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THE INFLATIONARY COSTS OF EXTREME WEATHER
Andreas Heinen
Universite Cergy-Pontoise
Jeetendra Khadan
International Development Bank
Eric Strobl
Ecole Polytechnique & SALISES
Abstract
We examine the inflationary costs of extreme weather. To this end we compile a monthly panel
data set of destruction indices for hurricanes and floods and combine these with price data for 15
Caribbean islands. Our econometric model shows that the impact of these extreme weather
events can be large, affecting both aggregate inflation as well as that food and housing and
utilities prices.
Section I: Introduction
Extreme weather has resulted in nearly US$3 trillion worth of damages globally over the
last 35 years, and the rate of growth of such losses may very well increase in the future due to
climate change.1 Unsurprisingly recent years have also witnessed a growing interest in the
economic implications of these potentially large negative shocks. In this regard most of the
academic literature has focused on the consequences for economic growth.2 However, a driving
factor behind the extent and duration of any longer term outcome, such as growth, is the nature
of the adjustment process in the immediate aftermath of the event. More particularly, the
physical losses and subsequent economic interruptions are likely to create at least temporary
shortages of many goods and services. Amongst other things, these shortages can in turn
translate into higher prices. Importantly, if the price hikes are sufficiently large and last long
enough, they could further increase the hardship of those already directly affected as well as
translate into larger costs for other consumers. Such inflationary costs may then further
exacerbate any long-term consequences, especially affecting the poor.3
From a policy maker‘s perspective, being able to predict possible price changes due to
extreme weather events can arguably aid in optimizing relief efforts, as well as choosing the
correct fiscal and monetary policies to limit any longer term economic growth impact. However,
as to date there is essentially no quantitative assessment of the inflationary costs of natural
disasters.4 The only exception is the novel study by Cavallo et al (2014) who examine the impacts
of the 2010 Chile and the 2011 Japan earthquake on product availability and prices. More
1 World Bank (2013).
2 See, for example, Noy and Cavallo (2011) and Klomp and Valckx (2014) for reviews of the literature.
3 Easterly and Fischer (2001) find that for a sample of 38 countries inflation is one of the primary concerns of
the poor. 4 As a matter of fact, as noted by Noy and Cavallo (2011) in their review of the literature on the economics of
natural disasters, the monetary aspects of disaster dynamics has been generally neglected. Notable exceptions in this regard are Keen and Pakko (2009) who evaluate the optimal response of monetary policy in a dynamic stochastic equilibrium model and Ramcharan (2007) who empirically examined the role of exchange rate policy in the degree of damages due to natural disasters.
specifically, using daily nationwide price and product listings collected from the websites of a
large international supermarket retailer in each country and comparing these before and after the
events, the authors find that there were sharp falls in good availability immediately ex-post,
amounting to 32 per cent in Chile and 17 per cent in Japan. However, surprisingly these
shortages did not translate into higher prices. As possible explanations for the lack of price
increases the authors suggest a reluctance by the large retailers to anger consumers, as well as that
price adjustments may have only be made for new inventory, the flow of which might have been
severely hemmed because of the earthquakes.5 Indeed, secondary evidence seems to suggest the
possibility of the consumer anger explanation for Chile, but the supply chain shock as the more
likely cause for Japan. Nevertheless, the finding of price stickiness after a natural disaster seems
to run counter-intuitive to the common perception and observation that these extreme events go
hand in hand with price increases, at least in many developing countries.6
In this paper we take a different approach to investigate potential inflationary costs of
natural disasters. More precisely, rather than focusing on single events we construct times series
of potential destructiveness for two types of extreme weather phenomena - namely hurricanes
and floods - for a large number of Caribbean islands over time. To do so we, in line with
Felbermayr and Groeschl (2014), not only consider the physical features of the events by using
meteorological data, but also, as shown in Strobl (2012) to be important, take account of their
localized nature and the local heterogeneity in exposure to them. We then combine these indices
with country specific monthly time series on prices, which provides us with a large panel of
cross-country, cross-time variation in prices and potential natural disaster events with which we
econometrically examine whether extreme weather can cause inflation. Our results show, in
contrast to Cavallo et al (2014), that there can indeed be large price increases due to natural
5 The authors provide secondary evidence that is consistent with the consumer anger explanation for Chile, but
the supply chain shock cause for Japan. 6 For example, internet searches on terms like `inflation’ and ‘storms’ and/or ‘floods’ quickly reveal the extent
of this view across countries typically subject to extreme weather events.
disasters. This effect is reflected in aggregate inflation as well as food and housing and utilities
prices.
