Learning income levels and inequality from spatial and sociodemographic data in Germany

This study explores the potential of predicting income inequality and income levels from attributes of the built, natural and social environment in Germany. Furthermore, it investigates differences in explanatory variables and estimation accuracy for municipalities with different social and spatial structure profiles. We use income tax data, the 2011 national census, and spatial data from various sources. The explanatory variables capture the spatial variation within the area of interest of characteristics of both the residents and the living environment. Our models explain 54% of the variability in inequality and 73% of the variability in median income levels for a sample of municipalities covering 97% of the country's population. Performance increases for the subsample of municipalities with at least 10,000 inhabitants, attaining 63% for inequality and 80% for income levels. Income inequality and top incomes are better identified in Western, urban, or central locations, while median income is best estimated in Eastern, rural and peripheral locations. The most important predictors are derived from attributes such as nationality, religious affiliation, household composition, residence construction year, as well as the size and density of residences and overall building stock. Our findings further the idea that the joint spatial analysis of population and the built environment can greatly improve our understanding of socioeconomic phenomena—at regional and local levels—beyond conventional data sources

The spatial and social structure of income inequality in Germany

Out of the multiple possible dimensions of inequality such as income, health or education, inequality of income is the highest. This study complements similar research on income inequality and socio-economic and spatial disparities with a comprehensive analysis of spatial descriptors of inequality in Germany. Using anonymized gross income tax declarations, we compute the Gini inequality index at the municipality level. We extract spatial variables from mostly open spatial datasets, and socioeconomic variables, from the openly available 2011 national census. We focus on measures of spatial variability, such as variation of spatial attributes or measures of population segregation and diversity between spatial units of one squared kilometer census cells within municipalities. Results show that a random forest model performs variable in predicting inequality across federal states, with a R2 statistic ranging between 0.3 and 0.58. Predictions are found significantly better for big municipalities with more than 10,000 inhabitants. For most states, inequality positively correlates with diversity of religion and nationality. Between the different states, inequality is associated with specific attributes of the built environment, from density of residential living space or size of residential annexes such as garages to coefficient of variation of building height or the amount of green spaces

Learning income levels and inequality from spatial and sociodemographic data in Germany - working paper

This study explores the potential of predicting income inequality and income levels from attributes of the built, natural and social environment in Germany. Furthermore, it investigates differences in explanatory variables and estimation accuracy for municipalities with different social and spatial structure profiles. We use income tax data, the 2011 national census, and spatial data from various sources. The explanatory variables capture the spatial variation within the area of interest of characteristics of both the residents and the living environment. Our models explain 54% of the variability in inequality and 73% of the variability in median income levels for a sample of municipalities covering 97% of the country's population. Performance increases for the subsample of municipalities with at least 10,000 inhabitants, attaining 63% for inequality and 80% for income levels. Income inequality and top incomes are better identified in Western, urban, or central locations, while median income is best estimated in Eastern, rural and peripheral locations. The most important predictors are derived from attributes such as nationality, religious affiliation, household composition, residence construction year, as well as the size and density of residences and overall building stock. Our findings further the idea that the joint spatial analysis of population and the built environment can greatly improve our understanding of socioeconomic phenomena—at regional and local levels—beyond conventional data sources

Learning income levels and inequality from spatial and sociodemographic data in Germany - working paper

This study explores the potential of predicting income inequality and income levels from attributes of the built, natural and social environment in Germany. Furthermore, it investigates differences in explanatory variables and estimation accuracy for municipalities with different social and spatial structure profiles. We use income tax data, the 2011 national census, and spatial data from various sources. The explanatory variables capture the spatial variation within the area of interest of characteristics of both the residents and the living environment. Our models explain 54% of the variability in inequality and 73% of the variability in median income levels for a sample of municipalities covering 97% of the country's population. Performance increases for the subsample of municipalities with at least 10,000 inhabitants, attaining 63% for inequality and 80% for income levels. Income inequality and top incomes are better identified in Western, urban, or central locations, while median income is best estimated in Eastern, rural and peripheral locations. The most important predictors are derived from attributes such as nationality, religious affiliation, household composition, residence construction year, as well as the size and density of residences and overall building stock. Our findings further the idea that the joint spatial analysis of population and the built environment can greatly improve our understanding of socioeconomic phenomena—at regional and local levels—beyond conventional data sources

Spatial distribution of income inequality and its determinants in Italy and Germany

The study is a comparative analysis of spatial and socioeconomic determinants of income inequality in municipalities in Italy and Germany. The general aim is determining the accuracy in estimating unknown inequality values by learning from different types of municipality subsamples, with a particular focus on regional differences and applications for actionable policy implications. For this purpose, we combine demographics and socioeconomic data, with natural and built environment data, at multiple spatial scales. We use machine learning for estimating variable importance for the prediction of income inequality variables extracted from tabulated income tax declarations. Results show that selected variables explain 52% of the variability in income inequality in Germany, and 59% of the variability in income inequality in Italy. Employment and housing factors associated with income inequality are largely dissimlar between East and West Germany. Education and mobility factors are largely dissimilar between South, Central and North Italy

Income inequality in Italy and Germany

The study is a comparative analysis of spatial and socioeconomic determinants of income inequality in municipalities in Italy and Germany. The general aim is determining the accuracy in estimating unknown inequality values by learning from different types of municipality subsamples, with a particular focus on regional differences and applications for actionable policy implications. For this purpose, we combine demographics and socioeconomic data, with natural and built environment data, at multiple spatial scales. We use machine learning for estimating variable importance for the prediction of income inequality variables extracted from tabulated income tax declarations. Results show that selected variables explain 51% of the variability in income inequality in Germany, and 81% of the variability in income inequality in Italy. Factors associated with income inequality are largely similar between East and West Germany, but dissimilar between South and North Italy. Commuting, employment and foreign citizenship status are factors associated with inequality in both countries. In Italy, education levels play a significant role in connection with inequality, while in Germany housing costs and built-up density are significant factors