Skip to main content
SpringerLink
Log in

Small Area Estimates for the Fraction of the Unemployed

  • Published:
Lithuanian Mathematical Journal Aims and scope Submit manuscript

Abstract

One of the main research trends in contemporary survey sampling and the need to improve the accuracy of the Lithuanian Labor force survey estimates in small geographic areas have stimulated this study. The aim of the paper is to compare area level models and estimation methods for the fraction of the unemployed using simulation based on the Lithuanian Labor Force Survey data. The Fay–Herriot area level model, estimated by empirical best linear unbiased prediction, and the unmatched logit-normal-normal and binomial-logit-normal models, estimated using hierarchical Bayes analysis, are applied. Bayesian imputation is used for areas without sample data. We suggest the composition of some model elements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. H.J. Boonstra, B. Buelens, K. Leufkens, and M. Smeets, Small area estimates of labour status in Dutchmunicipalities, Discussion paper No. 201102, Statistics Netherlands, The Hague, 2011.

  2. H.J. Boonstra, B. Buelens, and M. Smeets, Estimation of municipal unemployment fractions – a simulation study comparing different small area estimators, Project No. DMH-205714, Statistics Netherlands, The Hague, 2009.

  3. G. Datta and M. Ghosh, Small area shrinkage estimation, Stat. Sci., 27(1):95–114, 2012.

    Article  MathSciNet  Google Scholar 

  4. G.S. Datta, Model-based approach to small area estimation, in D. Pfeffermann and C.R. Rao (Eds.), Handbook of Statistics. Sample Surveys: Inference and Analysis, Vol. 29B, Elsevier, North Holland, 2009, pp. 251–288.

    Chapter  Google Scholar 

  5. G.S. Datta, P. Lahiri, T. Maiti, and K.L. Lu, Hierarchical Bayes estimation of unemployment rates for the states of the U.S., J. Am. Stat. Assoc., 94(448):1074–1082, 1999.

    Article  Google Scholar 

  6. E. Fabrizi, Hierarchical Bayesian models for the estimation of unemployment rates in small domains of the Italian Labour Force Survey, Statistica, LXII(4):603–618, 2002.

    MathSciNet  Google Scholar 

  7. P.J. Farrell, Bayesian inference for small area proportions, Sankhyā, Ser. B, 62(3):402–416, 2000.

    MATH  Google Scholar 

  8. R.E. Fay and R.A. Herriot, Estimates of income for small places: An application of James–Stein procedures to census data, J. Am. Stat. Assoc., 74(366):269–277, 1979.

    Article  MathSciNet  Google Scholar 

  9. J. Geweke, Contemporary Bayesian Econometrics and Statistics, Wiley Series in Probability and Statistics, John Wiley & Sons, Hoboken, NJ, 2005.

  10. M. Ghosh and G. Meeden, Bayesian Methods for Finite Population Sampling, Chapman & Hall, London, 1997.

    Book  Google Scholar 

  11. M. Ghosh and J.N.K. Rao, Small area estimation: An appraisal, Stat. Sci., 9(1):55–93, 1994.

    Article  MATH  MathSciNet  Google Scholar 

  12. T. Klimanek, Using indirect estimation with spatial autocorellation in social surveys in Poland, Przegl. Stat., 1(spec. issue):155–172, 2012.

    Google Scholar 

  13. D. Krapavickaitė, An example of small area estimation in finite population sampling, Liet. Mat. Rink., 43(spec. issue):497–503, 2003.

    Google Scholar 

  14. J. Kubacki, Application of the hierarchical Bayes estimation to the Polish Labour Force Survey, Statistics in Transition, 6(5):785–796, 2004.

    Google Scholar 

  15. R. Lehtonen and A. Veijanen, Design-based methods of estimation for domains and small areas, in D. Pfeffermann and C.R. Rao (Eds.), Handbook of Statistics. Sample Surveys: Inference and Analysis, Volume 29B, Elsevier, North Holland, 2009, pp. 219–249.

  16. D. Malec, J. Sedransk, C.L. Moriarity, and F.B. LeClere, Small area inference for binary variables in the national health interview survey, J. Am. Stat. Assoc., 92(439):815–826, 1997.

    Article  MATH  Google Scholar 

  17. J.N.K. Rao, Small Area Estimation, JohnWiley & Sons, Hoboken, NJ, 2003.

    Book  MATH  Google Scholar 

  18. C. Ritter and M. A. Tanner, Facilitating the Gibbs sampler: The Gibbs stopper and the griddy-Gibbs sampler, J. Am. Stat. Assoc., 87(419):861–868, 1992.

    Article  Google Scholar 

  19. C.P. Robert, The Bayesian Choice, Springer, New York, 2007.

    MATH  Google Scholar 

  20. M.P. Singh, J. Gambino, and H. Mantel, Issues and strategies for small area data, Survey Methodology, 20(1):3–22, 1994.

    Google Scholar 

  21. Statisticat, LLC, Laplacesdemon: Complete environment for Bayesian inference, R package version 14.04.05, 2014, available from: http://www.bayesian-inference.com/software.

  22. N. Torelli and M. Trevisani, Labour force estimates for small geografical domains in Italy: Problems, data and models, Working paper No. 118, University of Trieste, 2008.

  23. M. Trevisani and N. Torelli, Hierarchical Bayesian models for small area estimation with count data, Working paper No. 115, University of Trieste, 2007.

  24. Y. You and B. Chapman, Small area estimation using area level models and estimated sampling variances, Survey Methodology, 32(1):97–103, 2006.

    Google Scholar 

  25. Y. You, J.N.K. Rao, and J. Gambino, Model-based unemployment rate estimation for the Canadian Labour Force Survey: A hierarchical Bayes approach, Survey Methodology, 29(1):25–32, 2003.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vilnius Gediminas Technical University, Saulėtekio ave. 11, SRL-I, 425, LT-10223, Vilnius, Lithuania

    Danutė Krapavickaitė

  2. Vilnius University, Akademijos str. 4, LT-08663, Vilnius, Lithuania

    Tomas Rudys

Authors
  1. Danutė Krapavickaitė
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomas Rudys
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Danutė Krapavickaitė.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Krapavickaitė, D., Rudys, T. Small Area Estimates for the Fraction of the Unemployed. Lith Math J 55, 243–254 (2015). https://doi.org/10.1007/s10986-015-9277-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10986-015-9277-9

MSC

Keywords

Search

Navigation