The Caribbean is arguably an ideal case study for the impact of natural disasters in
general, and their potential inflationary costs in particular. Firstly, the region is known to be
subject to a large number and many different types of potential disastrous natural events,
including tropical storms, earthquakes, volcano outbreaks, landslides, floods, and droughts.7
Secondly, as a set of mostly small island developing states these countries/territories are
particularly vulnerable to such large natural shocks due to their small physical size, geographic
isolation, limited natural resources, rapid population growth, high population densities, low
economic diversification, and poorly developed infrastructure.8 Moreover, since they rely on
imports for a large part of their consumption goods, or at least cannot easily and quickly
substitute internationally produced goods for domestic ones, they are potential very sensitive to
shortages after a natural disaster.
With regard to the two types of natural disasters examined here, one should note that
hurricanes and floods are the most common of natural shocks in the Caribbean and have been
driving most of the observed damages, affecting some part of the region consistently almost
every year. Moreover, these events have often had disastrous impacts on the islands affected. For
example, in 2004 Hurricane Ivan is estimated to have resulted in losses of over 300 per cent of
Grenada's annual GDP, while the recent heavy rains due to a tropical trough system in St.
Vincent and the Grenadines during Christmas 2013 are believed to have caused damages
constituting nearly 15 per cent of its economic output. Worryingly, some studies estimate that
rising risks from hurricanes and other extreme weather events will cost Caribbean nations of up
7 The Caribbean is subject to a large number and types of disasters, including hurricanes, earthquakes, volcano
outbreaks, floods and droughts. In this regard the Easter Caribbean is considered as the most disaster struck region globally; see REFERENCE. 8 See Meheux (2007).
to 9% of annual GDP in damages and losses by 2030.9 Our paper suggests that the immediate
nominal costs in terms of inflation should also be of concern.
The remainder of the paper is organized as follows. In the next section we describe our
data and provide some summary statistics. Our econometric model and results are provided in
Section III. Concluding remarks are provided in the final section.
Section II: Data and Summary Statistics
II.1 Hurricane Destruction Index
Tropical cyclones that form in the North Atlantic and the North East Pacific region are
referred to as hurricanes if they are of sufficient strength.10 In terms of structure, a hurricane will
typically harbor an area of sinking air at the center of circulation, known as the ‗eye, where
weather in these is normally calm and free of clouds, though the sea may be extremely violent.
Outside of the eye curved bands of clouds and thunderstorms move away from the eye wall in a
spiral fashion, where these bands are capable of producing heavy bursts of rain, wind, and
tornadoes. Hurricane strength tropical cyclones are normally about 483 km wide, although this
can vary considerably. These storms can travel between 17 and 56 km/hr, depending on the
latitude. Importantly, they quickly lose wind speed and forward speed once they hit landfall.
Hurricane destruction can take the form of damages due to winds, heavy rainfall, and storm
surge. One may want to note that the latter two aspects tend to be heavily correlated with the
wind of the hurricane and thus wind is often used as a proxy for all.11
To capture the potential destruction due to hurricanes we use an index in the spirit of
Strobl (2012), which measures wind speed experienced at a very localized level and then uses
9 See CCRIF (2010).
10 Generally at least 119 km/hr.
11 See Emanuel (2005).
exposure weights to arrive at an island specific proxy.12 More specifically, for a set of hurricanes,
k=1,…, K, and a set of locations, i=1, …I, in island j we define hurricane destruction during
month t as:
K
k
tkij
I
i
titj WwH1
3max
,,,
1
1,, Wmax ≥ W* (1)
where Wmax is the maximum measured wind speed at point i during a storm k, W* is
threshold above which wind is damaging, and w is exposure weights in the previous month t-1 of
locations , i=1, …I, which aggregate to 1 at the island j level. As can be seen from (1), our index
H requires local wind speed and exposure weights as inputs. One may want to note that we allow
local destruction to vary with wind speed in a cubic manner, since, as noted by Emanuel (2011),
there are physical reasons doing so in that kinetic energy from a storm dissipates roughly to the
cubic power with respect to wind speed and that the energy release scales with the wind pressure
acting on a structure.13 As a starting point we set W* to equal 119 km/hr, i.e. the threshold
above which winds are considered to be of hurricane strength.14
II.1A Local Wind Speed (Wmax)
What level of wind a location will experience during a passing hurricane depends crucially
on that location‘s position relative to the storm and the storm‘s movement and features, and thus
requires explicit wind field modeling. In order to calculate the wind speed experienced due to a
hurricane we use Boose et al.‘s (2004) version of the well-known Holland (1980) wind field
model. More specifically, the wind experienced at time t due to hurricane k at any point P=i, i.e.,
Wik is given by:
12
Strobl (2012) shows that no weighting for local exposure can substantially underestimate the impact of hurricanes on economic growth. 13
See Kantha (2008) and ASCE (2006). 14
http://www.nhc.noaa.gov/aboutsshws.php.
