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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

Caine, N. (1980). ―The Rainfall Intensity-Duration Control of Shallow Landslides and Debris Flows‖, Geogrfiska Annaler, 62A, pp. 23-27.

Cannon, S., Boldt, E., Laber, J., Kean, J., and Staley, D. (2011). ―Rainfall Intensity-Duration Thresholds for Postfire Debris-Flow emergency-Response Planning, Natural Hazards, 59, pp. 209-236.

Caribbean Catastrophe Risk Insurance Facility [CCRIF] (2010). Enhancing the Climate Risk and Adaption Fact Base for the Caribbean, CCRIF, Cayman Islands.

Driscoll, J. and Kraay, A. (1998). ―Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data‖, Review of Economics of Statistics, 80, pp. 549-560.

Easterly, W., and S. Fischer (2001). ―Inflation and the poor‖, Journal of Money, Credit and

Banking, 33, pp.160—178.

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FEMA (2006). Multi-Hazard Loss Estimation Methodology. Flood Model – Technical Manual. Washington, DC. (http://www.fema.gov/media-library-data/20130726-1820-25045-8292/hzmh2_1_fl_tm.pdf)

Fujii, T. (2013). ―The Impact of Food Inflation on Poverty in the Philippines‖, Food Policy, 39, pp. 13-27.

Goodbody, I & Thomas-Hope, E 2002, Natural Resource Management for Sustainable Development in the Caribbean, Canoe Press, Jamaica.

Gumbricht, T. (1996). ―Landscape Interfaces and Transparency to Hydrological Functions‖, in Application of Geographic Information Systems in Hydrology and Water Resources Management, IAHS Publications, 235, pp. 115.-221.

Guzzetti, F, Peruccacci, S & Rossi, M 2005, Definition of Critical Threshold for Different Scenarios, 3B064, RISK Advanced Weather forecast system to Advise on Risk Events and management.

Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. (2008). ―The Rainfall Intensity-Duration control of Shallow Landslides and Debris Flows: An Update‖, Landslides, 5, pp. 3-17.

Hong, Y., Adler, R., Negri, A., and Huffman, G. (2007). ―Flood and Landslide Applications of Near Real-Time Satellite Rainfall Products‖, Natural Hazards, 43, pp. 285-294.

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http://www.fema.gov/media-library-data/20130726-1820-25045-8292/hzmh2_1_fl_tm.pdf

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Mandal, A., Wilson, M., Taylor, A., Nandi, T. Stephenson, C., Burgess., J., Campbell, S. Otuokon (2014). ―Flood Hazards In Jamaica With Special Emphasis On The Yallahs River Watershed: Climate Change, Future Flood Risk And Community Awareness‖, WCRP-CORDEX LAC Phase II The Caribbean, Santo Domingo Dominican Republic, 7th -9th April, 2014.

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Wu, H., Adler, R., Tian, Y., Huffman, G., Li, H., and Wang, J. (2014). ―Real-Time Global Flood Estimation Using Satellite-Based Precipitation and a Coupled Land Surface and Routing Model‖, Water Resources Research, 50, pp. 2693-2717.

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


.1.1

5.2

.25

.3

Fo

od

Sh

are

of C

onsu

mp

tion


Figure : Housing and Utilities Budget Share vs. Consumption per Capita

Notes: (a) Graph of the kernel regression estimate using a epanechnikov kernel and optimal


.05

.1.1

5.2

.25

Ho

usin

g &

Utilit

ies S

ha

re o

f C

on

sum

ption


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)


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




specifications.

Table 5:

(1) (2) (3) (4) (5) (6) (7) (8) (9)


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




specifications.

Table 6:

(1) (2) (3) (4) (5) (6) (7) (8) (9)


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




specifications.

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