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Iterative Kernel Density Estimation Applied to Grouped Data: Estimating Poverty and Inequality Indicators from the German Microcensus
[Zeitschriftenartikel]
Abstract 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 confi... mehr
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.... weniger
Thesaurusschlagwörter
Mikrozensus; Bundesrepublik Deutschland; Armut; Ungleichheit; Schätzung; Daten; Indikator
Klassifikation
Erhebungstechniken und Analysetechniken der Sozialwissenschaften
Freie Schlagwörter
direct estimation; interval-censored data; non-parametric estimation; OECD scale; prediction; Mikrozensus 2012
Sprache Dokument
Englisch
Publikationsjahr
2022
Seitenangabe
S. 599-635
Zeitschriftentitel
Journal of Official Statistics, 38 (2022) 2
DOI
https://doi.org/10.2478/jos-2022-0027
ISSN
2001-7367
Status
Veröffentlichungsversion; begutachtet (peer reviewed)
Lizenz
Creative Commons - Namensnennung, Nicht kommerz., Keine Bearbeitung 4.0