2
1
,,
,,
,,
,,,,
,,,,,, 1exp2
sin1
jtjt B
tki
tkm
B
tki
tkmtkh
tkitkmtkiR
R
R
RVTSVGFW (2)
where Vm is the maximum sustained wind velocity anywhere in the hurricane, T is the clockwise
angle between the forward path of the hurricane and a radial line from the hurricane center to the
pixel of interest, P=i, Vh is the forward velocity of the hurricane, Rm is the radius of maximum
winds, and R is the radial distance from the center of the hurricane to point P. The relationship
between these parameters and point P=i are depicted in Figure 1. The remaining ingredients in
(1) consist of the gust factor G and the scaling parameters F, S, and B, for surface friction,
asymmetry due to the forward motion of the storm, and the shape of the wind profile curve,
respectively.
In terms of implementing (1) one should note that Vm is given by the storm track data
described below, Vh can be directly calculated by following the storm‘s movements between
locations, and R and T are calculated relative to the pixel of interest P=i. All other parameters
have to be estimated or assumed. For instance, we have no information on the gust wind factor
G. However, a number of studies (e.g. Paulsen and Schroeder, 2005) have measured G to be
around 1.5, and we also use this value. For S we follow Boose et al. (2004) and assume it to be 1.
We also do not know the surface friction to directly determine F. However, Vickery et al. (2009)
note that in open water the reduction factor is about 0.7 and reduces by 14% on the coast and
28% further 50 km inland. We thus adopt a reduction factor that linearly decreases within this
range as we consider points i further inland from the coast. Finally, to determine B we employ
Holland‘s (2008) approximation method, whereas we use the parametric model estimated by Xiao
et al. (2009) to derive Rmax.
Our source for hurricane data is the HURDAT Best Track Data, which since 1951 has
provided six hourly data on all tropical cyclones in the North Atlantic Basin. Other information
includes the position of the eye of the storm and the maximum wind speed. We linearly
interpolate these to 3 hourly positions in order to be in congruence with our rainfall data,
described below. We also restrict the set of storms to those that came within 500 km of our
Caribbean islands and that achieved hurricane strength (at least 119 km/hr) at some stage.15
Figure 2 depicts the tracks of all remaining tropical storms for our sample period 2000 to 2012,
where the red portion of the tracks refers to the segment of the storm that reached hurricane
strength. All in all a total of ??? hurricane strength storms traversed the 500km radius of the
Caribbean during our sample period of 2000 to 2012.
II.1B Exposure Weights (w)
In order to derive island specific aggregate time varying measures of destruction we also
want to take exposure into account. Ideally we would like to have time varying information on
the degree of dispersion of economic activity within islands at the most spatially disaggregated
level as possible, given that wind speeds due to tropical storms can differ substantially across
space. To this end we employ nightlight imagery provided by the Defense Meteorological
Satellite Program (DMSP) satellites. One may want to note that nightlights have now found
widespread use of proxying local economic activity where no other measures are available; see,
for instance, Harari and La Ferrara (2013), Holder and Raschky (2014) and Michalopoulos and
Papaioannou (2014). In terms of coverage each DMSP satellite has a 101 minute near-polar orbit
at an altitude of about 800km above the surface of the earth, providing global coverage twice per
day, at the same local time each day. In the late 1990s, the National Oceanic and Atmospheric
Administration (NOAA) developed a methodology to generate ―stable, cloud-free nightlight data
sets by filtering out from the data transient light produced by for example forest fires and other
random noise events occurring in the same place less than three times‖ (see Elvidge et al. 1997
for a detailed description of the filtering process). The resulting images provide the percentage of
nightlight occurrences for each pixel per year normalized across satellites to a scale ranging from
15 Tropical cyclones generally do not exceed a diameter of 1000km.
0 (no light) to 63 (maximum light). The spatial resolution of the original images was about 0.008
degrees on a cylindrical projection and was later converted to a polyconic projection, giving
squares of about 1 km2 near the equator. Yearly values were then created as simple averages
across daily (filtered) values of grids, and are available from 1992. 16 In order to obtain monthly
time varying values for our weights w we linearly interpolated between yearly values.
II.2: Flood Events
A flood is a temporary water overflow of a normally dry area due to overflow of a body
of water, unusual buildup, runoff of surface waters, or abnormal erosion or undermining of
shoreline.17 In this regard there are several different types, including flash floods, coastal floods,
urban floods, fluvial floods, and pluvial floods. One should note that apart from causing
inundation of areas, floods are often also the trigger of landslides. At any rate, the main driving
factor behind floods is generally excessive rainfall.
