Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/272623 
Year of Publication: 
2023
Series/Report no.: 
IZA Discussion Papers No. 15996
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
This paper identifies individual and regional risk factors for hospitalizations caused by heat within the German population over 65 years of age. Using administrative insurance claims data and a machine-learning-based regression model, we causally estimate heterogeneous heat effects and explore the geographic, morbidity, and socioeconomic correlates of heat vulnerability. Our results indicate that health effects distribute highly unevenly across the population. The most vulnerable are more likely to suffer from chronic diseases such as dementia and Alzheimer's disease and live in rural areas with more old-age poverty and less nursing care. We project that unabated climate change might bring heat to areas with particularly vulnerable populations, which could lead to a five-fold increase in heat-related hospitalization by 2100.
Subjects: 
heat
climate change
hospitalization
risk factors
adaptation
machine learning
JEL: 
I14
I18
Q51
Q54
Q58
Document Type: 
Working Paper

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