Poor coverage of top incomes in surveys, also referred to as the “missing rich” problem, leads to severe underestimation of income inequality. At the regional level this shortcoming is even more ...eminent due to small regional sample sizes. Tax records contain more accurate income information at the top and cover all regions equally well. Top-income correction approaches tackle the missing rich problem by imputing top incomes from tax to survey data. While existing methods focus on adjustments at the national level, our paper provides corrections of the
income distributions in survey data by exploiting the tax data’s regional variability. We impute top incomes in the survey data from the German Microcensus based on region-specific Pareto and generalized Pareto distributions estimated from tax records. The combined survey and tax data provide new estimates of regional income inequality in Germany. Our findings indicate that inequality between and within the regions is much larger than previously understood with the magnitude of the adjustment depending on the federal states’ level of inequality in the tail.
Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health ...reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.
Hibernation is one of the most important behaviours of bats of the temperate zone. During winter, when little food or liquid water is available, hibernation in torpor lowers metabolic costs. However, ...the timing of emergence from hibernation is crucial for the resumption of the reproductive process in spring. Here, we investigate the spring emergence of six bat species or pairs of bat species of the genera Myotis and Plecotus at five hibernation sites in Central Europe over 5 years. Using generalized additive Poisson models (GAPMs), we analyze the influence of weather conditions (air and soil temperature, atmospheric pressure, atmospheric pressure trend, rain, wind, and cloud cover) as predictors of bat activity and separate these extrinsic triggers from residual motivation to emerge from hibernation (extrinsic factors not studied; intrinsic motivation). Although bats in a subterranean hibernaculum are more or less cut off from the outside world, all species showed weather dependence, albeit to varying degrees, with air temperature outside the hibernaculum having a significant positive effect in all species. The residual, potentially intrinsic motivation of species to emerge from their hibernacula corresponds to their general ecological adaptation, such as trophic specialization and roosting preferences. It allows the definition of three functional groups (high, medium and low residual activity groups) according to the degree of weather dependence of spring activity. A better knowledge of the interplay of extrinsic triggers and residual motivation (e.g., internal zeitgebers) for spring emergence will help to understand the flexibility of a species to adapt to a changing world.
Dynamic microsimulations are tools to stochastically project (synthetic) microdata into the future. In spatial microsimulations, regional discrepancies are of particular interest and must be ...considered accordingly. In practice, the probabilities for state changes are unknown and must be estimated, usually from survey data. However, estimating such models on the regional level is often not feasible due to limited sample size and lack of geographic information. Simply applying the model estimated at the national level to all geographies leads to biased state transitions due to regional differences in level and distribution. In this paper, we introduce a model-based alignment method to adapt predicted probabilities obtained from a nationally estimated model to subregions by integrating known marginal distributions to re-introduce regional heterogeneity and create more realistic trajectories, particularly in small areas. We show that the model-adjusted transition probabilities can capture region-specific patterns and lead to improved projections. Our findings are useful to researchers who want to harmonise model outputs with external information, in particular for the field of microsimulation.
Abstract
Income is an important economic indicator to measure living standards and individual well-being. In Germany, different data sources yield ambiguous evidence for analyzing the income ...distribution. The Tax Statistics (TS)—an income register recording the total population of more than 40 million taxpayers in Germany for the year 2014—contains the most reliable income information covering the full income distribution. However, it offers only a limited range of socio-demographic variables essential for income analysis. We tackle this challenge by enriching the tax data with information on education and working time from the Microcensus, a representative 1 percent sample of the German population. We examine two types of data fusion methods well suited to the specific data fusion scenario of the TS and the Microcensus: missing-data methods and performant prediction models. We conduct a simulation study and provide an empirical application comparing the proposed data fusion methods, and our results indicate that Multinomial Regression and Random Forest are the most suitable methods for our data fusion scenario.
In 2015, the European Commission has drafted a framework regulation for integrated European social statistics. This integration covers the Labour Force Survey, the Statistics on Income and Living ...conditions, and others. In order to avoid an inappropriate response burden, administrative and other sources shall be considered to achieve accurate survey estimates. Combining information from different data sources has become a field of growing research interest among statistical offices and other institutions. In the statistical literature this problem is known as data fusion or statistical matching, and is widely considered as a particular missing-data pattern. Assuming that budgets are limited, and that only some additional information can be obtained to improve the quality of the data fusion, we investigate different scenarios of using these limited resources within an integrated system of household surveys. Our main objective is to develop a framework that fosters on the one hand the estimation of statistical models using several surveys, and on the other hand classical totals for different sub-classes and areas which are of special interest for official statistics.
Modern systems of official statistics require the estimation and publication of business statistics for disaggregated domains, for example, industry domains and geographical regions. Outlier robust ...methods have proven to be useful for small-area estimation. Recently proposed outlier robust model-based small-area methods assume, however, uncorrected random effects. Spatial dependencies, resulting from similar industry domains or geographic regions, often occur. In this paper, we propose an outlier robust small-area methodology that allows for the presence of spatial correlation in the data. In particular, we present a robust predictive methodology that incorporates the potential spatial impact from other areas (domains) on the small area (domain) of interest. We further propose two parametric bootstrap methods for estimating the mean-squared error. Simulations indicate that the proposed methodology may lead to efficiency gains. The paper concludes with an illustrative application by using business data for estimating average labour costs in Italian provinces.
A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are ...appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.
Der Artikel diskutiert die verschiedenen Methoden bei der Datenerhebung von Mietspiegeln. Es werden Vor- und Nachteile der in der Praxis zu findenden Methoden diskutiert und aus dem statistischen ...Blickwinkel beleuchtet. Dabei gehen wir den drei Fragen nach: Wer wird befragt? Wie wird befragt? Wie erfolgt die Stichprobenziehung? Neben statistischen Aspekten werden die Mietspiegel der 30 größten Städte als Beispiel herangezogen, um aufzuzeigen, dass die angewandte Methodik in der Praxis sehr heterogen ist.
Zusammenfassung
Am 24. Oktober 2017 wurden beim Bundesverfassungsgericht die Normenkontrollklagen der Länder Berlin und Hamburg im Bezug auf das Zensusgesetz 2011 verhandelt. Ein zentraler Gegenstand ...der Klagen war eine methodische Ungleichbehandlung von Gemeinden unterschiedlicher Größe bei der Einwohnerzahlermittlung im Zensus 2011, da für Gemeinden kleiner als 10.000 Einwohner eine andere Methodik angewendet wurde als für Gemeinden mit mehr als 10.000 Einwohnern. Diese Grenze wurde im Zensusgesetz für den Zensus 2011 festgeschrieben und ist nach Bekanntwerden der Zensusergebnisse kritisiert worden. Im vorliegenden Beitrag werden verschiedene Möglichkeiten diskutiert, wie beim Zensus 2021 auf einen Methodenwechsel, wie er im Jahr 2011 stattfand, verzichtet werden kann und wie dahingehend die Methodik geeignet angepasst werden muss. Einen besonderen Schwerpunkt nimmt hierbei eine Adjustierung der Qualitätsmessung, die sogenannten Präzisionsvorgaben, ein. Eine Definition dieser für eine stichprobenbasierte Zensuserhebung muss insbesondere im Hinblick auf die Gemeindegröße geeignet gewählt werden. Zudem bedarf es einer Untersuchung der Auswirkung der Qualitätsmessung auf die regionalen Stichprobenumfänge und auf die Qualität der später zu schätzenden Einwohnerzahl. In den Untersuchungen werden methodische und praktische Überlegungen berücksichtigt.
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