Unfortunately there is no complete flood event database providing location and flooding
intensity for the Caribbean. An alternatively manner to identify flood occurrences is to use data
on precipitation and simulate water runoff using a hydrological model. However, the required
data to run a hydrological model is also not readily available on a Caribbean wide basis. But, as
shown by Montesarchio et al (2009), in regions where basin size is less than 400km2, as it is
essentially for all of the Caribbean, it is possible to perform flood detection based solely on
precipitation data, and we thus take this approach here. Nevertheless, even relying on
measurements of rainfall to detect floods, one still needs to decide on a threshold of precipitation
above which flooding is likely to occur. In this regard, since Caine (1980) there have been a large
number of studies who use intensity-duration precipitation thresholds for flood induced
16 For the years when satellites were replaced observations were available from both the new and old satellite. In this paper we use the imagery from the most recent satellite but as part of our sensitivity analysis we also re-estimated our results using an average of the two satellites and the older satellite only. The results of these latter two options were almost quantitatively and qualitatively identical. 17
Samaroo, M. (2010).
landslides and debris flow.18 More specifically, this approach entails taking information on the
duration and intensity of rainfall for known landslide events and estimating a power law
relationship between the two:
Intensity=α*Durationβ (3)
where α and β are the parameters to be estimated, and can be used to identify the threshold
rainfall intensity for a given rainfall duration that will induce landslides. More recently, this
method has also been employed to identify floods more generally; see, for example, Hurford et al
(2012). This can be justified on the grounds that for other types of floods, such as urban, river,
or flashfloods, the concept of a intensity-duration threshold is similar – a surface has a maximum
storage capacity above which surface runoff will occur; see Gumbricht (1996).
With regard to the Caribbean, Pathirana et al (2010) collected duration and intensity data
for flood events in Trinidad over the period 2004-2008 and in estimating (3) found α to be 4.064
and β -0.267. Here we use these estimates to infer flood events in the Caribbean. In this regard
we set duration to be equal 3 days, and the resultant implied intensity threshold is a cumulative 3-
day sum of 112 mm. Our choice of identifying flood events over three day windows rather than
some shorter or longer horizon was for two reasons. Firstly, Wu et al (2014) noted that the data
of precipitation that we use here, namely TRMM satellite derived rainfall, is much better suited to
identifying flood occurrences for 3-day windows than incidences of a shorter nature.19 Secondly,
cumulative 2-3 day rainfall is currently also used to identify excess rainfall events for the excess
rainfall product of the Caribbean Catastrophe Risk Insurance Facility (CCRIF).20 Using the 3-
day threshold implied by Pathirana et al (2010) we can proxy country level flood induced
potential destruction as:
18
See for instance, Guzzetti et al (2008), Cannon et al (2011), and Turkington et al (2014). 19
Similarly, Mathew et al (2014) find that 3-day cumulative rainfall derived from TRMM data can be a significant predictor of landslides 20
The CCRIF is a regional insurance scheme that offers Caribbean countries insurance to hedge against the immediate costs of natural disasters. It currently covers hurricanes, earthquakes, and excess rainfall.
t
d
d
td
djidji
I
i
tjitj rrIrwF1 3
,,,,1,,, *)( (4)
where F is the exposure weighted average of country level excess rainfall in a month, I() ins an
indicator function, r is the daily rainfall, and w are exposure weights for location i as defined in
(1) . r* is the cumulative 3-day threshold, as a starting point assumed to be 112 mm as inferred
by the results of Pathirana et al (2010). One may want to note that unlike for tropical storm wind
speed we are assuming that potential damages are linearly related to the extent of precipitation
during a flood event. This is generally in congruence with most estimated flood fragility curves;
see, for instance, those used by FEMA for damage estimation within their HAZUS flood
software.21
Our only required input in (4) is precipitation r. Since consistent series of rainfall
estimates from weather stations are available neither on a temporal nor on a spatial scale for the
Caribbean, we instead use the satellite derived TRMM-adjusted merged-infrared precipitation
(3B42 V7) product. These 3 hourly precipitation estimates were generated by first using the
TRMM VIRS and TMI orbit data (TRMM products 1B01 and 2A12) and the TMI/TRMM
Combined Instrument (TCI) calibration parameters (from TRMM product 3B31) to produce IR
calibration parameters. The derived IR calibration parameters were then employed to adjust the
merged-IR precipitation data, which consists of GMS, GOES-E, GOES-W, Meteosat-7,
Meteosat-5, and NOAA-12 data. The final gridded, adjusted merged-IR precipitation (mm/hr)
have a 3 hourly temporal resolution and a 0.25-degree by 0.25-degree spatial resolution and
extend from 50 degrees south to 50 degrees north latitude, and is available from 1998. Since the
TRMM grid cells are of greater size than the location pixels that we use for our hurricane index
and exposure weights, pixels located within the same TRMM pixels will necessarily have the same
local precipitation values.
