Estimating regional unemployment with mobile network data for Functional Urban Areas in Germany

Hadam, Sandra; Würz, Nora; Kreutzmann, Ann-Kristin

doi:10.17169/refubium-26791

Cited by 5 publications

(4 citation statements)

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“…2. A back-transformation with bias-correction (bc) following Sugawasa and Kubokawa (2017) and Hadam et al (2020):…”

Section: Extended Area-level Modelsmentioning

confidence: 99%

A Framework for Producing Small Area Estimates Based on Area-Level Models in R

Harmening,

Kreutzmann,

Schmidt

et al. 2023

The R Journal

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The R package emdi facilitates the estimation of regionally disaggregated indicators using small area estimation methods and provides tools for model building, diagnostics, presenting, and exporting the results. The package version 1.1.7 includes unit-level small area models that rely on access to micro data. The area-level model by Fay and Herriot (1979) and various extensions have been added to the package since the release of version 2.0.0. These extensions include (a) area-level models with back-transformations, (b) spatial and robust extensions, (c) adjusted variance estimation methods, and (d) area-level models that account for measurement errors. Corresponding mean squared error estimators are implemented for assessing the uncertainty. User-friendly tools like a stepwise variable selection, model diagnostics, benchmarking options, high quality maps and results exportation options enable a complete analysis procedure. The functionality of the package is illustrated by examples based on synthetic data for Austrian districts.

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“…2. A back-transformation with bias-correction (bc) following Sugawasa and Kubokawa (2017) and Hadam et al (2020):…”

Section: Extended Area-level Modelsmentioning

confidence: 99%

A Framework for Producing Small Area Estimates Based on Area-Level Models in R

Harmening,

Kreutzmann,

Schmidt

et al. 2023

The R Journal

View full text Add to dashboard Cite

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“…If analytical solutions for its estimation cannot be derived, bootstrap methods are often implemented instead. Here, the uncertainty of the estimated Gini coefficients is assessed using a bootstrap procedure following Gonzalez-Manteiga et al ( 2005) with an additional step of applying the bias-corrected back-transformation similar to Hadam et al (2020).…”

Section: Motivationmentioning

confidence: 99%

“…As naive inverse back-transformations (in this case the logistic function) usually introduce a bias for nonlinear functions, Sugasawa and Kubokawa (2017) present an asymptotically unbiased back-transformation for a general parametric transformation. Hadam et al (2020) applies this to the arcsine transformation, for example. Following Sugasawa and Kubokawa (2017) to obtain a bias-corrected backtransformation for ûFH d , the normal distribution of the transformed FH estimator on the logit-scale and the expected value (E) of a transformation (here the inverse logit) are used.…”

Section: Logit-transformed Fay-herriot Modelmentioning

confidence: 99%

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

Runge

2023

Journal of Official Statistics

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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.

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“…Given that the direct estimator used in the model is a ratio, like the SHEoF or the ARoFP rate, we decided to use a popular arcsin transformation (Casas-Cordero et al, 2016 , Schmid et al, 2017 and Jiang et al, 2001 ), with a back-transformation for bias correction proposed by Sugawasa and Kubokawa ( 2017 ) and Hadam et al ( 2020 ). The R package “emdi” (Kreutzmann et al, 2019 ) was used to compute the estimates.…”

Section: The Proposed Analytical Approachmentioning

confidence: 99%

The Economic Perspective of Food Poverty and (In)security: An Analytical Approach to Measuring and Estimation in Italy

Marchetti

Secondi

2022

Soc Indic Res

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The UN Sustainable Development Goals have set clear targets on global poverty, hunger and malnutrition to be achieved by 2030, which have prompted academics and policymakers to identify useful strategies and drivers. Moreover, the COVID19 pandemic has exacerbated inequalities at national and sub-national levels thus hampering the achievement of these goals. On considering the multifaceted nature of poverty, a recent research strand focuses on food poverty and insecurity issues in terms of economic access to food and healthy diet consumption, with moderate and extreme food insecurity affecting almost 9% of the population in Europe and North America. This paper aims to analyse food poverty and insecurity at regional level in Italy. Using micro-data from the Italian Household Budget Survey carried out by ISTAT, an analytical approach was proposed to define and measure the different degree of food poverty and insecurity. Moreover, to obtain insights into whether food poverty and insecurity can afford population healthy nutrition, inequality of the distributions of food expenditure categories are estimated. The results provided us with information on other important aspects of the poverty. Indeed, in Italy individuals who are at-risk-of-food-poverty or food insecure amount to 22.3% of the entire population. Furthermore, the at-risk-of-food-poverty-rate varies at regional level from 14.6% (Umbria) to 29.6% (Abruzzo), with high levels of food consumption inequalities observed above all for vegetables, meat and fish. All these issues could help policy makers to define economic intervention policies aimed at reducing social exclusion and achieving more equitable and sustainable living conditions for the entire population.

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Estimating regional unemployment with mobile network data for Functional Urban Areas in Germany

Cited by 5 publications

References 0 publications

A Framework for Producing Small Area Estimates Based on Area-Level Models in R

A Framework for Producing Small Area Estimates Based on Area-Level Models in R

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

The Economic Perspective of Food Poverty and (In)security: An Analytical Approach to Measuring and Estimation in Italy

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