is not listed on IDEAS
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    More about this item

    Keywords

    small area estimation; poverty rate; the spatial Fay-Herriot model;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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