21
See FEMA (2006) and Scawthorn (2006).
II.3: Inflation Data
Our source of inflation is monthly data on the consumer price index (CPI) for a group of 15
island economies in the Caribbean - Antigua and Barbuda, Bahamas, Barbados, Dominica,
Dominican Republic, Guadeloupe, Grenada, Haiti, Jamaica, St. Kitts & Nevis, St. Lucia,
Montserrat, Martinique, Trinidad & Tobago, St. Vincent & the Grenadines – where our choice of
economies was determined by data availability. The data are taken from the Eastern Caribbean
Central Bank databank and each country‘s respective Central Bank Bulletins. Overall our dataset
covers the period January 2001 to December 2012. Because of missing monthly data for the
Bahamas for the years 2001-02, our panel is marginally unbalanced. We use data on total CPI, as
well as on the price index for food and for housing and utilities as separate dependent variables.
Inflation in aggregate and by sub-category is simply the logged difference in monthly prices over
time.
II.3: Summary Statistics
In Table 2 we depict summary statistics for all variables used in the analysis. Accordingly,
average monthly aggregate inflation is about 0.4 per cent, although with considerable variation.
Also, the rate of food inflation is higher than that of housing and utilities, but less variable. If one
examines our benchmark extreme weather proxies (W*=119km/hr and r*=112mm) one
discovers that the variation is large relative to the mean over our sample period. In part this is
due to the number of non-damaging months for each. More precisely, for our total observations
of 2,340 island-months for H there were only 142 non-negative occurrences, with a
corresponding figure of 673 for F.
Section III: Econometric Results
III.1: Econometric Specification
Our goal is to estimate the impact of extreme weather events on inflation:
tjtj
S
s
stjst
S
s
stjsttj FHINFL ,
0
,
0
,,
(5)
where INF is the inflation rate, defined as the difference in logged CPI, H is our hurricane
destruction index, F is our flood index, µ is a vector of country specific indicator variables, λ is a
vector of year and month indicator variables, and ε is the error term. In order to take account of
the country specific time invariant factors, µ , we simply employ a fixed effects estimator. One
should note that we also allow for cross-sectional and serial correlation of up to 4 lags by using
Driscoll and Kraay (1998) standard errors.
III.2: Estimation Results
We started with regressing the overall inflation rate on the contemporaneous values of
our hurricane and our flood index as shown in the first column of Table 2. As can be seen, both
have a positive and significant effect on monthly inflation. In other words, if an extreme weather
event, either in the form of hurricane winds or excess rainfall occur, then overall prices in
Caribbean economies rise. To see whether there is persistence in these effects we included lags of
up to two after the event in the second and third columns, respectively. However, we find no
evidence of the inflationary impact lasting beyond a month of the events.22
We next investigated whether extreme weather increased prices for our two CPI sub-
categories. In this regard, columns four through six show qualitatively similar results for food
price inflation as the overall price series, i.e., there are only contemporaneous increases due to the
negative shocks. The quantitative impact is, however, substantially larger, about double that of
overall prices for both hurricanes and floods. In contrast, neither weather phenomena appears
to have played any role in increasing prices of housing and utilies‘ goods and services, as depicted
in the last three columns.
22
Further lags were also insignificant.
One potential problem with our two indices is that these are unlikely to be completely
independent. More precisely, many of the excess rainfall events driving our F index occur during
tropical storms. As a matter of fact, as noted for example by Jiang et al. (2008), the amount of
rain and the maximum wind speed during a storm tend to be highly positively correlated. One
solution could be to simply exclude all flood events that happened during a tropical storm.
However, in practice many tropical storms are not powerful or come close enough to a locality to
cause wind damage, but may still produce enough excess rainfall to cause flooding. For example,
although Tropical Storm Nicole never reached Hurricane strength, it caused a considerable
amount of damage, believed to be around US $239.6 million, in Jamaica.23 We thus redefined F
excluding flood events for a cell within an island during a storm if the corresponding estimated
wind speed was above the wind threshold value W*. In this context our H index captures both
wind and rainfall damage for a locality given that winds experienced were of at least hurricane
strength, while F is constructed to identify both non-tropical storm related events as well as flood
damage due to tropical storms that did not translate into local hurricane strength winds. One
should note that this reduced the correlation between the two potential damage indices from
0.2095 to 0.0128. We reproduced Table 2 with the new flood proxy in Table 3. Accordingly,
the findings remain qualitatively the same, except that there appears to a marginally significant
lagged effect for F in terms of food prices, although this is not robust to including further lags.
However, there are clear changes quantitatively. Specifically, not only does the size of the
coefficients on our significant coefficients notably increase, but also their associated t-statistics.
