IDEAS home Printed from https://ideas.repec.org/a/vrs/stintr/v22y2021i2p155-172n1.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.21307/stattrans-2021-021
    Download Restriction: no
    File URL: https://libkey.io/10.21307/stattrans-2021-021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Łukasz Wawrowski & Maciej Beręsewicz, 2021. "Small area estimates of the low work intensity indicator at voivodeship level in Poland," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 155-172, June.
    2. Simona Buscemi & Antonella Plaia, 2020. "Model selection in linear mixed-effect models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 529-575, December.
    3. Tomasz Ża̧dło, 2015. "On longitudinal moving average model for prediction of subpopulation total," Statistical Papers, Springer, vol. 56(3), pages 749-771, August.
    4. Molina, Isabel, 2022. "Disaggregating data in household surveys: Using small area estimation methodologies," Estudios Estadísticos 48107, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    5. Kordos Jan, 2016. "Development of Small Area Estimation in Official Statistics," Statistics in Transition New Series, Statistics Poland, vol. 17(1), pages 105-132, March.
    6. Jan Kordos, 2016. "Development Of Smallarea Estimation In Official Statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 105-132, March.
    7. repec:csb:stintr:v:17:y:2016:i:1:p:105-132 is not listed on IDEAS
    8. Whitney S Beck & Ed K Hall, 2018. "Confounding factors in algal phosphorus limitation experiments," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    9. Xindong Xue & W. Robert Reed & Robbie C.M. van Aert, 2022. "Social Capital and Economic Growth: A Meta-Analysis," Working Papers in Economics 22/20, University of Canterbury, Department of Economics and Finance.
    10. Bart Verkuil & Serpil Atasayi & Marc L Molendijk, 2015. "Workplace Bullying and Mental Health: A Meta-Analysis on Cross-Sectional and Longitudinal Data," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-16, August.
    11. Francesca Pilotto & Ingolf Kühn & Rita Adrian & Renate Alber & Audrey Alignier & Christopher Andrews & Jaana Bäck & Luc Barbaro & Deborah Beaumont & Natalie Beenaerts & Sue Benham & David S. Boukal & , 2020. "Meta-analysis of multidecadal biodiversity trends in Europe," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    12. Hemant Kulkarni & Jayabrata Biswas & Kiranmoy Das, 2019. "A joint quantile regression model for multiple longitudinal outcomes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 453-473, December.
    13. repec:cup:judgdm:v:15:y:2020:i:6:p:972-988 is not listed on IDEAS
    14. Jonas Schmidt & Tammo H. A. Bijmolt, 2020. "Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 499-518, May.
    15. Mario Herberz & Tobias Brosch & Ulf J. J. Hahnel, 2020. "Kilo what? Default units increase value sensitivity in joint evaluations of energy efficiency," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(6), pages 972-988, November.
    16. María Dolores Esteban & María José Lombardía & Esther López-Vizcaíno & Domingo Morales & Agustín Pérez, 2020. "Small area estimation of proportions under area-level compositional mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 793-818, September.
    17. Piers Steel & Sjoerd Beugelsdijk & Herman Aguinis, 2021. "The anatomy of an award-winning meta-analysis: Recommendations for authors, reviewers, and readers of meta-analytic reviews," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 52(1), pages 23-44, February.
    18. Jan Pablo Burgard & Domingo Morales & Anna-Lena Wölwer, 2022. "Small area estimation of socioeconomic indicators for sampled and unsampled domains," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 287-314, June.
    19. Michael Wegener & Göran Kauermann, 2017. "Forecasting in nonlinear univariate time series using penalized splines," Statistical Papers, Springer, vol. 58(3), pages 557-576, September.
    20. Dian Handayani & Henk Folmer & Anang Kurnia & Khairil Anwar Notodiputro, 2018. "The spatial empirical Bayes predictor of the small area mean for a lognormal variable of interest and spatially correlated random effects," Empirical Economics, Springer, vol. 55(1), pages 147-167, August.
    21. Molo, Fabio & Pawel, Samuel & Fraga González, Gorka, 2024. "A Robustness Reproduction of "A Systematic Review and Meta-Analysis of 90 Cohort Studies of Social Isolation, Loneliness and Mortality"," I4R Discussion Paper Series 169, The Institute for Replication (I4R).
    22. Augusteijn, Hilde Elisabeth Maria & van Aert, Robbie Cornelis Maria & van Assen, Marcel A. L. M., 2021. "Posterior Probabilities of Effect Sizes and Heterogeneity in Meta-Analysis: An Intuitive Approach of Dealing with Publication Bias," OSF Preprints avkgj, Center for Open Science.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:stintr:v:22:y:2021:i:2:p:155-172:n:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://stat.gov.pl/en/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.