Domain Prediction with Grouped Income Data

Walter, Paul; Groß, Marcus; Schmid, Timo; Tzavidis, Nikos

doi:10.1111/rssa.12736

Cited by 2 publications

(2 citation statements)

References 51 publications

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“…Working with real‐world income data as is the case in the design‐based simulation, we recognise that the adaptive log‐shift transformation adjusts to the underlying data and therefore yields better results than the fixed log transformation (bias and uncertainty). These results are in line with the results for the GB2 ‐scenario from the model‐based simulation study and the findings by Rojas‐Perilla et al (2020) and Walter et al (2021).…”

Section: Design‐based Simulation Studysupporting

confidence: 92%

Estimating Regional Income Indicators under Transformations and Access to Limited Population Auxiliary Information

Würz

Schmid

Tzavidis

2022

Journal of the Royal Statistical Society Series A: Statistics in Society

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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 microdata. 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.

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Section: Design‐based Simulation Studysupporting

confidence: 92%

Estimating Regional Income Indicators under Transformations and Access to Limited Population Auxiliary Information

Würz

Schmid

Tzavidis

2022

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…Reed and Wu (2008), Kleiber (2008) and Chen (2017) are primarily focusing on the estimation of statistical indicators from grouped data by fitting a parametric distributions to the data. Walter et al (2021) estimate linear and non-linear indicators for small areas by a nested error regression model when the response variable is grouped. Kakwani and Podder (2008) argue against the parametric estimation of the income distributions from grouped data due to its lack of precision and present a method that can be utilized to estimate the Lorenz curve directly from the grouped data in order to compute inequality indicators.…”

Section: Introductionmentioning

confidence: 99%

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

Walter

Groß

Schmid

et al. 2022

Journal of Official Statistics

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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.

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Domain Prediction with Grouped Income Data

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 2 publications

References 51 publications

Estimating Regional Income Indicators under Transformations and Access to Limited Population Auxiliary Information

Estimating Regional Income Indicators under Transformations and Access to Limited Population Auxiliary Information

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

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