We thus continue, for the rest of the analysis, to work with definitions of F that exclude cell
events with tropical winds above W*.
One can use our estimated coefficients in Table 3 to assess the economic impact of
extreme weather over our sample period. More precisely, average overall monthly inflation rose
by 0.003 percentage points due to damaging hurricanes. In those months when damage was non-
23
PlJ (2010).
zero the average impact was about 0.05, while the implied maximum price hike was 1.4
percentage points. In contrast, average monthly expected flood induced inflation was 0.024
percentage points. When flooding struck the average effect was about 0.083, whereas the implied
largest price hike was 0.604 percentage points. In terms of food inflation our results imply that
the monthly expected inflationary cost is about 0.006 percentage points for hurricane strikes, and
0.044 percentage points for flood events. Considering those months in which damage was
positive according to our indices, the suggested mean impacts are 0.104 and 0.148 percentage
points, for hurricanes and floods, respectively. Our estimates and data also suggest that the
largest inflationary boost was 2.785 and 1.083 percentage points for these extreme weather types,
respectively.
We thus far have assumed that hurricane wind and corresponding rainfall damage occurs
if localized winds are above 119 km/hr, i.e., of at least the Saffir-Simpson (SS) Intensity 1. In this
regard the NOAA notes that when winds are of SS Category 1 (119-153 km/hr), typically ―..well-
constructed frame homes could have damage to roof, shingles, vinyl siding and gutters…large
branches of trees will snap and shallowly rooted trees may be toppled…extensive damage to
power lines and poles likely will result in power outages that could last a few to several days.‖. If
one, in contrast, considers Category 3 (178-208km/hr) winds then ―…well-built framed homes
may incur major damage or removal of roof decking and gable ends…many trees will be snapped
or uprooted… electricity and water will be unavailable for several days to weeks after the storm
passes‖. 24 To investigate whether setting the threshold at Category 3 winds changes our findings,
we redefined H in (1) using W*=178. The results of replicating Table 3 using his proxy are given
in Table 4. Accordingly, there is now compared to Table 3 a lagged effect of hurricane damage
for overall and for food prices. Perhaps more importantly, we now find both significant
contemporaneous and lagged effect of hurricane strikes on housing and utilities prices.
24
http://www.nhc.noaa.gov/aboutsshws.php.
Using the coefficients in Table 4 suggests that when hurricanes winds were above 178
km/hr aggregate monthly prices to rise by around 0.080 percentage points in the month they
struck as well as 0.063 points in the subsequent month. The maximum observed effects were
1.533 and 1.212 percentage points, respectively. The monthly price effect for floods was similar
to when we used the benchmark hurricane threshold, standing at about 0.023 percentage points.
In the months that the flood occurred the effect was 0.075 on average and 0.514 at its highest
observed value. For food inflation the total expected hurricane induced inflationary costs was
0.008 percentage points, while the implied impacts during damaging months was 0.264 on
average and 5.061 at the maximum over our sample period. In contrast, food prices rose on
average by 0.047 percentage points due to flooding. In those months when there was flooding,
consumers were faced with a 0.152 percentage point hike in prices, and at its maximum 1.039
percentage points. Finally, in terms of housing and utilities goods and services, prices increased
on average by 0.004 percentage points. In those months in which H was positive, the implied
total impact was about 0.150 percentage points, while its maximum value suggested a rise in
inflation of 2.877 percentage points over two months.
With a similar line of reasoning as for our higher threshold for H, we also considered a
higher threshold for defining a flood event in (4). More specifically, in estimating the power law
for worldwide landslides using TRMM, Hong et al. (2007) found α and β to be 12.45 and -0.42,
respectively. In terms of our three day running sum window this suggests to set r* at 199 mm.
We used the corresponding new series of F and replicated Table 3 in Table 5.25 As can be seen,
while our findings on H still hold, now floods appear to have no discernable impact on inflation.
This suggests that setting a threshold too high may result in excluding too many flood events and
25
One should note that we kept H at its 119 km/hr threshold in order to adequately isolate what potentially different impact setting a higher flood threshold would be. However, using instead h*≥178 km/hr did not change our results qualitatively or noticeably quantitatively in this regard. Similarly, including our higher threshold F index with threshold did not change the results in Table 4.
thus introduce too much measurement error into F to accurately capture the price inflating
effects of floods.
CONCLUSION
In this paper we investigated how extreme weather can drive short-term inflation. To this
end we constructed hurricane and flood construction indices from weather and exposure data
and combined these with monthly price data for 15 Caribbean islands. Our econometric results
suggest that while the expected inflationary rise due to hurricanes and floods is small every
month, as is intrinsic to the very nature of extreme events, when these do strike the impact can
be multifold of average monthly inflation.
