Iterative Kernel Density Estimation Applied to Grouped Data : Estimating Poverty and Inequality Indicators from the German Microcensus

Faculty/Professorship: Statistics and Econometrics  
Author(s): Walter, Paul; Groß, Marcus; Schmid, Timo ; Weimer, Katja
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2022
Pages: 599-635
Source/Other editions: Journal of Official Statistics : JOS, 38 (2022), 2, S. 599-635 - ISSN: 2001-7367
is version of: 10.2478/jos-2022-0027
Year of first publication: 2022
Language(s): English
Licence: Creative Commons - CC BY-NC-ND - Attribution - NonCommercial - NoDerivatives 4.0 International 
URN: urn:nbn:de:bvb:473-irb-559830
The estimation of poverty and inequality indicators based on survey data is trivial as long as the variable of interest (e.g., income or consumption) is measured on a metric scale. However, estimation is not directly possible, using standard formulas, when the income variable is grouped due to confidentiality constraints or in order to decrease item nonresponse. We propose an iterative kernel density algorithm that generates metric pseudo samples from the grouped variable for the estimation of indicators. The corresponding standard errors are estimated by a non-parametric bootstrap that accounts for the additional uncertainty due to the grouping. The algorithm enables the use of survey weights and household equivalence scales. The proposed method is applied to the German Microcensus for estimating the regional distribution of poverty and inequality in Germany.
GND Keywords: Mikrozensus; Armut; Ungleichheit; Schätzung
Keywords: Direct estimation, Interval-censored data, non-parametric estimation, OECD scale, prediction
DDC Classification: 300 Social sciences, sociology & anthropology  
RVK Classification: QH 233   
Type: Article
Release Date: 20. October 2022

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