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PurposeUsing aggregated data at the municipality level allows the authors to assess the role of relative deprivation (RD) – a measure of income inequality – on top of absolute income in shaping internal migration in Austria.Design/methodology/approachIn this study, the authors analyse the effect of regional income inequality on emigration rates of Austrian municipalities using a unique spatial dataset that is constructed based on Austrian administrative register data. The register-based data contain information on the municipality of residence of all individuals aged 16 and over that have their main residency in Austria, as well as their income and socio-demographic characteristics.FindingsThe authors find that increases in relative deprivation in a municipality are related to higher emigration from the municipality. Allowing for heterogeneous effects across income, education and age groups reveals that the effect is stronger among those with comparably low levels of income and among low-skilled and young individuals.Originality/valueThe unique spatially disaggregated perspective is based on novel data from Austrian administrative registers, which comprehensively capture the economic situation and geographic movements of the whole Austrian population. Most importantly, this approach allows for measuring income inequality within local communities and for a direct identification of social groups that are more sensitive to inequality.

This work concerns the sintering of tungsten-based (i.e WMoTaNb) high entropy alloy (HEA) powders using the spark plasma sintering (SPS) technique and their mechanical properties. The synthesis was performed by a self-propagating high-temperature synthesis (SHS) type reaction in which the mixture of metallic oxides (WO3, MoO3 …) is reduced by magnesium. For this, a specific reactor has been developed. Different conditions including the addition of a moderator were tested. These powders are then densified by SPS technology which allows for keeping the initial microstructure of the powder. The optimization of sintering conditions was performed with the objective to control simultaneously the chemical composition, the grain growth and the densification stages.

The recent wave of populism sweeping Europe and the Americas generated considerable interest among political scientists, economists, sociologists and to some extent, geographers. The vast majority of these studies focuses on individual voter decisions or national comparisons over time but neglects the within-country spatial variation of the populist vote. This paper addresses this shortcoming and applies spatial econometric techniques to explore possible explanations for spatial variation in the increase of the populist right vote between the 2013 and 2017 national elections in Austria for 2118 municipalities. Spatial variation in voting shares can result from (1) compositional effects, regional differences in the composition of voters with different characteristics, (2) broad spatial, historically evolved institutional differences, such as membership to one of the nine states, (3) unequal integration of different types of regions into the global economy, such as peripheral regions, central urban regions, old industrial regions or tourist areas, (4) spatial vote spillovers due to localized social networks, and (5) unobserved spatial processes. We find that the populist right vote gains in Austrian municipalities are affected by all processes, but that the type of regions becomes insignificant once we correct for unobservable spatial structures in the regression framework. The increase in the share of foreigners, the share of foreigners, income and inequality levels, educational differences, selected state membership, as well as spatial spillovers of populist right voting are all important to explain spatial variation in the rise of the populist right vote.

We introduce a new measure of bivariate jointness to assess the degree of inclusion dependency between pairs of explanatory variables in Bayesian Model Averaging analysis. Building on the discussion concerning appropriate statistics to assess covariate inclusion dependency in this context, a set of desirable properties for bivariate jointness measures is proposed. We show that none of the proposed measures so far meets all these criteria and an alternative measure is presented which fulfils all of them. Our measure corresponds to a regularised version of the Yule’s Q association coefficient, obtained by combining the original measure with a Jeffreys prior to avoid problems in the case of zero counts. We provide an empirical illustration using cross-country data on economic growth and its determinants.

In 2011, Statistics Austria carried out its first register-based census. Advantages of using administrative data for statistical purposes are, among others, a reduced burden for respondents and lower cost for the National Statistical Institutes (NSI). However, new challenges, like need for a new approach to the quality assessment of this kind of data arise. Therefore, Statistics Austria developed a comprehensive standardized framework to evaluate data quality for register-based statistics. In this paper, we present the basic concept of this quality framework and provide detailed results from the quality evaluation of the Austrian census of 2011. More specifically, we derive a quality measure for each census attribute from four complementary hyperdimensions. The first three of these hyperdimensions address the documentation of data, the usability of records and an external data validation. The fourth hyperdimension focuses on the quality of data imputations. The proposed framework combines these different quality-related information sources for each attribute to form an overall quality indicator. This procedure allows to track changes in quality during data processing and to compare the quality of different census generations.

The income–inequality nexus in a developed country: small-scale regional evidence from Austria. Regional Studies. This paper analyses the relationship between regional inequality and average income in a small-scale framework for Austria. The empirical findings are based on a novel inequality database generated from individual wage tax information at the municipality level. This study researches the magnitude of regional spillovers of income and inequality using spatial econometric methods. The results show a pronounced positive relation between regional income levels and inequality, where especially high-income municipalities exhibit a large spread in the income distribution. Furthermore, it shows that higher levels of inequality are associated with income gains at the top of the distribution.

