Maison des Sciences Humaines
11, Porte des Sciences
L-4366 Esch-sur-Alzette / Belval
Conference room (1st floor)
We show how income inequality propagates through space and examines determinants of income inequality at different spatial scales. We use tabulated income data from the American Community Survey for the period 2005-2018 and recover full income distributions at the block group level for the entire US by employing recently developed Generalized Pareto curve methods. Given this parametrization of the income distribution, we show how to derive the distribution for any meaningful higher scale. Compared to previous research, our approach has two main advantages from a data perspective. First, by reconstructing the full distribution we are able to outline shifts based on different income inequality indicators focusing on different parts of the distribution. Second, while previous work has relied on different sources of information for different spatial scales and inequality statistics, we are able to work with a single and consistent source of information. This allows us to highlight the importance of the choice of the observational units and their different levels of spatial aggregation in estimating determinants of distributional change.