Tropical Cyclones,
Caribbean Economics and
Rethinking the Cost of
Climatic Change
Solomon Hsiang
Ph.D. student in Sustainable Development
NASA
Neumann
the question
• How will climatic change affect Caribbean
economies (through the mechanism of
tropical cyclones)?
the result
• History suggests that populations will
adapt, coping with (small) changes in
income and consumption following
additional storm events by expanding
government, leading to (large) reductions
in consumption and income growth.
this talk
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prior work
estimating historical storm incidence
theoretical framework
responses to events
responses to risk
comparison
is the response dynamic?
discussion
prior work
attribution & public literature
•
During 2005, the Jamaican economy recorded real Gross Domestic
Product growth of an estimated 1.4 per cent, however, the targets
established under the Medium Term Socio-Economic Policy
Framework were not fully realized.... During the year, growth
performance was adversely impacted by a number of challenges,
which included:
•
-the residual impact of Hurricane Ivan;
•
-drought conditions and bush fires during the first half of the year;
•
-the impact of Hurricanes Dennis and Emily which caused damage
to infrastructure and productive assets amounting to
approximately $6.0 billion; and
•
-record high international crude oil prices.
•
[Planning Institute of Jamaica,
2005]
economic literature
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Economic damage (Nordhaus, Pielke et al.)
Hurricanes and development (Barker, Mulcahy)
Income smoothing after storms (Bluedorn)
Projecting long run influence on economic
trajectories (Freeman)
• Environment and indirect effect - Institutions
(Acemoglu et al.)
climate change context
Knutson & Tuleya, 2004
Emanuel, 2005
the “we’ll adapt” assumption
•“Adaptation to climate change has the
potential to substantially reduce many of the
adverse impacts of climate change and
enhance beneficial impacts, though neither
without cost nor without leaving residual
damage.”
•- IPCC, 2001, Working Group 2,
Technical Summary
storm incidence
Tartaglione et al., Journal of Climate,
2003
estimating a storm incidence
reconstruction
44 million people
(Suzana Camargo, IRI)
land surface data
NOAA NGDC GLOBE Digital Elevation Model
1 km x 1 km
resolution
44 km long
St. Kitts and
Nevis
RMW = a + b x Vmax + c x LAT
(Kossin et al 2007)
socially relevant storm incidence
storm
motion
integrate
storm
measure
250 km
distance at
closest
approach
nice pictures.
where’s the economics?
defining terms
X(t)
• technical damages
• the “event effect”
• “technical adaptation”
• behavioral damages
• the “risk effect”
• “behavioral adaptation”
• eg. u(GPO) - u(Nash)
X
pdf(x)
X
theoretical framework
example: biking in Boston and
New York City
• biking risk: get hit by a car
• technical effect: hospitalization
• technical adaptation: helmet
• behavioral adaptation: bike less
• behavioral effect: fewer bikers in NYC
than Boston
climate change
X(t)
• current focus: events
• cyclones, drought,
floods, sea level rise,
etc.
• is a change in the set
of possible outcomes
(and risk)
X
pdf( x | climate_1 )
pdf( x | climate_2 )
data
• Penn World Tables
• GDP (PPP/c), consumption, investment, govt
•
•
•
•
1967 - 2004
16 nations
44M people
Controls:
• Precipitation, Surface Temperature
• Year
• Country fixed effects
• Area, GDP in 1970, population
the simplest cut
• is there any effect (event or risk) of
cyclones on outcomes?
‘low risk’
‘high risk’
There is a clear effect, but cannot identify event from risk.
quick aside:
short-run response to surface
temperature events
the “event effect”
&
“technical adaptation”
short-run response to
tropical cyclone events
Income shocks
and reallocation of
consumption
No statistical
evidence of
successful
technical
adaptation!?
the “risk effect”
&
“behavioral adaptation”
general equilibrium response
to tropical cyclone risk
comparing the magnitudes:
events vs risk
annual
ATE
annual
max of
2500 obs.
annual
is the response dynamic?
• Acemoglu et al:
–t = 0: environment produces institutions
–t > 0: institutions produce outcomes
• Or dynamic adjustment?
–t > 0: environment to institutions to outcomes
Antigua example
results
• direct temp effect:
–+1 degree C = -3.1 % growth
• storm event effect:
–ATE = [-4, +4] % growth
–ATE = [-0.5, +1.5] % income consumption
• storm risk effect:
–ATE = +16.0 % government spending
–ATE = -17.6 % income consumption
–ATE = -1.9 % growth
possible stories
• Durkheim’s “social effervescence”
• liability transfer to government (requires
good credit markets)
• inefficient mechanisms for public good
provision
• risk in cooperation games
• Mulcahy’s inequality and income transfers
• high taxation and incentives to invest (low
growth)
take home messages
• no evidence of frequently cited “technical
adaptation”
• strong “behavioral adaptation”
• a focus on observed “events” and
damages underestimates the impact of
climate (i.e. risk) change in general
equilibrium by 1-2 orders of magnitude
Thanks to Leigh Linden, Wolfram Schlenker, Jeffrey Sachs, Josh Graff
Zivin, John Mutter, Bernard Salanie, Scott Barrett, Adam Sobel,
Jennifer Hill, Wojciech Kopczuk, Bentley MacLeod, Kerry Emanuel,
Mark Cane, Suzana Camargo, Alessandra Giannini, Jim Kossin, John
Bluedorn, Dennis Shea, Ram Fishman, Jesse Antilla-Houghs, Tobias
Sigfried, Matthew Notowidigdo and Adam Sachs; and NSF-IGERT and
EPA-STAR for support.
extra slides
Radiation to space
Storm
Velocity
Sun
‘Eye’
Earth’s
rotation
Prevailing winds
Surface winds
Main Development Region
incidence measures
reducing attenuation bias
Basin
Energy
count, energy
storm
Basin Storm Count
spatial sensitivity to climate
Basin
Storm
Count
Basin
Integrated
Energy
10 x 10 km
Point
Energy
Sea Surface Temperature - NOAA NCDC ERSST v2
Smith & Reynolds, 2004
El Nino Souther Oscillation - ENSO 3.4
Kaplan et al. 1998, Reynolds et al. 2002
SST
ENSO
Total
Basin
Energy
integrated energy =
a
+ b x SST
+
c x ENSO3.4 + error
OLS
-2.3210e6
tstat
(-3.2277)***
heteroskedastic spherical disturbance
0.0892e6
(3.4027)***
-0.0478e6
(-4.3322)***
GLM
tstat
gamma (exp) errors
0.0769e6
(4.0376)***
-0.0433e6
(-5.9367)***
-1.9842e6
(-3.8047)***
Point-wise regressions
Energy = a + b x SST + c x ENSO
Energy =
a + b x SST
+ c x ENSO
b
c
mean
risk
sensitivity
to SST
country summary stats