We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian Model Averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model pace formed by linear regression models reveals interesting patterns of complementarity and substitutabiliy across economic growth determinants.

In der vorliegenden Arbeit wurde die Einkommensverteilung für die Periode 1959-2008 für Österreich analysiert. Dabei konnten aufgrund der mangelhaften Datenlage ausschließlich die Lohn- und Einkommensteuerstatistik untersucht werden. Die Entwicklung der personenbezogenen Kapitaleinkommen konnte mangels Daten nicht analysiert werden. Für die Lohnsteuerstatistik zeigt sich, dass die Einkommenskonzentration nach einer langen Stagnationsphase in den 70er- und 80er-Jahren seit 1994 kontinuierlich zu steigen begann. Einkommensteuerpflichtigen Einkommen sind zwar stärker konzentriert als reine Lohneinkommen, jedoch ist bei ersteren eine kontinuierliche Abnahme der Konzentration über den gesamten Beobachtungszeitraum zu beobachten. Diese gegenläufigen Tendenzen können statistische, rechtliche sowie strukturelle Ursachen haben. Insbesondere die Zusammensetzung der Einkommen innerhalb der Einkommensteuerstatistik hat sich in dieser Zeit stark verändert, wobei der starke Rückgang der Gewerbeeinkommen nahezu vollständig kompensiert wird durch die Zunahme der Nichtselbstständigen-Einkommen (NSA). Da aber Erstere eine ungleichere Verteilung aufweisen als Letztere (NSA), kann somit der Rückgang in der Konzentration in der Einkommensteuerstatistik insgesamt (mit) erklärt werden. Auch der höhere Erfassungsgrad von NSA-Einkommen sowie der Rechtsformwechsel von Personengesellschaften in Kapitalgesellschaften dürften eine Rolle spielen. Für eine umfassende Analyse der Entwicklung der Gesamteinkommen müssten auch die Entwicklung der Dividenden- (KESt I) und Zinserträge (KESt II) sowie die Körperschaftund andere Vermögensteuern miteinbezogen werden müssten. Für die Analyse der personellen Einkommensentwicklung wünschenswert wäre eine personelle Zuordnung aller Einkommensarten, insbesondere auch der Zins- und Dividendeneinkommen. Zu diesem Zwecke wäre eine De-Anonymisierung der in Österreich bestehenden Finanzvermögen notwendig. Dieser Schritt könnte über die Eindämmung von Steuerhinterziehung auch positive Auswirkungen auf das Steueraufkommen insgesamt haben.

Posterior analysis in Bayesian model averaging (BMA) applications often includes the assessment of measures of jointness (joint inclusion) across covariates. We link the discussion of jointness measures in the econometric literature to the literature on association rules in data mining exercises. We analyze a group of alternative jointness measures that include those proposed in the BMA literature and several others put forward in the field of data mining. The way these measures address the joint exclusion of covariates appears particularly important in terms of the conclusions that can be drawn from them. Using a dataset of economic growth determinants, we assess how the measurement of jointness in BMA can affect inference about the structure of bivariate inclusion patterns across covariates.

This article contributes a framework for the quality assessment of imputations within a broader structure to evaluate the quality of register-based data. Four quality-related hyperdimensions examine the data processing from the raw-data level to the final statistics. Our focus lies on the quality assessment of different imputation steps and their influence on overall data quality. We suggest classification rates as a measure of accuracy of imputation and derive several computational approaches.

This paper analyzes if neighborhood income inequality has an effect on informal regulation of environmental quality, using census tract-level data on industrial air pollution exposure from EPA’s Risk Screening Environmental Indicators and income and demographic variables from the American Community Survey and EPA’s Smart Location Database. Estimating a spatial lag model and controlling for formal regulation at the states level, we find evidence that overall neighborhood inequality – as measured by the ratio between the fourth and the second income quintile or the neighborhood Gini coefficient – increases local air pollution exposure, whereas a concentration of top incomes reduces local exposure. The positive coefficient of the general inequality measure is driven by urban neighborhoods, whereas the negative coefficient of top incomes is stronger in rural areas. We explain these findings by two contradicting effects of inequality: On the one hand, overall inequality reduces collective action and thus the organizing capacities for environmental improvements. On the other hand, a concentration of income at the top enhances the ability of rich residents to negotiate with regulators or polluting plants in their vicinity.

This paper investigates the nexus between regional income levels and inequality. We present a novel small-scale inequality database for Austrian municipalities to address this question. Our dataset combines individual tax data of Austrian wage tax payer on regionally disaggregated scale with census and geographical information. This setting allows us to investigate regional spillover effects of average income and various measures of income inequality. Using this data set we find distinct regional clusters of both high average wages and high earnings inequality in Austria. Furthermore we use spatial econometric regressions to quantify the effects between income levels and a number of inequality measures such as the Gini and 90/10 quantile ratios.