1) The document discusses losses from natural disasters as reported by different organizations, noting large variations in reported losses depending on the source. 2) It examines the concept of indirect losses from disasters, providing examples where indirect costs far exceeded direct property losses. 3) The document shows that disaster loss reporting is biased, with higher income countries reporting a greater percentage of losses. 4) Data on disaster losses can help policymakers understand total costs and how losses have trended over time as a share of GDP for different disaster types.

1. OECD Meeting on Disaster Losses
Debby Guha-Sapir
November, 2014

2. HOW MUCH DO LOSSES VARY BY SOURCE

3. Loss reporting differences by source
Earthquake-Tsunami, Japan (Fukushima)
0
50
100
150
200
250
300
350
OCHA AFP USGS World
Bank
Munich Re Aon Swiss Re
Billions
Economic damages (US$2011)

4. Flood in Thailand (2011)
0
5
10
15
20
25
30
35
40
45
50
Cat-i Aon Munich Re Swiss Re
Billions
Economic damages (US$2011)

5. WHAT DO INDIRECT LOSSES MEAN ?

6. Indirect Losses
Kobe - 17 January 1995
Northridge earthquake, USA,
1994
Hurricane Mitch, Nicaragua,
1998
Private property losses – 50b;
economic dislocation and
business interruption - 50b;
restore basic functions - 100b
Unprecedented business
interruption losses—$6.5 b
Losses of rent (estimation
based on rent for inhabitant to
move into a new houses)

7. HOW WELL ARE LOSSES REPORTED

8. Figure 15: Reporting bias: % of disasters reporting losses, colour coded by
income category (cf. Annex 1), Asian ADB countries, 1980-2012
0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00
Hong Kong (China)
Taipei (China)
Malaysia
Turkmenistan
China, P. Rep.
Philippines
Uzbekistan
Pakistan
India
Georgia
Viet Nam
Afghanistan
Nepal
Tajikistan
Cambodia

9. HOW CAN POLICY MAKERS USE LOSS DATA ?

10. Figure 14: Disaster types: total damages (2012 US$ billion) and percentage
of total damages, AADB countries, 1980-2012
Geophysical (Earthquakes &
tsunamis), 600, 47%
Hydrological (Floods), 432,
34%
Meteorological (Storms), 236,
19%

11. Figure 16: Time trend of losses as share of GDP for storms, earthquakes and floods, AADB
countries, 1980-2012
0
0.2
0.4
0.6
0.8
1
1.2
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Earthquake Flood Storm
Poly. (Earthquake) Poly. (Flood) Poly. (Storm)

12. THANK YOU
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