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Small area estimates of the low work intensity indicator at voivodeship level in Poland

Author

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  • Wawrowski Łukasz

    (Computer Science Research Centre, Research Network Łukasiewicz, Institute of Innovative Technologies EMAG, Poznań, ; Poland .)

  • Beresewicz Maciej

    (Department of Statistics, Poznań University of Economics and Business. Statistical Office in Poznań, Poznań, ; Centre for Small Area Estimation, Poland .)

Abstract

The EU Statistics on Income and Living Conditions (EU-SILC) has provided annual estimates of the number of labour market indicators for EU countries since 2003, with an almost exclusive focus on national rates. However, it is impossible to obtain reliable direct estimates of labour market statistics at low levels based on the EU-SILC survey. In such cases, model-based small area estimation can be used. In this paper, the low work intensity indicator for the spatial domains in Poland between 2005-2012 was estimated. The Rao and You (1994), Fay and Diallo (2012), and Marhuenda, Molina and Morales (2013) models were applied. The bootstrap MSE for the discussed methods was proposed. The results indicate that these models provide more reliable estimates than direct estimation.

Suggested Citation

  • Wawrowski Łukasz & Beresewicz Maciej, 2021. "Small area estimates of the low work intensity indicator at voivodeship level in Poland," Statistics in Transition New Series, Statistics Poland, vol. 22(2), pages 155-172, June.
    • Handle: RePEc:vrs:stintr:v:22:y:2021:i:2:p:155-172:n:1
    DOI: 10.21307/stattrans-2021-021
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    References listed on IDEAS

    as
    1. Marhuenda, Yolanda & Molina, Isabel & Morales, Domingo, 2013. "Small area estimation with spatio-temporal Fay–Herriot models," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 308-325.
    2. Marcin Szymkowiak & Andrzej Młodak & Łukasz Wawrowski, 2017. "Mapping Poverty At The Level Of Subregions In Poland Using Indirect Estimation," Statistics in Transition New Series, Polish Statistical Association, vol. 18(4), pages 609-635, December.
    3. Grażyna Dehnel & Łukasz Wawrowski, 2020. "Robust estimation of wages in small enterprises: the application to Poland’s districts," Statistics in Transition New Series, Polish Statistical Association, vol. 21(1), pages 137-157, March.
    4. Isabel Molina & Nicola Salvati & Monica Pratesi, 2009. "Bootstrap for estimating the MSE of the Spatial EBLUP," Computational Statistics, Springer, vol. 24(3), pages 441-458, August.
    5. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    6. Sonja Greven & Thomas Kneib, 2010. "On the behaviour of marginal and conditional AIC in linear mixed models," Biometrika, Biometrika Trust, vol. 97(4), pages 773-789.
    7. Terry Ward & Erhan Ozdemir, 2013. "Measuring low work intensity – an analysis of the indicator," ImPRovE Working Papers 13/09, Herman Deleeck Centre for Social Policy, University of Antwerp.
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