Abstract
This study compares the disaster damage records of two of the most widely used disaster databases - EM-DAT and DesInventar for a selected sample of 70 countries over the period 1995–2013. We consider four types of natural disasters – droughts, floods, earthquakes and storms and use descriptive statistics to compare the records of the two databases for the selected datasets in terms of all recorded events, matched events and large-scale events along with few country-specific comparisons. We note significant differences in the damage estimates reported in the two selected datasets. The comparison of the damage estimates for all recorded events shows that the DesInventar dataset has greater number of recorded events than the EM-DAT. The descriptive statistics of the former exhibits larger mean and standard deviation across all disaster types compared to that for the latter. The same is true for the comparison of large-scale disaster events and country-specific comparisons with the DesInventar dataset having higher number of recorded events and larger values for descriptive statistics for the selected country-year. On the contrary, for the hand-matched events data, EM-DAT shows larger mean disaster damages along with a higher statistical range compared to the DesInventar dataset. The basic structure of the datasets and the data collection methods may influence the magnitude of the recorded damages along with possible human errors while data entering. The present study further highlights the need for a more systematic and standardized disaster damages database which is critical to achieve the first priority action ‘understanding disaster risk’ of the Sendai Framework for Disaster Risk Reduction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The description of the selected disaster impact indicators is provided in Section 4.2 of this study.
It is well known now that there is major time bias in the EM-DAT data before the year 1995. Therefore, we deliberately avoid data before the year 1995. Further, in DesInventar dataset, consistent data are available for the selected 70 countries till 2013 only.
To make economic damages data comparable across the datasets, we convert it into US dollars at 2010 prices.
The website of PreventionWeb also provides list global regional and national datasets on natural and man-made disasters. The PreventionWeb is collaborative knowledge sharing platform on disaster risk which is managed by the UNISDR.
Though, the DesInventar datasets includes heads such as damages to crops, public utility, education facilities etc. but almost all such heads suffer from large number of missing observations.
To identify such large-scale events, we have normalized our human and economic impact indicators by total population and gross domestic product (GDP), respectively.
We include only those records of which we feel confident of being present in both the datasets based on the event type, time (date/month) and location of the event.
The EM-DAT reports cumulative damages for this flood event starting from August to December, 2011, whereas the DesInventar reports damages for four flood events during the same period. Based on the location and timing of the event, we have clubbed the damages of these four events to compare them with EM-DAT.
The exactly matching records include those with ‘zero’ or missing data common in both the datasets.
The GLobal IDEntifier number (GLIDE) is a globally common Unique ID code for disasters proposed by Asian Disaster Reduction Center (ADRC)
References
Bahinipati CS, Venkatachalam L (2016) Role of climate risks and socio-economic factors in influencing the impact of climatic extremes: a normalisation study in the context of Odisha, India. Reg Environ Chang 16(1):177–188
Bakkensen LA, Shi X, Zurita BD (2018) The impact of disaster data on estimating damage determinants and climate costs. Econ Disast Climate Change 2(1):49–71. https://doi.org/10.1007/s41885-017-0018-x
Cavallo E, Noy E (2011) Natural disasters and the economy — a survey. Int Rev Environ Resour Econ 5(1):63–102. https://doi.org/10.1561/101.00000039
Cavallo E, Galiani S, Noy I, Pantano J (2013) Catastrophic natural disasters and economic growth. Rev Econ Stat 95(5):1549–1561. https://doi.org/10.1162/REST_a_00413
Edmonds C, Noy I (2018) The economics of disaster risks and impacts in the Pacific. Disaster Prevention Manage: Int J 27(5):478–494. https://doi.org/10.1108/DPM-02-2018-0057
Felbermayr G, Gröschl J (2014) Naturally negative: the growth effects of natural disasters. J Dev Econ 111:92–106. https://doi.org/10.1016/j.jdeveco.2014.07.004