More generally our analysis suggests that the potential short-terms costs inflationary
pressures of good shortages after an extreme weather event should not be ignored. In this
regard, one may want to note that some governments, like the Philippines in recognition of this
has for many years employed deflationary policies. More recently the Caribbean Catastrophe Risk
Insurance Facility specifically introduced hurricane and excess rainfall products to deal with the
short-term impacts of extreme weather, such as the sudden rise in prices.
REFERENCES
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Figures
Figure 2: Hurricane Wind Field Model
Notes: (1) Sample diagram of input parameters into typhoon wind field model; (2) P: point of interest, R: distance
from storm eye to point of interest, Rmax: radius of maximum wind speed, T: angle of point relative to direction of
storm; Vh: forward speed of storm.
Figure 2: Tropical Cyclones in the Caribbean Region 2001-2012
Notes: Orange, red and black, portions of the tracks indicates tropical storm, hurricane Saffir-
Simpson Scale 1 (119-153 km/hr), and at least hurricane Saffir-Simpson Scale 3 (178 km/hr+)
strength storms.
Figure 2: Consumption per Capita Distribution in Jamaica (2012)
Notes: (a) Graph of the kernel density estimate using a epanechnikov kernel and optimal
bandwidth; (b) Red line indicates poverty threshold at J$12,000.
0
.000
01
.000
02
.000
03
.000
04
Pro
bab
ility
0 20000 40000 60000 80000 100000Consumption per Capita
Figure 3: Food Budget Share vs. Consumption per Capita
Notes: (a) Graph of the kernel regression estimate using an Epanechnikov kernel and optimal
bandwidth; (b) Red line indicates poverty threshold at J$12,000.
.1.1
5.2
.25
.3
Fo
od
Sh
are
of C
onsu
mp
tion
0 20000 40000 60000 80000 100000Consumption per Capita
Figure : Housing and Utilities Budget Share vs. Consumption per Capita
Notes: (a) Graph of the kernel regression estimate using a epanechnikov kernel and optimal
bandwidth; (b) Red line indicates poverty threshold at J$12,000.
.05
.1.1
5.2
.25
Ho
usin
g &
Utilit
ies S
ha
re o
f C
on
sum
ption
0 20000 40000 60000 80000 100000Consumption per Capita
Table 3:
(1) (2) (3) (4) (5) (6) (7) (8) (9)
INFL : ALL ALL ALL FOOD FOOD FOOD H&U H&U H&U
Ht 0.845* 0.853* 0.836* 1.662** 1.671** 1.669** 0.786 0.803 0.790
(0.398) (0.407) (0.417) (0.612) (0.622) (0.627) (0.485) (0.486) (0.497)
Ht-1 0.537 0.535 0.749 0.757 0.730 0.746
(0.399) (0.402) (0.670) (0.677) (0.599) (0.608)
Ht-2 -0.101 0.329 0.663
(0.289) (0.613) (0.352)
Ft 0.122* 0.126* 0.125* 0.253** 0.262** 0.261** 0.0484 0.0493 0.0460
(0.0575) (0.0592) (0.0602) (0.0765) (0.0811) (0.0824) (0.0841) (0.0837) (0.0845)
Ft-1 0.0410 0.0382 0.115 0.114 -0.0298 -0.0328
(0.0660) (0.0675) (0.0895) (0.0918) (0.0797) (0.0782)
Ft-2 -0.0429 -0.0356 -0.102
(0.0593) (0.0742) (0.115)
Wmax
: 119 119 119 119 119 119 119 119 119
r*: 200 200 200 200 200 200 200 200 200
Obs. 2,145 2,145 2,145 2,115 2,115 2,115 2,115 2,115 2,115
F-test(β=0) 7.188 6.901 6.905 8.097 8.418 8.791 4.118 4.424 4.469
R2
Notes: (1) H and F were divided by 1011
and 104, respectively, to make coefficients more readable. (3) Driscoll Kraay (1998) standard errors in
parentheses. (3) ** and * indicate 1 and 5 per cent significance levels, respectively. (4) Yearly and monthly dummies included in all
specifications.
