Estimating regional income indicators under transformations and access to limited population auxiliary information

Faculty/Professorship: Statistics and Econometrics  
Author(s): Würz, Nora ; Schmid, Timo ; Tzavidis, Nikos
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2023
Pages: 1679-1706
Source/Other editions: Journal of the Royal Statistical Society. Series A, 185 (2022), 4, S. 1679-1706 - ISSN: 1467-985X
is version of: 10.1111/rssa.12913
Year of first publication: 2022
Language(s): English
Licence: Creative Commons - CC BY - Attribution 4.0 International 
URN: urn:nbn:de:bvb:473-irb-582821
Spatially disaggregated income indicators are typically estimated by using model-based methods that assume access to auxiliary information from population micro-data. In many countries like Germany and the UK population micro-data are not publicly available. In this work we propose small area methodology when only aggregate population-level auxiliary information is available. We use data-driven transformations of the response to satisfy the parametric assumptions of the used models. In the absence of population micro-data, appropriate bias-corrections for small area prediction are needed. Under the approach we propose in this paper, aggregate statistics (means and covariances) and kernel density estimation are used to resolve the issue of not having access to population micro-data. We further explore the estimation of the mean squared error using the parametric bootstrap. Extensive model-based and design-based simulations are used to compare the proposed method to alternative methods. Finally, the proposed methodology is applied to the 2011 Socio-Economic Panel and aggregate census information from the same year to estimate the average income for 96 regional planning regions in Germany.
GND Keywords: Volkszählung; Dichteschätzung; Amtliche Statistik; Personenbezogene Daten
Keywords: census, density estimation, official statistics, small area estimation, unit-level models
DDC Classification: 330 Economics  
RVK Classification: QH 250   
Type: Article
Release Date: 17. February 2023
Project: Open-Access-Publikationskosten 2022 - 2024

File SizeFormat  
fisba58282.pdf2.2 MBPDFView/Open