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Spatial regression models provide the opportunity to analyse spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarises the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In contrast to previous simulations, this study evaluates the bias of the impacts rather than the regression coefficients and additionally provides results for situations with a non-spatial omitted variable bias. Results reveal that the most commonly used spatial autoregressive (SAR) and spatial error (SEM) specifications yield severe drawbacks. In contrast, spatial Durbin specifications (SDM and SDEM) as well as the simple SLX provide accurate estimates of direct impacts even in the case of misspecification. Regarding the indirect `spillover' effects, several - quite realistic - situations exist in which the SLX outperforms the more complex SDM and SDEM specifications.
Though environmental inequality research has gained extensive interest in the United States, it has received far less attention in Europe and Germany. The main objective of this book is to extend the research on environmental inequality in Germany. This book aims to shed more light on the question of whether minorities in Germany are affected by a disproportionately high burden of environmental pollution, and to increase the general knowledge about the causal mechanisms, which contribute to the unequal distribution of environmental hazards across the population.
To improve our knowledge about environmental inequality in Germany, this book extends previous research in several ways. First, to evaluate the extent of environmental inequality, this book relies on two different data sources. On the on hand, it uses household-level survey data and self-reports about the impairment through air pollution. On the other hand, it combines aggregated census data and objective register-based measures of industrial air pollution by using geographic information systems (GIS). Consequently, this book offers the first analysis of environmental inequality on the national level that uses objective measures of air pollution in Germany. Second, to evaluate the causes of environmental inequality, this book applies a panel data analysis on the household level, thereby offering the first longitudinal analysis of selective migration processes outside the United States. Third, it compares the level of environmental inequality between German metropolitan areas and evaluates to which extent the theoretical arguments of environmental inequality can explain differing levels of environmental inequality across the country. By doing so, this book not only investigates the impact of indicators derived by the standard strand of theoretical reasoning but also includes structural characteristics of the urban space.
All studies presented in this book confirm the disproportionate exposure of minorities to environmental pollution. Minorities live in more polluted areas in Germany but also in more polluted parts of the communities, and this disadvantage is most severe in metropolitan regions. Though this book finds evidence for selective migration processes contributing to the disproportionate exposure of minorities to environmental pollution, it also stresses the importance of urban conditions. Especially cities with centrally located industrial facilities yield a high level of environmental inequality. This poses the question of whether environmental inequality might be the result of two independent processes: 1) urban infrastructure confines residential choices of minorities to the urban core, and 2) urban infrastructure facilitates centrally located industries. In combination, both processes lead to a disproportionate burden of minority households.
Several studies now document the disproportionate distribution of environmental pollution across different groups, but many are based on aggregated data or subjective pollution measures. In this study, we describe the air quality disadvantage of migrants in Germany using objective pollution data linked to nationally representative individual-level survey data. We intersect 1 × 1 km2 grid geo-references from the German General Social Survey (ALLBUS) 2014, 2016, and 2018 with 2 × 2 km2 estimates of annually averaged air pollution by the German Environment Agency for nitrogen dioxide, ozone, and particulate matter. Respondents with a migration background are exposed to higher levels of nitrogen dioxide and particulate matter than people of German descent. Urbanity of residence partly explains these differences, up to 81 per cent for particulate matter and about 30 per cent for other pollutants. A larger proportion of immigrants live in larger cities, which are more prone to high levels of air pollution. This is especially true for second-generation migrants. Income differences, on the other hand, do not explain the migrant disadvantage. In city fixed effects models, the patterns for migration background point unambiguously in the direction of environmental disadvantage for all pollutants except ozone. However, the within-municipality associations are weak.