Fomby T, Ikeda Y, Loayza NV (2013) The growth aftermath of natural disasters. J Appl Econ 28(3):412–434. https://doi.org/10.1002/jae.1273
Gall M (2015) The suitability of disaster loss databases to measure loss and damage from climate change. Int J Global Warm 8(2):170. https://doi.org/10.1504/IJGW.2015.071966
Gall M, Borden KA, Cutter SL (2009) When do losses count? Six fallacies of natural hazards loss data. Bull Am Meteorol Soc 90(6):799–810. https://doi.org/10.1175/2008BAMS2721.1
Gall M, Borden KA, Emrich CT, Cutter SL (2011) The unsustainable trend of natural hazard losses in the United States. Sustainability. https://doi.org/10.3390/su3112157
Guha-Sapir D, Below R. (2002). The quality and accuracy of disaster data: A comparative analysis of three global data sets. (No. 191)
Guha-Sapir D, Below R, Hoyois P (2009). EM-DAT: The CRED/OFDA International Disaster Database. Retrieved from www.emdat.be
Hallegatte S, Dumas P (2009) Can natural disasters have positive consequences? Investigating the role of embodied technical change. Ecol Econ 68(3):777–786. https://doi.org/10.1016/j.ecolecon.2008.06.011
Hsiang SM, Narita D (2012) Adaptation to cyclone risk: evidence from the global cross-section. Clim Change Econ 3(02):1250011 https://doi.org/10.1142/S201000781250011X
Klomp J, Valckx K (2014) Natural disasters and economic growth: a meta-analysis. Glob Environ Chang 26(1):183–195. https://doi.org/10.1016/j.gloenvcha.2014.02.006
Kron W, Steuer M, Löw P, Wirtz A (2012) How to deal properly with a natural catastrophe database - analysis of flood losses. Nat Hazards Earth Syst Sci. https://doi.org/10.5194/nhess-12-535-2012
Loayza NV, Olaberría E, Rigolini J, Christiaensen L (2012) Natural disasters and growth: going beyond the averages. World Dev 40(7):1317–1336. https://doi.org/10.1016/j.worlddev.2012.03.002
Moriyama K, Sasaki D, Ono Y (2018) Comparison of global databases for disaster loss and damage data. J Disast Res 13(6):1007–1014
Noy I (2009) The macroeconomic consequences of disasters. J Dev Econ 88(2):221–231. https://doi.org/10.1016/j.jdeveco.2008.02.005
Noy I (2016) Natural disasters in the Pacific Island countries: new measurements of impacts. Nat Hazards 84(S1):7–18. https://doi.org/10.1007/s11069-015-1957-6
Osuteye E, Johnson C, Brown D (2017) The data gap: an analysis of data availability on disaster losses in sub-Saharan African cities. Int J Disast Risk Reduct. https://doi.org/10.1016/j.ijdrr.2017.09.026
Panwar V, Sen S (2019a) Economic impact of natural disasters: an empirical re-examination. Margin J Appl Econ Res 13(1):109–139. https://doi.org/10.1177/0973801018800087
Panwar V, Sen S (2019b) Examining the economic impact of floods in selected Indian states. Clim Dev. https://doi.org/10.1080/17565529.2019.1614897
Raddatz C (2007) Are external shocks responsible for the instability of output in low-income countries? J Dev Econ 84(1):155–187. https://doi.org/10.1016/j.jdeveco.2006.11.001
Raddatz C (2009) The wrath of god: macroeconomic costs of natural disasters, (September). https://doi.org/10.1596/1813-9450-5039
Skidmore M, Toya H (2002) Do natural disasters promote long-run growth? Econ Inq 40(4):664–687. https://doi.org/10.1093/ei/40.4.664
Smith AB, Katz RW (2013) US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases. Nat Hazards. https://doi.org/10.1007/s11069-013-0566-5
Tschoegl L, Below R, Guha-Sapir D (2006) An analytical review of selected data sets on natural disasters and impacts. Centre for Research on the Epidemiology of Disasters, Brussels, Belgium
UNISDR (2015a) Making development sustainable: the future of disaster risk management. Global assessment report on disaster risk reduction. International Stratergy for Disaster Reduction (ISDR). https://doi.org/9789211320282
UNISDR (2015b) Sendai framework for disaster risk reduction 2015-2030. In Third United Nations World Conference on Disaster Risk Reduction (pp. 1–25). https://doi.org/A/CONF.224/CRP.1
UNISDR. (n.d.). DesInventar. United Nations Office for disaster risk reduction. Retrieved from https://www.desinventar.net
UNISDR, U (2005) Hyogo framework for action 2005–2015: building the resilience of nations and communities to disasters. In Extract from the final report of the World Conference on Disaster Reduction (A/CONF. 206/6) (Vol. 380). The United Nations International Strategy for Sisaster Reduction Geneva
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Panwar, V., Sen, S. Disaster Damage Records of EM-DAT and DesInventar: A Systematic Comparison. EconDisCliCha 4, 295–317 (2020). https://doi.org/10.1007/s41885-019-00052-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41885-019-00052-0