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

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Abstract

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.

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Notes

  1. We note that in 2007, CRED and Munich Re spearheaded the “Disaster Category Classification and Peril Terminology for Operational Databases” that lays out common disaster definitions and terminology but an international standard does not yet exist (Wirtz et al. 2014).

  2. See Section 2 for a description of Guha-Sapir and Below’s analysis. Our data analysis differs from Guha-Sapir and Below in several ways. We assess a different country context (China). We also include China government data in addition to EM-DAT and Munich Re data (but not Swiss Re), which will allow for comparisons across an official government source. Finally, we originally contribute to the literature through our analysis of damages function and climate and economic change cost sensitivity to underlying damage data quality. Guha-Sapir and Below focus solely on comparisons across the historical observations.

  3. This statistic is calculated on a subset of the datasets that could be matched across datasets. See Section 3.1 for a description of the matching process.

  4. Concern over dataset differences is not unique to cyclone damages. For example, literature assesses differences in cyclone observations across meteorological centers (e.g., Yu et al. 2007; Liang et al. 2010).

  5. We were granted special access to the event-level data for typhoons in China for this study.

  6. Recall that differences in the underlying sample can be driven by different damage estimates as well as any missing data, either from incompleteness or due to events not meeting inclusion criteria, which may vary across damage datasets.

  7. If more than one province in affected, we use the GDP for the province of typhoon eyewall landfall.

  8. We collect year 2000 county data and assume that the county to country GDP ratio is fixed over time to estimate county-level GDP for our full sample.

  9. The EM-DAT database extends back to 1900 although we only select observations post 1979 for consistency with the other datasets.

  10. For example, if a province has a population density 20% higher than China as a whole, we assume it remains 20% more dense relative to China as a whole over the coming century.

  11. We do not present statistics on individual cyclone-landfall damages due to our confidentiality agreement with Munich Re.

  12. Original estimates were 28.7 billion yuan (in 2006 currency) and were converted to real 2009 $USD by the authors.

  13. For comparison, Guha-Sapir and Below (2002) find a 26% match rate across their three datasets.

  14. We test GDP elasticities across the three wind models and, separately, across the three pressure models.

  15. Bakkensen and Mendelsohn utilize EM-DAT data as well as damage data from Nordhaus (2010).

  16. The six TCIAM steps are described in Section 3.3 above.

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Acknowledgements

The authors would like to thank Munich RE’s NatCatSERVICE as well as EM-DAT for providing access to the respective datasets. We also thank Kerry Emanuel and Robert Mendelsohn.

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Authors and Affiliations

  1. School of Government and Public Policy, University of Arizona, Tucson, AZ, USA

    Laura A. Bakkensen & Brianna D. Zurita

  2. College of Environmental Sciences and Engineering, Peking University, Beijing, People’s Republic of China

    Xiangying Shi

  3. Shan Shui Conservation Center, Beijing, People’s Republic of China

    Xiangying Shi

Authors
  1. Laura A. Bakkensen
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  2. Xiangying Shi
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  3. Brianna D. Zurita
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Corresponding author

Correspondence to Laura A. Bakkensen.

Appendix

Appendix

Baseline Historical Damage Functions

Table 8 presents our tropical cyclone damages function using a parsimonious baseline specification with only province-level GDP and cyclone intensity (wind or pressure) included as explanatory variables. We find broad similarity across these and the main results damage functions presented in Table 3, with GDP elasticity values of around 0.2 and 0.3. In addition, we find the China Gov intensity elasticities to be the largest (farthest from zero) and the EM-DAT coefficients to be the closest to zero. Table 9 presents the same specification but with decade and province fixed effects. Again, similar to Table 6 in the main results, we find the GDP elasticity to vary in sign and significance across the three datasets. We continue to find China Gov intensity elasticities to be the largest in magnitude and EM-DAT to be closest to zero in magnitude and significance.

Table 8 Baseline typhoon damages functions using three damages datasets
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Table 9 Baseline cyclone damages functions with fixed effects using three damages datasets
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Integrated Assessment Model Additional Results

To estimate TCIAM sensitivity to model assumptions, we run the TCIAM using two additional base models. In Table 10, we present the results assuming our parsimonious baseline damage functions from Appendix Table 8, including only province-level GDP and cyclone intensity (wind or pressure). We find that without accounting for rain and underlying cyclone risk, the results are slightly lower, with estimated total future losses (MSLP) of between $3.36 and $10.09 billion on average per year, compared to between $4.04 and $11.26 billion in our main results. In Table 11, we present results using our main damage function results but assume that cyclone rainfall will increase by 5%, guided by Wang et al. (2012). Logically, holding all else equal and intensifying rainfall, we find that estimated future cyclone losses increase to between $4.4 and $12.85 billion. However, these additional estimates are still within approximately 10% of our original estimates, giving confidence in the estimated costs. We also note that the broad qualitative conclusions across all TCIAM runs remains the same, as we find that the EM-DAT model predicts smaller future increases and the China Gov model predicts highest future increases across a majority of the specifications.

Table 10 Current and future average annual cyclone damages in China ($ Billions) assuming baseline damage functions
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Table 11 Current and future average annual cyclone damages in China ($ Billions) assuming main results damage functions and 5% increase in cyclone precipitation
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Bakkensen, L.A., Shi, X. & Zurita, B.D. The Impact of Disaster Data on Estimating Damage Determinants and Climate Costs. EconDisCliCha 2, 49–71 (2018). https://doi.org/10.1007/s41885-017-0018-x

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