Both the developed as well as the developing world have witnessed substantial territorial changes at all tiers of government over the last few decades. However, causal evidence on the economic consequences of these territorial changes remains scarce. This project aims to fill this gap in the literature by making use of a new geo-referenced dataset on the location and shape of administrative borders (national as well as first and second subnational level, i.e. countries, provinces/states and counties/districts) around the world over the period 1990 to 2015. We match this data on administrative borders with geo-referenced grid-level raster datasets on night lights and population. To uncover a causal effect, we implement both a Difference-in-Difference on the regional level and a Spatial Regression Discontinuity Design (Spatial RDD) at the detailed pixel level, comparing (night lights and population) growth in administrative units subject to border changes with growth in neighboring but unaffected administrative units.