<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Income inequality is a persistent topic of public and political debate. In this context, the focus often shifts from the national level to a more detailed geographical level. In particular, inequality between or within local communities can be assessed. In this article, the estimation of inequality within regions, that is, between households, is considered at a regionally disaggregated level. From a methodological point of view, a small area estimation of the Gini coefficient is carried out using an area-level model linking survey data with related administrative data. Specifically, the Fay-Herriot model is applied using a logit-transformation followed by a bias-corrected back-transformation. The uncertainty of the point estimate is assessed using a parametric bootstrap procedure to estimate the mean squared error. The validity of the methodology is shown in a model-based simulation for the point estimator as well as for the uncertainty measure. The proposed methodology is illustrated by estimating model-based Gini coefficients for spatial planning regions in Germany, using survey data from the Socio-Economic Panel and aggregate data from the 2011 Census. The results show that intra-regional inequality is more diverse than a consideration only between East and West suggests.</p>
</abstract>

Income inequality is a persistent topic of public and political debate. In this context, the focus often shifts from the national level to a more detailed geographical level. In particular, inequality between or within local communities can be assessed. In this article, the estimation of inequality within regions, that is, between households, is considered at a regionally disaggregated level. From a methodological point of view, a small area estimation of the Gini coefficient is carried out using an area-level model linking survey data with related administrative data. Specifically, the Fay-Herriot model is applied using a logit-transformation followed by a bias-corrected back-transformation. The uncertainty of the point estimate is assessed using a parametric bootstrap procedure to estimate the mean squared error. The validity of the methodology is shown in a model-based simulation for the point estimator as well as for the uncertainty measure. The proposed methodology is illustrated by estimating model-based Gini coefficients for spatial planning regions in Germany, using survey data from the Socio-Economic Panel and aggregate data from the 2011 Census. The results show that intra-regional inequality is more diverse than a consideration only between East and West suggests.

Estimating Intra-Regional Inequality with an Application to German Spatial Planning Regions

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Income inequality is a persistent topic of public and political debate. In this context, the focus often shifts from the national level to a more detailed...

Estimating Intra-Regional Inequality with an Application to German Spatial Planning Regions

Data publikacji: 09 cze 2023

Zakres stron: 203 - 228

Otrzymano: 01 lut 2022

Przyjęty: 01 sty 2023

DOI: https://doi.org/10.2478/jos-2023-0010

Słowa kluczowe
Fay-Herriot model, Gini coefficient, small area estimation, survey statistics

© 2023 Marina Runge, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Estimating Intra-Regional Inequality with an Application to German Spatial Planning Regions

Data publikacji: 09 cze 2023

Zakres stron: 203 - 228

Otrzymano: 01 lut 2022

Przyjęty: 01 sty 2023

DOI: https://doi.org/10.2478/jos-2023-0010

Słowa kluczoweFay-Herriot model, Gini coefficient, small area estimation, survey statistics

© 2023 Marina Runge, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Fay-Herriot model, Gini coefficient, small area estimation, survey statistics