Human and economic impacts of natural disasters: can we trust the global data?

Author:

Jones Rebecca LouiseORCID,Guha-Sapir Debarati,Tubeuf SandyORCID

Abstract

AbstractReliable and complete data held in disaster databases are imperative to inform effective disaster preparedness and mitigation policies. Nonetheless, disaster databases are highly prone to missingness. In this article, we conduct a missing data diagnosis of the widely-cited, global disaster database, the Emergency Events Database (EM-DAT) to identify the extent and potential determinants of missing data within EM-DAT. In addition, through a review of prominent empirical literature, we contextualise how missing data within EM-DAT has been handled previously. A large proportion of missing data was identified for disasters attributed to natural hazards occurring between 1990 and 2020, particularly on the economic losses. The year the disaster occurred, income-classification of the affected country and disaster type were all significant predictors of missingness for key human and economic loss variables. Accordingly, data are unlikely to be missing completely at random. Advanced statistical methods to handle missing data are thus warranted when analysing disaster data to minimise the risk of biasing statistical inferences and to ensure global disaster data can be trusted.

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

Reference47 articles.

1. International Federation of Red Cross and Red Crescent Societies, National Disaster Reduction Centre of China & Academy of Disaster Reduction and Emergency Management. 2020 Global Natural Disaster Assessment Report. https://www.preventionweb.net/publication/2020-global-natural-disaster-assessment-report (2021).

2. Bevere, L. Natural catastrophes in 2020. Swiss RE sigma https://www.swissre.com/institute/research/sigma-research/sigma-2021-01.html (2021).

3. Wirtz, A., Kron, W., Löw, P. & Steuer, M. The need for data: natural disasters and the challenges of database management. Nat. Hazards. 70, 135–157 (2012).

4. Guha-Sapir, D. & Below, R. Quality and accuracy of disaster data: A comparative analyse of 3 global data sets. CRED Work. Pap. 1–18 (2002).

5. Rubin, D. B. Inference and Missing Data. Oxford Univ. Press 63, 581–592 (1976).

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