From start to finish: a framework for the production of small area official statistics

Nikos Tzavidis, Li Chun Zhang, Angela Luna, Timo Schmid, Natalia Rojas-Perilla

Research output: Contribution to journalArticlepeer-review

58 Citations (Scopus)


Small area estimation is a research area in official and survey statistics of great practical relevance for national statistical institutes and related organizations. Despite rapid developments in methodology and software, researchers and users would benefit from having practical guidelines for the process of small area estimation. We propose a general framework for the production of small area statistics that is governed by the principle of parsimony and is based on three broadly defined stages, namely specification, analysis and adaptation, and evaluation. Emphasis is given to the interaction between a user of small area statistics and the statistician in specifying the target geography and parameters in the light of the available data. Model-free and model-dependent methods are described with a focus on model selection and testing, model diagnostics and adaptations such as use of data transformations. Uncertainty measures and the use of model and design-based simulations for method evaluation are also at the centre of the paper. We illustrate the application of the proposed framework by using real data for the estimation of non-linear deprivation indicators. Linear statistics, e.g. averages, are included as special cases of the general framework.

Original languageEnglish
Pages (from-to)927-979
Number of pages53
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
Issue number4
Publication statusPublished - Oct 2018
Externally publishedYes


  • Census
  • Design-based methods
  • Diagnostics
  • Inequality
  • Model-based methods

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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