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Deriving small area estimates from information technology business surveys

Author

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  • A. F. Militino
  • M. D. Ugarte
  • T. Goicoa

Abstract

type="main" xml:id="rssa12105-abs-0001"> Knowledge of the current state of the art in information and communication technology of businesses (ICTB) is an important issue for governments, markets and policy makers, because information technology improves access to information and plays an important role in firms' competitiveness. Statistical agencies use normalized surveys to provide harmonized statistics about the use of technology in enterprises. Classical design-based estimators are appropriate for large domains, because direct estimates are consistent and easy to obtain by using sampling weights. However, to supply estimates for unplanned domains, where the sample size is random, model-based estimators are usually required. In this paper, alternative logistic model-based estimators are suggested to derive small area estimates from ICTB surveys. Final estimates are benchmarked to achieve coherence with direct estimates in larger domains, and standard errors are given by using bootstrap techniques. A Monte Carlo simulation study is conducted to compare the performance of the small area estimators proposed and to evaluate the behaviour of the mean-squared error estimator. Results are illustrated with the 2010 ICTB survey of the Basque country (Spain).

Suggested Citation

  • A. F. Militino & M. D. Ugarte & T. Goicoa, 2015. "Deriving small area estimates from information technology business surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 1051-1067, October.
    • Handle: RePEc:bla:jorssa:v:178:y:2015:i:4:p:1051-1067
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    File URL: http://hdl.handle.net/10.1111/rssa.2015.178.issue-4
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    Citations

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    Cited by:

    1. Joscha Krause & Jan Pablo Burgard & Domingo Morales, 2022. "Robust prediction of domain compositions from uncertain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(1), pages 65-96, February.
    2. Tomáš Hobza & Domingo Morales & Laureano Santamaría, 2018. "Small area estimation of poverty proportions under unit-level temporal binomial-logit mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 270-294, June.
    3. Paul A. Smith & Chiara Bocci & Nikos Tzavidis & Sabine Krieg & Marc J. E. Smeets, 2021. "Robust estimation for small domains in business surveys," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 312-334, March.
    4. Joscha Krause & Jan Pablo Burgard & Domingo Morales, 2022. "$$\ell _2$$ ℓ 2 -penalized approximate likelihood inference in logit mixed models for regional prevalence estimation under covariate rank-deficiency," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(4), pages 459-489, May.
    5. Domingo Morales & Joscha Krause & Jan Pablo Burgard, 2022. "On the Use of Aggregate Survey Data for Estimating Regional Major Depressive Disorder Prevalence," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 344-368, March.
    6. M. Giovanna Ranalli & Giorgio E. Montanari & Cecilia Vicarelli, 2018. "Estimation of small area counts with the benchmarking property," METRON, Springer;Sapienza Università di Roma, vol. 76(3), pages 349-378, December.

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