@article{d6ba362750554d76a4e9a6491c5ddf02,
title = "Differential Exposure to Climate Change? Evidence from the 2021 Floods in Germany",
abstract = "We analyze the exposure of different income groups to the 2021 floods in Germany, which serve as an exemplary case of natural disasters intensified by anthropogenic climate change. To this end, we link official geo-coded satellite data on flood-affected buildings to neighborhood-level information on socio-economic status. We then document the empirical relationship between flood damages and household income. We limit comparisons to the vicinity of affected rivers and absorb a rich set of regional fixed effects to assess the differential exposure at the local level. Average household income is around 1,500 euros or three percent lower in flood-affected neighborhoods than in non-affected neighborhoods nearby. Average flood exposure is more than three times as high in the bottom sixty than in the upper forty percent of neighborhoods in terms of average household income. Our study is the first to document this regressive exposure along the income distribution based on actual flood damage data in Europe.",
keywords = "Climate change, Differential exposure, Floods, Income distribution",
author = "Moritz Odersky and Max L{\"o}ffler",
note = "data sources: study combines various data sources discussed in more detail in the replication files. The two key underlying data sets can be retrieved from the respective data providers: The socio-economic grid-level data from the RWI-GEO-GRID: Socio-Economic Data on Grid Level data set (DOI: 10.7807/microm:v10) is provided by the Research Data Center Ruhr at RWI - Leibniz Institute for Economic Research in Essen, Germany. The satellite data on flood damages comes from data set EMSR517: Flood in Western Germany provided by the EU{\textquoteright}s Copernicus Emergency Management Service (URL: https://emergency.copernicus.eu/mapping/list-of-components/EMSR517). After obtaining these data sets, our empirical results will be fully replicable using our code which is available at Harvard Dataverse (https://doi.org/10.7910/DVN/PUU3NF).",
year = "2024",
month = sep,
doi = "10.1007/s10888-023-09605-6",
language = "English",
volume = "22",
pages = "551--576",
journal = "Journal of Economic Inequality",
issn = "1569-1721",
publisher = "Springer",
number = "3",
}