The Impact of Disaster Data on Estimating Damage Determinants and Climate Costs

An active literature utilizes natural disaster data to analyze damage determinants and estimate future costs of climate change. However, despite its importance in research and policy, no international standard exists to quantify damages, and the impact of damage data quality on empirical estimates remains an open question. Using the case of tropical cyclone landfalls in China, we analyze three damage datasets: official Chinese government records, CRED’s International Disaster Database, and Munich Re’s NatCatSERVICE. We begin by systematically comparing damage entries across the three datasets. We then use the data to estimate historical damage functions. Lastly, we utilize the damage functions to project the future costs of climate and economic change. We find that damage data quality matters. While the estimated economic determinants of historical damage functions are similar across the three datasets, we estimate differences in the cyclone intensity coefficients. These variations in damage functions lead to divergence in projections of future damages by almost three times, with average annual future loss estimates ranging between $4 and $11 billion. Similar to previous literature, we call for more internationally standardized disaster damage reporting.