Table 4:
(1) (2) (3) (4) (5) (6) (7) (8) (9)
INFL : ALL ALL ALL FOOD FOOD FOOD H&U H&U H&U
Ht 1.178** 1.210** 1.191** 2.339** 2.414** 2.405** 0.924 0.936 0.911
(0.328) (0.344) (0.356) (0.448) (0.470) (0.487) (0.524) (0.532) (0.544)
Ht-1 0.649 0.629 1.045 1.033 0.672 0.640
(0.396) (0.410) (0.634) (0.656) (0.568) (0.593)
Ht-2 -0.227 0.220 0.389
(0.357) (0.650) (0.378)
Ft 0.155** 0.159** 0.157** 0.278** 0.288** 0.286** 0.0970 0.0984 0.0923
(0.0512) (0.0519) (0.0522) (0.0769) (0.0796) (0.0808) (0.0896) (0.0891) (0.0886)
Ft-1 0.0392 0.0368 0.137* 0.136 -0.0264 -0.0299
(0.0527) (0.0533) (0.0679) (0.0696) (0.0883) (0.0869)
Ft-2 -0.0405 -0.0534 -0.130
(0.0492) (0.0807) (0.0942)
Wmax
: 119 119 119 119 119 119 119 119 119
r*: 200 200 200 200 200 200 200 200 200
Obs. 2,145 2,145 2,145 2,115 2,115 2,115 2,115 2,115 2,115
F-test(β=0) 8.101 7.708 7.482 10.19 11.21 12.15 4.013 4.591 4.724
R2
Notes: (1) H and F were divided by 1011
and 104, respectively, to make coefficients more readable. (3) Driscoll Kraay (1998) standard errors in
parentheses. (3) ** and * indicate 1 and 5 per cent significance levels, respectively. (4) Yearly and monthly dummies included in all
specifications.
Table 5:
(1) (2) (3) (4) (5) (6) (7) (8) (9)
INFL : ALL ALL ALL FOOD FOOD FOOD H&U H&U H&U
Ht 1.311** 1.336** 1.325** 2.764** 2.799** 2.801** 1.376** 1.406** 1.394**
(0.233) (0.244) (0.248) (0.347) (0.359) (0.363) (0.476) (0.470) (0.472)
Ht-1 1.058** 1.060** 1.613** 1.626** 1.096** 1.117**
(0.264) (0.267) (0.437) (0.445) (0.392) (0.400)
Ht-2 0.0618 0.475 0.702
(0.253) (0.586) (0.401)
Ft 0.119* 0.123* 0.122* 0.240** 0.249** 0.249** 0.0421 0.0430 0.0401
(0.0574) (0.0590) (0.0599) (0.0751) (0.0792) (0.0809) (0.0845) (0.0841) (0.0849)
Ft-1 0.0316 0.0295 0.102 0.101 -0.0371 -0.0402
(0.0672) (0.0686) (0.0918) (0.0938) (0.0794) (0.0780)
Ft-2 -0.0454 -0.0366 -0.103
(0.0624) (0.0771) (0.118)
Wmax
: 178 178 178 178 178 178 178 178 178
r*:
Obs. 2,145 2,145 2,145 2,115 2,115 2,115 2,115 2,115 2,115
F-test(β=0) 11.73 10.61 10.43 23.72 26.13 25.43 3.711 6.242 5.909
R2
Notes: (1) H and F were divided by 1011
and 104, respectively, to make coefficients more readable. (3) Driscoll Kraay (1998) standard errors in
parentheses. (3) ** and * indicate 1 and 5 per cent significance levels, respectively. (4) Yearly and monthly dummies included in all
specifications.
Table 6:
(1) (2) (3) (4) (5) (6) (7) (8) (9)
INFL : ALL ALL ALL FOOD FOOD FOOD H&U H&U H&U
Ht 1.164** 1.185** 1.166** 2.326** 2.371** 2.376** 0.913 0.916 0.922
(0.309) (0.324) (0.333) (0.415) (0.434) (0.439) (0.519) (0.527) (0.533)
Ht-1 0.185 0.189 0.186 0.453 0.461 0.461 0.0857 0.0881 0.0849
(0.122) (0.123) (0.123) (0.233) (0.234) (0.234) (0.117) (0.117) (0.118)
Ht-2 0.596 0.574 0.969 0.974 0.632 0.636
(0.389) (0.399) (0.598) (0.611) (0.569) (0.580)
Ft 0.0290 0.0263 0.140 0.141 -0.142 -0.140
(0.0849) (0.0875) (0.108) (0.111) (0.139) (0.140)
Ft-1 -0.261 0.141 0.389
(0.350) (0.623) (0.378)
Ft-2 -0.111 0.00705 -0.0630
(0.0807) (0.104) (0.163)
Wmax
: 178 178 178 178 178 178 178 178 178
r*:
Obs. 2,145 2,145 2,145 2,115 2,115 2,115 2,115 2,115 2,115
F-test(β=0) 8.102 7.496 7.717 9.312 10.53 11.38 3.804 3.952 3.892
R2
Notes: (1) H and F were divided by 1011
and 104, respectively, to make coefficients more readable. (3) Driscoll Kraay (1998) standard errors in
parentheses. (3) ** and * indicate 1 and 5 per cent significance levels, respectively. (4) Yearly and monthly dummies included in all
specifications.
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